diff --git a/lib/python3.11/site-packages/cuda/bindings/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2090eeb088f7c4f118bf044f0f4edbc03147685e
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/__pycache__/_version.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/__pycache__/_version.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0d84136add011a27263930fb12ec1bd6e35036ee
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/__pycache__/_version.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/__init__.py b/lib/python3.11/site-packages/cuda/bindings/_bindings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/_bindings/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ed9db3488f4988adb8b85a581ad6ec5647791ac9
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_bindings/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/cydriver.pxd b/lib/python3.11/site-packages/cuda/bindings/_bindings/cydriver.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7c848eec927f56745b9cf7b7799c6a9d67813084
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_bindings/cydriver.pxd
@@ -0,0 +1,987 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+from cuda.bindings.cydriver cimport *
+
+cdef CUresult _cuGetErrorString(CUresult error, const char** pStr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGetErrorName(CUresult error, const char** pStr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuInit(unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDriverGetVersion(int* driverVersion) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGet(CUdevice* device, int ordinal) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetCount(int* count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetName(char* name, int length, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetUuid_v2(CUuuid* uuid, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetLuid(char* luid, unsigned int* deviceNodeMask, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceTotalMem_v2(size_t* numbytes, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetTexture1DLinearMaxWidth(size_t* maxWidthInElements, CUarray_format pformat, unsigned numChannels, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetAttribute(int* pi, CUdevice_attribute attrib, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetHostAtomicCapabilities(unsigned int* capabilities, const CUatomicOperation* operations, unsigned int count, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetNvSciSyncAttributes(void* nvSciSyncAttrList, CUdevice dev, int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceSetMemPool(CUdevice dev, CUmemoryPool pool) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetMemPool(CUmemoryPool* pool, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetDefaultMemPool(CUmemoryPool* pool_out, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetExecAffinitySupport(int* pi, CUexecAffinityType typename, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFlushGPUDirectRDMAWrites(CUflushGPUDirectRDMAWritesTarget target, CUflushGPUDirectRDMAWritesScope scope) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetProperties(CUdevprop* prop, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceComputeCapability(int* major, int* minor, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevicePrimaryCtxRetain(CUcontext* pctx, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevicePrimaryCtxRelease_v2(CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevicePrimaryCtxSetFlags_v2(CUdevice dev, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevicePrimaryCtxGetState(CUdevice dev, unsigned int* flags, int* active) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevicePrimaryCtxReset_v2(CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxCreate_v4(CUcontext* pctx, CUctxCreateParams* ctxCreateParams, unsigned int flags, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxDestroy_v2(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxPushCurrent_v2(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxPopCurrent_v2(CUcontext* pctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSetCurrent(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetCurrent(CUcontext* pctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetDevice(CUdevice* device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetDevice_v2(CUdevice* device, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetFlags(unsigned int* flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSetFlags(unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetId(CUcontext ctx, unsigned long long* ctxId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSynchronize() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSynchronize_v2(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSetLimit(CUlimit limit, size_t value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetLimit(size_t* pvalue, CUlimit limit) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetCacheConfig(CUfunc_cache* pconfig) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSetCacheConfig(CUfunc_cache config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetApiVersion(CUcontext ctx, unsigned int* version) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetStreamPriorityRange(int* leastPriority, int* greatestPriority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxResetPersistingL2Cache() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetExecAffinity(CUexecAffinityParam* pExecAffinity, CUexecAffinityType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxRecordEvent(CUcontext hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxWaitEvent(CUcontext hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxAttach(CUcontext* pctx, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxDetach(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetSharedMemConfig(CUsharedconfig* pConfig) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxSetSharedMemConfig(CUsharedconfig config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleLoad(CUmodule* module, const char* fname) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleLoadData(CUmodule* module, const void* image) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleLoadDataEx(CUmodule* module, const void* image, unsigned int numOptions, CUjit_option* options, void** optionValues) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleLoadFatBinary(CUmodule* module, const void* fatCubin) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleUnload(CUmodule hmod) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleGetLoadingMode(CUmoduleLoadingMode* mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleGetFunction(CUfunction* hfunc, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleGetFunctionCount(unsigned int* count, CUmodule mod) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleEnumerateFunctions(CUfunction* functions, unsigned int numFunctions, CUmodule mod) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleGetGlobal_v2(CUdeviceptr* dptr, size_t* numbytes, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLinkCreate_v2(unsigned int numOptions, CUjit_option* options, void** optionValues, CUlinkState* stateOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLinkAddData_v2(CUlinkState state, CUjitInputType typename, void* data, size_t size, const char* name, unsigned int numOptions, CUjit_option* options, void** optionValues) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLinkAddFile_v2(CUlinkState state, CUjitInputType typename, const char* path, unsigned int numOptions, CUjit_option* options, void** optionValues) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLinkComplete(CUlinkState state, void** cubinOut, size_t* sizeOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLinkDestroy(CUlinkState state) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleGetTexRef(CUtexref* pTexRef, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuModuleGetSurfRef(CUsurfref* pSurfRef, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryLoadData(CUlibrary* library, const void* code, CUjit_option* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, CUlibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryLoadFromFile(CUlibrary* library, const char* fileName, CUjit_option* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, CUlibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryUnload(CUlibrary library) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryGetKernel(CUkernel* pKernel, CUlibrary library, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryGetKernelCount(unsigned int* count, CUlibrary lib) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryEnumerateKernels(CUkernel* kernels, unsigned int numKernels, CUlibrary lib) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryGetModule(CUmodule* pMod, CUlibrary library) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelGetFunction(CUfunction* pFunc, CUkernel kernel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelGetLibrary(CUlibrary* pLib, CUkernel kernel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryGetGlobal(CUdeviceptr* dptr, size_t* numbytes, CUlibrary library, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryGetManaged(CUdeviceptr* dptr, size_t* numbytes, CUlibrary library, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLibraryGetUnifiedFunction(void** fptr, CUlibrary library, const char* symbol) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelGetAttribute(int* pi, CUfunction_attribute attrib, CUkernel kernel, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelSetAttribute(CUfunction_attribute attrib, int val, CUkernel kernel, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelSetCacheConfig(CUkernel kernel, CUfunc_cache config, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelGetName(const char** name, CUkernel hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelGetParamInfo(CUkernel kernel, size_t paramIndex, size_t* paramOffset, size_t* paramSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuKernelGetParamCount(CUkernel kernel, size_t* paramCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetInfo_v2(size_t* free, size_t* total) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAlloc_v2(CUdeviceptr* dptr, size_t bytesize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAllocPitch_v2(CUdeviceptr* dptr, size_t* pPitch, size_t WidthInBytes, size_t Height, unsigned int ElementSizeBytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemFree_v2(CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetAddressRange_v2(CUdeviceptr* pbase, size_t* psize, CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAllocHost_v2(void** pp, size_t bytesize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemFreeHost(void* p) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemHostAlloc(void** pp, size_t bytesize, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemHostGetDevicePointer_v2(CUdeviceptr* pdptr, void* p, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemHostGetFlags(unsigned int* pFlags, void* p) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAllocManaged(CUdeviceptr* dptr, size_t bytesize, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceRegisterAsyncNotification(CUdevice device, CUasyncCallback callbackFunc, void* userData, CUasyncCallbackHandle* callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceUnregisterAsyncNotification(CUdevice device, CUasyncCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetByPCIBusId(CUdevice* dev, const char* pciBusId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetPCIBusId(char* pciBusId, int length, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuIpcGetEventHandle(CUipcEventHandle* pHandle, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuIpcOpenEventHandle(CUevent* phEvent, CUipcEventHandle handle) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuIpcGetMemHandle(CUipcMemHandle* pHandle, CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuIpcOpenMemHandle_v2(CUdeviceptr* pdptr, CUipcMemHandle handle, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuIpcCloseMemHandle(CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemHostRegister_v2(void* p, size_t bytesize, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemHostUnregister(void* p) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyHtoD_v2(CUdeviceptr dstDevice, const void* srcHost, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyDtoH_v2(void* dstHost, CUdeviceptr srcDevice, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyDtoD_v2(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyDtoA_v2(CUarray dstArray, size_t dstOffset, CUdeviceptr srcDevice, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyAtoD_v2(CUdeviceptr dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyHtoA_v2(CUarray dstArray, size_t dstOffset, const void* srcHost, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyAtoH_v2(void* dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyAtoA_v2(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy2D_v2(const CUDA_MEMCPY2D* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy2DUnaligned_v2(const CUDA_MEMCPY2D* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy3D_v2(const CUDA_MEMCPY3D* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyHtoDAsync_v2(CUdeviceptr dstDevice, const void* srcHost, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyDtoHAsync_v2(void* dstHost, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyDtoDAsync_v2(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyHtoAAsync_v2(CUarray dstArray, size_t dstOffset, const void* srcHost, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyAtoHAsync_v2(void* dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy2DAsync_v2(const CUDA_MEMCPY2D* pCopy, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy3DAsync_v2(const CUDA_MEMCPY3D* pCopy, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER* pCopy, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyBatchAsync_v2(CUdeviceptr* dsts, CUdeviceptr* srcs, size_t* sizes, size_t count, CUmemcpyAttributes* attrs, size_t* attrsIdxs, size_t numAttrs, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy3DBatchAsync_v2(size_t numOps, CUDA_MEMCPY3D_BATCH_OP* opList, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpyWithAttributesAsync(CUdeviceptr dst, CUdeviceptr src, size_t size, CUmemcpyAttributes* attr, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemcpy3DWithAttributesAsync(CUDA_MEMCPY3D_BATCH_OP* op, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD8_v2(CUdeviceptr dstDevice, unsigned char uc, size_t N) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD16_v2(CUdeviceptr dstDevice, unsigned short us, size_t N) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD32_v2(CUdeviceptr dstDevice, unsigned int ui, size_t N) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD2D8_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD2D16_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD2D32_v2(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc, size_t N, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD16Async(CUdeviceptr dstDevice, unsigned short us, size_t N, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD32Async(CUdeviceptr dstDevice, unsigned int ui, size_t N, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD2D8Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD2D16Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemsetD2D32Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArrayCreate_v2(CUarray* pHandle, const CUDA_ARRAY_DESCRIPTOR* pAllocateArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArrayGetDescriptor_v2(CUDA_ARRAY_DESCRIPTOR* pArrayDescriptor, CUarray hArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES* sparseProperties, CUarray array) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMipmappedArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES* sparseProperties, CUmipmappedArray mipmap) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS* memoryRequirements, CUarray array, CUdevice device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMipmappedArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS* memoryRequirements, CUmipmappedArray mipmap, CUdevice device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArrayGetPlane(CUarray* pPlaneArray, CUarray hArray, unsigned int planeIdx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArrayDestroy(CUarray hArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArray3DCreate_v2(CUarray* pHandle, const CUDA_ARRAY3D_DESCRIPTOR* pAllocateArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuArray3DGetDescriptor_v2(CUDA_ARRAY3D_DESCRIPTOR* pArrayDescriptor, CUarray hArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMipmappedArrayCreate(CUmipmappedArray* pHandle, const CUDA_ARRAY3D_DESCRIPTOR* pMipmappedArrayDesc, unsigned int numMipmapLevels) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMipmappedArrayGetLevel(CUarray* pLevelArray, CUmipmappedArray hMipmappedArray, unsigned int level) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMipmappedArrayDestroy(CUmipmappedArray hMipmappedArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetHandleForAddressRange(void* handle, CUdeviceptr dptr, size_t size, CUmemRangeHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemBatchDecompressAsync(CUmemDecompressParams* paramsArray, size_t count, unsigned int flags, size_t* errorIndex, CUstream stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAddressReserve(CUdeviceptr* ptr, size_t size, size_t alignment, CUdeviceptr addr, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAddressFree(CUdeviceptr ptr, size_t size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemCreate(CUmemGenericAllocationHandle* handle, size_t size, const CUmemAllocationProp* prop, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemRelease(CUmemGenericAllocationHandle handle) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemMap(CUdeviceptr ptr, size_t size, size_t offset, CUmemGenericAllocationHandle handle, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemMapArrayAsync(CUarrayMapInfo* mapInfoList, unsigned int count, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemUnmap(CUdeviceptr ptr, size_t size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemSetAccess(CUdeviceptr ptr, size_t size, const CUmemAccessDesc* desc, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetAccess(unsigned long long* flags, const CUmemLocation* location, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemExportToShareableHandle(void* shareableHandle, CUmemGenericAllocationHandle handle, CUmemAllocationHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemImportFromShareableHandle(CUmemGenericAllocationHandle* handle, void* osHandle, CUmemAllocationHandleType shHandleType) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetAllocationGranularity(size_t* granularity, const CUmemAllocationProp* prop, CUmemAllocationGranularity_flags option) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetAllocationPropertiesFromHandle(CUmemAllocationProp* prop, CUmemGenericAllocationHandle handle) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemRetainAllocationHandle(CUmemGenericAllocationHandle* handle, void* addr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemFreeAsync(CUdeviceptr dptr, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAllocAsync(CUdeviceptr* dptr, size_t bytesize, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolTrimTo(CUmemoryPool pool, size_t minBytesToKeep) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolSetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolGetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolSetAccess(CUmemoryPool pool, const CUmemAccessDesc* map, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolGetAccess(CUmemAccess_flags* flags, CUmemoryPool memPool, CUmemLocation* location) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolCreate(CUmemoryPool* pool, const CUmemPoolProps* poolProps) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolDestroy(CUmemoryPool pool) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetDefaultMemPool(CUmemoryPool* pool_out, CUmemLocation* location, CUmemAllocationType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemGetMemPool(CUmemoryPool* pool, CUmemLocation* location, CUmemAllocationType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemSetMemPool(CUmemLocation* location, CUmemAllocationType typename, CUmemoryPool pool) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAllocFromPoolAsync(CUdeviceptr* dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolExportToShareableHandle(void* handle_out, CUmemoryPool pool, CUmemAllocationHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolImportFromShareableHandle(CUmemoryPool* pool_out, void* handle, CUmemAllocationHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolExportPointer(CUmemPoolPtrExportData* shareData_out, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPoolImportPointer(CUdeviceptr* ptr_out, CUmemoryPool pool, CUmemPoolPtrExportData* shareData) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastCreate(CUmemGenericAllocationHandle* mcHandle, const CUmulticastObjectProp* prop) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastAddDevice(CUmemGenericAllocationHandle mcHandle, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastBindMem(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastBindMem_v2(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastBindAddr(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastBindAddr_v2(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastUnbind(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, size_t size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMulticastGetGranularity(size_t* granularity, const CUmulticastObjectProp* prop, CUmulticastGranularity_flags option) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuPointerGetAttribute(void* data, CUpointer_attribute attribute, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPrefetchAsync_v2(CUdeviceptr devPtr, size_t count, CUmemLocation location, unsigned int flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemAdvise_v2(CUdeviceptr devPtr, size_t count, CUmem_advise advice, CUmemLocation location) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemPrefetchBatchAsync(CUdeviceptr* dptrs, size_t* sizes, size_t count, CUmemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemDiscardBatchAsync(CUdeviceptr* dptrs, size_t* sizes, size_t count, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemDiscardAndPrefetchBatchAsync(CUdeviceptr* dptrs, size_t* sizes, size_t count, CUmemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemRangeGetAttribute(void* data, size_t dataSize, CUmem_range_attribute attribute, CUdeviceptr devPtr, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuMemRangeGetAttributes(void** data, size_t* dataSizes, CUmem_range_attribute* attributes, size_t numAttributes, CUdeviceptr devPtr, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuPointerSetAttribute(const void* value, CUpointer_attribute attribute, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuPointerGetAttributes(unsigned int numAttributes, CUpointer_attribute* attributes, void** data, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamCreate(CUstream* phStream, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamCreateWithPriority(CUstream* phStream, unsigned int flags, int priority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamBeginCaptureToCig(CUstream hStream, CUstreamCigCaptureParams* streamCigCaptureParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamEndCaptureToCig(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetPriority(CUstream hStream, int* priority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetDevice(CUstream hStream, CUdevice* device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetFlags(CUstream hStream, unsigned int* flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetId(CUstream hStream, unsigned long long* streamId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetCtx(CUstream hStream, CUcontext* pctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetCtx_v2(CUstream hStream, CUcontext* pCtx, CUgreenCtx* pGreenCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamWaitEvent(CUstream hStream, CUevent hEvent, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamAddCallback(CUstream hStream, CUstreamCallback callback, void* userData, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamBeginCapture_v2(CUstream hStream, CUstreamCaptureMode mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamBeginCaptureToGraph(CUstream hStream, CUgraph hGraph, const CUgraphNode* dependencies, const CUgraphEdgeData* dependencyData, size_t numDependencies, CUstreamCaptureMode mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuThreadExchangeStreamCaptureMode(CUstreamCaptureMode* mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamEndCapture(CUstream hStream, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamIsCapturing(CUstream hStream, CUstreamCaptureStatus* captureStatus) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetCaptureInfo_v3(CUstream hStream, CUstreamCaptureStatus* captureStatus_out, cuuint64_t* id_out, CUgraph* graph_out, const CUgraphNode** dependencies_out, const CUgraphEdgeData** edgeData_out, size_t* numDependencies_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamUpdateCaptureDependencies_v2(CUstream hStream, CUgraphNode* dependencies, const CUgraphEdgeData* dependencyData, size_t numDependencies, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamAttachMemAsync(CUstream hStream, CUdeviceptr dptr, size_t length, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamQuery(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamSynchronize(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamDestroy_v2(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamCopyAttributes(CUstream dst, CUstream src) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetAttribute(CUstream hStream, CUstreamAttrID attr, CUstreamAttrValue* value_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamSetAttribute(CUstream hStream, CUstreamAttrID attr, const CUstreamAttrValue* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventCreate(CUevent* phEvent, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventRecord(CUevent hEvent, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventRecordWithFlags(CUevent hEvent, CUstream hStream, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventQuery(CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventSynchronize(CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventDestroy_v2(CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventElapsedTime_v2(float* pMilliseconds, CUevent hStart, CUevent hEnd) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuImportExternalMemory(CUexternalMemory* extMem_out, const CUDA_EXTERNAL_MEMORY_HANDLE_DESC* memHandleDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuExternalMemoryGetMappedBuffer(CUdeviceptr* devPtr, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_BUFFER_DESC* bufferDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuExternalMemoryGetMappedMipmappedArray(CUmipmappedArray* mipmap, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC* mipmapDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDestroyExternalMemory(CUexternalMemory extMem) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuImportExternalSemaphore(CUexternalSemaphore* extSem_out, const CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC* semHandleDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuSignalExternalSemaphoresAsync(const CUexternalSemaphore* extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray, unsigned int numExtSems, CUstream stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuWaitExternalSemaphoresAsync(const CUexternalSemaphore* extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray, unsigned int numExtSems, CUstream stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDestroyExternalSemaphore(CUexternalSemaphore extSem) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamWaitValue32_v2(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamWaitValue64_v2(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamWriteValue32_v2(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamWriteValue64_v2(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamBatchMemOp_v2(CUstream stream, unsigned int count, CUstreamBatchMemOpParams* paramArray, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncGetAttribute(int* pi, CUfunction_attribute attrib, CUfunction hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncSetCacheConfig(CUfunction hfunc, CUfunc_cache config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncGetModule(CUmodule* hmod, CUfunction hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncGetName(const char** name, CUfunction hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncGetParamInfo(CUfunction func, size_t paramIndex, size_t* paramOffset, size_t* paramSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncGetParamCount(CUfunction func, size_t* paramCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncIsLoaded(CUfunctionLoadingState* state, CUfunction function) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncLoad(CUfunction function) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void** kernelParams, void** extra) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchKernelEx(const CUlaunchConfig* config, CUfunction f, void** kernelParams, void** extra) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchCooperativeKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void** kernelParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchCooperativeKernelMultiDevice(CUDA_LAUNCH_PARAMS* launchParamsList, unsigned int numDevices, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchHostFunc(CUstream hStream, CUhostFn fn, void* userData) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchHostFunc_v2(CUstream hStream, CUhostFn fn, void* userData, unsigned int syncMode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncSetBlockShape(CUfunction hfunc, int x, int y, int z) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncSetSharedSize(CUfunction hfunc, unsigned int numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuParamSetSize(CUfunction hfunc, unsigned int numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuParamSeti(CUfunction hfunc, int offset, unsigned int value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuParamSetf(CUfunction hfunc, int offset, float value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuParamSetv(CUfunction hfunc, int offset, void* ptr, unsigned int numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunch(CUfunction f) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchGrid(CUfunction f, int grid_width, int grid_height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLaunchGridAsync(CUfunction f, int grid_width, int grid_height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuParamSetTexRef(CUfunction hfunc, int texunit, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuFuncSetSharedMemConfig(CUfunction hfunc, CUsharedconfig config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphCreate(CUgraph* phGraph, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddKernelNode_v2(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphKernelNodeGetParams_v2(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphKernelNodeSetParams_v2(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddMemcpyNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_MEMCPY3D* copyParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphMemcpyNodeGetParams(CUgraphNode hNode, CUDA_MEMCPY3D* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphMemcpyNodeSetParams(CUgraphNode hNode, const CUDA_MEMCPY3D* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddMemsetNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_MEMSET_NODE_PARAMS* memsetParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphMemsetNodeGetParams(CUgraphNode hNode, CUDA_MEMSET_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphMemsetNodeSetParams(CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddHostNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphHostNodeGetParams(CUgraphNode hNode, CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphHostNodeSetParams(CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddChildGraphNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUgraph childGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphChildGraphNodeGetGraph(CUgraphNode hNode, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddEmptyNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddEventRecordNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphEventRecordNodeGetEvent(CUgraphNode hNode, CUevent* event_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphEventRecordNodeSetEvent(CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddEventWaitNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphEventWaitNodeGetEvent(CUgraphNode hNode, CUevent* event_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphEventWaitNodeSetEvent(CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddExternalSemaphoresSignalNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExternalSemaphoresSignalNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* params_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExternalSemaphoresSignalNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddExternalSemaphoresWaitNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_EXT_SEM_WAIT_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExternalSemaphoresWaitNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS* params_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExternalSemaphoresWaitNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddBatchMemOpNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphBatchMemOpNodeGetParams(CUgraphNode hNode, CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphBatchMemOpNodeSetParams(CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecBatchMemOpNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddMemAllocNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUDA_MEM_ALLOC_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphMemAllocNodeGetParams(CUgraphNode hNode, CUDA_MEM_ALLOC_NODE_PARAMS* params_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddMemFreeNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphMemFreeNodeGetParams(CUgraphNode hNode, CUdeviceptr* dptr_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGraphMemTrim(CUdevice device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceSetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphClone(CUgraph* phGraphClone, CUgraph originalGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeFindInClone(CUgraphNode* phNode, CUgraphNode hOriginalNode, CUgraph hClonedGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetType(CUgraphNode hNode, CUgraphNodeType* typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetContainingGraph(CUgraphNode hNode, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetLocalId(CUgraphNode hNode, unsigned int* nodeId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetToolsId(CUgraphNode hNode, unsigned long long* toolsNodeId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphGetId(CUgraph hGraph, unsigned int* graphId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecGetId(CUgraphExec hGraphExec, unsigned int* graphId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphGetNodes(CUgraph hGraph, CUgraphNode* nodes, size_t* numNodes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphGetRootNodes(CUgraph hGraph, CUgraphNode* rootNodes, size_t* numRootNodes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphGetEdges_v2(CUgraph hGraph, CUgraphNode* from_, CUgraphNode* to, CUgraphEdgeData* edgeData, size_t* numEdges) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetDependencies_v2(CUgraphNode hNode, CUgraphNode* dependencies, CUgraphEdgeData* edgeData, size_t* numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetDependentNodes_v2(CUgraphNode hNode, CUgraphNode* dependentNodes, CUgraphEdgeData* edgeData, size_t* numDependentNodes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddDependencies_v2(CUgraph hGraph, const CUgraphNode* from_, const CUgraphNode* to, const CUgraphEdgeData* edgeData, size_t numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphRemoveDependencies_v2(CUgraph hGraph, const CUgraphNode* from_, const CUgraphNode* to, const CUgraphEdgeData* edgeData, size_t numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphDestroyNode(CUgraphNode hNode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphInstantiateWithFlags(CUgraphExec* phGraphExec, CUgraph hGraph, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphInstantiateWithParams(CUgraphExec* phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS* instantiateParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecGetFlags(CUgraphExec hGraphExec, cuuint64_t* flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecKernelNodeSetParams_v2(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecMemcpyNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMCPY3D* copyParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecMemsetNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS* memsetParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecHostNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecChildGraphNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraph childGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecEventRecordNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecEventWaitNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecExternalSemaphoresSignalNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecExternalSemaphoresWaitNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeSetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int isEnabled) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int* isEnabled) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphUpload(CUgraphExec hGraphExec, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphLaunch(CUgraphExec hGraphExec, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecDestroy(CUgraphExec hGraphExec) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphDestroy(CUgraph hGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecUpdate_v2(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphExecUpdateResultInfo* resultInfo) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphKernelNodeCopyAttributes(CUgraphNode dst, CUgraphNode src) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphKernelNodeGetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr, CUkernelNodeAttrValue* value_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphKernelNodeSetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr, const CUkernelNodeAttrValue* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphDebugDotPrint(CUgraph hGraph, const char* path, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuUserObjectCreate(CUuserObject* object_out, void* ptr, CUhostFn destroy, unsigned int initialRefcount, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuUserObjectRetain(CUuserObject object, unsigned int count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuUserObjectRelease(CUuserObject object, unsigned int count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphRetainUserObject(CUgraph graph, CUuserObject object, unsigned int count, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphReleaseUserObject(CUgraph graph, CUuserObject object, unsigned int count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphAddNode_v2(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, const CUgraphEdgeData* dependencyData, size_t numDependencies, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeSetParams(CUgraphNode hNode, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphNodeGetParams(CUgraphNode hNode, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphExecNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphConditionalHandleCreate(CUgraphConditionalHandle* pHandle_out, CUgraph hGraph, CUcontext ctx, unsigned int defaultLaunchValue, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyMaxActiveBlocksPerMultiprocessor(int* numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int* numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyMaxPotentialBlockSize(int* minGridSize, int* blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyMaxPotentialBlockSizeWithFlags(int* minGridSize, int* blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyAvailableDynamicSMemPerBlock(size_t* dynamicSmemSize, CUfunction func, int numBlocks, int blockSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyMaxPotentialClusterSize(int* clusterSize, CUfunction func, const CUlaunchConfig* config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuOccupancyMaxActiveClusters(int* numClusters, CUfunction func, const CUlaunchConfig* config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetArray(CUtexref hTexRef, CUarray hArray, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetMipmappedArray(CUtexref hTexRef, CUmipmappedArray hMipmappedArray, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetAddress_v2(size_t* ByteOffset, CUtexref hTexRef, CUdeviceptr dptr, size_t numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetAddress2D_v3(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR* desc, CUdeviceptr dptr, size_t Pitch) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetFormat(CUtexref hTexRef, CUarray_format fmt, int NumPackedComponents) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetAddressMode(CUtexref hTexRef, int dim, CUaddress_mode am) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetFilterMode(CUtexref hTexRef, CUfilter_mode fm) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetMipmapFilterMode(CUtexref hTexRef, CUfilter_mode fm) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetMipmapLevelBias(CUtexref hTexRef, float bias) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetMipmapLevelClamp(CUtexref hTexRef, float minMipmapLevelClamp, float maxMipmapLevelClamp) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetMaxAnisotropy(CUtexref hTexRef, unsigned int maxAniso) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetBorderColor(CUtexref hTexRef, float* pBorderColor) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefSetFlags(CUtexref hTexRef, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetAddress_v2(CUdeviceptr* pdptr, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetArray(CUarray* phArray, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetMipmappedArray(CUmipmappedArray* phMipmappedArray, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetAddressMode(CUaddress_mode* pam, CUtexref hTexRef, int dim) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetFilterMode(CUfilter_mode* pfm, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetFormat(CUarray_format* pFormat, int* pNumChannels, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetMipmapFilterMode(CUfilter_mode* pfm, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetMipmapLevelBias(float* pbias, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetMipmapLevelClamp(float* pminMipmapLevelClamp, float* pmaxMipmapLevelClamp, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetMaxAnisotropy(int* pmaxAniso, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetBorderColor(float* pBorderColor, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefGetFlags(unsigned int* pFlags, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefCreate(CUtexref* pTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexRefDestroy(CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuSurfRefSetArray(CUsurfref hSurfRef, CUarray hArray, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuSurfRefGetArray(CUarray* phArray, CUsurfref hSurfRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexObjectCreate(CUtexObject* pTexObject, const CUDA_RESOURCE_DESC* pResDesc, const CUDA_TEXTURE_DESC* pTexDesc, const CUDA_RESOURCE_VIEW_DESC* pResViewDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexObjectDestroy(CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexObjectGetResourceDesc(CUDA_RESOURCE_DESC* pResDesc, CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexObjectGetTextureDesc(CUDA_TEXTURE_DESC* pTexDesc, CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTexObjectGetResourceViewDesc(CUDA_RESOURCE_VIEW_DESC* pResViewDesc, CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuSurfObjectCreate(CUsurfObject* pSurfObject, const CUDA_RESOURCE_DESC* pResDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuSurfObjectDestroy(CUsurfObject surfObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuSurfObjectGetResourceDesc(CUDA_RESOURCE_DESC* pResDesc, CUsurfObject surfObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTensorMapEncodeTiled(CUtensorMap* tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void* globalAddress, const cuuint64_t* globalDim, const cuuint64_t* globalStrides, const cuuint32_t* boxDim, const cuuint32_t* elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTensorMapEncodeIm2col(CUtensorMap* tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void* globalAddress, const cuuint64_t* globalDim, const cuuint64_t* globalStrides, const int* pixelBoxLowerCorner, const int* pixelBoxUpperCorner, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t* elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTensorMapEncodeIm2colWide(CUtensorMap* tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void* globalAddress, const cuuint64_t* globalDim, const cuuint64_t* globalStrides, int pixelBoxLowerCornerWidth, int pixelBoxUpperCornerWidth, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t* elementStrides, CUtensorMapInterleave interleave, CUtensorMapIm2ColWideMode mode, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuTensorMapReplaceAddress(CUtensorMap* tensorMap, void* globalAddress) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceCanAccessPeer(int* canAccessPeer, CUdevice dev, CUdevice peerDev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxEnablePeerAccess(CUcontext peerContext, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxDisablePeerAccess(CUcontext peerContext) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetP2PAttribute(int* value, CUdevice_P2PAttribute attrib, CUdevice srcDevice, CUdevice dstDevice) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetP2PAtomicCapabilities(unsigned int* capabilities, const CUatomicOperation* operations, unsigned int count, CUdevice srcDevice, CUdevice dstDevice) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsUnregisterResource(CUgraphicsResource resource) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsSubResourceGetMappedArray(CUarray* pArray, CUgraphicsResource resource, unsigned int arrayIndex, unsigned int mipLevel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsResourceGetMappedMipmappedArray(CUmipmappedArray* pMipmappedArray, CUgraphicsResource resource) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsResourceGetMappedPointer_v2(CUdeviceptr* pDevPtr, size_t* pSize, CUgraphicsResource resource) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsResourceSetMapFlags_v2(CUgraphicsResource resource, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsMapResources(unsigned int count, CUgraphicsResource* resources, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsUnmapResources(unsigned int count, CUgraphicsResource* resources, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGetProcAddress_v2(const char* symbol, void** pfn, int cudaVersion, cuuint64_t flags, CUdriverProcAddressQueryResult* symbolStatus) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpGetAttribute(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpGetAttributeGlobal(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpSetAttribute(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpSetAttributeGlobal(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpRegisterStartCallback(CUcoredumpStatusCallback callback, void* userData, CUcoredumpCallbackHandle* callbackOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpRegisterCompleteCallback(CUcoredumpStatusCallback callback, void* userData, CUcoredumpCallbackHandle* callbackOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpDeregisterStartCallback(CUcoredumpCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCoredumpDeregisterCompleteCallback(CUcoredumpCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGetExportTable(const void** ppExportTable, const CUuuid* pExportTableId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxCreate(CUgreenCtx* phCtx, CUdevResourceDesc desc, CUdevice dev, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxDestroy(CUgreenCtx hCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxFromGreenCtx(CUcontext* pContext, CUgreenCtx hCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDeviceGetDevResource(CUdevice device, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCtxGetDevResource(CUcontext hCtx, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxGetDevResource(CUgreenCtx hCtx, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevSmResourceSplitByCount(CUdevResource* result, unsigned int* nbGroups, const CUdevResource* input, CUdevResource* remainder, unsigned int flags, unsigned int minCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevSmResourceSplit(CUdevResource* result, unsigned int nbGroups, const CUdevResource* input, CUdevResource* remainder, unsigned int flags, CU_DEV_SM_RESOURCE_GROUP_PARAMS* groupParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuDevResourceGenerateDesc(CUdevResourceDesc* phDesc, CUdevResource* resources, unsigned int nbResources) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxRecordEvent(CUgreenCtx hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxWaitEvent(CUgreenCtx hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetGreenCtx(CUstream hStream, CUgreenCtx* phCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxStreamCreate(CUstream* phStream, CUgreenCtx greenCtx, unsigned int flags, int priority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGreenCtxGetId(CUgreenCtx greenCtx, unsigned long long* greenCtxId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuStreamGetDevResource(CUstream hStream, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLogsRegisterCallback(CUlogsCallback callbackFunc, void* userData, CUlogsCallbackHandle* callback_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLogsUnregisterCallback(CUlogsCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLogsCurrent(CUlogIterator* iterator_out, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLogsDumpToFile(CUlogIterator* iterator, const char* pathToFile, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuLogsDumpToMemory(CUlogIterator* iterator, char* buffer, size_t* size, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCheckpointProcessGetRestoreThreadId(int pid, int* tid) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCheckpointProcessGetState(int pid, CUprocessState* state) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCheckpointProcessLock(int pid, CUcheckpointLockArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCheckpointProcessCheckpoint(int pid, CUcheckpointCheckpointArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCheckpointProcessRestore(int pid, CUcheckpointRestoreArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuCheckpointProcessUnlock(int pid, CUcheckpointUnlockArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuProfilerStart() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuProfilerStop() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsEGLRegisterImage(CUgraphicsResource* pCudaResource, EGLImageKHR image, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamConsumerConnect(CUeglStreamConnection* conn, EGLStreamKHR stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamConsumerConnectWithFlags(CUeglStreamConnection* conn, EGLStreamKHR stream, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamConsumerDisconnect(CUeglStreamConnection* conn) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamConsumerAcquireFrame(CUeglStreamConnection* conn, CUgraphicsResource* pCudaResource, CUstream* pStream, unsigned int timeout) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamConsumerReleaseFrame(CUeglStreamConnection* conn, CUgraphicsResource pCudaResource, CUstream* pStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamProducerConnect(CUeglStreamConnection* conn, EGLStreamKHR stream, EGLint width, EGLint height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamProducerDisconnect(CUeglStreamConnection* conn) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamProducerPresentFrame(CUeglStreamConnection* conn, CUeglFrame eglframe, CUstream* pStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEGLStreamProducerReturnFrame(CUeglStreamConnection* conn, CUeglFrame* eglframe, CUstream* pStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsResourceGetMappedEglFrame(CUeglFrame* eglFrame, CUgraphicsResource resource, unsigned int index, unsigned int mipLevel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuEventCreateFromEGLSync(CUevent* phEvent, EGLSyncKHR eglSync, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsGLRegisterBuffer(CUgraphicsResource* pCudaResource, GLuint buffer, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsGLRegisterImage(CUgraphicsResource* pCudaResource, GLuint image, GLenum target, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGLGetDevices_v2(unsigned int* pCudaDeviceCount, CUdevice* pCudaDevices, unsigned int cudaDeviceCount, CUGLDeviceList deviceList) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuVDPAUGetDevice(CUdevice* pDevice, VdpDevice vdpDevice, VdpGetProcAddress* vdpGetProcAddress) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuVDPAUCtxCreate_v2(CUcontext* pCtx, unsigned int flags, CUdevice device, VdpDevice vdpDevice, VdpGetProcAddress* vdpGetProcAddress) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsVDPAURegisterVideoSurface(CUgraphicsResource* pCudaResource, VdpVideoSurface vdpSurface, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult _cuGraphicsVDPAURegisterOutputSurface(CUgraphicsResource* pCudaResource, VdpOutputSurface vdpSurface, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/_bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..a0b5412a6da6c23f5fab00ca8b38c8d35b4356c9
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/cynvrtc.pxd b/lib/python3.11/site-packages/cuda/bindings/_bindings/cynvrtc.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..e143f0e154dc5f0e20c61746b8452d91662b0f9d
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_bindings/cynvrtc.pxd
@@ -0,0 +1,57 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+from cuda.bindings.cynvrtc cimport *
+
+cdef const char* _nvrtcGetErrorString(nvrtcResult result) except ?NULL nogil
+
+cdef nvrtcResult _nvrtcVersion(int* major, int* minor) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetNumSupportedArchs(int* numArchs) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetSupportedArchs(int* supportedArchs) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcCreateProgram(nvrtcProgram* prog, const char* src, const char* name, int numHeaders, const char** headers, const char** includeNames) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcDestroyProgram(nvrtcProgram* prog) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcCompileProgram(nvrtcProgram prog, int numOptions, const char** options) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetPTXSize(nvrtcProgram prog, size_t* ptxSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetPTX(nvrtcProgram prog, char* ptx) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetCUBINSize(nvrtcProgram prog, size_t* cubinSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetCUBIN(nvrtcProgram prog, char* cubin) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetLTOIRSize(nvrtcProgram prog, size_t* LTOIRSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetLTOIR(nvrtcProgram prog, char* LTOIR) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetOptiXIRSize(nvrtcProgram prog, size_t* optixirSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetOptiXIR(nvrtcProgram prog, char* optixir) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetProgramLogSize(nvrtcProgram prog, size_t* logSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetProgramLog(nvrtcProgram prog, char* log) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcAddNameExpression(nvrtcProgram prog, const char* name_expression) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetLoweredName(nvrtcProgram prog, const char* name_expression, const char** lowered_name) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetPCHHeapSize(size_t* ret) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcSetPCHHeapSize(size_t size) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetPCHCreateStatus(nvrtcProgram prog) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetPCHHeapSizeRequired(nvrtcProgram prog, size_t* size) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcSetFlowCallback(nvrtcProgram prog, void* callback, void* payload) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetTileIRSize(nvrtcProgram prog, size_t* TileIRSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult _nvrtcGetTileIR(nvrtcProgram prog, char* TileIR) except ?NVRTC_ERROR_INVALID_INPUT nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/cyruntime.pxd b/lib/python3.11/site-packages/cuda/bindings/_bindings/cyruntime.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..210873029664b153b6c0c89dcb2e913f0def22f3
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_bindings/cyruntime.pxd
@@ -0,0 +1,647 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+include "../cyruntime_types.pxi"
+
+include "../_lib/cyruntime/cyruntime.pxd"
+
+cdef cudaError_t _cudaDeviceReset() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSynchronize() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetLimit(cudaLimit limit, size_t value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetLimit(size_t* pValue, cudaLimit limit) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetTexture1DLinearMaxWidth(size_t* maxWidthInElements, const cudaChannelFormatDesc* fmtDesc, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetCacheConfig(cudaFuncCache* pCacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetStreamPriorityRange(int* leastPriority, int* greatestPriority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetCacheConfig(cudaFuncCache cacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetByPCIBusId(int* device, const char* pciBusId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetPCIBusId(char* pciBusId, int length, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcGetEventHandle(cudaIpcEventHandle_t* handle, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcOpenEventHandle(cudaEvent_t* event, cudaIpcEventHandle_t handle) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcGetMemHandle(cudaIpcMemHandle_t* handle, void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcOpenMemHandle(void** devPtr, cudaIpcMemHandle_t handle, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcCloseMemHandle(void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceFlushGPUDirectRDMAWrites(cudaFlushGPUDirectRDMAWritesTarget target, cudaFlushGPUDirectRDMAWritesScope scope) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceRegisterAsyncNotification(int device, cudaAsyncCallback callbackFunc, void* userData, cudaAsyncCallbackHandle_t* callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceUnregisterAsyncNotification(int device, cudaAsyncCallbackHandle_t callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetSharedMemConfig(cudaSharedMemConfig* pConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetSharedMemConfig(cudaSharedMemConfig config) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetLastError() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaPeekAtLastError() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef const char* _cudaGetErrorName(cudaError_t error) except ?NULL nogil
+
+cdef const char* _cudaGetErrorString(cudaError_t error) except ?NULL nogil
+
+cdef cudaError_t _cudaGetDeviceCount(int* count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDeviceProperties(cudaDeviceProp* prop, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetAttribute(int* value, cudaDeviceAttr attr, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetHostAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetDefaultMemPool(cudaMemPool_t* memPool, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetMemPool(int device, cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetMemPool(cudaMemPool_t* memPool, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetNvSciSyncAttributes(void* nvSciSyncAttrList, int device, int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetP2PAttribute(int* value, cudaDeviceP2PAttr attr, int srcDevice, int dstDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetP2PAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int srcDevice, int dstDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaChooseDevice(int* device, const cudaDeviceProp* prop) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaInitDevice(int device, unsigned int deviceFlags, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaSetDevice(int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDevice(int* device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaSetDeviceFlags(unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDeviceFlags(unsigned int* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCreate(cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCreateWithFlags(cudaStream_t* pStream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCreateWithPriority(cudaStream_t* pStream, unsigned int flags, int priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetPriority(cudaStream_t hStream, int* priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetFlags(cudaStream_t hStream, unsigned int* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetId(cudaStream_t hStream, unsigned long long* streamId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetDevice(cudaStream_t hStream, int* device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaCtxResetPersistingL2Cache() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCopyAttributes(cudaStream_t dst, cudaStream_t src) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, cudaStreamAttrValue* value_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamSetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, const cudaStreamAttrValue* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamDestroy(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamAddCallback(cudaStream_t stream, cudaStreamCallback_t callback, void* userData, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamSynchronize(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamQuery(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamAttachMemAsync(cudaStream_t stream, void* devPtr, size_t length, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamBeginCapture(cudaStream_t stream, cudaStreamCaptureMode mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamBeginCaptureToGraph(cudaStream_t stream, cudaGraph_t graph, const cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaStreamCaptureMode mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaThreadExchangeStreamCaptureMode(cudaStreamCaptureMode* mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamEndCapture(cudaStream_t stream, cudaGraph_t* pGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamIsCapturing(cudaStream_t stream, cudaStreamCaptureStatus* pCaptureStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetCaptureInfo(cudaStream_t stream, cudaStreamCaptureStatus* captureStatus_out, unsigned long long* id_out, cudaGraph_t* graph_out, const cudaGraphNode_t** dependencies_out, const cudaGraphEdgeData** edgeData_out, size_t* numDependencies_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamUpdateCaptureDependencies(cudaStream_t stream, cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventCreate(cudaEvent_t* event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventCreateWithFlags(cudaEvent_t* event, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventRecord(cudaEvent_t event, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventQuery(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventSynchronize(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventDestroy(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventElapsedTime(float* ms, cudaEvent_t start, cudaEvent_t end) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaImportExternalMemory(cudaExternalMemory_t* extMem_out, const cudaExternalMemoryHandleDesc* memHandleDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExternalMemoryGetMappedBuffer(void** devPtr, cudaExternalMemory_t extMem, const cudaExternalMemoryBufferDesc* bufferDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExternalMemoryGetMappedMipmappedArray(cudaMipmappedArray_t* mipmap, cudaExternalMemory_t extMem, const cudaExternalMemoryMipmappedArrayDesc* mipmapDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroyExternalMemory(cudaExternalMemory_t extMem) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaImportExternalSemaphore(cudaExternalSemaphore_t* extSem_out, const cudaExternalSemaphoreHandleDesc* semHandleDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaSignalExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreSignalParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaWaitExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreWaitParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroyExternalSemaphore(cudaExternalSemaphore_t extSem) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncSetCacheConfig(const void* func, cudaFuncCache cacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncGetAttributes(cudaFuncAttributes* attr, const void* func) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncSetAttribute(const void* func, cudaFuncAttribute attr, int value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncGetParamCount(const void* func, size_t* paramCount) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLaunchHostFunc(cudaStream_t stream, cudaHostFn_t fn, void* userData) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLaunchHostFunc_v2(cudaStream_t stream, cudaHostFn_t fn, void* userData, unsigned int syncMode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncSetSharedMemConfig(const void* func, cudaSharedMemConfig config) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaOccupancyMaxActiveBlocksPerMultiprocessor(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaOccupancyAvailableDynamicSMemPerBlock(size_t* dynamicSmemSize, const void* func, int numBlocks, int blockSize) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocManaged(void** devPtr, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMalloc(void** devPtr, size_t size) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocHost(void** ptr, size_t size) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocPitch(void** devPtr, size_t* pitch, size_t width, size_t height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, size_t width, size_t height, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFree(void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeHost(void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeArray(cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeMipmappedArray(cudaMipmappedArray_t mipmappedArray) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostAlloc(void** pHost, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostRegister(void* ptr, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostUnregister(void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostGetDevicePointer(void** pDevice, void* pHost, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostGetFlags(unsigned int* pFlags, void* pHost) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMalloc3D(cudaPitchedPtr* pitchedDevPtr, cudaExtent extent) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMalloc3DArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocMipmappedArray(cudaMipmappedArray_t* mipmappedArray, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int numLevels, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetMipmappedArrayLevel(cudaArray_t* levelArray, cudaMipmappedArray_const_t mipmappedArray, unsigned int level) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3D(const cudaMemcpy3DParms* p) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DPeer(const cudaMemcpy3DPeerParms* p) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DAsync(const cudaMemcpy3DParms* p, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DPeerAsync(const cudaMemcpy3DPeerParms* p, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemGetInfo(size_t* free, size_t* total) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetInfo(cudaChannelFormatDesc* desc, cudaExtent* extent, unsigned int* flags, cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetPlane(cudaArray_t* pPlaneArray, cudaArray_t hArray, unsigned int planeIdx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaArray_t array, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMipmappedArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaMipmappedArray_t mipmap, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMipmappedArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaMipmappedArray_t mipmap) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy(void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyPeer(void* dst, int dstDevice, const void* src, int srcDevice, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2D(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DFromArray(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyAsync(void* dst, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyPeerAsync(void* dst, int dstDevice, const void* src, int srcDevice, size_t count, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyBatchAsync(const void** dsts, const void** srcs, const size_t* sizes, size_t count, cudaMemcpyAttributes* attrs, size_t* attrsIdxs, size_t numAttrs, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DBatchAsync(size_t numOps, cudaMemcpy3DBatchOp* opList, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyWithAttributesAsync(void* dst, const void* src, size_t size, cudaMemcpyAttributes* attr, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DWithAttributesAsync(cudaMemcpy3DBatchOp* op, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DAsync(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DFromArrayAsync(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset(void* devPtr, int value, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset2D(void* devPtr, size_t pitch, int value, size_t width, size_t height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset3D(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemsetAsync(void* devPtr, int value, size_t count, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset2DAsync(void* devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset3DAsync(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPrefetchAsync(const void* devPtr, size_t count, cudaMemLocation location, unsigned int flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemDiscardBatchAsync(void** dptrs, size_t* sizes, size_t count, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemDiscardAndPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemAdvise(const void* devPtr, size_t count, cudaMemoryAdvise advice, cudaMemLocation location) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemRangeGetAttribute(void* data, size_t dataSize, cudaMemRangeAttribute attribute, const void* devPtr, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemRangeGetAttributes(void** data, size_t* dataSizes, cudaMemRangeAttribute* attributes, size_t numAttributes, const void* devPtr, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyFromArray(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyFromArrayAsync(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocAsync(void** devPtr, size_t size, cudaStream_t hStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeAsync(void* devPtr, cudaStream_t hStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolTrimTo(cudaMemPool_t memPool, size_t minBytesToKeep) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolSetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolGetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolSetAccess(cudaMemPool_t memPool, const cudaMemAccessDesc* descList, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolGetAccess(cudaMemAccessFlags* flags, cudaMemPool_t memPool, cudaMemLocation* location) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolCreate(cudaMemPool_t* memPool, const cudaMemPoolProps* poolProps) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolDestroy(cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemGetDefaultMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemGetMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemSetMemPool(cudaMemLocation* location, cudaMemAllocationType typename, cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocFromPoolAsync(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolExportToShareableHandle(void* shareableHandle, cudaMemPool_t memPool, cudaMemAllocationHandleType handleType, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolImportFromShareableHandle(cudaMemPool_t* memPool, void* shareableHandle, cudaMemAllocationHandleType handleType, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolExportPointer(cudaMemPoolPtrExportData* exportData, void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolImportPointer(void** ptr, cudaMemPool_t memPool, cudaMemPoolPtrExportData* exportData) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaPointerGetAttributes(cudaPointerAttributes* attributes, const void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceCanAccessPeer(int* canAccessPeer, int device, int peerDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceEnablePeerAccess(int peerDevice, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceDisablePeerAccess(int peerDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsUnregisterResource(cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsResourceSetMapFlags(cudaGraphicsResource_t resource, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsMapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsUnmapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsResourceGetMappedPointer(void** devPtr, size_t* size, cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsSubResourceGetMappedArray(cudaArray_t* array, cudaGraphicsResource_t resource, unsigned int arrayIndex, unsigned int mipLevel) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsResourceGetMappedMipmappedArray(cudaMipmappedArray_t* mipmappedArray, cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetChannelDesc(cudaChannelFormatDesc* desc, cudaArray_const_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaChannelFormatDesc _cudaCreateChannelDesc(int x, int y, int z, int w, cudaChannelFormatKind f) except* nogil
+
+cdef cudaError_t _cudaCreateTextureObject(cudaTextureObject_t* pTexObject, const cudaResourceDesc* pResDesc, const cudaTextureDesc* pTexDesc, const cudaResourceViewDesc* pResViewDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroyTextureObject(cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetTextureObjectResourceDesc(cudaResourceDesc* pResDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetTextureObjectTextureDesc(cudaTextureDesc* pTexDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetTextureObjectResourceViewDesc(cudaResourceViewDesc* pResViewDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaCreateSurfaceObject(cudaSurfaceObject_t* pSurfObject, const cudaResourceDesc* pResDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroySurfaceObject(cudaSurfaceObject_t surfObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetSurfaceObjectResourceDesc(cudaResourceDesc* pResDesc, cudaSurfaceObject_t surfObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDriverGetVersion(int* driverVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaRuntimeGetVersion(int* runtimeVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsRegisterCallback(cudaLogsCallback_t callbackFunc, void* userData, cudaLogsCallbackHandle* callback_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsUnregisterCallback(cudaLogsCallbackHandle callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsCurrent(cudaLogIterator* iterator_out, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsDumpToFile(cudaLogIterator* iterator, const char* pathToFile, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsDumpToMemory(cudaLogIterator* iterator, char* buffer, size_t* size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphCreate(cudaGraph_t* pGraph, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddKernelNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeGetParams(cudaGraphNode_t node, cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeSetParams(cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeCopyAttributes(cudaGraphNode_t hDst, cudaGraphNode_t hSrc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeGetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, cudaKernelNodeAttrValue* value_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeSetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, const cudaKernelNodeAttrValue* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemcpyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemcpy3DParms* pCopyParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemcpyNode1D(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemcpyNodeGetParams(cudaGraphNode_t node, cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemcpyNodeSetParams(cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemcpyNodeSetParams1D(cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemsetNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemsetParams* pMemsetParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemsetNodeGetParams(cudaGraphNode_t node, cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemsetNodeSetParams(cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddHostNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphHostNodeGetParams(cudaGraphNode_t node, cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphHostNodeSetParams(cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddChildGraphNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaGraph_t childGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphChildGraphNodeGetGraph(cudaGraphNode_t node, cudaGraph_t* pGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddEmptyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddEventRecordNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventRecordNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventRecordNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddEventWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventWaitNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventWaitNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddExternalSemaphoresSignalNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresSignalNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreSignalNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresSignalNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddExternalSemaphoresWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresWaitNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreWaitNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresWaitNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemAllocNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaMemAllocNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemAllocNodeGetParams(cudaGraphNode_t node, cudaMemAllocNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemFreeNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemFreeNodeGetParams(cudaGraphNode_t node, void* dptr_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGraphMemTrim(int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphClone(cudaGraph_t* pGraphClone, cudaGraph_t originalGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeFindInClone(cudaGraphNode_t* pNode, cudaGraphNode_t originalNode, cudaGraph_t clonedGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetType(cudaGraphNode_t node, cudaGraphNodeType* pType) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetContainingGraph(cudaGraphNode_t hNode, cudaGraph_t* phGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetLocalId(cudaGraphNode_t hNode, unsigned int* nodeId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetToolsId(cudaGraphNode_t hNode, unsigned long long* toolsNodeId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetId(cudaGraph_t hGraph, unsigned int* graphID) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecGetId(cudaGraphExec_t hGraphExec, unsigned int* graphID) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetNodes(cudaGraph_t graph, cudaGraphNode_t* nodes, size_t* numNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetRootNodes(cudaGraph_t graph, cudaGraphNode_t* pRootNodes, size_t* pNumRootNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetEdges(cudaGraph_t graph, cudaGraphNode_t* from_, cudaGraphNode_t* to, cudaGraphEdgeData* edgeData, size_t* numEdges) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetDependencies(cudaGraphNode_t node, cudaGraphNode_t* pDependencies, cudaGraphEdgeData* edgeData, size_t* pNumDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetDependentNodes(cudaGraphNode_t node, cudaGraphNode_t* pDependentNodes, cudaGraphEdgeData* edgeData, size_t* pNumDependentNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphRemoveDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphDestroyNode(cudaGraphNode_t node) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphInstantiate(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphInstantiateWithFlags(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphInstantiateWithParams(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, cudaGraphInstantiateParams* instantiateParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecGetFlags(cudaGraphExec_t graphExec, unsigned long long* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecKernelNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecMemcpyNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecMemcpyNodeSetParams1D(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecMemsetNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecHostNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecChildGraphNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, cudaGraph_t childGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecEventRecordNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecEventWaitNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecExternalSemaphoresSignalNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecExternalSemaphoresWaitNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeSetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int isEnabled) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int* isEnabled) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecUpdate(cudaGraphExec_t hGraphExec, cudaGraph_t hGraph, cudaGraphExecUpdateResultInfo* resultInfo) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphUpload(cudaGraphExec_t graphExec, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecDestroy(cudaGraphExec_t graphExec) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphDestroy(cudaGraph_t graph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphDebugDotPrint(cudaGraph_t graph, const char* path, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaUserObjectCreate(cudaUserObject_t* object_out, void* ptr, cudaHostFn_t destroy, unsigned int initialRefcount, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaUserObjectRetain(cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaUserObjectRelease(cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphRetainUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphReleaseUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeSetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecNodeSetParams(cudaGraphExec_t graphExec, cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphConditionalHandleCreate(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, unsigned int defaultLaunchValue, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphConditionalHandleCreate_v2(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, cudaExecutionContext_t ctx, unsigned int defaultLaunchValue, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDriverEntryPoint(const char* symbol, void** funcPtr, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDriverEntryPointByVersion(const char* symbol, void** funcPtr, unsigned int cudaVersion, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryLoadData(cudaLibrary_t* library, const void* code, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryLoadFromFile(cudaLibrary_t* library, const char* fileName, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryUnload(cudaLibrary_t library) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetKernel(cudaKernel_t* pKernel, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetGlobal(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetManaged(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetUnifiedFunction(void** fptr, cudaLibrary_t library, const char* symbol) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetKernelCount(unsigned int* count, cudaLibrary_t lib) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryEnumerateKernels(cudaKernel_t* kernels, unsigned int numKernels, cudaLibrary_t lib) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaKernelSetAttributeForDevice(cudaKernel_t kernel, cudaFuncAttribute attr, int value, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetDevResource(int device, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDevSmResourceSplitByCount(cudaDevResource* result, unsigned int* nbGroups, const cudaDevResource* input, cudaDevResource* remaining, unsigned int flags, unsigned int minCount) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDevSmResourceSplit(cudaDevResource* result, unsigned int nbGroups, const cudaDevResource* input, cudaDevResource* remainder, unsigned int flags, cudaDevSmResourceGroupParams* groupParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDevResourceGenerateDesc(cudaDevResourceDesc_t* phDesc, cudaDevResource* resources, unsigned int nbResources) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGreenCtxCreate(cudaExecutionContext_t* phCtx, cudaDevResourceDesc_t desc, int device, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxDestroy(cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxGetDevResource(cudaExecutionContext_t ctx, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxGetDevice(int* device, cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxGetId(cudaExecutionContext_t ctx, unsigned long long* ctxId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxStreamCreate(cudaStream_t* phStream, cudaExecutionContext_t ctx, unsigned int flags, int priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxSynchronize(cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetDevResource(cudaStream_t hStream, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxRecordEvent(cudaExecutionContext_t ctx, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxWaitEvent(cudaExecutionContext_t ctx, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetExecutionCtx(cudaExecutionContext_t* ctx, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetExportTable(const void** ppExportTable, const cudaUUID_t* pExportTableId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetKernel(cudaKernel_t* kernelPtr, const void* entryFuncAddr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaPitchedPtr _make_cudaPitchedPtr(void* d, size_t p, size_t xsz, size_t ysz) except* nogil
+
+cdef cudaPos _make_cudaPos(size_t x, size_t y, size_t z) except* nogil
+
+cdef cudaExtent _make_cudaExtent(size_t w, size_t h, size_t d) except* nogil
+
+cdef cudaError_t _cudaProfilerStart() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaProfilerStop() except ?cudaErrorCallRequiresNewerDriver nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/cyruntime_ptds.pxd b/lib/python3.11/site-packages/cuda/bindings/_bindings/cyruntime_ptds.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..d95926fd2159086e3f38bbe5c4fe44d4acacd5e2
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_bindings/cyruntime_ptds.pxd
@@ -0,0 +1,650 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+cdef extern from "":
+    """
+    #define CUDA_API_PER_THREAD_DEFAULT_STREAM
+    """
+
+include "../cyruntime_types.pxi"
+
+cdef cudaError_t _cudaDeviceReset() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSynchronize() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetLimit(cudaLimit limit, size_t value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetLimit(size_t* pValue, cudaLimit limit) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetTexture1DLinearMaxWidth(size_t* maxWidthInElements, const cudaChannelFormatDesc* fmtDesc, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetCacheConfig(cudaFuncCache* pCacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetStreamPriorityRange(int* leastPriority, int* greatestPriority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetCacheConfig(cudaFuncCache cacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetByPCIBusId(int* device, const char* pciBusId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetPCIBusId(char* pciBusId, int length, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcGetEventHandle(cudaIpcEventHandle_t* handle, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcOpenEventHandle(cudaEvent_t* event, cudaIpcEventHandle_t handle) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcGetMemHandle(cudaIpcMemHandle_t* handle, void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcOpenMemHandle(void** devPtr, cudaIpcMemHandle_t handle, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaIpcCloseMemHandle(void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceFlushGPUDirectRDMAWrites(cudaFlushGPUDirectRDMAWritesTarget target, cudaFlushGPUDirectRDMAWritesScope scope) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceRegisterAsyncNotification(int device, cudaAsyncCallback callbackFunc, void* userData, cudaAsyncCallbackHandle_t* callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceUnregisterAsyncNotification(int device, cudaAsyncCallbackHandle_t callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetSharedMemConfig(cudaSharedMemConfig* pConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetSharedMemConfig(cudaSharedMemConfig config) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetLastError() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaPeekAtLastError() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef const char* _cudaGetErrorName(cudaError_t error) except ?NULL nogil
+
+cdef const char* _cudaGetErrorString(cudaError_t error) except ?NULL nogil
+
+cdef cudaError_t _cudaGetDeviceCount(int* count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDeviceProperties(cudaDeviceProp* prop, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetAttribute(int* value, cudaDeviceAttr attr, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetHostAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetDefaultMemPool(cudaMemPool_t* memPool, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetMemPool(int device, cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetMemPool(cudaMemPool_t* memPool, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetNvSciSyncAttributes(void* nvSciSyncAttrList, int device, int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetP2PAttribute(int* value, cudaDeviceP2PAttr attr, int srcDevice, int dstDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetP2PAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int srcDevice, int dstDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaChooseDevice(int* device, const cudaDeviceProp* prop) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaInitDevice(int device, unsigned int deviceFlags, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaSetDevice(int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDevice(int* device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaSetDeviceFlags(unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDeviceFlags(unsigned int* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCreate(cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCreateWithFlags(cudaStream_t* pStream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCreateWithPriority(cudaStream_t* pStream, unsigned int flags, int priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetPriority(cudaStream_t hStream, int* priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetFlags(cudaStream_t hStream, unsigned int* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetId(cudaStream_t hStream, unsigned long long* streamId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetDevice(cudaStream_t hStream, int* device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaCtxResetPersistingL2Cache() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamCopyAttributes(cudaStream_t dst, cudaStream_t src) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, cudaStreamAttrValue* value_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamSetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, const cudaStreamAttrValue* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamDestroy(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamAddCallback(cudaStream_t stream, cudaStreamCallback_t callback, void* userData, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamSynchronize(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamQuery(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamAttachMemAsync(cudaStream_t stream, void* devPtr, size_t length, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamBeginCapture(cudaStream_t stream, cudaStreamCaptureMode mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamBeginCaptureToGraph(cudaStream_t stream, cudaGraph_t graph, const cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaStreamCaptureMode mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaThreadExchangeStreamCaptureMode(cudaStreamCaptureMode* mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamEndCapture(cudaStream_t stream, cudaGraph_t* pGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamIsCapturing(cudaStream_t stream, cudaStreamCaptureStatus* pCaptureStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetCaptureInfo(cudaStream_t stream, cudaStreamCaptureStatus* captureStatus_out, unsigned long long* id_out, cudaGraph_t* graph_out, const cudaGraphNode_t** dependencies_out, const cudaGraphEdgeData** edgeData_out, size_t* numDependencies_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamUpdateCaptureDependencies(cudaStream_t stream, cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventCreate(cudaEvent_t* event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventCreateWithFlags(cudaEvent_t* event, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventRecord(cudaEvent_t event, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventQuery(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventSynchronize(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventDestroy(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaEventElapsedTime(float* ms, cudaEvent_t start, cudaEvent_t end) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaImportExternalMemory(cudaExternalMemory_t* extMem_out, const cudaExternalMemoryHandleDesc* memHandleDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExternalMemoryGetMappedBuffer(void** devPtr, cudaExternalMemory_t extMem, const cudaExternalMemoryBufferDesc* bufferDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExternalMemoryGetMappedMipmappedArray(cudaMipmappedArray_t* mipmap, cudaExternalMemory_t extMem, const cudaExternalMemoryMipmappedArrayDesc* mipmapDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroyExternalMemory(cudaExternalMemory_t extMem) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaImportExternalSemaphore(cudaExternalSemaphore_t* extSem_out, const cudaExternalSemaphoreHandleDesc* semHandleDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaSignalExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreSignalParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaWaitExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreWaitParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroyExternalSemaphore(cudaExternalSemaphore_t extSem) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncSetCacheConfig(const void* func, cudaFuncCache cacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncGetAttributes(cudaFuncAttributes* attr, const void* func) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncSetAttribute(const void* func, cudaFuncAttribute attr, int value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncGetParamCount(const void* func, size_t* paramCount) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLaunchHostFunc(cudaStream_t stream, cudaHostFn_t fn, void* userData) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLaunchHostFunc_v2(cudaStream_t stream, cudaHostFn_t fn, void* userData, unsigned int syncMode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFuncSetSharedMemConfig(const void* func, cudaSharedMemConfig config) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaOccupancyMaxActiveBlocksPerMultiprocessor(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaOccupancyAvailableDynamicSMemPerBlock(size_t* dynamicSmemSize, const void* func, int numBlocks, int blockSize) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocManaged(void** devPtr, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMalloc(void** devPtr, size_t size) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocHost(void** ptr, size_t size) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocPitch(void** devPtr, size_t* pitch, size_t width, size_t height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, size_t width, size_t height, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFree(void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeHost(void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeArray(cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeMipmappedArray(cudaMipmappedArray_t mipmappedArray) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostAlloc(void** pHost, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostRegister(void* ptr, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostUnregister(void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostGetDevicePointer(void** pDevice, void* pHost, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaHostGetFlags(unsigned int* pFlags, void* pHost) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMalloc3D(cudaPitchedPtr* pitchedDevPtr, cudaExtent extent) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMalloc3DArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocMipmappedArray(cudaMipmappedArray_t* mipmappedArray, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int numLevels, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetMipmappedArrayLevel(cudaArray_t* levelArray, cudaMipmappedArray_const_t mipmappedArray, unsigned int level) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3D(const cudaMemcpy3DParms* p) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DPeer(const cudaMemcpy3DPeerParms* p) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DAsync(const cudaMemcpy3DParms* p, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DPeerAsync(const cudaMemcpy3DPeerParms* p, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemGetInfo(size_t* free, size_t* total) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetInfo(cudaChannelFormatDesc* desc, cudaExtent* extent, unsigned int* flags, cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetPlane(cudaArray_t* pPlaneArray, cudaArray_t hArray, unsigned int planeIdx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaArray_t array, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMipmappedArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaMipmappedArray_t mipmap, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMipmappedArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaMipmappedArray_t mipmap) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy(void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyPeer(void* dst, int dstDevice, const void* src, int srcDevice, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2D(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DFromArray(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyAsync(void* dst, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyPeerAsync(void* dst, int dstDevice, const void* src, int srcDevice, size_t count, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyBatchAsync(const void** dsts, const void** srcs, const size_t* sizes, size_t count, cudaMemcpyAttributes* attrs, size_t* attrsIdxs, size_t numAttrs, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DBatchAsync(size_t numOps, cudaMemcpy3DBatchOp* opList, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyWithAttributesAsync(void* dst, const void* src, size_t size, cudaMemcpyAttributes* attr, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy3DWithAttributesAsync(cudaMemcpy3DBatchOp* op, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DAsync(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpy2DFromArrayAsync(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset(void* devPtr, int value, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset2D(void* devPtr, size_t pitch, int value, size_t width, size_t height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset3D(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemsetAsync(void* devPtr, int value, size_t count, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset2DAsync(void* devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemset3DAsync(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPrefetchAsync(const void* devPtr, size_t count, cudaMemLocation location, unsigned int flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemDiscardBatchAsync(void** dptrs, size_t* sizes, size_t count, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemDiscardAndPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemAdvise(const void* devPtr, size_t count, cudaMemoryAdvise advice, cudaMemLocation location) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemRangeGetAttribute(void* data, size_t dataSize, cudaMemRangeAttribute attribute, const void* devPtr, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemRangeGetAttributes(void** data, size_t* dataSizes, cudaMemRangeAttribute* attributes, size_t numAttributes, const void* devPtr, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyFromArray(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemcpyFromArrayAsync(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocAsync(void** devPtr, size_t size, cudaStream_t hStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaFreeAsync(void* devPtr, cudaStream_t hStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolTrimTo(cudaMemPool_t memPool, size_t minBytesToKeep) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolSetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolGetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolSetAccess(cudaMemPool_t memPool, const cudaMemAccessDesc* descList, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolGetAccess(cudaMemAccessFlags* flags, cudaMemPool_t memPool, cudaMemLocation* location) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolCreate(cudaMemPool_t* memPool, const cudaMemPoolProps* poolProps) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolDestroy(cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemGetDefaultMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemGetMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemSetMemPool(cudaMemLocation* location, cudaMemAllocationType typename, cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMallocFromPoolAsync(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolExportToShareableHandle(void* shareableHandle, cudaMemPool_t memPool, cudaMemAllocationHandleType handleType, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolImportFromShareableHandle(cudaMemPool_t* memPool, void* shareableHandle, cudaMemAllocationHandleType handleType, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolExportPointer(cudaMemPoolPtrExportData* exportData, void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaMemPoolImportPointer(void** ptr, cudaMemPool_t memPool, cudaMemPoolPtrExportData* exportData) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaPointerGetAttributes(cudaPointerAttributes* attributes, const void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceCanAccessPeer(int* canAccessPeer, int device, int peerDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceEnablePeerAccess(int peerDevice, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceDisablePeerAccess(int peerDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsUnregisterResource(cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsResourceSetMapFlags(cudaGraphicsResource_t resource, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsMapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsUnmapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsResourceGetMappedPointer(void** devPtr, size_t* size, cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsSubResourceGetMappedArray(cudaArray_t* array, cudaGraphicsResource_t resource, unsigned int arrayIndex, unsigned int mipLevel) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphicsResourceGetMappedMipmappedArray(cudaMipmappedArray_t* mipmappedArray, cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetChannelDesc(cudaChannelFormatDesc* desc, cudaArray_const_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaChannelFormatDesc _cudaCreateChannelDesc(int x, int y, int z, int w, cudaChannelFormatKind f) except* nogil
+
+cdef cudaError_t _cudaCreateTextureObject(cudaTextureObject_t* pTexObject, const cudaResourceDesc* pResDesc, const cudaTextureDesc* pTexDesc, const cudaResourceViewDesc* pResViewDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroyTextureObject(cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetTextureObjectResourceDesc(cudaResourceDesc* pResDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetTextureObjectTextureDesc(cudaTextureDesc* pTexDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetTextureObjectResourceViewDesc(cudaResourceViewDesc* pResViewDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaCreateSurfaceObject(cudaSurfaceObject_t* pSurfObject, const cudaResourceDesc* pResDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDestroySurfaceObject(cudaSurfaceObject_t surfObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetSurfaceObjectResourceDesc(cudaResourceDesc* pResDesc, cudaSurfaceObject_t surfObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDriverGetVersion(int* driverVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaRuntimeGetVersion(int* runtimeVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsRegisterCallback(cudaLogsCallback_t callbackFunc, void* userData, cudaLogsCallbackHandle* callback_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsUnregisterCallback(cudaLogsCallbackHandle callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsCurrent(cudaLogIterator* iterator_out, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsDumpToFile(cudaLogIterator* iterator, const char* pathToFile, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLogsDumpToMemory(cudaLogIterator* iterator, char* buffer, size_t* size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphCreate(cudaGraph_t* pGraph, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddKernelNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeGetParams(cudaGraphNode_t node, cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeSetParams(cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeCopyAttributes(cudaGraphNode_t hDst, cudaGraphNode_t hSrc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeGetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, cudaKernelNodeAttrValue* value_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphKernelNodeSetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, const cudaKernelNodeAttrValue* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemcpyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemcpy3DParms* pCopyParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemcpyNode1D(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemcpyNodeGetParams(cudaGraphNode_t node, cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemcpyNodeSetParams(cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemcpyNodeSetParams1D(cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemsetNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemsetParams* pMemsetParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemsetNodeGetParams(cudaGraphNode_t node, cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemsetNodeSetParams(cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddHostNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphHostNodeGetParams(cudaGraphNode_t node, cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphHostNodeSetParams(cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddChildGraphNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaGraph_t childGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphChildGraphNodeGetGraph(cudaGraphNode_t node, cudaGraph_t* pGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddEmptyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddEventRecordNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventRecordNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventRecordNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddEventWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventWaitNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphEventWaitNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddExternalSemaphoresSignalNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresSignalNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreSignalNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresSignalNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddExternalSemaphoresWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresWaitNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreWaitNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExternalSemaphoresWaitNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemAllocNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaMemAllocNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemAllocNodeGetParams(cudaGraphNode_t node, cudaMemAllocNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddMemFreeNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphMemFreeNodeGetParams(cudaGraphNode_t node, void* dptr_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGraphMemTrim(int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceSetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphClone(cudaGraph_t* pGraphClone, cudaGraph_t originalGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeFindInClone(cudaGraphNode_t* pNode, cudaGraphNode_t originalNode, cudaGraph_t clonedGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetType(cudaGraphNode_t node, cudaGraphNodeType* pType) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetContainingGraph(cudaGraphNode_t hNode, cudaGraph_t* phGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetLocalId(cudaGraphNode_t hNode, unsigned int* nodeId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetToolsId(cudaGraphNode_t hNode, unsigned long long* toolsNodeId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetId(cudaGraph_t hGraph, unsigned int* graphID) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecGetId(cudaGraphExec_t hGraphExec, unsigned int* graphID) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetNodes(cudaGraph_t graph, cudaGraphNode_t* nodes, size_t* numNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetRootNodes(cudaGraph_t graph, cudaGraphNode_t* pRootNodes, size_t* pNumRootNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphGetEdges(cudaGraph_t graph, cudaGraphNode_t* from_, cudaGraphNode_t* to, cudaGraphEdgeData* edgeData, size_t* numEdges) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetDependencies(cudaGraphNode_t node, cudaGraphNode_t* pDependencies, cudaGraphEdgeData* edgeData, size_t* pNumDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetDependentNodes(cudaGraphNode_t node, cudaGraphNode_t* pDependentNodes, cudaGraphEdgeData* edgeData, size_t* pNumDependentNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphRemoveDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphDestroyNode(cudaGraphNode_t node) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphInstantiate(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphInstantiateWithFlags(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphInstantiateWithParams(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, cudaGraphInstantiateParams* instantiateParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecGetFlags(cudaGraphExec_t graphExec, unsigned long long* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecKernelNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecMemcpyNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecMemcpyNodeSetParams1D(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecMemsetNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecHostNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecChildGraphNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, cudaGraph_t childGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecEventRecordNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecEventWaitNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecExternalSemaphoresSignalNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecExternalSemaphoresWaitNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeSetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int isEnabled) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int* isEnabled) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecUpdate(cudaGraphExec_t hGraphExec, cudaGraph_t hGraph, cudaGraphExecUpdateResultInfo* resultInfo) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphUpload(cudaGraphExec_t graphExec, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecDestroy(cudaGraphExec_t graphExec) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphDestroy(cudaGraph_t graph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphDebugDotPrint(cudaGraph_t graph, const char* path, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaUserObjectCreate(cudaUserObject_t* object_out, void* ptr, cudaHostFn_t destroy, unsigned int initialRefcount, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaUserObjectRetain(cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaUserObjectRelease(cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphRetainUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphReleaseUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphAddNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeSetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphNodeGetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphExecNodeSetParams(cudaGraphExec_t graphExec, cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphConditionalHandleCreate(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, unsigned int defaultLaunchValue, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGraphConditionalHandleCreate_v2(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, cudaExecutionContext_t ctx, unsigned int defaultLaunchValue, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDriverEntryPoint(const char* symbol, void** funcPtr, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetDriverEntryPointByVersion(const char* symbol, void** funcPtr, unsigned int cudaVersion, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryLoadData(cudaLibrary_t* library, const void* code, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryLoadFromFile(cudaLibrary_t* library, const char* fileName, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryUnload(cudaLibrary_t library) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetKernel(cudaKernel_t* pKernel, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetGlobal(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetManaged(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetUnifiedFunction(void** fptr, cudaLibrary_t library, const char* symbol) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryGetKernelCount(unsigned int* count, cudaLibrary_t lib) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaLibraryEnumerateKernels(cudaKernel_t* kernels, unsigned int numKernels, cudaLibrary_t lib) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaKernelSetAttributeForDevice(cudaKernel_t kernel, cudaFuncAttribute attr, int value, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetDevResource(int device, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDevSmResourceSplitByCount(cudaDevResource* result, unsigned int* nbGroups, const cudaDevResource* input, cudaDevResource* remaining, unsigned int flags, unsigned int minCount) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDevSmResourceSplit(cudaDevResource* result, unsigned int nbGroups, const cudaDevResource* input, cudaDevResource* remainder, unsigned int flags, cudaDevSmResourceGroupParams* groupParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDevResourceGenerateDesc(cudaDevResourceDesc_t* phDesc, cudaDevResource* resources, unsigned int nbResources) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGreenCtxCreate(cudaExecutionContext_t* phCtx, cudaDevResourceDesc_t desc, int device, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxDestroy(cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxGetDevResource(cudaExecutionContext_t ctx, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxGetDevice(int* device, cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxGetId(cudaExecutionContext_t ctx, unsigned long long* ctxId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxStreamCreate(cudaStream_t* phStream, cudaExecutionContext_t ctx, unsigned int flags, int priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxSynchronize(cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaStreamGetDevResource(cudaStream_t hStream, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxRecordEvent(cudaExecutionContext_t ctx, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaExecutionCtxWaitEvent(cudaExecutionContext_t ctx, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaDeviceGetExecutionCtx(cudaExecutionContext_t* ctx, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetExportTable(const void** ppExportTable, const cudaUUID_t* pExportTableId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaGetKernel(cudaKernel_t* kernelPtr, const void* entryFuncAddr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaPitchedPtr _make_cudaPitchedPtr(void* d, size_t p, size_t xsz, size_t ysz) except* nogil
+
+cdef cudaPos _make_cudaPos(size_t x, size_t y, size_t z) except* nogil
+
+cdef cudaExtent _make_cudaExtent(size_t w, size_t h, size_t d) except* nogil
+
+cdef cudaError_t _cudaProfilerStart() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t _cudaProfilerStop() except ?cudaErrorCallRequiresNewerDriver nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/loader.h b/lib/python3.11/site-packages/cuda/bindings/_bindings/loader.h
new file mode 100644
index 0000000000000000000000000000000000000000..2411037b03256b70ca6e260fc140d94b64b16e00
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_bindings/loader.h
@@ -0,0 +1,9 @@
+// SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+//
+// Please refer to the NVIDIA end user license agreement (EULA) associated
+// with this source code for terms and conditions that govern your use of
+// this software. Any use, reproduction, disclosure, or distribution of
+// this software and related documentation outside the terms of the EULA
+// is strictly prohibited.
+int getCUDALibraryPath(char *libPath, bool isBit64);
diff --git a/lib/python3.11/site-packages/cuda/bindings/_bindings/loader.pxd b/lib/python3.11/site-packages/cuda/bindings/_bindings/loader.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..805b849cc660295d97cba986c55444db5f21fc47
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_bindings/loader.pxd
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+cdef extern from "loader.h":
+    int getCUDALibraryPath(char *libPath, bint isBit64)
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/__init__.py b/lib/python3.11/site-packages/cuda/bindings/_internal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/_internal/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cdad9a9dc046950fa32061b289556db1f3273538
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_internal/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/__pycache__/_fast_enum.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/_internal/__pycache__/_fast_enum.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..04b32717b008cae25ab42b8856250cd7b1e92144
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_internal/__pycache__/_fast_enum.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/_fast_enum.py b/lib/python3.11/site-packages/cuda/bindings/_internal/_fast_enum.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c8e42cc938f516c1436624c694592b6648d7253
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/_fast_enum.py
@@ -0,0 +1,76 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+
+# This code was automatically generated across versions from 12.9.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+
+"""
+This is a replacement for the stdlib enum.IntEnum.
+
+Notably, it has much better import time performance, since it doesn't generate
+and evaluate Python code at startup time.
+
+It supports the most important subset of the IntEnum API.  See `test_enum` in
+`cuda_bindings/tests/test_basics.py` for details.
+"""
+
+from typing import Any, Iterator
+
+
+class FastEnumMetaclass(type):
+    def __init__(cls, name, bases, namespace):
+        super().__init__(name, bases, namespace)
+
+        cls.__singletons__ = {}
+        cls.__members__ = {}
+        for name, value in cls.__dict__.items():
+            if name.startswith("__") and name.endswith("__"):
+                continue
+
+            if isinstance(value, tuple):
+                value, doc = value
+            elif isinstance(value, int):
+                doc = None
+            else:
+                continue
+
+            singleton = int.__new__(cls, value)
+            singleton.__doc__ = doc
+            singleton._name = name
+            cls.__singletons__[value] = singleton
+            cls.__members__[name] = singleton
+
+        for name, member in cls.__members__.items():
+            setattr(cls, name, member)
+
+    def __repr__(cls) -> str:
+        return f"<enum '{cls.__name__}'>"
+
+    def __len__(cls) -> int:
+        return len(cls.__members__)
+
+    def __iter__(cls) -> Iterator["FastEnum"]:
+        return iter(cls.__members__.values())
+
+    def __contains__(cls, item: Any) -> bool:
+        return item in cls.__singletons__
+
+
+class FastEnum(int, metaclass=FastEnumMetaclass):
+    def __new__(cls, value: int) -> "FastEnum":
+        singleton: FastEnum = cls.__singletons__.get(value)
+        if singleton is None:
+            raise ValueError(f"{value} is not a valid {cls.__name__}")
+        return singleton
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}.{self._name}: {int(self)}>"
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def value(self) -> int:
+        return int(self)
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/cufile.pxd b/lib/python3.11/site-packages/cuda/bindings/_internal/cufile.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1e0da8beb736bf236b1fe93ba0f28f1ef76081f7
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/cufile.pxd
@@ -0,0 +1,56 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.9.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from ..cycufile cimport *
+
+
+###############################################################################
+# Wrapper functions
+###############################################################################
+
+cdef CUfileError_t _cuFileHandleRegister(CUfileHandle_t* fh, CUfileDescr_t* descr) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef void _cuFileHandleDeregister(CUfileHandle_t fh) except* nogil
+cdef CUfileError_t _cuFileBufRegister(const void* bufPtr_base, size_t length, int flags) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileBufDeregister(const void* bufPtr_base) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef ssize_t _cuFileRead(CUfileHandle_t fh, void* bufPtr_base, size_t size, off_t file_offset, off_t bufPtr_offset) except* nogil
+cdef ssize_t _cuFileWrite(CUfileHandle_t fh, const void* bufPtr_base, size_t size, off_t file_offset, off_t bufPtr_offset) except* nogil
+cdef CUfileError_t _cuFileDriverOpen() except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileDriverClose() except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileDriverClose_v2() except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef long _cuFileUseCount() except* nogil
+cdef CUfileError_t _cuFileDriverGetProperties(CUfileDrvProps_t* props) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileDriverSetPollMode(cpp_bool poll, size_t poll_threshold_size) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileDriverSetMaxDirectIOSize(size_t max_direct_io_size) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileDriverSetMaxCacheSize(size_t max_cache_size) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileDriverSetMaxPinnedMemSize(size_t max_pinned_size) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileBatchIOSetUp(CUfileBatchHandle_t* batch_idp, unsigned nr) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileBatchIOSubmit(CUfileBatchHandle_t batch_idp, unsigned nr, CUfileIOParams_t* iocbp, unsigned int flags) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileBatchIOGetStatus(CUfileBatchHandle_t batch_idp, unsigned min_nr, unsigned* nr, CUfileIOEvents_t* iocbp, timespec* timeout) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileBatchIOCancel(CUfileBatchHandle_t batch_idp) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef void _cuFileBatchIODestroy(CUfileBatchHandle_t batch_idp) except* nogil
+cdef CUfileError_t _cuFileReadAsync(CUfileHandle_t fh, void* bufPtr_base, size_t* size_p, off_t* file_offset_p, off_t* bufPtr_offset_p, ssize_t* bytes_read_p, CUstream stream) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileWriteAsync(CUfileHandle_t fh, void* bufPtr_base, size_t* size_p, off_t* file_offset_p, off_t* bufPtr_offset_p, ssize_t* bytes_written_p, CUstream stream) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileStreamRegister(CUstream stream, unsigned flags) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileStreamDeregister(CUstream stream) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetVersion(int* version) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetParameterSizeT(CUFileSizeTConfigParameter_t param, size_t* value) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetParameterBool(CUFileBoolConfigParameter_t param, cpp_bool* value) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetParameterString(CUFileStringConfigParameter_t param, char* desc_str, int len) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileSetParameterSizeT(CUFileSizeTConfigParameter_t param, size_t value) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileSetParameterBool(CUFileBoolConfigParameter_t param, cpp_bool value) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileSetParameterString(CUFileStringConfigParameter_t param, const char* desc_str) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetParameterMinMaxValue(CUFileSizeTConfigParameter_t param, size_t* min_value, size_t* max_value) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileSetStatsLevel(int level) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetStatsLevel(int* level) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileStatsStart() except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileStatsStop() except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileStatsReset() except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetStatsL1(CUfileStatsLevel1_t* stats) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetStatsL2(CUfileStatsLevel2_t* stats) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetStatsL3(CUfileStatsLevel3_t* stats) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetBARSizeInKB(int gpuIndex, size_t* barSize) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileSetParameterPosixPoolSlabArray(const size_t* size_values, const size_t* count_values, int len) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
+cdef CUfileError_t _cuFileGetParameterPosixPoolSlabArray(size_t* size_values, size_t* count_values, int len) except?<CUfileError_t>CUFILE_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvfatbin.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/_internal/nvfatbin.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..395df2c9c8335768e26027695245f85e1d3a790d
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_internal/nvfatbin.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvfatbin.pxd b/lib/python3.11/site-packages/cuda/bindings/_internal/nvfatbin.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..15617f8ad54a14e50027d503ed622c6432fb32a1
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/nvfatbin.pxd
@@ -0,0 +1,24 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.4.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from ..cynvfatbin cimport *
+
+
+###############################################################################
+# Wrapper functions
+###############################################################################
+
+cdef const char* _nvFatbinGetErrorString(nvFatbinResult result) except?NULL nogil
+cdef nvFatbinResult _nvFatbinCreate(nvFatbinHandle* handle_indirect, const char** options, size_t optionsCount) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinDestroy(nvFatbinHandle* handle_indirect) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinAddPTX(nvFatbinHandle handle, const char* code, size_t size, const char* arch, const char* identifier, const char* optionsCmdLine) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinAddCubin(nvFatbinHandle handle, const void* code, size_t size, const char* arch, const char* identifier) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinAddLTOIR(nvFatbinHandle handle, const void* code, size_t size, const char* arch, const char* identifier, const char* optionsCmdLine) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinSize(nvFatbinHandle handle, size_t* size) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinGet(nvFatbinHandle handle, void* buffer) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinVersion(unsigned int* major, unsigned int* minor) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinAddReloc(nvFatbinHandle handle, const void* code, size_t size) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult _nvFatbinAddTileIR(nvFatbinHandle handle, const void* code, size_t size, const char* identifier, const char* optionsCmdLine) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvjitlink.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/_internal/nvjitlink.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..7db7d5da85475778c6a28506d0991ecb6a7ada7d
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_internal/nvjitlink.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvjitlink.pxd b/lib/python3.11/site-packages/cuda/bindings/_internal/nvjitlink.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..6fd75c86822d8b9e01e9d6e160a4309971558d5f
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/nvjitlink.pxd
@@ -0,0 +1,27 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.0.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from ..cynvjitlink cimport *
+
+
+###############################################################################
+# Wrapper functions
+###############################################################################
+
+cdef nvJitLinkResult _nvJitLinkCreate(nvJitLinkHandle* handle, uint32_t numOptions, const char** options) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkDestroy(nvJitLinkHandle* handle) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkAddData(nvJitLinkHandle handle, nvJitLinkInputType inputType, const void* data, size_t size, const char* name) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkAddFile(nvJitLinkHandle handle, nvJitLinkInputType inputType, const char* fileName) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkComplete(nvJitLinkHandle handle) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetLinkedCubinSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetLinkedCubin(nvJitLinkHandle handle, void* cubin) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetLinkedPtxSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetLinkedPtx(nvJitLinkHandle handle, char* ptx) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetErrorLogSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetErrorLog(nvJitLinkHandle handle, char* log) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetInfoLogSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkGetInfoLog(nvJitLinkHandle handle, char* log) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult _nvJitLinkVersion(unsigned int* major, unsigned int* minor) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvml.pxd b/lib/python3.11/site-packages/cuda/bindings/_internal/nvml.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..40805378a892b85a605e5859594d5d7ec8c33c1b
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/nvml.pxd
@@ -0,0 +1,356 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.9.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from ..cynvml cimport *
+
+
+###############################################################################
+# Wrapper functions
+###############################################################################
+
+cdef nvmlReturn_t _nvmlInit_v2() except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlInitWithFlags(unsigned int flags) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlShutdown() except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef const char* _nvmlErrorString(nvmlReturn_t result) except?NULL nogil
+cdef nvmlReturn_t _nvmlSystemGetDriverVersion(char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetNVMLVersion(char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetCudaDriverVersion(int* cudaDriverVersion) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetCudaDriverVersion_v2(int* cudaDriverVersion) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetProcessName(unsigned int pid, char* name, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetHicVersion(unsigned int* hwbcCount, nvmlHwbcEntry_t* hwbcEntries) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetTopologyGpuSet(unsigned int cpuNumber, unsigned int* count, nvmlDevice_t* deviceArray) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetDriverBranch(nvmlSystemDriverBranchInfo_t* branchInfo, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetCount(unsigned int* unitCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetHandleByIndex(unsigned int index, nvmlUnit_t* unit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetUnitInfo(nvmlUnit_t unit, nvmlUnitInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetLedState(nvmlUnit_t unit, nvmlLedState_t* state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetPsuInfo(nvmlUnit_t unit, nvmlPSUInfo_t* psu) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetTemperature(nvmlUnit_t unit, unsigned int type, unsigned int* temp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetFanSpeedInfo(nvmlUnit_t unit, nvmlUnitFanSpeeds_t* fanSpeeds) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitGetDevices(nvmlUnit_t unit, unsigned int* deviceCount, nvmlDevice_t* devices) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCount_v2(unsigned int* deviceCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAttributes_v2(nvmlDevice_t device, nvmlDeviceAttributes_t* attributes) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHandleByIndex_v2(unsigned int index, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHandleBySerial(const char* serial, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHandleByUUID(const char* uuid, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHandleByUUIDV(const nvmlUUID_t* uuid, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHandleByPciBusId_v2(const char* pciBusId, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetName(nvmlDevice_t device, char* name, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetBrand(nvmlDevice_t device, nvmlBrandType_t* type) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetIndex(nvmlDevice_t device, unsigned int* index) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSerial(nvmlDevice_t device, char* serial, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetModuleId(nvmlDevice_t device, unsigned int* moduleId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetC2cModeInfoV(nvmlDevice_t device, nvmlC2cModeInfo_v1_t* c2cModeInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMemoryAffinity(nvmlDevice_t device, unsigned int nodeSetSize, unsigned long* nodeSet, nvmlAffinityScope_t scope) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCpuAffinityWithinScope(nvmlDevice_t device, unsigned int cpuSetSize, unsigned long* cpuSet, nvmlAffinityScope_t scope) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCpuAffinity(nvmlDevice_t device, unsigned int cpuSetSize, unsigned long* cpuSet) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetCpuAffinity(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceClearCpuAffinity(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNumaNodeId(nvmlDevice_t device, unsigned int* node) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTopologyCommonAncestor(nvmlDevice_t device1, nvmlDevice_t device2, nvmlGpuTopologyLevel_t* pathInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTopologyNearestGpus(nvmlDevice_t device, nvmlGpuTopologyLevel_t level, unsigned int* count, nvmlDevice_t* deviceArray) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetP2PStatus(nvmlDevice_t device1, nvmlDevice_t device2, nvmlGpuP2PCapsIndex_t p2pIndex, nvmlGpuP2PStatus_t* p2pStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetUUID(nvmlDevice_t device, char* uuid, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMinorNumber(nvmlDevice_t device, unsigned int* minorNumber) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetBoardPartNumber(nvmlDevice_t device, char* partNumber, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetInforomVersion(nvmlDevice_t device, nvmlInforomObject_t object, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetInforomImageVersion(nvmlDevice_t device, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetInforomConfigurationChecksum(nvmlDevice_t device, unsigned int* checksum) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceValidateInforom(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetLastBBXFlushTime(nvmlDevice_t device, unsigned long long* timestamp, unsigned long* durationUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDisplayMode(nvmlDevice_t device, nvmlEnableState_t* display) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDisplayActive(nvmlDevice_t device, nvmlEnableState_t* isActive) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPersistenceMode(nvmlDevice_t device, nvmlEnableState_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPciInfoExt(nvmlDevice_t device, nvmlPciInfoExt_t* pci) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPciInfo_v3(nvmlDevice_t device, nvmlPciInfo_t* pci) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMaxPcieLinkGeneration(nvmlDevice_t device, unsigned int* maxLinkGen) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuMaxPcieLinkGeneration(nvmlDevice_t device, unsigned int* maxLinkGenDevice) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMaxPcieLinkWidth(nvmlDevice_t device, unsigned int* maxLinkWidth) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCurrPcieLinkGeneration(nvmlDevice_t device, unsigned int* currLinkGen) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCurrPcieLinkWidth(nvmlDevice_t device, unsigned int* currLinkWidth) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPcieThroughput(nvmlDevice_t device, nvmlPcieUtilCounter_t counter, unsigned int* value) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPcieReplayCounter(nvmlDevice_t device, unsigned int* value) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetClockInfo(nvmlDevice_t device, nvmlClockType_t type, unsigned int* clock) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMaxClockInfo(nvmlDevice_t device, nvmlClockType_t type, unsigned int* clock) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpcClkVfOffset(nvmlDevice_t device, int* offset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetClock(nvmlDevice_t device, nvmlClockType_t clockType, nvmlClockId_t clockId, unsigned int* clockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMaxCustomerBoostClock(nvmlDevice_t device, nvmlClockType_t clockType, unsigned int* clockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSupportedMemoryClocks(nvmlDevice_t device, unsigned int* count, unsigned int* clocksMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSupportedGraphicsClocks(nvmlDevice_t device, unsigned int memoryClockMHz, unsigned int* count, unsigned int* clocksMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAutoBoostedClocksEnabled(nvmlDevice_t device, nvmlEnableState_t* isEnabled, nvmlEnableState_t* defaultIsEnabled) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFanSpeed(nvmlDevice_t device, unsigned int* speed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFanSpeed_v2(nvmlDevice_t device, unsigned int fan, unsigned int* speed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFanSpeedRPM(nvmlDevice_t device, nvmlFanSpeedInfo_t* fanSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTargetFanSpeed(nvmlDevice_t device, unsigned int fan, unsigned int* targetSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMinMaxFanSpeed(nvmlDevice_t device, unsigned int* minSpeed, unsigned int* maxSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFanControlPolicy_v2(nvmlDevice_t device, unsigned int fan, nvmlFanControlPolicy_t* policy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNumFans(nvmlDevice_t device, unsigned int* numFans) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCoolerInfo(nvmlDevice_t device, nvmlCoolerInfo_t* coolerInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTemperatureV(nvmlDevice_t device, nvmlTemperature_t* temperature) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTemperatureThreshold(nvmlDevice_t device, nvmlTemperatureThresholds_t thresholdType, unsigned int* temp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMarginTemperature(nvmlDevice_t device, nvmlMarginTemperature_t* marginTempInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetThermalSettings(nvmlDevice_t device, unsigned int sensorIndex, nvmlGpuThermalSettings_t* pThermalSettings) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPerformanceState(nvmlDevice_t device, nvmlPstates_t* pState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCurrentClocksEventReasons(nvmlDevice_t device, unsigned long long* clocksEventReasons) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSupportedClocksEventReasons(nvmlDevice_t device, unsigned long long* supportedClocksEventReasons) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerState(nvmlDevice_t device, nvmlPstates_t* pState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDynamicPstatesInfo(nvmlDevice_t device, nvmlGpuDynamicPstatesInfo_t* pDynamicPstatesInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMemClkVfOffset(nvmlDevice_t device, int* offset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMinMaxClockOfPState(nvmlDevice_t device, nvmlClockType_t type, nvmlPstates_t pstate, unsigned int* minClockMHz, unsigned int* maxClockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSupportedPerformanceStates(nvmlDevice_t device, nvmlPstates_t* pstates, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpcClkMinMaxVfOffset(nvmlDevice_t device, int* minOffset, int* maxOffset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMemClkMinMaxVfOffset(nvmlDevice_t device, int* minOffset, int* maxOffset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetClockOffsets(nvmlDevice_t device, nvmlClockOffset_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetClockOffsets(nvmlDevice_t device, nvmlClockOffset_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPerformanceModes(nvmlDevice_t device, nvmlDevicePerfModes_t* perfModes) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCurrentClockFreqs(nvmlDevice_t device, nvmlDeviceCurrentClockFreqs_t* currentClockFreqs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerManagementLimit(nvmlDevice_t device, unsigned int* limit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerManagementLimitConstraints(nvmlDevice_t device, unsigned int* minLimit, unsigned int* maxLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerManagementDefaultLimit(nvmlDevice_t device, unsigned int* defaultLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerUsage(nvmlDevice_t device, unsigned int* power) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTotalEnergyConsumption(nvmlDevice_t device, unsigned long long* energy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetEnforcedPowerLimit(nvmlDevice_t device, unsigned int* limit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuOperationMode(nvmlDevice_t device, nvmlGpuOperationMode_t* current, nvmlGpuOperationMode_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMemoryInfo_v2(nvmlDevice_t device, nvmlMemory_v2_t* memory) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetComputeMode(nvmlDevice_t device, nvmlComputeMode_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCudaComputeCapability(nvmlDevice_t device, int* major, int* minor) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDramEncryptionMode(nvmlDevice_t device, nvmlDramEncryptionInfo_t* current, nvmlDramEncryptionInfo_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetDramEncryptionMode(nvmlDevice_t device, const nvmlDramEncryptionInfo_t* dramEncryption) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetEccMode(nvmlDevice_t device, nvmlEnableState_t* current, nvmlEnableState_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDefaultEccMode(nvmlDevice_t device, nvmlEnableState_t* defaultMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetBoardId(nvmlDevice_t device, unsigned int* boardId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMultiGpuBoard(nvmlDevice_t device, unsigned int* multiGpuBool) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetTotalEccErrors(nvmlDevice_t device, nvmlMemoryErrorType_t errorType, nvmlEccCounterType_t counterType, unsigned long long* eccCounts) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMemoryErrorCounter(nvmlDevice_t device, nvmlMemoryErrorType_t errorType, nvmlEccCounterType_t counterType, nvmlMemoryLocation_t locationType, unsigned long long* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetUtilizationRates(nvmlDevice_t device, nvmlUtilization_t* utilization) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetEncoderUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetEncoderCapacity(nvmlDevice_t device, nvmlEncoderType_t encoderQueryType, unsigned int* encoderCapacity) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetEncoderStats(nvmlDevice_t device, unsigned int* sessionCount, unsigned int* averageFps, unsigned int* averageLatency) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetEncoderSessions(nvmlDevice_t device, unsigned int* sessionCount, nvmlEncoderSessionInfo_t* sessionInfos) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDecoderUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetJpgUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetOfaUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFBCStats(nvmlDevice_t device, nvmlFBCStats_t* fbcStats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFBCSessions(nvmlDevice_t device, unsigned int* sessionCount, nvmlFBCSessionInfo_t* sessionInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDriverModel_v2(nvmlDevice_t device, nvmlDriverModel_t* current, nvmlDriverModel_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVbiosVersion(nvmlDevice_t device, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetBridgeChipInfo(nvmlDevice_t device, nvmlBridgeChipHierarchy_t* bridgeHierarchy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetComputeRunningProcesses_v3(nvmlDevice_t device, unsigned int* infoCount, nvmlProcessInfo_t* infos) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMPSComputeRunningProcesses_v3(nvmlDevice_t device, unsigned int* infoCount, nvmlProcessInfo_t* infos) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRunningProcessDetailList(nvmlDevice_t device, nvmlProcessDetailList_t* plist) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceOnSameBoard(nvmlDevice_t device1, nvmlDevice_t device2, int* onSameBoard) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAPIRestriction(nvmlDevice_t device, nvmlRestrictedAPI_t apiType, nvmlEnableState_t* isRestricted) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSamples(nvmlDevice_t device, nvmlSamplingType_t type, unsigned long long lastSeenTimeStamp, nvmlValueType_t* sampleValType, unsigned int* sampleCount, nvmlSample_t* samples) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetBAR1MemoryInfo(nvmlDevice_t device, nvmlBAR1Memory_t* bar1Memory) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetIrqNum(nvmlDevice_t device, unsigned int* irqNum) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNumGpuCores(nvmlDevice_t device, unsigned int* numCores) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerSource(nvmlDevice_t device, nvmlPowerSource_t* powerSource) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMemoryBusWidth(nvmlDevice_t device, unsigned int* busWidth) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPcieLinkMaxSpeed(nvmlDevice_t device, unsigned int* maxSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPcieSpeed(nvmlDevice_t device, unsigned int* pcieSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAdaptiveClockInfoStatus(nvmlDevice_t device, unsigned int* adaptiveClockStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetBusType(nvmlDevice_t device, nvmlBusType_t* type) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuFabricInfoV(nvmlDevice_t device, nvmlGpuFabricInfoV_t* gpuFabricInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetConfComputeCapabilities(nvmlConfComputeSystemCaps_t* capabilities) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetConfComputeState(nvmlConfComputeSystemState_t* state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetConfComputeMemSizeInfo(nvmlDevice_t device, nvmlConfComputeMemSizeInfo_t* memInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetConfComputeGpusReadyState(unsigned int* isAcceptingWork) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetConfComputeProtectedMemoryUsage(nvmlDevice_t device, nvmlMemory_t* memory) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetConfComputeGpuCertificate(nvmlDevice_t device, nvmlConfComputeGpuCertificate_t* gpuCert) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetConfComputeGpuAttestationReport(nvmlDevice_t device, nvmlConfComputeGpuAttestationReport_t* gpuAtstReport) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetConfComputeKeyRotationThresholdInfo(nvmlConfComputeGetKeyRotationThresholdInfo_t* pKeyRotationThrInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetConfComputeUnprotectedMemSize(nvmlDevice_t device, unsigned long long sizeKiB) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemSetConfComputeGpusReadyState(unsigned int isAcceptingWork) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemSetConfComputeKeyRotationThresholdInfo(nvmlConfComputeSetKeyRotationThresholdInfo_t* pKeyRotationThrInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetConfComputeSettings(nvmlSystemConfComputeSettings_t* settings) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGspFirmwareVersion(nvmlDevice_t device, char* version) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGspFirmwareMode(nvmlDevice_t device, unsigned int* isEnabled, unsigned int* defaultMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSramEccErrorStatus(nvmlDevice_t device, nvmlEccSramErrorStatus_t* status) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAccountingMode(nvmlDevice_t device, nvmlEnableState_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAccountingStats(nvmlDevice_t device, unsigned int pid, nvmlAccountingStats_t* stats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAccountingPids(nvmlDevice_t device, unsigned int* count, unsigned int* pids) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAccountingBufferSize(nvmlDevice_t device, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRetiredPages(nvmlDevice_t device, nvmlPageRetirementCause_t cause, unsigned int* pageCount, unsigned long long* addresses) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRetiredPages_v2(nvmlDevice_t device, nvmlPageRetirementCause_t cause, unsigned int* pageCount, unsigned long long* addresses, unsigned long long* timestamps) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRetiredPagesPendingStatus(nvmlDevice_t device, nvmlEnableState_t* isPending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRemappedRows(nvmlDevice_t device, unsigned int* corrRows, unsigned int* uncRows, unsigned int* isPending, unsigned int* failureOccurred) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRowRemapperHistogram(nvmlDevice_t device, nvmlRowRemapperHistogramValues_t* values) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetArchitecture(nvmlDevice_t device, nvmlDeviceArchitecture_t* arch) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetClkMonStatus(nvmlDevice_t device, nvmlClkMonStatus_t* status) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetProcessUtilization(nvmlDevice_t device, nvmlProcessUtilizationSample_t* utilization, unsigned int* processSamplesCount, unsigned long long lastSeenTimeStamp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetProcessesUtilizationInfo(nvmlDevice_t device, nvmlProcessesUtilizationInfo_t* procesesUtilInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPlatformInfo(nvmlDevice_t device, nvmlPlatformInfo_t* platformInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlUnitSetLedState(nvmlUnit_t unit, nvmlLedColor_t color) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetPersistenceMode(nvmlDevice_t device, nvmlEnableState_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetComputeMode(nvmlDevice_t device, nvmlComputeMode_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetEccMode(nvmlDevice_t device, nvmlEnableState_t ecc) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceClearEccErrorCounts(nvmlDevice_t device, nvmlEccCounterType_t counterType) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetDriverModel(nvmlDevice_t device, nvmlDriverModel_t driverModel, unsigned int flags) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetGpuLockedClocks(nvmlDevice_t device, unsigned int minGpuClockMHz, unsigned int maxGpuClockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceResetGpuLockedClocks(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetMemoryLockedClocks(nvmlDevice_t device, unsigned int minMemClockMHz, unsigned int maxMemClockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceResetMemoryLockedClocks(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetAutoBoostedClocksEnabled(nvmlDevice_t device, nvmlEnableState_t enabled) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetDefaultAutoBoostedClocksEnabled(nvmlDevice_t device, nvmlEnableState_t enabled, unsigned int flags) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetDefaultFanSpeed_v2(nvmlDevice_t device, unsigned int fan) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetFanControlPolicy(nvmlDevice_t device, unsigned int fan, nvmlFanControlPolicy_t policy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetTemperatureThreshold(nvmlDevice_t device, nvmlTemperatureThresholds_t thresholdType, int* temp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetGpuOperationMode(nvmlDevice_t device, nvmlGpuOperationMode_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetAPIRestriction(nvmlDevice_t device, nvmlRestrictedAPI_t apiType, nvmlEnableState_t isRestricted) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetFanSpeed_v2(nvmlDevice_t device, unsigned int fan, unsigned int speed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetAccountingMode(nvmlDevice_t device, nvmlEnableState_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceClearAccountingPids(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetPowerManagementLimit_v2(nvmlDevice_t device, nvmlPowerValue_v2_t* powerValue) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkState(nvmlDevice_t device, unsigned int link, nvmlEnableState_t* isActive) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkVersion(nvmlDevice_t device, unsigned int link, unsigned int* version) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkCapability(nvmlDevice_t device, unsigned int link, nvmlNvLinkCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkRemotePciInfo_v2(nvmlDevice_t device, unsigned int link, nvmlPciInfo_t* pci) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkErrorCounter(nvmlDevice_t device, unsigned int link, nvmlNvLinkErrorCounter_t counter, unsigned long long* counterValue) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceResetNvLinkErrorCounters(nvmlDevice_t device, unsigned int link) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkRemoteDeviceType(nvmlDevice_t device, unsigned int link, nvmlIntNvLinkDeviceType_t* pNvLinkDeviceType) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetNvLinkDeviceLowPowerThreshold(nvmlDevice_t device, nvmlNvLinkPowerThres_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemSetNvlinkBwMode(unsigned int nvlinkBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemGetNvlinkBwMode(unsigned int* nvlinkBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvlinkSupportedBwModes(nvmlDevice_t device, nvmlNvlinkSupportedBwModes_t* supportedBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvlinkBwMode(nvmlDevice_t device, nvmlNvlinkGetBwMode_t* getBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetNvlinkBwMode(nvmlDevice_t device, nvmlNvlinkSetBwMode_t* setBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlEventSetCreate(nvmlEventSet_t* set) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceRegisterEvents(nvmlDevice_t device, unsigned long long eventTypes, nvmlEventSet_t set) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSupportedEventTypes(nvmlDevice_t device, unsigned long long* eventTypes) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlEventSetWait_v2(nvmlEventSet_t set, nvmlEventData_t* data, unsigned int timeoutms) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlEventSetFree(nvmlEventSet_t set) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemEventSetCreate(nvmlSystemEventSetCreateRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemEventSetFree(nvmlSystemEventSetFreeRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemRegisterEvents(nvmlSystemRegisterEventRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSystemEventSetWait(nvmlSystemEventSetWaitRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceModifyDrainState(nvmlPciInfo_t* pciInfo, nvmlEnableState_t newState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceQueryDrainState(nvmlPciInfo_t* pciInfo, nvmlEnableState_t* currentState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceRemoveGpu_v2(nvmlPciInfo_t* pciInfo, nvmlDetachGpuState_t gpuState, nvmlPcieLinkState_t linkState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceDiscoverGpus(nvmlPciInfo_t* pciInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetFieldValues(nvmlDevice_t device, int valuesCount, nvmlFieldValue_t* values) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceClearFieldValues(nvmlDevice_t device, int valuesCount, nvmlFieldValue_t* values) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVirtualizationMode(nvmlDevice_t device, nvmlGpuVirtualizationMode_t* pVirtualMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHostVgpuMode(nvmlDevice_t device, nvmlHostVgpuMode_t* pHostVgpuMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetVirtualizationMode(nvmlDevice_t device, nvmlGpuVirtualizationMode_t virtualMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuHeterogeneousMode(nvmlDevice_t device, nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetVgpuHeterogeneousMode(nvmlDevice_t device, const nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetPlacementId(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuPlacementId_t* pPlacement) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuTypeSupportedPlacements(nvmlDevice_t device, nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuPlacementList_t* pPlacementList) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuTypeCreatablePlacements(nvmlDevice_t device, nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuPlacementList_t* pPlacementList) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetGspHeapSize(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* gspHeapSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetFbReservation(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* fbReservation) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetRuntimeStateSize(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuRuntimeState_t* pState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetVgpuCapabilities(nvmlDevice_t device, nvmlDeviceVgpuCapability_t capability, nvmlEnableState_t state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGridLicensableFeatures_v4(nvmlDevice_t device, nvmlGridLicensableFeatures_t* pGridLicensableFeatures) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGetVgpuDriverCapabilities(nvmlVgpuDriverCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuCapabilities(nvmlDevice_t device, nvmlDeviceVgpuCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSupportedVgpus(nvmlDevice_t device, unsigned int* vgpuCount, nvmlVgpuTypeId_t* vgpuTypeIds) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCreatableVgpus(nvmlDevice_t device, unsigned int* vgpuCount, nvmlVgpuTypeId_t* vgpuTypeIds) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetClass(nvmlVgpuTypeId_t vgpuTypeId, char* vgpuTypeClass, unsigned int* size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetName(nvmlVgpuTypeId_t vgpuTypeId, char* vgpuTypeName, unsigned int* size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetGpuInstanceProfileId(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* gpuInstanceProfileId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetDeviceID(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* deviceID, unsigned long long* subsystemID) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetFramebufferSize(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* fbSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetNumDisplayHeads(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* numDisplayHeads) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetResolution(nvmlVgpuTypeId_t vgpuTypeId, unsigned int displayIndex, unsigned int* xdim, unsigned int* ydim) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetLicense(nvmlVgpuTypeId_t vgpuTypeId, char* vgpuTypeLicenseString, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetFrameRateLimit(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* frameRateLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetMaxInstances(nvmlDevice_t device, nvmlVgpuTypeId_t vgpuTypeId, unsigned int* vgpuInstanceCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetMaxInstancesPerVm(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* vgpuInstanceCountPerVm) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetBAR1Info(nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuTypeBar1Info_t* bar1Info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetActiveVgpus(nvmlDevice_t device, unsigned int* vgpuCount, nvmlVgpuInstance_t* vgpuInstances) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetVmID(nvmlVgpuInstance_t vgpuInstance, char* vmId, unsigned int size, nvmlVgpuVmIdType_t* vmIdType) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetUUID(nvmlVgpuInstance_t vgpuInstance, char* uuid, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetVmDriverVersion(nvmlVgpuInstance_t vgpuInstance, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetFbUsage(nvmlVgpuInstance_t vgpuInstance, unsigned long long* fbUsage) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetLicenseStatus(nvmlVgpuInstance_t vgpuInstance, unsigned int* licensed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetType(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuTypeId_t* vgpuTypeId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetFrameRateLimit(nvmlVgpuInstance_t vgpuInstance, unsigned int* frameRateLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetEccMode(nvmlVgpuInstance_t vgpuInstance, nvmlEnableState_t* eccMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetEncoderCapacity(nvmlVgpuInstance_t vgpuInstance, unsigned int* encoderCapacity) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceSetEncoderCapacity(nvmlVgpuInstance_t vgpuInstance, unsigned int encoderCapacity) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetEncoderStats(nvmlVgpuInstance_t vgpuInstance, unsigned int* sessionCount, unsigned int* averageFps, unsigned int* averageLatency) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetEncoderSessions(nvmlVgpuInstance_t vgpuInstance, unsigned int* sessionCount, nvmlEncoderSessionInfo_t* sessionInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetFBCStats(nvmlVgpuInstance_t vgpuInstance, nvmlFBCStats_t* fbcStats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetFBCSessions(nvmlVgpuInstance_t vgpuInstance, unsigned int* sessionCount, nvmlFBCSessionInfo_t* sessionInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetGpuInstanceId(nvmlVgpuInstance_t vgpuInstance, unsigned int* gpuInstanceId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetGpuPciId(nvmlVgpuInstance_t vgpuInstance, char* vgpuPciId, unsigned int* length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetCapabilities(nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetMdevUUID(nvmlVgpuInstance_t vgpuInstance, char* mdevUuid, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetCreatableVgpus(nvmlGpuInstance_t gpuInstance, nvmlVgpuTypeIdInfo_t* pVgpus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuTypeGetMaxInstancesPerGpuInstance(nvmlVgpuTypeMaxInstance_t* pMaxInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetActiveVgpus(nvmlGpuInstance_t gpuInstance, nvmlActiveVgpuInstanceInfo_t* pVgpuInstanceInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceSetVgpuSchedulerState(nvmlGpuInstance_t gpuInstance, nvmlVgpuSchedulerState_t* pScheduler) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetVgpuSchedulerState(nvmlGpuInstance_t gpuInstance, nvmlVgpuSchedulerStateInfo_t* pSchedulerStateInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetVgpuSchedulerLog(nvmlGpuInstance_t gpuInstance, nvmlVgpuSchedulerLogInfo_t* pSchedulerLogInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetVgpuTypeCreatablePlacements(nvmlGpuInstance_t gpuInstance, nvmlVgpuCreatablePlacementInfo_t* pCreatablePlacementInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetVgpuHeterogeneousMode(nvmlGpuInstance_t gpuInstance, nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceSetVgpuHeterogeneousMode(nvmlGpuInstance_t gpuInstance, const nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetMetadata(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuMetadata_t* vgpuMetadata, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuMetadata(nvmlDevice_t device, nvmlVgpuPgpuMetadata_t* pgpuMetadata, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGetVgpuCompatibility(nvmlVgpuMetadata_t* vgpuMetadata, nvmlVgpuPgpuMetadata_t* pgpuMetadata, nvmlVgpuPgpuCompatibility_t* compatibilityInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPgpuMetadataString(nvmlDevice_t device, char* pgpuMetadata, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuSchedulerLog(nvmlDevice_t device, nvmlVgpuSchedulerLog_t* pSchedulerLog) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuSchedulerState(nvmlDevice_t device, nvmlVgpuSchedulerGetState_t* pSchedulerState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuSchedulerCapabilities(nvmlDevice_t device, nvmlVgpuSchedulerCapabilities_t* pCapabilities) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetVgpuSchedulerState(nvmlDevice_t device, nvmlVgpuSchedulerSetState_t* pSchedulerState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGetVgpuVersion(nvmlVgpuVersion_t* supported, nvmlVgpuVersion_t* current) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlSetVgpuVersion(nvmlVgpuVersion_t* vgpuVersion) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuUtilization(nvmlDevice_t device, unsigned long long lastSeenTimeStamp, nvmlValueType_t* sampleValType, unsigned int* vgpuInstanceSamplesCount, nvmlVgpuInstanceUtilizationSample_t* utilizationSamples) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuInstancesUtilizationInfo(nvmlDevice_t device, nvmlVgpuInstancesUtilizationInfo_t* vgpuUtilInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuProcessUtilization(nvmlDevice_t device, unsigned long long lastSeenTimeStamp, unsigned int* vgpuProcessSamplesCount, nvmlVgpuProcessUtilizationSample_t* utilizationSamples) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetVgpuProcessesUtilizationInfo(nvmlDevice_t device, nvmlVgpuProcessesUtilizationInfo_t* vgpuProcUtilInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetAccountingMode(nvmlVgpuInstance_t vgpuInstance, nvmlEnableState_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetAccountingPids(nvmlVgpuInstance_t vgpuInstance, unsigned int* count, unsigned int* pids) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetAccountingStats(nvmlVgpuInstance_t vgpuInstance, unsigned int pid, nvmlAccountingStats_t* stats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceClearAccountingPids(nvmlVgpuInstance_t vgpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlVgpuInstanceGetLicenseInfo_v2(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuLicenseInfo_t* licenseInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGetExcludedDeviceCount(unsigned int* deviceCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGetExcludedDeviceInfoByIndex(unsigned int index, nvmlExcludedDeviceInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetMigMode(nvmlDevice_t device, unsigned int mode, nvmlReturn_t* activationStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMigMode(nvmlDevice_t device, unsigned int* currentMode, unsigned int* pendingMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstanceProfileInfoV(nvmlDevice_t device, unsigned int profile, nvmlGpuInstanceProfileInfo_v2_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstancePossiblePlacements_v2(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstancePlacement_t* placements, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstanceRemainingCapacity(nvmlDevice_t device, unsigned int profileId, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceCreateGpuInstance(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstance_t* gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceCreateGpuInstanceWithPlacement(nvmlDevice_t device, unsigned int profileId, const nvmlGpuInstancePlacement_t* placement, nvmlGpuInstance_t* gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceDestroy(nvmlGpuInstance_t gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstances(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstance_t* gpuInstances, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstanceById(nvmlDevice_t device, unsigned int id, nvmlGpuInstance_t* gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetInfo(nvmlGpuInstance_t gpuInstance, nvmlGpuInstanceInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetComputeInstanceProfileInfoV(nvmlGpuInstance_t gpuInstance, unsigned int profile, unsigned int engProfile, nvmlComputeInstanceProfileInfo_v2_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetComputeInstanceRemainingCapacity(nvmlGpuInstance_t gpuInstance, unsigned int profileId, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetComputeInstancePossiblePlacements(nvmlGpuInstance_t gpuInstance, unsigned int profileId, nvmlComputeInstancePlacement_t* placements, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceCreateComputeInstance(nvmlGpuInstance_t gpuInstance, unsigned int profileId, nvmlComputeInstance_t* computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceCreateComputeInstanceWithPlacement(nvmlGpuInstance_t gpuInstance, unsigned int profileId, const nvmlComputeInstancePlacement_t* placement, nvmlComputeInstance_t* computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlComputeInstanceDestroy(nvmlComputeInstance_t computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetComputeInstances(nvmlGpuInstance_t gpuInstance, unsigned int profileId, nvmlComputeInstance_t* computeInstances, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlGpuInstanceGetComputeInstanceById(nvmlGpuInstance_t gpuInstance, unsigned int id, nvmlComputeInstance_t* computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlComputeInstanceGetInfo_v2(nvmlComputeInstance_t computeInstance, nvmlComputeInstanceInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceIsMigDeviceHandle(nvmlDevice_t device, unsigned int* isMigDevice) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstanceId(nvmlDevice_t device, unsigned int* id) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetComputeInstanceId(nvmlDevice_t device, unsigned int* id) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMaxMigDeviceCount(nvmlDevice_t device, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetMigDeviceHandleByIndex(nvmlDevice_t device, unsigned int index, nvmlDevice_t* migDevice) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetDeviceHandleFromMigDeviceHandle(nvmlDevice_t migDevice, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetCapabilities(nvmlDevice_t device, nvmlDeviceCapabilities_t* caps) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDevicePowerSmoothingActivatePresetProfile(nvmlDevice_t device, nvmlPowerSmoothingProfile_t* profile) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDevicePowerSmoothingUpdatePresetProfileParam(nvmlDevice_t device, nvmlPowerSmoothingProfile_t* profile) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDevicePowerSmoothingSetState(nvmlDevice_t device, nvmlPowerSmoothingState_t* state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetAddressingMode(nvmlDevice_t device, nvmlDeviceAddressingMode_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetRepairStatus(nvmlDevice_t device, nvmlRepairStatus_t* repairStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPowerMizerMode_v1(nvmlDevice_t device, nvmlDevicePowerMizerModes_v1_t* powerMizerMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetPowerMizerMode_v1(nvmlDevice_t device, nvmlDevicePowerMizerModes_v1_t* powerMizerMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetPdi(nvmlDevice_t device, nvmlPdi_t* pdi) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetHostname_v1(nvmlDevice_t device, nvmlHostname_v1_t* hostname) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetHostname_v1(nvmlDevice_t device, nvmlHostname_v1_t* hostname) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetNvLinkInfo(nvmlDevice_t device, nvmlNvLinkInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceReadWritePRM_v1(nvmlDevice_t device, nvmlPRMTLV_v1_t* buffer) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetGpuInstanceProfileInfoByIdV(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstanceProfileInfo_v2_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetSramUniqueUncorrectedEccErrorCounts(nvmlDevice_t device, nvmlEccSramUniqueUncorrectedErrorCounts_t* errorCounts) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceGetUnrepairableMemoryFlag_v1(nvmlDevice_t device, nvmlUnrepairableMemoryStatus_v1_t* unrepairableMemoryStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceReadPRMCounters_v1(nvmlDevice_t device, nvmlPRMCounterList_v1_t* counterList) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t _nvmlDeviceSetRusdSettings_v1(nvmlDevice_t device, nvmlRusdSettings_v1_t* settings) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvvm.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/_internal/nvvm.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..38f64f914428bb6a32e680651ac0975ecbf99368
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_internal/nvvm.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/nvvm.pxd b/lib/python3.11/site-packages/cuda/bindings/_internal/nvvm.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..23427edd9b656581a601fb336e349c56978d7cd7
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/nvvm.pxd
@@ -0,0 +1,26 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.0.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from ..cynvvm cimport *
+
+
+###############################################################################
+# Wrapper functions
+###############################################################################
+
+cdef const char* _nvvmGetErrorString(nvvmResult result) except?NULL nogil
+cdef nvvmResult _nvvmVersion(int* major, int* minor) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmIRVersion(int* majorIR, int* minorIR, int* majorDbg, int* minorDbg) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmCreateProgram(nvvmProgram* prog) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmDestroyProgram(nvvmProgram* prog) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmAddModuleToProgram(nvvmProgram prog, const char* buffer, size_t size, const char* name) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmLazyAddModuleToProgram(nvvmProgram prog, const char* buffer, size_t size, const char* name) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmCompileProgram(nvvmProgram prog, int numOptions, const char** options) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmVerifyProgram(nvvmProgram prog, int numOptions, const char** options) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmGetCompiledResultSize(nvvmProgram prog, size_t* bufferSizeRet) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmGetCompiledResult(nvvmProgram prog, char* buffer) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmGetProgramLogSize(nvvmProgram prog, size_t* bufferSizeRet) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult _nvvmGetProgramLog(nvvmProgram prog, char* buffer) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_internal/utils.pxd b/lib/python3.11/site-packages/cuda/bindings/_internal/utils.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..50484727b7ade126a6af539241bd1a2365bad4b8
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_internal/utils.pxd
@@ -0,0 +1,167 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+from libc.stdint cimport int32_t, int64_t, intptr_t
+from libcpp.vector cimport vector
+from libcpp cimport bool as cppbool
+from libcpp cimport nullptr_t, nullptr
+from libcpp.memory cimport unique_ptr
+
+
+cdef extern from * nogil:
+    """
+    template<typename T>
+    class nullable_unique_ptr {
+      public:
+        nullable_unique_ptr() noexcept = default;
+
+        nullable_unique_ptr(std::nullptr_t) noexcept = delete;
+
+        explicit nullable_unique_ptr(T* data, bool own_data):
+            own_data_(own_data)
+        {
+            if (own_data)
+                manager_.reset(data);
+            else
+                raw_data_ = data;
+        }
+
+        nullable_unique_ptr(const nullable_unique_ptr&) = delete;
+
+        nullable_unique_ptr& operator=(const nullable_unique_ptr&) = delete;
+
+        nullable_unique_ptr(nullable_unique_ptr&& other) noexcept
+        {
+            own_data_ = other.own_data_;
+            other.own_data_ = false;  // ownership is transferred
+            if (own_data_)
+            {
+                manager_ = std::move(other.manager_);
+                raw_data_ = nullptr;  // just in case
+            }
+            else
+            {
+                manager_.reset(nullptr);  // just in case
+                raw_data_ = other.raw_data_;
+            }
+        }
+
+        nullable_unique_ptr& operator=(nullable_unique_ptr&& other) noexcept
+        {
+            own_data_ = other.own_data_;
+            other.own_data_ = false;  // ownership is transferred
+            if (own_data_)
+            {
+                manager_ = std::move(other.manager_);
+                raw_data_ = nullptr;  // just in case
+            }
+            else
+            {
+                manager_.reset(nullptr);  // just in case
+                raw_data_ = other.raw_data_;
+            }
+            return *this;
+        }
+
+        ~nullable_unique_ptr() = default;
+
+        void reset(T* data, bool own_data)
+        {
+            own_data_ = own_data;
+            if (own_data_)
+            {
+                manager_.reset(data);
+                raw_data_ = nullptr;
+            }
+            else
+            {
+                manager_.reset(nullptr);
+                raw_data_ = data;
+            }
+        }
+
+        void swap(nullable_unique_ptr& other) noexcept
+        {
+            std::swap(manager_, other.manager_);
+            std::swap(raw_data_, other.raw_data_);
+            std::swap(own_data_, other.own_data_);
+        }
+
+        /*
+         * Get the pointer to the underlying object (this is different from data()!).
+         */
+        T* get() const noexcept
+        {
+            if (own_data_)
+                return manager_.get();
+            else
+                return raw_data_;
+        }
+
+        /*
+         * Get the pointer to the underlying buffer (this is different from get()!).
+         */
+        void* data() noexcept
+        {
+            if (own_data_)
+                return manager_.get()->data();
+            else
+                return raw_data_;
+        }
+
+        T& operator*()
+        {
+            if (own_data_)
+                return *manager_;
+            else
+                return *raw_data_;
+        }
+
+      private:
+        std::unique_ptr<T> manager_{};
+        T* raw_data_{nullptr};
+        bool own_data_{false};
+    };
+    """
+    # xref: cython/Cython/Includes/libcpp/memory.pxd
+    cdef cppclass nullable_unique_ptr[T]:
+        nullable_unique_ptr()
+        nullable_unique_ptr(T*, cppbool)
+        nullable_unique_ptr(nullable_unique_ptr[T]&)
+
+        # Modifiers
+        void reset(T*, cppbool)
+        void swap(nullable_unique_ptr&)
+
+        # Observers
+        T* get()
+        T& operator*()
+        void* data()
+
+
+ctypedef fused ResT:
+    int
+    int32_t
+    int64_t
+    char
+    float
+    double
+
+
+ctypedef fused PtrT:
+    void
+
+
+cdef cppclass nested_resource[T]:
+    nullable_unique_ptr[ vector[intptr_t] ] ptrs
+    nullable_unique_ptr[ vector[vector[T]] ] nested_resource_ptr
+
+
+# accepts the output pointer as input to use the return value for exception propagation
+cdef int get_resource_ptr(nullable_unique_ptr[vector[ResT]] &in_out_ptr, object obj, ResT* __unused) except 1
+cdef int get_resource_ptrs(nullable_unique_ptr[ vector[PtrT*] ] &in_out_ptr, object obj, PtrT* __unused) except 1
+cdef int get_nested_resource_ptr(nested_resource[ResT] &in_out_ptr, object obj, ResT* __unused) except 1
+
+cdef bint is_nested_sequence(data)
+cdef void* get_buffer_pointer(buf, Py_ssize_t size, readonly=*) except*
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/__init__.py b/lib/python3.11/site-packages/cuda/bindings/_lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/_lib/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bca8682d9789ae6c5ad2b93d49ce82c1ee7cba18
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_lib/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/cyruntime/cyruntime.pxd b/lib/python3.11/site-packages/cuda/bindings/_lib/cyruntime/cyruntime.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..48f87f29caf4e93aef95c2d197c48064d7f4da19
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/cyruntime/cyruntime.pxd
@@ -0,0 +1,43 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+cimport cuda.bindings.cyruntime as cyruntime
+cimport cuda.bindings._bindings.cydriver as _cydriver
+
+# These graphics API are the reimplemented version of what's supported by CUDA Runtime.
+# Issue https://github.com/NVIDIA/cuda-python/issues/488 will remove them by letting us
+# use call into the static library directly.
+#
+# This is an ABI breaking change which can only happen in a major version bump.
+
+# This file is included from cuda/bindings/_bindings/cyruntime.pxd.in but kept in a
+# separate file to keep it separated from the auto-generated code there.
+
+# Prior to https://github.com/NVIDIA/cuda-python/pull/914, this was two
+# independent modules (c.b._lib.cyruntime.cyruntime and
+# c.b._lib.cyruntime.utils), but was merged into one.
+
+cdef cudaError_t _cudaEGLStreamProducerPresentFrame(cyruntime.cudaEglStreamConnection* conn, cyruntime.cudaEglFrame eglframe, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamProducerReturnFrame(cyruntime.cudaEglStreamConnection* conn, cyruntime.cudaEglFrame* eglframe, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGraphicsResourceGetMappedEglFrame(cyruntime.cudaEglFrame* eglFrame, cudaGraphicsResource_t resource, unsigned int index, unsigned int mipLevel) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaVDPAUSetVDPAUDevice(int device, cyruntime.VdpDevice vdpDevice, cyruntime.VdpGetProcAddress* vdpGetProcAddress) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaVDPAUGetDevice(int* device, cyruntime.VdpDevice vdpDevice, cyruntime.VdpGetProcAddress* vdpGetProcAddress) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGraphicsVDPAURegisterVideoSurface(cudaGraphicsResource** resource, cyruntime.VdpVideoSurface vdpSurface, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGraphicsVDPAURegisterOutputSurface(cudaGraphicsResource** resource, cyruntime.VdpOutputSurface vdpSurface, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGLGetDevices(unsigned int* pCudaDeviceCount, int* pCudaDevices, unsigned int cudaDeviceCount, cyruntime.cudaGLDeviceList deviceList) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGraphicsGLRegisterImage(cudaGraphicsResource** resource, cyruntime.GLuint image, cyruntime.GLenum target, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGraphicsGLRegisterBuffer(cudaGraphicsResource** resource, cyruntime.GLuint buffer, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaGraphicsEGLRegisterImage(cudaGraphicsResource_t* pCudaResource, cyruntime.EGLImageKHR image, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamConsumerConnect(cyruntime.cudaEglStreamConnection* conn, cyruntime.EGLStreamKHR eglStream) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamConsumerConnectWithFlags(cyruntime.cudaEglStreamConnection* conn, cyruntime.EGLStreamKHR eglStream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamConsumerDisconnect(cyruntime.cudaEglStreamConnection* conn) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamConsumerAcquireFrame(cyruntime.cudaEglStreamConnection* conn, cudaGraphicsResource_t* pCudaResource, cudaStream_t* pStream, unsigned int timeout) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamConsumerReleaseFrame(cyruntime.cudaEglStreamConnection* conn, cudaGraphicsResource_t pCudaResource, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamProducerConnect(cyruntime.cudaEglStreamConnection* conn, cyruntime.EGLStreamKHR eglStream, cyruntime.EGLint width, cyruntime.EGLint height) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEGLStreamProducerDisconnect(cyruntime.cudaEglStreamConnection* conn) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t _cudaEventCreateFromEGLSync(cudaEvent_t* phEvent, cyruntime.EGLSyncKHR eglSync, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+# utility functions
+
+cdef cudaError_t getDriverEglFrame(_cydriver.CUeglFrame *cuEglFrame, cyruntime.cudaEglFrame eglFrame) except ?cudaErrorCallRequiresNewerDriver nogil
+cdef cudaError_t getRuntimeEglFrame(cyruntime.cudaEglFrame *eglFrame, _cydriver.CUeglFrame cueglFrame) except ?cudaErrorCallRequiresNewerDriver nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/cyruntime/cyruntime.pxi b/lib/python3.11/site-packages/cuda/bindings/_lib/cyruntime/cyruntime.pxi
new file mode 100644
index 0000000000000000000000000000000000000000..c18bd1ca2ea4427e3dfc859bceae6e794b307277
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/cyruntime/cyruntime.pxi
@@ -0,0 +1,1176 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# These graphics API are the reimplemented version of what's supported by CUDA Runtime.
+# Issue https://github.com/NVIDIA/cuda-python/issues/488 will remove them by letting us
+# use call into the static library directly.
+
+# This file is included from cuda/bindings/_bindings/cyruntime.pyx.in but kept in a
+# separate file to keep it separated from the auto-generated code there.
+
+# Prior to https://github.com/NVIDIA/cuda-python/pull/914, this was two
+# independent modules (c.b._lib.cyruntime.cyruntime and
+# c.b._lib.cyruntime.utils), but was merged into one.
+
+from libc.string cimport memset
+cimport cuda.bindings._bindings.cydriver as cydriver
+
+cdef cudaError_t _cudaEGLStreamProducerPresentFrame(cyruntime.cudaEglStreamConnection* conn, cyruntime.cudaEglFrame eglframe, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    cdef cydriver.CUeglFrame cueglFrame
+    err = getDriverEglFrame(&cueglFrame, eglframe)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamProducerPresentFrame(<cydriver.CUeglStreamConnection*>conn, cueglFrame, pStream)
+    return err
+
+cdef cudaError_t _cudaEGLStreamProducerReturnFrame(cyruntime.cudaEglStreamConnection* conn, cyruntime.cudaEglFrame* eglframe, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    if eglframe == NULL:
+        err = cudaErrorInvalidResourceHandle
+        return err
+    cdef cydriver.CUeglFrame cueglFrame
+    err = <cudaError_t>cydriver._cuEGLStreamProducerReturnFrame(<cydriver.CUeglStreamConnection*>conn, &cueglFrame, pStream)
+    if err != cudaSuccess:
+        return err
+    err = getRuntimeEglFrame(eglframe, cueglFrame)
+    return err
+
+cdef cudaError_t _cudaGraphicsResourceGetMappedEglFrame(cyruntime.cudaEglFrame* eglFrame, cudaGraphicsResource_t resource, unsigned int index, unsigned int mipLevel) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    cdef cydriver.CUeglFrame cueglFrame
+    memset(&cueglFrame, 0, sizeof(cueglFrame))
+    err = <cudaError_t>cydriver._cuGraphicsResourceGetMappedEglFrame(&cueglFrame, <cydriver.CUgraphicsResource>resource, index, mipLevel)
+    if err != cudaSuccess:
+        return err
+    err = getRuntimeEglFrame(eglFrame, cueglFrame)
+    return err
+
+cdef cudaError_t _cudaVDPAUSetVDPAUDevice(int device, cyruntime.VdpDevice vdpDevice, cyruntime.VdpGetProcAddress* vdpGetProcAddress) except ?cudaErrorCallRequiresNewerDriver nogil:
+    return cudaErrorNotSupported
+
+cdef cudaError_t _cudaVDPAUGetDevice(int* device, cyruntime.VdpDevice vdpDevice, cyruntime.VdpGetProcAddress* vdpGetProcAddress) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuVDPAUGetDevice(<cydriver.CUdevice*>device, vdpDevice, vdpGetProcAddress)
+    return err
+
+cdef cudaError_t _cudaGraphicsVDPAURegisterVideoSurface(cudaGraphicsResource** resource, cyruntime.VdpVideoSurface vdpSurface, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuGraphicsVDPAURegisterVideoSurface(<cydriver.CUgraphicsResource*>resource, vdpSurface, flags)
+    return err
+
+cdef cudaError_t _cudaGraphicsVDPAURegisterOutputSurface(cudaGraphicsResource** resource, cyruntime.VdpOutputSurface vdpSurface, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuGraphicsVDPAURegisterOutputSurface(<cydriver.CUgraphicsResource*>resource, vdpSurface, flags)
+    return err
+
+cdef cudaError_t _cudaGLGetDevices(unsigned int* pCudaDeviceCount, int* pCudaDevices, unsigned int cudaDeviceCount, cyruntime.cudaGLDeviceList deviceList) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuGLGetDevices_v2(pCudaDeviceCount, <cydriver.CUdevice*>pCudaDevices, cudaDeviceCount, <cydriver.CUGLDeviceList>deviceList)
+    return err
+
+cdef cudaError_t _cudaGraphicsGLRegisterImage(cudaGraphicsResource** resource, cyruntime.GLuint image, cyruntime.GLenum target, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuGraphicsGLRegisterImage(<cydriver.CUgraphicsResource*>resource, image, target, flags)
+    return err
+
+cdef cudaError_t _cudaGraphicsGLRegisterBuffer(cudaGraphicsResource** resource, cyruntime.GLuint buffer, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuGraphicsGLRegisterBuffer(<cydriver.CUgraphicsResource*>resource, buffer, flags)
+    return err
+
+cdef cudaError_t _cudaGraphicsEGLRegisterImage(cudaGraphicsResource_t* pCudaResource, cyruntime.EGLImageKHR image, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuGraphicsEGLRegisterImage(<cydriver.CUgraphicsResource*>pCudaResource, image, flags)
+    return err
+
+cdef cudaError_t _cudaEGLStreamConsumerConnect(cyruntime.cudaEglStreamConnection* conn, cyruntime.EGLStreamKHR eglStream) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamConsumerConnect(<cydriver.CUeglStreamConnection*>conn, eglStream)
+    return err
+
+cdef cudaError_t _cudaEGLStreamConsumerConnectWithFlags(cyruntime.cudaEglStreamConnection* conn, cyruntime.EGLStreamKHR eglStream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamConsumerConnectWithFlags(<cydriver.CUeglStreamConnection*>conn, eglStream, flags)
+    return err
+
+cdef cudaError_t _cudaEGLStreamConsumerDisconnect(cyruntime.cudaEglStreamConnection* conn) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamConsumerDisconnect(<cydriver.CUeglStreamConnection*>conn)
+    return err
+
+cdef cudaError_t _cudaEGLStreamConsumerAcquireFrame(cyruntime.cudaEglStreamConnection* conn, cudaGraphicsResource_t* pCudaResource, cudaStream_t* pStream, unsigned int timeout) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamConsumerAcquireFrame(<cydriver.CUeglStreamConnection*>conn, <cydriver.CUgraphicsResource*>pCudaResource, <cydriver.CUstream*>pStream, timeout)
+    return err
+
+cdef cudaError_t _cudaEGLStreamConsumerReleaseFrame(cyruntime.cudaEglStreamConnection* conn, cudaGraphicsResource_t pCudaResource, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamConsumerReleaseFrame(<cydriver.CUeglStreamConnection*>conn, <cydriver.CUgraphicsResource>pCudaResource, <cydriver.CUstream*>pStream)
+    return err
+
+cdef cudaError_t _cudaEGLStreamProducerConnect(cyruntime.cudaEglStreamConnection* conn, cyruntime.EGLStreamKHR eglStream, cyruntime.EGLint width, cyruntime.EGLint height) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamProducerConnect(<cydriver.CUeglStreamConnection*>conn, eglStream, width, height)
+    return err
+
+cdef cudaError_t _cudaEGLStreamProducerDisconnect(cyruntime.cudaEglStreamConnection* conn) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEGLStreamProducerDisconnect(<cydriver.CUeglStreamConnection*>conn)
+    return err
+
+cdef cudaError_t _cudaEventCreateFromEGLSync(cudaEvent_t* phEvent, cyruntime.EGLSyncKHR eglSync, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    # cudaFree(0) is a NOP operations that initializes the context state
+    err = cudaFree(<void*>0)
+    if err != cudaSuccess:
+        return err
+    err = <cudaError_t>cydriver._cuEventCreateFromEGLSync(<cydriver.CUevent*>phEvent, eglSync, flags)
+    return err
+
+## utility functions
+
+cdef int case_desc(const cudaChannelFormatDesc* d, int x, int y, int z, int w, int f) except ?cudaErrorCallRequiresNewerDriver nogil:
+    return d[0].x == x and d[0].y == y and d[0].z == z and d[0].w == w and d[0].f == f
+
+
+cdef cudaError_t getDescInfo(const cudaChannelFormatDesc* d, int *numberOfChannels, cydriver.CUarray_format *format) except ?cudaErrorCallRequiresNewerDriver nogil:
+    # Check validity
+    if d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindSigned,
+                  cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        if (d[0].x != 8) and (d[0].x != 16) and (d[0].x != 32):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindFloat,):
+        if (d[0].x != 16) and (d[0].x != 32):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindNV12,):
+        if (d[0].x != 8) or (d[0].y != 8) or (d[0].z != 8) or (d[0].w != 0):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized8X1,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized8X2,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized8X4,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized8X1,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized8X2,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized8X4,):
+        if (d[0].x != 8):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized16X1,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized16X2,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized16X4,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized16X1,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized16X2,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized16X4,):
+        if (d[0].x != 16):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed1,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed1SRGB,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed2,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed2SRGB,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed3,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed3SRGB,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed4,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed4,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed5,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed5,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed7,
+                    cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed7SRGB,):
+        if (d[0].x != 8):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed6H,
+                    cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed6H,):
+        if (d[0].x != 16) or (d[0].y != 16) or (d[0].z != 16) or (d[0].w != 0):
+            return cudaErrorInvalidChannelDescriptor
+    elif d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized1010102,):
+        if (d[0].x != 10) or (d[0].y != 10) or (d[0].z != 10) or (d[0].w != 2):
+            return cudaErrorInvalidChannelDescriptor
+    else:
+        return cudaErrorInvalidChannelDescriptor
+
+    # If Y is non-zero, it must match X
+    # If Z is non-zero, it must match Y
+    # If W is non-zero, it must match Z
+    if (((d[0].y != 0) and (d[0].y != d[0].x)) or
+        ((d[0].z != 0) and (d[0].z != d[0].y)) or
+        ((d[0].w != 0) and (d[0].w != d[0].z))):
+        if d[0].f != cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized1010102:
+            return cudaErrorInvalidChannelDescriptor
+    if case_desc(d, 8, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT8
+    elif case_desc(d, 8, 8, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT8
+    elif case_desc(d, 8, 8, 8, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT8
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT8
+    elif case_desc(d, 8, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT8
+    elif case_desc(d, 8, 8, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT8
+    elif case_desc(d, 8, 8, 8, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT8
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT8
+    elif case_desc(d, 16, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT16
+    elif case_desc(d, 16, 16, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT16
+    elif case_desc(d, 16, 16, 16, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT16
+    elif case_desc(d, 16, 16, 16, 16, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT16
+    elif case_desc(d, 16, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT16
+    elif case_desc(d, 16, 16, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT16
+    elif case_desc(d, 16, 16, 16, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT16
+    elif case_desc(d, 16, 16, 16, 16, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT16
+    elif case_desc(d, 32, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT32
+    elif case_desc(d, 32, 32, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT32
+    elif case_desc(d, 32, 32, 32, 0, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT32
+    elif case_desc(d, 32, 32, 32, 32, cudaChannelFormatKind.cudaChannelFormatKindSigned):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_SIGNED_INT32
+    elif case_desc(d, 32, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT32
+    elif case_desc(d, 32, 32, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT32
+    elif case_desc(d, 32, 32, 32, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT32
+    elif case_desc(d, 32, 32, 32, 32, cudaChannelFormatKind.cudaChannelFormatKindUnsigned):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNSIGNED_INT32
+    elif case_desc(d, 16, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_HALF
+    elif case_desc(d, 16, 16, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_HALF
+    elif case_desc(d, 16, 16, 16, 0, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_HALF
+    elif case_desc(d, 16, 16, 16, 16, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_HALF
+    elif case_desc(d, 32, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_FLOAT
+    elif case_desc(d, 32, 32, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_FLOAT
+    elif case_desc(d, 32, 32, 32, 0, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_FLOAT
+    elif case_desc(d, 32, 32, 32, 32, cudaChannelFormatKind.cudaChannelFormatKindFloat):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_FLOAT
+    elif case_desc(d, 8, 8, 8, 0, cudaChannelFormatKind.cudaChannelFormatKindNV12):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_NV12
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed1):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC1_UNORM
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed1SRGB):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC1_UNORM_SRGB
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed2):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC2_UNORM
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed2SRGB):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC2_UNORM_SRGB
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed3):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC3_UNORM
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed3SRGB):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC3_UNORM_SRGB
+    elif case_desc(d, 8, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed4):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC4_UNORM
+    elif case_desc(d, 8, 0, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed4):
+        numberOfChannels[0] = 1
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC4_SNORM
+    elif case_desc(d, 8, 8, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed5):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC5_UNORM
+    elif case_desc(d, 8, 8, 0, 0, cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed5):
+        numberOfChannels[0] = 2
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC5_SNORM
+    elif case_desc(d, 16, 16, 16, 0, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed6H):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC6H_UF16
+    elif case_desc(d, 16, 16, 16, 0, cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed6H):
+        numberOfChannels[0] = 3
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC6H_SF16
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed7):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC7_UNORM
+    elif case_desc(d, 8, 8, 8, 8, cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed7SRGB):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_BC7_UNORM_SRGB
+    elif case_desc(d, 10, 10, 10, 2, cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized1010102):
+        numberOfChannels[0] = 4
+        format[0] = cydriver.CUarray_format_enum.CU_AD_FORMAT_UNORM_INT_101010_2
+    else:
+        return cudaErrorInvalidChannelDescriptor
+
+    if d[0].f in (cudaChannelFormatKind.cudaChannelFormatKindNV12,
+                  cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed6H,
+                  cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed6H,):
+        if numberOfChannels[0] != 3:
+            return cudaErrorInvalidChannelDescriptor
+    else:
+        if (numberOfChannels[0] != 1) and (numberOfChannels[0] != 2) and (numberOfChannels[0] != 4):
+            return cudaErrorInvalidChannelDescriptor
+    return cudaSuccess
+
+cdef cudaError_t getChannelFormatDescFromDriverDesc(cudaChannelFormatDesc* pRuntimeDesc, size_t* pDepth, size_t* pHeight, size_t* pWidth, const cydriver.CUDA_ARRAY3D_DESCRIPTOR_v2* pDriverDesc) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef int channel_size = 0
+    if pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNSIGNED_INT8:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsigned
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNSIGNED_INT16:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsigned
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNSIGNED_INT32:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsigned
+        channel_size = 32
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SIGNED_INT8:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSigned
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SIGNED_INT16:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSigned
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SIGNED_INT32:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSigned
+        channel_size = 32
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_HALF:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindFloat
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_FLOAT:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindFloat
+        channel_size = 32
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_NV12:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindNV12
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT8X1:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized8X1
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT8X2:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized8X2
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT8X4:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized8X4
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SNORM_INT8X1:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized8X1
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SNORM_INT8X2:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized8X2
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SNORM_INT8X4:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized8X4
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT16X1:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized16X1
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT16X2:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized16X2
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT16X4:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized16X4
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SNORM_INT16X1:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized16X1
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SNORM_INT16X2:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized16X2
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_SNORM_INT16X4:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedNormalized16X4
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC1_UNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed1
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC1_UNORM_SRGB:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed1SRGB
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC2_UNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed2
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC2_UNORM_SRGB:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed2SRGB
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC3_UNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed3
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC3_UNORM_SRGB:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed3SRGB
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC4_UNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed4
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC4_SNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed4
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC5_UNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed5
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC5_SNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed5
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC6H_UF16:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed6H
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC6H_SF16:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindSignedBlockCompressed6H
+        channel_size = 16
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC7_UNORM:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed7
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_BC7_UNORM_SRGB:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedBlockCompressed7SRGB
+        channel_size = 8
+    elif pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT_101010_2:
+        pRuntimeDesc[0].f = cudaChannelFormatKind.cudaChannelFormatKindUnsignedNormalized1010102
+    else:
+        return cudaErrorInvalidChannelDescriptor
+
+    # populate bits per channel
+    pRuntimeDesc[0].x = 0
+    pRuntimeDesc[0].y = 0
+    pRuntimeDesc[0].z = 0
+    pRuntimeDesc[0].w = 0
+
+    if pDriverDesc[0].Format == cydriver.CU_AD_FORMAT_UNORM_INT_101010_2 and pDriverDesc[0].NumChannels == 4:
+        pRuntimeDesc[0].w = 2
+        pRuntimeDesc[0].z = 10
+        pRuntimeDesc[0].y = 10
+        pRuntimeDesc[0].x = 10
+    else:
+        if pDriverDesc[0].NumChannels >= 4:
+            pRuntimeDesc[0].w = channel_size
+        if pDriverDesc[0].NumChannels >= 3:
+            pRuntimeDesc[0].z = channel_size
+        if pDriverDesc[0].NumChannels >= 2:
+            pRuntimeDesc[0].y = channel_size
+        if pDriverDesc[0].NumChannels >= 1:
+            pRuntimeDesc[0].x = channel_size
+
+    if pDriverDesc[0].NumChannels not in (4, 3, 2, 1):
+        return cudaErrorInvalidChannelDescriptor
+
+    # populate dimensions
+    if pDepth != NULL:
+        pDepth[0]  = pDriverDesc[0].Depth
+    if pHeight != NULL:
+        pHeight[0] = pDriverDesc[0].Height
+    if pWidth != NULL:
+        pWidth[0]  = pDriverDesc[0].Width
+    return cudaSuccess
+
+cdef cudaError_t getDriverEglFrame(cydriver.CUeglFrame *cuEglFrame, cyruntime.cudaEglFrame eglFrame) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    cdef unsigned int i = 0
+
+    err = getDescInfo(&eglFrame.planeDesc[0].channelDesc, <int*>&cuEglFrame[0].numChannels, &cuEglFrame[0].cuFormat)
+    if err != cudaSuccess:
+        return err
+    for i in range(eglFrame.planeCount):
+        if eglFrame.frameType == cyruntime.cudaEglFrameTypeArray:
+            cuEglFrame[0].frame.pArray[i] = <cydriver.CUarray>eglFrame.frame.pArray[i]
+        else:
+            cuEglFrame[0].frame.pPitch[i] = eglFrame.frame.pPitch[i].ptr
+    cuEglFrame[0].width = eglFrame.planeDesc[0].width
+    cuEglFrame[0].height = eglFrame.planeDesc[0].height
+    cuEglFrame[0].depth = eglFrame.planeDesc[0].depth
+    cuEglFrame[0].pitch = eglFrame.planeDesc[0].pitch
+    cuEglFrame[0].planeCount = eglFrame.planeCount
+    if eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420Planar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV422Planar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_PLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV422SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV444Planar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_PLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV444SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUYV422:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUYV_422
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatUYVY422:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_422
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatUYVY709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatUYVY709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatUYVY2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatARGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_ARGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatRGBA:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_RGBA
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatABGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_ABGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBGRA:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BGRA
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatL:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_L
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_R
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatA:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_A
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_RG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatAYUV:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_AYUV
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU444SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU422SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_444SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12V12U12_444SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12V12U12_420SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatVYUY_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_VYUY_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatUYVY_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUYV_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUYV_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVYU_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVYU_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUVA_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUVA_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatAYUV_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_AYUV_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV444Planar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_PLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV422Planar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_PLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420Planar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV444SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV422SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU444Planar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_PLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU422Planar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_PLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420Planar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU444SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU422SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerRGGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_RGGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerBGGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_BGGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerGRBG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_GRBG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerGBRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_GBRG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer10RGGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_RGGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer10BGGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_BGGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer10GRBG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_GRBG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer10GBRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_GBRG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12RGGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_RGGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12BGGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_BGGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12GRBG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_GRBG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12GBRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_GBRG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer14RGGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_RGGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer14BGGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_BGGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer14GRBG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_GRBG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer14GBRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_GBRG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer20RGGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_RGGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer20BGGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_BGGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer20GRBG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_GRBG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer20GBRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_GBRG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerIspRGGB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_RGGB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerIspBGGR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_BGGR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerIspGRBG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_GRBG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerIspGBRG:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_GBRG
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU444Planar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_PLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU422Planar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_PLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420Planar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerBCCR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_BCCR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerRCCB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_RCCB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerCRBC:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_CRBC
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayerCBRC:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_CBRC
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer10CCCC:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_CCCC
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12BCCR:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_BCCR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12RCCB:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_RCCB
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12CRBC:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_CRBC
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12CBRC:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_CBRC
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatBayer12CCCC:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_CCCC
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420SemiPlanar_2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420SemiPlanar_2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420Planar_2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420Planar_2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420SemiPlanar_709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420SemiPlanar_709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUV420Planar_709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVU420Planar_709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_422SemiPlanar_2020:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_422SemiPlanar:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_422SemiPlanar_709:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYUVA:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUVA
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatYVYU:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVYU
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatVYUY:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_VYUY
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_444SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat =  cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY10V10U10_444SemiPlanar_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12V12U12_420SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12V12U12_420SemiPlanar_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12V12U12_444SemiPlanar_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER
+    elif eglFrame.eglColorFormat == cyruntime.cudaEglColorFormatY12V12U12_444SemiPlanar_709_ER:
+        cuEglFrame[0].eglColorFormat = cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER
+    else:
+        return cudaErrorInvalidValue
+    if eglFrame.frameType == cyruntime.cudaEglFrameTypeArray:
+        cuEglFrame[0].frameType = cydriver.CUeglFrameType_enum.CU_EGL_FRAME_TYPE_ARRAY
+    elif eglFrame.frameType == cyruntime.cudaEglFrameTypePitch:
+        cuEglFrame[0].frameType = cydriver.CUeglFrameType_enum.CU_EGL_FRAME_TYPE_PITCH
+    else:
+        return cudaErrorInvalidValue
+
+@cython.show_performance_hints(False)
+cdef cudaError_t getRuntimeEglFrame(cyruntime.cudaEglFrame *eglFrame, cydriver.CUeglFrame cueglFrame) except ?cudaErrorCallRequiresNewerDriver nogil:
+    cdef cudaError_t err = cudaSuccess
+    cdef unsigned int i
+    cdef cydriver.CUDA_ARRAY3D_DESCRIPTOR_v2 ad
+    cdef cudaPitchedPtr pPtr
+    memset(eglFrame, 0, sizeof(eglFrame[0]))
+    memset(&ad, 0, sizeof(ad))
+    for i in range(cueglFrame.planeCount):
+        ad.Depth = cueglFrame.depth
+        ad.Flags = 0
+        ad.Format = cueglFrame.cuFormat
+        ad.Height = cueglFrame.height
+        ad.NumChannels = cueglFrame.numChannels
+        ad.Width = cueglFrame.width
+
+        err = getChannelFormatDescFromDriverDesc(&eglFrame[0].planeDesc[i].channelDesc, NULL, NULL, NULL, &ad)
+        if err != cudaSuccess:
+            return err
+
+        eglFrame[0].planeDesc[i].depth = cueglFrame.depth
+        eglFrame[0].planeDesc[i].numChannels = cueglFrame.numChannels
+        if i == 0:
+            eglFrame[0].planeDesc[i].width = cueglFrame.width
+            eglFrame[0].planeDesc[i].height = cueglFrame.height
+            eglFrame[0].planeDesc[i].pitch = cueglFrame.pitch
+        elif (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_2020 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_2020 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_709 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_709):
+            eglFrame[0].planeDesc[i].width = <unsigned int>(cueglFrame.width / 2)
+            eglFrame[0].planeDesc[i].height = <unsigned int>(cueglFrame.height / 2)
+            eglFrame[0].planeDesc[i].pitch = <unsigned int>(cueglFrame.pitch / 2)
+        elif (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_2020 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_2020 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_709 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_709 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER):
+            eglFrame[0].planeDesc[i].width = <unsigned int>(cueglFrame.width / 2)
+            eglFrame[0].planeDesc[i].height = <unsigned int>(cueglFrame.height / 2)
+            eglFrame[0].planeDesc[i].pitch = <unsigned int>(cueglFrame.pitch / 2)
+            eglFrame[0].planeDesc[1].channelDesc.y = 8
+            if (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709 or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020 or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER):
+                eglFrame[0].planeDesc[1].channelDesc.y = 16
+        elif (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_PLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_PLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_PLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_PLANAR_ER):
+            eglFrame[0].planeDesc[i].height = cueglFrame.height
+            eglFrame[0].planeDesc[i].width = <unsigned int>(cueglFrame.width / 2)
+            eglFrame[0].planeDesc[i].pitch = <unsigned int>(cueglFrame.pitch / 2)
+        elif (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020 or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709):
+            eglFrame[0].planeDesc[i].width = <unsigned int>(cueglFrame.width / 2)
+            eglFrame[0].planeDesc[i].height = cueglFrame.height
+            eglFrame[0].planeDesc[i].pitch = <unsigned int>(cueglFrame.pitch / 2)
+            eglFrame[0].planeDesc[1].channelDesc.y = 8
+            if (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020 or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709):
+                eglFrame[0].planeDesc[1].channelDesc.y = 16
+        elif (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_PLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_PLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_PLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_PLANAR_ER):
+            eglFrame[0].planeDesc[i].height = cueglFrame.height
+            eglFrame[0].planeDesc[i].width = cueglFrame.width
+            eglFrame[0].planeDesc[i].pitch = cueglFrame.pitch
+        elif (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER or
+              cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER):
+            eglFrame[0].planeDesc[i].height = cueglFrame.height
+            eglFrame[0].planeDesc[i].width = cueglFrame.width
+            eglFrame[0].planeDesc[i].pitch = cueglFrame.pitch
+            eglFrame[0].planeDesc[1].channelDesc.y = 8
+            if (cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER or
+                cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER):
+                eglFrame[0].planeDesc[1].channelDesc.y = 16
+        if cueglFrame.frameType == cydriver.CUeglFrameType_enum.CU_EGL_FRAME_TYPE_ARRAY:
+            eglFrame[0].frame.pArray[i] = <cudaArray_t>cueglFrame.frame.pArray[i]
+        else:
+            pPtr = make_cudaPitchedPtr(cueglFrame.frame.pPitch[i], eglFrame[0].planeDesc[i].pitch,
+                    eglFrame[0].planeDesc[i].width, eglFrame[0].planeDesc[i].height)
+            eglFrame[0].frame.pPitch[i] = pPtr
+
+    eglFrame[0].planeCount = cueglFrame.planeCount
+    if cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420Planar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_PLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV422Planar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV422SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_PLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV444Planar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV444SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUYV_422:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUYV422
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_422:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatUYVY422
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatUYVY709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatUYVY709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatUYVY2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_ARGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatARGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_RGBA:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatRGBA
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_ABGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatABGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BGRA:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBGRA
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_L:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatL
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_R:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_A:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatA
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_RG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_AYUV:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatAYUV
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU444SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU422SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_444SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12V12U12_444SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12V12U12_420SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_VYUY_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatVYUY_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_UYVY_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatUYVY_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUYV_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUYV_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVYU_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVYU_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUVA_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUVA_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_AYUV_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatAYUV_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_PLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV444Planar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_PLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV422Planar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420Planar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV444SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV422SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_PLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU444Planar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_PLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU422Planar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420Planar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU444SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU422SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_RGGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerRGGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_BGGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerBGGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_GRBG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerGRBG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_GBRG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerGBRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_RGGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer10RGGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_BGGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer10BGGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_GRBG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer10GRBG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_GBRG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer10GBRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_RGGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12RGGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_BGGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12BGGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_GRBG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12GRBG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_GBRG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12GBRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_RGGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer14RGGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_BGGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer14BGGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_GRBG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer14GRBG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER14_GBRG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer14GBRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_RGGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer20RGGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_BGGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer20BGGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_GRBG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer20GRBG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER20_GBRG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer20GBRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_RGGB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerIspRGGB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_BGGR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerIspBGGR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_GRBG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerIspGRBG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_ISP_GBRG:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerIspGBRG
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU444_PLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU444Planar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU422_PLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU422Planar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420Planar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_BCCR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerBCCR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_RCCB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerRCCB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_CRBC:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerCRBC
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER_CBRC:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayerCBRC
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER10_CCCC:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer10CCCC
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_BCCR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12BCCR
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_RCCB:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12RCCB
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_CRBC:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12CRBC
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_CBRC:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12CBRC
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_BAYER12_CCCC:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatBayer12CCCC
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420SemiPlanar_2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420SemiPlanar_2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420Planar_2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420Planar_2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420SemiPlanar_709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420SemiPlanar_709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUV420_PLANAR_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUV420Planar_709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVU420_PLANAR_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVU420Planar_709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_422SemiPlanar_2020
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_422SemiPlanar
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_422SemiPlanar_709
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY_709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10_709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12_709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YUVA:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYUVA
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_YVYU:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatYVYU
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_VYUY:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatVYUY
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_420SemiPlanar_709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_444SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY10V10U10_444SemiPlanar_709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12V12U12_420SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12V12U12_420SemiPlanar_709_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12V12U12_444SemiPlanar_ER
+    elif cueglFrame.eglColorFormat == cydriver.CUeglColorFormat_enum.CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER:
+        eglFrame[0].eglColorFormat = cyruntime.cudaEglColorFormatY12V12U12_444SemiPlanar_709_ER
+    else:
+        return cudaErrorInvalidValue
+    if cueglFrame.frameType == cydriver.CUeglFrameType_enum.CU_EGL_FRAME_TYPE_ARRAY:
+        eglFrame[0].frameType = cyruntime.cudaEglFrameTypeArray
+    elif cueglFrame.frameType == cydriver.CUeglFrameType_enum.CU_EGL_FRAME_TYPE_PITCH:
+        eglFrame[0].frameType = cyruntime.cudaEglFrameTypePitch
+    else:
+        return cudaErrorInvalidValue
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/dlfcn.pxd b/lib/python3.11/site-packages/cuda/bindings/_lib/dlfcn.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..2ae95814396cab6134db80754e52e6f7b68bf979
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/dlfcn.pxd
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+cdef extern from "<dlfcn.h>" nogil:
+    void *dlopen(const char *, int)
+    char *dlerror()
+    void *dlsym(void *, const char *)
+    int dlclose(void *)
+
+    enum:
+        RTLD_LAZY
+        RTLD_NOW
+        RTLD_GLOBAL
+        RTLD_LOCAL
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/param_packer.h b/lib/python3.11/site-packages/cuda/bindings/_lib/param_packer.h
new file mode 100644
index 0000000000000000000000000000000000000000..96c56b4fe4e0f2d3ec4d7dc87d7f66b3df545d79
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/param_packer.h
@@ -0,0 +1,152 @@
+// SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+// Please refer to the NVIDIA end user license agreement (EULA) associated
+// with this source code for terms and conditions that govern your use of
+// this software. Any use, reproduction, disclosure, or distribution of
+// this software and related documentation outside the terms of the EULA
+// is strictly prohibited.
+
+#include <Python.h>
+
+#include <map>
+#include <functional>
+#include <stdexcept>
+#include <string>
+
+static PyObject* ctypes_module = nullptr;
+
+static PyTypeObject* ctypes_c_char = nullptr;
+static PyTypeObject* ctypes_c_bool = nullptr;
+static PyTypeObject* ctypes_c_wchar = nullptr;
+static PyTypeObject* ctypes_c_byte = nullptr;
+static PyTypeObject* ctypes_c_ubyte = nullptr;
+static PyTypeObject* ctypes_c_short = nullptr;
+static PyTypeObject* ctypes_c_ushort = nullptr;
+static PyTypeObject* ctypes_c_int = nullptr;
+static PyTypeObject* ctypes_c_uint = nullptr;
+static PyTypeObject* ctypes_c_long = nullptr;
+static PyTypeObject* ctypes_c_ulong = nullptr;
+static PyTypeObject* ctypes_c_longlong = nullptr;
+static PyTypeObject* ctypes_c_ulonglong = nullptr;
+static PyTypeObject* ctypes_c_size_t = nullptr;
+static PyTypeObject* ctypes_c_float = nullptr;
+static PyTypeObject* ctypes_c_double = nullptr;
+static PyTypeObject* ctypes_c_void_p = nullptr;
+
+static void fetch_ctypes()
+{
+    ctypes_module = PyImport_ImportModule("ctypes");
+    if (ctypes_module == nullptr)
+        throw std::runtime_error("Cannot import ctypes module");
+    // get method addressof
+    PyObject* ctypes_dict = PyModule_GetDict(ctypes_module);
+    if (ctypes_dict == nullptr)
+        throw std::runtime_error(std::string("FAILURE @ ") + std::string(__FILE__) + " : " + std::to_string(__LINE__));
+    // supportedtypes
+    ctypes_c_char = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_char");
+    ctypes_c_bool = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_bool");
+    ctypes_c_wchar = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_wchar");
+    ctypes_c_byte = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_byte");
+    ctypes_c_ubyte = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_ubyte");
+    ctypes_c_short = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_short");
+    ctypes_c_ushort = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_ushort");
+    ctypes_c_int = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_int");
+    ctypes_c_uint = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_uint");
+    ctypes_c_long = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_long");
+    ctypes_c_ulong = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_ulong");
+    ctypes_c_longlong = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_longlong");
+    ctypes_c_ulonglong = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_ulonglong");
+    ctypes_c_size_t = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_size_t");
+    ctypes_c_float = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_float");
+    ctypes_c_double = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_double");
+    ctypes_c_void_p = (PyTypeObject*) PyDict_GetItemString(ctypes_dict, "c_void_p"); // == c_voidp
+}
+
+
+// (target type, source type)
+static std::map<std::pair<PyTypeObject*,PyTypeObject*>, std::function<int(void*, PyObject*)>> m_feeders;
+
+static void populate_feeders(PyTypeObject* target_t, PyTypeObject* source_t)
+{
+    if (target_t == ctypes_c_int)
+    {
+        if (source_t == &PyLong_Type)
+        {
+            m_feeders[{target_t,source_t}] = [](void* ptr, PyObject* value) -> int
+            {
+                *((int*)ptr) = (int)PyLong_AsLong(value);
+                return sizeof(int);
+            };
+            return;
+        }
+    } else if (target_t == ctypes_c_bool) {
+        if (source_t == &PyBool_Type)
+        {
+            m_feeders[{target_t,source_t}] = [](void* ptr, PyObject* value) -> int
+            {
+                *((bool*)ptr) = (value == Py_True);
+                return sizeof(bool);
+            };
+            return;
+        }
+    } else if (target_t == ctypes_c_byte) {
+        if (source_t == &PyLong_Type)
+        {
+            m_feeders[{target_t,source_t}] = [](void* ptr, PyObject* value) -> int
+            {
+                *((int8_t*)ptr) = (int8_t)PyLong_AsLong(value);
+                return sizeof(int8_t);
+            };
+            return;
+        }
+    } else if (target_t == ctypes_c_double) {
+        if (source_t == &PyFloat_Type)
+        {
+            m_feeders[{target_t,source_t}] = [](void* ptr, PyObject* value) -> int
+            {
+                *((double*)ptr) = (double)PyFloat_AsDouble(value);
+                return sizeof(double);
+            };
+            return;
+        }
+    } else if (target_t == ctypes_c_float) {
+        if (source_t == &PyFloat_Type)
+        {
+            m_feeders[{target_t,source_t}] = [](void* ptr, PyObject* value) -> int
+            {
+                *((float*)ptr) = (float)PyFloat_AsDouble(value);
+                return sizeof(float);
+            };
+            return;
+        }
+    } else if (target_t == ctypes_c_longlong) {
+        if (source_t == &PyLong_Type)
+        {
+            m_feeders[{target_t,source_t}] = [](void* ptr, PyObject* value) -> int
+            {
+                *((long long*)ptr) = (long long)PyLong_AsLongLong(value);
+                return sizeof(long long);
+            };
+            return;
+        }
+    }
+}
+
+static int feed(void* ptr, PyObject* value, PyObject* type)
+{
+    PyTypeObject* pto = (PyTypeObject*)type;
+    if (ctypes_c_int == nullptr)
+        fetch_ctypes();
+    auto found = m_feeders.find({pto,value->ob_type});
+    if (found == m_feeders.end())
+    {
+        populate_feeders(pto, value->ob_type);
+        found = m_feeders.find({pto,value->ob_type});
+    }
+    if (found != m_feeders.end())
+    {
+        return found->second(ptr, value);
+    }
+    return 0;
+}
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/param_packer.pxd b/lib/python3.11/site-packages/cuda/bindings/_lib/param_packer.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..ad7fd95668fb3065b8ffa142ddad22970d23ed55
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/param_packer.pxd
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# Include "param_packer.h" so its contents get compiled into every
+# Cython extension module that depends on param_packer.pxd.
+cdef extern from "param_packer.h":
+    int feed(void* ptr, object o, object ct)
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/utils.pxd b/lib/python3.11/site-packages/cuda/bindings/_lib/utils.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..94d4206affaec68af91c1967df3eb04987a7fb31
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/utils.pxd
@@ -0,0 +1,137 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+cimport cuda.bindings.driver as driver
+cimport cuda.bindings.cydriver as cydriver
+cimport cuda.bindings.cyruntime as cyruntime
+from libcpp.vector cimport vector
+from cpython.buffer cimport PyBuffer_Release, Py_buffer
+
+cdef class _HelperKernelParams:
+    cdef Py_buffer _pybuffer
+    cdef bint _pyobj_acquired
+    cdef void** _ckernelParams
+    cdef char* _ckernelParamsData
+    cdef int _length
+    cdef bint _malloc_list_created
+
+cdef struct _HelperInputVoidPtrStruct:
+    Py_buffer _pybuffer
+
+cdef class _HelperInputVoidPtr:
+    cdef _HelperInputVoidPtrStruct _helper 
+    cdef void* _cptr
+
+cdef void * _helper_input_void_ptr(ptr, _HelperInputVoidPtrStruct *buffer)
+
+cdef inline void * _helper_input_void_ptr_free(_HelperInputVoidPtrStruct *helper):
+    if helper[0]._pybuffer.buf != NULL:
+        PyBuffer_Release(&helper[0]._pybuffer)
+
+cdef class _HelperCUmemPool_attribute:
+    cdef void* _cptr
+    cdef cydriver.CUmemPool_attribute_enum _attr
+    cdef bint _is_getter
+
+    # Return values
+    cdef int _int_val
+    cdef driver.cuuint64_t _cuuint64_t_val
+
+cdef class _HelperCUmem_range_attribute:
+    cdef void* _cptr
+    cdef cydriver.CUmem_range_attribute_enum _attr
+    cdef size_t _data_size
+
+    # Return values
+    cdef int _int_val # 32 bit integer
+    cdef int* _int_val_list # 32 bit integer array
+
+cdef class _HelperCUpointer_attribute:
+    cdef void* _cptr
+    cdef cydriver.CUpointer_attribute_enum _attr
+    cdef bint _is_getter
+
+    # Return values
+    cdef driver.CUcontext _ctx
+    cdef unsigned int _uint
+    cdef int _int
+    cdef driver.CUdeviceptr _devptr
+    cdef void** _void
+    cdef driver.CUDA_POINTER_ATTRIBUTE_P2P_TOKENS _token
+    cdef bint _bool
+    cdef unsigned long long _ull
+    cdef size_t _size
+    cdef driver.CUmemoryPool _mempool
+
+cdef class _HelperCUgraphMem_attribute:
+    cdef void* _cptr
+    cdef cydriver.CUgraphMem_attribute_enum _attr
+    cdef bint _is_getter
+
+    # Return values
+    cdef driver.cuuint64_t _cuuint64_t_val
+
+cdef class _HelperCUjit_option:
+    cdef void* _cptr
+    cdef cydriver.CUjit_option_enum _attr
+
+    # Return values
+    cdef unsigned int _uint
+    cdef float _float
+    cdef char* _charstar
+    cdef cydriver.CUjit_target_enum _target
+    cdef cydriver.CUjit_fallback_enum _fallback
+    cdef int _int
+    cdef cydriver.CUjit_cacheMode_enum _cacheMode
+    cdef vector[char*] _charstarstar # list of names
+    cdef _InputVoidPtrPtrHelper _voidstarstar # list of addresses
+
+cdef class _HelperCudaJitOption:
+    cdef void* _cptr
+    cdef cyruntime.cudaJitOption _attr
+
+    # Return values
+    cdef unsigned int _uint
+    cdef float _float
+    cdef char* _charstar
+    cdef cyruntime.cudaJit_Fallback _fallback
+    cdef int _int
+    cdef cyruntime.cudaJit_CacheMode _cacheMode
+
+cdef class _HelperCUlibraryOption:
+    cdef void* _cptr
+    cdef cydriver.CUlibraryOption_enum _attr
+
+    # Return values
+    cdef unsigned int _uint
+
+cdef class _HelperCudaLibraryOption:
+    cdef void* _cptr
+    cdef cyruntime.cudaLibraryOption _attr
+
+    # Return values
+    cdef unsigned int _uint
+
+cdef class _HelperCUmemAllocationHandleType:
+    cdef void* _cptr
+    cdef cydriver.CUmemAllocationHandleType_enum _type
+
+    # Return values
+    cdef int _int
+    cdef void* _handle
+    cdef unsigned int _d3dkmt_handle
+    cdef driver.CUmemFabricHandle _mem_fabric_handle
+
+cdef class _InputVoidPtrPtrHelper:
+    cdef object _references
+    cdef void** _cptr
+
+cdef class _HelperCUcoredumpSettings:
+    cdef void* _cptr
+    cdef cydriver.CUcoredumpSettings_enum _attrib
+    cdef bint _is_getter
+    cdef size_t _size
+
+    # Return values
+    cdef bint _bool
+    cdef char* _charstar
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/utils.pxi b/lib/python3.11/site-packages/cuda/bindings/_lib/utils.pxi
new file mode 100644
index 0000000000000000000000000000000000000000..b19aac745c4f67753d88a4f5647f99adcd04dcfa
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/utils.pxi
@@ -0,0 +1,610 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+from cpython.buffer cimport PyObject_CheckBuffer, PyObject_GetBuffer, PyBuffer_Release, PyBUF_SIMPLE, PyBUF_ANY_CONTIGUOUS
+from libc.stdlib cimport calloc, free
+from libc.stdint cimport int32_t, uint32_t, int64_t, uint64_t
+from libc.stddef cimport wchar_t
+from libc.string cimport memcpy
+from cuda.bindings._internal._fast_enum import FastEnum as _FastEnum
+import ctypes as _ctypes
+cimport cuda.bindings.cydriver as cydriver
+cimport cuda.bindings._lib.param_packer as param_packer
+
+cdef void* _callocWrapper(length, size):
+    cdef void* out = calloc(length, size)
+    if out is NULL:
+        raise MemoryError('Failed to allocated length x size memory: {}x{}'.format(length, size))
+    return out
+
+cdef class _HelperKernelParams:
+    supported_types = { # excluding void_p and None, which are handled specially
+        _ctypes.c_bool,
+        _ctypes.c_char,
+        _ctypes.c_wchar,
+        _ctypes.c_byte,
+        _ctypes.c_ubyte,
+        _ctypes.c_short,
+        _ctypes.c_ushort,
+        _ctypes.c_int,
+        _ctypes.c_uint,
+        _ctypes.c_long,
+        _ctypes.c_ulong,
+        _ctypes.c_longlong,
+        _ctypes.c_ulonglong,
+        _ctypes.c_size_t,
+        _ctypes.c_float,
+        _ctypes.c_double
+    }
+
+    max_param_size = max(_ctypes.sizeof(max(_HelperKernelParams.supported_types, key=lambda t:_ctypes.sizeof(t))), sizeof(void_ptr))
+
+    def __cinit__(self, kernelParams):
+        self._pyobj_acquired = False
+        self._malloc_list_created = False
+        if kernelParams is None:
+            self._ckernelParams = NULL
+        elif isinstance(kernelParams, (int)):
+            # Easy run, user gave us an already configured void** address
+            self._ckernelParams = <void**><void_ptr>kernelParams
+        elif PyObject_CheckBuffer(kernelParams):
+            # Easy run, get address from Python Buffer Protocol
+            err_buffer = PyObject_GetBuffer(kernelParams, &self._pybuffer, PyBUF_SIMPLE | PyBUF_ANY_CONTIGUOUS)
+            if err_buffer == -1:
+                raise RuntimeError("Argument 'kernelParams' failed to retrieve buffer through Buffer Protocol")
+            self._pyobj_acquired = True
+            self._ckernelParams = <void**><void_ptr>self._pybuffer.buf
+        elif isinstance(kernelParams, (tuple)) and len(kernelParams) == 2 and isinstance(kernelParams[0], (tuple)) and isinstance(kernelParams[1], (tuple)):
+            # Hard run, construct and fill out contigues memory using provided kernel values and types based
+            if len(kernelParams[0]) != len(kernelParams[1]):
+                raise TypeError("Argument 'kernelParams' has tuples with different length")
+            if len(kernelParams[0]) != 0:
+                self._length = len(kernelParams[0])
+                self._ckernelParams = <void**>_callocWrapper(len(kernelParams[0]), sizeof(void*))
+                self._ckernelParamsData = <char*>_callocWrapper(len(kernelParams[0]), _HelperKernelParams.max_param_size)
+                self._malloc_list_created = True
+
+            idx = 0
+            data_idx = 0
+            for value, ctype in zip(kernelParams[0], kernelParams[1]):
+                if ctype is None:
+                    # special cases for None
+                    if callable(getattr(value, 'getPtr', None)):
+                        self._ckernelParams[idx] = <void*><void_ptr>value.getPtr()
+                    elif isinstance(value, (_ctypes.Structure)):
+                        self._ckernelParams[idx] = <void*><void_ptr>_ctypes.addressof(value)
+                    elif isinstance(value, (_FastEnum)):
+                        self._ckernelParams[idx] = &(self._ckernelParamsData[data_idx])
+                        (<int*>self._ckernelParams[idx])[0] = value.value
+                        data_idx += sizeof(int)
+                    else:
+                        raise TypeError("Provided argument is of type {} but expected Type {}, {} or CUDA Binding structure with getPtr() attribute".format(type(value), type(_ctypes.Structure), type(_ctypes.c_void_p)))
+                elif ctype in _HelperKernelParams.supported_types:
+                    self._ckernelParams[idx] = &(self._ckernelParamsData[data_idx])
+
+                    # handle case where a float is passed as a double
+                    if ctype == _ctypes.c_double and isinstance(value, _ctypes.c_float):
+                        value = ctype(value.value)
+                    if not isinstance(value, ctype): # make it a ctype
+                        size = param_packer.feed(self._ckernelParams[idx], value, ctype)
+                        if size == 0: # feed failed
+                            value = ctype(value)
+                            size = _ctypes.sizeof(ctype)
+                            addr = <void*>(<void_ptr>_ctypes.addressof(value))
+                            memcpy(self._ckernelParams[idx], addr, size)
+                    else:
+                        size = _ctypes.sizeof(ctype)
+                        addr = <void*>(<void_ptr>_ctypes.addressof(value))
+                        memcpy(self._ckernelParams[idx], addr, size)
+                    data_idx += size
+                elif ctype == _ctypes.c_void_p:
+                    # special cases for void_p
+                    if isinstance(value, (int, _ctypes.c_void_p)):
+                        self._ckernelParams[idx] = &(self._ckernelParamsData[data_idx])
+                        (<void_ptr*>self._ckernelParams[idx])[0] = value.value if isinstance(value, (_ctypes.c_void_p)) else value
+                        data_idx += sizeof(void_ptr)
+                    elif callable(getattr(value, 'getPtr', None)):
+                        self._ckernelParams[idx] = &(self._ckernelParamsData[data_idx])
+                        (<void_ptr*>self._ckernelParams[idx])[0] = value.getPtr()
+                        data_idx += sizeof(void_ptr)
+                    else:
+                        raise TypeError("Provided argument is of type {} but expected Type {}, {} or CUDA Binding structure with getPtr() attribute".format(type(value), type(int), type(_ctypes.c_void_p)))
+                else:
+                    raise TypeError("Unsupported type: " + str(type(ctype)))
+                idx += 1
+        else:
+            raise TypeError("Argument 'kernelParams' is not a valid type: tuple[tuple[Any, ...], tuple[Any, ...]] or PyObject implimenting Buffer Protocol or Int")
+
+    def __dealloc__(self):
+        if self._pyobj_acquired is True:
+            PyBuffer_Release(&self._pybuffer)
+        if self._malloc_list_created is True:
+            free(self._ckernelParams)
+            free(self._ckernelParamsData)
+
+    @property
+    def ckernelParams(self):
+        return <void_ptr>self._ckernelParams
+
+cdef class _HelperInputVoidPtr:
+    def __cinit__(self, ptr):
+        self._cptr = _helper_input_void_ptr(ptr, &self._helper)
+
+    def __dealloc__(self):
+        _helper_input_void_ptr_free(&self._helper)
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr 
+
+
+cdef void * _helper_input_void_ptr(ptr, _HelperInputVoidPtrStruct *helper):
+    helper[0]._pybuffer.buf = NULL
+    try:
+        return <void *><void_ptr>ptr        
+    except:
+        if ptr is None:
+            return NULL
+        elif PyObject_CheckBuffer(ptr):
+            # Easy run, get address from Python Buffer Protocol
+            err_buffer = PyObject_GetBuffer(ptr, &helper[0]._pybuffer, PyBUF_SIMPLE | PyBUF_ANY_CONTIGUOUS)
+            if err_buffer == -1:
+                raise RuntimeError("Failed to retrieve buffer through Buffer Protocol")
+            return <void*><void_ptr>(helper[0]._pybuffer.buf)
+        else:
+            raise TypeError("Provided argument is of type {} but expected Type {}, {} or object with Buffer Protocol".format(type(ptr), type(None), type(int)))
+
+
+cdef class _HelperCUmemPool_attribute:
+    def __cinit__(self, attr, init_value, is_getter=False):
+        self._is_getter = is_getter
+        self._attr = attr.value
+        if self._attr in (cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES,
+                          cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC,
+                          cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES,):
+            self._int_val = init_value
+            self._cptr = <void*>&self._int_val
+        elif self._attr in (cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_RELEASE_THRESHOLD,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_USED_MEM_CURRENT,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_USED_MEM_HIGH,):
+            if self._is_getter:
+                self._cuuint64_t_val = _driver["cuuint64_t"]()
+                self._cptr = <void*><void_ptr>self._cuuint64_t_val.getPtr()
+            else:
+                self._cptr = <void*><void_ptr>init_value.getPtr()
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+    def pyObj(self):
+        assert(self._is_getter == True)
+        if self._attr in (cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES,
+                          cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC,
+                          cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES,):
+            return self._int_val
+        elif self._attr in (cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_RELEASE_THRESHOLD,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_USED_MEM_CURRENT,
+                            cydriver.CUmemPool_attribute_enum.CU_MEMPOOL_ATTR_USED_MEM_HIGH,):
+            return self._cuuint64_t_val
+        else:
+            raise TypeError('Unsupported attribute value: {}'.format(self._attr))
+
+cdef class _HelperCUmem_range_attribute:
+    def __cinit__(self, attr, data_size):
+        self._data_size = data_size
+        self._attr = attr.value
+        if self._attr in (cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY,
+                          cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION,
+                          cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION,):
+            self._cptr = <void*>&self._int_val
+        elif self._attr in (cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY,):
+            self._cptr = _callocWrapper(1, self._data_size)
+            self._int_val_list = <int*>self._cptr
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        if self._attr in (cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY,):
+            free(self._cptr)
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+    def pyObj(self):
+        if self._attr in (cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY,
+                          cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION,
+                          cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION,):
+            return self._int_val
+        elif self._attr in (cydriver.CUmem_range_attribute_enum.CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY,):
+            return [self._int_val_list[idx] for idx in range(int(self._data_size/4))]
+        else:
+            raise TypeError('Unsupported attribute value: {}'.format(self._attr))
+
+cdef class _HelperCUpointer_attribute:
+    def __cinit__(self, attr, init_value, is_getter=False):
+        self._is_getter = is_getter
+        self._attr = attr.value
+        if self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_CONTEXT,):
+            if self._is_getter:
+                self._ctx = _driver["CUcontext"]()
+                self._cptr = <void*><void_ptr>self._ctx.getPtr()
+            else:
+                self._cptr = <void*><void_ptr>init_value.getPtr()
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_MEMORY_TYPE,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_ACCESS_FLAGS,):
+            self._uint = init_value
+            self._cptr = <void*>&self._uint
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL,):
+            self._int = init_value
+            self._cptr = <void*>&self._int
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_DEVICE_POINTER,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,):
+            if self._is_getter:
+                self._devptr = _driver["CUdeviceptr"]()
+                self._cptr = <void*><void_ptr>self._devptr.getPtr()
+            else:
+                self._cptr = <void*><void_ptr>init_value.getPtr()
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_HOST_POINTER,):
+            self._void = <void**><void_ptr>init_value
+            self._cptr = <void*>&self._void
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_P2P_TOKENS,):
+            if self._is_getter:
+                self._token = _driver["CUDA_POINTER_ATTRIBUTE_P2P_TOKENS"]()
+                self._cptr = <void*><void_ptr>self._token.getPtr()
+            else:
+                self._cptr = <void*><void_ptr>init_value.getPtr()
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_SYNC_MEMOPS,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_IS_MANAGED,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_MAPPED,):
+            self._bool = init_value
+            self._cptr = <void*>&self._bool
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_BUFFER_ID,):
+            self._ull = init_value
+            self._cptr = <void*>&self._ull
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_RANGE_SIZE,):
+            self._size = init_value
+            self._cptr = <void*>&self._size
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE,):
+            if self._is_getter:
+                self._mempool = _driver["CUmemoryPool"]()
+                self._cptr = <void*><void_ptr>self._mempool.getPtr()
+            else:
+                self._cptr = <void*><void_ptr>init_value.getPtr()
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+    def pyObj(self):
+        assert(self._is_getter == True)
+        if self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_CONTEXT,):
+            return self._ctx
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_MEMORY_TYPE,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_ACCESS_FLAGS,):
+            return self._uint
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_DEVICE_POINTER,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,):
+            return self._devptr
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_HOST_POINTER,):
+            return <void_ptr>self._void
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_P2P_TOKENS,):
+            return self._token
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_SYNC_MEMOPS,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_IS_MANAGED,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE,
+                            cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_MAPPED,):
+            return self._bool
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_BUFFER_ID,):
+            return self._ull
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_RANGE_SIZE,):
+            return self._size
+        elif self._attr in (cydriver.CUpointer_attribute_enum.CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE,):
+            return self._mempool
+        else:
+            raise TypeError('Unsupported attribute value: {}'.format(self._attr))
+
+cdef class _HelperCUgraphMem_attribute:
+    def __cinit__(self, attr, init_value, is_getter=False):
+        self._is_getter = is_getter
+        self._attr = attr.value
+        if self._attr in (cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT,
+                          cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_USED_MEM_HIGH,
+                          cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT,
+                          cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH,):
+            if self._is_getter:
+                self._cuuint64_t_val = _driver["cuuint64_t"]()
+                self._cptr = <void*><void_ptr>self._cuuint64_t_val.getPtr()
+            else:
+                self._cptr = <void*><void_ptr>init_value.getPtr()
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+    def pyObj(self):
+        assert(self._is_getter == True)
+        if self._attr in (cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT,
+                          cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_USED_MEM_HIGH,
+                          cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT,
+                          cydriver.CUgraphMem_attribute_enum.CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH,):
+            return self._cuuint64_t_val
+        else:
+            raise TypeError('Unsupported attribute value: {}'.format(self._attr))
+
+cdef class _HelperCUjit_option:
+    def __cinit__(self, attr, init_value):
+        self._attr = attr.value
+        if self._attr in (cydriver.CUjit_option_enum.CU_JIT_MAX_REGISTERS,
+                          cydriver.CUjit_option_enum.CU_JIT_THREADS_PER_BLOCK,
+                          cydriver.CUjit_option_enum.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
+                          cydriver.CUjit_option_enum.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
+                          cydriver.CUjit_option_enum.CU_JIT_OPTIMIZATION_LEVEL,
+                          cydriver.CUjit_option_enum.CU_JIT_GLOBAL_SYMBOL_COUNT,
+                          cydriver.CUjit_option_enum.CU_JIT_TARGET_FROM_CUCONTEXT,
+                          cydriver.CUjit_option_enum.CU_JIT_REFERENCED_KERNEL_COUNT,
+                          cydriver.CUjit_option_enum.CU_JIT_REFERENCED_VARIABLE_COUNT,
+                          cydriver.CUjit_option_enum.CU_JIT_MIN_CTA_PER_SM,
+                          cydriver.CUjit_option_enum.CU_JIT_SPLIT_COMPILE,):
+            self._uint = init_value
+            self._cptr = <void*><void_ptr>self._uint
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_WALL_TIME,):
+            self._float = init_value
+            self._cptr = <void*><void_ptr>self._float
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_INFO_LOG_BUFFER,
+                            cydriver.CUjit_option_enum.CU_JIT_ERROR_LOG_BUFFER):
+            self._charstar = init_value
+            self._cptr = <void*><void_ptr>self._charstar
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_TARGET,):
+            self._target = init_value.value
+            self._cptr = <void*><void_ptr>self._target
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_FALLBACK_STRATEGY,):
+            self._fallback = init_value.value
+            self._cptr = <void*><void_ptr>self._fallback
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_GENERATE_DEBUG_INFO,
+                            cydriver.CUjit_option_enum.CU_JIT_LOG_VERBOSE,
+                            cydriver.CUjit_option_enum.CU_JIT_GENERATE_LINE_INFO,
+                            cydriver.CUjit_option_enum.CU_JIT_LTO,
+                            cydriver.CUjit_option_enum.CU_JIT_FTZ,
+                            cydriver.CUjit_option_enum.CU_JIT_PREC_DIV,
+                            cydriver.CUjit_option_enum.CU_JIT_PREC_SQRT,
+                            cydriver.CUjit_option_enum.CU_JIT_FMA,
+                            cydriver.CUjit_option_enum.CU_JIT_OPTIMIZE_UNUSED_DEVICE_VARIABLES,):
+            self._int = init_value
+            self._cptr = <void*><void_ptr>self._int
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_CACHE_MODE,):
+            self._cacheMode = init_value.value
+            self._cptr = <void*><void_ptr>self._cacheMode
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_GLOBAL_SYMBOL_NAMES,
+                            cydriver.CUjit_option_enum.CU_JIT_REFERENCED_KERNEL_NAMES,
+                            cydriver.CUjit_option_enum.CU_JIT_REFERENCED_VARIABLE_NAMES,):
+            self._charstarstar = init_value
+            self._cptr = <void*>&self._charstarstar[0]
+        elif self._attr in (cydriver.CUjit_option_enum.CU_JIT_GLOBAL_SYMBOL_ADDRESSES,):
+            pylist = [_HelperInputVoidPtr(val) for val in init_value]
+            self._voidstarstar = _InputVoidPtrPtrHelper(pylist)
+            self._cptr = <void*><void_ptr>self._voidstarstar.cptr
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+cdef class _HelperCudaJitOption:
+    def __cinit__(self, attr, init_value):
+        self._attr = attr.value
+        if self._attr in (cyruntime.cudaJitOption.cudaJitMaxRegisters,
+                          cyruntime.cudaJitOption.cudaJitThreadsPerBlock,
+                          cyruntime.cudaJitOption.cudaJitInfoLogBufferSizeBytes,
+                          cyruntime.cudaJitOption.cudaJitErrorLogBufferSizeBytes,
+                          cyruntime.cudaJitOption.cudaJitOptimizationLevel,
+                          cyruntime.cudaJitOption.cudaJitMinCtaPerSm,):
+            self._uint = init_value
+            self._cptr = <void*><void_ptr>self._uint
+        elif self._attr in (cyruntime.cudaJitOption.cudaJitWallTime,):
+            self._float = init_value
+            self._cptr = <void*><void_ptr>self._float
+        elif self._attr in (cyruntime.cudaJitOption.cudaJitInfoLogBuffer,
+                            cyruntime.cudaJitOption.cudaJitErrorLogBuffer):
+            self._charstar = init_value
+            self._cptr = <void*><void_ptr>self._charstar
+        elif self._attr in (cyruntime.cudaJitOption.cudaJitFallbackStrategy,):
+            self._fallback = init_value.value
+            self._cptr = <void*><void_ptr>self._fallback
+        elif self._attr in (cyruntime.cudaJitOption.cudaJitGenerateDebugInfo,
+                            cyruntime.cudaJitOption.cudaJitLogVerbose,
+                            cyruntime.cudaJitOption.cudaJitGenerateLineInfo,
+                            cyruntime.cudaJitOption.cudaJitPositionIndependentCode,
+                            cyruntime.cudaJitOption.cudaJitMaxThreadsPerBlock,
+                            cyruntime.cudaJitOption.cudaJitOverrideDirectiveValues,):
+            self._int = init_value
+            self._cptr = <void*><void_ptr>self._int
+        elif self._attr in (cyruntime.cudaJitOption.cudaJitCacheMode,):
+            self._cacheMode = init_value.value
+            self._cptr = <void*><void_ptr>self._cacheMode
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+cdef class _HelperCUlibraryOption:
+    def __cinit__(self, attr, init_value):
+        self._attr = attr.value
+        if False:
+            pass
+        elif self._attr in (cydriver.CUlibraryOption_enum.CU_LIBRARY_HOST_UNIVERSAL_FUNCTION_AND_DATA_TABLE,):
+            self._cptr = <void*><void_ptr>init_value.getPtr()
+        elif self._attr in (cydriver.CUlibraryOption_enum.CU_LIBRARY_BINARY_IS_PRESERVED,):
+            self._uint = init_value
+            self._cptr = <void*><void_ptr>self._uint
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+cdef class _HelperCudaLibraryOption:
+    def __cinit__(self, attr, init_value):
+        self._attr = attr.value
+        if False:
+            pass
+        elif self._attr in (cyruntime.cudaLibraryOption.cudaLibraryHostUniversalFunctionAndDataTable,):
+            self._cptr = <void*><void_ptr>init_value.getPtr()
+        elif self._attr in (cyruntime.cudaLibraryOption.cudaLibraryBinaryIsPreserved,):
+            self._uint = init_value
+            self._cptr = <void*><void_ptr>self._uint
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+cdef class _HelperCUmemAllocationHandleType:
+    def __cinit__(self, attr):
+        self._type = attr.value
+        if False:
+            pass
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_NONE,):
+            self._cptr = <void*>&self._int
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR,):
+            self._cptr = <void*>&self._int
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_WIN32,):
+            self._cptr = <void*>&self._handle
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_WIN32_KMT,):
+            self._cptr = <void*>&self._d3dkmt_handle
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_FABRIC,):
+            self._mem_fabric_handle = _driver["CUmemFabricHandle"]()
+            self._cptr = <void*><void_ptr>self._mem_fabric_handle.getPtr()
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+    def pyObj(self):
+        if False:
+            pass
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_NONE,):
+            return self._int
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR,):
+            return self._int
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_WIN32,):
+            return <void_ptr>self._handle
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_WIN32_KMT,):
+            return self._d3dkmt_handle
+        elif self._type in (cydriver.CUmemAllocationHandleType_enum.CU_MEM_HANDLE_TYPE_FABRIC,):
+            return self._mem_fabric_handle
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(self._type))
+
+cdef class _InputVoidPtrPtrHelper:
+    def __cinit__(self, lst):
+        # Hold onto references to the original buffers so they
+        # won't be free'd behind our back
+        self._references = lst
+        self._cptr = <void**>_callocWrapper(len(lst), sizeof(void*))
+        for idx in range(len(lst)):
+            self._cptr[idx] = <void*><void_ptr>lst[idx].cptr
+
+    def __dealloc__(self):
+        free(self._cptr)
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+cdef class _HelperCUcoredumpSettings:
+    def __cinit__(self, attr, init_value, is_getter=False):
+        self._is_getter = is_getter
+        self._attrib = attr.value
+        if self._attrib in (cydriver.CUcoredumpSettings_enum.CU_COREDUMP_FILE,
+                          cydriver.CUcoredumpSettings_enum.CU_COREDUMP_PIPE,):
+            if self._is_getter:
+                self._charstar = <char*>_callocWrapper(1024, 1)
+                self._cptr = <void*><void_ptr>self._charstar
+                self._size = 1024
+            else:
+                self._charstar = init_value
+                self._cptr = <void*><void_ptr>self._charstar
+                self._size = len(init_value)
+        elif self._attrib in (cydriver.CUcoredumpSettings_enum.CU_COREDUMP_ENABLE_ON_EXCEPTION,
+                            cydriver.CUcoredumpSettings_enum.CU_COREDUMP_TRIGGER_HOST,
+                            cydriver.CUcoredumpSettings_enum.CU_COREDUMP_LIGHTWEIGHT,
+                            cydriver.CUcoredumpSettings_enum.CU_COREDUMP_ENABLE_USER_TRIGGER,):
+            if self._is_getter == False:
+                self._bool = init_value
+            
+            self._cptr = <void*>&self._bool
+            self._size = 1
+        else:
+            raise TypeError('Unsupported attribute: {}'.format(attr.name))
+
+    def __dealloc__(self):
+        pass
+
+    @property
+    def cptr(self):
+        return <void_ptr>self._cptr
+
+    def size(self):
+        return self._size
+
+    def pyObj(self):
+        assert(self._is_getter == True)
+        if self._attrib in (cydriver.CUcoredumpSettings_enum.CU_COREDUMP_FILE,
+                          cydriver.CUcoredumpSettings_enum.CU_COREDUMP_PIPE,):
+            return self._charstar
+        elif self._attrib in (cydriver.CUcoredumpSettings_enum.CU_COREDUMP_ENABLE_ON_EXCEPTION,
+                            cydriver.CUcoredumpSettings_enum.CU_COREDUMP_TRIGGER_HOST,
+                            cydriver.CUcoredumpSettings_enum.CU_COREDUMP_LIGHTWEIGHT,
+                            cydriver.CUcoredumpSettings_enum.CU_COREDUMP_ENABLE_USER_TRIGGER,):
+            return self._bool
+        else:
+            raise TypeError('Unsupported attribute value: {}'.format(self._attrib))
diff --git a/lib/python3.11/site-packages/cuda/bindings/_lib/windll.pxd b/lib/python3.11/site-packages/cuda/bindings/_lib/windll.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7b190f35959d98d17da3a5c7d1433dc7a8c17cc9
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_lib/windll.pxd
@@ -0,0 +1,45 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+from libc.stddef cimport wchar_t
+from libc.stdint cimport uintptr_t
+from cpython cimport PyUnicode_AsWideCharString, PyMem_Free
+
+cdef extern from "windows.h" nogil:
+    ctypedef void* HMODULE
+    ctypedef void* HANDLE
+    ctypedef void* FARPROC
+    ctypedef unsigned long DWORD
+    ctypedef const wchar_t *LPCWSTR
+    ctypedef const char *LPCSTR
+    ctypedef int BOOL
+
+    cdef DWORD LOAD_LIBRARY_SEARCH_SYSTEM32 = 0x00000800
+
+    HMODULE _LoadLibraryExW "LoadLibraryExW"(
+        LPCWSTR lpLibFileName,
+        HANDLE hFile,
+        DWORD dwFlags
+    )
+
+    FARPROC _GetProcAddress "GetProcAddress"(HMODULE hModule, LPCSTR lpProcName)
+
+    BOOL _FreeLibrary "FreeLibrary"(HMODULE hLibModule)
+
+cdef inline uintptr_t LoadLibraryExW(str path, HANDLE hFile, DWORD dwFlags):
+    cdef uintptr_t result
+    cdef wchar_t* wpath = PyUnicode_AsWideCharString(path, NULL)
+    with nogil:
+        result = <uintptr_t>_LoadLibraryExW(
+            wpath,
+            hFile,
+            dwFlags
+        )
+    PyMem_Free(wpath)
+    return result
+
+cdef inline FARPROC GetProcAddress(uintptr_t hModule, const char* lpProcName) nogil:
+    return _GetProcAddress(<HMODULE>hModule, lpProcName)
+
+cdef inline BOOL FreeLibrary(uintptr_t hLibModule) nogil:
+    return _FreeLibrary(<HMODULE>hLibModule)
diff --git a/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__init__.py b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6b171f9cd3721daade7339515b5c0ed8048d278
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__init__.py
@@ -0,0 +1,6 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+
+# This package contains test helper utilities that may also be useful for other libraries outside of `cuda.bindings`,
+# such as `cuda.core`. These utilities are not part of the public API of `cuda.bindings` and may change without notice.
diff --git a/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6e24be32b4903fa8b39b997da2c70fc11be9f525
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__pycache__/arch_check.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__pycache__/arch_check.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1026e25ab549f96e45d1dde6b3f782f33e02c0a8
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/__pycache__/arch_check.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/_test_helpers/arch_check.py b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/arch_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b1e5e23a72bce3e698cf42ce87494d8e77f4555
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/_test_helpers/arch_check.py
@@ -0,0 +1,68 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+
+from contextlib import contextmanager
+from functools import cache
+
+import pytest
+
+from cuda.bindings import nvml
+
+
+@cache
+def hardware_supports_nvml():
+    """
+    Tries to call the simplest NVML API possible to see if just the basics
+    works.  If not we are probably on one of the platforms where NVML is not
+    supported at all (e.g. Jetson Orin).
+    """
+    nvml.init_v2()
+    try:
+        nvml.system_get_driver_branch()
+    except (nvml.NotSupportedError, nvml.UnknownError):
+        return False
+    else:
+        return True
+    finally:
+        nvml.shutdown()
+
+
+@contextmanager
+def unsupported_before(device: int, expected_device_arch: nvml.DeviceArch | str | None):
+    device_arch = nvml.device_get_architecture(device)
+
+    if isinstance(expected_device_arch, nvml.DeviceArch):
+        expected_device_arch_int = int(expected_device_arch)
+    elif expected_device_arch == "FERMI":
+        expected_device_arch_int = 1
+    else:
+        expected_device_arch_int = 0
+
+    if expected_device_arch is None or expected_device_arch == "HAS_INFOROM" or device_arch == nvml.DeviceArch.UNKNOWN:
+        # In this case, we don't /know/ if it will fail, but we are ok if it
+        # does or does not.
+
+        # TODO: There are APIs that are documented as supported only if the
+        # device has an InfoROM, but I couldn't find a way to detect that.  For
+        # now, they are just handled as "possibly failing".
+
+        try:
+            yield
+        except nvml.NotSupportedError:
+            # The API call raised NotSupportedError, so we skip the test, but
+            # don't fail it
+            pytest.skip(
+                f"Unsupported call for device architecture {nvml.DeviceArch(device_arch).name} "
+                f"on device '{nvml.device_get_name(device)}'"
+            )
+        # If the API call worked, just continue
+    elif int(device_arch) < expected_device_arch_int:
+        # In this case, we /know/ if will fail, and we want to assert that it does.
+        with pytest.raises(nvml.NotSupportedError):
+            yield
+        # The above call was unsupported, so the rest of the test is skipped
+        pytest.skip(f"Unsupported before {expected_device_arch.name}, got {nvml.device_get_name(device)}")
+    else:
+        # In this case, we /know/ it should work, and if it fails, the test should fail.
+        yield
diff --git a/lib/python3.11/site-packages/cuda/bindings/cydriver.pxd b/lib/python3.11/site-packages/cuda/bindings/cydriver.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0b4f3843dbf1d7d899ab59f6b7c15a1280b82646
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cydriver.pxd
@@ -0,0 +1,3939 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "cuda.h":
+
+    ctypedef uint32_t cuuint32_t
+
+    ctypedef uint64_t cuuint64_t
+
+    ctypedef unsigned long long CUdeviceptr_v2
+
+    ctypedef CUdeviceptr_v2 CUdeviceptr
+
+    ctypedef int CUdevice_v1
+
+    ctypedef CUdevice_v1 CUdevice
+
+    cdef struct CUctx_st:
+        pass
+    ctypedef CUctx_st* CUcontext
+
+    cdef struct CUmod_st:
+        pass
+    ctypedef CUmod_st* CUmodule
+
+    cdef struct CUfunc_st:
+        pass
+    ctypedef CUfunc_st* CUfunction
+
+    cdef struct CUlib_st:
+        pass
+    ctypedef CUlib_st* CUlibrary
+
+    cdef struct CUkern_st:
+        pass
+    ctypedef CUkern_st* CUkernel
+
+    cdef struct CUarray_st:
+        pass
+    ctypedef CUarray_st* CUarray
+
+    cdef struct CUmipmappedArray_st:
+        pass
+    ctypedef CUmipmappedArray_st* CUmipmappedArray
+
+    cdef struct CUtexref_st:
+        pass
+    ctypedef CUtexref_st* CUtexref
+
+    cdef struct CUsurfref_st:
+        pass
+    ctypedef CUsurfref_st* CUsurfref
+
+    cdef struct CUevent_st:
+        pass
+    ctypedef CUevent_st* CUevent
+
+    cdef struct CUstream_st:
+        pass
+    ctypedef CUstream_st* CUstream
+
+    cdef struct CUgraphicsResource_st:
+        pass
+    ctypedef CUgraphicsResource_st* CUgraphicsResource
+
+    ctypedef unsigned long long CUtexObject_v1
+
+    ctypedef CUtexObject_v1 CUtexObject
+
+    ctypedef unsigned long long CUsurfObject_v1
+
+    ctypedef CUsurfObject_v1 CUsurfObject
+
+    cdef struct CUextMemory_st:
+        pass
+    ctypedef CUextMemory_st* CUexternalMemory
+
+    cdef struct CUextSemaphore_st:
+        pass
+    ctypedef CUextSemaphore_st* CUexternalSemaphore
+
+    cdef struct CUgraph_st:
+        pass
+    ctypedef CUgraph_st* CUgraph
+
+    cdef struct CUgraphNode_st:
+        pass
+    ctypedef CUgraphNode_st* CUgraphNode
+
+    cdef struct CUgraphExec_st:
+        pass
+    ctypedef CUgraphExec_st* CUgraphExec
+
+    cdef struct CUmemPoolHandle_st:
+        pass
+    ctypedef CUmemPoolHandle_st* CUmemoryPool
+
+    cdef struct CUuserObject_st:
+        pass
+    ctypedef CUuserObject_st* CUuserObject
+
+    ctypedef cuuint64_t CUgraphConditionalHandle
+
+    cdef struct CUgraphDeviceUpdatableNode_st:
+        pass
+    ctypedef CUgraphDeviceUpdatableNode_st* CUgraphDeviceNode
+
+    cdef struct CUasyncCallbackEntry_st:
+        pass
+    ctypedef CUasyncCallbackEntry_st* CUasyncCallbackHandle
+
+    cdef struct CUgreenCtx_st:
+        pass
+    ctypedef CUgreenCtx_st* CUgreenCtx
+
+    cdef struct CUuuid_st:
+        char bytes[16]
+
+    ctypedef CUuuid_st CUuuid
+
+    cdef struct CUmemFabricHandle_st:
+        unsigned char data[64]
+
+    ctypedef CUmemFabricHandle_st CUmemFabricHandle_v1
+
+    ctypedef CUmemFabricHandle_v1 CUmemFabricHandle
+
+    cdef struct CUipcEventHandle_st:
+        char reserved[64]
+
+    ctypedef CUipcEventHandle_st CUipcEventHandle_v1
+
+    ctypedef CUipcEventHandle_v1 CUipcEventHandle
+
+    cdef struct CUipcMemHandle_st:
+        char reserved[64]
+
+    ctypedef CUipcMemHandle_st CUipcMemHandle_v1
+
+    ctypedef CUipcMemHandle_v1 CUipcMemHandle
+
+    cdef enum CUipcMem_flags_enum:
+        CU_IPC_MEM_LAZY_ENABLE_PEER_ACCESS = 1
+
+    ctypedef CUipcMem_flags_enum CUipcMem_flags
+
+    cdef enum CUmemAttach_flags_enum:
+        CU_MEM_ATTACH_GLOBAL = 1
+        CU_MEM_ATTACH_HOST = 2
+        CU_MEM_ATTACH_SINGLE = 4
+
+    ctypedef CUmemAttach_flags_enum CUmemAttach_flags
+
+    cdef enum CUctx_flags_enum:
+        CU_CTX_SCHED_AUTO = 0
+        CU_CTX_SCHED_SPIN = 1
+        CU_CTX_SCHED_YIELD = 2
+        CU_CTX_SCHED_BLOCKING_SYNC = 4
+        CU_CTX_BLOCKING_SYNC = 4
+        CU_CTX_SCHED_MASK = 7
+        CU_CTX_MAP_HOST = 8
+        CU_CTX_LMEM_RESIZE_TO_MAX = 16
+        CU_CTX_COREDUMP_ENABLE = 32
+        CU_CTX_USER_COREDUMP_ENABLE = 64
+        CU_CTX_SYNC_MEMOPS = 128
+        CU_CTX_FLAGS_MASK = 255
+
+    ctypedef CUctx_flags_enum CUctx_flags
+
+    cdef enum CUevent_sched_flags_enum:
+        CU_EVENT_SCHED_AUTO = 0
+        CU_EVENT_SCHED_SPIN = 1
+        CU_EVENT_SCHED_YIELD = 2
+        CU_EVENT_SCHED_BLOCKING_SYNC = 4
+
+    ctypedef CUevent_sched_flags_enum CUevent_sched_flags
+
+    cdef enum cl_event_flags_enum:
+        NVCL_EVENT_SCHED_AUTO = 0
+        NVCL_EVENT_SCHED_SPIN = 1
+        NVCL_EVENT_SCHED_YIELD = 2
+        NVCL_EVENT_SCHED_BLOCKING_SYNC = 4
+
+    ctypedef cl_event_flags_enum cl_event_flags
+
+    cdef enum cl_context_flags_enum:
+        NVCL_CTX_SCHED_AUTO = 0
+        NVCL_CTX_SCHED_SPIN = 1
+        NVCL_CTX_SCHED_YIELD = 2
+        NVCL_CTX_SCHED_BLOCKING_SYNC = 4
+
+    ctypedef cl_context_flags_enum cl_context_flags
+
+    cdef enum CUhostTaskSyncMode_enum:
+        CU_HOST_TASK_BLOCKING = 0
+        CU_HOST_TASK_SPINWAIT = 1
+
+    ctypedef CUhostTaskSyncMode_enum CUhostTaskSyncMode
+
+    cdef enum CUstream_flags_enum:
+        CU_STREAM_DEFAULT = 0
+        CU_STREAM_NON_BLOCKING = 1
+
+    ctypedef CUstream_flags_enum CUstream_flags
+
+    cdef enum CUevent_flags_enum:
+        CU_EVENT_DEFAULT = 0
+        CU_EVENT_BLOCKING_SYNC = 1
+        CU_EVENT_DISABLE_TIMING = 2
+        CU_EVENT_INTERPROCESS = 4
+
+    ctypedef CUevent_flags_enum CUevent_flags
+
+    cdef enum CUevent_record_flags_enum:
+        CU_EVENT_RECORD_DEFAULT = 0
+        CU_EVENT_RECORD_EXTERNAL = 1
+
+    ctypedef CUevent_record_flags_enum CUevent_record_flags
+
+    cdef enum CUevent_wait_flags_enum:
+        CU_EVENT_WAIT_DEFAULT = 0
+        CU_EVENT_WAIT_EXTERNAL = 1
+
+    ctypedef CUevent_wait_flags_enum CUevent_wait_flags
+
+    cdef enum CUatomicOperation_enum:
+        CU_ATOMIC_OPERATION_INTEGER_ADD = 0
+        CU_ATOMIC_OPERATION_INTEGER_MIN = 1
+        CU_ATOMIC_OPERATION_INTEGER_MAX = 2
+        CU_ATOMIC_OPERATION_INTEGER_INCREMENT = 3
+        CU_ATOMIC_OPERATION_INTEGER_DECREMENT = 4
+        CU_ATOMIC_OPERATION_AND = 5
+        CU_ATOMIC_OPERATION_OR = 6
+        CU_ATOMIC_OPERATION_XOR = 7
+        CU_ATOMIC_OPERATION_EXCHANGE = 8
+        CU_ATOMIC_OPERATION_CAS = 9
+        CU_ATOMIC_OPERATION_FLOAT_ADD = 10
+        CU_ATOMIC_OPERATION_FLOAT_MIN = 11
+        CU_ATOMIC_OPERATION_FLOAT_MAX = 12
+        CU_ATOMIC_OPERATION_MAX = 13
+
+    ctypedef CUatomicOperation_enum CUatomicOperation
+
+    cdef enum CUatomicOperationCapability_enum:
+        CU_ATOMIC_CAPABILITY_SIGNED = 1
+        CU_ATOMIC_CAPABILITY_UNSIGNED = 2
+        CU_ATOMIC_CAPABILITY_REDUCTION = 4
+        CU_ATOMIC_CAPABILITY_SCALAR_32 = 8
+        CU_ATOMIC_CAPABILITY_SCALAR_64 = 16
+        CU_ATOMIC_CAPABILITY_SCALAR_128 = 32
+        CU_ATOMIC_CAPABILITY_VECTOR_32x4 = 64
+
+    ctypedef CUatomicOperationCapability_enum CUatomicOperationCapability
+
+    cdef enum CUstreamWaitValue_flags_enum:
+        CU_STREAM_WAIT_VALUE_GEQ = 0
+        CU_STREAM_WAIT_VALUE_EQ = 1
+        CU_STREAM_WAIT_VALUE_AND = 2
+        CU_STREAM_WAIT_VALUE_NOR = 3
+        CU_STREAM_WAIT_VALUE_FLUSH = 1073741824
+
+    ctypedef CUstreamWaitValue_flags_enum CUstreamWaitValue_flags
+
+    cdef enum CUstreamWriteValue_flags_enum:
+        CU_STREAM_WRITE_VALUE_DEFAULT = 0
+        CU_STREAM_WRITE_VALUE_NO_MEMORY_BARRIER = 1
+
+    ctypedef CUstreamWriteValue_flags_enum CUstreamWriteValue_flags
+
+    cdef enum CUstreamBatchMemOpType_enum:
+        CU_STREAM_MEM_OP_WAIT_VALUE_32 = 1
+        CU_STREAM_MEM_OP_WRITE_VALUE_32 = 2
+        CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES = 3
+        CU_STREAM_MEM_OP_WAIT_VALUE_64 = 4
+        CU_STREAM_MEM_OP_WRITE_VALUE_64 = 5
+        CU_STREAM_MEM_OP_BARRIER = 6
+        CU_STREAM_MEM_OP_ATOMIC_REDUCTION = 8
+
+    ctypedef CUstreamBatchMemOpType_enum CUstreamBatchMemOpType
+
+    cdef enum CUstreamMemoryBarrier_flags_enum:
+        CU_STREAM_MEMORY_BARRIER_TYPE_SYS = 0
+        CU_STREAM_MEMORY_BARRIER_TYPE_GPU = 1
+
+    ctypedef CUstreamMemoryBarrier_flags_enum CUstreamMemoryBarrier_flags
+
+    cdef enum CUstreamAtomicReductionOpType_enum:
+        CU_STREAM_ATOMIC_REDUCTION_OP_ADD = 0
+        CU_STREAM_ATOMIC_REDUCTION_OP_AND = 5
+        CU_STREAM_ATOMIC_REDUCTION_OP_OR = 6
+
+    ctypedef CUstreamAtomicReductionOpType_enum CUstreamAtomicReductionOpType
+
+    cdef enum CUstreamAtomicReductionDataType_enum:
+        CU_STREAM_ATOMIC_REDUCTION_UNSIGNED_32 = 14
+        CU_STREAM_ATOMIC_REDUCTION_UNSIGNED_64 = 22
+
+    ctypedef CUstreamAtomicReductionDataType_enum CUstreamAtomicReductionDataType
+
+    cdef struct CUstreamMemOpWaitValueParams_st:
+        CUstreamBatchMemOpType operation
+        CUdeviceptr address
+        cuuint32_t value
+        cuuint64_t value64
+        unsigned int flags
+        CUdeviceptr alias
+
+    cdef struct CUstreamMemOpWriteValueParams_st:
+        CUstreamBatchMemOpType operation
+        CUdeviceptr address
+        cuuint32_t value
+        cuuint64_t value64
+        unsigned int flags
+        CUdeviceptr alias
+
+    cdef struct CUstreamMemOpFlushRemoteWritesParams_st:
+        CUstreamBatchMemOpType operation
+        unsigned int flags
+
+    cdef struct CUstreamMemOpMemoryBarrierParams_st:
+        CUstreamBatchMemOpType operation
+        unsigned int flags
+
+    cdef struct CUstreamMemOpAtomicReductionParams_st:
+        CUstreamBatchMemOpType operation
+        unsigned int flags
+        CUstreamAtomicReductionOpType reductionOp
+        CUstreamAtomicReductionDataType dataType
+        CUdeviceptr address
+        cuuint64_t value
+        CUdeviceptr alias
+
+    cdef union CUstreamBatchMemOpParams_union:
+        CUstreamBatchMemOpType operation
+        CUstreamMemOpWaitValueParams_st waitValue
+        CUstreamMemOpWriteValueParams_st writeValue
+        CUstreamMemOpFlushRemoteWritesParams_st flushRemoteWrites
+        CUstreamMemOpMemoryBarrierParams_st memoryBarrier
+        CUstreamMemOpAtomicReductionParams_st atomicReduction
+        cuuint64_t pad[6]
+
+    ctypedef CUstreamBatchMemOpParams_union CUstreamBatchMemOpParams_v1
+
+    ctypedef CUstreamBatchMemOpParams_v1 CUstreamBatchMemOpParams
+
+    cdef struct CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st:
+        CUcontext ctx
+        unsigned int count
+        CUstreamBatchMemOpParams* paramArray
+        unsigned int flags
+
+    ctypedef CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st CUDA_BATCH_MEM_OP_NODE_PARAMS_v1
+
+    ctypedef CUDA_BATCH_MEM_OP_NODE_PARAMS_v1 CUDA_BATCH_MEM_OP_NODE_PARAMS
+
+    cdef struct CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st:
+        CUcontext ctx
+        unsigned int count
+        CUstreamBatchMemOpParams* paramArray
+        unsigned int flags
+
+    ctypedef CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st CUDA_BATCH_MEM_OP_NODE_PARAMS_v2
+
+    cdef enum CUoccupancy_flags_enum:
+        CU_OCCUPANCY_DEFAULT = 0
+        CU_OCCUPANCY_DISABLE_CACHING_OVERRIDE = 1
+
+    ctypedef CUoccupancy_flags_enum CUoccupancy_flags
+
+    cdef enum CUstreamUpdateCaptureDependencies_flags_enum:
+        CU_STREAM_ADD_CAPTURE_DEPENDENCIES = 0
+        CU_STREAM_SET_CAPTURE_DEPENDENCIES = 1
+
+    ctypedef CUstreamUpdateCaptureDependencies_flags_enum CUstreamUpdateCaptureDependencies_flags
+
+    cdef enum CUasyncNotificationType_enum:
+        CU_ASYNC_NOTIFICATION_TYPE_OVER_BUDGET = 1
+
+    ctypedef CUasyncNotificationType_enum CUasyncNotificationType
+
+    cdef struct anon_struct0:
+        unsigned long long bytesOverBudget
+
+    cdef union anon_union2:
+        anon_struct0 overBudget
+
+    cdef struct CUasyncNotificationInfo_st:
+        CUasyncNotificationType type
+        anon_union2 info
+
+    ctypedef CUasyncNotificationInfo_st CUasyncNotificationInfo
+
+    ctypedef void (*CUasyncCallback)(CUasyncNotificationInfo* info, void* userData, CUasyncCallbackHandle callback)
+
+    cdef enum CUarray_format_enum:
+        CU_AD_FORMAT_UNSIGNED_INT8 = 1
+        CU_AD_FORMAT_UNSIGNED_INT16 = 2
+        CU_AD_FORMAT_UNSIGNED_INT32 = 3
+        CU_AD_FORMAT_SIGNED_INT8 = 8
+        CU_AD_FORMAT_SIGNED_INT16 = 9
+        CU_AD_FORMAT_SIGNED_INT32 = 10
+        CU_AD_FORMAT_HALF = 16
+        CU_AD_FORMAT_FLOAT = 32
+        CU_AD_FORMAT_UNORM_INT_101010_2 = 80
+        CU_AD_FORMAT_UINT8_PACKED_422 = 81
+        CU_AD_FORMAT_UINT8_PACKED_444 = 82
+        CU_AD_FORMAT_UINT8_SEMIPLANAR_420 = 83
+        CU_AD_FORMAT_UINT16_SEMIPLANAR_420 = 84
+        CU_AD_FORMAT_UINT8_SEMIPLANAR_422 = 85
+        CU_AD_FORMAT_UINT16_SEMIPLANAR_422 = 86
+        CU_AD_FORMAT_UINT8_SEMIPLANAR_444 = 87
+        CU_AD_FORMAT_UINT16_SEMIPLANAR_444 = 88
+        CU_AD_FORMAT_UINT8_PLANAR_420 = 89
+        CU_AD_FORMAT_UINT16_PLANAR_420 = 90
+        CU_AD_FORMAT_UINT8_PLANAR_422 = 91
+        CU_AD_FORMAT_UINT16_PLANAR_422 = 92
+        CU_AD_FORMAT_UINT8_PLANAR_444 = 93
+        CU_AD_FORMAT_UINT16_PLANAR_444 = 94
+        CU_AD_FORMAT_BC1_UNORM = 145
+        CU_AD_FORMAT_BC1_UNORM_SRGB = 146
+        CU_AD_FORMAT_BC2_UNORM = 147
+        CU_AD_FORMAT_BC2_UNORM_SRGB = 148
+        CU_AD_FORMAT_BC3_UNORM = 149
+        CU_AD_FORMAT_BC3_UNORM_SRGB = 150
+        CU_AD_FORMAT_BC4_UNORM = 151
+        CU_AD_FORMAT_BC4_SNORM = 152
+        CU_AD_FORMAT_BC5_UNORM = 153
+        CU_AD_FORMAT_BC5_SNORM = 154
+        CU_AD_FORMAT_BC6H_UF16 = 155
+        CU_AD_FORMAT_BC6H_SF16 = 156
+        CU_AD_FORMAT_BC7_UNORM = 157
+        CU_AD_FORMAT_BC7_UNORM_SRGB = 158
+        CU_AD_FORMAT_P010 = 159
+        CU_AD_FORMAT_P016 = 161
+        CU_AD_FORMAT_NV16 = 162
+        CU_AD_FORMAT_P210 = 163
+        CU_AD_FORMAT_P216 = 164
+        CU_AD_FORMAT_YUY2 = 165
+        CU_AD_FORMAT_Y210 = 166
+        CU_AD_FORMAT_Y216 = 167
+        CU_AD_FORMAT_AYUV = 168
+        CU_AD_FORMAT_Y410 = 169
+        CU_AD_FORMAT_NV12 = 176
+        CU_AD_FORMAT_Y416 = 177
+        CU_AD_FORMAT_Y444_PLANAR8 = 178
+        CU_AD_FORMAT_Y444_PLANAR10 = 179
+        CU_AD_FORMAT_YUV444_8bit_SemiPlanar = 180
+        CU_AD_FORMAT_YUV444_16bit_SemiPlanar = 181
+        CU_AD_FORMAT_UNORM_INT8X1 = 192
+        CU_AD_FORMAT_UNORM_INT8X2 = 193
+        CU_AD_FORMAT_UNORM_INT8X4 = 194
+        CU_AD_FORMAT_UNORM_INT16X1 = 195
+        CU_AD_FORMAT_UNORM_INT16X2 = 196
+        CU_AD_FORMAT_UNORM_INT16X4 = 197
+        CU_AD_FORMAT_SNORM_INT8X1 = 198
+        CU_AD_FORMAT_SNORM_INT8X2 = 199
+        CU_AD_FORMAT_SNORM_INT8X4 = 200
+        CU_AD_FORMAT_SNORM_INT16X1 = 201
+        CU_AD_FORMAT_SNORM_INT16X2 = 202
+        CU_AD_FORMAT_SNORM_INT16X4 = 203
+        CU_AD_FORMAT_MAX = 2147483647
+
+    ctypedef CUarray_format_enum CUarray_format
+
+    cdef enum CUaddress_mode_enum:
+        CU_TR_ADDRESS_MODE_WRAP = 0
+        CU_TR_ADDRESS_MODE_CLAMP = 1
+        CU_TR_ADDRESS_MODE_MIRROR = 2
+        CU_TR_ADDRESS_MODE_BORDER = 3
+
+    ctypedef CUaddress_mode_enum CUaddress_mode
+
+    cdef enum CUfilter_mode_enum:
+        CU_TR_FILTER_MODE_POINT = 0
+        CU_TR_FILTER_MODE_LINEAR = 1
+
+    ctypedef CUfilter_mode_enum CUfilter_mode
+
+    cdef enum CUdevice_attribute_enum:
+        CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 1
+        CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X = 2
+        CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y = 3
+        CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z = 4
+        CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X = 5
+        CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y = 6
+        CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z = 7
+        CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK = 8
+        CU_DEVICE_ATTRIBUTE_SHARED_MEMORY_PER_BLOCK = 8
+        CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY = 9
+        CU_DEVICE_ATTRIBUTE_WARP_SIZE = 10
+        CU_DEVICE_ATTRIBUTE_MAX_PITCH = 11
+        CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK = 12
+        CU_DEVICE_ATTRIBUTE_REGISTERS_PER_BLOCK = 12
+        CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
+        CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT = 14
+        CU_DEVICE_ATTRIBUTE_GPU_OVERLAP = 15
+        CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16
+        CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT = 17
+        CU_DEVICE_ATTRIBUTE_INTEGRATED = 18
+        CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY = 19
+        CU_DEVICE_ATTRIBUTE_COMPUTE_MODE = 20
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH = 21
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH = 22
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT = 23
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH = 24
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT = 25
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH = 26
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH = 27
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_WIDTH = 27
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT = 28
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_HEIGHT = 28
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS = 29
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES = 29
+        CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT = 30
+        CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS = 31
+        CU_DEVICE_ATTRIBUTE_ECC_ENABLED = 32
+        CU_DEVICE_ATTRIBUTE_PCI_BUS_ID = 33
+        CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID = 34
+        CU_DEVICE_ATTRIBUTE_TCC_DRIVER = 35
+        CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
+        CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH = 37
+        CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE = 38
+        CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39
+        CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT = 40
+        CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING = 41
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH = 42
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS = 43
+        CU_DEVICE_ATTRIBUTE_CAN_TEX2D_GATHER = 44
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_WIDTH = 45
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_GATHER_HEIGHT = 46
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE = 47
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE = 48
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE = 49
+        CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID = 50
+        CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT = 51
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_WIDTH = 52
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH = 53
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS = 54
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_WIDTH = 55
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_WIDTH = 56
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_HEIGHT = 57
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_WIDTH = 58
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_HEIGHT = 59
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE3D_DEPTH = 60
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_WIDTH = 61
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE1D_LAYERED_LAYERS = 62
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_WIDTH = 63
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_HEIGHT = 64
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACE2D_LAYERED_LAYERS = 65
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_WIDTH = 66
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH = 67
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS = 68
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LINEAR_WIDTH = 69
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_WIDTH = 70
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_HEIGHT = 71
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LINEAR_PITCH = 72
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH = 73
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT = 74
+        CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR = 75
+        CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR = 76
+        CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH = 77
+        CU_DEVICE_ATTRIBUTE_STREAM_PRIORITIES_SUPPORTED = 78
+        CU_DEVICE_ATTRIBUTE_GLOBAL_L1_CACHE_SUPPORTED = 79
+        CU_DEVICE_ATTRIBUTE_LOCAL_L1_CACHE_SUPPORTED = 80
+        CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR = 81
+        CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_MULTIPROCESSOR = 82
+        CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY = 83
+        CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD = 84
+        CU_DEVICE_ATTRIBUTE_MULTI_GPU_BOARD_GROUP_ID = 85
+        CU_DEVICE_ATTRIBUTE_HOST_NATIVE_ATOMIC_SUPPORTED = 86
+        CU_DEVICE_ATTRIBUTE_SINGLE_TO_DOUBLE_PRECISION_PERF_RATIO = 87
+        CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS = 88
+        CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS = 89
+        CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED = 90
+        CU_DEVICE_ATTRIBUTE_CAN_USE_HOST_POINTER_FOR_REGISTERED_MEM = 91
+        CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_MEM_OPS_V1 = 92
+        CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS_V1 = 93
+        CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR_V1 = 94
+        CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH = 95
+        CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH = 96
+        CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN = 97
+        CU_DEVICE_ATTRIBUTE_CAN_FLUSH_REMOTE_WRITES = 98
+        CU_DEVICE_ATTRIBUTE_HOST_REGISTER_SUPPORTED = 99
+        CU_DEVICE_ATTRIBUTE_PAGEABLE_MEMORY_ACCESS_USES_HOST_PAGE_TABLES = 100
+        CU_DEVICE_ATTRIBUTE_DIRECT_MANAGED_MEM_ACCESS_FROM_HOST = 101
+        CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED = 102
+        CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED = 102
+        CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR_SUPPORTED = 103
+        CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_HANDLE_SUPPORTED = 104
+        CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_WIN32_KMT_HANDLE_SUPPORTED = 105
+        CU_DEVICE_ATTRIBUTE_MAX_BLOCKS_PER_MULTIPROCESSOR = 106
+        CU_DEVICE_ATTRIBUTE_GENERIC_COMPRESSION_SUPPORTED = 107
+        CU_DEVICE_ATTRIBUTE_MAX_PERSISTING_L2_CACHE_SIZE = 108
+        CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE = 109
+        CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WITH_CUDA_VMM_SUPPORTED = 110
+        CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK = 111
+        CU_DEVICE_ATTRIBUTE_SPARSE_CUDA_ARRAY_SUPPORTED = 112
+        CU_DEVICE_ATTRIBUTE_READ_ONLY_HOST_REGISTER_SUPPORTED = 113
+        CU_DEVICE_ATTRIBUTE_TIMELINE_SEMAPHORE_INTEROP_SUPPORTED = 114
+        CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED = 115
+        CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_SUPPORTED = 116
+        CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_FLUSH_WRITES_OPTIONS = 117
+        CU_DEVICE_ATTRIBUTE_GPU_DIRECT_RDMA_WRITES_ORDERING = 118
+        CU_DEVICE_ATTRIBUTE_MEMPOOL_SUPPORTED_HANDLE_TYPES = 119
+        CU_DEVICE_ATTRIBUTE_CLUSTER_LAUNCH = 120
+        CU_DEVICE_ATTRIBUTE_DEFERRED_MAPPING_CUDA_ARRAY_SUPPORTED = 121
+        CU_DEVICE_ATTRIBUTE_CAN_USE_64_BIT_STREAM_MEM_OPS = 122
+        CU_DEVICE_ATTRIBUTE_CAN_USE_STREAM_WAIT_VALUE_NOR = 123
+        CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED = 124
+        CU_DEVICE_ATTRIBUTE_IPC_EVENT_SUPPORTED = 125
+        CU_DEVICE_ATTRIBUTE_MEM_SYNC_DOMAIN_COUNT = 126
+        CU_DEVICE_ATTRIBUTE_TENSOR_MAP_ACCESS_SUPPORTED = 127
+        CU_DEVICE_ATTRIBUTE_HANDLE_TYPE_FABRIC_SUPPORTED = 128
+        CU_DEVICE_ATTRIBUTE_UNIFIED_FUNCTION_POINTERS = 129
+        CU_DEVICE_ATTRIBUTE_NUMA_CONFIG = 130
+        CU_DEVICE_ATTRIBUTE_NUMA_ID = 131
+        CU_DEVICE_ATTRIBUTE_MULTICAST_SUPPORTED = 132
+        CU_DEVICE_ATTRIBUTE_MPS_ENABLED = 133
+        CU_DEVICE_ATTRIBUTE_HOST_NUMA_ID = 134
+        CU_DEVICE_ATTRIBUTE_D3D12_CIG_SUPPORTED = 135
+        CU_DEVICE_ATTRIBUTE_MEM_DECOMPRESS_ALGORITHM_MASK = 136
+        CU_DEVICE_ATTRIBUTE_MEM_DECOMPRESS_MAXIMUM_LENGTH = 137
+        CU_DEVICE_ATTRIBUTE_VULKAN_CIG_SUPPORTED = 138
+        CU_DEVICE_ATTRIBUTE_GPU_PCI_DEVICE_ID = 139
+        CU_DEVICE_ATTRIBUTE_GPU_PCI_SUBSYSTEM_ID = 140
+        CU_DEVICE_ATTRIBUTE_HOST_NUMA_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED = 141
+        CU_DEVICE_ATTRIBUTE_HOST_NUMA_MEMORY_POOLS_SUPPORTED = 142
+        CU_DEVICE_ATTRIBUTE_HOST_NUMA_MULTINODE_IPC_SUPPORTED = 143
+        CU_DEVICE_ATTRIBUTE_HOST_MEMORY_POOLS_SUPPORTED = 144
+        CU_DEVICE_ATTRIBUTE_HOST_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED = 145
+        CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED = 146
+        CU_DEVICE_ATTRIBUTE_ONLY_PARTIAL_HOST_NATIVE_ATOMIC_SUPPORTED = 147
+        CU_DEVICE_ATTRIBUTE_ATOMIC_REDUCTION_SUPPORTED = 148
+        CU_DEVICE_ATTRIBUTE_MAX = 149
+
+    ctypedef CUdevice_attribute_enum CUdevice_attribute
+
+    cdef struct CUdevprop_st:
+        int maxThreadsPerBlock
+        int maxThreadsDim[3]
+        int maxGridSize[3]
+        int sharedMemPerBlock
+        int totalConstantMemory
+        int SIMDWidth
+        int memPitch
+        int regsPerBlock
+        int clockRate
+        int textureAlign
+
+    ctypedef CUdevprop_st CUdevprop_v1
+
+    ctypedef CUdevprop_v1 CUdevprop
+
+    cdef enum CUpointer_attribute_enum:
+        CU_POINTER_ATTRIBUTE_CONTEXT = 1
+        CU_POINTER_ATTRIBUTE_MEMORY_TYPE = 2
+        CU_POINTER_ATTRIBUTE_DEVICE_POINTER = 3
+        CU_POINTER_ATTRIBUTE_HOST_POINTER = 4
+        CU_POINTER_ATTRIBUTE_P2P_TOKENS = 5
+        CU_POINTER_ATTRIBUTE_SYNC_MEMOPS = 6
+        CU_POINTER_ATTRIBUTE_BUFFER_ID = 7
+        CU_POINTER_ATTRIBUTE_IS_MANAGED = 8
+        CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL = 9
+        CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE = 10
+        CU_POINTER_ATTRIBUTE_RANGE_START_ADDR = 11
+        CU_POINTER_ATTRIBUTE_RANGE_SIZE = 12
+        CU_POINTER_ATTRIBUTE_MAPPED = 13
+        CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES = 14
+        CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE = 15
+        CU_POINTER_ATTRIBUTE_ACCESS_FLAGS = 16
+        CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE = 17
+        CU_POINTER_ATTRIBUTE_MAPPING_SIZE = 18
+        CU_POINTER_ATTRIBUTE_MAPPING_BASE_ADDR = 19
+        CU_POINTER_ATTRIBUTE_MEMORY_BLOCK_ID = 20
+        CU_POINTER_ATTRIBUTE_IS_HW_DECOMPRESS_CAPABLE = 21
+
+    ctypedef CUpointer_attribute_enum CUpointer_attribute
+
+    cdef enum CUfunction_attribute_enum:
+        CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK = 0
+        CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES = 1
+        CU_FUNC_ATTRIBUTE_CONST_SIZE_BYTES = 2
+        CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES = 3
+        CU_FUNC_ATTRIBUTE_NUM_REGS = 4
+        CU_FUNC_ATTRIBUTE_PTX_VERSION = 5
+        CU_FUNC_ATTRIBUTE_BINARY_VERSION = 6
+        CU_FUNC_ATTRIBUTE_CACHE_MODE_CA = 7
+        CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES = 8
+        CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT = 9
+        CU_FUNC_ATTRIBUTE_CLUSTER_SIZE_MUST_BE_SET = 10
+        CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_WIDTH = 11
+        CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_HEIGHT = 12
+        CU_FUNC_ATTRIBUTE_REQUIRED_CLUSTER_DEPTH = 13
+        CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED = 14
+        CU_FUNC_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 15
+        CU_FUNC_ATTRIBUTE_MAX = 16
+
+    ctypedef CUfunction_attribute_enum CUfunction_attribute
+
+    cdef enum CUfunc_cache_enum:
+        CU_FUNC_CACHE_PREFER_NONE = 0
+        CU_FUNC_CACHE_PREFER_SHARED = 1
+        CU_FUNC_CACHE_PREFER_L1 = 2
+        CU_FUNC_CACHE_PREFER_EQUAL = 3
+
+    ctypedef CUfunc_cache_enum CUfunc_cache
+
+    cdef enum CUsharedconfig_enum:
+        CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE = 0
+        CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE = 1
+        CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE = 2
+
+    ctypedef CUsharedconfig_enum CUsharedconfig
+
+    cdef enum CUshared_carveout_enum:
+        CU_SHAREDMEM_CARVEOUT_DEFAULT = -1
+        CU_SHAREDMEM_CARVEOUT_MAX_L1 = 0
+        CU_SHAREDMEM_CARVEOUT_MAX_SHARED = 100
+
+    ctypedef CUshared_carveout_enum CUshared_carveout
+
+    cdef enum CUmemorytype_enum:
+        CU_MEMORYTYPE_HOST = 1
+        CU_MEMORYTYPE_DEVICE = 2
+        CU_MEMORYTYPE_ARRAY = 3
+        CU_MEMORYTYPE_UNIFIED = 4
+
+    ctypedef CUmemorytype_enum CUmemorytype
+
+    cdef enum CUcomputemode_enum:
+        CU_COMPUTEMODE_DEFAULT = 0
+        CU_COMPUTEMODE_PROHIBITED = 2
+        CU_COMPUTEMODE_EXCLUSIVE_PROCESS = 3
+
+    ctypedef CUcomputemode_enum CUcomputemode
+
+    cdef enum CUmem_advise_enum:
+        CU_MEM_ADVISE_SET_READ_MOSTLY = 1
+        CU_MEM_ADVISE_UNSET_READ_MOSTLY = 2
+        CU_MEM_ADVISE_SET_PREFERRED_LOCATION = 3
+        CU_MEM_ADVISE_UNSET_PREFERRED_LOCATION = 4
+        CU_MEM_ADVISE_SET_ACCESSED_BY = 5
+        CU_MEM_ADVISE_UNSET_ACCESSED_BY = 6
+
+    ctypedef CUmem_advise_enum CUmem_advise
+
+    cdef enum CUmem_range_attribute_enum:
+        CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY = 1
+        CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION = 2
+        CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY = 3
+        CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION = 4
+        CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_TYPE = 5
+        CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION_ID = 6
+        CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_TYPE = 7
+        CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION_ID = 8
+
+    ctypedef CUmem_range_attribute_enum CUmem_range_attribute
+
+    cdef enum CUjit_option_enum:
+        CU_JIT_MAX_REGISTERS = 0
+        CU_JIT_THREADS_PER_BLOCK = 1
+        CU_JIT_WALL_TIME = 2
+        CU_JIT_INFO_LOG_BUFFER = 3
+        CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES = 4
+        CU_JIT_ERROR_LOG_BUFFER = 5
+        CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES = 6
+        CU_JIT_OPTIMIZATION_LEVEL = 7
+        CU_JIT_TARGET_FROM_CUCONTEXT = 8
+        CU_JIT_TARGET = 9
+        CU_JIT_FALLBACK_STRATEGY = 10
+        CU_JIT_GENERATE_DEBUG_INFO = 11
+        CU_JIT_LOG_VERBOSE = 12
+        CU_JIT_GENERATE_LINE_INFO = 13
+        CU_JIT_CACHE_MODE = 14
+        CU_JIT_NEW_SM3X_OPT = 15
+        CU_JIT_FAST_COMPILE = 16
+        CU_JIT_GLOBAL_SYMBOL_NAMES = 17
+        CU_JIT_GLOBAL_SYMBOL_ADDRESSES = 18
+        CU_JIT_GLOBAL_SYMBOL_COUNT = 19
+        CU_JIT_LTO = 20
+        CU_JIT_FTZ = 21
+        CU_JIT_PREC_DIV = 22
+        CU_JIT_PREC_SQRT = 23
+        CU_JIT_FMA = 24
+        CU_JIT_REFERENCED_KERNEL_NAMES = 25
+        CU_JIT_REFERENCED_KERNEL_COUNT = 26
+        CU_JIT_REFERENCED_VARIABLE_NAMES = 27
+        CU_JIT_REFERENCED_VARIABLE_COUNT = 28
+        CU_JIT_OPTIMIZE_UNUSED_DEVICE_VARIABLES = 29
+        CU_JIT_POSITION_INDEPENDENT_CODE = 30
+        CU_JIT_MIN_CTA_PER_SM = 31
+        CU_JIT_MAX_THREADS_PER_BLOCK = 32
+        CU_JIT_OVERRIDE_DIRECTIVE_VALUES = 33
+        CU_JIT_SPLIT_COMPILE = 34
+        CU_JIT_BINARY_LOADER_THREAD_COUNT = 35
+        CU_JIT_NUM_OPTIONS = 36
+
+    ctypedef CUjit_option_enum CUjit_option
+
+    cdef enum CUjit_target_enum:
+        CU_TARGET_COMPUTE_30 = 30
+        CU_TARGET_COMPUTE_32 = 32
+        CU_TARGET_COMPUTE_35 = 35
+        CU_TARGET_COMPUTE_37 = 37
+        CU_TARGET_COMPUTE_50 = 50
+        CU_TARGET_COMPUTE_52 = 52
+        CU_TARGET_COMPUTE_53 = 53
+        CU_TARGET_COMPUTE_60 = 60
+        CU_TARGET_COMPUTE_61 = 61
+        CU_TARGET_COMPUTE_62 = 62
+        CU_TARGET_COMPUTE_70 = 70
+        CU_TARGET_COMPUTE_72 = 72
+        CU_TARGET_COMPUTE_75 = 75
+        CU_TARGET_COMPUTE_80 = 80
+        CU_TARGET_COMPUTE_86 = 86
+        CU_TARGET_COMPUTE_87 = 87
+        CU_TARGET_COMPUTE_89 = 89
+        CU_TARGET_COMPUTE_90 = 90
+        CU_TARGET_COMPUTE_100 = 100
+        CU_TARGET_COMPUTE_103 = 103
+        CU_TARGET_COMPUTE_110 = 110
+        CU_TARGET_COMPUTE_120 = 120
+        CU_TARGET_COMPUTE_121 = 121
+        CU_TARGET_COMPUTE_90A = 65626
+        CU_TARGET_COMPUTE_100A = 65636
+        CU_TARGET_COMPUTE_103A = 65639
+        CU_TARGET_COMPUTE_110A = 65646
+        CU_TARGET_COMPUTE_120A = 65656
+        CU_TARGET_COMPUTE_121A = 65657
+        CU_TARGET_COMPUTE_100F = 131172
+        CU_TARGET_COMPUTE_103F = 131175
+        CU_TARGET_COMPUTE_110F = 131182
+        CU_TARGET_COMPUTE_120F = 131192
+        CU_TARGET_COMPUTE_121F = 131193
+
+    ctypedef CUjit_target_enum CUjit_target
+
+    cdef enum CUjit_fallback_enum:
+        CU_PREFER_PTX = 0
+        CU_PREFER_BINARY = 1
+
+    ctypedef CUjit_fallback_enum CUjit_fallback
+
+    cdef enum CUjit_cacheMode_enum:
+        CU_JIT_CACHE_OPTION_NONE = 0
+        CU_JIT_CACHE_OPTION_CG = 1
+        CU_JIT_CACHE_OPTION_CA = 2
+
+    ctypedef CUjit_cacheMode_enum CUjit_cacheMode
+
+    cdef enum CUjitInputType_enum:
+        CU_JIT_INPUT_CUBIN = 0
+        CU_JIT_INPUT_PTX = 1
+        CU_JIT_INPUT_FATBINARY = 2
+        CU_JIT_INPUT_OBJECT = 3
+        CU_JIT_INPUT_LIBRARY = 4
+        CU_JIT_INPUT_NVVM = 5
+        CU_JIT_NUM_INPUT_TYPES = 6
+
+    ctypedef CUjitInputType_enum CUjitInputType
+
+    cdef struct CUlinkState_st:
+        pass
+    ctypedef CUlinkState_st* CUlinkState
+
+    cdef enum CUgraphicsRegisterFlags_enum:
+        CU_GRAPHICS_REGISTER_FLAGS_NONE = 0
+        CU_GRAPHICS_REGISTER_FLAGS_READ_ONLY = 1
+        CU_GRAPHICS_REGISTER_FLAGS_WRITE_DISCARD = 2
+        CU_GRAPHICS_REGISTER_FLAGS_SURFACE_LDST = 4
+        CU_GRAPHICS_REGISTER_FLAGS_TEXTURE_GATHER = 8
+
+    ctypedef CUgraphicsRegisterFlags_enum CUgraphicsRegisterFlags
+
+    cdef enum CUgraphicsMapResourceFlags_enum:
+        CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE = 0
+        CU_GRAPHICS_MAP_RESOURCE_FLAGS_READ_ONLY = 1
+        CU_GRAPHICS_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 2
+
+    ctypedef CUgraphicsMapResourceFlags_enum CUgraphicsMapResourceFlags
+
+    cdef enum CUarray_cubemap_face_enum:
+        CU_CUBEMAP_FACE_POSITIVE_X = 0
+        CU_CUBEMAP_FACE_NEGATIVE_X = 1
+        CU_CUBEMAP_FACE_POSITIVE_Y = 2
+        CU_CUBEMAP_FACE_NEGATIVE_Y = 3
+        CU_CUBEMAP_FACE_POSITIVE_Z = 4
+        CU_CUBEMAP_FACE_NEGATIVE_Z = 5
+
+    ctypedef CUarray_cubemap_face_enum CUarray_cubemap_face
+
+    cdef enum CUlimit_enum:
+        CU_LIMIT_STACK_SIZE = 0
+        CU_LIMIT_PRINTF_FIFO_SIZE = 1
+        CU_LIMIT_MALLOC_HEAP_SIZE = 2
+        CU_LIMIT_DEV_RUNTIME_SYNC_DEPTH = 3
+        CU_LIMIT_DEV_RUNTIME_PENDING_LAUNCH_COUNT = 4
+        CU_LIMIT_MAX_L2_FETCH_GRANULARITY = 5
+        CU_LIMIT_PERSISTING_L2_CACHE_SIZE = 6
+        CU_LIMIT_SHMEM_SIZE = 7
+        CU_LIMIT_CIG_ENABLED = 8
+        CU_LIMIT_CIG_SHMEM_FALLBACK_ENABLED = 9
+        CU_LIMIT_MAX = 10
+
+    ctypedef CUlimit_enum CUlimit
+
+    cdef enum CUresourcetype_enum:
+        CU_RESOURCE_TYPE_ARRAY = 0
+        CU_RESOURCE_TYPE_MIPMAPPED_ARRAY = 1
+        CU_RESOURCE_TYPE_LINEAR = 2
+        CU_RESOURCE_TYPE_PITCH2D = 3
+
+    ctypedef CUresourcetype_enum CUresourcetype
+
+    ctypedef void (*CUhostFn)(void* userData)
+
+    cdef enum CUaccessProperty_enum:
+        CU_ACCESS_PROPERTY_NORMAL = 0
+        CU_ACCESS_PROPERTY_STREAMING = 1
+        CU_ACCESS_PROPERTY_PERSISTING = 2
+
+    ctypedef CUaccessProperty_enum CUaccessProperty
+
+    cdef struct CUaccessPolicyWindow_st:
+        void* base_ptr
+        size_t num_bytes
+        float hitRatio
+        CUaccessProperty hitProp
+        CUaccessProperty missProp
+
+    ctypedef CUaccessPolicyWindow_st CUaccessPolicyWindow_v1
+
+    ctypedef CUaccessPolicyWindow_v1 CUaccessPolicyWindow
+
+    cdef struct CUDA_KERNEL_NODE_PARAMS_st:
+        CUfunction func
+        unsigned int gridDimX
+        unsigned int gridDimY
+        unsigned int gridDimZ
+        unsigned int blockDimX
+        unsigned int blockDimY
+        unsigned int blockDimZ
+        unsigned int sharedMemBytes
+        void** kernelParams
+        void** extra
+
+    ctypedef CUDA_KERNEL_NODE_PARAMS_st CUDA_KERNEL_NODE_PARAMS_v1
+
+    cdef struct CUDA_KERNEL_NODE_PARAMS_v2_st:
+        CUfunction func
+        unsigned int gridDimX
+        unsigned int gridDimY
+        unsigned int gridDimZ
+        unsigned int blockDimX
+        unsigned int blockDimY
+        unsigned int blockDimZ
+        unsigned int sharedMemBytes
+        void** kernelParams
+        void** extra
+        CUkernel kern
+        CUcontext ctx
+
+    ctypedef CUDA_KERNEL_NODE_PARAMS_v2_st CUDA_KERNEL_NODE_PARAMS_v2
+
+    ctypedef CUDA_KERNEL_NODE_PARAMS_v2 CUDA_KERNEL_NODE_PARAMS
+
+    cdef struct CUDA_KERNEL_NODE_PARAMS_v3_st:
+        CUfunction func
+        unsigned int gridDimX
+        unsigned int gridDimY
+        unsigned int gridDimZ
+        unsigned int blockDimX
+        unsigned int blockDimY
+        unsigned int blockDimZ
+        unsigned int sharedMemBytes
+        void** kernelParams
+        void** extra
+        CUkernel kern
+        CUcontext ctx
+
+    ctypedef CUDA_KERNEL_NODE_PARAMS_v3_st CUDA_KERNEL_NODE_PARAMS_v3
+
+    cdef struct CUDA_MEMSET_NODE_PARAMS_st:
+        CUdeviceptr dst
+        size_t pitch
+        unsigned int value
+        unsigned int elementSize
+        size_t width
+        size_t height
+
+    ctypedef CUDA_MEMSET_NODE_PARAMS_st CUDA_MEMSET_NODE_PARAMS_v1
+
+    ctypedef CUDA_MEMSET_NODE_PARAMS_v1 CUDA_MEMSET_NODE_PARAMS
+
+    cdef struct CUDA_MEMSET_NODE_PARAMS_v2_st:
+        CUdeviceptr dst
+        size_t pitch
+        unsigned int value
+        unsigned int elementSize
+        size_t width
+        size_t height
+        CUcontext ctx
+
+    ctypedef CUDA_MEMSET_NODE_PARAMS_v2_st CUDA_MEMSET_NODE_PARAMS_v2
+
+    cdef struct CUDA_HOST_NODE_PARAMS_st:
+        CUhostFn fn
+        void* userData
+
+    ctypedef CUDA_HOST_NODE_PARAMS_st CUDA_HOST_NODE_PARAMS_v1
+
+    ctypedef CUDA_HOST_NODE_PARAMS_v1 CUDA_HOST_NODE_PARAMS
+
+    cdef struct CUDA_HOST_NODE_PARAMS_v2_st:
+        CUhostFn fn
+        void* userData
+        unsigned int syncMode
+
+    ctypedef CUDA_HOST_NODE_PARAMS_v2_st CUDA_HOST_NODE_PARAMS_v2
+
+    cdef enum CUgraphConditionalNodeType_enum:
+        CU_GRAPH_COND_TYPE_IF = 0
+        CU_GRAPH_COND_TYPE_WHILE = 1
+        CU_GRAPH_COND_TYPE_SWITCH = 2
+
+    ctypedef CUgraphConditionalNodeType_enum CUgraphConditionalNodeType
+
+    cdef struct CUDA_CONDITIONAL_NODE_PARAMS:
+        CUgraphConditionalHandle handle
+        CUgraphConditionalNodeType type
+        unsigned int size
+        CUgraph* phGraph_out
+        CUcontext ctx
+
+    cdef enum CUgraphNodeType_enum:
+        CU_GRAPH_NODE_TYPE_KERNEL = 0
+        CU_GRAPH_NODE_TYPE_MEMCPY = 1
+        CU_GRAPH_NODE_TYPE_MEMSET = 2
+        CU_GRAPH_NODE_TYPE_HOST = 3
+        CU_GRAPH_NODE_TYPE_GRAPH = 4
+        CU_GRAPH_NODE_TYPE_EMPTY = 5
+        CU_GRAPH_NODE_TYPE_WAIT_EVENT = 6
+        CU_GRAPH_NODE_TYPE_EVENT_RECORD = 7
+        CU_GRAPH_NODE_TYPE_EXT_SEMAS_SIGNAL = 8
+        CU_GRAPH_NODE_TYPE_EXT_SEMAS_WAIT = 9
+        CU_GRAPH_NODE_TYPE_MEM_ALLOC = 10
+        CU_GRAPH_NODE_TYPE_MEM_FREE = 11
+        CU_GRAPH_NODE_TYPE_BATCH_MEM_OP = 12
+        CU_GRAPH_NODE_TYPE_CONDITIONAL = 13
+
+    ctypedef CUgraphNodeType_enum CUgraphNodeType
+
+    cdef enum CUgraphDependencyType_enum:
+        CU_GRAPH_DEPENDENCY_TYPE_DEFAULT = 0
+        CU_GRAPH_DEPENDENCY_TYPE_PROGRAMMATIC = 1
+
+    ctypedef CUgraphDependencyType_enum CUgraphDependencyType
+
+    cdef struct CUgraphEdgeData_st:
+        unsigned char from_port
+        unsigned char to_port
+        unsigned char type
+        unsigned char reserved[5]
+
+    ctypedef CUgraphEdgeData_st CUgraphEdgeData
+
+    cdef enum CUgraphInstantiateResult_enum:
+        CUDA_GRAPH_INSTANTIATE_SUCCESS = 0
+        CUDA_GRAPH_INSTANTIATE_ERROR = 1
+        CUDA_GRAPH_INSTANTIATE_INVALID_STRUCTURE = 2
+        CUDA_GRAPH_INSTANTIATE_NODE_OPERATION_NOT_SUPPORTED = 3
+        CUDA_GRAPH_INSTANTIATE_MULTIPLE_CTXS_NOT_SUPPORTED = 4
+        CUDA_GRAPH_INSTANTIATE_CONDITIONAL_HANDLE_UNUSED = 5
+
+    ctypedef CUgraphInstantiateResult_enum CUgraphInstantiateResult
+
+    cdef struct CUDA_GRAPH_INSTANTIATE_PARAMS_st:
+        cuuint64_t flags
+        CUstream hUploadStream
+        CUgraphNode hErrNode_out
+        CUgraphInstantiateResult result_out
+
+    ctypedef CUDA_GRAPH_INSTANTIATE_PARAMS_st CUDA_GRAPH_INSTANTIATE_PARAMS
+
+    cdef enum CUsynchronizationPolicy_enum:
+        CU_SYNC_POLICY_AUTO = 1
+        CU_SYNC_POLICY_SPIN = 2
+        CU_SYNC_POLICY_YIELD = 3
+        CU_SYNC_POLICY_BLOCKING_SYNC = 4
+
+    ctypedef CUsynchronizationPolicy_enum CUsynchronizationPolicy
+
+    cdef enum CUclusterSchedulingPolicy_enum:
+        CU_CLUSTER_SCHEDULING_POLICY_DEFAULT = 0
+        CU_CLUSTER_SCHEDULING_POLICY_SPREAD = 1
+        CU_CLUSTER_SCHEDULING_POLICY_LOAD_BALANCING = 2
+
+    ctypedef CUclusterSchedulingPolicy_enum CUclusterSchedulingPolicy
+
+    cdef enum CUlaunchMemSyncDomain_enum:
+        CU_LAUNCH_MEM_SYNC_DOMAIN_DEFAULT = 0
+        CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE = 1
+
+    ctypedef CUlaunchMemSyncDomain_enum CUlaunchMemSyncDomain
+
+    cdef struct CUlaunchMemSyncDomainMap_st:
+        unsigned char default_
+        unsigned char remote
+
+    ctypedef CUlaunchMemSyncDomainMap_st CUlaunchMemSyncDomainMap
+
+    cdef enum CUlaunchAttributePortableClusterMode_enum:
+        CU_LAUNCH_PORTABLE_CLUSTER_MODE_DEFAULT = 0
+        CU_LAUNCH_PORTABLE_CLUSTER_MODE_REQUIRE_PORTABLE = 1
+        CU_LAUNCH_PORTABLE_CLUSTER_MODE_ALLOW_NON_PORTABLE = 2
+
+    ctypedef CUlaunchAttributePortableClusterMode_enum CUlaunchAttributePortableClusterMode
+
+    cdef enum CUsharedMemoryMode_enum:
+        CU_SHARED_MEMORY_MODE_DEFAULT = 0
+        CU_SHARED_MEMORY_MODE_REQUIRE_PORTABLE = 1
+        CU_SHARED_MEMORY_MODE_ALLOW_NON_PORTABLE = 2
+
+    ctypedef CUsharedMemoryMode_enum CUsharedMemoryMode
+
+    cdef enum CUlaunchAttributeID_enum:
+        CU_LAUNCH_ATTRIBUTE_IGNORE = 0
+        CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW = 1
+        CU_LAUNCH_ATTRIBUTE_COOPERATIVE = 2
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY = 3
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION = 4
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 5
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION = 6
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT = 7
+        CU_LAUNCH_ATTRIBUTE_PRIORITY = 8
+        CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP = 9
+        CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN = 10
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION = 11
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT = 12
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE = 13
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT = 14
+        CU_LAUNCH_ATTRIBUTE_NVLINK_UTIL_CENTRIC_SCHEDULING = 16
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE = 17
+        CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE = 18
+
+    ctypedef CUlaunchAttributeID_enum CUlaunchAttributeID
+
+    cdef struct anon_struct1:
+        unsigned int x
+        unsigned int y
+        unsigned int z
+
+    cdef struct anon_struct2:
+        CUevent event
+        int flags
+        int triggerAtBlockStart
+
+    cdef struct anon_struct3:
+        CUevent event
+        int flags
+
+    cdef struct anon_struct4:
+        unsigned int x
+        unsigned int y
+        unsigned int z
+
+    cdef struct anon_struct5:
+        int deviceUpdatable
+        CUgraphDeviceNode devNode
+
+    cdef union CUlaunchAttributeValue_union:
+        char pad[64]
+        CUaccessPolicyWindow accessPolicyWindow
+        int cooperative
+        CUsynchronizationPolicy syncPolicy
+        anon_struct1 clusterDim
+        CUclusterSchedulingPolicy clusterSchedulingPolicyPreference
+        int programmaticStreamSerializationAllowed
+        anon_struct2 programmaticEvent
+        anon_struct3 launchCompletionEvent
+        int priority
+        CUlaunchMemSyncDomainMap memSyncDomainMap
+        CUlaunchMemSyncDomain memSyncDomain
+        anon_struct4 preferredClusterDim
+        anon_struct5 deviceUpdatableKernelNode
+        unsigned int sharedMemCarveout
+        unsigned int nvlinkUtilCentricScheduling
+        CUlaunchAttributePortableClusterMode portableClusterSizeMode
+        CUsharedMemoryMode sharedMemoryMode
+
+    ctypedef CUlaunchAttributeValue_union CUlaunchAttributeValue
+
+    cdef struct CUlaunchAttribute_st:
+        CUlaunchAttributeID id
+        CUlaunchAttributeValue value
+
+    ctypedef CUlaunchAttribute_st CUlaunchAttribute
+
+    cdef struct CUlaunchConfig_st:
+        unsigned int gridDimX
+        unsigned int gridDimY
+        unsigned int gridDimZ
+        unsigned int blockDimX
+        unsigned int blockDimY
+        unsigned int blockDimZ
+        unsigned int sharedMemBytes
+        CUstream hStream
+        CUlaunchAttribute* attrs
+        unsigned int numAttrs
+
+    ctypedef CUlaunchConfig_st CUlaunchConfig
+
+    ctypedef CUlaunchAttributeID CUkernelNodeAttrID
+
+    ctypedef CUlaunchAttributeValue CUkernelNodeAttrValue_v1
+
+    ctypedef CUkernelNodeAttrValue_v1 CUkernelNodeAttrValue
+
+    cdef enum CUstreamCaptureStatus_enum:
+        CU_STREAM_CAPTURE_STATUS_NONE = 0
+        CU_STREAM_CAPTURE_STATUS_ACTIVE = 1
+        CU_STREAM_CAPTURE_STATUS_INVALIDATED = 2
+
+    ctypedef CUstreamCaptureStatus_enum CUstreamCaptureStatus
+
+    cdef enum CUstreamCaptureMode_enum:
+        CU_STREAM_CAPTURE_MODE_GLOBAL = 0
+        CU_STREAM_CAPTURE_MODE_THREAD_LOCAL = 1
+        CU_STREAM_CAPTURE_MODE_RELAXED = 2
+
+    ctypedef CUstreamCaptureMode_enum CUstreamCaptureMode
+
+    ctypedef CUlaunchAttributeID CUstreamAttrID
+
+    ctypedef CUlaunchAttributeValue CUstreamAttrValue_v1
+
+    ctypedef CUstreamAttrValue_v1 CUstreamAttrValue
+
+    cdef enum CUdriverProcAddress_flags_enum:
+        CU_GET_PROC_ADDRESS_DEFAULT = 0
+        CU_GET_PROC_ADDRESS_LEGACY_STREAM = 1
+        CU_GET_PROC_ADDRESS_PER_THREAD_DEFAULT_STREAM = 2
+
+    ctypedef CUdriverProcAddress_flags_enum CUdriverProcAddress_flags
+
+    cdef enum CUdriverProcAddressQueryResult_enum:
+        CU_GET_PROC_ADDRESS_SUCCESS = 0
+        CU_GET_PROC_ADDRESS_SYMBOL_NOT_FOUND = 1
+        CU_GET_PROC_ADDRESS_VERSION_NOT_SUFFICIENT = 2
+
+    ctypedef CUdriverProcAddressQueryResult_enum CUdriverProcAddressQueryResult
+
+    cdef enum CUexecAffinityType_enum:
+        CU_EXEC_AFFINITY_TYPE_SM_COUNT = 0
+        CU_EXEC_AFFINITY_TYPE_MAX = 1
+
+    ctypedef CUexecAffinityType_enum CUexecAffinityType
+
+    cdef struct CUexecAffinitySmCount_st:
+        unsigned int val
+
+    ctypedef CUexecAffinitySmCount_st CUexecAffinitySmCount_v1
+
+    ctypedef CUexecAffinitySmCount_v1 CUexecAffinitySmCount
+
+    cdef union anon_union3:
+        CUexecAffinitySmCount smCount
+
+    cdef struct CUexecAffinityParam_st:
+        CUexecAffinityType type
+        anon_union3 param
+
+    ctypedef CUexecAffinityParam_st CUexecAffinityParam_v1
+
+    ctypedef CUexecAffinityParam_v1 CUexecAffinityParam
+
+    cdef enum CUcigDataType_enum:
+        CIG_DATA_TYPE_D3D12_COMMAND_QUEUE = 1
+        CIG_DATA_TYPE_NV_BLOB = 2
+
+    ctypedef CUcigDataType_enum CUcigDataType
+
+    cdef struct CUctxCigParam_st:
+        CUcigDataType sharedDataType
+        void* sharedData
+
+    ctypedef CUctxCigParam_st CUctxCigParam
+
+    cdef struct CUctxCreateParams_st:
+        CUexecAffinityParam* execAffinityParams
+        int numExecAffinityParams
+        CUctxCigParam* cigParams
+
+    ctypedef CUctxCreateParams_st CUctxCreateParams
+
+    cdef enum CUstreamCigDataType_enum:
+        STREAM_CIG_DATA_TYPE_D3D12_COMMAND_LIST = 1
+
+    ctypedef CUstreamCigDataType_enum CUstreamCigDataType
+
+    cdef struct CUstreamCigParam_st:
+        CUstreamCigDataType streamSharedDataType
+        void* streamSharedData
+
+    ctypedef CUstreamCigParam_st CUstreamCigParam
+
+    cdef struct CUstreamCigCaptureParams_st:
+        CUstreamCigParam* streamCigParams
+
+    ctypedef CUstreamCigCaptureParams_st CUstreamCigCaptureParams
+
+    cdef enum CUlibraryOption_enum:
+        CU_LIBRARY_HOST_UNIVERSAL_FUNCTION_AND_DATA_TABLE = 0
+        CU_LIBRARY_BINARY_IS_PRESERVED = 1
+        CU_LIBRARY_NUM_OPTIONS = 2
+
+    ctypedef CUlibraryOption_enum CUlibraryOption
+
+    cdef struct CUlibraryHostUniversalFunctionAndDataTable_st:
+        void* functionTable
+        size_t functionWindowSize
+        void* dataTable
+        size_t dataWindowSize
+
+    ctypedef CUlibraryHostUniversalFunctionAndDataTable_st CUlibraryHostUniversalFunctionAndDataTable
+
+    cdef enum cudaError_enum:
+        CUDA_SUCCESS = 0
+        CUDA_ERROR_INVALID_VALUE = 1
+        CUDA_ERROR_OUT_OF_MEMORY = 2
+        CUDA_ERROR_NOT_INITIALIZED = 3
+        CUDA_ERROR_DEINITIALIZED = 4
+        CUDA_ERROR_PROFILER_DISABLED = 5
+        CUDA_ERROR_PROFILER_NOT_INITIALIZED = 6
+        CUDA_ERROR_PROFILER_ALREADY_STARTED = 7
+        CUDA_ERROR_PROFILER_ALREADY_STOPPED = 8
+        CUDA_ERROR_STUB_LIBRARY = 34
+        CUDA_ERROR_CALL_REQUIRES_NEWER_DRIVER = 36
+        CUDA_ERROR_DEVICE_UNAVAILABLE = 46
+        CUDA_ERROR_NO_DEVICE = 100
+        CUDA_ERROR_INVALID_DEVICE = 101
+        CUDA_ERROR_DEVICE_NOT_LICENSED = 102
+        CUDA_ERROR_INVALID_IMAGE = 200
+        CUDA_ERROR_INVALID_CONTEXT = 201
+        CUDA_ERROR_CONTEXT_ALREADY_CURRENT = 202
+        CUDA_ERROR_MAP_FAILED = 205
+        CUDA_ERROR_UNMAP_FAILED = 206
+        CUDA_ERROR_ARRAY_IS_MAPPED = 207
+        CUDA_ERROR_ALREADY_MAPPED = 208
+        CUDA_ERROR_NO_BINARY_FOR_GPU = 209
+        CUDA_ERROR_ALREADY_ACQUIRED = 210
+        CUDA_ERROR_NOT_MAPPED = 211
+        CUDA_ERROR_NOT_MAPPED_AS_ARRAY = 212
+        CUDA_ERROR_NOT_MAPPED_AS_POINTER = 213
+        CUDA_ERROR_ECC_UNCORRECTABLE = 214
+        CUDA_ERROR_UNSUPPORTED_LIMIT = 215
+        CUDA_ERROR_CONTEXT_ALREADY_IN_USE = 216
+        CUDA_ERROR_PEER_ACCESS_UNSUPPORTED = 217
+        CUDA_ERROR_INVALID_PTX = 218
+        CUDA_ERROR_INVALID_GRAPHICS_CONTEXT = 219
+        CUDA_ERROR_NVLINK_UNCORRECTABLE = 220
+        CUDA_ERROR_JIT_COMPILER_NOT_FOUND = 221
+        CUDA_ERROR_UNSUPPORTED_PTX_VERSION = 222
+        CUDA_ERROR_JIT_COMPILATION_DISABLED = 223
+        CUDA_ERROR_UNSUPPORTED_EXEC_AFFINITY = 224
+        CUDA_ERROR_UNSUPPORTED_DEVSIDE_SYNC = 225
+        CUDA_ERROR_CONTAINED = 226
+        CUDA_ERROR_INVALID_SOURCE = 300
+        CUDA_ERROR_FILE_NOT_FOUND = 301
+        CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND = 302
+        CUDA_ERROR_SHARED_OBJECT_INIT_FAILED = 303
+        CUDA_ERROR_OPERATING_SYSTEM = 304
+        CUDA_ERROR_INVALID_HANDLE = 400
+        CUDA_ERROR_ILLEGAL_STATE = 401
+        CUDA_ERROR_LOSSY_QUERY = 402
+        CUDA_ERROR_NOT_FOUND = 500
+        CUDA_ERROR_NOT_READY = 600
+        CUDA_ERROR_ILLEGAL_ADDRESS = 700
+        CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES = 701
+        CUDA_ERROR_LAUNCH_TIMEOUT = 702
+        CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING = 703
+        CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED = 704
+        CUDA_ERROR_PEER_ACCESS_NOT_ENABLED = 705
+        CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE = 708
+        CUDA_ERROR_CONTEXT_IS_DESTROYED = 709
+        CUDA_ERROR_ASSERT = 710
+        CUDA_ERROR_TOO_MANY_PEERS = 711
+        CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED = 712
+        CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED = 713
+        CUDA_ERROR_HARDWARE_STACK_ERROR = 714
+        CUDA_ERROR_ILLEGAL_INSTRUCTION = 715
+        CUDA_ERROR_MISALIGNED_ADDRESS = 716
+        CUDA_ERROR_INVALID_ADDRESS_SPACE = 717
+        CUDA_ERROR_INVALID_PC = 718
+        CUDA_ERROR_LAUNCH_FAILED = 719
+        CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE = 720
+        CUDA_ERROR_TENSOR_MEMORY_LEAK = 721
+        CUDA_ERROR_NOT_PERMITTED = 800
+        CUDA_ERROR_NOT_SUPPORTED = 801
+        CUDA_ERROR_SYSTEM_NOT_READY = 802
+        CUDA_ERROR_SYSTEM_DRIVER_MISMATCH = 803
+        CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE = 804
+        CUDA_ERROR_MPS_CONNECTION_FAILED = 805
+        CUDA_ERROR_MPS_RPC_FAILURE = 806
+        CUDA_ERROR_MPS_SERVER_NOT_READY = 807
+        CUDA_ERROR_MPS_MAX_CLIENTS_REACHED = 808
+        CUDA_ERROR_MPS_MAX_CONNECTIONS_REACHED = 809
+        CUDA_ERROR_MPS_CLIENT_TERMINATED = 810
+        CUDA_ERROR_CDP_NOT_SUPPORTED = 811
+        CUDA_ERROR_CDP_VERSION_MISMATCH = 812
+        CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED = 900
+        CUDA_ERROR_STREAM_CAPTURE_INVALIDATED = 901
+        CUDA_ERROR_STREAM_CAPTURE_MERGE = 902
+        CUDA_ERROR_STREAM_CAPTURE_UNMATCHED = 903
+        CUDA_ERROR_STREAM_CAPTURE_UNJOINED = 904
+        CUDA_ERROR_STREAM_CAPTURE_ISOLATION = 905
+        CUDA_ERROR_STREAM_CAPTURE_IMPLICIT = 906
+        CUDA_ERROR_CAPTURED_EVENT = 907
+        CUDA_ERROR_STREAM_CAPTURE_WRONG_THREAD = 908
+        CUDA_ERROR_TIMEOUT = 909
+        CUDA_ERROR_GRAPH_EXEC_UPDATE_FAILURE = 910
+        CUDA_ERROR_EXTERNAL_DEVICE = 911
+        CUDA_ERROR_INVALID_CLUSTER_SIZE = 912
+        CUDA_ERROR_FUNCTION_NOT_LOADED = 913
+        CUDA_ERROR_INVALID_RESOURCE_TYPE = 914
+        CUDA_ERROR_INVALID_RESOURCE_CONFIGURATION = 915
+        CUDA_ERROR_KEY_ROTATION = 916
+        CUDA_ERROR_STREAM_DETACHED = 917
+        CUDA_ERROR_UNKNOWN = 999
+
+    ctypedef cudaError_enum CUresult
+
+    cdef enum CUdevice_P2PAttribute_enum:
+        CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK = 1
+        CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED = 2
+        CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED = 3
+        CU_DEVICE_P2P_ATTRIBUTE_ACCESS_ACCESS_SUPPORTED = 4
+        CU_DEVICE_P2P_ATTRIBUTE_CUDA_ARRAY_ACCESS_SUPPORTED = 4
+        CU_DEVICE_P2P_ATTRIBUTE_ONLY_PARTIAL_NATIVE_ATOMIC_SUPPORTED = 5
+
+    ctypedef CUdevice_P2PAttribute_enum CUdevice_P2PAttribute
+
+    ctypedef void (*CUstreamCallback)(CUstream hStream, CUresult status, void* userData)
+
+    ctypedef size_t (*CUoccupancyB2DSize)(int blockSize)
+
+    cdef struct CUDA_MEMCPY2D_st:
+        size_t srcXInBytes
+        size_t srcY
+        CUmemorytype srcMemoryType
+        const void* srcHost
+        CUdeviceptr srcDevice
+        CUarray srcArray
+        size_t srcPitch
+        size_t dstXInBytes
+        size_t dstY
+        CUmemorytype dstMemoryType
+        void* dstHost
+        CUdeviceptr dstDevice
+        CUarray dstArray
+        size_t dstPitch
+        size_t WidthInBytes
+        size_t Height
+
+    ctypedef CUDA_MEMCPY2D_st CUDA_MEMCPY2D_v2
+
+    ctypedef CUDA_MEMCPY2D_v2 CUDA_MEMCPY2D
+
+    cdef struct CUDA_MEMCPY3D_st:
+        size_t srcXInBytes
+        size_t srcY
+        size_t srcZ
+        size_t srcLOD
+        CUmemorytype srcMemoryType
+        const void* srcHost
+        CUdeviceptr srcDevice
+        CUarray srcArray
+        void* reserved0
+        size_t srcPitch
+        size_t srcHeight
+        size_t dstXInBytes
+        size_t dstY
+        size_t dstZ
+        size_t dstLOD
+        CUmemorytype dstMemoryType
+        void* dstHost
+        CUdeviceptr dstDevice
+        CUarray dstArray
+        void* reserved1
+        size_t dstPitch
+        size_t dstHeight
+        size_t WidthInBytes
+        size_t Height
+        size_t Depth
+
+    ctypedef CUDA_MEMCPY3D_st CUDA_MEMCPY3D_v2
+
+    ctypedef CUDA_MEMCPY3D_v2 CUDA_MEMCPY3D
+
+    cdef struct CUDA_MEMCPY3D_PEER_st:
+        size_t srcXInBytes
+        size_t srcY
+        size_t srcZ
+        size_t srcLOD
+        CUmemorytype srcMemoryType
+        const void* srcHost
+        CUdeviceptr srcDevice
+        CUarray srcArray
+        CUcontext srcContext
+        size_t srcPitch
+        size_t srcHeight
+        size_t dstXInBytes
+        size_t dstY
+        size_t dstZ
+        size_t dstLOD
+        CUmemorytype dstMemoryType
+        void* dstHost
+        CUdeviceptr dstDevice
+        CUarray dstArray
+        CUcontext dstContext
+        size_t dstPitch
+        size_t dstHeight
+        size_t WidthInBytes
+        size_t Height
+        size_t Depth
+
+    ctypedef CUDA_MEMCPY3D_PEER_st CUDA_MEMCPY3D_PEER_v1
+
+    ctypedef CUDA_MEMCPY3D_PEER_v1 CUDA_MEMCPY3D_PEER
+
+    cdef struct CUDA_MEMCPY_NODE_PARAMS_st:
+        int flags
+        int reserved
+        CUcontext copyCtx
+        CUDA_MEMCPY3D copyParams
+
+    ctypedef CUDA_MEMCPY_NODE_PARAMS_st CUDA_MEMCPY_NODE_PARAMS
+
+    cdef struct CUDA_ARRAY_DESCRIPTOR_st:
+        size_t Width
+        size_t Height
+        CUarray_format Format
+        unsigned int NumChannels
+
+    ctypedef CUDA_ARRAY_DESCRIPTOR_st CUDA_ARRAY_DESCRIPTOR_v2
+
+    ctypedef CUDA_ARRAY_DESCRIPTOR_v2 CUDA_ARRAY_DESCRIPTOR
+
+    cdef struct CUDA_ARRAY3D_DESCRIPTOR_st:
+        size_t Width
+        size_t Height
+        size_t Depth
+        CUarray_format Format
+        unsigned int NumChannels
+        unsigned int Flags
+
+    ctypedef CUDA_ARRAY3D_DESCRIPTOR_st CUDA_ARRAY3D_DESCRIPTOR_v2
+
+    ctypedef CUDA_ARRAY3D_DESCRIPTOR_v2 CUDA_ARRAY3D_DESCRIPTOR
+
+    cdef struct anon_struct6:
+        unsigned int width
+        unsigned int height
+        unsigned int depth
+
+    cdef struct CUDA_ARRAY_SPARSE_PROPERTIES_st:
+        anon_struct6 tileExtent
+        unsigned int miptailFirstLevel
+        unsigned long long miptailSize
+        unsigned int flags
+        unsigned int reserved[4]
+
+    ctypedef CUDA_ARRAY_SPARSE_PROPERTIES_st CUDA_ARRAY_SPARSE_PROPERTIES_v1
+
+    ctypedef CUDA_ARRAY_SPARSE_PROPERTIES_v1 CUDA_ARRAY_SPARSE_PROPERTIES
+
+    cdef struct CUDA_ARRAY_MEMORY_REQUIREMENTS_st:
+        size_t size
+        size_t alignment
+        unsigned int reserved[4]
+
+    ctypedef CUDA_ARRAY_MEMORY_REQUIREMENTS_st CUDA_ARRAY_MEMORY_REQUIREMENTS_v1
+
+    ctypedef CUDA_ARRAY_MEMORY_REQUIREMENTS_v1 CUDA_ARRAY_MEMORY_REQUIREMENTS
+
+    cdef struct anon_struct7:
+        CUarray hArray
+
+    cdef struct anon_struct8:
+        CUmipmappedArray hMipmappedArray
+
+    cdef struct anon_struct9:
+        CUdeviceptr devPtr
+        CUarray_format format
+        unsigned int numChannels
+        size_t sizeInBytes
+
+    cdef struct anon_struct10:
+        CUdeviceptr devPtr
+        CUarray_format format
+        unsigned int numChannels
+        size_t width
+        size_t height
+        size_t pitchInBytes
+
+    cdef struct anon_struct11:
+        int reserved[32]
+
+    cdef union anon_union4:
+        anon_struct7 array
+        anon_struct8 mipmap
+        anon_struct9 linear
+        anon_struct10 pitch2D
+        anon_struct11 reserved
+
+    cdef struct CUDA_RESOURCE_DESC_st:
+        CUresourcetype resType
+        anon_union4 res
+        unsigned int flags
+
+    ctypedef CUDA_RESOURCE_DESC_st CUDA_RESOURCE_DESC_v1
+
+    ctypedef CUDA_RESOURCE_DESC_v1 CUDA_RESOURCE_DESC
+
+    cdef struct CUDA_TEXTURE_DESC_st:
+        CUaddress_mode addressMode[3]
+        CUfilter_mode filterMode
+        unsigned int flags
+        unsigned int maxAnisotropy
+        CUfilter_mode mipmapFilterMode
+        float mipmapLevelBias
+        float minMipmapLevelClamp
+        float maxMipmapLevelClamp
+        float borderColor[4]
+        int reserved[12]
+
+    ctypedef CUDA_TEXTURE_DESC_st CUDA_TEXTURE_DESC_v1
+
+    ctypedef CUDA_TEXTURE_DESC_v1 CUDA_TEXTURE_DESC
+
+    cdef enum CUresourceViewFormat_enum:
+        CU_RES_VIEW_FORMAT_NONE = 0
+        CU_RES_VIEW_FORMAT_UINT_1X8 = 1
+        CU_RES_VIEW_FORMAT_UINT_2X8 = 2
+        CU_RES_VIEW_FORMAT_UINT_4X8 = 3
+        CU_RES_VIEW_FORMAT_SINT_1X8 = 4
+        CU_RES_VIEW_FORMAT_SINT_2X8 = 5
+        CU_RES_VIEW_FORMAT_SINT_4X8 = 6
+        CU_RES_VIEW_FORMAT_UINT_1X16 = 7
+        CU_RES_VIEW_FORMAT_UINT_2X16 = 8
+        CU_RES_VIEW_FORMAT_UINT_4X16 = 9
+        CU_RES_VIEW_FORMAT_SINT_1X16 = 10
+        CU_RES_VIEW_FORMAT_SINT_2X16 = 11
+        CU_RES_VIEW_FORMAT_SINT_4X16 = 12
+        CU_RES_VIEW_FORMAT_UINT_1X32 = 13
+        CU_RES_VIEW_FORMAT_UINT_2X32 = 14
+        CU_RES_VIEW_FORMAT_UINT_4X32 = 15
+        CU_RES_VIEW_FORMAT_SINT_1X32 = 16
+        CU_RES_VIEW_FORMAT_SINT_2X32 = 17
+        CU_RES_VIEW_FORMAT_SINT_4X32 = 18
+        CU_RES_VIEW_FORMAT_FLOAT_1X16 = 19
+        CU_RES_VIEW_FORMAT_FLOAT_2X16 = 20
+        CU_RES_VIEW_FORMAT_FLOAT_4X16 = 21
+        CU_RES_VIEW_FORMAT_FLOAT_1X32 = 22
+        CU_RES_VIEW_FORMAT_FLOAT_2X32 = 23
+        CU_RES_VIEW_FORMAT_FLOAT_4X32 = 24
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC1 = 25
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC2 = 26
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC3 = 27
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC4 = 28
+        CU_RES_VIEW_FORMAT_SIGNED_BC4 = 29
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC5 = 30
+        CU_RES_VIEW_FORMAT_SIGNED_BC5 = 31
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC6H = 32
+        CU_RES_VIEW_FORMAT_SIGNED_BC6H = 33
+        CU_RES_VIEW_FORMAT_UNSIGNED_BC7 = 34
+
+    ctypedef CUresourceViewFormat_enum CUresourceViewFormat
+
+    cdef struct CUDA_RESOURCE_VIEW_DESC_st:
+        CUresourceViewFormat format
+        size_t width
+        size_t height
+        size_t depth
+        unsigned int firstMipmapLevel
+        unsigned int lastMipmapLevel
+        unsigned int firstLayer
+        unsigned int lastLayer
+        unsigned int reserved[16]
+
+    ctypedef CUDA_RESOURCE_VIEW_DESC_st CUDA_RESOURCE_VIEW_DESC_v1
+
+    ctypedef CUDA_RESOURCE_VIEW_DESC_v1 CUDA_RESOURCE_VIEW_DESC
+
+    cdef struct CUtensorMap_st:
+        cuuint64_t opaque[16]
+
+    ctypedef CUtensorMap_st CUtensorMap
+
+    cdef enum CUtensorMapDataType_enum:
+        CU_TENSOR_MAP_DATA_TYPE_UINT8 = 0
+        CU_TENSOR_MAP_DATA_TYPE_UINT16 = 1
+        CU_TENSOR_MAP_DATA_TYPE_UINT32 = 2
+        CU_TENSOR_MAP_DATA_TYPE_INT32 = 3
+        CU_TENSOR_MAP_DATA_TYPE_UINT64 = 4
+        CU_TENSOR_MAP_DATA_TYPE_INT64 = 5
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT16 = 6
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32 = 7
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT64 = 8
+        CU_TENSOR_MAP_DATA_TYPE_BFLOAT16 = 9
+        CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ = 10
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32 = 11
+        CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ = 12
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN8B = 13
+        CU_TENSOR_MAP_DATA_TYPE_16U4_ALIGN16B = 14
+        CU_TENSOR_MAP_DATA_TYPE_16U6_ALIGN16B = 15
+
+    ctypedef CUtensorMapDataType_enum CUtensorMapDataType
+
+    cdef enum CUtensorMapInterleave_enum:
+        CU_TENSOR_MAP_INTERLEAVE_NONE = 0
+        CU_TENSOR_MAP_INTERLEAVE_16B = 1
+        CU_TENSOR_MAP_INTERLEAVE_32B = 2
+
+    ctypedef CUtensorMapInterleave_enum CUtensorMapInterleave
+
+    cdef enum CUtensorMapSwizzle_enum:
+        CU_TENSOR_MAP_SWIZZLE_NONE = 0
+        CU_TENSOR_MAP_SWIZZLE_32B = 1
+        CU_TENSOR_MAP_SWIZZLE_64B = 2
+        CU_TENSOR_MAP_SWIZZLE_128B = 3
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B = 4
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_32B_FLIP_8B = 5
+        CU_TENSOR_MAP_SWIZZLE_128B_ATOM_64B = 6
+
+    ctypedef CUtensorMapSwizzle_enum CUtensorMapSwizzle
+
+    cdef enum CUtensorMapL2promotion_enum:
+        CU_TENSOR_MAP_L2_PROMOTION_NONE = 0
+        CU_TENSOR_MAP_L2_PROMOTION_L2_64B = 1
+        CU_TENSOR_MAP_L2_PROMOTION_L2_128B = 2
+        CU_TENSOR_MAP_L2_PROMOTION_L2_256B = 3
+
+    ctypedef CUtensorMapL2promotion_enum CUtensorMapL2promotion
+
+    cdef enum CUtensorMapFloatOOBfill_enum:
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE = 0
+        CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA = 1
+
+    ctypedef CUtensorMapFloatOOBfill_enum CUtensorMapFloatOOBfill
+
+    cdef enum CUtensorMapIm2ColWideMode_enum:
+        CU_TENSOR_MAP_IM2COL_WIDE_MODE_W = 0
+        CU_TENSOR_MAP_IM2COL_WIDE_MODE_W128 = 1
+
+    ctypedef CUtensorMapIm2ColWideMode_enum CUtensorMapIm2ColWideMode
+
+    cdef struct CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st:
+        unsigned long long p2pToken
+        unsigned int vaSpaceToken
+
+    ctypedef CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1
+
+    ctypedef CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1 CUDA_POINTER_ATTRIBUTE_P2P_TOKENS
+
+    cdef enum CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS_enum:
+        CU_POINTER_ATTRIBUTE_ACCESS_FLAG_NONE = 0
+        CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READ = 1
+        CU_POINTER_ATTRIBUTE_ACCESS_FLAG_READWRITE = 3
+
+    ctypedef CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS_enum CUDA_POINTER_ATTRIBUTE_ACCESS_FLAGS
+
+    cdef struct CUDA_LAUNCH_PARAMS_st:
+        CUfunction function
+        unsigned int gridDimX
+        unsigned int gridDimY
+        unsigned int gridDimZ
+        unsigned int blockDimX
+        unsigned int blockDimY
+        unsigned int blockDimZ
+        unsigned int sharedMemBytes
+        CUstream hStream
+        void** kernelParams
+
+    ctypedef CUDA_LAUNCH_PARAMS_st CUDA_LAUNCH_PARAMS_v1
+
+    ctypedef CUDA_LAUNCH_PARAMS_v1 CUDA_LAUNCH_PARAMS
+
+    cdef enum CUexternalMemoryHandleType_enum:
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_FD = 1
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32 = 2
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_OPAQUE_WIN32_KMT = 3
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_HEAP = 4
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D12_RESOURCE = 5
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE = 6
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_D3D11_RESOURCE_KMT = 7
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF = 8
+        CU_EXTERNAL_MEMORY_HANDLE_TYPE_DMABUF_FD = 9
+
+    ctypedef CUexternalMemoryHandleType_enum CUexternalMemoryHandleType
+
+    cdef struct anon_struct12:
+        void* handle
+        const void* name
+
+    cdef union anon_union5:
+        int fd
+        anon_struct12 win32
+        const void* nvSciBufObject
+
+    cdef struct CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st:
+        CUexternalMemoryHandleType type
+        anon_union5 handle
+        unsigned long long size
+        unsigned int flags
+        unsigned int reserved[16]
+
+    ctypedef CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1
+
+    ctypedef CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1 CUDA_EXTERNAL_MEMORY_HANDLE_DESC
+
+    cdef struct CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st:
+        unsigned long long offset
+        unsigned long long size
+        unsigned int flags
+        unsigned int reserved[16]
+
+    ctypedef CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1
+
+    ctypedef CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1 CUDA_EXTERNAL_MEMORY_BUFFER_DESC
+
+    cdef struct CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st:
+        unsigned long long offset
+        CUDA_ARRAY3D_DESCRIPTOR arrayDesc
+        unsigned int numLevels
+        unsigned int reserved[16]
+
+    ctypedef CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1
+
+    ctypedef CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1 CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC
+
+    cdef enum CUexternalSemaphoreHandleType_enum:
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_FD = 1
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32 = 2
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_OPAQUE_WIN32_KMT = 3
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D12_FENCE = 4
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_FENCE = 5
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC = 6
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX = 7
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_D3D11_KEYED_MUTEX_KMT = 8
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_FD = 9
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_TIMELINE_SEMAPHORE_WIN32 = 10
+
+    ctypedef CUexternalSemaphoreHandleType_enum CUexternalSemaphoreHandleType
+
+    cdef struct anon_struct13:
+        void* handle
+        const void* name
+
+    cdef union anon_union6:
+        int fd
+        anon_struct13 win32
+        const void* nvSciSyncObj
+
+    cdef struct CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st:
+        CUexternalSemaphoreHandleType type
+        anon_union6 handle
+        unsigned int flags
+        unsigned int reserved[16]
+
+    ctypedef CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1
+
+    ctypedef CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1 CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC
+
+    cdef struct anon_struct14:
+        unsigned long long value
+
+    cdef union anon_union7:
+        void* fence
+        unsigned long long reserved
+
+    cdef struct anon_struct15:
+        unsigned long long key
+
+    cdef struct anon_struct16:
+        anon_struct14 fence
+        anon_union7 nvSciSync
+        anon_struct15 keyedMutex
+        unsigned int reserved[12]
+
+    cdef struct CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st:
+        anon_struct16 params
+        unsigned int flags
+        unsigned int reserved[16]
+
+    ctypedef CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1
+
+    ctypedef CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1 CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+
+    cdef struct anon_struct17:
+        unsigned long long value
+
+    cdef union anon_union8:
+        void* fence
+        unsigned long long reserved
+
+    cdef struct anon_struct18:
+        unsigned long long key
+        unsigned int timeoutMs
+
+    cdef struct anon_struct19:
+        anon_struct17 fence
+        anon_union8 nvSciSync
+        anon_struct18 keyedMutex
+        unsigned int reserved[10]
+
+    cdef struct CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st:
+        anon_struct19 params
+        unsigned int flags
+        unsigned int reserved[16]
+
+    ctypedef CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1
+
+    ctypedef CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1 CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+
+    cdef struct CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st:
+        CUexternalSemaphore* extSemArray
+        const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray
+        unsigned int numExtSems
+
+    ctypedef CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1
+
+    ctypedef CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1 CUDA_EXT_SEM_SIGNAL_NODE_PARAMS
+
+    cdef struct CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st:
+        CUexternalSemaphore* extSemArray
+        const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray
+        unsigned int numExtSems
+
+    ctypedef CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2
+
+    cdef struct CUDA_EXT_SEM_WAIT_NODE_PARAMS_st:
+        CUexternalSemaphore* extSemArray
+        const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray
+        unsigned int numExtSems
+
+    ctypedef CUDA_EXT_SEM_WAIT_NODE_PARAMS_st CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1
+
+    ctypedef CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1 CUDA_EXT_SEM_WAIT_NODE_PARAMS
+
+    cdef struct CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st:
+        CUexternalSemaphore* extSemArray
+        const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray
+        unsigned int numExtSems
+
+    ctypedef CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2
+
+    ctypedef unsigned long long CUmemGenericAllocationHandle_v1
+
+    ctypedef CUmemGenericAllocationHandle_v1 CUmemGenericAllocationHandle
+
+    cdef enum CUmemAllocationHandleType_enum:
+        CU_MEM_HANDLE_TYPE_NONE = 0
+        CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR = 1
+        CU_MEM_HANDLE_TYPE_WIN32 = 2
+        CU_MEM_HANDLE_TYPE_WIN32_KMT = 4
+        CU_MEM_HANDLE_TYPE_FABRIC = 8
+        CU_MEM_HANDLE_TYPE_MAX = 2147483647
+
+    ctypedef CUmemAllocationHandleType_enum CUmemAllocationHandleType
+
+    cdef enum CUmemAccess_flags_enum:
+        CU_MEM_ACCESS_FLAGS_PROT_NONE = 0
+        CU_MEM_ACCESS_FLAGS_PROT_READ = 1
+        CU_MEM_ACCESS_FLAGS_PROT_READWRITE = 3
+        CU_MEM_ACCESS_FLAGS_PROT_MAX = 2147483647
+
+    ctypedef CUmemAccess_flags_enum CUmemAccess_flags
+
+    cdef enum CUmemLocationType_enum:
+        CU_MEM_LOCATION_TYPE_INVALID = 0
+        CU_MEM_LOCATION_TYPE_NONE = 0
+        CU_MEM_LOCATION_TYPE_DEVICE = 1
+        CU_MEM_LOCATION_TYPE_HOST = 2
+        CU_MEM_LOCATION_TYPE_HOST_NUMA = 3
+        CU_MEM_LOCATION_TYPE_HOST_NUMA_CURRENT = 4
+        CU_MEM_LOCATION_TYPE_INVISIBLE = 5
+        CU_MEM_LOCATION_TYPE_MAX = 2147483647
+
+    ctypedef CUmemLocationType_enum CUmemLocationType
+
+    cdef enum CUmemAllocationType_enum:
+        CU_MEM_ALLOCATION_TYPE_INVALID = 0
+        CU_MEM_ALLOCATION_TYPE_PINNED = 1
+        CU_MEM_ALLOCATION_TYPE_MANAGED = 2
+        CU_MEM_ALLOCATION_TYPE_MAX = 2147483647
+
+    ctypedef CUmemAllocationType_enum CUmemAllocationType
+
+    cdef enum CUmemAllocationGranularity_flags_enum:
+        CU_MEM_ALLOC_GRANULARITY_MINIMUM = 0
+        CU_MEM_ALLOC_GRANULARITY_RECOMMENDED = 1
+
+    ctypedef CUmemAllocationGranularity_flags_enum CUmemAllocationGranularity_flags
+
+    cdef enum CUmemRangeHandleType_enum:
+        CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD = 1
+        CU_MEM_RANGE_HANDLE_TYPE_MAX = 2147483647
+
+    ctypedef CUmemRangeHandleType_enum CUmemRangeHandleType
+
+    cdef enum CUmemRangeFlags_enum:
+        CU_MEM_RANGE_FLAG_DMA_BUF_MAPPING_TYPE_PCIE = 1
+
+    ctypedef CUmemRangeFlags_enum CUmemRangeFlags
+
+    cdef enum CUarraySparseSubresourceType_enum:
+        CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_SPARSE_LEVEL = 0
+        CU_ARRAY_SPARSE_SUBRESOURCE_TYPE_MIPTAIL = 1
+
+    ctypedef CUarraySparseSubresourceType_enum CUarraySparseSubresourceType
+
+    cdef enum CUmemOperationType_enum:
+        CU_MEM_OPERATION_TYPE_MAP = 1
+        CU_MEM_OPERATION_TYPE_UNMAP = 2
+
+    ctypedef CUmemOperationType_enum CUmemOperationType
+
+    cdef enum CUmemHandleType_enum:
+        CU_MEM_HANDLE_TYPE_GENERIC = 0
+
+    ctypedef CUmemHandleType_enum CUmemHandleType
+
+    cdef union anon_union9:
+        CUmipmappedArray mipmap
+        CUarray array
+
+    cdef struct anon_struct20:
+        unsigned int level
+        unsigned int layer
+        unsigned int offsetX
+        unsigned int offsetY
+        unsigned int offsetZ
+        unsigned int extentWidth
+        unsigned int extentHeight
+        unsigned int extentDepth
+
+    cdef struct anon_struct21:
+        unsigned int layer
+        unsigned long long offset
+        unsigned long long size
+
+    cdef union anon_union10:
+        anon_struct20 sparseLevel
+        anon_struct21 miptail
+
+    cdef union anon_union11:
+        CUmemGenericAllocationHandle memHandle
+
+    cdef struct CUarrayMapInfo_st:
+        CUresourcetype resourceType
+        anon_union9 resource
+        CUarraySparseSubresourceType subresourceType
+        anon_union10 subresource
+        CUmemOperationType memOperationType
+        CUmemHandleType memHandleType
+        anon_union11 memHandle
+        unsigned long long offset
+        unsigned int deviceBitMask
+        unsigned int flags
+        unsigned int reserved[2]
+
+    ctypedef CUarrayMapInfo_st CUarrayMapInfo_v1
+
+    ctypedef CUarrayMapInfo_v1 CUarrayMapInfo
+
+    cdef struct CUmemLocation_st:
+        CUmemLocationType type
+        int id
+
+    ctypedef CUmemLocation_st CUmemLocation_v1
+
+    ctypedef CUmemLocation_v1 CUmemLocation
+
+    cdef enum CUmemAllocationCompType_enum:
+        CU_MEM_ALLOCATION_COMP_NONE = 0
+        CU_MEM_ALLOCATION_COMP_GENERIC = 1
+
+    ctypedef CUmemAllocationCompType_enum CUmemAllocationCompType
+
+    cdef struct anon_struct22:
+        unsigned char compressionType
+        unsigned char gpuDirectRDMACapable
+        unsigned short usage
+        unsigned char reserved[4]
+
+    cdef struct CUmemAllocationProp_st:
+        CUmemAllocationType type
+        CUmemAllocationHandleType requestedHandleTypes
+        CUmemLocation location
+        void* win32HandleMetaData
+        anon_struct22 allocFlags
+
+    ctypedef CUmemAllocationProp_st CUmemAllocationProp_v1
+
+    ctypedef CUmemAllocationProp_v1 CUmemAllocationProp
+
+    cdef enum CUmulticastGranularity_flags_enum:
+        CU_MULTICAST_GRANULARITY_MINIMUM = 0
+        CU_MULTICAST_GRANULARITY_RECOMMENDED = 1
+
+    ctypedef CUmulticastGranularity_flags_enum CUmulticastGranularity_flags
+
+    cdef struct CUmulticastObjectProp_st:
+        unsigned int numDevices
+        size_t size
+        unsigned long long handleTypes
+        unsigned long long flags
+
+    ctypedef CUmulticastObjectProp_st CUmulticastObjectProp_v1
+
+    ctypedef CUmulticastObjectProp_v1 CUmulticastObjectProp
+
+    cdef struct CUmemAccessDesc_st:
+        CUmemLocation location
+        CUmemAccess_flags flags
+
+    ctypedef CUmemAccessDesc_st CUmemAccessDesc_v1
+
+    ctypedef CUmemAccessDesc_v1 CUmemAccessDesc
+
+    cdef enum CUgraphExecUpdateResult_enum:
+        CU_GRAPH_EXEC_UPDATE_SUCCESS = 0
+        CU_GRAPH_EXEC_UPDATE_ERROR = 1
+        CU_GRAPH_EXEC_UPDATE_ERROR_TOPOLOGY_CHANGED = 2
+        CU_GRAPH_EXEC_UPDATE_ERROR_NODE_TYPE_CHANGED = 3
+        CU_GRAPH_EXEC_UPDATE_ERROR_FUNCTION_CHANGED = 4
+        CU_GRAPH_EXEC_UPDATE_ERROR_PARAMETERS_CHANGED = 5
+        CU_GRAPH_EXEC_UPDATE_ERROR_NOT_SUPPORTED = 6
+        CU_GRAPH_EXEC_UPDATE_ERROR_UNSUPPORTED_FUNCTION_CHANGE = 7
+        CU_GRAPH_EXEC_UPDATE_ERROR_ATTRIBUTES_CHANGED = 8
+
+    ctypedef CUgraphExecUpdateResult_enum CUgraphExecUpdateResult
+
+    cdef struct CUgraphExecUpdateResultInfo_st:
+        CUgraphExecUpdateResult result
+        CUgraphNode errorNode
+        CUgraphNode errorFromNode
+
+    ctypedef CUgraphExecUpdateResultInfo_st CUgraphExecUpdateResultInfo_v1
+
+    ctypedef CUgraphExecUpdateResultInfo_v1 CUgraphExecUpdateResultInfo
+
+    cdef enum CUmemPool_attribute_enum:
+        CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES = 1
+        CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC = 2
+        CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES = 3
+        CU_MEMPOOL_ATTR_RELEASE_THRESHOLD = 4
+        CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT = 5
+        CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH = 6
+        CU_MEMPOOL_ATTR_USED_MEM_CURRENT = 7
+        CU_MEMPOOL_ATTR_USED_MEM_HIGH = 8
+        CU_MEMPOOL_ATTR_ALLOCATION_TYPE = 9
+        CU_MEMPOOL_ATTR_EXPORT_HANDLE_TYPES = 10
+        CU_MEMPOOL_ATTR_LOCATION_ID = 11
+        CU_MEMPOOL_ATTR_LOCATION_TYPE = 12
+        CU_MEMPOOL_ATTR_MAX_POOL_SIZE = 13
+        CU_MEMPOOL_ATTR_HW_DECOMPRESS_ENABLED = 14
+
+    ctypedef CUmemPool_attribute_enum CUmemPool_attribute
+
+    cdef struct CUmemPoolProps_st:
+        CUmemAllocationType allocType
+        CUmemAllocationHandleType handleTypes
+        CUmemLocation location
+        void* win32SecurityAttributes
+        size_t maxSize
+        unsigned short usage
+        unsigned char reserved[54]
+
+    ctypedef CUmemPoolProps_st CUmemPoolProps_v1
+
+    ctypedef CUmemPoolProps_v1 CUmemPoolProps
+
+    cdef struct CUmemPoolPtrExportData_st:
+        unsigned char reserved[64]
+
+    ctypedef CUmemPoolPtrExportData_st CUmemPoolPtrExportData_v1
+
+    ctypedef CUmemPoolPtrExportData_v1 CUmemPoolPtrExportData
+
+    cdef enum CUmemcpyFlags_enum:
+        CU_MEMCPY_FLAG_DEFAULT = 0
+        CU_MEMCPY_FLAG_PREFER_OVERLAP_WITH_COMPUTE = 1
+
+    ctypedef CUmemcpyFlags_enum CUmemcpyFlags
+
+    cdef enum CUmemcpySrcAccessOrder_enum:
+        CU_MEMCPY_SRC_ACCESS_ORDER_INVALID = 0
+        CU_MEMCPY_SRC_ACCESS_ORDER_STREAM = 1
+        CU_MEMCPY_SRC_ACCESS_ORDER_DURING_API_CALL = 2
+        CU_MEMCPY_SRC_ACCESS_ORDER_ANY = 3
+        CU_MEMCPY_SRC_ACCESS_ORDER_MAX = 2147483647
+
+    ctypedef CUmemcpySrcAccessOrder_enum CUmemcpySrcAccessOrder
+
+    cdef struct CUmemcpyAttributes_st:
+        CUmemcpySrcAccessOrder srcAccessOrder
+        CUmemLocation srcLocHint
+        CUmemLocation dstLocHint
+        unsigned int flags
+
+    ctypedef CUmemcpyAttributes_st CUmemcpyAttributes_v1
+
+    ctypedef CUmemcpyAttributes_v1 CUmemcpyAttributes
+
+    cdef enum CUmemcpy3DOperandType_enum:
+        CU_MEMCPY_OPERAND_TYPE_POINTER = 1
+        CU_MEMCPY_OPERAND_TYPE_ARRAY = 2
+        CU_MEMCPY_OPERAND_TYPE_MAX = 2147483647
+
+    ctypedef CUmemcpy3DOperandType_enum CUmemcpy3DOperandType
+
+    cdef struct CUoffset3D_st:
+        size_t x
+        size_t y
+        size_t z
+
+    ctypedef CUoffset3D_st CUoffset3D_v1
+
+    ctypedef CUoffset3D_v1 CUoffset3D
+
+    cdef struct CUextent3D_st:
+        size_t width
+        size_t height
+        size_t depth
+
+    ctypedef CUextent3D_st CUextent3D_v1
+
+    ctypedef CUextent3D_v1 CUextent3D
+
+    cdef struct anon_struct23:
+        CUdeviceptr ptr
+        size_t rowLength
+        size_t layerHeight
+        CUmemLocation locHint
+
+    cdef struct anon_struct24:
+        CUarray array
+        CUoffset3D offset
+
+    cdef union anon_union13:
+        anon_struct23 ptr
+        anon_struct24 array
+
+    cdef struct CUmemcpy3DOperand_st:
+        CUmemcpy3DOperandType type
+        anon_union13 op
+
+    ctypedef CUmemcpy3DOperand_st CUmemcpy3DOperand_v1
+
+    ctypedef CUmemcpy3DOperand_v1 CUmemcpy3DOperand
+
+    cdef struct CUDA_MEMCPY3D_BATCH_OP_st:
+        CUmemcpy3DOperand src
+        CUmemcpy3DOperand dst
+        CUextent3D extent
+        CUmemcpySrcAccessOrder srcAccessOrder
+        unsigned int flags
+
+    ctypedef CUDA_MEMCPY3D_BATCH_OP_st CUDA_MEMCPY3D_BATCH_OP_v1
+
+    ctypedef CUDA_MEMCPY3D_BATCH_OP_v1 CUDA_MEMCPY3D_BATCH_OP
+
+    cdef struct CUDA_MEM_ALLOC_NODE_PARAMS_v1_st:
+        CUmemPoolProps poolProps
+        const CUmemAccessDesc* accessDescs
+        size_t accessDescCount
+        size_t bytesize
+        CUdeviceptr dptr
+
+    ctypedef CUDA_MEM_ALLOC_NODE_PARAMS_v1_st CUDA_MEM_ALLOC_NODE_PARAMS_v1
+
+    ctypedef CUDA_MEM_ALLOC_NODE_PARAMS_v1 CUDA_MEM_ALLOC_NODE_PARAMS
+
+    cdef struct CUDA_MEM_ALLOC_NODE_PARAMS_v2_st:
+        CUmemPoolProps poolProps
+        const CUmemAccessDesc* accessDescs
+        size_t accessDescCount
+        size_t bytesize
+        CUdeviceptr dptr
+
+    ctypedef CUDA_MEM_ALLOC_NODE_PARAMS_v2_st CUDA_MEM_ALLOC_NODE_PARAMS_v2
+
+    cdef struct CUDA_MEM_FREE_NODE_PARAMS_st:
+        CUdeviceptr dptr
+
+    ctypedef CUDA_MEM_FREE_NODE_PARAMS_st CUDA_MEM_FREE_NODE_PARAMS
+
+    cdef enum CUgraphMem_attribute_enum:
+        CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT = 0
+        CU_GRAPH_MEM_ATTR_USED_MEM_HIGH = 1
+        CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT = 2
+        CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH = 3
+
+    ctypedef CUgraphMem_attribute_enum CUgraphMem_attribute
+
+    cdef enum CUgraphChildGraphNodeOwnership_enum:
+        CU_GRAPH_CHILD_GRAPH_OWNERSHIP_INVALID = -1
+        CU_GRAPH_CHILD_GRAPH_OWNERSHIP_CLONE = 0
+        CU_GRAPH_CHILD_GRAPH_OWNERSHIP_MOVE = 1
+
+    ctypedef CUgraphChildGraphNodeOwnership_enum CUgraphChildGraphNodeOwnership
+
+    cdef struct CUDA_CHILD_GRAPH_NODE_PARAMS_st:
+        CUgraph graph
+        CUgraphChildGraphNodeOwnership ownership
+
+    ctypedef CUDA_CHILD_GRAPH_NODE_PARAMS_st CUDA_CHILD_GRAPH_NODE_PARAMS
+
+    cdef struct CUDA_EVENT_RECORD_NODE_PARAMS_st:
+        CUevent event
+
+    ctypedef CUDA_EVENT_RECORD_NODE_PARAMS_st CUDA_EVENT_RECORD_NODE_PARAMS
+
+    cdef struct CUDA_EVENT_WAIT_NODE_PARAMS_st:
+        CUevent event
+
+    ctypedef CUDA_EVENT_WAIT_NODE_PARAMS_st CUDA_EVENT_WAIT_NODE_PARAMS
+
+    cdef struct CUgraphNodeParams_st:
+        CUgraphNodeType type
+        int reserved0[3]
+        long long reserved1[29]
+        CUDA_KERNEL_NODE_PARAMS_v3 kernel
+        CUDA_MEMCPY_NODE_PARAMS memcpy
+        CUDA_MEMSET_NODE_PARAMS_v2 memset
+        CUDA_HOST_NODE_PARAMS_v2 host
+        CUDA_CHILD_GRAPH_NODE_PARAMS graph
+        CUDA_EVENT_WAIT_NODE_PARAMS eventWait
+        CUDA_EVENT_RECORD_NODE_PARAMS eventRecord
+        CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2 extSemSignal
+        CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2 extSemWait
+        CUDA_MEM_ALLOC_NODE_PARAMS_v2 alloc
+        CUDA_MEM_FREE_NODE_PARAMS free
+        CUDA_BATCH_MEM_OP_NODE_PARAMS_v2 memOp
+        CUDA_CONDITIONAL_NODE_PARAMS conditional
+        long long reserved2
+
+    ctypedef CUgraphNodeParams_st CUgraphNodeParams
+
+    cdef enum CUflushGPUDirectRDMAWritesOptions_enum:
+        CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_HOST = 1
+        CU_FLUSH_GPU_DIRECT_RDMA_WRITES_OPTION_MEMOPS = 2
+
+    ctypedef CUflushGPUDirectRDMAWritesOptions_enum CUflushGPUDirectRDMAWritesOptions
+
+    cdef enum CUGPUDirectRDMAWritesOrdering_enum:
+        CU_GPU_DIRECT_RDMA_WRITES_ORDERING_NONE = 0
+        CU_GPU_DIRECT_RDMA_WRITES_ORDERING_OWNER = 100
+        CU_GPU_DIRECT_RDMA_WRITES_ORDERING_ALL_DEVICES = 200
+
+    ctypedef CUGPUDirectRDMAWritesOrdering_enum CUGPUDirectRDMAWritesOrdering
+
+    cdef enum CUflushGPUDirectRDMAWritesScope_enum:
+        CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_OWNER = 100
+        CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TO_ALL_DEVICES = 200
+
+    ctypedef CUflushGPUDirectRDMAWritesScope_enum CUflushGPUDirectRDMAWritesScope
+
+    cdef enum CUflushGPUDirectRDMAWritesTarget_enum:
+        CU_FLUSH_GPU_DIRECT_RDMA_WRITES_TARGET_CURRENT_CTX = 0
+
+    ctypedef CUflushGPUDirectRDMAWritesTarget_enum CUflushGPUDirectRDMAWritesTarget
+
+    cdef enum CUgraphDebugDot_flags_enum:
+        CU_GRAPH_DEBUG_DOT_FLAGS_VERBOSE = 1
+        CU_GRAPH_DEBUG_DOT_FLAGS_RUNTIME_TYPES = 2
+        CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_PARAMS = 4
+        CU_GRAPH_DEBUG_DOT_FLAGS_MEMCPY_NODE_PARAMS = 8
+        CU_GRAPH_DEBUG_DOT_FLAGS_MEMSET_NODE_PARAMS = 16
+        CU_GRAPH_DEBUG_DOT_FLAGS_HOST_NODE_PARAMS = 32
+        CU_GRAPH_DEBUG_DOT_FLAGS_EVENT_NODE_PARAMS = 64
+        CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_SIGNAL_NODE_PARAMS = 128
+        CU_GRAPH_DEBUG_DOT_FLAGS_EXT_SEMAS_WAIT_NODE_PARAMS = 256
+        CU_GRAPH_DEBUG_DOT_FLAGS_KERNEL_NODE_ATTRIBUTES = 512
+        CU_GRAPH_DEBUG_DOT_FLAGS_HANDLES = 1024
+        CU_GRAPH_DEBUG_DOT_FLAGS_MEM_ALLOC_NODE_PARAMS = 2048
+        CU_GRAPH_DEBUG_DOT_FLAGS_MEM_FREE_NODE_PARAMS = 4096
+        CU_GRAPH_DEBUG_DOT_FLAGS_BATCH_MEM_OP_NODE_PARAMS = 8192
+        CU_GRAPH_DEBUG_DOT_FLAGS_EXTRA_TOPO_INFO = 16384
+        CU_GRAPH_DEBUG_DOT_FLAGS_CONDITIONAL_NODE_PARAMS = 32768
+
+    ctypedef CUgraphDebugDot_flags_enum CUgraphDebugDot_flags
+
+    cdef enum CUuserObject_flags_enum:
+        CU_USER_OBJECT_NO_DESTRUCTOR_SYNC = 1
+
+    ctypedef CUuserObject_flags_enum CUuserObject_flags
+
+    cdef enum CUuserObjectRetain_flags_enum:
+        CU_GRAPH_USER_OBJECT_MOVE = 1
+
+    ctypedef CUuserObjectRetain_flags_enum CUuserObjectRetain_flags
+
+    cdef enum CUgraphInstantiate_flags_enum:
+        CUDA_GRAPH_INSTANTIATE_FLAG_AUTO_FREE_ON_LAUNCH = 1
+        CUDA_GRAPH_INSTANTIATE_FLAG_UPLOAD = 2
+        CUDA_GRAPH_INSTANTIATE_FLAG_DEVICE_LAUNCH = 4
+        CUDA_GRAPH_INSTANTIATE_FLAG_USE_NODE_PRIORITY = 8
+
+    ctypedef CUgraphInstantiate_flags_enum CUgraphInstantiate_flags
+
+    cdef enum CUdeviceNumaConfig_enum:
+        CU_DEVICE_NUMA_CONFIG_NONE = 0
+        CU_DEVICE_NUMA_CONFIG_NUMA_NODE = 1
+
+    ctypedef CUdeviceNumaConfig_enum CUdeviceNumaConfig
+
+    cdef enum CUprocessState_enum:
+        CU_PROCESS_STATE_RUNNING = 0
+        CU_PROCESS_STATE_LOCKED = 1
+        CU_PROCESS_STATE_CHECKPOINTED = 2
+        CU_PROCESS_STATE_FAILED = 3
+
+    ctypedef CUprocessState_enum CUprocessState
+
+    cdef struct CUcheckpointLockArgs_st:
+        unsigned int timeoutMs
+        unsigned int reserved0
+        cuuint64_t reserved1[7]
+
+    ctypedef CUcheckpointLockArgs_st CUcheckpointLockArgs
+
+    cdef struct CUcheckpointCheckpointArgs_st:
+        cuuint64_t reserved[8]
+
+    ctypedef CUcheckpointCheckpointArgs_st CUcheckpointCheckpointArgs
+
+    cdef struct CUcheckpointGpuPair_st:
+        CUuuid oldUuid
+        CUuuid newUuid
+
+    ctypedef CUcheckpointGpuPair_st CUcheckpointGpuPair
+
+    cdef struct CUcheckpointRestoreArgs_st:
+        CUcheckpointGpuPair* gpuPairs
+        unsigned int gpuPairsCount
+        char reserved[44]
+        cuuint64_t reserved1
+
+    ctypedef CUcheckpointRestoreArgs_st CUcheckpointRestoreArgs
+
+    cdef struct CUcheckpointUnlockArgs_st:
+        cuuint64_t reserved[8]
+
+    ctypedef CUcheckpointUnlockArgs_st CUcheckpointUnlockArgs
+
+    cdef enum CUmoduleLoadingMode_enum:
+        CU_MODULE_EAGER_LOADING = 1
+        CU_MODULE_LAZY_LOADING = 2
+
+    ctypedef CUmoduleLoadingMode_enum CUmoduleLoadingMode
+
+    cdef enum CUmemDecompressAlgorithm_enum:
+        CU_MEM_DECOMPRESS_UNSUPPORTED = 0
+        CU_MEM_DECOMPRESS_ALGORITHM_DEFLATE = 1
+        CU_MEM_DECOMPRESS_ALGORITHM_SNAPPY = 2
+        CU_MEM_DECOMPRESS_ALGORITHM_LZ4 = 4
+
+    ctypedef CUmemDecompressAlgorithm_enum CUmemDecompressAlgorithm
+
+    cdef struct CUmemDecompressParams_st:
+        size_t srcNumBytes
+        size_t dstNumBytes
+        cuuint32_t* dstActBytes
+        const void* src
+        void* dst
+        CUmemDecompressAlgorithm algo
+        unsigned char padding[20]
+
+    ctypedef CUmemDecompressParams_st CUmemDecompressParams
+
+    cdef enum CUfunctionLoadingState_enum:
+        CU_FUNCTION_LOADING_STATE_UNLOADED = 0
+        CU_FUNCTION_LOADING_STATE_LOADED = 1
+        CU_FUNCTION_LOADING_STATE_MAX = 2
+
+    ctypedef CUfunctionLoadingState_enum CUfunctionLoadingState
+
+    cdef enum CUcoredumpSettings_enum:
+        CU_COREDUMP_ENABLE_ON_EXCEPTION = 1
+        CU_COREDUMP_TRIGGER_HOST = 2
+        CU_COREDUMP_LIGHTWEIGHT = 3
+        CU_COREDUMP_ENABLE_USER_TRIGGER = 4
+        CU_COREDUMP_FILE = 5
+        CU_COREDUMP_PIPE = 6
+        CU_COREDUMP_GENERATION_FLAGS = 7
+        CU_COREDUMP_MAX = 8
+
+    ctypedef CUcoredumpSettings_enum CUcoredumpSettings
+
+    cdef enum CUCoredumpGenerationFlags:
+        CU_COREDUMP_DEFAULT_FLAGS = 0
+        CU_COREDUMP_SKIP_NONRELOCATED_ELF_IMAGES = 1
+        CU_COREDUMP_SKIP_GLOBAL_MEMORY = 2
+        CU_COREDUMP_SKIP_SHARED_MEMORY = 4
+        CU_COREDUMP_SKIP_LOCAL_MEMORY = 8
+        CU_COREDUMP_SKIP_ABORT = 16
+        CU_COREDUMP_SKIP_CONSTBANK_MEMORY = 32
+        CU_COREDUMP_LIGHTWEIGHT_FLAGS = 47
+        CU_COREDUMP_GZIP_COMPRESS = 64
+
+    cdef struct CUcoredumpCallbackEntry_st:
+        pass
+    ctypedef CUcoredumpCallbackEntry_st* CUcoredumpCallbackHandle
+
+    ctypedef void (*CUcoredumpStatusCallback)(void* userData, int pid, CUdevice dev)
+
+    cdef struct CUdevResourceDesc_st:
+        pass
+    ctypedef CUdevResourceDesc_st* CUdevResourceDesc
+
+    ctypedef enum CUgreenCtxCreate_flags:
+        CU_GREEN_CTX_DEFAULT_STREAM = 1
+
+    ctypedef enum CUdevSmResourceGroup_flags:
+        CU_DEV_SM_RESOURCE_GROUP_DEFAULT = 0
+        CU_DEV_SM_RESOURCE_GROUP_BACKFILL = 1
+
+    ctypedef enum CUdevSmResourceSplitByCount_flags:
+        CU_DEV_SM_RESOURCE_SPLIT_IGNORE_SM_COSCHEDULING = 1
+        CU_DEV_SM_RESOURCE_SPLIT_MAX_POTENTIAL_CLUSTER_SIZE = 2
+
+    ctypedef enum CUdevResourceType:
+        CU_DEV_RESOURCE_TYPE_INVALID = 0
+        CU_DEV_RESOURCE_TYPE_SM = 1
+        CU_DEV_RESOURCE_TYPE_WORKQUEUE_CONFIG = 1000
+        CU_DEV_RESOURCE_TYPE_WORKQUEUE = 10000
+
+    cdef struct CUdevSmResource_st:
+        unsigned int smCount
+        unsigned int minSmPartitionSize
+        unsigned int smCoscheduledAlignment
+        unsigned int flags
+
+    ctypedef CUdevSmResource_st CUdevSmResource
+
+    ctypedef enum CUdevWorkqueueConfigScope:
+        CU_WORKQUEUE_SCOPE_DEVICE_CTX = 0
+        CU_WORKQUEUE_SCOPE_GREEN_CTX_BALANCED = 1
+
+    cdef struct CUdevWorkqueueConfigResource_st:
+        CUdevice device
+        unsigned int wqConcurrencyLimit
+        CUdevWorkqueueConfigScope sharingScope
+
+    ctypedef CUdevWorkqueueConfigResource_st CUdevWorkqueueConfigResource
+
+    cdef struct CUdevWorkqueueResource_st:
+        unsigned char reserved[40]
+
+    ctypedef CUdevWorkqueueResource_st CUdevWorkqueueResource
+
+    cdef struct CU_DEV_SM_RESOURCE_GROUP_PARAMS_st:
+        unsigned int smCount
+        unsigned int coscheduledSmCount
+        unsigned int preferredCoscheduledSmCount
+        unsigned int flags
+        unsigned int reserved[12]
+
+    ctypedef CU_DEV_SM_RESOURCE_GROUP_PARAMS_st CU_DEV_SM_RESOURCE_GROUP_PARAMS
+
+    cdef struct CUdevResource_st:
+        CUdevResourceType type
+        unsigned char _internal_padding[92]
+        CUdevSmResource sm
+        CUdevWorkqueueConfigResource wqConfig
+        CUdevWorkqueueResource wq
+        unsigned char _oversize[40]
+        CUdevResource_st* nextResource
+
+    ctypedef CUdevResource_st CUdevResource_v1
+
+    ctypedef CUdevResource_v1 CUdevResource
+
+    cdef enum CUlogLevel_enum:
+        CU_LOG_LEVEL_ERROR = 0
+        CU_LOG_LEVEL_WARNING = 1
+
+    ctypedef CUlogLevel_enum CUlogLevel
+
+    cdef struct CUlogsCallbackEntry_st:
+        pass
+    ctypedef CUlogsCallbackEntry_st* CUlogsCallbackHandle
+
+    ctypedef void (*CUlogsCallback)(void* data, CUlogLevel logLevel, char* message, size_t length)
+
+    ctypedef unsigned int CUlogIterator
+
+cdef extern from "cudaProfiler.h":
+
+    cdef enum CUoutput_mode_enum:
+        CU_OUT_KEY_VALUE_PAIR = 0
+        CU_OUT_CSV = 1
+
+    ctypedef CUoutput_mode_enum CUoutput_mode
+
+cdef enum CUeglFrameType_enum:
+    CU_EGL_FRAME_TYPE_ARRAY = 0
+    CU_EGL_FRAME_TYPE_PITCH = 1
+
+ctypedef CUeglFrameType_enum CUeglFrameType
+
+cdef enum CUeglResourceLocationFlags_enum:
+    CU_EGL_RESOURCE_LOCATION_SYSMEM = 0
+    CU_EGL_RESOURCE_LOCATION_VIDMEM = 1
+
+ctypedef CUeglResourceLocationFlags_enum CUeglResourceLocationFlags
+
+cdef enum CUeglColorFormat_enum:
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR = 0
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR = 1
+    CU_EGL_COLOR_FORMAT_YUV422_PLANAR = 2
+    CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR = 3
+    CU_EGL_COLOR_FORMAT_RGB = 4
+    CU_EGL_COLOR_FORMAT_BGR = 5
+    CU_EGL_COLOR_FORMAT_ARGB = 6
+    CU_EGL_COLOR_FORMAT_RGBA = 7
+    CU_EGL_COLOR_FORMAT_L = 8
+    CU_EGL_COLOR_FORMAT_R = 9
+    CU_EGL_COLOR_FORMAT_YUV444_PLANAR = 10
+    CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR = 11
+    CU_EGL_COLOR_FORMAT_YUYV_422 = 12
+    CU_EGL_COLOR_FORMAT_UYVY_422 = 13
+    CU_EGL_COLOR_FORMAT_ABGR = 14
+    CU_EGL_COLOR_FORMAT_BGRA = 15
+    CU_EGL_COLOR_FORMAT_A = 16
+    CU_EGL_COLOR_FORMAT_RG = 17
+    CU_EGL_COLOR_FORMAT_AYUV = 18
+    CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR = 19
+    CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR = 20
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR = 21
+    CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR = 22
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR = 23
+    CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR = 24
+    CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR = 25
+    CU_EGL_COLOR_FORMAT_VYUY_ER = 26
+    CU_EGL_COLOR_FORMAT_UYVY_ER = 27
+    CU_EGL_COLOR_FORMAT_YUYV_ER = 28
+    CU_EGL_COLOR_FORMAT_YVYU_ER = 29
+    CU_EGL_COLOR_FORMAT_YUV_ER = 30
+    CU_EGL_COLOR_FORMAT_YUVA_ER = 31
+    CU_EGL_COLOR_FORMAT_AYUV_ER = 32
+    CU_EGL_COLOR_FORMAT_YUV444_PLANAR_ER = 33
+    CU_EGL_COLOR_FORMAT_YUV422_PLANAR_ER = 34
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR_ER = 35
+    CU_EGL_COLOR_FORMAT_YUV444_SEMIPLANAR_ER = 36
+    CU_EGL_COLOR_FORMAT_YUV422_SEMIPLANAR_ER = 37
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_ER = 38
+    CU_EGL_COLOR_FORMAT_YVU444_PLANAR_ER = 39
+    CU_EGL_COLOR_FORMAT_YVU422_PLANAR_ER = 40
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR_ER = 41
+    CU_EGL_COLOR_FORMAT_YVU444_SEMIPLANAR_ER = 42
+    CU_EGL_COLOR_FORMAT_YVU422_SEMIPLANAR_ER = 43
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_ER = 44
+    CU_EGL_COLOR_FORMAT_BAYER_RGGB = 45
+    CU_EGL_COLOR_FORMAT_BAYER_BGGR = 46
+    CU_EGL_COLOR_FORMAT_BAYER_GRBG = 47
+    CU_EGL_COLOR_FORMAT_BAYER_GBRG = 48
+    CU_EGL_COLOR_FORMAT_BAYER10_RGGB = 49
+    CU_EGL_COLOR_FORMAT_BAYER10_BGGR = 50
+    CU_EGL_COLOR_FORMAT_BAYER10_GRBG = 51
+    CU_EGL_COLOR_FORMAT_BAYER10_GBRG = 52
+    CU_EGL_COLOR_FORMAT_BAYER12_RGGB = 53
+    CU_EGL_COLOR_FORMAT_BAYER12_BGGR = 54
+    CU_EGL_COLOR_FORMAT_BAYER12_GRBG = 55
+    CU_EGL_COLOR_FORMAT_BAYER12_GBRG = 56
+    CU_EGL_COLOR_FORMAT_BAYER14_RGGB = 57
+    CU_EGL_COLOR_FORMAT_BAYER14_BGGR = 58
+    CU_EGL_COLOR_FORMAT_BAYER14_GRBG = 59
+    CU_EGL_COLOR_FORMAT_BAYER14_GBRG = 60
+    CU_EGL_COLOR_FORMAT_BAYER20_RGGB = 61
+    CU_EGL_COLOR_FORMAT_BAYER20_BGGR = 62
+    CU_EGL_COLOR_FORMAT_BAYER20_GRBG = 63
+    CU_EGL_COLOR_FORMAT_BAYER20_GBRG = 64
+    CU_EGL_COLOR_FORMAT_YVU444_PLANAR = 65
+    CU_EGL_COLOR_FORMAT_YVU422_PLANAR = 66
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR = 67
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_RGGB = 68
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_BGGR = 69
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_GRBG = 70
+    CU_EGL_COLOR_FORMAT_BAYER_ISP_GBRG = 71
+    CU_EGL_COLOR_FORMAT_BAYER_BCCR = 72
+    CU_EGL_COLOR_FORMAT_BAYER_RCCB = 73
+    CU_EGL_COLOR_FORMAT_BAYER_CRBC = 74
+    CU_EGL_COLOR_FORMAT_BAYER_CBRC = 75
+    CU_EGL_COLOR_FORMAT_BAYER10_CCCC = 76
+    CU_EGL_COLOR_FORMAT_BAYER12_BCCR = 77
+    CU_EGL_COLOR_FORMAT_BAYER12_RCCB = 78
+    CU_EGL_COLOR_FORMAT_BAYER12_CRBC = 79
+    CU_EGL_COLOR_FORMAT_BAYER12_CBRC = 80
+    CU_EGL_COLOR_FORMAT_BAYER12_CCCC = 81
+    CU_EGL_COLOR_FORMAT_Y = 82
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_2020 = 83
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_2020 = 84
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR_2020 = 85
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR_2020 = 86
+    CU_EGL_COLOR_FORMAT_YUV420_SEMIPLANAR_709 = 87
+    CU_EGL_COLOR_FORMAT_YVU420_SEMIPLANAR_709 = 88
+    CU_EGL_COLOR_FORMAT_YUV420_PLANAR_709 = 89
+    CU_EGL_COLOR_FORMAT_YVU420_PLANAR_709 = 90
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709 = 91
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_2020 = 92
+    CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_2020 = 93
+    CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR = 94
+    CU_EGL_COLOR_FORMAT_Y10V10U10_422_SEMIPLANAR_709 = 95
+    CU_EGL_COLOR_FORMAT_Y_ER = 96
+    CU_EGL_COLOR_FORMAT_Y_709_ER = 97
+    CU_EGL_COLOR_FORMAT_Y10_ER = 98
+    CU_EGL_COLOR_FORMAT_Y10_709_ER = 99
+    CU_EGL_COLOR_FORMAT_Y12_ER = 100
+    CU_EGL_COLOR_FORMAT_Y12_709_ER = 101
+    CU_EGL_COLOR_FORMAT_YUVA = 102
+    CU_EGL_COLOR_FORMAT_YUV = 103
+    CU_EGL_COLOR_FORMAT_YVYU = 104
+    CU_EGL_COLOR_FORMAT_VYUY = 105
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_ER = 106
+    CU_EGL_COLOR_FORMAT_Y10V10U10_420_SEMIPLANAR_709_ER = 107
+    CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_ER = 108
+    CU_EGL_COLOR_FORMAT_Y10V10U10_444_SEMIPLANAR_709_ER = 109
+    CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_ER = 110
+    CU_EGL_COLOR_FORMAT_Y12V12U12_420_SEMIPLANAR_709_ER = 111
+    CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_ER = 112
+    CU_EGL_COLOR_FORMAT_Y12V12U12_444_SEMIPLANAR_709_ER = 113
+    CU_EGL_COLOR_FORMAT_UYVY_709 = 114
+    CU_EGL_COLOR_FORMAT_UYVY_709_ER = 115
+    CU_EGL_COLOR_FORMAT_UYVY_2020 = 116
+    CU_EGL_COLOR_FORMAT_MAX = 117
+
+ctypedef CUeglColorFormat_enum CUeglColorFormat
+
+cdef union anon_union16:
+    CUarray pArray[3]
+    void* pPitch[3]
+
+cdef struct CUeglFrame_st:
+    anon_union16 frame
+    unsigned int width
+    unsigned int height
+    unsigned int depth
+    unsigned int pitch
+    unsigned int planeCount
+    unsigned int numChannels
+    CUeglFrameType frameType
+    CUeglColorFormat eglColorFormat
+    CUarray_format cuFormat
+
+ctypedef CUeglFrame_st CUeglFrame_v1
+
+ctypedef CUeglFrame_v1 CUeglFrame
+
+cdef extern from "":
+    cdef struct CUeglStreamConnection_st:
+        pass
+ctypedef CUeglStreamConnection_st* CUeglStreamConnection
+
+cdef enum CUGLDeviceList_enum:
+    CU_GL_DEVICE_LIST_ALL = 1
+    CU_GL_DEVICE_LIST_CURRENT_FRAME = 2
+    CU_GL_DEVICE_LIST_NEXT_FRAME = 3
+
+ctypedef CUGLDeviceList_enum CUGLDeviceList
+
+cdef enum CUGLmap_flags_enum:
+    CU_GL_MAP_RESOURCE_FLAGS_NONE = 0
+    CU_GL_MAP_RESOURCE_FLAGS_READ_ONLY = 1
+    CU_GL_MAP_RESOURCE_FLAGS_WRITE_DISCARD = 2
+
+ctypedef CUGLmap_flags_enum CUGLmap_flags
+
+ctypedef unsigned int GLenum
+
+ctypedef unsigned int GLuint
+
+cdef extern from "":
+    cdef struct void:
+        pass
+ctypedef void* EGLImageKHR
+
+cdef extern from "":
+    cdef struct void:
+        pass
+ctypedef void* EGLStreamKHR
+
+ctypedef unsigned int EGLint
+
+cdef extern from "":
+    cdef struct void:
+        pass
+ctypedef void* EGLSyncKHR
+
+ctypedef uint32_t VdpDevice
+
+ctypedef unsigned long long VdpGetProcAddress
+
+ctypedef uint32_t VdpVideoSurface
+
+ctypedef uint32_t VdpOutputSurface
+
+cdef CUresult cuGetErrorString(CUresult error, const char** pStr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGetErrorName(CUresult error, const char** pStr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuInit(unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDriverGetVersion(int* driverVersion) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGet(CUdevice* device, int ordinal) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetCount(int* count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetName(char* name, int length, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetUuid(CUuuid* uuid, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetLuid(char* luid, unsigned int* deviceNodeMask, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceTotalMem(size_t* numbytes, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetTexture1DLinearMaxWidth(size_t* maxWidthInElements, CUarray_format pformat, unsigned numChannels, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetAttribute(int* pi, CUdevice_attribute attrib, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetHostAtomicCapabilities(unsigned int* capabilities, const CUatomicOperation* operations, unsigned int count, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetNvSciSyncAttributes(void* nvSciSyncAttrList, CUdevice dev, int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceSetMemPool(CUdevice dev, CUmemoryPool pool) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetMemPool(CUmemoryPool* pool, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetDefaultMemPool(CUmemoryPool* pool_out, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetExecAffinitySupport(int* pi, CUexecAffinityType typename, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFlushGPUDirectRDMAWrites(CUflushGPUDirectRDMAWritesTarget target, CUflushGPUDirectRDMAWritesScope scope) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetProperties(CUdevprop* prop, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceComputeCapability(int* major, int* minor, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevicePrimaryCtxRetain(CUcontext* pctx, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevicePrimaryCtxRelease(CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevicePrimaryCtxSetFlags(CUdevice dev, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevicePrimaryCtxGetState(CUdevice dev, unsigned int* flags, int* active) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevicePrimaryCtxReset(CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxCreate(CUcontext* pctx, CUctxCreateParams* ctxCreateParams, unsigned int flags, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxDestroy(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxPushCurrent(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxPopCurrent(CUcontext* pctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSetCurrent(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetCurrent(CUcontext* pctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetDevice(CUdevice* device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetDevice_v2(CUdevice* device, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetFlags(unsigned int* flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSetFlags(unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetId(CUcontext ctx, unsigned long long* ctxId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSynchronize() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSynchronize_v2(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSetLimit(CUlimit limit, size_t value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetLimit(size_t* pvalue, CUlimit limit) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetCacheConfig(CUfunc_cache* pconfig) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSetCacheConfig(CUfunc_cache config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetApiVersion(CUcontext ctx, unsigned int* version) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetStreamPriorityRange(int* leastPriority, int* greatestPriority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxResetPersistingL2Cache() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetExecAffinity(CUexecAffinityParam* pExecAffinity, CUexecAffinityType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxRecordEvent(CUcontext hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxWaitEvent(CUcontext hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxAttach(CUcontext* pctx, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxDetach(CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetSharedMemConfig(CUsharedconfig* pConfig) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxSetSharedMemConfig(CUsharedconfig config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleLoad(CUmodule* module, const char* fname) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleLoadData(CUmodule* module, const void* image) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleLoadDataEx(CUmodule* module, const void* image, unsigned int numOptions, CUjit_option* options, void** optionValues) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleLoadFatBinary(CUmodule* module, const void* fatCubin) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleUnload(CUmodule hmod) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleGetLoadingMode(CUmoduleLoadingMode* mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleGetFunction(CUfunction* hfunc, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleGetFunctionCount(unsigned int* count, CUmodule mod) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleEnumerateFunctions(CUfunction* functions, unsigned int numFunctions, CUmodule mod) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleGetGlobal(CUdeviceptr* dptr, size_t* numbytes, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLinkCreate(unsigned int numOptions, CUjit_option* options, void** optionValues, CUlinkState* stateOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLinkAddData(CUlinkState state, CUjitInputType typename, void* data, size_t size, const char* name, unsigned int numOptions, CUjit_option* options, void** optionValues) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLinkAddFile(CUlinkState state, CUjitInputType typename, const char* path, unsigned int numOptions, CUjit_option* options, void** optionValues) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLinkComplete(CUlinkState state, void** cubinOut, size_t* sizeOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLinkDestroy(CUlinkState state) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleGetTexRef(CUtexref* pTexRef, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuModuleGetSurfRef(CUsurfref* pSurfRef, CUmodule hmod, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryLoadData(CUlibrary* library, const void* code, CUjit_option* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, CUlibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryLoadFromFile(CUlibrary* library, const char* fileName, CUjit_option* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, CUlibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryUnload(CUlibrary library) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryGetKernel(CUkernel* pKernel, CUlibrary library, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryGetKernelCount(unsigned int* count, CUlibrary lib) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryEnumerateKernels(CUkernel* kernels, unsigned int numKernels, CUlibrary lib) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryGetModule(CUmodule* pMod, CUlibrary library) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelGetFunction(CUfunction* pFunc, CUkernel kernel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelGetLibrary(CUlibrary* pLib, CUkernel kernel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryGetGlobal(CUdeviceptr* dptr, size_t* numbytes, CUlibrary library, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryGetManaged(CUdeviceptr* dptr, size_t* numbytes, CUlibrary library, const char* name) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLibraryGetUnifiedFunction(void** fptr, CUlibrary library, const char* symbol) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelGetAttribute(int* pi, CUfunction_attribute attrib, CUkernel kernel, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelSetAttribute(CUfunction_attribute attrib, int val, CUkernel kernel, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelSetCacheConfig(CUkernel kernel, CUfunc_cache config, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelGetName(const char** name, CUkernel hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelGetParamInfo(CUkernel kernel, size_t paramIndex, size_t* paramOffset, size_t* paramSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuKernelGetParamCount(CUkernel kernel, size_t* paramCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetInfo(size_t* free, size_t* total) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAlloc(CUdeviceptr* dptr, size_t bytesize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAllocPitch(CUdeviceptr* dptr, size_t* pPitch, size_t WidthInBytes, size_t Height, unsigned int ElementSizeBytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemFree(CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetAddressRange(CUdeviceptr* pbase, size_t* psize, CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAllocHost(void** pp, size_t bytesize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemFreeHost(void* p) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemHostAlloc(void** pp, size_t bytesize, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemHostGetDevicePointer(CUdeviceptr* pdptr, void* p, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemHostGetFlags(unsigned int* pFlags, void* p) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAllocManaged(CUdeviceptr* dptr, size_t bytesize, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceRegisterAsyncNotification(CUdevice device, CUasyncCallback callbackFunc, void* userData, CUasyncCallbackHandle* callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceUnregisterAsyncNotification(CUdevice device, CUasyncCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetByPCIBusId(CUdevice* dev, const char* pciBusId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetPCIBusId(char* pciBusId, int length, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuIpcGetEventHandle(CUipcEventHandle* pHandle, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuIpcOpenEventHandle(CUevent* phEvent, CUipcEventHandle handle) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuIpcGetMemHandle(CUipcMemHandle* pHandle, CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuIpcOpenMemHandle(CUdeviceptr* pdptr, CUipcMemHandle handle, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuIpcCloseMemHandle(CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemHostRegister(void* p, size_t bytesize, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemHostUnregister(void* p) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyPeer(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyHtoD(CUdeviceptr dstDevice, const void* srcHost, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyDtoH(void* dstHost, CUdeviceptr srcDevice, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyDtoD(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyDtoA(CUarray dstArray, size_t dstOffset, CUdeviceptr srcDevice, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyAtoD(CUdeviceptr dstDevice, CUarray srcArray, size_t srcOffset, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyHtoA(CUarray dstArray, size_t dstOffset, const void* srcHost, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyAtoH(void* dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyAtoA(CUarray dstArray, size_t dstOffset, CUarray srcArray, size_t srcOffset, size_t ByteCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy2D(const CUDA_MEMCPY2D* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy2DUnaligned(const CUDA_MEMCPY2D* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy3D(const CUDA_MEMCPY3D* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy3DPeer(const CUDA_MEMCPY3D_PEER* pCopy) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyAsync(CUdeviceptr dst, CUdeviceptr src, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyPeerAsync(CUdeviceptr dstDevice, CUcontext dstContext, CUdeviceptr srcDevice, CUcontext srcContext, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyHtoDAsync(CUdeviceptr dstDevice, const void* srcHost, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyDtoHAsync(void* dstHost, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyDtoDAsync(CUdeviceptr dstDevice, CUdeviceptr srcDevice, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyHtoAAsync(CUarray dstArray, size_t dstOffset, const void* srcHost, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyAtoHAsync(void* dstHost, CUarray srcArray, size_t srcOffset, size_t ByteCount, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy2DAsync(const CUDA_MEMCPY2D* pCopy, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy3DAsync(const CUDA_MEMCPY3D* pCopy, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy3DPeerAsync(const CUDA_MEMCPY3D_PEER* pCopy, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyBatchAsync(CUdeviceptr* dsts, CUdeviceptr* srcs, size_t* sizes, size_t count, CUmemcpyAttributes* attrs, size_t* attrsIdxs, size_t numAttrs, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy3DBatchAsync(size_t numOps, CUDA_MEMCPY3D_BATCH_OP* opList, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpyWithAttributesAsync(CUdeviceptr dst, CUdeviceptr src, size_t size, CUmemcpyAttributes* attr, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemcpy3DWithAttributesAsync(CUDA_MEMCPY3D_BATCH_OP* op, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD8(CUdeviceptr dstDevice, unsigned char uc, size_t N) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD16(CUdeviceptr dstDevice, unsigned short us, size_t N) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD32(CUdeviceptr dstDevice, unsigned int ui, size_t N) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD2D8(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD2D16(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD2D32(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD8Async(CUdeviceptr dstDevice, unsigned char uc, size_t N, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD16Async(CUdeviceptr dstDevice, unsigned short us, size_t N, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD32Async(CUdeviceptr dstDevice, unsigned int ui, size_t N, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD2D8Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned char uc, size_t Width, size_t Height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD2D16Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned short us, size_t Width, size_t Height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemsetD2D32Async(CUdeviceptr dstDevice, size_t dstPitch, unsigned int ui, size_t Width, size_t Height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArrayCreate(CUarray* pHandle, const CUDA_ARRAY_DESCRIPTOR* pAllocateArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArrayGetDescriptor(CUDA_ARRAY_DESCRIPTOR* pArrayDescriptor, CUarray hArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES* sparseProperties, CUarray array) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMipmappedArrayGetSparseProperties(CUDA_ARRAY_SPARSE_PROPERTIES* sparseProperties, CUmipmappedArray mipmap) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS* memoryRequirements, CUarray array, CUdevice device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMipmappedArrayGetMemoryRequirements(CUDA_ARRAY_MEMORY_REQUIREMENTS* memoryRequirements, CUmipmappedArray mipmap, CUdevice device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArrayGetPlane(CUarray* pPlaneArray, CUarray hArray, unsigned int planeIdx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArrayDestroy(CUarray hArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArray3DCreate(CUarray* pHandle, const CUDA_ARRAY3D_DESCRIPTOR* pAllocateArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuArray3DGetDescriptor(CUDA_ARRAY3D_DESCRIPTOR* pArrayDescriptor, CUarray hArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMipmappedArrayCreate(CUmipmappedArray* pHandle, const CUDA_ARRAY3D_DESCRIPTOR* pMipmappedArrayDesc, unsigned int numMipmapLevels) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMipmappedArrayGetLevel(CUarray* pLevelArray, CUmipmappedArray hMipmappedArray, unsigned int level) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMipmappedArrayDestroy(CUmipmappedArray hMipmappedArray) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetHandleForAddressRange(void* handle, CUdeviceptr dptr, size_t size, CUmemRangeHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemBatchDecompressAsync(CUmemDecompressParams* paramsArray, size_t count, unsigned int flags, size_t* errorIndex, CUstream stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAddressReserve(CUdeviceptr* ptr, size_t size, size_t alignment, CUdeviceptr addr, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAddressFree(CUdeviceptr ptr, size_t size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemCreate(CUmemGenericAllocationHandle* handle, size_t size, const CUmemAllocationProp* prop, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemRelease(CUmemGenericAllocationHandle handle) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemMap(CUdeviceptr ptr, size_t size, size_t offset, CUmemGenericAllocationHandle handle, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemMapArrayAsync(CUarrayMapInfo* mapInfoList, unsigned int count, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemUnmap(CUdeviceptr ptr, size_t size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemSetAccess(CUdeviceptr ptr, size_t size, const CUmemAccessDesc* desc, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetAccess(unsigned long long* flags, const CUmemLocation* location, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemExportToShareableHandle(void* shareableHandle, CUmemGenericAllocationHandle handle, CUmemAllocationHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemImportFromShareableHandle(CUmemGenericAllocationHandle* handle, void* osHandle, CUmemAllocationHandleType shHandleType) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetAllocationGranularity(size_t* granularity, const CUmemAllocationProp* prop, CUmemAllocationGranularity_flags option) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetAllocationPropertiesFromHandle(CUmemAllocationProp* prop, CUmemGenericAllocationHandle handle) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemRetainAllocationHandle(CUmemGenericAllocationHandle* handle, void* addr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemFreeAsync(CUdeviceptr dptr, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAllocAsync(CUdeviceptr* dptr, size_t bytesize, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolTrimTo(CUmemoryPool pool, size_t minBytesToKeep) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolSetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolGetAttribute(CUmemoryPool pool, CUmemPool_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolSetAccess(CUmemoryPool pool, const CUmemAccessDesc* map, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolGetAccess(CUmemAccess_flags* flags, CUmemoryPool memPool, CUmemLocation* location) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolCreate(CUmemoryPool* pool, const CUmemPoolProps* poolProps) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolDestroy(CUmemoryPool pool) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetDefaultMemPool(CUmemoryPool* pool_out, CUmemLocation* location, CUmemAllocationType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemGetMemPool(CUmemoryPool* pool, CUmemLocation* location, CUmemAllocationType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemSetMemPool(CUmemLocation* location, CUmemAllocationType typename, CUmemoryPool pool) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAllocFromPoolAsync(CUdeviceptr* dptr, size_t bytesize, CUmemoryPool pool, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolExportToShareableHandle(void* handle_out, CUmemoryPool pool, CUmemAllocationHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolImportFromShareableHandle(CUmemoryPool* pool_out, void* handle, CUmemAllocationHandleType handleType, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolExportPointer(CUmemPoolPtrExportData* shareData_out, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPoolImportPointer(CUdeviceptr* ptr_out, CUmemoryPool pool, CUmemPoolPtrExportData* shareData) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastCreate(CUmemGenericAllocationHandle* mcHandle, const CUmulticastObjectProp* prop) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastAddDevice(CUmemGenericAllocationHandle mcHandle, CUdevice dev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastBindMem(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastBindMem_v2(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, CUmemGenericAllocationHandle memHandle, size_t memOffset, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastBindAddr(CUmemGenericAllocationHandle mcHandle, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastBindAddr_v2(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, CUdeviceptr memptr, size_t size, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastUnbind(CUmemGenericAllocationHandle mcHandle, CUdevice dev, size_t mcOffset, size_t size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMulticastGetGranularity(size_t* granularity, const CUmulticastObjectProp* prop, CUmulticastGranularity_flags option) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuPointerGetAttribute(void* data, CUpointer_attribute attribute, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPrefetchAsync(CUdeviceptr devPtr, size_t count, CUmemLocation location, unsigned int flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemAdvise(CUdeviceptr devPtr, size_t count, CUmem_advise advice, CUmemLocation location) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemPrefetchBatchAsync(CUdeviceptr* dptrs, size_t* sizes, size_t count, CUmemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemDiscardBatchAsync(CUdeviceptr* dptrs, size_t* sizes, size_t count, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemDiscardAndPrefetchBatchAsync(CUdeviceptr* dptrs, size_t* sizes, size_t count, CUmemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemRangeGetAttribute(void* data, size_t dataSize, CUmem_range_attribute attribute, CUdeviceptr devPtr, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuMemRangeGetAttributes(void** data, size_t* dataSizes, CUmem_range_attribute* attributes, size_t numAttributes, CUdeviceptr devPtr, size_t count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuPointerSetAttribute(const void* value, CUpointer_attribute attribute, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuPointerGetAttributes(unsigned int numAttributes, CUpointer_attribute* attributes, void** data, CUdeviceptr ptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamCreate(CUstream* phStream, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamCreateWithPriority(CUstream* phStream, unsigned int flags, int priority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamBeginCaptureToCig(CUstream hStream, CUstreamCigCaptureParams* streamCigCaptureParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamEndCaptureToCig(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetPriority(CUstream hStream, int* priority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetDevice(CUstream hStream, CUdevice* device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetFlags(CUstream hStream, unsigned int* flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetId(CUstream hStream, unsigned long long* streamId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetCtx(CUstream hStream, CUcontext* pctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetCtx_v2(CUstream hStream, CUcontext* pCtx, CUgreenCtx* pGreenCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamWaitEvent(CUstream hStream, CUevent hEvent, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamAddCallback(CUstream hStream, CUstreamCallback callback, void* userData, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamBeginCapture(CUstream hStream, CUstreamCaptureMode mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamBeginCaptureToGraph(CUstream hStream, CUgraph hGraph, const CUgraphNode* dependencies, const CUgraphEdgeData* dependencyData, size_t numDependencies, CUstreamCaptureMode mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuThreadExchangeStreamCaptureMode(CUstreamCaptureMode* mode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamEndCapture(CUstream hStream, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamIsCapturing(CUstream hStream, CUstreamCaptureStatus* captureStatus) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetCaptureInfo(CUstream hStream, CUstreamCaptureStatus* captureStatus_out, cuuint64_t* id_out, CUgraph* graph_out, const CUgraphNode** dependencies_out, const CUgraphEdgeData** edgeData_out, size_t* numDependencies_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamUpdateCaptureDependencies(CUstream hStream, CUgraphNode* dependencies, const CUgraphEdgeData* dependencyData, size_t numDependencies, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamAttachMemAsync(CUstream hStream, CUdeviceptr dptr, size_t length, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamQuery(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamSynchronize(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamDestroy(CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamCopyAttributes(CUstream dst, CUstream src) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetAttribute(CUstream hStream, CUstreamAttrID attr, CUstreamAttrValue* value_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamSetAttribute(CUstream hStream, CUstreamAttrID attr, const CUstreamAttrValue* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventCreate(CUevent* phEvent, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventRecord(CUevent hEvent, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventRecordWithFlags(CUevent hEvent, CUstream hStream, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventQuery(CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventSynchronize(CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventDestroy(CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventElapsedTime(float* pMilliseconds, CUevent hStart, CUevent hEnd) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuImportExternalMemory(CUexternalMemory* extMem_out, const CUDA_EXTERNAL_MEMORY_HANDLE_DESC* memHandleDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuExternalMemoryGetMappedBuffer(CUdeviceptr* devPtr, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_BUFFER_DESC* bufferDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuExternalMemoryGetMappedMipmappedArray(CUmipmappedArray* mipmap, CUexternalMemory extMem, const CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC* mipmapDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDestroyExternalMemory(CUexternalMemory extMem) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuImportExternalSemaphore(CUexternalSemaphore* extSem_out, const CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC* semHandleDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuSignalExternalSemaphoresAsync(const CUexternalSemaphore* extSemArray, const CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* paramsArray, unsigned int numExtSems, CUstream stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuWaitExternalSemaphoresAsync(const CUexternalSemaphore* extSemArray, const CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* paramsArray, unsigned int numExtSems, CUstream stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDestroyExternalSemaphore(CUexternalSemaphore extSem) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamWaitValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamWaitValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamWriteValue32(CUstream stream, CUdeviceptr addr, cuuint32_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamWriteValue64(CUstream stream, CUdeviceptr addr, cuuint64_t value, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamBatchMemOp(CUstream stream, unsigned int count, CUstreamBatchMemOpParams* paramArray, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncGetAttribute(int* pi, CUfunction_attribute attrib, CUfunction hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncSetCacheConfig(CUfunction hfunc, CUfunc_cache config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncGetModule(CUmodule* hmod, CUfunction hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncGetName(const char** name, CUfunction hfunc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncGetParamInfo(CUfunction func, size_t paramIndex, size_t* paramOffset, size_t* paramSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncGetParamCount(CUfunction func, size_t* paramCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncIsLoaded(CUfunctionLoadingState* state, CUfunction function) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncLoad(CUfunction function) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void** kernelParams, void** extra) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchKernelEx(const CUlaunchConfig* config, CUfunction f, void** kernelParams, void** extra) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchCooperativeKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void** kernelParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchCooperativeKernelMultiDevice(CUDA_LAUNCH_PARAMS* launchParamsList, unsigned int numDevices, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchHostFunc(CUstream hStream, CUhostFn fn, void* userData) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchHostFunc_v2(CUstream hStream, CUhostFn fn, void* userData, unsigned int syncMode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncSetBlockShape(CUfunction hfunc, int x, int y, int z) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncSetSharedSize(CUfunction hfunc, unsigned int numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuParamSetSize(CUfunction hfunc, unsigned int numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuParamSeti(CUfunction hfunc, int offset, unsigned int value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuParamSetf(CUfunction hfunc, int offset, float value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuParamSetv(CUfunction hfunc, int offset, void* ptr, unsigned int numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunch(CUfunction f) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchGrid(CUfunction f, int grid_width, int grid_height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLaunchGridAsync(CUfunction f, int grid_width, int grid_height, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuParamSetTexRef(CUfunction hfunc, int texunit, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuFuncSetSharedMemConfig(CUfunction hfunc, CUsharedconfig config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphCreate(CUgraph* phGraph, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddKernelNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphKernelNodeGetParams(CUgraphNode hNode, CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphKernelNodeSetParams(CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddMemcpyNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_MEMCPY3D* copyParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphMemcpyNodeGetParams(CUgraphNode hNode, CUDA_MEMCPY3D* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphMemcpyNodeSetParams(CUgraphNode hNode, const CUDA_MEMCPY3D* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddMemsetNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_MEMSET_NODE_PARAMS* memsetParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphMemsetNodeGetParams(CUgraphNode hNode, CUDA_MEMSET_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphMemsetNodeSetParams(CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddHostNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphHostNodeGetParams(CUgraphNode hNode, CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphHostNodeSetParams(CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddChildGraphNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUgraph childGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphChildGraphNodeGetGraph(CUgraphNode hNode, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddEmptyNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddEventRecordNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphEventRecordNodeGetEvent(CUgraphNode hNode, CUevent* event_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphEventRecordNodeSetEvent(CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddEventWaitNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphEventWaitNodeGetEvent(CUgraphNode hNode, CUevent* event_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphEventWaitNodeSetEvent(CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddExternalSemaphoresSignalNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExternalSemaphoresSignalNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* params_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExternalSemaphoresSignalNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddExternalSemaphoresWaitNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_EXT_SEM_WAIT_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExternalSemaphoresWaitNodeGetParams(CUgraphNode hNode, CUDA_EXT_SEM_WAIT_NODE_PARAMS* params_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExternalSemaphoresWaitNodeSetParams(CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddBatchMemOpNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, const CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphBatchMemOpNodeGetParams(CUgraphNode hNode, CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphBatchMemOpNodeSetParams(CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecBatchMemOpNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_BATCH_MEM_OP_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddMemAllocNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUDA_MEM_ALLOC_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphMemAllocNodeGetParams(CUgraphNode hNode, CUDA_MEM_ALLOC_NODE_PARAMS* params_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddMemFreeNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, size_t numDependencies, CUdeviceptr dptr) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphMemFreeNodeGetParams(CUgraphNode hNode, CUdeviceptr* dptr_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGraphMemTrim(CUdevice device) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceSetGraphMemAttribute(CUdevice device, CUgraphMem_attribute attr, void* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphClone(CUgraph* phGraphClone, CUgraph originalGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeFindInClone(CUgraphNode* phNode, CUgraphNode hOriginalNode, CUgraph hClonedGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetType(CUgraphNode hNode, CUgraphNodeType* typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetContainingGraph(CUgraphNode hNode, CUgraph* phGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetLocalId(CUgraphNode hNode, unsigned int* nodeId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetToolsId(CUgraphNode hNode, unsigned long long* toolsNodeId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphGetId(CUgraph hGraph, unsigned int* graphId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecGetId(CUgraphExec hGraphExec, unsigned int* graphId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphGetNodes(CUgraph hGraph, CUgraphNode* nodes, size_t* numNodes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphGetRootNodes(CUgraph hGraph, CUgraphNode* rootNodes, size_t* numRootNodes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphGetEdges(CUgraph hGraph, CUgraphNode* from_, CUgraphNode* to, CUgraphEdgeData* edgeData, size_t* numEdges) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetDependencies(CUgraphNode hNode, CUgraphNode* dependencies, CUgraphEdgeData* edgeData, size_t* numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetDependentNodes(CUgraphNode hNode, CUgraphNode* dependentNodes, CUgraphEdgeData* edgeData, size_t* numDependentNodes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddDependencies(CUgraph hGraph, const CUgraphNode* from_, const CUgraphNode* to, const CUgraphEdgeData* edgeData, size_t numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphRemoveDependencies(CUgraph hGraph, const CUgraphNode* from_, const CUgraphNode* to, const CUgraphEdgeData* edgeData, size_t numDependencies) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphDestroyNode(CUgraphNode hNode) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphInstantiate(CUgraphExec* phGraphExec, CUgraph hGraph, unsigned long long flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphInstantiateWithParams(CUgraphExec* phGraphExec, CUgraph hGraph, CUDA_GRAPH_INSTANTIATE_PARAMS* instantiateParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecGetFlags(CUgraphExec hGraphExec, cuuint64_t* flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecKernelNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_KERNEL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecMemcpyNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMCPY3D* copyParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecMemsetNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_MEMSET_NODE_PARAMS* memsetParams, CUcontext ctx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecHostNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_HOST_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecChildGraphNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraph childGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecEventRecordNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecEventWaitNodeSetEvent(CUgraphExec hGraphExec, CUgraphNode hNode, CUevent event) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecExternalSemaphoresSignalNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_SIGNAL_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecExternalSemaphoresWaitNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, const CUDA_EXT_SEM_WAIT_NODE_PARAMS* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeSetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int isEnabled) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetEnabled(CUgraphExec hGraphExec, CUgraphNode hNode, unsigned int* isEnabled) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphUpload(CUgraphExec hGraphExec, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphLaunch(CUgraphExec hGraphExec, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecDestroy(CUgraphExec hGraphExec) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphDestroy(CUgraph hGraph) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecUpdate(CUgraphExec hGraphExec, CUgraph hGraph, CUgraphExecUpdateResultInfo* resultInfo) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphKernelNodeCopyAttributes(CUgraphNode dst, CUgraphNode src) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphKernelNodeGetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr, CUkernelNodeAttrValue* value_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphKernelNodeSetAttribute(CUgraphNode hNode, CUkernelNodeAttrID attr, const CUkernelNodeAttrValue* value) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphDebugDotPrint(CUgraph hGraph, const char* path, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuUserObjectCreate(CUuserObject* object_out, void* ptr, CUhostFn destroy, unsigned int initialRefcount, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuUserObjectRetain(CUuserObject object, unsigned int count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuUserObjectRelease(CUuserObject object, unsigned int count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphRetainUserObject(CUgraph graph, CUuserObject object, unsigned int count, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphReleaseUserObject(CUgraph graph, CUuserObject object, unsigned int count) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphAddNode(CUgraphNode* phGraphNode, CUgraph hGraph, const CUgraphNode* dependencies, const CUgraphEdgeData* dependencyData, size_t numDependencies, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeSetParams(CUgraphNode hNode, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphNodeGetParams(CUgraphNode hNode, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphExecNodeSetParams(CUgraphExec hGraphExec, CUgraphNode hNode, CUgraphNodeParams* nodeParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphConditionalHandleCreate(CUgraphConditionalHandle* pHandle_out, CUgraph hGraph, CUcontext ctx, unsigned int defaultLaunchValue, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyMaxActiveBlocksPerMultiprocessor(int* numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int* numBlocks, CUfunction func, int blockSize, size_t dynamicSMemSize, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyMaxPotentialBlockSize(int* minGridSize, int* blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyMaxPotentialBlockSizeWithFlags(int* minGridSize, int* blockSize, CUfunction func, CUoccupancyB2DSize blockSizeToDynamicSMemSize, size_t dynamicSMemSize, int blockSizeLimit, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyAvailableDynamicSMemPerBlock(size_t* dynamicSmemSize, CUfunction func, int numBlocks, int blockSize) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyMaxPotentialClusterSize(int* clusterSize, CUfunction func, const CUlaunchConfig* config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuOccupancyMaxActiveClusters(int* numClusters, CUfunction func, const CUlaunchConfig* config) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetArray(CUtexref hTexRef, CUarray hArray, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetMipmappedArray(CUtexref hTexRef, CUmipmappedArray hMipmappedArray, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetAddress(size_t* ByteOffset, CUtexref hTexRef, CUdeviceptr dptr, size_t numbytes) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetAddress2D(CUtexref hTexRef, const CUDA_ARRAY_DESCRIPTOR* desc, CUdeviceptr dptr, size_t Pitch) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetFormat(CUtexref hTexRef, CUarray_format fmt, int NumPackedComponents) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetAddressMode(CUtexref hTexRef, int dim, CUaddress_mode am) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetFilterMode(CUtexref hTexRef, CUfilter_mode fm) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetMipmapFilterMode(CUtexref hTexRef, CUfilter_mode fm) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetMipmapLevelBias(CUtexref hTexRef, float bias) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetMipmapLevelClamp(CUtexref hTexRef, float minMipmapLevelClamp, float maxMipmapLevelClamp) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetMaxAnisotropy(CUtexref hTexRef, unsigned int maxAniso) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetBorderColor(CUtexref hTexRef, float* pBorderColor) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefSetFlags(CUtexref hTexRef, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetAddress(CUdeviceptr* pdptr, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetArray(CUarray* phArray, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetMipmappedArray(CUmipmappedArray* phMipmappedArray, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetAddressMode(CUaddress_mode* pam, CUtexref hTexRef, int dim) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetFilterMode(CUfilter_mode* pfm, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetFormat(CUarray_format* pFormat, int* pNumChannels, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetMipmapFilterMode(CUfilter_mode* pfm, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetMipmapLevelBias(float* pbias, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetMipmapLevelClamp(float* pminMipmapLevelClamp, float* pmaxMipmapLevelClamp, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetMaxAnisotropy(int* pmaxAniso, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetBorderColor(float* pBorderColor, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefGetFlags(unsigned int* pFlags, CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefCreate(CUtexref* pTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexRefDestroy(CUtexref hTexRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuSurfRefSetArray(CUsurfref hSurfRef, CUarray hArray, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuSurfRefGetArray(CUarray* phArray, CUsurfref hSurfRef) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexObjectCreate(CUtexObject* pTexObject, const CUDA_RESOURCE_DESC* pResDesc, const CUDA_TEXTURE_DESC* pTexDesc, const CUDA_RESOURCE_VIEW_DESC* pResViewDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexObjectDestroy(CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexObjectGetResourceDesc(CUDA_RESOURCE_DESC* pResDesc, CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexObjectGetTextureDesc(CUDA_TEXTURE_DESC* pTexDesc, CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTexObjectGetResourceViewDesc(CUDA_RESOURCE_VIEW_DESC* pResViewDesc, CUtexObject texObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuSurfObjectCreate(CUsurfObject* pSurfObject, const CUDA_RESOURCE_DESC* pResDesc) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuSurfObjectDestroy(CUsurfObject surfObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuSurfObjectGetResourceDesc(CUDA_RESOURCE_DESC* pResDesc, CUsurfObject surfObject) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTensorMapEncodeTiled(CUtensorMap* tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void* globalAddress, const cuuint64_t* globalDim, const cuuint64_t* globalStrides, const cuuint32_t* boxDim, const cuuint32_t* elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTensorMapEncodeIm2col(CUtensorMap* tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void* globalAddress, const cuuint64_t* globalDim, const cuuint64_t* globalStrides, const int* pixelBoxLowerCorner, const int* pixelBoxUpperCorner, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t* elementStrides, CUtensorMapInterleave interleave, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTensorMapEncodeIm2colWide(CUtensorMap* tensorMap, CUtensorMapDataType tensorDataType, cuuint32_t tensorRank, void* globalAddress, const cuuint64_t* globalDim, const cuuint64_t* globalStrides, int pixelBoxLowerCornerWidth, int pixelBoxUpperCornerWidth, cuuint32_t channelsPerPixel, cuuint32_t pixelsPerColumn, const cuuint32_t* elementStrides, CUtensorMapInterleave interleave, CUtensorMapIm2ColWideMode mode, CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion, CUtensorMapFloatOOBfill oobFill) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuTensorMapReplaceAddress(CUtensorMap* tensorMap, void* globalAddress) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceCanAccessPeer(int* canAccessPeer, CUdevice dev, CUdevice peerDev) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxEnablePeerAccess(CUcontext peerContext, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxDisablePeerAccess(CUcontext peerContext) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetP2PAttribute(int* value, CUdevice_P2PAttribute attrib, CUdevice srcDevice, CUdevice dstDevice) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetP2PAtomicCapabilities(unsigned int* capabilities, const CUatomicOperation* operations, unsigned int count, CUdevice srcDevice, CUdevice dstDevice) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsUnregisterResource(CUgraphicsResource resource) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsSubResourceGetMappedArray(CUarray* pArray, CUgraphicsResource resource, unsigned int arrayIndex, unsigned int mipLevel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsResourceGetMappedMipmappedArray(CUmipmappedArray* pMipmappedArray, CUgraphicsResource resource) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsResourceGetMappedPointer(CUdeviceptr* pDevPtr, size_t* pSize, CUgraphicsResource resource) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsResourceSetMapFlags(CUgraphicsResource resource, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsMapResources(unsigned int count, CUgraphicsResource* resources, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsUnmapResources(unsigned int count, CUgraphicsResource* resources, CUstream hStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGetProcAddress(const char* symbol, void** pfn, int cudaVersion, cuuint64_t flags, CUdriverProcAddressQueryResult* symbolStatus) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpGetAttribute(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpGetAttributeGlobal(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpSetAttribute(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpSetAttributeGlobal(CUcoredumpSettings attrib, void* value, size_t* size) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpRegisterStartCallback(CUcoredumpStatusCallback callback, void* userData, CUcoredumpCallbackHandle* callbackOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpRegisterCompleteCallback(CUcoredumpStatusCallback callback, void* userData, CUcoredumpCallbackHandle* callbackOut) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpDeregisterStartCallback(CUcoredumpCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCoredumpDeregisterCompleteCallback(CUcoredumpCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGetExportTable(const void** ppExportTable, const CUuuid* pExportTableId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxCreate(CUgreenCtx* phCtx, CUdevResourceDesc desc, CUdevice dev, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxDestroy(CUgreenCtx hCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxFromGreenCtx(CUcontext* pContext, CUgreenCtx hCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDeviceGetDevResource(CUdevice device, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCtxGetDevResource(CUcontext hCtx, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxGetDevResource(CUgreenCtx hCtx, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevSmResourceSplitByCount(CUdevResource* result, unsigned int* nbGroups, const CUdevResource* input, CUdevResource* remainder, unsigned int flags, unsigned int minCount) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevSmResourceSplit(CUdevResource* result, unsigned int nbGroups, const CUdevResource* input, CUdevResource* remainder, unsigned int flags, CU_DEV_SM_RESOURCE_GROUP_PARAMS* groupParams) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuDevResourceGenerateDesc(CUdevResourceDesc* phDesc, CUdevResource* resources, unsigned int nbResources) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxRecordEvent(CUgreenCtx hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxWaitEvent(CUgreenCtx hCtx, CUevent hEvent) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetGreenCtx(CUstream hStream, CUgreenCtx* phCtx) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxStreamCreate(CUstream* phStream, CUgreenCtx greenCtx, unsigned int flags, int priority) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGreenCtxGetId(CUgreenCtx greenCtx, unsigned long long* greenCtxId) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuStreamGetDevResource(CUstream hStream, CUdevResource* resource, CUdevResourceType typename) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLogsRegisterCallback(CUlogsCallback callbackFunc, void* userData, CUlogsCallbackHandle* callback_out) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLogsUnregisterCallback(CUlogsCallbackHandle callback) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLogsCurrent(CUlogIterator* iterator_out, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLogsDumpToFile(CUlogIterator* iterator, const char* pathToFile, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuLogsDumpToMemory(CUlogIterator* iterator, char* buffer, size_t* size, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCheckpointProcessGetRestoreThreadId(int pid, int* tid) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCheckpointProcessGetState(int pid, CUprocessState* state) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCheckpointProcessLock(int pid, CUcheckpointLockArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCheckpointProcessCheckpoint(int pid, CUcheckpointCheckpointArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCheckpointProcessRestore(int pid, CUcheckpointRestoreArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuCheckpointProcessUnlock(int pid, CUcheckpointUnlockArgs* args) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuProfilerStart() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuProfilerStop() except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsEGLRegisterImage(CUgraphicsResource* pCudaResource, EGLImageKHR image, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamConsumerConnect(CUeglStreamConnection* conn, EGLStreamKHR stream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamConsumerConnectWithFlags(CUeglStreamConnection* conn, EGLStreamKHR stream, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamConsumerDisconnect(CUeglStreamConnection* conn) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamConsumerAcquireFrame(CUeglStreamConnection* conn, CUgraphicsResource* pCudaResource, CUstream* pStream, unsigned int timeout) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamConsumerReleaseFrame(CUeglStreamConnection* conn, CUgraphicsResource pCudaResource, CUstream* pStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamProducerConnect(CUeglStreamConnection* conn, EGLStreamKHR stream, EGLint width, EGLint height) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamProducerDisconnect(CUeglStreamConnection* conn) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamProducerPresentFrame(CUeglStreamConnection* conn, CUeglFrame eglframe, CUstream* pStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEGLStreamProducerReturnFrame(CUeglStreamConnection* conn, CUeglFrame* eglframe, CUstream* pStream) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsResourceGetMappedEglFrame(CUeglFrame* eglFrame, CUgraphicsResource resource, unsigned int index, unsigned int mipLevel) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuEventCreateFromEGLSync(CUevent* phEvent, EGLSyncKHR eglSync, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsGLRegisterBuffer(CUgraphicsResource* pCudaResource, GLuint buffer, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsGLRegisterImage(CUgraphicsResource* pCudaResource, GLuint image, GLenum target, unsigned int Flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGLGetDevices(unsigned int* pCudaDeviceCount, CUdevice* pCudaDevices, unsigned int cudaDeviceCount, CUGLDeviceList deviceList) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuVDPAUGetDevice(CUdevice* pDevice, VdpDevice vdpDevice, VdpGetProcAddress* vdpGetProcAddress) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuVDPAUCtxCreate(CUcontext* pCtx, unsigned int flags, CUdevice device, VdpDevice vdpDevice, VdpGetProcAddress* vdpGetProcAddress) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsVDPAURegisterVideoSurface(CUgraphicsResource* pCudaResource, VdpVideoSurface vdpSurface, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef CUresult cuGraphicsVDPAURegisterOutputSurface(CUgraphicsResource* pCudaResource, VdpOutputSurface vdpSurface, unsigned int flags) except ?CUDA_ERROR_NOT_FOUND nogil
+
+cdef enum: CUDA_VERSION = 13020
+
+cdef enum: CU_IPC_HANDLE_SIZE = 64
+
+cdef enum: CU_STREAM_LEGACY = 1
+
+cdef enum: CU_STREAM_PER_THREAD = 2
+
+cdef enum: CU_COMPUTE_ACCELERATED_TARGET_BASE = 65536
+
+cdef enum: CU_COMPUTE_FAMILY_TARGET_BASE = 131072
+
+cdef enum: CU_GRAPH_COND_ASSIGN_DEFAULT = 1
+
+cdef enum: CU_GRAPH_KERNEL_NODE_PORT_DEFAULT = 0
+
+cdef enum: CU_GRAPH_KERNEL_NODE_PORT_PROGRAMMATIC = 1
+
+cdef enum: CU_GRAPH_KERNEL_NODE_PORT_LAUNCH_ORDER = 2
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_ACCESS_POLICY_WINDOW = 1
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_COOPERATIVE = 2
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_CLUSTER_DIMENSION = 4
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE = 5
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_PRIORITY = 8
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP = 9
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_MEM_SYNC_DOMAIN = 10
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION = 11
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE = 13
+
+cdef enum: CU_KERNEL_NODE_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT = 14
+
+cdef enum: CU_STREAM_ATTRIBUTE_ACCESS_POLICY_WINDOW = 1
+
+cdef enum: CU_STREAM_ATTRIBUTE_SYNCHRONIZATION_POLICY = 3
+
+cdef enum: CU_STREAM_ATTRIBUTE_PRIORITY = 8
+
+cdef enum: CU_STREAM_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP = 9
+
+cdef enum: CU_STREAM_ATTRIBUTE_MEM_SYNC_DOMAIN = 10
+
+cdef enum: CU_MEMHOSTALLOC_PORTABLE = 1
+
+cdef enum: CU_MEMHOSTALLOC_DEVICEMAP = 2
+
+cdef enum: CU_MEMHOSTALLOC_WRITECOMBINED = 4
+
+cdef enum: CU_MEMHOSTREGISTER_PORTABLE = 1
+
+cdef enum: CU_MEMHOSTREGISTER_DEVICEMAP = 2
+
+cdef enum: CU_MEMHOSTREGISTER_IOMEMORY = 4
+
+cdef enum: CU_MEMHOSTREGISTER_READ_ONLY = 8
+
+cdef enum: CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL = 1
+
+cdef enum: CU_TENSOR_MAP_NUM_QWORDS = 16
+
+cdef enum: CUDA_EXTERNAL_MEMORY_DEDICATED = 1
+
+cdef enum: CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC = 1
+
+cdef enum: CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC = 2
+
+cdef enum: CUDA_NVSCISYNC_ATTR_SIGNAL = 1
+
+cdef enum: CUDA_NVSCISYNC_ATTR_WAIT = 2
+
+cdef enum: CU_MEM_CREATE_USAGE_TILE_POOL = 1
+
+cdef enum: CU_MEM_CREATE_USAGE_HW_DECOMPRESS = 2
+
+cdef enum: CU_MEM_POOL_CREATE_USAGE_HW_DECOMPRESS = 2
+
+cdef enum: CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC = 1
+
+cdef enum: CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC = 2
+
+cdef enum: CUDA_ARRAY3D_LAYERED = 1
+
+cdef enum: CUDA_ARRAY3D_2DARRAY = 1
+
+cdef enum: CUDA_ARRAY3D_SURFACE_LDST = 2
+
+cdef enum: CUDA_ARRAY3D_CUBEMAP = 4
+
+cdef enum: CUDA_ARRAY3D_TEXTURE_GATHER = 8
+
+cdef enum: CUDA_ARRAY3D_DEPTH_TEXTURE = 16
+
+cdef enum: CUDA_ARRAY3D_COLOR_ATTACHMENT = 32
+
+cdef enum: CUDA_ARRAY3D_SPARSE = 64
+
+cdef enum: CUDA_ARRAY3D_DEFERRED_MAPPING = 128
+
+cdef enum: CUDA_ARRAY3D_VIDEO_ENCODE_DECODE = 256
+
+cdef enum: CU_TRSA_OVERRIDE_FORMAT = 1
+
+cdef enum: CU_TRSF_READ_AS_INTEGER = 1
+
+cdef enum: CU_TRSF_NORMALIZED_COORDINATES = 2
+
+cdef enum: CU_TRSF_SRGB = 16
+
+cdef enum: CU_TRSF_DISABLE_TRILINEAR_OPTIMIZATION = 32
+
+cdef enum: CU_TRSF_SEAMLESS_CUBEMAP = 64
+
+cdef enum: CU_LAUNCH_KERNEL_REQUIRED_BLOCK_DIM = 1
+
+cdef enum: CU_LAUNCH_PARAM_END_AS_INT = 0
+
+cdef enum: CU_LAUNCH_PARAM_END = 0
+
+cdef enum: CU_LAUNCH_PARAM_BUFFER_POINTER_AS_INT = 1
+
+cdef enum: CU_LAUNCH_PARAM_BUFFER_POINTER = 1
+
+cdef enum: CU_LAUNCH_PARAM_BUFFER_SIZE_AS_INT = 2
+
+cdef enum: CU_LAUNCH_PARAM_BUFFER_SIZE = 2
+
+cdef enum: CU_PARAM_TR_DEFAULT = -1
+
+cdef enum: CU_DEVICE_CPU = -1
+
+cdef enum: CU_DEVICE_INVALID = -2
+
+cdef enum: MAX_PLANES = 3
+
+cdef enum: CUDA_EGL_INFINITE_TIMEOUT = 4294967295
\ No newline at end of file
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvfatbin.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/cynvfatbin.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..54912aa88b6881d201a58c950e1693d70db0384e
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/cynvfatbin.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvfatbin.pxd b/lib/python3.11/site-packages/cuda/bindings/cynvfatbin.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..3cf5c542e257c6ca2342faa88c0d9d34de68e1d3
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cynvfatbin.pxd
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.4.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport intptr_t, uint32_t
+
+
+###############################################################################
+# Types (structs, enums, ...)
+###############################################################################
+
+# enums
+ctypedef enum nvFatbinResult "nvFatbinResult":
+    NVFATBIN_SUCCESS "NVFATBIN_SUCCESS" = 0
+    NVFATBIN_ERROR_INTERNAL "NVFATBIN_ERROR_INTERNAL"
+    NVFATBIN_ERROR_ELF_ARCH_MISMATCH "NVFATBIN_ERROR_ELF_ARCH_MISMATCH"
+    NVFATBIN_ERROR_ELF_SIZE_MISMATCH "NVFATBIN_ERROR_ELF_SIZE_MISMATCH"
+    NVFATBIN_ERROR_MISSING_PTX_VERSION "NVFATBIN_ERROR_MISSING_PTX_VERSION"
+    NVFATBIN_ERROR_NULL_POINTER "NVFATBIN_ERROR_NULL_POINTER"
+    NVFATBIN_ERROR_COMPRESSION_FAILED "NVFATBIN_ERROR_COMPRESSION_FAILED"
+    NVFATBIN_ERROR_COMPRESSED_SIZE_EXCEEDED "NVFATBIN_ERROR_COMPRESSED_SIZE_EXCEEDED"
+    NVFATBIN_ERROR_UNRECOGNIZED_OPTION "NVFATBIN_ERROR_UNRECOGNIZED_OPTION"
+    NVFATBIN_ERROR_INVALID_ARCH "NVFATBIN_ERROR_INVALID_ARCH"
+    NVFATBIN_ERROR_INVALID_NVVM "NVFATBIN_ERROR_INVALID_NVVM"
+    NVFATBIN_ERROR_EMPTY_INPUT "NVFATBIN_ERROR_EMPTY_INPUT"
+    NVFATBIN_ERROR_MISSING_PTX_ARCH "NVFATBIN_ERROR_MISSING_PTX_ARCH"
+    NVFATBIN_ERROR_PTX_ARCH_MISMATCH "NVFATBIN_ERROR_PTX_ARCH_MISMATCH"
+    NVFATBIN_ERROR_MISSING_FATBIN "NVFATBIN_ERROR_MISSING_FATBIN"
+    NVFATBIN_ERROR_INVALID_INDEX "NVFATBIN_ERROR_INVALID_INDEX"
+    NVFATBIN_ERROR_IDENTIFIER_REUSE "NVFATBIN_ERROR_IDENTIFIER_REUSE"
+    NVFATBIN_ERROR_INTERNAL_PTX_OPTION "NVFATBIN_ERROR_INTERNAL_PTX_OPTION"
+    _NVFATBINRESULT_INTERNAL_LOADING_ERROR "_NVFATBINRESULT_INTERNAL_LOADING_ERROR" = -42
+
+
+# types
+ctypedef void* nvFatbinHandle 'nvFatbinHandle'
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cdef const char* nvFatbinGetErrorString(nvFatbinResult result) except?NULL nogil
+cdef nvFatbinResult nvFatbinCreate(nvFatbinHandle* handle_indirect, const char** options, size_t optionsCount) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinDestroy(nvFatbinHandle* handle_indirect) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinAddPTX(nvFatbinHandle handle, const char* code, size_t size, const char* arch, const char* identifier, const char* optionsCmdLine) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinAddCubin(nvFatbinHandle handle, const void* code, size_t size, const char* arch, const char* identifier) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinAddLTOIR(nvFatbinHandle handle, const void* code, size_t size, const char* arch, const char* identifier, const char* optionsCmdLine) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinSize(nvFatbinHandle handle, size_t* size) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinGet(nvFatbinHandle handle, void* buffer) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinVersion(unsigned int* major, unsigned int* minor) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinAddReloc(nvFatbinHandle handle, const void* code, size_t size) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvFatbinResult nvFatbinAddTileIR(nvFatbinHandle handle, const void* code, size_t size, const char* identifier, const char* optionsCmdLine) except?_NVFATBINRESULT_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvjitlink.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/cynvjitlink.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..dcf7368f838a7fa76e65b9fbdcc487a6eda1ca2a
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/cynvjitlink.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvjitlink.pxd b/lib/python3.11/site-packages/cuda/bindings/cynvjitlink.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..50d817f13bdb8a3ce5f739c47d9739b01efbaf51
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cynvjitlink.pxd
@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.0.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport intptr_t, uint32_t
+
+
+###############################################################################
+# Types (structs, enums, ...)
+###############################################################################
+
+# enums
+ctypedef enum nvJitLinkResult "nvJitLinkResult":
+    NVJITLINK_SUCCESS "NVJITLINK_SUCCESS" = 0
+    NVJITLINK_ERROR_UNRECOGNIZED_OPTION "NVJITLINK_ERROR_UNRECOGNIZED_OPTION"
+    NVJITLINK_ERROR_MISSING_ARCH "NVJITLINK_ERROR_MISSING_ARCH"
+    NVJITLINK_ERROR_INVALID_INPUT "NVJITLINK_ERROR_INVALID_INPUT"
+    NVJITLINK_ERROR_PTX_COMPILE "NVJITLINK_ERROR_PTX_COMPILE"
+    NVJITLINK_ERROR_NVVM_COMPILE "NVJITLINK_ERROR_NVVM_COMPILE"
+    NVJITLINK_ERROR_INTERNAL "NVJITLINK_ERROR_INTERNAL"
+    NVJITLINK_ERROR_THREADPOOL "NVJITLINK_ERROR_THREADPOOL"
+    NVJITLINK_ERROR_UNRECOGNIZED_INPUT "NVJITLINK_ERROR_UNRECOGNIZED_INPUT"
+    NVJITLINK_ERROR_FINALIZE "NVJITLINK_ERROR_FINALIZE"
+    NVJITLINK_ERROR_NULL_INPUT "NVJITLINK_ERROR_NULL_INPUT"
+    NVJITLINK_ERROR_INCOMPATIBLE_OPTIONS "NVJITLINK_ERROR_INCOMPATIBLE_OPTIONS"
+    NVJITLINK_ERROR_INCORRECT_INPUT_TYPE "NVJITLINK_ERROR_INCORRECT_INPUT_TYPE"
+    NVJITLINK_ERROR_ARCH_MISMATCH "NVJITLINK_ERROR_ARCH_MISMATCH"
+    NVJITLINK_ERROR_OUTDATED_LIBRARY "NVJITLINK_ERROR_OUTDATED_LIBRARY"
+    NVJITLINK_ERROR_MISSING_FATBIN "NVJITLINK_ERROR_MISSING_FATBIN"
+    NVJITLINK_ERROR_UNRECOGNIZED_ARCH "NVJITLINK_ERROR_UNRECOGNIZED_ARCH"
+    NVJITLINK_ERROR_UNSUPPORTED_ARCH "NVJITLINK_ERROR_UNSUPPORTED_ARCH"
+    NVJITLINK_ERROR_LTO_NOT_ENABLED "NVJITLINK_ERROR_LTO_NOT_ENABLED"
+    _NVJITLINKRESULT_INTERNAL_LOADING_ERROR "_NVJITLINKRESULT_INTERNAL_LOADING_ERROR" = -42
+
+ctypedef enum nvJitLinkInputType "nvJitLinkInputType":
+    NVJITLINK_INPUT_NONE "NVJITLINK_INPUT_NONE" = 0
+    NVJITLINK_INPUT_CUBIN "NVJITLINK_INPUT_CUBIN" = 1
+    NVJITLINK_INPUT_PTX "NVJITLINK_INPUT_PTX"
+    NVJITLINK_INPUT_LTOIR "NVJITLINK_INPUT_LTOIR"
+    NVJITLINK_INPUT_FATBIN "NVJITLINK_INPUT_FATBIN"
+    NVJITLINK_INPUT_OBJECT "NVJITLINK_INPUT_OBJECT"
+    NVJITLINK_INPUT_LIBRARY "NVJITLINK_INPUT_LIBRARY"
+    NVJITLINK_INPUT_INDEX "NVJITLINK_INPUT_INDEX"
+    NVJITLINK_INPUT_ANY "NVJITLINK_INPUT_ANY" = 10
+
+
+# types
+ctypedef void* nvJitLinkHandle 'nvJitLinkHandle'
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cdef nvJitLinkResult nvJitLinkCreate(nvJitLinkHandle* handle, uint32_t numOptions, const char** options) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkDestroy(nvJitLinkHandle* handle) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkAddData(nvJitLinkHandle handle, nvJitLinkInputType inputType, const void* data, size_t size, const char* name) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkAddFile(nvJitLinkHandle handle, nvJitLinkInputType inputType, const char* fileName) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkComplete(nvJitLinkHandle handle) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetLinkedCubinSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetLinkedCubin(nvJitLinkHandle handle, void* cubin) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetLinkedPtxSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetLinkedPtx(nvJitLinkHandle handle, char* ptx) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetErrorLogSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetErrorLog(nvJitLinkHandle handle, char* log) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetInfoLogSize(nvJitLinkHandle handle, size_t* size) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkGetInfoLog(nvJitLinkHandle handle, char* log) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvJitLinkResult nvJitLinkVersion(unsigned int* major, unsigned int* minor) except?_NVJITLINKRESULT_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvml.pxd b/lib/python3.11/site-packages/cuda/bindings/cynvml.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..a1bb81ffb50d25f12f0c59f8da2b09dea52b5249
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cynvml.pxd
@@ -0,0 +1,2164 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.9.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport int64_t
+
+
+###############################################################################
+# Types (structs, enums, ...)
+###############################################################################
+
+# enums
+ctypedef enum nvmlBridgeChipType_t "nvmlBridgeChipType_t":
+    NVML_BRIDGE_CHIP_PLX "NVML_BRIDGE_CHIP_PLX" = 0
+    NVML_BRIDGE_CHIP_BRO4 "NVML_BRIDGE_CHIP_BRO4" = 1
+
+ctypedef enum nvmlNvLinkUtilizationCountUnits_t "nvmlNvLinkUtilizationCountUnits_t":
+    NVML_NVLINK_COUNTER_UNIT_CYCLES "NVML_NVLINK_COUNTER_UNIT_CYCLES" = 0
+    NVML_NVLINK_COUNTER_UNIT_PACKETS "NVML_NVLINK_COUNTER_UNIT_PACKETS" = 1
+    NVML_NVLINK_COUNTER_UNIT_BYTES "NVML_NVLINK_COUNTER_UNIT_BYTES" = 2
+    NVML_NVLINK_COUNTER_UNIT_RESERVED "NVML_NVLINK_COUNTER_UNIT_RESERVED" = 3
+    NVML_NVLINK_COUNTER_UNIT_COUNT "NVML_NVLINK_COUNTER_UNIT_COUNT"
+
+ctypedef enum nvmlNvLinkUtilizationCountPktTypes_t "nvmlNvLinkUtilizationCountPktTypes_t":
+    NVML_NVLINK_COUNTER_PKTFILTER_NOP "NVML_NVLINK_COUNTER_PKTFILTER_NOP" = 0x1
+    NVML_NVLINK_COUNTER_PKTFILTER_READ "NVML_NVLINK_COUNTER_PKTFILTER_READ" = 0x2
+    NVML_NVLINK_COUNTER_PKTFILTER_WRITE "NVML_NVLINK_COUNTER_PKTFILTER_WRITE" = 0x4
+    NVML_NVLINK_COUNTER_PKTFILTER_RATOM "NVML_NVLINK_COUNTER_PKTFILTER_RATOM" = 0x8
+    NVML_NVLINK_COUNTER_PKTFILTER_NRATOM "NVML_NVLINK_COUNTER_PKTFILTER_NRATOM" = 0x10
+    NVML_NVLINK_COUNTER_PKTFILTER_FLUSH "NVML_NVLINK_COUNTER_PKTFILTER_FLUSH" = 0x20
+    NVML_NVLINK_COUNTER_PKTFILTER_RESPDATA "NVML_NVLINK_COUNTER_PKTFILTER_RESPDATA" = 0x40
+    NVML_NVLINK_COUNTER_PKTFILTER_RESPNODATA "NVML_NVLINK_COUNTER_PKTFILTER_RESPNODATA" = 0x80
+    NVML_NVLINK_COUNTER_PKTFILTER_ALL "NVML_NVLINK_COUNTER_PKTFILTER_ALL" = 0xFF
+
+ctypedef enum nvmlNvLinkCapability_t "nvmlNvLinkCapability_t":
+    NVML_NVLINK_CAP_P2P_SUPPORTED "NVML_NVLINK_CAP_P2P_SUPPORTED" = 0
+    NVML_NVLINK_CAP_SYSMEM_ACCESS "NVML_NVLINK_CAP_SYSMEM_ACCESS" = 1
+    NVML_NVLINK_CAP_P2P_ATOMICS "NVML_NVLINK_CAP_P2P_ATOMICS" = 2
+    NVML_NVLINK_CAP_SYSMEM_ATOMICS "NVML_NVLINK_CAP_SYSMEM_ATOMICS" = 3
+    NVML_NVLINK_CAP_SLI_BRIDGE "NVML_NVLINK_CAP_SLI_BRIDGE" = 4
+    NVML_NVLINK_CAP_VALID "NVML_NVLINK_CAP_VALID" = 5
+    NVML_NVLINK_CAP_COUNT "NVML_NVLINK_CAP_COUNT"
+
+ctypedef enum nvmlNvLinkErrorCounter_t "nvmlNvLinkErrorCounter_t":
+    NVML_NVLINK_ERROR_DL_REPLAY "NVML_NVLINK_ERROR_DL_REPLAY" = 0
+    NVML_NVLINK_ERROR_DL_RECOVERY "NVML_NVLINK_ERROR_DL_RECOVERY" = 1
+    NVML_NVLINK_ERROR_DL_CRC_FLIT "NVML_NVLINK_ERROR_DL_CRC_FLIT" = 2
+    NVML_NVLINK_ERROR_DL_CRC_DATA "NVML_NVLINK_ERROR_DL_CRC_DATA" = 3
+    NVML_NVLINK_ERROR_DL_ECC_DATA "NVML_NVLINK_ERROR_DL_ECC_DATA" = 4
+    NVML_NVLINK_ERROR_COUNT "NVML_NVLINK_ERROR_COUNT"
+
+ctypedef enum nvmlIntNvLinkDeviceType_t "nvmlIntNvLinkDeviceType_t":
+    NVML_NVLINK_DEVICE_TYPE_GPU "NVML_NVLINK_DEVICE_TYPE_GPU" = 0x00
+    NVML_NVLINK_DEVICE_TYPE_IBMNPU "NVML_NVLINK_DEVICE_TYPE_IBMNPU" = 0x01
+    NVML_NVLINK_DEVICE_TYPE_SWITCH "NVML_NVLINK_DEVICE_TYPE_SWITCH" = 0x02
+    NVML_NVLINK_DEVICE_TYPE_UNKNOWN "NVML_NVLINK_DEVICE_TYPE_UNKNOWN" = 0xFF
+
+ctypedef enum nvmlGpuTopologyLevel_t "nvmlGpuTopologyLevel_t":
+    NVML_TOPOLOGY_INTERNAL "NVML_TOPOLOGY_INTERNAL" = 0
+    NVML_TOPOLOGY_SINGLE "NVML_TOPOLOGY_SINGLE" = 10
+    NVML_TOPOLOGY_MULTIPLE "NVML_TOPOLOGY_MULTIPLE" = 20
+    NVML_TOPOLOGY_HOSTBRIDGE "NVML_TOPOLOGY_HOSTBRIDGE" = 30
+    NVML_TOPOLOGY_NODE "NVML_TOPOLOGY_NODE" = 40
+    NVML_TOPOLOGY_SYSTEM "NVML_TOPOLOGY_SYSTEM" = 50
+
+ctypedef enum nvmlGpuP2PStatus_t "nvmlGpuP2PStatus_t":
+    NVML_P2P_STATUS_OK "NVML_P2P_STATUS_OK" = 0
+    NVML_P2P_STATUS_CHIPSET_NOT_SUPPORED "NVML_P2P_STATUS_CHIPSET_NOT_SUPPORED"
+    NVML_P2P_STATUS_CHIPSET_NOT_SUPPORTED "NVML_P2P_STATUS_CHIPSET_NOT_SUPPORTED" = NVML_P2P_STATUS_CHIPSET_NOT_SUPPORED
+    NVML_P2P_STATUS_GPU_NOT_SUPPORTED "NVML_P2P_STATUS_GPU_NOT_SUPPORTED"
+    NVML_P2P_STATUS_IOH_TOPOLOGY_NOT_SUPPORTED "NVML_P2P_STATUS_IOH_TOPOLOGY_NOT_SUPPORTED"
+    NVML_P2P_STATUS_DISABLED_BY_REGKEY "NVML_P2P_STATUS_DISABLED_BY_REGKEY"
+    NVML_P2P_STATUS_NOT_SUPPORTED "NVML_P2P_STATUS_NOT_SUPPORTED"
+    NVML_P2P_STATUS_UNKNOWN "NVML_P2P_STATUS_UNKNOWN"
+
+ctypedef enum nvmlGpuP2PCapsIndex_t "nvmlGpuP2PCapsIndex_t":
+    NVML_P2P_CAPS_INDEX_READ "NVML_P2P_CAPS_INDEX_READ" = 0
+    NVML_P2P_CAPS_INDEX_WRITE "NVML_P2P_CAPS_INDEX_WRITE" = 1
+    NVML_P2P_CAPS_INDEX_NVLINK "NVML_P2P_CAPS_INDEX_NVLINK" = 2
+    NVML_P2P_CAPS_INDEX_ATOMICS "NVML_P2P_CAPS_INDEX_ATOMICS" = 3
+    NVML_P2P_CAPS_INDEX_PCI "NVML_P2P_CAPS_INDEX_PCI" = 4
+    NVML_P2P_CAPS_INDEX_PROP "NVML_P2P_CAPS_INDEX_PROP" = NVML_P2P_CAPS_INDEX_PCI
+    NVML_P2P_CAPS_INDEX_UNKNOWN "NVML_P2P_CAPS_INDEX_UNKNOWN" = 5
+
+ctypedef enum nvmlSamplingType_t "nvmlSamplingType_t":
+    NVML_TOTAL_POWER_SAMPLES "NVML_TOTAL_POWER_SAMPLES" = 0
+    NVML_GPU_UTILIZATION_SAMPLES "NVML_GPU_UTILIZATION_SAMPLES" = 1
+    NVML_MEMORY_UTILIZATION_SAMPLES "NVML_MEMORY_UTILIZATION_SAMPLES" = 2
+    NVML_ENC_UTILIZATION_SAMPLES "NVML_ENC_UTILIZATION_SAMPLES" = 3
+    NVML_DEC_UTILIZATION_SAMPLES "NVML_DEC_UTILIZATION_SAMPLES" = 4
+    NVML_PROCESSOR_CLK_SAMPLES "NVML_PROCESSOR_CLK_SAMPLES" = 5
+    NVML_MEMORY_CLK_SAMPLES "NVML_MEMORY_CLK_SAMPLES" = 6
+    NVML_MODULE_POWER_SAMPLES "NVML_MODULE_POWER_SAMPLES" = 7
+    NVML_JPG_UTILIZATION_SAMPLES "NVML_JPG_UTILIZATION_SAMPLES" = 8
+    NVML_OFA_UTILIZATION_SAMPLES "NVML_OFA_UTILIZATION_SAMPLES" = 9
+    NVML_SAMPLINGTYPE_COUNT "NVML_SAMPLINGTYPE_COUNT"
+
+ctypedef enum nvmlPcieUtilCounter_t "nvmlPcieUtilCounter_t":
+    NVML_PCIE_UTIL_TX_BYTES "NVML_PCIE_UTIL_TX_BYTES" = 0
+    NVML_PCIE_UTIL_RX_BYTES "NVML_PCIE_UTIL_RX_BYTES" = 1
+    NVML_PCIE_UTIL_COUNT "NVML_PCIE_UTIL_COUNT"
+
+ctypedef enum nvmlValueType_t "nvmlValueType_t":
+    NVML_VALUE_TYPE_DOUBLE "NVML_VALUE_TYPE_DOUBLE" = 0
+    NVML_VALUE_TYPE_UNSIGNED_INT "NVML_VALUE_TYPE_UNSIGNED_INT" = 1
+    NVML_VALUE_TYPE_UNSIGNED_LONG "NVML_VALUE_TYPE_UNSIGNED_LONG" = 2
+    NVML_VALUE_TYPE_UNSIGNED_LONG_LONG "NVML_VALUE_TYPE_UNSIGNED_LONG_LONG" = 3
+    NVML_VALUE_TYPE_SIGNED_LONG_LONG "NVML_VALUE_TYPE_SIGNED_LONG_LONG" = 4
+    NVML_VALUE_TYPE_SIGNED_INT "NVML_VALUE_TYPE_SIGNED_INT" = 5
+    NVML_VALUE_TYPE_UNSIGNED_SHORT "NVML_VALUE_TYPE_UNSIGNED_SHORT" = 6
+    NVML_VALUE_TYPE_COUNT "NVML_VALUE_TYPE_COUNT"
+
+ctypedef enum nvmlPerfPolicyType_t "nvmlPerfPolicyType_t":
+    NVML_PERF_POLICY_POWER "NVML_PERF_POLICY_POWER" = 0
+    NVML_PERF_POLICY_THERMAL "NVML_PERF_POLICY_THERMAL" = 1
+    NVML_PERF_POLICY_SYNC_BOOST "NVML_PERF_POLICY_SYNC_BOOST" = 2
+    NVML_PERF_POLICY_BOARD_LIMIT "NVML_PERF_POLICY_BOARD_LIMIT" = 3
+    NVML_PERF_POLICY_LOW_UTILIZATION "NVML_PERF_POLICY_LOW_UTILIZATION" = 4
+    NVML_PERF_POLICY_RELIABILITY "NVML_PERF_POLICY_RELIABILITY" = 5
+    NVML_PERF_POLICY_TOTAL_APP_CLOCKS "NVML_PERF_POLICY_TOTAL_APP_CLOCKS" = 10
+    NVML_PERF_POLICY_TOTAL_BASE_CLOCKS "NVML_PERF_POLICY_TOTAL_BASE_CLOCKS" = 11
+    NVML_PERF_POLICY_COUNT "NVML_PERF_POLICY_COUNT"
+
+ctypedef enum nvmlThermalTarget_t "nvmlThermalTarget_t":
+    NVML_THERMAL_TARGET_NONE "NVML_THERMAL_TARGET_NONE" = 0
+    NVML_THERMAL_TARGET_GPU "NVML_THERMAL_TARGET_GPU" = 1
+    NVML_THERMAL_TARGET_MEMORY "NVML_THERMAL_TARGET_MEMORY" = 2
+    NVML_THERMAL_TARGET_POWER_SUPPLY "NVML_THERMAL_TARGET_POWER_SUPPLY" = 4
+    NVML_THERMAL_TARGET_BOARD "NVML_THERMAL_TARGET_BOARD" = 8
+    NVML_THERMAL_TARGET_VCD_BOARD "NVML_THERMAL_TARGET_VCD_BOARD" = 9
+    NVML_THERMAL_TARGET_VCD_INLET "NVML_THERMAL_TARGET_VCD_INLET" = 10
+    NVML_THERMAL_TARGET_VCD_OUTLET "NVML_THERMAL_TARGET_VCD_OUTLET" = 11
+    NVML_THERMAL_TARGET_ALL "NVML_THERMAL_TARGET_ALL" = 15
+    NVML_THERMAL_TARGET_UNKNOWN "NVML_THERMAL_TARGET_UNKNOWN" = -(1)
+
+ctypedef enum nvmlThermalController_t "nvmlThermalController_t":
+    NVML_THERMAL_CONTROLLER_NONE "NVML_THERMAL_CONTROLLER_NONE" = 0
+    NVML_THERMAL_CONTROLLER_GPU_INTERNAL "NVML_THERMAL_CONTROLLER_GPU_INTERNAL"
+    NVML_THERMAL_CONTROLLER_ADM1032 "NVML_THERMAL_CONTROLLER_ADM1032"
+    NVML_THERMAL_CONTROLLER_ADT7461 "NVML_THERMAL_CONTROLLER_ADT7461"
+    NVML_THERMAL_CONTROLLER_MAX6649 "NVML_THERMAL_CONTROLLER_MAX6649"
+    NVML_THERMAL_CONTROLLER_MAX1617 "NVML_THERMAL_CONTROLLER_MAX1617"
+    NVML_THERMAL_CONTROLLER_LM99 "NVML_THERMAL_CONTROLLER_LM99"
+    NVML_THERMAL_CONTROLLER_LM89 "NVML_THERMAL_CONTROLLER_LM89"
+    NVML_THERMAL_CONTROLLER_LM64 "NVML_THERMAL_CONTROLLER_LM64"
+    NVML_THERMAL_CONTROLLER_G781 "NVML_THERMAL_CONTROLLER_G781"
+    NVML_THERMAL_CONTROLLER_ADT7473 "NVML_THERMAL_CONTROLLER_ADT7473"
+    NVML_THERMAL_CONTROLLER_SBMAX6649 "NVML_THERMAL_CONTROLLER_SBMAX6649"
+    NVML_THERMAL_CONTROLLER_VBIOSEVT "NVML_THERMAL_CONTROLLER_VBIOSEVT"
+    NVML_THERMAL_CONTROLLER_OS "NVML_THERMAL_CONTROLLER_OS"
+    NVML_THERMAL_CONTROLLER_NVSYSCON_CANOAS "NVML_THERMAL_CONTROLLER_NVSYSCON_CANOAS"
+    NVML_THERMAL_CONTROLLER_NVSYSCON_E551 "NVML_THERMAL_CONTROLLER_NVSYSCON_E551"
+    NVML_THERMAL_CONTROLLER_MAX6649R "NVML_THERMAL_CONTROLLER_MAX6649R"
+    NVML_THERMAL_CONTROLLER_ADT7473S "NVML_THERMAL_CONTROLLER_ADT7473S"
+    NVML_THERMAL_CONTROLLER_UNKNOWN "NVML_THERMAL_CONTROLLER_UNKNOWN" = -(1)
+
+ctypedef enum nvmlCoolerControl_t "nvmlCoolerControl_t":
+    NVML_THERMAL_COOLER_SIGNAL_NONE "NVML_THERMAL_COOLER_SIGNAL_NONE" = 0
+    NVML_THERMAL_COOLER_SIGNAL_TOGGLE "NVML_THERMAL_COOLER_SIGNAL_TOGGLE" = 1
+    NVML_THERMAL_COOLER_SIGNAL_VARIABLE "NVML_THERMAL_COOLER_SIGNAL_VARIABLE" = 2
+    NVML_THERMAL_COOLER_SIGNAL_COUNT "NVML_THERMAL_COOLER_SIGNAL_COUNT"
+
+ctypedef enum nvmlCoolerTarget_t "nvmlCoolerTarget_t":
+    NVML_THERMAL_COOLER_TARGET_NONE "NVML_THERMAL_COOLER_TARGET_NONE" = (1 << 0)
+    NVML_THERMAL_COOLER_TARGET_GPU "NVML_THERMAL_COOLER_TARGET_GPU" = (1 << 1)
+    NVML_THERMAL_COOLER_TARGET_MEMORY "NVML_THERMAL_COOLER_TARGET_MEMORY" = (1 << 2)
+    NVML_THERMAL_COOLER_TARGET_POWER_SUPPLY "NVML_THERMAL_COOLER_TARGET_POWER_SUPPLY" = (1 << 3)
+    NVML_THERMAL_COOLER_TARGET_GPU_RELATED "NVML_THERMAL_COOLER_TARGET_GPU_RELATED" = ((NVML_THERMAL_COOLER_TARGET_GPU | NVML_THERMAL_COOLER_TARGET_MEMORY) | NVML_THERMAL_COOLER_TARGET_POWER_SUPPLY)
+
+ctypedef enum nvmlUUIDType_t "nvmlUUIDType_t":
+    NVML_UUID_TYPE_NONE "NVML_UUID_TYPE_NONE" = 0
+    NVML_UUID_TYPE_ASCII "NVML_UUID_TYPE_ASCII" = 1
+    NVML_UUID_TYPE_BINARY "NVML_UUID_TYPE_BINARY" = 2
+
+ctypedef enum nvmlEnableState_t "nvmlEnableState_t":
+    NVML_FEATURE_DISABLED "NVML_FEATURE_DISABLED" = 0
+    NVML_FEATURE_ENABLED "NVML_FEATURE_ENABLED" = 1
+
+ctypedef enum nvmlBrandType_t "nvmlBrandType_t":
+    NVML_BRAND_UNKNOWN "NVML_BRAND_UNKNOWN" = 0
+    NVML_BRAND_QUADRO "NVML_BRAND_QUADRO" = 1
+    NVML_BRAND_TESLA "NVML_BRAND_TESLA" = 2
+    NVML_BRAND_NVS "NVML_BRAND_NVS" = 3
+    NVML_BRAND_GRID "NVML_BRAND_GRID" = 4
+    NVML_BRAND_GEFORCE "NVML_BRAND_GEFORCE" = 5
+    NVML_BRAND_TITAN "NVML_BRAND_TITAN" = 6
+    NVML_BRAND_NVIDIA_VAPPS "NVML_BRAND_NVIDIA_VAPPS" = 7
+    NVML_BRAND_NVIDIA_VPC "NVML_BRAND_NVIDIA_VPC" = 8
+    NVML_BRAND_NVIDIA_VCS "NVML_BRAND_NVIDIA_VCS" = 9
+    NVML_BRAND_NVIDIA_VWS "NVML_BRAND_NVIDIA_VWS" = 10
+    NVML_BRAND_NVIDIA_CLOUD_GAMING "NVML_BRAND_NVIDIA_CLOUD_GAMING" = 11
+    NVML_BRAND_NVIDIA_VGAMING "NVML_BRAND_NVIDIA_VGAMING" = NVML_BRAND_NVIDIA_CLOUD_GAMING
+    NVML_BRAND_QUADRO_RTX "NVML_BRAND_QUADRO_RTX" = 12
+    NVML_BRAND_NVIDIA_RTX "NVML_BRAND_NVIDIA_RTX" = 13
+    NVML_BRAND_NVIDIA "NVML_BRAND_NVIDIA" = 14
+    NVML_BRAND_GEFORCE_RTX "NVML_BRAND_GEFORCE_RTX" = 15
+    NVML_BRAND_TITAN_RTX "NVML_BRAND_TITAN_RTX" = 16
+    NVML_BRAND_COUNT "NVML_BRAND_COUNT" = 18
+
+ctypedef enum nvmlTemperatureThresholds_t "nvmlTemperatureThresholds_t":
+    NVML_TEMPERATURE_THRESHOLD_SHUTDOWN "NVML_TEMPERATURE_THRESHOLD_SHUTDOWN" = 0
+    NVML_TEMPERATURE_THRESHOLD_SLOWDOWN "NVML_TEMPERATURE_THRESHOLD_SLOWDOWN" = 1
+    NVML_TEMPERATURE_THRESHOLD_MEM_MAX "NVML_TEMPERATURE_THRESHOLD_MEM_MAX" = 2
+    NVML_TEMPERATURE_THRESHOLD_GPU_MAX "NVML_TEMPERATURE_THRESHOLD_GPU_MAX" = 3
+    NVML_TEMPERATURE_THRESHOLD_ACOUSTIC_MIN "NVML_TEMPERATURE_THRESHOLD_ACOUSTIC_MIN" = 4
+    NVML_TEMPERATURE_THRESHOLD_ACOUSTIC_CURR "NVML_TEMPERATURE_THRESHOLD_ACOUSTIC_CURR" = 5
+    NVML_TEMPERATURE_THRESHOLD_ACOUSTIC_MAX "NVML_TEMPERATURE_THRESHOLD_ACOUSTIC_MAX" = 6
+    NVML_TEMPERATURE_THRESHOLD_GPS_CURR "NVML_TEMPERATURE_THRESHOLD_GPS_CURR" = 7
+    NVML_TEMPERATURE_THRESHOLD_COUNT "NVML_TEMPERATURE_THRESHOLD_COUNT"
+
+ctypedef enum nvmlTemperatureSensors_t "nvmlTemperatureSensors_t":
+    NVML_TEMPERATURE_GPU "NVML_TEMPERATURE_GPU" = 0
+    NVML_TEMPERATURE_COUNT "NVML_TEMPERATURE_COUNT"
+
+ctypedef enum nvmlComputeMode_t "nvmlComputeMode_t":
+    NVML_COMPUTEMODE_DEFAULT "NVML_COMPUTEMODE_DEFAULT" = 0
+    NVML_COMPUTEMODE_EXCLUSIVE_THREAD "NVML_COMPUTEMODE_EXCLUSIVE_THREAD" = 1
+    NVML_COMPUTEMODE_PROHIBITED "NVML_COMPUTEMODE_PROHIBITED" = 2
+    NVML_COMPUTEMODE_EXCLUSIVE_PROCESS "NVML_COMPUTEMODE_EXCLUSIVE_PROCESS" = 3
+    NVML_COMPUTEMODE_COUNT "NVML_COMPUTEMODE_COUNT"
+
+ctypedef enum nvmlMemoryErrorType_t "nvmlMemoryErrorType_t":
+    NVML_MEMORY_ERROR_TYPE_CORRECTED "NVML_MEMORY_ERROR_TYPE_CORRECTED" = 0
+    NVML_MEMORY_ERROR_TYPE_UNCORRECTED "NVML_MEMORY_ERROR_TYPE_UNCORRECTED" = 1
+    NVML_MEMORY_ERROR_TYPE_COUNT "NVML_MEMORY_ERROR_TYPE_COUNT"
+
+ctypedef enum nvmlNvlinkVersion_t "nvmlNvlinkVersion_t":
+    NVML_NVLINK_VERSION_INVALID "NVML_NVLINK_VERSION_INVALID" = 0
+    NVML_NVLINK_VERSION_1_0 "NVML_NVLINK_VERSION_1_0" = 1
+    NVML_NVLINK_VERSION_2_0 "NVML_NVLINK_VERSION_2_0" = 2
+    NVML_NVLINK_VERSION_2_2 "NVML_NVLINK_VERSION_2_2" = 3
+    NVML_NVLINK_VERSION_3_0 "NVML_NVLINK_VERSION_3_0" = 4
+    NVML_NVLINK_VERSION_3_1 "NVML_NVLINK_VERSION_3_1" = 5
+    NVML_NVLINK_VERSION_4_0 "NVML_NVLINK_VERSION_4_0" = 6
+    NVML_NVLINK_VERSION_5_0 "NVML_NVLINK_VERSION_5_0" = 7
+
+ctypedef enum nvmlEccCounterType_t "nvmlEccCounterType_t":
+    NVML_VOLATILE_ECC "NVML_VOLATILE_ECC" = 0
+    NVML_AGGREGATE_ECC "NVML_AGGREGATE_ECC" = 1
+    NVML_ECC_COUNTER_TYPE_COUNT "NVML_ECC_COUNTER_TYPE_COUNT"
+
+ctypedef enum nvmlClockType_t "nvmlClockType_t":
+    NVML_CLOCK_GRAPHICS "NVML_CLOCK_GRAPHICS" = 0
+    NVML_CLOCK_SM "NVML_CLOCK_SM" = 1
+    NVML_CLOCK_MEM "NVML_CLOCK_MEM" = 2
+    NVML_CLOCK_VIDEO "NVML_CLOCK_VIDEO" = 3
+    NVML_CLOCK_COUNT "NVML_CLOCK_COUNT"
+
+ctypedef enum nvmlClockId_t "nvmlClockId_t":
+    NVML_CLOCK_ID_CURRENT "NVML_CLOCK_ID_CURRENT" = 0
+    NVML_CLOCK_ID_APP_CLOCK_TARGET "NVML_CLOCK_ID_APP_CLOCK_TARGET" = 1
+    NVML_CLOCK_ID_APP_CLOCK_DEFAULT "NVML_CLOCK_ID_APP_CLOCK_DEFAULT" = 2
+    NVML_CLOCK_ID_CUSTOMER_BOOST_MAX "NVML_CLOCK_ID_CUSTOMER_BOOST_MAX" = 3
+    NVML_CLOCK_ID_COUNT "NVML_CLOCK_ID_COUNT"
+
+ctypedef enum nvmlDriverModel_t "nvmlDriverModel_t":
+    NVML_DRIVER_WDDM "NVML_DRIVER_WDDM" = 0
+    NVML_DRIVER_WDM "NVML_DRIVER_WDM" = 1
+    NVML_DRIVER_MCDM "NVML_DRIVER_MCDM" = 2
+
+ctypedef enum nvmlPstates_t "nvmlPstates_t":
+    NVML_PSTATE_0 "NVML_PSTATE_0" = 0
+    NVML_PSTATE_1 "NVML_PSTATE_1" = 1
+    NVML_PSTATE_2 "NVML_PSTATE_2" = 2
+    NVML_PSTATE_3 "NVML_PSTATE_3" = 3
+    NVML_PSTATE_4 "NVML_PSTATE_4" = 4
+    NVML_PSTATE_5 "NVML_PSTATE_5" = 5
+    NVML_PSTATE_6 "NVML_PSTATE_6" = 6
+    NVML_PSTATE_7 "NVML_PSTATE_7" = 7
+    NVML_PSTATE_8 "NVML_PSTATE_8" = 8
+    NVML_PSTATE_9 "NVML_PSTATE_9" = 9
+    NVML_PSTATE_10 "NVML_PSTATE_10" = 10
+    NVML_PSTATE_11 "NVML_PSTATE_11" = 11
+    NVML_PSTATE_12 "NVML_PSTATE_12" = 12
+    NVML_PSTATE_13 "NVML_PSTATE_13" = 13
+    NVML_PSTATE_14 "NVML_PSTATE_14" = 14
+    NVML_PSTATE_15 "NVML_PSTATE_15" = 15
+    NVML_PSTATE_UNKNOWN "NVML_PSTATE_UNKNOWN" = 32
+
+ctypedef enum nvmlGpuOperationMode_t "nvmlGpuOperationMode_t":
+    NVML_GOM_ALL_ON "NVML_GOM_ALL_ON" = 0
+    NVML_GOM_COMPUTE "NVML_GOM_COMPUTE" = 1
+    NVML_GOM_LOW_DP "NVML_GOM_LOW_DP" = 2
+
+ctypedef enum nvmlInforomObject_t "nvmlInforomObject_t":
+    NVML_INFOROM_OEM "NVML_INFOROM_OEM" = 0
+    NVML_INFOROM_ECC "NVML_INFOROM_ECC" = 1
+    NVML_INFOROM_POWER "NVML_INFOROM_POWER" = 2
+    NVML_INFOROM_DEN "NVML_INFOROM_DEN" = 3
+    NVML_INFOROM_COUNT "NVML_INFOROM_COUNT"
+
+ctypedef enum nvmlReturn_t "nvmlReturn_t":
+    NVML_SUCCESS "NVML_SUCCESS" = 0
+    NVML_ERROR_UNINITIALIZED "NVML_ERROR_UNINITIALIZED" = 1
+    NVML_ERROR_INVALID_ARGUMENT "NVML_ERROR_INVALID_ARGUMENT" = 2
+    NVML_ERROR_NOT_SUPPORTED "NVML_ERROR_NOT_SUPPORTED" = 3
+    NVML_ERROR_NO_PERMISSION "NVML_ERROR_NO_PERMISSION" = 4
+    NVML_ERROR_ALREADY_INITIALIZED "NVML_ERROR_ALREADY_INITIALIZED" = 5
+    NVML_ERROR_NOT_FOUND "NVML_ERROR_NOT_FOUND" = 6
+    NVML_ERROR_INSUFFICIENT_SIZE "NVML_ERROR_INSUFFICIENT_SIZE" = 7
+    NVML_ERROR_INSUFFICIENT_POWER "NVML_ERROR_INSUFFICIENT_POWER" = 8
+    NVML_ERROR_DRIVER_NOT_LOADED "NVML_ERROR_DRIVER_NOT_LOADED" = 9
+    NVML_ERROR_TIMEOUT "NVML_ERROR_TIMEOUT" = 10
+    NVML_ERROR_IRQ_ISSUE "NVML_ERROR_IRQ_ISSUE" = 11
+    NVML_ERROR_LIBRARY_NOT_FOUND "NVML_ERROR_LIBRARY_NOT_FOUND" = 12
+    NVML_ERROR_FUNCTION_NOT_FOUND "NVML_ERROR_FUNCTION_NOT_FOUND" = 13
+    NVML_ERROR_CORRUPTED_INFOROM "NVML_ERROR_CORRUPTED_INFOROM" = 14
+    NVML_ERROR_GPU_IS_LOST "NVML_ERROR_GPU_IS_LOST" = 15
+    NVML_ERROR_RESET_REQUIRED "NVML_ERROR_RESET_REQUIRED" = 16
+    NVML_ERROR_OPERATING_SYSTEM "NVML_ERROR_OPERATING_SYSTEM" = 17
+    NVML_ERROR_LIB_RM_VERSION_MISMATCH "NVML_ERROR_LIB_RM_VERSION_MISMATCH" = 18
+    NVML_ERROR_IN_USE "NVML_ERROR_IN_USE" = 19
+    NVML_ERROR_MEMORY "NVML_ERROR_MEMORY" = 20
+    NVML_ERROR_NO_DATA "NVML_ERROR_NO_DATA" = 21
+    NVML_ERROR_VGPU_ECC_NOT_SUPPORTED "NVML_ERROR_VGPU_ECC_NOT_SUPPORTED" = 22
+    NVML_ERROR_INSUFFICIENT_RESOURCES "NVML_ERROR_INSUFFICIENT_RESOURCES" = 23
+    NVML_ERROR_FREQ_NOT_SUPPORTED "NVML_ERROR_FREQ_NOT_SUPPORTED" = 24
+    NVML_ERROR_ARGUMENT_VERSION_MISMATCH "NVML_ERROR_ARGUMENT_VERSION_MISMATCH" = 25
+    NVML_ERROR_DEPRECATED "NVML_ERROR_DEPRECATED" = 26
+    NVML_ERROR_NOT_READY "NVML_ERROR_NOT_READY" = 27
+    NVML_ERROR_GPU_NOT_FOUND "NVML_ERROR_GPU_NOT_FOUND" = 28
+    NVML_ERROR_INVALID_STATE "NVML_ERROR_INVALID_STATE" = 29
+    NVML_ERROR_RESET_TYPE_NOT_SUPPORTED "NVML_ERROR_RESET_TYPE_NOT_SUPPORTED" = 30
+    NVML_ERROR_UNKNOWN "NVML_ERROR_UNKNOWN" = 999
+    _NVMLRETURN_T_INTERNAL_LOADING_ERROR "_NVMLRETURN_T_INTERNAL_LOADING_ERROR" = -42
+
+ctypedef enum nvmlMemoryLocation_t "nvmlMemoryLocation_t":
+    NVML_MEMORY_LOCATION_L1_CACHE "NVML_MEMORY_LOCATION_L1_CACHE" = 0
+    NVML_MEMORY_LOCATION_L2_CACHE "NVML_MEMORY_LOCATION_L2_CACHE" = 1
+    NVML_MEMORY_LOCATION_DRAM "NVML_MEMORY_LOCATION_DRAM" = 2
+    NVML_MEMORY_LOCATION_DEVICE_MEMORY "NVML_MEMORY_LOCATION_DEVICE_MEMORY" = 2
+    NVML_MEMORY_LOCATION_REGISTER_FILE "NVML_MEMORY_LOCATION_REGISTER_FILE" = 3
+    NVML_MEMORY_LOCATION_TEXTURE_MEMORY "NVML_MEMORY_LOCATION_TEXTURE_MEMORY" = 4
+    NVML_MEMORY_LOCATION_TEXTURE_SHM "NVML_MEMORY_LOCATION_TEXTURE_SHM" = 5
+    NVML_MEMORY_LOCATION_CBU "NVML_MEMORY_LOCATION_CBU" = 6
+    NVML_MEMORY_LOCATION_SRAM "NVML_MEMORY_LOCATION_SRAM" = 7
+    NVML_MEMORY_LOCATION_COUNT "NVML_MEMORY_LOCATION_COUNT"
+
+ctypedef enum nvmlPageRetirementCause_t "nvmlPageRetirementCause_t":
+    NVML_PAGE_RETIREMENT_CAUSE_MULTIPLE_SINGLE_BIT_ECC_ERRORS "NVML_PAGE_RETIREMENT_CAUSE_MULTIPLE_SINGLE_BIT_ECC_ERRORS" = 0
+    NVML_PAGE_RETIREMENT_CAUSE_DOUBLE_BIT_ECC_ERROR "NVML_PAGE_RETIREMENT_CAUSE_DOUBLE_BIT_ECC_ERROR" = 1
+    NVML_PAGE_RETIREMENT_CAUSE_COUNT "NVML_PAGE_RETIREMENT_CAUSE_COUNT"
+
+ctypedef enum nvmlRestrictedAPI_t "nvmlRestrictedAPI_t":
+    NVML_RESTRICTED_API_SET_APPLICATION_CLOCKS "NVML_RESTRICTED_API_SET_APPLICATION_CLOCKS" = 0
+    NVML_RESTRICTED_API_SET_AUTO_BOOSTED_CLOCKS "NVML_RESTRICTED_API_SET_AUTO_BOOSTED_CLOCKS" = 1
+    NVML_RESTRICTED_API_COUNT "NVML_RESTRICTED_API_COUNT"
+
+ctypedef enum nvmlGpuUtilizationDomainId_t "nvmlGpuUtilizationDomainId_t":
+    NVML_GPU_UTILIZATION_DOMAIN_GPU "NVML_GPU_UTILIZATION_DOMAIN_GPU" = 0
+    NVML_GPU_UTILIZATION_DOMAIN_FB "NVML_GPU_UTILIZATION_DOMAIN_FB" = 1
+    NVML_GPU_UTILIZATION_DOMAIN_VID "NVML_GPU_UTILIZATION_DOMAIN_VID" = 2
+    NVML_GPU_UTILIZATION_DOMAIN_BUS "NVML_GPU_UTILIZATION_DOMAIN_BUS" = 3
+
+ctypedef enum nvmlGpuVirtualizationMode_t "nvmlGpuVirtualizationMode_t":
+    NVML_GPU_VIRTUALIZATION_MODE_NONE "NVML_GPU_VIRTUALIZATION_MODE_NONE" = 0
+    NVML_GPU_VIRTUALIZATION_MODE_PASSTHROUGH "NVML_GPU_VIRTUALIZATION_MODE_PASSTHROUGH" = 1
+    NVML_GPU_VIRTUALIZATION_MODE_VGPU "NVML_GPU_VIRTUALIZATION_MODE_VGPU" = 2
+    NVML_GPU_VIRTUALIZATION_MODE_HOST_VGPU "NVML_GPU_VIRTUALIZATION_MODE_HOST_VGPU" = 3
+    NVML_GPU_VIRTUALIZATION_MODE_HOST_VSGA "NVML_GPU_VIRTUALIZATION_MODE_HOST_VSGA" = 4
+
+ctypedef enum nvmlHostVgpuMode_t "nvmlHostVgpuMode_t":
+    NVML_HOST_VGPU_MODE_NON_SRIOV "NVML_HOST_VGPU_MODE_NON_SRIOV" = 0
+    NVML_HOST_VGPU_MODE_SRIOV "NVML_HOST_VGPU_MODE_SRIOV" = 1
+
+ctypedef enum nvmlVgpuVmIdType_t "nvmlVgpuVmIdType_t":
+    NVML_VGPU_VM_ID_DOMAIN_ID "NVML_VGPU_VM_ID_DOMAIN_ID" = 0
+    NVML_VGPU_VM_ID_UUID "NVML_VGPU_VM_ID_UUID" = 1
+
+ctypedef enum nvmlVgpuGuestInfoState_t "nvmlVgpuGuestInfoState_t":
+    NVML_VGPU_INSTANCE_GUEST_INFO_STATE_UNINITIALIZED "NVML_VGPU_INSTANCE_GUEST_INFO_STATE_UNINITIALIZED" = 0
+    NVML_VGPU_INSTANCE_GUEST_INFO_STATE_INITIALIZED "NVML_VGPU_INSTANCE_GUEST_INFO_STATE_INITIALIZED" = 1
+
+ctypedef enum nvmlGridLicenseFeatureCode_t "nvmlGridLicenseFeatureCode_t":
+    NVML_GRID_LICENSE_FEATURE_CODE_UNKNOWN "NVML_GRID_LICENSE_FEATURE_CODE_UNKNOWN" = 0
+    NVML_GRID_LICENSE_FEATURE_CODE_VGPU "NVML_GRID_LICENSE_FEATURE_CODE_VGPU" = 1
+    NVML_GRID_LICENSE_FEATURE_CODE_NVIDIA_RTX "NVML_GRID_LICENSE_FEATURE_CODE_NVIDIA_RTX" = 2
+    NVML_GRID_LICENSE_FEATURE_CODE_VWORKSTATION "NVML_GRID_LICENSE_FEATURE_CODE_VWORKSTATION" = NVML_GRID_LICENSE_FEATURE_CODE_NVIDIA_RTX
+    NVML_GRID_LICENSE_FEATURE_CODE_GAMING "NVML_GRID_LICENSE_FEATURE_CODE_GAMING" = 3
+    NVML_GRID_LICENSE_FEATURE_CODE_COMPUTE "NVML_GRID_LICENSE_FEATURE_CODE_COMPUTE" = 4
+
+ctypedef enum nvmlVgpuCapability_t "nvmlVgpuCapability_t":
+    NVML_VGPU_CAP_NVLINK_P2P "NVML_VGPU_CAP_NVLINK_P2P" = 0
+    NVML_VGPU_CAP_GPUDIRECT "NVML_VGPU_CAP_GPUDIRECT" = 1
+    NVML_VGPU_CAP_MULTI_VGPU_EXCLUSIVE "NVML_VGPU_CAP_MULTI_VGPU_EXCLUSIVE" = 2
+    NVML_VGPU_CAP_EXCLUSIVE_TYPE "NVML_VGPU_CAP_EXCLUSIVE_TYPE" = 3
+    NVML_VGPU_CAP_EXCLUSIVE_SIZE "NVML_VGPU_CAP_EXCLUSIVE_SIZE" = 4
+    NVML_VGPU_CAP_COUNT "NVML_VGPU_CAP_COUNT"
+
+ctypedef enum nvmlVgpuDriverCapability_t "nvmlVgpuDriverCapability_t":
+    NVML_VGPU_DRIVER_CAP_HETEROGENEOUS_MULTI_VGPU "NVML_VGPU_DRIVER_CAP_HETEROGENEOUS_MULTI_VGPU" = 0
+    NVML_VGPU_DRIVER_CAP_WARM_UPDATE "NVML_VGPU_DRIVER_CAP_WARM_UPDATE" = 1
+    NVML_VGPU_DRIVER_CAP_COUNT "NVML_VGPU_DRIVER_CAP_COUNT"
+
+ctypedef enum nvmlDeviceVgpuCapability_t "nvmlDeviceVgpuCapability_t":
+    NVML_DEVICE_VGPU_CAP_FRACTIONAL_MULTI_VGPU "NVML_DEVICE_VGPU_CAP_FRACTIONAL_MULTI_VGPU" = 0
+    NVML_DEVICE_VGPU_CAP_HETEROGENEOUS_TIMESLICE_PROFILES "NVML_DEVICE_VGPU_CAP_HETEROGENEOUS_TIMESLICE_PROFILES" = 1
+    NVML_DEVICE_VGPU_CAP_HETEROGENEOUS_TIMESLICE_SIZES "NVML_DEVICE_VGPU_CAP_HETEROGENEOUS_TIMESLICE_SIZES" = 2
+    NVML_DEVICE_VGPU_CAP_READ_DEVICE_BUFFER_BW "NVML_DEVICE_VGPU_CAP_READ_DEVICE_BUFFER_BW" = 3
+    NVML_DEVICE_VGPU_CAP_WRITE_DEVICE_BUFFER_BW "NVML_DEVICE_VGPU_CAP_WRITE_DEVICE_BUFFER_BW" = 4
+    NVML_DEVICE_VGPU_CAP_DEVICE_STREAMING "NVML_DEVICE_VGPU_CAP_DEVICE_STREAMING" = 5
+    NVML_DEVICE_VGPU_CAP_MINI_QUARTER_GPU "NVML_DEVICE_VGPU_CAP_MINI_QUARTER_GPU" = 6
+    NVML_DEVICE_VGPU_CAP_COMPUTE_MEDIA_ENGINE_GPU "NVML_DEVICE_VGPU_CAP_COMPUTE_MEDIA_ENGINE_GPU" = 7
+    NVML_DEVICE_VGPU_CAP_WARM_UPDATE "NVML_DEVICE_VGPU_CAP_WARM_UPDATE" = 8
+    NVML_DEVICE_VGPU_CAP_HOMOGENEOUS_PLACEMENTS "NVML_DEVICE_VGPU_CAP_HOMOGENEOUS_PLACEMENTS" = 9
+    NVML_DEVICE_VGPU_CAP_MIG_TIMESLICING_SUPPORTED "NVML_DEVICE_VGPU_CAP_MIG_TIMESLICING_SUPPORTED" = 10
+    NVML_DEVICE_VGPU_CAP_MIG_TIMESLICING_ENABLED "NVML_DEVICE_VGPU_CAP_MIG_TIMESLICING_ENABLED" = 11
+    NVML_DEVICE_VGPU_CAP_COUNT "NVML_DEVICE_VGPU_CAP_COUNT"
+
+ctypedef enum nvmlDeviceGpuRecoveryAction_t "nvmlDeviceGpuRecoveryAction_t":
+    NVML_GPU_RECOVERY_ACTION_NONE "NVML_GPU_RECOVERY_ACTION_NONE" = 0
+    NVML_GPU_RECOVERY_ACTION_GPU_RESET "NVML_GPU_RECOVERY_ACTION_GPU_RESET" = 1
+    NVML_GPU_RECOVERY_ACTION_NODE_REBOOT "NVML_GPU_RECOVERY_ACTION_NODE_REBOOT" = 2
+    NVML_GPU_RECOVERY_ACTION_DRAIN_P2P "NVML_GPU_RECOVERY_ACTION_DRAIN_P2P" = 3
+    NVML_GPU_RECOVERY_ACTION_DRAIN_AND_RESET "NVML_GPU_RECOVERY_ACTION_DRAIN_AND_RESET" = 4
+
+ctypedef enum nvmlFanState_t "nvmlFanState_t":
+    NVML_FAN_NORMAL "NVML_FAN_NORMAL" = 0
+    NVML_FAN_FAILED "NVML_FAN_FAILED" = 1
+
+ctypedef enum nvmlLedColor_t "nvmlLedColor_t":
+    NVML_LED_COLOR_GREEN "NVML_LED_COLOR_GREEN" = 0
+    NVML_LED_COLOR_AMBER "NVML_LED_COLOR_AMBER" = 1
+
+ctypedef enum nvmlEncoderType_t "nvmlEncoderType_t":
+    NVML_ENCODER_QUERY_H264 "NVML_ENCODER_QUERY_H264" = 0x00
+    NVML_ENCODER_QUERY_HEVC "NVML_ENCODER_QUERY_HEVC" = 0x01
+    NVML_ENCODER_QUERY_AV1 "NVML_ENCODER_QUERY_AV1" = 0x02
+    NVML_ENCODER_QUERY_UNKNOWN "NVML_ENCODER_QUERY_UNKNOWN" = 0xFF
+
+ctypedef enum nvmlFBCSessionType_t "nvmlFBCSessionType_t":
+    NVML_FBC_SESSION_TYPE_UNKNOWN "NVML_FBC_SESSION_TYPE_UNKNOWN" = 0
+    NVML_FBC_SESSION_TYPE_TOSYS "NVML_FBC_SESSION_TYPE_TOSYS"
+    NVML_FBC_SESSION_TYPE_CUDA "NVML_FBC_SESSION_TYPE_CUDA"
+    NVML_FBC_SESSION_TYPE_VID "NVML_FBC_SESSION_TYPE_VID"
+    NVML_FBC_SESSION_TYPE_HWENC "NVML_FBC_SESSION_TYPE_HWENC"
+
+ctypedef enum nvmlDetachGpuState_t "nvmlDetachGpuState_t":
+    NVML_DETACH_GPU_KEEP "NVML_DETACH_GPU_KEEP" = 0
+    NVML_DETACH_GPU_REMOVE "NVML_DETACH_GPU_REMOVE"
+
+ctypedef enum nvmlPcieLinkState_t "nvmlPcieLinkState_t":
+    NVML_PCIE_LINK_KEEP "NVML_PCIE_LINK_KEEP" = 0
+    NVML_PCIE_LINK_SHUT_DOWN "NVML_PCIE_LINK_SHUT_DOWN"
+
+ctypedef enum nvmlClockLimitId_t "nvmlClockLimitId_t":
+    NVML_CLOCK_LIMIT_ID_RANGE_START "NVML_CLOCK_LIMIT_ID_RANGE_START" = 0xffffff00
+    NVML_CLOCK_LIMIT_ID_TDP "NVML_CLOCK_LIMIT_ID_TDP"
+    NVML_CLOCK_LIMIT_ID_UNLIMITED "NVML_CLOCK_LIMIT_ID_UNLIMITED"
+
+ctypedef enum nvmlVgpuVmCompatibility_t "nvmlVgpuVmCompatibility_t":
+    NVML_VGPU_VM_COMPATIBILITY_NONE "NVML_VGPU_VM_COMPATIBILITY_NONE" = 0x0
+    NVML_VGPU_VM_COMPATIBILITY_COLD "NVML_VGPU_VM_COMPATIBILITY_COLD" = 0x1
+    NVML_VGPU_VM_COMPATIBILITY_HIBERNATE "NVML_VGPU_VM_COMPATIBILITY_HIBERNATE" = 0x2
+    NVML_VGPU_VM_COMPATIBILITY_SLEEP "NVML_VGPU_VM_COMPATIBILITY_SLEEP" = 0x4
+    NVML_VGPU_VM_COMPATIBILITY_LIVE "NVML_VGPU_VM_COMPATIBILITY_LIVE" = 0x8
+
+ctypedef enum nvmlVgpuPgpuCompatibilityLimitCode_t "nvmlVgpuPgpuCompatibilityLimitCode_t":
+    NVML_VGPU_COMPATIBILITY_LIMIT_NONE "NVML_VGPU_COMPATIBILITY_LIMIT_NONE" = 0x0
+    NVML_VGPU_COMPATIBILITY_LIMIT_HOST_DRIVER "NVML_VGPU_COMPATIBILITY_LIMIT_HOST_DRIVER" = 0x1
+    NVML_VGPU_COMPATIBILITY_LIMIT_GUEST_DRIVER "NVML_VGPU_COMPATIBILITY_LIMIT_GUEST_DRIVER" = 0x2
+    NVML_VGPU_COMPATIBILITY_LIMIT_GPU "NVML_VGPU_COMPATIBILITY_LIMIT_GPU" = 0x4
+    NVML_VGPU_COMPATIBILITY_LIMIT_OTHER "NVML_VGPU_COMPATIBILITY_LIMIT_OTHER" = 0x80000000
+
+ctypedef enum nvmlGpmMetricId_t "nvmlGpmMetricId_t":
+    NVML_GPM_METRIC_GRAPHICS_UTIL "NVML_GPM_METRIC_GRAPHICS_UTIL" = 1
+    NVML_GPM_METRIC_SM_UTIL "NVML_GPM_METRIC_SM_UTIL" = 2
+    NVML_GPM_METRIC_SM_OCCUPANCY "NVML_GPM_METRIC_SM_OCCUPANCY" = 3
+    NVML_GPM_METRIC_INTEGER_UTIL "NVML_GPM_METRIC_INTEGER_UTIL" = 4
+    NVML_GPM_METRIC_ANY_TENSOR_UTIL "NVML_GPM_METRIC_ANY_TENSOR_UTIL" = 5
+    NVML_GPM_METRIC_DFMA_TENSOR_UTIL "NVML_GPM_METRIC_DFMA_TENSOR_UTIL" = 6
+    NVML_GPM_METRIC_HMMA_TENSOR_UTIL "NVML_GPM_METRIC_HMMA_TENSOR_UTIL" = 7
+    NVML_GPM_METRIC_DMMA_TENSOR_UTIL "NVML_GPM_METRIC_DMMA_TENSOR_UTIL" = 8
+    NVML_GPM_METRIC_IMMA_TENSOR_UTIL "NVML_GPM_METRIC_IMMA_TENSOR_UTIL" = 9
+    NVML_GPM_METRIC_DRAM_BW_UTIL "NVML_GPM_METRIC_DRAM_BW_UTIL" = 10
+    NVML_GPM_METRIC_FP64_UTIL "NVML_GPM_METRIC_FP64_UTIL" = 11
+    NVML_GPM_METRIC_FP32_UTIL "NVML_GPM_METRIC_FP32_UTIL" = 12
+    NVML_GPM_METRIC_FP16_UTIL "NVML_GPM_METRIC_FP16_UTIL" = 13
+    NVML_GPM_METRIC_PCIE_TX_PER_SEC "NVML_GPM_METRIC_PCIE_TX_PER_SEC" = 20
+    NVML_GPM_METRIC_PCIE_RX_PER_SEC "NVML_GPM_METRIC_PCIE_RX_PER_SEC" = 21
+    NVML_GPM_METRIC_NVDEC_0_UTIL "NVML_GPM_METRIC_NVDEC_0_UTIL" = 30
+    NVML_GPM_METRIC_NVDEC_1_UTIL "NVML_GPM_METRIC_NVDEC_1_UTIL" = 31
+    NVML_GPM_METRIC_NVDEC_2_UTIL "NVML_GPM_METRIC_NVDEC_2_UTIL" = 32
+    NVML_GPM_METRIC_NVDEC_3_UTIL "NVML_GPM_METRIC_NVDEC_3_UTIL" = 33
+    NVML_GPM_METRIC_NVDEC_4_UTIL "NVML_GPM_METRIC_NVDEC_4_UTIL" = 34
+    NVML_GPM_METRIC_NVDEC_5_UTIL "NVML_GPM_METRIC_NVDEC_5_UTIL" = 35
+    NVML_GPM_METRIC_NVDEC_6_UTIL "NVML_GPM_METRIC_NVDEC_6_UTIL" = 36
+    NVML_GPM_METRIC_NVDEC_7_UTIL "NVML_GPM_METRIC_NVDEC_7_UTIL" = 37
+    NVML_GPM_METRIC_NVJPG_0_UTIL "NVML_GPM_METRIC_NVJPG_0_UTIL" = 40
+    NVML_GPM_METRIC_NVJPG_1_UTIL "NVML_GPM_METRIC_NVJPG_1_UTIL" = 41
+    NVML_GPM_METRIC_NVJPG_2_UTIL "NVML_GPM_METRIC_NVJPG_2_UTIL" = 42
+    NVML_GPM_METRIC_NVJPG_3_UTIL "NVML_GPM_METRIC_NVJPG_3_UTIL" = 43
+    NVML_GPM_METRIC_NVJPG_4_UTIL "NVML_GPM_METRIC_NVJPG_4_UTIL" = 44
+    NVML_GPM_METRIC_NVJPG_5_UTIL "NVML_GPM_METRIC_NVJPG_5_UTIL" = 45
+    NVML_GPM_METRIC_NVJPG_6_UTIL "NVML_GPM_METRIC_NVJPG_6_UTIL" = 46
+    NVML_GPM_METRIC_NVJPG_7_UTIL "NVML_GPM_METRIC_NVJPG_7_UTIL" = 47
+    NVML_GPM_METRIC_NVOFA_0_UTIL "NVML_GPM_METRIC_NVOFA_0_UTIL" = 50
+    NVML_GPM_METRIC_NVOFA_1_UTIL "NVML_GPM_METRIC_NVOFA_1_UTIL" = 51
+    NVML_GPM_METRIC_NVLINK_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_TOTAL_RX_PER_SEC" = 60
+    NVML_GPM_METRIC_NVLINK_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_TOTAL_TX_PER_SEC" = 61
+    NVML_GPM_METRIC_NVLINK_L0_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L0_RX_PER_SEC" = 62
+    NVML_GPM_METRIC_NVLINK_L0_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L0_TX_PER_SEC" = 63
+    NVML_GPM_METRIC_NVLINK_L1_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L1_RX_PER_SEC" = 64
+    NVML_GPM_METRIC_NVLINK_L1_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L1_TX_PER_SEC" = 65
+    NVML_GPM_METRIC_NVLINK_L2_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L2_RX_PER_SEC" = 66
+    NVML_GPM_METRIC_NVLINK_L2_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L2_TX_PER_SEC" = 67
+    NVML_GPM_METRIC_NVLINK_L3_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L3_RX_PER_SEC" = 68
+    NVML_GPM_METRIC_NVLINK_L3_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L3_TX_PER_SEC" = 69
+    NVML_GPM_METRIC_NVLINK_L4_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L4_RX_PER_SEC" = 70
+    NVML_GPM_METRIC_NVLINK_L4_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L4_TX_PER_SEC" = 71
+    NVML_GPM_METRIC_NVLINK_L5_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L5_RX_PER_SEC" = 72
+    NVML_GPM_METRIC_NVLINK_L5_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L5_TX_PER_SEC" = 73
+    NVML_GPM_METRIC_NVLINK_L6_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L6_RX_PER_SEC" = 74
+    NVML_GPM_METRIC_NVLINK_L6_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L6_TX_PER_SEC" = 75
+    NVML_GPM_METRIC_NVLINK_L7_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L7_RX_PER_SEC" = 76
+    NVML_GPM_METRIC_NVLINK_L7_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L7_TX_PER_SEC" = 77
+    NVML_GPM_METRIC_NVLINK_L8_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L8_RX_PER_SEC" = 78
+    NVML_GPM_METRIC_NVLINK_L8_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L8_TX_PER_SEC" = 79
+    NVML_GPM_METRIC_NVLINK_L9_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L9_RX_PER_SEC" = 80
+    NVML_GPM_METRIC_NVLINK_L9_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L9_TX_PER_SEC" = 81
+    NVML_GPM_METRIC_NVLINK_L10_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L10_RX_PER_SEC" = 82
+    NVML_GPM_METRIC_NVLINK_L10_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L10_TX_PER_SEC" = 83
+    NVML_GPM_METRIC_NVLINK_L11_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L11_RX_PER_SEC" = 84
+    NVML_GPM_METRIC_NVLINK_L11_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L11_TX_PER_SEC" = 85
+    NVML_GPM_METRIC_NVLINK_L12_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L12_RX_PER_SEC" = 86
+    NVML_GPM_METRIC_NVLINK_L12_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L12_TX_PER_SEC" = 87
+    NVML_GPM_METRIC_NVLINK_L13_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L13_RX_PER_SEC" = 88
+    NVML_GPM_METRIC_NVLINK_L13_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L13_TX_PER_SEC" = 89
+    NVML_GPM_METRIC_NVLINK_L14_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L14_RX_PER_SEC" = 90
+    NVML_GPM_METRIC_NVLINK_L14_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L14_TX_PER_SEC" = 91
+    NVML_GPM_METRIC_NVLINK_L15_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L15_RX_PER_SEC" = 92
+    NVML_GPM_METRIC_NVLINK_L15_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L15_TX_PER_SEC" = 93
+    NVML_GPM_METRIC_NVLINK_L16_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L16_RX_PER_SEC" = 94
+    NVML_GPM_METRIC_NVLINK_L16_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L16_TX_PER_SEC" = 95
+    NVML_GPM_METRIC_NVLINK_L17_RX_PER_SEC "NVML_GPM_METRIC_NVLINK_L17_RX_PER_SEC" = 96
+    NVML_GPM_METRIC_NVLINK_L17_TX_PER_SEC "NVML_GPM_METRIC_NVLINK_L17_TX_PER_SEC" = 97
+    NVML_GPM_METRIC_C2C_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_TOTAL_TX_PER_SEC" = 100
+    NVML_GPM_METRIC_C2C_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_TOTAL_RX_PER_SEC" = 101
+    NVML_GPM_METRIC_C2C_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_DATA_TX_PER_SEC" = 102
+    NVML_GPM_METRIC_C2C_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_DATA_RX_PER_SEC" = 103
+    NVML_GPM_METRIC_C2C_LINK0_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK0_TOTAL_TX_PER_SEC" = 104
+    NVML_GPM_METRIC_C2C_LINK0_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK0_TOTAL_RX_PER_SEC" = 105
+    NVML_GPM_METRIC_C2C_LINK0_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK0_DATA_TX_PER_SEC" = 106
+    NVML_GPM_METRIC_C2C_LINK0_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK0_DATA_RX_PER_SEC" = 107
+    NVML_GPM_METRIC_C2C_LINK1_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK1_TOTAL_TX_PER_SEC" = 108
+    NVML_GPM_METRIC_C2C_LINK1_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK1_TOTAL_RX_PER_SEC" = 109
+    NVML_GPM_METRIC_C2C_LINK1_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK1_DATA_TX_PER_SEC" = 110
+    NVML_GPM_METRIC_C2C_LINK1_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK1_DATA_RX_PER_SEC" = 111
+    NVML_GPM_METRIC_C2C_LINK2_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK2_TOTAL_TX_PER_SEC" = 112
+    NVML_GPM_METRIC_C2C_LINK2_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK2_TOTAL_RX_PER_SEC" = 113
+    NVML_GPM_METRIC_C2C_LINK2_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK2_DATA_TX_PER_SEC" = 114
+    NVML_GPM_METRIC_C2C_LINK2_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK2_DATA_RX_PER_SEC" = 115
+    NVML_GPM_METRIC_C2C_LINK3_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK3_TOTAL_TX_PER_SEC" = 116
+    NVML_GPM_METRIC_C2C_LINK3_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK3_TOTAL_RX_PER_SEC" = 117
+    NVML_GPM_METRIC_C2C_LINK3_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK3_DATA_TX_PER_SEC" = 118
+    NVML_GPM_METRIC_C2C_LINK3_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK3_DATA_RX_PER_SEC" = 119
+    NVML_GPM_METRIC_C2C_LINK4_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK4_TOTAL_TX_PER_SEC" = 120
+    NVML_GPM_METRIC_C2C_LINK4_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK4_TOTAL_RX_PER_SEC" = 121
+    NVML_GPM_METRIC_C2C_LINK4_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK4_DATA_TX_PER_SEC" = 122
+    NVML_GPM_METRIC_C2C_LINK4_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK4_DATA_RX_PER_SEC" = 123
+    NVML_GPM_METRIC_C2C_LINK5_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK5_TOTAL_TX_PER_SEC" = 124
+    NVML_GPM_METRIC_C2C_LINK5_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK5_TOTAL_RX_PER_SEC" = 125
+    NVML_GPM_METRIC_C2C_LINK5_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK5_DATA_TX_PER_SEC" = 126
+    NVML_GPM_METRIC_C2C_LINK5_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK5_DATA_RX_PER_SEC" = 127
+    NVML_GPM_METRIC_C2C_LINK6_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK6_TOTAL_TX_PER_SEC" = 128
+    NVML_GPM_METRIC_C2C_LINK6_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK6_TOTAL_RX_PER_SEC" = 129
+    NVML_GPM_METRIC_C2C_LINK6_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK6_DATA_TX_PER_SEC" = 130
+    NVML_GPM_METRIC_C2C_LINK6_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK6_DATA_RX_PER_SEC" = 131
+    NVML_GPM_METRIC_C2C_LINK7_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK7_TOTAL_TX_PER_SEC" = 132
+    NVML_GPM_METRIC_C2C_LINK7_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK7_TOTAL_RX_PER_SEC" = 133
+    NVML_GPM_METRIC_C2C_LINK7_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK7_DATA_TX_PER_SEC" = 134
+    NVML_GPM_METRIC_C2C_LINK7_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK7_DATA_RX_PER_SEC" = 135
+    NVML_GPM_METRIC_C2C_LINK8_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK8_TOTAL_TX_PER_SEC" = 136
+    NVML_GPM_METRIC_C2C_LINK8_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK8_TOTAL_RX_PER_SEC" = 137
+    NVML_GPM_METRIC_C2C_LINK8_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK8_DATA_TX_PER_SEC" = 138
+    NVML_GPM_METRIC_C2C_LINK8_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK8_DATA_RX_PER_SEC" = 139
+    NVML_GPM_METRIC_C2C_LINK9_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK9_TOTAL_TX_PER_SEC" = 140
+    NVML_GPM_METRIC_C2C_LINK9_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK9_TOTAL_RX_PER_SEC" = 141
+    NVML_GPM_METRIC_C2C_LINK9_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK9_DATA_TX_PER_SEC" = 142
+    NVML_GPM_METRIC_C2C_LINK9_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK9_DATA_RX_PER_SEC" = 143
+    NVML_GPM_METRIC_C2C_LINK10_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK10_TOTAL_TX_PER_SEC" = 144
+    NVML_GPM_METRIC_C2C_LINK10_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK10_TOTAL_RX_PER_SEC" = 145
+    NVML_GPM_METRIC_C2C_LINK10_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK10_DATA_TX_PER_SEC" = 146
+    NVML_GPM_METRIC_C2C_LINK10_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK10_DATA_RX_PER_SEC" = 147
+    NVML_GPM_METRIC_C2C_LINK11_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK11_TOTAL_TX_PER_SEC" = 148
+    NVML_GPM_METRIC_C2C_LINK11_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK11_TOTAL_RX_PER_SEC" = 149
+    NVML_GPM_METRIC_C2C_LINK11_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK11_DATA_TX_PER_SEC" = 150
+    NVML_GPM_METRIC_C2C_LINK11_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK11_DATA_RX_PER_SEC" = 151
+    NVML_GPM_METRIC_C2C_LINK12_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK12_TOTAL_TX_PER_SEC" = 152
+    NVML_GPM_METRIC_C2C_LINK12_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK12_TOTAL_RX_PER_SEC" = 153
+    NVML_GPM_METRIC_C2C_LINK12_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK12_DATA_TX_PER_SEC" = 154
+    NVML_GPM_METRIC_C2C_LINK12_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK12_DATA_RX_PER_SEC" = 155
+    NVML_GPM_METRIC_C2C_LINK13_TOTAL_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK13_TOTAL_TX_PER_SEC" = 156
+    NVML_GPM_METRIC_C2C_LINK13_TOTAL_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK13_TOTAL_RX_PER_SEC" = 157
+    NVML_GPM_METRIC_C2C_LINK13_DATA_TX_PER_SEC "NVML_GPM_METRIC_C2C_LINK13_DATA_TX_PER_SEC" = 158
+    NVML_GPM_METRIC_C2C_LINK13_DATA_RX_PER_SEC "NVML_GPM_METRIC_C2C_LINK13_DATA_RX_PER_SEC" = 159
+    NVML_GPM_METRIC_HOSTMEM_CACHE_HIT "NVML_GPM_METRIC_HOSTMEM_CACHE_HIT" = 160
+    NVML_GPM_METRIC_HOSTMEM_CACHE_MISS "NVML_GPM_METRIC_HOSTMEM_CACHE_MISS" = 161
+    NVML_GPM_METRIC_PEERMEM_CACHE_HIT "NVML_GPM_METRIC_PEERMEM_CACHE_HIT" = 162
+    NVML_GPM_METRIC_PEERMEM_CACHE_MISS "NVML_GPM_METRIC_PEERMEM_CACHE_MISS" = 163
+    NVML_GPM_METRIC_DRAM_CACHE_HIT "NVML_GPM_METRIC_DRAM_CACHE_HIT" = 164
+    NVML_GPM_METRIC_DRAM_CACHE_MISS "NVML_GPM_METRIC_DRAM_CACHE_MISS" = 165
+    NVML_GPM_METRIC_NVENC_0_UTIL "NVML_GPM_METRIC_NVENC_0_UTIL" = 166
+    NVML_GPM_METRIC_NVENC_1_UTIL "NVML_GPM_METRIC_NVENC_1_UTIL" = 167
+    NVML_GPM_METRIC_NVENC_2_UTIL "NVML_GPM_METRIC_NVENC_2_UTIL" = 168
+    NVML_GPM_METRIC_NVENC_3_UTIL "NVML_GPM_METRIC_NVENC_3_UTIL" = 169
+    NVML_GPM_METRIC_GR0_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR0_CTXSW_CYCLES_ELAPSED" = 170
+    NVML_GPM_METRIC_GR0_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR0_CTXSW_CYCLES_ACTIVE" = 171
+    NVML_GPM_METRIC_GR0_CTXSW_REQUESTS "NVML_GPM_METRIC_GR0_CTXSW_REQUESTS" = 172
+    NVML_GPM_METRIC_GR0_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR0_CTXSW_CYCLES_PER_REQ" = 173
+    NVML_GPM_METRIC_GR0_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR0_CTXSW_ACTIVE_PCT" = 174
+    NVML_GPM_METRIC_GR1_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR1_CTXSW_CYCLES_ELAPSED" = 175
+    NVML_GPM_METRIC_GR1_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR1_CTXSW_CYCLES_ACTIVE" = 176
+    NVML_GPM_METRIC_GR1_CTXSW_REQUESTS "NVML_GPM_METRIC_GR1_CTXSW_REQUESTS" = 177
+    NVML_GPM_METRIC_GR1_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR1_CTXSW_CYCLES_PER_REQ" = 178
+    NVML_GPM_METRIC_GR1_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR1_CTXSW_ACTIVE_PCT" = 179
+    NVML_GPM_METRIC_GR2_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR2_CTXSW_CYCLES_ELAPSED" = 180
+    NVML_GPM_METRIC_GR2_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR2_CTXSW_CYCLES_ACTIVE" = 181
+    NVML_GPM_METRIC_GR2_CTXSW_REQUESTS "NVML_GPM_METRIC_GR2_CTXSW_REQUESTS" = 182
+    NVML_GPM_METRIC_GR2_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR2_CTXSW_CYCLES_PER_REQ" = 183
+    NVML_GPM_METRIC_GR2_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR2_CTXSW_ACTIVE_PCT" = 184
+    NVML_GPM_METRIC_GR3_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR3_CTXSW_CYCLES_ELAPSED" = 185
+    NVML_GPM_METRIC_GR3_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR3_CTXSW_CYCLES_ACTIVE" = 186
+    NVML_GPM_METRIC_GR3_CTXSW_REQUESTS "NVML_GPM_METRIC_GR3_CTXSW_REQUESTS" = 187
+    NVML_GPM_METRIC_GR3_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR3_CTXSW_CYCLES_PER_REQ" = 188
+    NVML_GPM_METRIC_GR3_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR3_CTXSW_ACTIVE_PCT" = 189
+    NVML_GPM_METRIC_GR4_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR4_CTXSW_CYCLES_ELAPSED" = 190
+    NVML_GPM_METRIC_GR4_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR4_CTXSW_CYCLES_ACTIVE" = 191
+    NVML_GPM_METRIC_GR4_CTXSW_REQUESTS "NVML_GPM_METRIC_GR4_CTXSW_REQUESTS" = 192
+    NVML_GPM_METRIC_GR4_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR4_CTXSW_CYCLES_PER_REQ" = 193
+    NVML_GPM_METRIC_GR4_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR4_CTXSW_ACTIVE_PCT" = 194
+    NVML_GPM_METRIC_GR5_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR5_CTXSW_CYCLES_ELAPSED" = 195
+    NVML_GPM_METRIC_GR5_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR5_CTXSW_CYCLES_ACTIVE" = 196
+    NVML_GPM_METRIC_GR5_CTXSW_REQUESTS "NVML_GPM_METRIC_GR5_CTXSW_REQUESTS" = 197
+    NVML_GPM_METRIC_GR5_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR5_CTXSW_CYCLES_PER_REQ" = 198
+    NVML_GPM_METRIC_GR5_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR5_CTXSW_ACTIVE_PCT" = 199
+    NVML_GPM_METRIC_GR6_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR6_CTXSW_CYCLES_ELAPSED" = 200
+    NVML_GPM_METRIC_GR6_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR6_CTXSW_CYCLES_ACTIVE" = 201
+    NVML_GPM_METRIC_GR6_CTXSW_REQUESTS "NVML_GPM_METRIC_GR6_CTXSW_REQUESTS" = 202
+    NVML_GPM_METRIC_GR6_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR6_CTXSW_CYCLES_PER_REQ" = 203
+    NVML_GPM_METRIC_GR6_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR6_CTXSW_ACTIVE_PCT" = 204
+    NVML_GPM_METRIC_GR7_CTXSW_CYCLES_ELAPSED "NVML_GPM_METRIC_GR7_CTXSW_CYCLES_ELAPSED" = 205
+    NVML_GPM_METRIC_GR7_CTXSW_CYCLES_ACTIVE "NVML_GPM_METRIC_GR7_CTXSW_CYCLES_ACTIVE" = 206
+    NVML_GPM_METRIC_GR7_CTXSW_REQUESTS "NVML_GPM_METRIC_GR7_CTXSW_REQUESTS" = 207
+    NVML_GPM_METRIC_GR7_CTXSW_CYCLES_PER_REQ "NVML_GPM_METRIC_GR7_CTXSW_CYCLES_PER_REQ" = 208
+    NVML_GPM_METRIC_GR7_CTXSW_ACTIVE_PCT "NVML_GPM_METRIC_GR7_CTXSW_ACTIVE_PCT" = 209
+    NVML_GPM_METRIC_SM_CYCLES_ELAPSED "NVML_GPM_METRIC_SM_CYCLES_ELAPSED" = 248
+    NVML_GPM_METRIC_SM_CYCLES_ACTIVE "NVML_GPM_METRIC_SM_CYCLES_ACTIVE" = 249
+    NVML_GPM_METRIC_MMA_CYCLES_ACTIVE "NVML_GPM_METRIC_MMA_CYCLES_ACTIVE" = 250
+    NVML_GPM_METRIC_DMMA_CYCLES_ACTIVE "NVML_GPM_METRIC_DMMA_CYCLES_ACTIVE" = 251
+    NVML_GPM_METRIC_HMMA_CYCLES_ACTIVE "NVML_GPM_METRIC_HMMA_CYCLES_ACTIVE" = 252
+    NVML_GPM_METRIC_IMMA_CYCLES_ACTIVE "NVML_GPM_METRIC_IMMA_CYCLES_ACTIVE" = 253
+    NVML_GPM_METRIC_DFMA_CYCLES_ACTIVE "NVML_GPM_METRIC_DFMA_CYCLES_ACTIVE" = 254
+    NVML_GPM_METRIC_PCIE_TX "NVML_GPM_METRIC_PCIE_TX" = 255
+    NVML_GPM_METRIC_PCIE_RX "NVML_GPM_METRIC_PCIE_RX" = 256
+    NVML_GPM_METRIC_INTEGER_CYCLES_ACTIVE "NVML_GPM_METRIC_INTEGER_CYCLES_ACTIVE" = 257
+    NVML_GPM_METRIC_FP64_CYCLES_ACTIVE "NVML_GPM_METRIC_FP64_CYCLES_ACTIVE" = 258
+    NVML_GPM_METRIC_FP32_CYCLES_ACTIVE "NVML_GPM_METRIC_FP32_CYCLES_ACTIVE" = 259
+    NVML_GPM_METRIC_FP16_CYCLES_ACTIVE "NVML_GPM_METRIC_FP16_CYCLES_ACTIVE" = 260
+    NVML_GPM_METRIC_NVLINK_L0_RX "NVML_GPM_METRIC_NVLINK_L0_RX" = 261
+    NVML_GPM_METRIC_NVLINK_L0_TX "NVML_GPM_METRIC_NVLINK_L0_TX" = 262
+    NVML_GPM_METRIC_NVLINK_L1_RX "NVML_GPM_METRIC_NVLINK_L1_RX" = 263
+    NVML_GPM_METRIC_NVLINK_L1_TX "NVML_GPM_METRIC_NVLINK_L1_TX" = 264
+    NVML_GPM_METRIC_NVLINK_L2_RX "NVML_GPM_METRIC_NVLINK_L2_RX" = 265
+    NVML_GPM_METRIC_NVLINK_L2_TX "NVML_GPM_METRIC_NVLINK_L2_TX" = 266
+    NVML_GPM_METRIC_NVLINK_L3_RX "NVML_GPM_METRIC_NVLINK_L3_RX" = 267
+    NVML_GPM_METRIC_NVLINK_L3_TX "NVML_GPM_METRIC_NVLINK_L3_TX" = 268
+    NVML_GPM_METRIC_NVLINK_L4_RX "NVML_GPM_METRIC_NVLINK_L4_RX" = 269
+    NVML_GPM_METRIC_NVLINK_L4_TX "NVML_GPM_METRIC_NVLINK_L4_TX" = 270
+    NVML_GPM_METRIC_NVLINK_L5_RX "NVML_GPM_METRIC_NVLINK_L5_RX" = 271
+    NVML_GPM_METRIC_NVLINK_L5_TX "NVML_GPM_METRIC_NVLINK_L5_TX" = 272
+    NVML_GPM_METRIC_NVLINK_L6_RX "NVML_GPM_METRIC_NVLINK_L6_RX" = 273
+    NVML_GPM_METRIC_NVLINK_L6_TX "NVML_GPM_METRIC_NVLINK_L6_TX" = 274
+    NVML_GPM_METRIC_NVLINK_L7_RX "NVML_GPM_METRIC_NVLINK_L7_RX" = 275
+    NVML_GPM_METRIC_NVLINK_L7_TX "NVML_GPM_METRIC_NVLINK_L7_TX" = 276
+    NVML_GPM_METRIC_NVLINK_L8_RX "NVML_GPM_METRIC_NVLINK_L8_RX" = 277
+    NVML_GPM_METRIC_NVLINK_L8_TX "NVML_GPM_METRIC_NVLINK_L8_TX" = 278
+    NVML_GPM_METRIC_NVLINK_L9_RX "NVML_GPM_METRIC_NVLINK_L9_RX" = 279
+    NVML_GPM_METRIC_NVLINK_L9_TX "NVML_GPM_METRIC_NVLINK_L9_TX" = 280
+    NVML_GPM_METRIC_NVLINK_L10_RX "NVML_GPM_METRIC_NVLINK_L10_RX" = 281
+    NVML_GPM_METRIC_NVLINK_L10_TX "NVML_GPM_METRIC_NVLINK_L10_TX" = 282
+    NVML_GPM_METRIC_NVLINK_L11_RX "NVML_GPM_METRIC_NVLINK_L11_RX" = 283
+    NVML_GPM_METRIC_NVLINK_L11_TX "NVML_GPM_METRIC_NVLINK_L11_TX" = 284
+    NVML_GPM_METRIC_NVLINK_L12_RX "NVML_GPM_METRIC_NVLINK_L12_RX" = 285
+    NVML_GPM_METRIC_NVLINK_L12_TX "NVML_GPM_METRIC_NVLINK_L12_TX" = 286
+    NVML_GPM_METRIC_NVLINK_L13_RX "NVML_GPM_METRIC_NVLINK_L13_RX" = 287
+    NVML_GPM_METRIC_NVLINK_L13_TX "NVML_GPM_METRIC_NVLINK_L13_TX" = 288
+    NVML_GPM_METRIC_NVLINK_L14_RX "NVML_GPM_METRIC_NVLINK_L14_RX" = 289
+    NVML_GPM_METRIC_NVLINK_L14_TX "NVML_GPM_METRIC_NVLINK_L14_TX" = 290
+    NVML_GPM_METRIC_NVLINK_L15_RX "NVML_GPM_METRIC_NVLINK_L15_RX" = 291
+    NVML_GPM_METRIC_NVLINK_L15_TX "NVML_GPM_METRIC_NVLINK_L15_TX" = 292
+    NVML_GPM_METRIC_NVLINK_L16_RX "NVML_GPM_METRIC_NVLINK_L16_RX" = 293
+    NVML_GPM_METRIC_NVLINK_L16_TX "NVML_GPM_METRIC_NVLINK_L16_TX" = 294
+    NVML_GPM_METRIC_NVLINK_L17_RX "NVML_GPM_METRIC_NVLINK_L17_RX" = 295
+    NVML_GPM_METRIC_NVLINK_L17_TX "NVML_GPM_METRIC_NVLINK_L17_TX" = 296
+    NVML_GPM_METRIC_MAX "NVML_GPM_METRIC_MAX" = 333
+
+ctypedef enum nvmlPowerProfileType_t "nvmlPowerProfileType_t":
+    NVML_POWER_PROFILE_MAX_P "NVML_POWER_PROFILE_MAX_P" = 0
+    NVML_POWER_PROFILE_MAX_Q "NVML_POWER_PROFILE_MAX_Q" = 1
+    NVML_POWER_PROFILE_COMPUTE "NVML_POWER_PROFILE_COMPUTE" = 2
+    NVML_POWER_PROFILE_MEMORY_BOUND "NVML_POWER_PROFILE_MEMORY_BOUND" = 3
+    NVML_POWER_PROFILE_NETWORK "NVML_POWER_PROFILE_NETWORK" = 4
+    NVML_POWER_PROFILE_BALANCED "NVML_POWER_PROFILE_BALANCED" = 5
+    NVML_POWER_PROFILE_LLM_INFERENCE "NVML_POWER_PROFILE_LLM_INFERENCE" = 6
+    NVML_POWER_PROFILE_LLM_TRAINING "NVML_POWER_PROFILE_LLM_TRAINING" = 7
+    NVML_POWER_PROFILE_RBM "NVML_POWER_PROFILE_RBM" = 8
+    NVML_POWER_PROFILE_DCPCIE "NVML_POWER_PROFILE_DCPCIE" = 9
+    NVML_POWER_PROFILE_HMMA_SPARSE "NVML_POWER_PROFILE_HMMA_SPARSE" = 10
+    NVML_POWER_PROFILE_HMMA_DENSE "NVML_POWER_PROFILE_HMMA_DENSE" = 11
+    NVML_POWER_PROFILE_SYNC_BALANCED "NVML_POWER_PROFILE_SYNC_BALANCED" = 12
+    NVML_POWER_PROFILE_HPC "NVML_POWER_PROFILE_HPC" = 13
+    NVML_POWER_PROFILE_MIG "NVML_POWER_PROFILE_MIG" = 14
+    NVML_POWER_PROFILE_MAX "NVML_POWER_PROFILE_MAX" = 15
+
+ctypedef enum nvmlDeviceAddressingModeType_t "nvmlDeviceAddressingModeType_t":
+    NVML_DEVICE_ADDRESSING_MODE_NONE "NVML_DEVICE_ADDRESSING_MODE_NONE" = 0
+    NVML_DEVICE_ADDRESSING_MODE_HMM "NVML_DEVICE_ADDRESSING_MODE_HMM" = 1
+    NVML_DEVICE_ADDRESSING_MODE_ATS "NVML_DEVICE_ADDRESSING_MODE_ATS" = 2
+
+ctypedef enum nvmlPRMCounterId_t "nvmlPRMCounterId_t":
+    NVML_PRM_COUNTER_ID_NONE "NVML_PRM_COUNTER_ID_NONE" = 0
+    NVML_PRM_COUNTER_ID_PPCNT_PHYSICAL_LAYER_CTRS_LINK_DOWN_EVENTS "NVML_PRM_COUNTER_ID_PPCNT_PHYSICAL_LAYER_CTRS_LINK_DOWN_EVENTS" = 1
+    NVML_PRM_COUNTER_ID_PPCNT_PHYSICAL_LAYER_CTRS_SUCCESSFUL_RECOVERY_EVENTS "NVML_PRM_COUNTER_ID_PPCNT_PHYSICAL_LAYER_CTRS_SUCCESSFUL_RECOVERY_EVENTS" = 2
+    NVML_PRM_COUNTER_ID_PPCNT_RECOVERY_CTRS_TOTAL_SUCCESSFUL_RECOVERY_EVENTS "NVML_PRM_COUNTER_ID_PPCNT_RECOVERY_CTRS_TOTAL_SUCCESSFUL_RECOVERY_EVENTS" = 101
+    NVML_PRM_COUNTER_ID_PPCNT_RECOVERY_CTRS_TIME_SINCE_LAST_RECOVERY "NVML_PRM_COUNTER_ID_PPCNT_RECOVERY_CTRS_TIME_SINCE_LAST_RECOVERY" = 102
+    NVML_PRM_COUNTER_ID_PPCNT_RECOVERY_CTRS_TIME_BETWEEN_LAST_TWO_RECOVERIES "NVML_PRM_COUNTER_ID_PPCNT_RECOVERY_CTRS_TIME_BETWEEN_LAST_TWO_RECOVERIES" = 103
+    NVML_PRM_COUNTER_ID_PPCNT_PORTCOUNTERS_PORT_XMIT_WAIT "NVML_PRM_COUNTER_ID_PPCNT_PORTCOUNTERS_PORT_XMIT_WAIT" = 201
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_RCV_CODES "NVML_PRM_COUNTER_ID_PPCNT_PLR_RCV_CODES" = 301
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_RCV_CODE_ERR "NVML_PRM_COUNTER_ID_PPCNT_PLR_RCV_CODE_ERR" = 302
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_RCV_UNCORRECTABLE_CODE "NVML_PRM_COUNTER_ID_PPCNT_PLR_RCV_UNCORRECTABLE_CODE" = 303
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_XMIT_CODES "NVML_PRM_COUNTER_ID_PPCNT_PLR_XMIT_CODES" = 304
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_XMIT_RETRY_CODES "NVML_PRM_COUNTER_ID_PPCNT_PLR_XMIT_RETRY_CODES" = 305
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_XMIT_RETRY_EVENTS "NVML_PRM_COUNTER_ID_PPCNT_PLR_XMIT_RETRY_EVENTS" = 306
+    NVML_PRM_COUNTER_ID_PPCNT_PLR_SYNC_EVENTS "NVML_PRM_COUNTER_ID_PPCNT_PLR_SYNC_EVENTS" = 307
+    NVML_PRM_COUNTER_ID_PPRM_OPER_RECOVERY "NVML_PRM_COUNTER_ID_PPRM_OPER_RECOVERY" = 1001
+
+ctypedef enum nvmlPowerProfileOperation_t "nvmlPowerProfileOperation_t":
+    NVML_POWER_PROFILE_OPERATION_CLEAR "NVML_POWER_PROFILE_OPERATION_CLEAR" = 0
+    NVML_POWER_PROFILE_OPERATION_SET "NVML_POWER_PROFILE_OPERATION_SET" = 1
+    NVML_POWER_PROFILE_OPERATION_SET_AND_OVERWRITE "NVML_POWER_PROFILE_OPERATION_SET_AND_OVERWRITE" = 2
+    NVML_POWER_PROFILE_OPERATION_MAX "NVML_POWER_PROFILE_OPERATION_MAX" = 3
+
+
+# types
+ctypedef struct nvmlPciInfoExt_v1_t 'nvmlPciInfoExt_v1_t':
+    unsigned int version
+    unsigned int domain
+    unsigned int bus
+    unsigned int device
+    unsigned int pciDeviceId
+    unsigned int pciSubSystemId
+    unsigned int baseClass
+    unsigned int subClass
+    char busId[32]
+
+ctypedef struct nvmlCoolerInfo_v1_t 'nvmlCoolerInfo_v1_t':
+    unsigned int version
+    unsigned int index
+    nvmlCoolerControl_t signalType
+    nvmlCoolerTarget_t target
+
+ctypedef struct nvmlDramEncryptionInfo_v1_t 'nvmlDramEncryptionInfo_v1_t':
+    unsigned int version
+    nvmlEnableState_t encryptionState
+
+ctypedef struct nvmlMarginTemperature_v1_t 'nvmlMarginTemperature_v1_t':
+    unsigned int version
+    int marginTemperature
+
+ctypedef struct nvmlClockOffset_v1_t 'nvmlClockOffset_v1_t':
+    unsigned int version
+    nvmlClockType_t type
+    nvmlPstates_t pstate
+    int clockOffsetMHz
+    int minClockOffsetMHz
+    int maxClockOffsetMHz
+
+ctypedef struct nvmlFanSpeedInfo_v1_t 'nvmlFanSpeedInfo_v1_t':
+    unsigned int version
+    unsigned int fan
+    unsigned int speed
+
+ctypedef struct nvmlDevicePerfModes_v1_t 'nvmlDevicePerfModes_v1_t':
+    unsigned int version
+    char str[2048]
+
+ctypedef struct nvmlDeviceCurrentClockFreqs_v1_t 'nvmlDeviceCurrentClockFreqs_v1_t':
+    unsigned int version
+    char str[2048]
+
+ctypedef struct nvmlEccSramErrorStatus_v1_t 'nvmlEccSramErrorStatus_v1_t':
+    unsigned int version
+    unsigned long long aggregateUncParity
+    unsigned long long aggregateUncSecDed
+    unsigned long long aggregateCor
+    unsigned long long volatileUncParity
+    unsigned long long volatileUncSecDed
+    unsigned long long volatileCor
+    unsigned long long aggregateUncBucketL2
+    unsigned long long aggregateUncBucketSm
+    unsigned long long aggregateUncBucketPcie
+    unsigned long long aggregateUncBucketMcu
+    unsigned long long aggregateUncBucketOther
+    unsigned int bThresholdExceeded
+
+ctypedef struct nvmlPlatformInfo_v2_t 'nvmlPlatformInfo_v2_t':
+    unsigned int version
+    unsigned char ibGuid[16]
+    unsigned char chassisSerialNumber[16]
+    unsigned char slotNumber
+    unsigned char trayIndex
+    unsigned char hostId
+    unsigned char peerType
+    unsigned char moduleId
+
+ctypedef unsigned int nvmlDeviceArchitecture_t 'nvmlDeviceArchitecture_t'
+ctypedef unsigned int nvmlBusType_t 'nvmlBusType_t'
+ctypedef unsigned int nvmlFanControlPolicy_t 'nvmlFanControlPolicy_t'
+ctypedef unsigned int nvmlPowerSource_t 'nvmlPowerSource_t'
+ctypedef unsigned char nvmlPowerScopeType_t 'nvmlPowerScopeType_t'
+ctypedef unsigned int nvmlVgpuTypeId_t 'nvmlVgpuTypeId_t'
+ctypedef unsigned int nvmlVgpuInstance_t 'nvmlVgpuInstance_t'
+ctypedef struct nvmlVgpuHeterogeneousMode_v1_t 'nvmlVgpuHeterogeneousMode_v1_t':
+    unsigned int version
+    unsigned int mode
+
+ctypedef struct nvmlVgpuPlacementId_v1_t 'nvmlVgpuPlacementId_v1_t':
+    unsigned int version
+    unsigned int placementId
+
+ctypedef struct nvmlVgpuPlacementList_v2_t 'nvmlVgpuPlacementList_v2_t':
+    unsigned int version
+    unsigned int placementSize
+    unsigned int count
+    unsigned int* placementIds
+    unsigned int mode
+
+ctypedef struct nvmlVgpuTypeBar1Info_v1_t 'nvmlVgpuTypeBar1Info_v1_t':
+    unsigned int version
+    unsigned long long bar1Size
+
+ctypedef struct nvmlVgpuRuntimeState_v1_t 'nvmlVgpuRuntimeState_v1_t':
+    unsigned int version
+    unsigned long long size
+
+ctypedef struct nvmlSystemConfComputeSettings_v1_t 'nvmlSystemConfComputeSettings_v1_t':
+    unsigned int version
+    unsigned int environment
+    unsigned int ccFeature
+    unsigned int devToolsMode
+    unsigned int multiGpuMode
+
+ctypedef struct nvmlConfComputeSetKeyRotationThresholdInfo_v1_t 'nvmlConfComputeSetKeyRotationThresholdInfo_v1_t':
+    unsigned int version
+    unsigned long long maxAttackerAdvantage
+
+ctypedef struct nvmlConfComputeGetKeyRotationThresholdInfo_v1_t 'nvmlConfComputeGetKeyRotationThresholdInfo_v1_t':
+    unsigned int version
+    unsigned long long attackerAdvantage
+
+ctypedef unsigned char nvmlGpuFabricState_t 'nvmlGpuFabricState_t'
+ctypedef struct nvmlSystemDriverBranchInfo_v1_t 'nvmlSystemDriverBranchInfo_v1_t':
+    unsigned int version
+    char branch[80]
+
+ctypedef unsigned int nvmlAffinityScope_t 'nvmlAffinityScope_t'
+ctypedef struct nvmlTemperature_v1_t 'nvmlTemperature_v1_t':
+    unsigned int version
+    nvmlTemperatureSensors_t sensorType
+    int temperature
+
+ctypedef struct nvmlNvlinkSupportedBwModes_v1_t 'nvmlNvlinkSupportedBwModes_v1_t':
+    unsigned int version
+    unsigned char bwModes[23]
+    unsigned char totalBwModes
+
+ctypedef struct nvmlNvlinkGetBwMode_v1_t 'nvmlNvlinkGetBwMode_v1_t':
+    unsigned int version
+    unsigned int bIsBest
+    unsigned char bwMode
+
+ctypedef struct nvmlNvlinkSetBwMode_v1_t 'nvmlNvlinkSetBwMode_v1_t':
+    unsigned int version
+    unsigned int bSetBest
+    unsigned char bwMode
+
+ctypedef struct nvmlDeviceCapabilities_v1_t 'nvmlDeviceCapabilities_v1_t':
+    unsigned int version
+    unsigned int capMask
+
+ctypedef struct nvmlPowerSmoothingProfile_v1_t 'nvmlPowerSmoothingProfile_v1_t':
+    unsigned int version
+    unsigned int profileId
+    unsigned int paramId
+    double value
+
+ctypedef struct nvmlPowerSmoothingState_v1_t 'nvmlPowerSmoothingState_v1_t':
+    unsigned int version
+    nvmlEnableState_t state
+
+ctypedef struct nvmlDeviceAddressingMode_v1_t 'nvmlDeviceAddressingMode_v1_t':
+    unsigned int version
+    unsigned int value
+
+ctypedef struct nvmlRepairStatus_v1_t 'nvmlRepairStatus_v1_t':
+    unsigned int version
+    unsigned int bChannelRepairPending
+    unsigned int bTpcRepairPending
+
+ctypedef struct nvmlPdi_v1_t 'nvmlPdi_v1_t':
+    unsigned int version
+    unsigned long long value
+
+ctypedef void* nvmlDevice_t 'nvmlDevice_t'
+ctypedef void* nvmlGpuInstance_t 'nvmlGpuInstance_t'
+ctypedef void* nvmlUnit_t 'nvmlUnit_t'
+ctypedef void* nvmlEventSet_t 'nvmlEventSet_t'
+ctypedef void* nvmlSystemEventSet_t 'nvmlSystemEventSet_t'
+ctypedef void* nvmlComputeInstance_t 'nvmlComputeInstance_t'
+ctypedef void* nvmlGpmSample_t 'nvmlGpmSample_t'
+ctypedef struct nvmlPciInfo_t 'nvmlPciInfo_t':
+    char busIdLegacy[16]
+    unsigned int domain
+    unsigned int bus
+    unsigned int device
+    unsigned int pciDeviceId
+    unsigned int pciSubSystemId
+    char busId[32]
+
+ctypedef struct nvmlEccErrorCounts_t 'nvmlEccErrorCounts_t':
+    unsigned long long l1Cache
+    unsigned long long l2Cache
+    unsigned long long deviceMemory
+    unsigned long long registerFile
+
+ctypedef struct nvmlUtilization_t 'nvmlUtilization_t':
+    unsigned int gpu
+    unsigned int memory
+
+ctypedef struct nvmlMemory_t 'nvmlMemory_t':
+    unsigned long long total
+    unsigned long long free
+    unsigned long long used
+
+ctypedef struct nvmlMemory_v2_t 'nvmlMemory_v2_t':
+    unsigned int version
+    unsigned long long total
+    unsigned long long reserved
+    unsigned long long free
+    unsigned long long used
+
+ctypedef struct nvmlBAR1Memory_t 'nvmlBAR1Memory_t':
+    unsigned long long bar1Total
+    unsigned long long bar1Free
+    unsigned long long bar1Used
+
+ctypedef struct nvmlProcessInfo_v1_t 'nvmlProcessInfo_v1_t':
+    unsigned int pid
+    unsigned long long usedGpuMemory
+
+ctypedef struct nvmlProcessInfo_v2_t 'nvmlProcessInfo_v2_t':
+    unsigned int pid
+    unsigned long long usedGpuMemory
+    unsigned int gpuInstanceId
+    unsigned int computeInstanceId
+
+ctypedef struct nvmlProcessInfo_t 'nvmlProcessInfo_t':
+    unsigned int pid
+    unsigned long long usedGpuMemory
+    unsigned int gpuInstanceId
+    unsigned int computeInstanceId
+
+ctypedef struct nvmlProcessDetail_v1_t 'nvmlProcessDetail_v1_t':
+    unsigned int pid
+    unsigned long long usedGpuMemory
+    unsigned int gpuInstanceId
+    unsigned int computeInstanceId
+    unsigned long long usedGpuCcProtectedMemory
+
+ctypedef struct nvmlDeviceAttributes_t 'nvmlDeviceAttributes_t':
+    unsigned int multiprocessorCount
+    unsigned int sharedCopyEngineCount
+    unsigned int sharedDecoderCount
+    unsigned int sharedEncoderCount
+    unsigned int sharedJpegCount
+    unsigned int sharedOfaCount
+    unsigned int gpuInstanceSliceCount
+    unsigned int computeInstanceSliceCount
+    unsigned long long memorySizeMB
+
+ctypedef struct nvmlC2cModeInfo_v1_t 'nvmlC2cModeInfo_v1_t':
+    unsigned int isC2cEnabled
+
+ctypedef struct nvmlRowRemapperHistogramValues_t 'nvmlRowRemapperHistogramValues_t':
+    unsigned int max
+    unsigned int high
+    unsigned int partial
+    unsigned int low
+    unsigned int none
+
+ctypedef struct nvmlNvLinkUtilizationControl_t 'nvmlNvLinkUtilizationControl_t':
+    nvmlNvLinkUtilizationCountUnits_t units
+    nvmlNvLinkUtilizationCountPktTypes_t pktfilter
+
+ctypedef struct nvmlBridgeChipInfo_t 'nvmlBridgeChipInfo_t':
+    nvmlBridgeChipType_t type
+    unsigned int fwVersion
+
+ctypedef union nvmlValue_t 'nvmlValue_t':
+    double dVal
+    int siVal
+    unsigned int uiVal
+    unsigned long ulVal
+    unsigned long long ullVal
+    signed long long sllVal
+    unsigned short usVal
+
+ctypedef struct nvmlViolationTime_t 'nvmlViolationTime_t':
+    unsigned long long referenceTime
+    unsigned long long violationTime
+
+ctypedef struct _anon_pod0 '_anon_pod0':
+    nvmlThermalController_t controller
+    int defaultMinTemp
+    int defaultMaxTemp
+    int currentTemp
+    nvmlThermalTarget_t target
+
+ctypedef union nvmlUUIDValue_t 'nvmlUUIDValue_t':
+    char str[41]
+    unsigned char bytes[16]
+
+ctypedef struct nvmlClkMonFaultInfo_t 'nvmlClkMonFaultInfo_t':
+    unsigned int clkApiDomain
+    unsigned int clkDomainFaultMask
+
+ctypedef struct nvmlProcessUtilizationSample_t 'nvmlProcessUtilizationSample_t':
+    unsigned int pid
+    unsigned long long timeStamp
+    unsigned int smUtil
+    unsigned int memUtil
+    unsigned int encUtil
+    unsigned int decUtil
+
+ctypedef struct nvmlProcessUtilizationInfo_v1_t 'nvmlProcessUtilizationInfo_v1_t':
+    unsigned long long timeStamp
+    unsigned int pid
+    unsigned int smUtil
+    unsigned int memUtil
+    unsigned int encUtil
+    unsigned int decUtil
+    unsigned int jpgUtil
+    unsigned int ofaUtil
+
+ctypedef struct nvmlPlatformInfo_v1_t 'nvmlPlatformInfo_v1_t':
+    unsigned int version
+    unsigned char ibGuid[16]
+    unsigned char rackGuid[16]
+    unsigned char chassisPhysicalSlotNumber
+    unsigned char computeSlotIndex
+    unsigned char nodeIndex
+    unsigned char peerType
+    unsigned char moduleId
+
+ctypedef struct _anon_pod1 '_anon_pod1':
+    unsigned int bIsPresent
+    unsigned int percentage
+    unsigned int incThreshold
+    unsigned int decThreshold
+
+ctypedef struct nvmlVgpuPlacementList_v1_t 'nvmlVgpuPlacementList_v1_t':
+    unsigned int version
+    unsigned int placementSize
+    unsigned int count
+    unsigned int* placementIds
+
+ctypedef struct _anon_pod2 '_anon_pod2':
+    unsigned int avgFactor
+    unsigned int timeslice
+
+ctypedef struct _anon_pod3 '_anon_pod3':
+    unsigned int timeslice
+
+ctypedef struct nvmlVgpuSchedulerLogEntry_t 'nvmlVgpuSchedulerLogEntry_t':
+    unsigned long long timestamp
+    unsigned long long timeRunTotal
+    unsigned long long timeRun
+    unsigned int swRunlistId
+    unsigned long long targetTimeSlice
+    unsigned long long cumulativePreemptionTime
+
+ctypedef struct _anon_pod4 '_anon_pod4':
+    unsigned int avgFactor
+    unsigned int frequency
+
+ctypedef struct _anon_pod5 '_anon_pod5':
+    unsigned int timeslice
+
+ctypedef struct nvmlVgpuSchedulerCapabilities_t 'nvmlVgpuSchedulerCapabilities_t':
+    unsigned int supportedSchedulers[3]
+    unsigned int maxTimeslice
+    unsigned int minTimeslice
+    unsigned int isArrModeSupported
+    unsigned int maxFrequencyForARR
+    unsigned int minFrequencyForARR
+    unsigned int maxAvgFactorForARR
+    unsigned int minAvgFactorForARR
+
+ctypedef struct nvmlVgpuLicenseExpiry_t 'nvmlVgpuLicenseExpiry_t':
+    unsigned int year
+    unsigned short month
+    unsigned short day
+    unsigned short hour
+    unsigned short min
+    unsigned short sec
+    unsigned char status
+
+ctypedef struct nvmlGridLicenseExpiry_t 'nvmlGridLicenseExpiry_t':
+    unsigned int year
+    unsigned short month
+    unsigned short day
+    unsigned short hour
+    unsigned short min
+    unsigned short sec
+    unsigned char status
+
+ctypedef struct nvmlNvLinkPowerThres_t 'nvmlNvLinkPowerThres_t':
+    unsigned int lowPwrThreshold
+
+ctypedef struct nvmlHwbcEntry_t 'nvmlHwbcEntry_t':
+    unsigned int hwbcId
+    char firmwareVersion[32]
+
+ctypedef struct nvmlLedState_t 'nvmlLedState_t':
+    char cause[256]
+    nvmlLedColor_t color
+
+ctypedef struct nvmlUnitInfo_t 'nvmlUnitInfo_t':
+    char name[96]
+    char id[96]
+    char serial[96]
+    char firmwareVersion[96]
+
+ctypedef struct nvmlPSUInfo_t 'nvmlPSUInfo_t':
+    char state[256]
+    unsigned int current
+    unsigned int voltage
+    unsigned int power
+
+ctypedef struct nvmlUnitFanInfo_t 'nvmlUnitFanInfo_t':
+    unsigned int speed
+    nvmlFanState_t state
+
+ctypedef struct nvmlSystemEventData_v1_t 'nvmlSystemEventData_v1_t':
+    unsigned long long eventType
+    unsigned int gpuId
+
+ctypedef struct nvmlAccountingStats_t 'nvmlAccountingStats_t':
+    unsigned int gpuUtilization
+    unsigned int memoryUtilization
+    unsigned long long maxMemoryUsage
+    unsigned long long time
+    unsigned long long startTime
+    unsigned int isRunning
+    unsigned int reserved[5]
+
+ctypedef struct nvmlFBCStats_t 'nvmlFBCStats_t':
+    unsigned int sessionsCount
+    unsigned int averageFPS
+    unsigned int averageLatency
+
+ctypedef struct nvmlConfComputeSystemCaps_t 'nvmlConfComputeSystemCaps_t':
+    unsigned int cpuCaps
+    unsigned int gpusCaps
+
+ctypedef struct nvmlConfComputeSystemState_t 'nvmlConfComputeSystemState_t':
+    unsigned int environment
+    unsigned int ccFeature
+    unsigned int devToolsMode
+
+ctypedef struct nvmlConfComputeMemSizeInfo_t 'nvmlConfComputeMemSizeInfo_t':
+    unsigned long long protectedMemSizeKib
+    unsigned long long unprotectedMemSizeKib
+
+ctypedef struct nvmlConfComputeGpuCertificate_t 'nvmlConfComputeGpuCertificate_t':
+    unsigned int certChainSize
+    unsigned int attestationCertChainSize
+    unsigned char certChain[0x1000]
+    unsigned char attestationCertChain[0x1400]
+
+ctypedef struct nvmlConfComputeGpuAttestationReport_t 'nvmlConfComputeGpuAttestationReport_t':
+    unsigned int isCecAttestationReportPresent
+    unsigned int attestationReportSize
+    unsigned int cecAttestationReportSize
+    unsigned char nonce[0x20]
+    unsigned char attestationReport[0x2000]
+    unsigned char cecAttestationReport[0x1000]
+
+ctypedef struct nvmlVgpuVersion_t 'nvmlVgpuVersion_t':
+    unsigned int minVersion
+    unsigned int maxVersion
+
+ctypedef struct nvmlVgpuMetadata_t 'nvmlVgpuMetadata_t':
+    unsigned int version
+    unsigned int revision
+    nvmlVgpuGuestInfoState_t guestInfoState
+    char guestDriverVersion[80]
+    char hostDriverVersion[80]
+    unsigned int reserved[6]
+    unsigned int vgpuVirtualizationCaps
+    unsigned int guestVgpuVersion
+    unsigned int opaqueDataSize
+    char opaqueData[4]
+
+ctypedef struct nvmlVgpuPgpuCompatibility_t 'nvmlVgpuPgpuCompatibility_t':
+    nvmlVgpuVmCompatibility_t vgpuVmCompatibility
+    nvmlVgpuPgpuCompatibilityLimitCode_t compatibilityLimitCode
+
+ctypedef struct nvmlGpuInstancePlacement_t 'nvmlGpuInstancePlacement_t':
+    unsigned int start
+    unsigned int size
+
+ctypedef struct nvmlGpuInstanceProfileInfo_t 'nvmlGpuInstanceProfileInfo_t':
+    unsigned int id
+    unsigned int isP2pSupported
+    unsigned int sliceCount
+    unsigned int instanceCount
+    unsigned int multiprocessorCount
+    unsigned int copyEngineCount
+    unsigned int decoderCount
+    unsigned int encoderCount
+    unsigned int jpegCount
+    unsigned int ofaCount
+    unsigned long long memorySizeMB
+
+ctypedef struct nvmlGpuInstanceProfileInfo_v2_t 'nvmlGpuInstanceProfileInfo_v2_t':
+    unsigned int version
+    unsigned int id
+    unsigned int isP2pSupported
+    unsigned int sliceCount
+    unsigned int instanceCount
+    unsigned int multiprocessorCount
+    unsigned int copyEngineCount
+    unsigned int decoderCount
+    unsigned int encoderCount
+    unsigned int jpegCount
+    unsigned int ofaCount
+    unsigned long long memorySizeMB
+    char name[96]
+
+ctypedef struct nvmlGpuInstanceProfileInfo_v3_t 'nvmlGpuInstanceProfileInfo_v3_t':
+    unsigned int version
+    unsigned int id
+    unsigned int sliceCount
+    unsigned int instanceCount
+    unsigned int multiprocessorCount
+    unsigned int copyEngineCount
+    unsigned int decoderCount
+    unsigned int encoderCount
+    unsigned int jpegCount
+    unsigned int ofaCount
+    unsigned long long memorySizeMB
+    char name[96]
+    unsigned int capabilities
+
+ctypedef struct nvmlComputeInstancePlacement_t 'nvmlComputeInstancePlacement_t':
+    unsigned int start
+    unsigned int size
+
+ctypedef struct nvmlComputeInstanceProfileInfo_t 'nvmlComputeInstanceProfileInfo_t':
+    unsigned int id
+    unsigned int sliceCount
+    unsigned int instanceCount
+    unsigned int multiprocessorCount
+    unsigned int sharedCopyEngineCount
+    unsigned int sharedDecoderCount
+    unsigned int sharedEncoderCount
+    unsigned int sharedJpegCount
+    unsigned int sharedOfaCount
+
+ctypedef struct nvmlComputeInstanceProfileInfo_v2_t 'nvmlComputeInstanceProfileInfo_v2_t':
+    unsigned int version
+    unsigned int id
+    unsigned int sliceCount
+    unsigned int instanceCount
+    unsigned int multiprocessorCount
+    unsigned int sharedCopyEngineCount
+    unsigned int sharedDecoderCount
+    unsigned int sharedEncoderCount
+    unsigned int sharedJpegCount
+    unsigned int sharedOfaCount
+    char name[96]
+
+ctypedef struct nvmlComputeInstanceProfileInfo_v3_t 'nvmlComputeInstanceProfileInfo_v3_t':
+    unsigned int version
+    unsigned int id
+    unsigned int sliceCount
+    unsigned int instanceCount
+    unsigned int multiprocessorCount
+    unsigned int sharedCopyEngineCount
+    unsigned int sharedDecoderCount
+    unsigned int sharedEncoderCount
+    unsigned int sharedJpegCount
+    unsigned int sharedOfaCount
+    char name[96]
+    unsigned int capabilities
+
+ctypedef struct _anon_pod6 '_anon_pod6':
+    char* shortName
+    char* longName
+    char* unit
+
+ctypedef struct nvmlGpmSupport_t 'nvmlGpmSupport_t':
+    unsigned int version
+    unsigned int isSupportedDevice
+
+ctypedef struct nvmlMask255_t 'nvmlMask255_t':
+    unsigned int mask[8]
+
+ctypedef struct nvmlDevicePowerMizerModes_v1_t 'nvmlDevicePowerMizerModes_v1_t':
+    unsigned int currentMode
+    unsigned int mode
+    unsigned int supportedPowerMizerModes
+
+ctypedef struct nvmlHostname_v1_t 'nvmlHostname_v1_t':
+    char value[64]
+
+ctypedef struct nvmlEccSramUniqueUncorrectedErrorEntry_v1_t 'nvmlEccSramUniqueUncorrectedErrorEntry_v1_t':
+    unsigned int unit
+    unsigned int location
+    unsigned int sublocation
+    unsigned int extlocation
+    unsigned int address
+    unsigned int isParity
+    unsigned int count
+
+ctypedef struct nvmlNvLinkInfo_v1_t 'nvmlNvLinkInfo_v1_t':
+    unsigned int version
+    unsigned int isNvleEnabled
+
+ctypedef struct nvmlNvlinkFirmwareVersion_t 'nvmlNvlinkFirmwareVersion_t':
+    unsigned char ucodeType
+    unsigned int major
+    unsigned int minor
+    unsigned int subMinor
+
+ctypedef union _anon_pod7 '_anon_pod7':
+    unsigned char inData[496]
+    unsigned char outData[496]
+
+ctypedef struct nvmlUnrepairableMemoryStatus_v1_t 'nvmlUnrepairableMemoryStatus_v1_t':
+    unsigned int bUnrepairableMemory
+
+ctypedef struct nvmlRusdSettings_v1_t 'nvmlRusdSettings_v1_t':
+    unsigned int version
+    unsigned long long pollMask
+
+ctypedef struct nvmlPRMCounterInput_v1_t 'nvmlPRMCounterInput_v1_t':
+    unsigned int localPort
+
+ctypedef struct nvmlVgpuSchedulerStateInfo_v2_t 'nvmlVgpuSchedulerStateInfo_v2_t':
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int avgFactor
+    unsigned int timeslice
+
+ctypedef struct nvmlVgpuSchedulerLogEntry_v2_t 'nvmlVgpuSchedulerLogEntry_v2_t':
+    unsigned long long timestamp
+    unsigned long long timeRunTotal
+    unsigned long long timeRun
+    unsigned int swRunlistId
+    unsigned long long targetTimeSlice
+    unsigned long long cumulativePreemptionTime
+    unsigned int weight
+
+ctypedef struct nvmlVgpuSchedulerState_v2_t 'nvmlVgpuSchedulerState_v2_t':
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int avgFactor
+    unsigned int frequency
+
+ctypedef nvmlPciInfoExt_v1_t nvmlPciInfoExt_t 'nvmlPciInfoExt_t'
+ctypedef nvmlCoolerInfo_v1_t nvmlCoolerInfo_t 'nvmlCoolerInfo_t'
+ctypedef nvmlDramEncryptionInfo_v1_t nvmlDramEncryptionInfo_t 'nvmlDramEncryptionInfo_t'
+ctypedef nvmlMarginTemperature_v1_t nvmlMarginTemperature_t 'nvmlMarginTemperature_t'
+ctypedef nvmlClockOffset_v1_t nvmlClockOffset_t 'nvmlClockOffset_t'
+ctypedef nvmlFanSpeedInfo_v1_t nvmlFanSpeedInfo_t 'nvmlFanSpeedInfo_t'
+ctypedef nvmlDevicePerfModes_v1_t nvmlDevicePerfModes_t 'nvmlDevicePerfModes_t'
+ctypedef nvmlDeviceCurrentClockFreqs_v1_t nvmlDeviceCurrentClockFreqs_t 'nvmlDeviceCurrentClockFreqs_t'
+ctypedef nvmlEccSramErrorStatus_v1_t nvmlEccSramErrorStatus_t 'nvmlEccSramErrorStatus_t'
+ctypedef nvmlPlatformInfo_v2_t nvmlPlatformInfo_t 'nvmlPlatformInfo_t'
+ctypedef struct nvmlPowerValue_v2_t 'nvmlPowerValue_v2_t':
+    unsigned int version
+    nvmlPowerScopeType_t powerScope
+    unsigned int powerValueMw
+
+ctypedef struct nvmlVgpuTypeIdInfo_v1_t 'nvmlVgpuTypeIdInfo_v1_t':
+    unsigned int version
+    unsigned int vgpuCount
+    nvmlVgpuTypeId_t* vgpuTypeIds
+
+ctypedef struct nvmlVgpuTypeMaxInstance_v1_t 'nvmlVgpuTypeMaxInstance_v1_t':
+    unsigned int version
+    nvmlVgpuTypeId_t vgpuTypeId
+    unsigned int maxInstancePerGI
+
+ctypedef struct nvmlVgpuCreatablePlacementInfo_v1_t 'nvmlVgpuCreatablePlacementInfo_v1_t':
+    unsigned int version
+    nvmlVgpuTypeId_t vgpuTypeId
+    unsigned int count
+    unsigned int* placementIds
+    unsigned int placementSize
+
+ctypedef struct nvmlVgpuProcessUtilizationSample_t 'nvmlVgpuProcessUtilizationSample_t':
+    nvmlVgpuInstance_t vgpuInstance
+    unsigned int pid
+    char processName[64]
+    unsigned long long timeStamp
+    unsigned int smUtil
+    unsigned int memUtil
+    unsigned int encUtil
+    unsigned int decUtil
+
+ctypedef struct nvmlVgpuProcessUtilizationInfo_v1_t 'nvmlVgpuProcessUtilizationInfo_v1_t':
+    char processName[64]
+    unsigned long long timeStamp
+    nvmlVgpuInstance_t vgpuInstance
+    unsigned int pid
+    unsigned int smUtil
+    unsigned int memUtil
+    unsigned int encUtil
+    unsigned int decUtil
+    unsigned int jpgUtil
+    unsigned int ofaUtil
+
+ctypedef struct nvmlActiveVgpuInstanceInfo_v1_t 'nvmlActiveVgpuInstanceInfo_v1_t':
+    unsigned int version
+    unsigned int vgpuCount
+    nvmlVgpuInstance_t* vgpuInstances
+
+ctypedef struct nvmlEncoderSessionInfo_t 'nvmlEncoderSessionInfo_t':
+    unsigned int sessionId
+    unsigned int pid
+    nvmlVgpuInstance_t vgpuInstance
+    nvmlEncoderType_t codecType
+    unsigned int hResolution
+    unsigned int vResolution
+    unsigned int averageFps
+    unsigned int averageLatency
+
+ctypedef struct nvmlFBCSessionInfo_t 'nvmlFBCSessionInfo_t':
+    unsigned int sessionId
+    unsigned int pid
+    nvmlVgpuInstance_t vgpuInstance
+    unsigned int displayOrdinal
+    nvmlFBCSessionType_t sessionType
+    unsigned int sessionFlags
+    unsigned int hMaxResolution
+    unsigned int vMaxResolution
+    unsigned int hResolution
+    unsigned int vResolution
+    unsigned int averageFPS
+    unsigned int averageLatency
+
+ctypedef nvmlVgpuHeterogeneousMode_v1_t nvmlVgpuHeterogeneousMode_t 'nvmlVgpuHeterogeneousMode_t'
+ctypedef nvmlVgpuPlacementId_v1_t nvmlVgpuPlacementId_t 'nvmlVgpuPlacementId_t'
+ctypedef nvmlVgpuPlacementList_v2_t nvmlVgpuPlacementList_t 'nvmlVgpuPlacementList_t'
+ctypedef nvmlVgpuTypeBar1Info_v1_t nvmlVgpuTypeBar1Info_t 'nvmlVgpuTypeBar1Info_t'
+ctypedef nvmlVgpuRuntimeState_v1_t nvmlVgpuRuntimeState_t 'nvmlVgpuRuntimeState_t'
+ctypedef nvmlSystemConfComputeSettings_v1_t nvmlSystemConfComputeSettings_t 'nvmlSystemConfComputeSettings_t'
+ctypedef nvmlConfComputeSetKeyRotationThresholdInfo_v1_t nvmlConfComputeSetKeyRotationThresholdInfo_t 'nvmlConfComputeSetKeyRotationThresholdInfo_t'
+ctypedef nvmlConfComputeGetKeyRotationThresholdInfo_v1_t nvmlConfComputeGetKeyRotationThresholdInfo_t 'nvmlConfComputeGetKeyRotationThresholdInfo_t'
+ctypedef struct nvmlGpuFabricInfo_t 'nvmlGpuFabricInfo_t':
+    unsigned char clusterUuid[16]
+    nvmlReturn_t status
+    unsigned int cliqueId
+    nvmlGpuFabricState_t state
+
+ctypedef struct nvmlGpuFabricInfo_v2_t 'nvmlGpuFabricInfo_v2_t':
+    unsigned int version
+    unsigned char clusterUuid[16]
+    nvmlReturn_t status
+    unsigned int cliqueId
+    nvmlGpuFabricState_t state
+    unsigned int healthMask
+
+ctypedef struct nvmlGpuFabricInfo_v3_t 'nvmlGpuFabricInfo_v3_t':
+    unsigned int version
+    unsigned char clusterUuid[16]
+    nvmlReturn_t status
+    unsigned int cliqueId
+    nvmlGpuFabricState_t state
+    unsigned int healthMask
+    unsigned char healthSummary
+
+ctypedef nvmlSystemDriverBranchInfo_v1_t nvmlSystemDriverBranchInfo_t 'nvmlSystemDriverBranchInfo_t'
+ctypedef nvmlTemperature_v1_t nvmlTemperature_t 'nvmlTemperature_t'
+ctypedef nvmlNvlinkSupportedBwModes_v1_t nvmlNvlinkSupportedBwModes_t 'nvmlNvlinkSupportedBwModes_t'
+ctypedef nvmlNvlinkGetBwMode_v1_t nvmlNvlinkGetBwMode_t 'nvmlNvlinkGetBwMode_t'
+ctypedef nvmlNvlinkSetBwMode_v1_t nvmlNvlinkSetBwMode_t 'nvmlNvlinkSetBwMode_t'
+ctypedef nvmlDeviceCapabilities_v1_t nvmlDeviceCapabilities_t 'nvmlDeviceCapabilities_t'
+ctypedef nvmlPowerSmoothingProfile_v1_t nvmlPowerSmoothingProfile_t 'nvmlPowerSmoothingProfile_t'
+ctypedef nvmlPowerSmoothingState_v1_t nvmlPowerSmoothingState_t 'nvmlPowerSmoothingState_t'
+ctypedef nvmlDeviceAddressingMode_v1_t nvmlDeviceAddressingMode_t 'nvmlDeviceAddressingMode_t'
+ctypedef nvmlRepairStatus_v1_t nvmlRepairStatus_t 'nvmlRepairStatus_t'
+ctypedef nvmlPdi_v1_t nvmlPdi_t 'nvmlPdi_t'
+ctypedef struct nvmlEventData_t 'nvmlEventData_t':
+    nvmlDevice_t device
+    unsigned long long eventType
+    unsigned long long eventData
+    unsigned int gpuInstanceId
+    unsigned int computeInstanceId
+
+ctypedef struct nvmlSystemEventSetCreateRequest_v1_t 'nvmlSystemEventSetCreateRequest_v1_t':
+    unsigned int version
+    nvmlSystemEventSet_t set
+
+ctypedef struct nvmlSystemEventSetFreeRequest_v1_t 'nvmlSystemEventSetFreeRequest_v1_t':
+    unsigned int version
+    nvmlSystemEventSet_t set
+
+ctypedef struct nvmlSystemRegisterEventRequest_v1_t 'nvmlSystemRegisterEventRequest_v1_t':
+    unsigned int version
+    unsigned long long eventTypes
+    nvmlSystemEventSet_t set
+
+ctypedef struct nvmlExcludedDeviceInfo_t 'nvmlExcludedDeviceInfo_t':
+    nvmlPciInfo_t pciInfo
+    char uuid[80]
+
+ctypedef struct nvmlProcessDetailList_v1_t 'nvmlProcessDetailList_v1_t':
+    unsigned int version
+    unsigned int mode
+    unsigned int numProcArrayEntries
+    nvmlProcessDetail_v1_t* procArray
+
+ctypedef struct nvmlBridgeChipHierarchy_t 'nvmlBridgeChipHierarchy_t':
+    unsigned char bridgeCount
+    nvmlBridgeChipInfo_t bridgeChipInfo[128]
+
+ctypedef struct nvmlSample_t 'nvmlSample_t':
+    unsigned long long timeStamp
+    nvmlValue_t sampleValue
+
+ctypedef struct nvmlVgpuInstanceUtilizationSample_t 'nvmlVgpuInstanceUtilizationSample_t':
+    nvmlVgpuInstance_t vgpuInstance
+    unsigned long long timeStamp
+    nvmlValue_t smUtil
+    nvmlValue_t memUtil
+    nvmlValue_t encUtil
+    nvmlValue_t decUtil
+
+ctypedef struct nvmlVgpuInstanceUtilizationInfo_v1_t 'nvmlVgpuInstanceUtilizationInfo_v1_t':
+    unsigned long long timeStamp
+    nvmlVgpuInstance_t vgpuInstance
+    nvmlValue_t smUtil
+    nvmlValue_t memUtil
+    nvmlValue_t encUtil
+    nvmlValue_t decUtil
+    nvmlValue_t jpgUtil
+    nvmlValue_t ofaUtil
+
+ctypedef struct nvmlFieldValue_t 'nvmlFieldValue_t':
+    unsigned int fieldId
+    unsigned int scopeId
+    long long timestamp
+    long long latencyUsec
+    nvmlValueType_t valueType
+    nvmlReturn_t nvmlReturn
+    nvmlValue_t value
+
+ctypedef struct nvmlPRMCounterValue_v1_t 'nvmlPRMCounterValue_v1_t':
+    nvmlReturn_t status
+    nvmlValueType_t outputType
+    nvmlValue_t outputValue
+
+ctypedef struct nvmlGpuThermalSettings_t 'nvmlGpuThermalSettings_t':
+    unsigned int count
+    _anon_pod0 sensor[3]
+
+ctypedef struct nvmlUUID_v1_t 'nvmlUUID_v1_t':
+    unsigned int version
+    unsigned int type
+    nvmlUUIDValue_t value
+
+ctypedef struct nvmlClkMonStatus_t 'nvmlClkMonStatus_t':
+    unsigned int bGlobalStatus
+    unsigned int clkMonListSize
+    nvmlClkMonFaultInfo_t clkMonList[32]
+
+ctypedef struct nvmlProcessesUtilizationInfo_v1_t 'nvmlProcessesUtilizationInfo_v1_t':
+    unsigned int version
+    unsigned int processSamplesCount
+    unsigned long long lastSeenTimeStamp
+    nvmlProcessUtilizationInfo_v1_t* procUtilArray
+
+ctypedef struct nvmlGpuDynamicPstatesInfo_t 'nvmlGpuDynamicPstatesInfo_t':
+    unsigned int flags
+    _anon_pod1 utilization[8]
+
+ctypedef union nvmlVgpuSchedulerParams_t 'nvmlVgpuSchedulerParams_t':
+    _anon_pod2 vgpuSchedDataWithARR
+    _anon_pod3 vgpuSchedData
+
+ctypedef union nvmlVgpuSchedulerSetParams_t 'nvmlVgpuSchedulerSetParams_t':
+    _anon_pod4 vgpuSchedDataWithARR
+    _anon_pod5 vgpuSchedData
+
+ctypedef struct nvmlVgpuLicenseInfo_t 'nvmlVgpuLicenseInfo_t':
+    unsigned char isLicensed
+    nvmlVgpuLicenseExpiry_t licenseExpiry
+    unsigned int currentState
+
+ctypedef struct nvmlGridLicensableFeature_t 'nvmlGridLicensableFeature_t':
+    nvmlGridLicenseFeatureCode_t featureCode
+    unsigned int featureState
+    char licenseInfo[128]
+    char productName[128]
+    unsigned int featureEnabled
+    nvmlGridLicenseExpiry_t licenseExpiry
+
+ctypedef struct nvmlUnitFanSpeeds_t 'nvmlUnitFanSpeeds_t':
+    nvmlUnitFanInfo_t fans[24]
+    unsigned int count
+
+ctypedef struct nvmlSystemEventSetWaitRequest_v1_t 'nvmlSystemEventSetWaitRequest_v1_t':
+    unsigned int version
+    unsigned int timeoutms
+    nvmlSystemEventSet_t set
+    nvmlSystemEventData_v1_t* data
+    unsigned int dataSize
+    unsigned int numEvent
+
+ctypedef struct nvmlVgpuPgpuMetadata_t 'nvmlVgpuPgpuMetadata_t':
+    unsigned int version
+    unsigned int revision
+    char hostDriverVersion[80]
+    unsigned int pgpuVirtualizationCaps
+    unsigned int reserved[5]
+    nvmlVgpuVersion_t hostSupportedVgpuRange
+    unsigned int opaqueDataSize
+    char opaqueData[4]
+
+ctypedef struct nvmlGpuInstanceInfo_t 'nvmlGpuInstanceInfo_t':
+    nvmlDevice_t device
+    unsigned int id
+    unsigned int profileId
+    nvmlGpuInstancePlacement_t placement
+
+ctypedef struct nvmlComputeInstanceInfo_t 'nvmlComputeInstanceInfo_t':
+    nvmlDevice_t device
+    nvmlGpuInstance_t gpuInstance
+    unsigned int id
+    unsigned int profileId
+    nvmlComputeInstancePlacement_t placement
+
+ctypedef struct nvmlGpmMetric_t 'nvmlGpmMetric_t':
+    unsigned int metricId
+    nvmlReturn_t nvmlReturn
+    double value
+    _anon_pod6 metricInfo
+
+ctypedef struct nvmlWorkloadPowerProfileInfo_v1_t 'nvmlWorkloadPowerProfileInfo_v1_t':
+    unsigned int version
+    unsigned int profileId
+    unsigned int priority
+    nvmlMask255_t conflictingMask
+
+ctypedef struct nvmlWorkloadPowerProfileCurrentProfiles_v1_t 'nvmlWorkloadPowerProfileCurrentProfiles_v1_t':
+    unsigned int version
+    nvmlMask255_t perfProfilesMask
+    nvmlMask255_t requestedProfilesMask
+    nvmlMask255_t enforcedProfilesMask
+
+ctypedef struct nvmlWorkloadPowerProfileRequestedProfiles_v1_t 'nvmlWorkloadPowerProfileRequestedProfiles_v1_t':
+    unsigned int version
+    nvmlMask255_t requestedProfilesMask
+
+ctypedef struct nvmlWorkloadPowerProfileUpdateProfiles_v1_t 'nvmlWorkloadPowerProfileUpdateProfiles_v1_t':
+    nvmlPowerProfileOperation_t operation
+    nvmlMask255_t updateProfilesMask
+
+ctypedef struct nvmlEccSramUniqueUncorrectedErrorCounts_v1_t 'nvmlEccSramUniqueUncorrectedErrorCounts_v1_t':
+    unsigned int version
+    unsigned int entryCount
+    nvmlEccSramUniqueUncorrectedErrorEntry_v1_t* entries
+
+ctypedef struct nvmlNvlinkFirmwareInfo_t 'nvmlNvlinkFirmwareInfo_t':
+    nvmlNvlinkFirmwareVersion_t firmwareVersion[100]
+    unsigned int numValidEntries
+
+ctypedef struct nvmlPRMTLV_v1_t 'nvmlPRMTLV_v1_t':
+    unsigned dataSize
+    unsigned status
+    _anon_pod7 _anon_pod_member0
+
+ctypedef struct nvmlVgpuSchedulerLogInfo_v2_t 'nvmlVgpuSchedulerLogInfo_v2_t':
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int avgFactor
+    unsigned int timeslice
+    unsigned int entriesCount
+    nvmlVgpuSchedulerLogEntry_v2_t logEntries[200]
+
+ctypedef nvmlVgpuTypeIdInfo_v1_t nvmlVgpuTypeIdInfo_t 'nvmlVgpuTypeIdInfo_t'
+ctypedef nvmlVgpuTypeMaxInstance_v1_t nvmlVgpuTypeMaxInstance_t 'nvmlVgpuTypeMaxInstance_t'
+ctypedef nvmlVgpuCreatablePlacementInfo_v1_t nvmlVgpuCreatablePlacementInfo_t 'nvmlVgpuCreatablePlacementInfo_t'
+ctypedef struct nvmlVgpuProcessesUtilizationInfo_v1_t 'nvmlVgpuProcessesUtilizationInfo_v1_t':
+    unsigned int version
+    unsigned int vgpuProcessCount
+    unsigned long long lastSeenTimeStamp
+    nvmlVgpuProcessUtilizationInfo_v1_t* vgpuProcUtilArray
+
+ctypedef nvmlActiveVgpuInstanceInfo_v1_t nvmlActiveVgpuInstanceInfo_t 'nvmlActiveVgpuInstanceInfo_t'
+ctypedef nvmlGpuFabricInfo_v3_t nvmlGpuFabricInfoV_t 'nvmlGpuFabricInfoV_t'
+ctypedef nvmlSystemEventSetCreateRequest_v1_t nvmlSystemEventSetCreateRequest_t 'nvmlSystemEventSetCreateRequest_t'
+ctypedef nvmlSystemEventSetFreeRequest_v1_t nvmlSystemEventSetFreeRequest_t 'nvmlSystemEventSetFreeRequest_t'
+ctypedef nvmlSystemRegisterEventRequest_v1_t nvmlSystemRegisterEventRequest_t 'nvmlSystemRegisterEventRequest_t'
+ctypedef nvmlProcessDetailList_v1_t nvmlProcessDetailList_t 'nvmlProcessDetailList_t'
+ctypedef struct nvmlVgpuInstancesUtilizationInfo_v1_t 'nvmlVgpuInstancesUtilizationInfo_v1_t':
+    unsigned int version
+    nvmlValueType_t sampleValType
+    unsigned int vgpuInstanceCount
+    unsigned long long lastSeenTimeStamp
+    nvmlVgpuInstanceUtilizationInfo_v1_t* vgpuUtilArray
+
+ctypedef struct nvmlPRMCounter_v1_t 'nvmlPRMCounter_v1_t':
+    unsigned int counterId
+    nvmlPRMCounterInput_v1_t inData
+    nvmlPRMCounterValue_v1_t counterValue
+
+ctypedef nvmlUUID_v1_t nvmlUUID_t 'nvmlUUID_t'
+ctypedef nvmlProcessesUtilizationInfo_v1_t nvmlProcessesUtilizationInfo_t 'nvmlProcessesUtilizationInfo_t'
+ctypedef struct nvmlVgpuSchedulerLog_t 'nvmlVgpuSchedulerLog_t':
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int arrMode
+    nvmlVgpuSchedulerParams_t schedulerParams
+    unsigned int entriesCount
+    nvmlVgpuSchedulerLogEntry_t logEntries[200]
+
+ctypedef struct nvmlVgpuSchedulerGetState_t 'nvmlVgpuSchedulerGetState_t':
+    unsigned int schedulerPolicy
+    unsigned int arrMode
+    nvmlVgpuSchedulerParams_t schedulerParams
+
+ctypedef struct nvmlVgpuSchedulerStateInfo_v1_t 'nvmlVgpuSchedulerStateInfo_v1_t':
+    unsigned int version
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int arrMode
+    nvmlVgpuSchedulerParams_t schedulerParams
+
+ctypedef struct nvmlVgpuSchedulerLogInfo_v1_t 'nvmlVgpuSchedulerLogInfo_v1_t':
+    unsigned int version
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int arrMode
+    nvmlVgpuSchedulerParams_t schedulerParams
+    unsigned int entriesCount
+    nvmlVgpuSchedulerLogEntry_t logEntries[200]
+
+ctypedef struct nvmlVgpuSchedulerSetState_t 'nvmlVgpuSchedulerSetState_t':
+    unsigned int schedulerPolicy
+    unsigned int enableARRMode
+    nvmlVgpuSchedulerSetParams_t schedulerParams
+
+ctypedef struct nvmlVgpuSchedulerState_v1_t 'nvmlVgpuSchedulerState_v1_t':
+    unsigned int version
+    unsigned int engineId
+    unsigned int schedulerPolicy
+    unsigned int enableARRMode
+    nvmlVgpuSchedulerSetParams_t schedulerParams
+
+ctypedef struct nvmlGridLicensableFeatures_t 'nvmlGridLicensableFeatures_t':
+    int isGridLicenseSupported
+    unsigned int licensableFeaturesCount
+    nvmlGridLicensableFeature_t gridLicensableFeatures[3]
+
+ctypedef nvmlSystemEventSetWaitRequest_v1_t nvmlSystemEventSetWaitRequest_t 'nvmlSystemEventSetWaitRequest_t'
+ctypedef struct nvmlGpmMetricsGet_t 'nvmlGpmMetricsGet_t':
+    unsigned int version
+    unsigned int numMetrics
+    nvmlGpmSample_t sample1
+    nvmlGpmSample_t sample2
+    nvmlGpmMetric_t metrics[333]
+
+ctypedef nvmlWorkloadPowerProfileInfo_v1_t nvmlWorkloadPowerProfileInfo_t 'nvmlWorkloadPowerProfileInfo_t'
+ctypedef nvmlWorkloadPowerProfileCurrentProfiles_v1_t nvmlWorkloadPowerProfileCurrentProfiles_t 'nvmlWorkloadPowerProfileCurrentProfiles_t'
+ctypedef nvmlWorkloadPowerProfileRequestedProfiles_v1_t nvmlWorkloadPowerProfileRequestedProfiles_t 'nvmlWorkloadPowerProfileRequestedProfiles_t'
+ctypedef nvmlEccSramUniqueUncorrectedErrorCounts_v1_t nvmlEccSramUniqueUncorrectedErrorCounts_t 'nvmlEccSramUniqueUncorrectedErrorCounts_t'
+ctypedef struct nvmlNvLinkInfo_v2_t 'nvmlNvLinkInfo_v2_t':
+    unsigned int version
+    unsigned int isNvleEnabled
+    nvmlNvlinkFirmwareInfo_t firmwareInfo
+
+ctypedef nvmlVgpuProcessesUtilizationInfo_v1_t nvmlVgpuProcessesUtilizationInfo_t 'nvmlVgpuProcessesUtilizationInfo_t'
+ctypedef nvmlVgpuInstancesUtilizationInfo_v1_t nvmlVgpuInstancesUtilizationInfo_t 'nvmlVgpuInstancesUtilizationInfo_t'
+ctypedef struct nvmlPRMCounterList_v1_t 'nvmlPRMCounterList_v1_t':
+    unsigned int numCounters
+    nvmlPRMCounter_v1_t* counters
+
+ctypedef nvmlVgpuSchedulerStateInfo_v1_t nvmlVgpuSchedulerStateInfo_t 'nvmlVgpuSchedulerStateInfo_t'
+ctypedef nvmlVgpuSchedulerLogInfo_v1_t nvmlVgpuSchedulerLogInfo_t 'nvmlVgpuSchedulerLogInfo_t'
+ctypedef nvmlVgpuSchedulerState_v1_t nvmlVgpuSchedulerState_t 'nvmlVgpuSchedulerState_t'
+ctypedef struct nvmlWorkloadPowerProfileProfilesInfo_v1_t 'nvmlWorkloadPowerProfileProfilesInfo_v1_t':
+    unsigned int version
+    nvmlMask255_t perfProfilesMask
+    nvmlWorkloadPowerProfileInfo_t perfProfile[255]
+
+ctypedef nvmlNvLinkInfo_v2_t nvmlNvLinkInfo_t 'nvmlNvLinkInfo_t'
+ctypedef nvmlWorkloadPowerProfileProfilesInfo_v1_t nvmlWorkloadPowerProfileProfilesInfo_t 'nvmlWorkloadPowerProfileProfilesInfo_t'
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cdef nvmlReturn_t nvmlInit_v2() except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlInitWithFlags(unsigned int flags) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlShutdown() except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef const char* nvmlErrorString(nvmlReturn_t result) except?NULL nogil
+cdef nvmlReturn_t nvmlSystemGetDriverVersion(char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetNVMLVersion(char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetCudaDriverVersion(int* cudaDriverVersion) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetCudaDriverVersion_v2(int* cudaDriverVersion) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetProcessName(unsigned int pid, char* name, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetHicVersion(unsigned int* hwbcCount, nvmlHwbcEntry_t* hwbcEntries) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetTopologyGpuSet(unsigned int cpuNumber, unsigned int* count, nvmlDevice_t* deviceArray) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetDriverBranch(nvmlSystemDriverBranchInfo_t* branchInfo, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetCount(unsigned int* unitCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetHandleByIndex(unsigned int index, nvmlUnit_t* unit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetUnitInfo(nvmlUnit_t unit, nvmlUnitInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetLedState(nvmlUnit_t unit, nvmlLedState_t* state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetPsuInfo(nvmlUnit_t unit, nvmlPSUInfo_t* psu) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetTemperature(nvmlUnit_t unit, unsigned int type, unsigned int* temp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetFanSpeedInfo(nvmlUnit_t unit, nvmlUnitFanSpeeds_t* fanSpeeds) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitGetDevices(nvmlUnit_t unit, unsigned int* deviceCount, nvmlDevice_t* devices) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCount_v2(unsigned int* deviceCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAttributes_v2(nvmlDevice_t device, nvmlDeviceAttributes_t* attributes) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHandleByIndex_v2(unsigned int index, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHandleBySerial(const char* serial, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHandleByUUID(const char* uuid, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHandleByUUIDV(const nvmlUUID_t* uuid, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHandleByPciBusId_v2(const char* pciBusId, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetName(nvmlDevice_t device, char* name, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetBrand(nvmlDevice_t device, nvmlBrandType_t* type) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetIndex(nvmlDevice_t device, unsigned int* index) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSerial(nvmlDevice_t device, char* serial, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetModuleId(nvmlDevice_t device, unsigned int* moduleId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetC2cModeInfoV(nvmlDevice_t device, nvmlC2cModeInfo_v1_t* c2cModeInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMemoryAffinity(nvmlDevice_t device, unsigned int nodeSetSize, unsigned long* nodeSet, nvmlAffinityScope_t scope) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCpuAffinityWithinScope(nvmlDevice_t device, unsigned int cpuSetSize, unsigned long* cpuSet, nvmlAffinityScope_t scope) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCpuAffinity(nvmlDevice_t device, unsigned int cpuSetSize, unsigned long* cpuSet) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetCpuAffinity(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceClearCpuAffinity(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNumaNodeId(nvmlDevice_t device, unsigned int* node) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTopologyCommonAncestor(nvmlDevice_t device1, nvmlDevice_t device2, nvmlGpuTopologyLevel_t* pathInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTopologyNearestGpus(nvmlDevice_t device, nvmlGpuTopologyLevel_t level, unsigned int* count, nvmlDevice_t* deviceArray) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetP2PStatus(nvmlDevice_t device1, nvmlDevice_t device2, nvmlGpuP2PCapsIndex_t p2pIndex, nvmlGpuP2PStatus_t* p2pStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetUUID(nvmlDevice_t device, char* uuid, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMinorNumber(nvmlDevice_t device, unsigned int* minorNumber) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetBoardPartNumber(nvmlDevice_t device, char* partNumber, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetInforomVersion(nvmlDevice_t device, nvmlInforomObject_t object, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetInforomImageVersion(nvmlDevice_t device, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetInforomConfigurationChecksum(nvmlDevice_t device, unsigned int* checksum) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceValidateInforom(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetLastBBXFlushTime(nvmlDevice_t device, unsigned long long* timestamp, unsigned long* durationUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDisplayMode(nvmlDevice_t device, nvmlEnableState_t* display) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDisplayActive(nvmlDevice_t device, nvmlEnableState_t* isActive) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPersistenceMode(nvmlDevice_t device, nvmlEnableState_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPciInfoExt(nvmlDevice_t device, nvmlPciInfoExt_t* pci) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPciInfo_v3(nvmlDevice_t device, nvmlPciInfo_t* pci) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMaxPcieLinkGeneration(nvmlDevice_t device, unsigned int* maxLinkGen) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuMaxPcieLinkGeneration(nvmlDevice_t device, unsigned int* maxLinkGenDevice) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMaxPcieLinkWidth(nvmlDevice_t device, unsigned int* maxLinkWidth) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCurrPcieLinkGeneration(nvmlDevice_t device, unsigned int* currLinkGen) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCurrPcieLinkWidth(nvmlDevice_t device, unsigned int* currLinkWidth) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPcieThroughput(nvmlDevice_t device, nvmlPcieUtilCounter_t counter, unsigned int* value) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPcieReplayCounter(nvmlDevice_t device, unsigned int* value) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetClockInfo(nvmlDevice_t device, nvmlClockType_t type, unsigned int* clock) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMaxClockInfo(nvmlDevice_t device, nvmlClockType_t type, unsigned int* clock) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpcClkVfOffset(nvmlDevice_t device, int* offset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetClock(nvmlDevice_t device, nvmlClockType_t clockType, nvmlClockId_t clockId, unsigned int* clockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMaxCustomerBoostClock(nvmlDevice_t device, nvmlClockType_t clockType, unsigned int* clockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSupportedMemoryClocks(nvmlDevice_t device, unsigned int* count, unsigned int* clocksMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSupportedGraphicsClocks(nvmlDevice_t device, unsigned int memoryClockMHz, unsigned int* count, unsigned int* clocksMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAutoBoostedClocksEnabled(nvmlDevice_t device, nvmlEnableState_t* isEnabled, nvmlEnableState_t* defaultIsEnabled) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFanSpeed(nvmlDevice_t device, unsigned int* speed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFanSpeed_v2(nvmlDevice_t device, unsigned int fan, unsigned int* speed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFanSpeedRPM(nvmlDevice_t device, nvmlFanSpeedInfo_t* fanSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTargetFanSpeed(nvmlDevice_t device, unsigned int fan, unsigned int* targetSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMinMaxFanSpeed(nvmlDevice_t device, unsigned int* minSpeed, unsigned int* maxSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFanControlPolicy_v2(nvmlDevice_t device, unsigned int fan, nvmlFanControlPolicy_t* policy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNumFans(nvmlDevice_t device, unsigned int* numFans) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCoolerInfo(nvmlDevice_t device, nvmlCoolerInfo_t* coolerInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTemperatureV(nvmlDevice_t device, nvmlTemperature_t* temperature) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTemperatureThreshold(nvmlDevice_t device, nvmlTemperatureThresholds_t thresholdType, unsigned int* temp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMarginTemperature(nvmlDevice_t device, nvmlMarginTemperature_t* marginTempInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetThermalSettings(nvmlDevice_t device, unsigned int sensorIndex, nvmlGpuThermalSettings_t* pThermalSettings) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPerformanceState(nvmlDevice_t device, nvmlPstates_t* pState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCurrentClocksEventReasons(nvmlDevice_t device, unsigned long long* clocksEventReasons) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSupportedClocksEventReasons(nvmlDevice_t device, unsigned long long* supportedClocksEventReasons) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerState(nvmlDevice_t device, nvmlPstates_t* pState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDynamicPstatesInfo(nvmlDevice_t device, nvmlGpuDynamicPstatesInfo_t* pDynamicPstatesInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMemClkVfOffset(nvmlDevice_t device, int* offset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMinMaxClockOfPState(nvmlDevice_t device, nvmlClockType_t type, nvmlPstates_t pstate, unsigned int* minClockMHz, unsigned int* maxClockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSupportedPerformanceStates(nvmlDevice_t device, nvmlPstates_t* pstates, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpcClkMinMaxVfOffset(nvmlDevice_t device, int* minOffset, int* maxOffset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMemClkMinMaxVfOffset(nvmlDevice_t device, int* minOffset, int* maxOffset) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetClockOffsets(nvmlDevice_t device, nvmlClockOffset_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetClockOffsets(nvmlDevice_t device, nvmlClockOffset_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPerformanceModes(nvmlDevice_t device, nvmlDevicePerfModes_t* perfModes) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCurrentClockFreqs(nvmlDevice_t device, nvmlDeviceCurrentClockFreqs_t* currentClockFreqs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerManagementLimit(nvmlDevice_t device, unsigned int* limit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerManagementLimitConstraints(nvmlDevice_t device, unsigned int* minLimit, unsigned int* maxLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerManagementDefaultLimit(nvmlDevice_t device, unsigned int* defaultLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerUsage(nvmlDevice_t device, unsigned int* power) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTotalEnergyConsumption(nvmlDevice_t device, unsigned long long* energy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetEnforcedPowerLimit(nvmlDevice_t device, unsigned int* limit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuOperationMode(nvmlDevice_t device, nvmlGpuOperationMode_t* current, nvmlGpuOperationMode_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMemoryInfo_v2(nvmlDevice_t device, nvmlMemory_v2_t* memory) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetComputeMode(nvmlDevice_t device, nvmlComputeMode_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCudaComputeCapability(nvmlDevice_t device, int* major, int* minor) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDramEncryptionMode(nvmlDevice_t device, nvmlDramEncryptionInfo_t* current, nvmlDramEncryptionInfo_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetDramEncryptionMode(nvmlDevice_t device, const nvmlDramEncryptionInfo_t* dramEncryption) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetEccMode(nvmlDevice_t device, nvmlEnableState_t* current, nvmlEnableState_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDefaultEccMode(nvmlDevice_t device, nvmlEnableState_t* defaultMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetBoardId(nvmlDevice_t device, unsigned int* boardId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMultiGpuBoard(nvmlDevice_t device, unsigned int* multiGpuBool) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetTotalEccErrors(nvmlDevice_t device, nvmlMemoryErrorType_t errorType, nvmlEccCounterType_t counterType, unsigned long long* eccCounts) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMemoryErrorCounter(nvmlDevice_t device, nvmlMemoryErrorType_t errorType, nvmlEccCounterType_t counterType, nvmlMemoryLocation_t locationType, unsigned long long* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetUtilizationRates(nvmlDevice_t device, nvmlUtilization_t* utilization) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetEncoderUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetEncoderCapacity(nvmlDevice_t device, nvmlEncoderType_t encoderQueryType, unsigned int* encoderCapacity) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetEncoderStats(nvmlDevice_t device, unsigned int* sessionCount, unsigned int* averageFps, unsigned int* averageLatency) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetEncoderSessions(nvmlDevice_t device, unsigned int* sessionCount, nvmlEncoderSessionInfo_t* sessionInfos) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDecoderUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetJpgUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetOfaUtilization(nvmlDevice_t device, unsigned int* utilization, unsigned int* samplingPeriodUs) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFBCStats(nvmlDevice_t device, nvmlFBCStats_t* fbcStats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFBCSessions(nvmlDevice_t device, unsigned int* sessionCount, nvmlFBCSessionInfo_t* sessionInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDriverModel_v2(nvmlDevice_t device, nvmlDriverModel_t* current, nvmlDriverModel_t* pending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVbiosVersion(nvmlDevice_t device, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetBridgeChipInfo(nvmlDevice_t device, nvmlBridgeChipHierarchy_t* bridgeHierarchy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetComputeRunningProcesses_v3(nvmlDevice_t device, unsigned int* infoCount, nvmlProcessInfo_t* infos) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMPSComputeRunningProcesses_v3(nvmlDevice_t device, unsigned int* infoCount, nvmlProcessInfo_t* infos) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRunningProcessDetailList(nvmlDevice_t device, nvmlProcessDetailList_t* plist) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceOnSameBoard(nvmlDevice_t device1, nvmlDevice_t device2, int* onSameBoard) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAPIRestriction(nvmlDevice_t device, nvmlRestrictedAPI_t apiType, nvmlEnableState_t* isRestricted) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSamples(nvmlDevice_t device, nvmlSamplingType_t type, unsigned long long lastSeenTimeStamp, nvmlValueType_t* sampleValType, unsigned int* sampleCount, nvmlSample_t* samples) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetBAR1MemoryInfo(nvmlDevice_t device, nvmlBAR1Memory_t* bar1Memory) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetIrqNum(nvmlDevice_t device, unsigned int* irqNum) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNumGpuCores(nvmlDevice_t device, unsigned int* numCores) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerSource(nvmlDevice_t device, nvmlPowerSource_t* powerSource) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMemoryBusWidth(nvmlDevice_t device, unsigned int* busWidth) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPcieLinkMaxSpeed(nvmlDevice_t device, unsigned int* maxSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPcieSpeed(nvmlDevice_t device, unsigned int* pcieSpeed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAdaptiveClockInfoStatus(nvmlDevice_t device, unsigned int* adaptiveClockStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetBusType(nvmlDevice_t device, nvmlBusType_t* type) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuFabricInfoV(nvmlDevice_t device, nvmlGpuFabricInfoV_t* gpuFabricInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetConfComputeCapabilities(nvmlConfComputeSystemCaps_t* capabilities) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetConfComputeState(nvmlConfComputeSystemState_t* state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetConfComputeMemSizeInfo(nvmlDevice_t device, nvmlConfComputeMemSizeInfo_t* memInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetConfComputeGpusReadyState(unsigned int* isAcceptingWork) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetConfComputeProtectedMemoryUsage(nvmlDevice_t device, nvmlMemory_t* memory) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetConfComputeGpuCertificate(nvmlDevice_t device, nvmlConfComputeGpuCertificate_t* gpuCert) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetConfComputeGpuAttestationReport(nvmlDevice_t device, nvmlConfComputeGpuAttestationReport_t* gpuAtstReport) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetConfComputeKeyRotationThresholdInfo(nvmlConfComputeGetKeyRotationThresholdInfo_t* pKeyRotationThrInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetConfComputeUnprotectedMemSize(nvmlDevice_t device, unsigned long long sizeKiB) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemSetConfComputeGpusReadyState(unsigned int isAcceptingWork) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemSetConfComputeKeyRotationThresholdInfo(nvmlConfComputeSetKeyRotationThresholdInfo_t* pKeyRotationThrInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetConfComputeSettings(nvmlSystemConfComputeSettings_t* settings) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGspFirmwareVersion(nvmlDevice_t device, char* version) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGspFirmwareMode(nvmlDevice_t device, unsigned int* isEnabled, unsigned int* defaultMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSramEccErrorStatus(nvmlDevice_t device, nvmlEccSramErrorStatus_t* status) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAccountingMode(nvmlDevice_t device, nvmlEnableState_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAccountingStats(nvmlDevice_t device, unsigned int pid, nvmlAccountingStats_t* stats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAccountingPids(nvmlDevice_t device, unsigned int* count, unsigned int* pids) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAccountingBufferSize(nvmlDevice_t device, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRetiredPages(nvmlDevice_t device, nvmlPageRetirementCause_t cause, unsigned int* pageCount, unsigned long long* addresses) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRetiredPages_v2(nvmlDevice_t device, nvmlPageRetirementCause_t cause, unsigned int* pageCount, unsigned long long* addresses, unsigned long long* timestamps) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRetiredPagesPendingStatus(nvmlDevice_t device, nvmlEnableState_t* isPending) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRemappedRows(nvmlDevice_t device, unsigned int* corrRows, unsigned int* uncRows, unsigned int* isPending, unsigned int* failureOccurred) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRowRemapperHistogram(nvmlDevice_t device, nvmlRowRemapperHistogramValues_t* values) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetArchitecture(nvmlDevice_t device, nvmlDeviceArchitecture_t* arch) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetClkMonStatus(nvmlDevice_t device, nvmlClkMonStatus_t* status) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetProcessUtilization(nvmlDevice_t device, nvmlProcessUtilizationSample_t* utilization, unsigned int* processSamplesCount, unsigned long long lastSeenTimeStamp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetProcessesUtilizationInfo(nvmlDevice_t device, nvmlProcessesUtilizationInfo_t* procesesUtilInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPlatformInfo(nvmlDevice_t device, nvmlPlatformInfo_t* platformInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlUnitSetLedState(nvmlUnit_t unit, nvmlLedColor_t color) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetPersistenceMode(nvmlDevice_t device, nvmlEnableState_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetComputeMode(nvmlDevice_t device, nvmlComputeMode_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetEccMode(nvmlDevice_t device, nvmlEnableState_t ecc) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceClearEccErrorCounts(nvmlDevice_t device, nvmlEccCounterType_t counterType) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetDriverModel(nvmlDevice_t device, nvmlDriverModel_t driverModel, unsigned int flags) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetGpuLockedClocks(nvmlDevice_t device, unsigned int minGpuClockMHz, unsigned int maxGpuClockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceResetGpuLockedClocks(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetMemoryLockedClocks(nvmlDevice_t device, unsigned int minMemClockMHz, unsigned int maxMemClockMHz) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceResetMemoryLockedClocks(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetAutoBoostedClocksEnabled(nvmlDevice_t device, nvmlEnableState_t enabled) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetDefaultAutoBoostedClocksEnabled(nvmlDevice_t device, nvmlEnableState_t enabled, unsigned int flags) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetDefaultFanSpeed_v2(nvmlDevice_t device, unsigned int fan) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetFanControlPolicy(nvmlDevice_t device, unsigned int fan, nvmlFanControlPolicy_t policy) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetTemperatureThreshold(nvmlDevice_t device, nvmlTemperatureThresholds_t thresholdType, int* temp) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetGpuOperationMode(nvmlDevice_t device, nvmlGpuOperationMode_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetAPIRestriction(nvmlDevice_t device, nvmlRestrictedAPI_t apiType, nvmlEnableState_t isRestricted) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetFanSpeed_v2(nvmlDevice_t device, unsigned int fan, unsigned int speed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetAccountingMode(nvmlDevice_t device, nvmlEnableState_t mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceClearAccountingPids(nvmlDevice_t device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetPowerManagementLimit_v2(nvmlDevice_t device, nvmlPowerValue_v2_t* powerValue) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkState(nvmlDevice_t device, unsigned int link, nvmlEnableState_t* isActive) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkVersion(nvmlDevice_t device, unsigned int link, unsigned int* version) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkCapability(nvmlDevice_t device, unsigned int link, nvmlNvLinkCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkRemotePciInfo_v2(nvmlDevice_t device, unsigned int link, nvmlPciInfo_t* pci) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkErrorCounter(nvmlDevice_t device, unsigned int link, nvmlNvLinkErrorCounter_t counter, unsigned long long* counterValue) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceResetNvLinkErrorCounters(nvmlDevice_t device, unsigned int link) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkRemoteDeviceType(nvmlDevice_t device, unsigned int link, nvmlIntNvLinkDeviceType_t* pNvLinkDeviceType) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetNvLinkDeviceLowPowerThreshold(nvmlDevice_t device, nvmlNvLinkPowerThres_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemSetNvlinkBwMode(unsigned int nvlinkBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemGetNvlinkBwMode(unsigned int* nvlinkBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvlinkSupportedBwModes(nvmlDevice_t device, nvmlNvlinkSupportedBwModes_t* supportedBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvlinkBwMode(nvmlDevice_t device, nvmlNvlinkGetBwMode_t* getBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetNvlinkBwMode(nvmlDevice_t device, nvmlNvlinkSetBwMode_t* setBwMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlEventSetCreate(nvmlEventSet_t* set) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceRegisterEvents(nvmlDevice_t device, unsigned long long eventTypes, nvmlEventSet_t set) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSupportedEventTypes(nvmlDevice_t device, unsigned long long* eventTypes) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlEventSetWait_v2(nvmlEventSet_t set, nvmlEventData_t* data, unsigned int timeoutms) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlEventSetFree(nvmlEventSet_t set) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemEventSetCreate(nvmlSystemEventSetCreateRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemEventSetFree(nvmlSystemEventSetFreeRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemRegisterEvents(nvmlSystemRegisterEventRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSystemEventSetWait(nvmlSystemEventSetWaitRequest_t* request) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceModifyDrainState(nvmlPciInfo_t* pciInfo, nvmlEnableState_t newState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceQueryDrainState(nvmlPciInfo_t* pciInfo, nvmlEnableState_t* currentState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceRemoveGpu_v2(nvmlPciInfo_t* pciInfo, nvmlDetachGpuState_t gpuState, nvmlPcieLinkState_t linkState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceDiscoverGpus(nvmlPciInfo_t* pciInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetFieldValues(nvmlDevice_t device, int valuesCount, nvmlFieldValue_t* values) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceClearFieldValues(nvmlDevice_t device, int valuesCount, nvmlFieldValue_t* values) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVirtualizationMode(nvmlDevice_t device, nvmlGpuVirtualizationMode_t* pVirtualMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHostVgpuMode(nvmlDevice_t device, nvmlHostVgpuMode_t* pHostVgpuMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetVirtualizationMode(nvmlDevice_t device, nvmlGpuVirtualizationMode_t virtualMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuHeterogeneousMode(nvmlDevice_t device, nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetVgpuHeterogeneousMode(nvmlDevice_t device, const nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetPlacementId(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuPlacementId_t* pPlacement) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuTypeSupportedPlacements(nvmlDevice_t device, nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuPlacementList_t* pPlacementList) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuTypeCreatablePlacements(nvmlDevice_t device, nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuPlacementList_t* pPlacementList) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetGspHeapSize(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* gspHeapSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetFbReservation(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* fbReservation) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetRuntimeStateSize(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuRuntimeState_t* pState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetVgpuCapabilities(nvmlDevice_t device, nvmlDeviceVgpuCapability_t capability, nvmlEnableState_t state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGridLicensableFeatures_v4(nvmlDevice_t device, nvmlGridLicensableFeatures_t* pGridLicensableFeatures) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGetVgpuDriverCapabilities(nvmlVgpuDriverCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuCapabilities(nvmlDevice_t device, nvmlDeviceVgpuCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSupportedVgpus(nvmlDevice_t device, unsigned int* vgpuCount, nvmlVgpuTypeId_t* vgpuTypeIds) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCreatableVgpus(nvmlDevice_t device, unsigned int* vgpuCount, nvmlVgpuTypeId_t* vgpuTypeIds) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetClass(nvmlVgpuTypeId_t vgpuTypeId, char* vgpuTypeClass, unsigned int* size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetName(nvmlVgpuTypeId_t vgpuTypeId, char* vgpuTypeName, unsigned int* size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetGpuInstanceProfileId(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* gpuInstanceProfileId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetDeviceID(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* deviceID, unsigned long long* subsystemID) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetFramebufferSize(nvmlVgpuTypeId_t vgpuTypeId, unsigned long long* fbSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetNumDisplayHeads(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* numDisplayHeads) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetResolution(nvmlVgpuTypeId_t vgpuTypeId, unsigned int displayIndex, unsigned int* xdim, unsigned int* ydim) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetLicense(nvmlVgpuTypeId_t vgpuTypeId, char* vgpuTypeLicenseString, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetFrameRateLimit(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* frameRateLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetMaxInstances(nvmlDevice_t device, nvmlVgpuTypeId_t vgpuTypeId, unsigned int* vgpuInstanceCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetMaxInstancesPerVm(nvmlVgpuTypeId_t vgpuTypeId, unsigned int* vgpuInstanceCountPerVm) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetBAR1Info(nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuTypeBar1Info_t* bar1Info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetActiveVgpus(nvmlDevice_t device, unsigned int* vgpuCount, nvmlVgpuInstance_t* vgpuInstances) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetVmID(nvmlVgpuInstance_t vgpuInstance, char* vmId, unsigned int size, nvmlVgpuVmIdType_t* vmIdType) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetUUID(nvmlVgpuInstance_t vgpuInstance, char* uuid, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetVmDriverVersion(nvmlVgpuInstance_t vgpuInstance, char* version, unsigned int length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetFbUsage(nvmlVgpuInstance_t vgpuInstance, unsigned long long* fbUsage) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetLicenseStatus(nvmlVgpuInstance_t vgpuInstance, unsigned int* licensed) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetType(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuTypeId_t* vgpuTypeId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetFrameRateLimit(nvmlVgpuInstance_t vgpuInstance, unsigned int* frameRateLimit) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetEccMode(nvmlVgpuInstance_t vgpuInstance, nvmlEnableState_t* eccMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetEncoderCapacity(nvmlVgpuInstance_t vgpuInstance, unsigned int* encoderCapacity) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceSetEncoderCapacity(nvmlVgpuInstance_t vgpuInstance, unsigned int encoderCapacity) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetEncoderStats(nvmlVgpuInstance_t vgpuInstance, unsigned int* sessionCount, unsigned int* averageFps, unsigned int* averageLatency) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetEncoderSessions(nvmlVgpuInstance_t vgpuInstance, unsigned int* sessionCount, nvmlEncoderSessionInfo_t* sessionInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetFBCStats(nvmlVgpuInstance_t vgpuInstance, nvmlFBCStats_t* fbcStats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetFBCSessions(nvmlVgpuInstance_t vgpuInstance, unsigned int* sessionCount, nvmlFBCSessionInfo_t* sessionInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetGpuInstanceId(nvmlVgpuInstance_t vgpuInstance, unsigned int* gpuInstanceId) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetGpuPciId(nvmlVgpuInstance_t vgpuInstance, char* vgpuPciId, unsigned int* length) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetCapabilities(nvmlVgpuTypeId_t vgpuTypeId, nvmlVgpuCapability_t capability, unsigned int* capResult) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetMdevUUID(nvmlVgpuInstance_t vgpuInstance, char* mdevUuid, unsigned int size) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetCreatableVgpus(nvmlGpuInstance_t gpuInstance, nvmlVgpuTypeIdInfo_t* pVgpus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuTypeGetMaxInstancesPerGpuInstance(nvmlVgpuTypeMaxInstance_t* pMaxInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetActiveVgpus(nvmlGpuInstance_t gpuInstance, nvmlActiveVgpuInstanceInfo_t* pVgpuInstanceInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceSetVgpuSchedulerState(nvmlGpuInstance_t gpuInstance, nvmlVgpuSchedulerState_t* pScheduler) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetVgpuSchedulerState(nvmlGpuInstance_t gpuInstance, nvmlVgpuSchedulerStateInfo_t* pSchedulerStateInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetVgpuSchedulerLog(nvmlGpuInstance_t gpuInstance, nvmlVgpuSchedulerLogInfo_t* pSchedulerLogInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetVgpuTypeCreatablePlacements(nvmlGpuInstance_t gpuInstance, nvmlVgpuCreatablePlacementInfo_t* pCreatablePlacementInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetVgpuHeterogeneousMode(nvmlGpuInstance_t gpuInstance, nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceSetVgpuHeterogeneousMode(nvmlGpuInstance_t gpuInstance, const nvmlVgpuHeterogeneousMode_t* pHeterogeneousMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetMetadata(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuMetadata_t* vgpuMetadata, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuMetadata(nvmlDevice_t device, nvmlVgpuPgpuMetadata_t* pgpuMetadata, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGetVgpuCompatibility(nvmlVgpuMetadata_t* vgpuMetadata, nvmlVgpuPgpuMetadata_t* pgpuMetadata, nvmlVgpuPgpuCompatibility_t* compatibilityInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPgpuMetadataString(nvmlDevice_t device, char* pgpuMetadata, unsigned int* bufferSize) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuSchedulerLog(nvmlDevice_t device, nvmlVgpuSchedulerLog_t* pSchedulerLog) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuSchedulerState(nvmlDevice_t device, nvmlVgpuSchedulerGetState_t* pSchedulerState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuSchedulerCapabilities(nvmlDevice_t device, nvmlVgpuSchedulerCapabilities_t* pCapabilities) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetVgpuSchedulerState(nvmlDevice_t device, nvmlVgpuSchedulerSetState_t* pSchedulerState) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGetVgpuVersion(nvmlVgpuVersion_t* supported, nvmlVgpuVersion_t* current) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlSetVgpuVersion(nvmlVgpuVersion_t* vgpuVersion) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuUtilization(nvmlDevice_t device, unsigned long long lastSeenTimeStamp, nvmlValueType_t* sampleValType, unsigned int* vgpuInstanceSamplesCount, nvmlVgpuInstanceUtilizationSample_t* utilizationSamples) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuInstancesUtilizationInfo(nvmlDevice_t device, nvmlVgpuInstancesUtilizationInfo_t* vgpuUtilInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuProcessUtilization(nvmlDevice_t device, unsigned long long lastSeenTimeStamp, unsigned int* vgpuProcessSamplesCount, nvmlVgpuProcessUtilizationSample_t* utilizationSamples) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetVgpuProcessesUtilizationInfo(nvmlDevice_t device, nvmlVgpuProcessesUtilizationInfo_t* vgpuProcUtilInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetAccountingMode(nvmlVgpuInstance_t vgpuInstance, nvmlEnableState_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetAccountingPids(nvmlVgpuInstance_t vgpuInstance, unsigned int* count, unsigned int* pids) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetAccountingStats(nvmlVgpuInstance_t vgpuInstance, unsigned int pid, nvmlAccountingStats_t* stats) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceClearAccountingPids(nvmlVgpuInstance_t vgpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlVgpuInstanceGetLicenseInfo_v2(nvmlVgpuInstance_t vgpuInstance, nvmlVgpuLicenseInfo_t* licenseInfo) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGetExcludedDeviceCount(unsigned int* deviceCount) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGetExcludedDeviceInfoByIndex(unsigned int index, nvmlExcludedDeviceInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetMigMode(nvmlDevice_t device, unsigned int mode, nvmlReturn_t* activationStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMigMode(nvmlDevice_t device, unsigned int* currentMode, unsigned int* pendingMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstanceProfileInfoV(nvmlDevice_t device, unsigned int profile, nvmlGpuInstanceProfileInfo_v2_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstancePossiblePlacements_v2(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstancePlacement_t* placements, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstanceRemainingCapacity(nvmlDevice_t device, unsigned int profileId, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceCreateGpuInstance(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstance_t* gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceCreateGpuInstanceWithPlacement(nvmlDevice_t device, unsigned int profileId, const nvmlGpuInstancePlacement_t* placement, nvmlGpuInstance_t* gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceDestroy(nvmlGpuInstance_t gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstances(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstance_t* gpuInstances, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstanceById(nvmlDevice_t device, unsigned int id, nvmlGpuInstance_t* gpuInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetInfo(nvmlGpuInstance_t gpuInstance, nvmlGpuInstanceInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetComputeInstanceProfileInfoV(nvmlGpuInstance_t gpuInstance, unsigned int profile, unsigned int engProfile, nvmlComputeInstanceProfileInfo_v2_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetComputeInstanceRemainingCapacity(nvmlGpuInstance_t gpuInstance, unsigned int profileId, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetComputeInstancePossiblePlacements(nvmlGpuInstance_t gpuInstance, unsigned int profileId, nvmlComputeInstancePlacement_t* placements, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceCreateComputeInstance(nvmlGpuInstance_t gpuInstance, unsigned int profileId, nvmlComputeInstance_t* computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceCreateComputeInstanceWithPlacement(nvmlGpuInstance_t gpuInstance, unsigned int profileId, const nvmlComputeInstancePlacement_t* placement, nvmlComputeInstance_t* computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlComputeInstanceDestroy(nvmlComputeInstance_t computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetComputeInstances(nvmlGpuInstance_t gpuInstance, unsigned int profileId, nvmlComputeInstance_t* computeInstances, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlGpuInstanceGetComputeInstanceById(nvmlGpuInstance_t gpuInstance, unsigned int id, nvmlComputeInstance_t* computeInstance) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlComputeInstanceGetInfo_v2(nvmlComputeInstance_t computeInstance, nvmlComputeInstanceInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceIsMigDeviceHandle(nvmlDevice_t device, unsigned int* isMigDevice) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstanceId(nvmlDevice_t device, unsigned int* id) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetComputeInstanceId(nvmlDevice_t device, unsigned int* id) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMaxMigDeviceCount(nvmlDevice_t device, unsigned int* count) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetMigDeviceHandleByIndex(nvmlDevice_t device, unsigned int index, nvmlDevice_t* migDevice) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetDeviceHandleFromMigDeviceHandle(nvmlDevice_t migDevice, nvmlDevice_t* device) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetCapabilities(nvmlDevice_t device, nvmlDeviceCapabilities_t* caps) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDevicePowerSmoothingActivatePresetProfile(nvmlDevice_t device, nvmlPowerSmoothingProfile_t* profile) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDevicePowerSmoothingUpdatePresetProfileParam(nvmlDevice_t device, nvmlPowerSmoothingProfile_t* profile) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDevicePowerSmoothingSetState(nvmlDevice_t device, nvmlPowerSmoothingState_t* state) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetAddressingMode(nvmlDevice_t device, nvmlDeviceAddressingMode_t* mode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetRepairStatus(nvmlDevice_t device, nvmlRepairStatus_t* repairStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPowerMizerMode_v1(nvmlDevice_t device, nvmlDevicePowerMizerModes_v1_t* powerMizerMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetPowerMizerMode_v1(nvmlDevice_t device, nvmlDevicePowerMizerModes_v1_t* powerMizerMode) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetPdi(nvmlDevice_t device, nvmlPdi_t* pdi) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetHostname_v1(nvmlDevice_t device, nvmlHostname_v1_t* hostname) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetHostname_v1(nvmlDevice_t device, nvmlHostname_v1_t* hostname) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetNvLinkInfo(nvmlDevice_t device, nvmlNvLinkInfo_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceReadWritePRM_v1(nvmlDevice_t device, nvmlPRMTLV_v1_t* buffer) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetGpuInstanceProfileInfoByIdV(nvmlDevice_t device, unsigned int profileId, nvmlGpuInstanceProfileInfo_v2_t* info) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetSramUniqueUncorrectedEccErrorCounts(nvmlDevice_t device, nvmlEccSramUniqueUncorrectedErrorCounts_t* errorCounts) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceGetUnrepairableMemoryFlag_v1(nvmlDevice_t device, nvmlUnrepairableMemoryStatus_v1_t* unrepairableMemoryStatus) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceReadPRMCounters_v1(nvmlDevice_t device, nvmlPRMCounterList_v1_t* counterList) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
+cdef nvmlReturn_t nvmlDeviceSetRusdSettings_v1(nvmlDevice_t device, nvmlRusdSettings_v1_t* settings) except?_NVMLRETURN_T_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..b39ebcf7671f85e905e29ddaba08d6e3ef6fa8ea
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvrtc.pxd b/lib/python3.11/site-packages/cuda/bindings/cynvrtc.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..e3cc80a38e69209047bc4e9618ca7767ddda7d4e
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cynvrtc.pxd
@@ -0,0 +1,84 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "nvrtc.h":
+
+    ctypedef enum nvrtcResult:
+        NVRTC_SUCCESS = 0
+        NVRTC_ERROR_OUT_OF_MEMORY = 1
+        NVRTC_ERROR_PROGRAM_CREATION_FAILURE = 2
+        NVRTC_ERROR_INVALID_INPUT = 3
+        NVRTC_ERROR_INVALID_PROGRAM = 4
+        NVRTC_ERROR_INVALID_OPTION = 5
+        NVRTC_ERROR_COMPILATION = 6
+        NVRTC_ERROR_BUILTIN_OPERATION_FAILURE = 7
+        NVRTC_ERROR_NO_NAME_EXPRESSIONS_AFTER_COMPILATION = 8
+        NVRTC_ERROR_NO_LOWERED_NAMES_BEFORE_COMPILATION = 9
+        NVRTC_ERROR_NAME_EXPRESSION_NOT_VALID = 10
+        NVRTC_ERROR_INTERNAL_ERROR = 11
+        NVRTC_ERROR_TIME_FILE_WRITE_FAILED = 12
+        NVRTC_ERROR_NO_PCH_CREATE_ATTEMPTED = 13
+        NVRTC_ERROR_PCH_CREATE_HEAP_EXHAUSTED = 14
+        NVRTC_ERROR_PCH_CREATE = 15
+        NVRTC_ERROR_CANCELLED = 16
+        NVRTC_ERROR_TIME_TRACE_FILE_WRITE_FAILED = 17
+
+    cdef struct _nvrtcProgram:
+        pass
+    ctypedef _nvrtcProgram* nvrtcProgram
+
+cdef const char* nvrtcGetErrorString(nvrtcResult result) except ?NULL nogil
+
+cdef nvrtcResult nvrtcVersion(int* major, int* minor) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetNumSupportedArchs(int* numArchs) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetSupportedArchs(int* supportedArchs) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcCreateProgram(nvrtcProgram* prog, const char* src, const char* name, int numHeaders, const char** headers, const char** includeNames) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcDestroyProgram(nvrtcProgram* prog) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcCompileProgram(nvrtcProgram prog, int numOptions, const char** options) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetPTXSize(nvrtcProgram prog, size_t* ptxSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetPTX(nvrtcProgram prog, char* ptx) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetCUBINSize(nvrtcProgram prog, size_t* cubinSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetCUBIN(nvrtcProgram prog, char* cubin) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetLTOIRSize(nvrtcProgram prog, size_t* LTOIRSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetLTOIR(nvrtcProgram prog, char* LTOIR) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetOptiXIRSize(nvrtcProgram prog, size_t* optixirSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetOptiXIR(nvrtcProgram prog, char* optixir) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetProgramLogSize(nvrtcProgram prog, size_t* logSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetProgramLog(nvrtcProgram prog, char* log) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcAddNameExpression(nvrtcProgram prog, const char* name_expression) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetLoweredName(nvrtcProgram prog, const char* name_expression, const char** lowered_name) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetPCHHeapSize(size_t* ret) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcSetPCHHeapSize(size_t size) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetPCHCreateStatus(nvrtcProgram prog) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetPCHHeapSizeRequired(nvrtcProgram prog, size_t* size) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcSetFlowCallback(nvrtcProgram prog, void* callback, void* payload) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetTileIRSize(nvrtcProgram prog, size_t* TileIRSizeRet) except ?NVRTC_ERROR_INVALID_INPUT nogil
+
+cdef nvrtcResult nvrtcGetTileIR(nvrtcProgram prog, char* TileIR) except ?NVRTC_ERROR_INVALID_INPUT nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvvm.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/cynvvm.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..961d87d3632f3cd25452fa0fad4e31ff5a5cccde
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/cynvvm.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/cynvvm.pxd b/lib/python3.11/site-packages/cuda/bindings/cynvvm.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..300123115fbcf349176a8e0b3cdad2c11ec6a014
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cynvvm.pxd
@@ -0,0 +1,48 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.0.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+
+###############################################################################
+# Types (structs, enums, ...)
+###############################################################################
+
+# enums
+ctypedef enum nvvmResult "nvvmResult":
+    NVVM_SUCCESS "NVVM_SUCCESS" = 0
+    NVVM_ERROR_OUT_OF_MEMORY "NVVM_ERROR_OUT_OF_MEMORY" = 1
+    NVVM_ERROR_PROGRAM_CREATION_FAILURE "NVVM_ERROR_PROGRAM_CREATION_FAILURE" = 2
+    NVVM_ERROR_IR_VERSION_MISMATCH "NVVM_ERROR_IR_VERSION_MISMATCH" = 3
+    NVVM_ERROR_INVALID_INPUT "NVVM_ERROR_INVALID_INPUT" = 4
+    NVVM_ERROR_INVALID_PROGRAM "NVVM_ERROR_INVALID_PROGRAM" = 5
+    NVVM_ERROR_INVALID_IR "NVVM_ERROR_INVALID_IR" = 6
+    NVVM_ERROR_INVALID_OPTION "NVVM_ERROR_INVALID_OPTION" = 7
+    NVVM_ERROR_NO_MODULE_IN_PROGRAM "NVVM_ERROR_NO_MODULE_IN_PROGRAM" = 8
+    NVVM_ERROR_COMPILATION "NVVM_ERROR_COMPILATION" = 9
+    NVVM_ERROR_CANCELLED "NVVM_ERROR_CANCELLED" = 10
+    _NVVMRESULT_INTERNAL_LOADING_ERROR "_NVVMRESULT_INTERNAL_LOADING_ERROR" = -42
+
+
+# types
+ctypedef void* nvvmProgram 'nvvmProgram'
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cdef const char* nvvmGetErrorString(nvvmResult result) except?NULL nogil
+cdef nvvmResult nvvmVersion(int* major, int* minor) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmIRVersion(int* majorIR, int* minorIR, int* majorDbg, int* minorDbg) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmCreateProgram(nvvmProgram* prog) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmDestroyProgram(nvvmProgram* prog) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmAddModuleToProgram(nvvmProgram prog, const char* buffer, size_t size, const char* name) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmLazyAddModuleToProgram(nvvmProgram prog, const char* buffer, size_t size, const char* name) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmCompileProgram(nvvmProgram prog, int numOptions, const char** options) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmVerifyProgram(nvvmProgram prog, int numOptions, const char** options) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmGetCompiledResultSize(nvvmProgram prog, size_t* bufferSizeRet) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmGetCompiledResult(nvvmProgram prog, char* buffer) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmGetProgramLogSize(nvvmProgram prog, size_t* bufferSizeRet) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
+cdef nvvmResult nvvmGetProgramLog(nvvmProgram prog, char* buffer) except?_NVVMRESULT_INTERNAL_LOADING_ERROR nogil
diff --git a/lib/python3.11/site-packages/cuda/bindings/cyruntime.pxd b/lib/python3.11/site-packages/cuda/bindings/cyruntime.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..d9fb27948ec820ecb389b34ca636015789e50075
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cyruntime.pxd
@@ -0,0 +1,1069 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+
+from libc.stdint cimport uint32_t, uint64_t
+
+include "cyruntime_types.pxi"
+
+ctypedef unsigned int GLenum
+
+ctypedef unsigned int GLuint
+
+cdef extern from "":
+    cdef struct void:
+        pass
+ctypedef void* EGLImageKHR
+
+cdef extern from "":
+    cdef struct void:
+        pass
+ctypedef void* EGLStreamKHR
+
+ctypedef unsigned int EGLint
+
+cdef extern from "":
+    cdef struct void:
+        pass
+ctypedef void* EGLSyncKHR
+
+ctypedef uint32_t VdpDevice
+
+ctypedef unsigned long long VdpGetProcAddress
+
+ctypedef uint32_t VdpVideoSurface
+
+ctypedef uint32_t VdpOutputSurface
+
+cdef enum cudaEglFrameType_enum:
+    cudaEglFrameTypeArray = 0
+    cudaEglFrameTypePitch = 1
+
+ctypedef cudaEglFrameType_enum cudaEglFrameType
+
+cdef enum cudaEglResourceLocationFlags_enum:
+    cudaEglResourceLocationSysmem = 0
+    cudaEglResourceLocationVidmem = 1
+
+ctypedef cudaEglResourceLocationFlags_enum cudaEglResourceLocationFlags
+
+cdef enum cudaEglColorFormat_enum:
+    cudaEglColorFormatYUV420Planar = 0
+    cudaEglColorFormatYUV420SemiPlanar = 1
+    cudaEglColorFormatYUV422Planar = 2
+    cudaEglColorFormatYUV422SemiPlanar = 3
+    cudaEglColorFormatARGB = 6
+    cudaEglColorFormatRGBA = 7
+    cudaEglColorFormatL = 8
+    cudaEglColorFormatR = 9
+    cudaEglColorFormatYUV444Planar = 10
+    cudaEglColorFormatYUV444SemiPlanar = 11
+    cudaEglColorFormatYUYV422 = 12
+    cudaEglColorFormatUYVY422 = 13
+    cudaEglColorFormatABGR = 14
+    cudaEglColorFormatBGRA = 15
+    cudaEglColorFormatA = 16
+    cudaEglColorFormatRG = 17
+    cudaEglColorFormatAYUV = 18
+    cudaEglColorFormatYVU444SemiPlanar = 19
+    cudaEglColorFormatYVU422SemiPlanar = 20
+    cudaEglColorFormatYVU420SemiPlanar = 21
+    cudaEglColorFormatY10V10U10_444SemiPlanar = 22
+    cudaEglColorFormatY10V10U10_420SemiPlanar = 23
+    cudaEglColorFormatY12V12U12_444SemiPlanar = 24
+    cudaEglColorFormatY12V12U12_420SemiPlanar = 25
+    cudaEglColorFormatVYUY_ER = 26
+    cudaEglColorFormatUYVY_ER = 27
+    cudaEglColorFormatYUYV_ER = 28
+    cudaEglColorFormatYVYU_ER = 29
+    cudaEglColorFormatYUVA_ER = 31
+    cudaEglColorFormatAYUV_ER = 32
+    cudaEglColorFormatYUV444Planar_ER = 33
+    cudaEglColorFormatYUV422Planar_ER = 34
+    cudaEglColorFormatYUV420Planar_ER = 35
+    cudaEglColorFormatYUV444SemiPlanar_ER = 36
+    cudaEglColorFormatYUV422SemiPlanar_ER = 37
+    cudaEglColorFormatYUV420SemiPlanar_ER = 38
+    cudaEglColorFormatYVU444Planar_ER = 39
+    cudaEglColorFormatYVU422Planar_ER = 40
+    cudaEglColorFormatYVU420Planar_ER = 41
+    cudaEglColorFormatYVU444SemiPlanar_ER = 42
+    cudaEglColorFormatYVU422SemiPlanar_ER = 43
+    cudaEglColorFormatYVU420SemiPlanar_ER = 44
+    cudaEglColorFormatBayerRGGB = 45
+    cudaEglColorFormatBayerBGGR = 46
+    cudaEglColorFormatBayerGRBG = 47
+    cudaEglColorFormatBayerGBRG = 48
+    cudaEglColorFormatBayer10RGGB = 49
+    cudaEglColorFormatBayer10BGGR = 50
+    cudaEglColorFormatBayer10GRBG = 51
+    cudaEglColorFormatBayer10GBRG = 52
+    cudaEglColorFormatBayer12RGGB = 53
+    cudaEglColorFormatBayer12BGGR = 54
+    cudaEglColorFormatBayer12GRBG = 55
+    cudaEglColorFormatBayer12GBRG = 56
+    cudaEglColorFormatBayer14RGGB = 57
+    cudaEglColorFormatBayer14BGGR = 58
+    cudaEglColorFormatBayer14GRBG = 59
+    cudaEglColorFormatBayer14GBRG = 60
+    cudaEglColorFormatBayer20RGGB = 61
+    cudaEglColorFormatBayer20BGGR = 62
+    cudaEglColorFormatBayer20GRBG = 63
+    cudaEglColorFormatBayer20GBRG = 64
+    cudaEglColorFormatYVU444Planar = 65
+    cudaEglColorFormatYVU422Planar = 66
+    cudaEglColorFormatYVU420Planar = 67
+    cudaEglColorFormatBayerIspRGGB = 68
+    cudaEglColorFormatBayerIspBGGR = 69
+    cudaEglColorFormatBayerIspGRBG = 70
+    cudaEglColorFormatBayerIspGBRG = 71
+    cudaEglColorFormatBayerBCCR = 72
+    cudaEglColorFormatBayerRCCB = 73
+    cudaEglColorFormatBayerCRBC = 74
+    cudaEglColorFormatBayerCBRC = 75
+    cudaEglColorFormatBayer10CCCC = 76
+    cudaEglColorFormatBayer12BCCR = 77
+    cudaEglColorFormatBayer12RCCB = 78
+    cudaEglColorFormatBayer12CRBC = 79
+    cudaEglColorFormatBayer12CBRC = 80
+    cudaEglColorFormatBayer12CCCC = 81
+    cudaEglColorFormatY = 82
+    cudaEglColorFormatYUV420SemiPlanar_2020 = 83
+    cudaEglColorFormatYVU420SemiPlanar_2020 = 84
+    cudaEglColorFormatYUV420Planar_2020 = 85
+    cudaEglColorFormatYVU420Planar_2020 = 86
+    cudaEglColorFormatYUV420SemiPlanar_709 = 87
+    cudaEglColorFormatYVU420SemiPlanar_709 = 88
+    cudaEglColorFormatYUV420Planar_709 = 89
+    cudaEglColorFormatYVU420Planar_709 = 90
+    cudaEglColorFormatY10V10U10_420SemiPlanar_709 = 91
+    cudaEglColorFormatY10V10U10_420SemiPlanar_2020 = 92
+    cudaEglColorFormatY10V10U10_422SemiPlanar_2020 = 93
+    cudaEglColorFormatY10V10U10_422SemiPlanar = 94
+    cudaEglColorFormatY10V10U10_422SemiPlanar_709 = 95
+    cudaEglColorFormatY_ER = 96
+    cudaEglColorFormatY_709_ER = 97
+    cudaEglColorFormatY10_ER = 98
+    cudaEglColorFormatY10_709_ER = 99
+    cudaEglColorFormatY12_ER = 100
+    cudaEglColorFormatY12_709_ER = 101
+    cudaEglColorFormatYUVA = 102
+    cudaEglColorFormatYVYU = 104
+    cudaEglColorFormatVYUY = 105
+    cudaEglColorFormatY10V10U10_420SemiPlanar_ER = 106
+    cudaEglColorFormatY10V10U10_420SemiPlanar_709_ER = 107
+    cudaEglColorFormatY10V10U10_444SemiPlanar_ER = 108
+    cudaEglColorFormatY10V10U10_444SemiPlanar_709_ER = 109
+    cudaEglColorFormatY12V12U12_420SemiPlanar_ER = 110
+    cudaEglColorFormatY12V12U12_420SemiPlanar_709_ER = 111
+    cudaEglColorFormatY12V12U12_444SemiPlanar_ER = 112
+    cudaEglColorFormatY12V12U12_444SemiPlanar_709_ER = 113
+    cudaEglColorFormatUYVY709 = 114
+    cudaEglColorFormatUYVY709_ER = 115
+    cudaEglColorFormatUYVY2020 = 116
+
+ctypedef cudaEglColorFormat_enum cudaEglColorFormat
+
+cdef struct cudaEglPlaneDesc_st:
+    unsigned int width
+    unsigned int height
+    unsigned int depth
+    unsigned int pitch
+    unsigned int numChannels
+    cudaChannelFormatDesc channelDesc
+    unsigned int reserved[4]
+
+ctypedef cudaEglPlaneDesc_st cudaEglPlaneDesc
+
+cdef union anon_union11:
+    cudaArray_t pArray[3]
+    cudaPitchedPtr pPitch[3]
+
+cdef struct cudaEglFrame_st:
+    anon_union11 frame
+    cudaEglPlaneDesc planeDesc[3]
+    unsigned int planeCount
+    cudaEglFrameType frameType
+    cudaEglColorFormat eglColorFormat
+
+ctypedef cudaEglFrame_st cudaEglFrame
+
+cdef extern from "":
+    cdef struct CUeglStreamConnection_st:
+        pass
+ctypedef CUeglStreamConnection_st* cudaEglStreamConnection
+
+cdef enum cudaGLDeviceList:
+    cudaGLDeviceListAll = 1
+    cudaGLDeviceListCurrentFrame = 2
+    cudaGLDeviceListNextFrame = 3
+
+cdef enum cudaGLMapFlags:
+    cudaGLMapFlagsNone = 0
+    cudaGLMapFlagsReadOnly = 1
+    cudaGLMapFlagsWriteDiscard = 2
+
+cdef cudaError_t cudaDeviceReset() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceSynchronize() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceSetLimit(cudaLimit limit, size_t value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetLimit(size_t* pValue, cudaLimit limit) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetTexture1DLinearMaxWidth(size_t* maxWidthInElements, const cudaChannelFormatDesc* fmtDesc, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetCacheConfig(cudaFuncCache* pCacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetStreamPriorityRange(int* leastPriority, int* greatestPriority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceSetCacheConfig(cudaFuncCache cacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetByPCIBusId(int* device, const char* pciBusId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetPCIBusId(char* pciBusId, int length, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaIpcGetEventHandle(cudaIpcEventHandle_t* handle, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaIpcOpenEventHandle(cudaEvent_t* event, cudaIpcEventHandle_t handle) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaIpcGetMemHandle(cudaIpcMemHandle_t* handle, void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaIpcOpenMemHandle(void** devPtr, cudaIpcMemHandle_t handle, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaIpcCloseMemHandle(void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceFlushGPUDirectRDMAWrites(cudaFlushGPUDirectRDMAWritesTarget target, cudaFlushGPUDirectRDMAWritesScope scope) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceRegisterAsyncNotification(int device, cudaAsyncCallback callbackFunc, void* userData, cudaAsyncCallbackHandle_t* callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceUnregisterAsyncNotification(int device, cudaAsyncCallbackHandle_t callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetSharedMemConfig(cudaSharedMemConfig* pConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceSetSharedMemConfig(cudaSharedMemConfig config) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetLastError() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaPeekAtLastError() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef const char* cudaGetErrorName(cudaError_t error) except ?NULL nogil
+
+cdef const char* cudaGetErrorString(cudaError_t error) except ?NULL nogil
+
+cdef cudaError_t cudaGetDeviceCount(int* count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetDeviceProperties(cudaDeviceProp* prop, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetAttribute(int* value, cudaDeviceAttr attr, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetHostAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetDefaultMemPool(cudaMemPool_t* memPool, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceSetMemPool(int device, cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetMemPool(cudaMemPool_t* memPool, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetNvSciSyncAttributes(void* nvSciSyncAttrList, int device, int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetP2PAttribute(int* value, cudaDeviceP2PAttr attr, int srcDevice, int dstDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetP2PAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int srcDevice, int dstDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaChooseDevice(int* device, const cudaDeviceProp* prop) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaInitDevice(int device, unsigned int deviceFlags, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaSetDevice(int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetDevice(int* device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaSetDeviceFlags(unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetDeviceFlags(unsigned int* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamCreate(cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamCreateWithFlags(cudaStream_t* pStream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamCreateWithPriority(cudaStream_t* pStream, unsigned int flags, int priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetPriority(cudaStream_t hStream, int* priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetFlags(cudaStream_t hStream, unsigned int* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetId(cudaStream_t hStream, unsigned long long* streamId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetDevice(cudaStream_t hStream, int* device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaCtxResetPersistingL2Cache() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamCopyAttributes(cudaStream_t dst, cudaStream_t src) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, cudaStreamAttrValue* value_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamSetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, const cudaStreamAttrValue* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamDestroy(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamAddCallback(cudaStream_t stream, cudaStreamCallback_t callback, void* userData, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamSynchronize(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamQuery(cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamAttachMemAsync(cudaStream_t stream, void* devPtr, size_t length, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamBeginCapture(cudaStream_t stream, cudaStreamCaptureMode mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamBeginCaptureToGraph(cudaStream_t stream, cudaGraph_t graph, const cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaStreamCaptureMode mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaThreadExchangeStreamCaptureMode(cudaStreamCaptureMode* mode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamEndCapture(cudaStream_t stream, cudaGraph_t* pGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamIsCapturing(cudaStream_t stream, cudaStreamCaptureStatus* pCaptureStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetCaptureInfo(cudaStream_t stream, cudaStreamCaptureStatus* captureStatus_out, unsigned long long* id_out, cudaGraph_t* graph_out, const cudaGraphNode_t** dependencies_out, const cudaGraphEdgeData** edgeData_out, size_t* numDependencies_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamUpdateCaptureDependencies(cudaStream_t stream, cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventCreate(cudaEvent_t* event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventCreateWithFlags(cudaEvent_t* event, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventRecord(cudaEvent_t event, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventQuery(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventSynchronize(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventDestroy(cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventElapsedTime(float* ms, cudaEvent_t start, cudaEvent_t end) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaImportExternalMemory(cudaExternalMemory_t* extMem_out, const cudaExternalMemoryHandleDesc* memHandleDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExternalMemoryGetMappedBuffer(void** devPtr, cudaExternalMemory_t extMem, const cudaExternalMemoryBufferDesc* bufferDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExternalMemoryGetMappedMipmappedArray(cudaMipmappedArray_t* mipmap, cudaExternalMemory_t extMem, const cudaExternalMemoryMipmappedArrayDesc* mipmapDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDestroyExternalMemory(cudaExternalMemory_t extMem) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaImportExternalSemaphore(cudaExternalSemaphore_t* extSem_out, const cudaExternalSemaphoreHandleDesc* semHandleDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaSignalExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreSignalParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaWaitExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreWaitParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDestroyExternalSemaphore(cudaExternalSemaphore_t extSem) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFuncSetCacheConfig(const void* func, cudaFuncCache cacheConfig) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFuncGetAttributes(cudaFuncAttributes* attr, const void* func) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFuncSetAttribute(const void* func, cudaFuncAttribute attr, int value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFuncGetParamCount(const void* func, size_t* paramCount) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLaunchHostFunc(cudaStream_t stream, cudaHostFn_t fn, void* userData) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLaunchHostFunc_v2(cudaStream_t stream, cudaHostFn_t fn, void* userData, unsigned int syncMode) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFuncSetSharedMemConfig(const void* func, cudaSharedMemConfig config) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaOccupancyAvailableDynamicSMemPerBlock(size_t* dynamicSmemSize, const void* func, int numBlocks, int blockSize) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocManaged(void** devPtr, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMalloc(void** devPtr, size_t size) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocHost(void** ptr, size_t size) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocPitch(void** devPtr, size_t* pitch, size_t width, size_t height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, size_t width, size_t height, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFree(void* devPtr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFreeHost(void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFreeArray(cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFreeMipmappedArray(cudaMipmappedArray_t mipmappedArray) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaHostAlloc(void** pHost, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaHostRegister(void* ptr, size_t size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaHostUnregister(void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaHostGetDevicePointer(void** pDevice, void* pHost, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaHostGetFlags(unsigned int* pFlags, void* pHost) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMalloc3D(cudaPitchedPtr* pitchedDevPtr, cudaExtent extent) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMalloc3DArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocMipmappedArray(cudaMipmappedArray_t* mipmappedArray, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int numLevels, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetMipmappedArrayLevel(cudaArray_t* levelArray, cudaMipmappedArray_const_t mipmappedArray, unsigned int level) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy3D(const cudaMemcpy3DParms* p) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy3DPeer(const cudaMemcpy3DPeerParms* p) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy3DAsync(const cudaMemcpy3DParms* p, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy3DPeerAsync(const cudaMemcpy3DPeerParms* p, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemGetInfo(size_t* free, size_t* total) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaArrayGetInfo(cudaChannelFormatDesc* desc, cudaExtent* extent, unsigned int* flags, cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaArrayGetPlane(cudaArray_t* pPlaneArray, cudaArray_t hArray, unsigned int planeIdx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaArray_t array, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMipmappedArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaMipmappedArray_t mipmap, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaArray_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMipmappedArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaMipmappedArray_t mipmap) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy(void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyPeer(void* dst, int dstDevice, const void* src, int srcDevice, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2D(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2DToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2DFromArray(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2DArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t width, size_t height, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyAsync(void* dst, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyPeerAsync(void* dst, int dstDevice, const void* src, int srcDevice, size_t count, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyBatchAsync(const void** dsts, const void** srcs, const size_t* sizes, size_t count, cudaMemcpyAttributes* attrs, size_t* attrsIdxs, size_t numAttrs, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy3DBatchAsync(size_t numOps, cudaMemcpy3DBatchOp* opList, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyWithAttributesAsync(void* dst, const void* src, size_t size, cudaMemcpyAttributes* attr, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy3DWithAttributesAsync(cudaMemcpy3DBatchOp* op, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2DAsync(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2DToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpy2DFromArrayAsync(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemset(void* devPtr, int value, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemset2D(void* devPtr, size_t pitch, int value, size_t width, size_t height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemset3D(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemsetAsync(void* devPtr, int value, size_t count, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemset2DAsync(void* devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemset3DAsync(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPrefetchAsync(const void* devPtr, size_t count, cudaMemLocation location, unsigned int flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemDiscardBatchAsync(void** dptrs, size_t* sizes, size_t count, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemDiscardAndPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemAdvise(const void* devPtr, size_t count, cudaMemoryAdvise advice, cudaMemLocation location) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemRangeGetAttribute(void* data, size_t dataSize, cudaMemRangeAttribute attribute, const void* devPtr, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemRangeGetAttributes(void** data, size_t* dataSizes, cudaMemRangeAttribute* attributes, size_t numAttributes, const void* devPtr, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyFromArray(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemcpyFromArrayAsync(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocAsync(void** devPtr, size_t size, cudaStream_t hStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaFreeAsync(void* devPtr, cudaStream_t hStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolTrimTo(cudaMemPool_t memPool, size_t minBytesToKeep) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolSetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolGetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolSetAccess(cudaMemPool_t memPool, const cudaMemAccessDesc* descList, size_t count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolGetAccess(cudaMemAccessFlags* flags, cudaMemPool_t memPool, cudaMemLocation* location) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolCreate(cudaMemPool_t* memPool, const cudaMemPoolProps* poolProps) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolDestroy(cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemGetDefaultMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemGetMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemSetMemPool(cudaMemLocation* location, cudaMemAllocationType typename, cudaMemPool_t memPool) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMallocFromPoolAsync(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolExportToShareableHandle(void* shareableHandle, cudaMemPool_t memPool, cudaMemAllocationHandleType handleType, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolImportFromShareableHandle(cudaMemPool_t* memPool, void* shareableHandle, cudaMemAllocationHandleType handleType, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolExportPointer(cudaMemPoolPtrExportData* exportData, void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaMemPoolImportPointer(void** ptr, cudaMemPool_t memPool, cudaMemPoolPtrExportData* exportData) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaPointerGetAttributes(cudaPointerAttributes* attributes, const void* ptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceCanAccessPeer(int* canAccessPeer, int device, int peerDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceEnablePeerAccess(int peerDevice, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceDisablePeerAccess(int peerDevice) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsUnregisterResource(cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsResourceSetMapFlags(cudaGraphicsResource_t resource, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsMapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsUnmapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsResourceGetMappedPointer(void** devPtr, size_t* size, cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsSubResourceGetMappedArray(cudaArray_t* array, cudaGraphicsResource_t resource, unsigned int arrayIndex, unsigned int mipLevel) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsResourceGetMappedMipmappedArray(cudaMipmappedArray_t* mipmappedArray, cudaGraphicsResource_t resource) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetChannelDesc(cudaChannelFormatDesc* desc, cudaArray_const_t array) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaChannelFormatDesc cudaCreateChannelDesc(int x, int y, int z, int w, cudaChannelFormatKind f) except* nogil
+
+cdef cudaError_t cudaCreateTextureObject(cudaTextureObject_t* pTexObject, const cudaResourceDesc* pResDesc, const cudaTextureDesc* pTexDesc, const cudaResourceViewDesc* pResViewDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDestroyTextureObject(cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetTextureObjectResourceDesc(cudaResourceDesc* pResDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetTextureObjectTextureDesc(cudaTextureDesc* pTexDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetTextureObjectResourceViewDesc(cudaResourceViewDesc* pResViewDesc, cudaTextureObject_t texObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaCreateSurfaceObject(cudaSurfaceObject_t* pSurfObject, const cudaResourceDesc* pResDesc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDestroySurfaceObject(cudaSurfaceObject_t surfObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetSurfaceObjectResourceDesc(cudaResourceDesc* pResDesc, cudaSurfaceObject_t surfObject) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDriverGetVersion(int* driverVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaRuntimeGetVersion(int* runtimeVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLogsRegisterCallback(cudaLogsCallback_t callbackFunc, void* userData, cudaLogsCallbackHandle* callback_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLogsUnregisterCallback(cudaLogsCallbackHandle callback) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLogsCurrent(cudaLogIterator* iterator_out, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLogsDumpToFile(cudaLogIterator* iterator, const char* pathToFile, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLogsDumpToMemory(cudaLogIterator* iterator, char* buffer, size_t* size, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphCreate(cudaGraph_t* pGraph, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddKernelNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphKernelNodeGetParams(cudaGraphNode_t node, cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphKernelNodeSetParams(cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphKernelNodeCopyAttributes(cudaGraphNode_t hDst, cudaGraphNode_t hSrc) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphKernelNodeGetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, cudaKernelNodeAttrValue* value_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphKernelNodeSetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, const cudaKernelNodeAttrValue* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddMemcpyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemcpy3DParms* pCopyParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddMemcpyNode1D(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemcpyNodeGetParams(cudaGraphNode_t node, cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemcpyNodeSetParams(cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemcpyNodeSetParams1D(cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddMemsetNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemsetParams* pMemsetParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemsetNodeGetParams(cudaGraphNode_t node, cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemsetNodeSetParams(cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddHostNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphHostNodeGetParams(cudaGraphNode_t node, cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphHostNodeSetParams(cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddChildGraphNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaGraph_t childGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphChildGraphNodeGetGraph(cudaGraphNode_t node, cudaGraph_t* pGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddEmptyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddEventRecordNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphEventRecordNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphEventRecordNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddEventWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphEventWaitNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphEventWaitNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddExternalSemaphoresSignalNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExternalSemaphoresSignalNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreSignalNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExternalSemaphoresSignalNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddExternalSemaphoresWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExternalSemaphoresWaitNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreWaitNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExternalSemaphoresWaitNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddMemAllocNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaMemAllocNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemAllocNodeGetParams(cudaGraphNode_t node, cudaMemAllocNodeParams* params_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddMemFreeNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dptr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphMemFreeNodeGetParams(cudaGraphNode_t node, void* dptr_out) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGraphMemTrim(int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceSetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphClone(cudaGraph_t* pGraphClone, cudaGraph_t originalGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeFindInClone(cudaGraphNode_t* pNode, cudaGraphNode_t originalNode, cudaGraph_t clonedGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetType(cudaGraphNode_t node, cudaGraphNodeType* pType) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetContainingGraph(cudaGraphNode_t hNode, cudaGraph_t* phGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetLocalId(cudaGraphNode_t hNode, unsigned int* nodeId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetToolsId(cudaGraphNode_t hNode, unsigned long long* toolsNodeId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphGetId(cudaGraph_t hGraph, unsigned int* graphID) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecGetId(cudaGraphExec_t hGraphExec, unsigned int* graphID) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphGetNodes(cudaGraph_t graph, cudaGraphNode_t* nodes, size_t* numNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphGetRootNodes(cudaGraph_t graph, cudaGraphNode_t* pRootNodes, size_t* pNumRootNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphGetEdges(cudaGraph_t graph, cudaGraphNode_t* from_, cudaGraphNode_t* to, cudaGraphEdgeData* edgeData, size_t* numEdges) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetDependencies(cudaGraphNode_t node, cudaGraphNode_t* pDependencies, cudaGraphEdgeData* edgeData, size_t* pNumDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetDependentNodes(cudaGraphNode_t node, cudaGraphNode_t* pDependentNodes, cudaGraphEdgeData* edgeData, size_t* pNumDependentNodes) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphRemoveDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphDestroyNode(cudaGraphNode_t node) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphInstantiate(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphInstantiateWithFlags(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphInstantiateWithParams(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, cudaGraphInstantiateParams* instantiateParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecGetFlags(cudaGraphExec_t graphExec, unsigned long long* flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecKernelNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecMemcpyNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecMemcpyNodeSetParams1D(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecMemsetNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecHostNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecChildGraphNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, cudaGraph_t childGraph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecEventRecordNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecEventWaitNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecExternalSemaphoresSignalNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecExternalSemaphoresWaitNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeSetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int isEnabled) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int* isEnabled) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecUpdate(cudaGraphExec_t hGraphExec, cudaGraph_t hGraph, cudaGraphExecUpdateResultInfo* resultInfo) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphUpload(cudaGraphExec_t graphExec, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecDestroy(cudaGraphExec_t graphExec) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphDestroy(cudaGraph_t graph) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphDebugDotPrint(cudaGraph_t graph, const char* path, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaUserObjectCreate(cudaUserObject_t* object_out, void* ptr, cudaHostFn_t destroy, unsigned int initialRefcount, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaUserObjectRetain(cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaUserObjectRelease(cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphRetainUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphReleaseUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphAddNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeSetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphNodeGetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphExecNodeSetParams(cudaGraphExec_t graphExec, cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphConditionalHandleCreate(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, unsigned int defaultLaunchValue, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphConditionalHandleCreate_v2(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, cudaExecutionContext_t ctx, unsigned int defaultLaunchValue, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetDriverEntryPoint(const char* symbol, void** funcPtr, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetDriverEntryPointByVersion(const char* symbol, void** funcPtr, unsigned int cudaVersion, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryLoadData(cudaLibrary_t* library, const void* code, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryLoadFromFile(cudaLibrary_t* library, const char* fileName, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryUnload(cudaLibrary_t library) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryGetKernel(cudaKernel_t* pKernel, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryGetGlobal(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryGetManaged(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryGetUnifiedFunction(void** fptr, cudaLibrary_t library, const char* symbol) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryGetKernelCount(unsigned int* count, cudaLibrary_t lib) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaLibraryEnumerateKernels(cudaKernel_t* kernels, unsigned int numKernels, cudaLibrary_t lib) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaKernelSetAttributeForDevice(cudaKernel_t kernel, cudaFuncAttribute attr, int value, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetDevResource(int device, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDevSmResourceSplitByCount(cudaDevResource* result, unsigned int* nbGroups, const cudaDevResource* input, cudaDevResource* remaining, unsigned int flags, unsigned int minCount) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDevSmResourceSplit(cudaDevResource* result, unsigned int nbGroups, const cudaDevResource* input, cudaDevResource* remainder, unsigned int flags, cudaDevSmResourceGroupParams* groupParams) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDevResourceGenerateDesc(cudaDevResourceDesc_t* phDesc, cudaDevResource* resources, unsigned int nbResources) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGreenCtxCreate(cudaExecutionContext_t* phCtx, cudaDevResourceDesc_t desc, int device, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxDestroy(cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxGetDevResource(cudaExecutionContext_t ctx, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxGetDevice(int* device, cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxGetId(cudaExecutionContext_t ctx, unsigned long long* ctxId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxStreamCreate(cudaStream_t* phStream, cudaExecutionContext_t ctx, unsigned int flags, int priority) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxSynchronize(cudaExecutionContext_t ctx) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaStreamGetDevResource(cudaStream_t hStream, cudaDevResource* resource, cudaDevResourceType typename) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxRecordEvent(cudaExecutionContext_t ctx, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaExecutionCtxWaitEvent(cudaExecutionContext_t ctx, cudaEvent_t event) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaDeviceGetExecutionCtx(cudaExecutionContext_t* ctx, int device) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetExportTable(const void** ppExportTable, const cudaUUID_t* pExportTableId) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGetKernel(cudaKernel_t* kernelPtr, const void* entryFuncAddr) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaPitchedPtr make_cudaPitchedPtr(void* d, size_t p, size_t xsz, size_t ysz) except* nogil
+
+cdef cudaPos make_cudaPos(size_t x, size_t y, size_t z) except* nogil
+
+cdef cudaExtent make_cudaExtent(size_t w, size_t h, size_t d) except* nogil
+
+cdef cudaError_t cudaGraphicsEGLRegisterImage(cudaGraphicsResource** pCudaResource, EGLImageKHR image, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamConsumerConnect(cudaEglStreamConnection* conn, EGLStreamKHR eglStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamConsumerConnectWithFlags(cudaEglStreamConnection* conn, EGLStreamKHR eglStream, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamConsumerDisconnect(cudaEglStreamConnection* conn) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamConsumerAcquireFrame(cudaEglStreamConnection* conn, cudaGraphicsResource_t* pCudaResource, cudaStream_t* pStream, unsigned int timeout) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamConsumerReleaseFrame(cudaEglStreamConnection* conn, cudaGraphicsResource_t pCudaResource, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamProducerConnect(cudaEglStreamConnection* conn, EGLStreamKHR eglStream, EGLint width, EGLint height) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamProducerDisconnect(cudaEglStreamConnection* conn) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamProducerPresentFrame(cudaEglStreamConnection* conn, cudaEglFrame eglframe, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEGLStreamProducerReturnFrame(cudaEglStreamConnection* conn, cudaEglFrame* eglframe, cudaStream_t* pStream) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsResourceGetMappedEglFrame(cudaEglFrame* eglFrame, cudaGraphicsResource_t resource, unsigned int index, unsigned int mipLevel) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaEventCreateFromEGLSync(cudaEvent_t* phEvent, EGLSyncKHR eglSync, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaProfilerStart() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaProfilerStop() except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGLGetDevices(unsigned int* pCudaDeviceCount, int* pCudaDevices, unsigned int cudaDeviceCount, cudaGLDeviceList deviceList) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsGLRegisterImage(cudaGraphicsResource** resource, GLuint image, GLenum target, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsGLRegisterBuffer(cudaGraphicsResource** resource, GLuint buffer, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaVDPAUGetDevice(int* device, VdpDevice vdpDevice, VdpGetProcAddress* vdpGetProcAddress) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaVDPAUSetVDPAUDevice(int device, VdpDevice vdpDevice, VdpGetProcAddress* vdpGetProcAddress) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsVDPAURegisterVideoSurface(cudaGraphicsResource** resource, VdpVideoSurface vdpSurface, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t cudaGraphicsVDPAURegisterOutputSurface(cudaGraphicsResource** resource, VdpOutputSurface vdpSurface, unsigned int flags) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef cudaError_t getLocalRuntimeVersion(int* runtimeVersion) except ?cudaErrorCallRequiresNewerDriver nogil
+
+cdef enum: cudaHostAllocDefault = 0
+
+cdef enum: cudaHostAllocPortable = 1
+
+cdef enum: cudaHostAllocMapped = 2
+
+cdef enum: cudaHostAllocWriteCombined = 4
+
+cdef enum: cudaHostRegisterDefault = 0
+
+cdef enum: cudaHostRegisterPortable = 1
+
+cdef enum: cudaHostRegisterMapped = 2
+
+cdef enum: cudaHostRegisterIoMemory = 4
+
+cdef enum: cudaHostRegisterReadOnly = 8
+
+cdef enum: cudaPeerAccessDefault = 0
+
+cdef enum: cudaStreamDefault = 0
+
+cdef enum: cudaStreamNonBlocking = 1
+
+cdef enum: cudaStreamLegacy = 1
+
+cdef enum: cudaStreamPerThread = 2
+
+cdef enum: cudaEventDefault = 0
+
+cdef enum: cudaEventBlockingSync = 1
+
+cdef enum: cudaEventDisableTiming = 2
+
+cdef enum: cudaEventInterprocess = 4
+
+cdef enum: cudaEventRecordDefault = 0
+
+cdef enum: cudaEventRecordExternal = 1
+
+cdef enum: cudaEventWaitDefault = 0
+
+cdef enum: cudaEventWaitExternal = 1
+
+cdef enum: cudaDeviceScheduleAuto = 0
+
+cdef enum: cudaDeviceScheduleSpin = 1
+
+cdef enum: cudaDeviceScheduleYield = 2
+
+cdef enum: cudaDeviceScheduleBlockingSync = 4
+
+cdef enum: cudaDeviceBlockingSync = 4
+
+cdef enum: cudaDeviceScheduleMask = 7
+
+cdef enum: cudaDeviceMapHost = 8
+
+cdef enum: cudaDeviceLmemResizeToMax = 16
+
+cdef enum: cudaDeviceSyncMemops = 128
+
+cdef enum: cudaDeviceMask = 255
+
+cdef enum: cudaArrayDefault = 0
+
+cdef enum: cudaArrayLayered = 1
+
+cdef enum: cudaArraySurfaceLoadStore = 2
+
+cdef enum: cudaArrayCubemap = 4
+
+cdef enum: cudaArrayTextureGather = 8
+
+cdef enum: cudaArrayColorAttachment = 32
+
+cdef enum: cudaArraySparse = 64
+
+cdef enum: cudaArrayDeferredMapping = 128
+
+cdef enum: cudaIpcMemLazyEnablePeerAccess = 1
+
+cdef enum: cudaMemAttachGlobal = 1
+
+cdef enum: cudaMemAttachHost = 2
+
+cdef enum: cudaMemAttachSingle = 4
+
+cdef enum: cudaOccupancyDefault = 0
+
+cdef enum: cudaOccupancyDisableCachingOverride = 1
+
+cdef enum: cudaCpuDeviceId = -1
+
+cdef enum: cudaInvalidDeviceId = -2
+
+cdef enum: cudaInitDeviceFlagsAreValid = 1
+
+cdef enum: cudaArraySparsePropertiesSingleMipTail = 1
+
+cdef enum: cudaMemPoolCreateUsageHwDecompress = 2
+
+cdef enum: CUDA_IPC_HANDLE_SIZE = 64
+
+cdef enum: cudaExternalMemoryDedicated = 1
+
+cdef enum: cudaExternalSemaphoreSignalSkipNvSciBufMemSync = 1
+
+cdef enum: cudaExternalSemaphoreWaitSkipNvSciBufMemSync = 2
+
+cdef enum: cudaNvSciSyncAttrSignal = 1
+
+cdef enum: cudaNvSciSyncAttrWait = 2
+
+cdef enum: cudaGraphKernelNodePortDefault = 0
+
+cdef enum: cudaGraphKernelNodePortProgrammatic = 1
+
+cdef enum: cudaGraphKernelNodePortLaunchCompletion = 2
+
+cdef enum: cudaStreamAttributeAccessPolicyWindow = 1
+
+cdef enum: cudaStreamAttributeSynchronizationPolicy = 3
+
+cdef enum: cudaStreamAttributeMemSyncDomainMap = 9
+
+cdef enum: cudaStreamAttributeMemSyncDomain = 10
+
+cdef enum: cudaStreamAttributePriority = 8
+
+cdef enum: cudaKernelNodeAttributeAccessPolicyWindow = 1
+
+cdef enum: cudaKernelNodeAttributeCooperative = 2
+
+cdef enum: cudaKernelNodeAttributePriority = 8
+
+cdef enum: cudaKernelNodeAttributeClusterDimension = 4
+
+cdef enum: cudaKernelNodeAttributeClusterSchedulingPolicyPreference = 5
+
+cdef enum: cudaKernelNodeAttributeMemSyncDomainMap = 9
+
+cdef enum: cudaKernelNodeAttributeMemSyncDomain = 10
+
+cdef enum: cudaKernelNodeAttributePreferredSharedMemoryCarveout = 14
+
+cdef enum: cudaKernelNodeAttributeDeviceUpdatableKernelNode = 13
+
+cdef enum: cudaKernelNodeAttributeNvlinkUtilCentricScheduling = 16
+
+cdef enum: cudaSurfaceType1D = 1
+
+cdef enum: cudaSurfaceType2D = 2
+
+cdef enum: cudaSurfaceType3D = 3
+
+cdef enum: cudaSurfaceTypeCubemap = 12
+
+cdef enum: cudaSurfaceType1DLayered = 241
+
+cdef enum: cudaSurfaceType2DLayered = 242
+
+cdef enum: cudaSurfaceTypeCubemapLayered = 252
+
+cdef enum: cudaTextureType1D = 1
+
+cdef enum: cudaTextureType2D = 2
+
+cdef enum: cudaTextureType3D = 3
+
+cdef enum: cudaTextureTypeCubemap = 12
+
+cdef enum: cudaTextureType1DLayered = 241
+
+cdef enum: cudaTextureType2DLayered = 242
+
+cdef enum: cudaTextureTypeCubemapLayered = 252
+
+cdef enum: CUDART_VERSION = 13020
+
+cdef enum: __CUDART_API_VERSION = 13020
+
+cdef enum: CUDA_EGL_MAX_PLANES = 3
\ No newline at end of file
diff --git a/lib/python3.11/site-packages/cuda/bindings/cyruntime_functions.pxi b/lib/python3.11/site-packages/cuda/bindings/cyruntime_functions.pxi
new file mode 100644
index 0000000000000000000000000000000000000000..3509c0e6aa39bca51e12790fe2a37107fd750718
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cyruntime_functions.pxi
@@ -0,0 +1,972 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+cdef extern from "cuda_runtime_api.h":
+
+
+    cudaError_t cudaDeviceReset() nogil
+
+
+    cudaError_t cudaDeviceSynchronize() nogil
+
+
+    cudaError_t cudaDeviceSetLimit(cudaLimit limit, size_t value) nogil
+
+
+    cudaError_t cudaDeviceGetLimit(size_t* pValue, cudaLimit limit) nogil
+
+
+    cudaError_t cudaDeviceGetTexture1DLinearMaxWidth(size_t* maxWidthInElements, const cudaChannelFormatDesc* fmtDesc, int device) nogil
+
+
+    cudaError_t cudaDeviceGetCacheConfig(cudaFuncCache* pCacheConfig) nogil
+
+
+    cudaError_t cudaDeviceGetStreamPriorityRange(int* leastPriority, int* greatestPriority) nogil
+
+
+    cudaError_t cudaDeviceSetCacheConfig(cudaFuncCache cacheConfig) nogil
+
+
+    cudaError_t cudaDeviceGetByPCIBusId(int* device, const char* pciBusId) nogil
+
+
+    cudaError_t cudaDeviceGetPCIBusId(char* pciBusId, int length, int device) nogil
+
+
+    cudaError_t cudaIpcGetEventHandle(cudaIpcEventHandle_t* handle, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaIpcOpenEventHandle(cudaEvent_t* event, cudaIpcEventHandle_t handle) nogil
+
+
+    cudaError_t cudaIpcGetMemHandle(cudaIpcMemHandle_t* handle, void* devPtr) nogil
+
+
+    cudaError_t cudaIpcOpenMemHandle(void** devPtr, cudaIpcMemHandle_t handle, unsigned int flags) nogil
+
+
+    cudaError_t cudaIpcCloseMemHandle(void* devPtr) nogil
+
+
+    cudaError_t cudaDeviceFlushGPUDirectRDMAWrites(cudaFlushGPUDirectRDMAWritesTarget target, cudaFlushGPUDirectRDMAWritesScope scope) nogil
+
+
+    cudaError_t cudaDeviceRegisterAsyncNotification(int device, cudaAsyncCallback callbackFunc, void* userData, cudaAsyncCallbackHandle_t* callback) nogil
+
+
+    cudaError_t cudaDeviceUnregisterAsyncNotification(int device, cudaAsyncCallbackHandle_t callback) nogil
+
+
+    cudaError_t cudaDeviceGetSharedMemConfig(cudaSharedMemConfig* pConfig) nogil
+
+
+    cudaError_t cudaDeviceSetSharedMemConfig(cudaSharedMemConfig config) nogil
+
+
+    cudaError_t cudaGetLastError() nogil
+
+
+    cudaError_t cudaPeekAtLastError() nogil
+
+
+    const char* cudaGetErrorName(cudaError_t error) nogil
+
+
+    const char* cudaGetErrorString(cudaError_t error) nogil
+
+
+    cudaError_t cudaGetDeviceCount(int* count) nogil
+
+
+    cudaError_t cudaGetDeviceProperties(cudaDeviceProp* prop, int device) nogil
+
+
+    cudaError_t cudaDeviceGetAttribute(int* value, cudaDeviceAttr attr, int device) nogil
+
+
+    cudaError_t cudaDeviceGetHostAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int device) nogil
+
+
+    cudaError_t cudaDeviceGetDefaultMemPool(cudaMemPool_t* memPool, int device) nogil
+
+
+    cudaError_t cudaDeviceSetMemPool(int device, cudaMemPool_t memPool) nogil
+
+
+    cudaError_t cudaDeviceGetMemPool(cudaMemPool_t* memPool, int device) nogil
+
+
+    cudaError_t cudaDeviceGetNvSciSyncAttributes(void* nvSciSyncAttrList, int device, int flags) nogil
+
+
+    cudaError_t cudaDeviceGetP2PAttribute(int* value, cudaDeviceP2PAttr attr, int srcDevice, int dstDevice) nogil
+
+
+    cudaError_t cudaDeviceGetP2PAtomicCapabilities(unsigned int* capabilities, const cudaAtomicOperation* operations, unsigned int count, int srcDevice, int dstDevice) nogil
+
+
+    cudaError_t cudaChooseDevice(int* device, const cudaDeviceProp* prop) nogil
+
+
+    cudaError_t cudaInitDevice(int device, unsigned int deviceFlags, unsigned int flags) nogil
+
+
+    cudaError_t cudaSetDevice(int device) nogil
+
+
+    cudaError_t cudaGetDevice(int* device) nogil
+
+
+    cudaError_t cudaSetDeviceFlags(unsigned int flags) nogil
+
+
+    cudaError_t cudaGetDeviceFlags(unsigned int* flags) nogil
+
+
+    cudaError_t cudaStreamCreate(cudaStream_t* pStream) nogil
+
+
+    cudaError_t cudaStreamCreateWithFlags(cudaStream_t* pStream, unsigned int flags) nogil
+
+
+    cudaError_t cudaStreamCreateWithPriority(cudaStream_t* pStream, unsigned int flags, int priority) nogil
+
+
+    cudaError_t cudaStreamGetPriority(cudaStream_t hStream, int* priority) nogil
+
+
+    cudaError_t cudaStreamGetFlags(cudaStream_t hStream, unsigned int* flags) nogil
+
+
+    cudaError_t cudaStreamGetId(cudaStream_t hStream, unsigned long long* streamId) nogil
+
+
+    cudaError_t cudaStreamGetDevice(cudaStream_t hStream, int* device) nogil
+
+
+    cudaError_t cudaCtxResetPersistingL2Cache() nogil
+
+
+    cudaError_t cudaStreamCopyAttributes(cudaStream_t dst, cudaStream_t src) nogil
+
+
+    cudaError_t cudaStreamGetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, cudaStreamAttrValue* value_out) nogil
+
+
+    cudaError_t cudaStreamSetAttribute(cudaStream_t hStream, cudaStreamAttrID attr, const cudaStreamAttrValue* value) nogil
+
+
+    cudaError_t cudaStreamDestroy(cudaStream_t stream) nogil
+
+
+    cudaError_t cudaStreamWaitEvent(cudaStream_t stream, cudaEvent_t event, unsigned int flags) nogil
+
+
+    cudaError_t cudaStreamAddCallback(cudaStream_t stream, cudaStreamCallback_t callback, void* userData, unsigned int flags) nogil
+
+
+    cudaError_t cudaStreamSynchronize(cudaStream_t stream) nogil
+
+
+    cudaError_t cudaStreamQuery(cudaStream_t stream) nogil
+
+
+    cudaError_t cudaStreamAttachMemAsync(cudaStream_t stream, void* devPtr, size_t length, unsigned int flags) nogil
+
+
+    cudaError_t cudaStreamBeginCapture(cudaStream_t stream, cudaStreamCaptureMode mode) nogil
+
+
+    cudaError_t cudaStreamBeginCaptureToGraph(cudaStream_t stream, cudaGraph_t graph, const cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaStreamCaptureMode mode) nogil
+
+
+    cudaError_t cudaThreadExchangeStreamCaptureMode(cudaStreamCaptureMode* mode) nogil
+
+
+    cudaError_t cudaStreamEndCapture(cudaStream_t stream, cudaGraph_t* pGraph) nogil
+
+
+    cudaError_t cudaStreamIsCapturing(cudaStream_t stream, cudaStreamCaptureStatus* pCaptureStatus) nogil
+
+
+    cudaError_t cudaStreamGetCaptureInfo(cudaStream_t stream, cudaStreamCaptureStatus* captureStatus_out, unsigned long long* id_out, cudaGraph_t* graph_out, const cudaGraphNode_t** dependencies_out, const cudaGraphEdgeData** edgeData_out, size_t* numDependencies_out) nogil
+
+
+    cudaError_t cudaStreamUpdateCaptureDependencies(cudaStream_t stream, cudaGraphNode_t* dependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, unsigned int flags) nogil
+
+
+    cudaError_t cudaEventCreate(cudaEvent_t* event) nogil
+
+
+    cudaError_t cudaEventCreateWithFlags(cudaEvent_t* event, unsigned int flags) nogil
+
+
+    cudaError_t cudaEventRecord(cudaEvent_t event, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaEventRecordWithFlags(cudaEvent_t event, cudaStream_t stream, unsigned int flags) nogil
+
+
+    cudaError_t cudaEventQuery(cudaEvent_t event) nogil
+
+
+    cudaError_t cudaEventSynchronize(cudaEvent_t event) nogil
+
+
+    cudaError_t cudaEventDestroy(cudaEvent_t event) nogil
+
+
+    cudaError_t cudaEventElapsedTime(float* ms, cudaEvent_t start, cudaEvent_t end) nogil
+
+
+    cudaError_t cudaImportExternalMemory(cudaExternalMemory_t* extMem_out, const cudaExternalMemoryHandleDesc* memHandleDesc) nogil
+
+
+    cudaError_t cudaExternalMemoryGetMappedBuffer(void** devPtr, cudaExternalMemory_t extMem, const cudaExternalMemoryBufferDesc* bufferDesc) nogil
+
+
+    cudaError_t cudaExternalMemoryGetMappedMipmappedArray(cudaMipmappedArray_t* mipmap, cudaExternalMemory_t extMem, const cudaExternalMemoryMipmappedArrayDesc* mipmapDesc) nogil
+
+
+    cudaError_t cudaDestroyExternalMemory(cudaExternalMemory_t extMem) nogil
+
+
+    cudaError_t cudaImportExternalSemaphore(cudaExternalSemaphore_t* extSem_out, const cudaExternalSemaphoreHandleDesc* semHandleDesc) nogil
+
+
+    cudaError_t cudaSignalExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreSignalParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaWaitExternalSemaphoresAsync(const cudaExternalSemaphore_t* extSemArray, const cudaExternalSemaphoreWaitParams* paramsArray, unsigned int numExtSems, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaDestroyExternalSemaphore(cudaExternalSemaphore_t extSem) nogil
+
+
+    cudaError_t cudaFuncSetCacheConfig(const void* func, cudaFuncCache cacheConfig) nogil
+
+
+    cudaError_t cudaFuncGetAttributes(cudaFuncAttributes* attr, const void* func) nogil
+
+
+    cudaError_t cudaFuncSetAttribute(const void* func, cudaFuncAttribute attr, int value) nogil
+
+
+    cudaError_t cudaFuncGetParamCount(const void* func, size_t* paramCount) nogil
+
+
+    cudaError_t cudaLaunchHostFunc(cudaStream_t stream, cudaHostFn_t fn, void* userData) nogil
+
+
+    cudaError_t cudaLaunchHostFunc_v2(cudaStream_t stream, cudaHostFn_t fn, void* userData, unsigned int syncMode) nogil
+
+
+    cudaError_t cudaFuncSetSharedMemConfig(const void* func, cudaSharedMemConfig config) nogil
+
+
+    cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize) nogil
+
+
+    cudaError_t cudaOccupancyAvailableDynamicSMemPerBlock(size_t* dynamicSmemSize, const void* func, int numBlocks, int blockSize) nogil
+
+
+    cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int* numBlocks, const void* func, int blockSize, size_t dynamicSMemSize, unsigned int flags) nogil
+
+
+    cudaError_t cudaMallocManaged(void** devPtr, size_t size, unsigned int flags) nogil
+
+
+    cudaError_t cudaMalloc(void** devPtr, size_t size) nogil
+
+
+    cudaError_t cudaMallocHost(void** ptr, size_t size) nogil
+
+
+    cudaError_t cudaMallocPitch(void** devPtr, size_t* pitch, size_t width, size_t height) nogil
+
+
+    cudaError_t cudaMallocArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, size_t width, size_t height, unsigned int flags) nogil
+
+
+    cudaError_t cudaFree(void* devPtr) nogil
+
+
+    cudaError_t cudaFreeHost(void* ptr) nogil
+
+
+    cudaError_t cudaFreeArray(cudaArray_t array) nogil
+
+
+    cudaError_t cudaFreeMipmappedArray(cudaMipmappedArray_t mipmappedArray) nogil
+
+
+    cudaError_t cudaHostAlloc(void** pHost, size_t size, unsigned int flags) nogil
+
+
+    cudaError_t cudaHostRegister(void* ptr, size_t size, unsigned int flags) nogil
+
+
+    cudaError_t cudaHostUnregister(void* ptr) nogil
+
+
+    cudaError_t cudaHostGetDevicePointer(void** pDevice, void* pHost, unsigned int flags) nogil
+
+
+    cudaError_t cudaHostGetFlags(unsigned int* pFlags, void* pHost) nogil
+
+
+    cudaError_t cudaMalloc3D(cudaPitchedPtr* pitchedDevPtr, cudaExtent extent) nogil
+
+
+    cudaError_t cudaMalloc3DArray(cudaArray_t* array, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int flags) nogil
+
+
+    cudaError_t cudaMallocMipmappedArray(cudaMipmappedArray_t* mipmappedArray, const cudaChannelFormatDesc* desc, cudaExtent extent, unsigned int numLevels, unsigned int flags) nogil
+
+
+    cudaError_t cudaGetMipmappedArrayLevel(cudaArray_t* levelArray, cudaMipmappedArray_const_t mipmappedArray, unsigned int level) nogil
+
+
+    cudaError_t cudaMemcpy3D(const cudaMemcpy3DParms* p) nogil
+
+
+    cudaError_t cudaMemcpy3DPeer(const cudaMemcpy3DPeerParms* p) nogil
+
+
+    cudaError_t cudaMemcpy3DAsync(const cudaMemcpy3DParms* p, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpy3DPeerAsync(const cudaMemcpy3DPeerParms* p, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemGetInfo(size_t* free, size_t* total) nogil
+
+
+    cudaError_t cudaArrayGetInfo(cudaChannelFormatDesc* desc, cudaExtent* extent, unsigned int* flags, cudaArray_t array) nogil
+
+
+    cudaError_t cudaArrayGetPlane(cudaArray_t* pPlaneArray, cudaArray_t hArray, unsigned int planeIdx) nogil
+
+
+    cudaError_t cudaArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaArray_t array, int device) nogil
+
+
+    cudaError_t cudaMipmappedArrayGetMemoryRequirements(cudaArrayMemoryRequirements* memoryRequirements, cudaMipmappedArray_t mipmap, int device) nogil
+
+
+    cudaError_t cudaArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaArray_t array) nogil
+
+
+    cudaError_t cudaMipmappedArrayGetSparseProperties(cudaArraySparseProperties* sparseProperties, cudaMipmappedArray_t mipmap) nogil
+
+
+    cudaError_t cudaMemcpy(void* dst, const void* src, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpyPeer(void* dst, int dstDevice, const void* src, int srcDevice, size_t count) nogil
+
+
+    cudaError_t cudaMemcpy2D(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpy2DToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpy2DFromArray(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpy2DArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t width, size_t height, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpyAsync(void* dst, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpyPeerAsync(void* dst, int dstDevice, const void* src, int srcDevice, size_t count, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpyBatchAsync(const void** dsts, const void** srcs, const size_t* sizes, size_t count, cudaMemcpyAttributes* attrs, size_t* attrsIdxs, size_t numAttrs, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpy3DBatchAsync(size_t numOps, cudaMemcpy3DBatchOp* opList, unsigned long long flags, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpyWithAttributesAsync(void* dst, const void* src, size_t size, cudaMemcpyAttributes* attr, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpy3DWithAttributesAsync(cudaMemcpy3DBatchOp* op, unsigned long long flags, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpy2DAsync(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpy2DToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t spitch, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpy2DFromArrayAsync(void* dst, size_t dpitch, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t width, size_t height, cudaMemcpyKind kind, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemset(void* devPtr, int value, size_t count) nogil
+
+
+    cudaError_t cudaMemset2D(void* devPtr, size_t pitch, int value, size_t width, size_t height) nogil
+
+
+    cudaError_t cudaMemset3D(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent) nogil
+
+
+    cudaError_t cudaMemsetAsync(void* devPtr, int value, size_t count, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemset2DAsync(void* devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemset3DAsync(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemPrefetchAsync(const void* devPtr, size_t count, cudaMemLocation location, unsigned int flags, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemDiscardBatchAsync(void** dptrs, size_t* sizes, size_t count, unsigned long long flags, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemDiscardAndPrefetchBatchAsync(void** dptrs, size_t* sizes, size_t count, cudaMemLocation* prefetchLocs, size_t* prefetchLocIdxs, size_t numPrefetchLocs, unsigned long long flags, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemAdvise(const void* devPtr, size_t count, cudaMemoryAdvise advice, cudaMemLocation location) nogil
+
+
+    cudaError_t cudaMemRangeGetAttribute(void* data, size_t dataSize, cudaMemRangeAttribute attribute, const void* devPtr, size_t count) nogil
+
+
+    cudaError_t cudaMemRangeGetAttributes(void** data, size_t* dataSizes, cudaMemRangeAttribute* attributes, size_t numAttributes, const void* devPtr, size_t count) nogil
+
+
+    cudaError_t cudaMemcpyToArray(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpyFromArray(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpyArrayToArray(cudaArray_t dst, size_t wOffsetDst, size_t hOffsetDst, cudaArray_const_t src, size_t wOffsetSrc, size_t hOffsetSrc, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaMemcpyToArrayAsync(cudaArray_t dst, size_t wOffset, size_t hOffset, const void* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemcpyFromArrayAsync(void* dst, cudaArray_const_t src, size_t wOffset, size_t hOffset, size_t count, cudaMemcpyKind kind, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMallocAsync(void** devPtr, size_t size, cudaStream_t hStream) nogil
+
+
+    cudaError_t cudaFreeAsync(void* devPtr, cudaStream_t hStream) nogil
+
+
+    cudaError_t cudaMemPoolTrimTo(cudaMemPool_t memPool, size_t minBytesToKeep) nogil
+
+
+    cudaError_t cudaMemPoolSetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) nogil
+
+
+    cudaError_t cudaMemPoolGetAttribute(cudaMemPool_t memPool, cudaMemPoolAttr attr, void* value) nogil
+
+
+    cudaError_t cudaMemPoolSetAccess(cudaMemPool_t memPool, const cudaMemAccessDesc* descList, size_t count) nogil
+
+
+    cudaError_t cudaMemPoolGetAccess(cudaMemAccessFlags* flags, cudaMemPool_t memPool, cudaMemLocation* location) nogil
+
+
+    cudaError_t cudaMemPoolCreate(cudaMemPool_t* memPool, const cudaMemPoolProps* poolProps) nogil
+
+
+    cudaError_t cudaMemPoolDestroy(cudaMemPool_t memPool) nogil
+
+
+    cudaError_t cudaMemGetDefaultMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) nogil
+
+
+    cudaError_t cudaMemGetMemPool(cudaMemPool_t* memPool, cudaMemLocation* location, cudaMemAllocationType typename) nogil
+
+
+    cudaError_t cudaMemSetMemPool(cudaMemLocation* location, cudaMemAllocationType typename, cudaMemPool_t memPool) nogil
+
+
+    cudaError_t cudaMallocFromPoolAsync(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaMemPoolExportToShareableHandle(void* shareableHandle, cudaMemPool_t memPool, cudaMemAllocationHandleType handleType, unsigned int flags) nogil
+
+
+    cudaError_t cudaMemPoolImportFromShareableHandle(cudaMemPool_t* memPool, void* shareableHandle, cudaMemAllocationHandleType handleType, unsigned int flags) nogil
+
+
+    cudaError_t cudaMemPoolExportPointer(cudaMemPoolPtrExportData* exportData, void* ptr) nogil
+
+
+    cudaError_t cudaMemPoolImportPointer(void** ptr, cudaMemPool_t memPool, cudaMemPoolPtrExportData* exportData) nogil
+
+
+    cudaError_t cudaPointerGetAttributes(cudaPointerAttributes* attributes, const void* ptr) nogil
+
+
+    cudaError_t cudaDeviceCanAccessPeer(int* canAccessPeer, int device, int peerDevice) nogil
+
+
+    cudaError_t cudaDeviceEnablePeerAccess(int peerDevice, unsigned int flags) nogil
+
+
+    cudaError_t cudaDeviceDisablePeerAccess(int peerDevice) nogil
+
+
+    cudaError_t cudaGraphicsUnregisterResource(cudaGraphicsResource_t resource) nogil
+
+
+    cudaError_t cudaGraphicsResourceSetMapFlags(cudaGraphicsResource_t resource, unsigned int flags) nogil
+
+
+    cudaError_t cudaGraphicsMapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaGraphicsUnmapResources(int count, cudaGraphicsResource_t* resources, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaGraphicsResourceGetMappedPointer(void** devPtr, size_t* size, cudaGraphicsResource_t resource) nogil
+
+
+    cudaError_t cudaGraphicsSubResourceGetMappedArray(cudaArray_t* array, cudaGraphicsResource_t resource, unsigned int arrayIndex, unsigned int mipLevel) nogil
+
+
+    cudaError_t cudaGraphicsResourceGetMappedMipmappedArray(cudaMipmappedArray_t* mipmappedArray, cudaGraphicsResource_t resource) nogil
+
+
+    cudaError_t cudaGetChannelDesc(cudaChannelFormatDesc* desc, cudaArray_const_t array) nogil
+
+
+    cudaChannelFormatDesc cudaCreateChannelDesc(int x, int y, int z, int w, cudaChannelFormatKind f) nogil
+
+
+    cudaError_t cudaCreateTextureObject(cudaTextureObject_t* pTexObject, const cudaResourceDesc* pResDesc, const cudaTextureDesc* pTexDesc, const cudaResourceViewDesc* pResViewDesc) nogil
+
+
+    cudaError_t cudaDestroyTextureObject(cudaTextureObject_t texObject) nogil
+
+
+    cudaError_t cudaGetTextureObjectResourceDesc(cudaResourceDesc* pResDesc, cudaTextureObject_t texObject) nogil
+
+
+    cudaError_t cudaGetTextureObjectTextureDesc(cudaTextureDesc* pTexDesc, cudaTextureObject_t texObject) nogil
+
+
+    cudaError_t cudaGetTextureObjectResourceViewDesc(cudaResourceViewDesc* pResViewDesc, cudaTextureObject_t texObject) nogil
+
+
+    cudaError_t cudaCreateSurfaceObject(cudaSurfaceObject_t* pSurfObject, const cudaResourceDesc* pResDesc) nogil
+
+
+    cudaError_t cudaDestroySurfaceObject(cudaSurfaceObject_t surfObject) nogil
+
+
+    cudaError_t cudaGetSurfaceObjectResourceDesc(cudaResourceDesc* pResDesc, cudaSurfaceObject_t surfObject) nogil
+
+
+    cudaError_t cudaDriverGetVersion(int* driverVersion) nogil
+
+
+    cudaError_t cudaRuntimeGetVersion(int* runtimeVersion) nogil
+
+
+    cudaError_t cudaLogsRegisterCallback(cudaLogsCallback_t callbackFunc, void* userData, cudaLogsCallbackHandle* callback_out) nogil
+
+
+    cudaError_t cudaLogsUnregisterCallback(cudaLogsCallbackHandle callback) nogil
+
+
+    cudaError_t cudaLogsCurrent(cudaLogIterator* iterator_out, unsigned int flags) nogil
+
+
+    cudaError_t cudaLogsDumpToFile(cudaLogIterator* iterator, const char* pathToFile, unsigned int flags) nogil
+
+
+    cudaError_t cudaLogsDumpToMemory(cudaLogIterator* iterator, char* buffer, size_t* size, unsigned int flags) nogil
+
+
+    cudaError_t cudaGraphCreate(cudaGraph_t* pGraph, unsigned int flags) nogil
+
+
+    cudaError_t cudaGraphAddKernelNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaKernelNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphKernelNodeGetParams(cudaGraphNode_t node, cudaKernelNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphKernelNodeSetParams(cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphKernelNodeCopyAttributes(cudaGraphNode_t hDst, cudaGraphNode_t hSrc) nogil
+
+
+    cudaError_t cudaGraphKernelNodeGetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, cudaKernelNodeAttrValue* value_out) nogil
+
+
+    cudaError_t cudaGraphKernelNodeSetAttribute(cudaGraphNode_t hNode, cudaKernelNodeAttrID attr, const cudaKernelNodeAttrValue* value) nogil
+
+
+    cudaError_t cudaGraphAddMemcpyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemcpy3DParms* pCopyParams) nogil
+
+
+    cudaError_t cudaGraphAddMemcpyNode1D(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dst, const void* src, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaGraphMemcpyNodeGetParams(cudaGraphNode_t node, cudaMemcpy3DParms* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphMemcpyNodeSetParams(cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphMemcpyNodeSetParams1D(cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaGraphAddMemsetNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaMemsetParams* pMemsetParams) nogil
+
+
+    cudaError_t cudaGraphMemsetNodeGetParams(cudaGraphNode_t node, cudaMemsetParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphMemsetNodeSetParams(cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphAddHostNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaHostNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphHostNodeGetParams(cudaGraphNode_t node, cudaHostNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphHostNodeSetParams(cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphAddChildGraphNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaGraph_t childGraph) nogil
+
+
+    cudaError_t cudaGraphChildGraphNodeGetGraph(cudaGraphNode_t node, cudaGraph_t* pGraph) nogil
+
+
+    cudaError_t cudaGraphAddEmptyNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies) nogil
+
+
+    cudaError_t cudaGraphAddEventRecordNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaGraphEventRecordNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) nogil
+
+
+    cudaError_t cudaGraphEventRecordNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaGraphAddEventWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaGraphEventWaitNodeGetEvent(cudaGraphNode_t node, cudaEvent_t* event_out) nogil
+
+
+    cudaError_t cudaGraphEventWaitNodeSetEvent(cudaGraphNode_t node, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaGraphAddExternalSemaphoresSignalNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreSignalNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphExternalSemaphoresSignalNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreSignalNodeParams* params_out) nogil
+
+
+    cudaError_t cudaGraphExternalSemaphoresSignalNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphAddExternalSemaphoresWaitNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, const cudaExternalSemaphoreWaitNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphExternalSemaphoresWaitNodeGetParams(cudaGraphNode_t hNode, cudaExternalSemaphoreWaitNodeParams* params_out) nogil
+
+
+    cudaError_t cudaGraphExternalSemaphoresWaitNodeSetParams(cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphAddMemAllocNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, cudaMemAllocNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphMemAllocNodeGetParams(cudaGraphNode_t node, cudaMemAllocNodeParams* params_out) nogil
+
+
+    cudaError_t cudaGraphAddMemFreeNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, size_t numDependencies, void* dptr) nogil
+
+
+    cudaError_t cudaGraphMemFreeNodeGetParams(cudaGraphNode_t node, void* dptr_out) nogil
+
+
+    cudaError_t cudaDeviceGraphMemTrim(int device) nogil
+
+
+    cudaError_t cudaDeviceGetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) nogil
+
+
+    cudaError_t cudaDeviceSetGraphMemAttribute(int device, cudaGraphMemAttributeType attr, void* value) nogil
+
+
+    cudaError_t cudaGraphClone(cudaGraph_t* pGraphClone, cudaGraph_t originalGraph) nogil
+
+
+    cudaError_t cudaGraphNodeFindInClone(cudaGraphNode_t* pNode, cudaGraphNode_t originalNode, cudaGraph_t clonedGraph) nogil
+
+
+    cudaError_t cudaGraphNodeGetType(cudaGraphNode_t node, cudaGraphNodeType* pType) nogil
+
+
+    cudaError_t cudaGraphNodeGetContainingGraph(cudaGraphNode_t hNode, cudaGraph_t* phGraph) nogil
+
+
+    cudaError_t cudaGraphNodeGetLocalId(cudaGraphNode_t hNode, unsigned int* nodeId) nogil
+
+
+    cudaError_t cudaGraphNodeGetToolsId(cudaGraphNode_t hNode, unsigned long long* toolsNodeId) nogil
+
+
+    cudaError_t cudaGraphGetId(cudaGraph_t hGraph, unsigned int* graphID) nogil
+
+
+    cudaError_t cudaGraphExecGetId(cudaGraphExec_t hGraphExec, unsigned int* graphID) nogil
+
+
+    cudaError_t cudaGraphGetNodes(cudaGraph_t graph, cudaGraphNode_t* nodes, size_t* numNodes) nogil
+
+
+    cudaError_t cudaGraphGetRootNodes(cudaGraph_t graph, cudaGraphNode_t* pRootNodes, size_t* pNumRootNodes) nogil
+
+
+    cudaError_t cudaGraphGetEdges(cudaGraph_t graph, cudaGraphNode_t* from_, cudaGraphNode_t* to, cudaGraphEdgeData* edgeData, size_t* numEdges) nogil
+
+
+    cudaError_t cudaGraphNodeGetDependencies(cudaGraphNode_t node, cudaGraphNode_t* pDependencies, cudaGraphEdgeData* edgeData, size_t* pNumDependencies) nogil
+
+
+    cudaError_t cudaGraphNodeGetDependentNodes(cudaGraphNode_t node, cudaGraphNode_t* pDependentNodes, cudaGraphEdgeData* edgeData, size_t* pNumDependentNodes) nogil
+
+
+    cudaError_t cudaGraphAddDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) nogil
+
+
+    cudaError_t cudaGraphRemoveDependencies(cudaGraph_t graph, const cudaGraphNode_t* from_, const cudaGraphNode_t* to, const cudaGraphEdgeData* edgeData, size_t numDependencies) nogil
+
+
+    cudaError_t cudaGraphDestroyNode(cudaGraphNode_t node) nogil
+
+
+    cudaError_t cudaGraphInstantiate(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) nogil
+
+
+    cudaError_t cudaGraphInstantiateWithFlags(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, unsigned long long flags) nogil
+
+
+    cudaError_t cudaGraphInstantiateWithParams(cudaGraphExec_t* pGraphExec, cudaGraph_t graph, cudaGraphInstantiateParams* instantiateParams) nogil
+
+
+    cudaError_t cudaGraphExecGetFlags(cudaGraphExec_t graphExec, unsigned long long* flags) nogil
+
+
+    cudaError_t cudaGraphExecKernelNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaKernelNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphExecMemcpyNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemcpy3DParms* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphExecMemcpyNodeSetParams1D(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, void* dst, const void* src, size_t count, cudaMemcpyKind kind) nogil
+
+
+    cudaError_t cudaGraphExecMemsetNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaMemsetParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphExecHostNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, const cudaHostNodeParams* pNodeParams) nogil
+
+
+    cudaError_t cudaGraphExecChildGraphNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t node, cudaGraph_t childGraph) nogil
+
+
+    cudaError_t cudaGraphExecEventRecordNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaGraphExecEventWaitNodeSetEvent(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaGraphExecExternalSemaphoresSignalNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreSignalNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphExecExternalSemaphoresWaitNodeSetParams(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, const cudaExternalSemaphoreWaitNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphNodeSetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int isEnabled) nogil
+
+
+    cudaError_t cudaGraphNodeGetEnabled(cudaGraphExec_t hGraphExec, cudaGraphNode_t hNode, unsigned int* isEnabled) nogil
+
+
+    cudaError_t cudaGraphExecUpdate(cudaGraphExec_t hGraphExec, cudaGraph_t hGraph, cudaGraphExecUpdateResultInfo* resultInfo) nogil
+
+
+    cudaError_t cudaGraphUpload(cudaGraphExec_t graphExec, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaGraphLaunch(cudaGraphExec_t graphExec, cudaStream_t stream) nogil
+
+
+    cudaError_t cudaGraphExecDestroy(cudaGraphExec_t graphExec) nogil
+
+
+    cudaError_t cudaGraphDestroy(cudaGraph_t graph) nogil
+
+
+    cudaError_t cudaGraphDebugDotPrint(cudaGraph_t graph, const char* path, unsigned int flags) nogil
+
+
+    cudaError_t cudaUserObjectCreate(cudaUserObject_t* object_out, void* ptr, cudaHostFn_t destroy, unsigned int initialRefcount, unsigned int flags) nogil
+
+
+    cudaError_t cudaUserObjectRetain(cudaUserObject_t object, unsigned int count) nogil
+
+
+    cudaError_t cudaUserObjectRelease(cudaUserObject_t object, unsigned int count) nogil
+
+
+    cudaError_t cudaGraphRetainUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count, unsigned int flags) nogil
+
+
+    cudaError_t cudaGraphReleaseUserObject(cudaGraph_t graph, cudaUserObject_t object, unsigned int count) nogil
+
+
+    cudaError_t cudaGraphAddNode(cudaGraphNode_t* pGraphNode, cudaGraph_t graph, const cudaGraphNode_t* pDependencies, const cudaGraphEdgeData* dependencyData, size_t numDependencies, cudaGraphNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphNodeSetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphNodeGetParams(cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphExecNodeSetParams(cudaGraphExec_t graphExec, cudaGraphNode_t node, cudaGraphNodeParams* nodeParams) nogil
+
+
+    cudaError_t cudaGraphConditionalHandleCreate(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, unsigned int defaultLaunchValue, unsigned int flags) nogil
+
+
+    cudaError_t cudaGraphConditionalHandleCreate_v2(cudaGraphConditionalHandle* pHandle_out, cudaGraph_t graph, cudaExecutionContext_t ctx, unsigned int defaultLaunchValue, unsigned int flags) nogil
+
+
+    cudaError_t cudaGetDriverEntryPoint(const char* symbol, void** funcPtr, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) nogil
+
+
+    cudaError_t cudaGetDriverEntryPointByVersion(const char* symbol, void** funcPtr, unsigned int cudaVersion, unsigned long long flags, cudaDriverEntryPointQueryResult* driverStatus) nogil
+
+
+    cudaError_t cudaLibraryLoadData(cudaLibrary_t* library, const void* code, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) nogil
+
+
+    cudaError_t cudaLibraryLoadFromFile(cudaLibrary_t* library, const char* fileName, cudaJitOption* jitOptions, void** jitOptionsValues, unsigned int numJitOptions, cudaLibraryOption* libraryOptions, void** libraryOptionValues, unsigned int numLibraryOptions) nogil
+
+
+    cudaError_t cudaLibraryUnload(cudaLibrary_t library) nogil
+
+
+    cudaError_t cudaLibraryGetKernel(cudaKernel_t* pKernel, cudaLibrary_t library, const char* name) nogil
+
+
+    cudaError_t cudaLibraryGetGlobal(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) nogil
+
+
+    cudaError_t cudaLibraryGetManaged(void** dptr, size_t* numbytes, cudaLibrary_t library, const char* name) nogil
+
+
+    cudaError_t cudaLibraryGetUnifiedFunction(void** fptr, cudaLibrary_t library, const char* symbol) nogil
+
+
+    cudaError_t cudaLibraryGetKernelCount(unsigned int* count, cudaLibrary_t lib) nogil
+
+
+    cudaError_t cudaLibraryEnumerateKernels(cudaKernel_t* kernels, unsigned int numKernels, cudaLibrary_t lib) nogil
+
+
+    cudaError_t cudaKernelSetAttributeForDevice(cudaKernel_t kernel, cudaFuncAttribute attr, int value, int device) nogil
+
+
+    cudaError_t cudaDeviceGetDevResource(int device, cudaDevResource* resource, cudaDevResourceType typename) nogil
+
+
+    cudaError_t cudaDevSmResourceSplitByCount(cudaDevResource* result, unsigned int* nbGroups, const cudaDevResource* input, cudaDevResource* remaining, unsigned int flags, unsigned int minCount) nogil
+
+
+    cudaError_t cudaDevSmResourceSplit(cudaDevResource* result, unsigned int nbGroups, const cudaDevResource* input, cudaDevResource* remainder, unsigned int flags, cudaDevSmResourceGroupParams* groupParams) nogil
+
+
+    cudaError_t cudaDevResourceGenerateDesc(cudaDevResourceDesc_t* phDesc, cudaDevResource* resources, unsigned int nbResources) nogil
+
+
+    cudaError_t cudaGreenCtxCreate(cudaExecutionContext_t* phCtx, cudaDevResourceDesc_t desc, int device, unsigned int flags) nogil
+
+
+    cudaError_t cudaExecutionCtxDestroy(cudaExecutionContext_t ctx) nogil
+
+
+    cudaError_t cudaExecutionCtxGetDevResource(cudaExecutionContext_t ctx, cudaDevResource* resource, cudaDevResourceType typename) nogil
+
+
+    cudaError_t cudaExecutionCtxGetDevice(int* device, cudaExecutionContext_t ctx) nogil
+
+
+    cudaError_t cudaExecutionCtxGetId(cudaExecutionContext_t ctx, unsigned long long* ctxId) nogil
+
+
+    cudaError_t cudaExecutionCtxStreamCreate(cudaStream_t* phStream, cudaExecutionContext_t ctx, unsigned int flags, int priority) nogil
+
+
+    cudaError_t cudaExecutionCtxSynchronize(cudaExecutionContext_t ctx) nogil
+
+
+    cudaError_t cudaStreamGetDevResource(cudaStream_t hStream, cudaDevResource* resource, cudaDevResourceType typename) nogil
+
+
+    cudaError_t cudaExecutionCtxRecordEvent(cudaExecutionContext_t ctx, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaExecutionCtxWaitEvent(cudaExecutionContext_t ctx, cudaEvent_t event) nogil
+
+
+    cudaError_t cudaDeviceGetExecutionCtx(cudaExecutionContext_t* ctx, int device) nogil
+
+
+    cudaError_t cudaGetExportTable(const void** ppExportTable, const cudaUUID_t* pExportTableId) nogil
+
+
+    cudaError_t cudaGetKernel(cudaKernel_t* kernelPtr, const void* entryFuncAddr) nogil
+
+
+cdef extern from "cuda_runtime.h":
+
+
+    cudaPitchedPtr make_cudaPitchedPtr(void* d, size_t p, size_t xsz, size_t ysz) nogil
+
+
+    cudaPos make_cudaPos(size_t x, size_t y, size_t z) nogil
+
+
+    cudaExtent make_cudaExtent(size_t w, size_t h, size_t d) nogil
+
+
+cdef extern from "cuda_profiler_api.h":
+
+
+    cudaError_t cudaProfilerStart() nogil
+
+
+    cudaError_t cudaProfilerStop() nogil
+
diff --git a/lib/python3.11/site-packages/cuda/bindings/cyruntime_types.pxi b/lib/python3.11/site-packages/cuda/bindings/cyruntime_types.pxi
new file mode 100644
index 0000000000000000000000000000000000000000..e4afa93f55008a6910c476437c09245dd9a8edf7
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/cyruntime_types.pxi
@@ -0,0 +1,1738 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+
+cdef extern from "vector_types.h":
+
+    cdef struct dim3:
+        unsigned int x
+        unsigned int y
+        unsigned int z
+
+cdef extern from "driver_types.h":
+
+    cdef enum cudaError:
+        cudaSuccess = 0
+        cudaErrorInvalidValue = 1
+        cudaErrorMemoryAllocation = 2
+        cudaErrorInitializationError = 3
+        cudaErrorCudartUnloading = 4
+        cudaErrorProfilerDisabled = 5
+        cudaErrorProfilerNotInitialized = 6
+        cudaErrorProfilerAlreadyStarted = 7
+        cudaErrorProfilerAlreadyStopped = 8
+        cudaErrorInvalidConfiguration = 9
+        cudaErrorVersionTranslation = 10
+        cudaErrorInvalidPitchValue = 12
+        cudaErrorInvalidSymbol = 13
+        cudaErrorInvalidHostPointer = 16
+        cudaErrorInvalidDevicePointer = 17
+        cudaErrorInvalidTexture = 18
+        cudaErrorInvalidTextureBinding = 19
+        cudaErrorInvalidChannelDescriptor = 20
+        cudaErrorInvalidMemcpyDirection = 21
+        cudaErrorAddressOfConstant = 22
+        cudaErrorTextureFetchFailed = 23
+        cudaErrorTextureNotBound = 24
+        cudaErrorSynchronizationError = 25
+        cudaErrorInvalidFilterSetting = 26
+        cudaErrorInvalidNormSetting = 27
+        cudaErrorMixedDeviceExecution = 28
+        cudaErrorNotYetImplemented = 31
+        cudaErrorMemoryValueTooLarge = 32
+        cudaErrorStubLibrary = 34
+        cudaErrorInsufficientDriver = 35
+        cudaErrorCallRequiresNewerDriver = 36
+        cudaErrorInvalidSurface = 37
+        cudaErrorDuplicateVariableName = 43
+        cudaErrorDuplicateTextureName = 44
+        cudaErrorDuplicateSurfaceName = 45
+        cudaErrorDevicesUnavailable = 46
+        cudaErrorIncompatibleDriverContext = 49
+        cudaErrorMissingConfiguration = 52
+        cudaErrorPriorLaunchFailure = 53
+        cudaErrorLaunchMaxDepthExceeded = 65
+        cudaErrorLaunchFileScopedTex = 66
+        cudaErrorLaunchFileScopedSurf = 67
+        cudaErrorSyncDepthExceeded = 68
+        cudaErrorLaunchPendingCountExceeded = 69
+        cudaErrorInvalidDeviceFunction = 98
+        cudaErrorNoDevice = 100
+        cudaErrorInvalidDevice = 101
+        cudaErrorDeviceNotLicensed = 102
+        cudaErrorSoftwareValidityNotEstablished = 103
+        cudaErrorStartupFailure = 127
+        cudaErrorInvalidKernelImage = 200
+        cudaErrorDeviceUninitialized = 201
+        cudaErrorMapBufferObjectFailed = 205
+        cudaErrorUnmapBufferObjectFailed = 206
+        cudaErrorArrayIsMapped = 207
+        cudaErrorAlreadyMapped = 208
+        cudaErrorNoKernelImageForDevice = 209
+        cudaErrorAlreadyAcquired = 210
+        cudaErrorNotMapped = 211
+        cudaErrorNotMappedAsArray = 212
+        cudaErrorNotMappedAsPointer = 213
+        cudaErrorECCUncorrectable = 214
+        cudaErrorUnsupportedLimit = 215
+        cudaErrorDeviceAlreadyInUse = 216
+        cudaErrorPeerAccessUnsupported = 217
+        cudaErrorInvalidPtx = 218
+        cudaErrorInvalidGraphicsContext = 219
+        cudaErrorNvlinkUncorrectable = 220
+        cudaErrorJitCompilerNotFound = 221
+        cudaErrorUnsupportedPtxVersion = 222
+        cudaErrorJitCompilationDisabled = 223
+        cudaErrorUnsupportedExecAffinity = 224
+        cudaErrorUnsupportedDevSideSync = 225
+        cudaErrorContained = 226
+        cudaErrorInvalidSource = 300
+        cudaErrorFileNotFound = 301
+        cudaErrorSharedObjectSymbolNotFound = 302
+        cudaErrorSharedObjectInitFailed = 303
+        cudaErrorOperatingSystem = 304
+        cudaErrorInvalidResourceHandle = 400
+        cudaErrorIllegalState = 401
+        cudaErrorLossyQuery = 402
+        cudaErrorSymbolNotFound = 500
+        cudaErrorNotReady = 600
+        cudaErrorIllegalAddress = 700
+        cudaErrorLaunchOutOfResources = 701
+        cudaErrorLaunchTimeout = 702
+        cudaErrorLaunchIncompatibleTexturing = 703
+        cudaErrorPeerAccessAlreadyEnabled = 704
+        cudaErrorPeerAccessNotEnabled = 705
+        cudaErrorSetOnActiveProcess = 708
+        cudaErrorContextIsDestroyed = 709
+        cudaErrorAssert = 710
+        cudaErrorTooManyPeers = 711
+        cudaErrorHostMemoryAlreadyRegistered = 712
+        cudaErrorHostMemoryNotRegistered = 713
+        cudaErrorHardwareStackError = 714
+        cudaErrorIllegalInstruction = 715
+        cudaErrorMisalignedAddress = 716
+        cudaErrorInvalidAddressSpace = 717
+        cudaErrorInvalidPc = 718
+        cudaErrorLaunchFailure = 719
+        cudaErrorCooperativeLaunchTooLarge = 720
+        cudaErrorTensorMemoryLeak = 721
+        cudaErrorNotPermitted = 800
+        cudaErrorNotSupported = 801
+        cudaErrorSystemNotReady = 802
+        cudaErrorSystemDriverMismatch = 803
+        cudaErrorCompatNotSupportedOnDevice = 804
+        cudaErrorMpsConnectionFailed = 805
+        cudaErrorMpsRpcFailure = 806
+        cudaErrorMpsServerNotReady = 807
+        cudaErrorMpsMaxClientsReached = 808
+        cudaErrorMpsMaxConnectionsReached = 809
+        cudaErrorMpsClientTerminated = 810
+        cudaErrorCdpNotSupported = 811
+        cudaErrorCdpVersionMismatch = 812
+        cudaErrorStreamCaptureUnsupported = 900
+        cudaErrorStreamCaptureInvalidated = 901
+        cudaErrorStreamCaptureMerge = 902
+        cudaErrorStreamCaptureUnmatched = 903
+        cudaErrorStreamCaptureUnjoined = 904
+        cudaErrorStreamCaptureIsolation = 905
+        cudaErrorStreamCaptureImplicit = 906
+        cudaErrorCapturedEvent = 907
+        cudaErrorStreamCaptureWrongThread = 908
+        cudaErrorTimeout = 909
+        cudaErrorGraphExecUpdateFailure = 910
+        cudaErrorExternalDevice = 911
+        cudaErrorInvalidClusterSize = 912
+        cudaErrorFunctionNotLoaded = 913
+        cudaErrorInvalidResourceType = 914
+        cudaErrorInvalidResourceConfiguration = 915
+        cudaErrorStreamDetached = 917
+        cudaErrorUnknown = 999
+        cudaErrorApiFailureBase = 10000
+
+    ctypedef cudaError cudaError_t
+
+    cdef struct CUdevResourceDesc_st:
+        pass
+    ctypedef CUdevResourceDesc_st* cudaDevResourceDesc_t
+
+    cdef struct cudaExecutionContext_st:
+        pass
+    ctypedef cudaExecutionContext_st* cudaExecutionContext_t
+
+    cdef struct cudaChannelFormatDesc:
+        int x
+        int y
+        int z
+        int w
+        cudaChannelFormatKind f
+
+    cdef struct cudaArray:
+        pass
+    ctypedef cudaArray* cudaArray_t
+
+    cdef struct cudaArray:
+        pass
+    ctypedef cudaArray* cudaArray_const_t
+
+    cdef struct cudaMipmappedArray:
+        pass
+    ctypedef cudaMipmappedArray* cudaMipmappedArray_t
+
+    cdef struct cudaMipmappedArray:
+        pass
+    ctypedef cudaMipmappedArray* cudaMipmappedArray_const_t
+
+    cdef struct anon_struct0:
+        unsigned int width
+        unsigned int height
+        unsigned int depth
+
+    cdef struct cudaArraySparseProperties:
+        anon_struct0 tileExtent
+        unsigned int miptailFirstLevel
+        unsigned long long miptailSize
+        unsigned int flags
+        unsigned int reserved[4]
+
+    cdef struct cudaArrayMemoryRequirements:
+        size_t size
+        size_t alignment
+        unsigned int reserved[4]
+
+    cdef struct cudaPitchedPtr:
+        void* ptr
+        size_t pitch
+        size_t xsize
+        size_t ysize
+
+    cdef struct cudaExtent:
+        size_t width
+        size_t height
+        size_t depth
+
+    cdef struct cudaPos:
+        size_t x
+        size_t y
+        size_t z
+
+    cdef struct cudaMemcpy3DParms:
+        cudaArray_t srcArray
+        cudaPos srcPos
+        cudaPitchedPtr srcPtr
+        cudaArray_t dstArray
+        cudaPos dstPos
+        cudaPitchedPtr dstPtr
+        cudaExtent extent
+        cudaMemcpyKind kind
+
+    cdef struct cudaMemcpyNodeParams:
+        int flags
+        int reserved
+        cudaExecutionContext_t ctx
+        cudaMemcpy3DParms copyParams
+
+    cdef struct cudaMemcpy3DPeerParms:
+        cudaArray_t srcArray
+        cudaPos srcPos
+        cudaPitchedPtr srcPtr
+        int srcDevice
+        cudaArray_t dstArray
+        cudaPos dstPos
+        cudaPitchedPtr dstPtr
+        int dstDevice
+        cudaExtent extent
+
+    cdef struct cudaMemsetParams:
+        void* dst
+        size_t pitch
+        unsigned int value
+        unsigned int elementSize
+        size_t width
+        size_t height
+
+    cdef struct cudaMemsetParamsV2:
+        void* dst
+        size_t pitch
+        unsigned int value
+        unsigned int elementSize
+        size_t width
+        size_t height
+        cudaExecutionContext_t ctx
+
+    cdef struct cudaAccessPolicyWindow:
+        void* base_ptr
+        size_t num_bytes
+        float hitRatio
+        cudaAccessProperty hitProp
+        cudaAccessProperty missProp
+
+    ctypedef void (*cudaHostFn_t)(void* userData)
+
+    cdef struct cudaHostNodeParams:
+        cudaHostFn_t fn
+        void* userData
+
+    cdef struct cudaHostNodeParamsV2:
+        cudaHostFn_t fn
+        void* userData
+        unsigned int syncMode
+
+    cdef struct anon_struct1:
+        cudaArray_t array
+
+    cdef struct anon_struct2:
+        cudaMipmappedArray_t mipmap
+
+    cdef struct anon_struct3:
+        void* devPtr
+        cudaChannelFormatDesc desc
+        size_t sizeInBytes
+
+    cdef struct anon_struct4:
+        void* devPtr
+        cudaChannelFormatDesc desc
+        size_t width
+        size_t height
+        size_t pitchInBytes
+
+    cdef struct anon_struct5:
+        int reserved[32]
+
+    cdef union anon_union0:
+        anon_struct1 array
+        anon_struct2 mipmap
+        anon_struct3 linear
+        anon_struct4 pitch2D
+        anon_struct5 reserved
+
+    cdef struct cudaResourceDesc:
+        cudaResourceType resType
+        anon_union0 res
+        unsigned int flags
+
+    cdef struct cudaResourceViewDesc:
+        cudaResourceViewFormat format
+        size_t width
+        size_t height
+        size_t depth
+        unsigned int firstMipmapLevel
+        unsigned int lastMipmapLevel
+        unsigned int firstLayer
+        unsigned int lastLayer
+        unsigned int reserved[16]
+
+    cdef struct cudaPointerAttributes:
+        cudaMemoryType type
+        int device
+        void* devicePointer
+        void* hostPointer
+        long reserved[8]
+
+    cdef struct cudaFuncAttributes:
+        size_t sharedSizeBytes
+        size_t constSizeBytes
+        size_t localSizeBytes
+        int maxThreadsPerBlock
+        int numRegs
+        int ptxVersion
+        int binaryVersion
+        int cacheModeCA
+        int maxDynamicSharedSizeBytes
+        int preferredShmemCarveout
+        int clusterDimMustBeSet
+        int requiredClusterWidth
+        int requiredClusterHeight
+        int requiredClusterDepth
+        int clusterSchedulingPolicyPreference
+        int nonPortableClusterSizeAllowed
+        int reserved0
+        int reserved[15]
+
+    cdef struct cudaMemLocation:
+        cudaMemLocationType type
+        int id
+
+    cdef struct cudaMemAccessDesc:
+        cudaMemLocation location
+        cudaMemAccessFlags flags
+
+    cdef struct cudaMemPoolProps:
+        cudaMemAllocationType allocType
+        cudaMemAllocationHandleType handleTypes
+        cudaMemLocation location
+        void* win32SecurityAttributes
+        size_t maxSize
+        unsigned short usage
+        unsigned char reserved[54]
+
+    cdef struct cudaMemPoolPtrExportData:
+        unsigned char reserved[64]
+
+    cdef struct cudaMemAllocNodeParams:
+        cudaMemPoolProps poolProps
+        const cudaMemAccessDesc* accessDescs
+        size_t accessDescCount
+        size_t bytesize
+        void* dptr
+
+    cdef struct cudaMemAllocNodeParamsV2:
+        cudaMemPoolProps poolProps
+        const cudaMemAccessDesc* accessDescs
+        size_t accessDescCount
+        size_t bytesize
+        void* dptr
+
+    cdef struct cudaMemFreeNodeParams:
+        void* dptr
+
+    cdef struct cudaMemcpyAttributes:
+        cudaMemcpySrcAccessOrder srcAccessOrder
+        cudaMemLocation srcLocHint
+        cudaMemLocation dstLocHint
+        unsigned int flags
+
+    cdef struct cudaOffset3D:
+        size_t x
+        size_t y
+        size_t z
+
+    cdef struct anon_struct6:
+        void* ptr
+        size_t rowLength
+        size_t layerHeight
+        cudaMemLocation locHint
+
+    cdef struct anon_struct7:
+        cudaArray_t array
+        cudaOffset3D offset
+
+    cdef union anon_union1:
+        anon_struct6 ptr
+        anon_struct7 array
+
+    cdef struct cudaMemcpy3DOperand:
+        cudaMemcpy3DOperandType type
+        anon_union1 op
+
+    cdef struct cudaMemcpy3DBatchOp:
+        cudaMemcpy3DOperand src
+        cudaMemcpy3DOperand dst
+        cudaExtent extent
+        cudaMemcpySrcAccessOrder srcAccessOrder
+        unsigned int flags
+
+    cdef struct CUuuid_st:
+        char bytes[16]
+
+    ctypedef CUuuid_st CUuuid
+
+    ctypedef CUuuid_st cudaUUID_t
+
+    cdef struct cudaDeviceProp:
+        char name[256]
+        cudaUUID_t uuid
+        char luid[8]
+        unsigned int luidDeviceNodeMask
+        size_t totalGlobalMem
+        size_t sharedMemPerBlock
+        int regsPerBlock
+        int warpSize
+        size_t memPitch
+        int maxThreadsPerBlock
+        int maxThreadsDim[3]
+        int maxGridSize[3]
+        size_t totalConstMem
+        int major
+        int minor
+        size_t textureAlignment
+        size_t texturePitchAlignment
+        int multiProcessorCount
+        int integrated
+        int canMapHostMemory
+        int maxTexture1D
+        int maxTexture1DMipmap
+        int maxTexture2D[2]
+        int maxTexture2DMipmap[2]
+        int maxTexture2DLinear[3]
+        int maxTexture2DGather[2]
+        int maxTexture3D[3]
+        int maxTexture3DAlt[3]
+        int maxTextureCubemap
+        int maxTexture1DLayered[2]
+        int maxTexture2DLayered[3]
+        int maxTextureCubemapLayered[2]
+        int maxSurface1D
+        int maxSurface2D[2]
+        int maxSurface3D[3]
+        int maxSurface1DLayered[2]
+        int maxSurface2DLayered[3]
+        int maxSurfaceCubemap
+        int maxSurfaceCubemapLayered[2]
+        size_t surfaceAlignment
+        int concurrentKernels
+        int ECCEnabled
+        int pciBusID
+        int pciDeviceID
+        int pciDomainID
+        int tccDriver
+        int asyncEngineCount
+        int unifiedAddressing
+        int memoryBusWidth
+        int l2CacheSize
+        int persistingL2CacheMaxSize
+        int maxThreadsPerMultiProcessor
+        int streamPrioritiesSupported
+        int globalL1CacheSupported
+        int localL1CacheSupported
+        size_t sharedMemPerMultiprocessor
+        int regsPerMultiprocessor
+        int managedMemory
+        int isMultiGpuBoard
+        int multiGpuBoardGroupID
+        int hostNativeAtomicSupported
+        int pageableMemoryAccess
+        int concurrentManagedAccess
+        int computePreemptionSupported
+        int canUseHostPointerForRegisteredMem
+        int cooperativeLaunch
+        size_t sharedMemPerBlockOptin
+        int pageableMemoryAccessUsesHostPageTables
+        int directManagedMemAccessFromHost
+        int maxBlocksPerMultiProcessor
+        int accessPolicyMaxWindowSize
+        size_t reservedSharedMemPerBlock
+        int hostRegisterSupported
+        int sparseCudaArraySupported
+        int hostRegisterReadOnlySupported
+        int timelineSemaphoreInteropSupported
+        int memoryPoolsSupported
+        int gpuDirectRDMASupported
+        unsigned int gpuDirectRDMAFlushWritesOptions
+        int gpuDirectRDMAWritesOrdering
+        unsigned int memoryPoolSupportedHandleTypes
+        int deferredMappingCudaArraySupported
+        int ipcEventSupported
+        int clusterLaunch
+        int unifiedFunctionPointers
+        int deviceNumaConfig
+        int deviceNumaId
+        int mpsEnabled
+        int hostNumaId
+        unsigned int gpuPciDeviceID
+        unsigned int gpuPciSubsystemID
+        int hostNumaMultinodeIpcSupported
+        int reserved[56]
+
+    cdef struct cudaIpcEventHandle_st:
+        char reserved[64]
+
+    ctypedef cudaIpcEventHandle_st cudaIpcEventHandle_t
+
+    cdef struct cudaIpcMemHandle_st:
+        char reserved[64]
+
+    ctypedef cudaIpcMemHandle_st cudaIpcMemHandle_t
+
+    cdef struct cudaMemFabricHandle_st:
+        char reserved[64]
+
+    ctypedef cudaMemFabricHandle_st cudaMemFabricHandle_t
+
+    cdef struct anon_struct8:
+        void* handle
+        const void* name
+
+    cdef union anon_union2:
+        int fd
+        anon_struct8 win32
+        const void* nvSciBufObject
+
+    cdef struct cudaExternalMemoryHandleDesc:
+        cudaExternalMemoryHandleType type
+        anon_union2 handle
+        unsigned long long size
+        unsigned int flags
+        unsigned int reserved[16]
+
+    cdef struct cudaExternalMemoryBufferDesc:
+        unsigned long long offset
+        unsigned long long size
+        unsigned int flags
+        unsigned int reserved[16]
+
+    cdef struct cudaExternalMemoryMipmappedArrayDesc:
+        unsigned long long offset
+        cudaChannelFormatDesc formatDesc
+        cudaExtent extent
+        unsigned int flags
+        unsigned int numLevels
+        unsigned int reserved[16]
+
+    cdef struct anon_struct9:
+        void* handle
+        const void* name
+
+    cdef union anon_union3:
+        int fd
+        anon_struct9 win32
+        const void* nvSciSyncObj
+
+    cdef struct cudaExternalSemaphoreHandleDesc:
+        cudaExternalSemaphoreHandleType type
+        anon_union3 handle
+        unsigned int flags
+        unsigned int reserved[16]
+
+    cdef struct anon_struct10:
+        unsigned long long value
+
+    cdef union anon_union4:
+        void* fence
+        unsigned long long reserved
+
+    cdef struct anon_struct11:
+        unsigned long long key
+
+    cdef struct anon_struct12:
+        anon_struct10 fence
+        anon_union4 nvSciSync
+        anon_struct11 keyedMutex
+        unsigned int reserved[12]
+
+    cdef struct cudaExternalSemaphoreSignalParams:
+        anon_struct12 params
+        unsigned int flags
+        unsigned int reserved[16]
+
+    cdef struct anon_struct13:
+        unsigned long long value
+
+    cdef union anon_union5:
+        void* fence
+        unsigned long long reserved
+
+    cdef struct anon_struct14:
+        unsigned long long key
+        unsigned int timeoutMs
+
+    cdef struct anon_struct15:
+        anon_struct13 fence
+        anon_union5 nvSciSync
+        anon_struct14 keyedMutex
+        unsigned int reserved[10]
+
+    cdef struct cudaExternalSemaphoreWaitParams:
+        anon_struct15 params
+        unsigned int flags
+        unsigned int reserved[16]
+
+    cdef struct cudaDevSmResource:
+        unsigned int smCount
+        unsigned int minSmPartitionSize
+        unsigned int smCoscheduledAlignment
+        unsigned int flags
+
+    cdef struct cudaDevWorkqueueConfigResource:
+        int device
+        unsigned int wqConcurrencyLimit
+        cudaDevWorkqueueConfigScope sharingScope
+
+    cdef struct cudaDevWorkqueueResource:
+        unsigned char reserved[40]
+
+    cdef struct cudaDevSmResourceGroupParams_st:
+        unsigned int smCount
+        unsigned int coscheduledSmCount
+        unsigned int preferredCoscheduledSmCount
+        unsigned int flags
+        unsigned int reserved[12]
+
+    ctypedef cudaDevSmResourceGroupParams_st cudaDevSmResourceGroupParams
+
+    cdef struct cudaDevResource_st:
+        cudaDevResourceType type
+        unsigned char _internal_padding[92]
+        cudaDevSmResource sm
+        cudaDevWorkqueueConfigResource wqConfig
+        cudaDevWorkqueueResource wq
+        unsigned char _oversize[40]
+        cudaDevResource_st* nextResource
+
+    ctypedef cudaDevResource_st cudaDevResource
+
+    cdef struct CUstream_st:
+        pass
+    ctypedef CUstream_st* cudaStream_t
+
+    cdef struct CUevent_st:
+        pass
+    ctypedef CUevent_st* cudaEvent_t
+
+    cdef struct cudaGraphicsResource:
+        pass
+    ctypedef cudaGraphicsResource* cudaGraphicsResource_t
+
+    cdef struct CUexternalMemory_st:
+        pass
+    ctypedef CUexternalMemory_st* cudaExternalMemory_t
+
+    cdef struct CUexternalSemaphore_st:
+        pass
+    ctypedef CUexternalSemaphore_st* cudaExternalSemaphore_t
+
+    cdef struct CUgraph_st:
+        pass
+    ctypedef CUgraph_st* cudaGraph_t
+
+    cdef struct CUgraphNode_st:
+        pass
+    ctypedef CUgraphNode_st* cudaGraphNode_t
+
+    cdef struct CUuserObject_st:
+        pass
+    ctypedef CUuserObject_st* cudaUserObject_t
+
+    ctypedef unsigned long long cudaGraphConditionalHandle
+
+    cdef struct CUfunc_st:
+        pass
+    ctypedef CUfunc_st* cudaFunction_t
+
+    cdef struct CUkern_st:
+        pass
+    ctypedef CUkern_st* cudaKernel_t
+
+    cdef struct cudalibraryHostUniversalFunctionAndDataTable:
+        void* functionTable
+        size_t functionWindowSize
+        void* dataTable
+        size_t dataWindowSize
+
+    cdef struct CUlib_st:
+        pass
+    ctypedef CUlib_st* cudaLibrary_t
+
+    cdef struct CUmemPoolHandle_st:
+        pass
+    ctypedef CUmemPoolHandle_st* cudaMemPool_t
+
+    cdef struct cudaKernelNodeParams:
+        void* func
+        dim3 gridDim
+        dim3 blockDim
+        unsigned int sharedMemBytes
+        void** kernelParams
+        void** extra
+
+    cdef struct cudaKernelNodeParamsV2:
+        void* func
+        cudaKernel_t kern
+        cudaFunction_t cuFunc
+        dim3 gridDim
+        dim3 blockDim
+        unsigned int sharedMemBytes
+        void** kernelParams
+        void** extra
+        cudaExecutionContext_t ctx
+        cudaKernelFunctionType functionType
+
+    cdef struct cudaExternalSemaphoreSignalNodeParams:
+        cudaExternalSemaphore_t* extSemArray
+        const cudaExternalSemaphoreSignalParams* paramsArray
+        unsigned int numExtSems
+
+    cdef struct cudaExternalSemaphoreSignalNodeParamsV2:
+        cudaExternalSemaphore_t* extSemArray
+        const cudaExternalSemaphoreSignalParams* paramsArray
+        unsigned int numExtSems
+
+    cdef struct cudaExternalSemaphoreWaitNodeParams:
+        cudaExternalSemaphore_t* extSemArray
+        const cudaExternalSemaphoreWaitParams* paramsArray
+        unsigned int numExtSems
+
+    cdef struct cudaExternalSemaphoreWaitNodeParamsV2:
+        cudaExternalSemaphore_t* extSemArray
+        const cudaExternalSemaphoreWaitParams* paramsArray
+        unsigned int numExtSems
+
+    cdef struct cudaConditionalNodeParams:
+        cudaGraphConditionalHandle handle
+        cudaGraphConditionalNodeType type
+        unsigned int size
+        cudaGraph_t* phGraph_out
+        cudaExecutionContext_t ctx
+
+    cdef struct cudaChildGraphNodeParams:
+        cudaGraph_t graph
+        cudaGraphChildGraphNodeOwnership ownership
+
+    cdef struct cudaEventRecordNodeParams:
+        cudaEvent_t event
+
+    cdef struct cudaEventWaitNodeParams:
+        cudaEvent_t event
+
+    cdef struct cudaGraphNodeParams:
+        cudaGraphNodeType type
+        int reserved0[3]
+        long long reserved1[29]
+        cudaKernelNodeParamsV2 kernel
+        cudaMemcpyNodeParams memcpy
+        cudaMemsetParamsV2 memset
+        cudaHostNodeParamsV2 host
+        cudaChildGraphNodeParams graph
+        cudaEventWaitNodeParams eventWait
+        cudaEventRecordNodeParams eventRecord
+        cudaExternalSemaphoreSignalNodeParamsV2 extSemSignal
+        cudaExternalSemaphoreWaitNodeParamsV2 extSemWait
+        cudaMemAllocNodeParamsV2 alloc
+        cudaMemFreeNodeParams free
+        cudaConditionalNodeParams conditional
+        long long reserved2
+
+    cdef enum cudaGraphDependencyType_enum:
+        cudaGraphDependencyTypeDefault = 0
+        cudaGraphDependencyTypeProgrammatic = 1
+
+    ctypedef cudaGraphDependencyType_enum cudaGraphDependencyType
+
+    cdef struct cudaGraphEdgeData_st:
+        unsigned char from_port
+        unsigned char to_port
+        unsigned char type
+        unsigned char reserved[5]
+
+    ctypedef cudaGraphEdgeData_st cudaGraphEdgeData
+
+    cdef struct CUgraphExec_st:
+        pass
+    ctypedef CUgraphExec_st* cudaGraphExec_t
+
+    cdef enum cudaGraphInstantiateResult:
+        cudaGraphInstantiateSuccess = 0
+        cudaGraphInstantiateError = 1
+        cudaGraphInstantiateInvalidStructure = 2
+        cudaGraphInstantiateNodeOperationNotSupported = 3
+        cudaGraphInstantiateMultipleDevicesNotSupported = 4
+        cudaGraphInstantiateConditionalHandleUnused = 5
+
+    cdef struct cudaGraphInstantiateParams_st:
+        unsigned long long flags
+        cudaStream_t uploadStream
+        cudaGraphNode_t errNode_out
+        cudaGraphInstantiateResult result_out
+
+    ctypedef cudaGraphInstantiateParams_st cudaGraphInstantiateParams
+
+    cdef struct cudaGraphExecUpdateResultInfo_st:
+        cudaGraphExecUpdateResult result
+        cudaGraphNode_t errorNode
+        cudaGraphNode_t errorFromNode
+
+    ctypedef cudaGraphExecUpdateResultInfo_st cudaGraphExecUpdateResultInfo
+
+    cdef struct CUgraphDeviceUpdatableNode_st:
+        pass
+    ctypedef CUgraphDeviceUpdatableNode_st* cudaGraphDeviceNode_t
+
+    cdef struct anon_struct16:
+        const void* pValue
+        size_t offset
+        size_t size
+
+    cdef union anon_union9:
+        dim3 gridDim
+        anon_struct16 param
+        unsigned int isEnabled
+
+    cdef struct cudaGraphKernelNodeUpdate:
+        cudaGraphDeviceNode_t node
+        cudaGraphKernelNodeField field
+        anon_union9 updateData
+
+    cdef enum cudaLaunchMemSyncDomain:
+        cudaLaunchMemSyncDomainDefault = 0
+        cudaLaunchMemSyncDomainRemote = 1
+
+    cdef struct cudaLaunchMemSyncDomainMap_st:
+        unsigned char default_
+        unsigned char remote
+
+    ctypedef cudaLaunchMemSyncDomainMap_st cudaLaunchMemSyncDomainMap
+
+    cdef enum cudaLaunchAttributePortableClusterMode:
+        cudaLaunchPortableClusterModeDefault = 0
+        cudaLaunchPortableClusterModeRequirePortable = 1
+        cudaLaunchPortableClusterModeAllowNonPortable = 2
+
+    cdef enum cudaSharedMemoryMode:
+        cudaSharedMemoryModeDefault = 0
+        cudaSharedMemoryModeRequirePortable = 1
+        cudaSharedMemoryModeAllowNonPortable = 2
+
+    cdef enum cudaLaunchAttributeID:
+        cudaLaunchAttributeIgnore = 0
+        cudaLaunchAttributeAccessPolicyWindow = 1
+        cudaLaunchAttributeCooperative = 2
+        cudaLaunchAttributeSynchronizationPolicy = 3
+        cudaLaunchAttributeClusterDimension = 4
+        cudaLaunchAttributeClusterSchedulingPolicyPreference = 5
+        cudaLaunchAttributeProgrammaticStreamSerialization = 6
+        cudaLaunchAttributeProgrammaticEvent = 7
+        cudaLaunchAttributePriority = 8
+        cudaLaunchAttributeMemSyncDomainMap = 9
+        cudaLaunchAttributeMemSyncDomain = 10
+        cudaLaunchAttributePreferredClusterDimension = 11
+        cudaLaunchAttributeLaunchCompletionEvent = 12
+        cudaLaunchAttributeDeviceUpdatableKernelNode = 13
+        cudaLaunchAttributePreferredSharedMemoryCarveout = 14
+        cudaLaunchAttributeNvlinkUtilCentricScheduling = 16
+        cudaLaunchAttributePortableClusterSizeMode = 17
+        cudaLaunchAttributeSharedMemoryMode = 18
+
+    cdef struct anon_struct17:
+        unsigned int x
+        unsigned int y
+        unsigned int z
+
+    cdef struct anon_struct18:
+        cudaEvent_t event
+        int flags
+        int triggerAtBlockStart
+
+    cdef struct anon_struct19:
+        unsigned int x
+        unsigned int y
+        unsigned int z
+
+    cdef struct anon_struct20:
+        cudaEvent_t event
+        int flags
+
+    cdef struct anon_struct21:
+        int deviceUpdatable
+        cudaGraphDeviceNode_t devNode
+
+    cdef union cudaLaunchAttributeValue:
+        char pad[64]
+        cudaAccessPolicyWindow accessPolicyWindow
+        int cooperative
+        cudaSynchronizationPolicy syncPolicy
+        anon_struct17 clusterDim
+        cudaClusterSchedulingPolicy clusterSchedulingPolicyPreference
+        int programmaticStreamSerializationAllowed
+        anon_struct18 programmaticEvent
+        int priority
+        cudaLaunchMemSyncDomainMap memSyncDomainMap
+        cudaLaunchMemSyncDomain memSyncDomain
+        anon_struct19 preferredClusterDim
+        anon_struct20 launchCompletionEvent
+        anon_struct21 deviceUpdatableKernelNode
+        unsigned int sharedMemCarveout
+        unsigned int nvlinkUtilCentricScheduling
+        cudaLaunchAttributePortableClusterMode portableClusterSizeMode
+        cudaSharedMemoryMode sharedMemoryMode
+
+    cdef struct cudaLaunchAttribute_st:
+        cudaLaunchAttributeID id
+        cudaLaunchAttributeValue val
+
+    ctypedef cudaLaunchAttribute_st cudaLaunchAttribute
+
+    cdef struct cudaAsyncCallbackEntry:
+        pass
+    ctypedef cudaAsyncCallbackEntry* cudaAsyncCallbackHandle_t
+
+    cdef enum cudaAsyncNotificationType_enum:
+        cudaAsyncNotificationTypeOverBudget = 1
+
+    ctypedef cudaAsyncNotificationType_enum cudaAsyncNotificationType
+
+    cdef struct anon_struct22:
+        unsigned long long bytesOverBudget
+
+    cdef union anon_union10:
+        anon_struct22 overBudget
+
+    cdef struct cudaAsyncNotificationInfo:
+        cudaAsyncNotificationType type
+        anon_union10 info
+
+    ctypedef cudaAsyncNotificationInfo cudaAsyncNotificationInfo_t
+
+    ctypedef void (*cudaAsyncCallback)(cudaAsyncNotificationInfo_t* , void* , cudaAsyncCallbackHandle_t )
+
+    cdef enum CUDAlogLevel_enum:
+        cudaLogLevelError = 0
+        cudaLogLevelWarning = 1
+
+    ctypedef CUDAlogLevel_enum cudaLogLevel
+
+    cdef struct CUlogsCallbackEntry_st:
+        pass
+    ctypedef CUlogsCallbackEntry_st* cudaLogsCallbackHandle
+
+    ctypedef unsigned int cudaLogIterator
+
+    cdef enum cudaChannelFormatKind:
+        cudaChannelFormatKindSigned = 0
+        cudaChannelFormatKindUnsigned = 1
+        cudaChannelFormatKindFloat = 2
+        cudaChannelFormatKindNone = 3
+        cudaChannelFormatKindNV12 = 4
+        cudaChannelFormatKindUnsignedNormalized8X1 = 5
+        cudaChannelFormatKindUnsignedNormalized8X2 = 6
+        cudaChannelFormatKindUnsignedNormalized8X4 = 7
+        cudaChannelFormatKindUnsignedNormalized16X1 = 8
+        cudaChannelFormatKindUnsignedNormalized16X2 = 9
+        cudaChannelFormatKindUnsignedNormalized16X4 = 10
+        cudaChannelFormatKindSignedNormalized8X1 = 11
+        cudaChannelFormatKindSignedNormalized8X2 = 12
+        cudaChannelFormatKindSignedNormalized8X4 = 13
+        cudaChannelFormatKindSignedNormalized16X1 = 14
+        cudaChannelFormatKindSignedNormalized16X2 = 15
+        cudaChannelFormatKindSignedNormalized16X4 = 16
+        cudaChannelFormatKindUnsignedBlockCompressed1 = 17
+        cudaChannelFormatKindUnsignedBlockCompressed1SRGB = 18
+        cudaChannelFormatKindUnsignedBlockCompressed2 = 19
+        cudaChannelFormatKindUnsignedBlockCompressed2SRGB = 20
+        cudaChannelFormatKindUnsignedBlockCompressed3 = 21
+        cudaChannelFormatKindUnsignedBlockCompressed3SRGB = 22
+        cudaChannelFormatKindUnsignedBlockCompressed4 = 23
+        cudaChannelFormatKindSignedBlockCompressed4 = 24
+        cudaChannelFormatKindUnsignedBlockCompressed5 = 25
+        cudaChannelFormatKindSignedBlockCompressed5 = 26
+        cudaChannelFormatKindUnsignedBlockCompressed6H = 27
+        cudaChannelFormatKindSignedBlockCompressed6H = 28
+        cudaChannelFormatKindUnsignedBlockCompressed7 = 29
+        cudaChannelFormatKindUnsignedBlockCompressed7SRGB = 30
+        cudaChannelFormatKindUnsignedNormalized1010102 = 31
+
+    cdef enum cudaMemoryType:
+        cudaMemoryTypeUnregistered = 0
+        cudaMemoryTypeHost = 1
+        cudaMemoryTypeDevice = 2
+        cudaMemoryTypeManaged = 3
+
+    cdef enum cudaMemcpyKind:
+        cudaMemcpyHostToHost = 0
+        cudaMemcpyHostToDevice = 1
+        cudaMemcpyDeviceToHost = 2
+        cudaMemcpyDeviceToDevice = 3
+        cudaMemcpyDefault = 4
+
+    cdef enum cudaAccessProperty:
+        cudaAccessPropertyNormal = 0
+        cudaAccessPropertyStreaming = 1
+        cudaAccessPropertyPersisting = 2
+
+    cdef enum cudaStreamCaptureStatus:
+        cudaStreamCaptureStatusNone = 0
+        cudaStreamCaptureStatusActive = 1
+        cudaStreamCaptureStatusInvalidated = 2
+
+    cdef enum cudaStreamCaptureMode:
+        cudaStreamCaptureModeGlobal = 0
+        cudaStreamCaptureModeThreadLocal = 1
+        cudaStreamCaptureModeRelaxed = 2
+
+    cdef enum cudaSynchronizationPolicy:
+        cudaSyncPolicyAuto = 1
+        cudaSyncPolicySpin = 2
+        cudaSyncPolicyYield = 3
+        cudaSyncPolicyBlockingSync = 4
+
+    cdef enum cudaClusterSchedulingPolicy:
+        cudaClusterSchedulingPolicyDefault = 0
+        cudaClusterSchedulingPolicySpread = 1
+        cudaClusterSchedulingPolicyLoadBalancing = 2
+
+    cdef enum cudaStreamUpdateCaptureDependenciesFlags:
+        cudaStreamAddCaptureDependencies = 0
+        cudaStreamSetCaptureDependencies = 1
+
+    cdef enum cudaUserObjectFlags:
+        cudaUserObjectNoDestructorSync = 1
+
+    cdef enum cudaUserObjectRetainFlags:
+        cudaGraphUserObjectMove = 1
+
+    cdef enum cudaHostTaskSyncMode:
+        cudaHostTaskBlocking = 0
+        cudaHostTaskSpinWait = 1
+
+    cdef enum cudaGraphicsRegisterFlags:
+        cudaGraphicsRegisterFlagsNone = 0
+        cudaGraphicsRegisterFlagsReadOnly = 1
+        cudaGraphicsRegisterFlagsWriteDiscard = 2
+        cudaGraphicsRegisterFlagsSurfaceLoadStore = 4
+        cudaGraphicsRegisterFlagsTextureGather = 8
+
+    cdef enum cudaGraphicsMapFlags:
+        cudaGraphicsMapFlagsNone = 0
+        cudaGraphicsMapFlagsReadOnly = 1
+        cudaGraphicsMapFlagsWriteDiscard = 2
+
+    cdef enum cudaGraphicsCubeFace:
+        cudaGraphicsCubeFacePositiveX = 0
+        cudaGraphicsCubeFaceNegativeX = 1
+        cudaGraphicsCubeFacePositiveY = 2
+        cudaGraphicsCubeFaceNegativeY = 3
+        cudaGraphicsCubeFacePositiveZ = 4
+        cudaGraphicsCubeFaceNegativeZ = 5
+
+    cdef enum cudaResourceType:
+        cudaResourceTypeArray = 0
+        cudaResourceTypeMipmappedArray = 1
+        cudaResourceTypeLinear = 2
+        cudaResourceTypePitch2D = 3
+
+    cdef enum cudaResourceViewFormat:
+        cudaResViewFormatNone = 0
+        cudaResViewFormatUnsignedChar1 = 1
+        cudaResViewFormatUnsignedChar2 = 2
+        cudaResViewFormatUnsignedChar4 = 3
+        cudaResViewFormatSignedChar1 = 4
+        cudaResViewFormatSignedChar2 = 5
+        cudaResViewFormatSignedChar4 = 6
+        cudaResViewFormatUnsignedShort1 = 7
+        cudaResViewFormatUnsignedShort2 = 8
+        cudaResViewFormatUnsignedShort4 = 9
+        cudaResViewFormatSignedShort1 = 10
+        cudaResViewFormatSignedShort2 = 11
+        cudaResViewFormatSignedShort4 = 12
+        cudaResViewFormatUnsignedInt1 = 13
+        cudaResViewFormatUnsignedInt2 = 14
+        cudaResViewFormatUnsignedInt4 = 15
+        cudaResViewFormatSignedInt1 = 16
+        cudaResViewFormatSignedInt2 = 17
+        cudaResViewFormatSignedInt4 = 18
+        cudaResViewFormatHalf1 = 19
+        cudaResViewFormatHalf2 = 20
+        cudaResViewFormatHalf4 = 21
+        cudaResViewFormatFloat1 = 22
+        cudaResViewFormatFloat2 = 23
+        cudaResViewFormatFloat4 = 24
+        cudaResViewFormatUnsignedBlockCompressed1 = 25
+        cudaResViewFormatUnsignedBlockCompressed2 = 26
+        cudaResViewFormatUnsignedBlockCompressed3 = 27
+        cudaResViewFormatUnsignedBlockCompressed4 = 28
+        cudaResViewFormatSignedBlockCompressed4 = 29
+        cudaResViewFormatUnsignedBlockCompressed5 = 30
+        cudaResViewFormatSignedBlockCompressed5 = 31
+        cudaResViewFormatUnsignedBlockCompressed6H = 32
+        cudaResViewFormatSignedBlockCompressed6H = 33
+        cudaResViewFormatUnsignedBlockCompressed7 = 34
+
+    cdef enum cudaFuncAttribute:
+        cudaFuncAttributeMaxDynamicSharedMemorySize = 8
+        cudaFuncAttributePreferredSharedMemoryCarveout = 9
+        cudaFuncAttributeClusterDimMustBeSet = 10
+        cudaFuncAttributeRequiredClusterWidth = 11
+        cudaFuncAttributeRequiredClusterHeight = 12
+        cudaFuncAttributeRequiredClusterDepth = 13
+        cudaFuncAttributeNonPortableClusterSizeAllowed = 14
+        cudaFuncAttributeClusterSchedulingPolicyPreference = 15
+        cudaFuncAttributeMax = 16
+
+    cdef enum cudaFuncCache:
+        cudaFuncCachePreferNone = 0
+        cudaFuncCachePreferShared = 1
+        cudaFuncCachePreferL1 = 2
+        cudaFuncCachePreferEqual = 3
+
+    cdef enum cudaSharedMemConfig:
+        cudaSharedMemBankSizeDefault = 0
+        cudaSharedMemBankSizeFourByte = 1
+        cudaSharedMemBankSizeEightByte = 2
+
+    cdef enum cudaSharedCarveout:
+        cudaSharedmemCarveoutDefault = -1
+        cudaSharedmemCarveoutMaxL1 = 0
+        cudaSharedmemCarveoutMaxShared = 100
+
+    cdef enum cudaComputeMode:
+        cudaComputeModeDefault = 0
+        cudaComputeModeExclusive = 1
+        cudaComputeModeProhibited = 2
+        cudaComputeModeExclusiveProcess = 3
+
+    cdef enum cudaLimit:
+        cudaLimitStackSize = 0
+        cudaLimitPrintfFifoSize = 1
+        cudaLimitMallocHeapSize = 2
+        cudaLimitDevRuntimeSyncDepth = 3
+        cudaLimitDevRuntimePendingLaunchCount = 4
+        cudaLimitMaxL2FetchGranularity = 5
+        cudaLimitPersistingL2CacheSize = 6
+
+    cdef enum cudaMemoryAdvise:
+        cudaMemAdviseSetReadMostly = 1
+        cudaMemAdviseUnsetReadMostly = 2
+        cudaMemAdviseSetPreferredLocation = 3
+        cudaMemAdviseUnsetPreferredLocation = 4
+        cudaMemAdviseSetAccessedBy = 5
+        cudaMemAdviseUnsetAccessedBy = 6
+
+    cdef enum cudaMemRangeAttribute:
+        cudaMemRangeAttributeReadMostly = 1
+        cudaMemRangeAttributePreferredLocation = 2
+        cudaMemRangeAttributeAccessedBy = 3
+        cudaMemRangeAttributeLastPrefetchLocation = 4
+        cudaMemRangeAttributePreferredLocationType = 5
+        cudaMemRangeAttributePreferredLocationId = 6
+        cudaMemRangeAttributeLastPrefetchLocationType = 7
+        cudaMemRangeAttributeLastPrefetchLocationId = 8
+
+    cdef enum cudaFlushGPUDirectRDMAWritesOptions:
+        cudaFlushGPUDirectRDMAWritesOptionHost = 1
+        cudaFlushGPUDirectRDMAWritesOptionMemOps = 2
+
+    cdef enum cudaGPUDirectRDMAWritesOrdering:
+        cudaGPUDirectRDMAWritesOrderingNone = 0
+        cudaGPUDirectRDMAWritesOrderingOwner = 100
+        cudaGPUDirectRDMAWritesOrderingAllDevices = 200
+
+    cdef enum cudaFlushGPUDirectRDMAWritesScope:
+        cudaFlushGPUDirectRDMAWritesToOwner = 100
+        cudaFlushGPUDirectRDMAWritesToAllDevices = 200
+
+    cdef enum cudaFlushGPUDirectRDMAWritesTarget:
+        cudaFlushGPUDirectRDMAWritesTargetCurrentDevice = 0
+
+    cdef enum cudaDeviceAttr:
+        cudaDevAttrMaxThreadsPerBlock = 1
+        cudaDevAttrMaxBlockDimX = 2
+        cudaDevAttrMaxBlockDimY = 3
+        cudaDevAttrMaxBlockDimZ = 4
+        cudaDevAttrMaxGridDimX = 5
+        cudaDevAttrMaxGridDimY = 6
+        cudaDevAttrMaxGridDimZ = 7
+        cudaDevAttrMaxSharedMemoryPerBlock = 8
+        cudaDevAttrTotalConstantMemory = 9
+        cudaDevAttrWarpSize = 10
+        cudaDevAttrMaxPitch = 11
+        cudaDevAttrMaxRegistersPerBlock = 12
+        cudaDevAttrClockRate = 13
+        cudaDevAttrTextureAlignment = 14
+        cudaDevAttrGpuOverlap = 15
+        cudaDevAttrMultiProcessorCount = 16
+        cudaDevAttrKernelExecTimeout = 17
+        cudaDevAttrIntegrated = 18
+        cudaDevAttrCanMapHostMemory = 19
+        cudaDevAttrComputeMode = 20
+        cudaDevAttrMaxTexture1DWidth = 21
+        cudaDevAttrMaxTexture2DWidth = 22
+        cudaDevAttrMaxTexture2DHeight = 23
+        cudaDevAttrMaxTexture3DWidth = 24
+        cudaDevAttrMaxTexture3DHeight = 25
+        cudaDevAttrMaxTexture3DDepth = 26
+        cudaDevAttrMaxTexture2DLayeredWidth = 27
+        cudaDevAttrMaxTexture2DLayeredHeight = 28
+        cudaDevAttrMaxTexture2DLayeredLayers = 29
+        cudaDevAttrSurfaceAlignment = 30
+        cudaDevAttrConcurrentKernels = 31
+        cudaDevAttrEccEnabled = 32
+        cudaDevAttrPciBusId = 33
+        cudaDevAttrPciDeviceId = 34
+        cudaDevAttrTccDriver = 35
+        cudaDevAttrMemoryClockRate = 36
+        cudaDevAttrGlobalMemoryBusWidth = 37
+        cudaDevAttrL2CacheSize = 38
+        cudaDevAttrMaxThreadsPerMultiProcessor = 39
+        cudaDevAttrAsyncEngineCount = 40
+        cudaDevAttrUnifiedAddressing = 41
+        cudaDevAttrMaxTexture1DLayeredWidth = 42
+        cudaDevAttrMaxTexture1DLayeredLayers = 43
+        cudaDevAttrMaxTexture2DGatherWidth = 45
+        cudaDevAttrMaxTexture2DGatherHeight = 46
+        cudaDevAttrMaxTexture3DWidthAlt = 47
+        cudaDevAttrMaxTexture3DHeightAlt = 48
+        cudaDevAttrMaxTexture3DDepthAlt = 49
+        cudaDevAttrPciDomainId = 50
+        cudaDevAttrTexturePitchAlignment = 51
+        cudaDevAttrMaxTextureCubemapWidth = 52
+        cudaDevAttrMaxTextureCubemapLayeredWidth = 53
+        cudaDevAttrMaxTextureCubemapLayeredLayers = 54
+        cudaDevAttrMaxSurface1DWidth = 55
+        cudaDevAttrMaxSurface2DWidth = 56
+        cudaDevAttrMaxSurface2DHeight = 57
+        cudaDevAttrMaxSurface3DWidth = 58
+        cudaDevAttrMaxSurface3DHeight = 59
+        cudaDevAttrMaxSurface3DDepth = 60
+        cudaDevAttrMaxSurface1DLayeredWidth = 61
+        cudaDevAttrMaxSurface1DLayeredLayers = 62
+        cudaDevAttrMaxSurface2DLayeredWidth = 63
+        cudaDevAttrMaxSurface2DLayeredHeight = 64
+        cudaDevAttrMaxSurface2DLayeredLayers = 65
+        cudaDevAttrMaxSurfaceCubemapWidth = 66
+        cudaDevAttrMaxSurfaceCubemapLayeredWidth = 67
+        cudaDevAttrMaxSurfaceCubemapLayeredLayers = 68
+        cudaDevAttrMaxTexture1DLinearWidth = 69
+        cudaDevAttrMaxTexture2DLinearWidth = 70
+        cudaDevAttrMaxTexture2DLinearHeight = 71
+        cudaDevAttrMaxTexture2DLinearPitch = 72
+        cudaDevAttrMaxTexture2DMipmappedWidth = 73
+        cudaDevAttrMaxTexture2DMipmappedHeight = 74
+        cudaDevAttrComputeCapabilityMajor = 75
+        cudaDevAttrComputeCapabilityMinor = 76
+        cudaDevAttrMaxTexture1DMipmappedWidth = 77
+        cudaDevAttrStreamPrioritiesSupported = 78
+        cudaDevAttrGlobalL1CacheSupported = 79
+        cudaDevAttrLocalL1CacheSupported = 80
+        cudaDevAttrMaxSharedMemoryPerMultiprocessor = 81
+        cudaDevAttrMaxRegistersPerMultiprocessor = 82
+        cudaDevAttrManagedMemory = 83
+        cudaDevAttrIsMultiGpuBoard = 84
+        cudaDevAttrMultiGpuBoardGroupID = 85
+        cudaDevAttrHostNativeAtomicSupported = 86
+        cudaDevAttrSingleToDoublePrecisionPerfRatio = 87
+        cudaDevAttrPageableMemoryAccess = 88
+        cudaDevAttrConcurrentManagedAccess = 89
+        cudaDevAttrComputePreemptionSupported = 90
+        cudaDevAttrCanUseHostPointerForRegisteredMem = 91
+        cudaDevAttrReserved92 = 92
+        cudaDevAttrReserved93 = 93
+        cudaDevAttrReserved94 = 94
+        cudaDevAttrCooperativeLaunch = 95
+        cudaDevAttrReserved96 = 96
+        cudaDevAttrMaxSharedMemoryPerBlockOptin = 97
+        cudaDevAttrCanFlushRemoteWrites = 98
+        cudaDevAttrHostRegisterSupported = 99
+        cudaDevAttrPageableMemoryAccessUsesHostPageTables = 100
+        cudaDevAttrDirectManagedMemAccessFromHost = 101
+        cudaDevAttrMaxBlocksPerMultiprocessor = 106
+        cudaDevAttrMaxPersistingL2CacheSize = 108
+        cudaDevAttrMaxAccessPolicyWindowSize = 109
+        cudaDevAttrReservedSharedMemoryPerBlock = 111
+        cudaDevAttrSparseCudaArraySupported = 112
+        cudaDevAttrHostRegisterReadOnlySupported = 113
+        cudaDevAttrTimelineSemaphoreInteropSupported = 114
+        cudaDevAttrMemoryPoolsSupported = 115
+        cudaDevAttrGPUDirectRDMASupported = 116
+        cudaDevAttrGPUDirectRDMAFlushWritesOptions = 117
+        cudaDevAttrGPUDirectRDMAWritesOrdering = 118
+        cudaDevAttrMemoryPoolSupportedHandleTypes = 119
+        cudaDevAttrClusterLaunch = 120
+        cudaDevAttrDeferredMappingCudaArraySupported = 121
+        cudaDevAttrReserved122 = 122
+        cudaDevAttrReserved123 = 123
+        cudaDevAttrReserved124 = 124
+        cudaDevAttrIpcEventSupport = 125
+        cudaDevAttrMemSyncDomainCount = 126
+        cudaDevAttrReserved127 = 127
+        cudaDevAttrReserved128 = 128
+        cudaDevAttrReserved129 = 129
+        cudaDevAttrNumaConfig = 130
+        cudaDevAttrNumaId = 131
+        cudaDevAttrReserved132 = 132
+        cudaDevAttrMpsEnabled = 133
+        cudaDevAttrHostNumaId = 134
+        cudaDevAttrD3D12CigSupported = 135
+        cudaDevAttrVulkanCigSupported = 138
+        cudaDevAttrGpuPciDeviceId = 139
+        cudaDevAttrGpuPciSubsystemId = 140
+        cudaDevAttrReserved141 = 141
+        cudaDevAttrHostNumaMemoryPoolsSupported = 142
+        cudaDevAttrHostNumaMultinodeIpcSupported = 143
+        cudaDevAttrHostMemoryPoolsSupported = 144
+        cudaDevAttrReserved145 = 145
+        cudaDevAttrOnlyPartialHostNativeAtomicSupported = 147
+        cudaDevAttrMax = 148
+
+    cdef enum cudaMemPoolAttr:
+        cudaMemPoolReuseFollowEventDependencies = 1
+        cudaMemPoolReuseAllowOpportunistic = 2
+        cudaMemPoolReuseAllowInternalDependencies = 3
+        cudaMemPoolAttrReleaseThreshold = 4
+        cudaMemPoolAttrReservedMemCurrent = 5
+        cudaMemPoolAttrReservedMemHigh = 6
+        cudaMemPoolAttrUsedMemCurrent = 7
+        cudaMemPoolAttrUsedMemHigh = 8
+        cudaMemPoolAttrAllocationType = 9
+        cudaMemPoolAttrExportHandleTypes = 10
+        cudaMemPoolAttrLocationId = 11
+        cudaMemPoolAttrLocationType = 12
+        cudaMemPoolAttrMaxPoolSize = 13
+        cudaMemPoolAttrHwDecompressEnabled = 14
+
+    cdef enum cudaMemLocationType:
+        cudaMemLocationTypeInvalid = 0
+        cudaMemLocationTypeNone = 0
+        cudaMemLocationTypeDevice = 1
+        cudaMemLocationTypeHost = 2
+        cudaMemLocationTypeHostNuma = 3
+        cudaMemLocationTypeHostNumaCurrent = 4
+        cudaMemLocationTypeInvisible = 5
+
+    cdef enum cudaMemAccessFlags:
+        cudaMemAccessFlagsProtNone = 0
+        cudaMemAccessFlagsProtRead = 1
+        cudaMemAccessFlagsProtReadWrite = 3
+
+    cdef enum cudaMemAllocationType:
+        cudaMemAllocationTypeInvalid = 0
+        cudaMemAllocationTypePinned = 1
+        cudaMemAllocationTypeManaged = 2
+        cudaMemAllocationTypeMax = 2147483647
+
+    cdef enum cudaMemAllocationHandleType:
+        cudaMemHandleTypeNone = 0
+        cudaMemHandleTypePosixFileDescriptor = 1
+        cudaMemHandleTypeWin32 = 2
+        cudaMemHandleTypeWin32Kmt = 4
+        cudaMemHandleTypeFabric = 8
+
+    cdef enum cudaGraphMemAttributeType:
+        cudaGraphMemAttrUsedMemCurrent = 0
+        cudaGraphMemAttrUsedMemHigh = 1
+        cudaGraphMemAttrReservedMemCurrent = 2
+        cudaGraphMemAttrReservedMemHigh = 3
+
+    cdef enum cudaMemcpyFlags:
+        cudaMemcpyFlagDefault = 0
+        cudaMemcpyFlagPreferOverlapWithCompute = 1
+
+    cdef enum cudaMemcpySrcAccessOrder:
+        cudaMemcpySrcAccessOrderInvalid = 0
+        cudaMemcpySrcAccessOrderStream = 1
+        cudaMemcpySrcAccessOrderDuringApiCall = 2
+        cudaMemcpySrcAccessOrderAny = 3
+        cudaMemcpySrcAccessOrderMax = 2147483647
+
+    cdef enum cudaMemcpy3DOperandType:
+        cudaMemcpyOperandTypePointer = 1
+        cudaMemcpyOperandTypeArray = 2
+        cudaMemcpyOperandTypeMax = 2147483647
+
+    cdef enum cudaDeviceP2PAttr:
+        cudaDevP2PAttrPerformanceRank = 1
+        cudaDevP2PAttrAccessSupported = 2
+        cudaDevP2PAttrNativeAtomicSupported = 3
+        cudaDevP2PAttrCudaArrayAccessSupported = 4
+        cudaDevP2PAttrOnlyPartialNativeAtomicSupported = 5
+
+    cdef enum cudaAtomicOperation:
+        cudaAtomicOperationIntegerAdd = 0
+        cudaAtomicOperationIntegerMin = 1
+        cudaAtomicOperationIntegerMax = 2
+        cudaAtomicOperationIntegerIncrement = 3
+        cudaAtomicOperationIntegerDecrement = 4
+        cudaAtomicOperationAnd = 5
+        cudaAtomicOperationOr = 6
+        cudaAtomicOperationXOR = 7
+        cudaAtomicOperationExchange = 8
+        cudaAtomicOperationCAS = 9
+        cudaAtomicOperationFloatAdd = 10
+        cudaAtomicOperationFloatMin = 11
+        cudaAtomicOperationFloatMax = 12
+
+    cdef enum cudaAtomicOperationCapability:
+        cudaAtomicCapabilitySigned = 1
+        cudaAtomicCapabilityUnsigned = 2
+        cudaAtomicCapabilityReduction = 4
+        cudaAtomicCapabilityScalar32 = 8
+        cudaAtomicCapabilityScalar64 = 16
+        cudaAtomicCapabilityScalar128 = 32
+        cudaAtomicCapabilityVector32x4 = 64
+
+    cdef enum cudaExternalMemoryHandleType:
+        cudaExternalMemoryHandleTypeOpaqueFd = 1
+        cudaExternalMemoryHandleTypeOpaqueWin32 = 2
+        cudaExternalMemoryHandleTypeOpaqueWin32Kmt = 3
+        cudaExternalMemoryHandleTypeD3D12Heap = 4
+        cudaExternalMemoryHandleTypeD3D12Resource = 5
+        cudaExternalMemoryHandleTypeD3D11Resource = 6
+        cudaExternalMemoryHandleTypeD3D11ResourceKmt = 7
+        cudaExternalMemoryHandleTypeNvSciBuf = 8
+
+    cdef enum cudaExternalSemaphoreHandleType:
+        cudaExternalSemaphoreHandleTypeOpaqueFd = 1
+        cudaExternalSemaphoreHandleTypeOpaqueWin32 = 2
+        cudaExternalSemaphoreHandleTypeOpaqueWin32Kmt = 3
+        cudaExternalSemaphoreHandleTypeD3D12Fence = 4
+        cudaExternalSemaphoreHandleTypeD3D11Fence = 5
+        cudaExternalSemaphoreHandleTypeNvSciSync = 6
+        cudaExternalSemaphoreHandleTypeKeyedMutex = 7
+        cudaExternalSemaphoreHandleTypeKeyedMutexKmt = 8
+        cudaExternalSemaphoreHandleTypeTimelineSemaphoreFd = 9
+        cudaExternalSemaphoreHandleTypeTimelineSemaphoreWin32 = 10
+
+    cdef enum cudaDevSmResourceGroup_flags:
+        cudaDevSmResourceGroupDefault = 0
+        cudaDevSmResourceGroupBackfill = 1
+
+    cdef enum cudaDevSmResourceSplitByCount_flags:
+        cudaDevSmResourceSplitIgnoreSmCoscheduling = 1
+        cudaDevSmResourceSplitMaxPotentialClusterSize = 2
+
+    cdef enum cudaDevResourceType:
+        cudaDevResourceTypeInvalid = 0
+        cudaDevResourceTypeSm = 1
+        cudaDevResourceTypeWorkqueueConfig = 1000
+        cudaDevResourceTypeWorkqueue = 10000
+
+    cdef enum cudaDevWorkqueueConfigScope:
+        cudaDevWorkqueueConfigScopeDeviceCtx = 0
+        cudaDevWorkqueueConfigScopeGreenCtxBalanced = 1
+
+    cdef enum cudaJitOption:
+        cudaJitMaxRegisters = 0
+        cudaJitThreadsPerBlock = 1
+        cudaJitWallTime = 2
+        cudaJitInfoLogBuffer = 3
+        cudaJitInfoLogBufferSizeBytes = 4
+        cudaJitErrorLogBuffer = 5
+        cudaJitErrorLogBufferSizeBytes = 6
+        cudaJitOptimizationLevel = 7
+        cudaJitFallbackStrategy = 10
+        cudaJitGenerateDebugInfo = 11
+        cudaJitLogVerbose = 12
+        cudaJitGenerateLineInfo = 13
+        cudaJitCacheMode = 14
+        cudaJitPositionIndependentCode = 30
+        cudaJitMinCtaPerSm = 31
+        cudaJitMaxThreadsPerBlock = 32
+        cudaJitOverrideDirectiveValues = 33
+
+    cdef enum cudaLibraryOption:
+        cudaLibraryHostUniversalFunctionAndDataTable = 0
+        cudaLibraryBinaryIsPreserved = 1
+
+    cdef enum cudaJit_CacheMode:
+        cudaJitCacheOptionNone = 0
+        cudaJitCacheOptionCG = 1
+        cudaJitCacheOptionCA = 2
+
+    cdef enum cudaJit_Fallback:
+        cudaPreferPtx = 0
+        cudaPreferBinary = 1
+
+    cdef enum cudaCGScope:
+        cudaCGScopeInvalid = 0
+        cudaCGScopeGrid = 1
+        cudaCGScopeReserved = 2
+
+    cdef enum cudaKernelFunctionType:
+        cudaKernelFunctionTypeUnspecified = 0
+        cudaKernelFunctionTypeDeviceEntry = 1
+        cudaKernelFunctionTypeKernel = 2
+        cudaKernelFunctionTypeFunction = 3
+
+    cdef enum cudaGraphConditionalHandleFlags:
+        cudaGraphCondAssignDefault = 1
+
+    cdef enum cudaGraphConditionalNodeType:
+        cudaGraphCondTypeIf = 0
+        cudaGraphCondTypeWhile = 1
+        cudaGraphCondTypeSwitch = 2
+
+    cdef enum cudaGraphNodeType:
+        cudaGraphNodeTypeKernel = 0
+        cudaGraphNodeTypeMemcpy = 1
+        cudaGraphNodeTypeMemset = 2
+        cudaGraphNodeTypeHost = 3
+        cudaGraphNodeTypeGraph = 4
+        cudaGraphNodeTypeEmpty = 5
+        cudaGraphNodeTypeWaitEvent = 6
+        cudaGraphNodeTypeEventRecord = 7
+        cudaGraphNodeTypeExtSemaphoreSignal = 8
+        cudaGraphNodeTypeExtSemaphoreWait = 9
+        cudaGraphNodeTypeMemAlloc = 10
+        cudaGraphNodeTypeMemFree = 11
+        cudaGraphNodeTypeConditional = 13
+        cudaGraphNodeTypeCount = 14
+
+    cdef enum cudaGraphChildGraphNodeOwnership:
+        cudaGraphChildGraphOwnershipInvalid = -1
+        cudaGraphChildGraphOwnershipClone = 0
+        cudaGraphChildGraphOwnershipMove = 1
+
+    cdef enum cudaGraphExecUpdateResult:
+        cudaGraphExecUpdateSuccess = 0
+        cudaGraphExecUpdateError = 1
+        cudaGraphExecUpdateErrorTopologyChanged = 2
+        cudaGraphExecUpdateErrorNodeTypeChanged = 3
+        cudaGraphExecUpdateErrorFunctionChanged = 4
+        cudaGraphExecUpdateErrorParametersChanged = 5
+        cudaGraphExecUpdateErrorNotSupported = 6
+        cudaGraphExecUpdateErrorUnsupportedFunctionChange = 7
+        cudaGraphExecUpdateErrorAttributesChanged = 8
+
+    cdef enum cudaGraphKernelNodeField:
+        cudaGraphKernelNodeFieldInvalid = 0
+        cudaGraphKernelNodeFieldGridDim = 1
+        cudaGraphKernelNodeFieldParam = 2
+        cudaGraphKernelNodeFieldEnabled = 3
+
+    cdef enum cudaGetDriverEntryPointFlags:
+        cudaEnableDefault = 0
+        cudaEnableLegacyStream = 1
+        cudaEnablePerThreadDefaultStream = 2
+
+    cdef enum cudaDriverEntryPointQueryResult:
+        cudaDriverEntryPointSuccess = 0
+        cudaDriverEntryPointSymbolNotFound = 1
+        cudaDriverEntryPointVersionNotSufficent = 2
+
+    cdef enum cudaGraphDebugDotFlags:
+        cudaGraphDebugDotFlagsVerbose = 1
+        cudaGraphDebugDotFlagsKernelNodeParams = 4
+        cudaGraphDebugDotFlagsMemcpyNodeParams = 8
+        cudaGraphDebugDotFlagsMemsetNodeParams = 16
+        cudaGraphDebugDotFlagsHostNodeParams = 32
+        cudaGraphDebugDotFlagsEventNodeParams = 64
+        cudaGraphDebugDotFlagsExtSemasSignalNodeParams = 128
+        cudaGraphDebugDotFlagsExtSemasWaitNodeParams = 256
+        cudaGraphDebugDotFlagsKernelNodeAttributes = 512
+        cudaGraphDebugDotFlagsHandles = 1024
+        cudaGraphDebugDotFlagsConditionalNodeParams = 32768
+
+    cdef enum cudaGraphInstantiateFlags:
+        cudaGraphInstantiateFlagAutoFreeOnLaunch = 1
+        cudaGraphInstantiateFlagUpload = 2
+        cudaGraphInstantiateFlagDeviceLaunch = 4
+        cudaGraphInstantiateFlagUseNodePriority = 8
+
+    cdef enum cudaDeviceNumaConfig:
+        cudaDeviceNumaConfigNone = 0
+        cudaDeviceNumaConfigNumaNode = 1
+
+cdef extern from "surface_types.h":
+
+    ctypedef unsigned long long cudaSurfaceObject_t
+
+    cdef enum cudaSurfaceBoundaryMode:
+        cudaBoundaryModeZero = 0
+        cudaBoundaryModeClamp = 1
+        cudaBoundaryModeTrap = 2
+
+    cdef enum cudaSurfaceFormatMode:
+        cudaFormatModeForced = 0
+        cudaFormatModeAuto = 1
+
+cdef extern from "texture_types.h":
+
+    cdef struct cudaTextureDesc:
+        cudaTextureAddressMode addressMode[3]
+        cudaTextureFilterMode filterMode
+        cudaTextureReadMode readMode
+        int sRGB
+        float borderColor[4]
+        int normalizedCoords
+        unsigned int maxAnisotropy
+        cudaTextureFilterMode mipmapFilterMode
+        float mipmapLevelBias
+        float minMipmapLevelClamp
+        float maxMipmapLevelClamp
+        int disableTrilinearOptimization
+        int seamlessCubemap
+
+    ctypedef unsigned long long cudaTextureObject_t
+
+    cdef enum cudaTextureAddressMode:
+        cudaAddressModeWrap = 0
+        cudaAddressModeClamp = 1
+        cudaAddressModeMirror = 2
+        cudaAddressModeBorder = 3
+
+    cdef enum cudaTextureFilterMode:
+        cudaFilterModePoint = 0
+        cudaFilterModeLinear = 1
+
+    cdef enum cudaTextureReadMode:
+        cudaReadModeElementType = 0
+        cudaReadModeNormalizedFloat = 1
+
+cdef extern from "library_types.h":
+
+    cdef enum cudaDataType_t:
+        CUDA_R_32F = 0
+        CUDA_R_64F = 1
+        CUDA_R_16F = 2
+        CUDA_R_8I = 3
+        CUDA_C_32F = 4
+        CUDA_C_64F = 5
+        CUDA_C_16F = 6
+        CUDA_C_8I = 7
+        CUDA_R_8U = 8
+        CUDA_C_8U = 9
+        CUDA_R_32I = 10
+        CUDA_C_32I = 11
+        CUDA_R_32U = 12
+        CUDA_C_32U = 13
+        CUDA_R_16BF = 14
+        CUDA_C_16BF = 15
+        CUDA_R_4I = 16
+        CUDA_C_4I = 17
+        CUDA_R_4U = 18
+        CUDA_C_4U = 19
+        CUDA_R_16I = 20
+        CUDA_C_16I = 21
+        CUDA_R_16U = 22
+        CUDA_C_16U = 23
+        CUDA_R_64I = 24
+        CUDA_C_64I = 25
+        CUDA_R_64U = 26
+        CUDA_C_64U = 27
+        CUDA_R_8F_E4M3 = 28
+        CUDA_R_8F_UE4M3 = 28
+        CUDA_R_8F_E5M2 = 29
+        CUDA_R_8F_UE8M0 = 30
+        CUDA_R_6F_E2M3 = 31
+        CUDA_R_6F_E3M2 = 32
+        CUDA_R_4F_E2M1 = 33
+
+    ctypedef cudaDataType_t cudaDataType
+
+    cdef enum cudaEmulationStrategy_t:
+        CUDA_EMULATION_STRATEGY_DEFAULT = 0
+        CUDA_EMULATION_STRATEGY_PERFORMANT = 1
+        CUDA_EMULATION_STRATEGY_EAGER = 2
+
+    ctypedef cudaEmulationStrategy_t cudaEmulationStrategy
+
+    cdef enum cudaEmulationMantissaControl_t:
+        CUDA_EMULATION_MANTISSA_CONTROL_DYNAMIC = 0
+        CUDA_EMULATION_MANTISSA_CONTROL_FIXED = 1
+
+    ctypedef cudaEmulationMantissaControl_t cudaEmulationMantissaControl
+
+    cdef enum cudaEmulationSpecialValuesSupport_t:
+        CUDA_EMULATION_SPECIAL_VALUES_SUPPORT_NONE = 0
+        CUDA_EMULATION_SPECIAL_VALUES_SUPPORT_INFINITY = 1
+        CUDA_EMULATION_SPECIAL_VALUES_SUPPORT_NAN = 2
+        CUDA_EMULATION_SPECIAL_VALUES_SUPPORT_DEFAULT = 65535
+
+    ctypedef cudaEmulationSpecialValuesSupport_t cudaEmulationSpecialValuesSupport
+
+    cdef enum libraryPropertyType_t:
+        MAJOR_VERSION = 0
+        MINOR_VERSION = 1
+        PATCH_LEVEL = 2
+
+    ctypedef libraryPropertyType_t libraryPropertyType
+
+cdef extern from "cuda_runtime_api.h":
+
+    ctypedef void (*cudaStreamCallback_t)(cudaStream_t stream, cudaError_t status, void* userData)
+
+    ctypedef void (*cudaLogsCallback_t)(void* data, cudaLogLevel logLevel, char* message, size_t length)
+
+cdef extern from "device_types.h":
+
+    cdef enum cudaRoundMode:
+        cudaRoundNearest = 0
+        cudaRoundZero = 1
+        cudaRoundPosInf = 2
+        cudaRoundMinInf = 3
+
+ctypedef cudaLaunchAttributeID cudaStreamAttrID
+
+ctypedef cudaLaunchAttributeID cudaKernelNodeAttrID
+
+ctypedef cudaLaunchAttributeValue cudaStreamAttrValue
+
+ctypedef cudaLaunchAttributeValue cudaKernelNodeAttrValue
diff --git a/lib/python3.11/site-packages/cuda/bindings/driver.pxd b/lib/python3.11/site-packages/cuda/bindings/driver.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..f2f76c9396418f1d5ba1749393f013618229f1f5
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/driver.pxd
@@ -0,0 +1,7979 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+cimport cuda.bindings.cydriver as cydriver
+
+include "_lib/utils.pxd"
+
+cdef class CUcontext:
+    """
+
+    A regular context handle
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUcontext  _pvt_val
+    cdef cydriver.CUcontext* _pvt_ptr
+
+cdef class CUmodule:
+    """
+
+    CUDA module
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUmodule  _pvt_val
+    cdef cydriver.CUmodule* _pvt_ptr
+
+cdef class CUfunction:
+    """
+
+    CUDA function
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUfunction  _pvt_val
+    cdef cydriver.CUfunction* _pvt_ptr
+
+cdef class CUlibrary:
+    """
+
+    CUDA library
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUlibrary  _pvt_val
+    cdef cydriver.CUlibrary* _pvt_ptr
+
+cdef class CUkernel:
+    """
+
+    CUDA kernel
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUkernel  _pvt_val
+    cdef cydriver.CUkernel* _pvt_ptr
+
+cdef class CUarray:
+    """
+
+    CUDA array
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUarray  _pvt_val
+    cdef cydriver.CUarray* _pvt_ptr
+
+cdef class CUmipmappedArray:
+    """
+
+    CUDA mipmapped array
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUmipmappedArray  _pvt_val
+    cdef cydriver.CUmipmappedArray* _pvt_ptr
+
+cdef class CUtexref:
+    """
+
+    CUDA texture reference
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUtexref  _pvt_val
+    cdef cydriver.CUtexref* _pvt_ptr
+
+cdef class CUsurfref:
+    """
+
+    CUDA surface reference
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUsurfref  _pvt_val
+    cdef cydriver.CUsurfref* _pvt_ptr
+
+cdef class CUevent:
+    """
+
+    CUDA event
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUevent  _pvt_val
+    cdef cydriver.CUevent* _pvt_ptr
+
+cdef class CUstream:
+    """
+
+    CUDA stream
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUstream  _pvt_val
+    cdef cydriver.CUstream* _pvt_ptr
+
+cdef class CUgraphicsResource:
+    """
+
+    CUDA graphics interop resource
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgraphicsResource  _pvt_val
+    cdef cydriver.CUgraphicsResource* _pvt_ptr
+
+cdef class CUexternalMemory:
+    """
+
+    CUDA external memory
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUexternalMemory  _pvt_val
+    cdef cydriver.CUexternalMemory* _pvt_ptr
+
+cdef class CUexternalSemaphore:
+    """
+
+    CUDA external semaphore
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUexternalSemaphore  _pvt_val
+    cdef cydriver.CUexternalSemaphore* _pvt_ptr
+
+cdef class CUgraph:
+    """
+
+    CUDA graph
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgraph  _pvt_val
+    cdef cydriver.CUgraph* _pvt_ptr
+
+cdef class CUgraphNode:
+    """
+
+    CUDA graph node
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgraphNode  _pvt_val
+    cdef cydriver.CUgraphNode* _pvt_ptr
+
+cdef class CUgraphExec:
+    """
+
+    CUDA executable graph
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgraphExec  _pvt_val
+    cdef cydriver.CUgraphExec* _pvt_ptr
+
+cdef class CUmemoryPool:
+    """
+
+    CUDA memory pool
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUmemoryPool  _pvt_val
+    cdef cydriver.CUmemoryPool* _pvt_ptr
+
+cdef class CUuserObject:
+    """
+
+    CUDA user object for graphs
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUuserObject  _pvt_val
+    cdef cydriver.CUuserObject* _pvt_ptr
+
+cdef class CUgraphDeviceNode:
+    """
+
+    CUDA graph device node handle
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgraphDeviceNode  _pvt_val
+    cdef cydriver.CUgraphDeviceNode* _pvt_ptr
+
+cdef class CUasyncCallbackHandle:
+    """
+
+    CUDA async notification callback handle
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUasyncCallbackHandle  _pvt_val
+    cdef cydriver.CUasyncCallbackHandle* _pvt_ptr
+
+cdef class CUgreenCtx:
+    """
+
+    A green context handle. This handle can be used safely from only one CPU thread at a time. Created via cuGreenCtxCreate
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgreenCtx  _pvt_val
+    cdef cydriver.CUgreenCtx* _pvt_ptr
+
+cdef class CUlinkState:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUlinkState  _pvt_val
+    cdef cydriver.CUlinkState* _pvt_ptr
+    cdef list _keepalive
+
+cdef class CUcoredumpCallbackHandle:
+    """ Opaque handle representing a registered coredump status callback.
+
+    This handle is returned when registering a callback and must be provided when deregistering the callback.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUcoredumpCallbackHandle  _pvt_val
+    cdef cydriver.CUcoredumpCallbackHandle* _pvt_ptr
+
+cdef class CUdevResourceDesc:
+    """
+
+    An opaque descriptor handle. The descriptor encapsulates multiple created and configured resources. Created via cuDevResourceGenerateDesc
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUdevResourceDesc  _pvt_val
+    cdef cydriver.CUdevResourceDesc* _pvt_ptr
+
+cdef class CUlogsCallbackHandle:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUlogsCallbackHandle  _pvt_val
+    cdef cydriver.CUlogsCallbackHandle* _pvt_ptr
+
+cdef class CUeglStreamConnection:
+    """
+
+    CUDA EGLSream Connection
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUeglStreamConnection  _pvt_val
+    cdef cydriver.CUeglStreamConnection* _pvt_ptr
+
+cdef class EGLImageKHR:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.EGLImageKHR  _pvt_val
+    cdef cydriver.EGLImageKHR* _pvt_ptr
+
+cdef class EGLStreamKHR:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.EGLStreamKHR  _pvt_val
+    cdef cydriver.EGLStreamKHR* _pvt_ptr
+
+cdef class EGLSyncKHR:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.EGLSyncKHR  _pvt_val
+    cdef cydriver.EGLSyncKHR* _pvt_ptr
+
+cdef class CUasyncCallback:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUasyncCallback  _pvt_val
+    cdef cydriver.CUasyncCallback* _pvt_ptr
+
+cdef class CUhostFn:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUhostFn  _pvt_val
+    cdef cydriver.CUhostFn* _pvt_ptr
+
+cdef class CUstreamCallback:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUstreamCallback  _pvt_val
+    cdef cydriver.CUstreamCallback* _pvt_ptr
+
+cdef class CUoccupancyB2DSize:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUoccupancyB2DSize  _pvt_val
+    cdef cydriver.CUoccupancyB2DSize* _pvt_ptr
+
+cdef class CUcoredumpStatusCallback:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUcoredumpStatusCallback  _pvt_val
+    cdef cydriver.CUcoredumpStatusCallback* _pvt_ptr
+
+cdef class CUlogsCallback:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUlogsCallback  _pvt_val
+    cdef cydriver.CUlogsCallback* _pvt_ptr
+
+cdef class CUuuid_st:
+    """
+    Attributes
+    ----------
+    bytes : bytes
+        < CUDA definition of UUID
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUuuid_st _pvt_val
+    cdef cydriver.CUuuid_st* _pvt_ptr
+
+cdef class CUmemFabricHandle_st:
+    """
+    Fabric handle - An opaque handle representing a memory allocation
+    that can be exported to processes in same or different nodes. For
+    IPC between processes on different nodes they must be connected via
+    the NVSwitch fabric.
+
+    Attributes
+    ----------
+    data : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemFabricHandle_st _pvt_val
+    cdef cydriver.CUmemFabricHandle_st* _pvt_ptr
+
+cdef class CUipcEventHandle_st:
+    """
+    CUDA IPC event handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUipcEventHandle_st _pvt_val
+    cdef cydriver.CUipcEventHandle_st* _pvt_ptr
+
+cdef class CUipcMemHandle_st:
+    """
+    CUDA IPC mem handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUipcMemHandle_st _pvt_val
+    cdef cydriver.CUipcMemHandle_st* _pvt_ptr
+
+cdef class CUstreamMemOpWaitValueParams_st:
+    """
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+
+    address : CUdeviceptr
+
+    value : cuuint32_t
+
+    value64 : cuuint64_t
+
+    flags : unsigned int
+        See CUstreamWaitValue_flags.
+    alias : CUdeviceptr
+        For driver internal use. Initial value is unimportant.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamBatchMemOpParams_union* _pvt_ptr
+    cdef CUdeviceptr _address
+    cdef cuuint32_t _value
+    cdef cuuint64_t _value64
+    cdef CUdeviceptr _alias
+
+cdef class CUstreamMemOpWriteValueParams_st:
+    """
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+
+    address : CUdeviceptr
+
+    value : cuuint32_t
+
+    value64 : cuuint64_t
+
+    flags : unsigned int
+        See CUstreamWriteValue_flags.
+    alias : CUdeviceptr
+        For driver internal use. Initial value is unimportant.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamBatchMemOpParams_union* _pvt_ptr
+    cdef CUdeviceptr _address
+    cdef cuuint32_t _value
+    cdef cuuint64_t _value64
+    cdef CUdeviceptr _alias
+
+cdef class CUstreamMemOpFlushRemoteWritesParams_st:
+    """
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+
+    flags : unsigned int
+        Must be 0.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamBatchMemOpParams_union* _pvt_ptr
+
+cdef class CUstreamMemOpMemoryBarrierParams_st:
+    """
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+        < Only supported in the _v2 API
+    flags : unsigned int
+        See CUstreamMemoryBarrier_flags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamBatchMemOpParams_union* _pvt_ptr
+
+cdef class CUstreamMemOpAtomicReductionParams_st:
+    """
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+
+    flags : unsigned int
+        Must be 0
+    reductionOp : CUstreamAtomicReductionOpType
+        See CUstreamAtomicReductionOpType
+    dataType : CUstreamAtomicReductionDataType
+        See CUstreamAtomicReductionDataType
+    address : CUdeviceptr
+        The address the atomic operation will be operated on
+    value : cuuint64_t
+        The operand value the atomic operation will operate with
+    alias : CUdeviceptr
+        For driver internal use. Initial value is unimportant.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamBatchMemOpParams_union* _pvt_ptr
+    cdef CUdeviceptr _address
+    cdef cuuint64_t _value
+    cdef CUdeviceptr _alias
+
+cdef class CUstreamBatchMemOpParams_union:
+    """
+    Per-operation parameters for cuStreamBatchMemOp
+
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+        Operation. This is the first field of all the union elemets and
+        acts as a TAG to determine which union member is valid.
+    waitValue : CUstreamMemOpWaitValueParams_st
+        Params for CU_STREAM_MEM_OP_WAIT_VALUE_32 and
+        CU_STREAM_MEM_OP_WAIT_VALUE_64 operations.
+    writeValue : CUstreamMemOpWriteValueParams_st
+        Params for CU_STREAM_MEM_OP_WRITE_VALUE_32 and
+        CU_STREAM_MEM_OP_WRITE_VALUE_64 operations.
+    flushRemoteWrites : CUstreamMemOpFlushRemoteWritesParams_st
+        Params for CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES operations.
+    memoryBarrier : CUstreamMemOpMemoryBarrierParams_st
+        Params for CU_STREAM_MEM_OP_BARRIER operations.
+    atomicReduction : CUstreamMemOpAtomicReductionParams_st
+
+    pad : list[cuuint64_t]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamBatchMemOpParams_union _pvt_val
+    cdef cydriver.CUstreamBatchMemOpParams_union* _pvt_ptr
+    cdef CUstreamMemOpWaitValueParams_st _waitValue
+    cdef CUstreamMemOpWriteValueParams_st _writeValue
+    cdef CUstreamMemOpFlushRemoteWritesParams_st _flushRemoteWrites
+    cdef CUstreamMemOpMemoryBarrierParams_st _memoryBarrier
+    cdef CUstreamMemOpAtomicReductionParams_st _atomicReduction
+
+cdef class CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st:
+    """
+    Batch memory operation node parameters  Used in the legacy
+    cuGraphAddBatchMemOpNode api. New code should use cuGraphAddNode()
+
+    Attributes
+    ----------
+    ctx : CUcontext
+
+    count : unsigned int
+
+    paramArray : CUstreamBatchMemOpParams
+
+    flags : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st _pvt_val
+    cdef cydriver.CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st* _pvt_ptr
+    cdef CUcontext _ctx
+    cdef size_t _paramArray_length
+    cdef cydriver.CUstreamBatchMemOpParams* _paramArray
+
+cdef class CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st:
+    """
+    Batch memory operation node parameters
+
+    Attributes
+    ----------
+    ctx : CUcontext
+        Context to use for the operations.
+    count : unsigned int
+        Number of operations in paramArray.
+    paramArray : CUstreamBatchMemOpParams
+        Array of batch memory operations.
+    flags : unsigned int
+        Flags to control the node.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef CUcontext _ctx
+    cdef size_t _paramArray_length
+    cdef cydriver.CUstreamBatchMemOpParams* _paramArray
+
+cdef class anon_struct0:
+    """
+    Attributes
+    ----------
+    bytesOverBudget : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUasyncNotificationInfo_st* _pvt_ptr
+
+cdef class anon_union2:
+    """
+    Attributes
+    ----------
+    overBudget : anon_struct0
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUasyncNotificationInfo_st* _pvt_ptr
+    cdef anon_struct0 _overBudget
+
+cdef class CUasyncNotificationInfo_st:
+    """
+    Information passed to the user via the async notification callback
+
+    Attributes
+    ----------
+    type : CUasyncNotificationType
+        The type of notification being sent
+    info : anon_union2
+        Information about the notification. `typename` must be checked in
+        order to interpret this field.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUasyncNotificationInfo_st* _val_ptr
+    cdef cydriver.CUasyncNotificationInfo_st* _pvt_ptr
+    cdef anon_union2 _info
+
+cdef class CUdevprop_st:
+    """
+    Legacy device properties
+
+    Attributes
+    ----------
+    maxThreadsPerBlock : int
+        Maximum number of threads per block
+    maxThreadsDim : list[int]
+        Maximum size of each dimension of a block
+    maxGridSize : list[int]
+        Maximum size of each dimension of a grid
+    sharedMemPerBlock : int
+        Shared memory available per block in bytes
+    totalConstantMemory : int
+        Constant memory available on device in bytes
+    SIMDWidth : int
+        Warp size in threads
+    memPitch : int
+        Maximum pitch in bytes allowed by memory copies
+    regsPerBlock : int
+        32-bit registers available per block
+    clockRate : int
+        Clock frequency in kilohertz
+    textureAlign : int
+        Alignment requirement for textures
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUdevprop_st _pvt_val
+    cdef cydriver.CUdevprop_st* _pvt_ptr
+
+cdef class CUaccessPolicyWindow_st:
+    """
+    Specifies an access policy for a window, a contiguous extent of
+    memory beginning at base_ptr and ending at base_ptr + num_bytes.
+    num_bytes is limited by
+    CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE. Partition into
+    many segments and assign segments such that: sum of "hit segments"
+    / window == approx. ratio. sum of "miss segments" / window ==
+    approx 1-ratio. Segments and ratio specifications are fitted to the
+    capabilities of the architecture. Accesses in a hit segment apply
+    the hitProp access policy. Accesses in a miss segment apply the
+    missProp access policy.
+
+    Attributes
+    ----------
+    base_ptr : Any
+        Starting address of the access policy window. CUDA driver may align
+        it.
+    num_bytes : size_t
+        Size in bytes of the window policy. CUDA driver may restrict the
+        maximum size and alignment.
+    hitRatio : float
+        hitRatio specifies percentage of lines assigned hitProp, rest are
+        assigned missProp.
+    hitProp : CUaccessProperty
+        CUaccessProperty set for hit.
+    missProp : CUaccessProperty
+        CUaccessProperty set for miss. Must be either NORMAL or STREAMING
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUaccessPolicyWindow_st _pvt_val
+    cdef cydriver.CUaccessPolicyWindow_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cybase_ptr
+
+cdef class CUDA_KERNEL_NODE_PARAMS_st:
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_KERNEL_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_KERNEL_NODE_PARAMS_st* _pvt_ptr
+    cdef CUfunction _func
+    cdef _HelperKernelParams _cykernelParams
+
+cdef class CUDA_KERNEL_NODE_PARAMS_v2_st:
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+    kern : CUkernel
+        Kernel to launch, will only be referenced if func is NULL
+    ctx : CUcontext
+        Context for the kernel task to run in. The value NULL will indicate
+        the current context should be used by the api. This field is
+        ignored if func is set.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_KERNEL_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_KERNEL_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef CUfunction _func
+    cdef _HelperKernelParams _cykernelParams
+    cdef CUkernel _kern
+    cdef CUcontext _ctx
+
+cdef class CUDA_KERNEL_NODE_PARAMS_v3_st:
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+    kern : CUkernel
+        Kernel to launch, will only be referenced if func is NULL
+    ctx : CUcontext
+        Context for the kernel task to run in. The value NULL will indicate
+        the current context should be used by the api. This field is
+        ignored if func is set.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_KERNEL_NODE_PARAMS_v3_st _pvt_val
+    cdef cydriver.CUDA_KERNEL_NODE_PARAMS_v3_st* _pvt_ptr
+    cdef CUfunction _func
+    cdef _HelperKernelParams _cykernelParams
+    cdef CUkernel _kern
+    cdef CUcontext _ctx
+
+cdef class CUDA_MEMSET_NODE_PARAMS_st:
+    """
+    Memset node parameters
+
+    Attributes
+    ----------
+    dst : CUdeviceptr
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMSET_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_MEMSET_NODE_PARAMS_st* _pvt_ptr
+    cdef CUdeviceptr _dst
+
+cdef class CUDA_MEMSET_NODE_PARAMS_v2_st:
+    """
+    Memset node parameters
+
+    Attributes
+    ----------
+    dst : CUdeviceptr
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+    ctx : CUcontext
+        Context on which to run the node
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMSET_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_MEMSET_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef CUdeviceptr _dst
+    cdef CUcontext _ctx
+
+cdef class CUDA_HOST_NODE_PARAMS_st:
+    """
+    Host node parameters
+
+    Attributes
+    ----------
+    fn : CUhostFn
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_HOST_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_HOST_NODE_PARAMS_st* _pvt_ptr
+    cdef CUhostFn _fn
+    cdef _HelperInputVoidPtr _cyuserData
+
+cdef class CUDA_HOST_NODE_PARAMS_v2_st:
+    """
+    Host node parameters
+
+    Attributes
+    ----------
+    fn : CUhostFn
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+    syncMode : unsigned int
+        The sync mode to use for the host task
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_HOST_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_HOST_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef CUhostFn _fn
+    cdef _HelperInputVoidPtr _cyuserData
+
+cdef class CUDA_CONDITIONAL_NODE_PARAMS:
+    """
+    Conditional node parameters
+
+    Attributes
+    ----------
+    handle : CUgraphConditionalHandle
+        Conditional node handle. Handles must be created in advance of
+        creating the node using cuGraphConditionalHandleCreate.
+    type : CUgraphConditionalNodeType
+        Type of conditional node.
+    size : unsigned int
+        Size of graph output array. Allowed values are 1 for
+        CU_GRAPH_COND_TYPE_WHILE, 1 or 2 for CU_GRAPH_COND_TYPE_IF, or any
+        value greater than zero for CU_GRAPH_COND_TYPE_SWITCH.
+    phGraph_out : CUgraph
+        CUDA-owned array populated with conditional node child graphs
+        during creation of the node. Valid for the lifetime of the
+        conditional node. The contents of the graph(s) are subject to the
+        following constraints:   - Allowed node types are kernel nodes,
+        empty nodes, child graphs, memsets, memcopies, and conditionals.
+        This applies recursively to child graphs and conditional bodies.
+        - All kernels, including kernels in nested conditionals or child
+        graphs at any level, must belong to the same CUDA context.
+        These graphs may be populated using graph node creation APIs or
+        cuStreamBeginCaptureToGraph.  CU_GRAPH_COND_TYPE_IF: phGraph_out[0]
+        is executed when the condition is non-zero. If `size` == 2,
+        phGraph_out[1] will be executed when the condition is zero.
+        CU_GRAPH_COND_TYPE_WHILE: phGraph_out[0] is executed as long as the
+        condition is non-zero. CU_GRAPH_COND_TYPE_SWITCH: phGraph_out[n] is
+        executed when the condition is equal to n. If the condition >=
+        `size`, no body graph is executed.
+    ctx : CUcontext
+        Context on which to run the node. Must match context used to create
+        the handle and all body nodes.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_CONDITIONAL_NODE_PARAMS _pvt_val
+    cdef cydriver.CUDA_CONDITIONAL_NODE_PARAMS* _pvt_ptr
+    cdef CUgraphConditionalHandle _handle
+    cdef size_t _phGraph_out_length
+    cdef cydriver.CUgraph* _phGraph_out
+    cdef CUcontext _ctx
+
+cdef class CUgraphEdgeData_st:
+    """
+    Optional annotation for edges in a CUDA graph. Note, all edges
+    implicitly have annotations and default to a zero-initialized value
+    if not specified. A zero-initialized struct indicates a standard
+    full serialization of two nodes with memory visibility.
+
+    Attributes
+    ----------
+    from_port : bytes
+        This indicates when the dependency is triggered from the upstream
+        node on the edge. The meaning is specfic to the node type. A value
+        of 0 in all cases means full completion of the upstream node, with
+        memory visibility to the downstream node or portion thereof
+        (indicated by `to_port`).   Only kernel nodes define non-zero
+        ports. A kernel node can use the following output port types:
+        CU_GRAPH_KERNEL_NODE_PORT_DEFAULT,
+        CU_GRAPH_KERNEL_NODE_PORT_PROGRAMMATIC, or
+        CU_GRAPH_KERNEL_NODE_PORT_LAUNCH_ORDER.
+    to_port : bytes
+        This indicates what portion of the downstream node is dependent on
+        the upstream node or portion thereof (indicated by `from_port`).
+        The meaning is specific to the node type. A value of 0 in all cases
+        means the entirety of the downstream node is dependent on the
+        upstream work.   Currently no node types define non-zero ports.
+        Accordingly, this field must be set to zero.
+    type : bytes
+        This should be populated with a value from CUgraphDependencyType.
+        (It is typed as char due to compiler-specific layout of bitfields.)
+        See CUgraphDependencyType.
+    reserved : bytes
+        These bytes are unused and must be zeroed. This ensures
+        compatibility if additional fields are added in the future.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUgraphEdgeData_st _pvt_val
+    cdef cydriver.CUgraphEdgeData_st* _pvt_ptr
+
+cdef class CUDA_GRAPH_INSTANTIATE_PARAMS_st:
+    """
+    Graph instantiation parameters
+
+    Attributes
+    ----------
+    flags : cuuint64_t
+        Instantiation flags
+    hUploadStream : CUstream
+        Upload stream
+    hErrNode_out : CUgraphNode
+        The node which caused instantiation to fail, if any
+    result_out : CUgraphInstantiateResult
+        Whether instantiation was successful. If it failed, the reason why
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_GRAPH_INSTANTIATE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_GRAPH_INSTANTIATE_PARAMS_st* _pvt_ptr
+    cdef cuuint64_t _flags
+    cdef CUstream _hUploadStream
+    cdef CUgraphNode _hErrNode_out
+
+cdef class CUlaunchMemSyncDomainMap_st:
+    """
+    Memory Synchronization Domain map  See ::cudaLaunchMemSyncDomain.
+    By default, kernels are launched in domain 0. Kernel launched with
+    CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE will have a different domain ID.
+    User may also alter the domain ID with CUlaunchMemSyncDomainMap for
+    a specific stream / graph node / kernel launch. See
+    CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.  Domain ID range is
+    available through CU_DEVICE_ATTRIBUTE_MEM_SYNC_DOMAIN_COUNT.
+
+    Attributes
+    ----------
+    default_ : bytes
+        The default domain ID to use for designated kernels
+    remote : bytes
+        The remote domain ID to use for designated kernels
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchMemSyncDomainMap_st _pvt_val
+    cdef cydriver.CUlaunchMemSyncDomainMap_st* _pvt_ptr
+
+cdef class anon_struct1:
+    """
+    Attributes
+    ----------
+    x : unsigned int
+
+    y : unsigned int
+
+    z : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttributeValue_union* _pvt_ptr
+
+cdef class anon_struct2:
+    """
+    Attributes
+    ----------
+    event : CUevent
+
+    flags : int
+
+    triggerAtBlockStart : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttributeValue_union* _pvt_ptr
+    cdef CUevent _event
+
+cdef class anon_struct3:
+    """
+    Attributes
+    ----------
+    event : CUevent
+
+    flags : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttributeValue_union* _pvt_ptr
+    cdef CUevent _event
+
+cdef class anon_struct4:
+    """
+    Attributes
+    ----------
+    x : unsigned int
+
+    y : unsigned int
+
+    z : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttributeValue_union* _pvt_ptr
+
+cdef class anon_struct5:
+    """
+    Attributes
+    ----------
+    deviceUpdatable : int
+
+    devNode : CUgraphDeviceNode
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttributeValue_union* _pvt_ptr
+    cdef CUgraphDeviceNode _devNode
+
+cdef class CUlaunchAttributeValue_union:
+    """
+    Launch attributes union; used as value field of CUlaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : CUaccessPolicyWindow
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW.
+    cooperative : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero
+        indicates a cooperative kernel (see cuLaunchCooperativeKernel).
+    syncPolicy : CUsynchronizationPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY.
+        ::CUsynchronizationPolicy for work queued up in this stream
+    clusterDim : anon_struct1
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+        that represents the desired cluster dimensions for the kernel.
+        Opaque type with the following fields: - `x` - The X dimension of
+        the cluster, in blocks. Must be a divisor of the grid X dimension.
+        - `y` - The Y dimension of the cluster, in blocks. Must be a
+        divisor of the grid Y dimension.    - `z` - The Z dimension of the
+        cluster, in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : CUclusterSchedulingPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.
+    programmaticEvent : anon_struct2
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+        with the following fields: - `CUevent` event - Event to fire when
+        all blocks trigger it.    - `Event` record flags, see
+        cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+        - `triggerAtBlockStart` - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    launchCompletionEvent : anon_struct3
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT with the following
+        fields: - `CUevent` event - Event to fire when the last block
+        launches    - `int` flags; - Event record flags, see
+        cuEventRecordWithFlags. Does not accept CU_EVENT_RECORD_EXTERNAL.
+    priority : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution
+        priority of the kernel.
+    memSyncDomainMap : CUlaunchMemSyncDomainMap
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+        See CUlaunchMemSyncDomainMap.
+    memSyncDomain : CUlaunchMemSyncDomain
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN.
+        See::CUlaunchMemSyncDomain
+    preferredClusterDim : anon_struct4
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        CUlaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        CUlaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        CUlaunchAttributeValue::clusterDim.
+    deviceUpdatableKernelNode : anon_struct5
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE. with the
+        following fields: - `int` deviceUpdatable - Whether or not the
+        resulting kernel node should be device-updatable.    -
+        `CUgraphDeviceNode` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+    nvlinkUtilCentricScheduling : unsigned int
+
+    portableClusterSizeMode : CUlaunchAttributePortableClusterMode
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE.
+    sharedMemoryMode : CUsharedMemoryMode
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE.
+        See CUsharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttributeValue_union _pvt_val
+    cdef cydriver.CUlaunchAttributeValue_union* _pvt_ptr
+    cdef CUaccessPolicyWindow _accessPolicyWindow
+    cdef anon_struct1 _clusterDim
+    cdef anon_struct2 _programmaticEvent
+    cdef anon_struct3 _launchCompletionEvent
+    cdef CUlaunchMemSyncDomainMap _memSyncDomainMap
+    cdef anon_struct4 _preferredClusterDim
+    cdef anon_struct5 _deviceUpdatableKernelNode
+
+cdef class CUlaunchAttribute_st:
+    """
+    Launch attribute
+
+    Attributes
+    ----------
+    id : CUlaunchAttributeID
+        Attribute to set
+    value : CUlaunchAttributeValue
+        Value of the attribute
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchAttribute_st _pvt_val
+    cdef cydriver.CUlaunchAttribute_st* _pvt_ptr
+    cdef CUlaunchAttributeValue _value
+
+cdef class CUlaunchConfig_st:
+    """
+    CUDA extensible launch configuration
+
+    Attributes
+    ----------
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    hStream : CUstream
+        Stream identifier
+    attrs : CUlaunchAttribute
+        List of attributes; nullable if CUlaunchConfig::numAttrs == 0
+    numAttrs : unsigned int
+        Number of attributes populated in CUlaunchConfig::attrs
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlaunchConfig_st _pvt_val
+    cdef cydriver.CUlaunchConfig_st* _pvt_ptr
+    cdef CUstream _hStream
+    cdef size_t _attrs_length
+    cdef cydriver.CUlaunchAttribute* _attrs
+
+cdef class CUexecAffinitySmCount_st:
+    """
+    Value for CU_EXEC_AFFINITY_TYPE_SM_COUNT
+
+    Attributes
+    ----------
+    val : unsigned int
+        The number of SMs the context is limited to use.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUexecAffinitySmCount_st _pvt_val
+    cdef cydriver.CUexecAffinitySmCount_st* _pvt_ptr
+
+cdef class anon_union3:
+    """
+    Attributes
+    ----------
+    smCount : CUexecAffinitySmCount
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUexecAffinityParam_st* _pvt_ptr
+    cdef CUexecAffinitySmCount _smCount
+
+cdef class CUexecAffinityParam_st:
+    """
+    Execution Affinity Parameters
+
+    Attributes
+    ----------
+    type : CUexecAffinityType
+        Type of execution affinity.
+    param : anon_union3
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUexecAffinityParam_st* _val_ptr
+    cdef cydriver.CUexecAffinityParam_st* _pvt_ptr
+    cdef anon_union3 _param
+
+cdef class CUctxCigParam_st:
+    """
+    CIG Context Create Params
+
+    Attributes
+    ----------
+    sharedDataType : CUcigDataType
+        Type of shared data from graphics client (D3D12 or Vulkan).
+    sharedData : Any
+        Graphics client data handle (ID3D12CommandQueue or Nvidia specific
+        data blob).
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUctxCigParam_st _pvt_val
+    cdef cydriver.CUctxCigParam_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cysharedData
+
+cdef class CUctxCreateParams_st:
+    """
+    Params for creating CUDA context. Both execAffinityParams and
+    cigParams cannot be non-NULL at the same time. If both are NULL,
+    the context will be created as a regular CUDA context.
+
+    Attributes
+    ----------
+    execAffinityParams : CUexecAffinityParam
+        Array of execution affinity parameters to limit context resources
+        (e.g., SM count). Only supported Volta+ MPS. Mutually exclusive
+        with cigParams.
+    numExecAffinityParams : int
+        Number of elements in execAffinityParams array. Must be 0 if
+        execAffinityParams is NULL.
+    cigParams : CUctxCigParam
+        CIG (CUDA in Graphics) parameters for sharing data from
+        D3D12/Vulkan graphics clients. Mutually exclusive with
+        execAffinityParams.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUctxCreateParams_st _pvt_val
+    cdef cydriver.CUctxCreateParams_st* _pvt_ptr
+    cdef size_t _execAffinityParams_length
+    cdef cydriver.CUexecAffinityParam* _execAffinityParams
+    cdef size_t _cigParams_length
+    cdef cydriver.CUctxCigParam* _cigParams
+
+cdef class CUstreamCigParam_st:
+    """
+    CIG Stream Capture Params
+
+    Attributes
+    ----------
+    streamSharedDataType : CUstreamCigDataType
+        Type of shared data from graphics client (D3D12).
+    streamSharedData : Any
+        Graphics client data handle
+        (ID3D12CommandList/ID3D12GraphicsCommandList).
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamCigParam_st _pvt_val
+    cdef cydriver.CUstreamCigParam_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cystreamSharedData
+
+cdef class CUstreamCigCaptureParams_st:
+    """
+    Params for capturing CUDA stream to CIG streamCigParams must be
+    non-NULL.
+
+    Attributes
+    ----------
+    streamCigParams : CUstreamCigParam
+        CIG (CUDA in Graphics) parameters for sharing command list data
+        from D3D12 graphics clients.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUstreamCigCaptureParams_st _pvt_val
+    cdef cydriver.CUstreamCigCaptureParams_st* _pvt_ptr
+    cdef size_t _streamCigParams_length
+    cdef cydriver.CUstreamCigParam* _streamCigParams
+
+cdef class CUlibraryHostUniversalFunctionAndDataTable_st:
+    """
+    Attributes
+    ----------
+    functionTable : Any
+
+    functionWindowSize : size_t
+
+    dataTable : Any
+
+    dataWindowSize : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUlibraryHostUniversalFunctionAndDataTable_st _pvt_val
+    cdef cydriver.CUlibraryHostUniversalFunctionAndDataTable_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfunctionTable
+    cdef _HelperInputVoidPtr _cydataTable
+
+cdef class CUDA_MEMCPY2D_st:
+    """
+    2D memory copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    WidthInBytes : size_t
+        Width of 2D memory copy in bytes
+    Height : size_t
+        Height of 2D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMCPY2D_st _pvt_val
+    cdef cydriver.CUDA_MEMCPY2D_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cysrcHost
+    cdef CUdeviceptr _srcDevice
+    cdef CUarray _srcArray
+    cdef _HelperInputVoidPtr _cydstHost
+    cdef CUdeviceptr _dstDevice
+    cdef CUarray _dstArray
+
+cdef class CUDA_MEMCPY3D_st:
+    """
+    3D memory copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcZ : size_t
+        Source Z
+    srcLOD : size_t
+        Source LOD
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    reserved0 : Any
+        Must be NULL
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    srcHeight : size_t
+        Source height (ignored when src is array; may be 0 if Depth==1)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstZ : size_t
+        Destination Z
+    dstLOD : size_t
+        Destination LOD
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    reserved1 : Any
+        Must be NULL
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    dstHeight : size_t
+        Destination height (ignored when dst is array; may be 0 if
+        Depth==1)
+    WidthInBytes : size_t
+        Width of 3D memory copy in bytes
+    Height : size_t
+        Height of 3D memory copy
+    Depth : size_t
+        Depth of 3D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMCPY3D_st _pvt_val
+    cdef cydriver.CUDA_MEMCPY3D_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cysrcHost
+    cdef CUdeviceptr _srcDevice
+    cdef CUarray _srcArray
+    cdef _HelperInputVoidPtr _cyreserved0
+    cdef _HelperInputVoidPtr _cydstHost
+    cdef CUdeviceptr _dstDevice
+    cdef CUarray _dstArray
+    cdef _HelperInputVoidPtr _cyreserved1
+
+cdef class CUDA_MEMCPY3D_PEER_st:
+    """
+    3D memory cross-context copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcZ : size_t
+        Source Z
+    srcLOD : size_t
+        Source LOD
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    srcContext : CUcontext
+        Source context (ignored with srcMemoryType is CU_MEMORYTYPE_ARRAY)
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    srcHeight : size_t
+        Source height (ignored when src is array; may be 0 if Depth==1)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstZ : size_t
+        Destination Z
+    dstLOD : size_t
+        Destination LOD
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    dstContext : CUcontext
+        Destination context (ignored with dstMemoryType is
+        CU_MEMORYTYPE_ARRAY)
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    dstHeight : size_t
+        Destination height (ignored when dst is array; may be 0 if
+        Depth==1)
+    WidthInBytes : size_t
+        Width of 3D memory copy in bytes
+    Height : size_t
+        Height of 3D memory copy
+    Depth : size_t
+        Depth of 3D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMCPY3D_PEER_st _pvt_val
+    cdef cydriver.CUDA_MEMCPY3D_PEER_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cysrcHost
+    cdef CUdeviceptr _srcDevice
+    cdef CUarray _srcArray
+    cdef CUcontext _srcContext
+    cdef _HelperInputVoidPtr _cydstHost
+    cdef CUdeviceptr _dstDevice
+    cdef CUarray _dstArray
+    cdef CUcontext _dstContext
+
+cdef class CUDA_MEMCPY_NODE_PARAMS_st:
+    """
+    Memcpy node parameters
+
+    Attributes
+    ----------
+    flags : int
+        Must be zero
+    reserved : int
+        Must be zero
+    copyCtx : CUcontext
+        Context on which to run the node
+    copyParams : CUDA_MEMCPY3D
+        Parameters for the memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMCPY_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_MEMCPY_NODE_PARAMS_st* _pvt_ptr
+    cdef CUcontext _copyCtx
+    cdef CUDA_MEMCPY3D _copyParams
+
+cdef class CUDA_ARRAY_DESCRIPTOR_st:
+    """
+    Array descriptor
+
+    Attributes
+    ----------
+    Width : size_t
+        Width of array
+    Height : size_t
+        Height of array
+    Format : CUarray_format
+        Array format
+    NumChannels : unsigned int
+        Channels per array element
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_ARRAY_DESCRIPTOR_st _pvt_val
+    cdef cydriver.CUDA_ARRAY_DESCRIPTOR_st* _pvt_ptr
+
+cdef class CUDA_ARRAY3D_DESCRIPTOR_st:
+    """
+    3D array descriptor
+
+    Attributes
+    ----------
+    Width : size_t
+        Width of 3D array
+    Height : size_t
+        Height of 3D array
+    Depth : size_t
+        Depth of 3D array
+    Format : CUarray_format
+        Array format
+    NumChannels : unsigned int
+        Channels per array element
+    Flags : unsigned int
+        Flags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_ARRAY3D_DESCRIPTOR_st _pvt_val
+    cdef cydriver.CUDA_ARRAY3D_DESCRIPTOR_st* _pvt_ptr
+
+cdef class anon_struct6:
+    """
+    Attributes
+    ----------
+    width : unsigned int
+
+    height : unsigned int
+
+    depth : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_ARRAY_SPARSE_PROPERTIES_st* _pvt_ptr
+
+cdef class CUDA_ARRAY_SPARSE_PROPERTIES_st:
+    """
+    CUDA array sparse properties
+
+    Attributes
+    ----------
+    tileExtent : anon_struct6
+
+    miptailFirstLevel : unsigned int
+        First mip level at which the mip tail begins.
+    miptailSize : unsigned long long
+        Total size of the mip tail.
+    flags : unsigned int
+        Flags will either be zero or
+        CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_ARRAY_SPARSE_PROPERTIES_st _pvt_val
+    cdef cydriver.CUDA_ARRAY_SPARSE_PROPERTIES_st* _pvt_ptr
+    cdef anon_struct6 _tileExtent
+
+cdef class CUDA_ARRAY_MEMORY_REQUIREMENTS_st:
+    """
+    CUDA array memory requirements
+
+    Attributes
+    ----------
+    size : size_t
+        Total required memory size
+    alignment : size_t
+        alignment requirement
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_ARRAY_MEMORY_REQUIREMENTS_st _pvt_val
+    cdef cydriver.CUDA_ARRAY_MEMORY_REQUIREMENTS_st* _pvt_ptr
+
+cdef class anon_struct7:
+    """
+    Attributes
+    ----------
+    hArray : CUarray
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+    cdef CUarray _hArray
+
+cdef class anon_struct8:
+    """
+    Attributes
+    ----------
+    hMipmappedArray : CUmipmappedArray
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+    cdef CUmipmappedArray _hMipmappedArray
+
+cdef class anon_struct9:
+    """
+    Attributes
+    ----------
+    devPtr : CUdeviceptr
+
+    format : CUarray_format
+
+    numChannels : unsigned int
+
+    sizeInBytes : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+    cdef CUdeviceptr _devPtr
+
+cdef class anon_struct10:
+    """
+    Attributes
+    ----------
+    devPtr : CUdeviceptr
+
+    format : CUarray_format
+
+    numChannels : unsigned int
+
+    width : size_t
+
+    height : size_t
+
+    pitchInBytes : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+    cdef CUdeviceptr _devPtr
+
+cdef class anon_struct11:
+    """
+    Attributes
+    ----------
+    reserved : list[int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+
+cdef class anon_union4:
+    """
+    Attributes
+    ----------
+    array : anon_struct7
+
+    mipmap : anon_struct8
+
+    linear : anon_struct9
+
+    pitch2D : anon_struct10
+
+    reserved : anon_struct11
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+    cdef anon_struct7 _array
+    cdef anon_struct8 _mipmap
+    cdef anon_struct9 _linear
+    cdef anon_struct10 _pitch2D
+    cdef anon_struct11 _reserved
+
+cdef class CUDA_RESOURCE_DESC_st:
+    """
+    CUDA Resource descriptor
+
+    Attributes
+    ----------
+    resType : CUresourcetype
+        Resource type
+    res : anon_union4
+
+    flags : unsigned int
+        Flags (must be zero)
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _val_ptr
+    cdef cydriver.CUDA_RESOURCE_DESC_st* _pvt_ptr
+    cdef anon_union4 _res
+
+cdef class CUDA_TEXTURE_DESC_st:
+    """
+    Texture descriptor
+
+    Attributes
+    ----------
+    addressMode : list[CUaddress_mode]
+        Address modes
+    filterMode : CUfilter_mode
+        Filter mode
+    flags : unsigned int
+        Flags
+    maxAnisotropy : unsigned int
+        Maximum anisotropy ratio
+    mipmapFilterMode : CUfilter_mode
+        Mipmap filter mode
+    mipmapLevelBias : float
+        Mipmap level bias
+    minMipmapLevelClamp : float
+        Mipmap minimum level clamp
+    maxMipmapLevelClamp : float
+        Mipmap maximum level clamp
+    borderColor : list[float]
+        Border Color
+    reserved : list[int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_TEXTURE_DESC_st _pvt_val
+    cdef cydriver.CUDA_TEXTURE_DESC_st* _pvt_ptr
+
+cdef class CUDA_RESOURCE_VIEW_DESC_st:
+    """
+    Resource view descriptor
+
+    Attributes
+    ----------
+    format : CUresourceViewFormat
+        Resource view format
+    width : size_t
+        Width of the resource view
+    height : size_t
+        Height of the resource view
+    depth : size_t
+        Depth of the resource view
+    firstMipmapLevel : unsigned int
+        First defined mipmap level
+    lastMipmapLevel : unsigned int
+        Last defined mipmap level
+    firstLayer : unsigned int
+        First layer index
+    lastLayer : unsigned int
+        Last layer index
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_RESOURCE_VIEW_DESC_st _pvt_val
+    cdef cydriver.CUDA_RESOURCE_VIEW_DESC_st* _pvt_ptr
+
+cdef class CUtensorMap_st:
+    """
+    Tensor map descriptor. Requires compiler support for aligning to
+    128 bytes.
+
+    Attributes
+    ----------
+    opaque : list[cuuint64_t]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUtensorMap_st _pvt_val
+    cdef cydriver.CUtensorMap_st* _pvt_ptr
+
+cdef class CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st:
+    """
+    GPU Direct v3 tokens
+
+    Attributes
+    ----------
+    p2pToken : unsigned long long
+
+    vaSpaceToken : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st _pvt_val
+    cdef cydriver.CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st* _pvt_ptr
+
+cdef class CUDA_LAUNCH_PARAMS_st:
+    """
+    Kernel launch parameters
+
+    Attributes
+    ----------
+    function : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    hStream : CUstream
+        Stream identifier
+    kernelParams : Any
+        Array of pointers to kernel parameters
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_LAUNCH_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_LAUNCH_PARAMS_st* _pvt_ptr
+    cdef CUfunction _function
+    cdef CUstream _hStream
+    cdef _HelperKernelParams _cykernelParams
+
+cdef class anon_struct12:
+    """
+    Attributes
+    ----------
+    handle : Any
+
+    name : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyhandle
+    cdef _HelperInputVoidPtr _cyname
+
+cdef class anon_union5:
+    """
+    Attributes
+    ----------
+    fd : int
+
+    win32 : anon_struct12
+
+    nvSciBufObject : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st* _pvt_ptr
+    cdef anon_struct12 _win32
+    cdef _HelperInputVoidPtr _cynvSciBufObject
+
+cdef class CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st:
+    """
+    External memory handle descriptor
+
+    Attributes
+    ----------
+    type : CUexternalMemoryHandleType
+        Type of the handle
+    handle : anon_union5
+
+    size : unsigned long long
+        Size of the memory allocation
+    flags : unsigned int
+        Flags must either be zero or CUDA_EXTERNAL_MEMORY_DEDICATED
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st* _val_ptr
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st* _pvt_ptr
+    cdef anon_union5 _handle
+
+cdef class CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st:
+    """
+    External memory buffer descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the buffer's base is
+    size : unsigned long long
+        Size of the buffer
+    flags : unsigned int
+        Flags reserved for future use. Must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st _pvt_val
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st* _pvt_ptr
+
+cdef class CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st:
+    """
+    External memory mipmap descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the base level of the mipmap
+        chain is.
+    arrayDesc : CUDA_ARRAY3D_DESCRIPTOR
+        Format, dimension and type of base level of the mipmap chain
+    numLevels : unsigned int
+        Total number of levels in the mipmap chain
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st _pvt_val
+    cdef cydriver.CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st* _pvt_ptr
+    cdef CUDA_ARRAY3D_DESCRIPTOR _arrayDesc
+
+cdef class anon_struct13:
+    """
+    Attributes
+    ----------
+    handle : Any
+
+    name : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyhandle
+    cdef _HelperInputVoidPtr _cyname
+
+cdef class anon_union6:
+    """
+    Attributes
+    ----------
+    fd : int
+
+    win32 : anon_struct13
+
+    nvSciSyncObj : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st* _pvt_ptr
+    cdef anon_struct13 _win32
+    cdef _HelperInputVoidPtr _cynvSciSyncObj
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st:
+    """
+    External semaphore handle descriptor
+
+    Attributes
+    ----------
+    type : CUexternalSemaphoreHandleType
+        Type of the handle
+    handle : anon_union6
+
+    flags : unsigned int
+        Flags reserved for the future. Must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st* _val_ptr
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st* _pvt_ptr
+    cdef anon_union6 _handle
+
+cdef class anon_struct14:
+    """
+    Attributes
+    ----------
+    value : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st* _pvt_ptr
+
+cdef class anon_union7:
+    """
+    Attributes
+    ----------
+    fence : Any
+
+    reserved : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfence
+
+cdef class anon_struct15:
+    """
+    Attributes
+    ----------
+    key : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st* _pvt_ptr
+
+cdef class anon_struct16:
+    """
+    Attributes
+    ----------
+    fence : anon_struct14
+
+    nvSciSync : anon_union7
+
+    keyedMutex : anon_struct15
+
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st* _pvt_ptr
+    cdef anon_struct14 _fence
+    cdef anon_union7 _nvSciSync
+    cdef anon_struct15 _keyedMutex
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st:
+    """
+    External semaphore signal parameters
+
+    Attributes
+    ----------
+    params : anon_struct16
+
+    flags : unsigned int
+        Only when ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to signal
+        a CUexternalSemaphore of type
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+        CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which
+        indicates that while signaling the CUexternalSemaphore, no memory
+        synchronization operations should be performed for any external
+        memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+        For all other types of CUexternalSemaphore, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st* _pvt_ptr
+    cdef anon_struct16 _params
+
+cdef class anon_struct17:
+    """
+    Attributes
+    ----------
+    value : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st* _pvt_ptr
+
+cdef class anon_union8:
+    """
+    Attributes
+    ----------
+    fence : Any
+
+    reserved : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfence
+
+cdef class anon_struct18:
+    """
+    Attributes
+    ----------
+    key : unsigned long long
+
+    timeoutMs : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st* _pvt_ptr
+
+cdef class anon_struct19:
+    """
+    Attributes
+    ----------
+    fence : anon_struct17
+
+    nvSciSync : anon_union8
+
+    keyedMutex : anon_struct18
+
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st* _pvt_ptr
+    cdef anon_struct17 _fence
+    cdef anon_union8 _nvSciSync
+    cdef anon_struct18 _keyedMutex
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st:
+    """
+    External semaphore wait parameters
+
+    Attributes
+    ----------
+    params : anon_struct19
+
+    flags : unsigned int
+        Only when ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on
+        a CUexternalSemaphore of type
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+        CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC which indicates
+        that while waiting for the CUexternalSemaphore, no memory
+        synchronization operations should be performed for any external
+        memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+        For all other types of CUexternalSemaphore, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st* _pvt_ptr
+    cdef anon_struct19 _params
+
+cdef class CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st:
+    """
+    Semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cydriver.CUexternalSemaphore* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* _paramsArray
+
+cdef class CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st:
+    """
+    Semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cydriver.CUexternalSemaphore* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS* _paramsArray
+
+cdef class CUDA_EXT_SEM_WAIT_NODE_PARAMS_st:
+    """
+    Semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXT_SEM_WAIT_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_EXT_SEM_WAIT_NODE_PARAMS_st* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cydriver.CUexternalSemaphore* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* _paramsArray
+
+cdef class CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st:
+    """
+    Semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cydriver.CUexternalSemaphore* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cydriver.CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS* _paramsArray
+
+cdef class anon_union9:
+    """
+    Attributes
+    ----------
+    mipmap : CUmipmappedArray
+
+    array : CUarray
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUarrayMapInfo_st* _pvt_ptr
+    cdef CUmipmappedArray _mipmap
+    cdef CUarray _array
+
+cdef class anon_struct20:
+    """
+    Attributes
+    ----------
+    level : unsigned int
+
+    layer : unsigned int
+
+    offsetX : unsigned int
+
+    offsetY : unsigned int
+
+    offsetZ : unsigned int
+
+    extentWidth : unsigned int
+
+    extentHeight : unsigned int
+
+    extentDepth : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUarrayMapInfo_st* _pvt_ptr
+
+cdef class anon_struct21:
+    """
+    Attributes
+    ----------
+    layer : unsigned int
+
+    offset : unsigned long long
+
+    size : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUarrayMapInfo_st* _pvt_ptr
+
+cdef class anon_union10:
+    """
+    Attributes
+    ----------
+    sparseLevel : anon_struct20
+
+    miptail : anon_struct21
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUarrayMapInfo_st* _pvt_ptr
+    cdef anon_struct20 _sparseLevel
+    cdef anon_struct21 _miptail
+
+cdef class anon_union11:
+    """
+    Attributes
+    ----------
+    memHandle : CUmemGenericAllocationHandle
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUarrayMapInfo_st* _pvt_ptr
+    cdef CUmemGenericAllocationHandle _memHandle
+
+cdef class CUarrayMapInfo_st:
+    """
+    Specifies the CUDA array or CUDA mipmapped array memory mapping
+    information
+
+    Attributes
+    ----------
+    resourceType : CUresourcetype
+        Resource type
+    resource : anon_union9
+
+    subresourceType : CUarraySparseSubresourceType
+        Sparse subresource type
+    subresource : anon_union10
+
+    memOperationType : CUmemOperationType
+        Memory operation type
+    memHandleType : CUmemHandleType
+        Memory handle type
+    memHandle : anon_union11
+
+    offset : unsigned long long
+        Offset within mip tail  Offset within the memory
+    deviceBitMask : unsigned int
+        Device ordinal bit mask
+    flags : unsigned int
+        flags for future use, must be zero now.
+    reserved : list[unsigned int]
+        Reserved for future use, must be zero now.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUarrayMapInfo_st* _val_ptr
+    cdef cydriver.CUarrayMapInfo_st* _pvt_ptr
+    cdef anon_union9 _resource
+    cdef anon_union10 _subresource
+    cdef anon_union11 _memHandle
+
+cdef class CUmemLocation_st:
+    """
+    Specifies a memory location.
+
+    Attributes
+    ----------
+    type : CUmemLocationType
+        Specifies the location type, which modifies the meaning of id.
+    id : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemLocation_st* _val_ptr
+    cdef cydriver.CUmemLocation_st* _pvt_ptr
+
+cdef class anon_struct22:
+    """
+    Attributes
+    ----------
+    compressionType : bytes
+
+    gpuDirectRDMACapable : bytes
+
+    usage : unsigned short
+
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemAllocationProp_st* _pvt_ptr
+
+cdef class CUmemAllocationProp_st:
+    """
+    Specifies the allocation properties for a allocation.
+
+    Attributes
+    ----------
+    type : CUmemAllocationType
+        Allocation type
+    requestedHandleTypes : CUmemAllocationHandleType
+        requested CUmemAllocationHandleType
+    location : CUmemLocation
+        Location of allocation
+    win32HandleMetaData : Any
+        Windows-specific POBJECT_ATTRIBUTES required when
+        CU_MEM_HANDLE_TYPE_WIN32 is specified. This object attributes
+        structure includes security attributes that define the scope of
+        which exported allocations may be transferred to other processes.
+        In all other cases, this field is required to be zero.
+    allocFlags : anon_struct22
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemAllocationProp_st _pvt_val
+    cdef cydriver.CUmemAllocationProp_st* _pvt_ptr
+    cdef CUmemLocation _location
+    cdef _HelperInputVoidPtr _cywin32HandleMetaData
+    cdef anon_struct22 _allocFlags
+
+cdef class CUmulticastObjectProp_st:
+    """
+    Specifies the properties for a multicast object.
+
+    Attributes
+    ----------
+    numDevices : unsigned int
+        The number of devices in the multicast team that will bind memory
+        to this object
+    size : size_t
+        The maximum amount of memory that can be bound to this multicast
+        object per device
+    handleTypes : unsigned long long
+        Bitmask of exportable handle types (see CUmemAllocationHandleType)
+        for this object
+    flags : unsigned long long
+        Flags for future use, must be zero now
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmulticastObjectProp_st _pvt_val
+    cdef cydriver.CUmulticastObjectProp_st* _pvt_ptr
+
+cdef class CUmemAccessDesc_st:
+    """
+    Memory access descriptor
+
+    Attributes
+    ----------
+    location : CUmemLocation
+        Location on which the request is to change it's accessibility
+    flags : CUmemAccess_flags
+        ::CUmemProt accessibility flags to set on the request
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemAccessDesc_st _pvt_val
+    cdef cydriver.CUmemAccessDesc_st* _pvt_ptr
+    cdef CUmemLocation _location
+
+cdef class CUgraphExecUpdateResultInfo_st:
+    """
+    Result information returned by cuGraphExecUpdate
+
+    Attributes
+    ----------
+    result : CUgraphExecUpdateResult
+        Gives more specific detail when a cuda graph update fails.
+    errorNode : CUgraphNode
+        The "to node" of the error edge when the topologies do not match.
+        The error node when the error is associated with a specific node.
+        NULL when the error is generic.
+    errorFromNode : CUgraphNode
+        The from node of error edge when the topologies do not match.
+        Otherwise NULL.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUgraphExecUpdateResultInfo_st _pvt_val
+    cdef cydriver.CUgraphExecUpdateResultInfo_st* _pvt_ptr
+    cdef CUgraphNode _errorNode
+    cdef CUgraphNode _errorFromNode
+
+cdef class CUmemPoolProps_st:
+    """
+    Specifies the properties of allocations made from the pool.
+
+    Attributes
+    ----------
+    allocType : CUmemAllocationType
+        Allocation type. Currently must be specified as
+        CU_MEM_ALLOCATION_TYPE_PINNED
+    handleTypes : CUmemAllocationHandleType
+        Handle types that will be supported by allocations from the pool.
+    location : CUmemLocation
+        Location where allocations should reside.
+    win32SecurityAttributes : Any
+        Windows-specific LPSECURITYATTRIBUTES required when
+        CU_MEM_HANDLE_TYPE_WIN32 is specified. This security attribute
+        defines the scope of which exported allocations may be transferred
+        to other processes. In all other cases, this field is required to
+        be zero.
+    maxSize : size_t
+        Maximum pool size. When set to 0, defaults to a system dependent
+        value.
+    usage : unsigned short
+        Bitmask indicating intended usage for the pool.
+    reserved : bytes
+        reserved for future use, must be 0
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemPoolProps_st _pvt_val
+    cdef cydriver.CUmemPoolProps_st* _pvt_ptr
+    cdef CUmemLocation _location
+    cdef _HelperInputVoidPtr _cywin32SecurityAttributes
+
+cdef class CUmemPoolPtrExportData_st:
+    """
+    Opaque data for exporting a pool allocation
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemPoolPtrExportData_st _pvt_val
+    cdef cydriver.CUmemPoolPtrExportData_st* _pvt_ptr
+
+cdef class CUmemcpyAttributes_st:
+    """
+    Attributes specific to copies within a batch. For more details on
+    usage see cuMemcpyBatchAsync.
+
+    Attributes
+    ----------
+    srcAccessOrder : CUmemcpySrcAccessOrder
+        Source access ordering to be observed for copies with this
+        attribute.
+    srcLocHint : CUmemLocation
+        Hint location for the source operand. Ignored when the pointers are
+        not managed memory or memory allocated outside CUDA.
+    dstLocHint : CUmemLocation
+        Hint location for the destination operand. Ignored when the
+        pointers are not managed memory or memory allocated outside CUDA.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See CUmemcpyFlags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemcpyAttributes_st _pvt_val
+    cdef cydriver.CUmemcpyAttributes_st* _pvt_ptr
+    cdef CUmemLocation _srcLocHint
+    cdef CUmemLocation _dstLocHint
+
+cdef class CUoffset3D_st:
+    """
+    Struct representing a 3D offset
+
+    Attributes
+    ----------
+    x : size_t
+
+    y : size_t
+
+    z : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUoffset3D_st _pvt_val
+    cdef cydriver.CUoffset3D_st* _pvt_ptr
+
+cdef class CUextent3D_st:
+    """
+    Struct representing width/height/depth of a CUarray in elements
+
+    Attributes
+    ----------
+    width : size_t
+
+    height : size_t
+
+    depth : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUextent3D_st _pvt_val
+    cdef cydriver.CUextent3D_st* _pvt_ptr
+
+cdef class anon_struct23:
+    """
+    Attributes
+    ----------
+    ptr : CUdeviceptr
+
+    rowLength : size_t
+
+    layerHeight : size_t
+
+    locHint : CUmemLocation
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemcpy3DOperand_st* _pvt_ptr
+    cdef CUdeviceptr _ptr
+    cdef CUmemLocation _locHint
+
+cdef class anon_struct24:
+    """
+    Attributes
+    ----------
+    array : CUarray
+
+    offset : CUoffset3D
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemcpy3DOperand_st* _pvt_ptr
+    cdef CUarray _array
+    cdef CUoffset3D _offset
+
+cdef class anon_union13:
+    """
+    Attributes
+    ----------
+    ptr : anon_struct23
+
+    array : anon_struct24
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemcpy3DOperand_st* _pvt_ptr
+    cdef anon_struct23 _ptr
+    cdef anon_struct24 _array
+
+cdef class CUmemcpy3DOperand_st:
+    """
+    Struct representing an operand for copy with cuMemcpy3DBatchAsync
+
+    Attributes
+    ----------
+    type : CUmemcpy3DOperandType
+
+    op : anon_union13
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemcpy3DOperand_st* _val_ptr
+    cdef cydriver.CUmemcpy3DOperand_st* _pvt_ptr
+    cdef anon_union13 _op
+
+cdef class CUDA_MEMCPY3D_BATCH_OP_st:
+    """
+    Attributes
+    ----------
+    src : CUmemcpy3DOperand
+        Source memcpy operand.
+    dst : CUmemcpy3DOperand
+        Destination memcpy operand.
+    extent : CUextent3D
+        Extents of the memcpy between src and dst. The width, height and
+        depth components must not be 0.
+    srcAccessOrder : CUmemcpySrcAccessOrder
+        Source access ordering to be observed for copy from src to dst.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See CUmemcpyFlags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEMCPY3D_BATCH_OP_st _pvt_val
+    cdef cydriver.CUDA_MEMCPY3D_BATCH_OP_st* _pvt_ptr
+    cdef CUmemcpy3DOperand _src
+    cdef CUmemcpy3DOperand _dst
+    cdef CUextent3D _extent
+
+cdef class CUDA_MEM_ALLOC_NODE_PARAMS_v1_st:
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : CUmemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be CU_MEM_HANDLE_TYPE_NONE. IPC is
+        not supported.
+    accessDescs : CUmemAccessDesc
+        in: array of memory access descriptors. Used to describe peer GPU
+        access
+    accessDescCount : size_t
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : CUdeviceptr
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEM_ALLOC_NODE_PARAMS_v1_st _pvt_val
+    cdef cydriver.CUDA_MEM_ALLOC_NODE_PARAMS_v1_st* _pvt_ptr
+    cdef CUmemPoolProps _poolProps
+    cdef size_t _accessDescs_length
+    cdef cydriver.CUmemAccessDesc* _accessDescs
+    cdef CUdeviceptr _dptr
+
+cdef class CUDA_MEM_ALLOC_NODE_PARAMS_v2_st:
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : CUmemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be CU_MEM_HANDLE_TYPE_NONE. IPC is
+        not supported.
+    accessDescs : CUmemAccessDesc
+        in: array of memory access descriptors. Used to describe peer GPU
+        access
+    accessDescCount : size_t
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : CUdeviceptr
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEM_ALLOC_NODE_PARAMS_v2_st _pvt_val
+    cdef cydriver.CUDA_MEM_ALLOC_NODE_PARAMS_v2_st* _pvt_ptr
+    cdef CUmemPoolProps _poolProps
+    cdef size_t _accessDescs_length
+    cdef cydriver.CUmemAccessDesc* _accessDescs
+    cdef CUdeviceptr _dptr
+
+cdef class CUDA_MEM_FREE_NODE_PARAMS_st:
+    """
+    Memory free node parameters
+
+    Attributes
+    ----------
+    dptr : CUdeviceptr
+        in: the pointer to free
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_MEM_FREE_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_MEM_FREE_NODE_PARAMS_st* _pvt_ptr
+    cdef CUdeviceptr _dptr
+
+cdef class CUDA_CHILD_GRAPH_NODE_PARAMS_st:
+    """
+    Child graph node parameters
+
+    Attributes
+    ----------
+    graph : CUgraph
+        The child graph to clone into the node for node creation, or a
+        handle to the graph owned by the node for node query. The graph
+        must not contain conditional nodes. Graphs containing memory
+        allocation or memory free nodes must set the ownership to be moved
+        to the parent.
+    ownership : CUgraphChildGraphNodeOwnership
+        The ownership relationship of the child graph node.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_CHILD_GRAPH_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_CHILD_GRAPH_NODE_PARAMS_st* _pvt_ptr
+    cdef CUgraph _graph
+
+cdef class CUDA_EVENT_RECORD_NODE_PARAMS_st:
+    """
+    Event record node parameters
+
+    Attributes
+    ----------
+    event : CUevent
+        The event to record when the node executes
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EVENT_RECORD_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_EVENT_RECORD_NODE_PARAMS_st* _pvt_ptr
+    cdef CUevent _event
+
+cdef class CUDA_EVENT_WAIT_NODE_PARAMS_st:
+    """
+    Event wait node parameters
+
+    Attributes
+    ----------
+    event : CUevent
+        The event to wait on from the node
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUDA_EVENT_WAIT_NODE_PARAMS_st _pvt_val
+    cdef cydriver.CUDA_EVENT_WAIT_NODE_PARAMS_st* _pvt_ptr
+    cdef CUevent _event
+
+cdef class CUgraphNodeParams_st:
+    """
+    Graph node parameters. See cuGraphAddNode.
+
+    Attributes
+    ----------
+    type : CUgraphNodeType
+        Type of the node
+    reserved0 : list[int]
+        Reserved. Must be zero.
+    reserved1 : list[long long]
+        Padding. Unused bytes must be zero.
+    kernel : CUDA_KERNEL_NODE_PARAMS_v3
+        Kernel node parameters.
+    memcpy : CUDA_MEMCPY_NODE_PARAMS
+        Memcpy node parameters.
+    memset : CUDA_MEMSET_NODE_PARAMS_v2
+        Memset node parameters.
+    host : CUDA_HOST_NODE_PARAMS_v2
+        Host node parameters.
+    graph : CUDA_CHILD_GRAPH_NODE_PARAMS
+        Child graph node parameters.
+    eventWait : CUDA_EVENT_WAIT_NODE_PARAMS
+        Event wait node parameters.
+    eventRecord : CUDA_EVENT_RECORD_NODE_PARAMS
+        Event record node parameters.
+    extSemSignal : CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2
+        External semaphore signal node parameters.
+    extSemWait : CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2
+        External semaphore wait node parameters.
+    alloc : CUDA_MEM_ALLOC_NODE_PARAMS_v2
+        Memory allocation node parameters.
+    free : CUDA_MEM_FREE_NODE_PARAMS
+        Memory free node parameters.
+    memOp : CUDA_BATCH_MEM_OP_NODE_PARAMS_v2
+        MemOp node parameters.
+    conditional : CUDA_CONDITIONAL_NODE_PARAMS
+        Conditional node parameters.
+    reserved2 : long long
+        Reserved bytes. Must be zero.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUgraphNodeParams_st* _val_ptr
+    cdef cydriver.CUgraphNodeParams_st* _pvt_ptr
+    cdef CUDA_KERNEL_NODE_PARAMS_v3 _kernel
+    cdef CUDA_MEMCPY_NODE_PARAMS _memcpy
+    cdef CUDA_MEMSET_NODE_PARAMS_v2 _memset
+    cdef CUDA_HOST_NODE_PARAMS_v2 _host
+    cdef CUDA_CHILD_GRAPH_NODE_PARAMS _graph
+    cdef CUDA_EVENT_WAIT_NODE_PARAMS _eventWait
+    cdef CUDA_EVENT_RECORD_NODE_PARAMS _eventRecord
+    cdef CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2 _extSemSignal
+    cdef CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2 _extSemWait
+    cdef CUDA_MEM_ALLOC_NODE_PARAMS_v2 _alloc
+    cdef CUDA_MEM_FREE_NODE_PARAMS _free
+    cdef CUDA_BATCH_MEM_OP_NODE_PARAMS_v2 _memOp
+    cdef CUDA_CONDITIONAL_NODE_PARAMS _conditional
+
+cdef class CUcheckpointLockArgs_st:
+    """
+    CUDA checkpoint optional lock arguments
+
+    Attributes
+    ----------
+    timeoutMs : unsigned int
+        Timeout in milliseconds to attempt to lock the process, 0 indicates
+        no timeout
+    reserved0 : unsigned int
+        Reserved for future use, must be zero
+    reserved1 : list[cuuint64_t]
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUcheckpointLockArgs_st _pvt_val
+    cdef cydriver.CUcheckpointLockArgs_st* _pvt_ptr
+
+cdef class CUcheckpointCheckpointArgs_st:
+    """
+    CUDA checkpoint optional checkpoint arguments
+
+    Attributes
+    ----------
+    reserved : list[cuuint64_t]
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUcheckpointCheckpointArgs_st _pvt_val
+    cdef cydriver.CUcheckpointCheckpointArgs_st* _pvt_ptr
+
+cdef class CUcheckpointGpuPair_st:
+    """
+    CUDA checkpoint GPU UUID pairs for device remapping during restore
+
+    Attributes
+    ----------
+    oldUuid : CUuuid
+        UUID of the GPU that was checkpointed
+    newUuid : CUuuid
+        UUID of the GPU to restore onto
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUcheckpointGpuPair_st _pvt_val
+    cdef cydriver.CUcheckpointGpuPair_st* _pvt_ptr
+    cdef CUuuid _oldUuid
+    cdef CUuuid _newUuid
+
+cdef class CUcheckpointRestoreArgs_st:
+    """
+    CUDA checkpoint optional restore arguments
+
+    Attributes
+    ----------
+    gpuPairs : CUcheckpointGpuPair
+        Pointer to array of gpu pairs that indicate how to remap GPUs
+        during restore
+    gpuPairsCount : unsigned int
+        Number of gpu pairs to remap
+    reserved : bytes
+        Reserved for future use, must be zeroed
+    reserved1 : cuuint64_t
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUcheckpointRestoreArgs_st _pvt_val
+    cdef cydriver.CUcheckpointRestoreArgs_st* _pvt_ptr
+    cdef size_t _gpuPairs_length
+    cdef cydriver.CUcheckpointGpuPair* _gpuPairs
+    cdef cuuint64_t _reserved1
+
+cdef class CUcheckpointUnlockArgs_st:
+    """
+    CUDA checkpoint optional unlock arguments
+
+    Attributes
+    ----------
+    reserved : list[cuuint64_t]
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUcheckpointUnlockArgs_st _pvt_val
+    cdef cydriver.CUcheckpointUnlockArgs_st* _pvt_ptr
+
+cdef class CUmemDecompressParams_st:
+    """
+    Structure describing the parameters that compose a single
+    decompression operation.
+
+    Attributes
+    ----------
+    srcNumBytes : size_t
+        The number of bytes to be read and decompressed from
+        CUmemDecompressParams_st.src.
+    dstNumBytes : size_t
+        The number of bytes that the decompression operation will be
+        expected to write to CUmemDecompressParams_st.dst. This value is
+        optional; if present, it may be used by the CUDA driver as a
+        heuristic for scheduling the individual decompression operations.
+    dstActBytes : cuuint32_t
+        After the decompression operation has completed, the actual number
+        of bytes written to CUmemDecompressParams.dst will be recorded as a
+        32-bit unsigned integer in the memory at this address.
+    src : Any
+        Pointer to a buffer of at least
+        CUmemDecompressParams_st.srcNumBytes compressed bytes.
+    dst : Any
+        Pointer to a buffer where the decompressed data will be written.
+        The number of bytes written to this location will be recorded in
+        the memory pointed to by CUmemDecompressParams_st.dstActBytes
+    algo : CUmemDecompressAlgorithm
+        The decompression algorithm to use.
+    padding : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUmemDecompressParams_st _pvt_val
+    cdef cydriver.CUmemDecompressParams_st* _pvt_ptr
+    cdef _HelperInputVoidPtr _cysrc
+    cdef _HelperInputVoidPtr _cydst
+
+cdef class CUdevSmResource_st:
+    """
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of streaming multiprocessors available in this resource.
+    minSmPartitionSize : unsigned int
+        The minimum number of streaming multiprocessors required to
+        partition this resource.
+    smCoscheduledAlignment : unsigned int
+        The number of streaming multiprocessors in this resource that are
+        guaranteed to be co-scheduled on the same GPU processing cluster.
+        smCount will be a multiple of this value, unless the backfill flag
+        is set.
+    flags : unsigned int
+        The flags set on this SM resource. For possible values see
+        CUdevSmResourceGroup_flags.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUdevSmResource_st _pvt_val
+    cdef cydriver.CUdevSmResource_st* _pvt_ptr
+
+cdef class CUdevWorkqueueConfigResource_st:
+    """
+    Attributes
+    ----------
+    device : CUdevice
+        The device on which the workqueue resources are available
+    wqConcurrencyLimit : unsigned int
+        The expected maximum number of concurrent stream-ordered workloads
+    sharingScope : CUdevWorkqueueConfigScope
+        The sharing scope for the workqueue resources
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUdevWorkqueueConfigResource_st _pvt_val
+    cdef cydriver.CUdevWorkqueueConfigResource_st* _pvt_ptr
+    cdef CUdevice _device
+
+cdef class CUdevWorkqueueResource_st:
+    """
+    Attributes
+    ----------
+    reserved : bytes
+        Reserved for future use
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUdevWorkqueueResource_st _pvt_val
+    cdef cydriver.CUdevWorkqueueResource_st* _pvt_ptr
+
+cdef class CU_DEV_SM_RESOURCE_GROUP_PARAMS_st:
+    """
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of SMs available in this resource.
+    coscheduledSmCount : unsigned int
+        The amount of co-scheduled SMs grouped together for locality
+        purposes.
+    preferredCoscheduledSmCount : unsigned int
+        When possible, combine co-scheduled groups together into larger
+        groups of this size.
+    flags : unsigned int
+        The flags set on this SM resource group. For possible values see
+        CUdevSmResourceGroup_flags.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CU_DEV_SM_RESOURCE_GROUP_PARAMS_st _pvt_val
+    cdef cydriver.CU_DEV_SM_RESOURCE_GROUP_PARAMS_st* _pvt_ptr
+
+cdef class CUdevResource_st:
+    """
+    Attributes
+    ----------
+    type : CUdevResourceType
+        Type of resource, dictates which union field was last set
+    _internal_padding : bytes
+
+    sm : CUdevSmResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_SM `typename`.
+    wqConfig : CUdevWorkqueueConfigResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_WORKQUEUE_CONFIG
+        `typename`.
+    wq : CUdevWorkqueueResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_WORKQUEUE
+        `typename`.
+    _oversize : bytes
+
+    nextResource : CUdevResource_st
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUdevResource_st* _val_ptr
+    cdef cydriver.CUdevResource_st* _pvt_ptr
+    cdef CUdevSmResource _sm
+    cdef CUdevWorkqueueConfigResource _wqConfig
+    cdef CUdevWorkqueueResource _wq
+    cdef size_t _nextResource_length
+    cdef cydriver.CUdevResource_st* _nextResource
+
+cdef class anon_union16:
+    """
+    Attributes
+    ----------
+    pArray : list[CUarray]
+
+    pPitch : list[Any]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUeglFrame_st* _pvt_ptr
+
+cdef class CUeglFrame_st:
+    """
+    CUDA EGLFrame structure Descriptor - structure defining one frame
+    of EGL.  Each frame may contain one or more planes depending on
+    whether the surface * is Multiplanar or not.
+
+    Attributes
+    ----------
+    frame : anon_union16
+
+    width : unsigned int
+        Width of first plane
+    height : unsigned int
+        Height of first plane
+    depth : unsigned int
+        Depth of first plane
+    pitch : unsigned int
+        Pitch of first plane
+    planeCount : unsigned int
+        Number of planes
+    numChannels : unsigned int
+        Number of channels for the plane
+    frameType : CUeglFrameType
+        Array or Pitch
+    eglColorFormat : CUeglColorFormat
+        CUDA EGL Color Format
+    cuFormat : CUarray_format
+        CUDA Array Format
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cydriver.CUeglFrame_st* _val_ptr
+    cdef cydriver.CUeglFrame_st* _pvt_ptr
+    cdef anon_union16 _frame
+
+cdef class CUdeviceptr:
+    """
+
+    CUDA device pointer CUdeviceptr is defined as an unsigned integer type whose size matches the size of a pointer on the target platform.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUdeviceptr  _pvt_val
+    cdef cydriver.CUdeviceptr* _pvt_ptr
+
+cdef class CUdevice:
+    """
+
+    CUDA device
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUdevice  _pvt_val
+    cdef cydriver.CUdevice* _pvt_ptr
+
+cdef class CUtexObject:
+    """
+
+    An opaque value that represents a CUDA texture object
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUtexObject  _pvt_val
+    cdef cydriver.CUtexObject* _pvt_ptr
+
+cdef class CUsurfObject:
+    """
+
+    An opaque value that represents a CUDA surface object
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUsurfObject  _pvt_val
+    cdef cydriver.CUsurfObject* _pvt_ptr
+
+cdef class CUgraphConditionalHandle:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUgraphConditionalHandle  _pvt_val
+    cdef cydriver.CUgraphConditionalHandle* _pvt_ptr
+
+cdef class CUuuid(CUuuid_st):
+    """
+    Attributes
+    ----------
+    bytes : bytes
+        < CUDA definition of UUID
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemFabricHandle_v1(CUmemFabricHandle_st):
+    """
+    Fabric handle - An opaque handle representing a memory allocation
+    that can be exported to processes in same or different nodes. For
+    IPC between processes on different nodes they must be connected via
+    the NVSwitch fabric.
+
+    Attributes
+    ----------
+    data : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemFabricHandle(CUmemFabricHandle_v1):
+    """
+    Fabric handle - An opaque handle representing a memory allocation
+    that can be exported to processes in same or different nodes. For
+    IPC between processes on different nodes they must be connected via
+    the NVSwitch fabric.
+
+    Attributes
+    ----------
+    data : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUipcEventHandle_v1(CUipcEventHandle_st):
+    """
+    CUDA IPC event handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUipcEventHandle(CUipcEventHandle_v1):
+    """
+    CUDA IPC event handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUipcMemHandle_v1(CUipcMemHandle_st):
+    """
+    CUDA IPC mem handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUipcMemHandle(CUipcMemHandle_v1):
+    """
+    CUDA IPC mem handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUstreamBatchMemOpParams_v1(CUstreamBatchMemOpParams_union):
+    """
+    Per-operation parameters for cuStreamBatchMemOp
+
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+        Operation. This is the first field of all the union elemets and
+        acts as a TAG to determine which union member is valid.
+    waitValue : CUstreamMemOpWaitValueParams_st
+        Params for CU_STREAM_MEM_OP_WAIT_VALUE_32 and
+        CU_STREAM_MEM_OP_WAIT_VALUE_64 operations.
+    writeValue : CUstreamMemOpWriteValueParams_st
+        Params for CU_STREAM_MEM_OP_WRITE_VALUE_32 and
+        CU_STREAM_MEM_OP_WRITE_VALUE_64 operations.
+    flushRemoteWrites : CUstreamMemOpFlushRemoteWritesParams_st
+        Params for CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES operations.
+    memoryBarrier : CUstreamMemOpMemoryBarrierParams_st
+        Params for CU_STREAM_MEM_OP_BARRIER operations.
+    atomicReduction : CUstreamMemOpAtomicReductionParams_st
+
+    pad : list[cuuint64_t]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUstreamBatchMemOpParams(CUstreamBatchMemOpParams_v1):
+    """
+    Per-operation parameters for cuStreamBatchMemOp
+
+    Attributes
+    ----------
+    operation : CUstreamBatchMemOpType
+        Operation. This is the first field of all the union elemets and
+        acts as a TAG to determine which union member is valid.
+    waitValue : CUstreamMemOpWaitValueParams_st
+        Params for CU_STREAM_MEM_OP_WAIT_VALUE_32 and
+        CU_STREAM_MEM_OP_WAIT_VALUE_64 operations.
+    writeValue : CUstreamMemOpWriteValueParams_st
+        Params for CU_STREAM_MEM_OP_WRITE_VALUE_32 and
+        CU_STREAM_MEM_OP_WRITE_VALUE_64 operations.
+    flushRemoteWrites : CUstreamMemOpFlushRemoteWritesParams_st
+        Params for CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES operations.
+    memoryBarrier : CUstreamMemOpMemoryBarrierParams_st
+        Params for CU_STREAM_MEM_OP_BARRIER operations.
+    atomicReduction : CUstreamMemOpAtomicReductionParams_st
+
+    pad : list[cuuint64_t]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_BATCH_MEM_OP_NODE_PARAMS_v1(CUDA_BATCH_MEM_OP_NODE_PARAMS_v1_st):
+    """
+    Batch memory operation node parameters  Used in the legacy
+    cuGraphAddBatchMemOpNode api. New code should use cuGraphAddNode()
+
+    Attributes
+    ----------
+    ctx : CUcontext
+
+    count : unsigned int
+
+    paramArray : CUstreamBatchMemOpParams
+
+    flags : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_BATCH_MEM_OP_NODE_PARAMS(CUDA_BATCH_MEM_OP_NODE_PARAMS_v1):
+    """
+    Batch memory operation node parameters  Used in the legacy
+    cuGraphAddBatchMemOpNode api. New code should use cuGraphAddNode()
+
+    Attributes
+    ----------
+    ctx : CUcontext
+
+    count : unsigned int
+
+    paramArray : CUstreamBatchMemOpParams
+
+    flags : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_BATCH_MEM_OP_NODE_PARAMS_v2(CUDA_BATCH_MEM_OP_NODE_PARAMS_v2_st):
+    """
+    Batch memory operation node parameters
+
+    Attributes
+    ----------
+    ctx : CUcontext
+        Context to use for the operations.
+    count : unsigned int
+        Number of operations in paramArray.
+    paramArray : CUstreamBatchMemOpParams
+        Array of batch memory operations.
+    flags : unsigned int
+        Flags to control the node.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUasyncNotificationInfo(CUasyncNotificationInfo_st):
+    """
+    Information passed to the user via the async notification callback
+
+    Attributes
+    ----------
+    type : CUasyncNotificationType
+        The type of notification being sent
+    info : anon_union2
+        Information about the notification. `typename` must be checked in
+        order to interpret this field.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevprop_v1(CUdevprop_st):
+    """
+    Legacy device properties
+
+    Attributes
+    ----------
+    maxThreadsPerBlock : int
+        Maximum number of threads per block
+    maxThreadsDim : list[int]
+        Maximum size of each dimension of a block
+    maxGridSize : list[int]
+        Maximum size of each dimension of a grid
+    sharedMemPerBlock : int
+        Shared memory available per block in bytes
+    totalConstantMemory : int
+        Constant memory available on device in bytes
+    SIMDWidth : int
+        Warp size in threads
+    memPitch : int
+        Maximum pitch in bytes allowed by memory copies
+    regsPerBlock : int
+        32-bit registers available per block
+    clockRate : int
+        Clock frequency in kilohertz
+    textureAlign : int
+        Alignment requirement for textures
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevprop(CUdevprop_v1):
+    """
+    Legacy device properties
+
+    Attributes
+    ----------
+    maxThreadsPerBlock : int
+        Maximum number of threads per block
+    maxThreadsDim : list[int]
+        Maximum size of each dimension of a block
+    maxGridSize : list[int]
+        Maximum size of each dimension of a grid
+    sharedMemPerBlock : int
+        Shared memory available per block in bytes
+    totalConstantMemory : int
+        Constant memory available on device in bytes
+    SIMDWidth : int
+        Warp size in threads
+    memPitch : int
+        Maximum pitch in bytes allowed by memory copies
+    regsPerBlock : int
+        32-bit registers available per block
+    clockRate : int
+        Clock frequency in kilohertz
+    textureAlign : int
+        Alignment requirement for textures
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUaccessPolicyWindow_v1(CUaccessPolicyWindow_st):
+    """
+    Specifies an access policy for a window, a contiguous extent of
+    memory beginning at base_ptr and ending at base_ptr + num_bytes.
+    num_bytes is limited by
+    CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE. Partition into
+    many segments and assign segments such that: sum of "hit segments"
+    / window == approx. ratio. sum of "miss segments" / window ==
+    approx 1-ratio. Segments and ratio specifications are fitted to the
+    capabilities of the architecture. Accesses in a hit segment apply
+    the hitProp access policy. Accesses in a miss segment apply the
+    missProp access policy.
+
+    Attributes
+    ----------
+    base_ptr : Any
+        Starting address of the access policy window. CUDA driver may align
+        it.
+    num_bytes : size_t
+        Size in bytes of the window policy. CUDA driver may restrict the
+        maximum size and alignment.
+    hitRatio : float
+        hitRatio specifies percentage of lines assigned hitProp, rest are
+        assigned missProp.
+    hitProp : CUaccessProperty
+        CUaccessProperty set for hit.
+    missProp : CUaccessProperty
+        CUaccessProperty set for miss. Must be either NORMAL or STREAMING
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUaccessPolicyWindow(CUaccessPolicyWindow_v1):
+    """
+    Specifies an access policy for a window, a contiguous extent of
+    memory beginning at base_ptr and ending at base_ptr + num_bytes.
+    num_bytes is limited by
+    CU_DEVICE_ATTRIBUTE_MAX_ACCESS_POLICY_WINDOW_SIZE. Partition into
+    many segments and assign segments such that: sum of "hit segments"
+    / window == approx. ratio. sum of "miss segments" / window ==
+    approx 1-ratio. Segments and ratio specifications are fitted to the
+    capabilities of the architecture. Accesses in a hit segment apply
+    the hitProp access policy. Accesses in a miss segment apply the
+    missProp access policy.
+
+    Attributes
+    ----------
+    base_ptr : Any
+        Starting address of the access policy window. CUDA driver may align
+        it.
+    num_bytes : size_t
+        Size in bytes of the window policy. CUDA driver may restrict the
+        maximum size and alignment.
+    hitRatio : float
+        hitRatio specifies percentage of lines assigned hitProp, rest are
+        assigned missProp.
+    hitProp : CUaccessProperty
+        CUaccessProperty set for hit.
+    missProp : CUaccessProperty
+        CUaccessProperty set for miss. Must be either NORMAL or STREAMING
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_KERNEL_NODE_PARAMS_v1(CUDA_KERNEL_NODE_PARAMS_st):
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_KERNEL_NODE_PARAMS_v2(CUDA_KERNEL_NODE_PARAMS_v2_st):
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+    kern : CUkernel
+        Kernel to launch, will only be referenced if func is NULL
+    ctx : CUcontext
+        Context for the kernel task to run in. The value NULL will indicate
+        the current context should be used by the api. This field is
+        ignored if func is set.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_KERNEL_NODE_PARAMS(CUDA_KERNEL_NODE_PARAMS_v2):
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+    kern : CUkernel
+        Kernel to launch, will only be referenced if func is NULL
+    ctx : CUcontext
+        Context for the kernel task to run in. The value NULL will indicate
+        the current context should be used by the api. This field is
+        ignored if func is set.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_KERNEL_NODE_PARAMS_v3(CUDA_KERNEL_NODE_PARAMS_v3_st):
+    """
+    GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to kernel parameters
+    extra : Any
+        Extra options
+    kern : CUkernel
+        Kernel to launch, will only be referenced if func is NULL
+    ctx : CUcontext
+        Context for the kernel task to run in. The value NULL will indicate
+        the current context should be used by the api. This field is
+        ignored if func is set.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMSET_NODE_PARAMS_v1(CUDA_MEMSET_NODE_PARAMS_st):
+    """
+    Memset node parameters
+
+    Attributes
+    ----------
+    dst : CUdeviceptr
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMSET_NODE_PARAMS(CUDA_MEMSET_NODE_PARAMS_v1):
+    """
+    Memset node parameters
+
+    Attributes
+    ----------
+    dst : CUdeviceptr
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMSET_NODE_PARAMS_v2(CUDA_MEMSET_NODE_PARAMS_v2_st):
+    """
+    Memset node parameters
+
+    Attributes
+    ----------
+    dst : CUdeviceptr
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+    ctx : CUcontext
+        Context on which to run the node
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_HOST_NODE_PARAMS_v1(CUDA_HOST_NODE_PARAMS_st):
+    """
+    Host node parameters
+
+    Attributes
+    ----------
+    fn : CUhostFn
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_HOST_NODE_PARAMS(CUDA_HOST_NODE_PARAMS_v1):
+    """
+    Host node parameters
+
+    Attributes
+    ----------
+    fn : CUhostFn
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_HOST_NODE_PARAMS_v2(CUDA_HOST_NODE_PARAMS_v2_st):
+    """
+    Host node parameters
+
+    Attributes
+    ----------
+    fn : CUhostFn
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+    syncMode : unsigned int
+        The sync mode to use for the host task
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUgraphEdgeData(CUgraphEdgeData_st):
+    """
+    Optional annotation for edges in a CUDA graph. Note, all edges
+    implicitly have annotations and default to a zero-initialized value
+    if not specified. A zero-initialized struct indicates a standard
+    full serialization of two nodes with memory visibility.
+
+    Attributes
+    ----------
+    from_port : bytes
+        This indicates when the dependency is triggered from the upstream
+        node on the edge. The meaning is specfic to the node type. A value
+        of 0 in all cases means full completion of the upstream node, with
+        memory visibility to the downstream node or portion thereof
+        (indicated by `to_port`).   Only kernel nodes define non-zero
+        ports. A kernel node can use the following output port types:
+        CU_GRAPH_KERNEL_NODE_PORT_DEFAULT,
+        CU_GRAPH_KERNEL_NODE_PORT_PROGRAMMATIC, or
+        CU_GRAPH_KERNEL_NODE_PORT_LAUNCH_ORDER.
+    to_port : bytes
+        This indicates what portion of the downstream node is dependent on
+        the upstream node or portion thereof (indicated by `from_port`).
+        The meaning is specific to the node type. A value of 0 in all cases
+        means the entirety of the downstream node is dependent on the
+        upstream work.   Currently no node types define non-zero ports.
+        Accordingly, this field must be set to zero.
+    type : bytes
+        This should be populated with a value from CUgraphDependencyType.
+        (It is typed as char due to compiler-specific layout of bitfields.)
+        See CUgraphDependencyType.
+    reserved : bytes
+        These bytes are unused and must be zeroed. This ensures
+        compatibility if additional fields are added in the future.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_GRAPH_INSTANTIATE_PARAMS(CUDA_GRAPH_INSTANTIATE_PARAMS_st):
+    """
+    Graph instantiation parameters
+
+    Attributes
+    ----------
+    flags : cuuint64_t
+        Instantiation flags
+    hUploadStream : CUstream
+        Upload stream
+    hErrNode_out : CUgraphNode
+        The node which caused instantiation to fail, if any
+    result_out : CUgraphInstantiateResult
+        Whether instantiation was successful. If it failed, the reason why
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUlaunchMemSyncDomainMap(CUlaunchMemSyncDomainMap_st):
+    """
+    Memory Synchronization Domain map  See ::cudaLaunchMemSyncDomain.
+    By default, kernels are launched in domain 0. Kernel launched with
+    CU_LAUNCH_MEM_SYNC_DOMAIN_REMOTE will have a different domain ID.
+    User may also alter the domain ID with CUlaunchMemSyncDomainMap for
+    a specific stream / graph node / kernel launch. See
+    CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.  Domain ID range is
+    available through CU_DEVICE_ATTRIBUTE_MEM_SYNC_DOMAIN_COUNT.
+
+    Attributes
+    ----------
+    default_ : bytes
+        The default domain ID to use for designated kernels
+    remote : bytes
+        The remote domain ID to use for designated kernels
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUlaunchAttributeValue(CUlaunchAttributeValue_union):
+    """
+    Launch attributes union; used as value field of CUlaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : CUaccessPolicyWindow
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW.
+    cooperative : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero
+        indicates a cooperative kernel (see cuLaunchCooperativeKernel).
+    syncPolicy : CUsynchronizationPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY.
+        ::CUsynchronizationPolicy for work queued up in this stream
+    clusterDim : anon_struct1
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+        that represents the desired cluster dimensions for the kernel.
+        Opaque type with the following fields: - `x` - The X dimension of
+        the cluster, in blocks. Must be a divisor of the grid X dimension.
+        - `y` - The Y dimension of the cluster, in blocks. Must be a
+        divisor of the grid Y dimension.    - `z` - The Z dimension of the
+        cluster, in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : CUclusterSchedulingPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.
+    programmaticEvent : anon_struct2
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+        with the following fields: - `CUevent` event - Event to fire when
+        all blocks trigger it.    - `Event` record flags, see
+        cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+        - `triggerAtBlockStart` - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    launchCompletionEvent : anon_struct3
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT with the following
+        fields: - `CUevent` event - Event to fire when the last block
+        launches    - `int` flags; - Event record flags, see
+        cuEventRecordWithFlags. Does not accept CU_EVENT_RECORD_EXTERNAL.
+    priority : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution
+        priority of the kernel.
+    memSyncDomainMap : CUlaunchMemSyncDomainMap
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+        See CUlaunchMemSyncDomainMap.
+    memSyncDomain : CUlaunchMemSyncDomain
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN.
+        See::CUlaunchMemSyncDomain
+    preferredClusterDim : anon_struct4
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        CUlaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        CUlaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        CUlaunchAttributeValue::clusterDim.
+    deviceUpdatableKernelNode : anon_struct5
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE. with the
+        following fields: - `int` deviceUpdatable - Whether or not the
+        resulting kernel node should be device-updatable.    -
+        `CUgraphDeviceNode` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+    nvlinkUtilCentricScheduling : unsigned int
+
+    portableClusterSizeMode : CUlaunchAttributePortableClusterMode
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE.
+    sharedMemoryMode : CUsharedMemoryMode
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE.
+        See CUsharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUlaunchAttribute(CUlaunchAttribute_st):
+    """
+    Launch attribute
+
+    Attributes
+    ----------
+    id : CUlaunchAttributeID
+        Attribute to set
+    value : CUlaunchAttributeValue
+        Value of the attribute
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUlaunchConfig(CUlaunchConfig_st):
+    """
+    CUDA extensible launch configuration
+
+    Attributes
+    ----------
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    hStream : CUstream
+        Stream identifier
+    attrs : CUlaunchAttribute
+        List of attributes; nullable if CUlaunchConfig::numAttrs == 0
+    numAttrs : unsigned int
+        Number of attributes populated in CUlaunchConfig::attrs
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUkernelNodeAttrValue_v1(CUlaunchAttributeValue):
+    """
+    Launch attributes union; used as value field of CUlaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : CUaccessPolicyWindow
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW.
+    cooperative : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero
+        indicates a cooperative kernel (see cuLaunchCooperativeKernel).
+    syncPolicy : CUsynchronizationPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY.
+        ::CUsynchronizationPolicy for work queued up in this stream
+    clusterDim : anon_struct1
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+        that represents the desired cluster dimensions for the kernel.
+        Opaque type with the following fields: - `x` - The X dimension of
+        the cluster, in blocks. Must be a divisor of the grid X dimension.
+        - `y` - The Y dimension of the cluster, in blocks. Must be a
+        divisor of the grid Y dimension.    - `z` - The Z dimension of the
+        cluster, in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : CUclusterSchedulingPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.
+    programmaticEvent : anon_struct2
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+        with the following fields: - `CUevent` event - Event to fire when
+        all blocks trigger it.    - `Event` record flags, see
+        cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+        - `triggerAtBlockStart` - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    launchCompletionEvent : anon_struct3
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT with the following
+        fields: - `CUevent` event - Event to fire when the last block
+        launches    - `int` flags; - Event record flags, see
+        cuEventRecordWithFlags. Does not accept CU_EVENT_RECORD_EXTERNAL.
+    priority : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution
+        priority of the kernel.
+    memSyncDomainMap : CUlaunchMemSyncDomainMap
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+        See CUlaunchMemSyncDomainMap.
+    memSyncDomain : CUlaunchMemSyncDomain
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN.
+        See::CUlaunchMemSyncDomain
+    preferredClusterDim : anon_struct4
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        CUlaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        CUlaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        CUlaunchAttributeValue::clusterDim.
+    deviceUpdatableKernelNode : anon_struct5
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE. with the
+        following fields: - `int` deviceUpdatable - Whether or not the
+        resulting kernel node should be device-updatable.    -
+        `CUgraphDeviceNode` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+    nvlinkUtilCentricScheduling : unsigned int
+
+    portableClusterSizeMode : CUlaunchAttributePortableClusterMode
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE.
+    sharedMemoryMode : CUsharedMemoryMode
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE.
+        See CUsharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUkernelNodeAttrValue(CUkernelNodeAttrValue_v1):
+    """
+    Launch attributes union; used as value field of CUlaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : CUaccessPolicyWindow
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW.
+    cooperative : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero
+        indicates a cooperative kernel (see cuLaunchCooperativeKernel).
+    syncPolicy : CUsynchronizationPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY.
+        ::CUsynchronizationPolicy for work queued up in this stream
+    clusterDim : anon_struct1
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+        that represents the desired cluster dimensions for the kernel.
+        Opaque type with the following fields: - `x` - The X dimension of
+        the cluster, in blocks. Must be a divisor of the grid X dimension.
+        - `y` - The Y dimension of the cluster, in blocks. Must be a
+        divisor of the grid Y dimension.    - `z` - The Z dimension of the
+        cluster, in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : CUclusterSchedulingPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.
+    programmaticEvent : anon_struct2
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+        with the following fields: - `CUevent` event - Event to fire when
+        all blocks trigger it.    - `Event` record flags, see
+        cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+        - `triggerAtBlockStart` - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    launchCompletionEvent : anon_struct3
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT with the following
+        fields: - `CUevent` event - Event to fire when the last block
+        launches    - `int` flags; - Event record flags, see
+        cuEventRecordWithFlags. Does not accept CU_EVENT_RECORD_EXTERNAL.
+    priority : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution
+        priority of the kernel.
+    memSyncDomainMap : CUlaunchMemSyncDomainMap
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+        See CUlaunchMemSyncDomainMap.
+    memSyncDomain : CUlaunchMemSyncDomain
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN.
+        See::CUlaunchMemSyncDomain
+    preferredClusterDim : anon_struct4
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        CUlaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        CUlaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        CUlaunchAttributeValue::clusterDim.
+    deviceUpdatableKernelNode : anon_struct5
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE. with the
+        following fields: - `int` deviceUpdatable - Whether or not the
+        resulting kernel node should be device-updatable.    -
+        `CUgraphDeviceNode` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+    nvlinkUtilCentricScheduling : unsigned int
+
+    portableClusterSizeMode : CUlaunchAttributePortableClusterMode
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE.
+    sharedMemoryMode : CUsharedMemoryMode
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE.
+        See CUsharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUstreamAttrValue_v1(CUlaunchAttributeValue):
+    """
+    Launch attributes union; used as value field of CUlaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : CUaccessPolicyWindow
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW.
+    cooperative : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero
+        indicates a cooperative kernel (see cuLaunchCooperativeKernel).
+    syncPolicy : CUsynchronizationPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY.
+        ::CUsynchronizationPolicy for work queued up in this stream
+    clusterDim : anon_struct1
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+        that represents the desired cluster dimensions for the kernel.
+        Opaque type with the following fields: - `x` - The X dimension of
+        the cluster, in blocks. Must be a divisor of the grid X dimension.
+        - `y` - The Y dimension of the cluster, in blocks. Must be a
+        divisor of the grid Y dimension.    - `z` - The Z dimension of the
+        cluster, in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : CUclusterSchedulingPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.
+    programmaticEvent : anon_struct2
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+        with the following fields: - `CUevent` event - Event to fire when
+        all blocks trigger it.    - `Event` record flags, see
+        cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+        - `triggerAtBlockStart` - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    launchCompletionEvent : anon_struct3
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT with the following
+        fields: - `CUevent` event - Event to fire when the last block
+        launches    - `int` flags; - Event record flags, see
+        cuEventRecordWithFlags. Does not accept CU_EVENT_RECORD_EXTERNAL.
+    priority : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution
+        priority of the kernel.
+    memSyncDomainMap : CUlaunchMemSyncDomainMap
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+        See CUlaunchMemSyncDomainMap.
+    memSyncDomain : CUlaunchMemSyncDomain
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN.
+        See::CUlaunchMemSyncDomain
+    preferredClusterDim : anon_struct4
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        CUlaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        CUlaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        CUlaunchAttributeValue::clusterDim.
+    deviceUpdatableKernelNode : anon_struct5
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE. with the
+        following fields: - `int` deviceUpdatable - Whether or not the
+        resulting kernel node should be device-updatable.    -
+        `CUgraphDeviceNode` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+    nvlinkUtilCentricScheduling : unsigned int
+
+    portableClusterSizeMode : CUlaunchAttributePortableClusterMode
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE.
+    sharedMemoryMode : CUsharedMemoryMode
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE.
+        See CUsharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUstreamAttrValue(CUstreamAttrValue_v1):
+    """
+    Launch attributes union; used as value field of CUlaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : CUaccessPolicyWindow
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_ACCESS_POLICY_WINDOW.
+    cooperative : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_COOPERATIVE. Nonzero
+        indicates a cooperative kernel (see cuLaunchCooperativeKernel).
+    syncPolicy : CUsynchronizationPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_SYNCHRONIZATION_POLICY.
+        ::CUsynchronizationPolicy for work queued up in this stream
+    clusterDim : anon_struct1
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION
+        that represents the desired cluster dimensions for the kernel.
+        Opaque type with the following fields: - `x` - The X dimension of
+        the cluster, in blocks. Must be a divisor of the grid X dimension.
+        - `y` - The Y dimension of the cluster, in blocks. Must be a
+        divisor of the grid Y dimension.    - `z` - The Z dimension of the
+        cluster, in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : CUclusterSchedulingPolicy
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.
+    programmaticEvent : anon_struct2
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_EVENT
+        with the following fields: - `CUevent` event - Event to fire when
+        all blocks trigger it.    - `Event` record flags, see
+        cuEventRecordWithFlags. Does not accept :CU_EVENT_RECORD_EXTERNAL.
+        - `triggerAtBlockStart` - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    launchCompletionEvent : anon_struct3
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_LAUNCH_COMPLETION_EVENT with the following
+        fields: - `CUevent` event - Event to fire when the last block
+        launches    - `int` flags; - Event record flags, see
+        cuEventRecordWithFlags. Does not accept CU_EVENT_RECORD_EXTERNAL.
+    priority : int
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_PRIORITY. Execution
+        priority of the kernel.
+    memSyncDomainMap : CUlaunchMemSyncDomainMap
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN_MAP.
+        See CUlaunchMemSyncDomainMap.
+    memSyncDomain : CUlaunchMemSyncDomain
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_MEM_SYNC_DOMAIN.
+        See::CUlaunchMemSyncDomain
+    preferredClusterDim : anon_struct4
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_CLUSTER_DIMENSION that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        CUlaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        CUlaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        CUlaunchAttributeValue::clusterDim.
+    deviceUpdatableKernelNode : anon_struct5
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_DEVICE_UPDATABLE_KERNEL_NODE. with the
+        following fields: - `int` deviceUpdatable - Whether or not the
+        resulting kernel node should be device-updatable.    -
+        `CUgraphDeviceNode` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT.
+    nvlinkUtilCentricScheduling : unsigned int
+
+    portableClusterSizeMode : CUlaunchAttributePortableClusterMode
+        Value of launch attribute
+        CU_LAUNCH_ATTRIBUTE_PORTABLE_CLUSTER_SIZE_MODE.
+    sharedMemoryMode : CUsharedMemoryMode
+        Value of launch attribute CU_LAUNCH_ATTRIBUTE_SHARED_MEMORY_MODE.
+        See CUsharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUexecAffinitySmCount_v1(CUexecAffinitySmCount_st):
+    """
+    Value for CU_EXEC_AFFINITY_TYPE_SM_COUNT
+
+    Attributes
+    ----------
+    val : unsigned int
+        The number of SMs the context is limited to use.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUexecAffinitySmCount(CUexecAffinitySmCount_v1):
+    """
+    Value for CU_EXEC_AFFINITY_TYPE_SM_COUNT
+
+    Attributes
+    ----------
+    val : unsigned int
+        The number of SMs the context is limited to use.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUexecAffinityParam_v1(CUexecAffinityParam_st):
+    """
+    Execution Affinity Parameters
+
+    Attributes
+    ----------
+    type : CUexecAffinityType
+        Type of execution affinity.
+    param : anon_union3
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUexecAffinityParam(CUexecAffinityParam_v1):
+    """
+    Execution Affinity Parameters
+
+    Attributes
+    ----------
+    type : CUexecAffinityType
+        Type of execution affinity.
+    param : anon_union3
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUctxCigParam(CUctxCigParam_st):
+    """
+    CIG Context Create Params
+
+    Attributes
+    ----------
+    sharedDataType : CUcigDataType
+        Type of shared data from graphics client (D3D12 or Vulkan).
+    sharedData : Any
+        Graphics client data handle (ID3D12CommandQueue or Nvidia specific
+        data blob).
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUctxCreateParams(CUctxCreateParams_st):
+    """
+    Params for creating CUDA context. Both execAffinityParams and
+    cigParams cannot be non-NULL at the same time. If both are NULL,
+    the context will be created as a regular CUDA context.
+
+    Attributes
+    ----------
+    execAffinityParams : CUexecAffinityParam
+        Array of execution affinity parameters to limit context resources
+        (e.g., SM count). Only supported Volta+ MPS. Mutually exclusive
+        with cigParams.
+    numExecAffinityParams : int
+        Number of elements in execAffinityParams array. Must be 0 if
+        execAffinityParams is NULL.
+    cigParams : CUctxCigParam
+        CIG (CUDA in Graphics) parameters for sharing data from
+        D3D12/Vulkan graphics clients. Mutually exclusive with
+        execAffinityParams.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUstreamCigParam(CUstreamCigParam_st):
+    """
+    CIG Stream Capture Params
+
+    Attributes
+    ----------
+    streamSharedDataType : CUstreamCigDataType
+        Type of shared data from graphics client (D3D12).
+    streamSharedData : Any
+        Graphics client data handle
+        (ID3D12CommandList/ID3D12GraphicsCommandList).
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUstreamCigCaptureParams(CUstreamCigCaptureParams_st):
+    """
+    Params for capturing CUDA stream to CIG streamCigParams must be
+    non-NULL.
+
+    Attributes
+    ----------
+    streamCigParams : CUstreamCigParam
+        CIG (CUDA in Graphics) parameters for sharing command list data
+        from D3D12 graphics clients.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUlibraryHostUniversalFunctionAndDataTable(CUlibraryHostUniversalFunctionAndDataTable_st):
+    """
+    Attributes
+    ----------
+    functionTable : Any
+
+    functionWindowSize : size_t
+
+    dataTable : Any
+
+    dataWindowSize : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY2D_v2(CUDA_MEMCPY2D_st):
+    """
+    2D memory copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    WidthInBytes : size_t
+        Width of 2D memory copy in bytes
+    Height : size_t
+        Height of 2D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY2D(CUDA_MEMCPY2D_v2):
+    """
+    2D memory copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    WidthInBytes : size_t
+        Width of 2D memory copy in bytes
+    Height : size_t
+        Height of 2D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY3D_v2(CUDA_MEMCPY3D_st):
+    """
+    3D memory copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcZ : size_t
+        Source Z
+    srcLOD : size_t
+        Source LOD
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    reserved0 : Any
+        Must be NULL
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    srcHeight : size_t
+        Source height (ignored when src is array; may be 0 if Depth==1)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstZ : size_t
+        Destination Z
+    dstLOD : size_t
+        Destination LOD
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    reserved1 : Any
+        Must be NULL
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    dstHeight : size_t
+        Destination height (ignored when dst is array; may be 0 if
+        Depth==1)
+    WidthInBytes : size_t
+        Width of 3D memory copy in bytes
+    Height : size_t
+        Height of 3D memory copy
+    Depth : size_t
+        Depth of 3D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY3D(CUDA_MEMCPY3D_v2):
+    """
+    3D memory copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcZ : size_t
+        Source Z
+    srcLOD : size_t
+        Source LOD
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    reserved0 : Any
+        Must be NULL
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    srcHeight : size_t
+        Source height (ignored when src is array; may be 0 if Depth==1)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstZ : size_t
+        Destination Z
+    dstLOD : size_t
+        Destination LOD
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    reserved1 : Any
+        Must be NULL
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    dstHeight : size_t
+        Destination height (ignored when dst is array; may be 0 if
+        Depth==1)
+    WidthInBytes : size_t
+        Width of 3D memory copy in bytes
+    Height : size_t
+        Height of 3D memory copy
+    Depth : size_t
+        Depth of 3D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY3D_PEER_v1(CUDA_MEMCPY3D_PEER_st):
+    """
+    3D memory cross-context copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcZ : size_t
+        Source Z
+    srcLOD : size_t
+        Source LOD
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    srcContext : CUcontext
+        Source context (ignored with srcMemoryType is CU_MEMORYTYPE_ARRAY)
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    srcHeight : size_t
+        Source height (ignored when src is array; may be 0 if Depth==1)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstZ : size_t
+        Destination Z
+    dstLOD : size_t
+        Destination LOD
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    dstContext : CUcontext
+        Destination context (ignored with dstMemoryType is
+        CU_MEMORYTYPE_ARRAY)
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    dstHeight : size_t
+        Destination height (ignored when dst is array; may be 0 if
+        Depth==1)
+    WidthInBytes : size_t
+        Width of 3D memory copy in bytes
+    Height : size_t
+        Height of 3D memory copy
+    Depth : size_t
+        Depth of 3D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY3D_PEER(CUDA_MEMCPY3D_PEER_v1):
+    """
+    3D memory cross-context copy parameters
+
+    Attributes
+    ----------
+    srcXInBytes : size_t
+        Source X in bytes
+    srcY : size_t
+        Source Y
+    srcZ : size_t
+        Source Z
+    srcLOD : size_t
+        Source LOD
+    srcMemoryType : CUmemorytype
+        Source memory type (host, device, array)
+    srcHost : Any
+        Source host pointer
+    srcDevice : CUdeviceptr
+        Source device pointer
+    srcArray : CUarray
+        Source array reference
+    srcContext : CUcontext
+        Source context (ignored with srcMemoryType is CU_MEMORYTYPE_ARRAY)
+    srcPitch : size_t
+        Source pitch (ignored when src is array)
+    srcHeight : size_t
+        Source height (ignored when src is array; may be 0 if Depth==1)
+    dstXInBytes : size_t
+        Destination X in bytes
+    dstY : size_t
+        Destination Y
+    dstZ : size_t
+        Destination Z
+    dstLOD : size_t
+        Destination LOD
+    dstMemoryType : CUmemorytype
+        Destination memory type (host, device, array)
+    dstHost : Any
+        Destination host pointer
+    dstDevice : CUdeviceptr
+        Destination device pointer
+    dstArray : CUarray
+        Destination array reference
+    dstContext : CUcontext
+        Destination context (ignored with dstMemoryType is
+        CU_MEMORYTYPE_ARRAY)
+    dstPitch : size_t
+        Destination pitch (ignored when dst is array)
+    dstHeight : size_t
+        Destination height (ignored when dst is array; may be 0 if
+        Depth==1)
+    WidthInBytes : size_t
+        Width of 3D memory copy in bytes
+    Height : size_t
+        Height of 3D memory copy
+    Depth : size_t
+        Depth of 3D memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY_NODE_PARAMS(CUDA_MEMCPY_NODE_PARAMS_st):
+    """
+    Memcpy node parameters
+
+    Attributes
+    ----------
+    flags : int
+        Must be zero
+    reserved : int
+        Must be zero
+    copyCtx : CUcontext
+        Context on which to run the node
+    copyParams : CUDA_MEMCPY3D
+        Parameters for the memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY_DESCRIPTOR_v2(CUDA_ARRAY_DESCRIPTOR_st):
+    """
+    Array descriptor
+
+    Attributes
+    ----------
+    Width : size_t
+        Width of array
+    Height : size_t
+        Height of array
+    Format : CUarray_format
+        Array format
+    NumChannels : unsigned int
+        Channels per array element
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY_DESCRIPTOR(CUDA_ARRAY_DESCRIPTOR_v2):
+    """
+    Array descriptor
+
+    Attributes
+    ----------
+    Width : size_t
+        Width of array
+    Height : size_t
+        Height of array
+    Format : CUarray_format
+        Array format
+    NumChannels : unsigned int
+        Channels per array element
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY3D_DESCRIPTOR_v2(CUDA_ARRAY3D_DESCRIPTOR_st):
+    """
+    3D array descriptor
+
+    Attributes
+    ----------
+    Width : size_t
+        Width of 3D array
+    Height : size_t
+        Height of 3D array
+    Depth : size_t
+        Depth of 3D array
+    Format : CUarray_format
+        Array format
+    NumChannels : unsigned int
+        Channels per array element
+    Flags : unsigned int
+        Flags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY3D_DESCRIPTOR(CUDA_ARRAY3D_DESCRIPTOR_v2):
+    """
+    3D array descriptor
+
+    Attributes
+    ----------
+    Width : size_t
+        Width of 3D array
+    Height : size_t
+        Height of 3D array
+    Depth : size_t
+        Depth of 3D array
+    Format : CUarray_format
+        Array format
+    NumChannels : unsigned int
+        Channels per array element
+    Flags : unsigned int
+        Flags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY_SPARSE_PROPERTIES_v1(CUDA_ARRAY_SPARSE_PROPERTIES_st):
+    """
+    CUDA array sparse properties
+
+    Attributes
+    ----------
+    tileExtent : anon_struct6
+
+    miptailFirstLevel : unsigned int
+        First mip level at which the mip tail begins.
+    miptailSize : unsigned long long
+        Total size of the mip tail.
+    flags : unsigned int
+        Flags will either be zero or
+        CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY_SPARSE_PROPERTIES(CUDA_ARRAY_SPARSE_PROPERTIES_v1):
+    """
+    CUDA array sparse properties
+
+    Attributes
+    ----------
+    tileExtent : anon_struct6
+
+    miptailFirstLevel : unsigned int
+        First mip level at which the mip tail begins.
+    miptailSize : unsigned long long
+        Total size of the mip tail.
+    flags : unsigned int
+        Flags will either be zero or
+        CU_ARRAY_SPARSE_PROPERTIES_SINGLE_MIPTAIL
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY_MEMORY_REQUIREMENTS_v1(CUDA_ARRAY_MEMORY_REQUIREMENTS_st):
+    """
+    CUDA array memory requirements
+
+    Attributes
+    ----------
+    size : size_t
+        Total required memory size
+    alignment : size_t
+        alignment requirement
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_ARRAY_MEMORY_REQUIREMENTS(CUDA_ARRAY_MEMORY_REQUIREMENTS_v1):
+    """
+    CUDA array memory requirements
+
+    Attributes
+    ----------
+    size : size_t
+        Total required memory size
+    alignment : size_t
+        alignment requirement
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_RESOURCE_DESC_v1(CUDA_RESOURCE_DESC_st):
+    """
+    CUDA Resource descriptor
+
+    Attributes
+    ----------
+    resType : CUresourcetype
+        Resource type
+    res : anon_union4
+
+    flags : unsigned int
+        Flags (must be zero)
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_RESOURCE_DESC(CUDA_RESOURCE_DESC_v1):
+    """
+    CUDA Resource descriptor
+
+    Attributes
+    ----------
+    resType : CUresourcetype
+        Resource type
+    res : anon_union4
+
+    flags : unsigned int
+        Flags (must be zero)
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_TEXTURE_DESC_v1(CUDA_TEXTURE_DESC_st):
+    """
+    Texture descriptor
+
+    Attributes
+    ----------
+    addressMode : list[CUaddress_mode]
+        Address modes
+    filterMode : CUfilter_mode
+        Filter mode
+    flags : unsigned int
+        Flags
+    maxAnisotropy : unsigned int
+        Maximum anisotropy ratio
+    mipmapFilterMode : CUfilter_mode
+        Mipmap filter mode
+    mipmapLevelBias : float
+        Mipmap level bias
+    minMipmapLevelClamp : float
+        Mipmap minimum level clamp
+    maxMipmapLevelClamp : float
+        Mipmap maximum level clamp
+    borderColor : list[float]
+        Border Color
+    reserved : list[int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_TEXTURE_DESC(CUDA_TEXTURE_DESC_v1):
+    """
+    Texture descriptor
+
+    Attributes
+    ----------
+    addressMode : list[CUaddress_mode]
+        Address modes
+    filterMode : CUfilter_mode
+        Filter mode
+    flags : unsigned int
+        Flags
+    maxAnisotropy : unsigned int
+        Maximum anisotropy ratio
+    mipmapFilterMode : CUfilter_mode
+        Mipmap filter mode
+    mipmapLevelBias : float
+        Mipmap level bias
+    minMipmapLevelClamp : float
+        Mipmap minimum level clamp
+    maxMipmapLevelClamp : float
+        Mipmap maximum level clamp
+    borderColor : list[float]
+        Border Color
+    reserved : list[int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_RESOURCE_VIEW_DESC_v1(CUDA_RESOURCE_VIEW_DESC_st):
+    """
+    Resource view descriptor
+
+    Attributes
+    ----------
+    format : CUresourceViewFormat
+        Resource view format
+    width : size_t
+        Width of the resource view
+    height : size_t
+        Height of the resource view
+    depth : size_t
+        Depth of the resource view
+    firstMipmapLevel : unsigned int
+        First defined mipmap level
+    lastMipmapLevel : unsigned int
+        Last defined mipmap level
+    firstLayer : unsigned int
+        First layer index
+    lastLayer : unsigned int
+        Last layer index
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_RESOURCE_VIEW_DESC(CUDA_RESOURCE_VIEW_DESC_v1):
+    """
+    Resource view descriptor
+
+    Attributes
+    ----------
+    format : CUresourceViewFormat
+        Resource view format
+    width : size_t
+        Width of the resource view
+    height : size_t
+        Height of the resource view
+    depth : size_t
+        Depth of the resource view
+    firstMipmapLevel : unsigned int
+        First defined mipmap level
+    lastMipmapLevel : unsigned int
+        Last defined mipmap level
+    firstLayer : unsigned int
+        First layer index
+    lastLayer : unsigned int
+        Last layer index
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUtensorMap(CUtensorMap_st):
+    """
+    Tensor map descriptor. Requires compiler support for aligning to
+    128 bytes.
+
+    Attributes
+    ----------
+    opaque : list[cuuint64_t]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1(CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_st):
+    """
+    GPU Direct v3 tokens
+
+    Attributes
+    ----------
+    p2pToken : unsigned long long
+
+    vaSpaceToken : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_POINTER_ATTRIBUTE_P2P_TOKENS(CUDA_POINTER_ATTRIBUTE_P2P_TOKENS_v1):
+    """
+    GPU Direct v3 tokens
+
+    Attributes
+    ----------
+    p2pToken : unsigned long long
+
+    vaSpaceToken : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_LAUNCH_PARAMS_v1(CUDA_LAUNCH_PARAMS_st):
+    """
+    Kernel launch parameters
+
+    Attributes
+    ----------
+    function : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    hStream : CUstream
+        Stream identifier
+    kernelParams : Any
+        Array of pointers to kernel parameters
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_LAUNCH_PARAMS(CUDA_LAUNCH_PARAMS_v1):
+    """
+    Kernel launch parameters
+
+    Attributes
+    ----------
+    function : CUfunction
+        Kernel to launch
+    gridDimX : unsigned int
+        Width of grid in blocks
+    gridDimY : unsigned int
+        Height of grid in blocks
+    gridDimZ : unsigned int
+        Depth of grid in blocks
+    blockDimX : unsigned int
+        X dimension of each thread block
+    blockDimY : unsigned int
+        Y dimension of each thread block
+    blockDimZ : unsigned int
+        Z dimension of each thread block
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    hStream : CUstream
+        Stream identifier
+    kernelParams : Any
+        Array of pointers to kernel parameters
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1(CUDA_EXTERNAL_MEMORY_HANDLE_DESC_st):
+    """
+    External memory handle descriptor
+
+    Attributes
+    ----------
+    type : CUexternalMemoryHandleType
+        Type of the handle
+    handle : anon_union5
+
+    size : unsigned long long
+        Size of the memory allocation
+    flags : unsigned int
+        Flags must either be zero or CUDA_EXTERNAL_MEMORY_DEDICATED
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_MEMORY_HANDLE_DESC(CUDA_EXTERNAL_MEMORY_HANDLE_DESC_v1):
+    """
+    External memory handle descriptor
+
+    Attributes
+    ----------
+    type : CUexternalMemoryHandleType
+        Type of the handle
+    handle : anon_union5
+
+    size : unsigned long long
+        Size of the memory allocation
+    flags : unsigned int
+        Flags must either be zero or CUDA_EXTERNAL_MEMORY_DEDICATED
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1(CUDA_EXTERNAL_MEMORY_BUFFER_DESC_st):
+    """
+    External memory buffer descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the buffer's base is
+    size : unsigned long long
+        Size of the buffer
+    flags : unsigned int
+        Flags reserved for future use. Must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_MEMORY_BUFFER_DESC(CUDA_EXTERNAL_MEMORY_BUFFER_DESC_v1):
+    """
+    External memory buffer descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the buffer's base is
+    size : unsigned long long
+        Size of the buffer
+    flags : unsigned int
+        Flags reserved for future use. Must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1(CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_st):
+    """
+    External memory mipmap descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the base level of the mipmap
+        chain is.
+    arrayDesc : CUDA_ARRAY3D_DESCRIPTOR
+        Format, dimension and type of base level of the mipmap chain
+    numLevels : unsigned int
+        Total number of levels in the mipmap chain
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC(CUDA_EXTERNAL_MEMORY_MIPMAPPED_ARRAY_DESC_v1):
+    """
+    External memory mipmap descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the base level of the mipmap
+        chain is.
+    arrayDesc : CUDA_ARRAY3D_DESCRIPTOR
+        Format, dimension and type of base level of the mipmap chain
+    numLevels : unsigned int
+        Total number of levels in the mipmap chain
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1(CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_st):
+    """
+    External semaphore handle descriptor
+
+    Attributes
+    ----------
+    type : CUexternalSemaphoreHandleType
+        Type of the handle
+    handle : anon_union6
+
+    flags : unsigned int
+        Flags reserved for the future. Must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC(CUDA_EXTERNAL_SEMAPHORE_HANDLE_DESC_v1):
+    """
+    External semaphore handle descriptor
+
+    Attributes
+    ----------
+    type : CUexternalSemaphoreHandleType
+        Type of the handle
+    handle : anon_union6
+
+    flags : unsigned int
+        Flags reserved for the future. Must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1(CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_st):
+    """
+    External semaphore signal parameters
+
+    Attributes
+    ----------
+    params : anon_struct16
+
+    flags : unsigned int
+        Only when ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to signal
+        a CUexternalSemaphore of type
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+        CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which
+        indicates that while signaling the CUexternalSemaphore, no memory
+        synchronization operations should be performed for any external
+        memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+        For all other types of CUexternalSemaphore, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS(CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS_v1):
+    """
+    External semaphore signal parameters
+
+    Attributes
+    ----------
+    params : anon_struct16
+
+    flags : unsigned int
+        Only when ::CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS is used to signal
+        a CUexternalSemaphore of type
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+        CUDA_EXTERNAL_SEMAPHORE_SIGNAL_SKIP_NVSCIBUF_MEMSYNC which
+        indicates that while signaling the CUexternalSemaphore, no memory
+        synchronization operations should be performed for any external
+        memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+        For all other types of CUexternalSemaphore, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1(CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_st):
+    """
+    External semaphore wait parameters
+
+    Attributes
+    ----------
+    params : anon_struct19
+
+    flags : unsigned int
+        Only when ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on
+        a CUexternalSemaphore of type
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+        CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC which indicates
+        that while waiting for the CUexternalSemaphore, no memory
+        synchronization operations should be performed for any external
+        memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+        For all other types of CUexternalSemaphore, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS(CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS_v1):
+    """
+    External semaphore wait parameters
+
+    Attributes
+    ----------
+    params : anon_struct19
+
+    flags : unsigned int
+        Only when ::CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS is used to wait on
+        a CUexternalSemaphore of type
+        CU_EXTERNAL_SEMAPHORE_HANDLE_TYPE_NVSCISYNC, the valid flag is
+        CUDA_EXTERNAL_SEMAPHORE_WAIT_SKIP_NVSCIBUF_MEMSYNC which indicates
+        that while waiting for the CUexternalSemaphore, no memory
+        synchronization operations should be performed for any external
+        memory object imported as CU_EXTERNAL_MEMORY_HANDLE_TYPE_NVSCIBUF.
+        For all other types of CUexternalSemaphore, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1(CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_st):
+    """
+    Semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXT_SEM_SIGNAL_NODE_PARAMS(CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v1):
+    """
+    Semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2(CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2_st):
+    """
+    Semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1(CUDA_EXT_SEM_WAIT_NODE_PARAMS_st):
+    """
+    Semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXT_SEM_WAIT_NODE_PARAMS(CUDA_EXT_SEM_WAIT_NODE_PARAMS_v1):
+    """
+    Semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2(CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2_st):
+    """
+    Semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : CUexternalSemaphore
+        Array of external semaphore handles.
+    paramsArray : CUDA_EXTERNAL_SEMAPHORE_WAIT_PARAMS
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemGenericAllocationHandle:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUmemGenericAllocationHandle  _pvt_val
+    cdef cydriver.CUmemGenericAllocationHandle* _pvt_ptr
+
+cdef class CUarrayMapInfo_v1(CUarrayMapInfo_st):
+    """
+    Specifies the CUDA array or CUDA mipmapped array memory mapping
+    information
+
+    Attributes
+    ----------
+    resourceType : CUresourcetype
+        Resource type
+    resource : anon_union9
+
+    subresourceType : CUarraySparseSubresourceType
+        Sparse subresource type
+    subresource : anon_union10
+
+    memOperationType : CUmemOperationType
+        Memory operation type
+    memHandleType : CUmemHandleType
+        Memory handle type
+    memHandle : anon_union11
+
+    offset : unsigned long long
+        Offset within mip tail  Offset within the memory
+    deviceBitMask : unsigned int
+        Device ordinal bit mask
+    flags : unsigned int
+        flags for future use, must be zero now.
+    reserved : list[unsigned int]
+        Reserved for future use, must be zero now.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUarrayMapInfo(CUarrayMapInfo_v1):
+    """
+    Specifies the CUDA array or CUDA mipmapped array memory mapping
+    information
+
+    Attributes
+    ----------
+    resourceType : CUresourcetype
+        Resource type
+    resource : anon_union9
+
+    subresourceType : CUarraySparseSubresourceType
+        Sparse subresource type
+    subresource : anon_union10
+
+    memOperationType : CUmemOperationType
+        Memory operation type
+    memHandleType : CUmemHandleType
+        Memory handle type
+    memHandle : anon_union11
+
+    offset : unsigned long long
+        Offset within mip tail  Offset within the memory
+    deviceBitMask : unsigned int
+        Device ordinal bit mask
+    flags : unsigned int
+        flags for future use, must be zero now.
+    reserved : list[unsigned int]
+        Reserved for future use, must be zero now.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemLocation_v1(CUmemLocation_st):
+    """
+    Specifies a memory location.
+
+    Attributes
+    ----------
+    type : CUmemLocationType
+        Specifies the location type, which modifies the meaning of id.
+    id : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemLocation(CUmemLocation_v1):
+    """
+    Specifies a memory location.
+
+    Attributes
+    ----------
+    type : CUmemLocationType
+        Specifies the location type, which modifies the meaning of id.
+    id : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemAllocationProp_v1(CUmemAllocationProp_st):
+    """
+    Specifies the allocation properties for a allocation.
+
+    Attributes
+    ----------
+    type : CUmemAllocationType
+        Allocation type
+    requestedHandleTypes : CUmemAllocationHandleType
+        requested CUmemAllocationHandleType
+    location : CUmemLocation
+        Location of allocation
+    win32HandleMetaData : Any
+        Windows-specific POBJECT_ATTRIBUTES required when
+        CU_MEM_HANDLE_TYPE_WIN32 is specified. This object attributes
+        structure includes security attributes that define the scope of
+        which exported allocations may be transferred to other processes.
+        In all other cases, this field is required to be zero.
+    allocFlags : anon_struct22
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemAllocationProp(CUmemAllocationProp_v1):
+    """
+    Specifies the allocation properties for a allocation.
+
+    Attributes
+    ----------
+    type : CUmemAllocationType
+        Allocation type
+    requestedHandleTypes : CUmemAllocationHandleType
+        requested CUmemAllocationHandleType
+    location : CUmemLocation
+        Location of allocation
+    win32HandleMetaData : Any
+        Windows-specific POBJECT_ATTRIBUTES required when
+        CU_MEM_HANDLE_TYPE_WIN32 is specified. This object attributes
+        structure includes security attributes that define the scope of
+        which exported allocations may be transferred to other processes.
+        In all other cases, this field is required to be zero.
+    allocFlags : anon_struct22
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmulticastObjectProp_v1(CUmulticastObjectProp_st):
+    """
+    Specifies the properties for a multicast object.
+
+    Attributes
+    ----------
+    numDevices : unsigned int
+        The number of devices in the multicast team that will bind memory
+        to this object
+    size : size_t
+        The maximum amount of memory that can be bound to this multicast
+        object per device
+    handleTypes : unsigned long long
+        Bitmask of exportable handle types (see CUmemAllocationHandleType)
+        for this object
+    flags : unsigned long long
+        Flags for future use, must be zero now
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmulticastObjectProp(CUmulticastObjectProp_v1):
+    """
+    Specifies the properties for a multicast object.
+
+    Attributes
+    ----------
+    numDevices : unsigned int
+        The number of devices in the multicast team that will bind memory
+        to this object
+    size : size_t
+        The maximum amount of memory that can be bound to this multicast
+        object per device
+    handleTypes : unsigned long long
+        Bitmask of exportable handle types (see CUmemAllocationHandleType)
+        for this object
+    flags : unsigned long long
+        Flags for future use, must be zero now
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemAccessDesc_v1(CUmemAccessDesc_st):
+    """
+    Memory access descriptor
+
+    Attributes
+    ----------
+    location : CUmemLocation
+        Location on which the request is to change it's accessibility
+    flags : CUmemAccess_flags
+        ::CUmemProt accessibility flags to set on the request
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemAccessDesc(CUmemAccessDesc_v1):
+    """
+    Memory access descriptor
+
+    Attributes
+    ----------
+    location : CUmemLocation
+        Location on which the request is to change it's accessibility
+    flags : CUmemAccess_flags
+        ::CUmemProt accessibility flags to set on the request
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUgraphExecUpdateResultInfo_v1(CUgraphExecUpdateResultInfo_st):
+    """
+    Result information returned by cuGraphExecUpdate
+
+    Attributes
+    ----------
+    result : CUgraphExecUpdateResult
+        Gives more specific detail when a cuda graph update fails.
+    errorNode : CUgraphNode
+        The "to node" of the error edge when the topologies do not match.
+        The error node when the error is associated with a specific node.
+        NULL when the error is generic.
+    errorFromNode : CUgraphNode
+        The from node of error edge when the topologies do not match.
+        Otherwise NULL.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUgraphExecUpdateResultInfo(CUgraphExecUpdateResultInfo_v1):
+    """
+    Result information returned by cuGraphExecUpdate
+
+    Attributes
+    ----------
+    result : CUgraphExecUpdateResult
+        Gives more specific detail when a cuda graph update fails.
+    errorNode : CUgraphNode
+        The "to node" of the error edge when the topologies do not match.
+        The error node when the error is associated with a specific node.
+        NULL when the error is generic.
+    errorFromNode : CUgraphNode
+        The from node of error edge when the topologies do not match.
+        Otherwise NULL.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemPoolProps_v1(CUmemPoolProps_st):
+    """
+    Specifies the properties of allocations made from the pool.
+
+    Attributes
+    ----------
+    allocType : CUmemAllocationType
+        Allocation type. Currently must be specified as
+        CU_MEM_ALLOCATION_TYPE_PINNED
+    handleTypes : CUmemAllocationHandleType
+        Handle types that will be supported by allocations from the pool.
+    location : CUmemLocation
+        Location where allocations should reside.
+    win32SecurityAttributes : Any
+        Windows-specific LPSECURITYATTRIBUTES required when
+        CU_MEM_HANDLE_TYPE_WIN32 is specified. This security attribute
+        defines the scope of which exported allocations may be transferred
+        to other processes. In all other cases, this field is required to
+        be zero.
+    maxSize : size_t
+        Maximum pool size. When set to 0, defaults to a system dependent
+        value.
+    usage : unsigned short
+        Bitmask indicating intended usage for the pool.
+    reserved : bytes
+        reserved for future use, must be 0
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemPoolProps(CUmemPoolProps_v1):
+    """
+    Specifies the properties of allocations made from the pool.
+
+    Attributes
+    ----------
+    allocType : CUmemAllocationType
+        Allocation type. Currently must be specified as
+        CU_MEM_ALLOCATION_TYPE_PINNED
+    handleTypes : CUmemAllocationHandleType
+        Handle types that will be supported by allocations from the pool.
+    location : CUmemLocation
+        Location where allocations should reside.
+    win32SecurityAttributes : Any
+        Windows-specific LPSECURITYATTRIBUTES required when
+        CU_MEM_HANDLE_TYPE_WIN32 is specified. This security attribute
+        defines the scope of which exported allocations may be transferred
+        to other processes. In all other cases, this field is required to
+        be zero.
+    maxSize : size_t
+        Maximum pool size. When set to 0, defaults to a system dependent
+        value.
+    usage : unsigned short
+        Bitmask indicating intended usage for the pool.
+    reserved : bytes
+        reserved for future use, must be 0
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemPoolPtrExportData_v1(CUmemPoolPtrExportData_st):
+    """
+    Opaque data for exporting a pool allocation
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemPoolPtrExportData(CUmemPoolPtrExportData_v1):
+    """
+    Opaque data for exporting a pool allocation
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemcpyAttributes_v1(CUmemcpyAttributes_st):
+    """
+    Attributes specific to copies within a batch. For more details on
+    usage see cuMemcpyBatchAsync.
+
+    Attributes
+    ----------
+    srcAccessOrder : CUmemcpySrcAccessOrder
+        Source access ordering to be observed for copies with this
+        attribute.
+    srcLocHint : CUmemLocation
+        Hint location for the source operand. Ignored when the pointers are
+        not managed memory or memory allocated outside CUDA.
+    dstLocHint : CUmemLocation
+        Hint location for the destination operand. Ignored when the
+        pointers are not managed memory or memory allocated outside CUDA.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See CUmemcpyFlags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemcpyAttributes(CUmemcpyAttributes_v1):
+    """
+    Attributes specific to copies within a batch. For more details on
+    usage see cuMemcpyBatchAsync.
+
+    Attributes
+    ----------
+    srcAccessOrder : CUmemcpySrcAccessOrder
+        Source access ordering to be observed for copies with this
+        attribute.
+    srcLocHint : CUmemLocation
+        Hint location for the source operand. Ignored when the pointers are
+        not managed memory or memory allocated outside CUDA.
+    dstLocHint : CUmemLocation
+        Hint location for the destination operand. Ignored when the
+        pointers are not managed memory or memory allocated outside CUDA.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See CUmemcpyFlags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUoffset3D_v1(CUoffset3D_st):
+    """
+    Struct representing a 3D offset
+
+    Attributes
+    ----------
+    x : size_t
+
+    y : size_t
+
+    z : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUoffset3D(CUoffset3D_v1):
+    """
+    Struct representing a 3D offset
+
+    Attributes
+    ----------
+    x : size_t
+
+    y : size_t
+
+    z : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUextent3D_v1(CUextent3D_st):
+    """
+    Struct representing width/height/depth of a CUarray in elements
+
+    Attributes
+    ----------
+    width : size_t
+
+    height : size_t
+
+    depth : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUextent3D(CUextent3D_v1):
+    """
+    Struct representing width/height/depth of a CUarray in elements
+
+    Attributes
+    ----------
+    width : size_t
+
+    height : size_t
+
+    depth : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemcpy3DOperand_v1(CUmemcpy3DOperand_st):
+    """
+    Struct representing an operand for copy with cuMemcpy3DBatchAsync
+
+    Attributes
+    ----------
+    type : CUmemcpy3DOperandType
+
+    op : anon_union13
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemcpy3DOperand(CUmemcpy3DOperand_v1):
+    """
+    Struct representing an operand for copy with cuMemcpy3DBatchAsync
+
+    Attributes
+    ----------
+    type : CUmemcpy3DOperandType
+
+    op : anon_union13
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY3D_BATCH_OP_v1(CUDA_MEMCPY3D_BATCH_OP_st):
+    """
+    Attributes
+    ----------
+    src : CUmemcpy3DOperand
+        Source memcpy operand.
+    dst : CUmemcpy3DOperand
+        Destination memcpy operand.
+    extent : CUextent3D
+        Extents of the memcpy between src and dst. The width, height and
+        depth components must not be 0.
+    srcAccessOrder : CUmemcpySrcAccessOrder
+        Source access ordering to be observed for copy from src to dst.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See CUmemcpyFlags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEMCPY3D_BATCH_OP(CUDA_MEMCPY3D_BATCH_OP_v1):
+    """
+    Attributes
+    ----------
+    src : CUmemcpy3DOperand
+        Source memcpy operand.
+    dst : CUmemcpy3DOperand
+        Destination memcpy operand.
+    extent : CUextent3D
+        Extents of the memcpy between src and dst. The width, height and
+        depth components must not be 0.
+    srcAccessOrder : CUmemcpySrcAccessOrder
+        Source access ordering to be observed for copy from src to dst.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See CUmemcpyFlags
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEM_ALLOC_NODE_PARAMS_v1(CUDA_MEM_ALLOC_NODE_PARAMS_v1_st):
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : CUmemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be CU_MEM_HANDLE_TYPE_NONE. IPC is
+        not supported.
+    accessDescs : CUmemAccessDesc
+        in: array of memory access descriptors. Used to describe peer GPU
+        access
+    accessDescCount : size_t
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : CUdeviceptr
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEM_ALLOC_NODE_PARAMS(CUDA_MEM_ALLOC_NODE_PARAMS_v1):
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : CUmemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be CU_MEM_HANDLE_TYPE_NONE. IPC is
+        not supported.
+    accessDescs : CUmemAccessDesc
+        in: array of memory access descriptors. Used to describe peer GPU
+        access
+    accessDescCount : size_t
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : CUdeviceptr
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEM_ALLOC_NODE_PARAMS_v2(CUDA_MEM_ALLOC_NODE_PARAMS_v2_st):
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : CUmemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be CU_MEM_HANDLE_TYPE_NONE. IPC is
+        not supported.
+    accessDescs : CUmemAccessDesc
+        in: array of memory access descriptors. Used to describe peer GPU
+        access
+    accessDescCount : size_t
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : CUdeviceptr
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_MEM_FREE_NODE_PARAMS(CUDA_MEM_FREE_NODE_PARAMS_st):
+    """
+    Memory free node parameters
+
+    Attributes
+    ----------
+    dptr : CUdeviceptr
+        in: the pointer to free
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_CHILD_GRAPH_NODE_PARAMS(CUDA_CHILD_GRAPH_NODE_PARAMS_st):
+    """
+    Child graph node parameters
+
+    Attributes
+    ----------
+    graph : CUgraph
+        The child graph to clone into the node for node creation, or a
+        handle to the graph owned by the node for node query. The graph
+        must not contain conditional nodes. Graphs containing memory
+        allocation or memory free nodes must set the ownership to be moved
+        to the parent.
+    ownership : CUgraphChildGraphNodeOwnership
+        The ownership relationship of the child graph node.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EVENT_RECORD_NODE_PARAMS(CUDA_EVENT_RECORD_NODE_PARAMS_st):
+    """
+    Event record node parameters
+
+    Attributes
+    ----------
+    event : CUevent
+        The event to record when the node executes
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUDA_EVENT_WAIT_NODE_PARAMS(CUDA_EVENT_WAIT_NODE_PARAMS_st):
+    """
+    Event wait node parameters
+
+    Attributes
+    ----------
+    event : CUevent
+        The event to wait on from the node
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUgraphNodeParams(CUgraphNodeParams_st):
+    """
+    Graph node parameters. See cuGraphAddNode.
+
+    Attributes
+    ----------
+    type : CUgraphNodeType
+        Type of the node
+    reserved0 : list[int]
+        Reserved. Must be zero.
+    reserved1 : list[long long]
+        Padding. Unused bytes must be zero.
+    kernel : CUDA_KERNEL_NODE_PARAMS_v3
+        Kernel node parameters.
+    memcpy : CUDA_MEMCPY_NODE_PARAMS
+        Memcpy node parameters.
+    memset : CUDA_MEMSET_NODE_PARAMS_v2
+        Memset node parameters.
+    host : CUDA_HOST_NODE_PARAMS_v2
+        Host node parameters.
+    graph : CUDA_CHILD_GRAPH_NODE_PARAMS
+        Child graph node parameters.
+    eventWait : CUDA_EVENT_WAIT_NODE_PARAMS
+        Event wait node parameters.
+    eventRecord : CUDA_EVENT_RECORD_NODE_PARAMS
+        Event record node parameters.
+    extSemSignal : CUDA_EXT_SEM_SIGNAL_NODE_PARAMS_v2
+        External semaphore signal node parameters.
+    extSemWait : CUDA_EXT_SEM_WAIT_NODE_PARAMS_v2
+        External semaphore wait node parameters.
+    alloc : CUDA_MEM_ALLOC_NODE_PARAMS_v2
+        Memory allocation node parameters.
+    free : CUDA_MEM_FREE_NODE_PARAMS
+        Memory free node parameters.
+    memOp : CUDA_BATCH_MEM_OP_NODE_PARAMS_v2
+        MemOp node parameters.
+    conditional : CUDA_CONDITIONAL_NODE_PARAMS
+        Conditional node parameters.
+    reserved2 : long long
+        Reserved bytes. Must be zero.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUcheckpointLockArgs(CUcheckpointLockArgs_st):
+    """
+    CUDA checkpoint optional lock arguments
+
+    Attributes
+    ----------
+    timeoutMs : unsigned int
+        Timeout in milliseconds to attempt to lock the process, 0 indicates
+        no timeout
+    reserved0 : unsigned int
+        Reserved for future use, must be zero
+    reserved1 : list[cuuint64_t]
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUcheckpointCheckpointArgs(CUcheckpointCheckpointArgs_st):
+    """
+    CUDA checkpoint optional checkpoint arguments
+
+    Attributes
+    ----------
+    reserved : list[cuuint64_t]
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUcheckpointGpuPair(CUcheckpointGpuPair_st):
+    """
+    CUDA checkpoint GPU UUID pairs for device remapping during restore
+
+    Attributes
+    ----------
+    oldUuid : CUuuid
+        UUID of the GPU that was checkpointed
+    newUuid : CUuuid
+        UUID of the GPU to restore onto
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUcheckpointRestoreArgs(CUcheckpointRestoreArgs_st):
+    """
+    CUDA checkpoint optional restore arguments
+
+    Attributes
+    ----------
+    gpuPairs : CUcheckpointGpuPair
+        Pointer to array of gpu pairs that indicate how to remap GPUs
+        during restore
+    gpuPairsCount : unsigned int
+        Number of gpu pairs to remap
+    reserved : bytes
+        Reserved for future use, must be zeroed
+    reserved1 : cuuint64_t
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUcheckpointUnlockArgs(CUcheckpointUnlockArgs_st):
+    """
+    CUDA checkpoint optional unlock arguments
+
+    Attributes
+    ----------
+    reserved : list[cuuint64_t]
+        Reserved for future use, must be zeroed
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUmemDecompressParams(CUmemDecompressParams_st):
+    """
+    Structure describing the parameters that compose a single
+    decompression operation.
+
+    Attributes
+    ----------
+    srcNumBytes : size_t
+        The number of bytes to be read and decompressed from
+        CUmemDecompressParams_st.src.
+    dstNumBytes : size_t
+        The number of bytes that the decompression operation will be
+        expected to write to CUmemDecompressParams_st.dst. This value is
+        optional; if present, it may be used by the CUDA driver as a
+        heuristic for scheduling the individual decompression operations.
+    dstActBytes : cuuint32_t
+        After the decompression operation has completed, the actual number
+        of bytes written to CUmemDecompressParams.dst will be recorded as a
+        32-bit unsigned integer in the memory at this address.
+    src : Any
+        Pointer to a buffer of at least
+        CUmemDecompressParams_st.srcNumBytes compressed bytes.
+    dst : Any
+        Pointer to a buffer where the decompressed data will be written.
+        The number of bytes written to this location will be recorded in
+        the memory pointed to by CUmemDecompressParams_st.dstActBytes
+    algo : CUmemDecompressAlgorithm
+        The decompression algorithm to use.
+    padding : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevSmResource(CUdevSmResource_st):
+    """
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of streaming multiprocessors available in this resource.
+    minSmPartitionSize : unsigned int
+        The minimum number of streaming multiprocessors required to
+        partition this resource.
+    smCoscheduledAlignment : unsigned int
+        The number of streaming multiprocessors in this resource that are
+        guaranteed to be co-scheduled on the same GPU processing cluster.
+        smCount will be a multiple of this value, unless the backfill flag
+        is set.
+    flags : unsigned int
+        The flags set on this SM resource. For possible values see
+        CUdevSmResourceGroup_flags.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevWorkqueueConfigResource(CUdevWorkqueueConfigResource_st):
+    """
+    Attributes
+    ----------
+    device : CUdevice
+        The device on which the workqueue resources are available
+    wqConcurrencyLimit : unsigned int
+        The expected maximum number of concurrent stream-ordered workloads
+    sharingScope : CUdevWorkqueueConfigScope
+        The sharing scope for the workqueue resources
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevWorkqueueResource(CUdevWorkqueueResource_st):
+    """
+    Attributes
+    ----------
+    reserved : bytes
+        Reserved for future use
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CU_DEV_SM_RESOURCE_GROUP_PARAMS(CU_DEV_SM_RESOURCE_GROUP_PARAMS_st):
+    """
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of SMs available in this resource.
+    coscheduledSmCount : unsigned int
+        The amount of co-scheduled SMs grouped together for locality
+        purposes.
+    preferredCoscheduledSmCount : unsigned int
+        When possible, combine co-scheduled groups together into larger
+        groups of this size.
+    flags : unsigned int
+        The flags set on this SM resource group. For possible values see
+        CUdevSmResourceGroup_flags.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevResource_v1(CUdevResource_st):
+    """
+    Attributes
+    ----------
+    type : CUdevResourceType
+        Type of resource, dictates which union field was last set
+    _internal_padding : bytes
+
+    sm : CUdevSmResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_SM `typename`.
+    wqConfig : CUdevWorkqueueConfigResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_WORKQUEUE_CONFIG
+        `typename`.
+    wq : CUdevWorkqueueResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_WORKQUEUE
+        `typename`.
+    _oversize : bytes
+
+    nextResource : CUdevResource_st
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUdevResource(CUdevResource_v1):
+    """
+    Attributes
+    ----------
+    type : CUdevResourceType
+        Type of resource, dictates which union field was last set
+    _internal_padding : bytes
+
+    sm : CUdevSmResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_SM `typename`.
+    wqConfig : CUdevWorkqueueConfigResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_WORKQUEUE_CONFIG
+        `typename`.
+    wq : CUdevWorkqueueResource
+        Resource corresponding to CU_DEV_RESOURCE_TYPE_WORKQUEUE
+        `typename`.
+    _oversize : bytes
+
+    nextResource : CUdevResource_st
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUeglFrame_v1(CUeglFrame_st):
+    """
+    CUDA EGLFrame structure Descriptor - structure defining one frame
+    of EGL.  Each frame may contain one or more planes depending on
+    whether the surface * is Multiplanar or not.
+
+    Attributes
+    ----------
+    frame : anon_union16
+
+    width : unsigned int
+        Width of first plane
+    height : unsigned int
+        Height of first plane
+    depth : unsigned int
+        Depth of first plane
+    pitch : unsigned int
+        Pitch of first plane
+    planeCount : unsigned int
+        Number of planes
+    numChannels : unsigned int
+        Number of channels for the plane
+    frameType : CUeglFrameType
+        Array or Pitch
+    eglColorFormat : CUeglColorFormat
+        CUDA EGL Color Format
+    cuFormat : CUarray_format
+        CUDA Array Format
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class CUeglFrame(CUeglFrame_v1):
+    """
+    CUDA EGLFrame structure Descriptor - structure defining one frame
+    of EGL.  Each frame may contain one or more planes depending on
+    whether the surface * is Multiplanar or not.
+
+    Attributes
+    ----------
+    frame : anon_union16
+
+    width : unsigned int
+        Width of first plane
+    height : unsigned int
+        Height of first plane
+    depth : unsigned int
+        Depth of first plane
+    pitch : unsigned int
+        Pitch of first plane
+    planeCount : unsigned int
+        Number of planes
+    numChannels : unsigned int
+        Number of channels for the plane
+    frameType : CUeglFrameType
+        Array or Pitch
+    eglColorFormat : CUeglColorFormat
+        CUDA EGL Color Format
+    cuFormat : CUarray_format
+        CUDA Array Format
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cuuint32_t:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.cuuint32_t  _pvt_val
+    cdef cydriver.cuuint32_t* _pvt_ptr
+
+cdef class cuuint64_t:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.cuuint64_t  _pvt_val
+    cdef cydriver.cuuint64_t* _pvt_ptr
+
+cdef class CUdeviceptr_v2:
+    """
+
+    CUDA device pointer CUdeviceptr is defined as an unsigned integer type whose size matches the size of a pointer on the target platform.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUdeviceptr_v2  _pvt_val
+    cdef cydriver.CUdeviceptr_v2* _pvt_ptr
+
+cdef class CUdevice_v1:
+    """
+
+    CUDA device
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUdevice_v1  _pvt_val
+    cdef cydriver.CUdevice_v1* _pvt_ptr
+
+cdef class CUtexObject_v1:
+    """
+
+    An opaque value that represents a CUDA texture object
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUtexObject_v1  _pvt_val
+    cdef cydriver.CUtexObject_v1* _pvt_ptr
+
+cdef class CUsurfObject_v1:
+    """
+
+    An opaque value that represents a CUDA surface object
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUsurfObject_v1  _pvt_val
+    cdef cydriver.CUsurfObject_v1* _pvt_ptr
+
+cdef class CUmemGenericAllocationHandle_v1:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUmemGenericAllocationHandle_v1  _pvt_val
+    cdef cydriver.CUmemGenericAllocationHandle_v1* _pvt_ptr
+
+cdef class CUlogIterator:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.CUlogIterator  _pvt_val
+    cdef cydriver.CUlogIterator* _pvt_ptr
+
+cdef class GLenum:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.GLenum  _pvt_val
+    cdef cydriver.GLenum* _pvt_ptr
+
+cdef class GLuint:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.GLuint  _pvt_val
+    cdef cydriver.GLuint* _pvt_ptr
+
+cdef class EGLint:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.EGLint  _pvt_val
+    cdef cydriver.EGLint* _pvt_ptr
+
+cdef class VdpDevice:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.VdpDevice  _pvt_val
+    cdef cydriver.VdpDevice* _pvt_ptr
+
+cdef class VdpGetProcAddress:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.VdpGetProcAddress  _pvt_val
+    cdef cydriver.VdpGetProcAddress* _pvt_ptr
+
+cdef class VdpVideoSurface:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.VdpVideoSurface  _pvt_val
+    cdef cydriver.VdpVideoSurface* _pvt_ptr
+
+cdef class VdpOutputSurface:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cydriver.VdpOutputSurface  _pvt_val
+    cdef cydriver.VdpOutputSurface* _pvt_ptr
diff --git a/lib/python3.11/site-packages/cuda/bindings/nvfatbin.pxd b/lib/python3.11/site-packages/cuda/bindings/nvfatbin.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..b117da600cf5ce12edaf11bba6ae951693a05172
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/nvfatbin.pxd
@@ -0,0 +1,38 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.4.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport intptr_t, uint32_t
+
+from .cynvfatbin cimport *
+
+
+###############################################################################
+# Types
+###############################################################################
+
+ctypedef nvFatbinHandle Handle
+
+
+###############################################################################
+# Enum
+###############################################################################
+
+ctypedef nvFatbinResult _Result
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cpdef intptr_t create(options, size_t options_count) except -1
+cpdef add_ptx(intptr_t handle, code, size_t size, arch, identifier, options_cmd_line)
+cpdef add_cubin(intptr_t handle, code, size_t size, arch, identifier)
+cpdef add_ltoir(intptr_t handle, code, size_t size, arch, identifier, options_cmd_line)
+cpdef size_t size(intptr_t handle) except? 0
+cpdef get(intptr_t handle, buffer)
+cpdef tuple version()
+cpdef add_reloc(intptr_t handle, code, size_t size)
+cpdef add_tile_ir(intptr_t handle, code, size_t size, identifier, options_cmd_line)
diff --git a/lib/python3.11/site-packages/cuda/bindings/nvjitlink.pxd b/lib/python3.11/site-packages/cuda/bindings/nvjitlink.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..e9090a6687291794f901d8c0c3e979fe04f90028
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/nvjitlink.pxd
@@ -0,0 +1,43 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.0.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport intptr_t, uint32_t
+
+from .cynvjitlink cimport *
+
+
+###############################################################################
+# Types
+###############################################################################
+
+ctypedef nvJitLinkHandle Handle
+
+
+###############################################################################
+# Enum
+###############################################################################
+
+ctypedef nvJitLinkResult _Result
+ctypedef nvJitLinkInputType _InputType
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cpdef intptr_t create(uint32_t num_options, options) except -1
+cpdef add_data(intptr_t handle, int input_type, data, size_t size, name)
+cpdef add_file(intptr_t handle, int input_type, file_name)
+cpdef complete(intptr_t handle)
+cpdef size_t get_linked_cubin_size(intptr_t handle) except? 0
+cpdef get_linked_cubin(intptr_t handle, cubin)
+cpdef size_t get_linked_ptx_size(intptr_t handle) except? 0
+cpdef get_linked_ptx(intptr_t handle, ptx)
+cpdef size_t get_error_log_size(intptr_t handle) except? 0
+cpdef get_error_log(intptr_t handle, log)
+cpdef size_t get_info_log_size(intptr_t handle) except? 0
+cpdef get_info_log(intptr_t handle, log)
+cpdef tuple version()
diff --git a/lib/python3.11/site-packages/cuda/bindings/nvml.pxd b/lib/python3.11/site-packages/cuda/bindings/nvml.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..3dc3f58e5d191a2a857e58d39c3d83af131faae3
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/nvml.pxd
@@ -0,0 +1,422 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.9.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport intptr_t
+
+from .cynvml cimport *
+
+
+###############################################################################
+# Types
+###############################################################################
+
+ctypedef nvmlDramEncryptionInfo_v1_t DramEncryptionInfo_v1
+ctypedef nvmlMarginTemperature_v1_t MarginTemperature_v1
+ctypedef nvmlFanSpeedInfo_v1_t FanSpeedInfo_v1
+ctypedef nvmlDevicePerfModes_v1_t DevicePerfModes_v1
+ctypedef nvmlDeviceCurrentClockFreqs_v1_t DeviceCurrentClockFreqs_v1
+ctypedef nvmlVgpuHeterogeneousMode_v1_t VgpuHeterogeneousMode_v1
+ctypedef nvmlVgpuPlacementId_v1_t VgpuPlacementId_v1
+ctypedef nvmlVgpuRuntimeState_v1_t VgpuRuntimeState_v1
+ctypedef nvmlConfComputeSetKeyRotationThresholdInfo_v1_t ConfComputeSetKeyRotationThresholdInfo_v1
+ctypedef nvmlConfComputeGetKeyRotationThresholdInfo_v1_t ConfComputeGetKeyRotationThresholdInfo_v1
+ctypedef nvmlSystemDriverBranchInfo_v1_t SystemDriverBranchInfo_v1
+ctypedef nvmlTemperature_v1_t Temperature_v1
+ctypedef nvmlDeviceCapabilities_v1_t DeviceCapabilities_v1
+ctypedef nvmlPowerSmoothingProfile_v1_t PowerSmoothingProfile_v1
+ctypedef nvmlPowerSmoothingState_v1_t PowerSmoothingState_v1
+ctypedef nvmlPdi_v1_t Pdi_v1
+ctypedef nvmlDevice_t Device
+ctypedef nvmlGpuInstance_t GpuInstance
+ctypedef nvmlUnit_t Unit
+ctypedef nvmlEventSet_t EventSet
+ctypedef nvmlSystemEventSet_t SystemEventSet
+ctypedef nvmlComputeInstance_t ComputeInstance
+ctypedef nvmlGpmSample_t GpmSample
+ctypedef nvmlEccErrorCounts_t EccErrorCounts
+ctypedef nvmlProcessInfo_v1_t ProcessInfo_v1
+ctypedef nvmlProcessInfo_v2_t ProcessInfo_v2
+ctypedef nvmlNvLinkUtilizationControl_t NvLinkUtilizationControl
+ctypedef nvmlViolationTime_t ViolationTime
+ctypedef nvmlUUIDValue_t UUIDValue
+ctypedef nvmlVgpuPlacementList_v1_t VgpuPlacementList_v1
+ctypedef nvmlNvLinkPowerThres_t NvLinkPowerThres
+ctypedef nvmlGpuInstanceProfileInfo_t GpuInstanceProfileInfo
+ctypedef nvmlGpuInstanceProfileInfo_v2_t GpuInstanceProfileInfo_v2
+ctypedef nvmlComputeInstanceProfileInfo_t ComputeInstanceProfileInfo
+ctypedef nvmlGpmSupport_t GpmSupport
+ctypedef nvmlMask255_t Mask255
+ctypedef nvmlHostname_v1_t Hostname_v1
+ctypedef nvmlUnrepairableMemoryStatus_v1_t UnrepairableMemoryStatus_v1
+ctypedef nvmlRusdSettings_v1_t RusdSettings_v1
+ctypedef nvmlVgpuSchedulerStateInfo_v2_t VgpuSchedulerStateInfo_v2
+ctypedef nvmlVgpuSchedulerLogEntry_v2_t VgpuSchedulerLogEntry_v2
+ctypedef nvmlVgpuSchedulerState_v2_t VgpuSchedulerState_v2
+ctypedef nvmlPowerValue_v2_t PowerValue_v2
+ctypedef nvmlVgpuTypeMaxInstance_v1_t VgpuTypeMaxInstance_v1
+ctypedef nvmlVgpuProcessUtilizationSample_t VgpuProcessUtilizationSample
+ctypedef nvmlGpuFabricInfo_t GpuFabricInfo
+ctypedef nvmlSystemEventSetCreateRequest_v1_t SystemEventSetCreateRequest_v1
+ctypedef nvmlSystemEventSetFreeRequest_v1_t SystemEventSetFreeRequest_v1
+ctypedef nvmlSystemRegisterEventRequest_v1_t SystemRegisterEventRequest_v1
+ctypedef nvmlUUID_v1_t UUID_v1
+ctypedef nvmlSystemEventSetWaitRequest_v1_t SystemEventSetWaitRequest_v1
+ctypedef nvmlGpmMetric_t GpmMetric
+ctypedef nvmlWorkloadPowerProfileInfo_v1_t WorkloadPowerProfileInfo_v1
+ctypedef nvmlWorkloadPowerProfileCurrentProfiles_v1_t WorkloadPowerProfileCurrentProfiles_v1
+ctypedef nvmlWorkloadPowerProfileRequestedProfiles_v1_t WorkloadPowerProfileRequestedProfiles_v1
+ctypedef nvmlWorkloadPowerProfileUpdateProfiles_v1_t WorkloadPowerProfileUpdateProfiles_v1
+ctypedef nvmlPRMTLV_v1_t PRMTLV_v1
+ctypedef nvmlVgpuSchedulerLogInfo_v2_t VgpuSchedulerLogInfo_v2
+ctypedef nvmlVgpuSchedulerSetState_t VgpuSchedulerSetState
+ctypedef nvmlGpmMetricsGet_t GpmMetricsGet
+ctypedef nvmlPRMCounterList_v1_t PRMCounterList_v1
+ctypedef nvmlWorkloadPowerProfileProfilesInfo_v1_t WorkloadPowerProfileProfilesInfo_v1
+
+
+###############################################################################
+# Enum
+###############################################################################
+
+ctypedef nvmlBridgeChipType_t _BridgeChipType
+ctypedef nvmlNvLinkUtilizationCountUnits_t _NvLinkUtilizationCountUnits
+ctypedef nvmlNvLinkUtilizationCountPktTypes_t _NvLinkUtilizationCountPktTypes
+ctypedef nvmlNvLinkCapability_t _NvLinkCapability
+ctypedef nvmlNvLinkErrorCounter_t _NvLinkErrorCounter
+ctypedef nvmlIntNvLinkDeviceType_t _IntNvLinkDeviceType
+ctypedef nvmlGpuTopologyLevel_t _GpuTopologyLevel
+ctypedef nvmlGpuP2PStatus_t _GpuP2PStatus
+ctypedef nvmlGpuP2PCapsIndex_t _GpuP2PCapsIndex
+ctypedef nvmlSamplingType_t _SamplingType
+ctypedef nvmlPcieUtilCounter_t _PcieUtilCounter
+ctypedef nvmlValueType_t _ValueType
+ctypedef nvmlPerfPolicyType_t _PerfPolicyType
+ctypedef nvmlThermalTarget_t _ThermalTarget
+ctypedef nvmlThermalController_t _ThermalController
+ctypedef nvmlCoolerControl_t _CoolerControl
+ctypedef nvmlCoolerTarget_t _CoolerTarget
+ctypedef nvmlUUIDType_t _UUIDType
+ctypedef nvmlEnableState_t _EnableState
+ctypedef nvmlBrandType_t _BrandType
+ctypedef nvmlTemperatureThresholds_t _TemperatureThresholds
+ctypedef nvmlTemperatureSensors_t _TemperatureSensors
+ctypedef nvmlComputeMode_t _ComputeMode
+ctypedef nvmlMemoryErrorType_t _MemoryErrorType
+ctypedef nvmlNvlinkVersion_t _NvlinkVersion
+ctypedef nvmlEccCounterType_t _EccCounterType
+ctypedef nvmlClockType_t _ClockType
+ctypedef nvmlClockId_t _ClockId
+ctypedef nvmlDriverModel_t _DriverModel
+ctypedef nvmlPstates_t _Pstates
+ctypedef nvmlGpuOperationMode_t _GpuOperationMode
+ctypedef nvmlInforomObject_t _InforomObject
+ctypedef nvmlReturn_t _Return
+ctypedef nvmlMemoryLocation_t _MemoryLocation
+ctypedef nvmlPageRetirementCause_t _PageRetirementCause
+ctypedef nvmlRestrictedAPI_t _RestrictedAPI
+ctypedef nvmlGpuUtilizationDomainId_t _GpuUtilizationDomainId
+ctypedef nvmlGpuVirtualizationMode_t _GpuVirtualizationMode
+ctypedef nvmlHostVgpuMode_t _HostVgpuMode
+ctypedef nvmlVgpuVmIdType_t _VgpuVmIdType
+ctypedef nvmlVgpuGuestInfoState_t _VgpuGuestInfoState
+ctypedef nvmlGridLicenseFeatureCode_t _GridLicenseFeatureCode
+ctypedef nvmlVgpuCapability_t _VgpuCapability
+ctypedef nvmlVgpuDriverCapability_t _VgpuDriverCapability
+ctypedef nvmlDeviceVgpuCapability_t _DeviceVgpuCapability
+ctypedef nvmlDeviceGpuRecoveryAction_t _DeviceGpuRecoveryAction
+ctypedef nvmlFanState_t _FanState
+ctypedef nvmlLedColor_t _LedColor
+ctypedef nvmlEncoderType_t _EncoderType
+ctypedef nvmlFBCSessionType_t _FBCSessionType
+ctypedef nvmlDetachGpuState_t _DetachGpuState
+ctypedef nvmlPcieLinkState_t _PcieLinkState
+ctypedef nvmlClockLimitId_t _ClockLimitId
+ctypedef nvmlVgpuVmCompatibility_t _VgpuVmCompatibility
+ctypedef nvmlVgpuPgpuCompatibilityLimitCode_t _VgpuPgpuCompatibilityLimitCode
+ctypedef nvmlGpmMetricId_t _GpmMetricId
+ctypedef nvmlPowerProfileType_t _PowerProfileType
+ctypedef nvmlDeviceAddressingModeType_t _DeviceAddressingModeType
+ctypedef nvmlPRMCounterId_t _PRMCounterId
+ctypedef nvmlPowerProfileOperation_t _PowerProfileOperation
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cpdef init_v2()
+cpdef init_with_flags(unsigned int flags)
+cpdef shutdown()
+cpdef str error_string(int result)
+cpdef str system_get_driver_version()
+cpdef str system_get_nvml_version()
+cpdef int system_get_cuda_driver_version() except *
+cpdef int system_get_cuda_driver_version_v2() except 0
+cpdef str system_get_process_name(unsigned int pid)
+cpdef object system_get_hic_version()
+cpdef unsigned int unit_get_count() except? 0
+cpdef intptr_t unit_get_handle_by_index(unsigned int ind_ex) except? 0
+cpdef object unit_get_unit_info(intptr_t unit)
+cpdef object unit_get_led_state(intptr_t unit)
+cpdef object unit_get_psu_info(intptr_t unit)
+cpdef unsigned int unit_get_temperature(intptr_t unit, unsigned int type) except? 0
+cpdef object unit_get_fan_speed_info(intptr_t unit)
+cpdef unsigned int device_get_count_v2() except? 0
+cpdef object device_get_attributes_v2(intptr_t device)
+cpdef intptr_t device_get_handle_by_index_v2(unsigned int ind_ex) except? 0
+cpdef intptr_t device_get_handle_by_serial(serial) except? 0
+cpdef intptr_t device_get_handle_by_uuid(uuid) except? 0
+cpdef intptr_t device_get_handle_by_pci_bus_id_v2(pci_bus_id) except? 0
+cpdef str device_get_name(intptr_t device)
+cpdef int device_get_brand(intptr_t device) except? -1
+cpdef unsigned int device_get_index(intptr_t device) except? 0
+cpdef str device_get_serial(intptr_t device)
+cpdef unsigned int device_get_module_id(intptr_t device) except? 0
+cpdef object device_get_c2c_mode_info_v(intptr_t device)
+cpdef object device_get_memory_affinity(intptr_t device, unsigned int node_set_size, unsigned int scope)
+cpdef object device_get_cpu_affinity_within_scope(intptr_t device, unsigned int cpu_set_size, unsigned int scope)
+cpdef object device_get_cpu_affinity(intptr_t device, unsigned int cpu_set_size)
+cpdef device_set_cpu_affinity(intptr_t device)
+cpdef device_clear_cpu_affinity(intptr_t device)
+cpdef unsigned int device_get_numa_node_id(intptr_t device) except? 0
+cpdef int device_get_topology_common_ancestor(intptr_t device1, intptr_t device2) except? -1
+cpdef int device_get_p2p_status(intptr_t device1, intptr_t device2, int p2p_ind_ex) except? -1
+cpdef str device_get_uuid(intptr_t device)
+cpdef unsigned int device_get_minor_number(intptr_t device) except? 0
+cpdef str device_get_board_part_number(intptr_t device)
+cpdef str device_get_inforom_version(intptr_t device, int object)
+cpdef str device_get_inforom_image_version(intptr_t device)
+cpdef unsigned int device_get_inforom_configuration_checksum(intptr_t device) except? 0
+cpdef device_validate_inforom(intptr_t device)
+cpdef tuple device_get_last_bbx_flush_time(intptr_t device)
+cpdef int device_get_display_mode(intptr_t device) except? -1
+cpdef int device_get_display_active(intptr_t device) except? -1
+cpdef int device_get_persistence_mode(intptr_t device) except? -1
+cpdef object device_get_pci_info_ext(intptr_t device)
+cpdef object device_get_pci_info_v3(intptr_t device)
+cpdef unsigned int device_get_max_pcie_link_generation(intptr_t device) except? 0
+cpdef unsigned int device_get_gpu_max_pcie_link_generation(intptr_t device) except? 0
+cpdef unsigned int device_get_max_pcie_link_width(intptr_t device) except? 0
+cpdef unsigned int device_get_curr_pcie_link_generation(intptr_t device) except? 0
+cpdef unsigned int device_get_curr_pcie_link_width(intptr_t device) except? 0
+cpdef unsigned int device_get_pcie_throughput(intptr_t device, int counter) except? 0
+cpdef unsigned int device_get_pcie_replay_counter(intptr_t device) except? 0
+cpdef unsigned int device_get_clock_info(intptr_t device, int type) except? 0
+cpdef unsigned int device_get_max_clock_info(intptr_t device, int type) except? 0
+cpdef int device_get_gpc_clk_vf_offset(intptr_t device) except? 0
+cpdef unsigned int device_get_clock(intptr_t device, int clock_type, int clock_id) except? 0
+cpdef unsigned int device_get_max_customer_boost_clock(intptr_t device, int clock_type) except? 0
+cpdef object device_get_supported_memory_clocks(intptr_t device)
+cpdef object device_get_supported_graphics_clocks(intptr_t device, unsigned int memory_clock_m_hz)
+cpdef tuple device_get_auto_boosted_clocks_enabled(intptr_t device)
+cpdef unsigned int device_get_fan_speed(intptr_t device) except? 0
+cpdef unsigned int device_get_fan_speed_v2(intptr_t device, unsigned int fan) except? 0
+cpdef unsigned int device_get_target_fan_speed(intptr_t device, unsigned int fan) except? 0
+cpdef tuple device_get_min_max_fan_speed(intptr_t device)
+cpdef unsigned int device_get_fan_control_policy_v2(intptr_t device, unsigned int fan) except *
+cpdef unsigned int device_get_num_fans(intptr_t device) except? 0
+cpdef object device_get_cooler_info(intptr_t device)
+cpdef unsigned int device_get_temperature_threshold(intptr_t device, int threshold_type) except? 0
+cpdef object device_get_thermal_settings(intptr_t device, unsigned int sensor_ind_ex)
+cpdef int device_get_performance_state(intptr_t device) except? -1
+cpdef unsigned long long device_get_current_clocks_event_reasons(intptr_t device) except? 0
+cpdef unsigned long long device_get_supported_clocks_event_reasons(intptr_t device) except? 0
+cpdef int device_get_power_state(intptr_t device) except? -1
+cpdef object device_get_dynamic_pstates_info(intptr_t device)
+cpdef int device_get_mem_clk_vf_offset(intptr_t device) except? 0
+cpdef tuple device_get_min_max_clock_of_p_state(intptr_t device, int type, int pstate)
+cpdef tuple device_get_gpc_clk_min_max_vf_offset(intptr_t device)
+cpdef tuple device_get_mem_clk_min_max_vf_offset(intptr_t device)
+cpdef device_set_clock_offsets(intptr_t device, intptr_t info)
+cpdef unsigned int device_get_power_management_limit(intptr_t device) except? 0
+cpdef tuple device_get_power_management_limit_constraints(intptr_t device)
+cpdef unsigned int device_get_power_management_default_limit(intptr_t device) except? 0
+cpdef unsigned int device_get_power_usage(intptr_t device) except? 0
+cpdef unsigned long long device_get_total_energy_consumption(intptr_t device) except? 0
+cpdef unsigned int device_get_enforced_power_limit(intptr_t device) except? 0
+cpdef tuple device_get_gpu_operation_mode(intptr_t device)
+cpdef object device_get_memory_info_v2(intptr_t device)
+cpdef int device_get_compute_mode(intptr_t device) except? -1
+cpdef tuple device_get_cuda_compute_capability(intptr_t device)
+cpdef tuple device_get_ecc_mode(intptr_t device)
+cpdef int device_get_default_ecc_mode(intptr_t device) except? -1
+cpdef unsigned int device_get_board_id(intptr_t device) except? 0
+cpdef unsigned int device_get_multi_gpu_board(intptr_t device) except? 0
+cpdef unsigned long long device_get_total_ecc_errors(intptr_t device, int error_type, int counter_type) except? 0
+cpdef unsigned long long device_get_memory_error_counter(intptr_t device, int error_type, int counter_type, int location_type) except? 0
+cpdef object device_get_utilization_rates(intptr_t device)
+cpdef tuple device_get_encoder_utilization(intptr_t device)
+cpdef unsigned int device_get_encoder_capacity(intptr_t device, int encoder_query_type) except? 0
+cpdef tuple device_get_encoder_stats(intptr_t device)
+cpdef object device_get_encoder_sessions(intptr_t device)
+cpdef tuple device_get_decoder_utilization(intptr_t device)
+cpdef tuple device_get_jpg_utilization(intptr_t device)
+cpdef tuple device_get_ofa_utilization(intptr_t device)
+cpdef object device_get_fbc_stats(intptr_t device)
+cpdef object device_get_fbc_sessions(intptr_t device)
+cpdef tuple device_get_driver_model_v2(intptr_t device)
+cpdef str device_get_vbios_version(intptr_t device)
+cpdef object device_get_bridge_chip_info(intptr_t device)
+cpdef object device_get_compute_running_processes_v3(intptr_t device)
+cpdef object device_get_mps_compute_running_processes_v3(intptr_t device)
+cpdef int device_on_same_board(intptr_t device1, intptr_t device2) except? 0
+cpdef int device_get_api_restriction(intptr_t device, int api_type) except? -1
+cpdef object device_get_bar1_memory_info(intptr_t device)
+cpdef unsigned int device_get_irq_num(intptr_t device) except? 0
+cpdef unsigned int device_get_num_gpu_cores(intptr_t device) except? 0
+cpdef unsigned int device_get_power_source(intptr_t device) except *
+cpdef unsigned int device_get_memory_bus_width(intptr_t device) except? 0
+cpdef unsigned int device_get_pcie_link_max_speed(intptr_t device) except? 0
+cpdef unsigned int device_get_pcie_speed(intptr_t device) except? 0
+cpdef unsigned int device_get_adaptive_clock_info_status(intptr_t device) except? 0
+cpdef unsigned int device_get_bus_type(intptr_t device) except? 0
+cpdef object system_get_conf_compute_capabilities()
+cpdef object system_get_conf_compute_state()
+cpdef object device_get_conf_compute_mem_size_info(intptr_t device)
+cpdef unsigned int system_get_conf_compute_gpus_ready_state() except? 0
+cpdef object device_get_conf_compute_protected_memory_usage(intptr_t device)
+cpdef object device_get_conf_compute_gpu_certificate(intptr_t device)
+cpdef device_set_conf_compute_unprotected_mem_size(intptr_t device, unsigned long long size_ki_b)
+cpdef system_set_conf_compute_gpus_ready_state(unsigned int is_accepting_work)
+cpdef object system_get_conf_compute_settings()
+cpdef char device_get_gsp_firmware_version(intptr_t device) except? 0
+cpdef tuple device_get_gsp_firmware_mode(intptr_t device)
+cpdef object device_get_sram_ecc_error_status(intptr_t device)
+cpdef int device_get_accounting_mode(intptr_t device) except? -1
+cpdef object device_get_accounting_stats(intptr_t device, unsigned int pid)
+cpdef object device_get_accounting_pids(intptr_t device)
+cpdef unsigned int device_get_accounting_buffer_size(intptr_t device) except? 0
+cpdef object device_get_retired_pages(intptr_t device, int cause)
+cpdef int device_get_retired_pages_pending_status(intptr_t device) except? -1
+cpdef tuple device_get_remapped_rows(intptr_t device)
+cpdef object device_get_row_remapper_histogram(intptr_t device)
+cpdef unsigned int device_get_architecture(intptr_t device) except? 0
+cpdef object device_get_clk_mon_status(intptr_t device)
+cpdef object device_get_process_utilization(intptr_t device, unsigned long long last_seen_time_stamp)
+cpdef unit_set_led_state(intptr_t unit, int color)
+cpdef device_set_persistence_mode(intptr_t device, int mode)
+cpdef device_set_compute_mode(intptr_t device, int mode)
+cpdef device_set_ecc_mode(intptr_t device, int ecc)
+cpdef device_clear_ecc_error_counts(intptr_t device, int counter_type)
+cpdef device_set_driver_model(intptr_t device, int driver_model, unsigned int flags)
+cpdef device_set_gpu_locked_clocks(intptr_t device, unsigned int min_gpu_clock_m_hz, unsigned int max_gpu_clock_m_hz)
+cpdef device_reset_gpu_locked_clocks(intptr_t device)
+cpdef device_set_memory_locked_clocks(intptr_t device, unsigned int min_mem_clock_m_hz, unsigned int max_mem_clock_m_hz)
+cpdef device_reset_memory_locked_clocks(intptr_t device)
+cpdef device_set_auto_boosted_clocks_enabled(intptr_t device, int enabled)
+cpdef device_set_default_auto_boosted_clocks_enabled(intptr_t device, int enabled, unsigned int flags)
+cpdef device_set_default_fan_speed_v2(intptr_t device, unsigned int fan)
+cpdef device_set_fan_control_policy(intptr_t device, unsigned int fan, unsigned int policy)
+cpdef device_set_gpu_operation_mode(intptr_t device, int mode)
+cpdef device_set_api_restriction(intptr_t device, int api_type, int is_restricted)
+cpdef device_set_fan_speed_v2(intptr_t device, unsigned int fan, unsigned int speed)
+cpdef device_set_accounting_mode(intptr_t device, int mode)
+cpdef device_clear_accounting_pids(intptr_t device)
+cpdef int device_get_nvlink_state(intptr_t device, unsigned int link) except? -1
+cpdef unsigned int device_get_nvlink_version(intptr_t device, unsigned int link) except? 0
+cpdef unsigned int device_get_nvlink_capability(intptr_t device, unsigned int link, int capability) except? 0
+cpdef object device_get_nvlink_remote_pci_info_v2(intptr_t device, unsigned int link)
+cpdef unsigned long long device_get_nvlink_error_counter(intptr_t device, unsigned int link, int counter) except? 0
+cpdef device_reset_nvlink_error_counters(intptr_t device, unsigned int link)
+cpdef int device_get_nvlink_remote_device_type(intptr_t device, unsigned int link) except? -1
+cpdef system_set_nvlink_bw_mode(unsigned int nvlink_bw_mode)
+cpdef unsigned int system_get_nvlink_bw_mode() except? 0
+cpdef object device_get_nvlink_supported_bw_modes(intptr_t device)
+cpdef object device_get_nvlink_bw_mode(intptr_t device)
+cpdef device_set_nvlink_bw_mode(intptr_t device, intptr_t set_bw_mode)
+cpdef intptr_t event_set_create() except? 0
+cpdef device_register_events(intptr_t device, unsigned long long event_types, intptr_t set)
+cpdef unsigned long long device_get_supported_event_types(intptr_t device) except? 0
+cpdef object event_set_wait_v2(intptr_t set, unsigned int timeoutms)
+cpdef event_set_free(intptr_t set)
+cpdef device_modify_drain_state(intptr_t pci_info, int new_state)
+cpdef int device_query_drain_state(intptr_t pci_info) except? -1
+cpdef device_remove_gpu_v2(intptr_t pci_info, int gpu_state, int link_state)
+cpdef device_discover_gpus(intptr_t pci_info)
+cpdef int device_get_virtualization_mode(intptr_t device) except? -1
+cpdef int device_get_host_vgpu_mode(intptr_t device) except? -1
+cpdef device_set_virtualization_mode(intptr_t device, int virtual_mode)
+cpdef unsigned long long vgpu_type_get_gsp_heap_size(unsigned int vgpu_type_id) except? 0
+cpdef unsigned long long vgpu_type_get_fb_reservation(unsigned int vgpu_type_id) except? 0
+cpdef device_set_vgpu_capabilities(intptr_t device, int capability, int state)
+cpdef object device_get_grid_licensable_features_v4(intptr_t device)
+cpdef unsigned int get_vgpu_driver_capabilities(int capability) except? 0
+cpdef unsigned int device_get_vgpu_capabilities(intptr_t device, int capability) except? 0
+cpdef str vgpu_type_get_class(unsigned int vgpu_type_id)
+cpdef unsigned int vgpu_type_get_gpu_instance_profile_id(unsigned int vgpu_type_id) except? 0
+cpdef tuple vgpu_type_get_device_id(unsigned int vgpu_type_id)
+cpdef unsigned long long vgpu_type_get_framebuffer_size(unsigned int vgpu_type_id) except? 0
+cpdef unsigned int vgpu_type_get_num_display_heads(unsigned int vgpu_type_id) except? 0
+cpdef tuple vgpu_type_get_resolution(unsigned int vgpu_type_id, unsigned int display_ind_ex)
+cpdef str vgpu_type_get_license(unsigned int vgpu_type_id)
+cpdef unsigned int vgpu_type_get_frame_rate_limit(unsigned int vgpu_type_id) except? 0
+cpdef unsigned int vgpu_type_get_max_instances(intptr_t device, unsigned int vgpu_type_id) except? 0
+cpdef unsigned int vgpu_type_get_max_instances_per_vm(unsigned int vgpu_type_id) except? 0
+cpdef object vgpu_type_get_bar1_info(unsigned int vgpu_type_id)
+cpdef str vgpu_instance_get_uuid(unsigned int vgpu_instance)
+cpdef str vgpu_instance_get_vm_driver_version(unsigned int vgpu_instance)
+cpdef unsigned long long vgpu_instance_get_fb_usage(unsigned int vgpu_instance) except? 0
+cpdef unsigned int vgpu_instance_get_license_status(unsigned int vgpu_instance) except? 0
+cpdef unsigned int vgpu_instance_get_type(unsigned int vgpu_instance) except? 0
+cpdef unsigned int vgpu_instance_get_frame_rate_limit(unsigned int vgpu_instance) except? 0
+cpdef int vgpu_instance_get_ecc_mode(unsigned int vgpu_instance) except? -1
+cpdef unsigned int vgpu_instance_get_encoder_capacity(unsigned int vgpu_instance) except? 0
+cpdef vgpu_instance_set_encoder_capacity(unsigned int vgpu_instance, unsigned int encoder_capacity)
+cpdef tuple vgpu_instance_get_encoder_stats(unsigned int vgpu_instance)
+cpdef object vgpu_instance_get_encoder_sessions(unsigned int vgpu_instance)
+cpdef object vgpu_instance_get_fbc_stats(unsigned int vgpu_instance)
+cpdef object vgpu_instance_get_fbc_sessions(unsigned int vgpu_instance)
+cpdef unsigned int vgpu_instance_get_gpu_instance_id(unsigned int vgpu_instance) except? 0
+cpdef str vgpu_instance_get_gpu_pci_id(unsigned int vgpu_instance)
+cpdef unsigned int vgpu_type_get_capabilities(unsigned int vgpu_type_id, int capability) except? 0
+cpdef str vgpu_instance_get_mdev_uuid(unsigned int vgpu_instance)
+cpdef gpu_instance_set_vgpu_scheduler_state(intptr_t gpu_instance, intptr_t p_scheduler)
+cpdef object gpu_instance_get_vgpu_scheduler_state(intptr_t gpu_instance)
+cpdef object gpu_instance_get_vgpu_scheduler_log(intptr_t gpu_instance)
+cpdef str device_get_pgpu_metadata_string(intptr_t device)
+cpdef object device_get_vgpu_scheduler_log(intptr_t device)
+cpdef object device_get_vgpu_scheduler_state(intptr_t device)
+cpdef object device_get_vgpu_scheduler_capabilities(intptr_t device)
+cpdef device_set_vgpu_scheduler_state(intptr_t device, intptr_t p_scheduler_state)
+cpdef set_vgpu_version(intptr_t vgpu_version)
+cpdef tuple device_get_vgpu_process_utilization(intptr_t device, unsigned long long last_seen_time_stamp)
+cpdef int vgpu_instance_get_accounting_mode(unsigned int vgpu_instance) except? -1
+cpdef object vgpu_instance_get_accounting_pids(unsigned int vgpu_instance)
+cpdef object vgpu_instance_get_accounting_stats(unsigned int vgpu_instance, unsigned int pid)
+cpdef vgpu_instance_clear_accounting_pids(unsigned int vgpu_instance)
+cpdef object vgpu_instance_get_license_info_v2(unsigned int vgpu_instance)
+cpdef unsigned int get_excluded_device_count() except? 0
+cpdef object get_excluded_device_info_by_index(unsigned int ind_ex)
+cpdef int device_set_mig_mode(intptr_t device, unsigned int mode) except? -1
+cpdef tuple device_get_mig_mode(intptr_t device)
+cpdef object device_get_gpu_instance_possible_placements_v2(intptr_t device, unsigned int profile_id)
+cpdef unsigned int device_get_gpu_instance_remaining_capacity(intptr_t device, unsigned int profile_id) except? 0
+cpdef intptr_t device_create_gpu_instance(intptr_t device, unsigned int profile_id) except? 0
+cpdef intptr_t device_create_gpu_instance_with_placement(intptr_t device, unsigned int profile_id, intptr_t placement) except? 0
+cpdef gpu_instance_destroy(intptr_t gpu_instance)
+cpdef intptr_t device_get_gpu_instance_by_id(intptr_t device, unsigned int id) except? 0
+cpdef object gpu_instance_get_info(intptr_t gpu_instance)
+cpdef object gpu_instance_get_compute_instance_profile_info_v(intptr_t gpu_instance, unsigned int profile, unsigned int eng_profile)
+cpdef unsigned int gpu_instance_get_compute_instance_remaining_capacity(intptr_t gpu_instance, unsigned int profile_id) except? 0
+cpdef object gpu_instance_get_compute_instance_possible_placements(intptr_t gpu_instance, unsigned int profile_id)
+cpdef intptr_t gpu_instance_create_compute_instance(intptr_t gpu_instance, unsigned int profile_id) except? 0
+cpdef intptr_t gpu_instance_create_compute_instance_with_placement(intptr_t gpu_instance, unsigned int profile_id, intptr_t placement) except? 0
+cpdef compute_instance_destroy(intptr_t compute_instance)
+cpdef intptr_t gpu_instance_get_compute_instance_by_id(intptr_t gpu_instance, unsigned int id) except? 0
+cpdef object compute_instance_get_info_v2(intptr_t compute_instance)
+cpdef unsigned int device_is_mig_device_handle(intptr_t device) except? 0
+cpdef unsigned int device_get_gpu_instance_id(intptr_t device) except? 0
+cpdef unsigned int device_get_compute_instance_id(intptr_t device) except? 0
+cpdef unsigned int device_get_max_mig_device_count(intptr_t device) except? 0
+cpdef intptr_t device_get_mig_device_handle_by_index(intptr_t device, unsigned int ind_ex) except? 0
+cpdef intptr_t device_get_device_handle_from_mig_device_handle(intptr_t mig_device) except? 0
+cpdef device_power_smoothing_activate_preset_profile(intptr_t device, intptr_t profile)
+cpdef device_power_smoothing_update_preset_profile_param(intptr_t device, intptr_t profile)
+cpdef device_power_smoothing_set_state(intptr_t device, intptr_t state)
+cpdef object device_get_addressing_mode(intptr_t device)
+cpdef object device_get_repair_status(intptr_t device)
+cpdef object device_get_power_mizer_mode_v1(intptr_t device)
+cpdef device_set_power_mizer_mode_v1(intptr_t device, intptr_t power_mizer_mode)
diff --git a/lib/python3.11/site-packages/cuda/bindings/nvrtc.pxd b/lib/python3.11/site-packages/cuda/bindings/nvrtc.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..3967e243220afbcb19003d52870a2d2a1f49ebef
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/nvrtc.pxd
@@ -0,0 +1,21 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+cimport cuda.bindings.cynvrtc as cynvrtc
+
+include "_lib/utils.pxd"
+
+cdef class nvrtcProgram:
+    """ nvrtcProgram is the unit of compilation, and an opaque handle for a program.
+
+    To compile a CUDA program string, an instance of nvrtcProgram must be created first with nvrtcCreateProgram, then compiled with nvrtcCompileProgram.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cynvrtc.nvrtcProgram  _pvt_val
+    cdef cynvrtc.nvrtcProgram* _pvt_ptr
diff --git a/lib/python3.11/site-packages/cuda/bindings/nvvm.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/cuda/bindings/nvvm.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..401bedcc5d41e108ae0b381d7b42b73cea132297
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/nvvm.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/nvvm.pxd b/lib/python3.11/site-packages/cuda/bindings/nvvm.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..0b17a143ca06cf2e5e13ef831e0c55e69ade476c
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/nvvm.pxd
@@ -0,0 +1,41 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+#
+# This code was automatically generated across versions from 12.0.1 to 13.2.0, generator version 0.3.1.dev1364+ged01d643e. Do not modify it directly.
+
+from libc.stdint cimport intptr_t
+
+from .cynvvm cimport *
+
+
+###############################################################################
+# Types
+###############################################################################
+
+ctypedef nvvmProgram Program
+
+
+###############################################################################
+# Enum
+###############################################################################
+
+ctypedef nvvmResult _Result
+
+
+###############################################################################
+# Functions
+###############################################################################
+
+cpdef str get_error_string(int result)
+cpdef tuple version()
+cpdef tuple ir_version()
+cpdef intptr_t create_program() except? 0
+cpdef add_module_to_program(intptr_t prog, buffer, size_t size, name)
+cpdef lazy_add_module_to_program(intptr_t prog, buffer, size_t size, name)
+cpdef compile_program(intptr_t prog, int num_options, options)
+cpdef verify_program(intptr_t prog, int num_options, options)
+cpdef size_t get_compiled_result_size(intptr_t prog) except? 0
+cpdef get_compiled_result(intptr_t prog, buffer)
+cpdef size_t get_program_log_size(intptr_t prog) except? 0
+cpdef get_program_log(intptr_t prog, buffer)
diff --git a/lib/python3.11/site-packages/cuda/bindings/runtime.pxd b/lib/python3.11/site-packages/cuda/bindings/runtime.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..6da217f47d49f1f75af85a0c887e0582ae84fe0f
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/runtime.pxd
@@ -0,0 +1,3873 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+# This code was automatically generated with version 13.2.0, generator version fa58871. Do not modify it directly.
+cimport cuda.bindings.cyruntime as cyruntime
+
+include "_lib/utils.pxd"
+cimport cuda.bindings.driver as driver
+
+cdef class cudaDevResourceDesc_t:
+    """
+
+    An opaque descriptor handle. The descriptor encapsulates multiple created and configured resources. Created via ::cudaDeviceResourceGenerateDesc
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaDevResourceDesc_t  _pvt_val
+    cdef cyruntime.cudaDevResourceDesc_t* _pvt_ptr
+
+cdef class cudaExecutionContext_t:
+    """
+
+    An opaque handle to a CUDA execution context. It represents an execution context created via CUDA Runtime APIs such as cudaGreenCtxCreate.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaExecutionContext_t  _pvt_val
+    cdef cyruntime.cudaExecutionContext_t* _pvt_ptr
+
+cdef class cudaArray_t:
+    """
+
+    CUDA array
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaArray_t  _pvt_val
+    cdef cyruntime.cudaArray_t* _pvt_ptr
+
+cdef class cudaArray_const_t:
+    """
+
+    CUDA array (as source copy argument)
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaArray_const_t  _pvt_val
+    cdef cyruntime.cudaArray_const_t* _pvt_ptr
+
+cdef class cudaMipmappedArray_t:
+    """
+
+    CUDA mipmapped array
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaMipmappedArray_t  _pvt_val
+    cdef cyruntime.cudaMipmappedArray_t* _pvt_ptr
+
+cdef class cudaMipmappedArray_const_t:
+    """
+
+    CUDA mipmapped array (as source argument)
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaMipmappedArray_const_t  _pvt_val
+    cdef cyruntime.cudaMipmappedArray_const_t* _pvt_ptr
+
+cdef class cudaGraphicsResource_t:
+    """
+
+    CUDA graphics resource types
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaGraphicsResource_t  _pvt_val
+    cdef cyruntime.cudaGraphicsResource_t* _pvt_ptr
+
+cdef class cudaExternalMemory_t:
+    """
+
+    CUDA external memory
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaExternalMemory_t  _pvt_val
+    cdef cyruntime.cudaExternalMemory_t* _pvt_ptr
+
+cdef class cudaExternalSemaphore_t:
+    """
+
+    CUDA external semaphore
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaExternalSemaphore_t  _pvt_val
+    cdef cyruntime.cudaExternalSemaphore_t* _pvt_ptr
+
+cdef class cudaKernel_t:
+    """
+
+    CUDA kernel
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaKernel_t  _pvt_val
+    cdef cyruntime.cudaKernel_t* _pvt_ptr
+
+cdef class cudaLibrary_t:
+    """
+
+    CUDA library
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaLibrary_t  _pvt_val
+    cdef cyruntime.cudaLibrary_t* _pvt_ptr
+
+cdef class cudaGraphDeviceNode_t:
+    """
+
+    CUDA device node handle for device-side node update
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaGraphDeviceNode_t  _pvt_val
+    cdef cyruntime.cudaGraphDeviceNode_t* _pvt_ptr
+
+cdef class cudaAsyncCallbackHandle_t:
+    """
+
+    CUDA async callback handle
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaAsyncCallbackHandle_t  _pvt_val
+    cdef cyruntime.cudaAsyncCallbackHandle_t* _pvt_ptr
+
+cdef class cudaLogsCallbackHandle:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaLogsCallbackHandle  _pvt_val
+    cdef cyruntime.cudaLogsCallbackHandle* _pvt_ptr
+
+cdef class EGLImageKHR:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.EGLImageKHR  _pvt_val
+    cdef cyruntime.EGLImageKHR* _pvt_ptr
+
+cdef class EGLStreamKHR:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.EGLStreamKHR  _pvt_val
+    cdef cyruntime.EGLStreamKHR* _pvt_ptr
+
+cdef class EGLSyncKHR:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.EGLSyncKHR  _pvt_val
+    cdef cyruntime.EGLSyncKHR* _pvt_ptr
+
+cdef class cudaHostFn_t:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaHostFn_t  _pvt_val
+    cdef cyruntime.cudaHostFn_t* _pvt_ptr
+
+cdef class cudaAsyncCallback:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaAsyncCallback  _pvt_val
+    cdef cyruntime.cudaAsyncCallback* _pvt_ptr
+
+cdef class cudaStreamCallback_t:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaStreamCallback_t  _pvt_val
+    cdef cyruntime.cudaStreamCallback_t* _pvt_ptr
+
+cdef class cudaLogsCallback_t:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaLogsCallback_t  _pvt_val
+    cdef cyruntime.cudaLogsCallback_t* _pvt_ptr
+
+cdef class dim3:
+    """
+    Attributes
+    ----------
+    x : unsigned int
+
+    y : unsigned int
+
+    z : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.dim3 _pvt_val
+    cdef cyruntime.dim3* _pvt_ptr
+
+cdef class cudaChannelFormatDesc:
+    """
+    CUDA Channel format descriptor
+
+    Attributes
+    ----------
+    x : int
+        x
+    y : int
+        y
+    z : int
+        z
+    w : int
+        w
+    f : cudaChannelFormatKind
+        Channel format kind
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaChannelFormatDesc _pvt_val
+    cdef cyruntime.cudaChannelFormatDesc* _pvt_ptr
+
+cdef class anon_struct0:
+    """
+    Attributes
+    ----------
+    width : unsigned int
+
+    height : unsigned int
+
+    depth : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaArraySparseProperties* _pvt_ptr
+
+cdef class cudaArraySparseProperties:
+    """
+    Sparse CUDA array and CUDA mipmapped array properties
+
+    Attributes
+    ----------
+    tileExtent : anon_struct0
+
+    miptailFirstLevel : unsigned int
+        First mip level at which the mip tail begins
+    miptailSize : unsigned long long
+        Total size of the mip tail.
+    flags : unsigned int
+        Flags will either be zero or cudaArraySparsePropertiesSingleMipTail
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaArraySparseProperties _pvt_val
+    cdef cyruntime.cudaArraySparseProperties* _pvt_ptr
+    cdef anon_struct0 _tileExtent
+
+cdef class cudaArrayMemoryRequirements:
+    """
+    CUDA array and CUDA mipmapped array memory requirements
+
+    Attributes
+    ----------
+    size : size_t
+        Total size of the array.
+    alignment : size_t
+        Alignment necessary for mapping the array.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaArrayMemoryRequirements _pvt_val
+    cdef cyruntime.cudaArrayMemoryRequirements* _pvt_ptr
+
+cdef class cudaPitchedPtr:
+    """
+    CUDA Pitched memory pointer  ::make_cudaPitchedPtr
+
+    Attributes
+    ----------
+    ptr : Any
+        Pointer to allocated memory
+    pitch : size_t
+        Pitch of allocated memory in bytes
+    xsize : size_t
+        Logical width of allocation in elements
+    ysize : size_t
+        Logical height of allocation in elements
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaPitchedPtr _pvt_val
+    cdef cyruntime.cudaPitchedPtr* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyptr
+
+cdef class cudaExtent:
+    """
+    CUDA extent  ::make_cudaExtent
+
+    Attributes
+    ----------
+    width : size_t
+        Width in elements when referring to array memory, in bytes when
+        referring to linear memory
+    height : size_t
+        Height in elements
+    depth : size_t
+        Depth in elements
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExtent _pvt_val
+    cdef cyruntime.cudaExtent* _pvt_ptr
+
+cdef class cudaPos:
+    """
+    CUDA 3D position  ::make_cudaPos
+
+    Attributes
+    ----------
+    x : size_t
+        x
+    y : size_t
+        y
+    z : size_t
+        z
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaPos _pvt_val
+    cdef cyruntime.cudaPos* _pvt_ptr
+
+cdef class cudaMemcpy3DParms:
+    """
+    CUDA 3D memory copying parameters
+
+    Attributes
+    ----------
+    srcArray : cudaArray_t
+        Source memory address
+    srcPos : cudaPos
+        Source position offset
+    srcPtr : cudaPitchedPtr
+        Pitched source memory address
+    dstArray : cudaArray_t
+        Destination memory address
+    dstPos : cudaPos
+        Destination position offset
+    dstPtr : cudaPitchedPtr
+        Pitched destination memory address
+    extent : cudaExtent
+        Requested memory copy size
+    kind : cudaMemcpyKind
+        Type of transfer
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DParms _pvt_val
+    cdef cyruntime.cudaMemcpy3DParms* _pvt_ptr
+    cdef cudaArray_t _srcArray
+    cdef cudaPos _srcPos
+    cdef cudaPitchedPtr _srcPtr
+    cdef cudaArray_t _dstArray
+    cdef cudaPos _dstPos
+    cdef cudaPitchedPtr _dstPtr
+    cdef cudaExtent _extent
+
+cdef class cudaMemcpyNodeParams:
+    """
+    Memcpy node parameters
+
+    Attributes
+    ----------
+    flags : int
+        Must be zero
+    reserved : int
+        Must be zero
+    ctx : cudaExecutionContext_t
+        Context in which to run the memcpy. If NULL will try to use the
+        current context.
+    copyParams : cudaMemcpy3DParms
+        Parameters for the memory copy
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpyNodeParams _pvt_val
+    cdef cyruntime.cudaMemcpyNodeParams* _pvt_ptr
+    cdef cudaExecutionContext_t _ctx
+    cdef cudaMemcpy3DParms _copyParams
+
+cdef class cudaMemcpy3DPeerParms:
+    """
+    CUDA 3D cross-device memory copying parameters
+
+    Attributes
+    ----------
+    srcArray : cudaArray_t
+        Source memory address
+    srcPos : cudaPos
+        Source position offset
+    srcPtr : cudaPitchedPtr
+        Pitched source memory address
+    srcDevice : int
+        Source device
+    dstArray : cudaArray_t
+        Destination memory address
+    dstPos : cudaPos
+        Destination position offset
+    dstPtr : cudaPitchedPtr
+        Pitched destination memory address
+    dstDevice : int
+        Destination device
+    extent : cudaExtent
+        Requested memory copy size
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DPeerParms _pvt_val
+    cdef cyruntime.cudaMemcpy3DPeerParms* _pvt_ptr
+    cdef cudaArray_t _srcArray
+    cdef cudaPos _srcPos
+    cdef cudaPitchedPtr _srcPtr
+    cdef cudaArray_t _dstArray
+    cdef cudaPos _dstPos
+    cdef cudaPitchedPtr _dstPtr
+    cdef cudaExtent _extent
+
+cdef class cudaMemsetParams:
+    """
+    CUDA Memset node parameters
+
+    Attributes
+    ----------
+    dst : Any
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemsetParams _pvt_val
+    cdef cyruntime.cudaMemsetParams* _pvt_ptr
+    cdef _HelperInputVoidPtr _cydst
+
+cdef class cudaMemsetParamsV2:
+    """
+    CUDA Memset node parameters
+
+    Attributes
+    ----------
+    dst : Any
+        Destination device pointer
+    pitch : size_t
+        Pitch of destination device pointer. Unused if height is 1
+    value : unsigned int
+        Value to be set
+    elementSize : unsigned int
+        Size of each element in bytes. Must be 1, 2, or 4.
+    width : size_t
+        Width of the row in elements
+    height : size_t
+        Number of rows
+    ctx : cudaExecutionContext_t
+        Context in which to run the memset. If NULL will try to use the
+        current context.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemsetParamsV2 _pvt_val
+    cdef cyruntime.cudaMemsetParamsV2* _pvt_ptr
+    cdef _HelperInputVoidPtr _cydst
+    cdef cudaExecutionContext_t _ctx
+
+cdef class cudaAccessPolicyWindow:
+    """
+    Specifies an access policy for a window, a contiguous extent of
+    memory beginning at base_ptr and ending at base_ptr + num_bytes.
+    Partition into many segments and assign segments such that. sum of
+    "hit segments" / window == approx. ratio. sum of "miss segments" /
+    window == approx 1-ratio. Segments and ratio specifications are
+    fitted to the capabilities of the architecture. Accesses in a hit
+    segment apply the hitProp access policy. Accesses in a miss segment
+    apply the missProp access policy.
+
+    Attributes
+    ----------
+    base_ptr : Any
+        Starting address of the access policy window. CUDA driver may align
+        it.
+    num_bytes : size_t
+        Size in bytes of the window policy. CUDA driver may restrict the
+        maximum size and alignment.
+    hitRatio : float
+        hitRatio specifies percentage of lines assigned hitProp, rest are
+        assigned missProp.
+    hitProp : cudaAccessProperty
+        ::CUaccessProperty set for hit.
+    missProp : cudaAccessProperty
+        ::CUaccessProperty set for miss. Must be either NORMAL or
+        STREAMING.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaAccessPolicyWindow _pvt_val
+    cdef cyruntime.cudaAccessPolicyWindow* _pvt_ptr
+    cdef _HelperInputVoidPtr _cybase_ptr
+
+cdef class cudaHostNodeParams:
+    """
+    CUDA host node parameters
+
+    Attributes
+    ----------
+    fn : cudaHostFn_t
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaHostNodeParams _pvt_val
+    cdef cyruntime.cudaHostNodeParams* _pvt_ptr
+    cdef cudaHostFn_t _fn
+    cdef _HelperInputVoidPtr _cyuserData
+
+cdef class cudaHostNodeParamsV2:
+    """
+    CUDA host node parameters
+
+    Attributes
+    ----------
+    fn : cudaHostFn_t
+        The function to call when the node executes
+    userData : Any
+        Argument to pass to the function
+    syncMode : unsigned int
+        The synchronization mode to use for the host task
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaHostNodeParamsV2 _pvt_val
+    cdef cyruntime.cudaHostNodeParamsV2* _pvt_ptr
+    cdef cudaHostFn_t _fn
+    cdef _HelperInputVoidPtr _cyuserData
+
+cdef class anon_struct1:
+    """
+    Attributes
+    ----------
+    array : cudaArray_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+    cdef cudaArray_t _array
+
+cdef class anon_struct2:
+    """
+    Attributes
+    ----------
+    mipmap : cudaMipmappedArray_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+    cdef cudaMipmappedArray_t _mipmap
+
+cdef class anon_struct3:
+    """
+    Attributes
+    ----------
+    devPtr : Any
+
+    desc : cudaChannelFormatDesc
+
+    sizeInBytes : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+    cdef _HelperInputVoidPtr _cydevPtr
+    cdef cudaChannelFormatDesc _desc
+
+cdef class anon_struct4:
+    """
+    Attributes
+    ----------
+    devPtr : Any
+
+    desc : cudaChannelFormatDesc
+
+    width : size_t
+
+    height : size_t
+
+    pitchInBytes : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+    cdef _HelperInputVoidPtr _cydevPtr
+    cdef cudaChannelFormatDesc _desc
+
+cdef class anon_struct5:
+    """
+    Attributes
+    ----------
+    reserved : list[int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+
+cdef class anon_union0:
+    """
+    Attributes
+    ----------
+    array : anon_struct1
+
+    mipmap : anon_struct2
+
+    linear : anon_struct3
+
+    pitch2D : anon_struct4
+
+    reserved : anon_struct5
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+    cdef anon_struct1 _array
+    cdef anon_struct2 _mipmap
+    cdef anon_struct3 _linear
+    cdef anon_struct4 _pitch2D
+    cdef anon_struct5 _reserved
+
+cdef class cudaResourceDesc:
+    """
+    CUDA resource descriptor
+
+    Attributes
+    ----------
+    resType : cudaResourceType
+        Resource type
+    res : anon_union0
+
+    flags : unsigned int
+        Flags (must be zero)
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceDesc* _val_ptr
+    cdef cyruntime.cudaResourceDesc* _pvt_ptr
+    cdef anon_union0 _res
+
+cdef class cudaResourceViewDesc:
+    """
+    CUDA resource view descriptor
+
+    Attributes
+    ----------
+    format : cudaResourceViewFormat
+        Resource view format
+    width : size_t
+        Width of the resource view
+    height : size_t
+        Height of the resource view
+    depth : size_t
+        Depth of the resource view
+    firstMipmapLevel : unsigned int
+        First defined mipmap level
+    lastMipmapLevel : unsigned int
+        Last defined mipmap level
+    firstLayer : unsigned int
+        First layer index
+    lastLayer : unsigned int
+        Last layer index
+    reserved : list[unsigned int]
+        Must be zero
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaResourceViewDesc _pvt_val
+    cdef cyruntime.cudaResourceViewDesc* _pvt_ptr
+
+cdef class cudaPointerAttributes:
+    """
+    CUDA pointer attributes
+
+    Attributes
+    ----------
+    type : cudaMemoryType
+        The type of memory - cudaMemoryTypeUnregistered,
+        cudaMemoryTypeHost, cudaMemoryTypeDevice or cudaMemoryTypeManaged.
+    device : int
+        The device against which the memory was allocated or registered. If
+        the memory type is cudaMemoryTypeDevice then this identifies the
+        device on which the memory referred physically resides. If the
+        memory type is cudaMemoryTypeHost or::cudaMemoryTypeManaged then
+        this identifies the device which was current when the memory was
+        allocated or registered (and if that device is deinitialized then
+        this allocation will vanish with that device's state).
+    devicePointer : Any
+        The address which may be dereferenced on the current device to
+        access the memory or NULL if no such address exists.
+    hostPointer : Any
+        The address which may be dereferenced on the host to access the
+        memory or NULL if no such address exists.  CUDA doesn't check if
+        unregistered memory is allocated so this field may contain invalid
+        pointer if an invalid pointer has been passed to CUDA.
+    reserved : list[long]
+        Must be zero
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaPointerAttributes _pvt_val
+    cdef cyruntime.cudaPointerAttributes* _pvt_ptr
+    cdef _HelperInputVoidPtr _cydevicePointer
+    cdef _HelperInputVoidPtr _cyhostPointer
+
+cdef class cudaFuncAttributes:
+    """
+    CUDA function attributes
+
+    Attributes
+    ----------
+    sharedSizeBytes : size_t
+        The size in bytes of statically-allocated shared memory per block
+        required by this function. This does not include dynamically-
+        allocated shared memory requested by the user at runtime.
+    constSizeBytes : size_t
+        The size in bytes of user-allocated constant memory required by
+        this function.
+    localSizeBytes : size_t
+        The size in bytes of local memory used by each thread of this
+        function.
+    maxThreadsPerBlock : int
+        The maximum number of threads per block, beyond which a launch of
+        the function would fail. This number depends on both the function
+        and the device on which the function is currently loaded.
+    numRegs : int
+        The number of registers used by each thread of this function.
+    ptxVersion : int
+        The PTX virtual architecture version for which the function was
+        compiled. This value is the major PTX version * 10 + the minor PTX
+        version, so a PTX version 1.3 function would return the value 13.
+    binaryVersion : int
+        The binary architecture version for which the function was
+        compiled. This value is the major binary version * 10 + the minor
+        binary version, so a binary version 1.3 function would return the
+        value 13.
+    cacheModeCA : int
+        The attribute to indicate whether the function has been compiled
+        with user specified option "-Xptxas --dlcm=ca" set.
+    maxDynamicSharedSizeBytes : int
+        The maximum size in bytes of dynamic shared memory per block for
+        this function. Any launch must have a dynamic shared memory size
+        smaller than this value.
+    preferredShmemCarveout : int
+        On devices where the L1 cache and shared memory use the same
+        hardware resources, this sets the shared memory carveout
+        preference, in percent of the maximum shared memory. Refer to
+        cudaDevAttrMaxSharedMemoryPerMultiprocessor. This is only a hint,
+        and the driver can choose a different ratio if required to execute
+        the function. See cudaFuncSetAttribute
+    clusterDimMustBeSet : int
+        If this attribute is set, the kernel must launch with a valid
+        cluster dimension specified.
+    requiredClusterWidth : int
+        The required cluster width/height/depth in blocks. The values must
+        either all be 0 or all be positive. The validity of the cluster
+        dimensions is otherwise checked at launch time.  If the value is
+        set during compile time, it cannot be set at runtime. Setting it at
+        runtime should return cudaErrorNotPermitted. See
+        cudaFuncSetAttribute
+    requiredClusterHeight : int
+
+    requiredClusterDepth : int
+
+    clusterSchedulingPolicyPreference : int
+        The block scheduling policy of a function. See cudaFuncSetAttribute
+    nonPortableClusterSizeAllowed : int
+        Whether the function can be launched with non-portable cluster
+        size. 1 is allowed, 0 is disallowed. A non-portable cluster size
+        may only function on the specific SKUs the program is tested on.
+        The launch might fail if the program is run on a different hardware
+        platform.  CUDA API provides cudaOccupancyMaxActiveClusters to
+        assist with checking whether the desired size can be launched on
+        the current device.  Portable Cluster Size  A portable cluster size
+        is guaranteed to be functional on all compute capabilities higher
+        than the target compute capability. The portable cluster size for
+        sm_90 is 8 blocks per cluster. This value may increase for future
+        compute capabilities.  The specific hardware unit may support
+        higher cluster sizes that’s not guaranteed to be portable. See
+        cudaFuncSetAttribute
+    reserved0 : int
+
+    reserved : list[int]
+        Reserved for future use.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaFuncAttributes _pvt_val
+    cdef cyruntime.cudaFuncAttributes* _pvt_ptr
+
+cdef class cudaMemLocation:
+    """
+    Specifies a memory location.  To specify a gpu, set type =
+    cudaMemLocationTypeDevice and set id = the gpu's device ordinal. To
+    specify a cpu NUMA node, set type = cudaMemLocationTypeHostNuma and
+    set id = host NUMA node id.
+
+    Attributes
+    ----------
+    type : cudaMemLocationType
+        Specifies the location type, which modifies the meaning of id.
+    id : int
+        identifier for a given this location's ::CUmemLocationType.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemLocation _pvt_val
+    cdef cyruntime.cudaMemLocation* _pvt_ptr
+
+cdef class cudaMemAccessDesc:
+    """
+    Memory access descriptor
+
+    Attributes
+    ----------
+    location : cudaMemLocation
+        Location on which the request is to change it's accessibility
+    flags : cudaMemAccessFlags
+        ::CUmemProt accessibility flags to set on the request
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemAccessDesc _pvt_val
+    cdef cyruntime.cudaMemAccessDesc* _pvt_ptr
+    cdef cudaMemLocation _location
+
+cdef class cudaMemPoolProps:
+    """
+    Specifies the properties of allocations made from the pool.
+
+    Attributes
+    ----------
+    allocType : cudaMemAllocationType
+        Allocation type. Currently must be specified as
+        cudaMemAllocationTypePinned
+    handleTypes : cudaMemAllocationHandleType
+        Handle types that will be supported by allocations from the pool.
+    location : cudaMemLocation
+        Location allocations should reside.
+    win32SecurityAttributes : Any
+        Windows-specific LPSECURITYATTRIBUTES required when
+        cudaMemHandleTypeWin32 is specified. This security attribute
+        defines the scope of which exported allocations may be tranferred
+        to other processes. In all other cases, this field is required to
+        be zero.
+    maxSize : size_t
+        Maximum pool size. When set to 0, defaults to a system dependent
+        value.
+    usage : unsigned short
+        Bitmask indicating intended usage for the pool.
+    reserved : bytes
+        reserved for future use, must be 0
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemPoolProps _pvt_val
+    cdef cyruntime.cudaMemPoolProps* _pvt_ptr
+    cdef cudaMemLocation _location
+    cdef _HelperInputVoidPtr _cywin32SecurityAttributes
+
+cdef class cudaMemPoolPtrExportData:
+    """
+    Opaque data for exporting a pool allocation
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemPoolPtrExportData _pvt_val
+    cdef cyruntime.cudaMemPoolPtrExportData* _pvt_ptr
+
+cdef class cudaMemAllocNodeParams:
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : cudaMemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be cudaMemHandleTypeNone. IPC is
+        not supported. in: array of memory access descriptors. Used to
+        describe peer GPU access
+    accessDescs : cudaMemAccessDesc
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    accessDescCount : size_t
+        in: Number of `accessDescs`s
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : Any
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemAllocNodeParams _pvt_val
+    cdef cyruntime.cudaMemAllocNodeParams* _pvt_ptr
+    cdef cudaMemPoolProps _poolProps
+    cdef size_t _accessDescs_length
+    cdef cyruntime.cudaMemAccessDesc* _accessDescs
+    cdef _HelperInputVoidPtr _cydptr
+
+cdef class cudaMemAllocNodeParamsV2:
+    """
+    Memory allocation node parameters
+
+    Attributes
+    ----------
+    poolProps : cudaMemPoolProps
+        in: location where the allocation should reside (specified in
+        ::location). ::handleTypes must be cudaMemHandleTypeNone. IPC is
+        not supported. in: array of memory access descriptors. Used to
+        describe peer GPU access
+    accessDescs : cudaMemAccessDesc
+        in: number of memory access descriptors. Must not exceed the number
+        of GPUs.
+    accessDescCount : size_t
+        in: Number of `accessDescs`s
+    bytesize : size_t
+        in: size in bytes of the requested allocation
+    dptr : Any
+        out: address of the allocation returned by CUDA
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemAllocNodeParamsV2 _pvt_val
+    cdef cyruntime.cudaMemAllocNodeParamsV2* _pvt_ptr
+    cdef cudaMemPoolProps _poolProps
+    cdef size_t _accessDescs_length
+    cdef cyruntime.cudaMemAccessDesc* _accessDescs
+    cdef _HelperInputVoidPtr _cydptr
+
+cdef class cudaMemFreeNodeParams:
+    """
+    Memory free node parameters
+
+    Attributes
+    ----------
+    dptr : Any
+        in: the pointer to free
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemFreeNodeParams _pvt_val
+    cdef cyruntime.cudaMemFreeNodeParams* _pvt_ptr
+    cdef _HelperInputVoidPtr _cydptr
+
+cdef class cudaMemcpyAttributes:
+    """
+    Attributes specific to copies within a batch. For more details on
+    usage see cudaMemcpyBatchAsync.
+
+    Attributes
+    ----------
+    srcAccessOrder : cudaMemcpySrcAccessOrder
+        Source access ordering to be observed for copies with this
+        attribute.
+    srcLocHint : cudaMemLocation
+        Hint location for the source operand. Ignored when the pointers are
+        not managed memory or memory allocated outside CUDA.
+    dstLocHint : cudaMemLocation
+        Hint location for the destination operand. Ignored when the
+        pointers are not managed memory or memory allocated outside CUDA.
+    flags : unsigned int
+        Additional flags for copies with this attribute. See
+        cudaMemcpyFlags.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpyAttributes _pvt_val
+    cdef cyruntime.cudaMemcpyAttributes* _pvt_ptr
+    cdef cudaMemLocation _srcLocHint
+    cdef cudaMemLocation _dstLocHint
+
+cdef class cudaOffset3D:
+    """
+    Struct representing offset into a cudaArray_t in elements
+
+    Attributes
+    ----------
+    x : size_t
+
+    y : size_t
+
+    z : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaOffset3D _pvt_val
+    cdef cyruntime.cudaOffset3D* _pvt_ptr
+
+cdef class anon_struct6:
+    """
+    Attributes
+    ----------
+    ptr : Any
+
+    rowLength : size_t
+
+    layerHeight : size_t
+
+    locHint : cudaMemLocation
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DOperand* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyptr
+    cdef cudaMemLocation _locHint
+
+cdef class anon_struct7:
+    """
+    Attributes
+    ----------
+    array : cudaArray_t
+
+    offset : cudaOffset3D
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DOperand* _pvt_ptr
+    cdef cudaArray_t _array
+    cdef cudaOffset3D _offset
+
+cdef class anon_union1:
+    """
+    Attributes
+    ----------
+    ptr : anon_struct6
+
+    array : anon_struct7
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DOperand* _pvt_ptr
+    cdef anon_struct6 _ptr
+    cdef anon_struct7 _array
+
+cdef class cudaMemcpy3DOperand:
+    """
+    Struct representing an operand for copy with cudaMemcpy3DBatchAsync
+
+    Attributes
+    ----------
+    type : cudaMemcpy3DOperandType
+
+    op : anon_union1
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DOperand* _val_ptr
+    cdef cyruntime.cudaMemcpy3DOperand* _pvt_ptr
+    cdef anon_union1 _op
+
+cdef class cudaMemcpy3DBatchOp:
+    """
+    Attributes
+    ----------
+    src : cudaMemcpy3DOperand
+        Source memcpy operand.
+    dst : cudaMemcpy3DOperand
+        Destination memcpy operand.
+    extent : cudaExtent
+        Extents of the memcpy between src and dst. The width, height and
+        depth components must not be 0.
+    srcAccessOrder : cudaMemcpySrcAccessOrder
+        Source access ordering to be observed for copy from src to dst.
+    flags : unsigned int
+        Additional flags for copy from src to dst. See cudaMemcpyFlags.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemcpy3DBatchOp _pvt_val
+    cdef cyruntime.cudaMemcpy3DBatchOp* _pvt_ptr
+    cdef cudaMemcpy3DOperand _src
+    cdef cudaMemcpy3DOperand _dst
+    cdef cudaExtent _extent
+
+cdef class CUuuid_st:
+    """
+    Attributes
+    ----------
+    bytes : bytes
+        < CUDA definition of UUID
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.CUuuid_st _pvt_val
+    cdef cyruntime.CUuuid_st* _pvt_ptr
+
+cdef class cudaDeviceProp:
+    """
+    CUDA device properties
+
+    Attributes
+    ----------
+    name : bytes
+        ASCII string identifying device
+    uuid : cudaUUID_t
+        16-byte unique identifier
+    luid : bytes
+        8-byte locally unique identifier. Value is undefined on TCC and
+        non-Windows platforms
+    luidDeviceNodeMask : unsigned int
+        LUID device node mask. Value is undefined on TCC and non-Windows
+        platforms
+    totalGlobalMem : size_t
+        Global memory available on device in bytes
+    sharedMemPerBlock : size_t
+        Shared memory available per block in bytes
+    regsPerBlock : int
+        32-bit registers available per block
+    warpSize : int
+        Warp size in threads
+    memPitch : size_t
+        Maximum pitch in bytes allowed by memory copies
+    maxThreadsPerBlock : int
+        Maximum number of threads per block
+    maxThreadsDim : list[int]
+        Maximum size of each dimension of a block
+    maxGridSize : list[int]
+        Maximum size of each dimension of a grid
+    totalConstMem : size_t
+        Constant memory available on device in bytes
+    major : int
+        Major compute capability
+    minor : int
+        Minor compute capability
+    textureAlignment : size_t
+        Alignment requirement for textures
+    texturePitchAlignment : size_t
+        Pitch alignment requirement for texture references bound to pitched
+        memory
+    multiProcessorCount : int
+        Number of multiprocessors on device
+    integrated : int
+        Device is integrated as opposed to discrete
+    canMapHostMemory : int
+        Device can map host memory with
+        cudaHostAlloc/cudaHostGetDevicePointer
+    maxTexture1D : int
+        Maximum 1D texture size
+    maxTexture1DMipmap : int
+        Maximum 1D mipmapped texture size
+    maxTexture2D : list[int]
+        Maximum 2D texture dimensions
+    maxTexture2DMipmap : list[int]
+        Maximum 2D mipmapped texture dimensions
+    maxTexture2DLinear : list[int]
+        Maximum dimensions (width, height, pitch) for 2D textures bound to
+        pitched memory
+    maxTexture2DGather : list[int]
+        Maximum 2D texture dimensions if texture gather operations have to
+        be performed
+    maxTexture3D : list[int]
+        Maximum 3D texture dimensions
+    maxTexture3DAlt : list[int]
+        Maximum alternate 3D texture dimensions
+    maxTextureCubemap : int
+        Maximum Cubemap texture dimensions
+    maxTexture1DLayered : list[int]
+        Maximum 1D layered texture dimensions
+    maxTexture2DLayered : list[int]
+        Maximum 2D layered texture dimensions
+    maxTextureCubemapLayered : list[int]
+        Maximum Cubemap layered texture dimensions
+    maxSurface1D : int
+        Maximum 1D surface size
+    maxSurface2D : list[int]
+        Maximum 2D surface dimensions
+    maxSurface3D : list[int]
+        Maximum 3D surface dimensions
+    maxSurface1DLayered : list[int]
+        Maximum 1D layered surface dimensions
+    maxSurface2DLayered : list[int]
+        Maximum 2D layered surface dimensions
+    maxSurfaceCubemap : int
+        Maximum Cubemap surface dimensions
+    maxSurfaceCubemapLayered : list[int]
+        Maximum Cubemap layered surface dimensions
+    surfaceAlignment : size_t
+        Alignment requirements for surfaces
+    concurrentKernels : int
+        Device can possibly execute multiple kernels concurrently
+    ECCEnabled : int
+        Device has ECC support enabled
+    pciBusID : int
+        PCI bus ID of the device
+    pciDeviceID : int
+        PCI device ID of the device
+    pciDomainID : int
+        PCI domain ID of the device
+    tccDriver : int
+        1 if device is a Tesla device using TCC driver, 0 otherwise
+    asyncEngineCount : int
+        Number of asynchronous engines
+    unifiedAddressing : int
+        Device shares a unified address space with the host
+    memoryBusWidth : int
+        Global memory bus width in bits
+    l2CacheSize : int
+        Size of L2 cache in bytes
+    persistingL2CacheMaxSize : int
+        Device's maximum l2 persisting lines capacity setting in bytes
+    maxThreadsPerMultiProcessor : int
+        Maximum resident threads per multiprocessor
+    streamPrioritiesSupported : int
+        Device supports stream priorities
+    globalL1CacheSupported : int
+        Device supports caching globals in L1
+    localL1CacheSupported : int
+        Device supports caching locals in L1
+    sharedMemPerMultiprocessor : size_t
+        Shared memory available per multiprocessor in bytes
+    regsPerMultiprocessor : int
+        32-bit registers available per multiprocessor
+    managedMemory : int
+        Device supports allocating managed memory on this system
+    isMultiGpuBoard : int
+        Device is on a multi-GPU board
+    multiGpuBoardGroupID : int
+        Unique identifier for a group of devices on the same multi-GPU
+        board
+    hostNativeAtomicSupported : int
+        Link between the device and the host supports native atomic
+        operations
+    pageableMemoryAccess : int
+        Device supports coherently accessing pageable memory without
+        calling cudaHostRegister on it
+    concurrentManagedAccess : int
+        Device can coherently access managed memory concurrently with the
+        CPU
+    computePreemptionSupported : int
+        Device supports Compute Preemption
+    canUseHostPointerForRegisteredMem : int
+        Device can access host registered memory at the same virtual
+        address as the CPU
+    cooperativeLaunch : int
+        Device supports launching cooperative kernels via
+        cudaLaunchCooperativeKernel
+    sharedMemPerBlockOptin : size_t
+        Per device maximum shared memory per block usable by special opt in
+    pageableMemoryAccessUsesHostPageTables : int
+        Device accesses pageable memory via the host's page tables
+    directManagedMemAccessFromHost : int
+        Host can directly access managed memory on the device without
+        migration.
+    maxBlocksPerMultiProcessor : int
+        Maximum number of resident blocks per multiprocessor
+    accessPolicyMaxWindowSize : int
+        The maximum value of cudaAccessPolicyWindow::num_bytes.
+    reservedSharedMemPerBlock : size_t
+        Shared memory reserved by CUDA driver per block in bytes
+    hostRegisterSupported : int
+        Device supports host memory registration via cudaHostRegister.
+    sparseCudaArraySupported : int
+        1 if the device supports sparse CUDA arrays and sparse CUDA
+        mipmapped arrays, 0 otherwise
+    hostRegisterReadOnlySupported : int
+        Device supports using the cudaHostRegister flag
+        cudaHostRegisterReadOnly to register memory that must be mapped as
+        read-only to the GPU
+    timelineSemaphoreInteropSupported : int
+        External timeline semaphore interop is supported on the device
+    memoryPoolsSupported : int
+        1 if the device supports using the cudaMallocAsync and cudaMemPool
+        family of APIs, 0 otherwise
+    gpuDirectRDMASupported : int
+        1 if the device supports GPUDirect RDMA APIs, 0 otherwise
+    gpuDirectRDMAFlushWritesOptions : unsigned int
+        Bitmask to be interpreted according to the
+        cudaFlushGPUDirectRDMAWritesOptions enum
+    gpuDirectRDMAWritesOrdering : int
+        See the cudaGPUDirectRDMAWritesOrdering enum for numerical values
+    memoryPoolSupportedHandleTypes : unsigned int
+        Bitmask of handle types supported with mempool-based IPC
+    deferredMappingCudaArraySupported : int
+        1 if the device supports deferred mapping CUDA arrays and CUDA
+        mipmapped arrays
+    ipcEventSupported : int
+        Device supports IPC Events.
+    clusterLaunch : int
+        Indicates device supports cluster launch
+    unifiedFunctionPointers : int
+        Indicates device supports unified pointers
+    deviceNumaConfig : int
+        NUMA configuration of a device: value is of type
+        cudaDeviceNumaConfig enum
+    deviceNumaId : int
+        NUMA node ID of the GPU memory
+    mpsEnabled : int
+        Indicates if contexts created on this device will be shared via MPS
+    hostNumaId : int
+        NUMA ID of the host node closest to the device or -1 when system
+        does not support NUMA
+    gpuPciDeviceID : unsigned int
+        The combined 16-bit PCI device ID and 16-bit PCI vendor ID
+    gpuPciSubsystemID : unsigned int
+        The combined 16-bit PCI subsystem ID and 16-bit PCI subsystem
+        vendor ID
+    hostNumaMultinodeIpcSupported : int
+        1 if the device supports HostNuma location IPC between nodes in a
+        multi-node system.
+    reserved : list[int]
+        Reserved for future use
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaDeviceProp _pvt_val
+    cdef cyruntime.cudaDeviceProp* _pvt_ptr
+    cdef cudaUUID_t _uuid
+
+cdef class cudaIpcEventHandle_st:
+    """
+    CUDA IPC event handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaIpcEventHandle_st _pvt_val
+    cdef cyruntime.cudaIpcEventHandle_st* _pvt_ptr
+
+cdef class cudaIpcMemHandle_st:
+    """
+    CUDA IPC memory handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaIpcMemHandle_st _pvt_val
+    cdef cyruntime.cudaIpcMemHandle_st* _pvt_ptr
+
+cdef class cudaMemFabricHandle_st:
+    """
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaMemFabricHandle_st _pvt_val
+    cdef cyruntime.cudaMemFabricHandle_st* _pvt_ptr
+
+cdef class anon_struct8:
+    """
+    Attributes
+    ----------
+    handle : Any
+
+    name : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalMemoryHandleDesc* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyhandle
+    cdef _HelperInputVoidPtr _cyname
+
+cdef class anon_union2:
+    """
+    Attributes
+    ----------
+    fd : int
+
+    win32 : anon_struct8
+
+    nvSciBufObject : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalMemoryHandleDesc* _pvt_ptr
+    cdef anon_struct8 _win32
+    cdef _HelperInputVoidPtr _cynvSciBufObject
+
+cdef class cudaExternalMemoryHandleDesc:
+    """
+    External memory handle descriptor
+
+    Attributes
+    ----------
+    type : cudaExternalMemoryHandleType
+        Type of the handle
+    handle : anon_union2
+
+    size : unsigned long long
+        Size of the memory allocation
+    flags : unsigned int
+        Flags must either be zero or cudaExternalMemoryDedicated
+    reserved : list[unsigned int]
+        Must be zero
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalMemoryHandleDesc* _val_ptr
+    cdef cyruntime.cudaExternalMemoryHandleDesc* _pvt_ptr
+    cdef anon_union2 _handle
+
+cdef class cudaExternalMemoryBufferDesc:
+    """
+    External memory buffer descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the buffer's base is
+    size : unsigned long long
+        Size of the buffer
+    flags : unsigned int
+        Flags reserved for future use. Must be zero.
+    reserved : list[unsigned int]
+        Must be zero
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalMemoryBufferDesc _pvt_val
+    cdef cyruntime.cudaExternalMemoryBufferDesc* _pvt_ptr
+
+cdef class cudaExternalMemoryMipmappedArrayDesc:
+    """
+    External memory mipmap descriptor
+
+    Attributes
+    ----------
+    offset : unsigned long long
+        Offset into the memory object where the base level of the mipmap
+        chain is.
+    formatDesc : cudaChannelFormatDesc
+        Format of base level of the mipmap chain
+    extent : cudaExtent
+        Dimensions of base level of the mipmap chain
+    flags : unsigned int
+        Flags associated with CUDA mipmapped arrays. See
+        cudaMallocMipmappedArray
+    numLevels : unsigned int
+        Total number of levels in the mipmap chain
+    reserved : list[unsigned int]
+        Must be zero
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalMemoryMipmappedArrayDesc _pvt_val
+    cdef cyruntime.cudaExternalMemoryMipmappedArrayDesc* _pvt_ptr
+    cdef cudaChannelFormatDesc _formatDesc
+    cdef cudaExtent _extent
+
+cdef class anon_struct9:
+    """
+    Attributes
+    ----------
+    handle : Any
+
+    name : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreHandleDesc* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyhandle
+    cdef _HelperInputVoidPtr _cyname
+
+cdef class anon_union3:
+    """
+    Attributes
+    ----------
+    fd : int
+
+    win32 : anon_struct9
+
+    nvSciSyncObj : Any
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreHandleDesc* _pvt_ptr
+    cdef anon_struct9 _win32
+    cdef _HelperInputVoidPtr _cynvSciSyncObj
+
+cdef class cudaExternalSemaphoreHandleDesc:
+    """
+    External semaphore handle descriptor
+
+    Attributes
+    ----------
+    type : cudaExternalSemaphoreHandleType
+        Type of the handle
+    handle : anon_union3
+
+    flags : unsigned int
+        Flags reserved for the future. Must be zero.
+    reserved : list[unsigned int]
+        Must be zero
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreHandleDesc* _val_ptr
+    cdef cyruntime.cudaExternalSemaphoreHandleDesc* _pvt_ptr
+    cdef anon_union3 _handle
+
+cdef class anon_struct10:
+    """
+    Attributes
+    ----------
+    value : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _pvt_ptr
+
+cdef class anon_union4:
+    """
+    Attributes
+    ----------
+    fence : Any
+
+    reserved : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfence
+
+cdef class anon_struct11:
+    """
+    Attributes
+    ----------
+    key : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _pvt_ptr
+
+cdef class anon_struct12:
+    """
+    Attributes
+    ----------
+    fence : anon_struct10
+
+    nvSciSync : anon_union4
+
+    keyedMutex : anon_struct11
+
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _pvt_ptr
+    cdef anon_struct10 _fence
+    cdef anon_union4 _nvSciSync
+    cdef anon_struct11 _keyedMutex
+
+cdef class cudaExternalSemaphoreSignalParams:
+    """
+    External semaphore signal parameters, compatible with driver type
+
+    Attributes
+    ----------
+    params : anon_struct12
+
+    flags : unsigned int
+        Only when cudaExternalSemaphoreSignalParams is used to signal a
+        cudaExternalSemaphore_t of type
+        cudaExternalSemaphoreHandleTypeNvSciSync, the valid flag is
+        cudaExternalSemaphoreSignalSkipNvSciBufMemSync: which indicates
+        that while signaling the cudaExternalSemaphore_t, no memory
+        synchronization operations should be performed for any external
+        memory object imported as cudaExternalMemoryHandleTypeNvSciBuf. For
+        all other types of cudaExternalSemaphore_t, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalParams _pvt_val
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _pvt_ptr
+    cdef anon_struct12 _params
+
+cdef class anon_struct13:
+    """
+    Attributes
+    ----------
+    value : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _pvt_ptr
+
+cdef class anon_union5:
+    """
+    Attributes
+    ----------
+    fence : Any
+
+    reserved : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfence
+
+cdef class anon_struct14:
+    """
+    Attributes
+    ----------
+    key : unsigned long long
+
+    timeoutMs : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _pvt_ptr
+
+cdef class anon_struct15:
+    """
+    Attributes
+    ----------
+    fence : anon_struct13
+
+    nvSciSync : anon_union5
+
+    keyedMutex : anon_struct14
+
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _pvt_ptr
+    cdef anon_struct13 _fence
+    cdef anon_union5 _nvSciSync
+    cdef anon_struct14 _keyedMutex
+
+cdef class cudaExternalSemaphoreWaitParams:
+    """
+    External semaphore wait parameters, compatible with driver type
+
+    Attributes
+    ----------
+    params : anon_struct15
+
+    flags : unsigned int
+        Only when cudaExternalSemaphoreSignalParams is used to signal a
+        cudaExternalSemaphore_t of type
+        cudaExternalSemaphoreHandleTypeNvSciSync, the valid flag is
+        cudaExternalSemaphoreSignalSkipNvSciBufMemSync: which indicates
+        that while waiting for the cudaExternalSemaphore_t, no memory
+        synchronization operations should be performed for any external
+        memory object imported as cudaExternalMemoryHandleTypeNvSciBuf. For
+        all other types of cudaExternalSemaphore_t, flags must be zero.
+    reserved : list[unsigned int]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitParams _pvt_val
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _pvt_ptr
+    cdef anon_struct15 _params
+
+cdef class cudaDevSmResource:
+    """
+    Data for SM-related resources All parameters in this structure are
+    OUTPUT only. Do not write to any of the fields in this structure.
+
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of streaming multiprocessors available in this resource.
+    minSmPartitionSize : unsigned int
+        The minimum number of streaming multiprocessors required to
+        partition this resource.
+    smCoscheduledAlignment : unsigned int
+        The number of streaming multiprocessors in this resource that are
+        guaranteed to be co-scheduled on the same GPU processing cluster.
+        smCount will be a multiple of this value, unless the backfill flag
+        is set.
+    flags : unsigned int
+        The flags set on this SM resource. For available flags see
+        cudaDevSmResourceGroup_flags.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaDevSmResource _pvt_val
+    cdef cyruntime.cudaDevSmResource* _pvt_ptr
+
+cdef class cudaDevWorkqueueConfigResource:
+    """
+    Data for workqueue configuration related resources
+
+    Attributes
+    ----------
+    device : int
+        The device on which the workqueue resources are available
+    wqConcurrencyLimit : unsigned int
+        The expected maximum number of concurrent stream-ordered workloads
+    sharingScope : cudaDevWorkqueueConfigScope
+        The sharing scope for the workqueue resources
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaDevWorkqueueConfigResource _pvt_val
+    cdef cyruntime.cudaDevWorkqueueConfigResource* _pvt_ptr
+
+cdef class cudaDevWorkqueueResource:
+    """
+    Handle to a pre-existing workqueue related resource
+
+    Attributes
+    ----------
+    reserved : bytes
+        Reserved for future use
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaDevWorkqueueResource _pvt_val
+    cdef cyruntime.cudaDevWorkqueueResource* _pvt_ptr
+
+cdef class cudaDevSmResourceGroupParams_st:
+    """
+    Input data for splitting SMs
+
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of SMs available in this resource.
+    coscheduledSmCount : unsigned int
+        The amount of co-scheduled SMs grouped together for locality
+        purposes.
+    preferredCoscheduledSmCount : unsigned int
+        When possible, combine co-scheduled groups together into larger
+        groups of this size.
+    flags : unsigned int
+        Combination of `cudaDevSmResourceGroup_flags` values to indicate
+        this this group is created.
+    reserved : list[unsigned int]
+        Reserved for future use - ensure this is is zero initialized.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaDevSmResourceGroupParams_st _pvt_val
+    cdef cyruntime.cudaDevSmResourceGroupParams_st* _pvt_ptr
+
+cdef class cudaDevResource_st:
+    """
+    A tagged union describing different resources identified by the
+    type field. This structure should not be directly modified outside
+    of the API that created it. struct enumcudaDevResourceTypetype;
+    union structcudaDevSmResourcesm;
+    structcudaDevWorkqueueConfigResourcewqConfig;
+    structcudaDevWorkqueueResourcewq; ; ;  - If `typename` is
+    `cudaDevResourceTypeInvalid`, this resoure is not valid and cannot
+    be further accessed.    - If `typename` is `cudaDevResourceTypeSm`,
+    the cudaDevSmResource structure `sm` is filled in. For example,
+    `sm.smCount` will reflect the amount of streaming multiprocessors
+    available in this resource.    - If `typename` is
+    `cudaDevResourceTypeWorkqueueConfig`, the
+    cudaDevWorkqueueConfigResource structure `wqConfig` is filled in.
+    - If `typename` is `cudaDevResourceTypeWorkqueue`, the
+    cudaDevWorkqueueResource structure `wq` is filled in.
+
+    Attributes
+    ----------
+    type : cudaDevResourceType
+        Type of resource, dictates which union field was last set
+    _internal_padding : bytes
+
+    sm : cudaDevSmResource
+        Resource corresponding to cudaDevResourceTypeSm `typename`.
+    wqConfig : cudaDevWorkqueueConfigResource
+        Resource corresponding to cudaDevResourceTypeWorkqueueConfig
+        `typename`.
+    wq : cudaDevWorkqueueResource
+        Resource corresponding to cudaDevResourceTypeWorkqueue `typename`.
+    _oversize : bytes
+
+    nextResource : cudaDevResource_st
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaDevResource_st* _val_ptr
+    cdef cyruntime.cudaDevResource_st* _pvt_ptr
+    cdef cudaDevSmResource _sm
+    cdef cudaDevWorkqueueConfigResource _wqConfig
+    cdef cudaDevWorkqueueResource _wq
+    cdef size_t _nextResource_length
+    cdef cyruntime.cudaDevResource_st* _nextResource
+
+cdef class cudalibraryHostUniversalFunctionAndDataTable:
+    """
+    Attributes
+    ----------
+    functionTable : Any
+
+    functionWindowSize : size_t
+
+    dataTable : Any
+
+    dataWindowSize : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudalibraryHostUniversalFunctionAndDataTable _pvt_val
+    cdef cyruntime.cudalibraryHostUniversalFunctionAndDataTable* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfunctionTable
+    cdef _HelperInputVoidPtr _cydataTable
+
+cdef class cudaKernelNodeParams:
+    """
+    CUDA GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : Any
+        Kernel to launch
+    gridDim : dim3
+        Grid dimensions
+    blockDim : dim3
+        Block dimensions
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to individual kernel arguments
+    extra : Any
+        Pointer to kernel arguments in the "extra" format
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaKernelNodeParams _pvt_val
+    cdef cyruntime.cudaKernelNodeParams* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfunc
+    cdef dim3 _gridDim
+    cdef dim3 _blockDim
+    cdef _HelperKernelParams _cykernelParams
+
+cdef class cudaKernelNodeParamsV2:
+    """
+    CUDA GPU kernel node parameters
+
+    Attributes
+    ----------
+    func : Any
+        functionType = cudaKernelFucntionTypeDevice
+    kern : cudaKernel_t
+        functionType = cudaKernelFucntionTypeKernel
+    cuFunc : cudaFunction_t
+        functionType = cudaKernelFucntionTypeFunction
+    gridDim : dim3
+        Grid dimensions
+    blockDim : dim3
+        Block dimensions
+    sharedMemBytes : unsigned int
+        Dynamic shared-memory size per thread block in bytes
+    kernelParams : Any
+        Array of pointers to individual kernel arguments
+    extra : Any
+        Pointer to kernel arguments in the "extra" format
+    ctx : cudaExecutionContext_t
+        Context in which to run the kernel. If NULL will try to use the
+        current context.
+    functionType : cudaKernelFunctionType
+        Type of handle passed in the func/kern/cuFunc union above
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaKernelNodeParamsV2* _val_ptr
+    cdef cyruntime.cudaKernelNodeParamsV2* _pvt_ptr
+    cdef _HelperInputVoidPtr _cyfunc
+    cdef cudaKernel_t _kern
+    cdef cudaFunction_t _cuFunc
+    cdef dim3 _gridDim
+    cdef dim3 _blockDim
+    cdef _HelperKernelParams _cykernelParams
+    cdef cudaExecutionContext_t _ctx
+
+cdef class cudaExternalSemaphoreSignalNodeParams:
+    """
+    External semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : cudaExternalSemaphore_t
+        Array of external semaphore handles.
+    paramsArray : cudaExternalSemaphoreSignalParams
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalNodeParams _pvt_val
+    cdef cyruntime.cudaExternalSemaphoreSignalNodeParams* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cyruntime.cudaExternalSemaphore_t* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _paramsArray
+
+cdef class cudaExternalSemaphoreSignalNodeParamsV2:
+    """
+    External semaphore signal node parameters
+
+    Attributes
+    ----------
+    extSemArray : cudaExternalSemaphore_t
+        Array of external semaphore handles.
+    paramsArray : cudaExternalSemaphoreSignalParams
+        Array of external semaphore signal parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreSignalNodeParamsV2 _pvt_val
+    cdef cyruntime.cudaExternalSemaphoreSignalNodeParamsV2* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cyruntime.cudaExternalSemaphore_t* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cyruntime.cudaExternalSemaphoreSignalParams* _paramsArray
+
+cdef class cudaExternalSemaphoreWaitNodeParams:
+    """
+    External semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : cudaExternalSemaphore_t
+        Array of external semaphore handles.
+    paramsArray : cudaExternalSemaphoreWaitParams
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitNodeParams _pvt_val
+    cdef cyruntime.cudaExternalSemaphoreWaitNodeParams* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cyruntime.cudaExternalSemaphore_t* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _paramsArray
+
+cdef class cudaExternalSemaphoreWaitNodeParamsV2:
+    """
+    External semaphore wait node parameters
+
+    Attributes
+    ----------
+    extSemArray : cudaExternalSemaphore_t
+        Array of external semaphore handles.
+    paramsArray : cudaExternalSemaphoreWaitParams
+        Array of external semaphore wait parameters.
+    numExtSems : unsigned int
+        Number of handles and parameters supplied in extSemArray and
+        paramsArray.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaExternalSemaphoreWaitNodeParamsV2 _pvt_val
+    cdef cyruntime.cudaExternalSemaphoreWaitNodeParamsV2* _pvt_ptr
+    cdef size_t _extSemArray_length
+    cdef cyruntime.cudaExternalSemaphore_t* _extSemArray
+    cdef size_t _paramsArray_length
+    cdef cyruntime.cudaExternalSemaphoreWaitParams* _paramsArray
+
+cdef class cudaConditionalNodeParams:
+    """
+    CUDA conditional node parameters
+
+    Attributes
+    ----------
+    handle : cudaGraphConditionalHandle
+        Conditional node handle. Handles must be created in advance of
+        creating the node using cudaGraphConditionalHandleCreate.
+    type : cudaGraphConditionalNodeType
+        Type of conditional node.
+    size : unsigned int
+        Size of graph output array. Allowed values are 1 for
+        cudaGraphCondTypeWhile, 1 or 2 for cudaGraphCondTypeIf, or any
+        value greater than zero for cudaGraphCondTypeSwitch.
+    phGraph_out : cudaGraph_t
+        CUDA-owned array populated with conditional node child graphs
+        during creation of the node. Valid for the lifetime of the
+        conditional node. The contents of the graph(s) are subject to the
+        following constraints:   - Allowed node types are kernel nodes,
+        empty nodes, child graphs, memsets, memcopies, and conditionals.
+        This applies recursively to child graphs and conditional bodies.
+        - All kernels, including kernels in nested conditionals or child
+        graphs at any level, must belong to the same CUDA context.
+        These graphs may be populated using graph node creation APIs or
+        cudaStreamBeginCaptureToGraph. cudaGraphCondTypeIf: phGraph_out[0]
+        is executed when the condition is non-zero. If `size` == 2,
+        phGraph_out[1] will be executed when the condition is zero.
+        cudaGraphCondTypeWhile: phGraph_out[0] is executed as long as the
+        condition is non-zero. cudaGraphCondTypeSwitch: phGraph_out[n] is
+        executed when the condition is equal to n. If the condition >=
+        `size`, no body graph is executed.
+    ctx : cudaExecutionContext_t
+        CUDA Execution Context
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaConditionalNodeParams _pvt_val
+    cdef cyruntime.cudaConditionalNodeParams* _pvt_ptr
+    cdef cudaGraphConditionalHandle _handle
+    cdef size_t _phGraph_out_length
+    cdef cyruntime.cudaGraph_t* _phGraph_out
+    cdef cudaExecutionContext_t _ctx
+
+cdef class cudaChildGraphNodeParams:
+    """
+    Child graph node parameters
+
+    Attributes
+    ----------
+    graph : cudaGraph_t
+        The child graph to clone into the node for node creation, or a
+        handle to the graph owned by the node for node query. The graph
+        must not contain conditional nodes. Graphs containing memory
+        allocation or memory free nodes must set the ownership to be moved
+        to the parent.
+    ownership : cudaGraphChildGraphNodeOwnership
+        The ownership relationship of the child graph node.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaChildGraphNodeParams _pvt_val
+    cdef cyruntime.cudaChildGraphNodeParams* _pvt_ptr
+    cdef cudaGraph_t _graph
+
+cdef class cudaEventRecordNodeParams:
+    """
+    Event record node parameters
+
+    Attributes
+    ----------
+    event : cudaEvent_t
+        The event to record when the node executes
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaEventRecordNodeParams _pvt_val
+    cdef cyruntime.cudaEventRecordNodeParams* _pvt_ptr
+    cdef cudaEvent_t _event
+
+cdef class cudaEventWaitNodeParams:
+    """
+    Event wait node parameters
+
+    Attributes
+    ----------
+    event : cudaEvent_t
+        The event to wait on from the node
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaEventWaitNodeParams _pvt_val
+    cdef cyruntime.cudaEventWaitNodeParams* _pvt_ptr
+    cdef cudaEvent_t _event
+
+cdef class cudaGraphNodeParams:
+    """
+    Graph node parameters. See cudaGraphAddNode.
+
+    Attributes
+    ----------
+    type : cudaGraphNodeType
+        Type of the node
+    reserved0 : list[int]
+        Reserved. Must be zero.
+    reserved1 : list[long long]
+        Padding. Unused bytes must be zero.
+    kernel : cudaKernelNodeParamsV2
+        Kernel node parameters.
+    memcpy : cudaMemcpyNodeParams
+        Memcpy node parameters.
+    memset : cudaMemsetParamsV2
+        Memset node parameters.
+    host : cudaHostNodeParamsV2
+        Host node parameters.
+    graph : cudaChildGraphNodeParams
+        Child graph node parameters.
+    eventWait : cudaEventWaitNodeParams
+        Event wait node parameters.
+    eventRecord : cudaEventRecordNodeParams
+        Event record node parameters.
+    extSemSignal : cudaExternalSemaphoreSignalNodeParamsV2
+        External semaphore signal node parameters.
+    extSemWait : cudaExternalSemaphoreWaitNodeParamsV2
+        External semaphore wait node parameters.
+    alloc : cudaMemAllocNodeParamsV2
+        Memory allocation node parameters.
+    free : cudaMemFreeNodeParams
+        Memory free node parameters.
+    conditional : cudaConditionalNodeParams
+        Conditional node parameters.
+    reserved2 : long long
+        Reserved bytes. Must be zero.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphNodeParams* _val_ptr
+    cdef cyruntime.cudaGraphNodeParams* _pvt_ptr
+    cdef cudaKernelNodeParamsV2 _kernel
+    cdef cudaMemcpyNodeParams _memcpy
+    cdef cudaMemsetParamsV2 _memset
+    cdef cudaHostNodeParamsV2 _host
+    cdef cudaChildGraphNodeParams _graph
+    cdef cudaEventWaitNodeParams _eventWait
+    cdef cudaEventRecordNodeParams _eventRecord
+    cdef cudaExternalSemaphoreSignalNodeParamsV2 _extSemSignal
+    cdef cudaExternalSemaphoreWaitNodeParamsV2 _extSemWait
+    cdef cudaMemAllocNodeParamsV2 _alloc
+    cdef cudaMemFreeNodeParams _free
+    cdef cudaConditionalNodeParams _conditional
+
+cdef class cudaGraphEdgeData_st:
+    """
+    Optional annotation for edges in a CUDA graph. Note, all edges
+    implicitly have annotations and default to a zero-initialized value
+    if not specified. A zero-initialized struct indicates a standard
+    full serialization of two nodes with memory visibility.
+
+    Attributes
+    ----------
+    from_port : bytes
+        This indicates when the dependency is triggered from the upstream
+        node on the edge. The meaning is specfic to the node type. A value
+        of 0 in all cases means full completion of the upstream node, with
+        memory visibility to the downstream node or portion thereof
+        (indicated by `to_port`).   Only kernel nodes define non-zero
+        ports. A kernel node can use the following output port types:
+        cudaGraphKernelNodePortDefault,
+        cudaGraphKernelNodePortProgrammatic, or
+        cudaGraphKernelNodePortLaunchCompletion.
+    to_port : bytes
+        This indicates what portion of the downstream node is dependent on
+        the upstream node or portion thereof (indicated by `from_port`).
+        The meaning is specific to the node type. A value of 0 in all cases
+        means the entirety of the downstream node is dependent on the
+        upstream work.   Currently no node types define non-zero ports.
+        Accordingly, this field must be set to zero.
+    type : bytes
+        This should be populated with a value from
+        ::cudaGraphDependencyType. (It is typed as char due to compiler-
+        specific layout of bitfields.) See ::cudaGraphDependencyType.
+    reserved : bytes
+        These bytes are unused and must be zeroed. This ensures
+        compatibility if additional fields are added in the future.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphEdgeData_st _pvt_val
+    cdef cyruntime.cudaGraphEdgeData_st* _pvt_ptr
+
+cdef class cudaGraphInstantiateParams_st:
+    """
+    Graph instantiation parameters
+
+    Attributes
+    ----------
+    flags : unsigned long long
+        Instantiation flags
+    uploadStream : cudaStream_t
+        Upload stream
+    errNode_out : cudaGraphNode_t
+        The node which caused instantiation to fail, if any
+    result_out : cudaGraphInstantiateResult
+        Whether instantiation was successful. If it failed, the reason why
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphInstantiateParams_st _pvt_val
+    cdef cyruntime.cudaGraphInstantiateParams_st* _pvt_ptr
+    cdef cudaStream_t _uploadStream
+    cdef cudaGraphNode_t _errNode_out
+
+cdef class cudaGraphExecUpdateResultInfo_st:
+    """
+    Result information returned by cudaGraphExecUpdate
+
+    Attributes
+    ----------
+    result : cudaGraphExecUpdateResult
+        Gives more specific detail when a cuda graph update fails.
+    errorNode : cudaGraphNode_t
+        The "to node" of the error edge when the topologies do not match.
+        The error node when the error is associated with a specific node.
+        NULL when the error is generic.
+    errorFromNode : cudaGraphNode_t
+        The from node of error edge when the topologies do not match.
+        Otherwise NULL.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphExecUpdateResultInfo_st _pvt_val
+    cdef cyruntime.cudaGraphExecUpdateResultInfo_st* _pvt_ptr
+    cdef cudaGraphNode_t _errorNode
+    cdef cudaGraphNode_t _errorFromNode
+
+cdef class anon_struct16:
+    """
+    Attributes
+    ----------
+    pValue : Any
+
+    offset : size_t
+
+    size : size_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphKernelNodeUpdate* _pvt_ptr
+    cdef _HelperInputVoidPtr _cypValue
+
+cdef class anon_union9:
+    """
+    Attributes
+    ----------
+    gridDim : dim3
+
+    param : anon_struct16
+
+    isEnabled : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphKernelNodeUpdate* _pvt_ptr
+    cdef dim3 _gridDim
+    cdef anon_struct16 _param
+
+cdef class cudaGraphKernelNodeUpdate:
+    """
+    Struct to specify a single node update to pass as part of a larger
+    array to ::cudaGraphKernelNodeUpdatesApply
+
+    Attributes
+    ----------
+    node : cudaGraphDeviceNode_t
+        Node to update
+    field : cudaGraphKernelNodeField
+        Which type of update to apply. Determines how updateData is
+        interpreted
+    updateData : anon_union9
+        Update data to apply. Which field is used depends on field's value
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaGraphKernelNodeUpdate* _val_ptr
+    cdef cyruntime.cudaGraphKernelNodeUpdate* _pvt_ptr
+    cdef cudaGraphDeviceNode_t _node
+    cdef anon_union9 _updateData
+
+cdef class cudaLaunchMemSyncDomainMap_st:
+    """
+    Memory Synchronization Domain map  See cudaLaunchMemSyncDomain.  By
+    default, kernels are launched in domain 0. Kernel launched with
+    cudaLaunchMemSyncDomainRemote will have a different domain ID. User
+    may also alter the domain ID with ::cudaLaunchMemSyncDomainMap for
+    a specific stream / graph node / kernel launch. See
+    cudaLaunchAttributeMemSyncDomainMap.  Domain ID range is available
+    through cudaDevAttrMemSyncDomainCount.
+
+    Attributes
+    ----------
+    default_ : bytes
+        The default domain ID to use for designated kernels
+    remote : bytes
+        The remote domain ID to use for designated kernels
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchMemSyncDomainMap_st _pvt_val
+    cdef cyruntime.cudaLaunchMemSyncDomainMap_st* _pvt_ptr
+
+cdef class anon_struct17:
+    """
+    Attributes
+    ----------
+    x : unsigned int
+
+    y : unsigned int
+
+    z : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttributeValue* _pvt_ptr
+
+cdef class anon_struct18:
+    """
+    Attributes
+    ----------
+    event : cudaEvent_t
+
+    flags : int
+
+    triggerAtBlockStart : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttributeValue* _pvt_ptr
+    cdef cudaEvent_t _event
+
+cdef class anon_struct19:
+    """
+    Attributes
+    ----------
+    x : unsigned int
+
+    y : unsigned int
+
+    z : unsigned int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttributeValue* _pvt_ptr
+
+cdef class anon_struct20:
+    """
+    Attributes
+    ----------
+    event : cudaEvent_t
+
+    flags : int
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttributeValue* _pvt_ptr
+    cdef cudaEvent_t _event
+
+cdef class anon_struct21:
+    """
+    Attributes
+    ----------
+    deviceUpdatable : int
+
+    devNode : cudaGraphDeviceNode_t
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttributeValue* _pvt_ptr
+    cdef cudaGraphDeviceNode_t _devNode
+
+cdef class cudaLaunchAttributeValue:
+    """
+    Launch attributes union; used as value field of
+    ::cudaLaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : cudaAccessPolicyWindow
+        Value of launch attribute cudaLaunchAttributeAccessPolicyWindow.
+    cooperative : int
+        Value of launch attribute cudaLaunchAttributeCooperative. Nonzero
+        indicates a cooperative kernel (see cudaLaunchCooperativeKernel).
+    syncPolicy : cudaSynchronizationPolicy
+        Value of launch attribute cudaLaunchAttributeSynchronizationPolicy.
+        ::cudaSynchronizationPolicy for work queued up in this stream.
+    clusterDim : anon_struct17
+        Value of launch attribute cudaLaunchAttributeClusterDimension that
+        represents the desired cluster dimensions for the kernel. Opaque
+        type with the following fields: - `x` - The X dimension of the
+        cluster, in blocks. Must be a divisor of the grid X dimension.    -
+        `y` - The Y dimension of the cluster, in blocks. Must be a divisor
+        of the grid Y dimension.    - `z` - The Z dimension of the cluster,
+        in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : cudaClusterSchedulingPolicy
+        Value of launch attribute
+        cudaLaunchAttributeClusterSchedulingPolicyPreference. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        cudaLaunchAttributeProgrammaticStreamSerialization.
+    programmaticEvent : anon_struct18
+        Value of launch attribute cudaLaunchAttributeProgrammaticEvent with
+        the following fields: - `cudaEvent_t` event - Event to fire when
+        all blocks trigger it.    - `int` flags; - Event record flags, see
+        cudaEventRecordWithFlags. Does not accept cudaEventRecordExternal.
+        - `int` triggerAtBlockStart - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    priority : int
+        Value of launch attribute cudaLaunchAttributePriority. Execution
+        priority of the kernel.
+    memSyncDomainMap : cudaLaunchMemSyncDomainMap
+        Value of launch attribute cudaLaunchAttributeMemSyncDomainMap. See
+        ::cudaLaunchMemSyncDomainMap.
+    memSyncDomain : cudaLaunchMemSyncDomain
+        Value of launch attribute cudaLaunchAttributeMemSyncDomain. See
+        cudaLaunchMemSyncDomain.
+    preferredClusterDim : anon_struct19
+        Value of launch attribute
+        cudaLaunchAttributePreferredClusterDimension that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        cudaLaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        cudaLaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        cudaLaunchAttributeValue::clusterDim.
+    launchCompletionEvent : anon_struct20
+        Value of launch attribute cudaLaunchAttributeLaunchCompletionEvent
+        with the following fields: - `cudaEvent_t` event - Event to fire
+        when the last block launches.    - `int` flags - Event record
+        flags, see cudaEventRecordWithFlags. Does not accept
+        cudaEventRecordExternal.
+    deviceUpdatableKernelNode : anon_struct21
+        Value of launch attribute
+        cudaLaunchAttributeDeviceUpdatableKernelNode with the following
+        fields: - `int` deviceUpdatable - Whether or not the resulting
+        kernel node should be device-updatable.    -
+        `cudaGraphDeviceNode_t` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        cudaLaunchAttributePreferredSharedMemoryCarveout.
+    nvlinkUtilCentricScheduling : unsigned int
+        Value of launch attribute
+        cudaLaunchAttributeNvlinkUtilCentricScheduling.
+    portableClusterSizeMode : cudaLaunchAttributePortableClusterMode
+        Value of launch attribute
+        cudaLaunchAttributePortableClusterSizeMode
+    sharedMemoryMode : cudaSharedMemoryMode
+        Value of launch attribute cudaLaunchAttributeSharedMemoryMode. See
+        cudaSharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttributeValue _pvt_val
+    cdef cyruntime.cudaLaunchAttributeValue* _pvt_ptr
+    cdef cudaAccessPolicyWindow _accessPolicyWindow
+    cdef anon_struct17 _clusterDim
+    cdef anon_struct18 _programmaticEvent
+    cdef cudaLaunchMemSyncDomainMap _memSyncDomainMap
+    cdef anon_struct19 _preferredClusterDim
+    cdef anon_struct20 _launchCompletionEvent
+    cdef anon_struct21 _deviceUpdatableKernelNode
+
+cdef class cudaLaunchAttribute_st:
+    """
+    Launch attribute
+
+    Attributes
+    ----------
+    id : cudaLaunchAttributeID
+        Attribute to set
+    val : cudaLaunchAttributeValue
+        Value of the attribute
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaLaunchAttribute_st _pvt_val
+    cdef cyruntime.cudaLaunchAttribute_st* _pvt_ptr
+    cdef cudaLaunchAttributeValue _val
+
+cdef class anon_struct22:
+    """
+    Attributes
+    ----------
+    bytesOverBudget : unsigned long long
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaAsyncNotificationInfo* _pvt_ptr
+
+cdef class anon_union10:
+    """
+    Attributes
+    ----------
+    overBudget : anon_struct22
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaAsyncNotificationInfo* _pvt_ptr
+    cdef anon_struct22 _overBudget
+
+cdef class cudaAsyncNotificationInfo:
+    """
+    Information describing an async notification event
+
+    Attributes
+    ----------
+    type : cudaAsyncNotificationType
+        The type of notification being sent
+    info : anon_union10
+        Information about the notification. `typename` must be checked in
+        order to interpret this field.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaAsyncNotificationInfo* _val_ptr
+    cdef cyruntime.cudaAsyncNotificationInfo* _pvt_ptr
+    cdef anon_union10 _info
+
+cdef class cudaTextureDesc:
+    """
+    CUDA texture descriptor
+
+    Attributes
+    ----------
+    addressMode : list[cudaTextureAddressMode]
+        Texture address mode for up to 3 dimensions
+    filterMode : cudaTextureFilterMode
+        Texture filter mode
+    readMode : cudaTextureReadMode
+        Texture read mode
+    sRGB : int
+        Perform sRGB->linear conversion during texture read
+    borderColor : list[float]
+        Texture Border Color
+    normalizedCoords : int
+        Indicates whether texture reads are normalized or not
+    maxAnisotropy : unsigned int
+        Limit to the anisotropy ratio
+    mipmapFilterMode : cudaTextureFilterMode
+        Mipmap filter mode
+    mipmapLevelBias : float
+        Offset applied to the supplied mipmap level
+    minMipmapLevelClamp : float
+        Lower end of the mipmap level range to clamp access to
+    maxMipmapLevelClamp : float
+        Upper end of the mipmap level range to clamp access to
+    disableTrilinearOptimization : int
+        Disable any trilinear filtering optimizations.
+    seamlessCubemap : int
+        Enable seamless cube map filtering.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaTextureDesc _pvt_val
+    cdef cyruntime.cudaTextureDesc* _pvt_ptr
+
+cdef class cudaEglPlaneDesc_st:
+    """
+    CUDA EGL Plane Descriptor - structure defining each plane of a CUDA
+    EGLFrame
+
+    Attributes
+    ----------
+    width : unsigned int
+        Width of plane
+    height : unsigned int
+        Height of plane
+    depth : unsigned int
+        Depth of plane
+    pitch : unsigned int
+        Pitch of plane
+    numChannels : unsigned int
+        Number of channels for the plane
+    channelDesc : cudaChannelFormatDesc
+        Channel Format Descriptor
+    reserved : list[unsigned int]
+        Reserved for future use
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaEglPlaneDesc_st _pvt_val
+    cdef cyruntime.cudaEglPlaneDesc_st* _pvt_ptr
+    cdef cudaChannelFormatDesc _channelDesc
+
+cdef class anon_union11:
+    """
+    Attributes
+    ----------
+    pArray : list[cudaArray_t]
+
+    pPitch : list[cudaPitchedPtr]
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaEglFrame_st* _pvt_ptr
+
+cdef class cudaEglFrame_st:
+    """
+    CUDA EGLFrame Descriptor - structure defining one frame of EGL.
+    Each frame may contain one or more planes depending on whether the
+    surface is Multiplanar or not. Each plane of EGLFrame is
+    represented by cudaEglPlaneDesc which is defined as:
+    typedefstructcudaEglPlaneDesc_st unsignedintwidth;
+    unsignedintheight; unsignedintdepth; unsignedintpitch;
+    unsignedintnumChannels; structcudaChannelFormatDescchannelDesc;
+    unsignedintreserved[4]; cudaEglPlaneDesc;
+
+    Attributes
+    ----------
+    frame : anon_union11
+
+    planeDesc : list[cudaEglPlaneDesc]
+        CUDA EGL Plane Descriptor cudaEglPlaneDesc
+    planeCount : unsigned int
+        Number of planes
+    frameType : cudaEglFrameType
+        Array or Pitch
+    eglColorFormat : cudaEglColorFormat
+        CUDA EGL Color Format
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    cdef cyruntime.cudaEglFrame_st* _val_ptr
+    cdef cyruntime.cudaEglFrame_st* _pvt_ptr
+    cdef anon_union11 _frame
+
+cdef class CUuuid(CUuuid_st):
+    """
+    Attributes
+    ----------
+    bytes : bytes
+        < CUDA definition of UUID
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaUUID_t(CUuuid_st):
+    """
+    Attributes
+    ----------
+    bytes : bytes
+        < CUDA definition of UUID
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaIpcEventHandle_t(cudaIpcEventHandle_st):
+    """
+    CUDA IPC event handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaIpcMemHandle_t(cudaIpcMemHandle_st):
+    """
+    CUDA IPC memory handle
+
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaMemFabricHandle_t(cudaMemFabricHandle_st):
+    """
+    Attributes
+    ----------
+    reserved : bytes
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaDevSmResourceGroupParams(cudaDevSmResourceGroupParams_st):
+    """
+    Input data for splitting SMs
+
+    Attributes
+    ----------
+    smCount : unsigned int
+        The amount of SMs available in this resource.
+    coscheduledSmCount : unsigned int
+        The amount of co-scheduled SMs grouped together for locality
+        purposes.
+    preferredCoscheduledSmCount : unsigned int
+        When possible, combine co-scheduled groups together into larger
+        groups of this size.
+    flags : unsigned int
+        Combination of `cudaDevSmResourceGroup_flags` values to indicate
+        this this group is created.
+    reserved : list[unsigned int]
+        Reserved for future use - ensure this is is zero initialized.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaDevResource(cudaDevResource_st):
+    """
+    A tagged union describing different resources identified by the
+    type field. This structure should not be directly modified outside
+    of the API that created it. struct enumcudaDevResourceTypetype;
+    union structcudaDevSmResourcesm;
+    structcudaDevWorkqueueConfigResourcewqConfig;
+    structcudaDevWorkqueueResourcewq; ; ;  - If `typename` is
+    `cudaDevResourceTypeInvalid`, this resoure is not valid and cannot
+    be further accessed.    - If `typename` is `cudaDevResourceTypeSm`,
+    the cudaDevSmResource structure `sm` is filled in. For example,
+    `sm.smCount` will reflect the amount of streaming multiprocessors
+    available in this resource.    - If `typename` is
+    `cudaDevResourceTypeWorkqueueConfig`, the
+    cudaDevWorkqueueConfigResource structure `wqConfig` is filled in.
+    - If `typename` is `cudaDevResourceTypeWorkqueue`, the
+    cudaDevWorkqueueResource structure `wq` is filled in.
+
+    Attributes
+    ----------
+    type : cudaDevResourceType
+        Type of resource, dictates which union field was last set
+    _internal_padding : bytes
+
+    sm : cudaDevSmResource
+        Resource corresponding to cudaDevResourceTypeSm `typename`.
+    wqConfig : cudaDevWorkqueueConfigResource
+        Resource corresponding to cudaDevResourceTypeWorkqueueConfig
+        `typename`.
+    wq : cudaDevWorkqueueResource
+        Resource corresponding to cudaDevResourceTypeWorkqueue `typename`.
+    _oversize : bytes
+
+    nextResource : cudaDevResource_st
+
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaGraphEdgeData(cudaGraphEdgeData_st):
+    """
+    Optional annotation for edges in a CUDA graph. Note, all edges
+    implicitly have annotations and default to a zero-initialized value
+    if not specified. A zero-initialized struct indicates a standard
+    full serialization of two nodes with memory visibility.
+
+    Attributes
+    ----------
+    from_port : bytes
+        This indicates when the dependency is triggered from the upstream
+        node on the edge. The meaning is specfic to the node type. A value
+        of 0 in all cases means full completion of the upstream node, with
+        memory visibility to the downstream node or portion thereof
+        (indicated by `to_port`).   Only kernel nodes define non-zero
+        ports. A kernel node can use the following output port types:
+        cudaGraphKernelNodePortDefault,
+        cudaGraphKernelNodePortProgrammatic, or
+        cudaGraphKernelNodePortLaunchCompletion.
+    to_port : bytes
+        This indicates what portion of the downstream node is dependent on
+        the upstream node or portion thereof (indicated by `from_port`).
+        The meaning is specific to the node type. A value of 0 in all cases
+        means the entirety of the downstream node is dependent on the
+        upstream work.   Currently no node types define non-zero ports.
+        Accordingly, this field must be set to zero.
+    type : bytes
+        This should be populated with a value from
+        ::cudaGraphDependencyType. (It is typed as char due to compiler-
+        specific layout of bitfields.) See ::cudaGraphDependencyType.
+    reserved : bytes
+        These bytes are unused and must be zeroed. This ensures
+        compatibility if additional fields are added in the future.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaGraphInstantiateParams(cudaGraphInstantiateParams_st):
+    """
+    Graph instantiation parameters
+
+    Attributes
+    ----------
+    flags : unsigned long long
+        Instantiation flags
+    uploadStream : cudaStream_t
+        Upload stream
+    errNode_out : cudaGraphNode_t
+        The node which caused instantiation to fail, if any
+    result_out : cudaGraphInstantiateResult
+        Whether instantiation was successful. If it failed, the reason why
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaGraphExecUpdateResultInfo(cudaGraphExecUpdateResultInfo_st):
+    """
+    Result information returned by cudaGraphExecUpdate
+
+    Attributes
+    ----------
+    result : cudaGraphExecUpdateResult
+        Gives more specific detail when a cuda graph update fails.
+    errorNode : cudaGraphNode_t
+        The "to node" of the error edge when the topologies do not match.
+        The error node when the error is associated with a specific node.
+        NULL when the error is generic.
+    errorFromNode : cudaGraphNode_t
+        The from node of error edge when the topologies do not match.
+        Otherwise NULL.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaLaunchMemSyncDomainMap(cudaLaunchMemSyncDomainMap_st):
+    """
+    Memory Synchronization Domain map  See cudaLaunchMemSyncDomain.  By
+    default, kernels are launched in domain 0. Kernel launched with
+    cudaLaunchMemSyncDomainRemote will have a different domain ID. User
+    may also alter the domain ID with ::cudaLaunchMemSyncDomainMap for
+    a specific stream / graph node / kernel launch. See
+    cudaLaunchAttributeMemSyncDomainMap.  Domain ID range is available
+    through cudaDevAttrMemSyncDomainCount.
+
+    Attributes
+    ----------
+    default_ : bytes
+        The default domain ID to use for designated kernels
+    remote : bytes
+        The remote domain ID to use for designated kernels
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaLaunchAttribute(cudaLaunchAttribute_st):
+    """
+    Launch attribute
+
+    Attributes
+    ----------
+    id : cudaLaunchAttributeID
+        Attribute to set
+    val : cudaLaunchAttributeValue
+        Value of the attribute
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaAsyncNotificationInfo_t(cudaAsyncNotificationInfo):
+    """
+    Information describing an async notification event
+
+    Attributes
+    ----------
+    type : cudaAsyncNotificationType
+        The type of notification being sent
+    info : anon_union10
+        Information about the notification. `typename` must be checked in
+        order to interpret this field.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaStreamAttrValue(cudaLaunchAttributeValue):
+    """
+    Launch attributes union; used as value field of
+    ::cudaLaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : cudaAccessPolicyWindow
+        Value of launch attribute cudaLaunchAttributeAccessPolicyWindow.
+    cooperative : int
+        Value of launch attribute cudaLaunchAttributeCooperative. Nonzero
+        indicates a cooperative kernel (see cudaLaunchCooperativeKernel).
+    syncPolicy : cudaSynchronizationPolicy
+        Value of launch attribute cudaLaunchAttributeSynchronizationPolicy.
+        ::cudaSynchronizationPolicy for work queued up in this stream.
+    clusterDim : anon_struct17
+        Value of launch attribute cudaLaunchAttributeClusterDimension that
+        represents the desired cluster dimensions for the kernel. Opaque
+        type with the following fields: - `x` - The X dimension of the
+        cluster, in blocks. Must be a divisor of the grid X dimension.    -
+        `y` - The Y dimension of the cluster, in blocks. Must be a divisor
+        of the grid Y dimension.    - `z` - The Z dimension of the cluster,
+        in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : cudaClusterSchedulingPolicy
+        Value of launch attribute
+        cudaLaunchAttributeClusterSchedulingPolicyPreference. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        cudaLaunchAttributeProgrammaticStreamSerialization.
+    programmaticEvent : anon_struct18
+        Value of launch attribute cudaLaunchAttributeProgrammaticEvent with
+        the following fields: - `cudaEvent_t` event - Event to fire when
+        all blocks trigger it.    - `int` flags; - Event record flags, see
+        cudaEventRecordWithFlags. Does not accept cudaEventRecordExternal.
+        - `int` triggerAtBlockStart - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    priority : int
+        Value of launch attribute cudaLaunchAttributePriority. Execution
+        priority of the kernel.
+    memSyncDomainMap : cudaLaunchMemSyncDomainMap
+        Value of launch attribute cudaLaunchAttributeMemSyncDomainMap. See
+        ::cudaLaunchMemSyncDomainMap.
+    memSyncDomain : cudaLaunchMemSyncDomain
+        Value of launch attribute cudaLaunchAttributeMemSyncDomain. See
+        cudaLaunchMemSyncDomain.
+    preferredClusterDim : anon_struct19
+        Value of launch attribute
+        cudaLaunchAttributePreferredClusterDimension that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        cudaLaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        cudaLaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        cudaLaunchAttributeValue::clusterDim.
+    launchCompletionEvent : anon_struct20
+        Value of launch attribute cudaLaunchAttributeLaunchCompletionEvent
+        with the following fields: - `cudaEvent_t` event - Event to fire
+        when the last block launches.    - `int` flags - Event record
+        flags, see cudaEventRecordWithFlags. Does not accept
+        cudaEventRecordExternal.
+    deviceUpdatableKernelNode : anon_struct21
+        Value of launch attribute
+        cudaLaunchAttributeDeviceUpdatableKernelNode with the following
+        fields: - `int` deviceUpdatable - Whether or not the resulting
+        kernel node should be device-updatable.    -
+        `cudaGraphDeviceNode_t` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        cudaLaunchAttributePreferredSharedMemoryCarveout.
+    nvlinkUtilCentricScheduling : unsigned int
+        Value of launch attribute
+        cudaLaunchAttributeNvlinkUtilCentricScheduling.
+    portableClusterSizeMode : cudaLaunchAttributePortableClusterMode
+        Value of launch attribute
+        cudaLaunchAttributePortableClusterSizeMode
+    sharedMemoryMode : cudaSharedMemoryMode
+        Value of launch attribute cudaLaunchAttributeSharedMemoryMode. See
+        cudaSharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaKernelNodeAttrValue(cudaLaunchAttributeValue):
+    """
+    Launch attributes union; used as value field of
+    ::cudaLaunchAttribute
+
+    Attributes
+    ----------
+    pad : bytes
+
+    accessPolicyWindow : cudaAccessPolicyWindow
+        Value of launch attribute cudaLaunchAttributeAccessPolicyWindow.
+    cooperative : int
+        Value of launch attribute cudaLaunchAttributeCooperative. Nonzero
+        indicates a cooperative kernel (see cudaLaunchCooperativeKernel).
+    syncPolicy : cudaSynchronizationPolicy
+        Value of launch attribute cudaLaunchAttributeSynchronizationPolicy.
+        ::cudaSynchronizationPolicy for work queued up in this stream.
+    clusterDim : anon_struct17
+        Value of launch attribute cudaLaunchAttributeClusterDimension that
+        represents the desired cluster dimensions for the kernel. Opaque
+        type with the following fields: - `x` - The X dimension of the
+        cluster, in blocks. Must be a divisor of the grid X dimension.    -
+        `y` - The Y dimension of the cluster, in blocks. Must be a divisor
+        of the grid Y dimension.    - `z` - The Z dimension of the cluster,
+        in blocks. Must be a divisor of the grid Z dimension.
+    clusterSchedulingPolicyPreference : cudaClusterSchedulingPolicy
+        Value of launch attribute
+        cudaLaunchAttributeClusterSchedulingPolicyPreference. Cluster
+        scheduling policy preference for the kernel.
+    programmaticStreamSerializationAllowed : int
+        Value of launch attribute
+        cudaLaunchAttributeProgrammaticStreamSerialization.
+    programmaticEvent : anon_struct18
+        Value of launch attribute cudaLaunchAttributeProgrammaticEvent with
+        the following fields: - `cudaEvent_t` event - Event to fire when
+        all blocks trigger it.    - `int` flags; - Event record flags, see
+        cudaEventRecordWithFlags. Does not accept cudaEventRecordExternal.
+        - `int` triggerAtBlockStart - If this is set to non-0, each block
+        launch will automatically trigger the event.
+    priority : int
+        Value of launch attribute cudaLaunchAttributePriority. Execution
+        priority of the kernel.
+    memSyncDomainMap : cudaLaunchMemSyncDomainMap
+        Value of launch attribute cudaLaunchAttributeMemSyncDomainMap. See
+        ::cudaLaunchMemSyncDomainMap.
+    memSyncDomain : cudaLaunchMemSyncDomain
+        Value of launch attribute cudaLaunchAttributeMemSyncDomain. See
+        cudaLaunchMemSyncDomain.
+    preferredClusterDim : anon_struct19
+        Value of launch attribute
+        cudaLaunchAttributePreferredClusterDimension that represents the
+        desired preferred cluster dimensions for the kernel. Opaque type
+        with the following fields: - `x` - The X dimension of the preferred
+        cluster, in blocks. Must be a divisor of the grid X dimension, and
+        must be a multiple of the `x` field of
+        cudaLaunchAttributeValue::clusterDim.    - `y` - The Y dimension of
+        the preferred cluster, in blocks. Must be a divisor of the grid Y
+        dimension, and must be a multiple of the `y` field of
+        cudaLaunchAttributeValue::clusterDim.    - `z` - The Z dimension of
+        the preferred cluster, in blocks. Must be equal to the `z` field of
+        cudaLaunchAttributeValue::clusterDim.
+    launchCompletionEvent : anon_struct20
+        Value of launch attribute cudaLaunchAttributeLaunchCompletionEvent
+        with the following fields: - `cudaEvent_t` event - Event to fire
+        when the last block launches.    - `int` flags - Event record
+        flags, see cudaEventRecordWithFlags. Does not accept
+        cudaEventRecordExternal.
+    deviceUpdatableKernelNode : anon_struct21
+        Value of launch attribute
+        cudaLaunchAttributeDeviceUpdatableKernelNode with the following
+        fields: - `int` deviceUpdatable - Whether or not the resulting
+        kernel node should be device-updatable.    -
+        `cudaGraphDeviceNode_t` devNode - Returns a handle to pass to the
+        various device-side update functions.
+    sharedMemCarveout : unsigned int
+        Value of launch attribute
+        cudaLaunchAttributePreferredSharedMemoryCarveout.
+    nvlinkUtilCentricScheduling : unsigned int
+        Value of launch attribute
+        cudaLaunchAttributeNvlinkUtilCentricScheduling.
+    portableClusterSizeMode : cudaLaunchAttributePortableClusterMode
+        Value of launch attribute
+        cudaLaunchAttributePortableClusterSizeMode
+    sharedMemoryMode : cudaSharedMemoryMode
+        Value of launch attribute cudaLaunchAttributeSharedMemoryMode. See
+        cudaSharedMemoryMode for acceptable values.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaEglPlaneDesc(cudaEglPlaneDesc_st):
+    """
+    CUDA EGL Plane Descriptor - structure defining each plane of a CUDA
+    EGLFrame
+
+    Attributes
+    ----------
+    width : unsigned int
+        Width of plane
+    height : unsigned int
+        Height of plane
+    depth : unsigned int
+        Depth of plane
+    pitch : unsigned int
+        Pitch of plane
+    numChannels : unsigned int
+        Number of channels for the plane
+    channelDesc : cudaChannelFormatDesc
+        Channel Format Descriptor
+    reserved : list[unsigned int]
+        Reserved for future use
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaEglFrame(cudaEglFrame_st):
+    """
+    CUDA EGLFrame Descriptor - structure defining one frame of EGL.
+    Each frame may contain one or more planes depending on whether the
+    surface is Multiplanar or not. Each plane of EGLFrame is
+    represented by cudaEglPlaneDesc which is defined as:
+    typedefstructcudaEglPlaneDesc_st unsignedintwidth;
+    unsignedintheight; unsignedintdepth; unsignedintpitch;
+    unsignedintnumChannels; structcudaChannelFormatDescchannelDesc;
+    unsignedintreserved[4]; cudaEglPlaneDesc;
+
+    Attributes
+    ----------
+    frame : anon_union11
+
+    planeDesc : list[cudaEglPlaneDesc]
+        CUDA EGL Plane Descriptor cudaEglPlaneDesc
+    planeCount : unsigned int
+        Number of planes
+    frameType : cudaEglFrameType
+        Array or Pitch
+    eglColorFormat : cudaEglColorFormat
+        CUDA EGL Color Format
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+    """
+    pass
+
+cdef class cudaStream_t(driver.CUstream):
+    """
+
+    CUDA stream
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaEvent_t(driver.CUevent):
+    """
+
+    CUDA event types
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaGraph_t(driver.CUgraph):
+    """
+
+    CUDA graph
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaGraphNode_t(driver.CUgraphNode):
+    """
+
+    CUDA graph node.
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaUserObject_t(driver.CUuserObject):
+    """
+
+    CUDA user object for graphs
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaFunction_t(driver.CUfunction):
+    """
+
+    CUDA function
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaMemPool_t(driver.CUmemoryPool):
+    """
+
+    CUDA memory pool
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaGraphExec_t(driver.CUgraphExec):
+    """
+
+    CUDA executable (launchable) graph
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaEglStreamConnection(driver.CUeglStreamConnection):
+    """
+
+    CUDA EGLSream Connection
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    pass
+
+cdef class cudaGraphConditionalHandle:
+    """
+
+    CUDA handle for conditional graph nodes
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaGraphConditionalHandle  _pvt_val
+    cdef cyruntime.cudaGraphConditionalHandle* _pvt_ptr
+
+cdef class cudaLogIterator:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaLogIterator  _pvt_val
+    cdef cyruntime.cudaLogIterator* _pvt_ptr
+
+cdef class cudaSurfaceObject_t:
+    """
+
+    An opaque value that represents a CUDA Surface object
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaSurfaceObject_t  _pvt_val
+    cdef cyruntime.cudaSurfaceObject_t* _pvt_ptr
+
+cdef class cudaTextureObject_t:
+    """
+
+    An opaque value that represents a CUDA texture object
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.cudaTextureObject_t  _pvt_val
+    cdef cyruntime.cudaTextureObject_t* _pvt_ptr
+
+cdef class GLenum:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.GLenum  _pvt_val
+    cdef cyruntime.GLenum* _pvt_ptr
+
+cdef class GLuint:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.GLuint  _pvt_val
+    cdef cyruntime.GLuint* _pvt_ptr
+
+cdef class EGLint:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.EGLint  _pvt_val
+    cdef cyruntime.EGLint* _pvt_ptr
+
+cdef class VdpDevice:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.VdpDevice  _pvt_val
+    cdef cyruntime.VdpDevice* _pvt_ptr
+
+cdef class VdpGetProcAddress:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.VdpGetProcAddress  _pvt_val
+    cdef cyruntime.VdpGetProcAddress* _pvt_ptr
+
+cdef class VdpVideoSurface:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.VdpVideoSurface  _pvt_val
+    cdef cyruntime.VdpVideoSurface* _pvt_ptr
+
+cdef class VdpOutputSurface:
+    """
+
+    Methods
+    -------
+    getPtr()
+        Get memory address of class instance
+
+    """
+    cdef cyruntime.VdpOutputSurface  _pvt_val
+    cdef cyruntime.VdpOutputSurface* _pvt_ptr
diff --git a/lib/python3.11/site-packages/cuda/bindings/utils/__init__.py b/lib/python3.11/site-packages/cuda/bindings/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4fcb15e8fddada5370a828ed46500c01ed8de0d
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/utils/__init__.py
@@ -0,0 +1,31 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+from typing import Any, Callable
+
+from ._ptx_utils import get_minimal_required_cuda_ver_from_ptx_ver, get_ptx_ver
+from ._version_check import warn_if_cuda_major_version_mismatch
+
+_handle_getters: dict[type, Callable[[Any], int]] = {}
+
+
+def _add_cuda_native_handle_getter(t: type, getter: Callable[[Any], int]) -> None:
+    _handle_getters[t] = getter
+
+
+def get_cuda_native_handle(obj: Any) -> int:
+    """Returns the address of the provided CUDA Python object as a Python int.
+
+    Parameters
+    ----------
+    obj : Any
+        CUDA Python object
+
+    Returns
+    -------
+    int : The object address.
+    """
+    obj_type = type(obj)
+    try:
+        return _handle_getters[obj_type](obj)
+    except KeyError:
+        raise TypeError("Unknown type: " + str(obj_type)) from None
diff --git a/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..059adf529f4d14ec24f400b218241f2bc658f31d
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/_ptx_utils.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/_ptx_utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..88401edbe4f5cbacfa786c53e5ecaf972cff14a5
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/_ptx_utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/_version_check.cpython-311.pyc b/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/_version_check.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1e3775b794e8374957a11990d80ea30aa8a9b3f6
Binary files /dev/null and b/lib/python3.11/site-packages/cuda/bindings/utils/__pycache__/_version_check.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/cuda/bindings/utils/_ptx_utils.py b/lib/python3.11/site-packages/cuda/bindings/utils/_ptx_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9df6503120a8a920dea0e0248566da47f21b844
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/utils/_ptx_utils.py
@@ -0,0 +1,134 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+import re
+
+# Mapping based on the official PTX ISA <-> CUDA Release table
+# https://docs.nvidia.com/cuda/parallel-thread-execution/#release-notes-ptx-release-history
+_ptx_to_cuda = {
+    "1.0": (1, 0),
+    "1.1": (1, 1),
+    "1.2": (2, 0),
+    "1.3": (2, 1),
+    "1.4": (2, 2),
+    "2.0": (3, 0),
+    "2.1": (3, 1),
+    "2.2": (3, 2),
+    "2.3": (4, 0),
+    "3.0": (4, 1),
+    "3.1": (5, 0),
+    "3.2": (5, 5),
+    "4.0": (6, 0),
+    "4.1": (6, 5),
+    "4.2": (7, 0),
+    "4.3": (7, 5),
+    "5.0": (8, 0),
+    "6.0": (9, 0),
+    "6.1": (9, 1),
+    "6.2": (9, 2),
+    "6.3": (10, 0),
+    "6.4": (10, 1),
+    "6.5": (10, 2),
+    "7.0": (11, 0),
+    "7.1": (11, 1),
+    "7.2": (11, 2),
+    "7.3": (11, 3),
+    "7.4": (11, 4),
+    "7.5": (11, 5),
+    "7.6": (11, 6),
+    "7.7": (11, 7),
+    "7.8": (11, 8),
+    "8.0": (12, 0),
+    "8.1": (12, 1),
+    "8.2": (12, 2),
+    "8.3": (12, 3),
+    "8.4": (12, 4),
+    "8.5": (12, 5),
+    "8.6": (12, 7),
+    "8.7": (12, 8),
+    "8.8": (12, 9),
+    "9.0": (13, 0),
+    "9.1": (13, 1),
+    "9.2": (13, 2),
+}
+
+
+def get_minimal_required_cuda_ver_from_ptx_ver(ptx_version: str) -> int:
+    """
+    Maps the PTX ISA version to the minimal CUDA driver, nvPTXCompiler, or nvJitLink version
+    that is needed to load a PTX of the given ISA version.
+
+    Parameters
+    ----------
+    ptx_version : str
+        PTX ISA version as a string, e.g. "8.8" for PTX ISA 8.8. This is the ``.version``
+        directive in the PTX header.
+
+    Returns
+    -------
+    int
+        Minimal CUDA version as 1000 * major + 10 * minor, e.g. 12090 for CUDA 12.9.
+
+    Raises
+    ------
+    ValueError
+        If the PTX version is unknown.
+
+    Examples
+    --------
+    >>> get_minimal_required_driver_ver_from_ptx_ver("8.8")
+    12090
+    >>> get_minimal_required_driver_ver_from_ptx_ver("7.0")
+    11000
+    """
+    try:
+        major, minor = _ptx_to_cuda[ptx_version]
+        return 1000 * major + 10 * minor
+    except KeyError:
+        raise ValueError(f"Unknown or unsupported PTX ISA version: {ptx_version}") from None
+
+
+# Regex pattern to match .version directive and capture the version number
+# TODO: if import speed is a concern, consider lazy-initializing it.
+_ptx_ver_pattern = re.compile(r"\.version\s+([0-9]+\.[0-9]+)")
+
+
+def get_ptx_ver(ptx: str) -> str:
+    """
+    Extract the PTX ISA version string from PTX source code.
+
+    Parameters
+    ----------
+    ptx : str
+        The PTX assembly source code as a string.
+
+    Returns
+    -------
+    str
+        The PTX ISA version string, e.g., "8.8".
+
+    Raises
+    ------
+    ValueError
+        If the .version directive is not found in the PTX source.
+
+    Examples
+    --------
+    >>> ptx = r'''
+    ... .version 8.8
+    ... .target sm_86
+    ... .address_size 64
+    ...
+    ... .visible .entry test_kernel()
+    ... {
+    ...     ret;
+    ... }
+    ... '''
+    >>> get_ptx_ver(ptx)
+    '8.8'
+    """
+    m = _ptx_ver_pattern.search(ptx)
+    if m:
+        return m.group(1)
+    else:
+        raise ValueError("No .version directive found in PTX source. Is it a valid PTX?")
diff --git a/lib/python3.11/site-packages/cuda/bindings/utils/_version_check.py b/lib/python3.11/site-packages/cuda/bindings/utils/_version_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ebd7f3d924208619676dd724371adadc504b881
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda/bindings/utils/_version_check.py
@@ -0,0 +1,61 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+import os
+import threading
+import warnings
+
+# Track whether we've already checked major version compatibility
+_major_version_compatibility_checked = False
+_lock = threading.Lock()
+
+
+def warn_if_cuda_major_version_mismatch():
+    """Warn if the CUDA driver major version is older than cuda-bindings compile-time version.
+
+    This function compares the CUDA major version that cuda-bindings was compiled
+    against with the CUDA major version supported by the installed driver. If the
+    compile-time major version is greater than the driver's major version, a warning
+    is issued.
+
+    The check runs only once per process. Subsequent calls are no-ops.
+
+    The warning can be suppressed by setting the environment variable
+    ``CUDA_PYTHON_DISABLE_MAJOR_VERSION_WARNING=1``.
+    """
+    global _major_version_compatibility_checked
+    if _major_version_compatibility_checked:
+        return
+    with _lock:
+        if _major_version_compatibility_checked:
+            return
+        _major_version_compatibility_checked = True
+
+    # Allow users to suppress the warning
+    if os.environ.get("CUDA_PYTHON_DISABLE_MAJOR_VERSION_WARNING"):
+        return
+
+    # Import here to avoid circular imports and allow lazy loading
+    from cuda.bindings import driver
+
+    # Get compile-time CUDA version from cuda-bindings
+    compile_version = driver.CUDA_VERSION  # e.g., 13010
+    compile_major = compile_version // 1000
+
+    # Get runtime driver version
+    err, runtime_version = driver.cuDriverGetVersion()
+    if err != driver.CUresult.CUDA_SUCCESS:
+        raise RuntimeError(f"Failed to query CUDA driver version: {err}")
+
+    runtime_major = runtime_version // 1000
+
+    if compile_major > runtime_major:
+        warnings.warn(
+            f"cuda-bindings was built for CUDA major version {compile_major}, but the "
+            f"NVIDIA driver only supports up to CUDA {runtime_major}. Some cuda-bindings "
+            f"features may not work correctly. Consider updating your NVIDIA driver, "
+            f"or using a cuda-bindings version built for CUDA {runtime_major}. "
+            f"(Set CUDA_PYTHON_DISABLE_MAJOR_VERSION_WARNING=1 to suppress this warning.)",
+            UserWarning,
+            stacklevel=3,
+        )
diff --git a/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/INSTALLER b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/METADATA b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..3828df1b3590a55c1b9706574b3222ecb9f4d2c9
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/METADATA
@@ -0,0 +1,46 @@
+Metadata-Version: 2.4
+Name: cuda-bindings
+Version: 13.2.0
+Summary: Python bindings for CUDA
+Author-email: NVIDIA Corporation <cuda-python-conduct@nvidia.com>
+License-Expression: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+Project-URL: Repository, https://github.com/NVIDIA/cuda-python
+Project-URL: Documentation, https://nvidia.github.io/cuda-python/
+Classifier: Intended Audience :: Developers
+Classifier: Topic :: Database
+Classifier: Topic :: Scientific/Engineering
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Environment :: GPU :: NVIDIA CUDA
+Requires-Python: >=3.10
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: cuda-pathfinder~=1.1
+Provides-Extra: all
+Requires-Dist: cuda-toolkit[nvfatbin,nvjitlink,nvrtc,nvvm]==13.*; extra == "all"
+Requires-Dist: cuda-toolkit[cufile]==13.*; sys_platform == "linux" and extra == "all"
+Dynamic: license-file
+
+.. SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+.. SPDX-License-Identifier: LicenseRef-NVIDIA-SOFTWARE-LICENSE
+
+****************************************
+cuda-bindings: Low-level CUDA interfaces
+****************************************
+
+.. image:: https://img.shields.io/badge/NVIDIA-black?logo=nvidia
+   :target: https://www.nvidia.com/
+   :alt: NVIDIA
+
+`cuda.bindings <https://nvidia.github.io/cuda-python/cuda-bindings/>`_ is a standard set of low-level interfaces, providing full coverage of and 1:1 access to the CUDA host APIs from Python. Checkout the `Overview <https://nvidia.github.io/cuda-python/cuda-bindings/latest/overview.html>`_ for the workflow and performance results.
+
+* `Repository <https://github.com/NVIDIA/cuda-python/tree/main/cuda_bindings>`_
+* `Documentation <https://nvidia.github.io/cuda-python/cuda-bindings/>`_
+* `Examples <https://github.com/NVIDIA/cuda-python/tree/main/cuda_bindings/examples>`_
+* `Issue tracker <https://github.com/NVIDIA/cuda-python/issues/>`_
+
+For the installation instruction, please refer to the `Installation <https://nvidia.github.io/cuda-python/cuda-bindings/latest/install.html>`_ page.
diff --git a/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/RECORD b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..ea04301affb51e97f2293f022990c3f7b10bfeed
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/RECORD
@@ -0,0 +1,93 @@
+cuda/bindings/__init__.pxd,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+cuda/bindings/__init__.py,sha256=MwaiGzsnjQRwjtwFMGS36SnuaElYmFDh_VOT5vAvzhI,246
+cuda/bindings/__pycache__/__init__.cpython-311.pyc,,
+cuda/bindings/__pycache__/_version.cpython-311.pyc,,
+cuda/bindings/_bindings/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+cuda/bindings/_bindings/__pycache__/__init__.cpython-311.pyc,,
+cuda/bindings/_bindings/cydriver.cpython-311-x86_64-linux-gnu.so,sha256=DFU3CmE96G9u3WGsF1aoG7oS3GoDUM55MWxHouA8vN4,467496
+cuda/bindings/_bindings/cydriver.pxd,sha256=Q_y7-jIEt7fWWCfYqXQZyeDiHPE-5uIXSYMUJflx1jo,68771
+cuda/bindings/_bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so,sha256=nG-T3jDxjNvrmvU-_98NXAD0dI46jVtP9tjYSM5TCCg,82600
+cuda/bindings/_bindings/cynvrtc.pxd,sha256=Wo9QkstkK7t2ElKgRcpwe-0CRpO5jW2LLa6tyLYGD_I,3297
+cuda/bindings/_bindings/cyruntime.cpython-311-x86_64-linux-gnu.so,sha256=84pbe7hJ2PRUhPdJpIIdNmL6WDI42gllZlUxtZO5qe8,933776
+cuda/bindings/_bindings/cyruntime.pxd,sha256=UiIClazo-MgORJ2aAai8eqpJ6I59BuulfQKHYp1sxCo,51329
+cuda/bindings/_bindings/cyruntime_ptds.cpython-311-x86_64-linux-gnu.so,sha256=yuO2aB9ecMQu6kKGmtG3cONYTG-it_HWIXW9oKCd4Gc,791632
+cuda/bindings/_bindings/cyruntime_ptds.pxd,sha256=9yYJOapFGTXpVPTCYbZ9We1DlL83L99RZdLunC-ptXg,51371
+cuda/bindings/_bindings/loader.h,sha256=nv8OzG_tSP4jszgK5o4-ELSW6K1iZEwaEekIECjP1dA,545
+cuda/bindings/_bindings/loader.pxd,sha256=vBMdQU-VrGguCEW58Y8V2HRzaUh5DDC0NkNWLJ_silI,252
+cuda/bindings/_internal/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+cuda/bindings/_internal/__pycache__/__init__.cpython-311.pyc,,
+cuda/bindings/_internal/__pycache__/_fast_enum.cpython-311.pyc,,
+cuda/bindings/_internal/_fast_enum.py,sha256=kuBsYOZ-dKPLegjNdeFIGiX79zGapvmjDEfQo_SZZmA,2331
+cuda/bindings/_internal/cufile.cpython-311-x86_64-linux-gnu.so,sha256=ZvSwb81Czzsi57z1-moYxiZKUCJx9n28J-kJVjYa0oA,103432
+cuda/bindings/_internal/cufile.pxd,sha256=xfyg-AsFaZPGSB4sYyyvbWfsE1Cpa9gcvo8ZnQBLOys,5985
+cuda/bindings/_internal/nvfatbin.cpython-311-x86_64-linux-gnu.so,sha256=Rm-0ZqVnrbr4QDI5QDtPEJWzR4XVYG-zLGUzMVGNMzQ,70312
+cuda/bindings/_internal/nvfatbin.pxd,sha256=-d4BC_Hoqggc6EcTB85gYLMHz9YNqWujZiACsLjCW7A,2224
+cuda/bindings/_internal/nvjitlink.cpython-311-x86_64-linux-gnu.so,sha256=QVhT5O6iAB0lm3hYYCfLofYeIKKCHXkRa1Xcm8gu8wM,74408
+cuda/bindings/_internal/nvjitlink.pxd,sha256=kCt6Qqwi11RNNT7akUxNE8RgYwyST4lzjO6r2b0Ms2Y,2507
+cuda/bindings/_internal/nvml.cpython-311-x86_64-linux-gnu.so,sha256=wxpNb0VEzNcgTD5UPFOmJDfSgwahE02RAxEacCzJ2ZA,449896
+cuda/bindings/_internal/nvml.pxd,sha256=yACnUUOAAzJ-uOnSqtouTA3McwtMdyw2000Zt84n31U,56153
+cuda/bindings/_internal/nvvm.cpython-311-x86_64-linux-gnu.so,sha256=cN0KXMt7YtHl_xYoKD70ONzw7MqQ_RyAD1caozvPPpo,74408
+cuda/bindings/_internal/nvvm.pxd,sha256=l9723vL2xP3iQoNlY9rbJVyEvf0rOhprWzLAngQEmg8,2131
+cuda/bindings/_internal/utils.cpython-311-x86_64-linux-gnu.so,sha256=XUC6fRA42VobDrrPje06462-_prs6a-j1PoI4Ve8dRs,127968
+cuda/bindings/_internal/utils.pxd,sha256=LlAp7vAy5_7XqZ8IIc0dCW0duu01V3aPt8seqgx37VM,4669
+cuda/bindings/_lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+cuda/bindings/_lib/__pycache__/__init__.cpython-311.pyc,,
+cuda/bindings/_lib/cyruntime/cyruntime.pxd,sha256=icR65C-YpWJ59uyCRMRg_wGbqNGt6qPQLh2ksFkuTis,4785
+cuda/bindings/_lib/cyruntime/cyruntime.pxi,sha256=xQGllvLx76JE8WEeKS3LjOungvwj335nP6QNmKulr8c,88191
+cuda/bindings/_lib/dlfcn.pxd,sha256=Hey780RLtX6IQwca7kzsBnoMJ6CsizjfaKtcY240GrE,406
+cuda/bindings/_lib/param_packer.h,sha256=ITzzEtJ4ykPY1Py7_SPlf8u1gY9tUlAelvQ6Z3b0vkc,6031
+cuda/bindings/_lib/param_packer.pxd,sha256=9dAhnMLzwYbdDhdiTW6EFXRPfIE5wvfnKwoZTVIhjgs,374
+cuda/bindings/_lib/utils.pxd,sha256=WxVRuS8qEv9Bn7JNAoL6vswIlMzVhJhsEAWW-1TVWPg,3799
+cuda/bindings/_lib/utils.pxi,sha256=m_ExKSMLe3-SUSIeI0pk5l399Cu9vZbI7AXvyIga3I4,31414
+cuda/bindings/_lib/windll.pxd,sha256=oOzOOJ_hpQNVnqareRx3h_ctJI_zpebBDfaRhxtpSk8,1445
+cuda/bindings/_test_helpers/__init__.py,sha256=cgHj5gSAhwMG-0TGW_v4V-rrqaPQqtshVyKh_WiQpEg,401
+cuda/bindings/_test_helpers/__pycache__/__init__.cpython-311.pyc,,
+cuda/bindings/_test_helpers/__pycache__/arch_check.cpython-311.pyc,,
+cuda/bindings/_test_helpers/arch_check.py,sha256=5JybKYuYW2h6wxcyUG5Erp9LIYKUp-58KnvLqaxsVXs,2515
+cuda/bindings/_version.py,sha256=YmFawMiMsMMopNM_zwbeX2OsUEwuEcxqVTzLAximZ_s,714
+cuda/bindings/cufile.cpython-311-x86_64-linux-gnu.so,sha256=X7Ld85g0J_Rv1TGnbG6SrsZdmaDxno1ogkHRKlLlMXU,765736
+cuda/bindings/cufile.pxd,sha256=OYdWHkVy-yXxJPo_NkcPRsqe5YU81ohUICMnPv7vuBE,3907
+cuda/bindings/cycufile.cpython-311-x86_64-linux-gnu.so,sha256=Itpk1IhWfv_4ynpAK6oVErVvfKz7eho8KxMG1SuLWN4,32320
+cuda/bindings/cycufile.pxd,sha256=Gm_O-vlSmwsxW04z6JZyuggA1j9kcQXGrtGPSyOO67E,18282
+cuda/bindings/cydriver.cpython-311-x86_64-linux-gnu.so,sha256=Cj7K1pquyzLmfN0SnsV5ni9TNcKnzmr2aj8eXMfAUnc,158272
+cuda/bindings/cydriver.pxd,sha256=Z7yWt_UQHuxVUq1o60SHpRKyR0dXkq2Ky_aP2YlkyIw,167883
+cuda/bindings/cynvfatbin.cpython-311-x86_64-linux-gnu.so,sha256=D377qTKblbM_CSRrpKAKa2N2vm5VqSCVn_1ui9j5tmk,23424
+cuda/bindings/cynvfatbin.pxd,sha256=P1a8P27aWe_CG62kEyPJxnCUU0YTykC_WZ8X26MFFKI,3834
+cuda/bindings/cynvjitlink.cpython-311-x86_64-linux-gnu.so,sha256=_68mkU909GnrjIyWpta86v7U0VXSNGL1yThFYodoomA,23424
+cuda/bindings/cynvjitlink.pxd,sha256=bemcE-jn-T_4kDTiTRKDQwipqnIHIQtsILQGzyBrevU,4688
+cuda/bindings/cynvml.cpython-311-x86_64-linux-gnu.so,sha256=SN0E0QaG26FkhVYIhtFyAIFN8vVwux1jyK0x6oC2fbQ,119040
+cuda/bindings/cynvml.pxd,sha256=6yvtCJdW3nphEUiMbR9TU2qIKHI3_igycaomck9GuLw,143031
+cuda/bindings/cynvrtc.cpython-311-x86_64-linux-gnu.so,sha256=ZjH5coPas3Lu6WajkXxuoBO-2JqC4RGEAK8N1RWe0z4,27520
+cuda/bindings/cynvrtc.pxd,sha256=_Z60qpczpJKLmBlhyNsMBnFdfDghfQiDyI7YyunIQz8,4228
+cuda/bindings/cynvvm.cpython-311-x86_64-linux-gnu.so,sha256=ITXlCgBopt9iXQZDuKynrnMtc9vRCAUhMkj16wx31X8,23424
+cuda/bindings/cynvvm.pxd,sha256=dyeql6-YjzOwzUaL0U4HvxHDGgSMomy7ynSRi98JLUY,3131
+cuda/bindings/cyruntime.cpython-311-x86_64-linux-gnu.so,sha256=-3QmlbkFswXim4cZ6-U_skKFRkwlbVatQjLIe4cDWwU,123200
+cuda/bindings/cyruntime.pxd,sha256=1_ZCuRPHCYA99HYsso0CSduC8jCN3YoV2TxGUHwntQI,64867
+cuda/bindings/cyruntime_functions.pxi,sha256=CuFzgwlbVfky7aR7K7RVmPPfHeLxHgE8QEhK16eE0XY,38120
+cuda/bindings/cyruntime_types.pxi,sha256=LQ3cnvX6aoTc2uaVdxTgdOGx21a1lkvtSufI2XSya8c,57832
+cuda/bindings/driver.cpython-311-x86_64-linux-gnu.so,sha256=Y1CMTJftsHzLTEDtjmv1wGdpoB9NfeKsgako0az2Zrg,7425568
+cuda/bindings/driver.pxd,sha256=60F-C8zO3DRRtDxJwYr_H1wbYHlLTJx1J_nXcP0qhMs,215545
+cuda/bindings/nvfatbin.cpython-311-x86_64-linux-gnu.so,sha256=DOIU-hFWvQmd7VTafPPiHctlMO9Fy7OShA-buU6vqm8,107752
+cuda/bindings/nvfatbin.pxd,sha256=abT8l_ivuJIKAhkrNUmSPqZZ_qEsuTab2SeAbXwxEjE,1512
+cuda/bindings/nvjitlink.cpython-311-x86_64-linux-gnu.so,sha256=dlhm-nqLzKkyMx20oLBcpxwgL8VqXk8Sk0Qzh6GDCCg,107848
+cuda/bindings/nvjitlink.pxd,sha256=18kkQ37F4qhP7bSV2bNZPaDLgj7zq-kefVQJUd2v0as,1675
+cuda/bindings/nvml.cpython-311-x86_64-linux-gnu.so,sha256=vKsDSQfgdwBzKjFqw9oQz5a4Sf1VbWKKeLPFfFVVWuM,4553864
+cuda/bindings/nvml.pxd,sha256=sdXyWvqrVdwYpXRf6RMa-SoYjSLKtBijF3En9hsQBjk,27138
+cuda/bindings/nvrtc.cpython-311-x86_64-linux-gnu.so,sha256=6p8oSvQ52VrlAznFlqHmwlTjv14HtRfQ6NwVu6zdciY,407904
+cuda/bindings/nvrtc.pxd,sha256=y70KzZ9RSfbPdQuMgwh6wIJL5S5xPZah3D8badAnd1o,786
+cuda/bindings/nvvm.cpython-311-x86_64-linux-gnu.so,sha256=bu6yJIXfQM-vXl6gZLFsMrvRm6waJMnyrm3N1C5HWfQ,99624
+cuda/bindings/nvvm.pxd,sha256=9cRYfaRYN-C8hYZLYc322on9UhwBGOsqjGff9Hq6XIs,1561
+cuda/bindings/runtime.cpython-311-x86_64-linux-gnu.so,sha256=KR7KPIYNO7PdOxN3HYRTqkzU0yr5UYq_jYS2poeQL-s,5464992
+cuda/bindings/runtime.pxd,sha256=hyJPpmaIRsZCiHzvPHCAzIFy5XwbOJWvqkGGcxqgImw,110880
+cuda/bindings/utils/__init__.py,sha256=ZBltMPUI7GQbJLp_5mnRb8LkrgGKoiJFXZmTCyXUNnM,942
+cuda/bindings/utils/__pycache__/__init__.cpython-311.pyc,,
+cuda/bindings/utils/__pycache__/_ptx_utils.cpython-311.pyc,,
+cuda/bindings/utils/__pycache__/_version_check.cpython-311.pyc,,
+cuda/bindings/utils/_ptx_utils.py,sha256=XsRSXDVnuFfQEF-xDcbU_A2LRZg_CdKZscMzuRw-ZTY,3225
+cuda/bindings/utils/_version_check.py,sha256=Wr-o20azz3b2wiWUrhUyzwd3RHEFBMpkbgz-6sPf_mE,2397
+cuda_bindings-13.2.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+cuda_bindings-13.2.0.dist-info/METADATA,sha256=OPFWg1a0gffJ_ISgNrrF__6YluqHVt4GEesTycDFqmk,2329
+cuda_bindings-13.2.0.dist-info/RECORD,,
+cuda_bindings-13.2.0.dist-info/WHEEL,sha256=eXcvYd-ZI2r9mJXN0bRPXPWgNEoR8Hvd23X3eVjySso,152
+cuda_bindings-13.2.0.dist-info/licenses/LICENSE,sha256=gHsN94kFVQ3-fC6hFBTLdOonjuxnrXrsJxtPC3MAy9M,11076
+cuda_bindings-13.2.0.dist-info/top_level.txt,sha256=U5vpnvwNpaJF8bl4KnoUluMDTRt0J972FipwXjgNQ3A,5
diff --git a/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/WHEEL b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..9a74c42ca259210d6a4a297a5e743e91472e1af4
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (13.2.0)
+Root-Is-Purelib: false
+Tag: cp311-cp311-manylinux_2_24_x86_64
+Tag: cp311-cp311-manylinux_2_28_x86_64
+
diff --git a/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/licenses/LICENSE b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a5a65097cdc5e452ffd79974f1d7621c06ddbd1f
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/licenses/LICENSE
@@ -0,0 +1,48 @@
+NVIDIA SOFTWARE LICENSE
+
+This license is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs your use of the NVIDIA CUDA Python software and materials provided hereunder ("SOFTWARE").
+
+This license can be accepted only by an adult of legal age of majority in the country in which the SOFTWARE is used. If you are under the legal age of majority, you must ask your parent or legal guardian to consent to this license. By taking delivery of the SOFTWARE, you affirm that you have reached the legal age of majority, you accept the terms of this license, and you take legal and financial responsibility for the actions of your permitted users.
+
+You agree to use the SOFTWARE only for purposes that are permitted by (a) this license, and (b) any applicable law, regulation or generally accepted practices or guidelines in the relevant jurisdictions.
+
+1. LICENSE. Subject to the terms of this license, NVIDIA grants you a non-exclusive limited license to: (a) install and use the SOFTWARE, and (b) distribute the SOFTWARE subject to the distribution requirements described in this license. NVIDIA reserves all rights, title and interest in and to the SOFTWARE not expressly granted to you under this license.
+
+2. DISTRIBUTION REQUIREMENTS. These are the distribution requirements for you to exercise the distribution grant:
+a.  The terms under which you distribute the SOFTWARE must be consistent with the terms of this license, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA's intellectual property rights.
+b.  You agree to notify NVIDIA in writing of any known or suspected distribution or use of the SOFTWARE not in compliance with the requirements of this license, and to enforce the terms of your agreements with respect to distributed SOFTWARE.
+
+3. LIMITATIONS. Your license to use the SOFTWARE is restricted as follows:
+a.  The SOFTWARE is licensed for you to develop applications only for use in systems with NVIDIA GPUs.
+b.  You may not reverse engineer, decompile or disassemble, or remove copyright or other proprietary notices from any portion of the SOFTWARE or copies of the SOFTWARE.
+c.  You may not modify or create derivative works of any portion of the SOFTWARE.
+d.  You may not bypass, disable, or circumvent any technical measure, encryption, security, digital rights management or authentication mechanism in the SOFTWARE.
+e.  You may not use the SOFTWARE in any manner that would cause it to become subject to an open source software license. As examples, licenses that require as a condition of use, modification, and/or distribution that the SOFTWARE be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge.
+f.  Unless you have an agreement with NVIDIA for this purpose, you may not use the SOFTWARE with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SOFTWARE for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses.
+g.  You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney's fees and costs incident to establishing the right of indemnification) arising out of or related to use of the SOFTWARE outside of the scope of this Agreement, or not in compliance with its terms.
+
+4. PRE-RELEASE. SOFTWARE versions identified as alpha, beta, preview, early access or otherwise as pre-release may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, availability, and reliability standards relative to commercial versions of NVIDIA software and materials. You may use a pre-release SOFTWARE version at your own risk, understanding that these versions are not intended for use in production or business-critical systems.
+
+5. OWNERSHIP. The SOFTWARE and the related intellectual property rights therein are and will remain the sole and exclusive property of NVIDIA or its licensors. The SOFTWARE is copyrighted and protected by the laws of the United States and other countries, and international treaty provisions. NVIDIA may make changes to the SOFTWARE, at any time without notice, but is not obligated to support or update the SOFTWARE.
+
+6. COMPONENTS UNDER OTHER LICENSES. The SOFTWARE may include NVIDIA or third-party components with separate legal notices or terms as may be described in proprietary notices accompanying the SOFTWARE. If and to the extent there is a conflict between the terms in this license and the license terms associated with a component, the license terms associated with the components control only to the extent necessary to resolve the conflict.
+
+7. FEEDBACK. You may, but don't have to, provide to NVIDIA any Feedback. "Feedback" means any suggestions, bug fixes, enhancements, modifications, feature requests or other feedback regarding the SOFTWARE. For any Feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) the Feedback without the payment of any royalties or fees to you. NVIDIA will use Feedback at its choice.
+
+8. NO WARRANTIES. THE SOFTWARE IS PROVIDED "AS IS" WITHOUT ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND INCLUDING, BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY, NONINFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. NVIDIA DOES NOT WARRANT THAT THE SOFTWARE WILL MEET YOUR REQUIREMENTS OR THAT THE OPERATION THEREOF WILL BE UNINTERRUPTED OR ERROR-FREE, OR THAT ALL ERRORS WILL BE CORRECTED.
+
+9. LIMITATIONS OF LIABILITY. TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, PROJECT DELAYS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS LICENSE OR THE USE OR PERFORMANCE OF THE SOFTWARE, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY, EVEN IF NVIDIA HAS PREVIOUSLY BEEN ADVISED OF, OR COULD REASONABLY HAVE FORESEEN, THE POSSIBILITY OF SUCH DAMAGES. IN NO EVENT WILL NVIDIA'S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS LICENSE EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT.
+
+10. TERMINATION. Your rights under this license will terminate automatically without notice from NVIDIA if you fail to comply with any term and condition of this license or if you commence or participate in any legal proceeding against NVIDIA with respect to the SOFTWARE. NVIDIA may terminate this license with advance written notice to you if NVIDIA decides to no longer provide the SOFTWARE in a country or, in NVIDIA's sole discretion, the continued use of it is no longer commercially viable. Upon any termination of this license, you agree to promptly discontinue use of the SOFTWARE and destroy all copies in your possession or control. Your prior distributions in accordance with this license are not affected by the termination of this license. All provisions of this license will survive termination, except for the license granted to you.
+
+11. APPLICABLE LAW. This license will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language. The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this license. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.
+
+12. NO ASSIGNMENT. This license and your rights and obligations thereunder may not be assigned by you by any means or operation of law without NVIDIA's permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect.
+
+13. EXPORT. The SOFTWARE is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SOFTWARE into any country, or use the SOFTWARE in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury's Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this license, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SOFTWARE.
+
+14. GOVERNMENT USE. The SOFTWARE has been developed entirely at private expense and is "commercial items" consisting of "commercial computer software" and "commercial computer software documentation" provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this license pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (b)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051.
+
+15. ENTIRE AGREEMENT. This license is the final, complete and exclusive agreement between the parties relating to the subject matter of this license and supersedes all prior or contemporaneous understandings and agreements relating to this subject matter, whether oral or written. If any court of competent jurisdiction determines that any provision of this license is illegal, invalid or unenforceable, the remaining provisions will remain in full force and effect. This license may only be modified in a writing signed by an authorized representative of each party.
+
+(v. May 12, 2021)
diff --git a/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/top_level.txt b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..089f044f442ab95aabf9de9eb52b63efbac9f7b9
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_bindings-13.2.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+cuda
diff --git a/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/INSTALLER b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/METADATA b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..3bee15e4cb53f574005642319f6282e65d1b017a
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/METADATA
@@ -0,0 +1,47 @@
+Metadata-Version: 2.4
+Name: cuda-pathfinder
+Version: 1.5.4
+Summary: Pathfinder for CUDA components
+Author-email: NVIDIA Corporation <cuda-python-conduct@nvidia.com>
+License-Expression: Apache-2.0
+Project-URL: Repository, https://github.com/NVIDIA/cuda-python
+Project-URL: Documentation, https://nvidia.github.io/cuda-python/
+Requires-Python: >=3.10
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Dynamic: license-file
+
+.. SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+.. SPDX-License-Identifier: Apache-2.0
+
+*******************************************************
+cuda-pathfinder: Utilities for locating CUDA components
+*******************************************************
+
+.. image:: https://img.shields.io/badge/NVIDIA-black?logo=nvidia
+   :target: https://www.nvidia.com/
+   :alt: NVIDIA
+
+`cuda.pathfinder <https://nvidia.github.io/cuda-python/cuda-pathfinder/>`_
+aims to be a one-stop solution for locating CUDA components. Currently
+it supports locating and loading dynamic libraries (``.so``, ``.dll``), and
+locating CTK header directories. Support for other artifacts is in progress.
+
+* `Documentation <https://nvidia.github.io/cuda-python/cuda-pathfinder/>`_
+* `Releases <https://nvidia.github.io/cuda-python/cuda-pathfinder/latest/release.html>`_
+* `Repository <https://github.com/NVIDIA/cuda-python/tree/main/cuda_pathfinder/>`_
+* `Issue tracker <https://github.com/NVIDIA/cuda-python/issues/>`_ (select component ``cuda.pathfinder``)
+
+``cuda.pathfinder`` is under active development. Feedback and suggestions are welcome.
+
+
+Installation
+============
+
+.. code-block:: bash
+
+   pip install cuda-pathfinder
+
+``cuda-pathfinder`` is `CUDA Toolkit (CTK) <https://developer.nvidia.com/cuda-toolkit>`_
+version-agnostic. It follows the general CUDA Toolkit support policy: the
+two most recent major versions are supported simultaneously.
diff --git a/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/RECORD b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..fe14dcdf612e72b8ed2c15f4ef46b5dc208bb68c
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/RECORD
@@ -0,0 +1,65 @@
+cuda/pathfinder/README.md,sha256=JPbUfq3xYFMGa1ZlnpHkKsGXjJi4Zr1nNU3SzWJIviw,123
+cuda/pathfinder/__init__.py,sha256=N88Ky8JCENHM0Bj5rv9kKKMswPm2BIWG2uhQIuE41ns,4203
+cuda/pathfinder/__pycache__/__init__.cpython-311.pyc,,
+cuda/pathfinder/__pycache__/_optional_cuda_import.cpython-311.pyc,,
+cuda/pathfinder/__pycache__/_version.cpython-311.pyc,,
+cuda/pathfinder/_binaries/__pycache__/find_nvidia_binary_utility.cpython-311.pyc,,
+cuda/pathfinder/_binaries/__pycache__/supported_nvidia_binaries.cpython-311.pyc,,
+cuda/pathfinder/_binaries/find_nvidia_binary_utility.py,sha256=Kosg5OLyX5BWW66yie6kWW8muHUsqKkk4cc7OVLWITs,4296
+cuda/pathfinder/_binaries/supported_nvidia_binaries.py,sha256=E6ed_ZL8EQy-zJYNjsUQC0hGBSN4zDgaMbFaEeca89M,1409
+cuda/pathfinder/_dynamic_libs/__pycache__/descriptor_catalog.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/dynamic_lib_subprocess.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/lib_descriptor.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/load_dl_common.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/load_dl_linux.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/load_dl_windows.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/load_nvidia_dynamic_lib.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/platform_loader.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/search_platform.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/search_steps.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/subprocess_protocol.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/__pycache__/supported_nvidia_libs.cpython-311.pyc,,
+cuda/pathfinder/_dynamic_libs/descriptor_catalog.py,sha256=iAJ35crcUDI0IU6zGWG1zLV_JYh6MjjKgZtBcYNnMS8,16315
+cuda/pathfinder/_dynamic_libs/dynamic_lib_subprocess.py,sha256=-jgshHMUUXwvPt49K1R4GcXMK0XhgNRRtyKKFczsoIc,5165
+cuda/pathfinder/_dynamic_libs/lib_descriptor.py,sha256=4TuEXVowm8Oq149M_tQvTgjWJGTQ_AJgouLz-x0I-Wg,753
+cuda/pathfinder/_dynamic_libs/load_dl_common.py,sha256=tQfuSm0VKEUzaYU7Y4HEHuHyU_Z4zYU2QQBYIxlvoU8,957
+cuda/pathfinder/_dynamic_libs/load_dl_linux.py,sha256=LOfpTbaZFUefoh-Uj8vahKeiYMjLz_wJJo3kx5MWNXE,8133
+cuda/pathfinder/_dynamic_libs/load_dl_windows.py,sha256=Y0-vxlvkA9GwEu0c1uHRE4EHOceKDbmcsYpZyNxS4gI,6441
+cuda/pathfinder/_dynamic_libs/load_nvidia_dynamic_lib.py,sha256=jXdnsB-605f1c6sValudTwI6vsr2M3MbfDevuFb7XLg,11815
+cuda/pathfinder/_dynamic_libs/platform_loader.py,sha256=9dwZFmVlWxsnmlpH3w23B99JxE1O-h0hFJKoiRiRbIE,1490
+cuda/pathfinder/_dynamic_libs/search_platform.py,sha256=eb7f1ayFpTgtbAdo5pst82wivaktkXwSEBNXhrXqylY,8250
+cuda/pathfinder/_dynamic_libs/search_steps.py,sha256=vMHCamMaLFQkn2w6KFGx8o82R4mG0JJAG4Dwc9deF7M,8122
+cuda/pathfinder/_dynamic_libs/subprocess_protocol.py,sha256=bieP_U7UujgbePzxvykszai1Xpn9tueO_qVv62_8CQA,2713
+cuda/pathfinder/_dynamic_libs/supported_nvidia_libs.py,sha256=L1IYyAzaIbV3CLPhTactV_YEGDoBcsLY7GAQhUAv7AM,4419
+cuda/pathfinder/_headers/__pycache__/find_nvidia_headers.cpython-311.pyc,,
+cuda/pathfinder/_headers/__pycache__/header_descriptor.cpython-311.pyc,,
+cuda/pathfinder/_headers/__pycache__/header_descriptor_catalog.cpython-311.pyc,,
+cuda/pathfinder/_headers/__pycache__/supported_nvidia_headers.cpython-311.pyc,,
+cuda/pathfinder/_headers/find_nvidia_headers.py,sha256=AzA2__iiJ47IRUknB-9qymPBEIfJnUZXe7NgW03giMs,8429
+cuda/pathfinder/_headers/header_descriptor.py,sha256=fdU_rg293TQqYxRJRPaHrPa1a5WDhgjHqTlGwAUNIcA,2172
+cuda/pathfinder/_headers/header_descriptor_catalog.py,sha256=1Zc_XN_K-LJtfctezu11gn4vEfR83y1pSqrc-311nYI,7499
+cuda/pathfinder/_headers/supported_nvidia_headers.py,sha256=DT5JHhBY59Q4CJz4iCMLcHyMRNM8rgesjYfGpX8FITs,3519
+cuda/pathfinder/_optional_cuda_import.py,sha256=1Vjsv3DvBmz7-yqeaxHD-TDe-Fzwdyu88S1df-CfRBo,1424
+cuda/pathfinder/_static_libs/__pycache__/find_bitcode_lib.cpython-311.pyc,,
+cuda/pathfinder/_static_libs/__pycache__/find_static_lib.cpython-311.pyc,,
+cuda/pathfinder/_static_libs/find_bitcode_lib.py,sha256=Ke0b3KmApVeF6QjVw5r020L6B_DiPG8qvvsdFRacuRo,6006
+cuda/pathfinder/_static_libs/find_static_lib.py,sha256=DhCLQLDUZ-a9X2ez6isSVgbwlSfWKHWx_PM0Vh4fd8I,5748
+cuda/pathfinder/_utils/__pycache__/driver_info.cpython-311.pyc,,
+cuda/pathfinder/_utils/__pycache__/env_vars.cpython-311.pyc,,
+cuda/pathfinder/_utils/__pycache__/find_site_packages_dll.cpython-311.pyc,,
+cuda/pathfinder/_utils/__pycache__/find_site_packages_so.cpython-311.pyc,,
+cuda/pathfinder/_utils/__pycache__/find_sub_dirs.cpython-311.pyc,,
+cuda/pathfinder/_utils/__pycache__/platform_aware.cpython-311.pyc,,
+cuda/pathfinder/_utils/driver_info.py,sha256=KYg8EXMdlJg9tn0VGk0cVzURholCHnFQAcyhGGcVV5g,3118
+cuda/pathfinder/_utils/env_vars.py,sha256=9VspktZnl7oQyGHVP85n61Snj6eXSqbqs6aiQsYxzqg,3813
+cuda/pathfinder/_utils/find_site_packages_dll.py,sha256=HixAy-Ubd5Gu9DZwCLAu2jvlEr1nfolOSZ_7GX3ZgbM,1143
+cuda/pathfinder/_utils/find_site_packages_so.py,sha256=_udOAI8RtTXRJWz-X6TUy-g9W_nW8PeAcG61k1AVSLw,1638
+cuda/pathfinder/_utils/find_sub_dirs.py,sha256=lr2Ls7COGIorf8H3snxLm66ClXkPNYb5f7g5ZBl5yBQ,2614
+cuda/pathfinder/_utils/platform_aware.py,sha256=oOCqy_VyNdEdISVM2ilde8Kl6rOeElItvsAnw0WD9b8,463
+cuda/pathfinder/_version.py,sha256=_S-t0YZNl_Vr7jaxF8oKdU6ukZIQDfvvY8oTOBVp45M,528
+cuda_pathfinder-1.5.4.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+cuda_pathfinder-1.5.4.dist-info/METADATA,sha256=2IMIfDnr-Ydd1gRF6K5AdxQX9D9jr4VH969rwNcYjL0,1906
+cuda_pathfinder-1.5.4.dist-info/RECORD,,
+cuda_pathfinder-1.5.4.dist-info/WHEEL,sha256=aeYiig01lYGDzBgS8HxWXOg3uV61G9ijOsup-k9o1sk,91
+cuda_pathfinder-1.5.4.dist-info/licenses/LICENSE,sha256=DVQuDIgE45qn836wDaWnYhSdxoLXgpRRKH4RuTjpRZQ,10174
+cuda_pathfinder-1.5.4.dist-info/top_level.txt,sha256=U5vpnvwNpaJF8bl4KnoUluMDTRt0J972FipwXjgNQ3A,5
diff --git a/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/WHEEL b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..14a883f292bc96b20c2b76a3081991f2676523a9
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (82.0.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/licenses/LICENSE b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/licenses/LICENSE
@@ -0,0 +1,177 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
diff --git a/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/top_level.txt b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..089f044f442ab95aabf9de9eb52b63efbac9f7b9
--- /dev/null
+++ b/lib/python3.11/site-packages/cuda_pathfinder-1.5.4.dist-info/top_level.txt
@@ -0,0 +1 @@
+cuda
diff --git a/lib/python3.11/site-packages/filelock/_api.py b/lib/python3.11/site-packages/filelock/_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5793c2e39f266536359496e7989f866499b8601
--- /dev/null
+++ b/lib/python3.11/site-packages/filelock/_api.py
@@ -0,0 +1,582 @@
+from __future__ import annotations
+
+import contextlib
+import inspect
+import logging
+import os
+import pathlib
+import sys
+import time
+import warnings
+from abc import ABCMeta, abstractmethod
+from dataclasses import dataclass
+from threading import local
+from typing import TYPE_CHECKING, Any, TypeVar
+from weakref import WeakValueDictionary
+
+from ._error import Timeout
+
+#: Sentinel indicating that no explicit file permission mode was passed.
+#: When used, lock files are created with 0o666 (letting umask and default ACLs control the final permissions)
+#: and fchmod is skipped so that POSIX default ACL inheritance is preserved.
+_UNSET_FILE_MODE: int = -1
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+    from types import TracebackType
+
+    from ._read_write import ReadWriteLock
+    from ._soft_rw import SoftReadWriteLock
+
+    if sys.version_info >= (3, 11):  # pragma: no cover (py311+)
+        from typing import Self
+    else:  # pragma: no cover (<py311)
+        from typing_extensions import Self
+
+
+_LOGGER = logging.getLogger("filelock")
+
+# On Windows os.path.realpath calls CreateFileW with share_mode=0, which blocks concurrent DeleteFileW and causes
+# livelocks under threaded contention with SoftFileLock. os.path.abspath is purely string-based and avoids this.
+_canonical = os.path.abspath if sys.platform == "win32" else os.path.realpath
+
+
+class _ThreadLocalRegistry(local):
+    def __init__(self) -> None:
+        super().__init__()
+        self.held: dict[str, int] = {}
+
+
+_registry = _ThreadLocalRegistry()
+
+
+# This is a helper class which is returned by :meth:`BaseFileLock.acquire` and wraps the lock to make sure __enter__
+# is not called twice when entering the with statement. If we would simply return *self*, the lock would be acquired
+# again in the *__enter__* method of the BaseFileLock, but not released again automatically. issue #37 (memory leak)
+class AcquireReturnProxy:
+    """A context-aware object that will release the lock file when exiting."""
+
+    def __init__(self, lock: BaseFileLock | ReadWriteLock | SoftReadWriteLock) -> None:
+        self.lock: BaseFileLock | ReadWriteLock | SoftReadWriteLock = lock
+
+    def __enter__(self) -> BaseFileLock | ReadWriteLock | SoftReadWriteLock:
+        return self.lock
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.lock.release()
+
+
+@dataclass
+class FileLockContext:
+    """A dataclass which holds the context for a ``BaseFileLock`` object."""
+
+    # The context is held in a separate class to allow optional use of thread local storage via the
+    # ThreadLocalFileContext class.
+
+    #: The path to the lock file.
+    lock_file: str
+
+    #: The default timeout value.
+    timeout: float
+
+    #: The mode for the lock files
+    mode: int
+
+    #: Whether the lock should be blocking or not
+    blocking: bool
+
+    #: The default polling interval value.
+    poll_interval: float
+
+    #: The lock lifetime in seconds; ``None`` means the lock never expires.
+    lifetime: float | None = None
+
+    #: The file descriptor for the *_lock_file* as it is returned by the os.open() function, not None when lock held
+    lock_file_fd: int | None = None
+
+    #: The lock counter is used for implementing the nested locking mechanism.
+    lock_counter: int = 0  # When the lock is acquired is increased and the lock is only released, when this value is 0
+
+
+class ThreadLocalFileContext(FileLockContext, local):
+    """A thread local version of the ``FileLockContext`` class."""
+
+
+_T = TypeVar("_T", bound="BaseFileLock")
+
+
+class FileLockMeta(ABCMeta):
+    _instances: WeakValueDictionary[str, BaseFileLock]
+
+    def __call__(  # noqa: PLR0913
+        cls: type[_T],
+        lock_file: str | os.PathLike[str],
+        timeout: float = -1,
+        mode: int = _UNSET_FILE_MODE,
+        thread_local: bool = True,  # noqa: FBT001, FBT002
+        *,
+        blocking: bool = True,
+        is_singleton: bool = False,
+        poll_interval: float = 0.05,
+        lifetime: float | None = None,
+        **kwargs: Any,  # capture remaining kwargs for subclasses  # noqa: ANN401
+    ) -> _T:
+        if is_singleton:
+            instance = cls._instances.get(str(lock_file))
+            if instance:
+                params_to_check = {
+                    "thread_local": (thread_local, instance.is_thread_local()),
+                    "timeout": (timeout, instance.timeout),
+                    "mode": (mode, instance._context.mode),  # noqa: SLF001
+                    "blocking": (blocking, instance.blocking),
+                    "poll_interval": (poll_interval, instance.poll_interval),
+                    "lifetime": (lifetime, instance.lifetime),
+                }
+
+                non_matching_params = {
+                    name: (passed_param, set_param)
+                    for name, (passed_param, set_param) in params_to_check.items()
+                    if passed_param != set_param
+                }
+                if not non_matching_params:
+                    return instance  # ty: ignore[invalid-return-type]  # https://github.com/astral-sh/ty/issues/3231
+
+                # parameters do not match; raise error
+                msg = "Singleton lock instances cannot be initialized with differing arguments"
+                msg += "\nNon-matching arguments: "
+                for param_name, (passed_param, set_param) in non_matching_params.items():
+                    msg += f"\n\t{param_name} (existing lock has {set_param} but {passed_param} was passed)"
+                raise ValueError(msg)
+
+        # Workaround to make `__init__`'s params optional in subclasses
+        # E.g. virtualenv changes the signature of the `__init__` method in the `BaseFileLock` class descendant
+        # (https://github.com/tox-dev/filelock/pull/340)
+
+        all_params = {
+            "timeout": timeout,
+            "mode": mode,
+            "thread_local": thread_local,
+            "blocking": blocking,
+            "is_singleton": is_singleton,
+            "poll_interval": poll_interval,
+            "lifetime": lifetime,
+            **kwargs,
+        }
+
+        present_params = inspect.signature(cls.__init__).parameters
+        init_params = {key: value for key, value in all_params.items() if key in present_params}
+
+        instance = super().__call__(lock_file, **init_params)
+
+        if is_singleton:
+            cls._instances[str(lock_file)] = instance
+
+        return instance
+
+
+class BaseFileLock(contextlib.ContextDecorator, metaclass=FileLockMeta):
+    """
+    Abstract base class for a file lock object.
+
+    Provides a reentrant, cross-process exclusive lock backed by OS-level primitives. Subclasses implement the actual
+    locking mechanism (:class:`UnixFileLock <filelock.UnixFileLock>`, :class:`WindowsFileLock
+    <filelock.WindowsFileLock>`, :class:`SoftFileLock <filelock.SoftFileLock>`).
+
+    """
+
+    _instances: WeakValueDictionary[str, BaseFileLock]
+
+    def __init_subclass__(cls, **kwargs: dict[str, Any]) -> None:
+        """Setup unique state for lock subclasses."""
+        super().__init_subclass__(**kwargs)
+        cls._instances = WeakValueDictionary()
+
+    def __init__(  # noqa: PLR0913
+        self,
+        lock_file: str | os.PathLike[str],
+        timeout: float = -1,
+        mode: int = _UNSET_FILE_MODE,
+        thread_local: bool = True,  # noqa: FBT001, FBT002
+        *,
+        blocking: bool = True,
+        is_singleton: bool = False,
+        poll_interval: float = 0.05,
+        lifetime: float | None = None,
+    ) -> None:
+        """
+        Create a new lock object.
+
+        :param lock_file: path to the file
+        :param timeout: default timeout when acquiring the lock, in seconds. It will be used as fallback value in the
+            acquire method, if no timeout value (``None``) is given. If you want to disable the timeout, set it to a
+            negative value. A timeout of 0 means that there is exactly one attempt to acquire the file lock.
+        :param mode: file permissions for the lockfile. When not specified, the OS controls permissions via umask and
+            default ACLs, preserving POSIX default ACL inheritance in shared directories.
+        :param thread_local: Whether this object's internal context should be thread local or not. If this is set to
+            ``False`` then the lock will be reentrant across threads.
+        :param blocking: whether the lock should be blocking or not
+        :param is_singleton: If this is set to ``True`` then only one instance of this class will be created per lock
+            file. This is useful if you want to use the lock object for reentrant locking without needing to pass the
+            same object around.
+        :param poll_interval: default interval for polling the lock file, in seconds. It will be used as fallback value
+            in the acquire method, if no poll_interval value (``None``) is given.
+        :param lifetime: maximum time in seconds a lock can be held before it is considered expired. When set, a waiting
+            process will break a lock whose file modification time is older than ``lifetime`` seconds. ``None`` (the
+            default) means locks never expire.
+
+        """
+        self._is_thread_local = thread_local
+        self._is_singleton = is_singleton
+
+        # Create the context. Note that external code should not work with the context directly and should instead use
+        # properties of this class.
+        kwargs: dict[str, Any] = {
+            "lock_file": os.fspath(lock_file),
+            "timeout": timeout,
+            "mode": mode,
+            "blocking": blocking,
+            "poll_interval": poll_interval,
+            "lifetime": lifetime,
+        }
+        self._context: FileLockContext = (ThreadLocalFileContext if thread_local else FileLockContext)(**kwargs)
+
+    def is_thread_local(self) -> bool:
+        """:returns: a flag indicating if this lock is thread local or not"""
+        return self._is_thread_local
+
+    @property
+    def is_singleton(self) -> bool:
+        """
+        :returns: a flag indicating if this lock is singleton or not
+
+        .. versionadded:: 3.13.0
+
+        """
+        return self._is_singleton
+
+    @property
+    def lock_file(self) -> str:
+        """:returns: path to the lock file"""
+        return self._context.lock_file
+
+    @property
+    def timeout(self) -> float:
+        """
+        :returns: the default timeout value, in seconds
+
+        .. versionadded:: 2.0.0
+
+        """
+        return self._context.timeout
+
+    @timeout.setter
+    def timeout(self, value: float | str) -> None:
+        """
+        Change the default timeout value.
+
+        :param value: the new value, in seconds
+
+        """
+        self._context.timeout = float(value)
+
+    @property
+    def blocking(self) -> bool:
+        """
+        :returns: whether the locking is blocking or not
+
+        .. versionadded:: 3.14.0
+
+        """
+        return self._context.blocking
+
+    @blocking.setter
+    def blocking(self, value: bool) -> None:
+        """
+        Change the default blocking value.
+
+        :param value: the new value as bool
+
+        """
+        self._context.blocking = value
+
+    @property
+    def poll_interval(self) -> float:
+        """
+        :returns: the default polling interval, in seconds
+
+        .. versionadded:: 3.24.0
+
+        """
+        return self._context.poll_interval
+
+    @poll_interval.setter
+    def poll_interval(self, value: float) -> None:
+        """
+        Change the default polling interval.
+
+        :param value: the new value, in seconds
+
+        """
+        self._context.poll_interval = value
+
+    @property
+    def lifetime(self) -> float | None:
+        """
+        :returns: the lock lifetime in seconds, or ``None`` if the lock never expires
+
+        .. versionadded:: 3.24.0
+
+        """
+        return self._context.lifetime
+
+    @lifetime.setter
+    def lifetime(self, value: float | None) -> None:
+        """
+        Change the lock lifetime.
+
+        :param value: the new value in seconds, or ``None`` to disable expiration
+
+        """
+        self._context.lifetime = value
+
+    @property
+    def mode(self) -> int:
+        """:returns: the file permissions for the lockfile"""
+        return 0o644 if self._context.mode == _UNSET_FILE_MODE else self._context.mode
+
+    @property
+    def has_explicit_mode(self) -> bool:
+        """:returns: whether the file permissions were explicitly set"""
+        return self._context.mode != _UNSET_FILE_MODE
+
+    def _open_mode(self) -> int:
+        """:returns: the mode for os.open() — 0o666 when unset (let umask/ACLs decide), else the explicit mode"""
+        return 0o666 if self._context.mode == _UNSET_FILE_MODE else self._context.mode
+
+    def _try_break_expired_lock(self) -> None:
+        """Remove the lock file if its modification time exceeds the configured :attr:`lifetime`."""
+        if (lifetime := self._context.lifetime) is None:
+            return
+        with contextlib.suppress(OSError):
+            if time.time() - pathlib.Path(self.lock_file).stat().st_mtime < lifetime:
+                return
+            break_path = f"{self.lock_file}.break.{os.getpid()}"
+            pathlib.Path(self.lock_file).rename(break_path)
+            pathlib.Path(break_path).unlink()
+
+    @abstractmethod
+    def _acquire(self) -> None:
+        """If the file lock could be acquired, self._context.lock_file_fd holds the file descriptor of the lock file."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def _release(self) -> None:
+        """Releases the lock and sets self._context.lock_file_fd to None."""
+        raise NotImplementedError
+
+    @property
+    def is_locked(self) -> bool:
+        """
+        :returns: A boolean indicating if the lock file is holding the lock currently.
+
+        .. versionchanged:: 2.0.0
+
+            This was previously a method and is now a property.
+
+        """
+        return self._context.lock_file_fd is not None
+
+    @property
+    def lock_counter(self) -> int:
+        """:returns: The number of times this lock has been acquired (but not yet released)."""
+        return self._context.lock_counter
+
+    @staticmethod
+    def _check_give_up(  # noqa: PLR0913
+        lock_id: int,
+        lock_filename: str,
+        *,
+        blocking: bool,
+        cancel_check: Callable[[], bool] | None,
+        timeout: float,
+        start_time: float,
+    ) -> bool:
+        if blocking is False:
+            _LOGGER.debug("Failed to immediately acquire lock %s on %s", lock_id, lock_filename)
+            return True
+        if cancel_check is not None and cancel_check():
+            _LOGGER.debug("Cancellation requested for lock %s on %s", lock_id, lock_filename)
+            return True
+        if 0 <= timeout < time.perf_counter() - start_time:
+            _LOGGER.debug("Timeout on acquiring lock %s on %s", lock_id, lock_filename)
+            return True
+        return False
+
+    def acquire(  # noqa: C901
+        self,
+        timeout: float | None = None,
+        poll_interval: float | None = None,
+        *,
+        poll_intervall: float | None = None,
+        blocking: bool | None = None,
+        cancel_check: Callable[[], bool] | None = None,
+    ) -> AcquireReturnProxy:
+        """
+        Try to acquire the file lock.
+
+        :param timeout: maximum wait time for acquiring the lock, ``None`` means use the default :attr:`~timeout` is and
+            if ``timeout < 0``, there is no timeout and this method will block until the lock could be acquired
+        :param poll_interval: interval of trying to acquire the lock file, ``None`` means use the default
+            :attr:`~poll_interval`
+        :param poll_intervall: deprecated, kept for backwards compatibility, use ``poll_interval`` instead
+        :param blocking: defaults to True. If False, function will return immediately if it cannot obtain a lock on the
+            first attempt. Otherwise, this method will block until the timeout expires or the lock is acquired.
+        :param cancel_check: a callable returning ``True`` when the acquisition should be canceled. Checked on each poll
+            iteration. When triggered, raises :class:`~Timeout` just like an expired timeout.
+
+        :returns: a context object that will unlock the file when the context is exited
+
+        :raises Timeout: if fails to acquire lock within the timeout period
+
+        .. code-block:: python
+
+            # You can use this method in the context manager (recommended)
+            with lock.acquire():
+                pass
+
+            # Or use an equivalent try-finally construct:
+            lock.acquire()
+            try:
+                pass
+            finally:
+                lock.release()
+
+        .. versionchanged:: 2.0.0
+
+            This method returns now a *proxy* object instead of *self*, so that it can be used in a with statement
+            without side effects.
+
+        """
+        # Use the default timeout, if no timeout is provided.
+        if timeout is None:
+            timeout = self._context.timeout
+
+        if blocking is None:
+            blocking = self._context.blocking
+
+        if poll_intervall is not None:
+            msg = "use poll_interval instead of poll_intervall"
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+            poll_interval = poll_intervall
+
+        poll_interval = poll_interval if poll_interval is not None else self._context.poll_interval
+
+        # Increment the number right at the beginning. We can still undo it, if something fails.
+        self._context.lock_counter += 1
+
+        lock_id = id(self)
+        lock_filename = self.lock_file
+        canonical = _canonical(lock_filename)
+
+        would_block = self._context.lock_counter == 1 and not self.is_locked and timeout < 0 and blocking
+        if would_block and (existing := _registry.held.get(canonical)) is not None and existing != lock_id:
+            self._context.lock_counter -= 1
+            msg = (
+                f"Deadlock: lock '{lock_filename}' is already held by a different "
+                f"FileLock instance in this thread. Use is_singleton=True to "
+                f"enable reentrant locking across instances."
+            )
+            raise RuntimeError(msg)
+
+        start_time = time.perf_counter()
+        try:
+            while True:
+                if not self.is_locked:
+                    self._try_break_expired_lock()
+                    _LOGGER.debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
+                    self._acquire()
+                if self.is_locked:
+                    _LOGGER.debug("Lock %s acquired on %s", lock_id, lock_filename)
+                    break
+                if self._check_give_up(
+                    lock_id,
+                    lock_filename,
+                    blocking=blocking,
+                    cancel_check=cancel_check,
+                    timeout=timeout,
+                    start_time=start_time,
+                ):
+                    raise Timeout(lock_filename)  # noqa: TRY301
+                msg = "Lock %s not acquired on %s, waiting %s seconds ..."
+                _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)
+                time.sleep(poll_interval)
+        except BaseException:
+            self._context.lock_counter = max(0, self._context.lock_counter - 1)
+            if self._context.lock_counter == 0:
+                _registry.held.pop(canonical, None)
+            raise
+        if self._context.lock_counter == 1:
+            _registry.held[canonical] = lock_id
+        return AcquireReturnProxy(lock=self)
+
+    def release(self, force: bool = False) -> None:  # noqa: FBT001, FBT002
+        """
+        Release the file lock. The lock is only completely released when the lock counter reaches 0. The lock file
+        itself is not automatically deleted.
+
+        :param force: If true, the lock counter is ignored and the lock is released in every case.
+
+        """
+        if self.is_locked:
+            self._context.lock_counter -= 1
+
+            if self._context.lock_counter == 0 or force:
+                lock_id, lock_filename = id(self), self.lock_file
+
+                _LOGGER.debug("Attempting to release lock %s on %s", lock_id, lock_filename)
+                self._release()
+                self._context.lock_counter = 0
+                _registry.held.pop(_canonical(lock_filename), None)
+                _LOGGER.debug("Lock %s released on %s", lock_id, lock_filename)
+
+    def __enter__(self) -> Self:
+        """
+        Acquire the lock.
+
+        :returns: the lock object
+
+        """
+        self.acquire()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        """
+        Release the lock.
+
+        :param exc_type: the exception type if raised
+        :param exc_value: the exception value if raised
+        :param traceback: the exception traceback if raised
+
+        """
+        self.release()
+
+    def __del__(self) -> None:
+        """Called when the lock object is deleted."""
+        self.release(force=True)
+
+
+__all__ = [
+    "_UNSET_FILE_MODE",
+    "AcquireReturnProxy",
+    "BaseFileLock",
+]
diff --git a/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/INSTALLER b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/METADATA b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..628d030edce19fa16d657b45f303c8460f6374c3
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/METADATA
@@ -0,0 +1,257 @@
+Metadata-Version: 2.4
+Name: fsspec
+Version: 2026.4.0
+Summary: File-system specification
+Project-URL: Changelog, https://filesystem-spec.readthedocs.io/en/latest/changelog.html
+Project-URL: Documentation, https://filesystem-spec.readthedocs.io/en/latest/
+Project-URL: Homepage, https://github.com/fsspec/filesystem_spec
+Maintainer-email: Martin Durant <mdurant@anaconda.com>
+License-Expression: BSD-3-Clause
+License-File: LICENSE
+Keywords: file
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Requires-Python: >=3.10
+Provides-Extra: abfs
+Requires-Dist: adlfs; extra == 'abfs'
+Provides-Extra: adl
+Requires-Dist: adlfs; extra == 'adl'
+Provides-Extra: arrow
+Requires-Dist: pyarrow>=1; extra == 'arrow'
+Provides-Extra: dask
+Requires-Dist: dask; extra == 'dask'
+Requires-Dist: distributed; extra == 'dask'
+Provides-Extra: dev
+Requires-Dist: pre-commit; extra == 'dev'
+Requires-Dist: ruff>=0.5; extra == 'dev'
+Provides-Extra: doc
+Requires-Dist: numpydoc; extra == 'doc'
+Requires-Dist: sphinx; extra == 'doc'
+Requires-Dist: sphinx-design; extra == 'doc'
+Requires-Dist: sphinx-rtd-theme; extra == 'doc'
+Requires-Dist: yarl; extra == 'doc'
+Provides-Extra: dropbox
+Requires-Dist: dropbox; extra == 'dropbox'
+Requires-Dist: dropboxdrivefs; extra == 'dropbox'
+Requires-Dist: requests; extra == 'dropbox'
+Provides-Extra: entrypoints
+Provides-Extra: full
+Requires-Dist: adlfs; extra == 'full'
+Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'full'
+Requires-Dist: dask; extra == 'full'
+Requires-Dist: distributed; extra == 'full'
+Requires-Dist: dropbox; extra == 'full'
+Requires-Dist: dropboxdrivefs; extra == 'full'
+Requires-Dist: fusepy; extra == 'full'
+Requires-Dist: gcsfs>2024.2.0; extra == 'full'
+Requires-Dist: libarchive-c; extra == 'full'
+Requires-Dist: ocifs; extra == 'full'
+Requires-Dist: panel; extra == 'full'
+Requires-Dist: paramiko; extra == 'full'
+Requires-Dist: pyarrow>=1; extra == 'full'
+Requires-Dist: pygit2; extra == 'full'
+Requires-Dist: requests; extra == 'full'
+Requires-Dist: s3fs>2024.2.0; extra == 'full'
+Requires-Dist: smbprotocol; extra == 'full'
+Requires-Dist: tqdm; extra == 'full'
+Provides-Extra: fuse
+Requires-Dist: fusepy; extra == 'fuse'
+Provides-Extra: gcs
+Requires-Dist: gcsfs>2024.2.0; extra == 'gcs'
+Provides-Extra: git
+Requires-Dist: pygit2; extra == 'git'
+Provides-Extra: github
+Requires-Dist: requests; extra == 'github'
+Provides-Extra: gs
+Requires-Dist: gcsfs; extra == 'gs'
+Provides-Extra: gui
+Requires-Dist: panel; extra == 'gui'
+Provides-Extra: hdfs
+Requires-Dist: pyarrow>=1; extra == 'hdfs'
+Provides-Extra: http
+Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'http'
+Provides-Extra: libarchive
+Requires-Dist: libarchive-c; extra == 'libarchive'
+Provides-Extra: oci
+Requires-Dist: ocifs; extra == 'oci'
+Provides-Extra: s3
+Requires-Dist: s3fs>2024.2.0; extra == 's3'
+Provides-Extra: sftp
+Requires-Dist: paramiko; extra == 'sftp'
+Provides-Extra: smb
+Requires-Dist: smbprotocol; extra == 'smb'
+Provides-Extra: ssh
+Requires-Dist: paramiko; extra == 'ssh'
+Provides-Extra: test
+Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'test'
+Requires-Dist: numpy; extra == 'test'
+Requires-Dist: pytest; extra == 'test'
+Requires-Dist: pytest-asyncio!=0.22.0; extra == 'test'
+Requires-Dist: pytest-benchmark; extra == 'test'
+Requires-Dist: pytest-cov; extra == 'test'
+Requires-Dist: pytest-mock; extra == 'test'
+Requires-Dist: pytest-recording; extra == 'test'
+Requires-Dist: pytest-rerunfailures; extra == 'test'
+Requires-Dist: requests; extra == 'test'
+Provides-Extra: test-downstream
+Requires-Dist: aiobotocore<3.0.0,>=2.5.4; extra == 'test-downstream'
+Requires-Dist: dask[dataframe,test]; extra == 'test-downstream'
+Requires-Dist: moto[server]<5,>4; extra == 'test-downstream'
+Requires-Dist: pytest-timeout; extra == 'test-downstream'
+Requires-Dist: xarray; extra == 'test-downstream'
+Provides-Extra: test-full
+Requires-Dist: adlfs; extra == 'test-full'
+Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'test-full'
+Requires-Dist: backports-zstd; (python_version < '3.14') and extra == 'test-full'
+Requires-Dist: cloudpickle; extra == 'test-full'
+Requires-Dist: dask; extra == 'test-full'
+Requires-Dist: distributed; extra == 'test-full'
+Requires-Dist: dropbox; extra == 'test-full'
+Requires-Dist: dropboxdrivefs; extra == 'test-full'
+Requires-Dist: fastparquet; extra == 'test-full'
+Requires-Dist: fusepy; extra == 'test-full'
+Requires-Dist: gcsfs; extra == 'test-full'
+Requires-Dist: jinja2; extra == 'test-full'
+Requires-Dist: kerchunk; extra == 'test-full'
+Requires-Dist: libarchive-c; extra == 'test-full'
+Requires-Dist: lz4; extra == 'test-full'
+Requires-Dist: notebook; extra == 'test-full'
+Requires-Dist: numpy; extra == 'test-full'
+Requires-Dist: ocifs; extra == 'test-full'
+Requires-Dist: pandas<3.0.0; extra == 'test-full'
+Requires-Dist: panel; extra == 'test-full'
+Requires-Dist: paramiko; extra == 'test-full'
+Requires-Dist: pyarrow; extra == 'test-full'
+Requires-Dist: pyarrow>=1; extra == 'test-full'
+Requires-Dist: pyftpdlib; extra == 'test-full'
+Requires-Dist: pygit2; extra == 'test-full'
+Requires-Dist: pytest; extra == 'test-full'
+Requires-Dist: pytest-asyncio!=0.22.0; extra == 'test-full'
+Requires-Dist: pytest-benchmark; extra == 'test-full'
+Requires-Dist: pytest-cov; extra == 'test-full'
+Requires-Dist: pytest-mock; extra == 'test-full'
+Requires-Dist: pytest-recording; extra == 'test-full'
+Requires-Dist: pytest-rerunfailures; extra == 'test-full'
+Requires-Dist: python-snappy; extra == 'test-full'
+Requires-Dist: requests; extra == 'test-full'
+Requires-Dist: smbprotocol; extra == 'test-full'
+Requires-Dist: tqdm; extra == 'test-full'
+Requires-Dist: urllib3; extra == 'test-full'
+Requires-Dist: zarr; extra == 'test-full'
+Requires-Dist: zstandard; (python_version < '3.14') and extra == 'test-full'
+Provides-Extra: tqdm
+Requires-Dist: tqdm; extra == 'tqdm'
+Description-Content-Type: text/markdown
+
+# filesystem_spec
+
+[![PyPI version](https://badge.fury.io/py/fsspec.svg)](https://pypi.python.org/pypi/fsspec/)
+[![Anaconda-Server Badge](https://anaconda.org/conda-forge/fsspec/badges/version.svg)](https://anaconda.org/conda-forge/fsspec)
+![Build](https://github.com/fsspec/filesystem_spec/workflows/CI/badge.svg)
+[![Docs](https://readthedocs.org/projects/filesystem-spec/badge/?version=latest)](https://filesystem-spec.readthedocs.io/en/latest/?badge=latest)
+
+A specification for pythonic filesystems.
+
+## Install
+
+```bash
+pip install fsspec
+```
+
+would install the base fsspec. Various optionally supported features might require specification of custom
+extra require, e.g. `pip install fsspec[ssh]` will install dependencies for `ssh` backends support.
+Use `pip install fsspec[full]` for installation of all known extra dependencies.
+
+Up-to-date package also provided through conda-forge distribution:
+
+```bash
+conda install -c conda-forge fsspec
+```
+
+
+## Purpose
+
+To produce a template or specification for a file-system interface, that specific implementations should follow,
+so that applications making use of them can rely on a common behaviour and not have to worry about the specific
+internal implementation decisions with any given backend. Many such implementations are included in this package,
+or in sister projects such as `s3fs` and `gcsfs`.
+
+In addition, if this is well-designed, then additional functionality, such as a key-value store or FUSE
+mounting of the file-system implementation may be available for all implementations "for free".
+
+## Documentation
+
+Please refer to [RTD](https://filesystem-spec.readthedocs.io/en/latest/?badge=latest)
+
+## Develop
+
+fsspec uses GitHub Actions for CI. Environment files can be found
+in the "ci/" directory. Note that the main environment is called "py38",
+but it is expected that the version of python installed be adjustable at
+CI runtime. For local use, pick a version suitable for you.
+
+```bash
+# For a new environment (mamba / conda).
+mamba create -n fsspec -c conda-forge  python=3.10 -y
+conda activate fsspec
+
+# Standard dev install with docs and tests.
+pip install -e ".[dev,doc,test]"
+
+# Full tests except for downstream
+pip install s3fs
+pip uninstall s3fs
+pip install -e .[dev,doc,test_full]
+pip install s3fs --no-deps
+pytest -v
+
+# Downstream tests.
+sh install_s3fs.sh
+# Windows powershell.
+install_s3fs.sh
+```
+
+### Testing
+
+Tests can be run in the dev environment, if activated, via ``pytest fsspec``.
+
+The full fsspec suite requires a system-level docker, docker-compose, and fuse
+installation. If only making changes to one backend implementation, it is
+not generally necessary to run all tests locally.
+
+It is expected that contributors ensure that any change to fsspec does not
+cause issues or regressions for either other fsspec-related packages such
+as gcsfs and s3fs, nor for downstream users of fsspec. The "downstream" CI
+run and corresponding environment file run a set of tests from the dask
+test suite, and very minimal tests against pandas and zarr from the
+test_downstream.py module in this repo.
+
+### Code Formatting
+
+fsspec uses [Black](https://black.readthedocs.io/en/stable) to ensure
+a consistent code format throughout the project.
+Run ``black fsspec`` from the root of the filesystem_spec repository to
+auto-format your code. Additionally, many editors have plugins that will apply
+``black`` as you edit files. ``black`` is included in the ``tox`` environments.
+
+Optionally, you may wish to setup [pre-commit hooks](https://pre-commit.com) to
+automatically run ``black`` when you make a git commit.
+Run ``pre-commit install --install-hooks`` from the root of the
+filesystem_spec repository to setup pre-commit hooks. ``black`` will now be run
+before you commit, reformatting any changed files. You can format without
+committing via ``pre-commit run`` or skip these checks with ``git commit
+--no-verify``.
+
+## Support
+
+Work on this repository is supported in part by:
+
+"Anaconda, Inc. - Advancing AI through open source."
+
+<a href="https://anaconda.com/"><img src="https://camo.githubusercontent.com/b8555ef2222598ed37ce38ac86955febbd25de7619931bb7dd3c58432181d3b6/68747470733a2f2f626565776172652e6f72672f636f6d6d756e6974792f6d656d626572732f616e61636f6e64612f616e61636f6e64612d6c617267652e706e67" alt="anaconda logo" width="40%"/></a>
diff --git a/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/RECORD b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..1435b852131aabe441a09f4e9d78487743b0a95e
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/RECORD
@@ -0,0 +1,119 @@
+fsspec-2026.4.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+fsspec-2026.4.0.dist-info/METADATA,sha256=j0nEQEtQcYKslT39RVFkHfC_Nsz2fgisimjM4yO_HUw,10524
+fsspec-2026.4.0.dist-info/RECORD,,
+fsspec-2026.4.0.dist-info/WHEEL,sha256=QccIxa26bgl1E6uMy58deGWi-0aeIkkangHcxk2kWfw,87
+fsspec-2026.4.0.dist-info/licenses/LICENSE,sha256=LcNUls5TpzB5FcAIqESq1T53K0mzTN0ARFBnaRQH7JQ,1513
+fsspec/__init__.py,sha256=L7qwNBU1iMNQd8Of87HYSNFT9gWlNMSESaJC8fY0AaQ,2053
+fsspec/__pycache__/__init__.cpython-311.pyc,,
+fsspec/__pycache__/_version.cpython-311.pyc,,
+fsspec/__pycache__/archive.cpython-311.pyc,,
+fsspec/__pycache__/asyn.cpython-311.pyc,,
+fsspec/__pycache__/caching.cpython-311.pyc,,
+fsspec/__pycache__/callbacks.cpython-311.pyc,,
+fsspec/__pycache__/compression.cpython-311.pyc,,
+fsspec/__pycache__/config.cpython-311.pyc,,
+fsspec/__pycache__/conftest.cpython-311.pyc,,
+fsspec/__pycache__/core.cpython-311.pyc,,
+fsspec/__pycache__/dircache.cpython-311.pyc,,
+fsspec/__pycache__/exceptions.cpython-311.pyc,,
+fsspec/__pycache__/fuse.cpython-311.pyc,,
+fsspec/__pycache__/generic.cpython-311.pyc,,
+fsspec/__pycache__/gui.cpython-311.pyc,,
+fsspec/__pycache__/json.cpython-311.pyc,,
+fsspec/__pycache__/mapping.cpython-311.pyc,,
+fsspec/__pycache__/parquet.cpython-311.pyc,,
+fsspec/__pycache__/registry.cpython-311.pyc,,
+fsspec/__pycache__/spec.cpython-311.pyc,,
+fsspec/__pycache__/transaction.cpython-311.pyc,,
+fsspec/__pycache__/utils.cpython-311.pyc,,
+fsspec/_version.py,sha256=pmVqnxq2zqVsksIat3OOIfF2Y5tX9d_HhLG4e1wXe80,526
+fsspec/archive.py,sha256=vM6t_lgV6lBWbBYwpm3S4ofBQFQxUPr5KkDQrrQcQro,2411
+fsspec/asyn.py,sha256=8vfpg8iNXerqo9byws0HmMzygZJ_LrMJ_jhS5b3sI10,37585
+fsspec/caching.py,sha256=YJm7SjQTxOgyavKvAE0CST_GR0mpnEjnIIWCdE1QbLk,34024
+fsspec/callbacks.py,sha256=BDIwLzK6rr_0V5ch557fSzsivCElpdqhXr5dZ9Te-EE,9210
+fsspec/compression.py,sha256=3v_Fe39gzRRWfaeXpzNjAGPqgTzmETYRCo3qHVqD3po,5132
+fsspec/config.py,sha256=mHKzAgjXa_LwqtO_VoTJJ568VqwYkbK3gUp4J6JZJp4,4238
+fsspec/conftest.py,sha256=uWfm_Qs5alPRxOhRpDfQ0-1jqSJ54pni4y96IxOREXM,3446
+fsspec/core.py,sha256=lc7XSnZU6_C6xljp7Z_xEGN3V7704hbeQLkxvPP0wds,24173
+fsspec/dircache.py,sha256=YzogWJrhEastHU7vWz-cJiJ7sdtLXFXhEpInGKd4EcM,2717
+fsspec/exceptions.py,sha256=pauSLDMxzTJMOjvX1WEUK0cMyFkrFxpWJsyFywav7A8,331
+fsspec/fuse.py,sha256=Q-3NOOyLqBfYa4Db5E19z_ZY36zzYHtIs1mOUasItBQ,10177
+fsspec/generic.py,sha256=9QHQYMNb-8w8-eYuIqShcTjO_LeHXFoQTyt8J5oEq5Q,13482
+fsspec/gui.py,sha256=CQ7QsrTpaDlWSLNOpwNoJc7khOcYXIZxmrAJN9bHWQU,14002
+fsspec/implementations/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+fsspec/implementations/__pycache__/__init__.cpython-311.pyc,,
+fsspec/implementations/__pycache__/arrow.cpython-311.pyc,,
+fsspec/implementations/__pycache__/asyn_wrapper.cpython-311.pyc,,
+fsspec/implementations/__pycache__/cache_mapper.cpython-311.pyc,,
+fsspec/implementations/__pycache__/cache_metadata.cpython-311.pyc,,
+fsspec/implementations/__pycache__/cached.cpython-311.pyc,,
+fsspec/implementations/__pycache__/chained.cpython-311.pyc,,
+fsspec/implementations/__pycache__/dask.cpython-311.pyc,,
+fsspec/implementations/__pycache__/data.cpython-311.pyc,,
+fsspec/implementations/__pycache__/dbfs.cpython-311.pyc,,
+fsspec/implementations/__pycache__/dirfs.cpython-311.pyc,,
+fsspec/implementations/__pycache__/ftp.cpython-311.pyc,,
+fsspec/implementations/__pycache__/gist.cpython-311.pyc,,
+fsspec/implementations/__pycache__/git.cpython-311.pyc,,
+fsspec/implementations/__pycache__/github.cpython-311.pyc,,
+fsspec/implementations/__pycache__/http.cpython-311.pyc,,
+fsspec/implementations/__pycache__/http_sync.cpython-311.pyc,,
+fsspec/implementations/__pycache__/jupyter.cpython-311.pyc,,
+fsspec/implementations/__pycache__/libarchive.cpython-311.pyc,,
+fsspec/implementations/__pycache__/local.cpython-311.pyc,,
+fsspec/implementations/__pycache__/memory.cpython-311.pyc,,
+fsspec/implementations/__pycache__/reference.cpython-311.pyc,,
+fsspec/implementations/__pycache__/sftp.cpython-311.pyc,,
+fsspec/implementations/__pycache__/smb.cpython-311.pyc,,
+fsspec/implementations/__pycache__/tar.cpython-311.pyc,,
+fsspec/implementations/__pycache__/webhdfs.cpython-311.pyc,,
+fsspec/implementations/__pycache__/zip.cpython-311.pyc,,
+fsspec/implementations/arrow.py,sha256=8FhvcvOYLZNMMegCYFFCEHgEqig8AkOU7Ehb8XfcgnA,8890
+fsspec/implementations/asyn_wrapper.py,sha256=3lfJkGs6D_AwRBdxTSYlL-RCVdaXBZ9Itys2P5o5Si0,3738
+fsspec/implementations/cache_mapper.py,sha256=W4wlxyPxZbSp9ItJ0pYRVBMh6bw9eFypgP6kUYuuiI4,2421
+fsspec/implementations/cache_metadata.py,sha256=_AGOfy46vIDseS7eQRk7IgmKMbKhEksOv9PuW7Lo5YA,8502
+fsspec/implementations/cached.py,sha256=38B4qVDDXRh65Zb_JXy2-lJOxtdgUdBgNdy-oBe6jGw,36375
+fsspec/implementations/chained.py,sha256=iGivpNaHUFjB_ea0-HAPhcmm6CL8qnDf270PSj7JwuE,680
+fsspec/implementations/dask.py,sha256=CXZbJzIVOhKV8ILcxuy3bTvcacCueAbyQxmvAkbPkrk,4466
+fsspec/implementations/data.py,sha256=IhOGDkacYp5gkl9jhEu4msQfZPb0gS5Q_ml7Mbr6dgQ,1627
+fsspec/implementations/dbfs.py,sha256=1cvvC6KBWOb8pBVpc01xavVbEPXO1xsgZvPD7H73M9k,16217
+fsspec/implementations/dirfs.py,sha256=5JAp_nn9v1OjKY3MwuRkLrqeZ8gGYlILSQRqkQQdz7k,12624
+fsspec/implementations/ftp.py,sha256=fJhaMIKq2RvzYlLwG3bewy2jq4iRqjVt1aIpwtUIRwI,13235
+fsspec/implementations/gist.py,sha256=Y6jTDrE-wuTwvpPyAQDuuOMBGxlajafKWoB1_yX6jdY,8528
+fsspec/implementations/git.py,sha256=qBDWMz5LNllPqVjr5jf_1FuNha4P5lyQI3IlhYg-wUE,3731
+fsspec/implementations/github.py,sha256=aCsZL8UvXZgdkcB1RUs3DdLeNrjLKcFsFYeQFDWbBFo,11653
+fsspec/implementations/http.py,sha256=1uWwxllOjPe8mQD54IrCYQJHU1YaAXh_cDmXBk-AoQ8,30894
+fsspec/implementations/http_sync.py,sha256=UmBqd938ebwVjYgVtzg-ysG3ZoGhIJw0wFtQAfxV3Aw,30332
+fsspec/implementations/jupyter.py,sha256=q1PlQ66AAswGFyr8MFKWyobaV2YekMWRtqENBDQtD28,4002
+fsspec/implementations/libarchive.py,sha256=SpIA1F-zf7kb2-VYUVuhMrXTBOhBxUXKgEW1RaAdDoA,7098
+fsspec/implementations/local.py,sha256=aVEUnAB2hkxmweXLCiB4XD_KPwDL-gMX4dN9Fz-pk7k,17161
+fsspec/implementations/memory.py,sha256=TDdLtSPWXxZKrrVGwmc3uS3oK_2mlcVTk2BiqR8IeII,10507
+fsspec/implementations/reference.py,sha256=9FDzS7nzxifOnd9azaeBiuSuspSDTQ0YiTYdflJrEFs,49198
+fsspec/implementations/sftp.py,sha256=v7t-LpD4hEycHnw1kbepyoNgrhClshXQXitFuY4mBWE,5951
+fsspec/implementations/smb.py,sha256=5fhu8h06nOLBPh2c48aT7WBRqh9cEcbIwtyu06wTjec,15236
+fsspec/implementations/tar.py,sha256=0gqTHdwmwQI1Ia8rNQjuAVjNgrEFKPbdPkmndR9vpmk,4451
+fsspec/implementations/webhdfs.py,sha256=osF2m0nhDil6sbMzYW_4DZzhxF4ygtb59XDiybd9Fyg,17589
+fsspec/implementations/zip.py,sha256=GPDJh4UtUNJmaqijUQ0eiEZIGpwusQjkIcbhsrmLSi0,6263
+fsspec/json.py,sha256=4EBZ-xOmRiyxmIqPIwxmDImosRQ7io7qBM2xjJPsEE4,3768
+fsspec/mapping.py,sha256=m2ndB_gtRBXYmNJg0Ie1-BVR75TFleHmIQBzC-yWhjU,8343
+fsspec/parquet.py,sha256=0SzfswUTc2k5nAj7xZNYPPyvUkFNXznFRXXtBRhr-iE,20506
+fsspec/registry.py,sha256=KeDNL9HOyHX_D4D4qQGAI-Y3DQhiLeUC1VK2I81NZaE,12344
+fsspec/spec.py,sha256=XKhR4bDdhuhaGcMhgKThCmi6sBwv6cSRMr0SSFmCBHE,77766
+fsspec/tests/abstract/__init__.py,sha256=4xUJrv7gDgc85xAOz1p-V_K1hrsdMWTSa0rviALlJk8,10181
+fsspec/tests/abstract/__pycache__/__init__.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/common.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/copy.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/get.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/mv.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/open.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/pipe.cpython-311.pyc,,
+fsspec/tests/abstract/__pycache__/put.cpython-311.pyc,,
+fsspec/tests/abstract/common.py,sha256=1GQwNo5AONzAnzZj0fWgn8NJPLXALehbsuGxS3FzWVU,4973
+fsspec/tests/abstract/copy.py,sha256=gU5-d97U3RSde35Vp4RxPY4rWwL744HiSrJ8IBOp9-8,19967
+fsspec/tests/abstract/get.py,sha256=vNR4HztvTR7Cj56AMo7_tx7TeYz1Jgr_2Wb8Lv-UiBY,20755
+fsspec/tests/abstract/mv.py,sha256=k8eUEBIrRrGMsBY5OOaDXdGnQUKGwDIfQyduB6YD3Ns,1982
+fsspec/tests/abstract/open.py,sha256=Fi2PBPYLbRqysF8cFm0rwnB41kMdQVYjq8cGyDXp3BU,329
+fsspec/tests/abstract/pipe.py,sha256=LFzIrLCB5GLXf9rzFKJmE8AdG7LQ_h4bJo70r8FLPqM,402
+fsspec/tests/abstract/put.py,sha256=7aih17OKB_IZZh1Mkq1eBDIjobhtMQmI8x-Pw-S_aZk,21201
+fsspec/transaction.py,sha256=xliRG6U2Zf3khG4xcw9WiB-yAoqJSHEGK_VjHOdtgo0,2398
+fsspec/utils.py,sha256=E24ji0XLWC6n3bw2sHA28OYxrGU9Wy_al2XydsRgrRk,23623
diff --git a/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/WHEEL b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..b1b94fd58e7e9ed0ef3449473bc48de68afcc3fe
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec-2026.4.0.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: hatchling 1.29.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c343c23003be59dec3da96cfb8926520c86a7f16
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/_version.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/_version.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4a4db25a3acb681bb02f138ee2a846480eaa3d21
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/_version.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/archive.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/archive.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6c97c06880f19185e4531f37ac5ceb9b5b884157
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/archive.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/asyn.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/asyn.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c564f578212483c3910a0e90a788cb4aac133e05
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/asyn.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/caching.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/caching.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..503912464813dd6e0363c5b104ac692b2da2702e
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/caching.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/callbacks.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/callbacks.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c34c868a426a1632afdda80429c264f2a03be015
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/callbacks.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/compression.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/compression.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d17b1a8b7c87ae18908fd7430492a1a556c7f0f2
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/compression.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/config.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/config.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e65fd47b5b389138c5bf2b5c54d17d46725ad5f
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/config.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/conftest.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/conftest.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..00a66735a5bf82fd419593ddf2a502480995f742
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/conftest.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/core.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/core.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6a841fe97e84bf07cf0c694f15575a25704a6872
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/core.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/dircache.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/dircache.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..43a881f33d4dfb704366f718d657ac87c497050d
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/dircache.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/exceptions.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/exceptions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7683adbc6e5d62606e2337c35658ad02a82d808f
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/exceptions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/fuse.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/fuse.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..68444f06af8fe3d6ae4185d6333568084d19ba83
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/fuse.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/generic.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/generic.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..28942d426a2c7abe76c8fe50874493f9f8c0bb1d
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/generic.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/gui.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/gui.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b36b1e6c126412b821ef8eb92bcf65a43cc120ee
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/gui.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/json.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/json.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cb2242ee5d5c2b06532f07283608b5ad8ff0d44e
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/json.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/mapping.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/mapping.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a7b1c48b89158cd383a1eae22617f79705e50033
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/mapping.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/parquet.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/parquet.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8260f85387c99bb1561bc2619b413e3391bc26df
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/parquet.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/registry.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/registry.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d92de15163e61deb0f7e135e4c2bb1c687a4eb60
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/registry.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/transaction.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/transaction.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..571aeb21d8678249ae00d15c253dcbddf3c364ef
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/transaction.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/__pycache__/utils.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/__pycache__/utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e39a99b4e4e5289829f9d23d8c9aef4d90dc0d4
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/__pycache__/utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__init__.py b/lib/python3.11/site-packages/fsspec/implementations/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..45ea769143b8ad0d2890ec453f8f1a5f8dcdef4a
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/arrow.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/arrow.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9e509fe8c1793534637b2ef6ed478232b9047ea1
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/arrow.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/asyn_wrapper.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/asyn_wrapper.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1016f6692882aa1bb0cccaccab0117b66821d897
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/asyn_wrapper.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cache_mapper.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cache_mapper.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5e4ca050750c433c5d0862a648664f251398d365
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cache_mapper.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cache_metadata.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cache_metadata.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d15f9923d12a19717b18a3d5f57ed085842f8209
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cache_metadata.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cached.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cached.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..77749bbe83a8babbd71d1061a228fa979a35e581
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/cached.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/chained.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/chained.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6bac8e1bae0bf8b0a40422285785bfe9c32dd1eb
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/chained.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dask.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dask.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2974dfbdea84e6c97cd37672a675598597a5db23
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dask.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/data.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/data.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..54d7c1eae03bfa6c69b3d8173cc07d8d775353d3
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/data.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dbfs.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dbfs.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fc05d2df4daf99b62b80cec80f8facf716750c10
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dbfs.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dirfs.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dirfs.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8739fa877bffd71ff003f5bd0ce55e89aae6908d
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/dirfs.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/ftp.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/ftp.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..51955b1072d87d52655ba03819d49f9e86d20e9f
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/ftp.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/gist.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/gist.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0600770c205ed1ec3bbd8abef2cbfd70b73125c2
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/gist.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/git.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/git.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..35fdc9deb3d61a7d1bc3d18783f2122fc44812cf
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/git.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/github.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/github.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..221857a760e902e8e2e086f57b31b551b40b3f3d
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/github.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/http.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/http.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..48b3bac71101335598f1a522a65659ad8f6a4090
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/http.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/http_sync.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/http_sync.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b663513849abc7f3d9a7f5c6c526c0551d2f9899
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/http_sync.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/jupyter.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/jupyter.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e75763f88ae0748b56c1f025f98b9c5c078432bd
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/jupyter.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/libarchive.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/libarchive.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..833f07ff623d0b97961075ff2b6747806e0b3985
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/libarchive.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/local.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/local.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..eaf8e25bfa7de0f89dafc420887d16cc13fe7a2f
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/local.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/memory.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/memory.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3e8a84dcf111c3eec0d8085664cce7acfbd037c5
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/memory.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/reference.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/reference.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b833504cec4f9d98b463b128c191dd2a52dae92b
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/reference.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/sftp.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/sftp.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..80340be55c3f0b28a9e83c69008fa031e651f47c
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/sftp.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/smb.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/smb.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b3339f50d78f7aa29da3e9aeb31513278a89308d
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/smb.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/tar.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/tar.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a47cb96f0151c719f2eefbd56623e4e82be0b445
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/tar.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/webhdfs.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/webhdfs.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e8e02c9c58db066d90753aabfa2ff11e55e97332
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/webhdfs.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/__pycache__/zip.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/zip.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..37930129217d19f2f4f00c508b3e2a6675569c04
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/implementations/__pycache__/zip.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/implementations/arrow.py b/lib/python3.11/site-packages/fsspec/implementations/arrow.py
new file mode 100644
index 0000000000000000000000000000000000000000..227d50930763f56bc8c01556249eb1306347350b
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/arrow.py
@@ -0,0 +1,312 @@
+import errno
+import io
+import os
+import secrets
+import shutil
+from contextlib import suppress
+from functools import cached_property, wraps
+from urllib.parse import parse_qs
+
+from fsspec.spec import AbstractFileSystem
+from fsspec.utils import (
+    get_package_version_without_import,
+    infer_storage_options,
+    mirror_from,
+    tokenize,
+)
+
+
+def wrap_exceptions(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except OSError as exception:
+            if not exception.args:
+                raise
+
+            message, *args = exception.args
+            if isinstance(message, str) and "does not exist" in message:
+                raise FileNotFoundError(errno.ENOENT, message) from exception
+            else:
+                raise
+
+    return wrapper
+
+
+PYARROW_VERSION = None
+
+
+class ArrowFSWrapper(AbstractFileSystem):
+    """FSSpec-compatible wrapper of pyarrow.fs.FileSystem.
+
+    Parameters
+    ----------
+    fs : pyarrow.fs.FileSystem
+
+    """
+
+    root_marker = "/"
+
+    def __init__(self, fs, **kwargs):
+        global PYARROW_VERSION
+        PYARROW_VERSION = get_package_version_without_import("pyarrow")
+        self.fs = fs
+        super().__init__(**kwargs)
+
+    @property
+    def protocol(self):
+        return self.fs.type_name
+
+    @cached_property
+    def fsid(self):
+        return "hdfs_" + tokenize(self.fs.host, self.fs.port)
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        ops = infer_storage_options(path)
+        path = ops["path"]
+        if path.startswith("//"):
+            # special case for "hdfs://path" (without the triple slash)
+            path = path[1:]
+        return path
+
+    def ls(self, path, detail=False, **kwargs):
+        path = self._strip_protocol(path)
+        from pyarrow.fs import FileSelector
+
+        try:
+            entries = [
+                self._make_entry(entry)
+                for entry in self.fs.get_file_info(FileSelector(path))
+            ]
+        except (FileNotFoundError, NotADirectoryError):
+            entries = [self.info(path, **kwargs)]
+        if detail:
+            return entries
+        else:
+            return [entry["name"] for entry in entries]
+
+    def info(self, path, **kwargs):
+        path = self._strip_protocol(path)
+        [info] = self.fs.get_file_info([path])
+        return self._make_entry(info)
+
+    def exists(self, path):
+        path = self._strip_protocol(path)
+        try:
+            self.info(path)
+        except FileNotFoundError:
+            return False
+        else:
+            return True
+
+    def _make_entry(self, info):
+        from pyarrow.fs import FileType
+
+        if info.type is FileType.Directory:
+            kind = "directory"
+        elif info.type is FileType.File:
+            kind = "file"
+        elif info.type is FileType.NotFound:
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), info.path)
+        else:
+            kind = "other"
+
+        return {
+            "name": info.path,
+            "size": info.size,
+            "type": kind,
+            "mtime": info.mtime,
+        }
+
+    @wrap_exceptions
+    def cp_file(self, path1, path2, **kwargs):
+        path1 = self._strip_protocol(path1).rstrip("/")
+        path2 = self._strip_protocol(path2).rstrip("/")
+
+        with self._open(path1, "rb") as lstream:
+            tmp_fname = f"{path2}.tmp.{secrets.token_hex(6)}"
+            try:
+                with self.open(tmp_fname, "wb") as rstream:
+                    shutil.copyfileobj(lstream, rstream)
+                self.fs.move(tmp_fname, path2)
+            except BaseException:
+                with suppress(FileNotFoundError):
+                    self.fs.delete_file(tmp_fname)
+                raise
+
+    @wrap_exceptions
+    def mv(self, path1, path2, **kwargs):
+        path1 = self._strip_protocol(path1).rstrip("/")
+        path2 = self._strip_protocol(path2).rstrip("/")
+        self.fs.move(path1, path2)
+
+    @wrap_exceptions
+    def rm_file(self, path):
+        path = self._strip_protocol(path)
+        self.fs.delete_file(path)
+
+    @wrap_exceptions
+    def rm(self, path, recursive=False, maxdepth=None):
+        path = self._strip_protocol(path).rstrip("/")
+        if self.isdir(path):
+            if recursive:
+                self.fs.delete_dir(path)
+            else:
+                raise ValueError("Can't delete directories without recursive=False")
+        else:
+            self.fs.delete_file(path)
+
+    @wrap_exceptions
+    def _open(self, path, mode="rb", block_size=None, seekable=True, **kwargs):
+        if mode == "rb":
+            if seekable:
+                method = self.fs.open_input_file
+            else:
+                method = self.fs.open_input_stream
+        elif mode == "wb":
+            method = self.fs.open_output_stream
+        elif mode == "ab":
+            method = self.fs.open_append_stream
+        else:
+            raise ValueError(f"unsupported mode for Arrow filesystem: {mode!r}")
+
+        _kwargs = {}
+        if mode != "rb" or not seekable:
+            if int(PYARROW_VERSION.split(".")[0]) >= 4:
+                # disable compression auto-detection
+                _kwargs["compression"] = None
+        stream = method(path, **_kwargs)
+
+        return ArrowFile(self, stream, path, mode, block_size, **kwargs)
+
+    @wrap_exceptions
+    def mkdir(self, path, create_parents=True, **kwargs):
+        path = self._strip_protocol(path)
+        if create_parents:
+            self.makedirs(path, exist_ok=True)
+        else:
+            self.fs.create_dir(path, recursive=False)
+
+    @wrap_exceptions
+    def makedirs(self, path, exist_ok=False):
+        path = self._strip_protocol(path)
+        self.fs.create_dir(path, recursive=True)
+
+    @wrap_exceptions
+    def rmdir(self, path):
+        path = self._strip_protocol(path)
+        self.fs.delete_dir(path)
+
+    @wrap_exceptions
+    def modified(self, path):
+        path = self._strip_protocol(path)
+        return self.fs.get_file_info(path).mtime
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        kwargs.setdefault("seekable", start not in [None, 0])
+        return super().cat_file(path, start=None, end=None, **kwargs)
+
+    def get_file(self, rpath, lpath, **kwargs):
+        kwargs.setdefault("seekable", False)
+        super().get_file(rpath, lpath, **kwargs)
+
+
+@mirror_from(
+    "stream",
+    [
+        "read",
+        "seek",
+        "tell",
+        "write",
+        "readable",
+        "writable",
+        "close",
+        "seekable",
+    ],
+)
+class ArrowFile(io.IOBase):
+    def __init__(self, fs, stream, path, mode, block_size=None, **kwargs):
+        self.path = path
+        self.mode = mode
+
+        self.fs = fs
+        self.stream = stream
+
+        self.blocksize = self.block_size = block_size
+        self.kwargs = kwargs
+
+    def __enter__(self):
+        return self
+
+    @property
+    def size(self):
+        if self.stream.seekable():
+            return self.stream.size()
+        return None
+
+    def __exit__(self, *args):
+        return self.close()
+
+
+class HadoopFileSystem(ArrowFSWrapper):
+    """A wrapper on top of the pyarrow.fs.HadoopFileSystem
+    to connect it's interface with fsspec"""
+
+    protocol = "hdfs"
+
+    def __init__(
+        self,
+        host="default",
+        port=0,
+        user=None,
+        kerb_ticket=None,
+        replication=3,
+        extra_conf=None,
+        **kwargs,
+    ):
+        """
+
+        Parameters
+        ----------
+        host: str
+            Hostname, IP or "default" to try to read from Hadoop config
+        port: int
+            Port to connect on, or default from Hadoop config if 0
+        user: str or None
+            If given, connect as this username
+        kerb_ticket: str or None
+            If given, use this ticket for authentication
+        replication: int
+            set replication factor of file for write operations. default value is 3.
+        extra_conf: None or dict
+            Passed on to HadoopFileSystem
+        """
+        from pyarrow.fs import HadoopFileSystem
+
+        fs = HadoopFileSystem(
+            host=host,
+            port=port,
+            user=user,
+            kerb_ticket=kerb_ticket,
+            replication=replication,
+            extra_conf=extra_conf,
+        )
+        super().__init__(fs=fs, **kwargs)
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        ops = infer_storage_options(path)
+        out = {}
+        if ops.get("host", None):
+            out["host"] = ops["host"]
+        if ops.get("username", None):
+            out["user"] = ops["username"]
+        if ops.get("port", None):
+            out["port"] = ops["port"]
+        if ops.get("url_query", None):
+            queries = parse_qs(ops["url_query"])
+            if queries.get("replication", None):
+                out["replication"] = int(queries["replication"][0])
+        return out
diff --git a/lib/python3.11/site-packages/fsspec/implementations/asyn_wrapper.py b/lib/python3.11/site-packages/fsspec/implementations/asyn_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..91db5eb48d00e36b46d9deb49504a7d2ad76d690
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/asyn_wrapper.py
@@ -0,0 +1,124 @@
+import asyncio
+import functools
+import inspect
+
+import fsspec
+from fsspec.asyn import AsyncFileSystem, running_async
+
+from .chained import ChainedFileSystem
+
+
+def async_wrapper(func, obj=None, semaphore=None):
+    """
+    Wraps a synchronous function to make it awaitable.
+
+    Parameters
+    ----------
+    func : callable
+        The synchronous function to wrap.
+    obj : object, optional
+        The instance to bind the function to, if applicable.
+    semaphore : asyncio.Semaphore, optional
+        A semaphore to limit concurrent calls.
+
+    Returns
+    -------
+    coroutine
+        An awaitable version of the function.
+    """
+
+    @functools.wraps(func)
+    async def wrapper(*args, **kwargs):
+        if semaphore:
+            async with semaphore:
+                return await asyncio.to_thread(func, *args, **kwargs)
+        return await asyncio.to_thread(func, *args, **kwargs)
+
+    return wrapper
+
+
+class AsyncFileSystemWrapper(AsyncFileSystem, ChainedFileSystem):
+    """
+    A wrapper class to convert a synchronous filesystem into an asynchronous one.
+
+    This class takes an existing synchronous filesystem implementation and wraps all
+    its methods to provide an asynchronous interface.
+
+    Parameters
+    ----------
+    sync_fs : AbstractFileSystem
+        The synchronous filesystem instance to wrap.
+    """
+
+    protocol = "asyncwrapper", "async_wrapper"
+    cachable = False
+
+    def __init__(
+        self,
+        fs=None,
+        asynchronous=None,
+        target_protocol=None,
+        target_options=None,
+        semaphore=None,
+        max_concurrent_tasks=None,
+        **kwargs,
+    ):
+        if asynchronous is None:
+            asynchronous = running_async()
+        super().__init__(asynchronous=asynchronous, **kwargs)
+        if fs is not None:
+            self.sync_fs = fs
+        else:
+            self.sync_fs = fsspec.filesystem(target_protocol, **target_options)
+        self.protocol = self.sync_fs.protocol
+        self.semaphore = semaphore
+        self._wrap_all_sync_methods()
+
+    @property
+    def fsid(self):
+        return f"async_{self.sync_fs.fsid}"
+
+    def _wrap_all_sync_methods(self):
+        """
+        Wrap all synchronous methods of the underlying filesystem with asynchronous versions.
+        """
+        excluded_methods = {"open"}
+        for method_name in dir(self.sync_fs):
+            if method_name.startswith("_") or method_name in excluded_methods:
+                continue
+
+            attr = inspect.getattr_static(self.sync_fs, method_name)
+            if isinstance(attr, property):
+                continue
+
+            method = getattr(self.sync_fs, method_name)
+            if callable(method) and not inspect.iscoroutinefunction(method):
+                async_method = async_wrapper(method, obj=self, semaphore=self.semaphore)
+                setattr(self, f"_{method_name}", async_method)
+
+    @classmethod
+    def wrap_class(cls, sync_fs_class):
+        """
+        Create a new class that can be used to instantiate an AsyncFileSystemWrapper
+        with lazy instantiation of the underlying synchronous filesystem.
+
+        Parameters
+        ----------
+        sync_fs_class : type
+            The class of the synchronous filesystem to wrap.
+
+        Returns
+        -------
+        type
+            A new class that wraps the provided synchronous filesystem class.
+        """
+
+        class GeneratedAsyncFileSystemWrapper(cls):
+            def __init__(self, *args, **kwargs):
+                sync_fs = sync_fs_class(*args, **kwargs)
+                super().__init__(sync_fs)
+
+        GeneratedAsyncFileSystemWrapper.__name__ = (
+            f"Async{sync_fs_class.__name__}Wrapper"
+        )
+        return GeneratedAsyncFileSystemWrapper
diff --git a/lib/python3.11/site-packages/fsspec/implementations/cache_mapper.py b/lib/python3.11/site-packages/fsspec/implementations/cache_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e7c7d88afdddf12f77b26bb635bd8bf1e2bd7f1
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/cache_mapper.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import abc
+import hashlib
+
+from fsspec.implementations.local import make_path_posix
+
+
+class AbstractCacheMapper(abc.ABC):
+    """Abstract super-class for mappers from remote URLs to local cached
+    basenames.
+    """
+
+    @abc.abstractmethod
+    def __call__(self, path: str) -> str: ...
+
+    def __eq__(self, other: object) -> bool:
+        # Identity only depends on class. When derived classes have attributes
+        # they will need to be included.
+        return isinstance(other, type(self))
+
+    def __hash__(self) -> int:
+        # Identity only depends on class. When derived classes have attributes
+        # they will need to be included.
+        return hash(type(self))
+
+
+class BasenameCacheMapper(AbstractCacheMapper):
+    """Cache mapper that uses the basename of the remote URL and a fixed number
+    of directory levels above this.
+
+    The default is zero directory levels, meaning different paths with the same
+    basename will have the same cached basename.
+    """
+
+    def __init__(self, directory_levels: int = 0):
+        if directory_levels < 0:
+            raise ValueError(
+                "BasenameCacheMapper requires zero or positive directory_levels"
+            )
+        self.directory_levels = directory_levels
+
+        # Separator for directories when encoded as strings.
+        self._separator = "_@_"
+
+    def __call__(self, path: str) -> str:
+        path = make_path_posix(path)
+        prefix, *bits = path.rsplit("/", self.directory_levels + 1)
+        if bits:
+            return self._separator.join(bits)
+        else:
+            return prefix  # No separator found, simple filename
+
+    def __eq__(self, other: object) -> bool:
+        return super().__eq__(other) and self.directory_levels == other.directory_levels
+
+    def __hash__(self) -> int:
+        return super().__hash__() ^ hash(self.directory_levels)
+
+
+class HashCacheMapper(AbstractCacheMapper):
+    """Cache mapper that uses a hash of the remote URL."""
+
+    def __call__(self, path: str) -> str:
+        return hashlib.sha256(path.encode()).hexdigest()
+
+
+def create_cache_mapper(same_names: bool) -> AbstractCacheMapper:
+    """Factory method to create cache mapper for backward compatibility with
+    ``CachingFileSystem`` constructor using ``same_names`` kwarg.
+    """
+    if same_names:
+        return BasenameCacheMapper()
+    else:
+        return HashCacheMapper()
diff --git a/lib/python3.11/site-packages/fsspec/implementations/cache_metadata.py b/lib/python3.11/site-packages/fsspec/implementations/cache_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..7690ff17228a9d489bef4ca937060c4f5855b0ce
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/cache_metadata.py
@@ -0,0 +1,231 @@
+from __future__ import annotations
+
+import os
+import pickle
+import time
+from typing import TYPE_CHECKING
+
+from fsspec.utils import atomic_write
+
+try:
+    import ujson as json
+except ImportError:
+    if not TYPE_CHECKING:
+        import json
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+    from typing import Any, Literal, TypeAlias
+
+    from .cached import CachingFileSystem
+
+    Detail: TypeAlias = dict[str, Any]
+
+
+class CacheMetadata:
+    """Cache metadata.
+
+    All reading and writing of cache metadata is performed by this class,
+    accessing the cached files and blocks is not.
+
+    Metadata is stored in a single file per storage directory in JSON format.
+    For backward compatibility, also reads metadata stored in pickle format
+    which is converted to JSON when next saved.
+    """
+
+    def __init__(self, storage: list[str]):
+        """
+
+        Parameters
+        ----------
+        storage: list[str]
+            Directories containing cached files, must be at least one. Metadata
+            is stored in the last of these directories by convention.
+        """
+        if not storage:
+            raise ValueError("CacheMetadata expects at least one storage location")
+
+        self._storage = storage
+        self.cached_files: list[Detail] = [{}]
+
+        # Private attribute to force saving of metadata in pickle format rather than
+        # JSON for use in tests to confirm can read both pickle and JSON formats.
+        self._force_save_pickle = False
+
+    def _load(self, fn: str) -> Detail:
+        """Low-level function to load metadata from specific file"""
+        try:
+            with open(fn, "r") as f:
+                loaded = json.load(f)
+        except ValueError:
+            with open(fn, "rb") as f:
+                loaded = pickle.load(f)
+        for c in loaded.values():
+            if isinstance(c.get("blocks"), list):
+                c["blocks"] = set(c["blocks"])
+        return loaded
+
+    def _save(self, metadata_to_save: Detail, fn: str) -> None:
+        """Low-level function to save metadata to specific file"""
+        if self._force_save_pickle:
+            with atomic_write(fn) as f:
+                pickle.dump(metadata_to_save, f)
+        else:
+            with atomic_write(fn, mode="w") as f:
+                json.dump(metadata_to_save, f)
+
+    def _scan_locations(
+        self, writable_only: bool = False
+    ) -> Iterator[tuple[str, str, bool]]:
+        """Yield locations (filenames) where metadata is stored, and whether
+        writable or not.
+
+        Parameters
+        ----------
+        writable: bool
+            Set to True to only yield writable locations.
+
+        Returns
+        -------
+        Yields (str, str, bool)
+        """
+        n = len(self._storage)
+        for i, storage in enumerate(self._storage):
+            writable = i == n - 1
+            if writable_only and not writable:
+                continue
+            yield os.path.join(storage, "cache"), storage, writable
+
+    def check_file(
+        self, path: str, cfs: CachingFileSystem | None
+    ) -> Literal[False] | tuple[Detail, str]:
+        """If path is in cache return its details, otherwise return ``False``.
+
+        If the optional CachingFileSystem is specified then it is used to
+        perform extra checks to reject possible matches, such as if they are
+        too old.
+        """
+        for (fn, base, _), cache in zip(self._scan_locations(), self.cached_files):
+            if path not in cache:
+                continue
+            detail = cache[path].copy()
+
+            if cfs is not None:
+                if cfs.check_files and detail["uid"] != cfs.fs.ukey(path):
+                    # Wrong file as determined by hash of file properties
+                    continue
+                if cfs.expiry and time.time() - detail["time"] > cfs.expiry:
+                    # Cached file has expired
+                    continue
+
+            fn = os.path.join(base, detail["fn"])
+            if os.path.exists(fn):
+                return detail, fn
+        return False
+
+    def clear_expired(self, expiry_time: int) -> tuple[list[str], bool]:
+        """Remove expired metadata from the cache.
+
+        Returns names of files corresponding to expired metadata and a boolean
+        flag indicating whether the writable cache is empty. Caller is
+        responsible for deleting the expired files.
+        """
+        expired_files = []
+        for path, detail in self.cached_files[-1].copy().items():
+            if time.time() - detail["time"] > expiry_time:
+                fn = detail.get("fn", "")
+                if not fn:
+                    raise RuntimeError(
+                        f"Cache metadata does not contain 'fn' for {path}"
+                    )
+                fn = os.path.join(self._storage[-1], fn)
+                expired_files.append(fn)
+                self.cached_files[-1].pop(path)
+
+        if self.cached_files[-1]:
+            cache_path = os.path.join(self._storage[-1], "cache")
+            self._save(self.cached_files[-1], cache_path)
+
+        writable_cache_empty = not self.cached_files[-1]
+        return expired_files, writable_cache_empty
+
+    def load(self) -> None:
+        """Load all metadata from disk and store in ``self.cached_files``"""
+        cached_files = []
+        for fn, _, _ in self._scan_locations():
+            if os.path.exists(fn):
+                # TODO: consolidate blocks here
+                cached_files.append(self._load(fn))
+            else:
+                cached_files.append({})
+        self.cached_files = cached_files or [{}]
+
+    def on_close_cached_file(self, f: Any, path: str) -> None:
+        """Perform side-effect actions on closing a cached file.
+
+        The actual closing of the file is the responsibility of the caller.
+        """
+        # File must be writable, so in self.cached_files[-1]
+        c = self.cached_files[-1][path]
+        if c["blocks"] is not True and len(c["blocks"]) * f.blocksize >= f.size:
+            c["blocks"] = True
+
+    def pop_file(self, path: str) -> str | None:
+        """Remove metadata of cached file.
+
+        If path is in the cache, return the filename of the cached file,
+        otherwise return ``None``.  Caller is responsible for deleting the
+        cached file.
+        """
+        details = self.check_file(path, None)
+        if not details:
+            return None
+        _, fn = details
+        if fn.startswith(self._storage[-1]):
+            self.cached_files[-1].pop(path)
+            self.save()
+        else:
+            raise PermissionError(
+                "Can only delete cached file in last, writable cache location"
+            )
+        return fn
+
+    def save(self) -> None:
+        """Save metadata to disk"""
+        for (fn, _, writable), cache in zip(self._scan_locations(), self.cached_files):
+            if not writable:
+                continue
+
+            if os.path.exists(fn):
+                cached_files = self._load(fn)
+                for k, c in cached_files.items():
+                    if k in cache:
+                        if c["blocks"] is True or cache[k]["blocks"] is True:
+                            c["blocks"] = True
+                        else:
+                            # self.cached_files[*][*]["blocks"] must continue to
+                            # point to the same set object so that updates
+                            # performed by MMapCache are propagated back to
+                            # self.cached_files.
+                            blocks = cache[k]["blocks"]
+                            blocks.update(c["blocks"])
+                            c["blocks"] = blocks
+                        c["time"] = max(c["time"], cache[k]["time"])
+                        c["uid"] = cache[k]["uid"]
+
+                # Files can be added to cache after it was written once
+                for k, c in cache.items():
+                    if k not in cached_files:
+                        cached_files[k] = c
+            else:
+                cached_files = cache
+            cache = {k: v.copy() for k, v in cached_files.items()}
+            for c in cache.values():
+                if isinstance(c["blocks"], set):
+                    c["blocks"] = list(c["blocks"])
+            self._save(cache, fn)
+            self.cached_files[-1] = cached_files
+
+    def update_file(self, path: str, detail: Detail) -> None:
+        """Update metadata for specific file in memory, do not save"""
+        self.cached_files[-1][path] = detail
diff --git a/lib/python3.11/site-packages/fsspec/implementations/cached.py b/lib/python3.11/site-packages/fsspec/implementations/cached.py
new file mode 100644
index 0000000000000000000000000000000000000000..c140f8d617d7cfe5781b01344ab3fb77e0fe3775
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/cached.py
@@ -0,0 +1,1024 @@
+from __future__ import annotations
+
+import inspect
+import logging
+import os
+import tempfile
+import time
+import weakref
+from collections.abc import Callable
+from shutil import rmtree
+from typing import TYPE_CHECKING, Any, ClassVar
+
+from fsspec import filesystem
+from fsspec.callbacks import DEFAULT_CALLBACK
+from fsspec.compression import compr
+from fsspec.core import BaseCache, MMapCache
+from fsspec.exceptions import BlocksizeMismatchError
+from fsspec.implementations.cache_mapper import create_cache_mapper
+from fsspec.implementations.cache_metadata import CacheMetadata
+from fsspec.implementations.chained import ChainedFileSystem
+from fsspec.implementations.local import LocalFileSystem
+from fsspec.spec import AbstractBufferedFile
+from fsspec.transaction import Transaction
+from fsspec.utils import infer_compression
+
+if TYPE_CHECKING:
+    from fsspec.implementations.cache_mapper import AbstractCacheMapper
+
+logger = logging.getLogger("fsspec.cached")
+
+
+class WriteCachedTransaction(Transaction):
+    def complete(self, commit=True):
+        rpaths = [f.path for f in self.files]
+        lpaths = [f.fn for f in self.files]
+        if commit:
+            self.fs.put(lpaths, rpaths)
+        self.files.clear()
+        self.fs._intrans = False
+        self.fs._transaction = None
+        self.fs = None  # break cycle
+
+
+class CachingFileSystem(ChainedFileSystem):
+    """Locally caching filesystem, layer over any other FS
+
+    This class implements chunk-wise local storage of remote files, for quick
+    access after the initial download. The files are stored in a given
+    directory with hashes of URLs for the filenames. If no directory is given,
+    a temporary one is used, which should be cleaned up by the OS after the
+    process ends. The files themselves are sparse (as implemented in
+    :class:`~fsspec.caching.MMapCache`), so only the data which is accessed
+    takes up space.
+
+    Restrictions:
+
+    - the block-size must be the same for each access of a given file, unless
+      all blocks of the file have already been read
+    - caching can only be applied to file-systems which produce files
+      derived from fsspec.spec.AbstractBufferedFile ; LocalFileSystem is also
+      allowed, for testing
+    """
+
+    protocol: ClassVar[str | tuple[str, ...]] = ("blockcache", "cached")
+    _strip_tokenize_options = ("fo",)
+
+    def __init__(
+        self,
+        target_protocol=None,
+        cache_storage="TMP",
+        cache_check=10,
+        check_files=False,
+        expiry_time=604800,
+        target_options=None,
+        fs=None,
+        same_names: bool | None = None,
+        compression=None,
+        cache_mapper: AbstractCacheMapper | None = None,
+        **kwargs,
+    ):
+        """
+
+        Parameters
+        ----------
+        target_protocol: str (optional)
+            Target filesystem protocol. Provide either this or ``fs``.
+        cache_storage: str or list(str)
+            Location to store files. If "TMP", this is a temporary directory,
+            and will be cleaned up by the OS when this process ends (or later).
+            If a list, each location will be tried in the order given, but
+            only the last will be considered writable.
+        cache_check: int
+            Number of seconds between reload of cache metadata
+        check_files: bool
+            Whether to explicitly see if the UID of the remote file matches
+            the stored one before using. Warning: some file systems such as
+            HTTP cannot reliably give a unique hash of the contents of some
+            path, so be sure to set this option to False.
+        expiry_time: int
+            The time in seconds after which a local copy is considered useless.
+            Set to falsy to prevent expiry. The default is equivalent to one
+            week.
+        target_options: dict or None
+            Passed to the instantiation of the FS, if fs is None.
+        fs: filesystem instance
+            The target filesystem to run against. Provide this or ``protocol``.
+        same_names: bool (optional)
+            By default, target URLs are hashed using a ``HashCacheMapper`` so
+            that files from different backends with the same basename do not
+            conflict. If this argument is ``true``, a ``BasenameCacheMapper``
+            is used instead. Other cache mapper options are available by using
+            the ``cache_mapper`` keyword argument. Only one of this and
+            ``cache_mapper`` should be specified.
+        compression: str (optional)
+            To decompress on download. Can be 'infer' (guess from the URL name),
+            one of the entries in ``fsspec.compression.compr``, or None for no
+            decompression.
+        cache_mapper: AbstractCacheMapper (optional)
+            The object use to map from original filenames to cached filenames.
+            Only one of this and ``same_names`` should be specified.
+        """
+        super().__init__(**kwargs)
+        if fs is None and target_protocol is None:
+            raise ValueError(
+                "Please provide filesystem instance(fs) or target_protocol"
+            )
+        if not (fs is None) ^ (target_protocol is None):
+            raise ValueError(
+                "Both filesystems (fs) and target_protocol may not be both given."
+            )
+        if cache_storage == "TMP":
+            tempdir = tempfile.mkdtemp()
+            storage = [tempdir]
+            weakref.finalize(self, self._remove_tempdir, tempdir)
+        else:
+            if isinstance(cache_storage, str):
+                storage = [cache_storage]
+            else:
+                storage = cache_storage
+        os.makedirs(storage[-1], exist_ok=True)
+        self.storage = storage
+        self.kwargs = target_options or {}
+        self.cache_check = cache_check
+        self.check_files = check_files
+        self.expiry = expiry_time
+        self.compression = compression
+
+        # Size of cache in bytes. If None then the size is unknown and will be
+        # recalculated the next time cache_size() is called. On writes to the
+        # cache this is reset to None.
+        self._cache_size = None
+
+        if same_names is not None and cache_mapper is not None:
+            raise ValueError(
+                "Cannot specify both same_names and cache_mapper in "
+                "CachingFileSystem.__init__"
+            )
+        if cache_mapper is not None:
+            self._mapper = cache_mapper
+        else:
+            self._mapper = create_cache_mapper(
+                same_names if same_names is not None else False
+            )
+
+        self.target_protocol = (
+            target_protocol
+            if isinstance(target_protocol, str)
+            else (fs.protocol if isinstance(fs.protocol, str) else fs.protocol[0])
+        )
+        self._metadata = CacheMetadata(self.storage)
+        self.load_cache()
+        self.fs = fs if fs is not None else filesystem(target_protocol, **self.kwargs)
+
+        def _strip_protocol(path):
+            # acts as a method, since each instance has a difference target
+            return self.fs._strip_protocol(type(self)._strip_protocol(path))
+
+        self._strip_protocol: Callable = _strip_protocol
+
+    @staticmethod
+    def _remove_tempdir(tempdir):
+        try:
+            rmtree(tempdir)
+        except Exception:
+            pass
+
+    def _mkcache(self):
+        os.makedirs(self.storage[-1], exist_ok=True)
+
+    def cache_size(self):
+        """Return size of cache in bytes.
+
+        If more than one cache directory is in use, only the size of the last
+        one (the writable cache directory) is returned.
+        """
+        if self._cache_size is None:
+            cache_dir = self.storage[-1]
+            self._cache_size = filesystem("file").du(cache_dir, withdirs=True)
+        return self._cache_size
+
+    def load_cache(self):
+        """Read set of stored blocks from file"""
+        self._metadata.load()
+        self._mkcache()
+        self.last_cache = time.time()
+
+    def save_cache(self):
+        """Save set of stored blocks from file"""
+        self._mkcache()
+        self._metadata.save()
+        self.last_cache = time.time()
+        self._cache_size = None
+
+    def _check_cache(self):
+        """Reload caches if time elapsed or any disappeared"""
+        self._mkcache()
+        if not self.cache_check:
+            # explicitly told not to bother checking
+            return
+        timecond = time.time() - self.last_cache > self.cache_check
+        existcond = all(os.path.exists(storage) for storage in self.storage)
+        if timecond or not existcond:
+            self.load_cache()
+
+    def _check_file(self, path):
+        """Is path in cache and still valid"""
+        path = self._strip_protocol(path)
+        self._check_cache()
+        return self._metadata.check_file(path, self)
+
+    def clear_cache(self):
+        """Remove all files and metadata from the cache
+
+        In the case of multiple cache locations, this clears only the last one,
+        which is assumed to be the read/write one.
+        """
+        rmtree(self.storage[-1])
+        self.load_cache()
+        self._cache_size = None
+
+    def clear_expired_cache(self, expiry_time=None):
+        """Remove all expired files and metadata from the cache
+
+        In the case of multiple cache locations, this clears only the last one,
+        which is assumed to be the read/write one.
+
+        Parameters
+        ----------
+        expiry_time: int
+            The time in seconds after which a local copy is considered useless.
+            If not defined the default is equivalent to the attribute from the
+            file caching instantiation.
+        """
+
+        if not expiry_time:
+            expiry_time = self.expiry
+
+        self._check_cache()
+
+        expired_files, writable_cache_empty = self._metadata.clear_expired(expiry_time)
+        for fn in expired_files:
+            if os.path.exists(fn):
+                os.remove(fn)
+
+        if writable_cache_empty:
+            rmtree(self.storage[-1])
+            self.load_cache()
+
+        self._cache_size = None
+
+    def pop_from_cache(self, path):
+        """Remove cached version of given file
+
+        Deletes local copy of the given (remote) path. If it is found in a cache
+        location which is not the last, it is assumed to be read-only, and
+        raises PermissionError
+        """
+        path = self._strip_protocol(path)
+        fn = self._metadata.pop_file(path)
+        if fn is not None:
+            os.remove(fn)
+        self._cache_size = None
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        """Wrap the target _open
+
+        If the whole file exists in the cache, just open it locally and
+        return that.
+
+        Otherwise, open the file on the target FS, and make it have a mmap
+        cache pointing to the location which we determine, in our cache.
+        The ``blocks`` instance is shared, so as the mmap cache instance
+        updates, so does the entry in our ``cached_files`` attribute.
+        We monkey-patch this file, so that when it closes, we call
+        ``close_and_update`` to save the state of the blocks.
+        """
+        path = self._strip_protocol(path)
+
+        path = self.fs._strip_protocol(path)
+        if "r" not in mode:
+            return self.fs._open(
+                path,
+                mode=mode,
+                block_size=block_size,
+                autocommit=autocommit,
+                cache_options=cache_options,
+                **kwargs,
+            )
+        detail = self._check_file(path)
+        if detail:
+            # file is in cache
+            detail, fn = detail
+            hash, blocks = detail["fn"], detail["blocks"]
+            if blocks is True:
+                # stored file is complete
+                logger.debug("Opening local copy of %s", path)
+                return open(fn, mode)
+            # TODO: action where partial file exists in read-only cache
+            logger.debug("Opening partially cached copy of %s", path)
+        else:
+            hash = self._mapper(path)
+            fn = os.path.join(self.storage[-1], hash)
+            blocks = set()
+            detail = {
+                "original": path,
+                "fn": hash,
+                "blocks": blocks,
+                "time": time.time(),
+                "uid": self.fs.ukey(path),
+            }
+            self._metadata.update_file(path, detail)
+            logger.debug("Creating local sparse file for %s", path)
+
+        # explicitly submitting the size to the open call will avoid extra
+        # operations when opening. This is particularly relevant
+        # for any file that is read over a network, e.g. S3.
+        size = detail.get("size")
+
+        # call target filesystems open
+        self._mkcache()
+        f = self.fs._open(
+            path,
+            mode=mode,
+            block_size=block_size,
+            autocommit=autocommit,
+            cache_options=cache_options,
+            cache_type="none",
+            size=size,
+            **kwargs,
+        )
+
+        # set size if not already set
+        if size is None:
+            detail["size"] = f.size
+            self._metadata.update_file(path, detail)
+
+        if self.compression:
+            comp = (
+                infer_compression(path)
+                if self.compression == "infer"
+                else self.compression
+            )
+            f = compr[comp](f, mode="rb")
+        if "blocksize" in detail:
+            if detail["blocksize"] != f.blocksize:
+                raise BlocksizeMismatchError(
+                    f"Cached file must be reopened with same block"
+                    f" size as original (old: {detail['blocksize']},"
+                    f" new {f.blocksize})"
+                )
+        else:
+            detail["blocksize"] = f.blocksize
+
+        def _fetch_ranges(ranges):
+            return self.fs.cat_ranges(
+                [path] * len(ranges),
+                [r[0] for r in ranges],
+                [r[1] for r in ranges],
+                **kwargs,
+            )
+
+        multi_fetcher = None if self.compression else _fetch_ranges
+        f.cache = MMapCache(
+            f.blocksize, f._fetch_range, f.size, fn, blocks, multi_fetcher=multi_fetcher
+        )
+        close = f.close
+        f.close = lambda: self.close_and_update(f, close)
+        self.save_cache()
+        return f
+
+    def _parent(self, path):
+        return self.fs._parent(path)
+
+    def hash_name(self, path: str, *args: Any) -> str:
+        # Kept for backward compatibility with downstream libraries.
+        # Ignores extra arguments, previously same_name boolean.
+        return self._mapper(path)
+
+    def close_and_update(self, f, close):
+        """Called when a file is closing, so store the set of blocks"""
+        if f.closed:
+            return
+        path = self._strip_protocol(f.path)
+        self._metadata.on_close_cached_file(f, path)
+        try:
+            logger.debug("going to save")
+            self.save_cache()
+            logger.debug("saved")
+        except OSError:
+            logger.debug("Cache saving failed while closing file")
+        except NameError:
+            logger.debug("Cache save failed due to interpreter shutdown")
+        close()
+        f.closed = True
+
+    def ls(self, path, detail=True):
+        return self.fs.ls(path, detail)
+
+    def __getattribute__(self, item):
+        if item in {
+            "load_cache",
+            "_get_cached_file_before_open",
+            "_open",
+            "save_cache",
+            "close_and_update",
+            "__init__",
+            "__getattribute__",
+            "__reduce__",
+            "_make_local_details",
+            "open",
+            "cat",
+            "cat_file",
+            "_cat_file",
+            "cat_ranges",
+            "_cat_ranges",
+            "get",
+            "read_block",
+            "tail",
+            "head",
+            "info",
+            "ls",
+            "exists",
+            "isfile",
+            "isdir",
+            "_check_file",
+            "_check_cache",
+            "_mkcache",
+            "clear_cache",
+            "clear_expired_cache",
+            "pop_from_cache",
+            "local_file",
+            "_paths_from_path",
+            "get_mapper",
+            "open_many",
+            "commit_many",
+            "hash_name",
+            "__hash__",
+            "__eq__",
+            "to_json",
+            "to_dict",
+            "cache_size",
+            "pipe_file",
+            "pipe",
+            "start_transaction",
+            "end_transaction",
+        }:
+            # all the methods defined in this class. Note `open` here, since
+            # it calls `_open`, but is actually in superclass
+            if hasattr(type(self), item):
+                return lambda *args, **kw: getattr(type(self), item).__get__(self)(
+                    *args, **kw
+                )
+            # method is in the whitelist but not defined on this subclass;
+            # fall through to delegate to the wrapped filesystem below
+        if item in ["__reduce_ex__"]:
+            raise AttributeError
+        if item in ["transaction"]:
+            # property
+            return type(self).transaction.__get__(self)
+        if item in {"_cache", "transaction_type", "protocol"}:
+            # class attributes
+            return getattr(type(self), item)
+        if item == "__class__":
+            return type(self)
+        d = object.__getattribute__(self, "__dict__")
+        fs = d.get("fs", None)  # fs is not immediately defined
+        if item in d:
+            return d[item]
+        elif fs is not None:
+            if item in fs.__dict__:
+                # attribute of instance
+                return fs.__dict__[item]
+            # attributed belonging to the target filesystem
+            cls = type(fs)
+            m = getattr(cls, item)
+            if (inspect.isfunction(m) or inspect.isdatadescriptor(m)) and (
+                not hasattr(m, "__self__") or m.__self__ is None
+            ):
+                # instance method
+                return m.__get__(fs, cls)
+            return m  # class method or attribute
+        else:
+            # attributes of the superclass, while target is being set up
+            return super().__getattribute__(item)
+
+    def __eq__(self, other):
+        """Test for equality."""
+        if self is other:
+            return True
+        if not isinstance(other, type(self)):
+            return False
+        return (
+            self.storage == other.storage
+            and self.kwargs == other.kwargs
+            and self.cache_check == other.cache_check
+            and self.check_files == other.check_files
+            and self.expiry == other.expiry
+            and self.compression == other.compression
+            and self._mapper == other._mapper
+            and self.target_protocol == other.target_protocol
+        )
+
+    def __hash__(self):
+        """Calculate hash."""
+        return (
+            hash(tuple(self.storage))
+            ^ hash(str(self.kwargs))
+            ^ hash(self.cache_check)
+            ^ hash(self.check_files)
+            ^ hash(self.expiry)
+            ^ hash(self.compression)
+            ^ hash(self._mapper)
+            ^ hash(self.target_protocol)
+        )
+
+
+class WholeFileCacheFileSystem(CachingFileSystem):
+    """Caches whole remote files on first access
+
+    This class is intended as a layer over any other file system, and
+    will make a local copy of each file accessed, so that all subsequent
+    reads are local. This is similar to ``CachingFileSystem``, but without
+    the block-wise functionality and so can work even when sparse files
+    are not allowed. See its docstring for definition of the init
+    arguments.
+
+    The class still needs access to the remote store for listing files,
+    and may refresh cached files.
+    """
+
+    protocol = "filecache"
+    local_file = True
+
+    def open_many(self, open_files, **kwargs):
+        paths = [of.path for of in open_files]
+        if "r" in open_files.mode:
+            self._mkcache()
+        else:
+            return [
+                LocalTempFile(
+                    self.fs,
+                    path,
+                    mode=open_files.mode,
+                    fn=os.path.join(self.storage[-1], self._mapper(path)),
+                    **kwargs,
+                )
+                for path in paths
+            ]
+
+        if self.compression:
+            raise NotImplementedError
+        details = [self._check_file(sp) for sp in paths]
+        downpath = [p for p, d in zip(paths, details) if not d]
+        downfn0 = [
+            os.path.join(self.storage[-1], self._mapper(p))
+            for p, d in zip(paths, details)
+        ]  # keep these path names for opening later
+        downfn = [fn for fn, d in zip(downfn0, details) if not d]
+        if downpath:
+            # skip if all files are already cached and up to date
+            self.fs.get(downpath, downfn)
+
+            # update metadata - only happens when downloads are successful
+            newdetail = [
+                {
+                    "original": path,
+                    "fn": self._mapper(path),
+                    "blocks": True,
+                    "time": time.time(),
+                    "uid": self.fs.ukey(path),
+                }
+                for path in downpath
+            ]
+            for path, detail in zip(downpath, newdetail):
+                self._metadata.update_file(path, detail)
+            self.save_cache()
+
+        def firstpart(fn):
+            # helper to adapt both whole-file and simple-cache
+            return fn[1] if isinstance(fn, tuple) else fn
+
+        return [
+            open(firstpart(fn0) if fn0 else fn1, mode=open_files.mode)
+            for fn0, fn1 in zip(details, downfn0)
+        ]
+
+    def commit_many(self, open_files):
+        self.fs.put([f.fn for f in open_files], [f.path for f in open_files])
+        [f.close() for f in open_files]
+        for f in open_files:
+            # in case autocommit is off, and so close did not already delete
+            try:
+                os.remove(f.name)
+            except FileNotFoundError:
+                pass
+        self._cache_size = None
+
+    def _make_local_details(self, path):
+        hash = self._mapper(path)
+        fn = os.path.join(self.storage[-1], hash)
+        detail = {
+            "original": path,
+            "fn": hash,
+            "blocks": True,
+            "time": time.time(),
+            "uid": self.fs.ukey(path),
+        }
+        self._metadata.update_file(path, detail)
+        logger.debug("Copying %s to local cache", path)
+        return fn
+
+    def cat(
+        self,
+        path,
+        recursive=False,
+        on_error="raise",
+        callback=DEFAULT_CALLBACK,
+        **kwargs,
+    ):
+        paths = self.expand_path(
+            path, recursive=recursive, maxdepth=kwargs.get("maxdepth")
+        )
+        getpaths = []
+        storepaths = []
+        fns = []
+        out = {}
+        for p in paths.copy():
+            try:
+                detail = self._check_file(p)
+                if not detail:
+                    fn = self._make_local_details(p)
+                    getpaths.append(p)
+                    storepaths.append(fn)
+                else:
+                    detail, fn = detail if isinstance(detail, tuple) else (None, detail)
+                fns.append(fn)
+            except Exception as e:
+                if on_error == "raise":
+                    raise
+                if on_error == "return":
+                    out[p] = e
+                paths.remove(p)
+
+        if getpaths:
+            self.fs.get(getpaths, storepaths)
+            self.save_cache()
+
+        callback.set_size(len(paths))
+        for p, fn in zip(paths, fns):
+            with open(fn, "rb") as f:
+                out[p] = f.read()
+            callback.relative_update(1)
+        if isinstance(path, str) and len(paths) == 1 and recursive is False:
+            out = out[paths[0]]
+        return out
+
+    def _get_cached_file_before_open(self, path, **kwargs):
+        fn = self._make_local_details(path)
+        # call target filesystems open
+        self._mkcache()
+        if self.compression:
+            with self.fs._open(path, mode="rb", **kwargs) as f, open(fn, "wb") as f2:
+                if isinstance(f, AbstractBufferedFile):
+                    # want no type of caching if just downloading whole thing
+                    f.cache = BaseCache(0, f.cache.fetcher, f.size)
+                comp = (
+                    infer_compression(path)
+                    if self.compression == "infer"
+                    else self.compression
+                )
+                f = compr[comp](f, mode="rb")
+                data = True
+                while data:
+                    block = getattr(f, "blocksize", 5 * 2**20)
+                    data = f.read(block)
+                    f2.write(data)
+        else:
+            self.fs.get_file(path, fn)
+        self.save_cache()
+
+    def _open(self, path, mode="rb", **kwargs):
+        path = self._strip_protocol(path)
+        # For read (or append), (try) download from remote
+        if "r" in mode or "a" in mode:
+            if not self._check_file(path):
+                if self.fs.exists(path):
+                    self._get_cached_file_before_open(path, **kwargs)
+                elif "r" in mode:
+                    raise FileNotFoundError(path)
+
+            detail, fn = self._check_file(path)
+            _, blocks = detail["fn"], detail["blocks"]
+            if blocks is True:
+                logger.debug("Opening local copy of %s", path)
+            else:
+                raise ValueError(
+                    f"Attempt to open partially cached file {path}"
+                    f" as a wholly cached file"
+                )
+
+        # Just reading does not need special file handling
+        if "r" in mode and "+" not in mode:
+            # In order to support downstream filesystems to be able to
+            # infer the compression from the original filename, like
+            # the `TarFileSystem`, let's extend the `io.BufferedReader`
+            # fileobject protocol by adding a dedicated attribute
+            # `original`.
+            f = open(fn, mode)
+            f.original = detail.get("original")
+            return f
+
+        hash = self._mapper(path)
+        fn = os.path.join(self.storage[-1], hash)
+        user_specified_kwargs = {
+            k: v
+            for k, v in kwargs.items()
+            # those kwargs were added by open(), we don't want them
+            if k not in ["autocommit", "block_size", "cache_options"]
+        }
+        return LocalTempFile(self, path, mode=mode, fn=fn, **user_specified_kwargs)
+
+    async def _cat_file(self, path, start=None, end=None, **kwargs):
+        logger.debug("async cat_file %s", path)
+        path = self._strip_protocol(path)
+        sha = self._mapper(path)
+        fn = self._check_file(path)
+
+        if not fn:
+            fn = os.path.join(self.storage[-1], sha)
+            await self.fs._get_file(path, fn, **kwargs)
+
+        with open(fn, "rb") as f:  # noqa ASYNC230
+            if start:
+                f.seek(start)
+            size = -1 if end is None else end - f.tell()
+            return f.read(size)
+
+    async def _cat_ranges(
+        self, paths, starts, ends, max_gap=None, on_error="return", **kwargs
+    ):
+        logger.debug("async cat ranges %s", paths)
+        lpaths = []
+        rset = set()
+        download = []
+        rpaths = []
+        for p in paths:
+            fn = self._check_file(p)
+            if fn is None and p not in rset:
+                sha = self._mapper(p)
+                fn = os.path.join(self.storage[-1], sha)
+                download.append(fn)
+                rset.add(p)
+                rpaths.append(p)
+            lpaths.append(fn)
+        if download:
+            await self.fs._get(rpaths, download, on_error=on_error)
+
+        return LocalFileSystem().cat_ranges(
+            lpaths, starts, ends, max_gap=max_gap, on_error=on_error, **kwargs
+        )
+
+
+class SimpleCacheFileSystem(WholeFileCacheFileSystem):
+    """Caches whole remote files on first access
+
+    This class is intended as a layer over any other file system, and
+    will make a local copy of each file accessed, so that all subsequent
+    reads are local. This implementation only copies whole files, and
+    does not keep any metadata about the download time or file details.
+    It is therefore safer to use in multi-threaded/concurrent situations.
+
+    This is the only of the caching filesystems that supports write: you will
+    be given a real local open file, and upon close and commit, it will be
+    uploaded to the target filesystem; the writability or the target URL is
+    not checked until that time.
+
+    """
+
+    protocol = "simplecache"
+    local_file = True
+    transaction_type = WriteCachedTransaction
+
+    def __init__(self, **kwargs):
+        kw = kwargs.copy()
+        for key in ["cache_check", "expiry_time", "check_files"]:
+            kw[key] = False
+        super().__init__(**kw)
+        for storage in self.storage:
+            if not os.path.exists(storage):
+                os.makedirs(storage, exist_ok=True)
+
+    def _check_file(self, path):
+        self._check_cache()
+        sha = self._mapper(path)
+        for storage in self.storage:
+            fn = os.path.join(storage, sha)
+            if os.path.exists(fn):
+                return fn
+
+    def save_cache(self):
+        pass
+
+    def load_cache(self):
+        pass
+
+    def pipe_file(self, path, value=None, **kwargs):
+        if self._intrans:
+            with self.open(path, "wb") as f:
+                f.write(value)
+        else:
+            super().pipe_file(path, value)
+
+    def ls(self, path, detail=True, **kwargs):
+        path = self._strip_protocol(path)
+        details = []
+        try:
+            details = self.fs.ls(
+                path, detail=True, **kwargs
+            ).copy()  # don't edit original!
+        except FileNotFoundError as e:
+            ex = e
+        else:
+            ex = None
+        if self._intrans:
+            path1 = path.rstrip("/") + "/"
+            for f in self.transaction.files:
+                if f.path == path:
+                    details.append(
+                        {"name": path, "size": f.size or f.tell(), "type": "file"}
+                    )
+                elif f.path.startswith(path1):
+                    if f.path.count("/") == path1.count("/"):
+                        details.append(
+                            {"name": f.path, "size": f.size or f.tell(), "type": "file"}
+                        )
+                    else:
+                        dname = "/".join(f.path.split("/")[: path1.count("/") + 1])
+                        details.append({"name": dname, "size": 0, "type": "directory"})
+        if ex is not None and not details:
+            raise ex
+        if detail:
+            return details
+        return sorted(_["name"] for _ in details)
+
+    def info(self, path, **kwargs):
+        path = self._strip_protocol(path)
+        if self._intrans:
+            f = [_ for _ in self.transaction.files if _.path == path]
+            if f:
+                size = os.path.getsize(f[0].fn) if f[0].closed else f[0].tell()
+                return {"name": path, "size": size, "type": "file"}
+            f = any(_.path.startswith(path + "/") for _ in self.transaction.files)
+            if f:
+                return {"name": path, "size": 0, "type": "directory"}
+        return self.fs.info(path, **kwargs)
+
+    def pipe(self, path, value=None, **kwargs):
+        if isinstance(path, str):
+            self.pipe_file(self._strip_protocol(path), value, **kwargs)
+        elif isinstance(path, dict):
+            for k, v in path.items():
+                self.pipe_file(self._strip_protocol(k), v, **kwargs)
+        else:
+            raise ValueError("path must be str or dict")
+
+    def cat_ranges(
+        self, paths, starts, ends, max_gap=None, on_error="return", **kwargs
+    ):
+        logger.debug("cat ranges %s", paths)
+        lpaths = [self._check_file(p) for p in paths]
+        rpaths = [p for l, p in zip(lpaths, paths) if l is False]
+        lpaths = [l for l, p in zip(lpaths, paths) if l is False]
+        self.fs.get(rpaths, lpaths)
+        paths = [self._check_file(p) for p in paths]
+        return LocalFileSystem().cat_ranges(
+            paths, starts, ends, max_gap=max_gap, on_error=on_error, **kwargs
+        )
+
+    def _get_cached_file_before_open(self, path, **kwargs):
+        sha = self._mapper(path)
+        fn = os.path.join(self.storage[-1], sha)
+        logger.debug("Copying %s to local cache", path)
+
+        self._mkcache()
+        self._cache_size = None
+
+        if self.compression:
+            with self.fs._open(path, mode="rb", **kwargs) as f, open(fn, "wb") as f2:
+                if isinstance(f, AbstractBufferedFile):
+                    # want no type of caching if just downloading whole thing
+                    f.cache = BaseCache(0, f.cache.fetcher, f.size)
+                comp = (
+                    infer_compression(path)
+                    if self.compression == "infer"
+                    else self.compression
+                )
+                f = compr[comp](f, mode="rb")
+                data = True
+                while data:
+                    block = getattr(f, "blocksize", 5 * 2**20)
+                    data = f.read(block)
+                    f2.write(data)
+        else:
+            self.fs.get_file(path, fn)
+
+    def _open(self, path, mode="rb", **kwargs):
+        path = self._strip_protocol(path)
+        sha = self._mapper(path)
+
+        # For read (or append), (try) download from remote
+        if "r" in mode or "a" in mode:
+            if not self._check_file(path):
+                # append does not require an existing file but read does
+                if self.fs.exists(path):
+                    self._get_cached_file_before_open(path, **kwargs)
+                elif "r" in mode:
+                    raise FileNotFoundError(path)
+
+        fn = self._check_file(path)
+        # Just reading does not need special file handling
+        if "r" in mode and "+" not in mode:
+            return open(fn, mode)
+
+        fn = os.path.join(self.storage[-1], sha)
+        user_specified_kwargs = {
+            k: v
+            for k, v in kwargs.items()
+            if k not in ["autocommit", "block_size", "cache_options"]
+        }  # those were added by open()
+        return LocalTempFile(
+            self,
+            path,
+            mode=mode,
+            autocommit=not self._intrans,
+            fn=fn,
+            **user_specified_kwargs,
+        )
+
+
+class LocalTempFile:
+    """A temporary local file, which will be uploaded on commit"""
+
+    def __init__(self, fs, path, fn, mode="wb", autocommit=True, seek=0, **kwargs):
+        self.fn = fn
+        self.fh = open(fn, mode)
+        self.mode = mode
+        if seek:
+            self.fh.seek(seek)
+        self.path = path
+        self.size = None
+        self.fs = fs
+        self.closed = False
+        self.autocommit = autocommit
+        self.kwargs = kwargs
+
+    def __reduce__(self):
+        # always open in r+b to allow continuing writing at a location
+        return (
+            LocalTempFile,
+            (self.fs, self.path, self.fn, "r+b", self.autocommit, self.tell()),
+        )
+
+    def __enter__(self):
+        return self.fh
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+    def close(self):
+        # self.size = self.fh.tell()
+        if self.closed:
+            return
+        self.fh.close()
+        self.closed = True
+        if self.autocommit:
+            self.commit()
+
+    def discard(self):
+        self.fh.close()
+        os.remove(self.fn)
+
+    def commit(self):
+        # calling put() with list arguments avoids path expansion and additional operations
+        # like isdir()
+        self.fs.put([self.fn], [self.path], **self.kwargs)
+        # we do not delete the local copy, it's still in the cache.
+
+    @property
+    def name(self):
+        return self.fn
+
+    def __repr__(self) -> str:
+        return f"LocalTempFile: {self.path}"
+
+    def __getattr__(self, item):
+        return getattr(self.fh, item)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/chained.py b/lib/python3.11/site-packages/fsspec/implementations/chained.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfce64334e8db0272eefa96b4428b23524b059f0
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/chained.py
@@ -0,0 +1,23 @@
+from typing import ClassVar
+
+from fsspec import AbstractFileSystem
+
+__all__ = ("ChainedFileSystem",)
+
+
+class ChainedFileSystem(AbstractFileSystem):
+    """Chained filesystem base class.
+
+    A chained filesystem is designed to be layered over another FS.
+    This is useful to implement things like caching.
+
+    This base class does very little on its own, but is used as a marker
+    that the class is designed for chaining.
+
+    Right now this is only used in `url_to_fs` to provide the path argument
+    (`fo`) to the chained filesystem from the underlying filesystem.
+
+    Additional functionality may be added in the future.
+    """
+
+    protocol: ClassVar[str] = "chained"
diff --git a/lib/python3.11/site-packages/fsspec/implementations/dask.py b/lib/python3.11/site-packages/fsspec/implementations/dask.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e1276463db6866665e6a0fe114efc247971b57e
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/dask.py
@@ -0,0 +1,152 @@
+import dask
+from distributed.client import Client, _get_global_client
+from distributed.worker import Worker
+
+from fsspec import filesystem
+from fsspec.spec import AbstractBufferedFile, AbstractFileSystem
+from fsspec.utils import infer_storage_options
+
+
+def _get_client(client):
+    if client is None:
+        return _get_global_client()
+    elif isinstance(client, Client):
+        return client
+    else:
+        # e.g., connection string
+        return Client(client)
+
+
+def _in_worker():
+    return bool(Worker._instances)
+
+
+class DaskWorkerFileSystem(AbstractFileSystem):
+    """View files accessible to a worker as any other remote file-system
+
+    When instances are run on the worker, uses the real filesystem. When
+    run on the client, they call the worker to provide information or data.
+
+    **Warning** this implementation is experimental, and read-only for now.
+    """
+
+    def __init__(
+        self, target_protocol=None, target_options=None, fs=None, client=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        if not (fs is None) ^ (target_protocol is None):
+            raise ValueError(
+                "Please provide one of filesystem instance (fs) or"
+                " target_protocol, not both"
+            )
+        self.target_protocol = target_protocol
+        self.target_options = target_options
+        self.worker = None
+        self.client = client
+        self.fs = fs
+        self._determine_worker()
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        so = infer_storage_options(path)
+        if "host" in so and "port" in so:
+            return {"client": f"{so['host']}:{so['port']}"}
+        else:
+            return {}
+
+    def _determine_worker(self):
+        if _in_worker():
+            self.worker = True
+            if self.fs is None:
+                self.fs = filesystem(
+                    self.target_protocol, **(self.target_options or {})
+                )
+        else:
+            self.worker = False
+            self.client = _get_client(self.client)
+            self.rfs = dask.delayed(self)
+
+    def mkdir(self, *args, **kwargs):
+        if self.worker:
+            self.fs.mkdir(*args, **kwargs)
+        else:
+            self.rfs.mkdir(*args, **kwargs).compute()
+
+    def rm(self, *args, **kwargs):
+        if self.worker:
+            self.fs.rm(*args, **kwargs)
+        else:
+            self.rfs.rm(*args, **kwargs).compute()
+
+    def copy(self, *args, **kwargs):
+        if self.worker:
+            self.fs.copy(*args, **kwargs)
+        else:
+            self.rfs.copy(*args, **kwargs).compute()
+
+    def mv(self, *args, **kwargs):
+        if self.worker:
+            self.fs.mv(*args, **kwargs)
+        else:
+            self.rfs.mv(*args, **kwargs).compute()
+
+    def ls(self, *args, **kwargs):
+        if self.worker:
+            return self.fs.ls(*args, **kwargs)
+        else:
+            return self.rfs.ls(*args, **kwargs).compute()
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        if self.worker:
+            return self.fs._open(
+                path,
+                mode=mode,
+                block_size=block_size,
+                autocommit=autocommit,
+                cache_options=cache_options,
+                **kwargs,
+            )
+        else:
+            return DaskFile(
+                fs=self,
+                path=path,
+                mode=mode,
+                block_size=block_size,
+                autocommit=autocommit,
+                cache_options=cache_options,
+                **kwargs,
+            )
+
+    def fetch_range(self, path, mode, start, end):
+        if self.worker:
+            with self._open(path, mode) as f:
+                f.seek(start)
+                return f.read(end - start)
+        else:
+            return self.rfs.fetch_range(path, mode, start, end).compute()
+
+
+class DaskFile(AbstractBufferedFile):
+    def __init__(self, mode="rb", **kwargs):
+        if mode != "rb":
+            raise ValueError('Remote dask files can only be opened in "rb" mode')
+        super().__init__(**kwargs)
+
+    def _upload_chunk(self, final=False):
+        pass
+
+    def _initiate_upload(self):
+        """Create remote file/upload"""
+        pass
+
+    def _fetch_range(self, start, end):
+        """Get the specified set of bytes from remote"""
+        return self.fs.fetch_range(self.path, self.mode, start, end)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/data.py b/lib/python3.11/site-packages/fsspec/implementations/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..f11542b48c98fd53fc367ade7425a00b38487619
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/data.py
@@ -0,0 +1,57 @@
+import base64
+import io
+from urllib.parse import unquote
+
+from fsspec import AbstractFileSystem
+
+
+class DataFileSystem(AbstractFileSystem):
+    """A handy decoder for data-URLs
+
+    Example
+    -------
+    >>> with fsspec.open("data:,Hello%2C%20World%21") as f:
+    ...     print(f.read())
+    b"Hello, World!"
+
+    See https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs
+    """
+
+    protocol = "data"
+
+    def __init__(self, **kwargs):
+        """No parameters for this filesystem"""
+        super().__init__(**kwargs)
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        pref, data = path.split(",", 1)
+        if pref.endswith("base64"):
+            return base64.b64decode(data)[start:end]
+        return unquote(data).encode()[start:end]
+
+    def info(self, path, **kwargs):
+        pref, name = path.split(",", 1)
+        data = self.cat_file(path)
+        mime = pref.split(":", 1)[1].split(";", 1)[0]
+        return {"name": name, "size": len(data), "type": "file", "mimetype": mime}
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        if "r" not in mode:
+            raise ValueError("Read only filesystem")
+        return io.BytesIO(self.cat_file(path))
+
+    @staticmethod
+    def encode(data: bytes, mime: str | None = None):
+        """Format the given data into data-URL syntax
+
+        This version always base64 encodes, even when the data is ascii/url-safe.
+        """
+        return f"data:{mime or ''};base64,{base64.b64encode(data).decode()}"
diff --git a/lib/python3.11/site-packages/fsspec/implementations/dbfs.py b/lib/python3.11/site-packages/fsspec/implementations/dbfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a7fc93d7389c894ecb5fc6267ce20abe4087068
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/dbfs.py
@@ -0,0 +1,496 @@
+from __future__ import annotations
+
+import base64
+import urllib
+
+import requests
+from requests.adapters import HTTPAdapter, Retry
+from typing_extensions import override
+
+from fsspec import AbstractFileSystem
+from fsspec.spec import AbstractBufferedFile
+
+
+class DatabricksException(Exception):
+    """
+    Helper class for exceptions raised in this module.
+    """
+
+    def __init__(self, error_code, message, details=None):
+        """Create a new DatabricksException"""
+        super().__init__(message)
+
+        self.error_code = error_code
+        self.message = message
+        self.details = details
+
+
+class DatabricksFileSystem(AbstractFileSystem):
+    """
+    Get access to the Databricks filesystem implementation over HTTP.
+    Can be used inside and outside of a databricks cluster.
+    """
+
+    def __init__(self, instance, token, **kwargs):
+        """
+        Create a new DatabricksFileSystem.
+
+        Parameters
+        ----------
+        instance: str
+            The instance URL of the databricks cluster.
+            For example for an Azure databricks cluster, this
+            has the form adb-<some-number>.<two digits>.azuredatabricks.net.
+        token: str
+            Your personal token. Find out more
+            here: https://docs.databricks.com/dev-tools/api/latest/authentication.html
+        """
+        self.instance = instance
+        self.token = token
+        self.session = requests.Session()
+        self.retries = Retry(
+            total=10,
+            backoff_factor=0.05,
+            status_forcelist=[408, 429, 500, 502, 503, 504],
+        )
+
+        self.session.mount("https://", HTTPAdapter(max_retries=self.retries))
+        self.session.headers.update({"Authorization": f"Bearer {self.token}"})
+
+        super().__init__(**kwargs)
+
+    @override
+    def _ls_from_cache(self, path) -> list[dict[str, str | int]] | None:
+        """Check cache for listing
+
+        Returns listing, if found (may be empty list for a directory that
+        exists but contains nothing), None if not in cache.
+        """
+        self.dircache.pop(path.rstrip("/"), None)
+
+        parent = self._parent(path)
+        if parent in self.dircache:
+            for entry in self.dircache[parent]:
+                if entry["name"] == path.rstrip("/"):
+                    if entry["type"] != "directory":
+                        return [entry]
+                    return []
+            raise FileNotFoundError(path)
+
+    def ls(self, path, detail=True, **kwargs):
+        """
+        List the contents of the given path.
+
+        Parameters
+        ----------
+        path: str
+            Absolute path
+        detail: bool
+            Return not only the list of filenames,
+            but also additional information on file sizes
+            and types.
+        """
+        try:
+            out = self._ls_from_cache(path)
+        except FileNotFoundError:
+            # This happens if the `path`'s parent was cached, but `path` is not
+            # there. This suggests that `path` is new since the parent was
+            # cached. Attempt to invalidate parent's cache before continuing.
+            self.dircache.pop(self._parent(path), None)
+            out = None
+
+        if not out:
+            try:
+                r = self._send_to_api(
+                    method="get", endpoint="list", json={"path": path}
+                )
+            except DatabricksException as e:
+                if e.error_code == "RESOURCE_DOES_NOT_EXIST":
+                    raise FileNotFoundError(e.message) from e
+
+                raise
+            files = r.get("files", [])
+            out = [
+                {
+                    "name": o["path"],
+                    "type": "directory" if o["is_dir"] else "file",
+                    "size": o["file_size"],
+                }
+                for o in files
+            ]
+            self.dircache[path] = out
+
+        if detail:
+            return out
+        return [o["name"] for o in out]
+
+    def makedirs(self, path, exist_ok=True):
+        """
+        Create a given absolute path and all of its parents.
+
+        Parameters
+        ----------
+        path: str
+            Absolute path to create
+        exist_ok: bool
+            If false, checks if the folder
+            exists before creating it (and raises an
+            Exception if this is the case)
+        """
+        if not exist_ok:
+            try:
+                # If the following succeeds, the path is already present
+                self._send_to_api(
+                    method="get", endpoint="get-status", json={"path": path}
+                )
+                raise FileExistsError(f"Path {path} already exists")
+            except DatabricksException as e:
+                if e.error_code == "RESOURCE_DOES_NOT_EXIST":
+                    pass
+
+        try:
+            self._send_to_api(method="post", endpoint="mkdirs", json={"path": path})
+        except DatabricksException as e:
+            if e.error_code == "RESOURCE_ALREADY_EXISTS":
+                raise FileExistsError(e.message) from e
+
+            raise
+        self.invalidate_cache(self._parent(path))
+
+    def mkdir(self, path, create_parents=True, **kwargs):
+        """
+        Create a given absolute path and all of its parents.
+
+        Parameters
+        ----------
+        path: str
+            Absolute path to create
+        create_parents: bool
+            Whether to create all parents or not.
+            "False" is not implemented so far.
+        """
+        if not create_parents:
+            raise NotImplementedError
+
+        self.mkdirs(path, **kwargs)
+
+    def rm(self, path, recursive=False, **kwargs):
+        """
+        Remove the file or folder at the given absolute path.
+
+        Parameters
+        ----------
+        path: str
+            Absolute path what to remove
+        recursive: bool
+            Recursively delete all files in a folder.
+        """
+        try:
+            self._send_to_api(
+                method="post",
+                endpoint="delete",
+                json={"path": path, "recursive": recursive},
+            )
+        except DatabricksException as e:
+            # This is not really an exception, it just means
+            # not everything was deleted so far
+            if e.error_code == "PARTIAL_DELETE":
+                self.rm(path=path, recursive=recursive)
+            elif e.error_code == "IO_ERROR":
+                # Using the same exception as the os module would use here
+                raise OSError(e.message) from e
+
+            raise
+        self.invalidate_cache(self._parent(path))
+
+    def mv(
+        self, source_path, destination_path, recursive=False, maxdepth=None, **kwargs
+    ):
+        """
+        Move a source to a destination path.
+
+        A note from the original [databricks API manual]
+        (https://docs.databricks.com/dev-tools/api/latest/dbfs.html#move).
+
+        When moving a large number of files the API call will time out after
+        approximately 60s, potentially resulting in partially moved data.
+        Therefore, for operations that move more than 10k files, we strongly
+        discourage using the DBFS REST API.
+
+        Parameters
+        ----------
+        source_path: str
+            From where to move (absolute path)
+        destination_path: str
+            To where to move (absolute path)
+        recursive: bool
+            Not implemented to far.
+        maxdepth:
+            Not implemented to far.
+        """
+        if recursive:
+            raise NotImplementedError
+        if maxdepth:
+            raise NotImplementedError
+
+        try:
+            self._send_to_api(
+                method="post",
+                endpoint="move",
+                json={"source_path": source_path, "destination_path": destination_path},
+            )
+        except DatabricksException as e:
+            if e.error_code == "RESOURCE_DOES_NOT_EXIST":
+                raise FileNotFoundError(e.message) from e
+            elif e.error_code == "RESOURCE_ALREADY_EXISTS":
+                raise FileExistsError(e.message) from e
+
+            raise
+        self.invalidate_cache(self._parent(source_path))
+        self.invalidate_cache(self._parent(destination_path))
+
+    def _open(self, path, mode="rb", block_size="default", **kwargs):
+        """
+        Overwrite the base class method to make sure to create a DBFile.
+        All arguments are copied from the base method.
+
+        Only the default blocksize is allowed.
+        """
+        return DatabricksFile(self, path, mode=mode, block_size=block_size, **kwargs)
+
+    def _send_to_api(self, method, endpoint, json):
+        """
+        Send the given json to the DBFS API
+        using a get or post request (specified by the argument `method`).
+
+        Parameters
+        ----------
+        method: str
+            Which http method to use for communication; "get" or "post".
+        endpoint: str
+            Where to send the request to (last part of the API URL)
+        json: dict
+            Dictionary of information to send
+        """
+        if method == "post":
+            session_call = self.session.post
+        elif method == "get":
+            session_call = self.session.get
+        else:
+            raise ValueError(f"Do not understand method {method}")
+
+        url = urllib.parse.urljoin(f"https://{self.instance}/api/2.0/dbfs/", endpoint)
+
+        r = session_call(url, json=json)
+
+        # The DBFS API will return a json, also in case of an exception.
+        # We want to preserve this information as good as possible.
+        try:
+            r.raise_for_status()
+        except requests.HTTPError as e:
+            # try to extract json error message
+            # if that fails, fall back to the original exception
+            try:
+                exception_json = e.response.json()
+            except Exception:
+                raise e from None
+
+            raise DatabricksException(**exception_json) from e
+
+        return r.json()
+
+    def _create_handle(self, path, overwrite=True):
+        """
+        Internal function to create a handle, which can be used to
+        write blocks of a file to DBFS.
+        A handle has a unique identifier which needs to be passed
+        whenever written during this transaction.
+        The handle is active for 10 minutes - after that a new
+        write transaction needs to be created.
+        Make sure to close the handle after you are finished.
+
+        Parameters
+        ----------
+        path: str
+            Absolute path for this file.
+        overwrite: bool
+            If a file already exist at this location, either overwrite
+            it or raise an exception.
+        """
+        try:
+            r = self._send_to_api(
+                method="post",
+                endpoint="create",
+                json={"path": path, "overwrite": overwrite},
+            )
+            return r["handle"]
+        except DatabricksException as e:
+            if e.error_code == "RESOURCE_ALREADY_EXISTS":
+                raise FileExistsError(e.message) from e
+
+            raise
+
+    def _close_handle(self, handle):
+        """
+        Close a handle, which was opened by :func:`_create_handle`.
+
+        Parameters
+        ----------
+        handle: str
+            Which handle to close.
+        """
+        try:
+            self._send_to_api(method="post", endpoint="close", json={"handle": handle})
+        except DatabricksException as e:
+            if e.error_code == "RESOURCE_DOES_NOT_EXIST":
+                raise FileNotFoundError(e.message) from e
+
+            raise
+
+    def _add_data(self, handle, data):
+        """
+        Upload data to an already opened file handle
+        (opened by :func:`_create_handle`).
+        The maximal allowed data size is 1MB after
+        conversion to base64.
+        Remember to close the handle when you are finished.
+
+        Parameters
+        ----------
+        handle: str
+            Which handle to upload data to.
+        data: bytes
+            Block of data to add to the handle.
+        """
+        data = base64.b64encode(data).decode()
+        try:
+            self._send_to_api(
+                method="post",
+                endpoint="add-block",
+                json={"handle": handle, "data": data},
+            )
+        except DatabricksException as e:
+            if e.error_code == "RESOURCE_DOES_NOT_EXIST":
+                raise FileNotFoundError(e.message) from e
+            elif e.error_code == "MAX_BLOCK_SIZE_EXCEEDED":
+                raise ValueError(e.message) from e
+
+            raise
+
+    def _get_data(self, path, start, end):
+        """
+        Download data in bytes from a given absolute path in a block
+        from [start, start+length].
+        The maximum number of allowed bytes to read is 1MB.
+
+        Parameters
+        ----------
+        path: str
+            Absolute path to download data from
+        start: int
+            Start position of the block
+        end: int
+            End position of the block
+        """
+        try:
+            r = self._send_to_api(
+                method="get",
+                endpoint="read",
+                json={"path": path, "offset": start, "length": end - start},
+            )
+            return base64.b64decode(r["data"])
+        except DatabricksException as e:
+            if e.error_code == "RESOURCE_DOES_NOT_EXIST":
+                raise FileNotFoundError(e.message) from e
+            elif e.error_code in ["INVALID_PARAMETER_VALUE", "MAX_READ_SIZE_EXCEEDED"]:
+                raise ValueError(e.message) from e
+
+            raise
+
+    def invalidate_cache(self, path=None):
+        if path is None:
+            self.dircache.clear()
+        else:
+            self.dircache.pop(path, None)
+        super().invalidate_cache(path)
+
+
+class DatabricksFile(AbstractBufferedFile):
+    """
+    Helper class for files referenced in the DatabricksFileSystem.
+    """
+
+    DEFAULT_BLOCK_SIZE = 1 * 2**20  # only allowed block size
+
+    def __init__(
+        self,
+        fs,
+        path,
+        mode="rb",
+        block_size="default",
+        autocommit=True,
+        cache_type="readahead",
+        cache_options=None,
+        **kwargs,
+    ):
+        """
+        Create a new instance of the DatabricksFile.
+
+        The blocksize needs to be the default one.
+        """
+        if block_size is None or block_size == "default":
+            block_size = self.DEFAULT_BLOCK_SIZE
+
+        assert block_size == self.DEFAULT_BLOCK_SIZE, (
+            f"Only the default block size is allowed, not {block_size}"
+        )
+
+        super().__init__(
+            fs,
+            path,
+            mode=mode,
+            block_size=block_size,
+            autocommit=autocommit,
+            cache_type=cache_type,
+            cache_options=cache_options or {},
+            **kwargs,
+        )
+
+    def _initiate_upload(self):
+        """Internal function to start a file upload"""
+        self.handle = self.fs._create_handle(self.path)
+
+    def _upload_chunk(self, final=False):
+        """Internal function to add a chunk of data to a started upload"""
+        self.buffer.seek(0)
+        data = self.buffer.getvalue()
+
+        data_chunks = [
+            data[start:end] for start, end in self._to_sized_blocks(len(data))
+        ]
+
+        for data_chunk in data_chunks:
+            self.fs._add_data(handle=self.handle, data=data_chunk)
+
+        if final:
+            self.fs._close_handle(handle=self.handle)
+            return True
+
+    def _fetch_range(self, start, end):
+        """Internal function to download a block of data"""
+        return_buffer = b""
+        length = end - start
+        for chunk_start, chunk_end in self._to_sized_blocks(length, start):
+            return_buffer += self.fs._get_data(
+                path=self.path, start=chunk_start, end=chunk_end
+            )
+
+        return return_buffer
+
+    def _to_sized_blocks(self, length, start=0):
+        """Helper function to split a range from 0 to total_length into blocksizes"""
+        end = start + length
+        for data_chunk in range(start, end, self.blocksize):
+            data_start = data_chunk
+            data_end = min(end, data_chunk + self.blocksize)
+            yield data_start, data_end
diff --git a/lib/python3.11/site-packages/fsspec/implementations/dirfs.py b/lib/python3.11/site-packages/fsspec/implementations/dirfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..65b9b5da1219eb3a2e642c6dfb7f508f629d4e37
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/dirfs.py
@@ -0,0 +1,404 @@
+from .. import filesystem
+from ..asyn import AsyncFileSystem
+from .chained import ChainedFileSystem
+
+
+class DirFileSystem(AsyncFileSystem, ChainedFileSystem):
+    """Directory prefix filesystem
+
+    The DirFileSystem is a filesystem-wrapper. It assumes every path it is dealing with
+    is relative to the `path`. After performing the necessary paths operation it
+    delegates everything to the wrapped filesystem.
+    """
+
+    protocol = "dir"
+
+    def __init__(
+        self,
+        path=None,
+        fs=None,
+        fo=None,
+        target_protocol=None,
+        target_options=None,
+        **storage_options,
+    ):
+        """
+        Parameters
+        ----------
+        path: str
+            Path to the directory.
+        fs: AbstractFileSystem
+            An instantiated filesystem to wrap.
+        target_protocol, target_options:
+            if fs is none, construct it from these
+        fo: str
+            Alternate for path; do not provide both
+        """
+        super().__init__(**storage_options)
+        if fs is None:
+            fs = filesystem(protocol=target_protocol, **(target_options or {}))
+        path = path or fo
+
+        if self.asynchronous and not fs.async_impl:
+            raise ValueError("can't use asynchronous with non-async fs")
+
+        if fs.async_impl and self.asynchronous != fs.asynchronous:
+            raise ValueError("both dirfs and fs should be in the same sync/async mode")
+
+        self.path = fs._strip_protocol(path)
+        self.fs = fs
+
+    def _join(self, path):
+        if isinstance(path, str):
+            if not self.path:
+                return path
+            if not path:
+                return self.path
+            return self.fs.sep.join((self.path, self._strip_protocol(path)))
+        if isinstance(path, dict):
+            return {self._join(_path): value for _path, value in path.items()}
+        return [self._join(_path) for _path in path]
+
+    def _relpath(self, path):
+        if isinstance(path, str):
+            if not self.path:
+                return path
+            # We need to account for S3FileSystem returning paths that do not
+            # start with a '/'
+            if path == self.path or (
+                self.path.startswith(self.fs.sep) and path == self.path[1:]
+            ):
+                return ""
+            prefix = self.path + self.fs.sep
+            if self.path.startswith(self.fs.sep) and not path.startswith(self.fs.sep):
+                prefix = prefix[1:]
+            assert path.startswith(prefix)
+            return path[len(prefix) :]
+        return [self._relpath(_path) for _path in path]
+
+    # Wrappers below
+
+    @property
+    def sep(self):
+        return self.fs.sep
+
+    async def set_session(self, *args, **kwargs):
+        return await self.fs.set_session(*args, **kwargs)
+
+    async def _rm_file(self, path, **kwargs):
+        return await self.fs._rm_file(self._join(path), **kwargs)
+
+    def rm_file(self, path, **kwargs):
+        return self.fs.rm_file(self._join(path), **kwargs)
+
+    async def _rm(self, path, *args, **kwargs):
+        return await self.fs._rm(self._join(path), *args, **kwargs)
+
+    def rm(self, path, *args, **kwargs):
+        return self.fs.rm(self._join(path), *args, **kwargs)
+
+    def delete(self, path, recursive=False, maxdepth=None):
+        return self.fs.delete(self._join(path), recursive=recursive, maxdepth=maxdepth)
+
+    async def _cp_file(self, path1, path2, **kwargs):
+        return await self.fs._cp_file(self._join(path1), self._join(path2), **kwargs)
+
+    def cp_file(self, path1, path2, **kwargs):
+        return self.fs.cp_file(self._join(path1), self._join(path2), **kwargs)
+
+    async def _copy(
+        self,
+        path1,
+        path2,
+        *args,
+        **kwargs,
+    ):
+        return await self.fs._copy(
+            self._join(path1),
+            self._join(path2),
+            *args,
+            **kwargs,
+        )
+
+    def copy(self, path1, path2, *args, **kwargs):
+        return self.fs.copy(
+            self._join(path1),
+            self._join(path2),
+            *args,
+            **kwargs,
+        )
+
+    async def _pipe(self, path, *args, **kwargs):
+        return await self.fs._pipe(self._join(path), *args, **kwargs)
+
+    def pipe(self, path, *args, **kwargs):
+        return self.fs.pipe(self._join(path), *args, **kwargs)
+
+    async def _pipe_file(self, path, *args, **kwargs):
+        return await self.fs._pipe_file(self._join(path), *args, **kwargs)
+
+    def pipe_file(self, path, *args, **kwargs):
+        return self.fs.pipe_file(self._join(path), *args, **kwargs)
+
+    def write_text(
+        self, path, value, encoding=None, errors=None, newline=None, **kwargs
+    ):
+        return self.fs.write_text(
+            self._join(path),
+            value,
+            encoding=encoding,
+            errors=errors,
+            newline=newline,
+            **kwargs,
+        )
+
+    async def _cat_file(self, path, *args, **kwargs):
+        return await self.fs._cat_file(self._join(path), *args, **kwargs)
+
+    def cat_file(self, path, *args, **kwargs):
+        return self.fs.cat_file(self._join(path), *args, **kwargs)
+
+    async def _cat(self, path, *args, **kwargs):
+        ret = await self.fs._cat(
+            self._join(path),
+            *args,
+            **kwargs,
+        )
+
+        if isinstance(ret, dict):
+            return {self._relpath(key): value for key, value in ret.items()}
+
+        return ret
+
+    def cat(self, path, *args, **kwargs):
+        ret = self.fs.cat(
+            self._join(path),
+            *args,
+            **kwargs,
+        )
+
+        if isinstance(ret, dict):
+            return {self._relpath(key): value for key, value in ret.items()}
+
+        return ret
+
+    async def _put_file(self, lpath, rpath, **kwargs):
+        return await self.fs._put_file(lpath, self._join(rpath), **kwargs)
+
+    def put_file(self, lpath, rpath, **kwargs):
+        return self.fs.put_file(lpath, self._join(rpath), **kwargs)
+
+    async def _put(
+        self,
+        lpath,
+        rpath,
+        *args,
+        **kwargs,
+    ):
+        return await self.fs._put(
+            lpath,
+            self._join(rpath),
+            *args,
+            **kwargs,
+        )
+
+    def put(self, lpath, rpath, *args, **kwargs):
+        return self.fs.put(
+            lpath,
+            self._join(rpath),
+            *args,
+            **kwargs,
+        )
+
+    async def _get_file(self, rpath, lpath, **kwargs):
+        return await self.fs._get_file(self._join(rpath), lpath, **kwargs)
+
+    def get_file(self, rpath, lpath, **kwargs):
+        return self.fs.get_file(self._join(rpath), lpath, **kwargs)
+
+    async def _get(self, rpath, *args, **kwargs):
+        return await self.fs._get(self._join(rpath), *args, **kwargs)
+
+    def get(self, rpath, *args, **kwargs):
+        return self.fs.get(self._join(rpath), *args, **kwargs)
+
+    async def _isfile(self, path):
+        return await self.fs._isfile(self._join(path))
+
+    def isfile(self, path):
+        return self.fs.isfile(self._join(path))
+
+    async def _isdir(self, path):
+        return await self.fs._isdir(self._join(path))
+
+    def isdir(self, path):
+        return self.fs.isdir(self._join(path))
+
+    async def _size(self, path):
+        return await self.fs._size(self._join(path))
+
+    def size(self, path):
+        return self.fs.size(self._join(path))
+
+    async def _exists(self, path):
+        return await self.fs._exists(self._join(path))
+
+    def exists(self, path):
+        return self.fs.exists(self._join(path))
+
+    async def _info(self, path, **kwargs):
+        info = await self.fs._info(self._join(path), **kwargs)
+        info = info.copy()
+        info["name"] = self._relpath(info["name"])
+        return info
+
+    def info(self, path, **kwargs):
+        info = self.fs.info(self._join(path), **kwargs)
+        info = info.copy()
+        info["name"] = self._relpath(info["name"])
+        return info
+
+    async def _ls(self, path, detail=True, **kwargs):
+        ret = (await self.fs._ls(self._join(path), detail=detail, **kwargs)).copy()
+        if detail:
+            out = []
+            for entry in ret:
+                entry = entry.copy()
+                entry["name"] = self._relpath(entry["name"])
+                out.append(entry)
+            return out
+
+        return self._relpath(ret)
+
+    def ls(self, path, detail=True, **kwargs):
+        ret = self.fs.ls(self._join(path), detail=detail, **kwargs).copy()
+        if detail:
+            out = []
+            for entry in ret:
+                entry = entry.copy()
+                entry["name"] = self._relpath(entry["name"])
+                out.append(entry)
+            return out
+
+        return self._relpath(ret)
+
+    async def _walk(self, path, *args, **kwargs):
+        async for root, dirs, files in self.fs._walk(self._join(path), *args, **kwargs):
+            yield self._relpath(root), dirs, files
+
+    def walk(self, path, *args, **kwargs):
+        for root, dirs, files in self.fs.walk(self._join(path), *args, **kwargs):
+            yield self._relpath(root), dirs, files
+
+    async def _glob(self, path, **kwargs):
+        detail = kwargs.get("detail", False)
+        ret = await self.fs._glob(self._join(path), **kwargs)
+        if detail:
+            return {self._relpath(path): info for path, info in ret.items()}
+        return self._relpath(ret)
+
+    def glob(self, path, **kwargs):
+        detail = kwargs.get("detail", False)
+        ret = self.fs.glob(self._join(path), **kwargs)
+        if detail:
+            return {self._relpath(path): info for path, info in ret.items()}
+        return self._relpath(ret)
+
+    async def _du(self, path, *args, **kwargs):
+        total = kwargs.get("total", True)
+        ret = await self.fs._du(self._join(path), *args, **kwargs)
+        if total:
+            return ret
+
+        return {self._relpath(path): size for path, size in ret.items()}
+
+    def du(self, path, *args, **kwargs):
+        total = kwargs.get("total", True)
+        ret = self.fs.du(self._join(path), *args, **kwargs)
+        if total:
+            return ret
+
+        return {self._relpath(path): size for path, size in ret.items()}
+
+    async def _find(self, path, *args, **kwargs):
+        detail = kwargs.get("detail", False)
+        ret = await self.fs._find(self._join(path), *args, **kwargs)
+        if detail:
+            return {self._relpath(path): info for path, info in ret.items()}
+        return self._relpath(ret)
+
+    def find(self, path, *args, **kwargs):
+        detail = kwargs.get("detail", False)
+        ret = self.fs.find(self._join(path), *args, **kwargs)
+        if detail:
+            return {self._relpath(path): info for path, info in ret.items()}
+        return self._relpath(ret)
+
+    async def _expand_path(self, path, *args, **kwargs):
+        return self._relpath(
+            await self.fs._expand_path(self._join(path), *args, **kwargs)
+        )
+
+    def expand_path(self, path, *args, **kwargs):
+        return self._relpath(self.fs.expand_path(self._join(path), *args, **kwargs))
+
+    async def _mkdir(self, path, *args, **kwargs):
+        return await self.fs._mkdir(self._join(path), *args, **kwargs)
+
+    def mkdir(self, path, *args, **kwargs):
+        return self.fs.mkdir(self._join(path), *args, **kwargs)
+
+    async def _makedirs(self, path, *args, **kwargs):
+        return await self.fs._makedirs(self._join(path), *args, **kwargs)
+
+    def makedirs(self, path, *args, **kwargs):
+        return self.fs.makedirs(self._join(path), *args, **kwargs)
+
+    def rmdir(self, path):
+        return self.fs.rmdir(self._join(path))
+
+    def mv(self, path1, path2, **kwargs):
+        return self.fs.mv(
+            self._join(path1),
+            self._join(path2),
+            **kwargs,
+        )
+
+    def touch(self, path, **kwargs):
+        return self.fs.touch(self._join(path), **kwargs)
+
+    def created(self, path):
+        return self.fs.created(self._join(path))
+
+    def modified(self, path):
+        return self.fs.modified(self._join(path))
+
+    def sign(self, path, *args, **kwargs):
+        return self.fs.sign(self._join(path), *args, **kwargs)
+
+    def __repr__(self):
+        return f"{self.__class__.__qualname__}(path='{self.path}', fs={self.fs})"
+
+    def open(
+        self,
+        path,
+        *args,
+        **kwargs,
+    ):
+        return self.fs.open(
+            self._join(path),
+            *args,
+            **kwargs,
+        )
+
+    async def open_async(
+        self,
+        path,
+        *args,
+        **kwargs,
+    ):
+        return await self.fs.open_async(
+            self._join(path),
+            *args,
+            **kwargs,
+        )
diff --git a/lib/python3.11/site-packages/fsspec/implementations/ftp.py b/lib/python3.11/site-packages/fsspec/implementations/ftp.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca151c6ea3cc2bb514701cd95fd5258bed3c9899
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/ftp.py
@@ -0,0 +1,437 @@
+import os
+import ssl
+import uuid
+from ftplib import FTP, FTP_TLS, Error, error_perm
+from typing import Any
+
+from ..spec import AbstractBufferedFile, AbstractFileSystem
+from ..utils import infer_storage_options, isfilelike
+
+SECURITY_PROTOCOL_MAP = {
+    "tls": ssl.PROTOCOL_TLS,
+    "tlsv1": ssl.PROTOCOL_TLSv1,
+    "tlsv1_1": ssl.PROTOCOL_TLSv1_1,
+    "tlsv1_2": ssl.PROTOCOL_TLSv1_2,
+    "sslv23": ssl.PROTOCOL_SSLv23,
+}
+
+
+class ImplicitFTPTLS(FTP_TLS):
+    """
+    FTP_TLS subclass that automatically wraps sockets in SSL
+    to support implicit FTPS.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._sock = None
+
+    @property
+    def sock(self):
+        """Return the socket."""
+        return self._sock
+
+    @sock.setter
+    def sock(self, value):
+        """When modifying the socket, ensure that it is ssl wrapped."""
+        if value is not None and not isinstance(value, ssl.SSLSocket):
+            value = self.context.wrap_socket(value)
+        self._sock = value
+
+
+class FTPFileSystem(AbstractFileSystem):
+    """A filesystem over classic FTP"""
+
+    root_marker = "/"
+    cachable = False
+    protocol = "ftp"
+
+    def __init__(
+        self,
+        host,
+        port=21,
+        username=None,
+        password=None,
+        acct=None,
+        block_size=None,
+        tempdir=None,
+        timeout=30,
+        encoding="utf-8",
+        tls=False,
+        **kwargs,
+    ):
+        """
+        You can use _get_kwargs_from_urls to get some kwargs from
+        a reasonable FTP url.
+
+        Authentication will be anonymous if username/password are not
+        given.
+
+        Parameters
+        ----------
+        host: str
+            The remote server name/ip to connect to
+        port: int
+            Port to connect with
+        username: str or None
+            If authenticating, the user's identifier
+        password: str of None
+            User's password on the server, if using
+        acct: str or None
+            Some servers also need an "account" string for auth
+        block_size: int or None
+            If given, the read-ahead or write buffer size.
+        tempdir: str
+            Directory on remote to put temporary files when in a transaction
+        timeout: int
+            Timeout of the ftp connection in seconds
+        encoding: str
+            Encoding to use for directories and filenames in FTP connection
+        tls: bool or str
+            Enable FTP-TLS for secure connections:
+                - False: Plain FTP (default)
+                - True: Explicit TLS (FTPS with AUTH TLS command)
+                - "tls": Auto-negotiate highest protocol
+                - "tlsv1": TLS v1.0
+                - "tlsv1_1": TLS v1.1
+                - "tlsv1_2": TLS v1.2
+        """
+        super().__init__(**kwargs)
+        self.host = host
+        self.port = port
+        self.tempdir = tempdir or "/tmp"
+        self.cred = username or "", password or "", acct or ""
+        self.timeout = timeout
+        self.encoding = encoding
+        if block_size is not None:
+            self.blocksize = block_size
+        else:
+            self.blocksize = 2**16
+        self.tls = tls
+        self._connect()
+        if isinstance(self.tls, bool) and self.tls:
+            self.ftp.prot_p()
+
+    def _connect(self):
+        security = None
+        if self.tls:
+            if isinstance(self.tls, str):
+                ftp_cls = ImplicitFTPTLS
+                security = SECURITY_PROTOCOL_MAP.get(
+                    self.tls,
+                    f"Not supported {self.tls} protocol",
+                )
+                if isinstance(security, str):
+                    raise ValueError(security)
+            else:
+                ftp_cls = FTP_TLS
+        else:
+            ftp_cls = FTP
+        self.ftp = ftp_cls(timeout=self.timeout, encoding=self.encoding)
+        if security:
+            self.ftp.ssl_version = security
+        self.ftp.connect(self.host, self.port)
+        self.ftp.login(*self.cred)
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        return "/" + infer_storage_options(path)["path"].lstrip("/").rstrip("/")
+
+    @staticmethod
+    def _get_kwargs_from_urls(urlpath):
+        out = infer_storage_options(urlpath)
+        out.pop("path", None)
+        out.pop("protocol", None)
+        return out
+
+    def ls(self, path, detail=True, **kwargs):
+        path = self._strip_protocol(path)
+        out = []
+        if path not in self.dircache:
+            try:
+                try:
+                    out = [
+                        (fn, details)
+                        for (fn, details) in self.ftp.mlsd(path)
+                        if fn not in [".", ".."]
+                        and details["type"] not in ["pdir", "cdir"]
+                    ]
+                except error_perm:
+                    out = _mlsd2(self.ftp, path)  # Not platform independent
+                for fn, details in out:
+                    details["name"] = "/".join(
+                        ["" if path == "/" else path, fn.lstrip("/")]
+                    )
+                    if details["type"] == "file":
+                        details["size"] = int(details["size"])
+                    else:
+                        details["size"] = 0
+                    if details["type"] == "dir":
+                        details["type"] = "directory"
+                self.dircache[path] = out
+            except Error:
+                try:
+                    info = self.info(path)
+                    if info["type"] == "file":
+                        out = [(path, info)]
+                except (Error, IndexError) as exc:
+                    raise FileNotFoundError(path) from exc
+        files = self.dircache.get(path, out)
+        if not detail:
+            return sorted([fn for fn, details in files])
+        return [details for fn, details in files]
+
+    def info(self, path, **kwargs):
+        # implement with direct method
+        path = self._strip_protocol(path)
+        if path == "/":
+            # special case, since this dir has no real entry
+            return {"name": "/", "size": 0, "type": "directory"}
+        files = self.ls(self._parent(path).lstrip("/"), True)
+        try:
+            out = next(f for f in files if f["name"] == path)
+        except StopIteration as exc:
+            raise FileNotFoundError(path) from exc
+        return out
+
+    def get_file(self, rpath, lpath, **kwargs):
+        if self.isdir(rpath):
+            if not os.path.exists(lpath):
+                os.mkdir(lpath)
+            return
+        if isfilelike(lpath):
+            outfile = lpath
+        else:
+            outfile = open(lpath, "wb")
+
+        def cb(x):
+            outfile.write(x)
+
+        self.ftp.retrbinary(
+            f"RETR {rpath}",
+            blocksize=self.blocksize,
+            callback=cb,
+        )
+        if not isfilelike(lpath):
+            outfile.close()
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        if end is not None:
+            return super().cat_file(path, start, end, **kwargs)
+        out = []
+
+        def cb(x):
+            out.append(x)
+
+        try:
+            self.ftp.retrbinary(
+                f"RETR {path}",
+                blocksize=self.blocksize,
+                rest=start,
+                callback=cb,
+            )
+        except (Error, error_perm) as orig_exc:
+            raise FileNotFoundError(path) from orig_exc
+        return b"".join(out)
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        cache_options=None,
+        autocommit=True,
+        **kwargs,
+    ):
+        path = self._strip_protocol(path)
+        block_size = block_size or self.blocksize
+        return FTPFile(
+            self,
+            path,
+            mode=mode,
+            block_size=block_size,
+            tempdir=self.tempdir,
+            autocommit=autocommit,
+            cache_options=cache_options,
+        )
+
+    def _rm(self, path):
+        path = self._strip_protocol(path)
+        self.ftp.delete(path)
+        self.invalidate_cache(self._parent(path))
+
+    def rm(self, path, recursive=False, maxdepth=None):
+        paths = self.expand_path(path, recursive=recursive, maxdepth=maxdepth)
+        for p in reversed(paths):
+            if self.isfile(p):
+                self.rm_file(p)
+            else:
+                self.rmdir(p)
+
+    def mkdir(self, path: str, create_parents: bool = True, **kwargs: Any) -> None:
+        path = self._strip_protocol(path)
+        parent = self._parent(path)
+        if parent != self.root_marker and not self.exists(parent) and create_parents:
+            self.mkdir(parent, create_parents=create_parents)
+
+        self.ftp.mkd(path)
+        self.invalidate_cache(self._parent(path))
+
+    def makedirs(self, path: str, exist_ok: bool = False) -> None:
+        path = self._strip_protocol(path)
+        if self.exists(path):
+            # NB: "/" does not "exist" as it has no directory entry
+            if not exist_ok:
+                raise FileExistsError(f"{path} exists without `exist_ok`")
+            # exists_ok=True -> no-op
+        else:
+            self.mkdir(path, create_parents=True)
+
+    def rmdir(self, path):
+        path = self._strip_protocol(path)
+        self.ftp.rmd(path)
+        self.invalidate_cache(self._parent(path))
+
+    def mv(self, path1, path2, **kwargs):
+        path1 = self._strip_protocol(path1)
+        path2 = self._strip_protocol(path2)
+        self.ftp.rename(path1, path2)
+        self.invalidate_cache(self._parent(path1))
+        self.invalidate_cache(self._parent(path2))
+
+    def __del__(self):
+        self.ftp.close()
+
+    def invalidate_cache(self, path=None):
+        if path is None:
+            self.dircache.clear()
+        else:
+            self.dircache.pop(path, None)
+        super().invalidate_cache(path)
+
+
+class TransferDone(Exception):
+    """Internal exception to break out of transfer"""
+
+    pass
+
+
+class FTPFile(AbstractBufferedFile):
+    """Interact with a remote FTP file with read/write buffering"""
+
+    def __init__(
+        self,
+        fs,
+        path,
+        mode="rb",
+        block_size="default",
+        autocommit=True,
+        cache_type="readahead",
+        cache_options=None,
+        **kwargs,
+    ):
+        super().__init__(
+            fs,
+            path,
+            mode=mode,
+            block_size=block_size,
+            autocommit=autocommit,
+            cache_type=cache_type,
+            cache_options=cache_options,
+            **kwargs,
+        )
+        if not autocommit:
+            self.target = self.path
+            self.path = "/".join([kwargs["tempdir"], str(uuid.uuid4())])
+
+    def commit(self):
+        self.fs.mv(self.path, self.target)
+
+    def discard(self):
+        self.fs.rm(self.path)
+
+    def _fetch_range(self, start, end):
+        """Get bytes between given byte limits
+
+        Implemented by raising an exception in the fetch callback when the
+        number of bytes received reaches the requested amount.
+
+        Will fail if the server does not respect the REST command on
+        retrieve requests.
+        """
+        out = []
+        total = [0]
+
+        def callback(x):
+            total[0] += len(x)
+            if total[0] > end - start:
+                out.append(x[: (end - start) - total[0]])
+                if end < self.size:
+                    raise TransferDone
+            else:
+                out.append(x)
+
+            if total[0] == end - start and end < self.size:
+                raise TransferDone
+
+        try:
+            self.fs.ftp.retrbinary(
+                f"RETR {self.path}",
+                blocksize=self.blocksize,
+                rest=start,
+                callback=callback,
+            )
+        except TransferDone:
+            try:
+                # stop transfer, we got enough bytes for this block
+                self.fs.ftp.abort()
+                self.fs.ftp.getmultiline()
+            except Error:
+                self.fs._connect()
+
+        return b"".join(out)
+
+    def _upload_chunk(self, final=False):
+        self.buffer.seek(0)
+        self.fs.ftp.storbinary(
+            f"STOR {self.path}", self.buffer, blocksize=self.blocksize, rest=self.offset
+        )
+        return True
+
+
+def _mlsd2(ftp, path="."):
+    """
+    Fall back to using `dir` instead of `mlsd` if not supported.
+
+    This parses a Linux style `ls -l` response to `dir`, but the response may
+    be platform dependent.
+
+    Parameters
+    ----------
+    ftp: ftplib.FTP
+    path: str
+        Expects to be given path, but defaults to ".".
+    """
+    lines = []
+    minfo = []
+    ftp.dir(path, lines.append)
+    for line in lines:
+        split_line = line.split()
+        if len(split_line) < 9:
+            continue
+        this = (
+            split_line[-1],
+            {
+                "modify": " ".join(split_line[5:8]),
+                "unix.owner": split_line[2],
+                "unix.group": split_line[3],
+                "unix.mode": split_line[0],
+                "size": split_line[4],
+            },
+        )
+        if this[1]["unix.mode"][0] == "d":
+            this[1]["type"] = "dir"
+        else:
+            this[1]["type"] = "file"
+        minfo.append(this)
+    return minfo
diff --git a/lib/python3.11/site-packages/fsspec/implementations/gist.py b/lib/python3.11/site-packages/fsspec/implementations/gist.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad9ac0b6a1cdbcfba6188e2cdeab2350bb9aad0a
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/gist.py
@@ -0,0 +1,241 @@
+import requests
+
+from ..spec import AbstractFileSystem
+from ..utils import infer_storage_options
+from .memory import MemoryFile
+
+
+class GistFileSystem(AbstractFileSystem):
+    """
+    Interface to files in a single GitHub Gist.
+
+    Provides read-only access to a gist's files. Gists do not contain
+    subdirectories, so file listing is straightforward.
+
+    Parameters
+    ----------
+    gist_id: str
+        The ID of the gist you want to access (the long hex value from the URL).
+    filenames: list[str] (optional)
+        If provided, only make a file system representing these files, and do not fetch
+        the list of all files for this gist.
+    sha: str (optional)
+        If provided, fetch a particular revision of the gist. If omitted,
+        the latest revision is used.
+    username: str (optional)
+        GitHub username for authentication.
+    token: str (optional)
+        GitHub personal access token (required if username is given), or.
+    timeout: (float, float) or float, optional
+        Connect and read timeouts for requests (default 60s each).
+    kwargs: dict
+        Stored on `self.request_kw` and passed to `requests.get` when fetching Gist
+        metadata or reading ("opening") a file.
+    """
+
+    protocol = "gist"
+    gist_url = "https://api.github.com/gists/{gist_id}"
+    gist_rev_url = "https://api.github.com/gists/{gist_id}/{sha}"
+
+    def __init__(
+        self,
+        gist_id,
+        filenames=None,
+        sha=None,
+        username=None,
+        token=None,
+        timeout=None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.gist_id = gist_id
+        self.filenames = filenames
+        self.sha = sha  # revision of the gist (optional)
+        if username is not None and token is None:
+            raise ValueError("User auth requires a token")
+        self.username = username
+        self.token = token
+        self.request_kw = kwargs
+        # Default timeouts to 60s connect/read if none provided
+        self.timeout = timeout if timeout is not None else (60, 60)
+
+        # We use a single-level "directory" cache, because a gist is essentially flat
+        self.dircache[""] = self._fetch_file_list()
+
+    @property
+    def kw(self):
+        """Auth parameters passed to 'requests' if we have username/token."""
+        kw = {
+            "headers": {
+                "Accept": "application/vnd.github+json",
+                "X-GitHub-Api-Version": "2022-11-28",
+            }
+        }
+        kw.update(self.request_kw)
+        if self.username and self.token:
+            kw["auth"] = (self.username, self.token)
+        elif self.token:
+            kw["headers"]["Authorization"] = f"Bearer {self.token}"
+        return kw
+
+    def _fetch_gist_metadata(self):
+        """
+        Fetch the JSON metadata for this gist (possibly for a specific revision).
+        """
+        if self.sha:
+            url = self.gist_rev_url.format(gist_id=self.gist_id, sha=self.sha)
+        else:
+            url = self.gist_url.format(gist_id=self.gist_id)
+
+        r = requests.get(url, timeout=self.timeout, **self.kw)
+        if r.status_code == 404:
+            raise FileNotFoundError(
+                f"Gist not found: {self.gist_id}@{self.sha or 'latest'}"
+            )
+        r.raise_for_status()
+        return r.json()
+
+    def _fetch_file_list(self):
+        """
+        Returns a list of dicts describing each file in the gist. These get stored
+        in self.dircache[""].
+        """
+        meta = self._fetch_gist_metadata()
+        if self.filenames:
+            available_files = meta.get("files", {})
+            files = {}
+            for fn in self.filenames:
+                if fn not in available_files:
+                    raise FileNotFoundError(fn)
+                files[fn] = available_files[fn]
+        else:
+            files = meta.get("files", {})
+
+        out = []
+        for fname, finfo in files.items():
+            if finfo is None:
+                # Occasionally GitHub returns a file entry with null if it was deleted
+                continue
+            # Build a directory entry
+            out.append(
+                {
+                    "name": fname,  # file's name
+                    "type": "file",  # gists have no subdirectories
+                    "size": finfo.get("size", 0),  # file size in bytes
+                    "raw_url": finfo.get("raw_url"),
+                }
+            )
+        return out
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        """
+        Remove 'gist://' from the path, if present.
+        """
+        # The default infer_storage_options can handle gist://username:token@id/file
+        # or gist://id/file, but let's ensure we handle a normal usage too.
+        # We'll just strip the protocol prefix if it exists.
+        path = infer_storage_options(path).get("path", path)
+        return path.lstrip("/")
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        """
+        Parse 'gist://' style URLs into GistFileSystem constructor kwargs.
+        For example:
+          gist://:TOKEN@<gist_id>/file.txt
+          gist://username:TOKEN@<gist_id>/file.txt
+        """
+        so = infer_storage_options(path)
+        out = {}
+        if "username" in so and so["username"]:
+            out["username"] = so["username"]
+        if "password" in so and so["password"]:
+            out["token"] = so["password"]
+        if "host" in so and so["host"]:
+            # We interpret 'host' as the gist ID
+            out["gist_id"] = so["host"]
+
+        # Extract SHA and filename from path
+        if "path" in so and so["path"]:
+            path_parts = so["path"].rsplit("/", 2)[-2:]
+            if len(path_parts) == 2:
+                if path_parts[0]:  # SHA present
+                    out["sha"] = path_parts[0]
+                if path_parts[1]:  # filename also present
+                    out["filenames"] = [path_parts[1]]
+
+        return out
+
+    def ls(self, path="", detail=False, **kwargs):
+        """
+        List files in the gist. Gists are single-level, so any 'path' is basically
+        the filename, or empty for all files.
+
+        Parameters
+        ----------
+        path : str, optional
+            The filename to list. If empty, returns all files in the gist.
+        detail : bool, default False
+            If True, return a list of dicts; if False, return a list of filenames.
+        """
+        path = self._strip_protocol(path or "")
+        # If path is empty, return all
+        if path == "":
+            results = self.dircache[""]
+        else:
+            # We want just the single file with this name
+            all_files = self.dircache[""]
+            results = [f for f in all_files if f["name"] == path]
+            if not results:
+                raise FileNotFoundError(path)
+        if detail:
+            return results
+        else:
+            return sorted(f["name"] for f in results)
+
+    def _open(self, path, mode="rb", block_size=None, **kwargs):
+        """
+        Read a single file from the gist.
+        """
+        if mode != "rb":
+            raise NotImplementedError("GitHub Gist FS is read-only (no write).")
+
+        path = self._strip_protocol(path)
+        # Find the file entry in our dircache
+        matches = [f for f in self.dircache[""] if f["name"] == path]
+        if not matches:
+            raise FileNotFoundError(path)
+        finfo = matches[0]
+
+        raw_url = finfo.get("raw_url")
+        if not raw_url:
+            raise FileNotFoundError(f"No raw_url for file: {path}")
+
+        r = requests.get(raw_url, timeout=self.timeout, **self.kw)
+        if r.status_code == 404:
+            raise FileNotFoundError(path)
+        r.raise_for_status()
+        return MemoryFile(path, None, r.content)
+
+    def cat(self, path, recursive=False, on_error="raise", **kwargs):
+        """
+        Return {path: contents} for the given file or files. If 'recursive' is True,
+        and path is empty, returns all files in the gist.
+        """
+        paths = self.expand_path(path, recursive=recursive)
+        out = {}
+        for p in paths:
+            try:
+                with self.open(p, "rb") as f:
+                    out[p] = f.read()
+            except FileNotFoundError as e:
+                if on_error == "raise":
+                    raise e
+                elif on_error == "omit":
+                    pass  # skip
+                else:
+                    out[p] = e
+        if len(paths) == 1 and paths[0] == path:
+            return out[path]
+        return out
diff --git a/lib/python3.11/site-packages/fsspec/implementations/git.py b/lib/python3.11/site-packages/fsspec/implementations/git.py
new file mode 100644
index 0000000000000000000000000000000000000000..808d293a1c991ea87d19a2129f3e56d9b813daaa
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/git.py
@@ -0,0 +1,114 @@
+import os
+
+import pygit2
+
+from fsspec.spec import AbstractFileSystem
+
+from .memory import MemoryFile
+
+
+class GitFileSystem(AbstractFileSystem):
+    """Browse the files of a local git repo at any hash/tag/branch
+
+    (experimental backend)
+    """
+
+    root_marker = ""
+    cachable = True
+
+    def __init__(self, path=None, fo=None, ref=None, **kwargs):
+        """
+
+        Parameters
+        ----------
+        path: str (optional)
+            Local location of the repo (uses current directory if not given).
+            May be deprecated in favour of ``fo``. When used with a higher
+            level function such as fsspec.open(), may be of the form
+            "git://[path-to-repo[:]][ref@]path/to/file" (but the actual
+            file path should not contain "@" or ":").
+        fo: str (optional)
+            Same as ``path``, but passed as part of a chained URL. This one
+            takes precedence if both are given.
+        ref: str (optional)
+            Reference to work with, could be a hash, tag or branch name. Defaults
+            to current working tree. Note that ``ls`` and ``open`` also take hash,
+            so this becomes the default for those operations
+        kwargs
+        """
+        super().__init__(**kwargs)
+        self.repo = pygit2.Repository(fo or path or os.getcwd())
+        self.ref = ref or "master"
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        path = super()._strip_protocol(path).lstrip("/")
+        if ":" in path:
+            path = path.split(":", 1)[1]
+        if "@" in path:
+            path = path.split("@", 1)[1]
+        return path.lstrip("/")
+
+    def _path_to_object(self, path, ref):
+        comm, ref = self.repo.resolve_refish(ref or self.ref)
+        parts = path.split("/")
+        tree = comm.tree
+        for part in parts:
+            if part and isinstance(tree, pygit2.Tree):
+                if part not in tree:
+                    raise FileNotFoundError(path)
+                tree = tree[part]
+        return tree
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        path = path.removeprefix("git://")
+        out = {}
+        if ":" in path:
+            out["path"], path = path.split(":", 1)
+        if "@" in path:
+            out["ref"], path = path.split("@", 1)
+        return out
+
+    @staticmethod
+    def _object_to_info(obj, path=None):
+        # obj.name and obj.filemode are None for the root tree!
+        is_dir = isinstance(obj, pygit2.Tree)
+        return {
+            "type": "directory" if is_dir else "file",
+            "name": (
+                "/".join([path, obj.name or ""]).lstrip("/") if path else obj.name
+            ),
+            "hex": str(obj.id),
+            "mode": "100644" if obj.filemode is None else f"{obj.filemode:o}",
+            "size": 0 if is_dir else obj.size,
+        }
+
+    def ls(self, path, detail=True, ref=None, **kwargs):
+        tree = self._path_to_object(self._strip_protocol(path), ref)
+        return [
+            GitFileSystem._object_to_info(obj, path)
+            if detail
+            else GitFileSystem._object_to_info(obj, path)["name"]
+            for obj in (tree if isinstance(tree, pygit2.Tree) else [tree])
+        ]
+
+    def info(self, path, ref=None, **kwargs):
+        tree = self._path_to_object(self._strip_protocol(path), ref)
+        return GitFileSystem._object_to_info(tree, path)
+
+    def ukey(self, path, ref=None):
+        return self.info(path, ref=ref)["hex"]
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        ref=None,
+        **kwargs,
+    ):
+        obj = self._path_to_object(path, ref or self.ref)
+        return MemoryFile(data=obj.data)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/github.py b/lib/python3.11/site-packages/fsspec/implementations/github.py
new file mode 100644
index 0000000000000000000000000000000000000000..3630f6db54413e2c396f6cc1b6b10cd379200043
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/github.py
@@ -0,0 +1,333 @@
+import base64
+import re
+
+import requests
+
+from ..spec import AbstractFileSystem
+from ..utils import infer_storage_options
+from .memory import MemoryFile
+
+
+class GithubFileSystem(AbstractFileSystem):
+    """Interface to files in github
+
+    An instance of this class provides the files residing within a remote github
+    repository. You may specify a point in the repos history, by SHA, branch
+    or tag (default is current master).
+
+    For files less than 1 MB in size, file content is returned directly in a
+    MemoryFile. For larger files, or for files tracked by git-lfs, file content
+    is returned as an HTTPFile wrapping the ``download_url`` provided by the
+    GitHub API.
+
+    When using fsspec.open, allows URIs of the form:
+
+    - "github://path/file", in which case you must specify org, repo and
+      may specify sha in the extra args
+    - 'github://org:repo@/precip/catalog.yml', where the org and repo are
+      part of the URI
+    - 'github://org:repo@sha/precip/catalog.yml', where the sha is also included
+
+    ``sha`` can be the full or abbreviated hex of the commit you want to fetch
+    from, or a branch or tag name (so long as it doesn't contain special characters
+    like "/", "?", which would have to be HTTP-encoded).
+
+    For authorised access, you must provide username and token, which can be made
+    at https://github.com/settings/tokens
+    """
+
+    url = "https://api.github.com/repos/{org}/{repo}/git/trees/{sha}"
+    content_url = "https://api.github.com/repos/{org}/{repo}/contents/{path}?ref={sha}"
+    protocol = "github"
+    timeout = (60, 60)  # connect, read timeouts
+
+    def __init__(
+        self, org, repo, sha=None, username=None, token=None, timeout=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.org = org
+        self.repo = repo
+        if (username is None) ^ (token is None):
+            raise ValueError("Auth required both username and token")
+        self.username = username
+        self.token = token
+        if timeout is not None:
+            self.timeout = timeout
+        if sha is None:
+            # look up default branch (not necessarily "master")
+            u = "https://api.github.com/repos/{org}/{repo}"
+            r = requests.get(
+                u.format(org=org, repo=repo), timeout=self.timeout, **self.kw
+            )
+            r.raise_for_status()
+            sha = r.json()["default_branch"]
+
+        self.root = sha
+        self.ls("")
+        try:
+            from .http import HTTPFileSystem
+
+            self.http_fs = HTTPFileSystem(**kwargs)
+        except ImportError:
+            self.http_fs = None
+
+    @property
+    def kw(self):
+        if self.username:
+            return {"auth": (self.username, self.token)}
+        return {}
+
+    @classmethod
+    def repos(cls, org_or_user, is_org=True):
+        """List repo names for given org or user
+
+        This may become the top level of the FS
+
+        Parameters
+        ----------
+        org_or_user: str
+            Name of the github org or user to query
+        is_org: bool (default True)
+            Whether the name is an organisation (True) or user (False)
+
+        Returns
+        -------
+        List of string
+        """
+        r = requests.get(
+            f"https://api.github.com/{['users', 'orgs'][is_org]}/{org_or_user}/repos",
+            timeout=cls.timeout,
+        )
+        r.raise_for_status()
+        return [repo["name"] for repo in r.json()]
+
+    @property
+    def tags(self):
+        """Names of tags in the repo"""
+        r = requests.get(
+            f"https://api.github.com/repos/{self.org}/{self.repo}/tags",
+            timeout=self.timeout,
+            **self.kw,
+        )
+        r.raise_for_status()
+        return [t["name"] for t in r.json()]
+
+    @property
+    def branches(self):
+        """Names of branches in the repo"""
+        r = requests.get(
+            f"https://api.github.com/repos/{self.org}/{self.repo}/branches",
+            timeout=self.timeout,
+            **self.kw,
+        )
+        r.raise_for_status()
+        return [t["name"] for t in r.json()]
+
+    @property
+    def refs(self):
+        """Named references, tags and branches"""
+        return {"tags": self.tags, "branches": self.branches}
+
+    def ls(self, path, detail=False, sha=None, _sha=None, **kwargs):
+        """List files at given path
+
+        Parameters
+        ----------
+        path: str
+            Location to list, relative to repo root
+        detail: bool
+            If True, returns list of dicts, one per file; if False, returns
+            list of full filenames only
+        sha: str (optional)
+            List at the given point in the repo history, branch or tag name or commit
+            SHA
+        _sha: str (optional)
+            List this specific tree object (used internally to descend into trees)
+        """
+        path = self._strip_protocol(path)
+        if path == "":
+            _sha = sha or self.root
+        if _sha is None:
+            parts = path.rstrip("/").split("/")
+            so_far = ""
+            _sha = sha or self.root
+            for part in parts:
+                out = self.ls(so_far, True, sha=sha, _sha=_sha)
+                so_far += "/" + part if so_far else part
+                out = [o for o in out if o["name"] == so_far]
+                if not out:
+                    raise FileNotFoundError(path)
+                out = out[0]
+                if out["type"] == "file":
+                    if detail:
+                        return [out]
+                    else:
+                        return path
+                _sha = out["sha"]
+        if path not in self.dircache or sha not in [self.root, None]:
+            r = requests.get(
+                self.url.format(org=self.org, repo=self.repo, sha=_sha),
+                timeout=self.timeout,
+                **self.kw,
+            )
+            if r.status_code == 404:
+                raise FileNotFoundError(path)
+            r.raise_for_status()
+            types = {"blob": "file", "tree": "directory"}
+            out = [
+                {
+                    "name": path + "/" + f["path"] if path else f["path"],
+                    "mode": f["mode"],
+                    "type": types[f["type"]],
+                    "size": f.get("size", 0),
+                    "sha": f["sha"],
+                }
+                for f in r.json()["tree"]
+                if f["type"] in types
+            ]
+            if sha in [self.root, None]:
+                self.dircache[path] = out
+        else:
+            out = self.dircache[path]
+        if detail:
+            return out
+        else:
+            return sorted([f["name"] for f in out])
+
+    def invalidate_cache(self, path=None):
+        self.dircache.clear()
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        opts = infer_storage_options(path)
+        if "username" not in opts:
+            return super()._strip_protocol(path)
+        return opts["path"].lstrip("/")
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        opts = infer_storage_options(path)
+        if "username" not in opts:
+            return {}
+        out = {"org": opts["username"], "repo": opts["password"]}
+        if opts["host"]:
+            out["sha"] = opts["host"]
+        return out
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        cache_options=None,
+        sha=None,
+        **kwargs,
+    ):
+        if mode != "rb":
+            raise NotImplementedError
+
+        # construct a url to hit the GitHub API's repo contents API
+        url = self.content_url.format(
+            org=self.org, repo=self.repo, path=path, sha=sha or self.root
+        )
+
+        # make a request to this API, and parse the response as JSON
+        r = requests.get(url, timeout=self.timeout, **self.kw)
+        if r.status_code == 404:
+            raise FileNotFoundError(path)
+        r.raise_for_status()
+        content_json = r.json()
+
+        # if the response's content key is not empty, try to parse it as base64
+        if content_json["content"]:
+            content = base64.b64decode(content_json["content"])
+
+            # as long as the content does not start with the string
+            # "version https://git-lfs.github.com/"
+            # then it is probably not a git-lfs pointer and we can just return
+            # the content directly
+            if not content.startswith(b"version https://git-lfs.github.com/"):
+                return MemoryFile(None, None, content)
+
+        # we land here if the content was not present in the first response
+        # (regular file over 1MB or git-lfs tracked file)
+        # in this case, we get let the HTTPFileSystem handle the download
+        if self.http_fs is None:
+            raise ImportError(
+                "Please install fsspec[http] to access github files >1 MB "
+                "or git-lfs tracked files."
+            )
+        return self.http_fs.open(
+            content_json["download_url"],
+            mode=mode,
+            block_size=block_size,
+            cache_options=cache_options,
+            **kwargs,
+        )
+
+    def rm(self, path, recursive=False, maxdepth=None, message=None):
+        path = self.expand_path(path, recursive=recursive, maxdepth=maxdepth)
+        for p in reversed(path):
+            self.rm_file(p, message=message)
+
+    def rm_file(self, path, message=None, **kwargs):
+        """
+        Remove a file from a specified branch using a given commit message.
+
+        Since Github DELETE operation requires a branch name, and we can't reliably
+        determine whether the provided SHA refers to a branch, tag, or commit, we
+        assume it's a branch. If it's not, the user will encounter an error when
+        attempting to retrieve the file SHA or delete the file.
+
+        Parameters
+        ----------
+        path: str
+            The file's location relative to the repository root.
+        message: str, optional
+            The commit message for the deletion.
+        """
+
+        if not self.username:
+            raise ValueError("Authentication required")
+
+        path = self._strip_protocol(path)
+
+        # Attempt to get SHA from cache or Github API
+        sha = self._get_sha_from_cache(path)
+        if not sha:
+            url = self.content_url.format(
+                org=self.org, repo=self.repo, path=path.lstrip("/"), sha=self.root
+            )
+            r = requests.get(url, timeout=self.timeout, **self.kw)
+            if r.status_code == 404:
+                raise FileNotFoundError(path)
+            r.raise_for_status()
+            sha = r.json()["sha"]
+
+        # Delete the file
+        delete_url = self.content_url.format(
+            org=self.org, repo=self.repo, path=path, sha=self.root
+        )
+        branch = self.root
+        data = {
+            "message": message or f"Delete {path}",
+            "sha": sha,
+            **({"branch": branch} if branch else {}),
+        }
+
+        r = requests.delete(delete_url, json=data, timeout=self.timeout, **self.kw)
+        error_message = r.json().get("message", "")
+        if re.search(r"Branch .+ not found", error_message):
+            error = "Remove only works when the filesystem is initialised from a branch or default (None)"
+            raise ValueError(error)
+        r.raise_for_status()
+
+        self.invalidate_cache(path)
+
+    def _get_sha_from_cache(self, path):
+        for entries in self.dircache.values():
+            for entry in entries:
+                entry_path = entry.get("name")
+                if entry_path and entry_path == path and "sha" in entry:
+                    return entry["sha"]
+        return None
diff --git a/lib/python3.11/site-packages/fsspec/implementations/http.py b/lib/python3.11/site-packages/fsspec/implementations/http.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff1a955c27091229513b988627de9e806dca8c5
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/http.py
@@ -0,0 +1,902 @@
+import asyncio
+import io
+import logging
+import re
+import weakref
+from copy import copy
+from urllib.parse import urlparse
+
+import aiohttp
+import yarl
+
+from fsspec.asyn import AbstractAsyncStreamedFile, AsyncFileSystem, sync, sync_wrapper
+from fsspec.callbacks import DEFAULT_CALLBACK
+from fsspec.exceptions import FSTimeoutError
+from fsspec.spec import AbstractBufferedFile
+from fsspec.utils import (
+    DEFAULT_BLOCK_SIZE,
+    glob_translate,
+    isfilelike,
+    nullcontext,
+    tokenize,
+)
+
+from ..caching import AllBytes
+
+# https://stackoverflow.com/a/15926317/3821154
+ex = re.compile(r"""<(a|A)\s+(?:[^>]*?\s+)?(href|HREF)=["'](?P<url>[^"']+)""")
+ex2 = re.compile(r"""(?P<url>http[s]?://[-a-zA-Z0-9@:%_+.~#?&/=]+)""")
+logger = logging.getLogger("fsspec.http")
+
+
+async def get_client(**kwargs):
+    return aiohttp.ClientSession(**kwargs)
+
+
+class HTTPFileSystem(AsyncFileSystem):
+    """
+    Simple File-System for fetching data via HTTP(S)
+
+    ``ls()`` is implemented by loading the parent page and doing a regex
+    match on the result. If simple_link=True, anything of the form
+    "http(s)://server.com/stuff?thing=other"; otherwise only links within
+    HTML href tags will be used.
+
+    URLs are passed unfiltered to aiohttp, so all addresses are accessible. Where URLs are
+    supplied by a user, the calling application may wish to filter to prevent scanning.
+    """
+
+    protocol = ("http", "https")
+    sep = "/"
+
+    def __init__(
+        self,
+        simple_links=True,
+        block_size=None,
+        same_scheme=True,
+        size_policy=None,
+        cache_type="bytes",
+        cache_options=None,
+        asynchronous=False,
+        loop=None,
+        client_kwargs=None,
+        get_client=get_client,
+        encoded=False,
+        **storage_options,
+    ):
+        """
+        NB: if this is called async, you must await set_client
+
+        Parameters
+        ----------
+        block_size: int
+            Blocks to read bytes; if 0, will default to raw requests file-like
+            objects instead of HTTPFile instances
+        simple_links: bool
+            If True, will consider both HTML <a> tags and anything that looks
+            like a URL; if False, will consider only the former.
+        same_scheme: True
+            When doing ls/glob, if this is True, only consider paths that have
+            http/https matching the input URLs.
+        size_policy: this argument is deprecated
+        client_kwargs: dict
+            Passed to aiohttp.ClientSession, see
+            https://docs.aiohttp.org/en/stable/client_reference.html
+            For example, ``{'auth': aiohttp.BasicAuth('user', 'pass')}``
+        get_client: Callable[..., aiohttp.ClientSession]
+            A callable, which takes keyword arguments and constructs
+            an aiohttp.ClientSession. Its state will be managed by
+            the HTTPFileSystem class.
+        storage_options: key-value
+            Any other parameters passed on to requests
+        cache_type, cache_options: defaults used in open()
+        """
+        super().__init__(self, asynchronous=asynchronous, loop=loop, **storage_options)
+        self.block_size = block_size if block_size is not None else DEFAULT_BLOCK_SIZE
+        self.simple_links = simple_links
+        self.same_schema = same_scheme
+        self.cache_type = cache_type
+        self.cache_options = cache_options
+        self.client_kwargs = client_kwargs or {}
+        self.get_client = get_client
+        self.encoded = encoded
+        self.kwargs = storage_options
+        self._session = None
+
+        # Clean caching-related parameters from `storage_options`
+        # before propagating them as `request_options` through `self.kwargs`.
+        # TODO: Maybe rename `self.kwargs` to `self.request_options` to make
+        #       it clearer.
+        request_options = copy(storage_options)
+        self.use_listings_cache = request_options.pop("use_listings_cache", False)
+        request_options.pop("listings_expiry_time", None)
+        request_options.pop("max_paths", None)
+        request_options.pop("skip_instance_cache", None)
+        self.kwargs = request_options
+
+    @property
+    def fsid(self):
+        return "http"
+
+    def encode_url(self, url):
+        return yarl.URL(url, encoded=self.encoded)
+
+    @staticmethod
+    def close_session(loop, session):
+        if loop is not None and loop.is_running():
+            try:
+                sync(loop, session.close, timeout=0.1)
+                return
+            except (TimeoutError, FSTimeoutError, NotImplementedError):
+                pass
+        connector = getattr(session, "_connector", None)
+        if connector is not None:
+            # close after loop is dead
+            connector._close()
+
+    async def set_session(self):
+        if self._session is None:
+            self._session = await self.get_client(loop=self.loop, **self.client_kwargs)
+            if not self.asynchronous:
+                weakref.finalize(self, self.close_session, self.loop, self._session)
+        return self._session
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        """For HTTP, we always want to keep the full URL"""
+        return path
+
+    @classmethod
+    def _parent(cls, path):
+        # override, since _strip_protocol is different for URLs
+        par = super()._parent(path)
+        if len(par) > 7:  # "http://..."
+            return par
+        return ""
+
+    async def _ls_real(self, url, detail=True, **kwargs):
+        # ignoring URL-encoded arguments
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        logger.debug(url)
+        session = await self.set_session()
+        async with session.get(self.encode_url(url), **self.kwargs) as r:
+            self._raise_not_found_for_status(r, url)
+
+            if "Content-Type" in r.headers:
+                mimetype = r.headers["Content-Type"].partition(";")[0]
+            else:
+                mimetype = None
+
+            if mimetype in ("text/html", None):
+                try:
+                    text = await r.text(errors="ignore")
+                    if self.simple_links:
+                        links = ex2.findall(text) + [u[2] for u in ex.findall(text)]
+                    else:
+                        links = [u[2] for u in ex.findall(text)]
+                except UnicodeDecodeError:
+                    links = []  # binary, not HTML
+            else:
+                links = []
+
+        out = set()
+        parts = urlparse(url)
+        for l in links:
+            if isinstance(l, tuple):
+                l = l[1]
+            if l.startswith("/") and len(l) > 1:
+                # absolute URL on this server
+                l = f"{parts.scheme}://{parts.netloc}{l}"
+            if l.startswith("http"):
+                if self.same_schema and l.startswith(url.rstrip("/") + "/"):
+                    out.add(l)
+                elif l.replace("https", "http").startswith(
+                    url.replace("https", "http").rstrip("/") + "/"
+                ):
+                    # allowed to cross http <-> https
+                    out.add(l)
+            else:
+                if l not in ["..", "../"]:
+                    # Ignore FTP-like "parent"
+                    out.add("/".join([url.rstrip("/"), l.lstrip("/")]))
+        if not out and url.endswith("/"):
+            out = await self._ls_real(url.rstrip("/"), detail=False)
+        if detail:
+            return [
+                {
+                    "name": u,
+                    "size": None,
+                    "type": "directory" if u.endswith("/") else "file",
+                }
+                for u in out
+            ]
+        else:
+            return sorted(out)
+
+    async def _ls(self, url, detail=True, **kwargs):
+        if self.use_listings_cache and url in self.dircache:
+            out = self.dircache[url]
+        else:
+            out = await self._ls_real(url, detail=detail, **kwargs)
+            self.dircache[url] = out
+        return out
+
+    ls = sync_wrapper(_ls)
+
+    def _raise_not_found_for_status(self, response, url):
+        """
+        Raises FileNotFoundError for 404s, otherwise uses raise_for_status.
+        """
+        if response.status == 404:
+            raise FileNotFoundError(url)
+        response.raise_for_status()
+
+    async def _cat_file(self, url, start=None, end=None, **kwargs):
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        logger.debug(url)
+
+        if start is not None or end is not None:
+            if start == end:
+                return b""
+            headers = kw.pop("headers", {}).copy()
+
+            headers["Range"] = await self._process_limits(url, start, end)
+            kw["headers"] = headers
+        session = await self.set_session()
+        async with session.get(self.encode_url(url), **kw) as r:
+            out = await r.read()
+            self._raise_not_found_for_status(r, url)
+        return out
+
+    async def _get_file(
+        self, rpath, lpath, chunk_size=5 * 2**20, callback=DEFAULT_CALLBACK, **kwargs
+    ):
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        logger.debug(rpath)
+        session = await self.set_session()
+        async with session.get(self.encode_url(rpath), **kw) as r:
+            try:
+                size = int(r.headers["content-length"])
+            except (ValueError, KeyError):
+                size = None
+
+            callback.set_size(size)
+            self._raise_not_found_for_status(r, rpath)
+            if isfilelike(lpath):
+                outfile = lpath
+            else:
+                outfile = open(lpath, "wb")  # noqa: ASYNC230
+
+            try:
+                chunk = True
+                while chunk:
+                    chunk = await r.content.read(chunk_size)
+                    outfile.write(chunk)
+                    callback.relative_update(len(chunk))
+            finally:
+                if not isfilelike(lpath):
+                    outfile.close()
+
+    async def _put_file(
+        self,
+        lpath,
+        rpath,
+        chunk_size=5 * 2**20,
+        callback=DEFAULT_CALLBACK,
+        method="post",
+        mode="overwrite",
+        **kwargs,
+    ):
+        if mode != "overwrite":
+            raise NotImplementedError("Exclusive write")
+
+        async def gen_chunks():
+            # Support passing arbitrary file-like objects
+            # and use them instead of streams.
+            if isinstance(lpath, io.IOBase):
+                context = nullcontext(lpath)
+                use_seek = False  # might not support seeking
+            else:
+                context = open(lpath, "rb")  # noqa: ASYNC230
+                use_seek = True
+
+            with context as f:
+                if use_seek:
+                    callback.set_size(f.seek(0, 2))
+                    f.seek(0)
+                else:
+                    callback.set_size(getattr(f, "size", None))
+
+                chunk = f.read(chunk_size)
+                while chunk:
+                    yield chunk
+                    callback.relative_update(len(chunk))
+                    chunk = f.read(chunk_size)
+
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        session = await self.set_session()
+
+        method = method.lower()
+        if method not in ("post", "put"):
+            raise ValueError(
+                f"method has to be either 'post' or 'put', not: {method!r}"
+            )
+
+        meth = getattr(session, method)
+        async with meth(self.encode_url(rpath), data=gen_chunks(), **kw) as resp:
+            self._raise_not_found_for_status(resp, rpath)
+
+    async def _exists(self, path, strict=False, **kwargs):
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        try:
+            logger.debug(path)
+            session = await self.set_session()
+            r = await session.get(self.encode_url(path), **kw)
+            async with r:
+                if strict:
+                    self._raise_not_found_for_status(r, path)
+                return r.status < 400
+        except FileNotFoundError:
+            return False
+        except aiohttp.ClientError:
+            if strict:
+                raise
+            return False
+
+    async def _isfile(self, path, **kwargs):
+        return await self._exists(path, **kwargs)
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=None,  # XXX: This differs from the base class.
+        cache_type=None,
+        cache_options=None,
+        size=None,
+        **kwargs,
+    ):
+        """Make a file-like object
+
+        Parameters
+        ----------
+        path: str
+            Full URL with protocol
+        mode: string
+            must be "rb"
+        block_size: int or None
+            Bytes to download in one request; use instance value if None. If
+            zero, will return a streaming Requests file-like instance.
+        kwargs: key-value
+            Any other parameters, passed to requests calls
+        """
+        if mode != "rb":
+            raise NotImplementedError
+        block_size = block_size if block_size is not None else self.block_size
+        kw = self.kwargs.copy()
+        kw["asynchronous"] = self.asynchronous
+        kw.update(kwargs)
+        info = {}
+        size = size or info.update(self.info(path, **kwargs)) or info["size"]
+        session = sync(self.loop, self.set_session)
+        if block_size and size and info.get("partial", True):
+            return HTTPFile(
+                self,
+                path,
+                session=session,
+                block_size=block_size,
+                mode=mode,
+                size=size,
+                cache_type=cache_type or self.cache_type,
+                cache_options=cache_options or self.cache_options,
+                loop=self.loop,
+                **kw,
+            )
+        else:
+            return HTTPStreamFile(
+                self,
+                path,
+                mode=mode,
+                loop=self.loop,
+                session=session,
+                **kw,
+            )
+
+    async def open_async(self, path, mode="rb", size=None, **kwargs):
+        session = await self.set_session()
+        if size is None:
+            try:
+                size = (await self._info(path, **kwargs))["size"]
+            except FileNotFoundError:
+                pass
+        return AsyncStreamFile(
+            self,
+            path,
+            loop=self.loop,
+            session=session,
+            size=size,
+            **kwargs,
+        )
+
+    def ukey(self, url):
+        """Unique identifier; assume HTTP files are static, unchanging"""
+        return tokenize(url, self.kwargs, self.protocol)
+
+    async def _info(self, url, **kwargs):
+        """Get info of URL
+
+        Tries to access location via HEAD, and then GET methods, but does
+        not fetch the data.
+
+        It is possible that the server does not supply any size information, in
+        which case size will be given as None (and certain operations on the
+        corresponding file will not work).
+        """
+        info = {}
+        session = await self.set_session()
+
+        for policy in ["head", "get"]:
+            try:
+                info.update(
+                    await _file_info(
+                        self.encode_url(url),
+                        size_policy=policy,
+                        session=session,
+                        **self.kwargs,
+                        **kwargs,
+                    )
+                )
+                if info.get("size") is not None:
+                    break
+            except Exception as exc:
+                if policy == "get":
+                    # If get failed, then raise a FileNotFoundError
+                    raise FileNotFoundError(url) from exc
+                logger.debug("", exc_info=exc)
+
+        return {"name": url, "size": None, **info, "type": "file"}
+
+    async def _glob(self, path, maxdepth=None, **kwargs):
+        """
+        Find files by glob-matching.
+
+        This implementation is idntical to the one in AbstractFileSystem,
+        but "?" is not considered as a character for globbing, because it is
+        so common in URLs, often identifying the "query" part.
+        """
+        if maxdepth is not None and maxdepth < 1:
+            raise ValueError("maxdepth must be at least 1")
+        import re
+
+        ends_with_slash = path.endswith("/")  # _strip_protocol strips trailing slash
+        path = self._strip_protocol(path)
+        append_slash_to_dirname = ends_with_slash or path.endswith(("/**", "/*"))
+        idx_star = path.find("*") if path.find("*") >= 0 else len(path)
+        idx_brace = path.find("[") if path.find("[") >= 0 else len(path)
+
+        min_idx = min(idx_star, idx_brace)
+
+        detail = kwargs.pop("detail", False)
+
+        if not has_magic(path):
+            if await self._exists(path, **kwargs):
+                if not detail:
+                    return [path]
+                else:
+                    return {path: await self._info(path, **kwargs)}
+            else:
+                if not detail:
+                    return []  # glob of non-existent returns empty
+                else:
+                    return {}
+        elif "/" in path[:min_idx]:
+            min_idx = path[:min_idx].rindex("/")
+            root = path[: min_idx + 1]
+            depth = path[min_idx + 1 :].count("/") + 1
+        else:
+            root = ""
+            depth = path[min_idx + 1 :].count("/") + 1
+
+        if "**" in path:
+            if maxdepth is not None:
+                idx_double_stars = path.find("**")
+                depth_double_stars = path[idx_double_stars:].count("/") + 1
+                depth = depth - depth_double_stars + maxdepth
+            else:
+                depth = None
+
+        allpaths = await self._find(
+            root, maxdepth=depth, withdirs=True, detail=True, **kwargs
+        )
+
+        pattern = glob_translate(path + ("/" if ends_with_slash else ""))
+        pattern = re.compile(pattern)
+
+        out = {
+            (
+                p.rstrip("/")
+                if not append_slash_to_dirname
+                and info["type"] == "directory"
+                and p.endswith("/")
+                else p
+            ): info
+            for p, info in sorted(allpaths.items())
+            if pattern.match(p.rstrip("/"))
+        }
+
+        if detail:
+            return out
+        else:
+            return list(out)
+
+    async def _isdir(self, path):
+        # override, since all URLs are (also) files
+        try:
+            return bool(await self._ls(path))
+        except (FileNotFoundError, ValueError):
+            return False
+
+    async def _pipe_file(self, path, value, mode="overwrite", **kwargs):
+        """
+        Write bytes to a remote file over HTTP.
+
+        Parameters
+        ----------
+        path : str
+            Target URL where the data should be written
+        value : bytes
+            Data to be written
+        mode : str
+            How to write to the file - 'overwrite' or 'append'
+        **kwargs : dict
+            Additional parameters to pass to the HTTP request
+        """
+        url = self._strip_protocol(path)
+        headers = kwargs.pop("headers", {})
+        headers["Content-Length"] = str(len(value))
+
+        session = await self.set_session()
+
+        async with session.put(
+            self.encode_url(url), data=value, headers=headers, **kwargs
+        ) as r:
+            r.raise_for_status()
+
+
+class HTTPFile(AbstractBufferedFile):
+    """
+    A file-like object pointing to a remote HTTP(S) resource
+
+    Supports only reading, with read-ahead of a predetermined block-size.
+
+    In the case that the server does not supply the filesize, only reading of
+    the complete file in one go is supported.
+
+    Parameters
+    ----------
+    url: str
+        Full URL of the remote resource, including the protocol
+    session: aiohttp.ClientSession or None
+        All calls will be made within this session, to avoid restarting
+        connections where the server allows this
+    block_size: int or None
+        The amount of read-ahead to do, in bytes. Default is 5MB, or the value
+        configured for the FileSystem creating this file
+    size: None or int
+        If given, this is the size of the file in bytes, and we don't attempt
+        to call the server to find the value.
+    kwargs: all other key-values are passed to requests calls.
+    """
+
+    def __init__(
+        self,
+        fs,
+        url,
+        session=None,
+        block_size=None,
+        mode="rb",
+        cache_type="bytes",
+        cache_options=None,
+        size=None,
+        loop=None,
+        asynchronous=False,
+        **kwargs,
+    ):
+        if mode != "rb":
+            raise NotImplementedError("File mode not supported")
+        self.asynchronous = asynchronous
+        self.loop = loop
+        self.url = url
+        self.session = session
+        self.details = {"name": url, "size": size, "type": "file"}
+        super().__init__(
+            fs=fs,
+            path=url,
+            mode=mode,
+            block_size=block_size,
+            cache_type=cache_type,
+            cache_options=cache_options,
+            **kwargs,
+        )
+
+    def read(self, length=-1):
+        """Read bytes from file
+
+        Parameters
+        ----------
+        length: int
+            Read up to this many bytes. If negative, read all content to end of
+            file. If the server has not supplied the filesize, attempting to
+            read only part of the data will raise a ValueError.
+        """
+        if (
+            (length < 0 and self.loc == 0)  # explicit read all
+            # but not when the size is known and fits into a block anyways
+            and not (self.size is not None and self.size <= self.blocksize)
+        ):
+            self._fetch_all()
+        if self.size is None:
+            if length < 0:
+                self._fetch_all()
+        else:
+            length = min(self.size - self.loc, length)
+        return super().read(length)
+
+    async def async_fetch_all(self):
+        """Read whole file in one shot, without caching
+
+        This is only called when position is still at zero,
+        and read() is called without a byte-count.
+        """
+        logger.debug(f"Fetch all for {self}")
+        if not isinstance(self.cache, AllBytes):
+            r = await self.session.get(self.fs.encode_url(self.url), **self.kwargs)
+            async with r:
+                r.raise_for_status()
+                out = await r.read()
+                self.cache = AllBytes(
+                    size=len(out), fetcher=None, blocksize=None, data=out
+                )
+                self.size = len(out)
+
+    _fetch_all = sync_wrapper(async_fetch_all)
+
+    def _parse_content_range(self, headers):
+        """Parse the Content-Range header"""
+        s = headers.get("Content-Range", "")
+        m = re.match(r"bytes (\d+-\d+|\*)/(\d+|\*)", s)
+        if not m:
+            return None, None, None
+
+        if m[1] == "*":
+            start = end = None
+        else:
+            start, end = [int(x) for x in m[1].split("-")]
+        total = None if m[2] == "*" else int(m[2])
+        return start, end, total
+
+    async def async_fetch_range(self, start, end):
+        """Download a block of data
+
+        The expectation is that the server returns only the requested bytes,
+        with HTTP code 206. If this is not the case, we first check the headers,
+        and then stream the output - if the data size is bigger than we
+        requested, an exception is raised.
+        """
+        logger.debug(f"Fetch range for {self}: {start}-{end}")
+        kwargs = self.kwargs.copy()
+        headers = kwargs.pop("headers", {}).copy()
+        headers["Range"] = f"bytes={start}-{end - 1}"
+        logger.debug(f"{self.url} : {headers['Range']}")
+        r = await self.session.get(
+            self.fs.encode_url(self.url), headers=headers, **kwargs
+        )
+        async with r:
+            if r.status == 416:
+                # range request outside file
+                return b""
+            r.raise_for_status()
+
+            # If the server has handled the range request, it should reply
+            # with status 206 (partial content). But we'll guess that a suitable
+            # Content-Range header or a Content-Length no more than the
+            # requested range also mean we have got the desired range.
+            response_is_range = (
+                r.status == 206
+                or self._parse_content_range(r.headers)[0] == start
+                or int(r.headers.get("Content-Length", end + 1)) <= end - start
+            )
+
+            if response_is_range:
+                # partial content, as expected
+                out = await r.read()
+            elif start > 0:
+                raise ValueError(
+                    "The HTTP server doesn't appear to support range requests. "
+                    "Only reading this file from the beginning is supported. "
+                    "Open with block_size=0 for a streaming file interface."
+                )
+            else:
+                # Response is not a range, but we want the start of the file,
+                # so we can read the required amount anyway.
+                cl = 0
+                out = []
+                while True:
+                    chunk = await r.content.read(2**20)
+                    # data size unknown, let's read until we have enough
+                    if chunk:
+                        out.append(chunk)
+                        cl += len(chunk)
+                        if cl > end - start:
+                            break
+                    else:
+                        break
+                out = b"".join(out)[: end - start]
+            return out
+
+    _fetch_range = sync_wrapper(async_fetch_range)
+
+
+magic_check = re.compile("([*[])")
+
+
+def has_magic(s):
+    match = magic_check.search(s)
+    return match is not None
+
+
+class HTTPStreamFile(AbstractBufferedFile):
+    def __init__(self, fs, url, mode="rb", loop=None, session=None, **kwargs):
+        self.asynchronous = kwargs.pop("asynchronous", False)
+        self.url = url
+        self.loop = loop
+        self.session = session
+        if mode != "rb":
+            raise ValueError
+        self.details = {"name": url, "size": None}
+        super().__init__(fs=fs, path=url, mode=mode, cache_type="none", **kwargs)
+
+        async def cor():
+            r = await self.session.get(self.fs.encode_url(url), **kwargs).__aenter__()
+            self.fs._raise_not_found_for_status(r, url)
+            return r
+
+        self.r = sync(self.loop, cor)
+        self.loop = fs.loop
+
+    def seek(self, loc, whence=0):
+        if loc == 0 and whence == 1:
+            return
+        if loc == self.loc and whence == 0:
+            return
+        raise ValueError("Cannot seek streaming HTTP file")
+
+    async def _read(self, num=-1):
+        out = await self.r.content.read(num)
+        self.loc += len(out)
+        return out
+
+    read = sync_wrapper(_read)
+
+    async def _close(self):
+        self.r.close()
+
+    def close(self):
+        asyncio.run_coroutine_threadsafe(self._close(), self.loop)
+        super().close()
+
+
+class AsyncStreamFile(AbstractAsyncStreamedFile):
+    def __init__(
+        self, fs, url, mode="rb", loop=None, session=None, size=None, **kwargs
+    ):
+        self.url = url
+        self.session = session
+        self.r = None
+        if mode != "rb":
+            raise ValueError
+        self.details = {"name": url, "size": None}
+        self.kwargs = kwargs
+        super().__init__(fs=fs, path=url, mode=mode, cache_type="none")
+        self.size = size
+
+    async def read(self, num=-1):
+        if self.r is None:
+            r = await self.session.get(
+                self.fs.encode_url(self.url), **self.kwargs
+            ).__aenter__()
+            self.fs._raise_not_found_for_status(r, self.url)
+            self.r = r
+        out = await self.r.content.read(num)
+        self.loc += len(out)
+        return out
+
+    async def close(self):
+        if self.r is not None:
+            self.r.close()
+            self.r = None
+        await super().close()
+
+
+async def get_range(session, url, start, end, file=None, **kwargs):
+    # explicit get a range when we know it must be safe
+    kwargs = kwargs.copy()
+    headers = kwargs.pop("headers", {}).copy()
+    headers["Range"] = f"bytes={start}-{end - 1}"
+    r = await session.get(url, headers=headers, **kwargs)
+    r.raise_for_status()
+    async with r:
+        out = await r.read()
+    if file:
+        with open(file, "r+b") as f:  # noqa: ASYNC230
+            f.seek(start)
+            f.write(out)
+    else:
+        return out
+
+
+async def _file_info(url, session, size_policy="head", **kwargs):
+    """Call HEAD on the server to get details about the file (size/checksum etc.)
+
+    Default operation is to explicitly allow redirects and use encoding
+    'identity' (no compression) to get the true size of the target.
+    """
+    logger.debug("Retrieve file size for %s", url)
+    kwargs = kwargs.copy()
+    ar = kwargs.pop("allow_redirects", True)
+    head = kwargs.get("headers", {}).copy()
+    head["Accept-Encoding"] = "identity"
+    kwargs["headers"] = head
+
+    info = {}
+    if size_policy == "head":
+        r = await session.head(url, allow_redirects=ar, **kwargs)
+    elif size_policy == "get":
+        r = await session.get(url, allow_redirects=ar, **kwargs)
+    else:
+        raise TypeError(f'size_policy must be "head" or "get", got {size_policy}')
+    async with r:
+        r.raise_for_status()
+
+        if "Content-Length" in r.headers:
+            # Some servers may choose to ignore Accept-Encoding and return
+            # compressed content, in which case the returned size is unreliable.
+            if "Content-Encoding" not in r.headers or r.headers["Content-Encoding"] in [
+                "identity",
+                "",
+            ]:
+                info["size"] = int(r.headers["Content-Length"])
+        elif "Content-Range" in r.headers:
+            info["size"] = int(r.headers["Content-Range"].split("/")[1])
+
+        if "Content-Type" in r.headers:
+            info["mimetype"] = r.headers["Content-Type"].partition(";")[0]
+
+        if r.headers.get("Accept-Ranges") == "none":
+            # Some servers may explicitly discourage partial content requests, but
+            # the lack of "Accept-Ranges" does not always indicate they would fail
+            info["partial"] = False
+
+        info["url"] = str(r.url)
+
+        for checksum_field in ["ETag", "Content-MD5", "Digest", "Last-Modified"]:
+            if r.headers.get(checksum_field):
+                info[checksum_field] = r.headers[checksum_field]
+
+    return info
+
+
+async def _file_size(url, session=None, *args, **kwargs):
+    if session is None:
+        session = await get_client()
+    info = await _file_info(url, session=session, *args, **kwargs)
+    return info.get("size")
+
+
+file_size = sync_wrapper(_file_size)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/http_sync.py b/lib/python3.11/site-packages/fsspec/implementations/http_sync.py
new file mode 100644
index 0000000000000000000000000000000000000000..a67ea3ea5fee9e6b51f7f3f66773e8cf65735e52
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/http_sync.py
@@ -0,0 +1,937 @@
+"""This file is largely copied from http.py"""
+
+import io
+import logging
+import re
+import urllib.error
+import urllib.parse
+from copy import copy
+from json import dumps, loads
+from urllib.parse import urlparse
+
+try:
+    import yarl
+except (ImportError, ModuleNotFoundError, OSError):
+    yarl = False
+
+from fsspec.callbacks import _DEFAULT_CALLBACK
+from fsspec.registry import register_implementation
+from fsspec.spec import AbstractBufferedFile, AbstractFileSystem
+from fsspec.utils import DEFAULT_BLOCK_SIZE, isfilelike, nullcontext, tokenize
+
+from ..caching import AllBytes
+
+# https://stackoverflow.com/a/15926317/3821154
+ex = re.compile(r"""<(a|A)\s+(?:[^>]*?\s+)?(href|HREF)=["'](?P<url>[^"']+)""")
+ex2 = re.compile(r"""(?P<url>http[s]?://[-a-zA-Z0-9@:%_+.~#?&/=]+)""")
+logger = logging.getLogger("fsspec.http")
+
+
+class JsHttpException(urllib.error.HTTPError): ...
+
+
+class StreamIO(io.BytesIO):
+    # fake class, so you can set attributes on it
+    # will eventually actually stream
+    ...
+
+
+class ResponseProxy:
+    """Looks like a requests response"""
+
+    def __init__(self, req, stream=False):
+        self.request = req
+        self.stream = stream
+        self._data = None
+        self._headers = None
+
+    @property
+    def raw(self):
+        if self._data is None:
+            b = self.request.response.to_bytes()
+            if self.stream:
+                self._data = StreamIO(b)
+            else:
+                self._data = b
+        return self._data
+
+    def close(self):
+        if hasattr(self, "_data"):
+            del self._data
+
+    @property
+    def headers(self):
+        if self._headers is None:
+            self._headers = dict(
+                [
+                    _.split(": ")
+                    for _ in self.request.getAllResponseHeaders().strip().split("\r\n")
+                ]
+            )
+        return self._headers
+
+    @property
+    def status_code(self):
+        return int(self.request.status)
+
+    def raise_for_status(self):
+        if not self.ok:
+            raise JsHttpException(
+                self.url, self.status_code, self.reason, self.headers, None
+            )
+
+    def iter_content(self, chunksize, *_, **__):
+        while True:
+            out = self.raw.read(chunksize)
+            if out:
+                yield out
+            else:
+                break
+
+    @property
+    def reason(self):
+        return self.request.statusText
+
+    @property
+    def ok(self):
+        return self.status_code < 400
+
+    @property
+    def url(self):
+        return self.request.response.responseURL
+
+    @property
+    def text(self):
+        # TODO: encoding from headers
+        return self.content.decode()
+
+    @property
+    def content(self):
+        self.stream = False
+        return self.raw
+
+    def json(self):
+        return loads(self.text)
+
+
+class RequestsSessionShim:
+    def __init__(self):
+        self.headers = {}
+
+    def request(
+        self,
+        method,
+        url,
+        params=None,
+        data=None,
+        headers=None,
+        cookies=None,
+        files=None,
+        auth=None,
+        timeout=None,
+        allow_redirects=None,
+        proxies=None,
+        hooks=None,
+        stream=None,
+        verify=None,
+        cert=None,
+        json=None,
+    ):
+        from js import Blob, XMLHttpRequest
+
+        logger.debug("JS request: %s %s", method, url)
+
+        if cert or verify or proxies or files or cookies or hooks:
+            raise NotImplementedError
+        if data and json:
+            raise ValueError("Use json= or data=, not both")
+        req = XMLHttpRequest.new()
+        extra = auth if auth else ()
+        if params:
+            url = f"{url}?{urllib.parse.urlencode(params)}"
+        req.open(method, url, False, *extra)
+        if timeout:
+            req.timeout = timeout
+        if headers:
+            for k, v in headers.items():
+                req.setRequestHeader(k, v)
+
+        req.setRequestHeader("Accept", "application/octet-stream")
+        req.responseType = "arraybuffer"
+        if json:
+            blob = Blob.new([dumps(data)], {type: "application/json"})
+            req.send(blob)
+        elif data:
+            if isinstance(data, io.IOBase):
+                data = data.read()
+            blob = Blob.new([data], {type: "application/octet-stream"})
+            req.send(blob)
+        else:
+            req.send(None)
+        return ResponseProxy(req, stream=stream)
+
+    def get(self, url, **kwargs):
+        return self.request("GET", url, **kwargs)
+
+    def head(self, url, **kwargs):
+        return self.request("HEAD", url, **kwargs)
+
+    def post(self, url, **kwargs):
+        return self.request("POST}", url, **kwargs)
+
+    def put(self, url, **kwargs):
+        return self.request("PUT", url, **kwargs)
+
+    def patch(self, url, **kwargs):
+        return self.request("PATCH", url, **kwargs)
+
+    def delete(self, url, **kwargs):
+        return self.request("DELETE", url, **kwargs)
+
+
+class HTTPFileSystem(AbstractFileSystem):
+    """
+    Simple File-System for fetching data via HTTP(S)
+
+    This is the BLOCKING version of the normal HTTPFileSystem. It uses
+    requests in normal python and the JS runtime in pyodide.
+
+    ***This implementation is extremely experimental, do not use unless
+    you are testing pyodide/pyscript integration***
+    """
+
+    protocol = ("http", "https", "sync-http", "sync-https")
+    sep = "/"
+
+    def __init__(
+        self,
+        simple_links=True,
+        block_size=None,
+        same_scheme=True,
+        cache_type="readahead",
+        cache_options=None,
+        client_kwargs=None,
+        encoded=False,
+        **storage_options,
+    ):
+        """
+
+        Parameters
+        ----------
+        block_size: int
+            Blocks to read bytes; if 0, will default to raw requests file-like
+            objects instead of HTTPFile instances
+        simple_links: bool
+            If True, will consider both HTML <a> tags and anything that looks
+            like a URL; if False, will consider only the former.
+        same_scheme: True
+            When doing ls/glob, if this is True, only consider paths that have
+            http/https matching the input URLs.
+        size_policy: this argument is deprecated
+        client_kwargs: dict
+            Passed to aiohttp.ClientSession, see
+            https://docs.aiohttp.org/en/stable/client_reference.html
+            For example, ``{'auth': aiohttp.BasicAuth('user', 'pass')}``
+        storage_options: key-value
+            Any other parameters passed on to requests
+        cache_type, cache_options: defaults used in open
+        """
+        super().__init__(self, **storage_options)
+        self.block_size = block_size if block_size is not None else DEFAULT_BLOCK_SIZE
+        self.simple_links = simple_links
+        self.same_schema = same_scheme
+        self.cache_type = cache_type
+        self.cache_options = cache_options
+        self.client_kwargs = client_kwargs or {}
+        self.encoded = encoded
+        self.kwargs = storage_options
+
+        try:
+            import js  # noqa: F401
+
+            logger.debug("Starting JS session")
+            self.session = RequestsSessionShim()
+            self.js = True
+        except Exception as e:
+            import requests
+
+            logger.debug("Starting cpython session because of: %s", e)
+            self.session = requests.Session(**(client_kwargs or {}))
+            self.js = False
+
+        request_options = copy(storage_options)
+        self.use_listings_cache = request_options.pop("use_listings_cache", False)
+        request_options.pop("listings_expiry_time", None)
+        request_options.pop("max_paths", None)
+        request_options.pop("skip_instance_cache", None)
+        self.kwargs = request_options
+
+    @property
+    def fsid(self):
+        return "sync-http"
+
+    def encode_url(self, url):
+        if yarl:
+            return yarl.URL(url, encoded=self.encoded)
+        return url
+
+    @classmethod
+    def _strip_protocol(cls, path: str) -> str:
+        """For HTTP, we always want to keep the full URL"""
+        path = path.replace("sync-http://", "http://").replace(
+            "sync-https://", "https://"
+        )
+        return path
+
+    @classmethod
+    def _parent(cls, path):
+        # override, since _strip_protocol is different for URLs
+        par = super()._parent(path)
+        if len(par) > 7:  # "http://..."
+            return par
+        return ""
+
+    def _ls_real(self, url, detail=True, **kwargs):
+        # ignoring URL-encoded arguments
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        logger.debug(url)
+        r = self.session.get(self.encode_url(url), **self.kwargs)
+        self._raise_not_found_for_status(r, url)
+        text = r.text
+        if self.simple_links:
+            links = ex2.findall(text) + [u[2] for u in ex.findall(text)]
+        else:
+            links = [u[2] for u in ex.findall(text)]
+        out = set()
+        parts = urlparse(url)
+        for l in links:
+            if isinstance(l, tuple):
+                l = l[1]
+            if l.startswith("/") and len(l) > 1:
+                # absolute URL on this server
+                l = parts.scheme + "://" + parts.netloc + l
+            if l.startswith("http"):
+                if self.same_schema and l.startswith(url.rstrip("/") + "/"):
+                    out.add(l)
+                elif l.replace("https", "http").startswith(
+                    url.replace("https", "http").rstrip("/") + "/"
+                ):
+                    # allowed to cross http <-> https
+                    out.add(l)
+            else:
+                if l not in ["..", "../"]:
+                    # Ignore FTP-like "parent"
+                    out.add("/".join([url.rstrip("/"), l.lstrip("/")]))
+        if not out and url.endswith("/"):
+            out = self._ls_real(url.rstrip("/"), detail=False)
+        if detail:
+            return [
+                {
+                    "name": u,
+                    "size": None,
+                    "type": "directory" if u.endswith("/") else "file",
+                }
+                for u in out
+            ]
+        else:
+            return sorted(out)
+
+    def ls(self, url, detail=True, **kwargs):
+        if self.use_listings_cache and url in self.dircache:
+            out = self.dircache[url]
+        else:
+            out = self._ls_real(url, detail=detail, **kwargs)
+            self.dircache[url] = out
+        return out
+
+    def _raise_not_found_for_status(self, response, url):
+        """
+        Raises FileNotFoundError for 404s, otherwise uses raise_for_status.
+        """
+        if response.status_code == 404:
+            raise FileNotFoundError(url)
+        response.raise_for_status()
+
+    def cat_file(self, url, start=None, end=None, **kwargs):
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        logger.debug(url)
+
+        if start is not None or end is not None:
+            if start == end:
+                return b""
+            headers = kw.pop("headers", {}).copy()
+
+            headers["Range"] = self._process_limits(url, start, end)
+            kw["headers"] = headers
+        r = self.session.get(self.encode_url(url), **kw)
+        self._raise_not_found_for_status(r, url)
+        return r.content
+
+    def get_file(
+        self, rpath, lpath, chunk_size=5 * 2**20, callback=_DEFAULT_CALLBACK, **kwargs
+    ):
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        logger.debug(rpath)
+        r = self.session.get(self.encode_url(rpath), **kw)
+        try:
+            size = int(
+                r.headers.get("content-length", None)
+                or r.headers.get("Content-Length", None)
+            )
+        except (ValueError, KeyError, TypeError):
+            size = None
+
+        callback.set_size(size)
+        self._raise_not_found_for_status(r, rpath)
+        if not isfilelike(lpath):
+            lpath = open(lpath, "wb")
+        for chunk in r.iter_content(chunk_size, decode_unicode=False):
+            lpath.write(chunk)
+            callback.relative_update(len(chunk))
+
+    def put_file(
+        self,
+        lpath,
+        rpath,
+        chunk_size=5 * 2**20,
+        callback=_DEFAULT_CALLBACK,
+        method="post",
+        **kwargs,
+    ):
+        def gen_chunks():
+            # Support passing arbitrary file-like objects
+            # and use them instead of streams.
+            if isinstance(lpath, io.IOBase):
+                context = nullcontext(lpath)
+                use_seek = False  # might not support seeking
+            else:
+                context = open(lpath, "rb")
+                use_seek = True
+
+            with context as f:
+                if use_seek:
+                    callback.set_size(f.seek(0, 2))
+                    f.seek(0)
+                else:
+                    callback.set_size(getattr(f, "size", None))
+
+                chunk = f.read(chunk_size)
+                while chunk:
+                    yield chunk
+                    callback.relative_update(len(chunk))
+                    chunk = f.read(chunk_size)
+
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+
+        method = method.lower()
+        if method not in ("post", "put"):
+            raise ValueError(
+                f"method has to be either 'post' or 'put', not: {method!r}"
+            )
+
+        meth = getattr(self.session, method)
+        resp = meth(rpath, data=gen_chunks(), **kw)
+        self._raise_not_found_for_status(resp, rpath)
+
+    def _process_limits(self, url, start, end):
+        """Helper for "Range"-based _cat_file"""
+        size = None
+        suff = False
+        if start is not None and start < 0:
+            # if start is negative and end None, end is the "suffix length"
+            if end is None:
+                end = -start
+                start = ""
+                suff = True
+            else:
+                size = size or self.info(url)["size"]
+                start = size + start
+        elif start is None:
+            start = 0
+        if not suff:
+            if end is not None and end < 0:
+                if start is not None:
+                    size = size or self.info(url)["size"]
+                    end = size + end
+            elif end is None:
+                end = ""
+            if isinstance(end, int):
+                end -= 1  # bytes range is inclusive
+        return f"bytes={start}-{end}"
+
+    def exists(self, path, strict=False, **kwargs):
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        try:
+            logger.debug(path)
+            r = self.session.get(self.encode_url(path), **kw)
+            if strict:
+                self._raise_not_found_for_status(r, path)
+            return r.status_code < 400
+        except FileNotFoundError:
+            return False
+        except Exception:
+            if strict:
+                raise
+            return False
+
+    def isfile(self, path, **kwargs):
+        return self.exists(path, **kwargs)
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=None,  # XXX: This differs from the base class.
+        cache_type=None,
+        cache_options=None,
+        size=None,
+        **kwargs,
+    ):
+        """Make a file-like object
+
+        Parameters
+        ----------
+        path: str
+            Full URL with protocol
+        mode: string
+            must be "rb"
+        block_size: int or None
+            Bytes to download in one request; use instance value if None. If
+            zero, will return a streaming Requests file-like instance.
+        kwargs: key-value
+            Any other parameters, passed to requests calls
+        """
+        if mode != "rb":
+            raise NotImplementedError
+        block_size = block_size if block_size is not None else self.block_size
+        kw = self.kwargs.copy()
+        kw.update(kwargs)
+        size = size or self.info(path, **kwargs)["size"]
+        if block_size and size:
+            return HTTPFile(
+                self,
+                path,
+                session=self.session,
+                block_size=block_size,
+                mode=mode,
+                size=size,
+                cache_type=cache_type or self.cache_type,
+                cache_options=cache_options or self.cache_options,
+                **kw,
+            )
+        else:
+            return HTTPStreamFile(
+                self,
+                path,
+                mode=mode,
+                session=self.session,
+                **kw,
+            )
+
+    def ukey(self, url):
+        """Unique identifier; assume HTTP files are static, unchanging"""
+        return tokenize(url, self.kwargs, self.protocol)
+
+    def info(self, url, **kwargs):
+        """Get info of URL
+
+        Tries to access location via HEAD, and then GET methods, but does
+        not fetch the data.
+
+        It is possible that the server does not supply any size information, in
+        which case size will be given as None (and certain operations on the
+        corresponding file will not work).
+        """
+        info = {}
+        for policy in ["head", "get"]:
+            try:
+                info.update(
+                    _file_info(
+                        self.encode_url(url),
+                        size_policy=policy,
+                        session=self.session,
+                        **self.kwargs,
+                        **kwargs,
+                    )
+                )
+                if info.get("size") is not None:
+                    break
+            except Exception as exc:
+                if policy == "get":
+                    # If get failed, then raise a FileNotFoundError
+                    raise FileNotFoundError(url) from exc
+                logger.debug(str(exc))
+
+        return {"name": url, "size": None, **info, "type": "file"}
+
+    def glob(self, path, maxdepth=None, **kwargs):
+        """
+        Find files by glob-matching.
+
+        This implementation is idntical to the one in AbstractFileSystem,
+        but "?" is not considered as a character for globbing, because it is
+        so common in URLs, often identifying the "query" part.
+        """
+        import re
+
+        ends = path.endswith("/")
+        path = self._strip_protocol(path)
+        indstar = path.find("*") if path.find("*") >= 0 else len(path)
+        indbrace = path.find("[") if path.find("[") >= 0 else len(path)
+
+        ind = min(indstar, indbrace)
+
+        detail = kwargs.pop("detail", False)
+
+        if not has_magic(path):
+            root = path
+            depth = 1
+            if ends:
+                path += "/*"
+            elif self.exists(path):
+                if not detail:
+                    return [path]
+                else:
+                    return {path: self.info(path)}
+            else:
+                if not detail:
+                    return []  # glob of non-existent returns empty
+                else:
+                    return {}
+        elif "/" in path[:ind]:
+            ind2 = path[:ind].rindex("/")
+            root = path[: ind2 + 1]
+            depth = None if "**" in path else path[ind2 + 1 :].count("/") + 1
+        else:
+            root = ""
+            depth = None if "**" in path else path[ind + 1 :].count("/") + 1
+
+        allpaths = self.find(
+            root, maxdepth=maxdepth or depth, withdirs=True, detail=True, **kwargs
+        )
+        # Escape characters special to python regex, leaving our supported
+        # special characters in place.
+        # See https://www.gnu.org/software/bash/manual/html_node/Pattern-Matching.html
+        # for shell globbing details.
+        pattern = (
+            "^"
+            + (
+                path.replace("\\", r"\\")
+                .replace(".", r"\.")
+                .replace("+", r"\+")
+                .replace("//", "/")
+                .replace("(", r"\(")
+                .replace(")", r"\)")
+                .replace("|", r"\|")
+                .replace("^", r"\^")
+                .replace("$", r"\$")
+                .replace("{", r"\{")
+                .replace("}", r"\}")
+                .rstrip("/")
+            )
+            + "$"
+        )
+        pattern = re.sub("[*]{2}", "=PLACEHOLDER=", pattern)
+        pattern = re.sub("[*]", "[^/]*", pattern)
+        pattern = re.compile(pattern.replace("=PLACEHOLDER=", ".*"))
+        out = {
+            p: allpaths[p]
+            for p in sorted(allpaths)
+            if pattern.match(p.replace("//", "/").rstrip("/"))
+        }
+        if detail:
+            return out
+        else:
+            return list(out)
+
+    def isdir(self, path):
+        # override, since all URLs are (also) files
+        try:
+            return bool(self.ls(path))
+        except (FileNotFoundError, ValueError):
+            return False
+
+
+class HTTPFile(AbstractBufferedFile):
+    """
+    A file-like object pointing to a remove HTTP(S) resource
+
+    Supports only reading, with read-ahead of a predermined block-size.
+
+    In the case that the server does not supply the filesize, only reading of
+    the complete file in one go is supported.
+
+    Parameters
+    ----------
+    url: str
+        Full URL of the remote resource, including the protocol
+    session: requests.Session or None
+        All calls will be made within this session, to avoid restarting
+        connections where the server allows this
+    block_size: int or None
+        The amount of read-ahead to do, in bytes. Default is 5MB, or the value
+        configured for the FileSystem creating this file
+    size: None or int
+        If given, this is the size of the file in bytes, and we don't attempt
+        to call the server to find the value.
+    kwargs: all other key-values are passed to requests calls.
+    """
+
+    def __init__(
+        self,
+        fs,
+        url,
+        session=None,
+        block_size=None,
+        mode="rb",
+        cache_type="bytes",
+        cache_options=None,
+        size=None,
+        **kwargs,
+    ):
+        if mode != "rb":
+            raise NotImplementedError("File mode not supported")
+        self.url = url
+        self.session = session
+        self.details = {"name": url, "size": size, "type": "file"}
+        super().__init__(
+            fs=fs,
+            path=url,
+            mode=mode,
+            block_size=block_size,
+            cache_type=cache_type,
+            cache_options=cache_options,
+            **kwargs,
+        )
+
+    def read(self, length=-1):
+        """Read bytes from file
+
+        Parameters
+        ----------
+        length: int
+            Read up to this many bytes. If negative, read all content to end of
+            file. If the server has not supplied the filesize, attempting to
+            read only part of the data will raise a ValueError.
+        """
+        if (
+            (length < 0 and self.loc == 0)  # explicit read all
+            # but not when the size is known and fits into a block anyways
+            and not (self.size is not None and self.size <= self.blocksize)
+        ):
+            self._fetch_all()
+        if self.size is None:
+            if length < 0:
+                self._fetch_all()
+        else:
+            length = min(self.size - self.loc, length)
+        return super().read(length)
+
+    def _fetch_all(self):
+        """Read whole file in one shot, without caching
+
+        This is only called when position is still at zero,
+        and read() is called without a byte-count.
+        """
+        logger.debug(f"Fetch all for {self}")
+        if not isinstance(self.cache, AllBytes):
+            r = self.session.get(self.fs.encode_url(self.url), **self.kwargs)
+            r.raise_for_status()
+            out = r.content
+            self.cache = AllBytes(size=len(out), fetcher=None, blocksize=None, data=out)
+            self.size = len(out)
+
+    def _parse_content_range(self, headers):
+        """Parse the Content-Range header"""
+        s = headers.get("Content-Range", "")
+        m = re.match(r"bytes (\d+-\d+|\*)/(\d+|\*)", s)
+        if not m:
+            return None, None, None
+
+        if m[1] == "*":
+            start = end = None
+        else:
+            start, end = [int(x) for x in m[1].split("-")]
+        total = None if m[2] == "*" else int(m[2])
+        return start, end, total
+
+    def _fetch_range(self, start, end):
+        """Download a block of data
+
+        The expectation is that the server returns only the requested bytes,
+        with HTTP code 206. If this is not the case, we first check the headers,
+        and then stream the output - if the data size is bigger than we
+        requested, an exception is raised.
+        """
+        logger.debug(f"Fetch range for {self}: {start}-{end}")
+        kwargs = self.kwargs.copy()
+        headers = kwargs.pop("headers", {}).copy()
+        headers["Range"] = f"bytes={start}-{end - 1}"
+        logger.debug("%s : %s", self.url, headers["Range"])
+        r = self.session.get(self.fs.encode_url(self.url), headers=headers, **kwargs)
+        if r.status_code == 416:
+            # range request outside file
+            return b""
+        r.raise_for_status()
+
+        # If the server has handled the range request, it should reply
+        # with status 206 (partial content). But we'll guess that a suitable
+        # Content-Range header or a Content-Length no more than the
+        # requested range also mean we have got the desired range.
+        cl = r.headers.get("Content-Length", r.headers.get("content-length", end + 1))
+        response_is_range = (
+            r.status_code == 206
+            or self._parse_content_range(r.headers)[0] == start
+            or int(cl) <= end - start
+        )
+
+        if response_is_range:
+            # partial content, as expected
+            out = r.content
+        elif start > 0:
+            raise ValueError(
+                "The HTTP server doesn't appear to support range requests. "
+                "Only reading this file from the beginning is supported. "
+                "Open with block_size=0 for a streaming file interface."
+            )
+        else:
+            # Response is not a range, but we want the start of the file,
+            # so we can read the required amount anyway.
+            cl = 0
+            out = []
+            for chunk in r.iter_content(2**20, False):
+                out.append(chunk)
+                cl += len(chunk)
+            out = b"".join(out)[: end - start]
+        return out
+
+
+magic_check = re.compile("([*[])")
+
+
+def has_magic(s):
+    match = magic_check.search(s)
+    return match is not None
+
+
+class HTTPStreamFile(AbstractBufferedFile):
+    def __init__(self, fs, url, mode="rb", session=None, **kwargs):
+        self.url = url
+        self.session = session
+        if mode != "rb":
+            raise ValueError
+        self.details = {"name": url, "size": None}
+        super().__init__(fs=fs, path=url, mode=mode, cache_type="readahead", **kwargs)
+
+        r = self.session.get(self.fs.encode_url(url), stream=True, **kwargs)
+        self.fs._raise_not_found_for_status(r, url)
+        self.it = r.iter_content(1024, False)
+        self.leftover = b""
+
+        self.r = r
+
+    def seek(self, *args, **kwargs):
+        raise ValueError("Cannot seek streaming HTTP file")
+
+    def read(self, num=-1):
+        bufs = [self.leftover]
+        leng = len(self.leftover)
+        while leng < num or num < 0:
+            try:
+                out = self.it.__next__()
+            except StopIteration:
+                break
+            if out:
+                bufs.append(out)
+            else:
+                break
+            leng += len(out)
+        out = b"".join(bufs)
+        if num >= 0:
+            self.leftover = out[num:]
+            out = out[:num]
+        else:
+            self.leftover = b""
+        self.loc += len(out)
+        return out
+
+    def close(self):
+        self.r.close()
+        self.closed = True
+
+
+def get_range(session, url, start, end, **kwargs):
+    # explicit get a range when we know it must be safe
+    kwargs = kwargs.copy()
+    headers = kwargs.pop("headers", {}).copy()
+    headers["Range"] = f"bytes={start}-{end - 1}"
+    r = session.get(url, headers=headers, **kwargs)
+    r.raise_for_status()
+    return r.content
+
+
+def _file_info(url, session, size_policy="head", **kwargs):
+    """Call HEAD on the server to get details about the file (size/checksum etc.)
+
+    Default operation is to explicitly allow redirects and use encoding
+    'identity' (no compression) to get the true size of the target.
+    """
+    logger.debug("Retrieve file size for %s", url)
+    kwargs = kwargs.copy()
+    ar = kwargs.pop("allow_redirects", True)
+    head = kwargs.get("headers", {}).copy()
+    # TODO: not allowed in JS
+    # head["Accept-Encoding"] = "identity"
+    kwargs["headers"] = head
+
+    info = {}
+    if size_policy == "head":
+        r = session.head(url, allow_redirects=ar, **kwargs)
+    elif size_policy == "get":
+        r = session.get(url, allow_redirects=ar, **kwargs)
+    else:
+        raise TypeError(f'size_policy must be "head" or "get", got {size_policy}')
+    r.raise_for_status()
+
+    # TODO:
+    #  recognise lack of 'Accept-Ranges',
+    #                 or 'Accept-Ranges': 'none' (not 'bytes')
+    #  to mean streaming only, no random access => return None
+    if "Content-Length" in r.headers:
+        info["size"] = int(r.headers["Content-Length"])
+    elif "Content-Range" in r.headers:
+        info["size"] = int(r.headers["Content-Range"].split("/")[1])
+    elif "content-length" in r.headers:
+        info["size"] = int(r.headers["content-length"])
+    elif "content-range" in r.headers:
+        info["size"] = int(r.headers["content-range"].split("/")[1])
+
+    for checksum_field in ["ETag", "Content-MD5", "Digest"]:
+        if r.headers.get(checksum_field):
+            info[checksum_field] = r.headers[checksum_field]
+
+    return info
+
+
+# importing this is enough to register it
+def register():
+    register_implementation("http", HTTPFileSystem, clobber=True)
+    register_implementation("https", HTTPFileSystem, clobber=True)
+    register_implementation("sync-http", HTTPFileSystem, clobber=True)
+    register_implementation("sync-https", HTTPFileSystem, clobber=True)
+
+
+register()
+
+
+def unregister():
+    from fsspec.implementations.http import HTTPFileSystem
+
+    register_implementation("http", HTTPFileSystem, clobber=True)
+    register_implementation("https", HTTPFileSystem, clobber=True)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/jupyter.py b/lib/python3.11/site-packages/fsspec/implementations/jupyter.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5571ed56582170051f3b7cd903093eed4c65244
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/jupyter.py
@@ -0,0 +1,129 @@
+import base64
+import io
+import re
+
+import requests
+
+import fsspec
+
+
+class JupyterFileSystem(fsspec.AbstractFileSystem):
+    """View of the files as seen by a Jupyter server (notebook or lab)"""
+
+    protocol = ("jupyter", "jlab")
+
+    def __init__(self, url, tok=None, **kwargs):
+        """
+
+        Parameters
+        ----------
+        url : str
+            Base URL of the server, like "http://127.0.0.1:8888". May include
+            token in the string, which is given by the process when starting up
+        tok : str
+            If the token is obtained separately, can be given here
+        kwargs
+        """
+        if "?" in url:
+            if tok is None:
+                try:
+                    tok = re.findall("token=([a-z0-9]+)", url)[0]
+                except IndexError as e:
+                    raise ValueError("Could not determine token") from e
+            url = url.split("?", 1)[0]
+        self.url = url.rstrip("/") + "/api/contents"
+        self.session = requests.Session()
+        if tok:
+            self.session.headers["Authorization"] = f"token {tok}"
+
+        super().__init__(**kwargs)
+
+    def ls(self, path, detail=True, **kwargs):
+        path = self._strip_protocol(path)
+        r = self.session.get(f"{self.url}/{path}")
+        if r.status_code == 404:
+            raise FileNotFoundError(path)
+        r.raise_for_status()
+        out = r.json()
+
+        if out["type"] == "directory":
+            out = out["content"]
+        else:
+            out = [out]
+        for o in out:
+            o["name"] = o.pop("path")
+            o.pop("content")
+            if o["type"] == "notebook":
+                o["type"] = "file"
+        if detail:
+            return out
+        return [o["name"] for o in out]
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        path = self._strip_protocol(path)
+        r = self.session.get(f"{self.url}/{path}")
+        if r.status_code == 404:
+            raise FileNotFoundError(path)
+        r.raise_for_status()
+        out = r.json()
+        if out["format"] == "text":
+            # data should be binary
+            b = out["content"].encode()
+        else:
+            b = base64.b64decode(out["content"])
+        return b[start:end]
+
+    def pipe_file(self, path, value, **_):
+        path = self._strip_protocol(path)
+        json = {
+            "name": path.rsplit("/", 1)[-1],
+            "path": path,
+            "size": len(value),
+            "content": base64.b64encode(value).decode(),
+            "format": "base64",
+            "type": "file",
+        }
+        self.session.put(f"{self.url}/{path}", json=json)
+
+    def mkdir(self, path, create_parents=True, **kwargs):
+        path = self._strip_protocol(path)
+        if create_parents and "/" in path:
+            self.mkdir(path.rsplit("/", 1)[0], True)
+        json = {
+            "name": path.rsplit("/", 1)[-1],
+            "path": path,
+            "size": None,
+            "content": None,
+            "type": "directory",
+        }
+        self.session.put(f"{self.url}/{path}", json=json)
+
+    def mv(self, path1, path2, recursive=False, maxdepth=None, **kwargs):
+        if path1 == path2:
+            return
+        self.session.patch(f"{self.url}/{path1}", json={"path": path2})
+
+    def _rm(self, path):
+        path = self._strip_protocol(path)
+        self.session.delete(f"{self.url}/{path}")
+
+    def _open(self, path, mode="rb", **kwargs):
+        path = self._strip_protocol(path)
+        if mode == "rb":
+            data = self.cat_file(path)
+            return io.BytesIO(data)
+        else:
+            return SimpleFileWriter(self, path, mode="wb")
+
+
+class SimpleFileWriter(fsspec.spec.AbstractBufferedFile):
+    def _upload_chunk(self, final=False):
+        """Never uploads a chunk until file is done
+
+        Not suitable for large files
+        """
+        if final is False:
+            return False
+        self.buffer.seek(0)
+        data = self.buffer.read()
+        self.fs.pipe_file(self.path, data)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/libarchive.py b/lib/python3.11/site-packages/fsspec/implementations/libarchive.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f8e750002df72865d611b48022e6634f9572614
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/libarchive.py
@@ -0,0 +1,213 @@
+from contextlib import contextmanager
+from ctypes import (
+    CFUNCTYPE,
+    POINTER,
+    c_int,
+    c_longlong,
+    c_void_p,
+    cast,
+    create_string_buffer,
+)
+
+import libarchive
+import libarchive.ffi as ffi
+
+from fsspec import open_files
+from fsspec.archive import AbstractArchiveFileSystem
+from fsspec.implementations.memory import MemoryFile
+from fsspec.utils import DEFAULT_BLOCK_SIZE
+
+# Libarchive requires seekable files or memory only for certain archive
+# types. However, since we read the directory first to cache the contents
+# and also allow random access to any file, the file-like object needs
+# to be seekable no matter what.
+
+# Seek call-backs (not provided in the libarchive python wrapper)
+SEEK_CALLBACK = CFUNCTYPE(c_longlong, c_int, c_void_p, c_longlong, c_int)
+read_set_seek_callback = ffi.ffi(
+    "read_set_seek_callback", [ffi.c_archive_p, SEEK_CALLBACK], c_int, ffi.check_int
+)
+new_api = hasattr(ffi, "NO_OPEN_CB")
+
+
+@contextmanager
+def custom_reader(file, format_name="all", filter_name="all", block_size=ffi.page_size):
+    """Read an archive from a seekable file-like object.
+
+    The `file` object must support the standard `readinto` and 'seek' methods.
+    """
+    buf = create_string_buffer(block_size)
+    buf_p = cast(buf, c_void_p)
+
+    def read_func(archive_p, context, ptrptr):
+        # readinto the buffer, returns number of bytes read
+        length = file.readinto(buf)
+        # write the address of the buffer into the pointer
+        ptrptr = cast(ptrptr, POINTER(c_void_p))
+        ptrptr[0] = buf_p
+        # tell libarchive how much data was written into the buffer
+        return length
+
+    def seek_func(archive_p, context, offset, whence):
+        file.seek(offset, whence)
+        # tell libarchvie the current position
+        return file.tell()
+
+    read_cb = ffi.READ_CALLBACK(read_func)
+    seek_cb = SEEK_CALLBACK(seek_func)
+
+    if new_api:
+        open_cb = ffi.NO_OPEN_CB
+        close_cb = ffi.NO_CLOSE_CB
+    else:
+        open_cb = libarchive.read.OPEN_CALLBACK(ffi.VOID_CB)
+        close_cb = libarchive.read.CLOSE_CALLBACK(ffi.VOID_CB)
+
+    with libarchive.read.new_archive_read(format_name, filter_name) as archive_p:
+        read_set_seek_callback(archive_p, seek_cb)
+        ffi.read_open(archive_p, None, open_cb, read_cb, close_cb)
+        yield libarchive.read.ArchiveRead(archive_p)
+
+
+class LibArchiveFileSystem(AbstractArchiveFileSystem):
+    """Compressed archives as a file-system (read-only)
+
+    Supports the following formats:
+    tar, pax , cpio, ISO9660, zip, mtree, shar, ar, raw, xar, lha/lzh, rar
+    Microsoft CAB, 7-Zip, WARC
+
+    See the libarchive documentation for further restrictions.
+    https://www.libarchive.org/
+
+    Keeps file object open while instance lives. It only works in seekable
+    file-like objects. In case the filesystem does not support this kind of
+    file object, it is recommended to cache locally.
+
+    This class is pickleable, but not necessarily thread-safe (depends on the
+    platform). See libarchive documentation for details.
+    """
+
+    root_marker = ""
+    protocol = "libarchive"
+    cachable = False
+
+    def __init__(
+        self,
+        fo="",
+        mode="r",
+        target_protocol=None,
+        target_options=None,
+        block_size=DEFAULT_BLOCK_SIZE,
+        **kwargs,
+    ):
+        """
+        Parameters
+        ----------
+        fo: str or file-like
+            Contains ZIP, and must exist. If a str, will fetch file using
+            :meth:`~fsspec.open_files`, which must return one file exactly.
+        mode: str
+            Currently, only 'r' accepted
+        target_protocol: str (optional)
+            If ``fo`` is a string, this value can be used to override the
+            FS protocol inferred from a URL
+        target_options: dict (optional)
+            Kwargs passed when instantiating the target FS, if ``fo`` is
+            a string.
+        """
+        super().__init__(self, **kwargs)
+        if mode != "r":
+            raise ValueError("Only read from archive files accepted")
+        if isinstance(fo, str):
+            files = open_files(fo, protocol=target_protocol, **(target_options or {}))
+            if len(files) != 1:
+                raise ValueError(
+                    f'Path "{fo}" did not resolve to exactly one file: "{files}"'
+                )
+            fo = files[0]
+        self.of = fo
+        self.fo = fo.__enter__()  # the whole instance is a context
+        self.block_size = block_size
+        self.dir_cache = None
+
+    @contextmanager
+    def _open_archive(self):
+        self.fo.seek(0)
+        with custom_reader(self.fo, block_size=self.block_size) as arc:
+            yield arc
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        # file paths are always relative to the archive root
+        return super()._strip_protocol(path).lstrip("/")
+
+    def _get_dirs(self):
+        fields = {
+            "name": "pathname",
+            "size": "size",
+            "created": "ctime",
+            "mode": "mode",
+            "uid": "uid",
+            "gid": "gid",
+            "mtime": "mtime",
+        }
+
+        if self.dir_cache is not None:
+            return
+
+        self.dir_cache = {}
+        list_names = []
+        with self._open_archive() as arc:
+            for entry in arc:
+                if not entry.isdir and not entry.isfile:
+                    # Skip symbolic links, fifo entries, etc.
+                    continue
+                self.dir_cache.update(
+                    {
+                        dirname: {"name": dirname, "size": 0, "type": "directory"}
+                        for dirname in self._all_dirnames(set(entry.name))
+                    }
+                )
+                f = {key: getattr(entry, fields[key]) for key in fields}
+                f["type"] = "directory" if entry.isdir else "file"
+                list_names.append(entry.name)
+
+                self.dir_cache[f["name"]] = f
+        # libarchive does not seem to return an entry for the directories (at least
+        # not in all formats), so get the directories names from the files names
+        self.dir_cache.update(
+            {
+                dirname: {"name": dirname, "size": 0, "type": "directory"}
+                for dirname in self._all_dirnames(list_names)
+            }
+        )
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        path = self._strip_protocol(path)
+        if mode != "rb":
+            raise NotImplementedError
+
+        data = b""
+        with self._open_archive() as arc:
+            for entry in arc:
+                if entry.pathname != path:
+                    continue
+
+                if entry.size == 0:
+                    # empty file, so there are no blocks
+                    break
+
+                for block in entry.get_blocks(entry.size):
+                    data = block
+                    break
+                else:
+                    raise ValueError
+        return MemoryFile(fs=self, path=path, data=data)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/local.py b/lib/python3.11/site-packages/fsspec/implementations/local.py
new file mode 100644
index 0000000000000000000000000000000000000000..b549b93e22c56fc67c00a3a87718999d2039d4c2
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/local.py
@@ -0,0 +1,517 @@
+import datetime
+import io
+import logging
+import os
+import os.path as osp
+import shutil
+import stat
+import tempfile
+from functools import lru_cache
+
+from fsspec import AbstractFileSystem
+from fsspec.compression import compr
+from fsspec.core import get_compression
+from fsspec.utils import isfilelike, stringify_path
+
+logger = logging.getLogger("fsspec.local")
+
+
+class LocalFileSystem(AbstractFileSystem):
+    """Interface to files on local storage
+
+    Parameters
+    ----------
+    auto_mkdir: bool
+        Whether, when opening a file, the directory containing it should
+        be created (if it doesn't already exist). This is assumed by pyarrow
+        code.
+    """
+
+    root_marker = "/"
+    protocol = "file", "local"
+    local_file = True
+
+    def __init__(self, auto_mkdir=False, **kwargs):
+        super().__init__(**kwargs)
+        self.auto_mkdir = auto_mkdir
+
+    @property
+    def fsid(self):
+        return "local"
+
+    def mkdir(self, path, create_parents=True, **kwargs):
+        path = self._strip_protocol(path)
+        if self.exists(path):
+            raise FileExistsError(path)
+        if create_parents:
+            self.makedirs(path, exist_ok=True)
+        else:
+            os.mkdir(path, **kwargs)
+
+    def makedirs(self, path, exist_ok=False):
+        path = self._strip_protocol(path)
+        os.makedirs(path, exist_ok=exist_ok)
+
+    def rmdir(self, path):
+        path = self._strip_protocol(path)
+        os.rmdir(path)
+
+    def ls(self, path, detail=False, **kwargs):
+        path = self._strip_protocol(path)
+        path_info = self.info(path)
+        infos = []
+        if path_info["type"] == "directory":
+            with os.scandir(path) as it:
+                for f in it:
+                    try:
+                        # Only get the info if requested since it is a bit expensive (the stat call inside)
+                        # The strip_protocol is also used in info() and calls make_path_posix to always return posix paths
+                        info = self.info(f) if detail else self._strip_protocol(f.path)
+                        infos.append(info)
+                    except FileNotFoundError:
+                        pass
+        else:
+            infos = [path_info] if detail else [path_info["name"]]
+
+        return infos
+
+    def info(self, path, **kwargs):
+        if isinstance(path, os.DirEntry):
+            # scandir DirEntry
+            out = path.stat(follow_symlinks=False)
+            link = path.is_symlink()
+            if path.is_dir(follow_symlinks=False):
+                t = "directory"
+            elif path.is_file(follow_symlinks=False):
+                t = "file"
+            else:
+                t = "other"
+
+            size = out.st_size
+            if link:
+                try:
+                    out2 = path.stat(follow_symlinks=True)
+                    size = out2.st_size
+                except OSError:
+                    size = 0
+            path = self._strip_protocol(path.path)
+        else:
+            # str or path-like
+            path = self._strip_protocol(path)
+            out = os.stat(path, follow_symlinks=False)
+            link = stat.S_ISLNK(out.st_mode)
+            if link:
+                out = os.stat(path, follow_symlinks=True)
+            size = out.st_size
+            if stat.S_ISDIR(out.st_mode):
+                t = "directory"
+            elif stat.S_ISREG(out.st_mode):
+                t = "file"
+            else:
+                t = "other"
+
+        # Check for the 'st_birthtime' attribute, which is not always present; fallback to st_ctime
+        created_time = getattr(out, "st_birthtime", out.st_ctime)
+
+        result = {
+            "name": path,
+            "size": size,
+            "type": t,
+            "created": created_time,
+            "islink": link,
+        }
+        for field in ["mode", "uid", "gid", "mtime", "ino", "nlink"]:
+            result[field] = getattr(out, f"st_{field}")
+        if link:
+            result["destination"] = os.readlink(path)
+        return result
+
+    def lexists(self, path, **kwargs):
+        return osp.lexists(path)
+
+    def cp_file(self, path1, path2, **kwargs):
+        path1 = self._strip_protocol(path1)
+        path2 = self._strip_protocol(path2)
+        if self.auto_mkdir:
+            self.makedirs(self._parent(path2), exist_ok=True)
+        if self.isfile(path1):
+            shutil.copyfile(path1, path2)
+        elif self.isdir(path1):
+            self.mkdirs(path2, exist_ok=True)
+        else:
+            raise FileNotFoundError(path1)
+
+    def isfile(self, path):
+        path = self._strip_protocol(path)
+        return os.path.isfile(path)
+
+    def isdir(self, path):
+        path = self._strip_protocol(path)
+        return os.path.isdir(path)
+
+    def get_file(self, path1, path2, callback=None, **kwargs):
+        if isfilelike(path2):
+            with open(path1, "rb") as f:
+                shutil.copyfileobj(f, path2)
+        else:
+            return self.cp_file(path1, path2, **kwargs)
+
+    def put_file(self, path1, path2, callback=None, **kwargs):
+        return self.cp_file(path1, path2, **kwargs)
+
+    def mv(self, path1, path2, recursive: bool = True, **kwargs):
+        """Move files/directories
+        For the specific case of local, all ops on directories are recursive and
+        the recursive= kwarg is ignored.
+        """
+        path1 = self._strip_protocol(path1)
+        path2 = self._strip_protocol(path2)
+
+        if self.auto_mkdir:
+            self.makedirs(self._parent(path2), exist_ok=True)
+
+        shutil.move(path1, path2)
+
+    def link(self, src, dst, **kwargs):
+        src = self._strip_protocol(src)
+        dst = self._strip_protocol(dst)
+        os.link(src, dst, **kwargs)
+
+    def symlink(self, src, dst, **kwargs):
+        src = self._strip_protocol(src)
+        dst = self._strip_protocol(dst)
+        os.symlink(src, dst, **kwargs)
+
+    def islink(self, path) -> bool:
+        return os.path.islink(self._strip_protocol(path))
+
+    def rm_file(self, path):
+        os.remove(self._strip_protocol(path))
+
+    def rm(self, path, recursive=False, maxdepth=None):
+        if not isinstance(path, list):
+            path = [path]
+
+        for p in path:
+            p = self._strip_protocol(p)
+            if self.isdir(p):
+                if not recursive:
+                    raise ValueError("Cannot delete directory, set recursive=True")
+                if osp.abspath(p) == os.getcwd():
+                    raise ValueError("Cannot delete current working directory")
+                shutil.rmtree(p)
+            else:
+                os.remove(p)
+
+    def unstrip_protocol(self, name):
+        protocol = self.protocol if isinstance(self.protocol, str) else self.protocol[0]
+        name = self._strip_protocol(name)  # normalise for local/win/...
+        return f"{protocol}://{name}"
+
+    def _open(self, path, mode="rb", block_size=None, **kwargs):
+        path = self._strip_protocol(path)
+        if self.auto_mkdir and "w" in mode:
+            self.makedirs(self._parent(path), exist_ok=True)
+        return LocalFileOpener(path, mode, fs=self, **kwargs)
+
+    def touch(self, path, truncate=True, **kwargs):
+        path = self._strip_protocol(path)
+        if self.auto_mkdir:
+            self.makedirs(self._parent(path), exist_ok=True)
+        if self.exists(path):
+            os.utime(path, None)
+        else:
+            open(path, "a").close()
+        if truncate:
+            os.truncate(path, 0)
+
+    def created(self, path):
+        info = self.info(path=path)
+        return datetime.datetime.fromtimestamp(
+            info["created"], tz=datetime.timezone.utc
+        )
+
+    def modified(self, path):
+        info = self.info(path=path)
+        return datetime.datetime.fromtimestamp(info["mtime"], tz=datetime.timezone.utc)
+
+    @classmethod
+    def _parent(cls, path):
+        path = cls._strip_protocol(path)
+        if os.sep == "/":
+            # posix native
+            return path.rsplit("/", 1)[0] or "/"
+        else:
+            # NT
+            path_ = path.rsplit("/", 1)[0]
+            if len(path_) <= 3:
+                if path_[1:2] == ":":
+                    # nt root (something like c:/)
+                    return path_[0] + ":/"
+            # More cases may be required here
+            return path_
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        path = stringify_path(path)
+        protos = (cls.protocol,) if isinstance(cls.protocol, str) else cls.protocol
+        prefixes = (protocol + sep for protocol in protos for sep in ("://", ":"))
+        for prefix in prefixes:
+            if path.startswith(prefix):
+                path = path.removeprefix(prefix)
+                break
+
+        path = make_path_posix(path)
+        if os.sep != "/":
+            # This code-path is a stripped down version of
+            # > drive, path = ntpath.splitdrive(path)
+            if path[1:2] == ":":
+                # Absolute drive-letter path, e.g. X:\Windows
+                # Relative path with drive, e.g. X:Windows
+                drive, path = path[:2], path[2:]
+            elif path[:2] == "//":
+                # UNC drives, e.g. \\server\share or \\?\UNC\server\share
+                # Device drives, e.g. \\.\device or \\?\device
+                if (index1 := path.find("/", 2)) == -1 or (
+                    index2 := path.find("/", index1 + 1)
+                ) == -1:
+                    drive, path = path, ""
+                else:
+                    drive, path = path[:index2], path[index2:]
+            else:
+                # Relative path, e.g. Windows
+                drive = ""
+
+            path = path.rstrip("/") or cls.root_marker
+            return drive + path
+
+        else:
+            return path.rstrip("/") or cls.root_marker
+
+    def _isfilestore(self):
+        # Inheriting from DaskFileSystem makes this False (S3, etc. were)
+        # the original motivation. But we are a posix-like file system.
+        # See https://github.com/dask/dask/issues/5526
+        return True
+
+    def chmod(self, path, mode):
+        path = stringify_path(path)
+        return os.chmod(path, mode)
+
+
+def make_path_posix(path):
+    """Make path generic and absolute for current OS"""
+    if not isinstance(path, str):
+        if isinstance(path, (list, set, tuple)):
+            return type(path)(make_path_posix(p) for p in path)
+        else:
+            path = stringify_path(path)
+            if not isinstance(path, str):
+                raise TypeError(f"could not convert {path!r} to string")
+    if os.sep == "/":
+        # Native posix
+        if path.startswith("/"):
+            # most common fast case for posix
+            return path
+        elif path.startswith("~"):
+            return osp.expanduser(path)
+        elif path.startswith("./"):
+            path = path[2:]
+        elif path == ".":
+            path = ""
+        return f"{os.getcwd()}/{path}"
+    else:
+        # NT handling
+        if path[0:1] == "/" and path[2:3] == ":":
+            # path is like "/c:/local/path"
+            path = path[1:]
+        if path[1:2] == ":":
+            # windows full path like "C:\\local\\path"
+            if len(path) <= 3:
+                # nt root (something like c:/)
+                return path[0] + ":/"
+            path = path.replace("\\", "/")
+            return path
+        elif path[0:1] == "~":
+            return make_path_posix(osp.expanduser(path))
+        elif path.startswith(("\\\\", "//")):
+            # windows UNC/DFS-style paths
+            return "//" + path[2:].replace("\\", "/")
+        elif path.startswith(("\\", "/")):
+            # windows relative path with root
+            path = path.replace("\\", "/")
+            return f"{osp.splitdrive(os.getcwd())[0]}{path}"
+        else:
+            path = path.replace("\\", "/")
+            if path.startswith("./"):
+                path = path[2:]
+            elif path == ".":
+                path = ""
+            return f"{make_path_posix(os.getcwd())}/{path}"
+
+
+def trailing_sep(path):
+    """Return True if the path ends with a path separator.
+
+    A forward slash is always considered a path separator, even on Operating
+    Systems that normally use a backslash.
+    """
+    # TODO: if all incoming paths were posix-compliant then separator would
+    # always be a forward slash, simplifying this function.
+    # See https://github.com/fsspec/filesystem_spec/pull/1250
+    return path.endswith(os.sep) or (os.altsep is not None and path.endswith(os.altsep))
+
+
+@lru_cache(maxsize=1)
+def get_umask(mask: int = 0o666) -> int:
+    """Get the current umask.
+
+    Follows https://stackoverflow.com/a/44130549 to get the umask.
+    Temporarily sets the umask to the given value, and then resets it to the
+    original value.
+    """
+    value = os.umask(mask)
+    os.umask(value)
+    return value
+
+
+class LocalFileOpener(io.IOBase):
+    def __init__(
+        self, path, mode, autocommit=True, fs=None, compression=None, **kwargs
+    ):
+        logger.debug("open file: %s", path)
+        self.path = path
+        self.mode = mode
+        self.fs = fs
+        self.f = None
+        self.autocommit = autocommit
+        self.compression = get_compression(path, compression)
+        self.blocksize = io.DEFAULT_BUFFER_SIZE
+        self._open()
+
+    def _open(self):
+        if self.f is None or self.f.closed:
+            if self.autocommit or "w" not in self.mode:
+                self.f = open(self.path, mode=self.mode)
+                if self.compression:
+                    compress = compr[self.compression]
+                    self.f = compress(self.f, mode=self.mode)
+            else:
+                # TODO: check if path is writable?
+                i, name = tempfile.mkstemp()
+                os.close(i)  # we want normal open and normal buffered file
+                self.temp = name
+                self.f = open(name, mode=self.mode)
+            if "w" not in self.mode:
+                self.size = self.f.seek(0, 2)
+                self.f.seek(0)
+                self.f.size = self.size
+
+    def _fetch_range(self, start, end):
+        # probably only used by cached FS
+        if "r" not in self.mode:
+            raise ValueError
+        self._open()
+        self.f.seek(start)
+        return self.f.read(end - start)
+
+    def __setstate__(self, state):
+        self.f = None
+        loc = state.pop("loc", None)
+        self.__dict__.update(state)
+        if "r" in state["mode"]:
+            self.f = None
+            self._open()
+            self.f.seek(loc)
+
+    def __getstate__(self):
+        d = self.__dict__.copy()
+        d.pop("f")
+        if "r" in self.mode:
+            d["loc"] = self.f.tell()
+        else:
+            if not self.f.closed:
+                raise ValueError("Cannot serialise open write-mode local file")
+        return d
+
+    def commit(self):
+        if self.autocommit:
+            raise RuntimeError("Can only commit if not already set to autocommit")
+        try:
+            shutil.move(self.temp, self.path)
+        except PermissionError as e:
+            # shutil.move raises PermissionError if os.rename
+            # and the default copy2 fallback with shutil.copystats fail.
+            # The file should be there nonetheless, but without copied permissions.
+            # If it doesn't exist, there was no permission to create the file.
+            if not os.path.exists(self.path):
+                raise e
+        else:
+            # If PermissionError is not raised, permissions can be set.
+            try:
+                mask = 0o666
+                os.chmod(self.path, mask & ~get_umask(mask))
+            except RuntimeError:
+                pass
+
+    def discard(self):
+        if self.autocommit:
+            raise RuntimeError("Cannot discard if set to autocommit")
+        os.remove(self.temp)
+
+    def readable(self) -> bool:
+        return True
+
+    def writable(self) -> bool:
+        return "r" not in self.mode
+
+    def read(self, *args, **kwargs):
+        return self.f.read(*args, **kwargs)
+
+    def write(self, *args, **kwargs):
+        return self.f.write(*args, **kwargs)
+
+    def tell(self, *args, **kwargs):
+        return self.f.tell(*args, **kwargs)
+
+    def seek(self, *args, **kwargs):
+        return self.f.seek(*args, **kwargs)
+
+    def seekable(self, *args, **kwargs):
+        return self.f.seekable(*args, **kwargs)
+
+    def readline(self, *args, **kwargs):
+        return self.f.readline(*args, **kwargs)
+
+    def readlines(self, *args, **kwargs):
+        return self.f.readlines(*args, **kwargs)
+
+    def close(self):
+        return self.f.close()
+
+    def truncate(self, size=None) -> int:
+        return self.f.truncate(size)
+
+    @property
+    def closed(self):
+        return self.f.closed
+
+    def fileno(self):
+        return self.raw.fileno()
+
+    def flush(self) -> None:
+        self.f.flush()
+
+    def __iter__(self):
+        return self.f.__iter__()
+
+    def __getattr__(self, item):
+        return getattr(self.f, item)
+
+    def __enter__(self):
+        self._incontext = True
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._incontext = False
+        self.f.__exit__(exc_type, exc_value, traceback)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/memory.py b/lib/python3.11/site-packages/fsspec/implementations/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6b67bbc84e7aa625eee5609c20f6a893ddd349e
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/memory.py
@@ -0,0 +1,311 @@
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from errno import ENOTEMPTY
+from io import BytesIO
+from pathlib import PurePath, PureWindowsPath
+from typing import Any, ClassVar
+
+from fsspec import AbstractFileSystem
+from fsspec.implementations.local import LocalFileSystem
+from fsspec.utils import stringify_path
+
+logger = logging.getLogger("fsspec.memoryfs")
+
+
+class MemoryFileSystem(AbstractFileSystem):
+    """A filesystem based on a dict of BytesIO objects
+
+    This is a global filesystem so instances of this class all point to the same
+    in memory filesystem.
+    """
+
+    store: ClassVar[dict[str, Any]] = {}  # global, do not overwrite!
+    pseudo_dirs = [""]  # global, do not overwrite!
+    protocol = "memory"
+    root_marker = "/"
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        if isinstance(path, PurePath):
+            if isinstance(path, PureWindowsPath):
+                return LocalFileSystem._strip_protocol(path)
+            else:
+                path = stringify_path(path)
+
+        path = path.removeprefix("memory://")
+        if "::" in path or "://" in path:
+            return path.rstrip("/")
+        path = path.lstrip("/").rstrip("/")
+        return "/" + path if path else ""
+
+    def ls(self, path, detail=True, **kwargs):
+        path = self._strip_protocol(path)
+        if path in self.store:
+            # there is a key with this exact name
+            if not detail:
+                return [path]
+            return [
+                {
+                    "name": path,
+                    "size": self.store[path].size,
+                    "type": "file",
+                    "created": self.store[path].created.timestamp(),
+                }
+            ]
+        paths = set()
+        starter = path + "/"
+        out = []
+        for p2 in tuple(self.store):
+            if p2.startswith(starter):
+                if "/" not in p2[len(starter) :]:
+                    # exact child
+                    out.append(
+                        {
+                            "name": p2,
+                            "size": self.store[p2].size,
+                            "type": "file",
+                            "created": self.store[p2].created.timestamp(),
+                        }
+                    )
+                elif len(p2) > len(starter):
+                    # implied child directory
+                    ppath = starter + p2[len(starter) :].split("/", 1)[0]
+                    if ppath not in paths:
+                        out = out or []
+                        out.append(
+                            {
+                                "name": ppath,
+                                "size": 0,
+                                "type": "directory",
+                            }
+                        )
+                        paths.add(ppath)
+        for p2 in self.pseudo_dirs:
+            if p2.startswith(starter):
+                if "/" not in p2[len(starter) :]:
+                    # exact child pdir
+                    if p2 not in paths:
+                        out.append({"name": p2, "size": 0, "type": "directory"})
+                        paths.add(p2)
+                else:
+                    # directory implied by deeper pdir
+                    ppath = starter + p2[len(starter) :].split("/", 1)[0]
+                    if ppath not in paths:
+                        out.append({"name": ppath, "size": 0, "type": "directory"})
+                        paths.add(ppath)
+        if not out:
+            if path in self.pseudo_dirs:
+                # empty dir
+                return []
+            raise FileNotFoundError(path)
+        if detail:
+            return out
+        return sorted([f["name"] for f in out])
+
+    def mkdir(self, path, create_parents=True, **kwargs):
+        path = self._strip_protocol(path)
+        if path in self.store or path in self.pseudo_dirs:
+            raise FileExistsError(path)
+        if self._parent(path).strip("/") and self.isfile(self._parent(path)):
+            raise NotADirectoryError(self._parent(path))
+        if create_parents and self._parent(path).strip("/"):
+            try:
+                self.mkdir(self._parent(path), create_parents, **kwargs)
+            except FileExistsError:
+                pass
+        if path and path not in self.pseudo_dirs:
+            self.pseudo_dirs.append(path)
+
+    def makedirs(self, path, exist_ok=False):
+        try:
+            self.mkdir(path, create_parents=True)
+        except FileExistsError:
+            if not exist_ok:
+                raise
+
+    def pipe_file(self, path, value, mode="overwrite", **kwargs):
+        """Set the bytes of given file
+
+        Avoids copies of the data if possible
+        """
+        mode = "xb" if mode == "create" else "wb"
+        self.open(path, mode=mode, data=value)
+
+    def rmdir(self, path):
+        path = self._strip_protocol(path)
+        if path == "":
+            # silently avoid deleting FS root
+            return
+        if path in self.pseudo_dirs:
+            if not self.ls(path):
+                self.pseudo_dirs.remove(path)
+            else:
+                raise OSError(ENOTEMPTY, "Directory not empty", path)
+        else:
+            raise FileNotFoundError(path)
+
+    def info(self, path, **kwargs):
+        logger.debug("info: %s", path)
+        path = self._strip_protocol(path)
+        if path in self.pseudo_dirs or any(
+            p.startswith(path + "/") for p in list(self.store) + self.pseudo_dirs
+        ):
+            return {
+                "name": path,
+                "size": 0,
+                "type": "directory",
+            }
+        elif path in self.store:
+            filelike = self.store[path]
+            return {
+                "name": path,
+                "size": filelike.size,
+                "type": "file",
+                "created": getattr(filelike, "created", None),
+            }
+        else:
+            raise FileNotFoundError(path)
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        path = self._strip_protocol(path)
+        if "x" in mode and self.exists(path):
+            raise FileExistsError
+        if path in self.pseudo_dirs:
+            raise IsADirectoryError(path)
+        parent = path
+        while len(parent) > 1:
+            parent = self._parent(parent)
+            if self.isfile(parent):
+                raise FileExistsError(parent)
+        if mode in ["rb", "ab", "r+b", "a+b"]:
+            if path in self.store:
+                f = self.store[path]
+                if "a" in mode:
+                    # position at the end of file
+                    f.seek(0, 2)
+                else:
+                    # position at the beginning of file
+                    f.seek(0)
+                return f
+            else:
+                raise FileNotFoundError(path)
+        elif mode in {"wb", "w+b", "xb", "x+b"}:
+            if "x" in mode and self.exists(path):
+                raise FileExistsError
+            m = MemoryFile(self, path, kwargs.get("data"))
+            if not self._intrans:
+                m.commit()
+            return m
+        else:
+            name = self.__class__.__name__
+            raise ValueError(f"unsupported file mode for {name}: {mode!r}")
+
+    def cp_file(self, path1, path2, **kwargs):
+        path1 = self._strip_protocol(path1)
+        path2 = self._strip_protocol(path2)
+        if self.isfile(path1):
+            self.store[path2] = MemoryFile(
+                self, path2, self.store[path1].getvalue()
+            )  # implicit copy
+        elif self.isdir(path1):
+            if path2 not in self.pseudo_dirs:
+                self.pseudo_dirs.append(path2)
+        else:
+            raise FileNotFoundError(path1)
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        logger.debug("cat: %s", path)
+        path = self._strip_protocol(path)
+        try:
+            return bytes(self.store[path].getbuffer()[start:end])
+        except KeyError as e:
+            raise FileNotFoundError(path) from e
+
+    def _rm(self, path):
+        path = self._strip_protocol(path)
+        try:
+            del self.store[path]
+        except KeyError as e:
+            raise FileNotFoundError(path) from e
+
+    def modified(self, path):
+        path = self._strip_protocol(path)
+        try:
+            return self.store[path].modified
+        except KeyError as e:
+            raise FileNotFoundError(path) from e
+
+    def created(self, path):
+        path = self._strip_protocol(path)
+        try:
+            return self.store[path].created
+        except KeyError as e:
+            raise FileNotFoundError(path) from e
+
+    def isfile(self, path):
+        path = self._strip_protocol(path)
+        return path in self.store
+
+    def rm(self, path, recursive=False, maxdepth=None):
+        if isinstance(path, str):
+            path = self._strip_protocol(path)
+        else:
+            path = [self._strip_protocol(p) for p in path]
+        paths = self.expand_path(path, recursive=recursive, maxdepth=maxdepth)
+        for p in reversed(paths):
+            if self.isfile(p):
+                self.rm_file(p)
+            # If the expanded path doesn't exist, it is only because the expanded
+            # path was a directory that does not exist in self.pseudo_dirs. This
+            # is possible if you directly create files without making the
+            # directories first.
+            elif not self.exists(p):
+                continue
+            else:
+                self.rmdir(p)
+
+
+class MemoryFile(BytesIO):
+    """A BytesIO which can't close and works as a context manager
+
+    Can initialise with data. Each path should only be active once at any moment.
+
+    No need to provide fs, path if auto-committing (default)
+    """
+
+    def __init__(self, fs=None, path=None, data=None):
+        logger.debug("open file %s", path)
+        self.fs = fs
+        self.path = path
+        self.created = datetime.now(tz=timezone.utc)
+        self.modified = datetime.now(tz=timezone.utc)
+        if data:
+            super().__init__(data)
+            self.seek(0)
+
+    @property
+    def size(self):
+        return self.getbuffer().nbytes
+
+    def __enter__(self):
+        return self
+
+    def close(self):
+        pass
+
+    def discard(self):
+        pass
+
+    def commit(self):
+        self.fs.store[self.path] = self
+        self.modified = datetime.now(tz=timezone.utc)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/reference.py b/lib/python3.11/site-packages/fsspec/implementations/reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..562e76160dbf8b65b8bd9d38d6ffe082514e96bc
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/reference.py
@@ -0,0 +1,1316 @@
+import base64
+import collections
+import io
+import itertools
+import logging
+import math
+import os
+from functools import lru_cache
+from itertools import chain
+from typing import TYPE_CHECKING, Literal
+
+import fsspec.core
+from fsspec.spec import AbstractBufferedFile
+
+try:
+    import ujson as json
+except ImportError:
+    if not TYPE_CHECKING:
+        import json
+
+from fsspec.asyn import AsyncFileSystem
+from fsspec.callbacks import DEFAULT_CALLBACK
+from fsspec.core import filesystem, open, split_protocol
+from fsspec.implementations.asyn_wrapper import AsyncFileSystemWrapper
+from fsspec.utils import (
+    isfilelike,
+    merge_offset_ranges,
+    other_paths,
+)
+
+logger = logging.getLogger("fsspec.reference")
+
+
+class ReferenceNotReachable(RuntimeError):
+    def __init__(self, reference, target, *args):
+        super().__init__(*args)
+        self.reference = reference
+        self.target = target
+
+    def __str__(self):
+        return f'Reference "{self.reference}" failed to fetch target {self.target}'
+
+
+def _first(d):
+    return next(iter(d.values()))
+
+
+def _prot_in_references(path, references):
+    ref = references.get(path)
+    if isinstance(ref, (list, tuple)) and isinstance(ref[0], str):
+        return split_protocol(ref[0])[0] if ref[0] else ref[0]
+
+
+def _protocol_groups(paths, references):
+    if isinstance(paths, str):
+        return {_prot_in_references(paths, references): [paths]}
+    out = {}
+    for path in paths:
+        protocol = _prot_in_references(path, references)
+        out.setdefault(protocol, []).append(path)
+    return out
+
+
+class RefsValuesView(collections.abc.ValuesView):
+    def __iter__(self):
+        for val in self._mapping.zmetadata.values():
+            yield json.dumps(val).encode()
+        yield from self._mapping._items.values()
+        for field in self._mapping.listdir():
+            chunk_sizes = self._mapping._get_chunk_sizes(field)
+            if len(chunk_sizes) == 0:
+                yield self._mapping[field + "/0"]
+                continue
+            yield from self._mapping._generate_all_records(field)
+
+
+class RefsItemsView(collections.abc.ItemsView):
+    def __iter__(self):
+        return zip(self._mapping.keys(), self._mapping.values())
+
+
+def ravel_multi_index(idx, sizes):
+    val = 0
+    mult = 1
+    for i, s in zip(idx[::-1], sizes[::-1]):
+        val += i * mult
+        mult *= s
+    return val
+
+
+class LazyReferenceMapper(collections.abc.MutableMapping):
+    """This interface can be used to read/write references from Parquet stores.
+    It is not intended for other types of references.
+    It can be used with Kerchunk's MultiZarrToZarr method to combine
+    references into a parquet store.
+    Examples of this use-case can be found here:
+    https://fsspec.github.io/kerchunk/advanced.html?highlight=parquet#parquet-storage"""
+
+    # import is class level to prevent numpy dep requirement for fsspec
+    @property
+    def np(self):
+        import numpy as np
+
+        return np
+
+    @property
+    def pd(self):
+        import pandas as pd
+
+        return pd
+
+    def __init__(
+        self,
+        root,
+        fs=None,
+        out_root=None,
+        cache_size=128,
+        categorical_threshold=10,
+        engine: Literal["fastparquet", "pyarrow"] = "fastparquet",
+    ):
+        """
+
+        This instance will be writable, storing changes in memory until full partitions
+        are accumulated or .flush() is called.
+
+        To create an empty lazy store, use .create()
+
+        Parameters
+        ----------
+        root : str
+            Root of parquet store
+        fs : fsspec.AbstractFileSystem
+            fsspec filesystem object, default is local filesystem.
+        cache_size : int, default=128
+            Maximum size of LRU cache, where cache_size*record_size denotes
+            the total number of references that can be loaded in memory at once.
+        categorical_threshold : int
+            Encode urls as pandas.Categorical to reduce memory footprint if the ratio
+            of the number of unique urls to total number of refs for each variable
+            is greater than or equal to this number. (default 10)
+        engine: Literal["fastparquet","pyarrow"]
+            Engine choice for reading parquet files. (default is "fastparquet")
+        """
+
+        self.root = root
+        self.chunk_sizes = {}
+        self.cat_thresh = categorical_threshold
+        self.engine = engine
+        self.cache_size = cache_size
+        self.url = self.root + "/{field}/refs.{record}.parq"
+        # TODO: derive fs from `root`
+        self.fs = fsspec.filesystem("file") if fs is None else fs
+        self.out_root = self.fs.unstrip_protocol(out_root or self.root)
+
+        from importlib.util import find_spec
+
+        if self.engine == "pyarrow" and find_spec("pyarrow") is None:
+            raise ImportError("engine choice `pyarrow` is not installed.")
+
+        # Apply `lru_cache` decorator manually per instance.
+        # This way `self` reference is not held on class level.
+        # WARNING: However, this means that self and its members are not reflected
+        # in the cache key, so we expect they won't be mutated once a value is cached.
+        self.listdir = lru_cache()(self.listdir)
+        self._key_to_record = lru_cache(maxsize=4096)(self._key_to_record)
+
+    def __getattr__(self, item):
+        if item in ("_items", "record_size", "zmetadata"):
+            self.setup()
+            # avoid possible recursion if setup fails somehow
+            return self.__dict__[item]
+        raise AttributeError(item)
+
+    def setup(self):
+        self._items = {}
+        self._items[".zmetadata"] = self.fs.cat_file(
+            "/".join([self.root, ".zmetadata"])
+        )
+        met = json.loads(self._items[".zmetadata"])
+        self.record_size = met["record_size"]
+        self.zmetadata = met["metadata"]
+
+        # Define function to open and decompress refs
+        @lru_cache(maxsize=self.cache_size)
+        def open_refs(field, record):
+            """cached parquet file loader"""
+            path = self.url.format(field=field, record=record)
+            data = io.BytesIO(self.fs.cat_file(path))
+            try:
+                df = self.pd.read_parquet(data, engine=self.engine)
+                refs = {c: df[c].to_numpy() for c in df.columns}
+            except OSError:
+                refs = None
+            return refs
+
+        self.open_refs = open_refs
+
+    @staticmethod
+    def create(root, storage_options=None, fs=None, record_size=10000, **kwargs):
+        """Make empty parquet reference set
+
+        First deletes the contents of the given directory, if it exists.
+
+        Parameters
+        ----------
+        root: str
+            Directory to contain the output; will be created
+        storage_options: dict | None
+            For making the filesystem to use for writing is fs is None
+        fs: FileSystem | None
+            Filesystem for writing
+        record_size: int
+            Number of references per parquet file
+        kwargs: passed to __init__
+
+        Returns
+        -------
+        LazyReferenceMapper instance
+        """
+        met = {"metadata": {}, "record_size": record_size}
+        if fs is None:
+            fs, root = fsspec.core.url_to_fs(root, **(storage_options or {}))
+        if fs.exists(root):
+            fs.rm(root, recursive=True)
+        fs.makedirs(root, exist_ok=True)
+        fs.pipe("/".join([root, ".zmetadata"]), json.dumps(met).encode())
+        return LazyReferenceMapper(root, fs, **kwargs)
+
+    def listdir(self):
+        """List top-level directories"""
+        dirs = (p.rsplit("/", 1)[0] for p in self.zmetadata if not p.startswith(".z"))
+        return set(dirs)
+
+    def ls(self, path="", detail=True):
+        """Shortcut file listings"""
+        path = path.rstrip("/")
+        pathdash = path + "/" if path else ""
+        dirnames = self.listdir()
+        dirs = [
+            d
+            for d in dirnames
+            if d.startswith(pathdash) and "/" not in d.lstrip(pathdash)
+        ]
+        if dirs:
+            others = {
+                f
+                for f in chain(
+                    [".zmetadata"],
+                    (name for name in self.zmetadata),
+                    (name for name in self._items),
+                )
+                if f.startswith(pathdash) and "/" not in f.lstrip(pathdash)
+            }
+            if detail is False:
+                others.update(dirs)
+                return sorted(others)
+            dirinfo = [{"name": name, "type": "directory", "size": 0} for name in dirs]
+            fileinfo = [
+                {
+                    "name": name,
+                    "type": "file",
+                    "size": len(
+                        json.dumps(self.zmetadata[name])
+                        if name in self.zmetadata
+                        else self._items[name]
+                    ),
+                }
+                for name in others
+            ]
+            return sorted(dirinfo + fileinfo, key=lambda s: s["name"])
+        field = path
+        others = set(
+            [name for name in self.zmetadata if name.startswith(f"{path}/")]
+            + [name for name in self._items if name.startswith(f"{path}/")]
+        )
+        fileinfo = [
+            {
+                "name": name,
+                "type": "file",
+                "size": len(
+                    json.dumps(self.zmetadata[name])
+                    if name in self.zmetadata
+                    else self._items[name]
+                ),
+            }
+            for name in others
+        ]
+        keys = self._keys_in_field(field)
+
+        if detail is False:
+            return list(others) + list(keys)
+        recs = self._generate_all_records(field)
+        recinfo = [
+            {"name": name, "type": "file", "size": rec[-1]}
+            for name, rec in zip(keys, recs)
+            if rec[0]  # filters out path==None, deleted/missing
+        ]
+        return fileinfo + recinfo
+
+    def _load_one_key(self, key):
+        """Get the reference for one key
+
+        Returns bytes, one-element list or three-element list.
+        """
+        if key in self._items:
+            return self._items[key]
+        elif key in self.zmetadata:
+            return json.dumps(self.zmetadata[key]).encode()
+        elif "/" not in key or self._is_meta(key):
+            raise KeyError(key)
+        field, _ = key.rsplit("/", 1)
+        record, ri, chunk_size = self._key_to_record(key)
+        maybe = self._items.get((field, record), {}).get(ri, False)
+        if maybe is None:
+            # explicitly deleted
+            raise KeyError
+        elif maybe:
+            return maybe
+        elif chunk_size == 0:
+            return b""
+
+        # Chunk keys can be loaded from row group and cached in LRU cache
+        try:
+            refs = self.open_refs(field, record)
+        except (ValueError, TypeError, FileNotFoundError) as exc:
+            raise KeyError(key) from exc
+        columns = ["path", "offset", "size", "raw"]
+        selection = [refs[c][ri] if c in refs else None for c in columns]
+        raw = selection[-1]
+        if raw is not None:
+            return raw
+        if selection[0] is None:
+            raise KeyError("This reference does not exist or has been deleted")
+        if selection[1:3] == [0, 0]:
+            # URL only
+            return selection[:1]
+        # URL, offset, size
+        return selection[:3]
+
+    def _key_to_record(self, key):
+        """Details needed to construct a reference for one key"""
+        field, chunk = key.rsplit("/", 1)
+        chunk_sizes = self._get_chunk_sizes(field)
+        if len(chunk_sizes) == 0:
+            return 0, 0, 0
+        chunk_idx = [int(c) for c in chunk.split(".")]
+        chunk_number = ravel_multi_index(chunk_idx, chunk_sizes)
+        record = chunk_number // self.record_size
+        ri = chunk_number % self.record_size
+        return record, ri, len(chunk_sizes)
+
+    def _get_chunk_sizes(self, field):
+        """The number of chunks along each axis for a given field"""
+        if field not in self.chunk_sizes:
+            zarray = self.zmetadata[f"{field}/.zarray"]
+            size_ratio = [
+                math.ceil(s / c) for s, c in zip(zarray["shape"], zarray["chunks"])
+            ]
+            self.chunk_sizes[field] = size_ratio or [1]
+        return self.chunk_sizes[field]
+
+    def _generate_record(self, field, record):
+        """The references for a given parquet file of a given field"""
+        refs = self.open_refs(field, record)
+        it = iter(zip(*refs.values()))
+        if len(refs) == 3:
+            # All urls
+            return (list(t) for t in it)
+        elif len(refs) == 1:
+            # All raws
+            return refs["raw"]
+        else:
+            # Mix of urls and raws
+            return (list(t[:3]) if not t[3] else t[3] for t in it)
+
+    def _generate_all_records(self, field):
+        """Load all the references within a field by iterating over the parquet files"""
+        nrec = 1
+        for ch in self._get_chunk_sizes(field):
+            nrec *= ch
+        nrec = math.ceil(nrec / self.record_size)
+        for record in range(nrec):
+            yield from self._generate_record(field, record)
+
+    def values(self):
+        return RefsValuesView(self)
+
+    def items(self):
+        return RefsItemsView(self)
+
+    def __hash__(self):
+        return id(self)
+
+    def __getitem__(self, key):
+        return self._load_one_key(key)
+
+    def __setitem__(self, key, value):
+        if "/" in key and not self._is_meta(key):
+            field, chunk = key.rsplit("/", 1)
+            record, i, _ = self._key_to_record(key)
+            subdict = self._items.setdefault((field, record), {})
+            subdict[i] = value
+            if len(subdict) == self.record_size:
+                self.write(field, record)
+        else:
+            # metadata or top-level
+            if hasattr(value, "to_bytes"):
+                val = value.to_bytes().decode()
+            elif isinstance(value, bytes):
+                val = value.decode()
+            else:
+                val = value
+            self._items[key] = val
+            new_value = json.loads(val)
+            self.zmetadata[key] = {**self.zmetadata.get(key, {}), **new_value}
+
+    @staticmethod
+    def _is_meta(key):
+        return key.startswith(".z") or "/.z" in key
+
+    def __delitem__(self, key):
+        if key in self._items:
+            del self._items[key]
+        elif key in self.zmetadata:
+            del self.zmetadata[key]
+        else:
+            if "/" in key and not self._is_meta(key):
+                field, _ = key.rsplit("/", 1)
+                record, i, _ = self._key_to_record(key)
+                subdict = self._items.setdefault((field, record), {})
+                subdict[i] = None
+                if len(subdict) == self.record_size:
+                    self.write(field, record)
+            else:
+                # metadata or top-level
+                self._items[key] = None
+
+    def write(self, field, record, base_url=None, storage_options=None):
+        # extra requirements if writing
+        import kerchunk.df
+        import numpy as np
+        import pandas as pd
+
+        partition = self._items[(field, record)]
+        original = False
+        if len(partition) < self.record_size:
+            try:
+                original = self.open_refs(field, record)
+            except OSError:
+                pass
+
+        if original:
+            paths = original["path"]
+            offsets = original["offset"]
+            sizes = original["size"]
+            raws = original["raw"]
+        else:
+            paths = np.full(self.record_size, np.nan, dtype="O")
+            offsets = np.zeros(self.record_size, dtype="int64")
+            sizes = np.zeros(self.record_size, dtype="int64")
+            raws = np.full(self.record_size, np.nan, dtype="O")
+        for j, data in partition.items():
+            if isinstance(data, list):
+                if (
+                    str(paths.dtype) == "category"
+                    and data[0] not in paths.dtype.categories
+                ):
+                    paths = paths.add_categories(data[0])
+                paths[j] = data[0]
+                if len(data) > 1:
+                    offsets[j] = data[1]
+                    sizes[j] = data[2]
+            elif data is None:
+                # delete
+                paths[j] = None
+                offsets[j] = 0
+                sizes[j] = 0
+                raws[j] = None
+            else:
+                # this is the only call into kerchunk, could remove
+                raws[j] = kerchunk.df._proc_raw(data)
+        # TODO: only save needed columns
+        df = pd.DataFrame(
+            {
+                "path": paths,
+                "offset": offsets,
+                "size": sizes,
+                "raw": raws,
+            },
+            copy=False,
+        )
+        if df.path.count() / (df.path.nunique() or 1) > self.cat_thresh:
+            df["path"] = df["path"].astype("category")
+        object_encoding = {"raw": "bytes", "path": "utf8"}
+        has_nulls = ["path", "raw"]
+
+        fn = f"{base_url or self.out_root}/{field}/refs.{record}.parq"
+        self.fs.mkdirs(f"{base_url or self.out_root}/{field}", exist_ok=True)
+
+        if self.engine == "pyarrow":
+            df_backend_kwargs = {"write_statistics": False}
+        elif self.engine == "fastparquet":
+            df_backend_kwargs = {
+                "stats": False,
+                "object_encoding": object_encoding,
+                "has_nulls": has_nulls,
+            }
+        else:
+            raise NotImplementedError(f"{self.engine} not supported")
+        df.to_parquet(
+            fn,
+            engine=self.engine,
+            storage_options=storage_options
+            or getattr(self.fs, "storage_options", None),
+            compression="zstd",
+            index=False,
+            **df_backend_kwargs,
+        )
+
+        partition.clear()
+        self._items.pop((field, record))
+
+    def flush(self, base_url=None, storage_options=None):
+        """Output any modified or deleted keys
+
+        Parameters
+        ----------
+        base_url: str
+            Location of the output
+        """
+
+        # write what we have so far and clear sub chunks
+        for thing in list(self._items):
+            if isinstance(thing, tuple):
+                field, record = thing
+                self.write(
+                    field,
+                    record,
+                    base_url=base_url,
+                    storage_options=storage_options,
+                )
+
+        # gather .zmetadata from self._items and write that too
+        for k in list(self._items):
+            if k != ".zmetadata" and ".z" in k:
+                self.zmetadata[k] = json.loads(self._items.pop(k))
+        met = {"metadata": self.zmetadata, "record_size": self.record_size}
+        self._items.clear()
+        self._items[".zmetadata"] = json.dumps(met).encode()
+        self.fs.pipe(
+            "/".join([base_url or self.out_root, ".zmetadata"]),
+            self._items[".zmetadata"],
+        )
+
+        # TODO: only clear those that we wrote to?
+        self.open_refs.cache_clear()
+
+    def __len__(self):
+        # Caveat: This counts expected references, not actual - but is fast
+        count = 0
+        for field in self.listdir():
+            if field.startswith("."):
+                count += 1
+            else:
+                count += math.prod(self._get_chunk_sizes(field))
+        count += len(self.zmetadata)  # all metadata keys
+        # any other files not in reference partitions
+        count += sum(1 for _ in self._items if not isinstance(_, tuple))
+        return count
+
+    def __iter__(self):
+        # Caveat: returns only existing keys, so the number of these does not
+        #  match len(self)
+        metas = set(self.zmetadata)
+        metas.update(self._items)
+        for bit in metas:
+            if isinstance(bit, str):
+                yield bit
+        for field in self.listdir():
+            for k in self._keys_in_field(field):
+                if k in self:
+                    yield k
+
+    def __contains__(self, item):
+        try:
+            self._load_one_key(item)
+            return True
+        except KeyError:
+            return False
+
+    def _keys_in_field(self, field):
+        """List key names in given field
+
+        Produces strings like "field/x.y" appropriate from the chunking of the array
+        """
+        chunk_sizes = self._get_chunk_sizes(field)
+        if len(chunk_sizes) == 0:
+            yield field + "/0"
+            return
+        inds = itertools.product(*(range(i) for i in chunk_sizes))
+        for ind in inds:
+            yield field + "/" + ".".join([str(c) for c in ind])
+
+
+class ReferenceFileSystem(AsyncFileSystem):
+    """View byte ranges of some other file as a file system
+    Initial version: single file system target, which must support
+    async, and must allow start and end args in _cat_file. Later versions
+    may allow multiple arbitrary URLs for the targets.
+    This FileSystem is read-only. It is designed to be used with async
+    targets (for now). We do not get original file details from the target FS.
+    Configuration is by passing a dict of references at init, or a URL to
+    a JSON file containing the same; this dict
+    can also contain concrete data for some set of paths.
+    Reference dict format:
+    {path0: bytes_data, path1: (target_url, offset, size)}
+    https://github.com/fsspec/kerchunk/blob/main/README.md
+    """
+
+    protocol = "reference"
+    cachable = False
+
+    def __init__(
+        self,
+        fo,
+        target=None,
+        ref_storage_args=None,
+        target_protocol=None,
+        target_options=None,
+        remote_protocol=None,
+        remote_options=None,
+        fs=None,
+        template_overrides=None,
+        simple_templates=True,
+        max_gap=64_000,
+        max_block=256_000_000,
+        cache_size=128,
+        **kwargs,
+    ):
+        """
+        Parameters
+        ----------
+        fo : dict or str
+            The set of references to use for this instance, with a structure as above.
+            If str referencing a JSON file, will use fsspec.open, in conjunction
+            with target_options and target_protocol to open and parse JSON at this
+            location. If a directory, then assume references are a set of parquet
+            files to be loaded lazily.
+        target : str
+            For any references having target_url as None, this is the default file
+            target to use
+        ref_storage_args : dict
+            If references is a str, use these kwargs for loading the JSON file.
+            Deprecated: use target_options instead.
+        target_protocol : str
+            Used for loading the reference file, if it is a path. If None, protocol
+            will be derived from the given path
+        target_options : dict
+            Extra FS options for loading the reference file ``fo``, if given as a path
+        remote_protocol : str
+            The protocol of the filesystem on which the references will be evaluated
+            (unless fs is provided). If not given, will be derived from the first
+            URL that has a protocol in the templates or in the references, in that
+            order.
+        remote_options : dict
+            kwargs to go with remote_protocol
+        fs : AbstractFileSystem | dict(str, (AbstractFileSystem | dict))
+            Directly provide a file system(s):
+                - a single filesystem instance
+                - a dict of protocol:filesystem, where each value is either a filesystem
+                  instance, or a dict of kwargs that can be used to create in
+                  instance for the given protocol
+
+            If this is given, remote_options and remote_protocol are ignored.
+        template_overrides : dict
+            Swap out any templates in the references file with these - useful for
+            testing.
+        simple_templates: bool
+            Whether templates can be processed with simple replace (True) or if
+            jinja  is needed (False, much slower). All reference sets produced by
+            ``kerchunk`` are simple in this sense, but the spec allows for complex.
+        max_gap, max_block: int
+            For merging multiple concurrent requests to the same remote file.
+            Neighboring byte ranges will only be merged when their
+            inter-range gap is <= ``max_gap``. Default is 64KB. Set to 0
+            to only merge when it requires no extra bytes. Pass a negative
+            number to disable merging, appropriate for local target files.
+            Neighboring byte ranges will only be merged when the size of
+            the aggregated range is <= ``max_block``. Default is 256MB.
+        cache_size : int
+            Maximum size of LRU cache, where cache_size*record_size denotes
+            the total number of references that can be loaded in memory at once.
+            Only used for lazily loaded references.
+        kwargs : passed to parent class
+        """
+        super().__init__(**kwargs)
+        self.target = target
+        self.template_overrides = template_overrides
+        self.simple_templates = simple_templates
+        self.templates = {}
+        self.fss = {}
+        self._dircache = {}
+        self.max_gap = max_gap
+        self.max_block = max_block
+        if isinstance(fo, str):
+            dic = dict(
+                **(ref_storage_args or target_options or {}), protocol=target_protocol
+            )
+            ref_fs, fo2 = fsspec.core.url_to_fs(fo, **dic)
+            if ".json" not in fo2 and (
+                fo.endswith(("parq", "parquet", "/")) or ref_fs.isdir(fo2)
+            ):
+                # Lazy parquet refs
+                logger.info("Open lazy reference dict from URL %s", fo)
+                self.references = LazyReferenceMapper(
+                    fo2,
+                    fs=ref_fs,
+                    cache_size=cache_size,
+                )
+            else:
+                # text JSON
+                with fsspec.open(fo, "rb", **dic) as f:
+                    logger.info("Read reference from URL %s", fo)
+                    text = json.load(f)
+                self._process_references(text, template_overrides)
+        else:
+            # dictionaries
+            self._process_references(fo, template_overrides)
+        if isinstance(fs, dict):
+            self.fss = {
+                k: (
+                    fsspec.filesystem(k.split(":", 1)[0], **opts)
+                    if isinstance(opts, dict)
+                    else opts
+                )
+                for k, opts in fs.items()
+            }
+            if None not in self.fss:
+                self.fss[None] = filesystem("file")
+            return
+        if fs is not None:
+            # single remote FS
+            remote_protocol = (
+                fs.protocol[0] if isinstance(fs.protocol, tuple) else fs.protocol
+            )
+            self.fss[remote_protocol] = fs
+
+        if remote_protocol is None:
+            # get single protocol from any templates
+            for ref in self.templates.values():
+                if callable(ref):
+                    ref = ref()
+                protocol, _ = fsspec.core.split_protocol(ref)
+                if protocol and protocol not in self.fss:
+                    fs = filesystem(protocol, **(remote_options or {}))
+                    self.fss[protocol] = fs
+        if remote_protocol is None:
+            # get single protocol from references
+            # TODO: warning here, since this can be very expensive?
+            for ref in self.references.values():
+                if callable(ref):
+                    ref = ref()
+                if isinstance(ref, list) and ref[0]:
+                    protocol, _ = fsspec.core.split_protocol(ref[0])
+                    if protocol not in self.fss:
+                        fs = filesystem(protocol, **(remote_options or {}))
+                        self.fss[protocol] = fs
+                        # only use first remote URL
+                        break
+
+        if remote_protocol and remote_protocol not in self.fss:
+            fs = filesystem(remote_protocol, **(remote_options or {}))
+            self.fss[remote_protocol] = fs
+
+        self.fss[None] = fs or filesystem("file")  # default one
+        # Wrap any non-async filesystems to ensure async methods are available below
+        for k, f in self.fss.items():
+            if not f.async_impl:
+                self.fss[k] = AsyncFileSystemWrapper(f, asynchronous=self.asynchronous)
+            elif self.asynchronous ^ f.asynchronous:
+                raise ValueError(
+                    "Reference-FS's target filesystem must have same value "
+                    "of asynchronous"
+                )
+
+    def _cat_common(self, path, start=None, end=None):
+        path = self._strip_protocol(path)
+        logger.debug(f"cat: {path}")
+        try:
+            part = self.references[path]
+        except KeyError as exc:
+            raise FileNotFoundError(path) from exc
+        if isinstance(part, str):
+            part = part.encode()
+        if hasattr(part, "to_bytes"):
+            part = part.to_bytes()
+        if isinstance(part, bytes):
+            logger.debug(f"Reference: {path}, type bytes")
+            if part.startswith(b"base64:"):
+                part = base64.b64decode(part[7:])
+            return part, None, None
+
+        if len(part) == 1:
+            logger.debug(f"Reference: {path}, whole file => {part}")
+            url = part[0]
+            start1, end1 = start, end
+        else:
+            url, start0, size = part
+            logger.debug(f"Reference: {path} => {url}, offset {start0}, size {size}")
+            end0 = start0 + size
+
+            if start is not None:
+                if start >= 0:
+                    start1 = start0 + start
+                else:
+                    start1 = end0 + start
+            else:
+                start1 = start0
+            if end is not None:
+                if end >= 0:
+                    end1 = start0 + end
+                else:
+                    end1 = end0 + end
+            else:
+                end1 = end0
+        if url is None:
+            url = self.target
+        return url, start1, end1
+
+    async def _cat_file(self, path, start=None, end=None, **kwargs):
+        part_or_url, start0, end0 = self._cat_common(path, start=start, end=end)
+        if isinstance(part_or_url, bytes):
+            return part_or_url[start:end]
+        protocol, _ = split_protocol(part_or_url)
+        try:
+            return await self.fss[protocol]._cat_file(
+                part_or_url, start=start0, end=end0
+            )
+        except Exception as e:
+            raise ReferenceNotReachable(path, part_or_url) from e
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        part_or_url, start0, end0 = self._cat_common(path, start=start, end=end)
+        if isinstance(part_or_url, bytes):
+            return part_or_url[start:end]
+        protocol, _ = split_protocol(part_or_url)
+        try:
+            return self.fss[protocol].cat_file(part_or_url, start=start0, end=end0)
+        except Exception as e:
+            raise ReferenceNotReachable(path, part_or_url) from e
+
+    def pipe_file(self, path, value, **_):
+        """Temporarily add binary data or reference as a file"""
+        self.references[path] = value
+
+    async def _get_file(self, rpath, lpath, **kwargs):
+        if self.isdir(rpath):
+            return os.makedirs(lpath, exist_ok=True)
+        data = await self._cat_file(rpath)
+        with open(lpath, "wb") as f:
+            f.write(data)
+
+    def get_file(self, rpath, lpath, callback=DEFAULT_CALLBACK, **kwargs):
+        if self.isdir(rpath):
+            return os.makedirs(lpath, exist_ok=True)
+        data = self.cat_file(rpath, **kwargs)
+        callback.set_size(len(data))
+        if isfilelike(lpath):
+            lpath.write(data)
+        else:
+            with open(lpath, "wb") as f:
+                f.write(data)
+        callback.absolute_update(len(data))
+
+    def get(self, rpath, lpath, recursive=False, **kwargs):
+        if recursive:
+            # trigger directory build
+            self.ls("")
+        rpath = self.expand_path(rpath, recursive=recursive)
+        fs = fsspec.filesystem("file", auto_mkdir=True)
+        targets = other_paths(rpath, lpath)
+        if recursive:
+            data = self.cat([r for r in rpath if not self.isdir(r)])
+        else:
+            data = self.cat(rpath)
+        for remote, local in zip(rpath, targets):
+            if remote in data:
+                fs.pipe_file(local, data[remote])
+
+    def cat(self, path, recursive=False, on_error="raise", **kwargs):
+        if isinstance(path, str) and recursive:
+            raise NotImplementedError
+        if isinstance(path, list) and (recursive or any("*" in p for p in path)):
+            raise NotImplementedError
+        # TODO: if references is lazy, pre-fetch all paths in batch before access
+        proto_dict = _protocol_groups(path, self.references)
+        out = {}
+        for proto, paths in proto_dict.items():
+            fs = self.fss[proto]
+            urls, starts, ends, valid_paths = [], [], [], []
+            for p in paths:
+                # find references or label not-found. Early exit if any not
+                # found and on_error is "raise"
+                try:
+                    u, s, e = self._cat_common(p)
+                    if not isinstance(u, (bytes, str)):
+                        # nan/None from parquet
+                        continue
+                except FileNotFoundError as err:
+                    if on_error == "raise":
+                        raise
+                    if on_error != "omit":
+                        out[p] = err
+                else:
+                    urls.append(u)
+                    starts.append(s)
+                    ends.append(e)
+                    valid_paths.append(p)
+
+            # process references into form for merging
+            urls2 = []
+            starts2 = []
+            ends2 = []
+            paths2 = []
+            whole_files = set()
+            for u, s, e, p in zip(urls, starts, ends, valid_paths):
+                if isinstance(u, bytes):
+                    # data
+                    out[p] = u
+                elif s is None:
+                    # whole file - limits are None, None, but no further
+                    # entries take for this file
+                    whole_files.add(u)
+                    urls2.append(u)
+                    starts2.append(s)
+                    ends2.append(e)
+                    paths2.append(p)
+            for u, s, e, p in zip(urls, starts, ends, valid_paths):
+                # second run to account for files that are to be loaded whole
+                if s is not None and u not in whole_files:
+                    urls2.append(u)
+                    starts2.append(s)
+                    ends2.append(e)
+                    paths2.append(p)
+
+            # merge and fetch consolidated ranges
+            new_paths, new_starts, new_ends = merge_offset_ranges(
+                list(urls2),
+                list(starts2),
+                list(ends2),
+                sort=True,
+                max_gap=self.max_gap,
+                max_block=self.max_block,
+            )
+            bytes_out = fs.cat_ranges(new_paths, new_starts, new_ends)
+
+            # unbundle from merged bytes - simple approach
+            for u, s, e, p in zip(urls, starts, ends, valid_paths):
+                if p in out:
+                    continue  # was bytes, already handled
+                for np, ns, ne, b in zip(new_paths, new_starts, new_ends, bytes_out):
+                    if np == u and (ns is None or ne is None):
+                        if isinstance(b, Exception):
+                            out[p] = b
+                        else:
+                            out[p] = b[s:e]
+                    elif np == u and s >= ns and e <= ne:
+                        if isinstance(b, Exception):
+                            out[p] = b
+                        else:
+                            out[p] = b[s - ns : (e - ne) or None]
+
+        for k, v in out.copy().items():
+            # these were valid references, but fetch failed, so transform exc
+            if isinstance(v, Exception) and k in self.references:
+                ex = out[k]
+                new_ex = ReferenceNotReachable(k, self.references[k])
+                new_ex.__cause__ = ex
+                if on_error == "raise":
+                    raise new_ex
+                elif on_error != "omit":
+                    out[k] = new_ex
+
+        if len(out) == 1 and isinstance(path, str) and "*" not in path:
+            return _first(out)
+        return out
+
+    def _process_references(self, references, template_overrides=None):
+        vers = references.get("version", None)
+        if vers is None:
+            self._process_references0(references)
+        elif vers == 1:
+            self._process_references1(references, template_overrides=template_overrides)
+        else:
+            raise ValueError(f"Unknown reference spec version: {vers}")
+        # TODO: we make dircache by iterating over all entries, but for Spec >= 1,
+        #  can replace with programmatic. Is it even needed for mapper interface?
+
+    def _process_references0(self, references):
+        """Make reference dict for Spec Version 0"""
+        if isinstance(references, dict):
+            # do not do this for lazy/parquet backend, which will not make dicts,
+            # but must remain writable in the original object
+            references = {
+                key: json.dumps(val) if isinstance(val, dict) else val
+                for key, val in references.items()
+            }
+        self.references = references
+
+    def _process_references1(self, references, template_overrides=None):
+        if not self.simple_templates or self.templates:
+            import jinja2
+        self.references = {}
+        self._process_templates(references.get("templates", {}))
+
+        @lru_cache(1000)
+        def _render_jinja(u):
+            return jinja2.Template(u).render(**self.templates)
+
+        for k, v in references.get("refs", {}).items():
+            if isinstance(v, str):
+                if v.startswith("base64:"):
+                    self.references[k] = base64.b64decode(v[7:])
+                self.references[k] = v
+            elif isinstance(v, dict):
+                self.references[k] = json.dumps(v)
+            elif self.templates:
+                u = v[0]
+                if "{{" in u:
+                    if self.simple_templates:
+                        u = (
+                            u.replace("{{", "{")
+                            .replace("}}", "}")
+                            .format(**self.templates)
+                        )
+                    else:
+                        u = _render_jinja(u)
+                self.references[k] = [u] if len(v) == 1 else [u, v[1], v[2]]
+            else:
+                self.references[k] = v
+        self.references.update(self._process_gen(references.get("gen", [])))
+
+    def _process_templates(self, tmp):
+        self.templates = {}
+        if self.template_overrides is not None:
+            tmp.update(self.template_overrides)
+        for k, v in tmp.items():
+            if "{{" in v:
+                import jinja2
+
+                self.templates[k] = lambda temp=v, **kwargs: jinja2.Template(
+                    temp
+                ).render(**kwargs)
+            else:
+                self.templates[k] = v
+
+    def _process_gen(self, gens):
+        out = {}
+        for gen in gens:
+            dimension = {
+                k: (
+                    v
+                    if isinstance(v, list)
+                    else range(v.get("start", 0), v["stop"], v.get("step", 1))
+                )
+                for k, v in gen["dimensions"].items()
+            }
+            products = (
+                dict(zip(dimension.keys(), values))
+                for values in itertools.product(*dimension.values())
+            )
+            for pr in products:
+                import jinja2
+
+                key = jinja2.Template(gen["key"]).render(**pr, **self.templates)
+                url = jinja2.Template(gen["url"]).render(**pr, **self.templates)
+                if ("offset" in gen) and ("length" in gen):
+                    offset = int(
+                        jinja2.Template(gen["offset"]).render(**pr, **self.templates)
+                    )
+                    length = int(
+                        jinja2.Template(gen["length"]).render(**pr, **self.templates)
+                    )
+                    out[key] = [url, offset, length]
+                elif ("offset" in gen) ^ ("length" in gen):
+                    raise ValueError(
+                        "Both 'offset' and 'length' are required for a "
+                        "reference generator entry if either is provided."
+                    )
+                else:
+                    out[key] = [url]
+        return out
+
+    def _dircache_from_items(self):
+        self.dircache = {"": []}
+        it = self.references.items()
+        for path, part in it:
+            if isinstance(part, (bytes, str)) or hasattr(part, "to_bytes"):
+                size = len(part)
+            elif len(part) == 1:
+                size = None
+            else:
+                _, _, size = part
+            par = path.rsplit("/", 1)[0] if "/" in path else ""
+            par0 = par
+            subdirs = [par0]
+            while par0 and par0 not in self.dircache:
+                # collect parent directories
+                par0 = self._parent(par0)
+                subdirs.append(par0)
+
+            subdirs.reverse()
+            for parent, child in zip(subdirs, subdirs[1:]):
+                # register newly discovered directories
+                assert child not in self.dircache
+                assert parent in self.dircache
+                self.dircache[parent].append(
+                    {"name": child, "type": "directory", "size": 0}
+                )
+                self.dircache[child] = []
+
+            self.dircache[par].append({"name": path, "type": "file", "size": size})
+
+    def _open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs):
+        part_or_url, start0, end0 = self._cat_common(path)
+        # This logic is kept outside `ReferenceFile` to avoid unnecessary redirection.
+        # That does mean `_cat_common` gets called twice if it eventually reaches `ReferenceFile`.
+        if isinstance(part_or_url, bytes):
+            return io.BytesIO(part_or_url[start0:end0])
+
+        protocol, _ = split_protocol(part_or_url)
+        if start0 is None and end0 is None:
+            return self.fss[protocol]._open(
+                part_or_url,
+                mode,
+                block_size=block_size,
+                cache_options=cache_options,
+                **kwargs,
+            )
+
+        return ReferenceFile(
+            self,
+            path,
+            mode,
+            block_size=block_size,
+            cache_options=cache_options,
+            **kwargs,
+        )
+
+    def ls(self, path, detail=True, **kwargs):
+        logger.debug("list %s", path)
+        path = self._strip_protocol(path)
+        if isinstance(self.references, LazyReferenceMapper):
+            try:
+                return self.references.ls(path, detail)
+            except KeyError:
+                pass
+            raise FileNotFoundError(f"'{path}' is not a known key")
+        if not self.dircache:
+            self._dircache_from_items()
+        out = self._ls_from_cache(path)
+        if out is None:
+            raise FileNotFoundError(path)
+        if detail:
+            return out
+        return [o["name"] for o in out]
+
+    def exists(self, path, **kwargs):  # overwrite auto-sync version
+        return self.isdir(path) or self.isfile(path)
+
+    def isdir(self, path):  # overwrite auto-sync version
+        if self.dircache:
+            return path in self.dircache
+        elif isinstance(self.references, LazyReferenceMapper):
+            return path in self.references.listdir()
+        else:
+            # this may be faster than building dircache for single calls, but
+            # by looping will be slow for many calls; could cache it?
+            return any(_.startswith(f"{path}/") for _ in self.references)
+
+    def isfile(self, path):  # overwrite auto-sync version
+        return path in self.references
+
+    async def _ls(self, path, detail=True, **kwargs):  # calls fast sync code
+        return self.ls(path, detail, **kwargs)
+
+    def find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
+        if withdirs:
+            return super().find(
+                path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs
+            )
+        if path:
+            path = self._strip_protocol(path)
+            r = sorted(k for k in self.references if k.startswith(path))
+        else:
+            r = sorted(self.references)
+        if detail:
+            if not self.dircache:
+                self._dircache_from_items()
+            return {k: self._ls_from_cache(k)[0] for k in r}
+        else:
+            return r
+
+    def info(self, path, **kwargs):
+        out = self.references.get(path)
+        if out is not None:
+            if isinstance(out, (str, bytes)):
+                # decode base64 here
+                return {"name": path, "type": "file", "size": len(out)}
+            elif len(out) > 1:
+                return {"name": path, "type": "file", "size": out[2]}
+            else:
+                out0 = [{"name": path, "type": "file", "size": None}]
+        else:
+            out = self.ls(path, True)
+            out0 = [o for o in out if o["name"] == path]
+            if not out0:
+                return {"name": path, "type": "directory", "size": 0}
+        if out0[0]["size"] is None:
+            # if this is a whole remote file, update size using remote FS
+            prot, _ = split_protocol(self.references[path][0])
+            out0[0]["size"] = self.fss[prot].size(self.references[path][0])
+        return out0[0]
+
+    async def _info(self, path, **kwargs):  # calls fast sync code
+        return self.info(path)
+
+    async def _rm_file(self, path, **kwargs):
+        self.references.pop(
+            path, None
+        )  # ignores FileNotFound, just as well for directories
+        self.dircache.clear()  # this is a bit heavy handed
+
+    async def _pipe_file(self, path, data, mode="overwrite", **kwargs):
+        if mode == "create" and self.exists(path):
+            raise FileExistsError
+        # can be str or bytes
+        self.references[path] = data
+        self.dircache.clear()  # this is a bit heavy handed
+
+    async def _put_file(self, lpath, rpath, mode="overwrite", **kwargs):
+        # puts binary
+        if mode == "create" and self.exists(rpath):
+            raise FileExistsError
+        with open(lpath, "rb") as f:
+            self.references[rpath] = f.read()
+        self.dircache.clear()  # this is a bit heavy handed
+
+    def save_json(self, url, **storage_options):
+        """Write modified references into new location"""
+        out = {}
+        for k, v in self.references.items():
+            if isinstance(v, bytes):
+                try:
+                    out[k] = v.decode("ascii")
+                except UnicodeDecodeError:
+                    out[k] = (b"base64:" + base64.b64encode(v)).decode()
+            else:
+                out[k] = v
+        with fsspec.open(url, "wb", **storage_options) as f:
+            f.write(json.dumps({"version": 1, "refs": out}).encode())
+
+
+class ReferenceFile(AbstractBufferedFile):
+    def __init__(
+        self,
+        fs,
+        path,
+        mode="rb",
+        block_size="default",
+        autocommit=True,
+        cache_type="readahead",
+        cache_options=None,
+        size=None,
+        **kwargs,
+    ):
+        super().__init__(
+            fs,
+            path,
+            mode=mode,
+            block_size=block_size,
+            autocommit=autocommit,
+            size=size,
+            cache_type=cache_type,
+            cache_options=cache_options,
+            **kwargs,
+        )
+        part_or_url, self.start, self.end = self.fs._cat_common(self.path)
+        protocol, _ = split_protocol(part_or_url)
+        self.src_fs = self.fs.fss[protocol]
+        self.src_path = part_or_url
+        self._f = None
+
+    @property
+    def f(self):
+        if self._f is None or self._f.closed:
+            self._f = self.src_fs._open(
+                self.src_path,
+                mode=self.mode,
+                block_size=self.blocksize,
+                autocommit=self.autocommit,
+                cache_type="none",
+                **self.kwargs,
+            )
+        return self._f
+
+    def close(self):
+        if self._f is not None:
+            self._f.close()
+        return super().close()
+
+    def _fetch_range(self, start, end):
+        start = start + self.start
+        end = min(end + self.start, self.end)
+        self.f.seek(start)
+        return self.f.read(end - start)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/sftp.py b/lib/python3.11/site-packages/fsspec/implementations/sftp.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c347963d692d50390b131225a56477b328f7a3c
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/sftp.py
@@ -0,0 +1,187 @@
+import datetime
+import logging
+import os
+import types
+import uuid
+from stat import S_ISDIR, S_ISLNK
+
+import paramiko
+
+from .. import AbstractFileSystem
+from ..utils import infer_storage_options
+
+logger = logging.getLogger("fsspec.sftp")
+
+
+class SFTPFileSystem(AbstractFileSystem):
+    """Files over SFTP/SSH
+
+    Peer-to-peer filesystem over SSH using paramiko.
+
+    Note: if using this with the ``open`` or ``open_files``, with full URLs,
+    there is no way to tell if a path is relative, so all paths are assumed
+    to be absolute.
+    """
+
+    protocol = "sftp", "ssh"
+
+    def __init__(self, host, **ssh_kwargs):
+        """
+
+        Parameters
+        ----------
+        host: str
+            Hostname or IP as a string
+        temppath: str
+            Location on the server to put files, when within a transaction
+        ssh_kwargs: dict
+            Parameters passed on to connection. See details in
+            https://docs.paramiko.org/en/3.3/api/client.html#paramiko.client.SSHClient.connect
+            May include port, username, password...
+        """
+        if self._cached:
+            return
+        super().__init__(**ssh_kwargs)
+        self.temppath = ssh_kwargs.pop("temppath", "/tmp")  # remote temp directory
+        self.host = host
+        self.ssh_kwargs = ssh_kwargs
+        self._connect()
+
+    def _connect(self):
+        logger.debug("Connecting to SFTP server %s", self.host)
+        self.client = paramiko.SSHClient()
+        self.client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
+        self.client.connect(self.host, **self.ssh_kwargs)
+        self.ftp = self.client.open_sftp()
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        return infer_storage_options(path)["path"]
+
+    @staticmethod
+    def _get_kwargs_from_urls(urlpath):
+        out = infer_storage_options(urlpath)
+        out.pop("path", None)
+        out.pop("protocol", None)
+        return out
+
+    def mkdir(self, path, create_parents=True, mode=511):
+        path = self._strip_protocol(path)
+        logger.debug("Creating folder %s", path)
+        if self.exists(path):
+            raise FileExistsError(f"File exists: {path}")
+
+        if create_parents:
+            self.makedirs(path)
+        else:
+            self.ftp.mkdir(path, mode)
+
+    def makedirs(self, path, exist_ok=False, mode=511):
+        if self.exists(path) and not exist_ok:
+            raise FileExistsError(f"File exists: {path}")
+
+        parts = path.split("/")
+        new_path = "/" if path[:1] == "/" else ""
+
+        for part in parts:
+            if part:
+                new_path = f"{new_path}/{part}" if new_path else part
+                if not self.exists(new_path):
+                    self.ftp.mkdir(new_path, mode)
+
+    def rmdir(self, path):
+        path = self._strip_protocol(path)
+        logger.debug("Removing folder %s", path)
+        self.ftp.rmdir(path)
+
+    def info(self, path):
+        path = self._strip_protocol(path)
+        stat = self._decode_stat(self.ftp.stat(path))
+        stat["name"] = path
+        return stat
+
+    @staticmethod
+    def _decode_stat(stat, parent_path=None):
+        if S_ISDIR(stat.st_mode):
+            t = "directory"
+        elif S_ISLNK(stat.st_mode):
+            t = "link"
+        else:
+            t = "file"
+        out = {
+            "name": "",
+            "size": stat.st_size,
+            "type": t,
+            "uid": stat.st_uid,
+            "gid": stat.st_gid,
+            "time": datetime.datetime.fromtimestamp(
+                stat.st_atime, tz=datetime.timezone.utc
+            ),
+            "mtime": datetime.datetime.fromtimestamp(
+                stat.st_mtime, tz=datetime.timezone.utc
+            ),
+        }
+        if parent_path:
+            out["name"] = "/".join([parent_path.rstrip("/"), stat.filename])
+        return out
+
+    def ls(self, path, detail=False):
+        path = self._strip_protocol(path)
+        logger.debug("Listing folder %s", path)
+        stats = [self._decode_stat(stat, path) for stat in self.ftp.listdir_iter(path)]
+        if detail:
+            return stats
+        else:
+            paths = [stat["name"] for stat in stats]
+            return sorted(paths)
+
+    def put_file(self, lpath, rpath, callback=None, **kwargs):
+        self.mkdirs(self._parent(os.fspath(rpath)), exist_ok=True)
+        logger.debug("Put file %s into %s", lpath, rpath)
+        self.ftp.put(lpath, rpath)
+
+    def get_file(self, rpath, lpath, **kwargs):
+        if self.isdir(rpath):
+            os.makedirs(lpath, exist_ok=True)
+        else:
+            self.ftp.get(self._strip_protocol(rpath), lpath)
+
+    def _open(self, path, mode="rb", block_size=None, **kwargs):
+        """
+        block_size: int or None
+            If 0, no buffering, if 1, line buffering, if >1, buffer that many
+            bytes, if None use default from paramiko.
+        """
+        logger.debug("Opening file %s", path)
+        if kwargs.get("autocommit", True) is False:
+            # writes to temporary file, move on commit
+            path2 = "/".join([self.temppath, str(uuid.uuid4())])
+            f = self.ftp.open(path2, mode, bufsize=block_size if block_size else -1)
+            f.temppath = path2
+            f.targetpath = path
+            f.fs = self
+            f.commit = types.MethodType(commit_a_file, f)
+            f.discard = types.MethodType(discard_a_file, f)
+        else:
+            f = self.ftp.open(path, mode, bufsize=block_size if block_size else -1)
+        return f
+
+    def _rm(self, path):
+        if self.isdir(path):
+            self.ftp.rmdir(path)
+        else:
+            self.ftp.remove(path)
+
+    def mv(self, old, new):
+        new = self._strip_protocol(new)
+        old = self._strip_protocol(old)
+        logger.debug("Renaming %s into %s", old, new)
+        self.ftp.posix_rename(old, new)
+
+
+def commit_a_file(self):
+    self.fs.mv(self.temppath, self.targetpath)
+
+
+def discard_a_file(self):
+    self.fs._rm(self.temppath)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/smb.py b/lib/python3.11/site-packages/fsspec/implementations/smb.py
new file mode 100644
index 0000000000000000000000000000000000000000..db6b3f5c3702de90cf121ccca49f3ca2b580df9f
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/smb.py
@@ -0,0 +1,416 @@
+"""
+This module contains SMBFileSystem class responsible for handling access to
+Windows Samba network shares by using package smbprotocol
+"""
+
+import datetime
+import re
+import uuid
+from stat import S_ISDIR, S_ISLNK
+
+import smbclient
+import smbprotocol.exceptions
+
+from .. import AbstractFileSystem
+from ..utils import infer_storage_options
+
+# ! pylint: disable=bad-continuation
+
+
+class SMBFileSystem(AbstractFileSystem):
+    """Allow reading and writing to Windows and Samba network shares.
+
+    When using `fsspec.open()` for getting a file-like object the URI
+    should be specified as this format:
+    ``smb://workgroup;user:password@server:port/share/folder/file.csv``.
+
+    Example::
+
+        >>> import fsspec
+        >>> with fsspec.open(
+        ...     'smb://myuser:mypassword@myserver.com/' 'share/folder/file.csv'
+        ... ) as smbfile:
+        ...     df = pd.read_csv(smbfile, sep='|', header=None)
+
+    Note that you need to pass in a valid hostname or IP address for the host
+    component of the URL. Do not use the Windows/NetBIOS machine name for the
+    host component.
+
+    The first component of the path in the URL points to the name of the shared
+    folder. Subsequent path components will point to the directory/folder/file.
+
+    The URL components ``workgroup`` , ``user``, ``password`` and ``port`` may be
+    optional.
+
+    .. note::
+
+        For working this source require `smbprotocol`_ to be installed, e.g.::
+
+            $ pip install smbprotocol
+            # or
+            # pip install smbprotocol[kerberos]
+
+    .. _smbprotocol: https://github.com/jborean93/smbprotocol#requirements
+
+    Note: if using this with the ``open`` or ``open_files``, with full URLs,
+    there is no way to tell if a path is relative, so all paths are assumed
+    to be absolute.
+    """
+
+    protocol = "smb"
+
+    # pylint: disable=too-many-arguments
+    def __init__(
+        self,
+        host,
+        port=None,
+        username=None,
+        password=None,
+        timeout=60,
+        encrypt=None,
+        share_access=None,
+        register_session_retries=4,
+        register_session_retry_wait=1,
+        register_session_retry_factor=10,
+        auto_mkdir=False,
+        **kwargs,
+    ):
+        """
+        You can use _get_kwargs_from_urls to get some kwargs from
+        a reasonable SMB url.
+
+        Authentication will be anonymous or integrated if username/password are not
+        given.
+
+        Parameters
+        ----------
+        host: str
+            The remote server name/ip to connect to
+        port: int or None
+            Port to connect with. Usually 445, sometimes 139.
+        username: str or None
+            Username to connect with. Required if Kerberos auth is not being used.
+        password: str or None
+            User's password on the server, if using username
+        timeout: int
+            Connection timeout in seconds
+        encrypt: bool
+            Whether to force encryption or not, once this has been set to True
+            the session cannot be changed back to False.
+        share_access: str or None
+            Specifies the default access applied to file open operations
+            performed with this file system object.
+            This affects whether other processes can concurrently open a handle
+            to the same file.
+
+            - None (the default): exclusively locks the file until closed.
+            - 'r': Allow other handles to be opened with read access.
+            - 'w': Allow other handles to be opened with write access.
+            - 'd': Allow other handles to be opened with delete access.
+        register_session_retries: int
+            Number of retries to register a session with the server. Retries are not performed
+            for authentication errors, as they are considered as invalid credentials and not network
+            issues. If set to negative value, no register attempts will be performed.
+        register_session_retry_wait: int
+            Time in seconds to wait between each retry. Number must be non-negative.
+        register_session_retry_factor: int
+            Base factor for the wait time between each retry. The wait time
+            is calculated using exponential function. For factor=1 all wait times
+            will be equal to `register_session_retry_wait`. For any number of retries,
+            the last wait time will be equal to `register_session_retry_wait` and for retries>1
+            the first wait time will be equal to `register_session_retry_wait / factor`.
+            Number must be equal to or greater than 1. Optimal factor is 10.
+        auto_mkdir: bool
+            Whether, when opening a file, the directory containing it should
+            be created (if it doesn't already exist). This is assumed by pyarrow
+            and zarr-python code.
+        """
+        super().__init__(**kwargs)
+        self.host = host
+        self.port = port
+        self.username = username
+        self.password = password
+        self.timeout = timeout
+        self.encrypt = encrypt
+        self.temppath = kwargs.pop("temppath", "")
+        self.share_access = share_access
+        self.register_session_retries = register_session_retries
+        if register_session_retry_wait < 0:
+            raise ValueError(
+                "register_session_retry_wait must be a non-negative integer"
+            )
+        self.register_session_retry_wait = register_session_retry_wait
+        if register_session_retry_factor < 1:
+            raise ValueError(
+                "register_session_retry_factor must be a positive "
+                "integer equal to or greater than 1"
+            )
+        self.register_session_retry_factor = register_session_retry_factor
+        self.auto_mkdir = auto_mkdir
+        self._connect()
+
+    @property
+    def _port(self):
+        return 445 if self.port is None else self.port
+
+    def _connect(self):
+        import time
+
+        if self.register_session_retries <= -1:
+            return
+
+        retried_errors = []
+
+        wait_time = self.register_session_retry_wait
+        n_waits = (
+            self.register_session_retries - 1
+        )  # -1 = No wait time after the last retry
+        factor = self.register_session_retry_factor
+
+        # Generate wait times for each retry attempt.
+        # Wait times are calculated using exponential function. For factor=1 all wait times
+        # will be equal to `wait`. For any number of retries the last wait time will be
+        # equal to `wait` and for retries>2 the first wait time will be equal to `wait / factor`.
+        wait_times = iter(
+            factor ** (n / n_waits - 1) * wait_time for n in range(0, n_waits + 1)
+        )
+
+        for attempt in range(self.register_session_retries + 1):
+            try:
+                smbclient.register_session(
+                    self.host,
+                    username=self.username,
+                    password=self.password,
+                    port=self._port,
+                    encrypt=self.encrypt,
+                    connection_timeout=self.timeout,
+                )
+                return
+            except (
+                smbprotocol.exceptions.SMBAuthenticationError,
+                smbprotocol.exceptions.LogonFailure,
+            ):
+                # These exceptions should not be repeated, as they clearly indicate
+                # that the credentials are invalid and not a network issue.
+                raise
+            except ValueError as exc:
+                if re.findall(r"\[Errno -\d+]", str(exc)):
+                    # This exception is raised by the smbprotocol.transport:Tcp.connect
+                    # and originates from socket.gaierror (OSError). These exceptions might
+                    # be raised due to network instability. We will retry to connect.
+                    retried_errors.append(exc)
+                else:
+                    # All another ValueError exceptions should be raised, as they are not
+                    # related to network issues.
+                    raise
+            except Exception as exc:
+                # Save the exception and retry to connect. This except might be dropped
+                # in the future, once all exceptions suited for retry are identified.
+                retried_errors.append(exc)
+
+            if attempt < self.register_session_retries:
+                time.sleep(next(wait_times))
+
+        # Raise last exception to inform user about the connection issues.
+        # Note: Should we use ExceptionGroup to raise all exceptions?
+        raise retried_errors[-1]
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        return infer_storage_options(path)["path"]
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        # smb://workgroup;user:password@host:port/share/folder/file.csv
+        out = infer_storage_options(path)
+        out.pop("path", None)
+        out.pop("protocol", None)
+        return out
+
+    def mkdir(self, path, create_parents=True, **kwargs):
+        wpath = _as_unc_path(self.host, path)
+        if create_parents:
+            smbclient.makedirs(wpath, exist_ok=False, port=self._port, **kwargs)
+        else:
+            smbclient.mkdir(wpath, port=self._port, **kwargs)
+
+    def makedirs(self, path, exist_ok=False):
+        if _share_has_path(path):
+            wpath = _as_unc_path(self.host, path)
+            smbclient.makedirs(wpath, exist_ok=exist_ok, port=self._port)
+
+    def rmdir(self, path):
+        if _share_has_path(path):
+            wpath = _as_unc_path(self.host, path)
+            smbclient.rmdir(wpath, port=self._port)
+
+    def info(self, path, **kwargs):
+        wpath = _as_unc_path(self.host, path)
+        stats = smbclient.stat(wpath, port=self._port, **kwargs)
+        if S_ISDIR(stats.st_mode):
+            stype = "directory"
+        elif S_ISLNK(stats.st_mode):
+            stype = "link"
+        else:
+            stype = "file"
+        res = {
+            "name": path + "/" if stype == "directory" else path,
+            "size": stats.st_size,
+            "type": stype,
+            "uid": stats.st_uid,
+            "gid": stats.st_gid,
+            "time": stats.st_atime,
+            "mtime": stats.st_mtime,
+        }
+        return res
+
+    def created(self, path):
+        """Return the created timestamp of a file as a datetime.datetime"""
+        wpath = _as_unc_path(self.host, path)
+        stats = smbclient.stat(wpath, port=self._port)
+        return datetime.datetime.fromtimestamp(stats.st_ctime, tz=datetime.timezone.utc)
+
+    def modified(self, path):
+        """Return the modified timestamp of a file as a datetime.datetime"""
+        wpath = _as_unc_path(self.host, path)
+        stats = smbclient.stat(wpath, port=self._port)
+        return datetime.datetime.fromtimestamp(stats.st_mtime, tz=datetime.timezone.utc)
+
+    def ls(self, path, detail=True, **kwargs):
+        unc = _as_unc_path(self.host, path)
+        listed = smbclient.listdir(unc, port=self._port, **kwargs)
+        dirs = ["/".join([path.rstrip("/"), p]) for p in listed]
+        if detail:
+            dirs = [self.info(d) for d in dirs]
+        return dirs
+
+    # pylint: disable=too-many-arguments
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=-1,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        """
+        block_size: int or None
+            If 0, no buffering, 1, line buffering, >1, buffer that many bytes
+
+        Notes
+        -----
+        By specifying 'share_access' in 'kwargs' it is possible to override the
+        default shared access setting applied in the constructor of this object.
+        """
+        if self.auto_mkdir and "w" in mode:
+            self.makedirs(self._parent(path), exist_ok=True)
+        bls = block_size if block_size is not None and block_size >= 0 else -1
+        wpath = _as_unc_path(self.host, path)
+        share_access = kwargs.pop("share_access", self.share_access)
+        if "w" in mode and autocommit is False:
+            temp = _as_temp_path(self.host, path, self.temppath)
+            return SMBFileOpener(
+                wpath, temp, mode, port=self._port, block_size=bls, **kwargs
+            )
+        return smbclient.open_file(
+            wpath,
+            mode,
+            buffering=bls,
+            share_access=share_access,
+            port=self._port,
+            **kwargs,
+        )
+
+    def copy(self, path1, path2, **kwargs):
+        """Copy within two locations in the same filesystem"""
+        wpath1 = _as_unc_path(self.host, path1)
+        wpath2 = _as_unc_path(self.host, path2)
+        if self.auto_mkdir:
+            self.makedirs(self._parent(path2), exist_ok=True)
+        smbclient.copyfile(wpath1, wpath2, port=self._port, **kwargs)
+
+    def _rm(self, path):
+        if _share_has_path(path):
+            wpath = _as_unc_path(self.host, path)
+            stats = smbclient.stat(wpath, port=self._port)
+            if S_ISDIR(stats.st_mode):
+                smbclient.rmdir(wpath, port=self._port)
+            else:
+                smbclient.remove(wpath, port=self._port)
+
+    def mv(self, path1, path2, recursive=None, maxdepth=None, **kwargs):
+        wpath1 = _as_unc_path(self.host, path1)
+        wpath2 = _as_unc_path(self.host, path2)
+        smbclient.rename(wpath1, wpath2, port=self._port, **kwargs)
+
+
+def _as_unc_path(host, path):
+    rpath = path.replace("/", "\\")
+    unc = f"\\\\{host}{rpath}"
+    return unc
+
+
+def _as_temp_path(host, path, temppath):
+    share = path.split("/")[1]
+    temp_file = f"/{share}{temppath}/{uuid.uuid4()}"
+    unc = _as_unc_path(host, temp_file)
+    return unc
+
+
+def _share_has_path(path):
+    parts = path.count("/")
+    if path.endswith("/"):
+        return parts > 2
+    return parts > 1
+
+
+class SMBFileOpener:
+    """writes to remote temporary file, move on commit"""
+
+    def __init__(self, path, temp, mode, port=445, block_size=-1, **kwargs):
+        self.path = path
+        self.temp = temp
+        self.mode = mode
+        self.block_size = block_size
+        self.kwargs = kwargs
+        self.smbfile = None
+        self._incontext = False
+        self.port = port
+        self._open()
+
+    def _open(self):
+        if self.smbfile is None or self.smbfile.closed:
+            self.smbfile = smbclient.open_file(
+                self.temp,
+                self.mode,
+                port=self.port,
+                buffering=self.block_size,
+                **self.kwargs,
+            )
+
+    def commit(self):
+        """Move temp file to definitive on success."""
+        # TODO: use transaction support in SMB protocol
+        smbclient.replace(self.temp, self.path, port=self.port)
+
+    def discard(self):
+        """Remove the temp file on failure."""
+        smbclient.remove(self.temp, port=self.port)
+
+    def __fspath__(self):
+        return self.path
+
+    def __iter__(self):
+        return self.smbfile.__iter__()
+
+    def __getattr__(self, item):
+        return getattr(self.smbfile, item)
+
+    def __enter__(self):
+        self._incontext = True
+        return self.smbfile.__enter__()
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._incontext = False
+        self.smbfile.__exit__(exc_type, exc_value, traceback)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/tar.py b/lib/python3.11/site-packages/fsspec/implementations/tar.py
new file mode 100644
index 0000000000000000000000000000000000000000..41bd894a4e3f1b3b2318f9b28d690dada72d670a
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/tar.py
@@ -0,0 +1,135 @@
+import logging
+import tarfile
+
+import fsspec
+from fsspec.archive import AbstractArchiveFileSystem
+from fsspec.compression import compr
+from fsspec.utils import infer_compression
+
+typemap = {b"0": "file", b"5": "directory"}
+
+logger = logging.getLogger("tar")
+
+
+class TarFileSystem(AbstractArchiveFileSystem):
+    """Compressed Tar archives as a file-system (read-only)
+
+    Supports the following formats:
+    tar.gz, tar.bz2, tar.xz
+    """
+
+    root_marker = ""
+    protocol = "tar"
+    cachable = False
+
+    def __init__(
+        self,
+        fo="",
+        index_store=None,
+        target_options=None,
+        target_protocol=None,
+        compression=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        target_options = target_options or {}
+
+        if isinstance(fo, str):
+            self.of = fsspec.open(fo, protocol=target_protocol, **target_options)
+            fo = self.of.open()  # keep the reference
+
+        # Try to infer compression.
+        if compression is None:
+            name = None
+
+            # Try different ways to get hold of the filename. `fo` might either
+            # be a `fsspec.LocalFileOpener`, an `io.BufferedReader` or an
+            # `fsspec.AbstractFileSystem` instance.
+            try:
+                # Amended io.BufferedReader or similar.
+                # This uses a "protocol extension" where original filenames are
+                # propagated to archive-like filesystems in order to let them
+                # infer the right compression appropriately.
+                if hasattr(fo, "original"):
+                    name = fo.original
+
+                # fsspec.LocalFileOpener
+                elif hasattr(fo, "path"):
+                    name = fo.path
+
+                # io.BufferedReader
+                elif hasattr(fo, "name"):
+                    name = fo.name
+
+                # fsspec.AbstractFileSystem
+                elif hasattr(fo, "info"):
+                    name = fo.info()["name"]
+
+            except Exception as ex:
+                logger.warning(
+                    f"Unable to determine file name, not inferring compression: {ex}"
+                )
+
+            if name is not None:
+                compression = infer_compression(name)
+                logger.info(f"Inferred compression {compression} from file name {name}")
+
+        if compression is not None:
+            # TODO: tarfile already implements compression with modes like "'r:gz'",
+            #  but then would seek to offset in the file work?
+            fo = compr[compression](fo)
+
+        self._fo_ref = fo
+        self.fo = fo  # the whole instance is a context
+        self.tar = tarfile.TarFile(fileobj=self.fo)
+        self.dir_cache = None
+
+        self.index_store = index_store
+        self.index = None
+        self._index()
+
+    def _index(self):
+        # TODO: load and set saved index, if exists
+        out = {}
+        for ti in self.tar:
+            info = ti.get_info()
+            info["type"] = typemap.get(info["type"], "file")
+            name = ti.get_info()["name"].rstrip("/")
+            out[name] = (info, ti.offset_data)
+
+        self.index = out
+        # TODO: save index to self.index_store here, if set
+
+    def _get_dirs(self):
+        if self.dir_cache is not None:
+            return
+
+        # This enables ls to get directories as children as well as files
+        self.dir_cache = {
+            dirname: {"name": dirname, "size": 0, "type": "directory"}
+            for dirname in self._all_dirnames(self.tar.getnames())
+        }
+        for member in self.tar.getmembers():
+            info = member.get_info()
+            info["name"] = info["name"].rstrip("/")
+            info["type"] = typemap.get(info["type"], "file")
+            self.dir_cache[info["name"]] = info
+
+    def _open(self, path, mode="rb", **kwargs):
+        if mode != "rb":
+            raise ValueError("Read-only filesystem implementation")
+        details, offset = self.index[path]
+        if details["type"] != "file":
+            raise ValueError("Can only handle regular files")
+        return self.tar.extractfile(path)
+
+    def close(self):
+        """Commits any write changes to the file. Done on ``del`` too."""
+        self.tar.close()
+
+    def __del__(self):
+        if hasattr(self, "tar"):
+            self.close()
+            del self.tar
+        if hasattr(self, "of") and hasattr(self.of, "__exit__"):
+            self.of.__exit__(None, None, None)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/webhdfs.py b/lib/python3.11/site-packages/fsspec/implementations/webhdfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3048b6a3638cf34e400804a3057521e720b6e81
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/webhdfs.py
@@ -0,0 +1,503 @@
+# https://hadoop.apache.org/docs/r1.0.4/webhdfs.html
+
+import logging
+import os
+import secrets
+import shutil
+import tempfile
+import uuid
+from contextlib import suppress
+from datetime import datetime
+from urllib.parse import quote
+
+import requests
+
+from ..spec import AbstractBufferedFile, AbstractFileSystem
+from ..utils import infer_storage_options, tokenize
+
+logger = logging.getLogger("webhdfs")
+
+
+class WebHDFS(AbstractFileSystem):
+    """
+    Interface to HDFS over HTTP using the WebHDFS API. Supports also HttpFS gateways.
+
+    Four auth mechanisms are supported:
+
+    insecure: no auth is done, and the user is assumed to be whoever they
+        say they are (parameter ``user``), or a predefined value such as
+        "dr.who" if not given
+    spnego: when kerberos authentication is enabled, auth is negotiated by
+        requests_kerberos https://github.com/requests/requests-kerberos .
+        This establishes a session based on existing kinit login and/or
+        specified principal/password; parameters are passed with ``kerb_kwargs``
+    token: uses an existing Hadoop delegation token from another secured
+        service. Indeed, this client can also generate such tokens when
+        not insecure. Note that tokens expire, but can be renewed (by a
+        previously specified user) and may allow for proxying.
+    basic-auth: used when both parameter ``user`` and parameter ``password``
+        are provided.
+
+    """
+
+    tempdir = str(tempfile.gettempdir())
+    protocol = "webhdfs", "webHDFS"
+
+    def __init__(
+        self,
+        host,
+        port=50070,
+        kerberos=False,
+        token=None,
+        user=None,
+        password=None,
+        proxy_to=None,
+        kerb_kwargs=None,
+        data_proxy=None,
+        use_https=False,
+        session_cert=None,
+        session_verify=True,
+        **kwargs,
+    ):
+        """
+        Parameters
+        ----------
+        host: str
+            Name-node address
+        port: int
+            Port for webHDFS
+        kerberos: bool
+            Whether to authenticate with kerberos for this connection
+        token: str or None
+            If given, use this token on every call to authenticate. A user
+            and user-proxy may be encoded in the token and should not be also
+            given
+        user: str or None
+            If given, assert the user name to connect with
+        password: str or None
+            If given, assert the password to use for basic auth. If password
+            is provided, user must be provided also
+        proxy_to: str or None
+            If given, the user has the authority to proxy, and this value is
+            the user in who's name actions are taken
+        kerb_kwargs: dict
+            Any extra arguments for HTTPKerberosAuth, see
+            `<https://github.com/requests/requests-kerberos/blob/master/requests_kerberos/kerberos_.py>`_
+        data_proxy: dict, callable or None
+            If given, map data-node addresses. This can be necessary if the
+            HDFS cluster is behind a proxy, running on Docker or otherwise has
+            a mismatch between the host-names given by the name-node and the
+            address by which to refer to them from the client. If a dict,
+            maps host names ``host->data_proxy[host]``; if a callable, full
+            URLs are passed, and function must conform to
+            ``url->data_proxy(url)``.
+        use_https: bool
+            Whether to connect to the Name-node using HTTPS instead of HTTP
+        session_cert: str or Tuple[str, str] or None
+            Path to a certificate file, or tuple of (cert, key) files to use
+            for the requests.Session
+        session_verify: str, bool or None
+            Path to a certificate file to use for verifying the requests.Session.
+        kwargs
+        """
+        if self._cached:
+            return
+        super().__init__(**kwargs)
+        self.url = f"{'https' if use_https else 'http'}://{host}:{port}/webhdfs/v1"
+        self.kerb = kerberos
+        self.kerb_kwargs = kerb_kwargs or {}
+        self.pars = {}
+        self.proxy = data_proxy or {}
+        if token is not None:
+            if user is not None or proxy_to is not None:
+                raise ValueError(
+                    "If passing a delegation token, must not set "
+                    "user or proxy_to, as these are encoded in the"
+                    " token"
+                )
+            self.pars["delegation"] = token
+        self.user = user
+        self.password = password
+
+        if password is not None:
+            if user is None:
+                raise ValueError(
+                    "If passing a password, the user must also be"
+                    "set in order to set up the basic-auth"
+                )
+        else:
+            if user is not None:
+                self.pars["user.name"] = user
+
+        if proxy_to is not None:
+            self.pars["doas"] = proxy_to
+        if kerberos and user is not None:
+            raise ValueError(
+                "If using Kerberos auth, do not specify the "
+                "user, this is handled by kinit."
+            )
+
+        self.session_cert = session_cert
+        self.session_verify = session_verify
+
+        self._connect()
+
+        self._fsid = f"webhdfs_{tokenize(host, port)}"
+
+    @property
+    def fsid(self):
+        return self._fsid
+
+    def _connect(self):
+        self.session = requests.Session()
+
+        if self.session_cert:
+            self.session.cert = self.session_cert
+
+        self.session.verify = self.session_verify
+
+        if self.kerb:
+            from requests_kerberos import HTTPKerberosAuth
+
+            self.session.auth = HTTPKerberosAuth(**self.kerb_kwargs)
+
+        if self.user is not None and self.password is not None:
+            from requests.auth import HTTPBasicAuth
+
+            self.session.auth = HTTPBasicAuth(self.user, self.password)
+
+    def _call(self, op, method="get", path=None, data=None, redirect=True, **kwargs):
+        path = self._strip_protocol(path) if path is not None else ""
+        url = self._apply_proxy(self.url + quote(path, safe="/="))
+        args = kwargs.copy()
+        args.update(self.pars)
+        args["op"] = op.upper()
+        logger.debug("sending %s with %s", url, method)
+        out = self.session.request(
+            method=method.upper(),
+            url=url,
+            params=args,
+            data=data,
+            allow_redirects=redirect,
+        )
+        if out.status_code in [400, 401, 403, 404, 500]:
+            try:
+                err = out.json()
+                msg = err["RemoteException"]["message"]
+                exp = err["RemoteException"]["exception"]
+            except (ValueError, KeyError):
+                pass
+            else:
+                if exp in ["IllegalArgumentException", "UnsupportedOperationException"]:
+                    raise ValueError(msg)
+                elif exp in ["SecurityException", "AccessControlException"]:
+                    raise PermissionError(msg)
+                elif exp in ["FileNotFoundException"]:
+                    raise FileNotFoundError(msg)
+                else:
+                    raise RuntimeError(msg)
+        out.raise_for_status()
+        return out
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        replication=None,
+        permissions=None,
+        **kwargs,
+    ):
+        """
+
+        Parameters
+        ----------
+        path: str
+            File location
+        mode: str
+            'rb', 'wb', etc.
+        block_size: int
+            Client buffer size for read-ahead or write buffer
+        autocommit: bool
+            If False, writes to temporary file that only gets put in final
+            location upon commit
+        replication: int
+            Number of copies of file on the cluster, write mode only
+        permissions: str or int
+            posix permissions, write mode only
+        kwargs
+
+        Returns
+        -------
+        WebHDFile instance
+        """
+        block_size = block_size or self.blocksize
+        return WebHDFile(
+            self,
+            path,
+            mode=mode,
+            block_size=block_size,
+            tempdir=self.tempdir,
+            autocommit=autocommit,
+            replication=replication,
+            permissions=permissions,
+        )
+
+    @staticmethod
+    def _process_info(info):
+        info["type"] = info["type"].lower()
+        info["size"] = info["length"]
+        return info
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        return infer_storage_options(path)["path"]
+
+    @staticmethod
+    def _get_kwargs_from_urls(urlpath):
+        out = infer_storage_options(urlpath)
+        out.pop("path", None)
+        out.pop("protocol", None)
+        if "username" in out:
+            out["user"] = out.pop("username")
+        return out
+
+    def info(self, path):
+        out = self._call("GETFILESTATUS", path=path)
+        info = out.json()["FileStatus"]
+        info["name"] = path
+        return self._process_info(info)
+
+    def created(self, path):
+        """Return the created timestamp of a file as a datetime.datetime"""
+        # The API does not provide creation time, so we use modification time
+        info = self.info(path)
+        mtime = info.get("modificationTime", None)
+        if mtime is not None:
+            return datetime.fromtimestamp(mtime / 1000)
+        raise RuntimeError("Could not retrieve creation time (modification time).")
+
+    def modified(self, path):
+        """Return the modified timestamp of a file as a datetime.datetime"""
+        info = self.info(path)
+        mtime = info.get("modificationTime", None)
+        if mtime is not None:
+            return datetime.fromtimestamp(mtime / 1000)
+        raise RuntimeError("Could not retrieve modification time.")
+
+    def ls(self, path, detail=False, **kwargs):
+        out = self._call("LISTSTATUS", path=path)
+        infos = out.json()["FileStatuses"]["FileStatus"]
+        for info in infos:
+            self._process_info(info)
+            info["name"] = path.rstrip("/") + "/" + info["pathSuffix"]
+        if detail:
+            return sorted(infos, key=lambda i: i["name"])
+        else:
+            return sorted(info["name"] for info in infos)
+
+    def content_summary(self, path):
+        """Total numbers of files, directories and bytes under path"""
+        out = self._call("GETCONTENTSUMMARY", path=path)
+        return out.json()["ContentSummary"]
+
+    def ukey(self, path):
+        """Checksum info of file, giving method and result"""
+        out = self._call("GETFILECHECKSUM", path=path, redirect=False)
+        if "Location" in out.headers:
+            location = self._apply_proxy(out.headers["Location"])
+            out2 = self.session.get(location)
+            out2.raise_for_status()
+            return out2.json()["FileChecksum"]
+        else:
+            out.raise_for_status()
+            return out.json()["FileChecksum"]
+
+    def home_directory(self):
+        """Get user's home directory"""
+        out = self._call("GETHOMEDIRECTORY")
+        return out.json()["Path"]
+
+    def get_delegation_token(self, renewer=None):
+        """Retrieve token which can give the same authority to other uses
+
+        Parameters
+        ----------
+        renewer: str or None
+            User who may use this token; if None, will be current user
+        """
+        if renewer:
+            out = self._call("GETDELEGATIONTOKEN", renewer=renewer)
+        else:
+            out = self._call("GETDELEGATIONTOKEN")
+        t = out.json()["Token"]
+        if t is None:
+            raise ValueError("No token available for this user/security context")
+        return t["urlString"]
+
+    def renew_delegation_token(self, token):
+        """Make token live longer. Returns new expiry time"""
+        out = self._call("RENEWDELEGATIONTOKEN", method="put", token=token)
+        return out.json()["long"]
+
+    def cancel_delegation_token(self, token):
+        """Stop the token from being useful"""
+        self._call("CANCELDELEGATIONTOKEN", method="put", token=token)
+
+    def chmod(self, path, mod):
+        """Set the permission at path
+
+        Parameters
+        ----------
+        path: str
+            location to set (file or directory)
+        mod: str or int
+            posix epresentation or permission, give as oct string, e.g, '777'
+            or 0o777
+        """
+        self._call("SETPERMISSION", method="put", path=path, permission=mod)
+
+    def chown(self, path, owner=None, group=None):
+        """Change owning user and/or group"""
+        kwargs = {}
+        if owner is not None:
+            kwargs["owner"] = owner
+        if group is not None:
+            kwargs["group"] = group
+        self._call("SETOWNER", method="put", path=path, **kwargs)
+
+    def set_replication(self, path, replication):
+        """
+        Set file replication factor
+
+        Parameters
+        ----------
+        path: str
+            File location (not for directories)
+        replication: int
+            Number of copies of file on the cluster. Should be smaller than
+            number of data nodes; normally 3 on most systems.
+        """
+        self._call("SETREPLICATION", path=path, method="put", replication=replication)
+
+    def mkdir(self, path, **kwargs):
+        self._call("MKDIRS", method="put", path=path)
+
+    def makedirs(self, path, exist_ok=False):
+        if exist_ok is False and self.exists(path):
+            raise FileExistsError(path)
+        self.mkdir(path)
+
+    def mv(self, path1, path2, **kwargs):
+        self._call("RENAME", method="put", path=path1, destination=path2)
+
+    def rm(self, path, recursive=False, **kwargs):
+        self._call(
+            "DELETE",
+            method="delete",
+            path=path,
+            recursive="true" if recursive else "false",
+        )
+
+    def rm_file(self, path, **kwargs):
+        self.rm(path)
+
+    def cp_file(self, lpath, rpath, **kwargs):
+        with self.open(lpath) as lstream:
+            tmp_fname = "/".join([self._parent(rpath), f".tmp.{secrets.token_hex(16)}"])
+            # Perform an atomic copy (stream to a temporary file and
+            # move it to the actual destination).
+            try:
+                with self.open(tmp_fname, "wb") as rstream:
+                    shutil.copyfileobj(lstream, rstream)
+                self.mv(tmp_fname, rpath)
+            except BaseException:
+                with suppress(FileNotFoundError):
+                    self.rm(tmp_fname)
+                raise
+
+    def _apply_proxy(self, location):
+        if self.proxy and callable(self.proxy):
+            location = self.proxy(location)
+        elif self.proxy:
+            # as a dict
+            for k, v in self.proxy.items():
+                location = location.replace(k, v, 1)
+        return location
+
+
+class WebHDFile(AbstractBufferedFile):
+    """A file living in HDFS over webHDFS"""
+
+    def __init__(self, fs, path, **kwargs):
+        super().__init__(fs, path, **kwargs)
+        kwargs = kwargs.copy()
+        if kwargs.get("permissions", None) is None:
+            kwargs.pop("permissions", None)
+        if kwargs.get("replication", None) is None:
+            kwargs.pop("replication", None)
+        self.permissions = kwargs.pop("permissions", 511)
+        tempdir = kwargs.pop("tempdir")
+        if kwargs.pop("autocommit", False) is False:
+            self.target = self.path
+            self.path = os.path.join(tempdir, str(uuid.uuid4()))
+
+    def _upload_chunk(self, final=False):
+        """Write one part of a multi-block file upload
+
+        Parameters
+        ==========
+        final: bool
+            This is the last block, so should complete file, if
+            self.autocommit is True.
+        """
+        out = self.fs.session.post(
+            self.location,
+            data=self.buffer.getvalue(),
+            headers={"content-type": "application/octet-stream"},
+        )
+        out.raise_for_status()
+        return True
+
+    def _initiate_upload(self):
+        """Create remote file/upload"""
+        kwargs = self.kwargs.copy()
+        if "a" in self.mode:
+            op, method = "APPEND", "POST"
+        else:
+            op, method = "CREATE", "PUT"
+            kwargs["overwrite"] = "true"
+        out = self.fs._call(op, method, self.path, redirect=False, **kwargs)
+        location = self.fs._apply_proxy(out.headers["Location"])
+        if "w" in self.mode:
+            # create empty file to append to
+            out2 = self.fs.session.put(
+                location, headers={"content-type": "application/octet-stream"}
+            )
+            out2.raise_for_status()
+            # after creating empty file, change location to append to
+            out2 = self.fs._call("APPEND", "POST", self.path, redirect=False, **kwargs)
+            self.location = self.fs._apply_proxy(out2.headers["Location"])
+
+    def _fetch_range(self, start, end):
+        start = max(start, 0)
+        end = min(self.size, end)
+        if start >= end or start >= self.size:
+            return b""
+        out = self.fs._call(
+            "OPEN", path=self.path, offset=start, length=end - start, redirect=False
+        )
+        out.raise_for_status()
+        if "Location" in out.headers:
+            location = out.headers["Location"]
+            out2 = self.fs.session.get(self.fs._apply_proxy(location))
+            return out2.content
+        else:
+            return out.content
+
+    def commit(self):
+        self.fs.mv(self.path, self.target)
+
+    def discard(self):
+        self.fs.rm(self.path)
diff --git a/lib/python3.11/site-packages/fsspec/implementations/zip.py b/lib/python3.11/site-packages/fsspec/implementations/zip.py
new file mode 100644
index 0000000000000000000000000000000000000000..485307a34f8daba70eb30bfda713c95893bbdb0b
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/implementations/zip.py
@@ -0,0 +1,183 @@
+import os
+import zipfile
+
+import fsspec
+from fsspec.archive import AbstractArchiveFileSystem
+
+
+class ZipFileSystem(AbstractArchiveFileSystem):
+    """Read/Write contents of ZIP archive as a file-system
+
+    Keeps file object open while instance lives.
+
+    This class is pickleable, but not necessarily thread-safe
+    """
+
+    root_marker = ""
+    protocol = "zip"
+    cachable = False
+
+    def __init__(
+        self,
+        fo="",
+        mode="r",
+        target_protocol=None,
+        target_options=None,
+        compression=zipfile.ZIP_STORED,
+        allowZip64=True,
+        compresslevel=None,
+        **kwargs,
+    ):
+        """
+        Parameters
+        ----------
+        fo: str or file-like
+            Contains ZIP, and must exist. If a str, will fetch file using
+            :meth:`~fsspec.open_files`, which must return one file exactly.
+        mode: str
+            Accept: "r", "w", "a"
+        target_protocol: str (optional)
+            If ``fo`` is a string, this value can be used to override the
+            FS protocol inferred from a URL
+        target_options: dict (optional)
+            Kwargs passed when instantiating the target FS, if ``fo`` is
+            a string.
+        compression, allowZip64, compresslevel: passed to ZipFile
+            Only relevant when creating a ZIP
+        """
+        super().__init__(self, **kwargs)
+        if mode not in set("rwa"):
+            raise ValueError(f"mode '{mode}' no understood")
+        self.mode = mode
+        if isinstance(fo, (str, os.PathLike)):
+            if mode == "a":
+                m = "r+b"
+            else:
+                m = mode + "b"
+            fo = fsspec.open(
+                fo, mode=m, protocol=target_protocol, **(target_options or {})
+            )
+        self.force_zip_64 = allowZip64
+        self.of = fo
+        self.fo = fo.__enter__()  # the whole instance is a context
+        self.zip = zipfile.ZipFile(
+            self.fo,
+            mode=mode,
+            compression=compression,
+            allowZip64=allowZip64,
+            compresslevel=compresslevel,
+        )
+        self.dir_cache = None
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        # zip file paths are always relative to the archive root
+        return super()._strip_protocol(path).lstrip("/")
+
+    def __del__(self):
+        if hasattr(self, "zip"):
+            self.close()
+            del self.zip
+        if hasattr(self, "of") and hasattr(self.of, "__exit__"):
+            self.of.__exit__(None, None, None)
+
+    def close(self):
+        """Commits any write changes to the file. Done on ``del`` too."""
+        self.zip.close()
+
+    def _get_dirs(self):
+        if self.dir_cache is None or self.mode in set("wa"):
+            # when writing, dir_cache is always in the ZipFile's attributes,
+            # not read from the file.
+            files = self.zip.infolist()
+            self.dir_cache = {
+                dirname.rstrip("/"): {
+                    "name": dirname.rstrip("/"),
+                    "size": 0,
+                    "type": "directory",
+                }
+                for dirname in self._all_dirnames(self.zip.namelist())
+            }
+            for z in files:
+                f = {s: getattr(z, s, None) for s in zipfile.ZipInfo.__slots__}
+                f.update(
+                    {
+                        "name": z.filename.rstrip("/"),
+                        "size": z.file_size,
+                        "type": ("directory" if z.is_dir() else "file"),
+                    }
+                )
+                self.dir_cache[f["name"]] = f
+
+    def pipe_file(self, path, value, **kwargs):
+        # override upstream, because we know the exact file size in this case
+        self.zip.writestr(path, value, **kwargs)
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        path = self._strip_protocol(path)
+        if "r" in mode and self.mode in set("wa"):
+            if self.exists(path):
+                raise OSError("ZipFS can only be open for reading or writing, not both")
+            raise FileNotFoundError(path)
+        if "r" in self.mode and "w" in mode:
+            raise OSError("ZipFS can only be open for reading or writing, not both")
+        out = self.zip.open(path, mode.strip("b"), force_zip64=self.force_zip_64)
+        if "r" in mode:
+            info = self.info(path)
+            out.size = info["size"]
+            out.name = info["name"]
+        return out
+
+    def find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
+        if maxdepth is not None and maxdepth < 1:
+            raise ValueError("maxdepth must be at least 1")
+
+        def to_parts(_path: str):
+            return list(filter(None, _path.replace("\\", "/").split("/")))
+
+        if not isinstance(path, str):
+            path = str(path)
+
+        # Remove the leading slash, as the zip file paths are always
+        # given without a leading slash
+        path = path.lstrip("/")
+        path_parts = to_parts(path)
+        path_depth = len(path_parts)
+
+        self._get_dirs()
+
+        result = {}
+        # To match posix find, if an exact file name is given, we should
+        # return only that file
+        if path in self.dir_cache and self.dir_cache[path]["type"] == "file":
+            result[path] = self.dir_cache[path]
+            return result if detail else [path]
+
+        for file_path, file_info in self.dir_cache.items():
+            if len(file_parts := to_parts(file_path)) < path_depth or any(
+                a != b for a, b in zip(path_parts, file_parts)
+            ):
+                # skip parent folders and mismatching paths
+                continue
+
+            if file_info["type"] == "directory":
+                if withdirs and file_path not in result:
+                    result[file_path.strip("/")] = file_info
+                continue
+
+            if file_path not in result:
+                result[file_path] = file_info if detail else None
+
+        if maxdepth:
+            result = {
+                k: v for k, v in result.items() if k.count("/") < maxdepth + path_depth
+            }
+        return result if detail else sorted(result)
diff --git a/lib/python3.11/site-packages/fsspec/spec.py b/lib/python3.11/site-packages/fsspec/spec.py
new file mode 100644
index 0000000000000000000000000000000000000000..237391eb9836f4efe464a0761e7a95e910130640
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/spec.py
@@ -0,0 +1,2284 @@
+from __future__ import annotations
+
+import io
+import json
+import logging
+import os
+import threading
+import warnings
+import weakref
+from errno import ESPIPE
+from glob import has_magic
+from hashlib import sha256
+from typing import Any, ClassVar
+
+from .callbacks import DEFAULT_CALLBACK
+from .config import apply_config, conf
+from .dircache import DirCache
+from .transaction import Transaction
+from .utils import (
+    _unstrip_protocol,
+    glob_translate,
+    isfilelike,
+    other_paths,
+    read_block,
+    stringify_path,
+    tokenize,
+)
+
+logger = logging.getLogger("fsspec")
+
+
+def make_instance(cls, args, kwargs):
+    return cls(*args, **kwargs)
+
+
+class _Cached(type):
+    """
+    Metaclass for caching file system instances.
+
+    Notes
+    -----
+    Instances are cached according to
+
+    * The values of the class attributes listed in `_extra_tokenize_attributes`
+    * The arguments passed to ``__init__``.
+
+    This creates an additional reference to the filesystem, which prevents the
+    filesystem from being garbage collected when all *user* references go away.
+    A call to the :meth:`AbstractFileSystem.clear_instance_cache` must *also*
+    be made for a filesystem instance to be garbage collected.
+    """
+
+    def __init__(cls, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # Note: we intentionally create a reference here, to avoid garbage
+        # collecting instances when all other references are gone. To really
+        # delete a FileSystem, the cache must be cleared.
+        if conf.get("weakref_instance_cache"):  # pragma: no cover
+            # debug option for analysing fork/spawn conditions
+            cls._cache = weakref.WeakValueDictionary()
+        else:
+            cls._cache = {}
+        cls._pid = os.getpid()
+
+    def __call__(cls, *args, **kwargs):
+        kwargs = apply_config(cls, kwargs)
+        extra_tokens = tuple(
+            getattr(cls, attr, None) for attr in cls._extra_tokenize_attributes
+        )
+        strip_tokenize_options = {
+            k: kwargs.pop(k) for k in cls._strip_tokenize_options if k in kwargs
+        }
+        token = tokenize(
+            cls, cls._pid, threading.get_ident(), *args, *extra_tokens, **kwargs
+        )
+        skip = kwargs.pop("skip_instance_cache", False)
+        if os.getpid() != cls._pid:
+            cls._cache.clear()
+            cls._pid = os.getpid()
+        if not skip and cls.cachable and token in cls._cache:
+            cls._latest = token
+            return cls._cache[token]
+        else:
+            obj = super().__call__(*args, **kwargs, **strip_tokenize_options)
+            # Setting _fs_token here causes some static linters to complain.
+            obj._fs_token_ = token
+            obj.storage_args = args
+            obj.storage_options = kwargs
+            if obj.async_impl and obj.mirror_sync_methods:
+                from .asyn import mirror_sync_methods
+
+                mirror_sync_methods(obj)
+
+            if cls.cachable and not skip:
+                cls._latest = token
+                cls._cache[token] = obj
+            return obj
+
+
+class AbstractFileSystem(metaclass=_Cached):
+    """
+    An abstract super-class for pythonic file-systems
+
+    Implementations are expected to be compatible with or, better, subclass
+    from here.
+    """
+
+    cachable = True  # this class can be cached, instances reused
+    _cached = False
+    blocksize = 2**22
+    sep = "/"
+    protocol: ClassVar[str | tuple[str, ...]] = "abstract"
+    _latest = None
+    async_impl = False
+    mirror_sync_methods = False
+    root_marker = ""  # For some FSs, may require leading '/' or other character
+    transaction_type = Transaction
+
+    #: Extra *class attributes* that should be considered when hashing.
+    _extra_tokenize_attributes = ()
+    #: *storage options* that should not be considered when hashing.
+    _strip_tokenize_options = ()
+
+    # Set by _Cached metaclass
+    storage_args: tuple[Any, ...]
+    storage_options: dict[str, Any]
+
+    def __init__(self, *args, **storage_options):
+        """Create and configure file-system instance
+
+        Instances may be cachable, so if similar enough arguments are seen
+        a new instance is not required. The token attribute exists to allow
+        implementations to cache instances if they wish.
+
+        A reasonable default should be provided if there are no arguments.
+
+        Subclasses should call this method.
+
+        Parameters
+        ----------
+        use_listings_cache, listings_expiry_time, max_paths:
+            passed to ``DirCache``, if the implementation supports
+            directory listing caching. Pass use_listings_cache=False
+            to disable such caching.
+        skip_instance_cache: bool
+            If this is a cachable implementation, pass True here to force
+            creating a new instance even if a matching instance exists, and prevent
+            storing this instance.
+        asynchronous: bool
+        loop: asyncio-compatible IOLoop or None
+        """
+        if self._cached:
+            # reusing instance, don't change
+            return
+        self._cached = True
+        self._intrans = False
+        self._transaction = None
+        self._invalidated_caches_in_transaction = []
+        self.dircache = DirCache(**storage_options)
+
+        if storage_options.pop("add_docs", None):
+            warnings.warn("add_docs is no longer supported.", FutureWarning)
+
+        if storage_options.pop("add_aliases", None):
+            warnings.warn("add_aliases has been removed.", FutureWarning)
+        # This is set in _Cached
+        self._fs_token_ = None
+
+    @property
+    def fsid(self):
+        """Persistent filesystem id that can be used to compare filesystems
+        across sessions.
+        """
+        raise NotImplementedError
+
+    @property
+    def _fs_token(self):
+        return self._fs_token_
+
+    def __dask_tokenize__(self):
+        return self._fs_token
+
+    def __hash__(self):
+        return int(self._fs_token, 16)
+
+    def __eq__(self, other):
+        return isinstance(other, type(self)) and self._fs_token == other._fs_token
+
+    def __reduce__(self):
+        return make_instance, (type(self), self.storage_args, self.storage_options)
+
+    @classmethod
+    def _strip_protocol(cls, path):
+        """Turn path from fully-qualified to file-system-specific
+
+        May require FS-specific handling, e.g., for relative paths or links.
+        """
+        if isinstance(path, list):
+            return [cls._strip_protocol(p) for p in path]
+        path = stringify_path(path)
+        protos = (cls.protocol,) if isinstance(cls.protocol, str) else cls.protocol
+        for protocol in protos:
+            if path.startswith(protocol + "://"):
+                path = path[len(protocol) + 3 :]
+            elif path.startswith(protocol + "::"):
+                path = path[len(protocol) + 2 :]
+        path = path.rstrip("/")
+        # use of root_marker to make minimum required path, e.g., "/"
+        return path or cls.root_marker
+
+    def unstrip_protocol(self, name: str) -> str:
+        """Format FS-specific path to generic, including protocol"""
+        protos = (self.protocol,) if isinstance(self.protocol, str) else self.protocol
+        for protocol in protos:
+            if name.startswith(f"{protocol}://"):
+                return name
+        return f"{protos[0]}://{name}"
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        """If kwargs can be encoded in the paths, extract them here
+
+        This should happen before instantiation of the class; incoming paths
+        then should be amended to strip the options in methods.
+
+        Examples may look like an sftp path "sftp://user@host:/my/path", where
+        the user and host should become kwargs and later get stripped.
+        """
+        # by default, nothing happens
+        return {}
+
+    @classmethod
+    def current(cls):
+        """Return the most recently instantiated FileSystem
+
+        If no instance has been created, then create one with defaults
+        """
+        if cls._latest in cls._cache:
+            return cls._cache[cls._latest]
+        return cls()
+
+    @property
+    def transaction(self):
+        """A context within which files are committed together upon exit
+
+        Requires the file class to implement `.commit()` and `.discard()`
+        for the normal and exception cases.
+        """
+        if self._transaction is None:
+            self._transaction = self.transaction_type(self)
+        return self._transaction
+
+    def start_transaction(self):
+        """Begin write transaction for deferring files, non-context version"""
+        self._intrans = True
+        self._transaction = self.transaction_type(self)
+        return self.transaction
+
+    def end_transaction(self):
+        """Finish write transaction, non-context version"""
+        self.transaction.complete()
+        self._transaction = None
+        # The invalid cache must be cleared after the transaction is completed.
+        for path in self._invalidated_caches_in_transaction:
+            self.invalidate_cache(path)
+        self._invalidated_caches_in_transaction.clear()
+
+    def invalidate_cache(self, path=None):
+        """
+        Discard any cached directory information
+
+        Parameters
+        ----------
+        path: string or None
+            If None, clear all listings cached else listings at or under given
+            path.
+        """
+        # Not necessary to implement invalidation mechanism, may have no cache.
+        # But if have, you should call this method of parent class from your
+        # subclass to ensure expiring caches after transacations correctly.
+        # See the implementation of FTPFileSystem in ftp.py
+        if self._intrans:
+            self._invalidated_caches_in_transaction.append(path)
+
+    def mkdir(self, path, create_parents=True, **kwargs):
+        """
+        Create directory entry at path
+
+        For systems that don't have true directories, may create an for
+        this instance only and not touch the real filesystem
+
+        Parameters
+        ----------
+        path: str
+            location
+        create_parents: bool
+            if True, this is equivalent to ``makedirs``
+        kwargs:
+            may be permissions, etc.
+        """
+        pass  # not necessary to implement, may not have directories
+
+    def makedirs(self, path, exist_ok=False):
+        """Recursively make directories
+
+        Creates directory at path and any intervening required directories.
+        Raises exception if, for instance, the path already exists but is a
+        file.
+
+        Parameters
+        ----------
+        path: str
+            leaf directory name
+        exist_ok: bool (False)
+            If False, will error if the target already exists
+        """
+        pass  # not necessary to implement, may not have directories
+
+    def rmdir(self, path):
+        """Remove a directory, if empty"""
+        pass  # not necessary to implement, may not have directories
+
+    def ls(self, path, detail=True, **kwargs):
+        """List objects at path.
+
+        This should include subdirectories and files at that location. The
+        difference between a file and a directory must be clear when details
+        are requested.
+
+        The specific keys, or perhaps a FileInfo class, or similar, is TBD,
+        but must be consistent across implementations.
+        Must include:
+
+        - full path to the entry (without protocol)
+        - size of the entry, in bytes. If the value cannot be determined, will
+          be ``None``.
+        - type of entry, "file", "directory" or other
+
+        Additional information
+        may be present, appropriate to the file-system, e.g., generation,
+        checksum, etc.
+
+        May use refresh=True|False to allow use of self._ls_from_cache to
+        check for a saved listing and avoid calling the backend. This would be
+        common where listing may be expensive.
+
+        Parameters
+        ----------
+        path: str
+        detail: bool
+            if True, gives a list of dictionaries, where each is the same as
+            the result of ``info(path)``. If False, gives a list of paths
+            (str).
+        kwargs: may have additional backend-specific options, such as version
+            information
+
+        Returns
+        -------
+        List of strings if detail is False, or list of directory information
+        dicts if detail is True.
+        """
+        raise NotImplementedError
+
+    def _ls_from_cache(self, path):
+        """Check cache for listing
+
+        Returns listing, if found (may be empty list for a directly that exists
+        but contains nothing), None if not in cache.
+        """
+        parent = self._parent(path)
+        try:
+            return self.dircache[path.rstrip("/")]
+        except KeyError:
+            pass
+        try:
+            files = [
+                f
+                for f in self.dircache[parent]
+                if f["name"] == path
+                or (f["name"] == path.rstrip("/") and f["type"] == "directory")
+            ]
+            if len(files) == 0:
+                # parent dir was listed but did not contain this file
+                raise FileNotFoundError(path)
+            return files
+        except KeyError:
+            pass
+
+    def walk(self, path, maxdepth=None, topdown=True, on_error="omit", **kwargs):
+        """Return all files under the given path.
+
+        List all files, recursing into subdirectories; output is iterator-style,
+        like ``os.walk()``. For a simple list of files, ``find()`` is available.
+
+        When topdown is True, the caller can modify the dirnames list in-place (perhaps
+        using del or slice assignment), and walk() will
+        only recurse into the subdirectories whose names remain in dirnames;
+        this can be used to prune the search, impose a specific order of visiting,
+        or even to inform walk() about directories the caller creates or renames before
+        it resumes walk() again.
+        Modifying dirnames when topdown is False has no effect. (see os.walk)
+
+        Note that the "files" outputted will include anything that is not
+        a directory, such as links.
+
+        Parameters
+        ----------
+        path: str
+            Root to recurse into
+        maxdepth: int
+            Maximum recursion depth. None means limitless, but not recommended
+            on link-based file-systems.
+        topdown: bool (True)
+            Whether to walk the directory tree from the top downwards or from
+            the bottom upwards.
+        on_error: "omit", "raise", a callable
+            if omit (default), path with exception will simply be empty;
+            If raise, an underlying exception will be raised;
+            if callable, it will be called with a single OSError instance as argument
+        kwargs: passed to ``ls``
+        """
+        if maxdepth is not None and maxdepth < 1:
+            raise ValueError("maxdepth must be at least 1")
+
+        path = self._strip_protocol(path)
+        full_dirs = {}
+        dirs = {}
+        files = {}
+
+        detail = kwargs.pop("detail", False)
+        try:
+            listing = self.ls(path, detail=True, **kwargs)
+        except (FileNotFoundError, OSError) as e:
+            if on_error == "raise":
+                raise
+            if callable(on_error):
+                on_error(e)
+            return
+
+        for info in listing:
+            # each info name must be at least [path]/part , but here
+            # we check also for names like [path]/part/
+            pathname = info["name"].rstrip("/")
+            name = pathname.rsplit("/", 1)[-1]
+            if info["type"] == "directory" and pathname != path:
+                # do not include "self" path
+                full_dirs[name] = pathname
+                dirs[name] = info
+            elif pathname == path:
+                # file-like with same name as give path
+                files[""] = info
+            else:
+                files[name] = info
+
+        if not detail:
+            dirs = list(dirs)
+            files = list(files)
+
+        if topdown:
+            # Yield before recursion if walking top down
+            yield path, dirs, files
+
+        if maxdepth is not None:
+            maxdepth -= 1
+            if maxdepth < 1:
+                if not topdown:
+                    yield path, dirs, files
+                return
+
+        for d in dirs:
+            yield from self.walk(
+                full_dirs[d],
+                maxdepth=maxdepth,
+                detail=detail,
+                topdown=topdown,
+                **kwargs,
+            )
+
+        if not topdown:
+            # Yield after recursion if walking bottom up
+            yield path, dirs, files
+
+    def find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
+        """List all files below path.
+
+        Like posix ``find`` command without conditions
+
+        Parameters
+        ----------
+        path : str
+        maxdepth: int or None
+            If not None, the maximum number of levels to descend
+        withdirs: bool
+            Whether to include directory paths in the output. This is True
+            when used by glob, but users usually only want files.
+        kwargs are passed to ``ls``.
+        """
+        # TODO: allow equivalent of -name parameter
+        path = self._strip_protocol(path)
+        out = {}
+
+        # Add the root directory if withdirs is requested
+        # This is needed for posix glob compliance
+        if withdirs and path != "" and self.isdir(path):
+            out[path] = self.info(path)
+
+        for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs):
+            if withdirs:
+                files.update(dirs)
+            out.update({info["name"]: info for name, info in files.items()})
+        if not out and self.isfile(path):
+            # walk works on directories, but find should also return [path]
+            # when path happens to be a file
+            out[path] = {}
+        names = sorted(out)
+        if not detail:
+            return names
+        else:
+            return {name: out[name] for name in names}
+
+    def du(self, path, total=True, maxdepth=None, withdirs=False, **kwargs):
+        """Space used by files and optionally directories within a path
+
+        Directory size does not include the size of its contents.
+
+        Parameters
+        ----------
+        path: str
+        total: bool
+            Whether to sum all the file sizes
+        maxdepth: int or None
+            Maximum number of directory levels to descend, None for unlimited.
+        withdirs: bool
+            Whether to include directory paths in the output.
+        kwargs: passed to ``find``
+
+        Returns
+        -------
+        Dict of {path: size} if total=False, or int otherwise, where numbers
+        refer to bytes used.
+        """
+        sizes = {}
+        if withdirs and self.isdir(path):
+            # Include top-level directory in output
+            info = self.info(path)
+            sizes[info["name"]] = info["size"]
+        for f in self.find(path, maxdepth=maxdepth, withdirs=withdirs, **kwargs):
+            info = self.info(f)
+            sizes[info["name"]] = info["size"]
+        if total:
+            return sum(sizes.values())
+        else:
+            return sizes
+
+    def glob(self, path, maxdepth=None, **kwargs):
+        """Find files by glob-matching.
+
+        Pattern matching capabilities for finding files that match the given pattern.
+
+        Parameters
+        ----------
+        path: str
+            The glob pattern to match against
+        maxdepth: int or None
+            Maximum depth for ``'**'`` patterns. Applied on the first ``'**'`` found.
+            Must be at least 1 if provided.
+        kwargs:
+            Additional arguments passed to ``find`` (e.g., detail=True)
+
+        Returns
+        -------
+        List of matched paths, or dict of paths and their info if detail=True
+
+        Notes
+        -----
+        Supported patterns:
+        - '*': Matches any sequence of characters within a single directory level
+        - ``'**'``: Matches any number of directory levels (must be an entire path component)
+        - '?': Matches exactly one character
+        - '[abc]': Matches any character in the set
+        - '[a-z]': Matches any character in the range
+        - '[!abc]': Matches any character NOT in the set
+
+        Special behaviors:
+        - If the path ends with '/', only folders are returned
+        - Consecutive '*' characters are compressed into a single '*'
+        - Empty brackets '[]' never match anything
+        - Negated empty brackets '[!]' match any single character
+        - Special characters in character classes are escaped properly
+
+        Limitations:
+        - ``'**'`` must be a complete path component (e.g., ``'a/**/b'``, not ``'a**b'``)
+        - No brace expansion ('{a,b}.txt')
+        - No extended glob patterns ('+(pattern)', '!(pattern)')
+        """
+        if maxdepth is not None and maxdepth < 1:
+            raise ValueError("maxdepth must be at least 1")
+
+        import re
+
+        seps = (os.path.sep, os.path.altsep) if os.path.altsep else (os.path.sep,)
+        ends_with_sep = path.endswith(seps)  # _strip_protocol strips trailing slash
+        path = self._strip_protocol(path)
+        append_slash_to_dirname = ends_with_sep or path.endswith(
+            tuple(sep + "**" for sep in seps)
+        )
+        idx_star = path.find("*") if path.find("*") >= 0 else len(path)
+        idx_qmark = path.find("?") if path.find("?") >= 0 else len(path)
+        idx_brace = path.find("[") if path.find("[") >= 0 else len(path)
+
+        min_idx = min(idx_star, idx_qmark, idx_brace)
+
+        detail = kwargs.pop("detail", False)
+        withdirs = kwargs.pop("withdirs", True)
+
+        if not has_magic(path):
+            if self.exists(path, **kwargs):
+                if not detail:
+                    return [path]
+                else:
+                    return {path: self.info(path, **kwargs)}
+            else:
+                if not detail:
+                    return []  # glob of non-existent returns empty
+                else:
+                    return {}
+        elif "/" in path[:min_idx]:
+            min_idx = path[:min_idx].rindex("/")
+            root = path[: min_idx + 1]
+            depth = path[min_idx + 1 :].count("/") + 1
+        else:
+            root = ""
+            depth = path[min_idx + 1 :].count("/") + 1
+
+        if "**" in path:
+            if maxdepth is not None:
+                idx_double_stars = path.find("**")
+                depth_double_stars = path[idx_double_stars:].count("/") + 1
+                depth = depth - depth_double_stars + maxdepth
+            else:
+                depth = None
+
+        allpaths = self.find(
+            root, maxdepth=depth, withdirs=withdirs, detail=True, **kwargs
+        )
+
+        pattern = glob_translate(path + ("/" if ends_with_sep else ""))
+        pattern = re.compile(pattern)
+
+        out = {
+            p: info
+            for p, info in sorted(allpaths.items())
+            if pattern.match(
+                p + "/"
+                if append_slash_to_dirname and info["type"] == "directory"
+                else p
+            )
+        }
+
+        if detail:
+            return out
+        else:
+            return list(out)
+
+    def exists(self, path, **kwargs):
+        """Is there a file at the given path"""
+        try:
+            self.info(path, **kwargs)
+            return True
+        except:  # noqa: E722
+            # any exception allowed bar FileNotFoundError?
+            return False
+
+    def lexists(self, path, **kwargs):
+        """If there is a file at the given path (including
+        broken links)"""
+        return self.exists(path)
+
+    def info(self, path, **kwargs):
+        """Give details of entry at path
+
+        Returns a single dictionary, with exactly the same information as ``ls``
+        would with ``detail=True``.
+
+        The default implementation calls ls and could be overridden by a
+        shortcut. kwargs are passed on to ```ls()``.
+
+        Some file systems might not be able to measure the file's size, in
+        which case, the returned dict will include ``'size': None``.
+
+        Returns
+        -------
+        dict with keys: name (full path in the FS), size (in bytes), type (file,
+        directory, or something else) and other FS-specific keys.
+        """
+        path = self._strip_protocol(path)
+        out = self.ls(self._parent(path), detail=True, **kwargs)
+        out = [o for o in out if o["name"].rstrip("/") == path]
+        if out:
+            return out[0]
+        out = self.ls(path, detail=True, **kwargs)
+        path = path.rstrip("/")
+        out1 = [o for o in out if o["name"].rstrip("/") == path]
+        if len(out1) == 1:
+            if "size" not in out1[0]:
+                out1[0]["size"] = None
+            return out1[0]
+        elif len(out1) > 1 or out:
+            return {"name": path, "size": 0, "type": "directory"}
+        else:
+            raise FileNotFoundError(path)
+
+    def checksum(self, path):
+        """Unique value for current version of file
+
+        If the checksum is the same from one moment to another, the contents
+        are guaranteed to be the same. If the checksum changes, the contents
+        *might* have changed.
+
+        This should normally be overridden; default will probably capture
+        creation/modification timestamp (which would be good) or maybe
+        access timestamp (which would be bad)
+        """
+        return int(tokenize(self.info(path)), 16)
+
+    def size(self, path):
+        """Size in bytes of file"""
+        return self.info(path).get("size", None)
+
+    def sizes(self, paths):
+        """Size in bytes of each file in a list of paths"""
+        return [self.size(p) for p in paths]
+
+    def isdir(self, path):
+        """Is this entry directory-like?"""
+        try:
+            return self.info(path)["type"] == "directory"
+        except OSError:
+            return False
+
+    def isfile(self, path):
+        """Is this entry file-like?"""
+        try:
+            return self.info(path)["type"] == "file"
+        except:  # noqa: E722
+            return False
+
+    def read_text(self, path, encoding=None, errors=None, newline=None, **kwargs):
+        """Get the contents of the file as a string.
+
+        Parameters
+        ----------
+        path: str
+            URL of file on this filesystems
+        encoding, errors, newline: same as `open`.
+        """
+        with self.open(
+            path,
+            mode="r",
+            encoding=encoding,
+            errors=errors,
+            newline=newline,
+            **kwargs,
+        ) as f:
+            return f.read()
+
+    def write_text(
+        self, path, value, encoding=None, errors=None, newline=None, **kwargs
+    ):
+        """Write the text to the given file.
+
+        An existing file will be overwritten.
+
+        Parameters
+        ----------
+        path: str
+            URL of file on this filesystems
+        value: str
+            Text to write.
+        encoding, errors, newline: same as `open`.
+        """
+        with self.open(
+            path,
+            mode="w",
+            encoding=encoding,
+            errors=errors,
+            newline=newline,
+            **kwargs,
+        ) as f:
+            return f.write(value)
+
+    def cat_file(self, path, start=None, end=None, **kwargs):
+        """Get the content of a file
+
+        Parameters
+        ----------
+        path: URL of file on this filesystems
+        start, end: int
+            Bytes limits of the read. If negative, backwards from end,
+            like usual python slices. Either can be None for start or
+            end of file, respectively
+        kwargs: passed to ``open()``.
+        """
+        # explicitly set buffering off?
+        with self.open(path, "rb", **kwargs) as f:
+            if start is not None:
+                if start >= 0:
+                    f.seek(start)
+                else:
+                    f.seek(max(0, f.size + start))
+            if end is not None:
+                if end < 0:
+                    end = f.size + end
+                return f.read(end - f.tell())
+            return f.read()
+
+    def pipe_file(self, path, value, mode="overwrite", **kwargs):
+        """Set the bytes of given file"""
+        if mode == "create" and self.exists(path):
+            # non-atomic but simple way; or could use "xb" in open(), which is likely
+            # not as well supported
+            raise FileExistsError
+        with self.open(path, "wb", **kwargs) as f:
+            f.write(value)
+
+    def pipe(self, path, value=None, **kwargs):
+        """Put value into path
+
+        (counterpart to ``cat``)
+
+        Parameters
+        ----------
+        path: string or dict(str, bytes)
+            If a string, a single remote location to put ``value`` bytes; if a dict,
+            a mapping of {path: bytesvalue}.
+        value: bytes, optional
+            If using a single path, these are the bytes to put there. Ignored if
+            ``path`` is a dict
+        """
+        if isinstance(path, str):
+            self.pipe_file(self._strip_protocol(path), value, **kwargs)
+        elif isinstance(path, dict):
+            for k, v in path.items():
+                self.pipe_file(self._strip_protocol(k), v, **kwargs)
+        else:
+            raise ValueError("path must be str or dict")
+
+    def cat_ranges(
+        self, paths, starts, ends, max_gap=None, on_error="return", **kwargs
+    ):
+        """Get the contents of byte ranges from one or more files
+
+        Parameters
+        ----------
+        paths: list
+            A list of of filepaths on this filesystems
+        starts, ends: int or list
+            Bytes limits of the read. If using a single int, the same value will be
+            used to read all the specified files.
+        """
+        if max_gap is not None:
+            raise NotImplementedError
+        if not isinstance(paths, list):
+            raise TypeError
+        if not isinstance(starts, list):
+            starts = [starts] * len(paths)
+        if not isinstance(ends, list):
+            ends = [ends] * len(paths)
+        if len(starts) != len(paths) or len(ends) != len(paths):
+            raise ValueError
+        out = []
+        for p, s, e in zip(paths, starts, ends):
+            try:
+                out.append(self.cat_file(p, s, e))
+            except Exception as e:
+                if on_error == "return":
+                    out.append(e)
+                else:
+                    raise
+        return out
+
+    def cat(self, path, recursive=False, on_error="raise", **kwargs):
+        """Fetch (potentially multiple) paths' contents
+
+        Parameters
+        ----------
+        recursive: bool
+            If True, assume the path(s) are directories, and get all the
+            contained files
+        on_error : "raise", "omit", "return"
+            If raise, an underlying exception will be raised (converted to KeyError
+            if the type is in self.missing_exceptions); if omit, keys with exception
+            will simply not be included in the output; if "return", all keys are
+            included in the output, but the value will be bytes or an exception
+            instance.
+        kwargs: passed to cat_file
+
+        Returns
+        -------
+        dict of {path: contents} if there are multiple paths
+        or the path has been otherwise expanded
+        """
+        paths = self.expand_path(path, recursive=recursive, **kwargs)
+        if (
+            len(paths) > 1
+            or isinstance(path, list)
+            or paths[0] != self._strip_protocol(path)
+        ):
+            out = {}
+            for path in paths:
+                try:
+                    out[path] = self.cat_file(path, **kwargs)
+                except Exception as e:
+                    if on_error == "raise":
+                        raise
+                    if on_error == "return":
+                        out[path] = e
+            return out
+        else:
+            return self.cat_file(paths[0], **kwargs)
+
+    def get_file(self, rpath, lpath, callback=DEFAULT_CALLBACK, outfile=None, **kwargs):
+        """Copy single remote file to local"""
+        from .implementations.local import LocalFileSystem
+
+        if isfilelike(lpath):
+            outfile = lpath
+        elif self.isdir(rpath):
+            os.makedirs(lpath, exist_ok=True)
+            return None
+
+        fs = LocalFileSystem(auto_mkdir=True)
+        fs.makedirs(fs._parent(lpath), exist_ok=True)
+
+        with self.open(rpath, "rb", **kwargs) as f1:
+            if outfile is None:
+                outfile = open(lpath, "wb")
+
+            try:
+                callback.set_size(getattr(f1, "size", None))
+                data = True
+                while data:
+                    data = f1.read(self.blocksize)
+                    segment_len = outfile.write(data)
+                    if segment_len is None:
+                        segment_len = len(data)
+                    callback.relative_update(segment_len)
+            finally:
+                if not isfilelike(lpath):
+                    outfile.close()
+
+    def get(
+        self,
+        rpath,
+        lpath,
+        recursive=False,
+        callback=DEFAULT_CALLBACK,
+        maxdepth=None,
+        **kwargs,
+    ):
+        """Copy file(s) to local.
+
+        Copies a specific file or tree of files (if recursive=True). If lpath
+        ends with a "/", it will be assumed to be a directory, and target files
+        will go within. Can submit a list of paths, which may be glob-patterns
+        and will be expanded.
+
+        Calls get_file for each source.
+        """
+        if isinstance(lpath, list) and isinstance(rpath, list):
+            # No need to expand paths when both source and destination
+            # are provided as lists
+            rpaths = rpath
+            lpaths = lpath
+        else:
+            from .implementations.local import (
+                LocalFileSystem,
+                make_path_posix,
+                trailing_sep,
+            )
+
+            source_is_str = isinstance(rpath, str)
+            rpaths = self.expand_path(
+                rpath, recursive=recursive, maxdepth=maxdepth, **kwargs
+            )
+            if source_is_str and (not recursive or maxdepth is not None):
+                # Non-recursive glob does not copy directories
+                rpaths = [p for p in rpaths if not (trailing_sep(p) or self.isdir(p))]
+                if not rpaths:
+                    return
+
+            if isinstance(lpath, str):
+                lpath = make_path_posix(lpath)
+
+            source_is_file = len(rpaths) == 1
+            dest_is_dir = isinstance(lpath, str) and (
+                trailing_sep(lpath) or LocalFileSystem().isdir(lpath)
+            )
+
+            exists = source_is_str and (
+                (has_magic(rpath) and source_is_file)
+                or (not has_magic(rpath) and dest_is_dir and not trailing_sep(rpath))
+            )
+            lpaths = other_paths(
+                rpaths,
+                lpath,
+                exists=exists,
+                flatten=not source_is_str,
+            )
+
+        callback.set_size(len(lpaths))
+        for lpath, rpath in callback.wrap(zip(lpaths, rpaths)):
+            with callback.branched(rpath, lpath) as child:
+                self.get_file(rpath, lpath, callback=child, **kwargs)
+
+    def put_file(
+        self, lpath, rpath, callback=DEFAULT_CALLBACK, mode="overwrite", **kwargs
+    ):
+        """Copy single file to remote"""
+        if mode == "create" and self.exists(rpath):
+            raise FileExistsError
+        if os.path.isdir(lpath):
+            self.makedirs(rpath, exist_ok=True)
+            return None
+
+        with open(lpath, "rb") as f1:
+            size = f1.seek(0, 2)
+            callback.set_size(size)
+            f1.seek(0)
+
+            self.mkdirs(self._parent(os.fspath(rpath)), exist_ok=True)
+            with self.open(rpath, "wb", **kwargs) as f2:
+                while f1.tell() < size:
+                    data = f1.read(self.blocksize)
+                    segment_len = f2.write(data)
+                    if segment_len is None:
+                        segment_len = len(data)
+                    callback.relative_update(segment_len)
+
+    def put(
+        self,
+        lpath,
+        rpath,
+        recursive=False,
+        callback=DEFAULT_CALLBACK,
+        maxdepth=None,
+        **kwargs,
+    ):
+        """Copy file(s) from local.
+
+        Copies a specific file or tree of files (if recursive=True). If rpath
+        ends with a "/", it will be assumed to be a directory, and target files
+        will go within.
+
+        Calls put_file for each source.
+        """
+        if isinstance(lpath, list) and isinstance(rpath, list):
+            # No need to expand paths when both source and destination
+            # are provided as lists
+            rpaths = rpath
+            lpaths = lpath
+        else:
+            from .implementations.local import (
+                LocalFileSystem,
+                make_path_posix,
+                trailing_sep,
+            )
+
+            source_is_str = isinstance(lpath, str)
+            if source_is_str:
+                lpath = make_path_posix(lpath)
+            fs = LocalFileSystem()
+            lpaths = fs.expand_path(
+                lpath, recursive=recursive, maxdepth=maxdepth, **kwargs
+            )
+            if source_is_str and (not recursive or maxdepth is not None):
+                # Non-recursive glob does not copy directories
+                lpaths = [p for p in lpaths if not (trailing_sep(p) or fs.isdir(p))]
+                if not lpaths:
+                    return
+
+            source_is_file = len(lpaths) == 1
+            dest_is_dir = isinstance(rpath, str) and (
+                trailing_sep(rpath) or self.isdir(rpath)
+            )
+
+            rpath = (
+                self._strip_protocol(rpath)
+                if isinstance(rpath, str)
+                else [self._strip_protocol(p) for p in rpath]
+            )
+            exists = source_is_str and (
+                (has_magic(lpath) and source_is_file)
+                or (not has_magic(lpath) and dest_is_dir and not trailing_sep(lpath))
+            )
+            rpaths = other_paths(
+                lpaths,
+                rpath,
+                exists=exists,
+                flatten=not source_is_str,
+            )
+
+        callback.set_size(len(rpaths))
+        for lpath, rpath in callback.wrap(zip(lpaths, rpaths)):
+            with callback.branched(lpath, rpath) as child:
+                self.put_file(lpath, rpath, callback=child, **kwargs)
+
+    def head(self, path, size=1024):
+        """Get the first ``size`` bytes from file"""
+        with self.open(path, "rb") as f:
+            return f.read(size)
+
+    def tail(self, path, size=1024):
+        """Get the last ``size`` bytes from file"""
+        with self.open(path, "rb") as f:
+            f.seek(max(-size, -f.size), 2)
+            return f.read()
+
+    def cp_file(self, path1, path2, **kwargs):
+        raise NotImplementedError
+
+    def copy(
+        self, path1, path2, recursive=False, maxdepth=None, on_error=None, **kwargs
+    ):
+        """Copy within two locations in the filesystem
+
+        on_error : "raise", "ignore"
+            If raise, any not-found exceptions will be raised; if ignore any
+            not-found exceptions will cause the path to be skipped; defaults to
+            raise unless recursive is true, where the default is ignore
+        """
+        if on_error is None and recursive:
+            on_error = "ignore"
+        elif on_error is None:
+            on_error = "raise"
+
+        if isinstance(path1, list) and isinstance(path2, list):
+            # No need to expand paths when both source and destination
+            # are provided as lists
+            paths1 = path1
+            paths2 = path2
+        else:
+            from .implementations.local import trailing_sep
+
+            source_is_str = isinstance(path1, str)
+            paths1 = self.expand_path(
+                path1, recursive=recursive, maxdepth=maxdepth, **kwargs
+            )
+            if source_is_str and (not recursive or maxdepth is not None):
+                # Non-recursive glob does not copy directories
+                paths1 = [p for p in paths1 if not (trailing_sep(p) or self.isdir(p))]
+                if not paths1:
+                    return
+
+            source_is_file = len(paths1) == 1
+            dest_is_dir = isinstance(path2, str) and (
+                trailing_sep(path2) or self.isdir(path2)
+            )
+
+            exists = source_is_str and (
+                (has_magic(path1) and source_is_file)
+                or (not has_magic(path1) and dest_is_dir and not trailing_sep(path1))
+            )
+            paths2 = other_paths(
+                paths1,
+                path2,
+                exists=exists,
+                flatten=not source_is_str,
+            )
+
+        for p1, p2 in zip(paths1, paths2):
+            try:
+                self.cp_file(p1, p2, **kwargs)
+            except FileNotFoundError:
+                if on_error == "raise":
+                    raise
+
+    def expand_path(self, path, recursive=False, maxdepth=None, **kwargs):
+        """Turn one or more globs or directories into a list of all matching paths
+        to files or directories.
+
+        kwargs are passed to ``glob`` or ``find``, which may in turn call ``ls``
+        """
+
+        if maxdepth is not None and maxdepth < 1:
+            raise ValueError("maxdepth must be at least 1")
+
+        if isinstance(path, (str, os.PathLike)):
+            out = self.expand_path([path], recursive, maxdepth, **kwargs)
+        else:
+            out = set()
+            path = [self._strip_protocol(p) for p in path]
+            for p in path:
+                if has_magic(p):
+                    bit = set(self.glob(p, maxdepth=maxdepth, **kwargs))
+                    out |= bit
+                    if recursive:
+                        # glob call above expanded one depth so if maxdepth is defined
+                        # then decrement it in expand_path call below. If it is zero
+                        # after decrementing then avoid expand_path call.
+                        if maxdepth is not None and maxdepth <= 1:
+                            continue
+                        out |= set(
+                            self.expand_path(
+                                list(bit),
+                                recursive=recursive,
+                                maxdepth=maxdepth - 1 if maxdepth is not None else None,
+                                **kwargs,
+                            )
+                        )
+                    continue
+                elif recursive:
+                    rec = set(
+                        self.find(
+                            p, maxdepth=maxdepth, withdirs=True, detail=False, **kwargs
+                        )
+                    )
+                    out |= rec
+                if p not in out and (recursive is False or self.exists(p)):
+                    # should only check once, for the root
+                    out.add(p)
+        if not out:
+            raise FileNotFoundError(path)
+        return sorted(out)
+
+    def mv(self, path1, path2, recursive=False, maxdepth=None, **kwargs):
+        """Move file(s) from one location to another"""
+        if path1 == path2:
+            logger.debug("%s mv: The paths are the same, so no files were moved.", self)
+        else:
+            # explicitly raise exception to prevent data corruption
+            self.copy(
+                path1, path2, recursive=recursive, maxdepth=maxdepth, onerror="raise"
+            )
+            self.rm(path1, recursive=recursive)
+
+    def rm_file(self, path):
+        """Delete a file"""
+        self._rm(path)
+
+    def _rm(self, path):
+        """Delete one file"""
+        # this is the old name for the method, prefer rm_file
+        raise NotImplementedError
+
+    def rm(self, path, recursive=False, maxdepth=None):
+        """Delete files.
+
+        Parameters
+        ----------
+        path: str or list of str
+            File(s) to delete.
+        recursive: bool
+            If file(s) are directories, recursively delete contents and then
+            also remove the directory
+        maxdepth: int or None
+            Depth to pass to walk for finding files to delete, if recursive.
+            If None, there will be no limit and infinite recursion may be
+            possible.
+        """
+        path = self.expand_path(path, recursive=recursive, maxdepth=maxdepth)
+        for p in reversed(path):
+            self.rm_file(p)
+
+    @classmethod
+    def _parent(cls, path):
+        path = cls._strip_protocol(path)
+        if "/" in path:
+            parent = path.rsplit("/", 1)[0].lstrip(cls.root_marker)
+            return cls.root_marker + parent
+        else:
+            return cls.root_marker
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        """Return raw bytes-mode file-like from the file-system"""
+        return AbstractBufferedFile(
+            self,
+            path,
+            mode,
+            block_size,
+            autocommit,
+            cache_options=cache_options,
+            **kwargs,
+        )
+
+    def open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        cache_options=None,
+        compression=None,
+        **kwargs,
+    ):
+        """
+        Return a file-like object from the filesystem
+
+        The resultant instance must function correctly in a context ``with``
+        block.
+
+        Parameters
+        ----------
+        path: str
+            Target file
+        mode: str like 'rb', 'w'
+            See builtin ``open()``
+            Mode "x" (exclusive write) may be implemented by the backend. Even if
+            it is, whether  it is checked up front or on commit, and whether it is
+            atomic is implementation-dependent.
+        block_size: int
+            Some indication of buffering - this is a value in bytes
+        cache_options : dict, optional
+            Extra arguments to pass through to the cache.
+        compression: string or None
+            If given, open file using compression codec. Can either be a compression
+            name (a key in ``fsspec.compression.compr``) or "infer" to guess the
+            compression from the filename suffix.
+        encoding, errors, newline: passed on to TextIOWrapper for text mode
+        """
+        import io
+
+        path = self._strip_protocol(path)
+        if "b" not in mode:
+            mode = mode.replace("t", "") + "b"
+
+            text_kwargs = {
+                k: kwargs.pop(k)
+                for k in ["encoding", "errors", "newline"]
+                if k in kwargs
+            }
+            return io.TextIOWrapper(
+                self.open(
+                    path,
+                    mode,
+                    block_size=block_size,
+                    cache_options=cache_options,
+                    compression=compression,
+                    **kwargs,
+                ),
+                **text_kwargs,
+            )
+        else:
+            ac = kwargs.pop("autocommit", not self._intrans)
+            f = self._open(
+                path,
+                mode=mode,
+                block_size=block_size,
+                autocommit=ac,
+                cache_options=cache_options,
+                **kwargs,
+            )
+            if compression is not None:
+                from fsspec.compression import compr
+                from fsspec.core import get_compression
+
+                compression = get_compression(path, compression)
+                compress = compr[compression]
+                f = compress(f, mode=mode[0])
+
+            if not ac and "r" not in mode:
+                self.transaction.files.append(f)
+            return f
+
+    def touch(self, path, truncate=True, **kwargs):
+        """Create empty file, or update timestamp
+
+        Parameters
+        ----------
+        path: str
+            file location
+        truncate: bool
+            If True, always set file size to 0; if False, update timestamp and
+            leave file unchanged, if backend allows this
+        """
+        if truncate or not self.exists(path):
+            with self.open(path, "wb", **kwargs):
+                pass
+        else:
+            raise NotImplementedError  # update timestamp, if possible
+
+    def ukey(self, path):
+        """Hash of file properties, to tell if it has changed"""
+        return sha256(str(self.info(path)).encode()).hexdigest()
+
+    def read_block(self, fn, offset, length, delimiter=None):
+        """Read a block of bytes from
+
+        Starting at ``offset`` of the file, read ``length`` bytes.  If
+        ``delimiter`` is set then we ensure that the read starts and stops at
+        delimiter boundaries that follow the locations ``offset`` and ``offset
+        + length``.  If ``offset`` is zero then we start at zero.  The
+        bytestring returned WILL include the end delimiter string.
+
+        If offset+length is beyond the eof, reads to eof.
+
+        Parameters
+        ----------
+        fn: string
+            Path to filename
+        offset: int
+            Byte offset to start read
+        length: int
+            Number of bytes to read. If None, read to end.
+        delimiter: bytes (optional)
+            Ensure reading starts and stops at delimiter bytestring
+
+        Examples
+        --------
+        >>> fs.read_block('data/file.csv', 0, 13)  # doctest: +SKIP
+        b'Alice, 100\\nBo'
+        >>> fs.read_block('data/file.csv', 0, 13, delimiter=b'\\n')  # doctest: +SKIP
+        b'Alice, 100\\nBob, 200\\n'
+
+        Use ``length=None`` to read to the end of the file.
+        >>> fs.read_block('data/file.csv', 0, None, delimiter=b'\\n')  # doctest: +SKIP
+        b'Alice, 100\\nBob, 200\\nCharlie, 300'
+
+        See Also
+        --------
+        :func:`fsspec.utils.read_block`
+        """
+        with self.open(fn, "rb") as f:
+            size = f.size
+            if length is None:
+                length = size
+            if size is not None and offset + length > size:
+                length = size - offset
+            return read_block(f, offset, length, delimiter)
+
+    def to_json(self, *, include_password: bool = True) -> str:
+        """
+        JSON representation of this filesystem instance.
+
+        Parameters
+        ----------
+        include_password: bool, default True
+            Whether to include the password (if any) in the output.
+
+        Returns
+        -------
+        JSON string with keys ``cls`` (the python location of this class),
+        protocol (text name of this class's protocol, first one in case of
+        multiple), ``args`` (positional args, usually empty), and all other
+        keyword arguments as their own keys.
+
+        Warnings
+        --------
+        Serialized filesystems may contain sensitive information which have been
+        passed to the constructor, such as passwords and tokens. Make sure you
+        store and send them in a secure environment!
+        """
+        from .json import FilesystemJSONEncoder
+
+        return json.dumps(
+            self,
+            cls=type(
+                "_FilesystemJSONEncoder",
+                (FilesystemJSONEncoder,),
+                {"include_password": include_password},
+            ),
+        )
+
+    @staticmethod
+    def from_json(blob: str) -> AbstractFileSystem:
+        """
+        Recreate a filesystem instance from JSON representation.
+
+        See ``.to_json()`` for the expected structure of the input.
+
+        Parameters
+        ----------
+        blob: str
+
+        Returns
+        -------
+        file system instance, not necessarily of this particular class.
+
+        Warnings
+        --------
+        This can import arbitrary modules (as determined by the ``cls`` key).
+        Make sure you haven't installed any modules that may execute malicious code
+        at import time.
+        """
+        from .json import FilesystemJSONDecoder
+
+        return json.loads(blob, cls=FilesystemJSONDecoder)
+
+    def to_dict(self, *, include_password: bool = True) -> dict[str, Any]:
+        """
+        JSON-serializable dictionary representation of this filesystem instance.
+
+        Parameters
+        ----------
+        include_password: bool, default True
+            Whether to include the password (if any) in the output.
+
+        Returns
+        -------
+        Dictionary with keys ``cls`` (the python location of this class),
+        protocol (text name of this class's protocol, first one in case of
+        multiple), ``args`` (positional args, usually empty), and all other
+        keyword arguments as their own keys.
+
+        Warnings
+        --------
+        Serialized filesystems may contain sensitive information which have been
+        passed to the constructor, such as passwords and tokens. Make sure you
+        store and send them in a secure environment!
+        """
+        from .json import FilesystemJSONEncoder
+
+        json_encoder = FilesystemJSONEncoder()
+
+        cls = type(self)
+        proto = self.protocol
+
+        storage_options = dict(self.storage_options)
+        if not include_password:
+            storage_options.pop("password", None)
+
+        return dict(
+            cls=f"{cls.__module__}:{cls.__name__}",
+            protocol=proto[0] if isinstance(proto, (tuple, list)) else proto,
+            args=json_encoder.make_serializable(self.storage_args),
+            **json_encoder.make_serializable(storage_options),
+        )
+
+    @staticmethod
+    def from_dict(dct: dict[str, Any]) -> AbstractFileSystem:
+        """
+        Recreate a filesystem instance from dictionary representation.
+
+        See ``.to_dict()`` for the expected structure of the input.
+
+        Parameters
+        ----------
+        dct: Dict[str, Any]
+
+        Returns
+        -------
+        file system instance, not necessarily of this particular class.
+
+        Warnings
+        --------
+        This can import arbitrary modules (as determined by the ``cls`` key).
+        Make sure you haven't installed any modules that may execute malicious code
+        at import time.
+        """
+        from .json import FilesystemJSONDecoder
+
+        json_decoder = FilesystemJSONDecoder()
+
+        dct = dict(dct)  # Defensive copy
+
+        cls = FilesystemJSONDecoder.try_resolve_fs_cls(dct)
+        if cls is None:
+            raise ValueError("Not a serialized AbstractFileSystem")
+
+        dct.pop("cls", None)
+        dct.pop("protocol", None)
+
+        return cls(
+            *json_decoder.unmake_serializable(dct.pop("args", ())),
+            **json_decoder.unmake_serializable(dct),
+        )
+
+    def _get_pyarrow_filesystem(self):
+        """
+        Make a version of the FS instance which will be acceptable to pyarrow
+        """
+        # all instances already also derive from pyarrow
+        return self
+
+    def get_mapper(self, root="", check=False, create=False, missing_exceptions=None):
+        """Create key/value store based on this file-system
+
+        Makes a MutableMapping interface to the FS at the given root path.
+        See ``fsspec.mapping.FSMap`` for further details.
+        """
+        from .mapping import FSMap
+
+        return FSMap(
+            root,
+            self,
+            check=check,
+            create=create,
+            missing_exceptions=missing_exceptions,
+        )
+
+    @classmethod
+    def clear_instance_cache(cls):
+        """
+        Clear the cache of filesystem instances.
+
+        Notes
+        -----
+        Unless overridden by setting the ``cachable`` class attribute to False,
+        the filesystem class stores a reference to newly created instances. This
+        prevents Python's normal rules around garbage collection from working,
+        since the instances refcount will not drop to zero until
+        ``clear_instance_cache`` is called.
+        """
+        cls._cache.clear()
+
+    def created(self, path):
+        """Return the created timestamp of a file as a datetime.datetime"""
+        raise NotImplementedError
+
+    def modified(self, path):
+        """Return the modified timestamp of a file as a datetime.datetime"""
+        raise NotImplementedError
+
+    def tree(
+        self,
+        path: str = "/",
+        recursion_limit: int = 2,
+        max_display: int = 25,
+        display_size: bool = False,
+        prefix: str = "",
+        is_last: bool = True,
+        first: bool = True,
+        indent_size: int = 4,
+    ) -> str:
+        """
+        Return a tree-like structure of the filesystem starting from the given path as a string.
+
+        Parameters
+        ----------
+            path: Root path to start traversal from
+            recursion_limit: Maximum depth of directory traversal
+            max_display: Maximum number of items to display per directory
+            display_size: Whether to display file sizes
+            prefix: Current line prefix for visual tree structure
+            is_last: Whether current item is last in its level
+            first: Whether this is the first call (displays root path)
+            indent_size: Number of spaces by indent
+
+        Returns
+        -------
+            str: A string representing the tree structure.
+
+        Example
+        -------
+            >>> from fsspec import filesystem
+
+            >>> fs = filesystem('ftp', host='test.rebex.net', user='demo', password='password')
+            >>> tree = fs.tree(display_size=True, recursion_limit=3, indent_size=8, max_display=10)
+            >>> print(tree)
+        """
+
+        def format_bytes(n: int) -> str:
+            """Format bytes as text."""
+            for prefix, k in (
+                ("P", 2**50),
+                ("T", 2**40),
+                ("G", 2**30),
+                ("M", 2**20),
+                ("k", 2**10),
+            ):
+                if n >= 0.9 * k:
+                    return f"{n / k:.2f} {prefix}b"
+            return f"{n}B"
+
+        result = []
+
+        if first:
+            result.append(path)
+
+        if recursion_limit:
+            indent = " " * indent_size
+            contents = self.ls(path, detail=True)
+            contents.sort(
+                key=lambda x: (x.get("type") != "directory", x.get("name", ""))
+            )
+
+            if max_display is not None and len(contents) > max_display:
+                displayed_contents = contents[:max_display]
+                remaining_count = len(contents) - max_display
+            else:
+                displayed_contents = contents
+                remaining_count = 0
+
+            for i, item in enumerate(displayed_contents):
+                is_last_item = (i == len(displayed_contents) - 1) and (
+                    remaining_count == 0
+                )
+
+                branch = (
+                    "└" + ("─" * (indent_size - 2))
+                    if is_last_item
+                    else "├" + ("─" * (indent_size - 2))
+                )
+                branch += " "
+                new_prefix = prefix + (
+                    indent if is_last_item else "│" + " " * (indent_size - 1)
+                )
+
+                name = os.path.basename(item.get("name", ""))
+
+                if display_size and item.get("type") == "directory":
+                    sub_contents = self.ls(item.get("name", ""), detail=True)
+                    num_files = sum(
+                        1 for sub_item in sub_contents if sub_item.get("type") == "file"
+                    )
+                    num_folders = sum(
+                        1
+                        for sub_item in sub_contents
+                        if sub_item.get("type") == "directory"
+                    )
+
+                    if num_files == 0 and num_folders == 0:
+                        size = " (empty folder)"
+                    elif num_files == 0:
+                        size = f" ({num_folders} subfolder{'s' if num_folders > 1 else ''})"
+                    elif num_folders == 0:
+                        size = f" ({num_files} file{'s' if num_files > 1 else ''})"
+                    else:
+                        size = f" ({num_files} file{'s' if num_files > 1 else ''}, {num_folders} subfolder{'s' if num_folders > 1 else ''})"
+                elif display_size and item.get("type") == "file":
+                    size = f" ({format_bytes(item.get('size', 0))})"
+                else:
+                    size = ""
+
+                result.append(f"{prefix}{branch}{name}{size}")
+
+                if item.get("type") == "directory" and recursion_limit > 0:
+                    result.append(
+                        self.tree(
+                            path=item.get("name", ""),
+                            recursion_limit=recursion_limit - 1,
+                            max_display=max_display,
+                            display_size=display_size,
+                            prefix=new_prefix,
+                            is_last=is_last_item,
+                            first=False,
+                            indent_size=indent_size,
+                        )
+                    )
+
+            if remaining_count > 0:
+                more_message = f"{remaining_count} more item(s) not displayed."
+                result.append(
+                    f"{prefix}{'└' + ('─' * (indent_size - 2))} {more_message}"
+                )
+
+        return "\n".join(_ for _ in result if _)
+
+    # ------------------------------------------------------------------------
+    # Aliases
+
+    def read_bytes(self, path, start=None, end=None, **kwargs):
+        """Alias of `AbstractFileSystem.cat_file`."""
+        return self.cat_file(path, start=start, end=end, **kwargs)
+
+    def write_bytes(self, path, value, **kwargs):
+        """Alias of `AbstractFileSystem.pipe_file`."""
+        self.pipe_file(path, value, **kwargs)
+
+    def makedir(self, path, create_parents=True, **kwargs):
+        """Alias of `AbstractFileSystem.mkdir`."""
+        return self.mkdir(path, create_parents=create_parents, **kwargs)
+
+    def mkdirs(self, path, exist_ok=False):
+        """Alias of `AbstractFileSystem.makedirs`."""
+        return self.makedirs(path, exist_ok=exist_ok)
+
+    def listdir(self, path, detail=True, **kwargs):
+        """Alias of `AbstractFileSystem.ls`."""
+        return self.ls(path, detail=detail, **kwargs)
+
+    def cp(self, path1, path2, **kwargs):
+        """Alias of `AbstractFileSystem.copy`."""
+        return self.copy(path1, path2, **kwargs)
+
+    def move(self, path1, path2, **kwargs):
+        """Alias of `AbstractFileSystem.mv`."""
+        return self.mv(path1, path2, **kwargs)
+
+    def stat(self, path, **kwargs):
+        """Alias of `AbstractFileSystem.info`."""
+        return self.info(path, **kwargs)
+
+    def disk_usage(self, path, total=True, maxdepth=None, **kwargs):
+        """Alias of `AbstractFileSystem.du`."""
+        return self.du(path, total=total, maxdepth=maxdepth, **kwargs)
+
+    def rename(self, path1, path2, **kwargs):
+        """Alias of `AbstractFileSystem.mv`."""
+        return self.mv(path1, path2, **kwargs)
+
+    def delete(self, path, recursive=False, maxdepth=None):
+        """Alias of `AbstractFileSystem.rm`."""
+        return self.rm(path, recursive=recursive, maxdepth=maxdepth)
+
+    def upload(self, lpath, rpath, recursive=False, **kwargs):
+        """Alias of `AbstractFileSystem.put`."""
+        return self.put(lpath, rpath, recursive=recursive, **kwargs)
+
+    def download(self, rpath, lpath, recursive=False, **kwargs):
+        """Alias of `AbstractFileSystem.get`."""
+        return self.get(rpath, lpath, recursive=recursive, **kwargs)
+
+    def sign(self, path, expiration=100, **kwargs):
+        """Create a signed URL representing the given path
+
+        Some implementations allow temporary URLs to be generated, as a
+        way of delegating credentials.
+
+        Parameters
+        ----------
+        path : str
+             The path on the filesystem
+        expiration : int
+            Number of seconds to enable the URL for (if supported)
+
+        Returns
+        -------
+        URL : str
+            The signed URL
+
+        Raises
+        ------
+        NotImplementedError : if method is not implemented for a filesystem
+        """
+        raise NotImplementedError("Sign is not implemented for this filesystem")
+
+    def _isfilestore(self):
+        # Originally inherited from pyarrow DaskFileSystem. Keeping this
+        # here for backwards compatibility as long as pyarrow uses its
+        # legacy fsspec-compatible filesystems and thus accepts fsspec
+        # filesystems as well
+        return False
+
+
+class AbstractBufferedFile(io.IOBase):
+    """Convenient class to derive from to provide buffering
+
+    In the case that the backend does not provide a pythonic file-like object
+    already, this class contains much of the logic to build one. The only
+    methods that need to be overridden are ``_upload_chunk``,
+    ``_initiate_upload`` and ``_fetch_range``.
+    """
+
+    DEFAULT_BLOCK_SIZE = 5 * 2**20
+    _details = None
+
+    def __init__(
+        self,
+        fs,
+        path,
+        mode="rb",
+        block_size="default",
+        autocommit=True,
+        cache_type="readahead",
+        cache_options=None,
+        size=None,
+        **kwargs,
+    ):
+        """
+        Template for files with buffered reading and writing
+
+        Parameters
+        ----------
+        fs: instance of FileSystem
+        path: str
+            location in file-system
+        mode: str
+            Normal file modes. Currently only 'wb', 'ab' or 'rb'. Some file
+            systems may be read-only, and some may not support append.
+        block_size: int
+            Buffer size for reading or writing, 'default' for class default
+        autocommit: bool
+            Whether to write to final destination; may only impact what
+            happens when file is being closed.
+        cache_type: {"readahead", "none", "mmap", "bytes"}, default "readahead"
+            Caching policy in read mode. See the definitions in ``core``.
+        cache_options : dict
+            Additional options passed to the constructor for the cache specified
+            by `cache_type`.
+        size: int
+            If given and in read mode, suppressed having to look up the file size
+        kwargs:
+            Gets stored as self.kwargs
+        """
+        from .core import caches
+
+        self.path = path
+        self.fs = fs
+        self.mode = mode
+        self.blocksize = (
+            self.DEFAULT_BLOCK_SIZE if block_size in ["default", None] else block_size
+        )
+        self.loc = 0
+        self.autocommit = autocommit
+        self.end = None
+        self.start = None
+        self.closed = False
+
+        if cache_options is None:
+            cache_options = {}
+
+        if "trim" in kwargs:
+            warnings.warn(
+                "Passing 'trim' to control the cache behavior has been deprecated. "
+                "Specify it within the 'cache_options' argument instead.",
+                FutureWarning,
+            )
+            cache_options["trim"] = kwargs.pop("trim")
+
+        self.kwargs = kwargs
+
+        if mode not in {"ab", "rb", "wb", "xb"}:
+            raise NotImplementedError("File mode not supported")
+        if mode == "rb":
+            if size is not None:
+                self.size = size
+            else:
+                self.size = self.details["size"]
+            self.cache = caches[cache_type](
+                self.blocksize, self._fetch_range, self.size, **cache_options
+            )
+        else:
+            self.buffer = io.BytesIO()
+            self.offset = None
+            self.forced = False
+            self.location = None
+
+    @property
+    def details(self):
+        if self._details is None:
+            self._details = self.fs.info(self.path)
+        return self._details
+
+    @details.setter
+    def details(self, value):
+        self._details = value
+        self.size = value["size"]
+
+    @property
+    def full_name(self):
+        return _unstrip_protocol(self.path, self.fs)
+
+    @property
+    def closed(self):
+        # get around this attr being read-only in IOBase
+        # use getattr here, since this can be called during del
+        return getattr(self, "_closed", True)
+
+    @closed.setter
+    def closed(self, c):
+        self._closed = c
+
+    def __hash__(self):
+        if "w" in self.mode:
+            return id(self)
+        else:
+            return int(tokenize(self.details), 16)
+
+    def __eq__(self, other):
+        """Files are equal if they have the same checksum, only in read mode"""
+        if self is other:
+            return True
+        return (
+            isinstance(other, type(self))
+            and self.mode == "rb"
+            and other.mode == "rb"
+            and hash(self) == hash(other)
+        )
+
+    def commit(self):
+        """Move from temp to final destination"""
+
+    def discard(self):
+        """Throw away temporary file"""
+
+    def info(self):
+        """File information about this path"""
+        if self.readable():
+            return self.details
+        else:
+            raise ValueError("Info not available while writing")
+
+    def tell(self):
+        """Current file location"""
+        return self.loc
+
+    def seek(self, loc, whence=0):
+        """Set current file location
+
+        Parameters
+        ----------
+        loc: int
+            byte location
+        whence: {0, 1, 2}
+            from start of file, current location or end of file, resp.
+        """
+        loc = int(loc)
+        if not self.mode == "rb":
+            raise OSError(ESPIPE, "Seek only available in read mode")
+        if whence == 0:
+            nloc = loc
+        elif whence == 1:
+            nloc = self.loc + loc
+        elif whence == 2:
+            nloc = self.size + loc
+        else:
+            raise ValueError(f"invalid whence ({whence}, should be 0, 1 or 2)")
+        if nloc < 0:
+            raise ValueError("Seek before start of file")
+        self.loc = nloc
+        return self.loc
+
+    def write(self, data):
+        """
+        Write data to buffer.
+
+        Buffer only sent on flush() or if buffer is greater than
+        or equal to blocksize.
+
+        Parameters
+        ----------
+        data: bytes
+            Set of bytes to be written.
+        """
+        if not self.writable():
+            raise ValueError("File not in write mode")
+        if self.closed:
+            raise ValueError("I/O operation on closed file.")
+        if self.forced:
+            raise ValueError("This file has been force-flushed, can only close")
+        out = self.buffer.write(data)
+        self.loc += out
+        if self.buffer.tell() >= self.blocksize:
+            self.flush()
+        return out
+
+    def flush(self, force=False):
+        """
+        Write buffered data to backend store.
+
+        Writes the current buffer, if it is larger than the block-size, or if
+        the file is being closed.
+
+        Parameters
+        ----------
+        force: bool
+            When closing, write the last block even if it is smaller than
+            blocks are allowed to be. Disallows further writing to this file.
+        """
+
+        if self.closed:
+            raise ValueError("Flush on closed file")
+        if force and self.forced:
+            raise ValueError("Force flush cannot be called more than once")
+        if force:
+            self.forced = True
+
+        if self.readable():
+            # no-op to flush on read-mode
+            return
+
+        if not force and self.buffer.tell() < self.blocksize:
+            # Defer write on small block
+            return
+
+        if self.offset is None:
+            # Initialize a multipart upload
+            self.offset = 0
+            try:
+                self._initiate_upload()
+            except:
+                self.closed = True
+                raise
+
+        if self._upload_chunk(final=force) is not False:
+            self.offset += self.buffer.seek(0, 2)
+            self.buffer = io.BytesIO()
+
+    def _upload_chunk(self, final=False):
+        """Write one part of a multi-block file upload
+
+        Parameters
+        ==========
+        final: bool
+            This is the last block, so should complete file, if
+            self.autocommit is True.
+        """
+        # may not yet have been initialized, may need to call _initialize_upload
+
+    def _initiate_upload(self):
+        """Create remote file/upload"""
+        pass
+
+    def _fetch_range(self, start, end):
+        """Get the specified set of bytes from remote"""
+        return self.fs.cat_file(self.path, start=start, end=end)
+
+    def read(self, length=-1):
+        """
+        Return data from cache, or fetch pieces as necessary
+
+        Parameters
+        ----------
+        length: int (-1)
+            Number of bytes to read; if <0, all remaining bytes.
+        """
+        length = -1 if length is None else int(length)
+        if self.mode != "rb":
+            raise ValueError("File not in read mode")
+        if length < 0:
+            length = self.size - self.loc
+        if self.closed:
+            raise ValueError("I/O operation on closed file.")
+        if length == 0:
+            # don't even bother calling fetch
+            return b""
+        out = self.cache._fetch(self.loc, self.loc + length)
+
+        logger.debug(
+            "%s read: %i - %i %s",
+            self,
+            self.loc,
+            self.loc + length,
+            self.cache._log_stats(),
+        )
+        self.loc += len(out)
+        return out
+
+    def readinto(self, b):
+        """mirrors builtin file's readinto method
+
+        https://docs.python.org/3/library/io.html#io.RawIOBase.readinto
+        """
+        out = memoryview(b).cast("B")
+        data = self.read(out.nbytes)
+        out[: len(data)] = data
+        return len(data)
+
+    def readuntil(self, char=b"\n", blocks=None):
+        """Return data between current position and first occurrence of char
+
+        char is included in the output, except if the end of the tile is
+        encountered first.
+
+        Parameters
+        ----------
+        char: bytes
+            Thing to find
+        blocks: None or int
+            How much to read in each go. Defaults to file blocksize - which may
+            mean a new read on every call.
+        """
+        out = []
+        while True:
+            start = self.tell()
+            part = self.read(blocks or self.blocksize)
+            if len(part) == 0:
+                break
+            found = part.find(char)
+            if found > -1:
+                out.append(part[: found + len(char)])
+                self.seek(start + found + len(char))
+                break
+            out.append(part)
+        return b"".join(out)
+
+    def readline(self):
+        """Read until and including the first occurrence of newline character
+
+        Note that, because of character encoding, this is not necessarily a
+        true line ending.
+        """
+        return self.readuntil(b"\n")
+
+    def __next__(self):
+        out = self.readline()
+        if out:
+            return out
+        raise StopIteration
+
+    def __iter__(self):
+        return self
+
+    def readlines(self):
+        """Return all data, split by the newline character, including the newline character"""
+        data = self.read()
+        lines = data.split(b"\n")
+        out = [l + b"\n" for l in lines[:-1]]
+        if data.endswith(b"\n"):
+            return out
+        else:
+            return out + [lines[-1]]
+        # return list(self)  ???
+
+    def readinto1(self, b):
+        return self.readinto(b)
+
+    def close(self):
+        """Close file
+
+        Finalizes writes, discards cache
+        """
+        if getattr(self, "_unclosable", False):
+            return
+        if self.closed:
+            return
+        try:
+            if self.mode == "rb":
+                self.cache = None
+            else:
+                if not self.forced:
+                    self.flush(force=True)
+
+                if self.fs is not None:
+                    self.fs.invalidate_cache(self.path)
+                    self.fs.invalidate_cache(self.fs._parent(self.path))
+        finally:
+            self.closed = True
+
+    def readable(self):
+        """Whether opened for reading"""
+        return "r" in self.mode and not self.closed
+
+    def seekable(self):
+        """Whether is seekable (only in read mode)"""
+        return self.readable()
+
+    def writable(self):
+        """Whether opened for writing"""
+        return self.mode in {"wb", "ab", "xb"} and not self.closed
+
+    def __reduce__(self):
+        if self.mode != "rb":
+            raise RuntimeError("Pickling a writeable file is not supported")
+
+        return reopen, (
+            self.fs,
+            self.path,
+            self.mode,
+            self.blocksize,
+            self.loc,
+            self.size,
+            self.autocommit,
+            self.cache.name if self.cache else "none",
+            self.kwargs,
+        )
+
+    def __del__(self):
+        if not self.closed:
+            self.close()
+
+    def __str__(self):
+        return f"<File-like object {type(self.fs).__name__}, {self.path}>"
+
+    __repr__ = __str__
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+
+def reopen(fs, path, mode, blocksize, loc, size, autocommit, cache_type, kwargs):
+    file = fs.open(
+        path,
+        mode=mode,
+        block_size=blocksize,
+        autocommit=autocommit,
+        cache_type=cache_type,
+        size=size,
+        **kwargs,
+    )
+    if loc > 0:
+        file.seek(loc)
+    return file
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__init__.py b/lib/python3.11/site-packages/fsspec/tests/abstract/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ed2ad802ecaf021106c25c03112f29e75c7b2f8
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/__init__.py
@@ -0,0 +1,289 @@
+import os
+from hashlib import md5
+
+import pytest
+
+from fsspec.implementations.local import LocalFileSystem
+from fsspec.tests.abstract.copy import AbstractCopyTests  # noqa: F401
+from fsspec.tests.abstract.get import AbstractGetTests  # noqa: F401
+from fsspec.tests.abstract.open import AbstractOpenTests  # noqa: F401
+from fsspec.tests.abstract.pipe import AbstractPipeTests  # noqa: F401
+from fsspec.tests.abstract.put import AbstractPutTests  # noqa: F401
+
+
+class BaseAbstractFixtures:
+    """
+    Abstract base class containing fixtures that are used by but never need to
+    be overridden in derived filesystem-specific classes to run the abstract
+    tests on such filesystems.
+    """
+
+    @pytest.fixture
+    def fs_bulk_operations_scenario_0(self, fs, fs_join, fs_path):
+        """
+        Scenario on remote filesystem that is used for many cp/get/put tests.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._bulk_operations_scenario_0(fs, fs_join, fs_path)
+        yield source
+        fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def fs_glob_edge_cases_files(self, fs, fs_join, fs_path):
+        """
+        Scenario on remote filesystem that is used for glob edge cases cp/get/put tests.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._glob_edge_cases_files(fs, fs_join, fs_path)
+        yield source
+        fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def fs_dir_and_file_with_same_name_prefix(self, fs, fs_join, fs_path):
+        """
+        Scenario on remote filesystem that is used to check cp/get/put on directory
+        and file with the same name prefixes.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._dir_and_file_with_same_name_prefix(fs, fs_join, fs_path)
+        yield source
+        fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def fs_10_files_with_hashed_names(self, fs, fs_join, fs_path):
+        """
+        Scenario on remote filesystem that is used to check cp/get/put files order
+        when source and destination are lists.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._10_files_with_hashed_names(fs, fs_join, fs_path)
+        yield source
+        fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def fs_target(self, fs, fs_join, fs_path):
+        """
+        Return name of remote directory that does not yet exist to copy into.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        target = fs_join(fs_path, "target")
+        yield target
+        if fs.exists(target):
+            fs.rm(target, recursive=True)
+
+    @pytest.fixture
+    def local_bulk_operations_scenario_0(self, local_fs, local_join, local_path):
+        """
+        Scenario on local filesystem that is used for many cp/get/put tests.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._bulk_operations_scenario_0(local_fs, local_join, local_path)
+        yield source
+        local_fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def local_glob_edge_cases_files(self, local_fs, local_join, local_path):
+        """
+        Scenario on local filesystem that is used for glob edge cases cp/get/put tests.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._glob_edge_cases_files(local_fs, local_join, local_path)
+        yield source
+        local_fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def local_dir_and_file_with_same_name_prefix(
+        self, local_fs, local_join, local_path
+    ):
+        """
+        Scenario on local filesystem that is used to check cp/get/put on directory
+        and file with the same name prefixes.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._dir_and_file_with_same_name_prefix(
+            local_fs, local_join, local_path
+        )
+        yield source
+        local_fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def local_10_files_with_hashed_names(self, local_fs, local_join, local_path):
+        """
+        Scenario on local filesystem that is used to check cp/get/put files order
+        when source and destination are lists.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        source = self._10_files_with_hashed_names(local_fs, local_join, local_path)
+        yield source
+        local_fs.rm(source, recursive=True)
+
+    @pytest.fixture
+    def local_target(self, local_fs, local_join, local_path):
+        """
+        Return name of local directory that does not yet exist to copy into.
+
+        Cleans up at the end of each test it which it is used.
+        """
+        target = local_join(local_path, "target")
+        yield target
+        if local_fs.exists(target):
+            local_fs.rm(target, recursive=True)
+
+    def _glob_edge_cases_files(self, some_fs, some_join, some_path):
+        """
+        Scenario that is used for glob edge cases cp/get/put tests.
+        Creates the following directory and file structure:
+
+        📁 source
+        ├── 📄 file1
+        ├── 📄 file2
+        ├── 📁 subdir0
+        │   ├── 📄 subfile1
+        │   ├── 📄 subfile2
+        │   └── 📁 nesteddir
+        │       └── 📄 nestedfile
+        └── 📁 subdir1
+            ├── 📄 subfile1
+            ├── 📄 subfile2
+            └── 📁 nesteddir
+                └── 📄 nestedfile
+        """
+        source = some_join(some_path, "source")
+        some_fs.touch(some_join(source, "file1"))
+        some_fs.touch(some_join(source, "file2"))
+
+        for subdir_idx in range(2):
+            subdir = some_join(source, f"subdir{subdir_idx}")
+            nesteddir = some_join(subdir, "nesteddir")
+            some_fs.makedirs(nesteddir)
+            some_fs.touch(some_join(subdir, "subfile1"))
+            some_fs.touch(some_join(subdir, "subfile2"))
+            some_fs.touch(some_join(nesteddir, "nestedfile"))
+
+        return source
+
+    def _bulk_operations_scenario_0(self, some_fs, some_join, some_path):
+        """
+        Scenario that is used for many cp/get/put tests. Creates the following
+        directory and file structure:
+
+        📁 source
+        ├── 📄 file1
+        ├── 📄 file2
+        └── 📁 subdir
+            ├── 📄 subfile1
+            ├── 📄 subfile2
+            └── 📁 nesteddir
+                └── 📄 nestedfile
+        """
+        source = some_join(some_path, "source")
+        subdir = some_join(source, "subdir")
+        nesteddir = some_join(subdir, "nesteddir")
+        some_fs.makedirs(nesteddir)
+        some_fs.touch(some_join(source, "file1"))
+        some_fs.touch(some_join(source, "file2"))
+        some_fs.touch(some_join(subdir, "subfile1"))
+        some_fs.touch(some_join(subdir, "subfile2"))
+        some_fs.touch(some_join(nesteddir, "nestedfile"))
+        return source
+
+    def _dir_and_file_with_same_name_prefix(self, some_fs, some_join, some_path):
+        """
+        Scenario that is used to check cp/get/put on directory and file with
+        the same name prefixes. Creates the following directory and file structure:
+
+        📁 source
+        ├── 📄 subdir.txt
+        └── 📁 subdir
+            └── 📄 subfile.txt
+        """
+        source = some_join(some_path, "source")
+        subdir = some_join(source, "subdir")
+        file = some_join(source, "subdir.txt")
+        subfile = some_join(subdir, "subfile.txt")
+        some_fs.makedirs(subdir)
+        some_fs.touch(file)
+        some_fs.touch(subfile)
+        return source
+
+    def _10_files_with_hashed_names(self, some_fs, some_join, some_path):
+        """
+        Scenario that is used to check cp/get/put files order when source and
+        destination are lists. Creates the following directory and file structure:
+
+        📁 source
+        └── 📄 {hashed([0-9])}.txt
+        """
+        source = some_join(some_path, "source")
+        for i in range(10):
+            hashed_i = md5(str(i).encode("utf-8")).hexdigest()
+            path = some_join(source, f"{hashed_i}.txt")
+            some_fs.pipe(path=path, value=f"{i}".encode())
+        return source
+
+
+class AbstractFixtures(BaseAbstractFixtures):
+    """
+    Abstract base class containing fixtures that may be overridden in derived
+    filesystem-specific classes to run the abstract tests on such filesystems.
+
+    For any particular filesystem some of these fixtures must be overridden,
+    such as ``fs`` and ``fs_path``, and others may be overridden if the
+    default functions here are not appropriate, such as ``fs_join``.
+    """
+
+    @pytest.fixture
+    def fs(self):
+        raise NotImplementedError("This function must be overridden in derived classes")
+
+    @pytest.fixture
+    def fs_join(self):
+        """
+        Return a function that joins its arguments together into a path.
+
+        Most fsspec implementations join paths in a platform-dependent way,
+        but some will override this to always use a forward slash.
+        """
+        return os.path.join
+
+    @pytest.fixture
+    def fs_path(self):
+        raise NotImplementedError("This function must be overridden in derived classes")
+
+    @pytest.fixture(scope="class")
+    def local_fs(self):
+        # Maybe need an option for auto_mkdir=False?  This is only relevant
+        # for certain implementations.
+        return LocalFileSystem(auto_mkdir=True)
+
+    @pytest.fixture
+    def local_join(self):
+        """
+        Return a function that joins its arguments together into a path, on
+        the local filesystem.
+        """
+        return os.path.join
+
+    @pytest.fixture
+    def local_path(self, tmpdir):
+        return tmpdir
+
+    @pytest.fixture
+    def supports_empty_directories(self):
+        """
+        Return whether this implementation supports empty directories.
+        """
+        return True
+
+    @pytest.fixture
+    def fs_sanitize_path(self):
+        return lambda x: x
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b3871cc5d7c84989ec6442150c4359caa7a26df2
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/common.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/common.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..597e53d83ffa86e5be05cb475555e03bce53d29e
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/common.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/copy.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/copy.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..de7b1ed0e907e6ceb01efd82ba008b69dc0bf675
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/copy.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/get.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/get.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e7cf151ebb2a277d7039320e2b4fba49200635f9
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/get.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/mv.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/mv.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9210070eab13814a11bd2bdcc63734174ca1ac68
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/mv.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/open.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/open.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..36a7740c8cae9eba326220c181a89fa7d97d9bcd
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/open.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/pipe.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/pipe.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..465507f02ec6a0297221e5fcc853f5b39290990e
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/pipe.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/put.cpython-311.pyc b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/put.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6b718986aae24c783028b7c741ea478a96b3826f
Binary files /dev/null and b/lib/python3.11/site-packages/fsspec/tests/abstract/__pycache__/put.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/common.py b/lib/python3.11/site-packages/fsspec/tests/abstract/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..22e7c4140404ab2a8928689721419cf05c2760b9
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/common.py
@@ -0,0 +1,175 @@
+GLOB_EDGE_CASES_TESTS = {
+    "argnames": ("path", "recursive", "maxdepth", "expected"),
+    "argvalues": [
+        ("fil?1", False, None, ["file1"]),
+        ("fil?1", True, None, ["file1"]),
+        ("file[1-2]", False, None, ["file1", "file2"]),
+        ("file[1-2]", True, None, ["file1", "file2"]),
+        ("*", False, None, ["file1", "file2"]),
+        (
+            "*",
+            True,
+            None,
+            [
+                "file1",
+                "file2",
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir0/nesteddir/nestedfile",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        ("*", True, 1, ["file1", "file2"]),
+        (
+            "*",
+            True,
+            2,
+            [
+                "file1",
+                "file2",
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+            ],
+        ),
+        ("*1", False, None, ["file1"]),
+        (
+            "*1",
+            True,
+            None,
+            [
+                "file1",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        ("*1", True, 2, ["file1", "subdir1/subfile1", "subdir1/subfile2"]),
+        (
+            "**",
+            False,
+            None,
+            [
+                "file1",
+                "file2",
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir0/nesteddir/nestedfile",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        (
+            "**",
+            True,
+            None,
+            [
+                "file1",
+                "file2",
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir0/nesteddir/nestedfile",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        ("**", True, 1, ["file1", "file2"]),
+        (
+            "**",
+            True,
+            2,
+            [
+                "file1",
+                "file2",
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir0/nesteddir/nestedfile",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        (
+            "**",
+            False,
+            2,
+            [
+                "file1",
+                "file2",
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+            ],
+        ),
+        ("**/*1", False, None, ["file1", "subdir0/subfile1", "subdir1/subfile1"]),
+        (
+            "**/*1",
+            True,
+            None,
+            [
+                "file1",
+                "subdir0/subfile1",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        ("**/*1", True, 1, ["file1"]),
+        (
+            "**/*1",
+            True,
+            2,
+            ["file1", "subdir0/subfile1", "subdir1/subfile1", "subdir1/subfile2"],
+        ),
+        ("**/*1", False, 2, ["file1", "subdir0/subfile1", "subdir1/subfile1"]),
+        ("**/subdir0", False, None, []),
+        ("**/subdir0", True, None, ["subfile1", "subfile2", "nesteddir/nestedfile"]),
+        ("**/subdir0/nested*", False, 2, []),
+        ("**/subdir0/nested*", True, 2, ["nestedfile"]),
+        ("subdir[1-2]", False, None, []),
+        ("subdir[1-2]", True, None, ["subfile1", "subfile2", "nesteddir/nestedfile"]),
+        ("subdir[1-2]", True, 2, ["subfile1", "subfile2"]),
+        ("subdir[0-1]", False, None, []),
+        (
+            "subdir[0-1]",
+            True,
+            None,
+            [
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir0/nesteddir/nestedfile",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+                "subdir1/nesteddir/nestedfile",
+            ],
+        ),
+        (
+            "subdir[0-1]/*fil[e]*",
+            False,
+            None,
+            [
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+            ],
+        ),
+        (
+            "subdir[0-1]/*fil[e]*",
+            True,
+            None,
+            [
+                "subdir0/subfile1",
+                "subdir0/subfile2",
+                "subdir1/subfile1",
+                "subdir1/subfile2",
+            ],
+        ),
+    ],
+}
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/copy.py b/lib/python3.11/site-packages/fsspec/tests/abstract/copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..e39e57e5f7d52bfda8ab5e2398b04cc2303630a0
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/copy.py
@@ -0,0 +1,557 @@
+from hashlib import md5
+from itertools import product
+
+import pytest
+
+from fsspec.tests.abstract.common import GLOB_EDGE_CASES_TESTS
+
+
+class AbstractCopyTests:
+    def test_copy_file_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        fs_target,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1a
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            fs.touch(fs_join(target, "dummy"))
+        assert fs.isdir(target)
+
+        target_file2 = fs_join(target, "file2")
+        target_subfile1 = fs_join(target, "subfile1")
+
+        # Copy from source directory
+        fs.cp(fs_join(source, "file2"), target)
+        assert fs.isfile(target_file2)
+
+        # Copy from sub directory
+        fs.cp(fs_join(source, "subdir", "subfile1"), target)
+        assert fs.isfile(target_subfile1)
+
+        # Remove copied files
+        fs.rm([target_file2, target_subfile1])
+        assert not fs.exists(target_file2)
+        assert not fs.exists(target_subfile1)
+
+        # Repeat with trailing slash on target
+        fs.cp(fs_join(source, "file2"), target + "/")
+        assert fs.isdir(target)
+        assert fs.isfile(target_file2)
+
+        fs.cp(fs_join(source, "subdir", "subfile1"), target + "/")
+        assert fs.isfile(target_subfile1)
+
+    def test_copy_file_to_new_directory(
+        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
+    ):
+        # Copy scenario 1b
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        fs.cp(
+            fs_join(source, "subdir", "subfile1"), fs_join(target, "newdir/")
+        )  # Note trailing slash
+        assert fs.isdir(target)
+        assert fs.isdir(fs_join(target, "newdir"))
+        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+
+    def test_copy_file_to_file_in_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        fs_target,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1c
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            fs.touch(fs_join(target, "dummy"))
+        assert fs.isdir(target)
+
+        fs.cp(fs_join(source, "subdir", "subfile1"), fs_join(target, "newfile"))
+        assert fs.isfile(fs_join(target, "newfile"))
+
+    def test_copy_file_to_file_in_new_directory(
+        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
+    ):
+        # Copy scenario 1d
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        fs.cp(
+            fs_join(source, "subdir", "subfile1"), fs_join(target, "newdir", "newfile")
+        )
+        assert fs.isdir(fs_join(target, "newdir"))
+        assert fs.isfile(fs_join(target, "newdir", "newfile"))
+
+    def test_copy_directory_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        fs_target,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1e
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            dummy = fs_join(target, "dummy")
+            fs.touch(dummy)
+        assert fs.isdir(target)
+
+        for source_slash, target_slash in zip([False, True], [False, True]):
+            s = fs_join(source, "subdir")
+            if source_slash:
+                s += "/"
+            t = target + "/" if target_slash else target
+
+            # Without recursive does nothing
+            fs.cp(s, t)
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+            # With recursive
+            fs.cp(s, t, recursive=True)
+            if source_slash:
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert fs.isdir(fs_join(target, "nesteddir"))
+                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                        fs_join(target, "nesteddir"),
+                    ],
+                    recursive=True,
+                )
+            else:
+                assert fs.isdir(fs_join(target, "subdir"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
+                assert fs.isdir(fs_join(target, "subdir", "nesteddir"))
+                assert fs.isfile(fs_join(target, "subdir", "nesteddir", "nestedfile"))
+
+                fs.rm(fs_join(target, "subdir"), recursive=True)
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+            # Limit recursive by maxdepth
+            fs.cp(s, t, recursive=True, maxdepth=1)
+            if source_slash:
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert not fs.exists(fs_join(target, "nesteddir"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                    ],
+                    recursive=True,
+                )
+            else:
+                assert fs.isdir(fs_join(target, "subdir"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
+                assert not fs.exists(fs_join(target, "subdir", "nesteddir"))
+
+                fs.rm(fs_join(target, "subdir"), recursive=True)
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+    def test_copy_directory_to_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        fs_target,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1f
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        for source_slash, target_slash in zip([False, True], [False, True]):
+            s = fs_join(source, "subdir")
+            if source_slash:
+                s += "/"
+            t = fs_join(target, "newdir")
+            if target_slash:
+                t += "/"
+
+            # Without recursive does nothing
+            fs.cp(s, t)
+            if supports_empty_directories:
+                assert fs.ls(target) == []
+            else:
+                with pytest.raises(FileNotFoundError):
+                    fs.ls(target)
+
+            # With recursive
+            fs.cp(s, t, recursive=True)
+            assert fs.isdir(fs_join(target, "newdir"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+            assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
+            assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
+            assert not fs.exists(fs_join(target, "subdir"))
+
+            fs.rm(fs_join(target, "newdir"), recursive=True)
+            assert not fs.exists(fs_join(target, "newdir"))
+
+            # Limit recursive by maxdepth
+            fs.cp(s, t, recursive=True, maxdepth=1)
+            assert fs.isdir(fs_join(target, "newdir"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
+            assert not fs.exists(fs_join(target, "subdir"))
+
+            fs.rm(fs_join(target, "newdir"), recursive=True)
+            assert not fs.exists(fs_join(target, "newdir"))
+
+    def test_copy_glob_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        fs_target,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1g
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            dummy = fs_join(target, "dummy")
+            fs.touch(dummy)
+        assert fs.isdir(target)
+
+        for target_slash in [False, True]:
+            t = target + "/" if target_slash else target
+
+            # Without recursive
+            fs.cp(fs_join(source, "subdir", "*"), t)
+            assert fs.isfile(fs_join(target, "subfile1"))
+            assert fs.isfile(fs_join(target, "subfile2"))
+            assert not fs.isdir(fs_join(target, "nesteddir"))
+            assert not fs.exists(fs_join(target, "nesteddir", "nestedfile"))
+            assert not fs.exists(fs_join(target, "subdir"))
+
+            fs.rm(
+                [
+                    fs_join(target, "subfile1"),
+                    fs_join(target, "subfile2"),
+                ],
+                recursive=True,
+            )
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+            # With recursive
+            for glob, recursive in zip(["*", "**"], [True, False]):
+                fs.cp(fs_join(source, "subdir", glob), t, recursive=recursive)
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert fs.isdir(fs_join(target, "nesteddir"))
+                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                        fs_join(target, "nesteddir"),
+                    ],
+                    recursive=True,
+                )
+                assert fs.ls(target, detail=False) == (
+                    [] if supports_empty_directories else [dummy]
+                )
+
+                # Limit recursive by maxdepth
+                fs.cp(
+                    fs_join(source, "subdir", glob), t, recursive=recursive, maxdepth=1
+                )
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert not fs.exists(fs_join(target, "nesteddir"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                    ],
+                    recursive=True,
+                )
+                assert fs.ls(target, detail=False) == (
+                    [] if supports_empty_directories else [dummy]
+                )
+
+    def test_copy_glob_to_new_directory(
+        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
+    ):
+        # Copy scenario 1h
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        for target_slash in [False, True]:
+            t = fs_join(target, "newdir")
+            if target_slash:
+                t += "/"
+
+            # Without recursive
+            fs.cp(fs_join(source, "subdir", "*"), t)
+            assert fs.isdir(fs_join(target, "newdir"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
+            assert not fs.exists(fs_join(target, "newdir", "nesteddir", "nestedfile"))
+            assert not fs.exists(fs_join(target, "subdir"))
+            assert not fs.exists(fs_join(target, "newdir", "subdir"))
+
+            fs.rm(fs_join(target, "newdir"), recursive=True)
+            assert not fs.exists(fs_join(target, "newdir"))
+
+            # With recursive
+            for glob, recursive in zip(["*", "**"], [True, False]):
+                fs.cp(fs_join(source, "subdir", glob), t, recursive=recursive)
+                assert fs.isdir(fs_join(target, "newdir"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+                assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
+                assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
+                assert not fs.exists(fs_join(target, "subdir"))
+                assert not fs.exists(fs_join(target, "newdir", "subdir"))
+
+                fs.rm(fs_join(target, "newdir"), recursive=True)
+                assert not fs.exists(fs_join(target, "newdir"))
+
+                # Limit recursive by maxdepth
+                fs.cp(
+                    fs_join(source, "subdir", glob), t, recursive=recursive, maxdepth=1
+                )
+                assert fs.isdir(fs_join(target, "newdir"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+                assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
+                assert not fs.exists(fs_join(target, "subdir"))
+                assert not fs.exists(fs_join(target, "newdir", "subdir"))
+
+                fs.rm(fs_join(target, "newdir"), recursive=True)
+                assert not fs.exists(fs_join(target, "newdir"))
+
+    @pytest.mark.parametrize(
+        GLOB_EDGE_CASES_TESTS["argnames"],
+        GLOB_EDGE_CASES_TESTS["argvalues"],
+    )
+    def test_copy_glob_edge_cases(
+        self,
+        path,
+        recursive,
+        maxdepth,
+        expected,
+        fs,
+        fs_join,
+        fs_glob_edge_cases_files,
+        fs_target,
+        fs_sanitize_path,
+    ):
+        # Copy scenario 1g
+        source = fs_glob_edge_cases_files
+
+        target = fs_target
+
+        for new_dir, target_slash in product([True, False], [True, False]):
+            fs.mkdir(target)
+
+            t = fs_join(target, "newdir") if new_dir else target
+            t = t + "/" if target_slash else t
+
+            fs.copy(fs_join(source, path), t, recursive=recursive, maxdepth=maxdepth)
+
+            output = fs.find(target)
+            if new_dir:
+                prefixed_expected = [
+                    fs_sanitize_path(fs_join(target, "newdir", p)) for p in expected
+                ]
+            else:
+                prefixed_expected = [
+                    fs_sanitize_path(fs_join(target, p)) for p in expected
+                ]
+            assert sorted(output) == sorted(prefixed_expected)
+
+            try:
+                fs.rm(target, recursive=True)
+            except FileNotFoundError:
+                pass
+
+    def test_copy_list_of_files_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        fs_target,
+        supports_empty_directories,
+    ):
+        # Copy scenario 2a
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            dummy = fs_join(target, "dummy")
+            fs.touch(dummy)
+        assert fs.isdir(target)
+
+        source_files = [
+            fs_join(source, "file1"),
+            fs_join(source, "file2"),
+            fs_join(source, "subdir", "subfile1"),
+        ]
+
+        for target_slash in [False, True]:
+            t = target + "/" if target_slash else target
+
+            fs.cp(source_files, t)
+            assert fs.isfile(fs_join(target, "file1"))
+            assert fs.isfile(fs_join(target, "file2"))
+            assert fs.isfile(fs_join(target, "subfile1"))
+
+            fs.rm(
+                [
+                    fs_join(target, "file1"),
+                    fs_join(target, "file2"),
+                    fs_join(target, "subfile1"),
+                ],
+                recursive=True,
+            )
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+    def test_copy_list_of_files_to_new_directory(
+        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
+    ):
+        # Copy scenario 2b
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        source_files = [
+            fs_join(source, "file1"),
+            fs_join(source, "file2"),
+            fs_join(source, "subdir", "subfile1"),
+        ]
+
+        fs.cp(source_files, fs_join(target, "newdir") + "/")  # Note trailing slash
+        assert fs.isdir(fs_join(target, "newdir"))
+        assert fs.isfile(fs_join(target, "newdir", "file1"))
+        assert fs.isfile(fs_join(target, "newdir", "file2"))
+        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+
+    def test_copy_two_files_new_directory(
+        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
+    ):
+        # This is a duplicate of test_copy_list_of_files_to_new_directory and
+        # can eventually be removed.
+        source = fs_bulk_operations_scenario_0
+
+        target = fs_target
+        assert not fs.exists(target)
+        fs.cp([fs_join(source, "file1"), fs_join(source, "file2")], target)
+
+        assert fs.isdir(target)
+        assert fs.isfile(fs_join(target, "file1"))
+        assert fs.isfile(fs_join(target, "file2"))
+
+    def test_copy_directory_without_files_with_same_name_prefix(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        fs_dir_and_file_with_same_name_prefix,
+        supports_empty_directories,
+    ):
+        # Create the test dirs
+        source = fs_dir_and_file_with_same_name_prefix
+        target = fs_target
+
+        # Test without glob
+        fs.cp(fs_join(source, "subdir"), target, recursive=True)
+
+        assert fs.isfile(fs_join(target, "subfile.txt"))
+        assert not fs.isfile(fs_join(target, "subdir.txt"))
+
+        fs.rm([fs_join(target, "subfile.txt")])
+        if supports_empty_directories:
+            assert fs.ls(target) == []
+        else:
+            assert not fs.exists(target)
+
+        # Test with glob
+        fs.cp(fs_join(source, "subdir*"), target, recursive=True)
+
+        assert fs.isdir(fs_join(target, "subdir"))
+        assert fs.isfile(fs_join(target, "subdir", "subfile.txt"))
+        assert fs.isfile(fs_join(target, "subdir.txt"))
+
+    def test_copy_with_source_and_destination_as_list(
+        self, fs, fs_target, fs_join, fs_10_files_with_hashed_names
+    ):
+        # Create the test dir
+        source = fs_10_files_with_hashed_names
+        target = fs_target
+
+        # Create list of files for source and destination
+        source_files = []
+        destination_files = []
+        for i in range(10):
+            hashed_i = md5(str(i).encode("utf-8")).hexdigest()
+            source_files.append(fs_join(source, f"{hashed_i}.txt"))
+            destination_files.append(fs_join(target, f"{hashed_i}.txt"))
+
+        # Copy and assert order was kept
+        fs.copy(path1=source_files, path2=destination_files)
+
+        for i in range(10):
+            file_content = fs.cat(destination_files[i]).decode("utf-8")
+            assert file_content == str(i)
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/get.py b/lib/python3.11/site-packages/fsspec/tests/abstract/get.py
new file mode 100644
index 0000000000000000000000000000000000000000..851ab81ee581e74cac41c64c83ef0af75826d6b0
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/get.py
@@ -0,0 +1,587 @@
+from hashlib import md5
+from itertools import product
+
+import pytest
+
+from fsspec.implementations.local import make_path_posix
+from fsspec.tests.abstract.common import GLOB_EDGE_CASES_TESTS
+
+
+class AbstractGetTests:
+    def test_get_file_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1a
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+        assert local_fs.isdir(target)
+
+        target_file2 = local_join(target, "file2")
+        target_subfile1 = local_join(target, "subfile1")
+
+        # Copy from source directory
+        fs.get(fs_join(source, "file2"), target)
+        assert local_fs.isfile(target_file2)
+
+        # Copy from sub directory
+        fs.get(fs_join(source, "subdir", "subfile1"), target)
+        assert local_fs.isfile(target_subfile1)
+
+        # Remove copied files
+        local_fs.rm([target_file2, target_subfile1])
+        assert not local_fs.exists(target_file2)
+        assert not local_fs.exists(target_subfile1)
+
+        # Repeat with trailing slash on target
+        fs.get(fs_join(source, "file2"), target + "/")
+        assert local_fs.isdir(target)
+        assert local_fs.isfile(target_file2)
+
+        fs.get(fs_join(source, "subdir", "subfile1"), target + "/")
+        assert local_fs.isfile(target_subfile1)
+
+    def test_get_file_to_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1b
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        fs.get(
+            fs_join(source, "subdir", "subfile1"), local_join(target, "newdir/")
+        )  # Note trailing slash
+
+        assert local_fs.isdir(target)
+        assert local_fs.isdir(local_join(target, "newdir"))
+        assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+
+    def test_get_file_to_file_in_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1c
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        fs.get(fs_join(source, "subdir", "subfile1"), local_join(target, "newfile"))
+        assert local_fs.isfile(local_join(target, "newfile"))
+
+    def test_get_file_to_file_in_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1d
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        fs.get(
+            fs_join(source, "subdir", "subfile1"),
+            local_join(target, "newdir", "newfile"),
+        )
+        assert local_fs.isdir(local_join(target, "newdir"))
+        assert local_fs.isfile(local_join(target, "newdir", "newfile"))
+
+    def test_get_directory_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1e
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+        assert local_fs.isdir(target)
+
+        for source_slash, target_slash in zip([False, True], [False, True]):
+            s = fs_join(source, "subdir")
+            if source_slash:
+                s += "/"
+            t = target + "/" if target_slash else target
+
+            # Without recursive does nothing
+            fs.get(s, t)
+            assert local_fs.ls(target) == []
+
+            # With recursive
+            fs.get(s, t, recursive=True)
+            if source_slash:
+                assert local_fs.isfile(local_join(target, "subfile1"))
+                assert local_fs.isfile(local_join(target, "subfile2"))
+                assert local_fs.isdir(local_join(target, "nesteddir"))
+                assert local_fs.isfile(local_join(target, "nesteddir", "nestedfile"))
+                assert not local_fs.exists(local_join(target, "subdir"))
+
+                local_fs.rm(
+                    [
+                        local_join(target, "subfile1"),
+                        local_join(target, "subfile2"),
+                        local_join(target, "nesteddir"),
+                    ],
+                    recursive=True,
+                )
+            else:
+                assert local_fs.isdir(local_join(target, "subdir"))
+                assert local_fs.isfile(local_join(target, "subdir", "subfile1"))
+                assert local_fs.isfile(local_join(target, "subdir", "subfile2"))
+                assert local_fs.isdir(local_join(target, "subdir", "nesteddir"))
+                assert local_fs.isfile(
+                    local_join(target, "subdir", "nesteddir", "nestedfile")
+                )
+
+                local_fs.rm(local_join(target, "subdir"), recursive=True)
+            assert local_fs.ls(target) == []
+
+            # Limit recursive by maxdepth
+            fs.get(s, t, recursive=True, maxdepth=1)
+            if source_slash:
+                assert local_fs.isfile(local_join(target, "subfile1"))
+                assert local_fs.isfile(local_join(target, "subfile2"))
+                assert not local_fs.exists(local_join(target, "nesteddir"))
+                assert not local_fs.exists(local_join(target, "subdir"))
+
+                local_fs.rm(
+                    [
+                        local_join(target, "subfile1"),
+                        local_join(target, "subfile2"),
+                    ],
+                    recursive=True,
+                )
+            else:
+                assert local_fs.isdir(local_join(target, "subdir"))
+                assert local_fs.isfile(local_join(target, "subdir", "subfile1"))
+                assert local_fs.isfile(local_join(target, "subdir", "subfile2"))
+                assert not local_fs.exists(local_join(target, "subdir", "nesteddir"))
+
+                local_fs.rm(local_join(target, "subdir"), recursive=True)
+            assert local_fs.ls(target) == []
+
+    def test_get_directory_to_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1f
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        for source_slash, target_slash in zip([False, True], [False, True]):
+            s = fs_join(source, "subdir")
+            if source_slash:
+                s += "/"
+            t = local_join(target, "newdir")
+            if target_slash:
+                t += "/"
+
+            # Without recursive does nothing
+            fs.get(s, t)
+            assert local_fs.ls(target) == []
+
+            # With recursive
+            fs.get(s, t, recursive=True)
+            assert local_fs.isdir(local_join(target, "newdir"))
+            assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+            assert local_fs.isfile(local_join(target, "newdir", "subfile2"))
+            assert local_fs.isdir(local_join(target, "newdir", "nesteddir"))
+            assert local_fs.isfile(
+                local_join(target, "newdir", "nesteddir", "nestedfile")
+            )
+            assert not local_fs.exists(local_join(target, "subdir"))
+
+            local_fs.rm(local_join(target, "newdir"), recursive=True)
+            assert local_fs.ls(target) == []
+
+            # Limit recursive by maxdepth
+            fs.get(s, t, recursive=True, maxdepth=1)
+            assert local_fs.isdir(local_join(target, "newdir"))
+            assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+            assert local_fs.isfile(local_join(target, "newdir", "subfile2"))
+            assert not local_fs.exists(local_join(target, "newdir", "nesteddir"))
+            assert not local_fs.exists(local_join(target, "subdir"))
+
+            local_fs.rm(local_join(target, "newdir"), recursive=True)
+            assert not local_fs.exists(local_join(target, "newdir"))
+
+    def test_get_glob_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1g
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        for target_slash in [False, True]:
+            t = target + "/" if target_slash else target
+
+            # Without recursive
+            fs.get(fs_join(source, "subdir", "*"), t)
+            assert local_fs.isfile(local_join(target, "subfile1"))
+            assert local_fs.isfile(local_join(target, "subfile2"))
+            assert not local_fs.isdir(local_join(target, "nesteddir"))
+            assert not local_fs.exists(local_join(target, "nesteddir", "nestedfile"))
+            assert not local_fs.exists(local_join(target, "subdir"))
+
+            local_fs.rm(
+                [
+                    local_join(target, "subfile1"),
+                    local_join(target, "subfile2"),
+                ],
+                recursive=True,
+            )
+            assert local_fs.ls(target) == []
+
+            # With recursive
+            for glob, recursive in zip(["*", "**"], [True, False]):
+                fs.get(fs_join(source, "subdir", glob), t, recursive=recursive)
+                assert local_fs.isfile(local_join(target, "subfile1"))
+                assert local_fs.isfile(local_join(target, "subfile2"))
+                assert local_fs.isdir(local_join(target, "nesteddir"))
+                assert local_fs.isfile(local_join(target, "nesteddir", "nestedfile"))
+                assert not local_fs.exists(local_join(target, "subdir"))
+
+                local_fs.rm(
+                    [
+                        local_join(target, "subfile1"),
+                        local_join(target, "subfile2"),
+                        local_join(target, "nesteddir"),
+                    ],
+                    recursive=True,
+                )
+                assert local_fs.ls(target) == []
+
+                # Limit recursive by maxdepth
+                fs.get(
+                    fs_join(source, "subdir", glob), t, recursive=recursive, maxdepth=1
+                )
+                assert local_fs.isfile(local_join(target, "subfile1"))
+                assert local_fs.isfile(local_join(target, "subfile2"))
+                assert not local_fs.exists(local_join(target, "nesteddir"))
+                assert not local_fs.exists(local_join(target, "subdir"))
+
+                local_fs.rm(
+                    [
+                        local_join(target, "subfile1"),
+                        local_join(target, "subfile2"),
+                    ],
+                    recursive=True,
+                )
+                assert local_fs.ls(target) == []
+
+    def test_get_glob_to_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1h
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        for target_slash in [False, True]:
+            t = fs_join(target, "newdir")
+            if target_slash:
+                t += "/"
+
+            # Without recursive
+            fs.get(fs_join(source, "subdir", "*"), t)
+            assert local_fs.isdir(local_join(target, "newdir"))
+            assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+            assert local_fs.isfile(local_join(target, "newdir", "subfile2"))
+            assert not local_fs.exists(local_join(target, "newdir", "nesteddir"))
+            assert not local_fs.exists(
+                local_join(target, "newdir", "nesteddir", "nestedfile")
+            )
+            assert not local_fs.exists(local_join(target, "subdir"))
+            assert not local_fs.exists(local_join(target, "newdir", "subdir"))
+
+            local_fs.rm(local_join(target, "newdir"), recursive=True)
+            assert local_fs.ls(target) == []
+
+            # With recursive
+            for glob, recursive in zip(["*", "**"], [True, False]):
+                fs.get(fs_join(source, "subdir", glob), t, recursive=recursive)
+                assert local_fs.isdir(local_join(target, "newdir"))
+                assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+                assert local_fs.isfile(local_join(target, "newdir", "subfile2"))
+                assert local_fs.isdir(local_join(target, "newdir", "nesteddir"))
+                assert local_fs.isfile(
+                    local_join(target, "newdir", "nesteddir", "nestedfile")
+                )
+                assert not local_fs.exists(local_join(target, "subdir"))
+                assert not local_fs.exists(local_join(target, "newdir", "subdir"))
+
+                local_fs.rm(local_join(target, "newdir"), recursive=True)
+                assert not local_fs.exists(local_join(target, "newdir"))
+
+                # Limit recursive by maxdepth
+                fs.get(
+                    fs_join(source, "subdir", glob), t, recursive=recursive, maxdepth=1
+                )
+                assert local_fs.isdir(local_join(target, "newdir"))
+                assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+                assert local_fs.isfile(local_join(target, "newdir", "subfile2"))
+                assert not local_fs.exists(local_join(target, "newdir", "nesteddir"))
+                assert not local_fs.exists(local_join(target, "subdir"))
+                assert not local_fs.exists(local_join(target, "newdir", "subdir"))
+
+                local_fs.rm(local_fs.ls(target, detail=False), recursive=True)
+                assert not local_fs.exists(local_join(target, "newdir"))
+
+    @pytest.mark.parametrize(
+        GLOB_EDGE_CASES_TESTS["argnames"],
+        GLOB_EDGE_CASES_TESTS["argvalues"],
+    )
+    def test_get_glob_edge_cases(
+        self,
+        path,
+        recursive,
+        maxdepth,
+        expected,
+        fs,
+        fs_join,
+        fs_glob_edge_cases_files,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 1g
+        source = fs_glob_edge_cases_files
+
+        target = local_target
+
+        for new_dir, target_slash in product([True, False], [True, False]):
+            local_fs.mkdir(target)
+
+            t = local_join(target, "newdir") if new_dir else target
+            t = t + "/" if target_slash else t
+
+            fs.get(fs_join(source, path), t, recursive=recursive, maxdepth=maxdepth)
+
+            output = local_fs.find(target)
+            if new_dir:
+                prefixed_expected = [
+                    make_path_posix(local_join(target, "newdir", p)) for p in expected
+                ]
+            else:
+                prefixed_expected = [
+                    make_path_posix(local_join(target, p)) for p in expected
+                ]
+            assert sorted(output) == sorted(prefixed_expected)
+
+            try:
+                local_fs.rm(target, recursive=True)
+            except FileNotFoundError:
+                pass
+
+    def test_get_list_of_files_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 2a
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        source_files = [
+            fs_join(source, "file1"),
+            fs_join(source, "file2"),
+            fs_join(source, "subdir", "subfile1"),
+        ]
+
+        for target_slash in [False, True]:
+            t = target + "/" if target_slash else target
+
+            fs.get(source_files, t)
+            assert local_fs.isfile(local_join(target, "file1"))
+            assert local_fs.isfile(local_join(target, "file2"))
+            assert local_fs.isfile(local_join(target, "subfile1"))
+
+            local_fs.rm(
+                [
+                    local_join(target, "file1"),
+                    local_join(target, "file2"),
+                    local_join(target, "subfile1"),
+                ],
+                recursive=True,
+            )
+            assert local_fs.ls(target) == []
+
+    def test_get_list_of_files_to_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_bulk_operations_scenario_0,
+        local_fs,
+        local_join,
+        local_target,
+    ):
+        # Copy scenario 2b
+        source = fs_bulk_operations_scenario_0
+
+        target = local_target
+        local_fs.mkdir(target)
+
+        source_files = [
+            fs_join(source, "file1"),
+            fs_join(source, "file2"),
+            fs_join(source, "subdir", "subfile1"),
+        ]
+
+        fs.get(source_files, local_join(target, "newdir") + "/")  # Note trailing slash
+        assert local_fs.isdir(local_join(target, "newdir"))
+        assert local_fs.isfile(local_join(target, "newdir", "file1"))
+        assert local_fs.isfile(local_join(target, "newdir", "file2"))
+        assert local_fs.isfile(local_join(target, "newdir", "subfile1"))
+
+    def test_get_directory_recursive(
+        self, fs, fs_join, fs_path, local_fs, local_join, local_target
+    ):
+        # https://github.com/fsspec/filesystem_spec/issues/1062
+        # Recursive cp/get/put of source directory into non-existent target directory.
+        src = fs_join(fs_path, "src")
+        src_file = fs_join(src, "file")
+        fs.mkdir(src)
+        fs.touch(src_file)
+
+        target = local_target
+
+        # get without slash
+        assert not local_fs.exists(target)
+        for loop in range(2):
+            fs.get(src, target, recursive=True)
+            assert local_fs.isdir(target)
+
+            if loop == 0:
+                assert local_fs.isfile(local_join(target, "file"))
+                assert not local_fs.exists(local_join(target, "src"))
+            else:
+                assert local_fs.isfile(local_join(target, "file"))
+                assert local_fs.isdir(local_join(target, "src"))
+                assert local_fs.isfile(local_join(target, "src", "file"))
+
+        local_fs.rm(target, recursive=True)
+
+        # get with slash
+        assert not local_fs.exists(target)
+        for loop in range(2):
+            fs.get(src + "/", target, recursive=True)
+            assert local_fs.isdir(target)
+            assert local_fs.isfile(local_join(target, "file"))
+            assert not local_fs.exists(local_join(target, "src"))
+
+    def test_get_directory_without_files_with_same_name_prefix(
+        self,
+        fs,
+        fs_join,
+        local_fs,
+        local_join,
+        local_target,
+        fs_dir_and_file_with_same_name_prefix,
+    ):
+        # Create the test dirs
+        source = fs_dir_and_file_with_same_name_prefix
+        target = local_target
+
+        # Test without glob
+        fs.get(fs_join(source, "subdir"), target, recursive=True)
+
+        assert local_fs.isfile(local_join(target, "subfile.txt"))
+        assert not local_fs.isfile(local_join(target, "subdir.txt"))
+
+        local_fs.rm([local_join(target, "subfile.txt")])
+        assert local_fs.ls(target) == []
+
+        # Test with glob
+        fs.get(fs_join(source, "subdir*"), target, recursive=True)
+
+        assert local_fs.isdir(local_join(target, "subdir"))
+        assert local_fs.isfile(local_join(target, "subdir", "subfile.txt"))
+        assert local_fs.isfile(local_join(target, "subdir.txt"))
+
+    def test_get_with_source_and_destination_as_list(
+        self,
+        fs,
+        fs_join,
+        local_fs,
+        local_join,
+        local_target,
+        fs_10_files_with_hashed_names,
+    ):
+        # Create the test dir
+        source = fs_10_files_with_hashed_names
+        target = local_target
+
+        # Create list of files for source and destination
+        source_files = []
+        destination_files = []
+        for i in range(10):
+            hashed_i = md5(str(i).encode("utf-8")).hexdigest()
+            source_files.append(fs_join(source, f"{hashed_i}.txt"))
+            destination_files.append(
+                make_path_posix(local_join(target, f"{hashed_i}.txt"))
+            )
+
+        # Copy and assert order was kept
+        fs.get(rpath=source_files, lpath=destination_files)
+
+        for i in range(10):
+            file_content = local_fs.cat(destination_files[i]).decode("utf-8")
+            assert file_content == str(i)
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/mv.py b/lib/python3.11/site-packages/fsspec/tests/abstract/mv.py
new file mode 100644
index 0000000000000000000000000000000000000000..39f6caa3de815e024fa84de2acecc986c823ed29
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/mv.py
@@ -0,0 +1,57 @@
+import os
+
+import pytest
+
+import fsspec
+
+
+def test_move_raises_error_with_tmpdir(tmpdir):
+    # Create a file in the temporary directory
+    source = tmpdir.join("source_file.txt")
+    source.write("content")
+
+    # Define a destination that simulates a protected or invalid path
+    destination = tmpdir.join("non_existent_directory/destination_file.txt")
+
+    # Instantiate the filesystem (assuming the local file system interface)
+    fs = fsspec.filesystem("file")
+
+    # Use the actual file paths as string
+    with pytest.raises(FileNotFoundError):
+        fs.mv(str(source), str(destination))
+
+
+@pytest.mark.parametrize("recursive", (True, False))
+def test_move_raises_error_with_tmpdir_permission(recursive, tmpdir):
+    # Create a file in the temporary directory
+    source = tmpdir.join("source_file.txt")
+    source.write("content")
+
+    # Create a protected directory (non-writable)
+    protected_dir = tmpdir.mkdir("protected_directory")
+    protected_path = str(protected_dir)
+
+    # Set the directory to read-only
+    if os.name == "nt":
+        os.system(f'icacls "{protected_path}" /deny Everyone:(W)')
+    else:
+        os.chmod(protected_path, 0o555)  # Sets the directory to read-only
+
+    # Define a destination inside the protected directory
+    destination = protected_dir.join("destination_file.txt")
+
+    # Instantiate the filesystem (assuming the local file system interface)
+    fs = fsspec.filesystem("file")
+
+    # Try to move the file to the read-only directory, expecting a permission error
+    with pytest.raises(PermissionError):
+        fs.mv(str(source), str(destination), recursive=recursive)
+
+    # Assert the file was not created in the destination
+    assert not os.path.exists(destination)
+
+    # Cleanup: Restore permissions so the directory can be cleaned up
+    if os.name == "nt":
+        os.system(f'icacls "{protected_path}" /remove:d Everyone')
+    else:
+        os.chmod(protected_path, 0o755)  # Restore write permission for cleanup
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/open.py b/lib/python3.11/site-packages/fsspec/tests/abstract/open.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb75ea852276fb8d834345883813b8e27a0ae24c
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/open.py
@@ -0,0 +1,11 @@
+import pytest
+
+
+class AbstractOpenTests:
+    def test_open_exclusive(self, fs, fs_target):
+        with fs.open(fs_target, "wb") as f:
+            f.write(b"data")
+        with fs.open(fs_target, "rb") as f:
+            assert f.read() == b"data"
+        with pytest.raises(FileExistsError):
+            fs.open(fs_target, "xb")
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/pipe.py b/lib/python3.11/site-packages/fsspec/tests/abstract/pipe.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ecca96e9d23ff268a253c48269d5cca451ea270
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/pipe.py
@@ -0,0 +1,11 @@
+import pytest
+
+
+class AbstractPipeTests:
+    def test_pipe_exclusive(self, fs, fs_target):
+        fs.pipe_file(fs_target, b"data")
+        assert fs.cat_file(fs_target) == b"data"
+        with pytest.raises(FileExistsError):
+            fs.pipe_file(fs_target, b"data", mode="create")
+        fs.pipe_file(fs_target, b"new data", mode="overwrite")
+        assert fs.cat_file(fs_target) == b"new data"
diff --git a/lib/python3.11/site-packages/fsspec/tests/abstract/put.py b/lib/python3.11/site-packages/fsspec/tests/abstract/put.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fc349977f0384d9fc86126498be5c6ad99a21d3
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/tests/abstract/put.py
@@ -0,0 +1,591 @@
+from hashlib import md5
+from itertools import product
+
+import pytest
+
+from fsspec.tests.abstract.common import GLOB_EDGE_CASES_TESTS
+
+
+class AbstractPutTests:
+    def test_put_file_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_join,
+        local_bulk_operations_scenario_0,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1a
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            fs.touch(fs_join(target, "dummy"))
+        assert fs.isdir(target)
+
+        target_file2 = fs_join(target, "file2")
+        target_subfile1 = fs_join(target, "subfile1")
+
+        # Copy from source directory
+        fs.put(local_join(source, "file2"), target)
+        assert fs.isfile(target_file2)
+
+        # Copy from sub directory
+        fs.put(local_join(source, "subdir", "subfile1"), target)
+        assert fs.isfile(target_subfile1)
+
+        # Remove copied files
+        fs.rm([target_file2, target_subfile1])
+        assert not fs.exists(target_file2)
+        assert not fs.exists(target_subfile1)
+
+        # Repeat with trailing slash on target
+        fs.put(local_join(source, "file2"), target + "/")
+        assert fs.isdir(target)
+        assert fs.isfile(target_file2)
+
+        fs.put(local_join(source, "subdir", "subfile1"), target + "/")
+        assert fs.isfile(target_subfile1)
+
+    def test_put_file_to_new_directory(
+        self, fs, fs_join, fs_target, local_join, local_bulk_operations_scenario_0
+    ):
+        # Copy scenario 1b
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        fs.put(
+            local_join(source, "subdir", "subfile1"), fs_join(target, "newdir/")
+        )  # Note trailing slash
+        assert fs.isdir(target)
+        assert fs.isdir(fs_join(target, "newdir"))
+        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+
+    def test_put_file_to_file_in_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_join,
+        supports_empty_directories,
+        local_bulk_operations_scenario_0,
+    ):
+        # Copy scenario 1c
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            fs.touch(fs_join(target, "dummy"))
+        assert fs.isdir(target)
+
+        fs.put(local_join(source, "subdir", "subfile1"), fs_join(target, "newfile"))
+        assert fs.isfile(fs_join(target, "newfile"))
+
+    def test_put_file_to_file_in_new_directory(
+        self, fs, fs_join, fs_target, local_join, local_bulk_operations_scenario_0
+    ):
+        # Copy scenario 1d
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        fs.put(
+            local_join(source, "subdir", "subfile1"),
+            fs_join(target, "newdir", "newfile"),
+        )
+        assert fs.isdir(fs_join(target, "newdir"))
+        assert fs.isfile(fs_join(target, "newdir", "newfile"))
+
+    def test_put_directory_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_bulk_operations_scenario_0,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1e
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            dummy = fs_join(target, "dummy")
+            fs.touch(dummy)
+        assert fs.isdir(target)
+
+        for source_slash, target_slash in zip([False, True], [False, True]):
+            s = fs_join(source, "subdir")
+            if source_slash:
+                s += "/"
+            t = target + "/" if target_slash else target
+
+            # Without recursive does nothing
+            fs.put(s, t)
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+            # With recursive
+            fs.put(s, t, recursive=True)
+            if source_slash:
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert fs.isdir(fs_join(target, "nesteddir"))
+                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                        fs_join(target, "nesteddir"),
+                    ],
+                    recursive=True,
+                )
+            else:
+                assert fs.isdir(fs_join(target, "subdir"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
+                assert fs.isdir(fs_join(target, "subdir", "nesteddir"))
+                assert fs.isfile(fs_join(target, "subdir", "nesteddir", "nestedfile"))
+
+                fs.rm(fs_join(target, "subdir"), recursive=True)
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+            # Limit recursive by maxdepth
+            fs.put(s, t, recursive=True, maxdepth=1)
+            if source_slash:
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert not fs.exists(fs_join(target, "nesteddir"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                    ],
+                    recursive=True,
+                )
+            else:
+                assert fs.isdir(fs_join(target, "subdir"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
+                assert not fs.exists(fs_join(target, "subdir", "nesteddir"))
+
+                fs.rm(fs_join(target, "subdir"), recursive=True)
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+    def test_put_directory_to_new_directory(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_bulk_operations_scenario_0,
+        supports_empty_directories,
+    ):
+        # Copy scenario 1f
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        for source_slash, target_slash in zip([False, True], [False, True]):
+            s = fs_join(source, "subdir")
+            if source_slash:
+                s += "/"
+            t = fs_join(target, "newdir")
+            if target_slash:
+                t += "/"
+
+            # Without recursive does nothing
+            fs.put(s, t)
+            if supports_empty_directories:
+                assert fs.ls(target) == []
+            else:
+                with pytest.raises(FileNotFoundError):
+                    fs.ls(target)
+
+            # With recursive
+            fs.put(s, t, recursive=True)
+            assert fs.isdir(fs_join(target, "newdir"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+            assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
+            assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
+            assert not fs.exists(fs_join(target, "subdir"))
+
+            fs.rm(fs_join(target, "newdir"), recursive=True)
+            assert not fs.exists(fs_join(target, "newdir"))
+
+            # Limit recursive by maxdepth
+            fs.put(s, t, recursive=True, maxdepth=1)
+            assert fs.isdir(fs_join(target, "newdir"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
+            assert not fs.exists(fs_join(target, "subdir"))
+
+            fs.rm(fs_join(target, "newdir"), recursive=True)
+            assert not fs.exists(fs_join(target, "newdir"))
+
+    def test_put_glob_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_join,
+        supports_empty_directories,
+        local_bulk_operations_scenario_0,
+    ):
+        # Copy scenario 1g
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            dummy = fs_join(target, "dummy")
+            fs.touch(dummy)
+        assert fs.isdir(target)
+
+        for target_slash in [False, True]:
+            t = target + "/" if target_slash else target
+
+            # Without recursive
+            fs.put(local_join(source, "subdir", "*"), t)
+            assert fs.isfile(fs_join(target, "subfile1"))
+            assert fs.isfile(fs_join(target, "subfile2"))
+            assert not fs.isdir(fs_join(target, "nesteddir"))
+            assert not fs.exists(fs_join(target, "nesteddir", "nestedfile"))
+            assert not fs.exists(fs_join(target, "subdir"))
+
+            fs.rm(
+                [
+                    fs_join(target, "subfile1"),
+                    fs_join(target, "subfile2"),
+                ],
+                recursive=True,
+            )
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+            # With recursive
+            for glob, recursive in zip(["*", "**"], [True, False]):
+                fs.put(local_join(source, "subdir", glob), t, recursive=recursive)
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert fs.isdir(fs_join(target, "nesteddir"))
+                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                        fs_join(target, "nesteddir"),
+                    ],
+                    recursive=True,
+                )
+                assert fs.ls(target, detail=False) == (
+                    [] if supports_empty_directories else [dummy]
+                )
+
+                # Limit recursive by maxdepth
+                fs.put(
+                    local_join(source, "subdir", glob),
+                    t,
+                    recursive=recursive,
+                    maxdepth=1,
+                )
+                assert fs.isfile(fs_join(target, "subfile1"))
+                assert fs.isfile(fs_join(target, "subfile2"))
+                assert not fs.exists(fs_join(target, "nesteddir"))
+                assert not fs.exists(fs_join(target, "subdir"))
+
+                fs.rm(
+                    [
+                        fs_join(target, "subfile1"),
+                        fs_join(target, "subfile2"),
+                    ],
+                    recursive=True,
+                )
+                assert fs.ls(target, detail=False) == (
+                    [] if supports_empty_directories else [dummy]
+                )
+
+    def test_put_glob_to_new_directory(
+        self, fs, fs_join, fs_target, local_join, local_bulk_operations_scenario_0
+    ):
+        # Copy scenario 1h
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        for target_slash in [False, True]:
+            t = fs_join(target, "newdir")
+            if target_slash:
+                t += "/"
+
+            # Without recursive
+            fs.put(local_join(source, "subdir", "*"), t)
+            assert fs.isdir(fs_join(target, "newdir"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
+            assert not fs.exists(fs_join(target, "newdir", "nesteddir", "nestedfile"))
+            assert not fs.exists(fs_join(target, "subdir"))
+            assert not fs.exists(fs_join(target, "newdir", "subdir"))
+
+            fs.rm(fs_join(target, "newdir"), recursive=True)
+            assert not fs.exists(fs_join(target, "newdir"))
+
+            # With recursive
+            for glob, recursive in zip(["*", "**"], [True, False]):
+                fs.put(local_join(source, "subdir", glob), t, recursive=recursive)
+                assert fs.isdir(fs_join(target, "newdir"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+                assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
+                assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
+                assert not fs.exists(fs_join(target, "subdir"))
+                assert not fs.exists(fs_join(target, "newdir", "subdir"))
+
+                fs.rm(fs_join(target, "newdir"), recursive=True)
+                assert not fs.exists(fs_join(target, "newdir"))
+
+                # Limit recursive by maxdepth
+                fs.put(
+                    local_join(source, "subdir", glob),
+                    t,
+                    recursive=recursive,
+                    maxdepth=1,
+                )
+                assert fs.isdir(fs_join(target, "newdir"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+                assert fs.isfile(fs_join(target, "newdir", "subfile2"))
+                assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
+                assert not fs.exists(fs_join(target, "subdir"))
+                assert not fs.exists(fs_join(target, "newdir", "subdir"))
+
+                fs.rm(fs_join(target, "newdir"), recursive=True)
+                assert not fs.exists(fs_join(target, "newdir"))
+
+    @pytest.mark.parametrize(
+        GLOB_EDGE_CASES_TESTS["argnames"],
+        GLOB_EDGE_CASES_TESTS["argvalues"],
+    )
+    def test_put_glob_edge_cases(
+        self,
+        path,
+        recursive,
+        maxdepth,
+        expected,
+        fs,
+        fs_join,
+        fs_target,
+        local_glob_edge_cases_files,
+        local_join,
+        fs_sanitize_path,
+    ):
+        # Copy scenario 1g
+        source = local_glob_edge_cases_files
+
+        target = fs_target
+
+        for new_dir, target_slash in product([True, False], [True, False]):
+            fs.mkdir(target)
+
+            t = fs_join(target, "newdir") if new_dir else target
+            t = t + "/" if target_slash else t
+
+            fs.put(local_join(source, path), t, recursive=recursive, maxdepth=maxdepth)
+
+            output = fs.find(target)
+            if new_dir:
+                prefixed_expected = [
+                    fs_sanitize_path(fs_join(target, "newdir", p)) for p in expected
+                ]
+            else:
+                prefixed_expected = [
+                    fs_sanitize_path(fs_join(target, p)) for p in expected
+                ]
+            assert sorted(output) == sorted(prefixed_expected)
+
+            try:
+                fs.rm(target, recursive=True)
+            except FileNotFoundError:
+                pass
+
+    def test_put_list_of_files_to_existing_directory(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_join,
+        local_bulk_operations_scenario_0,
+        supports_empty_directories,
+    ):
+        # Copy scenario 2a
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+        if not supports_empty_directories:
+            # Force target directory to exist by adding a dummy file
+            dummy = fs_join(target, "dummy")
+            fs.touch(dummy)
+        assert fs.isdir(target)
+
+        source_files = [
+            local_join(source, "file1"),
+            local_join(source, "file2"),
+            local_join(source, "subdir", "subfile1"),
+        ]
+
+        for target_slash in [False, True]:
+            t = target + "/" if target_slash else target
+
+            fs.put(source_files, t)
+            assert fs.isfile(fs_join(target, "file1"))
+            assert fs.isfile(fs_join(target, "file2"))
+            assert fs.isfile(fs_join(target, "subfile1"))
+
+            fs.rm(
+                [
+                    fs_join(target, "file1"),
+                    fs_join(target, "file2"),
+                    fs_join(target, "subfile1"),
+                ],
+                recursive=True,
+            )
+            assert fs.ls(target, detail=False) == (
+                [] if supports_empty_directories else [dummy]
+            )
+
+    def test_put_list_of_files_to_new_directory(
+        self, fs, fs_join, fs_target, local_join, local_bulk_operations_scenario_0
+    ):
+        # Copy scenario 2b
+        source = local_bulk_operations_scenario_0
+
+        target = fs_target
+        fs.mkdir(target)
+
+        source_files = [
+            local_join(source, "file1"),
+            local_join(source, "file2"),
+            local_join(source, "subdir", "subfile1"),
+        ]
+
+        fs.put(source_files, fs_join(target, "newdir") + "/")  # Note trailing slash
+        assert fs.isdir(fs_join(target, "newdir"))
+        assert fs.isfile(fs_join(target, "newdir", "file1"))
+        assert fs.isfile(fs_join(target, "newdir", "file2"))
+        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
+
+    def test_put_directory_recursive(
+        self, fs, fs_join, fs_target, local_fs, local_join, local_path
+    ):
+        # https://github.com/fsspec/filesystem_spec/issues/1062
+        # Recursive cp/get/put of source directory into non-existent target directory.
+        src = local_join(local_path, "src")
+        src_file = local_join(src, "file")
+        local_fs.mkdir(src)
+        local_fs.touch(src_file)
+
+        target = fs_target
+
+        # put without slash
+        assert not fs.exists(target)
+        for loop in range(2):
+            fs.put(src, target, recursive=True)
+            assert fs.isdir(target)
+
+            if loop == 0:
+                assert fs.isfile(fs_join(target, "file"))
+                assert not fs.exists(fs_join(target, "src"))
+            else:
+                assert fs.isfile(fs_join(target, "file"))
+                assert fs.isdir(fs_join(target, "src"))
+                assert fs.isfile(fs_join(target, "src", "file"))
+
+        fs.rm(target, recursive=True)
+
+        # put with slash
+        assert not fs.exists(target)
+        for loop in range(2):
+            fs.put(src + "/", target, recursive=True)
+            assert fs.isdir(target)
+            assert fs.isfile(fs_join(target, "file"))
+            assert not fs.exists(fs_join(target, "src"))
+
+    def test_put_directory_without_files_with_same_name_prefix(
+        self,
+        fs,
+        fs_join,
+        fs_target,
+        local_join,
+        local_dir_and_file_with_same_name_prefix,
+        supports_empty_directories,
+    ):
+        # Create the test dirs
+        source = local_dir_and_file_with_same_name_prefix
+        target = fs_target
+
+        # Test without glob
+        fs.put(local_join(source, "subdir"), fs_target, recursive=True)
+
+        assert fs.isfile(fs_join(fs_target, "subfile.txt"))
+        assert not fs.isfile(fs_join(fs_target, "subdir.txt"))
+
+        fs.rm([fs_join(target, "subfile.txt")])
+        if supports_empty_directories:
+            assert fs.ls(target) == []
+        else:
+            assert not fs.exists(target)
+
+        # Test with glob
+        fs.put(local_join(source, "subdir*"), fs_target, recursive=True)
+
+        assert fs.isdir(fs_join(fs_target, "subdir"))
+        assert fs.isfile(fs_join(fs_target, "subdir", "subfile.txt"))
+        assert fs.isfile(fs_join(fs_target, "subdir.txt"))
+
+    def test_copy_with_source_and_destination_as_list(
+        self, fs, fs_target, fs_join, local_join, local_10_files_with_hashed_names
+    ):
+        # Create the test dir
+        source = local_10_files_with_hashed_names
+        target = fs_target
+
+        # Create list of files for source and destination
+        source_files = []
+        destination_files = []
+        for i in range(10):
+            hashed_i = md5(str(i).encode("utf-8")).hexdigest()
+            source_files.append(local_join(source, f"{hashed_i}.txt"))
+            destination_files.append(fs_join(target, f"{hashed_i}.txt"))
+
+        # Copy and assert order was kept
+        fs.put(lpath=source_files, rpath=destination_files)
+
+        for i in range(10):
+            file_content = fs.cat(destination_files[i]).decode("utf-8")
+            assert file_content == str(i)
diff --git a/lib/python3.11/site-packages/fsspec/transaction.py b/lib/python3.11/site-packages/fsspec/transaction.py
new file mode 100644
index 0000000000000000000000000000000000000000..77293f63ecc5f611e19d849ef236d53e9c258efc
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/transaction.py
@@ -0,0 +1,90 @@
+from collections import deque
+
+
+class Transaction:
+    """Filesystem transaction write context
+
+    Gathers files for deferred commit or discard, so that several write
+    operations can be finalized semi-atomically. This works by having this
+    instance as the ``.transaction`` attribute of the given filesystem
+    """
+
+    def __init__(self, fs, **kwargs):
+        """
+        Parameters
+        ----------
+        fs: FileSystem instance
+        """
+        self.fs = fs
+        self.files = deque()
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """End transaction and commit, if exit is not due to exception"""
+        # only commit if there was no exception
+        self.complete(commit=exc_type is None)
+        if self.fs:
+            self.fs._intrans = False
+            self.fs._transaction = None
+            self.fs = None
+
+    def start(self):
+        """Start a transaction on this FileSystem"""
+        self.files = deque()  # clean up after previous failed completions
+        self.fs._intrans = True
+
+    def complete(self, commit=True):
+        """Finish transaction: commit or discard all deferred files"""
+        while self.files:
+            f = self.files.popleft()
+            if commit:
+                f.commit()
+            else:
+                f.discard()
+        self.fs._intrans = False
+        self.fs._transaction = None
+        self.fs = None
+
+
+class FileActor:
+    def __init__(self):
+        self.files = []
+
+    def commit(self):
+        for f in self.files:
+            f.commit()
+        self.files.clear()
+
+    def discard(self):
+        for f in self.files:
+            f.discard()
+        self.files.clear()
+
+    def append(self, f):
+        self.files.append(f)
+
+
+class DaskTransaction(Transaction):
+    def __init__(self, fs):
+        """
+        Parameters
+        ----------
+        fs: FileSystem instance
+        """
+        import distributed
+
+        super().__init__(fs)
+        client = distributed.default_client()
+        self.files = client.submit(FileActor, actor=True).result()
+
+    def complete(self, commit=True):
+        """Finish transaction: commit or discard all deferred files"""
+        if commit:
+            self.files.commit().result()
+        else:
+            self.files.discard().result()
+        self.fs._intrans = False
+        self.fs = None
diff --git a/lib/python3.11/site-packages/fsspec/utils.py b/lib/python3.11/site-packages/fsspec/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b06dd581417eca1e90a19ef25fb14dcd3cf9c9c
--- /dev/null
+++ b/lib/python3.11/site-packages/fsspec/utils.py
@@ -0,0 +1,748 @@
+from __future__ import annotations
+
+import contextlib
+import logging
+import math
+import os
+import re
+import sys
+import tempfile
+from collections.abc import Callable, Iterable, Iterator, Sequence
+from functools import partial
+from hashlib import md5
+from importlib.metadata import version
+from typing import IO, TYPE_CHECKING, Any, TypeVar
+from urllib.parse import urlsplit
+
+if TYPE_CHECKING:
+    import pathlib
+    from typing import TypeGuard
+
+    from fsspec.spec import AbstractFileSystem
+
+
+DEFAULT_BLOCK_SIZE = 5 * 2**20
+
+T = TypeVar("T")
+
+
+def infer_storage_options(
+    urlpath: str, inherit_storage_options: dict[str, Any] | None = None
+) -> dict[str, Any]:
+    """Infer storage options from URL path and merge it with existing storage
+    options.
+
+    Parameters
+    ----------
+    urlpath: str or unicode
+        Either local absolute file path or URL (hdfs://namenode:8020/file.csv)
+    inherit_storage_options: dict (optional)
+        Its contents will get merged with the inferred information from the
+        given path
+
+    Returns
+    -------
+    Storage options dict.
+
+    Examples
+    --------
+    >>> infer_storage_options('/mnt/datasets/test.csv')  # doctest: +SKIP
+    {"protocol": "file", "path", "/mnt/datasets/test.csv"}
+    >>> infer_storage_options(
+    ...     'hdfs://username:pwd@node:123/mnt/datasets/test.csv?q=1',
+    ...     inherit_storage_options={'extra': 'value'},
+    ... )  # doctest: +SKIP
+    {"protocol": "hdfs", "username": "username", "password": "pwd",
+    "host": "node", "port": 123, "path": "/mnt/datasets/test.csv",
+    "url_query": "q=1", "extra": "value"}
+    """
+    # Handle Windows paths including disk name in this special case
+    if (
+        re.match(r"^[a-zA-Z]:[\\/]", urlpath)
+        or re.match(r"^[a-zA-Z0-9]+://", urlpath) is None
+    ):
+        return {"protocol": "file", "path": urlpath}
+
+    parsed_path = urlsplit(urlpath)
+    protocol = parsed_path.scheme or "file"
+    if parsed_path.fragment:
+        path = "#".join([parsed_path.path, parsed_path.fragment])
+    else:
+        path = parsed_path.path
+    if protocol == "file":
+        # Special case parsing file protocol URL on Windows according to:
+        # https://msdn.microsoft.com/en-us/library/jj710207.aspx
+        windows_path = re.match(r"^/([a-zA-Z])[:|]([\\/].*)$", path)
+        if windows_path:
+            drive, path = windows_path.groups()
+            path = f"{drive}:{path}"
+
+    if protocol in ["http", "https"]:
+        # for HTTP, we don't want to parse, as requests will anyway
+        return {"protocol": protocol, "path": urlpath}
+
+    options: dict[str, Any] = {"protocol": protocol, "path": path}
+
+    if parsed_path.netloc:
+        # Parse `hostname` from netloc manually because `parsed_path.hostname`
+        # lowercases the hostname which is not always desirable (e.g. in S3):
+        # https://github.com/dask/dask/issues/1417
+        options["host"] = parsed_path.netloc.rsplit("@", 1)[-1].rsplit(":", 1)[0]
+
+        if protocol in ("s3", "s3a", "gcs", "gs"):
+            options["path"] = options["host"] + options["path"]
+        else:
+            options["host"] = options["host"]
+        if parsed_path.port:
+            options["port"] = parsed_path.port
+        if parsed_path.username:
+            options["username"] = parsed_path.username
+        if parsed_path.password:
+            options["password"] = parsed_path.password
+
+    if parsed_path.query:
+        options["url_query"] = parsed_path.query
+    if parsed_path.fragment:
+        options["url_fragment"] = parsed_path.fragment
+
+    if inherit_storage_options:
+        update_storage_options(options, inherit_storage_options)
+
+    return options
+
+
+def update_storage_options(
+    options: dict[str, Any], inherited: dict[str, Any] | None = None
+) -> None:
+    if not inherited:
+        inherited = {}
+    collisions = set(options) & set(inherited)
+    if collisions:
+        for collision in collisions:
+            if options.get(collision) != inherited.get(collision):
+                raise KeyError(
+                    f"Collision between inferred and specified storage "
+                    f"option:\n{collision}"
+                )
+    options.update(inherited)
+
+
+# Compression extensions registered via fsspec.compression.register_compression
+compressions: dict[str, str] = {}
+
+
+def infer_compression(filename: str) -> str | None:
+    """Infer compression, if available, from filename.
+
+    Infer a named compression type, if registered and available, from filename
+    extension. This includes builtin (gz, bz2, zip) compressions, as well as
+    optional compressions. See fsspec.compression.register_compression.
+    """
+    extension = os.path.splitext(filename)[-1].strip(".").lower()
+    if extension in compressions:
+        return compressions[extension]
+    return None
+
+
+def build_name_function(max_int: float) -> Callable[[int], str]:
+    """Returns a function that receives a single integer
+    and returns it as a string padded by enough zero characters
+    to align with maximum possible integer
+
+    >>> name_f = build_name_function(57)
+
+    >>> name_f(7)
+    '07'
+    >>> name_f(31)
+    '31'
+    >>> build_name_function(1000)(42)
+    '0042'
+    >>> build_name_function(999)(42)
+    '042'
+    >>> build_name_function(0)(0)
+    '0'
+    """
+    # handle corner cases max_int is 0 or exact power of 10
+    max_int += 1e-8
+
+    pad_length = int(math.ceil(math.log10(max_int)))
+
+    def name_function(i: int) -> str:
+        return str(i).zfill(pad_length)
+
+    return name_function
+
+
+def seek_delimiter(file: IO[bytes], delimiter: bytes, blocksize: int) -> bool:
+    r"""Seek current file to file start, file end, or byte after delimiter seq.
+
+    Seeks file to next chunk delimiter, where chunks are defined on file start,
+    a delimiting sequence, and file end. Use file.tell() to see location afterwards.
+    Note that file start is a valid split, so must be at offset > 0 to seek for
+    delimiter.
+
+    Parameters
+    ----------
+    file: a file
+    delimiter: bytes
+        a delimiter like ``b'\n'`` or message sentinel, matching file .read() type
+    blocksize: int
+        Number of bytes to read from the file at once.
+
+
+    Returns
+    -------
+    Returns True if a delimiter was found, False if at file start or end.
+
+    """
+
+    if file.tell() == 0:
+        # beginning-of-file, return without seek
+        return False
+
+    # Interface is for binary IO, with delimiter as bytes, but initialize last
+    # with result of file.read to preserve compatibility with text IO.
+    last: bytes | None = None
+    while True:
+        current = file.read(blocksize)
+        if not current:
+            # end-of-file without delimiter
+            return False
+        full = last + current if last else current
+        try:
+            if delimiter in full:
+                i = full.index(delimiter)
+                file.seek(file.tell() - (len(full) - i) + len(delimiter))
+                return True
+            elif len(current) < blocksize:
+                # end-of-file without delimiter
+                return False
+        except (OSError, ValueError):
+            pass
+        last = full[-len(delimiter) :]
+
+
+def read_block(
+    f: IO[bytes],
+    offset: int,
+    length: int | None,
+    delimiter: bytes | None = None,
+    split_before: bool = False,
+) -> bytes:
+    """Read a block of bytes from a file
+
+    Parameters
+    ----------
+    f: File
+        Open file
+    offset: int
+        Byte offset to start read
+    length: int
+        Number of bytes to read, read through end of file if None
+    delimiter: bytes (optional)
+        Ensure reading starts and stops at delimiter bytestring
+    split_before: bool (optional)
+        Start/stop read *before* delimiter bytestring.
+
+
+    If using the ``delimiter=`` keyword argument we ensure that the read
+    starts and stops at delimiter boundaries that follow the locations
+    ``offset`` and ``offset + length``.  If ``offset`` is zero then we
+    start at zero, regardless of delimiter.  The bytestring returned WILL
+    include the terminating delimiter string.
+
+    Examples
+    --------
+
+    >>> from io import BytesIO  # doctest: +SKIP
+    >>> f = BytesIO(b'Alice, 100\\nBob, 200\\nCharlie, 300')  # doctest: +SKIP
+    >>> read_block(f, 0, 13)  # doctest: +SKIP
+    b'Alice, 100\\nBo'
+
+    >>> read_block(f, 0, 13, delimiter=b'\\n')  # doctest: +SKIP
+    b'Alice, 100\\nBob, 200\\n'
+
+    >>> read_block(f, 10, 10, delimiter=b'\\n')  # doctest: +SKIP
+    b'Bob, 200\\nCharlie, 300'
+    """
+    if delimiter:
+        f.seek(offset)
+        found_start_delim = seek_delimiter(f, delimiter, 2**16)
+        if length is None:
+            return f.read()
+        start = f.tell()
+        length -= start - offset
+
+        f.seek(start + length)
+        found_end_delim = seek_delimiter(f, delimiter, 2**16)
+        end = f.tell()
+
+        # Adjust split location to before delimiter if seek found the
+        # delimiter sequence, not start or end of file.
+        if found_start_delim and split_before:
+            start -= len(delimiter)
+
+        if found_end_delim and split_before:
+            end -= len(delimiter)
+
+        offset = start
+        length = end - start
+
+    f.seek(offset)
+
+    # TODO: allow length to be None and read to the end of the file?
+    assert length is not None
+    b = f.read(length)
+    return b
+
+
+def tokenize(*args: Any, **kwargs: Any) -> str:
+    """Deterministic token
+
+    (modified from dask.base)
+
+    >>> tokenize([1, 2, '3'])
+    '9d71491b50023b06fc76928e6eddb952'
+
+    >>> tokenize('Hello') == tokenize('Hello')
+    True
+    """
+    if kwargs:
+        args += (kwargs,)
+    try:
+        h = md5(str(args).encode())
+    except ValueError:
+        # FIPS systems: https://github.com/fsspec/filesystem_spec/issues/380
+        h = md5(str(args).encode(), usedforsecurity=False)
+    return h.hexdigest()
+
+
+def stringify_path(filepath: str | os.PathLike[str] | pathlib.Path) -> str:
+    """Attempt to convert a path-like object to a string.
+
+    Parameters
+    ----------
+    filepath: object to be converted
+
+    Returns
+    -------
+    filepath_str: maybe a string version of the object
+
+    Notes
+    -----
+    Objects supporting the fspath protocol are coerced according to its
+    __fspath__ method.
+
+    For backwards compatibility with older Python version, pathlib.Path
+    objects are specially coerced.
+
+    Any other object is passed through unchanged, which includes bytes,
+    strings, buffers, or anything else that's not even path-like.
+    """
+    if isinstance(filepath, str):
+        return filepath
+    elif hasattr(filepath, "__fspath__"):
+        return filepath.__fspath__()
+    elif hasattr(filepath, "path"):
+        return filepath.path
+    else:
+        return filepath  # type: ignore[return-value]
+
+
+def make_instance(
+    cls: Callable[..., T], args: Sequence[Any], kwargs: dict[str, Any]
+) -> T:
+    inst = cls(*args, **kwargs)
+    inst._determine_worker()  # type: ignore[attr-defined]
+    return inst
+
+
+def common_prefix(paths: Iterable[str]) -> str:
+    """For a list of paths, find the shortest prefix common to all"""
+    parts = [p.split("/") for p in paths]
+    lmax = min(len(p) for p in parts)
+    end = 0
+    for i in range(lmax):
+        end = all(p[i] == parts[0][i] for p in parts)
+        if not end:
+            break
+    i += end
+    return "/".join(parts[0][:i])
+
+
+def other_paths(
+    paths: list[str],
+    path2: str | list[str],
+    exists: bool = False,
+    flatten: bool = False,
+) -> list[str]:
+    """In bulk file operations, construct a new file tree from a list of files
+
+    Parameters
+    ----------
+    paths: list of str
+        The input file tree
+    path2: str or list of str
+        Root to construct the new list in. If this is already a list of str, we just
+        assert it has the right number of elements.
+    exists: bool (optional)
+        For a str destination, it is already exists (and is a dir), files should
+        end up inside.
+    flatten: bool (optional)
+        Whether to flatten the input directory tree structure so that the output files
+        are in the same directory.
+
+    Returns
+    -------
+    list of str
+    """
+
+    if isinstance(path2, str):
+        path2 = path2.rstrip("/")
+
+        if flatten:
+            path2 = ["/".join((path2, p.split("/")[-1])) for p in paths]
+        else:
+            cp = common_prefix(paths)
+            if exists:
+                cp = cp.rsplit("/", 1)[0]
+            if not cp and all(not s.startswith("/") for s in paths):
+                path2 = ["/".join([path2, p]) for p in paths]
+            else:
+                path2 = [p.replace(cp, path2, 1) for p in paths]
+    else:
+        assert len(paths) == len(path2)
+    return path2
+
+
+def is_exception(obj: Any) -> bool:
+    return isinstance(obj, BaseException)
+
+
+def isfilelike(f: Any) -> TypeGuard[IO[bytes]]:
+    return all(hasattr(f, attr) for attr in ["read", "close", "tell"])
+
+
+def get_protocol(url: str) -> str:
+    url = stringify_path(url)
+    parts = re.split(r"(\:\:|\://)", url, maxsplit=1)
+    if len(parts) > 1:
+        return parts[0]
+    return "file"
+
+
+def get_file_extension(url: str) -> str:
+    url = stringify_path(url)
+    ext_parts = url.rsplit(".", 1)
+    if len(ext_parts) > 1:
+        return ext_parts[-1]
+    return ""
+
+
+def can_be_local(path: str) -> bool:
+    """Can the given URL be used with open_local?"""
+    from fsspec import get_filesystem_class
+
+    try:
+        return getattr(get_filesystem_class(get_protocol(path)), "local_file", False)
+    except (ValueError, ImportError):
+        # not in registry or import failed
+        return False
+
+
+def get_package_version_without_import(name: str) -> str | None:
+    """For given package name, try to find the version without importing it
+
+    Import and package.__version__ is still the backup here, so an import
+    *might* happen.
+
+    Returns either the version string, or None if the package
+    or the version was not readily  found.
+    """
+    if name in sys.modules:
+        mod = sys.modules[name]
+        if hasattr(mod, "__version__"):
+            return mod.__version__
+    try:
+        return version(name)
+    except:  # noqa: E722
+        pass
+    try:
+        import importlib
+
+        mod = importlib.import_module(name)
+        return mod.__version__
+    except (ImportError, AttributeError):
+        return None
+
+
+def setup_logging(
+    logger: logging.Logger | None = None,
+    logger_name: str | None = None,
+    level: str = "DEBUG",
+    clear: bool = True,
+) -> logging.Logger:
+    if logger is None and logger_name is None:
+        raise ValueError("Provide either logger object or logger name")
+    logger = logger or logging.getLogger(logger_name)
+    handle = logging.StreamHandler()
+    formatter = logging.Formatter(
+        "%(asctime)s - %(name)s - %(levelname)s - %(funcName)s -- %(message)s"
+    )
+    handle.setFormatter(formatter)
+    if clear:
+        logger.handlers.clear()
+    logger.addHandler(handle)
+    logger.setLevel(level)
+    return logger
+
+
+def _unstrip_protocol(name: str, fs: AbstractFileSystem) -> str:
+    return fs.unstrip_protocol(name)
+
+
+def mirror_from(
+    origin_name: str, methods: Iterable[str]
+) -> Callable[[type[T]], type[T]]:
+    """Mirror attributes and methods from the given
+    origin_name attribute of the instance to the
+    decorated class"""
+
+    def origin_getter(method: str, self: Any) -> Any:
+        origin = getattr(self, origin_name)
+        return getattr(origin, method)
+
+    def wrapper(cls: type[T]) -> type[T]:
+        for method in methods:
+            wrapped_method = partial(origin_getter, method)
+            setattr(cls, method, property(wrapped_method))
+        return cls
+
+    return wrapper
+
+
+@contextlib.contextmanager
+def nullcontext(obj: T) -> Iterator[T]:
+    yield obj
+
+
+def merge_offset_ranges(
+    paths: list[str],
+    starts: list[int] | int,
+    ends: list[int] | int,
+    max_gap: int = 0,
+    max_block: int | None = None,
+    sort: bool = True,
+) -> tuple[list[str], list[int], list[int]]:
+    """Merge adjacent byte-offset ranges when the inter-range
+    gap is <= `max_gap`, and when the merged byte range does not
+    exceed `max_block` (if specified). By default, this function
+    will re-order the input paths and byte ranges to ensure sorted
+    order. If the user can guarantee that the inputs are already
+    sorted, passing `sort=False` will skip the re-ordering.
+    """
+    # Check input
+    if not isinstance(paths, list):
+        raise TypeError
+    if not isinstance(starts, list):
+        starts = [starts] * len(paths)
+    if not isinstance(ends, list):
+        ends = [ends] * len(paths)
+    if len(starts) != len(paths) or len(ends) != len(paths):
+        raise ValueError
+
+    # Early Return
+    if len(starts) <= 1:
+        return paths, starts, ends
+
+    starts = [s or 0 for s in starts]
+    # Sort by paths and then ranges if `sort=True`
+    if sort:
+        paths, starts, ends = (
+            list(v)
+            for v in zip(
+                *sorted(
+                    zip(paths, starts, ends),
+                )
+            )
+        )
+    remove = []
+    for i, (path, start, end) in enumerate(zip(paths, starts, ends)):
+        if any(
+            e is not None and p == path and start >= s and end <= e and i != i2
+            for i2, (p, s, e) in enumerate(zip(paths, starts, ends))
+        ):
+            remove.append(i)
+    paths = [p for i, p in enumerate(paths) if i not in remove]
+    starts = [s for i, s in enumerate(starts) if i not in remove]
+    ends = [e for i, e in enumerate(ends) if i not in remove]
+
+    if paths:
+        # Loop through the coupled `paths`, `starts`, and
+        # `ends`, and merge adjacent blocks when appropriate
+        new_paths = paths[:1]
+        new_starts = starts[:1]
+        new_ends = ends[:1]
+        for i in range(1, len(paths)):
+            if paths[i] == paths[i - 1] and new_ends[-1] is None:
+                continue
+            elif (
+                paths[i] != paths[i - 1]
+                or ((starts[i] - new_ends[-1]) > max_gap)
+                or (max_block is not None and (ends[i] - new_starts[-1]) > max_block)
+            ):
+                # Cannot merge with previous block.
+                # Add new `paths`, `starts`, and `ends` elements
+                new_paths.append(paths[i])
+                new_starts.append(starts[i])
+                new_ends.append(ends[i])
+            else:
+                # Merge with the previous block by updating the
+                # last element of `ends`
+                new_ends[-1] = ends[i]
+        return new_paths, new_starts, new_ends
+
+    # `paths` is empty. Just return input lists
+    return paths, starts, ends
+
+
+def file_size(filelike: IO[bytes]) -> int:
+    """Find length of any open read-mode file-like"""
+    pos = filelike.tell()
+    try:
+        return filelike.seek(0, 2)
+    finally:
+        filelike.seek(pos)
+
+
+@contextlib.contextmanager
+def atomic_write(path: str, mode: str = "wb"):
+    """
+    A context manager that opens a temporary file next to `path` and, on exit,
+    replaces `path` with the temporary file, thereby updating `path`
+    atomically.
+    """
+    fd, fn = tempfile.mkstemp(
+        dir=os.path.dirname(path), prefix=os.path.basename(path) + "-"
+    )
+    try:
+        with open(fd, mode) as fp:
+            yield fp
+    except BaseException:
+        with contextlib.suppress(FileNotFoundError):
+            os.unlink(fn)
+        raise
+    else:
+        os.replace(fn, path)
+
+
+def _translate(pat, STAR, QUESTION_MARK):
+    # Copied from: https://github.com/python/cpython/pull/106703.
+    res: list[str] = []
+    add = res.append
+    i, n = 0, len(pat)
+    while i < n:
+        c = pat[i]
+        i = i + 1
+        if c == "*":
+            # compress consecutive `*` into one
+            if (not res) or res[-1] is not STAR:
+                add(STAR)
+        elif c == "?":
+            add(QUESTION_MARK)
+        elif c == "[":
+            j = i
+            if j < n and pat[j] == "!":
+                j = j + 1
+            if j < n and pat[j] == "]":
+                j = j + 1
+            while j < n and pat[j] != "]":
+                j = j + 1
+            if j >= n:
+                add("\\[")
+            else:
+                stuff = pat[i:j]
+                if "-" not in stuff:
+                    stuff = stuff.replace("\\", r"\\")
+                else:
+                    chunks = []
+                    k = i + 2 if pat[i] == "!" else i + 1
+                    while True:
+                        k = pat.find("-", k, j)
+                        if k < 0:
+                            break
+                        chunks.append(pat[i:k])
+                        i = k + 1
+                        k = k + 3
+                    chunk = pat[i:j]
+                    if chunk:
+                        chunks.append(chunk)
+                    else:
+                        chunks[-1] += "-"
+                    # Remove empty ranges -- invalid in RE.
+                    for k in range(len(chunks) - 1, 0, -1):
+                        if chunks[k - 1][-1] > chunks[k][0]:
+                            chunks[k - 1] = chunks[k - 1][:-1] + chunks[k][1:]
+                            del chunks[k]
+                    # Escape backslashes and hyphens for set difference (--).
+                    # Hyphens that create ranges shouldn't be escaped.
+                    stuff = "-".join(
+                        s.replace("\\", r"\\").replace("-", r"\-") for s in chunks
+                    )
+                # Escape set operations (&&, ~~ and ||).
+                stuff = re.sub(r"([&~|])", r"\\\1", stuff)
+                i = j + 1
+                if not stuff:
+                    # Empty range: never match.
+                    add("(?!)")
+                elif stuff == "!":
+                    # Negated empty range: match any character.
+                    add(".")
+                else:
+                    if stuff[0] == "!":
+                        stuff = "^" + stuff[1:]
+                    elif stuff[0] in ("^", "["):
+                        stuff = "\\" + stuff
+                    add(f"[{stuff}]")
+        else:
+            add(re.escape(c))
+    assert i == n
+    return res
+
+
+def glob_translate(pat):
+    # Copied from: https://github.com/python/cpython/pull/106703.
+    # The keyword parameters' values are fixed to:
+    # recursive=True, include_hidden=True, seps=None
+    """Translate a pathname with shell wildcards to a regular expression."""
+    if os.path.altsep:
+        seps = os.path.sep + os.path.altsep
+    else:
+        seps = os.path.sep
+    escaped_seps = "".join(map(re.escape, seps))
+    any_sep = f"[{escaped_seps}]" if len(seps) > 1 else escaped_seps
+    not_sep = f"[^{escaped_seps}]"
+    one_last_segment = f"{not_sep}+"
+    one_segment = f"{one_last_segment}{any_sep}"
+    any_segments = f"(?:.+{any_sep})?"
+    any_last_segments = ".*"
+    results = []
+    parts = re.split(any_sep, pat)
+    last_part_idx = len(parts) - 1
+    for idx, part in enumerate(parts):
+        if part == "*":
+            results.append(one_segment if idx < last_part_idx else one_last_segment)
+            continue
+        if part == "**":
+            results.append(any_segments if idx < last_part_idx else any_last_segments)
+            continue
+        elif "**" in part:
+            raise ValueError(
+                "Invalid pattern: '**' can only be an entire path component"
+            )
+        if part:
+            results.extend(_translate(part, f"{not_sep}*", not_sep))
+        if idx < last_part_idx:
+            results.append(any_sep)
+    res = "".join(results)
+    return rf"(?s:{res})\Z"
diff --git a/lib/python3.11/site-packages/functorch/__init__.py b/lib/python3.11/site-packages/functorch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0aef38c8a9bb84a9833c4c2c9c34ad528d564b32
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/__init__.py
@@ -0,0 +1,39 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+from torch._functorch.deprecated import (
+    combine_state_for_ensemble,
+    functionalize,
+    grad,
+    grad_and_value,
+    hessian,
+    jacfwd,
+    jacrev,
+    jvp,
+    make_functional,
+    make_functional_with_buffers,
+    vjp,
+    vmap,
+)
+
+# utilities. Maybe these should go in their own namespace in the future?
+from torch._functorch.make_functional import (
+    FunctionalModule,
+    FunctionalModuleWithBuffers,
+)
+
+# Was never documented
+from torch._functorch.python_key import make_fx
+
+
+# Top-level APIs. Please think carefully before adding something to the
+# top-level namespace:
+# - private helper functions should go into torch._functorch
+# - very experimental things should go into functorch.experimental
+# - compilation related things should go into functorch.compile
+
+
+__version__ = torch.__version__
diff --git a/lib/python3.11/site-packages/functorch/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4f8153e42e13aae28ebff11a6ab09a0c309969bd
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/_src/__init__.py b/lib/python3.11/site-packages/functorch/_src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/functorch/_src/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/_src/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f337069e743866f4d6847fd7c8aa7e3eade89431
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/_src/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/_src/aot_autograd/__init__.py b/lib/python3.11/site-packages/functorch/_src/aot_autograd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..94f258df84ba8730208768fc44222bee4b3ebc33
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/_src/aot_autograd/__init__.py
@@ -0,0 +1,8 @@
+# This file has moved to under torch/_functorch. It is not public API.
+# If you are not a PyTorch developer and you are relying on the following
+# imports, please file an issue.
+from torch._functorch.aot_autograd import (
+    aot_autograd_decompositions,
+    KNOWN_TYPES,
+    PytreeThunk,
+)
diff --git a/lib/python3.11/site-packages/functorch/_src/aot_autograd/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/_src/aot_autograd/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e64f253b42c9b050507a4404403acd9ba8f6440
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/_src/aot_autograd/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/_src/eager_transforms/__init__.py b/lib/python3.11/site-packages/functorch/_src/eager_transforms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6052b5548f4af3dbc6d9d45b0ffe72a8d5013d41
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/_src/eager_transforms/__init__.py
@@ -0,0 +1,7 @@
+# This file has moved to under torch/_functorch. It is not public API.
+# If you are not a PyTorch developer and you are relying on the following
+# imports, please file an issue.
+from torch._functorch.eager_transforms import (
+    _assert_wrapped_functional,
+    _unwrap_functional_tensor,
+)
diff --git a/lib/python3.11/site-packages/functorch/_src/eager_transforms/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/_src/eager_transforms/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d7e2628eddefaca396523debab739faab319d844
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/_src/eager_transforms/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/_src/make_functional/__init__.py b/lib/python3.11/site-packages/functorch/_src/make_functional/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3de7787df0c3304207b42b51e9fb62da9d33c7d0
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/_src/make_functional/__init__.py
@@ -0,0 +1,4 @@
+# This file has moved to under torch/_functorch. It is not public API.
+# If you are not a PyTorch developer and you are relying on the following
+# imports, please file an issue.
+from torch._functorch.make_functional import _swap_state
diff --git a/lib/python3.11/site-packages/functorch/_src/make_functional/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/_src/make_functional/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4d920058d5e2f17aaae8fc8706513f511102629d
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/_src/make_functional/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/_src/vmap/__init__.py b/lib/python3.11/site-packages/functorch/_src/vmap/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc90517753e50f92362ba954248e31f69f7cfcd5
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/_src/vmap/__init__.py
@@ -0,0 +1,16 @@
+# This file has moved to under torch/_functorch. It is not public API.
+# If you are not a PyTorch developer and you are relying on the following
+# imports, please file an issue.
+from torch._functorch.vmap import (
+    _add_batch_dim,
+    _broadcast_to_and_flatten,
+    _create_batched_inputs,
+    _get_name,
+    _process_batched_inputs,
+    _remove_batch_dim,
+    _unwrap_batched,
+    _validate_and_get_batch_size,
+    Tensor,
+    tree_flatten,
+    tree_unflatten,
+)
diff --git a/lib/python3.11/site-packages/functorch/_src/vmap/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/_src/vmap/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4de49f4023ace8205f9c45a72b4418c949f9e99c
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/_src/vmap/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/compile/__init__.py b/lib/python3.11/site-packages/functorch/compile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7548a5ff6b91bae4fa561f0de7ad5d3492eda05
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/compile/__init__.py
@@ -0,0 +1,30 @@
+from torch._functorch import config
+from torch._functorch.aot_autograd import (
+    aot_function,
+    aot_module,
+    aot_module_simplified,
+    compiled_function,
+    compiled_module,
+    get_aot_compilation_context,
+    get_aot_graph_name,
+    get_graph_being_compiled,
+    make_boxed_compiler,
+    make_boxed_func,
+)
+from torch._functorch.compilers import (
+    debug_compile,
+    default_decompositions,
+    draw_graph_compile,
+    memory_efficient_fusion,
+    nnc_jit,
+    nop,
+    print_compile,
+    ts_compile,
+)
+from torch._functorch.fx_minifier import minifier
+from torch._functorch.partitioners import (
+    default_partition,
+    draw_graph,
+    min_cut_rematerialization_partition,
+)
+from torch._functorch.python_key import pythonkey_decompose
diff --git a/lib/python3.11/site-packages/functorch/compile/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/compile/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3646732683a13a7c22d939033025b9313fa0bf9c
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/compile/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__init__.py b/lib/python3.11/site-packages/functorch/dim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6de6ad59e9501b13cf5ba934c810e53ad61f6a3
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/__init__.py
@@ -0,0 +1,181 @@
+import dis
+import inspect
+from typing import Sequence, Union
+
+import functorch._C
+import torch
+from functorch._C import dim as _C
+
+from .tree_map import tree_flatten, tree_map
+from .wrap_type import wrap_type
+
+
+_C._patch_tensor_class()
+dims, DimList, dimlists = _C.dims, _C.DimList, _C.dimlists
+
+
+class DimensionMismatchError(Exception):
+    pass
+
+
+class DimensionBindError(Exception):
+    pass
+
+
+from . import op_properties
+
+
+# use dict to avoid writing C++ bindings for set
+pointwise = dict.fromkeys(op_properties.pointwise, True)
+
+use_c = True
+if not use_c:
+    from . import reference
+
+
+class _Tensor:
+    # fast path around slow wrapping/unwrapping logic for simply queries used
+    # by the implementation...
+
+    @property
+    def dims(self):
+        return tuple(d for d in self._levels if isinstance(d, Dim))
+
+    def dim(self):
+        return self.ndim
+
+    if use_c:
+        __torch_function__ = classmethod(_C.__torch_function__)
+        expand = _C._instancemethod(_C.expand)
+    else:
+        __torch_function__ = reference.__torch_function__
+        expand = reference.expand
+
+    index = _C._instancemethod(_C.index)
+
+    def __repr__(self):
+        tensor, levels, ndim = self._tensor, self._levels, self.ndim
+        return f"{tensor}\nwith dims={tuple(l + ndim if isinstance(l, int) else l for l in levels)} sizes={tuple(tensor.size())}"
+
+
+TensorLike = (_Tensor, torch.Tensor)
+
+
+class Dim(_C.Dim, _Tensor):
+    # note that _C.Dim comes before tensor because we want the Dim API for things like size to take precendence.
+    # Tensor defines format, but we want to print Dims with special formatting
+    __format__ = object.__format__
+
+
+class Tensor(_Tensor, _C.Tensor):
+    if not use_c:
+        from_batched = staticmethod(_C.Tensor_from_batched)
+    from_positional = staticmethod(_C.Tensor_from_positional)
+    sum = _C._instancemethod(_C.Tensor_sum)
+
+
+def cat(tensors, dim, new_dim):
+    n = dims()
+    return stack(tensors, n, dim).index([n, dim], new_dim)
+
+
+if use_c:
+    _wrap = _C._wrap
+
+    def _def(name, *args, **kwargs):
+        orig = getattr(torch.Tensor, name)
+        setattr(_Tensor, name, _C._instancemethod(_wrap(orig, *args, **kwargs)))
+
+    t__getitem__ = _C._instancemethod(_C.__getitem__)
+    stack = _C.stack
+    split = _C._instancemethod(_C.split)
+else:
+    _wrap, _def = reference._wrap, reference._def
+    t__getitem__ = reference.t__getitem__
+    stack = reference.stack
+    split = reference.split
+
+# note: there is no python reference
+t__setitem__ = _C._instancemethod(_C.__setitem__)
+# this is patched in the C API because otherwise torch.Tensor will
+# no longer be considered a sequence and things will break
+# torch.Tensor.__getitem__ = t__getitem__
+
+_Tensor.__getitem__ = t__getitem__
+# torch.Tensor.__setitem__ = t__setitem__
+_Tensor.__setitem__ = t__setitem__
+
+torch.Tensor.split = split
+_Tensor.split = split
+torch.Tensor.expand = _C._instancemethod(_C.expand)
+torch.Tensor.index = _C._instancemethod(_C.index)
+wrap_type(use_c, _Tensor, torch.Tensor, _Tensor.__torch_function__)
+del _Tensor.ndim
+
+if use_c:
+    _Tensor.order = _C._instancemethod(_C.order)
+else:
+    _Tensor.order = reference.positional
+
+_def("mean")
+_def("sum")
+_def("all")
+_def("amax")
+_def("amin")
+_def("aminmax")
+_def("any")
+_def("count_nonzero")
+_def("logsumexp")
+_def("nanmean")
+_def("nansum")
+_def("prod")
+_def("std", keepdim_offset=2)
+_def("var", keepdim_offset=2)
+_def("max", single_dim=True)
+_def("min", single_dim=True)
+_def("argmax", single_dim=True)
+_def("argmin", single_dim=True)
+_def("kthvalue", single_dim=True)
+_def("median", single_dim=True)
+_def("nanmedian", single_dim=True)
+_def("mode", single_dim=True)
+_def("sort", reduce=False)
+_def("argsort", reduce=False)
+_def("unbind", single_dim=True)
+_def("chunk", dim_offset=1, reduce=False)
+_def("cummax", single_dim=True, reduce=False)
+_def("cummin", single_dim=True, reduce=False)
+_def("cumprod", single_dim=True, reduce=False)
+_def("cumprod_", single_dim=True, reduce=False)
+_def("cumsum", single_dim=True, reduce=False)
+_def("cumsum_", single_dim=True, reduce=False)
+_def("logcumsumexp", single_dim=True, reduce=False)
+_def("renorm", dim_offset=1, single_dim=True, reduce=False)
+_def("softmax", single_dim=True, reduce=False)
+softmax = _wrap(torch.nn.functional.softmax, single_dim=True, reduce=False)
+
+# stuff to handle in the future, because they require special
+# binding logic for dims
+# cross
+# diag_embed
+# diagonal
+# diagonal_scatter
+# diff
+# nanquantile
+# quantile
+# roll
+# rot90
+# topk (new dimes on output)
+# should these all be subsumed by inplace indexing?
+# index_add_
+# index_add
+# index_copy
+# index_copy_
+# index_fill
+# index_fill_
+# index_select
+# scatter
+# scatter_
+# scatter_add
+# scatter_add_
+# scatter_reduce
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3c475e8efc3eb5bd71d5e827213ab928e1a39164
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/batch_tensor.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/batch_tensor.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1af43876fa09c9e1cfb6fe5fa44068df9b8bad84
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/batch_tensor.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/delayed_mul_tensor.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/delayed_mul_tensor.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e0769a308f6d2b62f49c7c250c10f1482a65574a
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/delayed_mul_tensor.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/dim.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/dim.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e6e272a016e56682d3875d84bbd448a28819df40
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/dim.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/magic_trace.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/magic_trace.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c89990b570c8550ca0d130c95eb839bcd79ea3b9
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/magic_trace.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/op_properties.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/op_properties.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3a8ebf083a87c799c8b45c06085b83109c0dbddb
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/op_properties.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/reference.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/reference.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..27231eb98d60bcc30a62fde10f8cc053252003a7
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/reference.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/tree_map.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/tree_map.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..06f89bde79b895012f095d105f4d41e061360d2e
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/tree_map.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/__pycache__/wrap_type.cpython-311.pyc b/lib/python3.11/site-packages/functorch/dim/__pycache__/wrap_type.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cab5ec3900620b9b50057c7a8c0630ca24514209
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/dim/__pycache__/wrap_type.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/dim/batch_tensor.py b/lib/python3.11/site-packages/functorch/dim/batch_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..dae9b270896e988151741409b666a82ba1aba5a4
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/batch_tensor.py
@@ -0,0 +1,26 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+from contextlib import contextmanager
+
+from torch._C._functorch import _vmap_add_layers, _vmap_remove_layers
+
+
+_enabled = False
+
+
+@contextmanager
+def _enable_layers(dims):
+    global _enabled
+    assert not _enabled
+    input = sorted((d._level, d.size) for d in dims if not isinstance(d, int))
+    n = len(input)
+    try:
+        _vmap_add_layers(input)
+        _enabled = True
+        yield
+    finally:
+        _enabled = False
+        _vmap_remove_layers(n)
diff --git a/lib/python3.11/site-packages/functorch/dim/delayed_mul_tensor.py b/lib/python3.11/site-packages/functorch/dim/delayed_mul_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c136cfe1247dbd7dcc38777e6326b360619941b
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/delayed_mul_tensor.py
@@ -0,0 +1,76 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from . import _Tensor, Tensor
+from .reference import _dims, _enable_layers, llist, ltuple
+
+
+class DelayedMulTensor(_Tensor):
+    def __init__(self, lhs, rhs):
+        self._lhs, self._rhs = lhs, rhs
+        self._data = None
+        self._levels_data = None
+        self._has_device = lhs._has_device or rhs._has_device
+        self._batchtensor_data = None
+        self._tensor_data = None
+
+    @property
+    def _levels(self):
+        if self._levels_data is None:
+            levels = llist(self._lhs._levels)
+            for l in self._rhs._levels:
+                if l not in levels:
+                    levels.append(l)
+            self._levels_data = ltuple(levels)
+        return self._levels_data
+
+    @property
+    def _batchtensor(self):
+        if self._batchtensor_data is None:
+            with _enable_layers(self._levels):
+                print("bt multiply fallback")
+                self._batchtensor_data = self._lhs._batchtensor * self._rhs._batchtensor
+        return self._batchtensor_data
+
+    @property
+    def _tensor(self):
+        if self._tensor_data is None:
+            self._tensor_data = Tensor.from_batched(
+                self._batchtensor, self._has_device
+            )._tensor
+        return self._tensor_data
+
+    @property
+    def ndim(self):
+        return self._batchtensor.ndim
+
+    @property
+    def dims(self):
+        return ltuple(super().dims)
+
+    def sum(self, dim):
+        dims = _dims(dim, 0, False, False)
+        n = ord("a")
+        all_levels = self._levels
+
+        def to_char(d):
+            return chr(n + all_levels.index(d))
+
+        plhs, levelslhs = self._lhs._tensor, self._lhs._levels
+        prhs, levelsrhs = self._rhs._tensor, self._rhs._levels
+        new_levels = [l for l in self._levels if l not in dims]
+        fmt = "".join(
+            [
+                *(to_char(d) for d in levelslhs),
+                ",",
+                *(to_char(d) for d in levelsrhs),
+                "->",
+                *(to_char(d) for d in new_levels),
+            ]
+        )
+        result_data = torch.einsum(fmt, (plhs, prhs))
+        return Tensor.from_positional(result_data, new_levels, True)
diff --git a/lib/python3.11/site-packages/functorch/dim/dim.py b/lib/python3.11/site-packages/functorch/dim/dim.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a4b56866484905cc0bd693c865d03c937aa1b75
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/dim.py
@@ -0,0 +1,120 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import dis
+import inspect
+from dataclasses import dataclass
+from typing import Union
+
+from . import DimList
+
+
+_vmap_levels = []
+
+
+@dataclass
+class LevelInfo:
+    level: int
+    alive: bool = True
+
+
+class Dim:
+    def __init__(self, name: str, size: Union[None, int] = None):
+        self.name = name
+        self._size = None
+        self._vmap_level = None
+        if size is not None:
+            self.size = size
+
+    def __del__(self):
+        if self._vmap_level is not None:
+            _vmap_active_levels[self._vmap_stack].alive = False  # noqa: F821
+            while (
+                not _vmap_levels[-1].alive and current_level() == _vmap_levels[-1].level  # noqa: F821
+            ):
+                _vmap_decrement_nesting()  # noqa: F821
+                _vmap_levels.pop()
+
+    @property
+    def size(self):
+        assert self.is_bound
+        return self._size
+
+    @size.setter
+    def size(self, size: int):
+        from . import DimensionBindError
+
+        if self._size is None:
+            self._size = size
+            self._vmap_level = _vmap_increment_nesting(size, "same")  # noqa: F821
+            self._vmap_stack = len(_vmap_levels)
+            _vmap_levels.append(LevelInfo(self._vmap_level))
+
+        elif self._size != size:
+            raise DimensionBindError(
+                f"Dim '{self}' previously bound to a dimension of size {self._size} cannot bind to a dimension of size {size}"
+            )
+
+    @property
+    def is_bound(self):
+        return self._size is not None
+
+    def __repr__(self):
+        return self.name
+
+
+def extract_name(inst):
+    assert inst.opname == "STORE_FAST" or inst.opname == "STORE_NAME"
+    return inst.argval
+
+
+_cache = {}
+
+
+def dims(lists=0):
+    frame = inspect.currentframe()
+    assert frame is not None
+    calling_frame = frame.f_back
+    assert calling_frame is not None
+    code, lasti = calling_frame.f_code, calling_frame.f_lasti
+    key = (code, lasti)
+    if key not in _cache:
+        first = lasti // 2 + 1
+        instructions = list(dis.get_instructions(calling_frame.f_code))
+        unpack = instructions[first]
+
+        if unpack.opname == "STORE_FAST" or unpack.opname == "STORE_NAME":
+            # just a single dim, not a list
+            name = unpack.argval
+            ctor = Dim if lists == 0 else DimList
+            _cache[key] = lambda: ctor(name=name)
+        else:
+            assert unpack.opname == "UNPACK_SEQUENCE"
+            ndims = unpack.argval
+            names = tuple(
+                extract_name(instructions[first + 1 + i]) for i in range(ndims)
+            )
+            first_list = len(names) - lists
+            _cache[key] = lambda: tuple(
+                Dim(n) if i < first_list else DimList(name=n)
+                for i, n in enumerate(names)
+            )
+    return _cache[key]()
+
+
+def _dim_set(positional, arg):
+    def convert(a):
+        if isinstance(a, Dim):
+            return a
+        else:
+            assert isinstance(a, int)
+            return positional[a]
+
+    if arg is None:
+        return positional
+    elif not isinstance(arg, (Dim, int)):
+        return tuple(convert(a) for a in arg)
+    else:
+        return (convert(arg),)
diff --git a/lib/python3.11/site-packages/functorch/dim/magic_trace.py b/lib/python3.11/site-packages/functorch/dim/magic_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c962a898ca79cfe3d8af7432aacc3802d4f4ade
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/magic_trace.py
@@ -0,0 +1,42 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import signal
+import subprocess
+from contextlib import contextmanager
+
+
+@contextmanager
+def magic_trace(output="trace.fxt", magic_trace_cache="/tmp/magic-trace"):
+    pid = os.getpid()
+    if not os.path.exists(magic_trace_cache):
+        print(f"Downloading magic_trace to: {magic_trace_cache}")
+        subprocess.run(
+            [
+                "wget",
+                "-O",
+                magic_trace_cache,
+                "-q",
+                "https://github.com/janestreet/magic-trace/releases/download/v1.0.2/magic-trace",
+            ]
+        )
+        subprocess.run(["chmod", "+x", magic_trace_cache])
+    args = [magic_trace_cache, "attach", "-pid", str(pid), "-o", output]
+    p = subprocess.Popen(args, stderr=subprocess.PIPE, encoding="utf-8")
+    while True:
+        x = p.stderr.readline()
+        print(x)
+        if "Attached" in x:
+            break
+    try:
+        yield
+    finally:
+        p.send_signal(signal.SIGINT)
+        r = p.wait()
+        print(p.stderr.read())
+        p.stderr.close()
+        if r != 0:
+            raise ValueError(f"magic_trace exited abnormally: {r}")
diff --git a/lib/python3.11/site-packages/functorch/dim/op_properties.py b/lib/python3.11/site-packages/functorch/dim/op_properties.py
new file mode 100644
index 0000000000000000000000000000000000000000..01313f71f030d58ce76c15c7f8516c4a0bdcf48a
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/op_properties.py
@@ -0,0 +1,312 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+
+# pointwise operators can go through a faster pathway
+
+tensor_magic_methods = ["add", ""]
+pointwise_magic_methods_with_reverse = (
+    "add",
+    "sub",
+    "mul",
+    "floordiv",
+    "div",
+    "truediv",
+    "mod",
+    "pow",
+    "lshift",
+    "rshift",
+    "and",
+    "or",
+    "xor",
+)
+pointwise_magic_methods = (
+    *(x for m in pointwise_magic_methods_with_reverse for x in (m, "r" + m)),
+    "eq",
+    "gt",
+    "le",
+    "lt",
+    "ge",
+    "gt",
+    "ne",
+    "neg",
+    "pos",
+    "abs",
+    "invert",
+    "iadd",
+    "isub",
+    "imul",
+    "ifloordiv",
+    "idiv",
+    "itruediv",
+    "imod",
+    "ipow",
+    "ilshift",
+    "irshift",
+    "iand",
+    "ior",
+    "ixor",
+    "int",
+    "long",
+    "float",
+    "complex",
+)
+
+pointwise_methods = (*(f"__{m}__" for m in pointwise_magic_methods),)
+
+pointwise = (
+    *(getattr(torch.Tensor, m) for m in pointwise_methods),
+    torch.nn.functional.dropout,
+    torch.where,
+    torch.Tensor.abs,
+    torch.abs,
+    torch.Tensor.acos,
+    torch.acos,
+    torch.Tensor.acosh,
+    torch.acosh,
+    torch.Tensor.add,
+    torch.add,
+    torch.Tensor.addcdiv,
+    torch.addcdiv,
+    torch.Tensor.addcmul,
+    torch.addcmul,
+    torch.Tensor.addr,
+    torch.addr,
+    torch.Tensor.angle,
+    torch.angle,
+    torch.Tensor.asin,
+    torch.asin,
+    torch.Tensor.asinh,
+    torch.asinh,
+    torch.Tensor.atan,
+    torch.atan,
+    torch.Tensor.atan2,
+    torch.atan2,
+    torch.Tensor.atanh,
+    torch.atanh,
+    torch.Tensor.bitwise_and,
+    torch.bitwise_and,
+    torch.Tensor.bitwise_left_shift,
+    torch.bitwise_left_shift,
+    torch.Tensor.bitwise_not,
+    torch.bitwise_not,
+    torch.Tensor.bitwise_or,
+    torch.bitwise_or,
+    torch.Tensor.bitwise_right_shift,
+    torch.bitwise_right_shift,
+    torch.Tensor.bitwise_xor,
+    torch.bitwise_xor,
+    torch.Tensor.ceil,
+    torch.ceil,
+    torch.celu,
+    torch.nn.functional.celu,
+    torch.Tensor.clamp,
+    torch.clamp,
+    torch.Tensor.clamp_max,
+    torch.clamp_max,
+    torch.Tensor.clamp_min,
+    torch.clamp_min,
+    torch.Tensor.copysign,
+    torch.copysign,
+    torch.Tensor.cos,
+    torch.cos,
+    torch.Tensor.cosh,
+    torch.cosh,
+    torch.Tensor.deg2rad,
+    torch.deg2rad,
+    torch.Tensor.digamma,
+    torch.digamma,
+    torch.Tensor.div,
+    torch.div,
+    torch.dropout,
+    torch.nn.functional.dropout,
+    torch.nn.functional.elu,
+    torch.Tensor.eq,
+    torch.eq,
+    torch.Tensor.erf,
+    torch.erf,
+    torch.Tensor.erfc,
+    torch.erfc,
+    torch.Tensor.erfinv,
+    torch.erfinv,
+    torch.Tensor.exp,
+    torch.exp,
+    torch.Tensor.exp2,
+    torch.exp2,
+    torch.Tensor.expm1,
+    torch.expm1,
+    torch.feature_dropout,
+    torch.Tensor.float_power,
+    torch.float_power,
+    torch.Tensor.floor,
+    torch.floor,
+    torch.Tensor.floor_divide,
+    torch.floor_divide,
+    torch.Tensor.fmod,
+    torch.fmod,
+    torch.Tensor.frac,
+    torch.frac,
+    torch.Tensor.frexp,
+    torch.frexp,
+    torch.Tensor.gcd,
+    torch.gcd,
+    torch.Tensor.ge,
+    torch.ge,
+    torch.nn.functional.gelu,
+    torch.nn.functional.glu,
+    torch.Tensor.gt,
+    torch.gt,
+    torch.Tensor.hardshrink,
+    torch.hardshrink,
+    torch.nn.functional.hardshrink,
+    torch.nn.functional.hardsigmoid,
+    torch.nn.functional.hardswish,
+    torch.nn.functional.hardtanh,
+    torch.Tensor.heaviside,
+    torch.heaviside,
+    torch.Tensor.hypot,
+    torch.hypot,
+    torch.Tensor.i0,
+    torch.i0,
+    torch.Tensor.igamma,
+    torch.igamma,
+    torch.Tensor.igammac,
+    torch.igammac,
+    torch.Tensor.isclose,
+    torch.isclose,
+    torch.Tensor.isfinite,
+    torch.isfinite,
+    torch.Tensor.isinf,
+    torch.isinf,
+    torch.Tensor.isnan,
+    torch.isnan,
+    torch.Tensor.isneginf,
+    torch.isneginf,
+    torch.Tensor.isposinf,
+    torch.isposinf,
+    torch.Tensor.isreal,
+    torch.isreal,
+    torch.Tensor.kron,
+    torch.kron,
+    torch.Tensor.lcm,
+    torch.lcm,
+    torch.Tensor.ldexp,
+    torch.ldexp,
+    torch.Tensor.le,
+    torch.le,
+    torch.nn.functional.leaky_relu,
+    torch.Tensor.lerp,
+    torch.lerp,
+    torch.Tensor.lgamma,
+    torch.lgamma,
+    torch.Tensor.log,
+    torch.log,
+    torch.Tensor.log10,
+    torch.log10,
+    torch.Tensor.log1p,
+    torch.log1p,
+    torch.Tensor.log2,
+    torch.log2,
+    torch.nn.functional.logsigmoid,
+    torch.Tensor.logical_and,
+    torch.logical_and,
+    torch.Tensor.logical_not,
+    torch.logical_not,
+    torch.Tensor.logical_or,
+    torch.logical_or,
+    torch.Tensor.logical_xor,
+    torch.logical_xor,
+    torch.Tensor.logit,
+    torch.logit,
+    torch.Tensor.lt,
+    torch.lt,
+    torch.Tensor.maximum,
+    torch.maximum,
+    torch.Tensor.minimum,
+    torch.minimum,
+    torch.nn.functional.mish,
+    torch.Tensor.mvlgamma,
+    torch.mvlgamma,
+    torch.Tensor.nan_to_num,
+    torch.nan_to_num,
+    torch.Tensor.ne,
+    torch.ne,
+    torch.Tensor.neg,
+    torch.neg,
+    torch.Tensor.nextafter,
+    torch.nextafter,
+    torch.Tensor.outer,
+    torch.outer,
+    torch.polar,
+    torch.Tensor.polygamma,
+    torch.polygamma,
+    torch.Tensor.positive,
+    torch.positive,
+    torch.Tensor.pow,
+    torch.pow,
+    torch.Tensor.prelu,
+    torch.prelu,
+    torch.nn.functional.prelu,
+    torch.Tensor.rad2deg,
+    torch.rad2deg,
+    torch.Tensor.reciprocal,
+    torch.reciprocal,
+    torch.Tensor.relu,
+    torch.relu,
+    torch.nn.functional.relu,
+    torch.nn.functional.relu6,
+    torch.Tensor.remainder,
+    torch.remainder,
+    torch.Tensor.round,
+    torch.round,
+    torch.rrelu,
+    torch.nn.functional.rrelu,
+    torch.Tensor.rsqrt,
+    torch.rsqrt,
+    torch.rsub,
+    torch.selu,
+    torch.nn.functional.selu,
+    torch.Tensor.sgn,
+    torch.sgn,
+    torch.Tensor.sigmoid,
+    torch.sigmoid,
+    torch.nn.functional.sigmoid,
+    torch.Tensor.sign,
+    torch.sign,
+    torch.Tensor.signbit,
+    torch.signbit,
+    torch.nn.functional.silu,
+    torch.Tensor.sin,
+    torch.sin,
+    torch.Tensor.sinc,
+    torch.sinc,
+    torch.Tensor.sinh,
+    torch.sinh,
+    torch.nn.functional.softplus,
+    torch.nn.functional.softshrink,
+    torch.Tensor.sqrt,
+    torch.sqrt,
+    torch.Tensor.square,
+    torch.square,
+    torch.Tensor.sub,
+    torch.sub,
+    torch.Tensor.tan,
+    torch.tan,
+    torch.Tensor.tanh,
+    torch.tanh,
+    torch.nn.functional.tanh,
+    torch.threshold,
+    torch.nn.functional.threshold,
+    torch.trapz,
+    torch.Tensor.true_divide,
+    torch.true_divide,
+    torch.Tensor.trunc,
+    torch.trunc,
+    torch.Tensor.xlogy,
+    torch.xlogy,
+    torch.rand_like,
+)
diff --git a/lib/python3.11/site-packages/functorch/dim/reference.py b/lib/python3.11/site-packages/functorch/dim/reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..6453a441b9445b58a02e3f917ff0746790908eae
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/reference.py
@@ -0,0 +1,643 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# reference python implementations for C ops
+import torch
+from functorch._C import dim as _C
+
+from . import op_properties
+from .batch_tensor import _enable_layers
+from .tree_map import tree_flatten, tree_map
+
+
+DimList = _C.DimList
+import operator
+from functools import reduce
+
+
+# use dict to avoid writing C++ bindings for set
+pointwise = set(op_properties.pointwise)
+
+
+def prod(x):
+    return reduce(operator.mul, x, 1)
+
+
+def _wrap_dim(d, N, keepdim):
+    from . import Dim
+
+    if isinstance(d, Dim):
+        assert not keepdim, "cannot preserve first-class dimensions with keepdim=True"
+        return d
+    elif d >= 0:
+        return d - N
+    else:
+        return d
+
+
+def _dims(d, N, keepdim, single_dim):
+    from . import Dim
+
+    if isinstance(d, (Dim, int)):
+        return ltuple((_wrap_dim(d, N, keepdim),))
+    assert not single_dim, f"expected a single dimension or int but found: {d}"
+    return ltuple(_wrap_dim(x, N, keepdim) for x in d)
+
+
+def _bind_dims_to_size(lhs_size, rhs, lhs_debug):
+    from . import DimensionMismatchError
+
+    not_bound = tuple((i, r) for i, r in enumerate(rhs) if not r.is_bound)
+    if len(not_bound) == 1:
+        idx, d = not_bound[0]
+        rhs_so_far = prod(r.size for r in rhs if r.is_bound)
+        if lhs_size % rhs_so_far != 0:
+            rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs)
+            raise DimensionMismatchError(
+                f"inferred dimension does not evenly fit into larger dimension: {lhs_size} vs {rhs_s}"
+            )
+        new_size = lhs_size // rhs_so_far
+        d.size = new_size
+    elif len(not_bound) > 1:
+        rhs_s = tuple("?" if not r.is_bound else str(r.size) for r in rhs)
+        raise DimensionMismatchError(
+            f"cannot infer the size of two dimensions at once: {rhs} with sizes {rhs_s}"
+        )
+    else:
+        rhs_size = prod(r.size for r in rhs)
+        if lhs_size != rhs_size:
+            raise DimensionMismatchError(
+                f"Dimension sizes to do not match ({lhs_size} != {rhs_size}) when matching {lhs_debug} to {rhs}"
+            )
+
+
+def _tensor_levels(inp):
+    from . import _Tensor
+
+    if isinstance(inp, _Tensor):
+        return inp._tensor, llist(inp._levels), inp._has_device
+    else:
+        return inp, llist(range(-inp.ndim, 0)), True
+
+
+def _match_levels(v, from_levels, to_levels):
+    view = []
+    permute = []
+    requires_view = False
+    size = v.size()
+    for t in to_levels:
+        try:
+            idx = from_levels.index(t)
+            permute.append(idx)
+            view.append(size[idx])
+        except ValueError:
+            view.append(1)
+            requires_view = True
+    if permute != list(range(len(permute))):
+        v = v.permute(*permute)
+    if requires_view:
+        v = v.view(*view)
+    return v
+
+
+# make a single dimension positional but do not permute it,
+# used to do multi-tensor operators where the dim being acted on
+# should not physically move if possible
+def _positional_no_permute(self, dim, expand_dim=False):
+    from . import Tensor
+
+    ptensor, levels = self._tensor, llist(self._levels)
+    try:
+        idx = levels.index(dim)
+    except ValueError:
+        if not expand_dim:
+            raise
+        idx = 0
+        ptensor = ptensor.expand(dim.size, *ptensor.size())
+        levels.insert(0, 0)
+    idx_batched = 0
+    for i in range(idx):
+        if isinstance(levels[i], int):
+            levels[i] -= 1
+            idx_batched += 1
+    levels[idx] = -idx_batched - 1
+    return Tensor.from_positional(ptensor, levels, self._has_device), idx_batched
+
+
+def seq(a, b):
+    from . import Dim
+
+    if isinstance(a, Dim) != isinstance(b, Dim):
+        return False
+    if isinstance(a, Dim):
+        return a is b
+    else:
+        return a == b
+
+
+class isin:
+    def __contains__(self, item):
+        for x in self:
+            if seq(item, x):
+                return True
+        return False
+
+    def index(self, item):
+        for i, x in enumerate(self):
+            if seq(item, x):
+                return i
+        raise ValueError
+
+
+class llist(isin, list):
+    pass
+
+
+class ltuple(isin, tuple):
+    pass
+
+
+empty_dict = {}
+
+
+@classmethod
+def __torch_function__(self, orig, cls, args, kwargs=empty_dict):
+    from . import _Tensor, Tensor, TensorLike
+    from .delayed_mul_tensor import DelayedMulTensor
+
+    if orig is torch.Tensor.__mul__:
+        lhs, rhs = args
+        if (
+            isinstance(lhs, _Tensor)
+            and isinstance(rhs, _Tensor)
+            and lhs.ndim == 0
+            and rhs.ndim == 0
+        ):
+            return DelayedMulTensor(lhs, rhs)
+    all_dims = llist()
+    flat_args, unflatten = tree_flatten((args, kwargs))
+    device_holding_tensor = None
+    for f in flat_args:
+        if isinstance(f, _Tensor):
+            if f._has_device:
+                device_holding_tensor = f._batchtensor
+            for d in f.dims:
+                if d not in all_dims:
+                    all_dims.append(d)
+
+    def unwrap(t):
+        if isinstance(t, _Tensor):
+            r = t._batchtensor
+            if device_holding_tensor is not None and not t._has_device:
+                r = r.to(device=device_holding_tensor.device)
+            return r
+        return t
+
+    if orig in pointwise:
+        result_levels = llist()
+        to_expand = []
+        for i, f in enumerate(flat_args):
+            if isinstance(f, TensorLike):
+                ptensor, levels, _ = _tensor_levels(f)
+                if (
+                    isinstance(f, _Tensor)
+                    and not f._has_device
+                    and device_holding_tensor is not None
+                ):
+                    ptensor = ptensor.to(device=device_holding_tensor.device)
+                flat_args[i] = ptensor
+                for l in levels:
+                    if l not in result_levels:
+                        result_levels.append(l)
+                to_expand.append((i, levels))
+
+        for i, levels in to_expand:
+            flat_args[i] = _match_levels(flat_args[i], levels, result_levels)
+        args, kwargs = unflatten(flat_args)
+        result = orig(*args, **kwargs)
+
+        def wrap(t):
+            if isinstance(t, TensorLike):
+                return Tensor.from_positional(
+                    t, result_levels, device_holding_tensor is not None
+                )
+            return t
+
+        return tree_map(wrap, result)
+    else:
+
+        def wrap(t):
+            if isinstance(t, TensorLike):
+                return Tensor.from_batched(t, device_holding_tensor is not None)
+            return t
+
+        with _enable_layers(all_dims):
+            print(f"batch_tensor for {orig}")
+            args, kwargs = unflatten(unwrap(f) for f in flat_args)
+            result = orig(*args, **kwargs)
+            # print("END", orig)
+            return tree_map(wrap, result)
+
+
+def positional(self, *dims):
+    from . import Dim, DimensionBindError, Tensor
+
+    ptensor, levels = self._tensor, llist(self._levels)
+    flat_dims = llist()
+    view = []
+    needs_view = False
+    ndim = self.ndim
+    for d in dims:
+        if isinstance(d, DimList):
+            flat_dims.extend(d)
+            view.extend(e.size for e in d)
+        elif isinstance(d, Dim):
+            flat_dims.append(d)
+            view.append(d.size)
+        elif isinstance(d, int):
+            d = _wrap_dim(d, ndim, False)
+            flat_dims.append(d)
+            view.append(ptensor.size(d))
+        else:
+            flat_dims.extend(d)
+            view.append(prod(e.size for e in d))
+            needs_view = True
+
+    permute = list(range(len(levels)))
+    for i, d in enumerate(flat_dims):
+        try:
+            idx = levels.index(d)
+        except ValueError as e:
+            raise DimensionBindError(
+                f"tensor of dimensions {self.dims} does not contain dim {d}"
+            ) from e
+        p = permute[idx]
+        del levels[idx]
+        del permute[idx]
+        levels.insert(i, 0)
+        permute.insert(i, p)
+    ptensor = ptensor.permute(*permute)
+    seen = 0
+    for i in range(len(levels) - 1, -1, -1):
+        if isinstance(levels[i], int):
+            seen += 1
+            levels[i] = -seen
+    result = Tensor.from_positional(ptensor, levels, self._has_device)
+    if needs_view:
+        result = result.reshape(*view, *result.size()[len(flat_dims) :])
+    return result
+
+
+def _contains_dim(input):
+    from . import Dim
+
+    for i in input:
+        if isinstance(i, Dim):
+            return True
+
+
+def expand(self, *sizes):
+    if not _contains_dim(sizes):
+        return self.__torch_function__(torch.Tensor.expand, None, (self, *sizes))
+    dims = sizes
+    sizes = [d.size for d in dims] + [-1] * self.ndim
+    self = self.expand(*sizes)
+    return self[dims]
+
+
+_not_present = object()
+
+
+def _getarg(name, offset, args, kwargs, default):
+    if len(args) > offset:
+        return args[offset]
+    return kwargs.get(name, default)
+
+
+def _patcharg(name, offset, args, kwargs, value):
+    if len(args) > offset:
+        args[offset] = value
+    else:
+        kwargs[name] = value
+
+
+def _wrap(
+    orig, dim_offset=0, keepdim_offset=1, dim_name="dim", single_dim=False, reduce=True
+):
+    from . import Dim, Tensor, TensorLike
+
+    def fn(self, *args, **kwargs):
+        dim = _getarg(dim_name, dim_offset, args, kwargs, _not_present)
+        if dim is _not_present or (single_dim and not isinstance(dim, Dim)):
+            with _enable_layers(self.dims):
+                print(f"dim fallback batch_tensor for {orig}")
+                return Tensor.from_batched(
+                    orig(self._batchtensor, *args, **kwargs), self._has_device
+                )
+        keepdim = (
+            _getarg("keepdim", keepdim_offset, args, kwargs, False) if reduce else False
+        )
+        t, levels = self._tensor, llist(self._levels)
+        dims = _dims(dim, self._batchtensor.ndim, keepdim, single_dim)
+        dim_indices = tuple(levels.index(d) for d in dims)
+        if reduce and not keepdim:
+            new_levels = [l for i, l in enumerate(levels) if i not in dim_indices]
+        else:
+            new_levels = levels
+
+        if len(dim_indices) == 1:
+            dim_indices = dim_indices[
+                0
+            ]  # so that dims that really only take a single argument work...
+        args = list(args)
+        _patcharg(dim_name, dim_offset, args, kwargs, dim_indices)
+
+        def wrap(t):
+            if isinstance(t, TensorLike):
+                return Tensor.from_positional(t, new_levels, self._has_device)
+            return t
+
+        with _enable_layers(new_levels):
+            print(f"dim used batch_tensor for {orig}")
+            r = orig(t, *args, **kwargs)
+            return tree_map(wrap, r)
+
+    return fn
+
+
+def _def(name, *args, **kwargs):
+    from . import _Tensor
+
+    orig = getattr(torch.Tensor, name)
+    setattr(_Tensor, name, _wrap(orig, *args, **kwargs))
+
+
+no_slice = slice(None)
+
+_orig_getitem = torch.Tensor.__getitem__
+
+
+class dim_tracker:
+    def __init__(self) -> None:
+        self.dims = llist()
+        self.count = []
+
+    def record(self, d):
+        if d not in self.dims:
+            self.dims.append(d)
+            self.count.append(1)
+
+    def __getitem__(self, d):
+        return self.count[self.dims.index(d)]
+
+
+def t__getitem__(self, input):
+    from . import _Tensor, Dim, DimensionBindError, DimList, Tensor, TensorLike
+
+    # * bail to original example if we have a single non-Dim tensor, or a non-tensor
+    # * locate ... or an unbound tensor list, and determine its size, bind dim list
+    #   (remember that None does not count to the total dim count)
+    # * bind simple dims and dim-packs to their sizes, count the number of uses of each dim,
+    #   produce the re-view if needed
+    # * for each single-use dim index, replace with no_slice and mark that it will be added
+    #   (keep track of whether we have to call super)
+    # * call super if needed
+    # * if we have dims to bind, bind them (it will help if we eliminated ... and None before)
+    # this handles bool indexing handling, as well as some other simple cases.
+
+    is_simple = (
+        not isinstance(input, Dim)
+        and not isinstance(input, (tuple, list))
+        and
+        # WAR for functorch bug where zero time tensors in getitem are not handled correctly.
+        not (isinstance(input, TensorLike) and input.ndim == 0)
+    )
+
+    if is_simple:
+        if isinstance(self, _Tensor):
+            return _Tensor.__torch_function__(_orig_getitem, None, (self, input))
+        else:
+            return _orig_getitem(self, input)
+
+    # can further optimize this case
+    if not isinstance(input, tuple):
+        input = [input]
+    else:
+        input = list(input)
+
+    dims_indexed = 0
+    expanding_object = None
+    dimlists = []
+    for i, s in enumerate(input):
+        if s is ... or isinstance(s, DimList) and not s.is_bound:
+            if expanding_object is not None:
+                msg = (
+                    "at most one ... or unbound dimension list can exist in indexing list but"
+                    f" found 2 at offsets {i} and {expanding_object}"
+                )
+                raise DimensionBindError(msg)
+            expanding_object = i
+
+        if isinstance(s, DimList):
+            dims_indexed += len(s) if s.is_bound else 0
+            dimlists.append(i)
+        elif s is not None and s is not ...:
+            dims_indexed += 1
+
+    ndim = self.ndim
+    if dims_indexed > ndim:
+        raise IndexError(
+            f"at least {dims_indexed} indices were supplied but the tensor only has {ndim} dimensions."
+        )
+    if expanding_object is not None:
+        expanding_ndims = ndim - dims_indexed
+        obj = input[expanding_object]
+        if obj is ...:
+            input[expanding_object : expanding_object + 1] = [
+                no_slice
+            ] * expanding_ndims
+        else:
+            obj.bind_len(expanding_ndims)
+    # flatten the dimslists into the indexing
+    for i in reversed(dimlists):
+        input[i : i + 1] = input[i]
+    dims_indexed = 0
+    requires_view = False
+    size = self.size()
+    view_sizes = []
+    dims_seen = dim_tracker()
+
+    def add_dims(t):
+        if not isinstance(t, _Tensor):
+            return
+        for d in t.dims:
+            dims_seen.record(d)
+
+    add_dims(self)
+    dim_packs = []
+    for i, idx in enumerate(input):
+        if idx is None:
+            input[i] = no_slice
+            view_sizes.append(1)
+            requires_view = True
+        else:
+            sz = size[dims_indexed]
+            if isinstance(idx, Dim):
+                idx.size = sz
+                dims_seen.record(idx)
+                view_sizes.append(sz)
+            elif isinstance(idx, (tuple, list)) and idx and isinstance(idx[0], Dim):
+                for d in idx:
+                    dims_seen.record(idx)
+                _bind_dims_to_size(sz, idx, f"offset {i}")
+                view_sizes.extend(d.size for d in idx)
+                requires_view = True
+                dim_packs.append(i)
+            else:
+                add_dims(idx)
+                view_sizes.append(sz)
+            dims_indexed += 1
+    if requires_view:
+        self = self.view(*view_sizes)
+    for i in reversed(dim_packs):
+        input[i : i + 1] = input[i]
+
+    # currenty:
+    # input is flat, containing either Dim, or Tensor, or something valid for standard indexing
+    # self may have first-class dims as well.
+
+    # to index:
+    # drop the first class dims from self, they just become direct indices of their positions
+
+    # figure out the dimensions of the indexing tensors: union of all the dims in the tensors in the index.
+    # these dimensions will appear and need to be bound at the first place tensor occures
+
+    if isinstance(self, _Tensor):
+        ptensor_self, levels = self._tensor, list(self._levels)
+        # indices to ptensor rather than self which has first-class dimensions
+        input_it = iter(input)
+        flat_inputs = [next(input_it) if isinstance(l, int) else l for l in levels]
+        has_device = self._has_device
+        to_pad = 0
+    else:
+        ptensor_self, flat_inputs = self, input
+        to_pad = ptensor_self.ndim - len(flat_inputs)
+        has_device = True
+
+    result_levels = []
+    index_levels = []
+    tensor_insert_point = None
+    to_expand = {}
+    requires_getindex = False
+    for i, inp in enumerate(flat_inputs):
+        if isinstance(inp, Dim) and dims_seen[inp] == 1:
+            flat_inputs[i] = no_slice
+            result_levels.append(inp)
+        elif isinstance(inp, TensorLike):
+            requires_getindex = True
+            if tensor_insert_point is None:
+                tensor_insert_point = len(result_levels)
+            ptensor, levels, _ = _tensor_levels(inp)
+            to_expand[i] = levels
+            flat_inputs[i] = ptensor
+            for l in levels:
+                if l not in index_levels:
+                    index_levels.append(l)
+        else:
+            requires_getindex = True
+            result_levels.append(0)
+
+    if tensor_insert_point is not None:
+        result_levels[tensor_insert_point:tensor_insert_point] = index_levels
+
+    for i, levels in to_expand.items():
+        flat_inputs[i] = _match_levels(flat_inputs[i], levels, index_levels)
+
+    if requires_getindex:
+        result = _orig_getitem(ptensor_self, flat_inputs)
+    else:
+        result = ptensor_self
+
+    next_positional = -1
+    if to_pad > 0:
+        result_levels.extend([0] * to_pad)
+    for i, r in enumerate(reversed(result_levels)):
+        if isinstance(r, int):
+            result_levels[-1 - i] = next_positional
+            next_positional -= 1
+
+    return Tensor.from_positional(result, result_levels, has_device)
+
+
+# XXX - dim is optional and can be the outer-most dimension...
+def stack(tensors, new_dim, dim=0, out=None):
+    if isinstance(dim, int):
+        return torch.stack(tensors, dim, out).index(dim, new_dim)
+    index = None
+    if out is not None:
+        out, index = _positional_no_permute(out, dim, expand_dim=True)
+    ptensors = []
+    for t in tensors:
+        pt, pi = _positional_no_permute(t, dim, expand_dim=True)
+        if index is not None and pi != index:
+            pt = pt.move_dim(pi, index)
+        else:
+            index = pi
+        ptensors.append(pt)
+    pr = torch.stack(ptensors, index, out=out)
+    return pr.index((index, index + 1), (new_dim, dim))
+
+
+_orig_split = torch.Tensor.split
+
+
+def split(self, split_size_or_sections, dim=0):
+    from . import _Tensor, Dim
+
+    if isinstance(split_size_or_sections, int) or any(
+        isinstance(t, int) for t in split_size_or_sections
+    ):
+        if isinstance(dim, Dim):
+            raise ValueError(
+                "when dim is specified as a Dim object, split sizes must also be dimensions."
+            )
+        return _orig_split(self, split_size_or_sections, dim=dim)
+
+    if isinstance(dim, Dim):
+        assert isinstance(self, _Tensor), f"Tensor does not have dimension {dim}"
+        self, dim = _positional_no_permute(self, dim)
+
+    size = self.size(dim)
+    total_bound_size = 0
+    unbound = []
+    sizes = []
+    for i, d in enumerate(split_size_or_sections):
+        if d.is_bound:
+            sizes.append(d.size)
+            total_bound_size += d.size
+        else:
+            sizes.append(0)
+            unbound.append(i)
+
+    if unbound:
+        assert (
+            total_bound_size <= size
+        ), f"result dimensions are larger than original: {total_bound_size} vs {size} ({split_size_or_sections})"
+        remaining_size = size - total_bound_size
+        chunk_size = -(-remaining_size // len(unbound))
+        for u in unbound:
+            sz = min(chunk_size, remaining_size)
+            split_size_or_sections[u].size = sz
+            sizes[u] = sz
+            remaining_size -= sz
+    else:
+        assert (
+            total_bound_size == size
+        ), f"result dimensions do not match original: {total_bound_size} vs {size} ({split_size_or_sections})"
+    return tuple(
+        t.index(dim, d)
+        for d, t in zip(split_size_or_sections, _orig_split(self, sizes, dim=dim))
+    )
diff --git a/lib/python3.11/site-packages/functorch/dim/tree_map.py b/lib/python3.11/site-packages/functorch/dim/tree_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d2eae0582c85619e0d28e648af58cbd847eee97
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/tree_map.py
@@ -0,0 +1,15 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functorch._C import dim
+
+
+tree_flatten = dim.tree_flatten
+
+
+def tree_map(fn, tree):
+    vs, unflatten = tree_flatten(tree)
+    return unflatten(fn(v) for v in vs)
diff --git a/lib/python3.11/site-packages/functorch/dim/wrap_type.py b/lib/python3.11/site-packages/functorch/dim/wrap_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..aae543b91a896e2d5cef3b03d725c3d91ce1305d
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/dim/wrap_type.py
@@ -0,0 +1,72 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from types import (
+    BuiltinMethodType,
+    FunctionType,
+    GetSetDescriptorType,
+    MethodDescriptorType,
+    WrapperDescriptorType,
+)
+
+from functorch._C import dim as _C
+
+
+_wrap_method = _C._wrap_method
+
+FUNC_TYPES = (
+    FunctionType,
+    MethodDescriptorType,
+    BuiltinMethodType,
+    WrapperDescriptorType,
+)
+PROPERTY_TYPES = (GetSetDescriptorType, property)
+
+
+def _py_wrap_method(orig, __torch_function__):
+    def impl(*args, **kwargs):
+        return __torch_function__(orig, None, args, kwargs)
+
+    return impl
+
+
+def wrap_type(use_c, to_patch, pattern, __torch_function__):
+    if use_c:
+        wrap_method = _wrap_method
+    else:
+        wrap_method = _py_wrap_method
+
+    all = {}
+    for t in reversed(pattern.mro()[:-1]):  # skip object
+        all.update(t.__dict__)
+
+    def wrap_attr(orig):
+        return property(wrap_method(orig.__get__, __torch_function__))
+
+    for name, obj in all.items():
+        if name in (
+            "__dict__",
+            "__new__",
+            "__init__",
+            "__repr__",
+            "__weakref__",
+            "__doc__",
+            "__module__",
+            "__dir__",
+        ):
+            continue
+
+        # skip things that have been overloaded
+        # things that come from object like `__eq__` still need to be patched, however.
+        if hasattr(to_patch, name) and getattr(to_patch, name) is not getattr(
+            object, name, None
+        ):
+            continue
+
+        if isinstance(obj, FUNC_TYPES):
+            setattr(to_patch, name, wrap_method(obj, __torch_function__))
+        elif isinstance(obj, PROPERTY_TYPES):
+            setattr(to_patch, name, wrap_attr(obj))
diff --git a/lib/python3.11/site-packages/functorch/einops/__init__.py b/lib/python3.11/site-packages/functorch/einops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7ac34f7a3722010fc0fde97fd1cd72e76fa88b7
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/einops/__init__.py
@@ -0,0 +1,4 @@
+from .rearrange import rearrange
+
+
+__all__ = ["rearrange"]
diff --git a/lib/python3.11/site-packages/functorch/einops/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/einops/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..320f772debdb7a1d7b0d0cbaf1b48cbd6056f27f
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/einops/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/einops/__pycache__/_parsing.cpython-311.pyc b/lib/python3.11/site-packages/functorch/einops/__pycache__/_parsing.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..126db25dc4ce7ac5b1e3c542502531642c1d9141
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/einops/__pycache__/_parsing.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/einops/__pycache__/rearrange.cpython-311.pyc b/lib/python3.11/site-packages/functorch/einops/__pycache__/rearrange.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..69f0f2cbbe76a39b3726123851b0dfc7c95ead80
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/einops/__pycache__/rearrange.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/einops/_parsing.py b/lib/python3.11/site-packages/functorch/einops/_parsing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee69aa60d1a58e44dcdf888d8c5e1a568f0151d6
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/einops/_parsing.py
@@ -0,0 +1,304 @@
+"""Adapted from https://github.com/arogozhnikov/einops/blob/36c7bb16e57d6e57f8f3050f9e07abdf3f00469f/einops/parsing.py.
+
+MIT License
+
+Copyright (c) 2018 Alex Rogozhnikov
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+from __future__ import annotations
+
+import keyword
+import warnings
+from typing import Collection, List, Mapping, Optional, Set, Tuple, Union
+
+
+_ellipsis: str = "\u2026"  # NB, this is a single unicode symbol. String is used as it is not a list, but can be iterated
+
+
+class AnonymousAxis:
+    """Used by `ParsedExpression` to represent an axis with a size (> 1), but no associated identifier.
+
+    Note: Different instances of this class are not equal to each other, even if they have the same value.
+    """
+
+    def __init__(self, value: str) -> None:
+        self.value = int(value)
+        if self.value < 1:
+            raise ValueError(
+                f"Anonymous axis should have positive length, not {self.value}"
+            )
+
+    def __repr__(self) -> str:
+        return f"{self.value}-axis"
+
+
+class ParsedExpression:
+    """Structure containing information about one side of an `einops`-style pattern (e.g. 'b c (h w)')."""
+
+    def __init__(
+        self,
+        expression: str,
+        *,
+        allow_underscore: bool = False,
+        allow_duplicates: bool = False,
+    ) -> None:
+        """Parse the expression and store relevant metadata.
+
+        Args:
+            expression (str): the `einops`-pattern to parse
+            allow_underscore (bool): whether to allow axis identifier names to begin with an underscore
+            allow_duplicates (bool): whether to allow an identifier to appear more than once in the expression
+        """
+        self.has_ellipsis: bool = False
+        self.has_ellipsis_parenthesized: Optional[bool] = None
+        self.identifiers: Set[Union[str, AnonymousAxis]] = set()
+        # that's axes like 2, 3, 4 or 5. Axes with size 1 are exceptional and replaced with empty composition
+        self.has_non_unitary_anonymous_axes: bool = False
+        # composition keeps structure of composite axes, see how different corner cases are handled in tests
+        self.composition: List[Union[List[Union[str, AnonymousAxis]], str]] = []
+        if "." in expression:
+            if "..." not in expression:
+                raise ValueError(
+                    "Expression may contain dots only inside ellipsis (...)"
+                )
+            if str.count(expression, "...") != 1 or str.count(expression, ".") != 3:
+                raise ValueError(
+                    "Expression may contain dots only inside ellipsis (...); only one ellipsis for tensor "
+                )
+            expression = expression.replace("...", _ellipsis)
+            self.has_ellipsis = True
+
+        bracket_group: Optional[List[Union[str, AnonymousAxis]]] = None
+
+        def add_axis_name(x: str) -> None:
+            if x in self.identifiers:
+                if not (allow_underscore and x == "_") and not allow_duplicates:
+                    raise ValueError(
+                        f"Indexing expression contains duplicate dimension '{x}'"
+                    )
+            if x == _ellipsis:
+                self.identifiers.add(_ellipsis)
+                if bracket_group is None:
+                    self.composition.append(_ellipsis)
+                    self.has_ellipsis_parenthesized = False
+                else:
+                    bracket_group.append(_ellipsis)
+                    self.has_ellipsis_parenthesized = True
+            else:
+                is_number = str.isdecimal(x)
+                if is_number and int(x) == 1:
+                    # handling the case of anonymous axis of length 1
+                    if bracket_group is None:
+                        self.composition.append([])
+                    else:
+                        pass  # no need to think about 1s inside parenthesis
+                    return
+                is_axis_name, reason = self.check_axis_name_return_reason(
+                    x, allow_underscore=allow_underscore
+                )
+                if not (is_number or is_axis_name):
+                    raise ValueError(f"Invalid axis identifier: {x}\n{reason}")
+                axis_name: Union[str, AnonymousAxis] = (
+                    AnonymousAxis(x) if is_number else x
+                )
+                self.identifiers.add(axis_name)
+                if is_number:
+                    self.has_non_unitary_anonymous_axes = True
+                if bracket_group is None:
+                    self.composition.append([axis_name])
+                else:
+                    bracket_group.append(axis_name)
+
+        current_identifier = None
+        for char in expression:
+            if char in "() ":
+                if current_identifier is not None:
+                    add_axis_name(current_identifier)
+                current_identifier = None
+                if char == "(":
+                    if bracket_group is not None:
+                        raise ValueError(
+                            "Axis composition is one-level (brackets inside brackets not allowed)"
+                        )
+                    bracket_group = []
+                elif char == ")":
+                    if bracket_group is None:
+                        raise ValueError("Brackets are not balanced")
+                    self.composition.append(bracket_group)
+                    bracket_group = None
+            elif str.isalnum(char) or char in ["_", _ellipsis]:
+                if current_identifier is None:
+                    current_identifier = char
+                else:
+                    current_identifier += char
+            else:
+                raise ValueError(f"Unknown character '{char}'")
+
+        if bracket_group is not None:
+            raise ValueError(f"Imbalanced parentheses in expression: '{expression}'")
+        if current_identifier is not None:
+            add_axis_name(current_identifier)
+
+    @staticmethod
+    def check_axis_name_return_reason(
+        name: str, allow_underscore: bool = False
+    ) -> Tuple[bool, str]:
+        """Check if the given axis name is valid, and a message explaining why if not.
+
+        Valid axes names are python identifiers except keywords, and should not start or end with an underscore.
+
+        Args:
+            name (str): the axis name to check
+            allow_underscore (bool): whether axis names are allowed to start with an underscore
+
+        Returns:
+            Tuple[bool, str]: whether the axis name is valid, a message explaining why if not
+        """
+        if not str.isidentifier(name):
+            return False, "not a valid python identifier"
+        elif name[0] == "_" or name[-1] == "_":
+            if name == "_" and allow_underscore:
+                return True, ""
+            return False, "axis name should should not start or end with underscore"
+        else:
+            if keyword.iskeyword(name):
+                warnings.warn(
+                    f"It is discouraged to use axes names that are keywords: {name}",
+                    RuntimeWarning,
+                )
+            if name in ["axis"]:
+                warnings.warn(
+                    "It is discouraged to use 'axis' as an axis name and will raise an error in future",
+                    FutureWarning,
+                )
+            return True, ""
+
+    @staticmethod
+    def check_axis_name(name: str) -> bool:
+        """Check if the name is a valid axis name.
+
+        Args:
+            name (str): the axis name to check
+
+        Returns:
+            bool: whether the axis name is valid
+        """
+        is_valid, _ = ParsedExpression.check_axis_name_return_reason(name)
+        return is_valid
+
+
+def parse_pattern(
+    pattern: str, axes_lengths: Mapping[str, int]
+) -> Tuple[ParsedExpression, ParsedExpression]:
+    """Parse an `einops`-style pattern into a left-hand side and right-hand side `ParsedExpression` object.
+
+    Args:
+        pattern (str): the `einops`-style rearrangement pattern
+        axes_lengths (Mapping[str, int]): any additional length specifications for dimensions
+
+    Returns:
+       Tuple[ParsedExpression, ParsedExpression]: a tuple containing the left-hand side and right-hand side expressions
+    """
+    # adapted from einops.einops._prepare_transformation_recipe
+    # https://github.com/arogozhnikov/einops/blob/230ac1526c1f42c9e1f7373912c7f8047496df11/einops/einops.py
+    try:
+        left_str, right_str = pattern.split("->")
+    except ValueError:
+        raise ValueError("Pattern must contain a single '->' separator") from None
+
+    if _ellipsis in axes_lengths:
+        raise ValueError(f"'{_ellipsis}' is not an allowed axis identifier")
+
+    left = ParsedExpression(left_str)
+    right = ParsedExpression(right_str)
+
+    if not left.has_ellipsis and right.has_ellipsis:
+        raise ValueError(
+            f"Ellipsis found in right side, but not left side of a pattern {pattern}"
+        )
+    if left.has_ellipsis and left.has_ellipsis_parenthesized:
+        raise ValueError(
+            f"Ellipsis is parenthesis in the left side is not allowed: {pattern}"
+        )
+
+    return left, right
+
+
+def validate_rearrange_expressions(
+    left: ParsedExpression, right: ParsedExpression, axes_lengths: Mapping[str, int]
+) -> None:
+    """Perform expression validations that are specific to the `rearrange` operation.
+
+    Args:
+        left (ParsedExpression): left-hand side expression
+        right (ParsedExpression): right-hand side expression
+        axes_lengths (Mapping[str, int]): any additional length specifications for dimensions
+    """
+    for length in axes_lengths.values():
+        if (length_type := type(length)) is not int:
+            raise TypeError(
+                f"rearrange axis lengths must be integers, got: {length_type}"
+            )
+
+    if left.has_non_unitary_anonymous_axes or right.has_non_unitary_anonymous_axes:
+        raise ValueError("rearrange only supports unnamed axes of size 1")
+
+    difference = set.symmetric_difference(left.identifiers, right.identifiers)
+    if len(difference) > 0:
+        raise ValueError(
+            f"Identifiers only on one side of rearrange expression (should be on both): {difference}"
+        )
+
+    unmatched_axes = axes_lengths.keys() - left.identifiers
+    if len(unmatched_axes) > 0:
+        raise ValueError(
+            f"Identifiers not found in rearrange expression: {unmatched_axes}"
+        )
+
+
+def comma_separate(collection: Collection[Union[str, Collection[str]]]) -> str:
+    """Convert a collection of strings representing first class dims into a comma-separated string.
+
+    Args:
+        collection (Collection[Union[str, Collection[str]]]): the collection of strings to convert
+
+    Returns:
+        str: the comma-separated string
+
+    Examples:
+        >>> comma_separate(("d0",))
+        'd0'
+
+        >>> comma_separate(("d0", "d1", "d2", "d3"))
+        'd0, d1, d2, d3'
+
+        >>> comma_separate([("d1", "d4")])
+        '(d1, d4)'
+
+        >>> comma_separate([("d0",), (), ("d1",), ("d2",), ("d3", "d4")])
+        '(d0,), (), (d1,), (d2,), (d3, d4)'
+    """
+    return ", ".join(
+        item
+        if isinstance(item, str)
+        else f"({comma_separate(item)}{',' if len(item) == 1 else ''})"
+        for item in collection
+    )
diff --git a/lib/python3.11/site-packages/functorch/einops/rearrange.py b/lib/python3.11/site-packages/functorch/einops/rearrange.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0bceed7388347faefe35b14e1d31759fdb5fa5f
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/einops/rearrange.py
@@ -0,0 +1,208 @@
+from __future__ import annotations
+
+import functools
+from typing import Callable, Dict, List, Sequence, Tuple, Union
+
+import torch
+from functorch._C import dim as _C
+
+from ._parsing import (
+    _ellipsis,
+    AnonymousAxis,
+    comma_separate,
+    parse_pattern,
+    validate_rearrange_expressions,
+)
+
+
+__all__ = ["rearrange"]
+
+dims = _C.dims
+
+
+@functools.lru_cache(256)
+def _create_rearrange_callable(
+    tensor_ndim: int, pattern: str, **axes_lengths: int
+) -> Callable[[torch.Tensor], torch.Tensor]:
+    r"""Translate an `einops`-style pattern into a callable that performs the rearrange using first-class dimensions.
+
+    Since the an equivalent result is computed for tensors with the same number of dimensions, with the same pattern and
+    specified axes lengths, this function can be memoized.
+
+    Args:
+        tensor_ndim (int): the number of dimensions in the tensor to rearrange
+        pattern (str): the `einops`-style rearrangement pattern
+        axes_lengths (int): any additional length specifications for dimensions
+
+    Returns:
+        Callable[[torch.Tensor], torch.Tensor]: a callable that performs the rearrangement
+    """
+    left, right = parse_pattern(pattern, axes_lengths)
+    validate_rearrange_expressions(left, right, axes_lengths)
+
+    n_anon_dims = sum(not dim for dim in left.composition)
+    if left.has_ellipsis:
+        n_ellipsis_dims = tensor_ndim - (len(left.composition) - 1)
+        n_named_dims = len(left.identifiers) - 1
+
+        if (pattern_ndim := n_anon_dims + n_named_dims) > tensor_ndim:
+            raise ValueError(
+                f"Number of dimensions in pattern ({pattern_ndim}) must be less than or equal to the number of "
+                f"dimensions in the tensor ({tensor_ndim})"
+            )
+    else:
+        n_ellipsis_dims = 0
+        n_named_dims = len(left.identifiers)
+
+        if (pattern_ndim := len(left.composition)) != tensor_ndim:
+            raise ValueError(
+                f"Number of dimensions in pattern ({pattern_ndim}) must be equal to the number of dimensions in "
+                f"the tensor ({tensor_ndim})"
+            )
+    n_dims = n_named_dims + n_ellipsis_dims + n_anon_dims
+
+    if n_dims == 0:
+        # an identity rearrangement on a 0-dimension tensor
+        return lambda tensor: tensor
+
+    first_class_dims: Tuple[str, ...] = tuple(f"d{i}" for i in range(n_dims))
+    identifier_dim_map: Dict[Union[str, AnonymousAxis], Tuple[str, ...]] = {}
+    anon_axes: List[AnonymousAxis] = []
+
+    # map the left-hand side identifiers to strings representing first class dims
+    dims_i = 0
+    for dimension in left.composition:
+        if isinstance(dimension, list):
+            for identifier in dimension:
+                # non-unitary anon axes are not allowed in rearrange & unitary anon axes are represented as empty lists
+                assert isinstance(identifier, str)
+                identifier_dim_map[identifier] = (first_class_dims[dims_i],)
+                dims_i += 1
+            if not dimension:
+                # unitary anonymous axis
+                anon_axis = AnonymousAxis("1")
+                identifier_dim_map[anon_axis] = (first_class_dims[dims_i],)
+                anon_axes.append(anon_axis)
+                dimension.append(anon_axis)
+                dims_i += 1
+        elif dimension == _ellipsis:
+            identifier = _ellipsis
+            identifier_dim_map[identifier] = tuple(
+                first_class_dims[dims_i + j] for j in range(n_ellipsis_dims)
+            )
+            dims_i += n_ellipsis_dims
+        else:
+            raise ValueError(f"Unexpected dimension: {dimension}")
+
+    def composition_to_dims(
+        composition: Sequence[Union[List[Union[str, AnonymousAxis]], str]],
+    ) -> List[Union[str, Tuple[str, ...]]]:
+        """Convert a `ParsedExpression.composition` into a `Tensor.__getitem__` index of strings representing first
+        class dims."""
+        dim_composition: List[Union[str, Tuple[str, ...]]] = []
+        for dimension in composition:
+            if isinstance(dimension, list):
+                dim_composition.append(
+                    tuple(
+                        dim
+                        for identifier in dimension
+                        for dim in identifier_dim_map[identifier]
+                    )
+                )
+            elif dimension == _ellipsis:
+                dim_composition.extend(identifier_dim_map[_ellipsis])
+            else:
+                raise ValueError(f"Unexpected dimension: {dimension}")
+        return dim_composition
+
+    left_dims = composition_to_dims(left.composition)
+    right_dims = composition_to_dims(right.composition)
+    anon_dims = tuple(identifier_dim_map[axis][0] for axis in anon_axes)
+    specified_lengths = tuple(
+        (identifier_dim_map[axis][0], length) for axis, length in axes_lengths.items()
+    )
+
+    custom_rearrange_callable_name = "do_rearrange"
+    custom_rearrange_callable_code = (
+        (
+            f"def {custom_rearrange_callable_name}(tensor):\n"
+            f"    {comma_separate(first_class_dims)} = dims({n_dims})\n"
+        )
+        + (
+            "".join(
+                f"    {dim}.size = {length}\n" for (dim, length) in specified_lengths
+            )
+            if specified_lengths
+            else ""
+        )
+        + f"    tensor = tensor[{comma_separate(left_dims)}].order({comma_separate(right_dims)})\n"
+        + (
+            f"    return tensor.sum({comma_separate([anon_dims])}, keepdim=False)\n"
+            if anon_dims
+            else "    return tensor\n"
+        )
+    )
+
+    exec(custom_rearrange_callable_code)
+    return locals()[custom_rearrange_callable_name]
+
+
+def rearrange(
+    tensor: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor, ...]],
+    pattern: str,
+    **axes_lengths: int,
+) -> torch.Tensor:
+    r"""A native implementation of `einops.rearrange`, a reader-friendly smart element reordering for multidimensional
+    tensors. This operation includes functionality of transpose (axes permutation), reshape (view), squeeze, unsqueeze,
+    stack, concatenate and other operations.
+
+    See: https://einops.rocks/api/rearrange/
+
+    Args:
+        tensor (Tensor or sequence of Tensor): the tensor(s) to rearrange
+        pattern (str): the rearrangement pattern
+        axes_lengths (int): any additional length specifications for dimensions
+
+    Returns:
+        Tensor: the rearranged tensor
+
+    Examples:
+        >>> # suppose we have a set of 32 images in "h w c" format (height-width-channel)
+        >>> images = torch.randn((32, 30, 40, 3))
+
+        >>> # stack along first (batch) axis, output is a single array
+        >>> rearrange(images, "b h w c -> b h w c").shape
+        torch.Size([32, 30, 40, 3])
+
+        >>> # concatenate images along height (vertical axis), 960 = 32 * 30
+        >>> rearrange(images, "b h w c -> (b h) w c").shape
+        torch.Size([960, 40, 3])
+
+        >>> # concatenated images along horizontal axis, 1280 = 32 * 40
+        >>> rearrange(images, "b h w c -> h (b w) c").shape
+        torch.Size([30, 1280, 3])
+
+        >>> # reordered axes to "b c h w" format for deep learning
+        >>> rearrange(images, "b h w c -> b c h w").shape
+        torch.Size([32, 3, 30, 40])
+
+        >>> # flattened each image into a vector, 3600 = 30 * 40 * 3
+        >>> rearrange(images, "b h w c -> b (c h w)").shape
+        torch.Size([32, 3600])
+
+        >>> # split each image into 4 smaller (top-left, top-right, bottom-left, bottom-right), 128 = 32 * 2 * 2
+        >>> rearrange(images, "b (h1 h) (w1 w) c -> (b h1 w1) h w c", h1=2, w1=2).shape
+        torch.Size([128, 15, 20, 3])
+
+        >>> # space-to-depth operation
+        >>> rearrange(images, "b (h h1) (w w1) c -> b h w (c h1 w1)", h1=2, w1=2).shape
+        torch.Size([32, 15, 20, 12])
+    """
+    if not isinstance(tensor, torch.Tensor):
+        tensor = torch.stack(tensor)
+
+    rearrange_callable = _create_rearrange_callable(
+        tensor.ndim, pattern, **axes_lengths
+    )
+
+    return rearrange_callable(tensor)
diff --git a/lib/python3.11/site-packages/functorch/experimental/__init__.py b/lib/python3.11/site-packages/functorch/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3941f6d96e1f6df532966d06de4f72a952a58465
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/experimental/__init__.py
@@ -0,0 +1,5 @@
+# PyTorch forward-mode is not mature yet
+from functorch import functionalize
+from torch._functorch.apis import chunk_vmap
+from torch._functorch.batch_norm_replacement import replace_all_batch_norm_modules_
+from torch._functorch.eager_transforms import hessian, jacfwd, jvp
diff --git a/lib/python3.11/site-packages/functorch/experimental/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/functorch/experimental/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e7019dc8a0c375caabebede2c65bab906e9333f7
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/experimental/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/experimental/__pycache__/control_flow.cpython-311.pyc b/lib/python3.11/site-packages/functorch/experimental/__pycache__/control_flow.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bbe3eef5e15f37f09014cac56fb25f3aeb85ad56
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/experimental/__pycache__/control_flow.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/experimental/__pycache__/ops.cpython-311.pyc b/lib/python3.11/site-packages/functorch/experimental/__pycache__/ops.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2c06a3121092eb7ba701418d0dbfdd8684cefa73
Binary files /dev/null and b/lib/python3.11/site-packages/functorch/experimental/__pycache__/ops.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/functorch/experimental/control_flow.py b/lib/python3.11/site-packages/functorch/experimental/control_flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbfd76d184cc224042a1ee19454d6957c2221b3b
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/experimental/control_flow.py
@@ -0,0 +1,7 @@
+from torch import cond  # noqa: F401
+from torch._higher_order_ops.cond import UnsupportedAliasMutationException  # noqa: F401
+from torch._higher_order_ops.map import (  # noqa: F401
+    _stack_pytree,
+    _unstack_pytree,
+    map,
+)
diff --git a/lib/python3.11/site-packages/functorch/experimental/ops.py b/lib/python3.11/site-packages/functorch/experimental/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a502ef2b002cd824e7b67d08fccac872b313110
--- /dev/null
+++ b/lib/python3.11/site-packages/functorch/experimental/ops.py
@@ -0,0 +1 @@
+from torch._ops import HigherOrderOperator  # noqa: F401
diff --git a/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/INSTALLER b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/METADATA b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..ffef2ff3bfa0c42b6e6e3eefda700391d181c9a0
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/METADATA
@@ -0,0 +1,84 @@
+Metadata-Version: 2.4
+Name: Jinja2
+Version: 3.1.6
+Summary: A very fast and expressive template engine.
+Maintainer-email: Pallets <contact@palletsprojects.com>
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Classifier: Topic :: Text Processing :: Markup :: HTML
+Classifier: Typing :: Typed
+License-File: LICENSE.txt
+Requires-Dist: MarkupSafe>=2.0
+Requires-Dist: Babel>=2.7 ; extra == "i18n"
+Project-URL: Changes, https://jinja.palletsprojects.com/changes/
+Project-URL: Chat, https://discord.gg/pallets
+Project-URL: Documentation, https://jinja.palletsprojects.com/
+Project-URL: Donate, https://palletsprojects.com/donate
+Project-URL: Source, https://github.com/pallets/jinja/
+Provides-Extra: i18n
+
+# Jinja
+
+Jinja is a fast, expressive, extensible templating engine. Special
+placeholders in the template allow writing code similar to Python
+syntax. Then the template is passed data to render the final document.
+
+It includes:
+
+-   Template inheritance and inclusion.
+-   Define and import macros within templates.
+-   HTML templates can use autoescaping to prevent XSS from untrusted
+    user input.
+-   A sandboxed environment can safely render untrusted templates.
+-   AsyncIO support for generating templates and calling async
+    functions.
+-   I18N support with Babel.
+-   Templates are compiled to optimized Python code just-in-time and
+    cached, or can be compiled ahead-of-time.
+-   Exceptions point to the correct line in templates to make debugging
+    easier.
+-   Extensible filters, tests, functions, and even syntax.
+
+Jinja's philosophy is that while application logic belongs in Python if
+possible, it shouldn't make the template designer's job difficult by
+restricting functionality too much.
+
+
+## In A Nutshell
+
+```jinja
+{% extends "base.html" %}
+{% block title %}Members{% endblock %}
+{% block content %}
+  <ul>
+  {% for user in users %}
+    <li><a href="{{ user.url }}">{{ user.username }}</a></li>
+  {% endfor %}
+  </ul>
+{% endblock %}
+```
+
+## Donate
+
+The Pallets organization develops and supports Jinja and other popular
+packages. In order to grow the community of contributors and users, and
+allow the maintainers to devote more time to the projects, [please
+donate today][].
+
+[please donate today]: https://palletsprojects.com/donate
+
+## Contributing
+
+See our [detailed contributing documentation][contrib] for many ways to
+contribute, including reporting issues, requesting features, asking or answering
+questions, and making PRs.
+
+[contrib]: https://palletsprojects.com/contributing/
+
diff --git a/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/RECORD b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..4b48b91fa45686b153737ec87eeddd846c2f56c6
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/RECORD
@@ -0,0 +1,57 @@
+jinja2-3.1.6.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+jinja2-3.1.6.dist-info/METADATA,sha256=aMVUj7Z8QTKhOJjZsx7FDGvqKr3ZFdkh8hQ1XDpkmcg,2871
+jinja2-3.1.6.dist-info/RECORD,,
+jinja2-3.1.6.dist-info/WHEEL,sha256=_2ozNFCLWc93bK4WKHCO-eDUENDlo-dgc9cU3qokYO4,82
+jinja2-3.1.6.dist-info/entry_points.txt,sha256=OL85gYU1eD8cuPlikifFngXpeBjaxl6rIJ8KkC_3r-I,58
+jinja2-3.1.6.dist-info/licenses/LICENSE.txt,sha256=O0nc7kEF6ze6wQ-vG-JgQI_oXSUrjp3y4JefweCUQ3s,1475
+jinja2/__init__.py,sha256=xxepO9i7DHsqkQrgBEduLtfoz2QCuT6_gbL4XSN1hbU,1928
+jinja2/__pycache__/__init__.cpython-311.pyc,,
+jinja2/__pycache__/_identifier.cpython-311.pyc,,
+jinja2/__pycache__/async_utils.cpython-311.pyc,,
+jinja2/__pycache__/bccache.cpython-311.pyc,,
+jinja2/__pycache__/compiler.cpython-311.pyc,,
+jinja2/__pycache__/constants.cpython-311.pyc,,
+jinja2/__pycache__/debug.cpython-311.pyc,,
+jinja2/__pycache__/defaults.cpython-311.pyc,,
+jinja2/__pycache__/environment.cpython-311.pyc,,
+jinja2/__pycache__/exceptions.cpython-311.pyc,,
+jinja2/__pycache__/ext.cpython-311.pyc,,
+jinja2/__pycache__/filters.cpython-311.pyc,,
+jinja2/__pycache__/idtracking.cpython-311.pyc,,
+jinja2/__pycache__/lexer.cpython-311.pyc,,
+jinja2/__pycache__/loaders.cpython-311.pyc,,
+jinja2/__pycache__/meta.cpython-311.pyc,,
+jinja2/__pycache__/nativetypes.cpython-311.pyc,,
+jinja2/__pycache__/nodes.cpython-311.pyc,,
+jinja2/__pycache__/optimizer.cpython-311.pyc,,
+jinja2/__pycache__/parser.cpython-311.pyc,,
+jinja2/__pycache__/runtime.cpython-311.pyc,,
+jinja2/__pycache__/sandbox.cpython-311.pyc,,
+jinja2/__pycache__/tests.cpython-311.pyc,,
+jinja2/__pycache__/utils.cpython-311.pyc,,
+jinja2/__pycache__/visitor.cpython-311.pyc,,
+jinja2/_identifier.py,sha256=_zYctNKzRqlk_murTNlzrju1FFJL7Va_Ijqqd7ii2lU,1958
+jinja2/async_utils.py,sha256=vK-PdsuorOMnWSnEkT3iUJRIkTnYgO2T6MnGxDgHI5o,2834
+jinja2/bccache.py,sha256=gh0qs9rulnXo0PhX5jTJy2UHzI8wFnQ63o_vw7nhzRg,14061
+jinja2/compiler.py,sha256=9RpCQl5X88BHllJiPsHPh295Hh0uApvwFJNQuutULeM,74131
+jinja2/constants.py,sha256=GMoFydBF_kdpaRKPoM5cl5MviquVRLVyZtfp5-16jg0,1433
+jinja2/debug.py,sha256=CnHqCDHd-BVGvti_8ZsTolnXNhA3ECsY-6n_2pwU8Hw,6297
+jinja2/defaults.py,sha256=boBcSw78h-lp20YbaXSJsqkAI2uN_mD_TtCydpeq5wU,1267
+jinja2/environment.py,sha256=9nhrP7Ch-NbGX00wvyr4yy-uhNHq2OCc60ggGrni_fk,61513
+jinja2/exceptions.py,sha256=ioHeHrWwCWNaXX1inHmHVblvc4haO7AXsjCp3GfWvx0,5071
+jinja2/ext.py,sha256=5PF5eHfh8mXAIxXHHRB2xXbXohi8pE3nHSOxa66uS7E,31875
+jinja2/filters.py,sha256=PQ_Egd9n9jSgtnGQYyF4K5j2nYwhUIulhPnyimkdr-k,55212
+jinja2/idtracking.py,sha256=-ll5lIp73pML3ErUYiIJj7tdmWxcH_IlDv3yA_hiZYo,10555
+jinja2/lexer.py,sha256=LYiYio6br-Tep9nPcupWXsPEtjluw3p1mU-lNBVRUfk,29786
+jinja2/loaders.py,sha256=wIrnxjvcbqh5VwW28NSkfotiDq8qNCxIOSFbGUiSLB4,24055
+jinja2/meta.py,sha256=OTDPkaFvU2Hgvx-6akz7154F8BIWaRmvJcBFvwopHww,4397
+jinja2/nativetypes.py,sha256=7GIGALVJgdyL80oZJdQUaUfwSt5q2lSSZbXt0dNf_M4,4210
+jinja2/nodes.py,sha256=m1Duzcr6qhZI8JQ6VyJgUNinjAf5bQzijSmDnMsvUx8,34579
+jinja2/optimizer.py,sha256=rJnCRlQ7pZsEEmMhsQDgC_pKyDHxP5TPS6zVPGsgcu8,1651
+jinja2/parser.py,sha256=lLOFy3sEmHc5IaEHRiH1sQVnId2moUQzhyeJZTtdY30,40383
+jinja2/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+jinja2/runtime.py,sha256=gDk-GvdriJXqgsGbHgrcKTP0Yp6zPXzhzrIpCFH3jAU,34249
+jinja2/sandbox.py,sha256=Mw2aitlY2I8la7FYhcX2YG9BtUYcLnD0Gh3d29cDWrY,15009
+jinja2/tests.py,sha256=VLsBhVFnWg-PxSBz1MhRnNWgP1ovXk3neO1FLQMeC9Q,5926
+jinja2/utils.py,sha256=rRp3o9e7ZKS4fyrWRbELyLcpuGVTFcnooaOa1qx_FIk,24129
+jinja2/visitor.py,sha256=EcnL1PIwf_4RVCOMxsRNuR8AXHbS1qfAdMOE2ngKJz4,3557
diff --git a/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/WHEEL b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..23d2d7e9a5d381ef8a375db09f82052144d1fd96
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.11.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/entry_points.txt b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..abc3eae3b3bc573957cf7401711948799b3465c0
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/entry_points.txt
@@ -0,0 +1,3 @@
+[babel.extractors]
+jinja2=jinja2.ext:babel_extract[i18n]
+
diff --git a/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/licenses/LICENSE.txt b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/licenses/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c37cae49ec77ad6ebb25568c1605f1fee5313cfb
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2-3.1.6.dist-info/licenses/LICENSE.txt
@@ -0,0 +1,28 @@
+Copyright 2007 Pallets
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1.  Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/lib/python3.11/site-packages/jinja2/__init__.py b/lib/python3.11/site-packages/jinja2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a423a3eac16ac550e8e4008d7f5d79401b50e0f
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/__init__.py
@@ -0,0 +1,38 @@
+"""Jinja is a template engine written in pure Python. It provides a
+non-XML syntax that supports inline expressions and an optional
+sandboxed environment.
+"""
+
+from .bccache import BytecodeCache as BytecodeCache
+from .bccache import FileSystemBytecodeCache as FileSystemBytecodeCache
+from .bccache import MemcachedBytecodeCache as MemcachedBytecodeCache
+from .environment import Environment as Environment
+from .environment import Template as Template
+from .exceptions import TemplateAssertionError as TemplateAssertionError
+from .exceptions import TemplateError as TemplateError
+from .exceptions import TemplateNotFound as TemplateNotFound
+from .exceptions import TemplateRuntimeError as TemplateRuntimeError
+from .exceptions import TemplatesNotFound as TemplatesNotFound
+from .exceptions import TemplateSyntaxError as TemplateSyntaxError
+from .exceptions import UndefinedError as UndefinedError
+from .loaders import BaseLoader as BaseLoader
+from .loaders import ChoiceLoader as ChoiceLoader
+from .loaders import DictLoader as DictLoader
+from .loaders import FileSystemLoader as FileSystemLoader
+from .loaders import FunctionLoader as FunctionLoader
+from .loaders import ModuleLoader as ModuleLoader
+from .loaders import PackageLoader as PackageLoader
+from .loaders import PrefixLoader as PrefixLoader
+from .runtime import ChainableUndefined as ChainableUndefined
+from .runtime import DebugUndefined as DebugUndefined
+from .runtime import make_logging_undefined as make_logging_undefined
+from .runtime import StrictUndefined as StrictUndefined
+from .runtime import Undefined as Undefined
+from .utils import clear_caches as clear_caches
+from .utils import is_undefined as is_undefined
+from .utils import pass_context as pass_context
+from .utils import pass_environment as pass_environment
+from .utils import pass_eval_context as pass_eval_context
+from .utils import select_autoescape as select_autoescape
+
+__version__ = "3.1.6"
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a28f855f2c77159dcc4ac93f7dbed9faf7c85b71
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/_identifier.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/_identifier.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c6c92eb9f51e5f6d3b8f9a64c75f0463579d6035
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/_identifier.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/async_utils.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/async_utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ac452dbf36ddc9a25ad25483d2cdbebda50e56e0
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/async_utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/bccache.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/bccache.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..100a701576de5d02eb808ab17f3d0a3cd2c6345e
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/bccache.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/constants.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/constants.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5f35b5066ab26f1bffc5ada47b6824d2e268081b
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/constants.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/debug.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/debug.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..abb3496e3a79a9f7ffa1057d0dc688588f1a7c56
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/debug.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/defaults.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/defaults.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..058e72c5934fee9ddb0502c91981ca3ed59fa933
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/defaults.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/environment.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/environment.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9e59d92e54fa184021731d9f46699e76a3fe8f6f
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/environment.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/exceptions.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/exceptions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ba949df1751d6bca2b7312fde8ea2f04216db641
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/exceptions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/ext.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/ext.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0677b6f3140402c77ed371336ceca81a2060d1d0
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/ext.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/filters.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/filters.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2c67358fb3723f51ece0b25fca1beba16489e00c
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/filters.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/idtracking.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/idtracking.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a38de12a6540a1e43b00dd8d99265d2bf76b5403
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/idtracking.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/lexer.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/lexer.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6b2489a2fc54c78aac3d0b1b6ccd9c3c271da70f
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/lexer.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/loaders.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/loaders.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1ed91905d29a1e4ef99961adce0a56bccbbe9974
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/loaders.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/meta.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/meta.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a2970ccb6fcca1d3edb9e51afa313c8b7528e2f2
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/meta.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/nativetypes.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/nativetypes.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..215d04b12ef5d23b57424756f1ce9947dab9ce5c
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/nativetypes.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/nodes.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/nodes.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..76a6ab96a997ae9f53ebdf0173e8f51c651bd20b
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/nodes.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/optimizer.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/optimizer.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0bfee3bc0020bd61d1dd6487a280ae32336a5f72
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/optimizer.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/parser.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/parser.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c8987d5e78f3533352672efda6f1bf8b7ef8ff5c
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/parser.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/runtime.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/runtime.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5cb2775c88f633bde9a6b363830c57bd655dde5f
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/runtime.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/sandbox.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/sandbox.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..258e87e7050917d570992ddf613c15965d893423
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/sandbox.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/tests.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/tests.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..aeb1148af1b53f37b458a81059ca55528c3319ea
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/tests.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/utils.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9d9c858a71481f421bcb1a51e29b74fd50b670ec
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/__pycache__/visitor.cpython-311.pyc b/lib/python3.11/site-packages/jinja2/__pycache__/visitor.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3b970e23b3c013f2ddf8e546706657c8d986bf8d
Binary files /dev/null and b/lib/python3.11/site-packages/jinja2/__pycache__/visitor.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/jinja2/_identifier.py b/lib/python3.11/site-packages/jinja2/_identifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..928c1503c7d414a8a86bbf5a82c68d42cb089bd2
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/_identifier.py
@@ -0,0 +1,6 @@
+import re
+
+# generated by scripts/generate_identifier_pattern.py
+pattern = re.compile(
+    r"[\w·̀-ͯ·҃-֑҇-ׇֽֿׁׂׅׄؐ-ًؚ-ٰٟۖ-ۜ۟-۪ۤۧۨ-ܑۭܰ-݊ަ-ް߫-߽߳ࠖ-࠙ࠛ-ࠣࠥ-ࠧࠩ-࡙࠭-࡛࣓-ࣣ࣡-ःऺ-़ा-ॏ॑-ॗॢॣঁ-ঃ়া-ৄেৈো-্ৗৢৣ৾ਁ-ਃ਼ਾ-ੂੇੈੋ-੍ੑੰੱੵઁ-ઃ઼ા-ૅે-ૉો-્ૢૣૺ-૿ଁ-ଃ଼ା-ୄେୈୋ-୍ୖୗୢୣஂா-ூெ-ைொ-்ௗఀ-ఄా-ౄె-ైొ-్ౕౖౢౣಁ-ಃ಼ಾ-ೄೆ-ೈೊ-್ೕೖೢೣഀ-ഃ഻഼ാ-ൄെ-ൈൊ-്ൗൢൣංඃ්ා-ුූෘ-ෟෲෳัิ-ฺ็-๎ັິ-ູົຼ່-ໍ༹༘༙༵༷༾༿ཱ-྄྆྇ྍ-ྗྙ-ྼ࿆ါ-ှၖ-ၙၞ-ၠၢ-ၤၧ-ၭၱ-ၴႂ-ႍႏႚ-ႝ፝-፟ᜒ-᜔ᜲ-᜴ᝒᝓᝲᝳ឴-៓៝᠋-᠍ᢅᢆᢩᤠ-ᤫᤰ-᤻ᨗ-ᨛᩕ-ᩞ᩠-᩿᩼᪰-᪽ᬀ-ᬄ᬴-᭄᭫-᭳ᮀ-ᮂᮡ-ᮭ᯦-᯳ᰤ-᰷᳐-᳔᳒-᳨᳭ᳲ-᳴᳷-᳹᷀-᷹᷻-᷿‿⁀⁔⃐-⃥⃜⃡-⃰℘℮⳯-⵿⳱ⷠ-〪ⷿ-゙゚〯꙯ꙴ-꙽ꚞꚟ꛰꛱ꠂ꠆ꠋꠣ-ꠧꢀꢁꢴ-ꣅ꣠-꣱ꣿꤦ-꤭ꥇ-꥓ꦀ-ꦃ꦳-꧀ꧥꨩ-ꨶꩃꩌꩍꩻ-ꩽꪰꪲ-ꪴꪷꪸꪾ꪿꫁ꫫ-ꫯꫵ꫶ꯣ-ꯪ꯬꯭ﬞ︀-️︠-︯︳︴﹍-﹏＿𐇽𐋠𐍶-𐍺𐨁-𐨃𐨅𐨆𐨌-𐨏𐨸-𐨿𐨺𐫦𐫥𐴤-𐽆𐴧-𐽐𑀀-𑀂𑀸-𑁆𑁿-𑂂𑂰-𑂺𑄀-𑄂𑄧-𑄴𑅅𑅆𑅳𑆀-𑆂𑆳-𑇀𑇉-𑇌𑈬-𑈷𑈾𑋟-𑋪𑌀-𑌃𑌻𑌼𑌾-𑍄𑍇𑍈𑍋-𑍍𑍗𑍢𑍣𑍦-𑍬𑍰-𑍴𑐵-𑑆𑑞𑒰-𑓃𑖯-𑖵𑖸-𑗀𑗜𑗝𑘰-𑙀𑚫-𑚷𑜝-𑜫𑠬-𑠺𑨁-𑨊𑨳-𑨹𑨻-𑨾𑩇𑩑-𑩛𑪊-𑪙𑰯-𑰶𑰸-𑰿𑲒-𑲧𑲩-𑲶𑴱-𑴶𑴺𑴼𑴽𑴿-𑵅𑵇𑶊-𑶎𑶐𑶑𑶓-𑶗𑻳-𑻶𖫰-𖫴𖬰-𖬶𖽑-𖽾𖾏-𖾒𛲝𛲞𝅥-𝅩𝅭-𝅲𝅻-𝆂𝆅-𝆋𝆪-𝆭𝉂-𝉄𝨀-𝨶𝨻-𝩬𝩵𝪄𝪛-𝪟𝪡-𝪯𞀀-𞀆𞀈-𞀘𞀛-𞀡𞀣𞀤𞀦-𞣐𞀪-𞣖𞥄-𞥊󠄀-󠇯]+"  # noqa: B950
+)
diff --git a/lib/python3.11/site-packages/jinja2/async_utils.py b/lib/python3.11/site-packages/jinja2/async_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0c140205c50a3df9863ce1ab610b0c62a483f1b
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/async_utils.py
@@ -0,0 +1,99 @@
+import inspect
+import typing as t
+from functools import WRAPPER_ASSIGNMENTS
+from functools import wraps
+
+from .utils import _PassArg
+from .utils import pass_eval_context
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+V = t.TypeVar("V")
+
+
+def async_variant(normal_func):  # type: ignore
+    def decorator(async_func):  # type: ignore
+        pass_arg = _PassArg.from_obj(normal_func)
+        need_eval_context = pass_arg is None
+
+        if pass_arg is _PassArg.environment:
+
+            def is_async(args: t.Any) -> bool:
+                return t.cast(bool, args[0].is_async)
+
+        else:
+
+            def is_async(args: t.Any) -> bool:
+                return t.cast(bool, args[0].environment.is_async)
+
+        # Take the doc and annotations from the sync function, but the
+        # name from the async function. Pallets-Sphinx-Themes
+        # build_function_directive expects __wrapped__ to point to the
+        # sync function.
+        async_func_attrs = ("__module__", "__name__", "__qualname__")
+        normal_func_attrs = tuple(set(WRAPPER_ASSIGNMENTS).difference(async_func_attrs))
+
+        @wraps(normal_func, assigned=normal_func_attrs)
+        @wraps(async_func, assigned=async_func_attrs, updated=())
+        def wrapper(*args, **kwargs):  # type: ignore
+            b = is_async(args)
+
+            if need_eval_context:
+                args = args[1:]
+
+            if b:
+                return async_func(*args, **kwargs)
+
+            return normal_func(*args, **kwargs)
+
+        if need_eval_context:
+            wrapper = pass_eval_context(wrapper)
+
+        wrapper.jinja_async_variant = True  # type: ignore[attr-defined]
+        return wrapper
+
+    return decorator
+
+
+_common_primitives = {int, float, bool, str, list, dict, tuple, type(None)}
+
+
+async def auto_await(value: t.Union[t.Awaitable["V"], "V"]) -> "V":
+    # Avoid a costly call to isawaitable
+    if type(value) in _common_primitives:
+        return t.cast("V", value)
+
+    if inspect.isawaitable(value):
+        return await t.cast("t.Awaitable[V]", value)
+
+    return value
+
+
+class _IteratorToAsyncIterator(t.Generic[V]):
+    def __init__(self, iterator: "t.Iterator[V]"):
+        self._iterator = iterator
+
+    def __aiter__(self) -> "te.Self":
+        return self
+
+    async def __anext__(self) -> V:
+        try:
+            return next(self._iterator)
+        except StopIteration as e:
+            raise StopAsyncIteration(e.value) from e
+
+
+def auto_aiter(
+    iterable: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+) -> "t.AsyncIterator[V]":
+    if hasattr(iterable, "__aiter__"):
+        return iterable.__aiter__()
+    else:
+        return _IteratorToAsyncIterator(iter(iterable))
+
+
+async def auto_to_list(
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+) -> t.List["V"]:
+    return [x async for x in auto_aiter(value)]
diff --git a/lib/python3.11/site-packages/jinja2/bccache.py b/lib/python3.11/site-packages/jinja2/bccache.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada8b099ff251ea9c6da4c42e1383f37e359f06a
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/bccache.py
@@ -0,0 +1,408 @@
+"""The optional bytecode cache system. This is useful if you have very
+complex template situations and the compilation of all those templates
+slows down your application too much.
+
+Situations where this is useful are often forking web applications that
+are initialized on the first request.
+"""
+
+import errno
+import fnmatch
+import marshal
+import os
+import pickle
+import stat
+import sys
+import tempfile
+import typing as t
+from hashlib import sha1
+from io import BytesIO
+from types import CodeType
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .environment import Environment
+
+    class _MemcachedClient(te.Protocol):
+        def get(self, key: str) -> bytes: ...
+
+        def set(
+            self, key: str, value: bytes, timeout: t.Optional[int] = None
+        ) -> None: ...
+
+
+bc_version = 5
+# Magic bytes to identify Jinja bytecode cache files. Contains the
+# Python major and minor version to avoid loading incompatible bytecode
+# if a project upgrades its Python version.
+bc_magic = (
+    b"j2"
+    + pickle.dumps(bc_version, 2)
+    + pickle.dumps((sys.version_info[0] << 24) | sys.version_info[1], 2)
+)
+
+
+class Bucket:
+    """Buckets are used to store the bytecode for one template.  It's created
+    and initialized by the bytecode cache and passed to the loading functions.
+
+    The buckets get an internal checksum from the cache assigned and use this
+    to automatically reject outdated cache material.  Individual bytecode
+    cache subclasses don't have to care about cache invalidation.
+    """
+
+    def __init__(self, environment: "Environment", key: str, checksum: str) -> None:
+        self.environment = environment
+        self.key = key
+        self.checksum = checksum
+        self.reset()
+
+    def reset(self) -> None:
+        """Resets the bucket (unloads the bytecode)."""
+        self.code: t.Optional[CodeType] = None
+
+    def load_bytecode(self, f: t.BinaryIO) -> None:
+        """Loads bytecode from a file or file like object."""
+        # make sure the magic header is correct
+        magic = f.read(len(bc_magic))
+        if magic != bc_magic:
+            self.reset()
+            return
+        # the source code of the file changed, we need to reload
+        checksum = pickle.load(f)
+        if self.checksum != checksum:
+            self.reset()
+            return
+        # if marshal_load fails then we need to reload
+        try:
+            self.code = marshal.load(f)
+        except (EOFError, ValueError, TypeError):
+            self.reset()
+            return
+
+    def write_bytecode(self, f: t.IO[bytes]) -> None:
+        """Dump the bytecode into the file or file like object passed."""
+        if self.code is None:
+            raise TypeError("can't write empty bucket")
+        f.write(bc_magic)
+        pickle.dump(self.checksum, f, 2)
+        marshal.dump(self.code, f)
+
+    def bytecode_from_string(self, string: bytes) -> None:
+        """Load bytecode from bytes."""
+        self.load_bytecode(BytesIO(string))
+
+    def bytecode_to_string(self) -> bytes:
+        """Return the bytecode as bytes."""
+        out = BytesIO()
+        self.write_bytecode(out)
+        return out.getvalue()
+
+
+class BytecodeCache:
+    """To implement your own bytecode cache you have to subclass this class
+    and override :meth:`load_bytecode` and :meth:`dump_bytecode`.  Both of
+    these methods are passed a :class:`~jinja2.bccache.Bucket`.
+
+    A very basic bytecode cache that saves the bytecode on the file system::
+
+        from os import path
+
+        class MyCache(BytecodeCache):
+
+            def __init__(self, directory):
+                self.directory = directory
+
+            def load_bytecode(self, bucket):
+                filename = path.join(self.directory, bucket.key)
+                if path.exists(filename):
+                    with open(filename, 'rb') as f:
+                        bucket.load_bytecode(f)
+
+            def dump_bytecode(self, bucket):
+                filename = path.join(self.directory, bucket.key)
+                with open(filename, 'wb') as f:
+                    bucket.write_bytecode(f)
+
+    A more advanced version of a filesystem based bytecode cache is part of
+    Jinja.
+    """
+
+    def load_bytecode(self, bucket: Bucket) -> None:
+        """Subclasses have to override this method to load bytecode into a
+        bucket.  If they are not able to find code in the cache for the
+        bucket, it must not do anything.
+        """
+        raise NotImplementedError()
+
+    def dump_bytecode(self, bucket: Bucket) -> None:
+        """Subclasses have to override this method to write the bytecode
+        from a bucket back to the cache.  If it unable to do so it must not
+        fail silently but raise an exception.
+        """
+        raise NotImplementedError()
+
+    def clear(self) -> None:
+        """Clears the cache.  This method is not used by Jinja but should be
+        implemented to allow applications to clear the bytecode cache used
+        by a particular environment.
+        """
+
+    def get_cache_key(
+        self, name: str, filename: t.Optional[t.Union[str]] = None
+    ) -> str:
+        """Returns the unique hash key for this template name."""
+        hash = sha1(name.encode("utf-8"))
+
+        if filename is not None:
+            hash.update(f"|{filename}".encode())
+
+        return hash.hexdigest()
+
+    def get_source_checksum(self, source: str) -> str:
+        """Returns a checksum for the source."""
+        return sha1(source.encode("utf-8")).hexdigest()
+
+    def get_bucket(
+        self,
+        environment: "Environment",
+        name: str,
+        filename: t.Optional[str],
+        source: str,
+    ) -> Bucket:
+        """Return a cache bucket for the given template.  All arguments are
+        mandatory but filename may be `None`.
+        """
+        key = self.get_cache_key(name, filename)
+        checksum = self.get_source_checksum(source)
+        bucket = Bucket(environment, key, checksum)
+        self.load_bytecode(bucket)
+        return bucket
+
+    def set_bucket(self, bucket: Bucket) -> None:
+        """Put the bucket into the cache."""
+        self.dump_bytecode(bucket)
+
+
+class FileSystemBytecodeCache(BytecodeCache):
+    """A bytecode cache that stores bytecode on the filesystem.  It accepts
+    two arguments: The directory where the cache items are stored and a
+    pattern string that is used to build the filename.
+
+    If no directory is specified a default cache directory is selected.  On
+    Windows the user's temp directory is used, on UNIX systems a directory
+    is created for the user in the system temp directory.
+
+    The pattern can be used to have multiple separate caches operate on the
+    same directory.  The default pattern is ``'__jinja2_%s.cache'``.  ``%s``
+    is replaced with the cache key.
+
+    >>> bcc = FileSystemBytecodeCache('/tmp/jinja_cache', '%s.cache')
+
+    This bytecode cache supports clearing of the cache using the clear method.
+    """
+
+    def __init__(
+        self, directory: t.Optional[str] = None, pattern: str = "__jinja2_%s.cache"
+    ) -> None:
+        if directory is None:
+            directory = self._get_default_cache_dir()
+        self.directory = directory
+        self.pattern = pattern
+
+    def _get_default_cache_dir(self) -> str:
+        def _unsafe_dir() -> "te.NoReturn":
+            raise RuntimeError(
+                "Cannot determine safe temp directory.  You "
+                "need to explicitly provide one."
+            )
+
+        tmpdir = tempfile.gettempdir()
+
+        # On windows the temporary directory is used specific unless
+        # explicitly forced otherwise.  We can just use that.
+        if os.name == "nt":
+            return tmpdir
+        if not hasattr(os, "getuid"):
+            _unsafe_dir()
+
+        dirname = f"_jinja2-cache-{os.getuid()}"
+        actual_dir = os.path.join(tmpdir, dirname)
+
+        try:
+            os.mkdir(actual_dir, stat.S_IRWXU)
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+        try:
+            os.chmod(actual_dir, stat.S_IRWXU)
+            actual_dir_stat = os.lstat(actual_dir)
+            if (
+                actual_dir_stat.st_uid != os.getuid()
+                or not stat.S_ISDIR(actual_dir_stat.st_mode)
+                or stat.S_IMODE(actual_dir_stat.st_mode) != stat.S_IRWXU
+            ):
+                _unsafe_dir()
+        except OSError as e:
+            if e.errno != errno.EEXIST:
+                raise
+
+        actual_dir_stat = os.lstat(actual_dir)
+        if (
+            actual_dir_stat.st_uid != os.getuid()
+            or not stat.S_ISDIR(actual_dir_stat.st_mode)
+            or stat.S_IMODE(actual_dir_stat.st_mode) != stat.S_IRWXU
+        ):
+            _unsafe_dir()
+
+        return actual_dir
+
+    def _get_cache_filename(self, bucket: Bucket) -> str:
+        return os.path.join(self.directory, self.pattern % (bucket.key,))
+
+    def load_bytecode(self, bucket: Bucket) -> None:
+        filename = self._get_cache_filename(bucket)
+
+        # Don't test for existence before opening the file, since the
+        # file could disappear after the test before the open.
+        try:
+            f = open(filename, "rb")
+        except (FileNotFoundError, IsADirectoryError, PermissionError):
+            # PermissionError can occur on Windows when an operation is
+            # in progress, such as calling clear().
+            return
+
+        with f:
+            bucket.load_bytecode(f)
+
+    def dump_bytecode(self, bucket: Bucket) -> None:
+        # Write to a temporary file, then rename to the real name after
+        # writing. This avoids another process reading the file before
+        # it is fully written.
+        name = self._get_cache_filename(bucket)
+        f = tempfile.NamedTemporaryFile(
+            mode="wb",
+            dir=os.path.dirname(name),
+            prefix=os.path.basename(name),
+            suffix=".tmp",
+            delete=False,
+        )
+
+        def remove_silent() -> None:
+            try:
+                os.remove(f.name)
+            except OSError:
+                # Another process may have called clear(). On Windows,
+                # another program may be holding the file open.
+                pass
+
+        try:
+            with f:
+                bucket.write_bytecode(f)
+        except BaseException:
+            remove_silent()
+            raise
+
+        try:
+            os.replace(f.name, name)
+        except OSError:
+            # Another process may have called clear(). On Windows,
+            # another program may be holding the file open.
+            remove_silent()
+        except BaseException:
+            remove_silent()
+            raise
+
+    def clear(self) -> None:
+        # imported lazily here because google app-engine doesn't support
+        # write access on the file system and the function does not exist
+        # normally.
+        from os import remove
+
+        files = fnmatch.filter(os.listdir(self.directory), self.pattern % ("*",))
+        for filename in files:
+            try:
+                remove(os.path.join(self.directory, filename))
+            except OSError:
+                pass
+
+
+class MemcachedBytecodeCache(BytecodeCache):
+    """This class implements a bytecode cache that uses a memcache cache for
+    storing the information.  It does not enforce a specific memcache library
+    (tummy's memcache or cmemcache) but will accept any class that provides
+    the minimal interface required.
+
+    Libraries compatible with this class:
+
+    -   `cachelib <https://github.com/pallets/cachelib>`_
+    -   `python-memcached <https://pypi.org/project/python-memcached/>`_
+
+    (Unfortunately the django cache interface is not compatible because it
+    does not support storing binary data, only text. You can however pass
+    the underlying cache client to the bytecode cache which is available
+    as `django.core.cache.cache._client`.)
+
+    The minimal interface for the client passed to the constructor is this:
+
+    .. class:: MinimalClientInterface
+
+        .. method:: set(key, value[, timeout])
+
+            Stores the bytecode in the cache.  `value` is a string and
+            `timeout` the timeout of the key.  If timeout is not provided
+            a default timeout or no timeout should be assumed, if it's
+            provided it's an integer with the number of seconds the cache
+            item should exist.
+
+        .. method:: get(key)
+
+            Returns the value for the cache key.  If the item does not
+            exist in the cache the return value must be `None`.
+
+    The other arguments to the constructor are the prefix for all keys that
+    is added before the actual cache key and the timeout for the bytecode in
+    the cache system.  We recommend a high (or no) timeout.
+
+    This bytecode cache does not support clearing of used items in the cache.
+    The clear method is a no-operation function.
+
+    .. versionadded:: 2.7
+       Added support for ignoring memcache errors through the
+       `ignore_memcache_errors` parameter.
+    """
+
+    def __init__(
+        self,
+        client: "_MemcachedClient",
+        prefix: str = "jinja2/bytecode/",
+        timeout: t.Optional[int] = None,
+        ignore_memcache_errors: bool = True,
+    ):
+        self.client = client
+        self.prefix = prefix
+        self.timeout = timeout
+        self.ignore_memcache_errors = ignore_memcache_errors
+
+    def load_bytecode(self, bucket: Bucket) -> None:
+        try:
+            code = self.client.get(self.prefix + bucket.key)
+        except Exception:
+            if not self.ignore_memcache_errors:
+                raise
+        else:
+            bucket.bytecode_from_string(code)
+
+    def dump_bytecode(self, bucket: Bucket) -> None:
+        key = self.prefix + bucket.key
+        value = bucket.bytecode_to_string()
+
+        try:
+            if self.timeout is not None:
+                self.client.set(key, value, self.timeout)
+            else:
+                self.client.set(key, value)
+        except Exception:
+            if not self.ignore_memcache_errors:
+                raise
diff --git a/lib/python3.11/site-packages/jinja2/compiler.py b/lib/python3.11/site-packages/jinja2/compiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4ff6a1b11af3e1a868d1a74c48d842390259b43
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/compiler.py
@@ -0,0 +1,1998 @@
+"""Compiles nodes from the parser into Python code."""
+
+import typing as t
+from contextlib import contextmanager
+from functools import update_wrapper
+from io import StringIO
+from itertools import chain
+from keyword import iskeyword as is_python_keyword
+
+from markupsafe import escape
+from markupsafe import Markup
+
+from . import nodes
+from .exceptions import TemplateAssertionError
+from .idtracking import Symbols
+from .idtracking import VAR_LOAD_ALIAS
+from .idtracking import VAR_LOAD_PARAMETER
+from .idtracking import VAR_LOAD_RESOLVE
+from .idtracking import VAR_LOAD_UNDEFINED
+from .nodes import EvalContext
+from .optimizer import Optimizer
+from .utils import _PassArg
+from .utils import concat
+from .visitor import NodeVisitor
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .environment import Environment
+
+F = t.TypeVar("F", bound=t.Callable[..., t.Any])
+
+operators = {
+    "eq": "==",
+    "ne": "!=",
+    "gt": ">",
+    "gteq": ">=",
+    "lt": "<",
+    "lteq": "<=",
+    "in": "in",
+    "notin": "not in",
+}
+
+
+def optimizeconst(f: F) -> F:
+    def new_func(
+        self: "CodeGenerator", node: nodes.Expr, frame: "Frame", **kwargs: t.Any
+    ) -> t.Any:
+        # Only optimize if the frame is not volatile
+        if self.optimizer is not None and not frame.eval_ctx.volatile:
+            new_node = self.optimizer.visit(node, frame.eval_ctx)
+
+            if new_node != node:
+                return self.visit(new_node, frame)
+
+        return f(self, node, frame, **kwargs)
+
+    return update_wrapper(new_func, f)  # type: ignore[return-value]
+
+
+def _make_binop(op: str) -> t.Callable[["CodeGenerator", nodes.BinExpr, "Frame"], None]:
+    @optimizeconst
+    def visitor(self: "CodeGenerator", node: nodes.BinExpr, frame: Frame) -> None:
+        if (
+            self.environment.sandboxed and op in self.environment.intercepted_binops  # type: ignore
+        ):
+            self.write(f"environment.call_binop(context, {op!r}, ")
+            self.visit(node.left, frame)
+            self.write(", ")
+            self.visit(node.right, frame)
+        else:
+            self.write("(")
+            self.visit(node.left, frame)
+            self.write(f" {op} ")
+            self.visit(node.right, frame)
+
+        self.write(")")
+
+    return visitor
+
+
+def _make_unop(
+    op: str,
+) -> t.Callable[["CodeGenerator", nodes.UnaryExpr, "Frame"], None]:
+    @optimizeconst
+    def visitor(self: "CodeGenerator", node: nodes.UnaryExpr, frame: Frame) -> None:
+        if (
+            self.environment.sandboxed and op in self.environment.intercepted_unops  # type: ignore
+        ):
+            self.write(f"environment.call_unop(context, {op!r}, ")
+            self.visit(node.node, frame)
+        else:
+            self.write("(" + op)
+            self.visit(node.node, frame)
+
+        self.write(")")
+
+    return visitor
+
+
+def generate(
+    node: nodes.Template,
+    environment: "Environment",
+    name: t.Optional[str],
+    filename: t.Optional[str],
+    stream: t.Optional[t.TextIO] = None,
+    defer_init: bool = False,
+    optimized: bool = True,
+) -> t.Optional[str]:
+    """Generate the python source for a node tree."""
+    if not isinstance(node, nodes.Template):
+        raise TypeError("Can't compile non template nodes")
+
+    generator = environment.code_generator_class(
+        environment, name, filename, stream, defer_init, optimized
+    )
+    generator.visit(node)
+
+    if stream is None:
+        return generator.stream.getvalue()  # type: ignore
+
+    return None
+
+
+def has_safe_repr(value: t.Any) -> bool:
+    """Does the node have a safe representation?"""
+    if value is None or value is NotImplemented or value is Ellipsis:
+        return True
+
+    if type(value) in {bool, int, float, complex, range, str, Markup}:
+        return True
+
+    if type(value) in {tuple, list, set, frozenset}:
+        return all(has_safe_repr(v) for v in value)
+
+    if type(value) is dict:  # noqa E721
+        return all(has_safe_repr(k) and has_safe_repr(v) for k, v in value.items())
+
+    return False
+
+
+def find_undeclared(
+    nodes: t.Iterable[nodes.Node], names: t.Iterable[str]
+) -> t.Set[str]:
+    """Check if the names passed are accessed undeclared.  The return value
+    is a set of all the undeclared names from the sequence of names found.
+    """
+    visitor = UndeclaredNameVisitor(names)
+    try:
+        for node in nodes:
+            visitor.visit(node)
+    except VisitorExit:
+        pass
+    return visitor.undeclared
+
+
+class MacroRef:
+    def __init__(self, node: t.Union[nodes.Macro, nodes.CallBlock]) -> None:
+        self.node = node
+        self.accesses_caller = False
+        self.accesses_kwargs = False
+        self.accesses_varargs = False
+
+
+class Frame:
+    """Holds compile time information for us."""
+
+    def __init__(
+        self,
+        eval_ctx: EvalContext,
+        parent: t.Optional["Frame"] = None,
+        level: t.Optional[int] = None,
+    ) -> None:
+        self.eval_ctx = eval_ctx
+
+        # the parent of this frame
+        self.parent = parent
+
+        if parent is None:
+            self.symbols = Symbols(level=level)
+
+            # in some dynamic inheritance situations the compiler needs to add
+            # write tests around output statements.
+            self.require_output_check = False
+
+            # inside some tags we are using a buffer rather than yield statements.
+            # this for example affects {% filter %} or {% macro %}.  If a frame
+            # is buffered this variable points to the name of the list used as
+            # buffer.
+            self.buffer: t.Optional[str] = None
+
+            # the name of the block we're in, otherwise None.
+            self.block: t.Optional[str] = None
+
+        else:
+            self.symbols = Symbols(parent.symbols, level=level)
+            self.require_output_check = parent.require_output_check
+            self.buffer = parent.buffer
+            self.block = parent.block
+
+        # a toplevel frame is the root + soft frames such as if conditions.
+        self.toplevel = False
+
+        # the root frame is basically just the outermost frame, so no if
+        # conditions.  This information is used to optimize inheritance
+        # situations.
+        self.rootlevel = False
+
+        # variables set inside of loops and blocks should not affect outer frames,
+        # but they still needs to be kept track of as part of the active context.
+        self.loop_frame = False
+        self.block_frame = False
+
+        # track whether the frame is being used in an if-statement or conditional
+        # expression as it determines which errors should be raised during runtime
+        # or compile time.
+        self.soft_frame = False
+
+    def copy(self) -> "te.Self":
+        """Create a copy of the current one."""
+        rv = object.__new__(self.__class__)
+        rv.__dict__.update(self.__dict__)
+        rv.symbols = self.symbols.copy()
+        return rv
+
+    def inner(self, isolated: bool = False) -> "Frame":
+        """Return an inner frame."""
+        if isolated:
+            return Frame(self.eval_ctx, level=self.symbols.level + 1)
+        return Frame(self.eval_ctx, self)
+
+    def soft(self) -> "te.Self":
+        """Return a soft frame.  A soft frame may not be modified as
+        standalone thing as it shares the resources with the frame it
+        was created of, but it's not a rootlevel frame any longer.
+
+        This is only used to implement if-statements and conditional
+        expressions.
+        """
+        rv = self.copy()
+        rv.rootlevel = False
+        rv.soft_frame = True
+        return rv
+
+    __copy__ = copy
+
+
+class VisitorExit(RuntimeError):
+    """Exception used by the `UndeclaredNameVisitor` to signal a stop."""
+
+
+class DependencyFinderVisitor(NodeVisitor):
+    """A visitor that collects filter and test calls."""
+
+    def __init__(self) -> None:
+        self.filters: t.Set[str] = set()
+        self.tests: t.Set[str] = set()
+
+    def visit_Filter(self, node: nodes.Filter) -> None:
+        self.generic_visit(node)
+        self.filters.add(node.name)
+
+    def visit_Test(self, node: nodes.Test) -> None:
+        self.generic_visit(node)
+        self.tests.add(node.name)
+
+    def visit_Block(self, node: nodes.Block) -> None:
+        """Stop visiting at blocks."""
+
+
+class UndeclaredNameVisitor(NodeVisitor):
+    """A visitor that checks if a name is accessed without being
+    declared.  This is different from the frame visitor as it will
+    not stop at closure frames.
+    """
+
+    def __init__(self, names: t.Iterable[str]) -> None:
+        self.names = set(names)
+        self.undeclared: t.Set[str] = set()
+
+    def visit_Name(self, node: nodes.Name) -> None:
+        if node.ctx == "load" and node.name in self.names:
+            self.undeclared.add(node.name)
+            if self.undeclared == self.names:
+                raise VisitorExit()
+        else:
+            self.names.discard(node.name)
+
+    def visit_Block(self, node: nodes.Block) -> None:
+        """Stop visiting a blocks."""
+
+
+class CompilerExit(Exception):
+    """Raised if the compiler encountered a situation where it just
+    doesn't make sense to further process the code.  Any block that
+    raises such an exception is not further processed.
+    """
+
+
+class CodeGenerator(NodeVisitor):
+    def __init__(
+        self,
+        environment: "Environment",
+        name: t.Optional[str],
+        filename: t.Optional[str],
+        stream: t.Optional[t.TextIO] = None,
+        defer_init: bool = False,
+        optimized: bool = True,
+    ) -> None:
+        if stream is None:
+            stream = StringIO()
+        self.environment = environment
+        self.name = name
+        self.filename = filename
+        self.stream = stream
+        self.created_block_context = False
+        self.defer_init = defer_init
+        self.optimizer: t.Optional[Optimizer] = None
+
+        if optimized:
+            self.optimizer = Optimizer(environment)
+
+        # aliases for imports
+        self.import_aliases: t.Dict[str, str] = {}
+
+        # a registry for all blocks.  Because blocks are moved out
+        # into the global python scope they are registered here
+        self.blocks: t.Dict[str, nodes.Block] = {}
+
+        # the number of extends statements so far
+        self.extends_so_far = 0
+
+        # some templates have a rootlevel extends.  In this case we
+        # can safely assume that we're a child template and do some
+        # more optimizations.
+        self.has_known_extends = False
+
+        # the current line number
+        self.code_lineno = 1
+
+        # registry of all filters and tests (global, not block local)
+        self.tests: t.Dict[str, str] = {}
+        self.filters: t.Dict[str, str] = {}
+
+        # the debug information
+        self.debug_info: t.List[t.Tuple[int, int]] = []
+        self._write_debug_info: t.Optional[int] = None
+
+        # the number of new lines before the next write()
+        self._new_lines = 0
+
+        # the line number of the last written statement
+        self._last_line = 0
+
+        # true if nothing was written so far.
+        self._first_write = True
+
+        # used by the `temporary_identifier` method to get new
+        # unique, temporary identifier
+        self._last_identifier = 0
+
+        # the current indentation
+        self._indentation = 0
+
+        # Tracks toplevel assignments
+        self._assign_stack: t.List[t.Set[str]] = []
+
+        # Tracks parameter definition blocks
+        self._param_def_block: t.List[t.Set[str]] = []
+
+        # Tracks the current context.
+        self._context_reference_stack = ["context"]
+
+    @property
+    def optimized(self) -> bool:
+        return self.optimizer is not None
+
+    # -- Various compilation helpers
+
+    def fail(self, msg: str, lineno: int) -> "te.NoReturn":
+        """Fail with a :exc:`TemplateAssertionError`."""
+        raise TemplateAssertionError(msg, lineno, self.name, self.filename)
+
+    def temporary_identifier(self) -> str:
+        """Get a new unique identifier."""
+        self._last_identifier += 1
+        return f"t_{self._last_identifier}"
+
+    def buffer(self, frame: Frame) -> None:
+        """Enable buffering for the frame from that point onwards."""
+        frame.buffer = self.temporary_identifier()
+        self.writeline(f"{frame.buffer} = []")
+
+    def return_buffer_contents(
+        self, frame: Frame, force_unescaped: bool = False
+    ) -> None:
+        """Return the buffer contents of the frame."""
+        if not force_unescaped:
+            if frame.eval_ctx.volatile:
+                self.writeline("if context.eval_ctx.autoescape:")
+                self.indent()
+                self.writeline(f"return Markup(concat({frame.buffer}))")
+                self.outdent()
+                self.writeline("else:")
+                self.indent()
+                self.writeline(f"return concat({frame.buffer})")
+                self.outdent()
+                return
+            elif frame.eval_ctx.autoescape:
+                self.writeline(f"return Markup(concat({frame.buffer}))")
+                return
+        self.writeline(f"return concat({frame.buffer})")
+
+    def indent(self) -> None:
+        """Indent by one."""
+        self._indentation += 1
+
+    def outdent(self, step: int = 1) -> None:
+        """Outdent by step."""
+        self._indentation -= step
+
+    def start_write(self, frame: Frame, node: t.Optional[nodes.Node] = None) -> None:
+        """Yield or write into the frame buffer."""
+        if frame.buffer is None:
+            self.writeline("yield ", node)
+        else:
+            self.writeline(f"{frame.buffer}.append(", node)
+
+    def end_write(self, frame: Frame) -> None:
+        """End the writing process started by `start_write`."""
+        if frame.buffer is not None:
+            self.write(")")
+
+    def simple_write(
+        self, s: str, frame: Frame, node: t.Optional[nodes.Node] = None
+    ) -> None:
+        """Simple shortcut for start_write + write + end_write."""
+        self.start_write(frame, node)
+        self.write(s)
+        self.end_write(frame)
+
+    def blockvisit(self, nodes: t.Iterable[nodes.Node], frame: Frame) -> None:
+        """Visit a list of nodes as block in a frame.  If the current frame
+        is no buffer a dummy ``if 0: yield None`` is written automatically.
+        """
+        try:
+            self.writeline("pass")
+            for node in nodes:
+                self.visit(node, frame)
+        except CompilerExit:
+            pass
+
+    def write(self, x: str) -> None:
+        """Write a string into the output stream."""
+        if self._new_lines:
+            if not self._first_write:
+                self.stream.write("\n" * self._new_lines)
+                self.code_lineno += self._new_lines
+                if self._write_debug_info is not None:
+                    self.debug_info.append((self._write_debug_info, self.code_lineno))
+                    self._write_debug_info = None
+            self._first_write = False
+            self.stream.write("    " * self._indentation)
+            self._new_lines = 0
+        self.stream.write(x)
+
+    def writeline(
+        self, x: str, node: t.Optional[nodes.Node] = None, extra: int = 0
+    ) -> None:
+        """Combination of newline and write."""
+        self.newline(node, extra)
+        self.write(x)
+
+    def newline(self, node: t.Optional[nodes.Node] = None, extra: int = 0) -> None:
+        """Add one or more newlines before the next write."""
+        self._new_lines = max(self._new_lines, 1 + extra)
+        if node is not None and node.lineno != self._last_line:
+            self._write_debug_info = node.lineno
+            self._last_line = node.lineno
+
+    def signature(
+        self,
+        node: t.Union[nodes.Call, nodes.Filter, nodes.Test],
+        frame: Frame,
+        extra_kwargs: t.Optional[t.Mapping[str, t.Any]] = None,
+    ) -> None:
+        """Writes a function call to the stream for the current node.
+        A leading comma is added automatically.  The extra keyword
+        arguments may not include python keywords otherwise a syntax
+        error could occur.  The extra keyword arguments should be given
+        as python dict.
+        """
+        # if any of the given keyword arguments is a python keyword
+        # we have to make sure that no invalid call is created.
+        kwarg_workaround = any(
+            is_python_keyword(t.cast(str, k))
+            for k in chain((x.key for x in node.kwargs), extra_kwargs or ())
+        )
+
+        for arg in node.args:
+            self.write(", ")
+            self.visit(arg, frame)
+
+        if not kwarg_workaround:
+            for kwarg in node.kwargs:
+                self.write(", ")
+                self.visit(kwarg, frame)
+            if extra_kwargs is not None:
+                for key, value in extra_kwargs.items():
+                    self.write(f", {key}={value}")
+        if node.dyn_args:
+            self.write(", *")
+            self.visit(node.dyn_args, frame)
+
+        if kwarg_workaround:
+            if node.dyn_kwargs is not None:
+                self.write(", **dict({")
+            else:
+                self.write(", **{")
+            for kwarg in node.kwargs:
+                self.write(f"{kwarg.key!r}: ")
+                self.visit(kwarg.value, frame)
+                self.write(", ")
+            if extra_kwargs is not None:
+                for key, value in extra_kwargs.items():
+                    self.write(f"{key!r}: {value}, ")
+            if node.dyn_kwargs is not None:
+                self.write("}, **")
+                self.visit(node.dyn_kwargs, frame)
+                self.write(")")
+            else:
+                self.write("}")
+
+        elif node.dyn_kwargs is not None:
+            self.write(", **")
+            self.visit(node.dyn_kwargs, frame)
+
+    def pull_dependencies(self, nodes: t.Iterable[nodes.Node]) -> None:
+        """Find all filter and test names used in the template and
+        assign them to variables in the compiled namespace. Checking
+        that the names are registered with the environment is done when
+        compiling the Filter and Test nodes. If the node is in an If or
+        CondExpr node, the check is done at runtime instead.
+
+        .. versionchanged:: 3.0
+            Filters and tests in If and CondExpr nodes are checked at
+            runtime instead of compile time.
+        """
+        visitor = DependencyFinderVisitor()
+
+        for node in nodes:
+            visitor.visit(node)
+
+        for id_map, names, dependency in (
+            (self.filters, visitor.filters, "filters"),
+            (
+                self.tests,
+                visitor.tests,
+                "tests",
+            ),
+        ):
+            for name in sorted(names):
+                if name not in id_map:
+                    id_map[name] = self.temporary_identifier()
+
+                # add check during runtime that dependencies used inside of executed
+                # blocks are defined, as this step may be skipped during compile time
+                self.writeline("try:")
+                self.indent()
+                self.writeline(f"{id_map[name]} = environment.{dependency}[{name!r}]")
+                self.outdent()
+                self.writeline("except KeyError:")
+                self.indent()
+                self.writeline("@internalcode")
+                self.writeline(f"def {id_map[name]}(*unused):")
+                self.indent()
+                self.writeline(
+                    f'raise TemplateRuntimeError("No {dependency[:-1]}'
+                    f' named {name!r} found.")'
+                )
+                self.outdent()
+                self.outdent()
+
+    def enter_frame(self, frame: Frame) -> None:
+        undefs = []
+        for target, (action, param) in frame.symbols.loads.items():
+            if action == VAR_LOAD_PARAMETER:
+                pass
+            elif action == VAR_LOAD_RESOLVE:
+                self.writeline(f"{target} = {self.get_resolve_func()}({param!r})")
+            elif action == VAR_LOAD_ALIAS:
+                self.writeline(f"{target} = {param}")
+            elif action == VAR_LOAD_UNDEFINED:
+                undefs.append(target)
+            else:
+                raise NotImplementedError("unknown load instruction")
+        if undefs:
+            self.writeline(f"{' = '.join(undefs)} = missing")
+
+    def leave_frame(self, frame: Frame, with_python_scope: bool = False) -> None:
+        if not with_python_scope:
+            undefs = []
+            for target in frame.symbols.loads:
+                undefs.append(target)
+            if undefs:
+                self.writeline(f"{' = '.join(undefs)} = missing")
+
+    def choose_async(self, async_value: str = "async ", sync_value: str = "") -> str:
+        return async_value if self.environment.is_async else sync_value
+
+    def func(self, name: str) -> str:
+        return f"{self.choose_async()}def {name}"
+
+    def macro_body(
+        self, node: t.Union[nodes.Macro, nodes.CallBlock], frame: Frame
+    ) -> t.Tuple[Frame, MacroRef]:
+        """Dump the function def of a macro or call block."""
+        frame = frame.inner()
+        frame.symbols.analyze_node(node)
+        macro_ref = MacroRef(node)
+
+        explicit_caller = None
+        skip_special_params = set()
+        args = []
+
+        for idx, arg in enumerate(node.args):
+            if arg.name == "caller":
+                explicit_caller = idx
+            if arg.name in ("kwargs", "varargs"):
+                skip_special_params.add(arg.name)
+            args.append(frame.symbols.ref(arg.name))
+
+        undeclared = find_undeclared(node.body, ("caller", "kwargs", "varargs"))
+
+        if "caller" in undeclared:
+            # In older Jinja versions there was a bug that allowed caller
+            # to retain the special behavior even if it was mentioned in
+            # the argument list.  However thankfully this was only really
+            # working if it was the last argument.  So we are explicitly
+            # checking this now and error out if it is anywhere else in
+            # the argument list.
+            if explicit_caller is not None:
+                try:
+                    node.defaults[explicit_caller - len(node.args)]
+                except IndexError:
+                    self.fail(
+                        "When defining macros or call blocks the "
+                        'special "caller" argument must be omitted '
+                        "or be given a default.",
+                        node.lineno,
+                    )
+            else:
+                args.append(frame.symbols.declare_parameter("caller"))
+            macro_ref.accesses_caller = True
+        if "kwargs" in undeclared and "kwargs" not in skip_special_params:
+            args.append(frame.symbols.declare_parameter("kwargs"))
+            macro_ref.accesses_kwargs = True
+        if "varargs" in undeclared and "varargs" not in skip_special_params:
+            args.append(frame.symbols.declare_parameter("varargs"))
+            macro_ref.accesses_varargs = True
+
+        # macros are delayed, they never require output checks
+        frame.require_output_check = False
+        frame.symbols.analyze_node(node)
+        self.writeline(f"{self.func('macro')}({', '.join(args)}):", node)
+        self.indent()
+
+        self.buffer(frame)
+        self.enter_frame(frame)
+
+        self.push_parameter_definitions(frame)
+        for idx, arg in enumerate(node.args):
+            ref = frame.symbols.ref(arg.name)
+            self.writeline(f"if {ref} is missing:")
+            self.indent()
+            try:
+                default = node.defaults[idx - len(node.args)]
+            except IndexError:
+                self.writeline(
+                    f'{ref} = undefined("parameter {arg.name!r} was not provided",'
+                    f" name={arg.name!r})"
+                )
+            else:
+                self.writeline(f"{ref} = ")
+                self.visit(default, frame)
+            self.mark_parameter_stored(ref)
+            self.outdent()
+        self.pop_parameter_definitions()
+
+        self.blockvisit(node.body, frame)
+        self.return_buffer_contents(frame, force_unescaped=True)
+        self.leave_frame(frame, with_python_scope=True)
+        self.outdent()
+
+        return frame, macro_ref
+
+    def macro_def(self, macro_ref: MacroRef, frame: Frame) -> None:
+        """Dump the macro definition for the def created by macro_body."""
+        arg_tuple = ", ".join(repr(x.name) for x in macro_ref.node.args)
+        name = getattr(macro_ref.node, "name", None)
+        if len(macro_ref.node.args) == 1:
+            arg_tuple += ","
+        self.write(
+            f"Macro(environment, macro, {name!r}, ({arg_tuple}),"
+            f" {macro_ref.accesses_kwargs!r}, {macro_ref.accesses_varargs!r},"
+            f" {macro_ref.accesses_caller!r}, context.eval_ctx.autoescape)"
+        )
+
+    def position(self, node: nodes.Node) -> str:
+        """Return a human readable position for the node."""
+        rv = f"line {node.lineno}"
+        if self.name is not None:
+            rv = f"{rv} in {self.name!r}"
+        return rv
+
+    def dump_local_context(self, frame: Frame) -> str:
+        items_kv = ", ".join(
+            f"{name!r}: {target}"
+            for name, target in frame.symbols.dump_stores().items()
+        )
+        return f"{{{items_kv}}}"
+
+    def write_commons(self) -> None:
+        """Writes a common preamble that is used by root and block functions.
+        Primarily this sets up common local helpers and enforces a generator
+        through a dead branch.
+        """
+        self.writeline("resolve = context.resolve_or_missing")
+        self.writeline("undefined = environment.undefined")
+        self.writeline("concat = environment.concat")
+        # always use the standard Undefined class for the implicit else of
+        # conditional expressions
+        self.writeline("cond_expr_undefined = Undefined")
+        self.writeline("if 0: yield None")
+
+    def push_parameter_definitions(self, frame: Frame) -> None:
+        """Pushes all parameter targets from the given frame into a local
+        stack that permits tracking of yet to be assigned parameters.  In
+        particular this enables the optimization from `visit_Name` to skip
+        undefined expressions for parameters in macros as macros can reference
+        otherwise unbound parameters.
+        """
+        self._param_def_block.append(frame.symbols.dump_param_targets())
+
+    def pop_parameter_definitions(self) -> None:
+        """Pops the current parameter definitions set."""
+        self._param_def_block.pop()
+
+    def mark_parameter_stored(self, target: str) -> None:
+        """Marks a parameter in the current parameter definitions as stored.
+        This will skip the enforced undefined checks.
+        """
+        if self._param_def_block:
+            self._param_def_block[-1].discard(target)
+
+    def push_context_reference(self, target: str) -> None:
+        self._context_reference_stack.append(target)
+
+    def pop_context_reference(self) -> None:
+        self._context_reference_stack.pop()
+
+    def get_context_ref(self) -> str:
+        return self._context_reference_stack[-1]
+
+    def get_resolve_func(self) -> str:
+        target = self._context_reference_stack[-1]
+        if target == "context":
+            return "resolve"
+        return f"{target}.resolve"
+
+    def derive_context(self, frame: Frame) -> str:
+        return f"{self.get_context_ref()}.derived({self.dump_local_context(frame)})"
+
+    def parameter_is_undeclared(self, target: str) -> bool:
+        """Checks if a given target is an undeclared parameter."""
+        if not self._param_def_block:
+            return False
+        return target in self._param_def_block[-1]
+
+    def push_assign_tracking(self) -> None:
+        """Pushes a new layer for assignment tracking."""
+        self._assign_stack.append(set())
+
+    def pop_assign_tracking(self, frame: Frame) -> None:
+        """Pops the topmost level for assignment tracking and updates the
+        context variables if necessary.
+        """
+        vars = self._assign_stack.pop()
+        if (
+            not frame.block_frame
+            and not frame.loop_frame
+            and not frame.toplevel
+            or not vars
+        ):
+            return
+        public_names = [x for x in vars if x[:1] != "_"]
+        if len(vars) == 1:
+            name = next(iter(vars))
+            ref = frame.symbols.ref(name)
+            if frame.loop_frame:
+                self.writeline(f"_loop_vars[{name!r}] = {ref}")
+                return
+            if frame.block_frame:
+                self.writeline(f"_block_vars[{name!r}] = {ref}")
+                return
+            self.writeline(f"context.vars[{name!r}] = {ref}")
+        else:
+            if frame.loop_frame:
+                self.writeline("_loop_vars.update({")
+            elif frame.block_frame:
+                self.writeline("_block_vars.update({")
+            else:
+                self.writeline("context.vars.update({")
+            for idx, name in enumerate(sorted(vars)):
+                if idx:
+                    self.write(", ")
+                ref = frame.symbols.ref(name)
+                self.write(f"{name!r}: {ref}")
+            self.write("})")
+        if not frame.block_frame and not frame.loop_frame and public_names:
+            if len(public_names) == 1:
+                self.writeline(f"context.exported_vars.add({public_names[0]!r})")
+            else:
+                names_str = ", ".join(map(repr, sorted(public_names)))
+                self.writeline(f"context.exported_vars.update(({names_str}))")
+
+    # -- Statement Visitors
+
+    def visit_Template(
+        self, node: nodes.Template, frame: t.Optional[Frame] = None
+    ) -> None:
+        assert frame is None, "no root frame allowed"
+        eval_ctx = EvalContext(self.environment, self.name)
+
+        from .runtime import async_exported
+        from .runtime import exported
+
+        if self.environment.is_async:
+            exported_names = sorted(exported + async_exported)
+        else:
+            exported_names = sorted(exported)
+
+        self.writeline("from jinja2.runtime import " + ", ".join(exported_names))
+
+        # if we want a deferred initialization we cannot move the
+        # environment into a local name
+        envenv = "" if self.defer_init else ", environment=environment"
+
+        # do we have an extends tag at all?  If not, we can save some
+        # overhead by just not processing any inheritance code.
+        have_extends = node.find(nodes.Extends) is not None
+
+        # find all blocks
+        for block in node.find_all(nodes.Block):
+            if block.name in self.blocks:
+                self.fail(f"block {block.name!r} defined twice", block.lineno)
+            self.blocks[block.name] = block
+
+        # find all imports and import them
+        for import_ in node.find_all(nodes.ImportedName):
+            if import_.importname not in self.import_aliases:
+                imp = import_.importname
+                self.import_aliases[imp] = alias = self.temporary_identifier()
+                if "." in imp:
+                    module, obj = imp.rsplit(".", 1)
+                    self.writeline(f"from {module} import {obj} as {alias}")
+                else:
+                    self.writeline(f"import {imp} as {alias}")
+
+        # add the load name
+        self.writeline(f"name = {self.name!r}")
+
+        # generate the root render function.
+        self.writeline(
+            f"{self.func('root')}(context, missing=missing{envenv}):", extra=1
+        )
+        self.indent()
+        self.write_commons()
+
+        # process the root
+        frame = Frame(eval_ctx)
+        if "self" in find_undeclared(node.body, ("self",)):
+            ref = frame.symbols.declare_parameter("self")
+            self.writeline(f"{ref} = TemplateReference(context)")
+        frame.symbols.analyze_node(node)
+        frame.toplevel = frame.rootlevel = True
+        frame.require_output_check = have_extends and not self.has_known_extends
+        if have_extends:
+            self.writeline("parent_template = None")
+        self.enter_frame(frame)
+        self.pull_dependencies(node.body)
+        self.blockvisit(node.body, frame)
+        self.leave_frame(frame, with_python_scope=True)
+        self.outdent()
+
+        # make sure that the parent root is called.
+        if have_extends:
+            if not self.has_known_extends:
+                self.indent()
+                self.writeline("if parent_template is not None:")
+            self.indent()
+            if not self.environment.is_async:
+                self.writeline("yield from parent_template.root_render_func(context)")
+            else:
+                self.writeline("agen = parent_template.root_render_func(context)")
+                self.writeline("try:")
+                self.indent()
+                self.writeline("async for event in agen:")
+                self.indent()
+                self.writeline("yield event")
+                self.outdent()
+                self.outdent()
+                self.writeline("finally: await agen.aclose()")
+            self.outdent(1 + (not self.has_known_extends))
+
+        # at this point we now have the blocks collected and can visit them too.
+        for name, block in self.blocks.items():
+            self.writeline(
+                f"{self.func('block_' + name)}(context, missing=missing{envenv}):",
+                block,
+                1,
+            )
+            self.indent()
+            self.write_commons()
+            # It's important that we do not make this frame a child of the
+            # toplevel template.  This would cause a variety of
+            # interesting issues with identifier tracking.
+            block_frame = Frame(eval_ctx)
+            block_frame.block_frame = True
+            undeclared = find_undeclared(block.body, ("self", "super"))
+            if "self" in undeclared:
+                ref = block_frame.symbols.declare_parameter("self")
+                self.writeline(f"{ref} = TemplateReference(context)")
+            if "super" in undeclared:
+                ref = block_frame.symbols.declare_parameter("super")
+                self.writeline(f"{ref} = context.super({name!r}, block_{name})")
+            block_frame.symbols.analyze_node(block)
+            block_frame.block = name
+            self.writeline("_block_vars = {}")
+            self.enter_frame(block_frame)
+            self.pull_dependencies(block.body)
+            self.blockvisit(block.body, block_frame)
+            self.leave_frame(block_frame, with_python_scope=True)
+            self.outdent()
+
+        blocks_kv_str = ", ".join(f"{x!r}: block_{x}" for x in self.blocks)
+        self.writeline(f"blocks = {{{blocks_kv_str}}}", extra=1)
+        debug_kv_str = "&".join(f"{k}={v}" for k, v in self.debug_info)
+        self.writeline(f"debug_info = {debug_kv_str!r}")
+
+    def visit_Block(self, node: nodes.Block, frame: Frame) -> None:
+        """Call a block and register it for the template."""
+        level = 0
+        if frame.toplevel:
+            # if we know that we are a child template, there is no need to
+            # check if we are one
+            if self.has_known_extends:
+                return
+            if self.extends_so_far > 0:
+                self.writeline("if parent_template is None:")
+                self.indent()
+                level += 1
+
+        if node.scoped:
+            context = self.derive_context(frame)
+        else:
+            context = self.get_context_ref()
+
+        if node.required:
+            self.writeline(f"if len(context.blocks[{node.name!r}]) <= 1:", node)
+            self.indent()
+            self.writeline(
+                f'raise TemplateRuntimeError("Required block {node.name!r} not found")',
+                node,
+            )
+            self.outdent()
+
+        if not self.environment.is_async and frame.buffer is None:
+            self.writeline(
+                f"yield from context.blocks[{node.name!r}][0]({context})", node
+            )
+        else:
+            self.writeline(f"gen = context.blocks[{node.name!r}][0]({context})")
+            self.writeline("try:")
+            self.indent()
+            self.writeline(
+                f"{self.choose_async()}for event in gen:",
+                node,
+            )
+            self.indent()
+            self.simple_write("event", frame)
+            self.outdent()
+            self.outdent()
+            self.writeline(
+                f"finally: {self.choose_async('await gen.aclose()', 'gen.close()')}"
+            )
+
+        self.outdent(level)
+
+    def visit_Extends(self, node: nodes.Extends, frame: Frame) -> None:
+        """Calls the extender."""
+        if not frame.toplevel:
+            self.fail("cannot use extend from a non top-level scope", node.lineno)
+
+        # if the number of extends statements in general is zero so
+        # far, we don't have to add a check if something extended
+        # the template before this one.
+        if self.extends_so_far > 0:
+            # if we have a known extends we just add a template runtime
+            # error into the generated code.  We could catch that at compile
+            # time too, but i welcome it not to confuse users by throwing the
+            # same error at different times just "because we can".
+            if not self.has_known_extends:
+                self.writeline("if parent_template is not None:")
+                self.indent()
+            self.writeline('raise TemplateRuntimeError("extended multiple times")')
+
+            # if we have a known extends already we don't need that code here
+            # as we know that the template execution will end here.
+            if self.has_known_extends:
+                raise CompilerExit()
+            else:
+                self.outdent()
+
+        self.writeline("parent_template = environment.get_template(", node)
+        self.visit(node.template, frame)
+        self.write(f", {self.name!r})")
+        self.writeline("for name, parent_block in parent_template.blocks.items():")
+        self.indent()
+        self.writeline("context.blocks.setdefault(name, []).append(parent_block)")
+        self.outdent()
+
+        # if this extends statement was in the root level we can take
+        # advantage of that information and simplify the generated code
+        # in the top level from this point onwards
+        if frame.rootlevel:
+            self.has_known_extends = True
+
+        # and now we have one more
+        self.extends_so_far += 1
+
+    def visit_Include(self, node: nodes.Include, frame: Frame) -> None:
+        """Handles includes."""
+        if node.ignore_missing:
+            self.writeline("try:")
+            self.indent()
+
+        func_name = "get_or_select_template"
+        if isinstance(node.template, nodes.Const):
+            if isinstance(node.template.value, str):
+                func_name = "get_template"
+            elif isinstance(node.template.value, (tuple, list)):
+                func_name = "select_template"
+        elif isinstance(node.template, (nodes.Tuple, nodes.List)):
+            func_name = "select_template"
+
+        self.writeline(f"template = environment.{func_name}(", node)
+        self.visit(node.template, frame)
+        self.write(f", {self.name!r})")
+        if node.ignore_missing:
+            self.outdent()
+            self.writeline("except TemplateNotFound:")
+            self.indent()
+            self.writeline("pass")
+            self.outdent()
+            self.writeline("else:")
+            self.indent()
+
+        def loop_body() -> None:
+            self.indent()
+            self.simple_write("event", frame)
+            self.outdent()
+
+        if node.with_context:
+            self.writeline(
+                f"gen = template.root_render_func("
+                "template.new_context(context.get_all(), True,"
+                f" {self.dump_local_context(frame)}))"
+            )
+            self.writeline("try:")
+            self.indent()
+            self.writeline(f"{self.choose_async()}for event in gen:")
+            loop_body()
+            self.outdent()
+            self.writeline(
+                f"finally: {self.choose_async('await gen.aclose()', 'gen.close()')}"
+            )
+        elif self.environment.is_async:
+            self.writeline(
+                "for event in (await template._get_default_module_async())"
+                "._body_stream:"
+            )
+            loop_body()
+        else:
+            self.writeline("yield from template._get_default_module()._body_stream")
+
+        if node.ignore_missing:
+            self.outdent()
+
+    def _import_common(
+        self, node: t.Union[nodes.Import, nodes.FromImport], frame: Frame
+    ) -> None:
+        self.write(f"{self.choose_async('await ')}environment.get_template(")
+        self.visit(node.template, frame)
+        self.write(f", {self.name!r}).")
+
+        if node.with_context:
+            f_name = f"make_module{self.choose_async('_async')}"
+            self.write(
+                f"{f_name}(context.get_all(), True, {self.dump_local_context(frame)})"
+            )
+        else:
+            self.write(f"_get_default_module{self.choose_async('_async')}(context)")
+
+    def visit_Import(self, node: nodes.Import, frame: Frame) -> None:
+        """Visit regular imports."""
+        self.writeline(f"{frame.symbols.ref(node.target)} = ", node)
+        if frame.toplevel:
+            self.write(f"context.vars[{node.target!r}] = ")
+
+        self._import_common(node, frame)
+
+        if frame.toplevel and not node.target.startswith("_"):
+            self.writeline(f"context.exported_vars.discard({node.target!r})")
+
+    def visit_FromImport(self, node: nodes.FromImport, frame: Frame) -> None:
+        """Visit named imports."""
+        self.newline(node)
+        self.write("included_template = ")
+        self._import_common(node, frame)
+        var_names = []
+        discarded_names = []
+        for name in node.names:
+            if isinstance(name, tuple):
+                name, alias = name
+            else:
+                alias = name
+            self.writeline(
+                f"{frame.symbols.ref(alias)} ="
+                f" getattr(included_template, {name!r}, missing)"
+            )
+            self.writeline(f"if {frame.symbols.ref(alias)} is missing:")
+            self.indent()
+            # The position will contain the template name, and will be formatted
+            # into a string that will be compiled into an f-string. Curly braces
+            # in the name must be replaced with escapes so that they will not be
+            # executed as part of the f-string.
+            position = self.position(node).replace("{", "{{").replace("}", "}}")
+            message = (
+                "the template {included_template.__name__!r}"
+                f" (imported on {position})"
+                f" does not export the requested name {name!r}"
+            )
+            self.writeline(
+                f"{frame.symbols.ref(alias)} = undefined(f{message!r}, name={name!r})"
+            )
+            self.outdent()
+            if frame.toplevel:
+                var_names.append(alias)
+                if not alias.startswith("_"):
+                    discarded_names.append(alias)
+
+        if var_names:
+            if len(var_names) == 1:
+                name = var_names[0]
+                self.writeline(f"context.vars[{name!r}] = {frame.symbols.ref(name)}")
+            else:
+                names_kv = ", ".join(
+                    f"{name!r}: {frame.symbols.ref(name)}" for name in var_names
+                )
+                self.writeline(f"context.vars.update({{{names_kv}}})")
+        if discarded_names:
+            if len(discarded_names) == 1:
+                self.writeline(f"context.exported_vars.discard({discarded_names[0]!r})")
+            else:
+                names_str = ", ".join(map(repr, discarded_names))
+                self.writeline(
+                    f"context.exported_vars.difference_update(({names_str}))"
+                )
+
+    def visit_For(self, node: nodes.For, frame: Frame) -> None:
+        loop_frame = frame.inner()
+        loop_frame.loop_frame = True
+        test_frame = frame.inner()
+        else_frame = frame.inner()
+
+        # try to figure out if we have an extended loop.  An extended loop
+        # is necessary if the loop is in recursive mode if the special loop
+        # variable is accessed in the body if the body is a scoped block.
+        extended_loop = (
+            node.recursive
+            or "loop"
+            in find_undeclared(node.iter_child_nodes(only=("body",)), ("loop",))
+            or any(block.scoped for block in node.find_all(nodes.Block))
+        )
+
+        loop_ref = None
+        if extended_loop:
+            loop_ref = loop_frame.symbols.declare_parameter("loop")
+
+        loop_frame.symbols.analyze_node(node, for_branch="body")
+        if node.else_:
+            else_frame.symbols.analyze_node(node, for_branch="else")
+
+        if node.test:
+            loop_filter_func = self.temporary_identifier()
+            test_frame.symbols.analyze_node(node, for_branch="test")
+            self.writeline(f"{self.func(loop_filter_func)}(fiter):", node.test)
+            self.indent()
+            self.enter_frame(test_frame)
+            self.writeline(self.choose_async("async for ", "for "))
+            self.visit(node.target, loop_frame)
+            self.write(" in ")
+            self.write(self.choose_async("auto_aiter(fiter)", "fiter"))
+            self.write(":")
+            self.indent()
+            self.writeline("if ", node.test)
+            self.visit(node.test, test_frame)
+            self.write(":")
+            self.indent()
+            self.writeline("yield ")
+            self.visit(node.target, loop_frame)
+            self.outdent(3)
+            self.leave_frame(test_frame, with_python_scope=True)
+
+        # if we don't have an recursive loop we have to find the shadowed
+        # variables at that point.  Because loops can be nested but the loop
+        # variable is a special one we have to enforce aliasing for it.
+        if node.recursive:
+            self.writeline(
+                f"{self.func('loop')}(reciter, loop_render_func, depth=0):", node
+            )
+            self.indent()
+            self.buffer(loop_frame)
+
+            # Use the same buffer for the else frame
+            else_frame.buffer = loop_frame.buffer
+
+        # make sure the loop variable is a special one and raise a template
+        # assertion error if a loop tries to write to loop
+        if extended_loop:
+            self.writeline(f"{loop_ref} = missing")
+
+        for name in node.find_all(nodes.Name):
+            if name.ctx == "store" and name.name == "loop":
+                self.fail(
+                    "Can't assign to special loop variable in for-loop target",
+                    name.lineno,
+                )
+
+        if node.else_:
+            iteration_indicator = self.temporary_identifier()
+            self.writeline(f"{iteration_indicator} = 1")
+
+        self.writeline(self.choose_async("async for ", "for "), node)
+        self.visit(node.target, loop_frame)
+        if extended_loop:
+            self.write(f", {loop_ref} in {self.choose_async('Async')}LoopContext(")
+        else:
+            self.write(" in ")
+
+        if node.test:
+            self.write(f"{loop_filter_func}(")
+        if node.recursive:
+            self.write("reciter")
+        else:
+            if self.environment.is_async and not extended_loop:
+                self.write("auto_aiter(")
+            self.visit(node.iter, frame)
+            if self.environment.is_async and not extended_loop:
+                self.write(")")
+        if node.test:
+            self.write(")")
+
+        if node.recursive:
+            self.write(", undefined, loop_render_func, depth):")
+        else:
+            self.write(", undefined):" if extended_loop else ":")
+
+        self.indent()
+        self.enter_frame(loop_frame)
+
+        self.writeline("_loop_vars = {}")
+        self.blockvisit(node.body, loop_frame)
+        if node.else_:
+            self.writeline(f"{iteration_indicator} = 0")
+        self.outdent()
+        self.leave_frame(
+            loop_frame, with_python_scope=node.recursive and not node.else_
+        )
+
+        if node.else_:
+            self.writeline(f"if {iteration_indicator}:")
+            self.indent()
+            self.enter_frame(else_frame)
+            self.blockvisit(node.else_, else_frame)
+            self.leave_frame(else_frame)
+            self.outdent()
+
+        # if the node was recursive we have to return the buffer contents
+        # and start the iteration code
+        if node.recursive:
+            self.return_buffer_contents(loop_frame)
+            self.outdent()
+            self.start_write(frame, node)
+            self.write(f"{self.choose_async('await ')}loop(")
+            if self.environment.is_async:
+                self.write("auto_aiter(")
+            self.visit(node.iter, frame)
+            if self.environment.is_async:
+                self.write(")")
+            self.write(", loop)")
+            self.end_write(frame)
+
+        # at the end of the iteration, clear any assignments made in the
+        # loop from the top level
+        if self._assign_stack:
+            self._assign_stack[-1].difference_update(loop_frame.symbols.stores)
+
+    def visit_If(self, node: nodes.If, frame: Frame) -> None:
+        if_frame = frame.soft()
+        self.writeline("if ", node)
+        self.visit(node.test, if_frame)
+        self.write(":")
+        self.indent()
+        self.blockvisit(node.body, if_frame)
+        self.outdent()
+        for elif_ in node.elif_:
+            self.writeline("elif ", elif_)
+            self.visit(elif_.test, if_frame)
+            self.write(":")
+            self.indent()
+            self.blockvisit(elif_.body, if_frame)
+            self.outdent()
+        if node.else_:
+            self.writeline("else:")
+            self.indent()
+            self.blockvisit(node.else_, if_frame)
+            self.outdent()
+
+    def visit_Macro(self, node: nodes.Macro, frame: Frame) -> None:
+        macro_frame, macro_ref = self.macro_body(node, frame)
+        self.newline()
+        if frame.toplevel:
+            if not node.name.startswith("_"):
+                self.write(f"context.exported_vars.add({node.name!r})")
+            self.writeline(f"context.vars[{node.name!r}] = ")
+        self.write(f"{frame.symbols.ref(node.name)} = ")
+        self.macro_def(macro_ref, macro_frame)
+
+    def visit_CallBlock(self, node: nodes.CallBlock, frame: Frame) -> None:
+        call_frame, macro_ref = self.macro_body(node, frame)
+        self.writeline("caller = ")
+        self.macro_def(macro_ref, call_frame)
+        self.start_write(frame, node)
+        self.visit_Call(node.call, frame, forward_caller=True)
+        self.end_write(frame)
+
+    def visit_FilterBlock(self, node: nodes.FilterBlock, frame: Frame) -> None:
+        filter_frame = frame.inner()
+        filter_frame.symbols.analyze_node(node)
+        self.enter_frame(filter_frame)
+        self.buffer(filter_frame)
+        self.blockvisit(node.body, filter_frame)
+        self.start_write(frame, node)
+        self.visit_Filter(node.filter, filter_frame)
+        self.end_write(frame)
+        self.leave_frame(filter_frame)
+
+    def visit_With(self, node: nodes.With, frame: Frame) -> None:
+        with_frame = frame.inner()
+        with_frame.symbols.analyze_node(node)
+        self.enter_frame(with_frame)
+        for target, expr in zip(node.targets, node.values):
+            self.newline()
+            self.visit(target, with_frame)
+            self.write(" = ")
+            self.visit(expr, frame)
+        self.blockvisit(node.body, with_frame)
+        self.leave_frame(with_frame)
+
+    def visit_ExprStmt(self, node: nodes.ExprStmt, frame: Frame) -> None:
+        self.newline(node)
+        self.visit(node.node, frame)
+
+    class _FinalizeInfo(t.NamedTuple):
+        const: t.Optional[t.Callable[..., str]]
+        src: t.Optional[str]
+
+    @staticmethod
+    def _default_finalize(value: t.Any) -> t.Any:
+        """The default finalize function if the environment isn't
+        configured with one. Or, if the environment has one, this is
+        called on that function's output for constants.
+        """
+        return str(value)
+
+    _finalize: t.Optional[_FinalizeInfo] = None
+
+    def _make_finalize(self) -> _FinalizeInfo:
+        """Build the finalize function to be used on constants and at
+        runtime. Cached so it's only created once for all output nodes.
+
+        Returns a ``namedtuple`` with the following attributes:
+
+        ``const``
+            A function to finalize constant data at compile time.
+
+        ``src``
+            Source code to output around nodes to be evaluated at
+            runtime.
+        """
+        if self._finalize is not None:
+            return self._finalize
+
+        finalize: t.Optional[t.Callable[..., t.Any]]
+        finalize = default = self._default_finalize
+        src = None
+
+        if self.environment.finalize:
+            src = "environment.finalize("
+            env_finalize = self.environment.finalize
+            pass_arg = {
+                _PassArg.context: "context",
+                _PassArg.eval_context: "context.eval_ctx",
+                _PassArg.environment: "environment",
+            }.get(
+                _PassArg.from_obj(env_finalize)  # type: ignore
+            )
+            finalize = None
+
+            if pass_arg is None:
+
+                def finalize(value: t.Any) -> t.Any:  # noqa: F811
+                    return default(env_finalize(value))
+
+            else:
+                src = f"{src}{pass_arg}, "
+
+                if pass_arg == "environment":
+
+                    def finalize(value: t.Any) -> t.Any:  # noqa: F811
+                        return default(env_finalize(self.environment, value))
+
+        self._finalize = self._FinalizeInfo(finalize, src)
+        return self._finalize
+
+    def _output_const_repr(self, group: t.Iterable[t.Any]) -> str:
+        """Given a group of constant values converted from ``Output``
+        child nodes, produce a string to write to the template module
+        source.
+        """
+        return repr(concat(group))
+
+    def _output_child_to_const(
+        self, node: nodes.Expr, frame: Frame, finalize: _FinalizeInfo
+    ) -> str:
+        """Try to optimize a child of an ``Output`` node by trying to
+        convert it to constant, finalized data at compile time.
+
+        If :exc:`Impossible` is raised, the node is not constant and
+        will be evaluated at runtime. Any other exception will also be
+        evaluated at runtime for easier debugging.
+        """
+        const = node.as_const(frame.eval_ctx)
+
+        if frame.eval_ctx.autoescape:
+            const = escape(const)
+
+        # Template data doesn't go through finalize.
+        if isinstance(node, nodes.TemplateData):
+            return str(const)
+
+        return finalize.const(const)  # type: ignore
+
+    def _output_child_pre(
+        self, node: nodes.Expr, frame: Frame, finalize: _FinalizeInfo
+    ) -> None:
+        """Output extra source code before visiting a child of an
+        ``Output`` node.
+        """
+        if frame.eval_ctx.volatile:
+            self.write("(escape if context.eval_ctx.autoescape else str)(")
+        elif frame.eval_ctx.autoescape:
+            self.write("escape(")
+        else:
+            self.write("str(")
+
+        if finalize.src is not None:
+            self.write(finalize.src)
+
+    def _output_child_post(
+        self, node: nodes.Expr, frame: Frame, finalize: _FinalizeInfo
+    ) -> None:
+        """Output extra source code after visiting a child of an
+        ``Output`` node.
+        """
+        self.write(")")
+
+        if finalize.src is not None:
+            self.write(")")
+
+    def visit_Output(self, node: nodes.Output, frame: Frame) -> None:
+        # If an extends is active, don't render outside a block.
+        if frame.require_output_check:
+            # A top-level extends is known to exist at compile time.
+            if self.has_known_extends:
+                return
+
+            self.writeline("if parent_template is None:")
+            self.indent()
+
+        finalize = self._make_finalize()
+        body: t.List[t.Union[t.List[t.Any], nodes.Expr]] = []
+
+        # Evaluate constants at compile time if possible. Each item in
+        # body will be either a list of static data or a node to be
+        # evaluated at runtime.
+        for child in node.nodes:
+            try:
+                if not (
+                    # If the finalize function requires runtime context,
+                    # constants can't be evaluated at compile time.
+                    finalize.const
+                    # Unless it's basic template data that won't be
+                    # finalized anyway.
+                    or isinstance(child, nodes.TemplateData)
+                ):
+                    raise nodes.Impossible()
+
+                const = self._output_child_to_const(child, frame, finalize)
+            except (nodes.Impossible, Exception):
+                # The node was not constant and needs to be evaluated at
+                # runtime. Or another error was raised, which is easier
+                # to debug at runtime.
+                body.append(child)
+                continue
+
+            if body and isinstance(body[-1], list):
+                body[-1].append(const)
+            else:
+                body.append([const])
+
+        if frame.buffer is not None:
+            if len(body) == 1:
+                self.writeline(f"{frame.buffer}.append(")
+            else:
+                self.writeline(f"{frame.buffer}.extend((")
+
+            self.indent()
+
+        for item in body:
+            if isinstance(item, list):
+                # A group of constant data to join and output.
+                val = self._output_const_repr(item)
+
+                if frame.buffer is None:
+                    self.writeline("yield " + val)
+                else:
+                    self.writeline(val + ",")
+            else:
+                if frame.buffer is None:
+                    self.writeline("yield ", item)
+                else:
+                    self.newline(item)
+
+                # A node to be evaluated at runtime.
+                self._output_child_pre(item, frame, finalize)
+                self.visit(item, frame)
+                self._output_child_post(item, frame, finalize)
+
+                if frame.buffer is not None:
+                    self.write(",")
+
+        if frame.buffer is not None:
+            self.outdent()
+            self.writeline(")" if len(body) == 1 else "))")
+
+        if frame.require_output_check:
+            self.outdent()
+
+    def visit_Assign(self, node: nodes.Assign, frame: Frame) -> None:
+        self.push_assign_tracking()
+
+        # ``a.b`` is allowed for assignment, and is parsed as an NSRef. However,
+        # it is only valid if it references a Namespace object. Emit a check for
+        # that for each ref here, before assignment code is emitted. This can't
+        # be done in visit_NSRef as the ref could be in the middle of a tuple.
+        seen_refs: t.Set[str] = set()
+
+        for nsref in node.find_all(nodes.NSRef):
+            if nsref.name in seen_refs:
+                # Only emit the check for each reference once, in case the same
+                # ref is used multiple times in a tuple, `ns.a, ns.b = c, d`.
+                continue
+
+            seen_refs.add(nsref.name)
+            ref = frame.symbols.ref(nsref.name)
+            self.writeline(f"if not isinstance({ref}, Namespace):")
+            self.indent()
+            self.writeline(
+                "raise TemplateRuntimeError"
+                '("cannot assign attribute on non-namespace object")'
+            )
+            self.outdent()
+
+        self.newline(node)
+        self.visit(node.target, frame)
+        self.write(" = ")
+        self.visit(node.node, frame)
+        self.pop_assign_tracking(frame)
+
+    def visit_AssignBlock(self, node: nodes.AssignBlock, frame: Frame) -> None:
+        self.push_assign_tracking()
+        block_frame = frame.inner()
+        # This is a special case.  Since a set block always captures we
+        # will disable output checks.  This way one can use set blocks
+        # toplevel even in extended templates.
+        block_frame.require_output_check = False
+        block_frame.symbols.analyze_node(node)
+        self.enter_frame(block_frame)
+        self.buffer(block_frame)
+        self.blockvisit(node.body, block_frame)
+        self.newline(node)
+        self.visit(node.target, frame)
+        self.write(" = (Markup if context.eval_ctx.autoescape else identity)(")
+        if node.filter is not None:
+            self.visit_Filter(node.filter, block_frame)
+        else:
+            self.write(f"concat({block_frame.buffer})")
+        self.write(")")
+        self.pop_assign_tracking(frame)
+        self.leave_frame(block_frame)
+
+    # -- Expression Visitors
+
+    def visit_Name(self, node: nodes.Name, frame: Frame) -> None:
+        if node.ctx == "store" and (
+            frame.toplevel or frame.loop_frame or frame.block_frame
+        ):
+            if self._assign_stack:
+                self._assign_stack[-1].add(node.name)
+        ref = frame.symbols.ref(node.name)
+
+        # If we are looking up a variable we might have to deal with the
+        # case where it's undefined.  We can skip that case if the load
+        # instruction indicates a parameter which are always defined.
+        if node.ctx == "load":
+            load = frame.symbols.find_load(ref)
+            if not (
+                load is not None
+                and load[0] == VAR_LOAD_PARAMETER
+                and not self.parameter_is_undeclared(ref)
+            ):
+                self.write(
+                    f"(undefined(name={node.name!r}) if {ref} is missing else {ref})"
+                )
+                return
+
+        self.write(ref)
+
+    def visit_NSRef(self, node: nodes.NSRef, frame: Frame) -> None:
+        # NSRef is a dotted assignment target a.b=c, but uses a[b]=c internally.
+        # visit_Assign emits code to validate that each ref is to a Namespace
+        # object only. That can't be emitted here as the ref could be in the
+        # middle of a tuple assignment.
+        ref = frame.symbols.ref(node.name)
+        self.writeline(f"{ref}[{node.attr!r}]")
+
+    def visit_Const(self, node: nodes.Const, frame: Frame) -> None:
+        val = node.as_const(frame.eval_ctx)
+        if isinstance(val, float):
+            self.write(str(val))
+        else:
+            self.write(repr(val))
+
+    def visit_TemplateData(self, node: nodes.TemplateData, frame: Frame) -> None:
+        try:
+            self.write(repr(node.as_const(frame.eval_ctx)))
+        except nodes.Impossible:
+            self.write(
+                f"(Markup if context.eval_ctx.autoescape else identity)({node.data!r})"
+            )
+
+    def visit_Tuple(self, node: nodes.Tuple, frame: Frame) -> None:
+        self.write("(")
+        idx = -1
+        for idx, item in enumerate(node.items):
+            if idx:
+                self.write(", ")
+            self.visit(item, frame)
+        self.write(",)" if idx == 0 else ")")
+
+    def visit_List(self, node: nodes.List, frame: Frame) -> None:
+        self.write("[")
+        for idx, item in enumerate(node.items):
+            if idx:
+                self.write(", ")
+            self.visit(item, frame)
+        self.write("]")
+
+    def visit_Dict(self, node: nodes.Dict, frame: Frame) -> None:
+        self.write("{")
+        for idx, item in enumerate(node.items):
+            if idx:
+                self.write(", ")
+            self.visit(item.key, frame)
+            self.write(": ")
+            self.visit(item.value, frame)
+        self.write("}")
+
+    visit_Add = _make_binop("+")
+    visit_Sub = _make_binop("-")
+    visit_Mul = _make_binop("*")
+    visit_Div = _make_binop("/")
+    visit_FloorDiv = _make_binop("//")
+    visit_Pow = _make_binop("**")
+    visit_Mod = _make_binop("%")
+    visit_And = _make_binop("and")
+    visit_Or = _make_binop("or")
+    visit_Pos = _make_unop("+")
+    visit_Neg = _make_unop("-")
+    visit_Not = _make_unop("not ")
+
+    @optimizeconst
+    def visit_Concat(self, node: nodes.Concat, frame: Frame) -> None:
+        if frame.eval_ctx.volatile:
+            func_name = "(markup_join if context.eval_ctx.volatile else str_join)"
+        elif frame.eval_ctx.autoescape:
+            func_name = "markup_join"
+        else:
+            func_name = "str_join"
+        self.write(f"{func_name}((")
+        for arg in node.nodes:
+            self.visit(arg, frame)
+            self.write(", ")
+        self.write("))")
+
+    @optimizeconst
+    def visit_Compare(self, node: nodes.Compare, frame: Frame) -> None:
+        self.write("(")
+        self.visit(node.expr, frame)
+        for op in node.ops:
+            self.visit(op, frame)
+        self.write(")")
+
+    def visit_Operand(self, node: nodes.Operand, frame: Frame) -> None:
+        self.write(f" {operators[node.op]} ")
+        self.visit(node.expr, frame)
+
+    @optimizeconst
+    def visit_Getattr(self, node: nodes.Getattr, frame: Frame) -> None:
+        if self.environment.is_async:
+            self.write("(await auto_await(")
+
+        self.write("environment.getattr(")
+        self.visit(node.node, frame)
+        self.write(f", {node.attr!r})")
+
+        if self.environment.is_async:
+            self.write("))")
+
+    @optimizeconst
+    def visit_Getitem(self, node: nodes.Getitem, frame: Frame) -> None:
+        # slices bypass the environment getitem method.
+        if isinstance(node.arg, nodes.Slice):
+            self.visit(node.node, frame)
+            self.write("[")
+            self.visit(node.arg, frame)
+            self.write("]")
+        else:
+            if self.environment.is_async:
+                self.write("(await auto_await(")
+
+            self.write("environment.getitem(")
+            self.visit(node.node, frame)
+            self.write(", ")
+            self.visit(node.arg, frame)
+            self.write(")")
+
+            if self.environment.is_async:
+                self.write("))")
+
+    def visit_Slice(self, node: nodes.Slice, frame: Frame) -> None:
+        if node.start is not None:
+            self.visit(node.start, frame)
+        self.write(":")
+        if node.stop is not None:
+            self.visit(node.stop, frame)
+        if node.step is not None:
+            self.write(":")
+            self.visit(node.step, frame)
+
+    @contextmanager
+    def _filter_test_common(
+        self, node: t.Union[nodes.Filter, nodes.Test], frame: Frame, is_filter: bool
+    ) -> t.Iterator[None]:
+        if self.environment.is_async:
+            self.write("(await auto_await(")
+
+        if is_filter:
+            self.write(f"{self.filters[node.name]}(")
+            func = self.environment.filters.get(node.name)
+        else:
+            self.write(f"{self.tests[node.name]}(")
+            func = self.environment.tests.get(node.name)
+
+        # When inside an If or CondExpr frame, allow the filter to be
+        # undefined at compile time and only raise an error if it's
+        # actually called at runtime. See pull_dependencies.
+        if func is None and not frame.soft_frame:
+            type_name = "filter" if is_filter else "test"
+            self.fail(f"No {type_name} named {node.name!r}.", node.lineno)
+
+        pass_arg = {
+            _PassArg.context: "context",
+            _PassArg.eval_context: "context.eval_ctx",
+            _PassArg.environment: "environment",
+        }.get(
+            _PassArg.from_obj(func)  # type: ignore
+        )
+
+        if pass_arg is not None:
+            self.write(f"{pass_arg}, ")
+
+        # Back to the visitor function to handle visiting the target of
+        # the filter or test.
+        yield
+
+        self.signature(node, frame)
+        self.write(")")
+
+        if self.environment.is_async:
+            self.write("))")
+
+    @optimizeconst
+    def visit_Filter(self, node: nodes.Filter, frame: Frame) -> None:
+        with self._filter_test_common(node, frame, True):
+            # if the filter node is None we are inside a filter block
+            # and want to write to the current buffer
+            if node.node is not None:
+                self.visit(node.node, frame)
+            elif frame.eval_ctx.volatile:
+                self.write(
+                    f"(Markup(concat({frame.buffer}))"
+                    f" if context.eval_ctx.autoescape else concat({frame.buffer}))"
+                )
+            elif frame.eval_ctx.autoescape:
+                self.write(f"Markup(concat({frame.buffer}))")
+            else:
+                self.write(f"concat({frame.buffer})")
+
+    @optimizeconst
+    def visit_Test(self, node: nodes.Test, frame: Frame) -> None:
+        with self._filter_test_common(node, frame, False):
+            self.visit(node.node, frame)
+
+    @optimizeconst
+    def visit_CondExpr(self, node: nodes.CondExpr, frame: Frame) -> None:
+        frame = frame.soft()
+
+        def write_expr2() -> None:
+            if node.expr2 is not None:
+                self.visit(node.expr2, frame)
+                return
+
+            self.write(
+                f'cond_expr_undefined("the inline if-expression on'
+                f" {self.position(node)} evaluated to false and no else"
+                f' section was defined.")'
+            )
+
+        self.write("(")
+        self.visit(node.expr1, frame)
+        self.write(" if ")
+        self.visit(node.test, frame)
+        self.write(" else ")
+        write_expr2()
+        self.write(")")
+
+    @optimizeconst
+    def visit_Call(
+        self, node: nodes.Call, frame: Frame, forward_caller: bool = False
+    ) -> None:
+        if self.environment.is_async:
+            self.write("(await auto_await(")
+        if self.environment.sandboxed:
+            self.write("environment.call(context, ")
+        else:
+            self.write("context.call(")
+        self.visit(node.node, frame)
+        extra_kwargs = {"caller": "caller"} if forward_caller else None
+        loop_kwargs = {"_loop_vars": "_loop_vars"} if frame.loop_frame else {}
+        block_kwargs = {"_block_vars": "_block_vars"} if frame.block_frame else {}
+        if extra_kwargs:
+            extra_kwargs.update(loop_kwargs, **block_kwargs)
+        elif loop_kwargs or block_kwargs:
+            extra_kwargs = dict(loop_kwargs, **block_kwargs)
+        self.signature(node, frame, extra_kwargs)
+        self.write(")")
+        if self.environment.is_async:
+            self.write("))")
+
+    def visit_Keyword(self, node: nodes.Keyword, frame: Frame) -> None:
+        self.write(node.key + "=")
+        self.visit(node.value, frame)
+
+    # -- Unused nodes for extensions
+
+    def visit_MarkSafe(self, node: nodes.MarkSafe, frame: Frame) -> None:
+        self.write("Markup(")
+        self.visit(node.expr, frame)
+        self.write(")")
+
+    def visit_MarkSafeIfAutoescape(
+        self, node: nodes.MarkSafeIfAutoescape, frame: Frame
+    ) -> None:
+        self.write("(Markup if context.eval_ctx.autoescape else identity)(")
+        self.visit(node.expr, frame)
+        self.write(")")
+
+    def visit_EnvironmentAttribute(
+        self, node: nodes.EnvironmentAttribute, frame: Frame
+    ) -> None:
+        self.write("environment." + node.name)
+
+    def visit_ExtensionAttribute(
+        self, node: nodes.ExtensionAttribute, frame: Frame
+    ) -> None:
+        self.write(f"environment.extensions[{node.identifier!r}].{node.name}")
+
+    def visit_ImportedName(self, node: nodes.ImportedName, frame: Frame) -> None:
+        self.write(self.import_aliases[node.importname])
+
+    def visit_InternalName(self, node: nodes.InternalName, frame: Frame) -> None:
+        self.write(node.name)
+
+    def visit_ContextReference(
+        self, node: nodes.ContextReference, frame: Frame
+    ) -> None:
+        self.write("context")
+
+    def visit_DerivedContextReference(
+        self, node: nodes.DerivedContextReference, frame: Frame
+    ) -> None:
+        self.write(self.derive_context(frame))
+
+    def visit_Continue(self, node: nodes.Continue, frame: Frame) -> None:
+        self.writeline("continue", node)
+
+    def visit_Break(self, node: nodes.Break, frame: Frame) -> None:
+        self.writeline("break", node)
+
+    def visit_Scope(self, node: nodes.Scope, frame: Frame) -> None:
+        scope_frame = frame.inner()
+        scope_frame.symbols.analyze_node(node)
+        self.enter_frame(scope_frame)
+        self.blockvisit(node.body, scope_frame)
+        self.leave_frame(scope_frame)
+
+    def visit_OverlayScope(self, node: nodes.OverlayScope, frame: Frame) -> None:
+        ctx = self.temporary_identifier()
+        self.writeline(f"{ctx} = {self.derive_context(frame)}")
+        self.writeline(f"{ctx}.vars = ")
+        self.visit(node.context, frame)
+        self.push_context_reference(ctx)
+
+        scope_frame = frame.inner(isolated=True)
+        scope_frame.symbols.analyze_node(node)
+        self.enter_frame(scope_frame)
+        self.blockvisit(node.body, scope_frame)
+        self.leave_frame(scope_frame)
+        self.pop_context_reference()
+
+    def visit_EvalContextModifier(
+        self, node: nodes.EvalContextModifier, frame: Frame
+    ) -> None:
+        for keyword in node.options:
+            self.writeline(f"context.eval_ctx.{keyword.key} = ")
+            self.visit(keyword.value, frame)
+            try:
+                val = keyword.value.as_const(frame.eval_ctx)
+            except nodes.Impossible:
+                frame.eval_ctx.volatile = True
+            else:
+                setattr(frame.eval_ctx, keyword.key, val)
+
+    def visit_ScopedEvalContextModifier(
+        self, node: nodes.ScopedEvalContextModifier, frame: Frame
+    ) -> None:
+        old_ctx_name = self.temporary_identifier()
+        saved_ctx = frame.eval_ctx.save()
+        self.writeline(f"{old_ctx_name} = context.eval_ctx.save()")
+        self.visit_EvalContextModifier(node, frame)
+        for child in node.body:
+            self.visit(child, frame)
+        frame.eval_ctx.revert(saved_ctx)
+        self.writeline(f"context.eval_ctx.revert({old_ctx_name})")
diff --git a/lib/python3.11/site-packages/jinja2/constants.py b/lib/python3.11/site-packages/jinja2/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..41a1c23b0a7fe134b1f662545876eb65b31b071e
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/constants.py
@@ -0,0 +1,20 @@
+#: list of lorem ipsum words used by the lipsum() helper function
+LOREM_IPSUM_WORDS = """\
+a ac accumsan ad adipiscing aenean aliquam aliquet amet ante aptent arcu at
+auctor augue bibendum blandit class commodo condimentum congue consectetuer
+consequat conubia convallis cras cubilia cum curabitur curae cursus dapibus
+diam dictum dictumst dignissim dis dolor donec dui duis egestas eget eleifend
+elementum elit enim erat eros est et etiam eu euismod facilisi facilisis fames
+faucibus felis fermentum feugiat fringilla fusce gravida habitant habitasse hac
+hendrerit hymenaeos iaculis id imperdiet in inceptos integer interdum ipsum
+justo lacinia lacus laoreet lectus leo libero ligula litora lobortis lorem
+luctus maecenas magna magnis malesuada massa mattis mauris metus mi molestie
+mollis montes morbi mus nam nascetur natoque nec neque netus nibh nisi nisl non
+nonummy nostra nulla nullam nunc odio orci ornare parturient pede pellentesque
+penatibus per pharetra phasellus placerat platea porta porttitor posuere
+potenti praesent pretium primis proin pulvinar purus quam quis quisque rhoncus
+ridiculus risus rutrum sagittis sapien scelerisque sed sem semper senectus sit
+sociis sociosqu sodales sollicitudin suscipit suspendisse taciti tellus tempor
+tempus tincidunt torquent tortor tristique turpis ullamcorper ultrices
+ultricies urna ut varius vehicula vel velit venenatis vestibulum vitae vivamus
+viverra volutpat vulputate"""
diff --git a/lib/python3.11/site-packages/jinja2/debug.py b/lib/python3.11/site-packages/jinja2/debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..eeeeee78b620f5d0745133b4629647973cd7af87
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/debug.py
@@ -0,0 +1,191 @@
+import sys
+import typing as t
+from types import CodeType
+from types import TracebackType
+
+from .exceptions import TemplateSyntaxError
+from .utils import internal_code
+from .utils import missing
+
+if t.TYPE_CHECKING:
+    from .runtime import Context
+
+
+def rewrite_traceback_stack(source: t.Optional[str] = None) -> BaseException:
+    """Rewrite the current exception to replace any tracebacks from
+    within compiled template code with tracebacks that look like they
+    came from the template source.
+
+    This must be called within an ``except`` block.
+
+    :param source: For ``TemplateSyntaxError``, the original source if
+        known.
+    :return: The original exception with the rewritten traceback.
+    """
+    _, exc_value, tb = sys.exc_info()
+    exc_value = t.cast(BaseException, exc_value)
+    tb = t.cast(TracebackType, tb)
+
+    if isinstance(exc_value, TemplateSyntaxError) and not exc_value.translated:
+        exc_value.translated = True
+        exc_value.source = source
+        # Remove the old traceback, otherwise the frames from the
+        # compiler still show up.
+        exc_value.with_traceback(None)
+        # Outside of runtime, so the frame isn't executing template
+        # code, but it still needs to point at the template.
+        tb = fake_traceback(
+            exc_value, None, exc_value.filename or "<unknown>", exc_value.lineno
+        )
+    else:
+        # Skip the frame for the render function.
+        tb = tb.tb_next
+
+    stack = []
+
+    # Build the stack of traceback object, replacing any in template
+    # code with the source file and line information.
+    while tb is not None:
+        # Skip frames decorated with @internalcode. These are internal
+        # calls that aren't useful in template debugging output.
+        if tb.tb_frame.f_code in internal_code:
+            tb = tb.tb_next
+            continue
+
+        template = tb.tb_frame.f_globals.get("__jinja_template__")
+
+        if template is not None:
+            lineno = template.get_corresponding_lineno(tb.tb_lineno)
+            fake_tb = fake_traceback(exc_value, tb, template.filename, lineno)
+            stack.append(fake_tb)
+        else:
+            stack.append(tb)
+
+        tb = tb.tb_next
+
+    tb_next = None
+
+    # Assign tb_next in reverse to avoid circular references.
+    for tb in reversed(stack):
+        tb.tb_next = tb_next
+        tb_next = tb
+
+    return exc_value.with_traceback(tb_next)
+
+
+def fake_traceback(  # type: ignore
+    exc_value: BaseException, tb: t.Optional[TracebackType], filename: str, lineno: int
+) -> TracebackType:
+    """Produce a new traceback object that looks like it came from the
+    template source instead of the compiled code. The filename, line
+    number, and location name will point to the template, and the local
+    variables will be the current template context.
+
+    :param exc_value: The original exception to be re-raised to create
+        the new traceback.
+    :param tb: The original traceback to get the local variables and
+        code info from.
+    :param filename: The template filename.
+    :param lineno: The line number in the template source.
+    """
+    if tb is not None:
+        # Replace the real locals with the context that would be
+        # available at that point in the template.
+        locals = get_template_locals(tb.tb_frame.f_locals)
+        locals.pop("__jinja_exception__", None)
+    else:
+        locals = {}
+
+    globals = {
+        "__name__": filename,
+        "__file__": filename,
+        "__jinja_exception__": exc_value,
+    }
+    # Raise an exception at the correct line number.
+    code: CodeType = compile(
+        "\n" * (lineno - 1) + "raise __jinja_exception__", filename, "exec"
+    )
+
+    # Build a new code object that points to the template file and
+    # replaces the location with a block name.
+    location = "template"
+
+    if tb is not None:
+        function = tb.tb_frame.f_code.co_name
+
+        if function == "root":
+            location = "top-level template code"
+        elif function.startswith("block_"):
+            location = f"block {function[6:]!r}"
+
+    if sys.version_info >= (3, 8):
+        code = code.replace(co_name=location)
+    else:
+        code = CodeType(
+            code.co_argcount,
+            code.co_kwonlyargcount,
+            code.co_nlocals,
+            code.co_stacksize,
+            code.co_flags,
+            code.co_code,
+            code.co_consts,
+            code.co_names,
+            code.co_varnames,
+            code.co_filename,
+            location,
+            code.co_firstlineno,
+            code.co_lnotab,
+            code.co_freevars,
+            code.co_cellvars,
+        )
+
+    # Execute the new code, which is guaranteed to raise, and return
+    # the new traceback without this frame.
+    try:
+        exec(code, globals, locals)
+    except BaseException:
+        return sys.exc_info()[2].tb_next  # type: ignore
+
+
+def get_template_locals(real_locals: t.Mapping[str, t.Any]) -> t.Dict[str, t.Any]:
+    """Based on the runtime locals, get the context that would be
+    available at that point in the template.
+    """
+    # Start with the current template context.
+    ctx: t.Optional[Context] = real_locals.get("context")
+
+    if ctx is not None:
+        data: t.Dict[str, t.Any] = ctx.get_all().copy()
+    else:
+        data = {}
+
+    # Might be in a derived context that only sets local variables
+    # rather than pushing a context. Local variables follow the scheme
+    # l_depth_name. Find the highest-depth local that has a value for
+    # each name.
+    local_overrides: t.Dict[str, t.Tuple[int, t.Any]] = {}
+
+    for name, value in real_locals.items():
+        if not name.startswith("l_") or value is missing:
+            # Not a template variable, or no longer relevant.
+            continue
+
+        try:
+            _, depth_str, name = name.split("_", 2)
+            depth = int(depth_str)
+        except ValueError:
+            continue
+
+        cur_depth = local_overrides.get(name, (-1,))[0]
+
+        if cur_depth < depth:
+            local_overrides[name] = (depth, value)
+
+    # Modify the context with any derived context.
+    for name, (_, value) in local_overrides.items():
+        if value is missing:
+            data.pop(name, None)
+        else:
+            data[name] = value
+
+    return data
diff --git a/lib/python3.11/site-packages/jinja2/defaults.py b/lib/python3.11/site-packages/jinja2/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..638cad3d2d8907330bde56e2b76c9b185c523b45
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/defaults.py
@@ -0,0 +1,48 @@
+import typing as t
+
+from .filters import FILTERS as DEFAULT_FILTERS  # noqa: F401
+from .tests import TESTS as DEFAULT_TESTS  # noqa: F401
+from .utils import Cycler
+from .utils import generate_lorem_ipsum
+from .utils import Joiner
+from .utils import Namespace
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+# defaults for the parser / lexer
+BLOCK_START_STRING = "{%"
+BLOCK_END_STRING = "%}"
+VARIABLE_START_STRING = "{{"
+VARIABLE_END_STRING = "}}"
+COMMENT_START_STRING = "{#"
+COMMENT_END_STRING = "#}"
+LINE_STATEMENT_PREFIX: t.Optional[str] = None
+LINE_COMMENT_PREFIX: t.Optional[str] = None
+TRIM_BLOCKS = False
+LSTRIP_BLOCKS = False
+NEWLINE_SEQUENCE: "te.Literal['\\n', '\\r\\n', '\\r']" = "\n"
+KEEP_TRAILING_NEWLINE = False
+
+# default filters, tests and namespace
+
+DEFAULT_NAMESPACE = {
+    "range": range,
+    "dict": dict,
+    "lipsum": generate_lorem_ipsum,
+    "cycler": Cycler,
+    "joiner": Joiner,
+    "namespace": Namespace,
+}
+
+# default policies
+DEFAULT_POLICIES: t.Dict[str, t.Any] = {
+    "compiler.ascii_str": True,
+    "urlize.rel": "noopener",
+    "urlize.target": None,
+    "urlize.extra_schemes": None,
+    "truncate.leeway": 5,
+    "json.dumps_function": None,
+    "json.dumps_kwargs": {"sort_keys": True},
+    "ext.i18n.trimmed": False,
+}
diff --git a/lib/python3.11/site-packages/jinja2/environment.py b/lib/python3.11/site-packages/jinja2/environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fc6e5be87ab8273f6056ddfede07e1be28f1495
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/environment.py
@@ -0,0 +1,1672 @@
+"""Classes for managing templates and their runtime and compile time
+options.
+"""
+
+import os
+import typing
+import typing as t
+import weakref
+from collections import ChainMap
+from functools import lru_cache
+from functools import partial
+from functools import reduce
+from types import CodeType
+
+from markupsafe import Markup
+
+from . import nodes
+from .compiler import CodeGenerator
+from .compiler import generate
+from .defaults import BLOCK_END_STRING
+from .defaults import BLOCK_START_STRING
+from .defaults import COMMENT_END_STRING
+from .defaults import COMMENT_START_STRING
+from .defaults import DEFAULT_FILTERS  # type: ignore[attr-defined]
+from .defaults import DEFAULT_NAMESPACE
+from .defaults import DEFAULT_POLICIES
+from .defaults import DEFAULT_TESTS  # type: ignore[attr-defined]
+from .defaults import KEEP_TRAILING_NEWLINE
+from .defaults import LINE_COMMENT_PREFIX
+from .defaults import LINE_STATEMENT_PREFIX
+from .defaults import LSTRIP_BLOCKS
+from .defaults import NEWLINE_SEQUENCE
+from .defaults import TRIM_BLOCKS
+from .defaults import VARIABLE_END_STRING
+from .defaults import VARIABLE_START_STRING
+from .exceptions import TemplateNotFound
+from .exceptions import TemplateRuntimeError
+from .exceptions import TemplatesNotFound
+from .exceptions import TemplateSyntaxError
+from .exceptions import UndefinedError
+from .lexer import get_lexer
+from .lexer import Lexer
+from .lexer import TokenStream
+from .nodes import EvalContext
+from .parser import Parser
+from .runtime import Context
+from .runtime import new_context
+from .runtime import Undefined
+from .utils import _PassArg
+from .utils import concat
+from .utils import consume
+from .utils import import_string
+from .utils import internalcode
+from .utils import LRUCache
+from .utils import missing
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .bccache import BytecodeCache
+    from .ext import Extension
+    from .loaders import BaseLoader
+
+_env_bound = t.TypeVar("_env_bound", bound="Environment")
+
+
+# for direct template usage we have up to ten living environments
+@lru_cache(maxsize=10)
+def get_spontaneous_environment(cls: t.Type[_env_bound], *args: t.Any) -> _env_bound:
+    """Return a new spontaneous environment. A spontaneous environment
+    is used for templates created directly rather than through an
+    existing environment.
+
+    :param cls: Environment class to create.
+    :param args: Positional arguments passed to environment.
+    """
+    env = cls(*args)
+    env.shared = True
+    return env
+
+
+def create_cache(
+    size: int,
+) -> t.Optional[t.MutableMapping[t.Tuple["weakref.ref[t.Any]", str], "Template"]]:
+    """Return the cache class for the given size."""
+    if size == 0:
+        return None
+
+    if size < 0:
+        return {}
+
+    return LRUCache(size)  # type: ignore
+
+
+def copy_cache(
+    cache: t.Optional[t.MutableMapping[t.Any, t.Any]],
+) -> t.Optional[t.MutableMapping[t.Tuple["weakref.ref[t.Any]", str], "Template"]]:
+    """Create an empty copy of the given cache."""
+    if cache is None:
+        return None
+
+    if type(cache) is dict:  # noqa E721
+        return {}
+
+    return LRUCache(cache.capacity)  # type: ignore
+
+
+def load_extensions(
+    environment: "Environment",
+    extensions: t.Sequence[t.Union[str, t.Type["Extension"]]],
+) -> t.Dict[str, "Extension"]:
+    """Load the extensions from the list and bind it to the environment.
+    Returns a dict of instantiated extensions.
+    """
+    result = {}
+
+    for extension in extensions:
+        if isinstance(extension, str):
+            extension = t.cast(t.Type["Extension"], import_string(extension))
+
+        result[extension.identifier] = extension(environment)
+
+    return result
+
+
+def _environment_config_check(environment: _env_bound) -> _env_bound:
+    """Perform a sanity check on the environment."""
+    assert issubclass(
+        environment.undefined, Undefined
+    ), "'undefined' must be a subclass of 'jinja2.Undefined'."
+    assert (
+        environment.block_start_string
+        != environment.variable_start_string
+        != environment.comment_start_string
+    ), "block, variable and comment start strings must be different."
+    assert environment.newline_sequence in {
+        "\r",
+        "\r\n",
+        "\n",
+    }, "'newline_sequence' must be one of '\\n', '\\r\\n', or '\\r'."
+    return environment
+
+
+class Environment:
+    r"""The core component of Jinja is the `Environment`.  It contains
+    important shared variables like configuration, filters, tests,
+    globals and others.  Instances of this class may be modified if
+    they are not shared and if no template was loaded so far.
+    Modifications on environments after the first template was loaded
+    will lead to surprising effects and undefined behavior.
+
+    Here are the possible initialization parameters:
+
+        `block_start_string`
+            The string marking the beginning of a block.  Defaults to ``'{%'``.
+
+        `block_end_string`
+            The string marking the end of a block.  Defaults to ``'%}'``.
+
+        `variable_start_string`
+            The string marking the beginning of a print statement.
+            Defaults to ``'{{'``.
+
+        `variable_end_string`
+            The string marking the end of a print statement.  Defaults to
+            ``'}}'``.
+
+        `comment_start_string`
+            The string marking the beginning of a comment.  Defaults to ``'{#'``.
+
+        `comment_end_string`
+            The string marking the end of a comment.  Defaults to ``'#}'``.
+
+        `line_statement_prefix`
+            If given and a string, this will be used as prefix for line based
+            statements.  See also :ref:`line-statements`.
+
+        `line_comment_prefix`
+            If given and a string, this will be used as prefix for line based
+            comments.  See also :ref:`line-statements`.
+
+            .. versionadded:: 2.2
+
+        `trim_blocks`
+            If this is set to ``True`` the first newline after a block is
+            removed (block, not variable tag!).  Defaults to `False`.
+
+        `lstrip_blocks`
+            If this is set to ``True`` leading spaces and tabs are stripped
+            from the start of a line to a block.  Defaults to `False`.
+
+        `newline_sequence`
+            The sequence that starts a newline.  Must be one of ``'\r'``,
+            ``'\n'`` or ``'\r\n'``.  The default is ``'\n'`` which is a
+            useful default for Linux and OS X systems as well as web
+            applications.
+
+        `keep_trailing_newline`
+            Preserve the trailing newline when rendering templates.
+            The default is ``False``, which causes a single newline,
+            if present, to be stripped from the end of the template.
+
+            .. versionadded:: 2.7
+
+        `extensions`
+            List of Jinja extensions to use.  This can either be import paths
+            as strings or extension classes.  For more information have a
+            look at :ref:`the extensions documentation <jinja-extensions>`.
+
+        `optimized`
+            should the optimizer be enabled?  Default is ``True``.
+
+        `undefined`
+            :class:`Undefined` or a subclass of it that is used to represent
+            undefined values in the template.
+
+        `finalize`
+            A callable that can be used to process the result of a variable
+            expression before it is output.  For example one can convert
+            ``None`` implicitly into an empty string here.
+
+        `autoescape`
+            If set to ``True`` the XML/HTML autoescaping feature is enabled by
+            default.  For more details about autoescaping see
+            :class:`~markupsafe.Markup`.  As of Jinja 2.4 this can also
+            be a callable that is passed the template name and has to
+            return ``True`` or ``False`` depending on autoescape should be
+            enabled by default.
+
+            .. versionchanged:: 2.4
+               `autoescape` can now be a function
+
+        `loader`
+            The template loader for this environment.
+
+        `cache_size`
+            The size of the cache.  Per default this is ``400`` which means
+            that if more than 400 templates are loaded the loader will clean
+            out the least recently used template.  If the cache size is set to
+            ``0`` templates are recompiled all the time, if the cache size is
+            ``-1`` the cache will not be cleaned.
+
+            .. versionchanged:: 2.8
+               The cache size was increased to 400 from a low 50.
+
+        `auto_reload`
+            Some loaders load templates from locations where the template
+            sources may change (ie: file system or database).  If
+            ``auto_reload`` is set to ``True`` (default) every time a template is
+            requested the loader checks if the source changed and if yes, it
+            will reload the template.  For higher performance it's possible to
+            disable that.
+
+        `bytecode_cache`
+            If set to a bytecode cache object, this object will provide a
+            cache for the internal Jinja bytecode so that templates don't
+            have to be parsed if they were not changed.
+
+            See :ref:`bytecode-cache` for more information.
+
+        `enable_async`
+            If set to true this enables async template execution which
+            allows using async functions and generators.
+    """
+
+    #: if this environment is sandboxed.  Modifying this variable won't make
+    #: the environment sandboxed though.  For a real sandboxed environment
+    #: have a look at jinja2.sandbox.  This flag alone controls the code
+    #: generation by the compiler.
+    sandboxed = False
+
+    #: True if the environment is just an overlay
+    overlayed = False
+
+    #: the environment this environment is linked to if it is an overlay
+    linked_to: t.Optional["Environment"] = None
+
+    #: shared environments have this set to `True`.  A shared environment
+    #: must not be modified
+    shared = False
+
+    #: the class that is used for code generation.  See
+    #: :class:`~jinja2.compiler.CodeGenerator` for more information.
+    code_generator_class: t.Type["CodeGenerator"] = CodeGenerator
+
+    concat = "".join
+
+    #: the context class that is used for templates.  See
+    #: :class:`~jinja2.runtime.Context` for more information.
+    context_class: t.Type[Context] = Context
+
+    template_class: t.Type["Template"]
+
+    def __init__(
+        self,
+        block_start_string: str = BLOCK_START_STRING,
+        block_end_string: str = BLOCK_END_STRING,
+        variable_start_string: str = VARIABLE_START_STRING,
+        variable_end_string: str = VARIABLE_END_STRING,
+        comment_start_string: str = COMMENT_START_STRING,
+        comment_end_string: str = COMMENT_END_STRING,
+        line_statement_prefix: t.Optional[str] = LINE_STATEMENT_PREFIX,
+        line_comment_prefix: t.Optional[str] = LINE_COMMENT_PREFIX,
+        trim_blocks: bool = TRIM_BLOCKS,
+        lstrip_blocks: bool = LSTRIP_BLOCKS,
+        newline_sequence: "te.Literal['\\n', '\\r\\n', '\\r']" = NEWLINE_SEQUENCE,
+        keep_trailing_newline: bool = KEEP_TRAILING_NEWLINE,
+        extensions: t.Sequence[t.Union[str, t.Type["Extension"]]] = (),
+        optimized: bool = True,
+        undefined: t.Type[Undefined] = Undefined,
+        finalize: t.Optional[t.Callable[..., t.Any]] = None,
+        autoescape: t.Union[bool, t.Callable[[t.Optional[str]], bool]] = False,
+        loader: t.Optional["BaseLoader"] = None,
+        cache_size: int = 400,
+        auto_reload: bool = True,
+        bytecode_cache: t.Optional["BytecodeCache"] = None,
+        enable_async: bool = False,
+    ):
+        # !!Important notice!!
+        #   The constructor accepts quite a few arguments that should be
+        #   passed by keyword rather than position.  However it's important to
+        #   not change the order of arguments because it's used at least
+        #   internally in those cases:
+        #       -   spontaneous environments (i18n extension and Template)
+        #       -   unittests
+        #   If parameter changes are required only add parameters at the end
+        #   and don't change the arguments (or the defaults!) of the arguments
+        #   existing already.
+
+        # lexer / parser information
+        self.block_start_string = block_start_string
+        self.block_end_string = block_end_string
+        self.variable_start_string = variable_start_string
+        self.variable_end_string = variable_end_string
+        self.comment_start_string = comment_start_string
+        self.comment_end_string = comment_end_string
+        self.line_statement_prefix = line_statement_prefix
+        self.line_comment_prefix = line_comment_prefix
+        self.trim_blocks = trim_blocks
+        self.lstrip_blocks = lstrip_blocks
+        self.newline_sequence = newline_sequence
+        self.keep_trailing_newline = keep_trailing_newline
+
+        # runtime information
+        self.undefined: t.Type[Undefined] = undefined
+        self.optimized = optimized
+        self.finalize = finalize
+        self.autoescape = autoescape
+
+        # defaults
+        self.filters = DEFAULT_FILTERS.copy()
+        self.tests = DEFAULT_TESTS.copy()
+        self.globals = DEFAULT_NAMESPACE.copy()
+
+        # set the loader provided
+        self.loader = loader
+        self.cache = create_cache(cache_size)
+        self.bytecode_cache = bytecode_cache
+        self.auto_reload = auto_reload
+
+        # configurable policies
+        self.policies = DEFAULT_POLICIES.copy()
+
+        # load extensions
+        self.extensions = load_extensions(self, extensions)
+
+        self.is_async = enable_async
+        _environment_config_check(self)
+
+    def add_extension(self, extension: t.Union[str, t.Type["Extension"]]) -> None:
+        """Adds an extension after the environment was created.
+
+        .. versionadded:: 2.5
+        """
+        self.extensions.update(load_extensions(self, [extension]))
+
+    def extend(self, **attributes: t.Any) -> None:
+        """Add the items to the instance of the environment if they do not exist
+        yet.  This is used by :ref:`extensions <writing-extensions>` to register
+        callbacks and configuration values without breaking inheritance.
+        """
+        for key, value in attributes.items():
+            if not hasattr(self, key):
+                setattr(self, key, value)
+
+    def overlay(
+        self,
+        block_start_string: str = missing,
+        block_end_string: str = missing,
+        variable_start_string: str = missing,
+        variable_end_string: str = missing,
+        comment_start_string: str = missing,
+        comment_end_string: str = missing,
+        line_statement_prefix: t.Optional[str] = missing,
+        line_comment_prefix: t.Optional[str] = missing,
+        trim_blocks: bool = missing,
+        lstrip_blocks: bool = missing,
+        newline_sequence: "te.Literal['\\n', '\\r\\n', '\\r']" = missing,
+        keep_trailing_newline: bool = missing,
+        extensions: t.Sequence[t.Union[str, t.Type["Extension"]]] = missing,
+        optimized: bool = missing,
+        undefined: t.Type[Undefined] = missing,
+        finalize: t.Optional[t.Callable[..., t.Any]] = missing,
+        autoescape: t.Union[bool, t.Callable[[t.Optional[str]], bool]] = missing,
+        loader: t.Optional["BaseLoader"] = missing,
+        cache_size: int = missing,
+        auto_reload: bool = missing,
+        bytecode_cache: t.Optional["BytecodeCache"] = missing,
+        enable_async: bool = missing,
+    ) -> "te.Self":
+        """Create a new overlay environment that shares all the data with the
+        current environment except for cache and the overridden attributes.
+        Extensions cannot be removed for an overlayed environment.  An overlayed
+        environment automatically gets all the extensions of the environment it
+        is linked to plus optional extra extensions.
+
+        Creating overlays should happen after the initial environment was set
+        up completely.  Not all attributes are truly linked, some are just
+        copied over so modifications on the original environment may not shine
+        through.
+
+        .. versionchanged:: 3.1.5
+            ``enable_async`` is applied correctly.
+
+        .. versionchanged:: 3.1.2
+            Added the ``newline_sequence``, ``keep_trailing_newline``,
+            and ``enable_async`` parameters to match ``__init__``.
+        """
+        args = dict(locals())
+        del args["self"], args["cache_size"], args["extensions"], args["enable_async"]
+
+        rv = object.__new__(self.__class__)
+        rv.__dict__.update(self.__dict__)
+        rv.overlayed = True
+        rv.linked_to = self
+
+        for key, value in args.items():
+            if value is not missing:
+                setattr(rv, key, value)
+
+        if cache_size is not missing:
+            rv.cache = create_cache(cache_size)
+        else:
+            rv.cache = copy_cache(self.cache)
+
+        rv.extensions = {}
+        for key, value in self.extensions.items():
+            rv.extensions[key] = value.bind(rv)
+        if extensions is not missing:
+            rv.extensions.update(load_extensions(rv, extensions))
+
+        if enable_async is not missing:
+            rv.is_async = enable_async
+
+        return _environment_config_check(rv)
+
+    @property
+    def lexer(self) -> Lexer:
+        """The lexer for this environment."""
+        return get_lexer(self)
+
+    def iter_extensions(self) -> t.Iterator["Extension"]:
+        """Iterates over the extensions by priority."""
+        return iter(sorted(self.extensions.values(), key=lambda x: x.priority))
+
+    def getitem(
+        self, obj: t.Any, argument: t.Union[str, t.Any]
+    ) -> t.Union[t.Any, Undefined]:
+        """Get an item or attribute of an object but prefer the item."""
+        try:
+            return obj[argument]
+        except (AttributeError, TypeError, LookupError):
+            if isinstance(argument, str):
+                try:
+                    attr = str(argument)
+                except Exception:
+                    pass
+                else:
+                    try:
+                        return getattr(obj, attr)
+                    except AttributeError:
+                        pass
+            return self.undefined(obj=obj, name=argument)
+
+    def getattr(self, obj: t.Any, attribute: str) -> t.Any:
+        """Get an item or attribute of an object but prefer the attribute.
+        Unlike :meth:`getitem` the attribute *must* be a string.
+        """
+        try:
+            return getattr(obj, attribute)
+        except AttributeError:
+            pass
+        try:
+            return obj[attribute]
+        except (TypeError, LookupError, AttributeError):
+            return self.undefined(obj=obj, name=attribute)
+
+    def _filter_test_common(
+        self,
+        name: t.Union[str, Undefined],
+        value: t.Any,
+        args: t.Optional[t.Sequence[t.Any]],
+        kwargs: t.Optional[t.Mapping[str, t.Any]],
+        context: t.Optional[Context],
+        eval_ctx: t.Optional[EvalContext],
+        is_filter: bool,
+    ) -> t.Any:
+        if is_filter:
+            env_map = self.filters
+            type_name = "filter"
+        else:
+            env_map = self.tests
+            type_name = "test"
+
+        func = env_map.get(name)  # type: ignore
+
+        if func is None:
+            msg = f"No {type_name} named {name!r}."
+
+            if isinstance(name, Undefined):
+                try:
+                    name._fail_with_undefined_error()
+                except Exception as e:
+                    msg = f"{msg} ({e}; did you forget to quote the callable name?)"
+
+            raise TemplateRuntimeError(msg)
+
+        args = [value, *(args if args is not None else ())]
+        kwargs = kwargs if kwargs is not None else {}
+        pass_arg = _PassArg.from_obj(func)
+
+        if pass_arg is _PassArg.context:
+            if context is None:
+                raise TemplateRuntimeError(
+                    f"Attempted to invoke a context {type_name} without context."
+                )
+
+            args.insert(0, context)
+        elif pass_arg is _PassArg.eval_context:
+            if eval_ctx is None:
+                if context is not None:
+                    eval_ctx = context.eval_ctx
+                else:
+                    eval_ctx = EvalContext(self)
+
+            args.insert(0, eval_ctx)
+        elif pass_arg is _PassArg.environment:
+            args.insert(0, self)
+
+        return func(*args, **kwargs)
+
+    def call_filter(
+        self,
+        name: str,
+        value: t.Any,
+        args: t.Optional[t.Sequence[t.Any]] = None,
+        kwargs: t.Optional[t.Mapping[str, t.Any]] = None,
+        context: t.Optional[Context] = None,
+        eval_ctx: t.Optional[EvalContext] = None,
+    ) -> t.Any:
+        """Invoke a filter on a value the same way the compiler does.
+
+        This might return a coroutine if the filter is running from an
+        environment in async mode and the filter supports async
+        execution. It's your responsibility to await this if needed.
+
+        .. versionadded:: 2.7
+        """
+        return self._filter_test_common(
+            name, value, args, kwargs, context, eval_ctx, True
+        )
+
+    def call_test(
+        self,
+        name: str,
+        value: t.Any,
+        args: t.Optional[t.Sequence[t.Any]] = None,
+        kwargs: t.Optional[t.Mapping[str, t.Any]] = None,
+        context: t.Optional[Context] = None,
+        eval_ctx: t.Optional[EvalContext] = None,
+    ) -> t.Any:
+        """Invoke a test on a value the same way the compiler does.
+
+        This might return a coroutine if the test is running from an
+        environment in async mode and the test supports async execution.
+        It's your responsibility to await this if needed.
+
+        .. versionchanged:: 3.0
+            Tests support ``@pass_context``, etc. decorators. Added
+            the ``context`` and ``eval_ctx`` parameters.
+
+        .. versionadded:: 2.7
+        """
+        return self._filter_test_common(
+            name, value, args, kwargs, context, eval_ctx, False
+        )
+
+    @internalcode
+    def parse(
+        self,
+        source: str,
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+    ) -> nodes.Template:
+        """Parse the sourcecode and return the abstract syntax tree.  This
+        tree of nodes is used by the compiler to convert the template into
+        executable source- or bytecode.  This is useful for debugging or to
+        extract information from templates.
+
+        If you are :ref:`developing Jinja extensions <writing-extensions>`
+        this gives you a good overview of the node tree generated.
+        """
+        try:
+            return self._parse(source, name, filename)
+        except TemplateSyntaxError:
+            self.handle_exception(source=source)
+
+    def _parse(
+        self, source: str, name: t.Optional[str], filename: t.Optional[str]
+    ) -> nodes.Template:
+        """Internal parsing function used by `parse` and `compile`."""
+        return Parser(self, source, name, filename).parse()
+
+    def lex(
+        self,
+        source: str,
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+    ) -> t.Iterator[t.Tuple[int, str, str]]:
+        """Lex the given sourcecode and return a generator that yields
+        tokens as tuples in the form ``(lineno, token_type, value)``.
+        This can be useful for :ref:`extension development <writing-extensions>`
+        and debugging templates.
+
+        This does not perform preprocessing.  If you want the preprocessing
+        of the extensions to be applied you have to filter source through
+        the :meth:`preprocess` method.
+        """
+        source = str(source)
+        try:
+            return self.lexer.tokeniter(source, name, filename)
+        except TemplateSyntaxError:
+            self.handle_exception(source=source)
+
+    def preprocess(
+        self,
+        source: str,
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+    ) -> str:
+        """Preprocesses the source with all extensions.  This is automatically
+        called for all parsing and compiling methods but *not* for :meth:`lex`
+        because there you usually only want the actual source tokenized.
+        """
+        return reduce(
+            lambda s, e: e.preprocess(s, name, filename),
+            self.iter_extensions(),
+            str(source),
+        )
+
+    def _tokenize(
+        self,
+        source: str,
+        name: t.Optional[str],
+        filename: t.Optional[str] = None,
+        state: t.Optional[str] = None,
+    ) -> TokenStream:
+        """Called by the parser to do the preprocessing and filtering
+        for all the extensions.  Returns a :class:`~jinja2.lexer.TokenStream`.
+        """
+        source = self.preprocess(source, name, filename)
+        stream = self.lexer.tokenize(source, name, filename, state)
+
+        for ext in self.iter_extensions():
+            stream = ext.filter_stream(stream)  # type: ignore
+
+            if not isinstance(stream, TokenStream):
+                stream = TokenStream(stream, name, filename)
+
+        return stream
+
+    def _generate(
+        self,
+        source: nodes.Template,
+        name: t.Optional[str],
+        filename: t.Optional[str],
+        defer_init: bool = False,
+    ) -> str:
+        """Internal hook that can be overridden to hook a different generate
+        method in.
+
+        .. versionadded:: 2.5
+        """
+        return generate(  # type: ignore
+            source,
+            self,
+            name,
+            filename,
+            defer_init=defer_init,
+            optimized=self.optimized,
+        )
+
+    def _compile(self, source: str, filename: str) -> CodeType:
+        """Internal hook that can be overridden to hook a different compile
+        method in.
+
+        .. versionadded:: 2.5
+        """
+        return compile(source, filename, "exec")
+
+    @typing.overload
+    def compile(
+        self,
+        source: t.Union[str, nodes.Template],
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+        raw: "te.Literal[False]" = False,
+        defer_init: bool = False,
+    ) -> CodeType: ...
+
+    @typing.overload
+    def compile(
+        self,
+        source: t.Union[str, nodes.Template],
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+        raw: "te.Literal[True]" = ...,
+        defer_init: bool = False,
+    ) -> str: ...
+
+    @internalcode
+    def compile(
+        self,
+        source: t.Union[str, nodes.Template],
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+        raw: bool = False,
+        defer_init: bool = False,
+    ) -> t.Union[str, CodeType]:
+        """Compile a node or template source code.  The `name` parameter is
+        the load name of the template after it was joined using
+        :meth:`join_path` if necessary, not the filename on the file system.
+        the `filename` parameter is the estimated filename of the template on
+        the file system.  If the template came from a database or memory this
+        can be omitted.
+
+        The return value of this method is a python code object.  If the `raw`
+        parameter is `True` the return value will be a string with python
+        code equivalent to the bytecode returned otherwise.  This method is
+        mainly used internally.
+
+        `defer_init` is use internally to aid the module code generator.  This
+        causes the generated code to be able to import without the global
+        environment variable to be set.
+
+        .. versionadded:: 2.4
+           `defer_init` parameter added.
+        """
+        source_hint = None
+        try:
+            if isinstance(source, str):
+                source_hint = source
+                source = self._parse(source, name, filename)
+            source = self._generate(source, name, filename, defer_init=defer_init)
+            if raw:
+                return source
+            if filename is None:
+                filename = "<template>"
+            return self._compile(source, filename)
+        except TemplateSyntaxError:
+            self.handle_exception(source=source_hint)
+
+    def compile_expression(
+        self, source: str, undefined_to_none: bool = True
+    ) -> "TemplateExpression":
+        """A handy helper method that returns a callable that accepts keyword
+        arguments that appear as variables in the expression.  If called it
+        returns the result of the expression.
+
+        This is useful if applications want to use the same rules as Jinja
+        in template "configuration files" or similar situations.
+
+        Example usage:
+
+        >>> env = Environment()
+        >>> expr = env.compile_expression('foo == 42')
+        >>> expr(foo=23)
+        False
+        >>> expr(foo=42)
+        True
+
+        Per default the return value is converted to `None` if the
+        expression returns an undefined value.  This can be changed
+        by setting `undefined_to_none` to `False`.
+
+        >>> env.compile_expression('var')() is None
+        True
+        >>> env.compile_expression('var', undefined_to_none=False)()
+        Undefined
+
+        .. versionadded:: 2.1
+        """
+        parser = Parser(self, source, state="variable")
+        try:
+            expr = parser.parse_expression()
+            if not parser.stream.eos:
+                raise TemplateSyntaxError(
+                    "chunk after expression", parser.stream.current.lineno, None, None
+                )
+            expr.set_environment(self)
+        except TemplateSyntaxError:
+            self.handle_exception(source=source)
+
+        body = [nodes.Assign(nodes.Name("result", "store"), expr, lineno=1)]
+        template = self.from_string(nodes.Template(body, lineno=1))
+        return TemplateExpression(template, undefined_to_none)
+
+    def compile_templates(
+        self,
+        target: t.Union[str, "os.PathLike[str]"],
+        extensions: t.Optional[t.Collection[str]] = None,
+        filter_func: t.Optional[t.Callable[[str], bool]] = None,
+        zip: t.Optional[str] = "deflated",
+        log_function: t.Optional[t.Callable[[str], None]] = None,
+        ignore_errors: bool = True,
+    ) -> None:
+        """Finds all the templates the loader can find, compiles them
+        and stores them in `target`.  If `zip` is `None`, instead of in a
+        zipfile, the templates will be stored in a directory.
+        By default a deflate zip algorithm is used. To switch to
+        the stored algorithm, `zip` can be set to ``'stored'``.
+
+        `extensions` and `filter_func` are passed to :meth:`list_templates`.
+        Each template returned will be compiled to the target folder or
+        zipfile.
+
+        By default template compilation errors are ignored.  In case a
+        log function is provided, errors are logged.  If you want template
+        syntax errors to abort the compilation you can set `ignore_errors`
+        to `False` and you will get an exception on syntax errors.
+
+        .. versionadded:: 2.4
+        """
+        from .loaders import ModuleLoader
+
+        if log_function is None:
+
+            def log_function(x: str) -> None:
+                pass
+
+        assert log_function is not None
+        assert self.loader is not None, "No loader configured."
+
+        def write_file(filename: str, data: str) -> None:
+            if zip:
+                info = ZipInfo(filename)
+                info.external_attr = 0o755 << 16
+                zip_file.writestr(info, data)
+            else:
+                with open(os.path.join(target, filename), "wb") as f:
+                    f.write(data.encode("utf8"))
+
+        if zip is not None:
+            from zipfile import ZIP_DEFLATED
+            from zipfile import ZIP_STORED
+            from zipfile import ZipFile
+            from zipfile import ZipInfo
+
+            zip_file = ZipFile(
+                target, "w", dict(deflated=ZIP_DEFLATED, stored=ZIP_STORED)[zip]
+            )
+            log_function(f"Compiling into Zip archive {target!r}")
+        else:
+            if not os.path.isdir(target):
+                os.makedirs(target)
+            log_function(f"Compiling into folder {target!r}")
+
+        try:
+            for name in self.list_templates(extensions, filter_func):
+                source, filename, _ = self.loader.get_source(self, name)
+                try:
+                    code = self.compile(source, name, filename, True, True)
+                except TemplateSyntaxError as e:
+                    if not ignore_errors:
+                        raise
+                    log_function(f'Could not compile "{name}": {e}')
+                    continue
+
+                filename = ModuleLoader.get_module_filename(name)
+
+                write_file(filename, code)
+                log_function(f'Compiled "{name}" as {filename}')
+        finally:
+            if zip:
+                zip_file.close()
+
+        log_function("Finished compiling templates")
+
+    def list_templates(
+        self,
+        extensions: t.Optional[t.Collection[str]] = None,
+        filter_func: t.Optional[t.Callable[[str], bool]] = None,
+    ) -> t.List[str]:
+        """Returns a list of templates for this environment.  This requires
+        that the loader supports the loader's
+        :meth:`~BaseLoader.list_templates` method.
+
+        If there are other files in the template folder besides the
+        actual templates, the returned list can be filtered.  There are two
+        ways: either `extensions` is set to a list of file extensions for
+        templates, or a `filter_func` can be provided which is a callable that
+        is passed a template name and should return `True` if it should end up
+        in the result list.
+
+        If the loader does not support that, a :exc:`TypeError` is raised.
+
+        .. versionadded:: 2.4
+        """
+        assert self.loader is not None, "No loader configured."
+        names = self.loader.list_templates()
+
+        if extensions is not None:
+            if filter_func is not None:
+                raise TypeError(
+                    "either extensions or filter_func can be passed, but not both"
+                )
+
+            def filter_func(x: str) -> bool:
+                return "." in x and x.rsplit(".", 1)[1] in extensions
+
+        if filter_func is not None:
+            names = [name for name in names if filter_func(name)]
+
+        return names
+
+    def handle_exception(self, source: t.Optional[str] = None) -> "te.NoReturn":
+        """Exception handling helper.  This is used internally to either raise
+        rewritten exceptions or return a rendered traceback for the template.
+        """
+        from .debug import rewrite_traceback_stack
+
+        raise rewrite_traceback_stack(source=source)
+
+    def join_path(self, template: str, parent: str) -> str:
+        """Join a template with the parent.  By default all the lookups are
+        relative to the loader root so this method returns the `template`
+        parameter unchanged, but if the paths should be relative to the
+        parent template, this function can be used to calculate the real
+        template name.
+
+        Subclasses may override this method and implement template path
+        joining here.
+        """
+        return template
+
+    @internalcode
+    def _load_template(
+        self, name: str, globals: t.Optional[t.MutableMapping[str, t.Any]]
+    ) -> "Template":
+        if self.loader is None:
+            raise TypeError("no loader for this environment specified")
+        cache_key = (weakref.ref(self.loader), name)
+        if self.cache is not None:
+            template = self.cache.get(cache_key)
+            if template is not None and (
+                not self.auto_reload or template.is_up_to_date
+            ):
+                # template.globals is a ChainMap, modifying it will only
+                # affect the template, not the environment globals.
+                if globals:
+                    template.globals.update(globals)
+
+                return template
+
+        template = self.loader.load(self, name, self.make_globals(globals))
+
+        if self.cache is not None:
+            self.cache[cache_key] = template
+        return template
+
+    @internalcode
+    def get_template(
+        self,
+        name: t.Union[str, "Template"],
+        parent: t.Optional[str] = None,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        """Load a template by name with :attr:`loader` and return a
+        :class:`Template`. If the template does not exist a
+        :exc:`TemplateNotFound` exception is raised.
+
+        :param name: Name of the template to load. When loading
+            templates from the filesystem, "/" is used as the path
+            separator, even on Windows.
+        :param parent: The name of the parent template importing this
+            template. :meth:`join_path` can be used to implement name
+            transformations with this.
+        :param globals: Extend the environment :attr:`globals` with
+            these extra variables available for all renders of this
+            template. If the template has already been loaded and
+            cached, its globals are updated with any new items.
+
+        .. versionchanged:: 3.0
+            If a template is loaded from cache, ``globals`` will update
+            the template's globals instead of ignoring the new values.
+
+        .. versionchanged:: 2.4
+            If ``name`` is a :class:`Template` object it is returned
+            unchanged.
+        """
+        if isinstance(name, Template):
+            return name
+        if parent is not None:
+            name = self.join_path(name, parent)
+
+        return self._load_template(name, globals)
+
+    @internalcode
+    def select_template(
+        self,
+        names: t.Iterable[t.Union[str, "Template"]],
+        parent: t.Optional[str] = None,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        """Like :meth:`get_template`, but tries loading multiple names.
+        If none of the names can be loaded a :exc:`TemplatesNotFound`
+        exception is raised.
+
+        :param names: List of template names to try loading in order.
+        :param parent: The name of the parent template importing this
+            template. :meth:`join_path` can be used to implement name
+            transformations with this.
+        :param globals: Extend the environment :attr:`globals` with
+            these extra variables available for all renders of this
+            template. If the template has already been loaded and
+            cached, its globals are updated with any new items.
+
+        .. versionchanged:: 3.0
+            If a template is loaded from cache, ``globals`` will update
+            the template's globals instead of ignoring the new values.
+
+        .. versionchanged:: 2.11
+            If ``names`` is :class:`Undefined`, an :exc:`UndefinedError`
+            is raised instead. If no templates were found and ``names``
+            contains :class:`Undefined`, the message is more helpful.
+
+        .. versionchanged:: 2.4
+            If ``names`` contains a :class:`Template` object it is
+            returned unchanged.
+
+        .. versionadded:: 2.3
+        """
+        if isinstance(names, Undefined):
+            names._fail_with_undefined_error()
+
+        if not names:
+            raise TemplatesNotFound(
+                message="Tried to select from an empty list of templates."
+            )
+
+        for name in names:
+            if isinstance(name, Template):
+                return name
+            if parent is not None:
+                name = self.join_path(name, parent)
+            try:
+                return self._load_template(name, globals)
+            except (TemplateNotFound, UndefinedError):
+                pass
+        raise TemplatesNotFound(names)  # type: ignore
+
+    @internalcode
+    def get_or_select_template(
+        self,
+        template_name_or_list: t.Union[
+            str, "Template", t.List[t.Union[str, "Template"]]
+        ],
+        parent: t.Optional[str] = None,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        """Use :meth:`select_template` if an iterable of template names
+        is given, or :meth:`get_template` if one name is given.
+
+        .. versionadded:: 2.3
+        """
+        if isinstance(template_name_or_list, (str, Undefined)):
+            return self.get_template(template_name_or_list, parent, globals)
+        elif isinstance(template_name_or_list, Template):
+            return template_name_or_list
+        return self.select_template(template_name_or_list, parent, globals)
+
+    def from_string(
+        self,
+        source: t.Union[str, nodes.Template],
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+        template_class: t.Optional[t.Type["Template"]] = None,
+    ) -> "Template":
+        """Load a template from a source string without using
+        :attr:`loader`.
+
+        :param source: Jinja source to compile into a template.
+        :param globals: Extend the environment :attr:`globals` with
+            these extra variables available for all renders of this
+            template. If the template has already been loaded and
+            cached, its globals are updated with any new items.
+        :param template_class: Return an instance of this
+            :class:`Template` class.
+        """
+        gs = self.make_globals(globals)
+        cls = template_class or self.template_class
+        return cls.from_code(self, self.compile(source), gs, None)
+
+    def make_globals(
+        self, d: t.Optional[t.MutableMapping[str, t.Any]]
+    ) -> t.MutableMapping[str, t.Any]:
+        """Make the globals map for a template. Any given template
+        globals overlay the environment :attr:`globals`.
+
+        Returns a :class:`collections.ChainMap`. This allows any changes
+        to a template's globals to only affect that template, while
+        changes to the environment's globals are still reflected.
+        However, avoid modifying any globals after a template is loaded.
+
+        :param d: Dict of template-specific globals.
+
+        .. versionchanged:: 3.0
+            Use :class:`collections.ChainMap` to always prevent mutating
+            environment globals.
+        """
+        if d is None:
+            d = {}
+
+        return ChainMap(d, self.globals)
+
+
+class Template:
+    """A compiled template that can be rendered.
+
+    Use the methods on :class:`Environment` to create or load templates.
+    The environment is used to configure how templates are compiled and
+    behave.
+
+    It is also possible to create a template object directly. This is
+    not usually recommended. The constructor takes most of the same
+    arguments as :class:`Environment`. All templates created with the
+    same environment arguments share the same ephemeral ``Environment``
+    instance behind the scenes.
+
+    A template object should be considered immutable. Modifications on
+    the object are not supported.
+    """
+
+    #: Type of environment to create when creating a template directly
+    #: rather than through an existing environment.
+    environment_class: t.Type[Environment] = Environment
+
+    environment: Environment
+    globals: t.MutableMapping[str, t.Any]
+    name: t.Optional[str]
+    filename: t.Optional[str]
+    blocks: t.Dict[str, t.Callable[[Context], t.Iterator[str]]]
+    root_render_func: t.Callable[[Context], t.Iterator[str]]
+    _module: t.Optional["TemplateModule"]
+    _debug_info: str
+    _uptodate: t.Optional[t.Callable[[], bool]]
+
+    def __new__(
+        cls,
+        source: t.Union[str, nodes.Template],
+        block_start_string: str = BLOCK_START_STRING,
+        block_end_string: str = BLOCK_END_STRING,
+        variable_start_string: str = VARIABLE_START_STRING,
+        variable_end_string: str = VARIABLE_END_STRING,
+        comment_start_string: str = COMMENT_START_STRING,
+        comment_end_string: str = COMMENT_END_STRING,
+        line_statement_prefix: t.Optional[str] = LINE_STATEMENT_PREFIX,
+        line_comment_prefix: t.Optional[str] = LINE_COMMENT_PREFIX,
+        trim_blocks: bool = TRIM_BLOCKS,
+        lstrip_blocks: bool = LSTRIP_BLOCKS,
+        newline_sequence: "te.Literal['\\n', '\\r\\n', '\\r']" = NEWLINE_SEQUENCE,
+        keep_trailing_newline: bool = KEEP_TRAILING_NEWLINE,
+        extensions: t.Sequence[t.Union[str, t.Type["Extension"]]] = (),
+        optimized: bool = True,
+        undefined: t.Type[Undefined] = Undefined,
+        finalize: t.Optional[t.Callable[..., t.Any]] = None,
+        autoescape: t.Union[bool, t.Callable[[t.Optional[str]], bool]] = False,
+        enable_async: bool = False,
+    ) -> t.Any:  # it returns a `Template`, but this breaks the sphinx build...
+        env = get_spontaneous_environment(
+            cls.environment_class,  # type: ignore
+            block_start_string,
+            block_end_string,
+            variable_start_string,
+            variable_end_string,
+            comment_start_string,
+            comment_end_string,
+            line_statement_prefix,
+            line_comment_prefix,
+            trim_blocks,
+            lstrip_blocks,
+            newline_sequence,
+            keep_trailing_newline,
+            frozenset(extensions),
+            optimized,
+            undefined,  # type: ignore
+            finalize,
+            autoescape,
+            None,
+            0,
+            False,
+            None,
+            enable_async,
+        )
+        return env.from_string(source, template_class=cls)
+
+    @classmethod
+    def from_code(
+        cls,
+        environment: Environment,
+        code: CodeType,
+        globals: t.MutableMapping[str, t.Any],
+        uptodate: t.Optional[t.Callable[[], bool]] = None,
+    ) -> "Template":
+        """Creates a template object from compiled code and the globals.  This
+        is used by the loaders and environment to create a template object.
+        """
+        namespace = {"environment": environment, "__file__": code.co_filename}
+        exec(code, namespace)
+        rv = cls._from_namespace(environment, namespace, globals)
+        rv._uptodate = uptodate
+        return rv
+
+    @classmethod
+    def from_module_dict(
+        cls,
+        environment: Environment,
+        module_dict: t.MutableMapping[str, t.Any],
+        globals: t.MutableMapping[str, t.Any],
+    ) -> "Template":
+        """Creates a template object from a module.  This is used by the
+        module loader to create a template object.
+
+        .. versionadded:: 2.4
+        """
+        return cls._from_namespace(environment, module_dict, globals)
+
+    @classmethod
+    def _from_namespace(
+        cls,
+        environment: Environment,
+        namespace: t.MutableMapping[str, t.Any],
+        globals: t.MutableMapping[str, t.Any],
+    ) -> "Template":
+        t: Template = object.__new__(cls)
+        t.environment = environment
+        t.globals = globals
+        t.name = namespace["name"]
+        t.filename = namespace["__file__"]
+        t.blocks = namespace["blocks"]
+
+        # render function and module
+        t.root_render_func = namespace["root"]
+        t._module = None
+
+        # debug and loader helpers
+        t._debug_info = namespace["debug_info"]
+        t._uptodate = None
+
+        # store the reference
+        namespace["environment"] = environment
+        namespace["__jinja_template__"] = t
+
+        return t
+
+    def render(self, *args: t.Any, **kwargs: t.Any) -> str:
+        """This method accepts the same arguments as the `dict` constructor:
+        A dict, a dict subclass or some keyword arguments.  If no arguments
+        are given the context will be empty.  These two calls do the same::
+
+            template.render(knights='that say nih')
+            template.render({'knights': 'that say nih'})
+
+        This will return the rendered template as a string.
+        """
+        if self.environment.is_async:
+            import asyncio
+
+            return asyncio.run(self.render_async(*args, **kwargs))
+
+        ctx = self.new_context(dict(*args, **kwargs))
+
+        try:
+            return self.environment.concat(self.root_render_func(ctx))  # type: ignore
+        except Exception:
+            self.environment.handle_exception()
+
+    async def render_async(self, *args: t.Any, **kwargs: t.Any) -> str:
+        """This works similar to :meth:`render` but returns a coroutine
+        that when awaited returns the entire rendered template string.  This
+        requires the async feature to be enabled.
+
+        Example usage::
+
+            await template.render_async(knights='that say nih; asynchronously')
+        """
+        if not self.environment.is_async:
+            raise RuntimeError(
+                "The environment was not created with async mode enabled."
+            )
+
+        ctx = self.new_context(dict(*args, **kwargs))
+
+        try:
+            return self.environment.concat(  # type: ignore
+                [n async for n in self.root_render_func(ctx)]  # type: ignore
+            )
+        except Exception:
+            return self.environment.handle_exception()
+
+    def stream(self, *args: t.Any, **kwargs: t.Any) -> "TemplateStream":
+        """Works exactly like :meth:`generate` but returns a
+        :class:`TemplateStream`.
+        """
+        return TemplateStream(self.generate(*args, **kwargs))
+
+    def generate(self, *args: t.Any, **kwargs: t.Any) -> t.Iterator[str]:
+        """For very large templates it can be useful to not render the whole
+        template at once but evaluate each statement after another and yield
+        piece for piece.  This method basically does exactly that and returns
+        a generator that yields one item after another as strings.
+
+        It accepts the same arguments as :meth:`render`.
+        """
+        if self.environment.is_async:
+            import asyncio
+
+            async def to_list() -> t.List[str]:
+                return [x async for x in self.generate_async(*args, **kwargs)]
+
+            yield from asyncio.run(to_list())
+            return
+
+        ctx = self.new_context(dict(*args, **kwargs))
+
+        try:
+            yield from self.root_render_func(ctx)
+        except Exception:
+            yield self.environment.handle_exception()
+
+    async def generate_async(
+        self, *args: t.Any, **kwargs: t.Any
+    ) -> t.AsyncGenerator[str, object]:
+        """An async version of :meth:`generate`.  Works very similarly but
+        returns an async iterator instead.
+        """
+        if not self.environment.is_async:
+            raise RuntimeError(
+                "The environment was not created with async mode enabled."
+            )
+
+        ctx = self.new_context(dict(*args, **kwargs))
+
+        try:
+            agen = self.root_render_func(ctx)
+            try:
+                async for event in agen:  # type: ignore
+                    yield event
+            finally:
+                # we can't use async with aclosing(...) because that's only
+                # in 3.10+
+                await agen.aclose()  # type: ignore
+        except Exception:
+            yield self.environment.handle_exception()
+
+    def new_context(
+        self,
+        vars: t.Optional[t.Dict[str, t.Any]] = None,
+        shared: bool = False,
+        locals: t.Optional[t.Mapping[str, t.Any]] = None,
+    ) -> Context:
+        """Create a new :class:`Context` for this template.  The vars
+        provided will be passed to the template.  Per default the globals
+        are added to the context.  If shared is set to `True` the data
+        is passed as is to the context without adding the globals.
+
+        `locals` can be a dict of local variables for internal usage.
+        """
+        return new_context(
+            self.environment, self.name, self.blocks, vars, shared, self.globals, locals
+        )
+
+    def make_module(
+        self,
+        vars: t.Optional[t.Dict[str, t.Any]] = None,
+        shared: bool = False,
+        locals: t.Optional[t.Mapping[str, t.Any]] = None,
+    ) -> "TemplateModule":
+        """This method works like the :attr:`module` attribute when called
+        without arguments but it will evaluate the template on every call
+        rather than caching it.  It's also possible to provide
+        a dict which is then used as context.  The arguments are the same
+        as for the :meth:`new_context` method.
+        """
+        ctx = self.new_context(vars, shared, locals)
+        return TemplateModule(self, ctx)
+
+    async def make_module_async(
+        self,
+        vars: t.Optional[t.Dict[str, t.Any]] = None,
+        shared: bool = False,
+        locals: t.Optional[t.Mapping[str, t.Any]] = None,
+    ) -> "TemplateModule":
+        """As template module creation can invoke template code for
+        asynchronous executions this method must be used instead of the
+        normal :meth:`make_module` one.  Likewise the module attribute
+        becomes unavailable in async mode.
+        """
+        ctx = self.new_context(vars, shared, locals)
+        return TemplateModule(
+            self,
+            ctx,
+            [x async for x in self.root_render_func(ctx)],  # type: ignore
+        )
+
+    @internalcode
+    def _get_default_module(self, ctx: t.Optional[Context] = None) -> "TemplateModule":
+        """If a context is passed in, this means that the template was
+        imported. Imported templates have access to the current
+        template's globals by default, but they can only be accessed via
+        the context during runtime.
+
+        If there are new globals, we need to create a new module because
+        the cached module is already rendered and will not have access
+        to globals from the current context. This new module is not
+        cached because the template can be imported elsewhere, and it
+        should have access to only the current template's globals.
+        """
+        if self.environment.is_async:
+            raise RuntimeError("Module is not available in async mode.")
+
+        if ctx is not None:
+            keys = ctx.globals_keys - self.globals.keys()
+
+            if keys:
+                return self.make_module({k: ctx.parent[k] for k in keys})
+
+        if self._module is None:
+            self._module = self.make_module()
+
+        return self._module
+
+    async def _get_default_module_async(
+        self, ctx: t.Optional[Context] = None
+    ) -> "TemplateModule":
+        if ctx is not None:
+            keys = ctx.globals_keys - self.globals.keys()
+
+            if keys:
+                return await self.make_module_async({k: ctx.parent[k] for k in keys})
+
+        if self._module is None:
+            self._module = await self.make_module_async()
+
+        return self._module
+
+    @property
+    def module(self) -> "TemplateModule":
+        """The template as module.  This is used for imports in the
+        template runtime but is also useful if one wants to access
+        exported template variables from the Python layer:
+
+        >>> t = Template('{% macro foo() %}42{% endmacro %}23')
+        >>> str(t.module)
+        '23'
+        >>> t.module.foo() == u'42'
+        True
+
+        This attribute is not available if async mode is enabled.
+        """
+        return self._get_default_module()
+
+    def get_corresponding_lineno(self, lineno: int) -> int:
+        """Return the source line number of a line number in the
+        generated bytecode as they are not in sync.
+        """
+        for template_line, code_line in reversed(self.debug_info):
+            if code_line <= lineno:
+                return template_line
+        return 1
+
+    @property
+    def is_up_to_date(self) -> bool:
+        """If this variable is `False` there is a newer version available."""
+        if self._uptodate is None:
+            return True
+        return self._uptodate()
+
+    @property
+    def debug_info(self) -> t.List[t.Tuple[int, int]]:
+        """The debug info mapping."""
+        if self._debug_info:
+            return [
+                tuple(map(int, x.split("=")))  # type: ignore
+                for x in self._debug_info.split("&")
+            ]
+
+        return []
+
+    def __repr__(self) -> str:
+        if self.name is None:
+            name = f"memory:{id(self):x}"
+        else:
+            name = repr(self.name)
+        return f"<{type(self).__name__} {name}>"
+
+
+class TemplateModule:
+    """Represents an imported template.  All the exported names of the
+    template are available as attributes on this object.  Additionally
+    converting it into a string renders the contents.
+    """
+
+    def __init__(
+        self,
+        template: Template,
+        context: Context,
+        body_stream: t.Optional[t.Iterable[str]] = None,
+    ) -> None:
+        if body_stream is None:
+            if context.environment.is_async:
+                raise RuntimeError(
+                    "Async mode requires a body stream to be passed to"
+                    " a template module. Use the async methods of the"
+                    " API you are using."
+                )
+
+            body_stream = list(template.root_render_func(context))
+
+        self._body_stream = body_stream
+        self.__dict__.update(context.get_exported())
+        self.__name__ = template.name
+
+    def __html__(self) -> Markup:
+        return Markup(concat(self._body_stream))
+
+    def __str__(self) -> str:
+        return concat(self._body_stream)
+
+    def __repr__(self) -> str:
+        if self.__name__ is None:
+            name = f"memory:{id(self):x}"
+        else:
+            name = repr(self.__name__)
+        return f"<{type(self).__name__} {name}>"
+
+
+class TemplateExpression:
+    """The :meth:`jinja2.Environment.compile_expression` method returns an
+    instance of this object.  It encapsulates the expression-like access
+    to the template with an expression it wraps.
+    """
+
+    def __init__(self, template: Template, undefined_to_none: bool) -> None:
+        self._template = template
+        self._undefined_to_none = undefined_to_none
+
+    def __call__(self, *args: t.Any, **kwargs: t.Any) -> t.Optional[t.Any]:
+        context = self._template.new_context(dict(*args, **kwargs))
+        consume(self._template.root_render_func(context))
+        rv = context.vars["result"]
+        if self._undefined_to_none and isinstance(rv, Undefined):
+            rv = None
+        return rv
+
+
+class TemplateStream:
+    """A template stream works pretty much like an ordinary python generator
+    but it can buffer multiple items to reduce the number of total iterations.
+    Per default the output is unbuffered which means that for every unbuffered
+    instruction in the template one string is yielded.
+
+    If buffering is enabled with a buffer size of 5, five items are combined
+    into a new string.  This is mainly useful if you are streaming
+    big templates to a client via WSGI which flushes after each iteration.
+    """
+
+    def __init__(self, gen: t.Iterator[str]) -> None:
+        self._gen = gen
+        self.disable_buffering()
+
+    def dump(
+        self,
+        fp: t.Union[str, t.IO[bytes]],
+        encoding: t.Optional[str] = None,
+        errors: t.Optional[str] = "strict",
+    ) -> None:
+        """Dump the complete stream into a file or file-like object.
+        Per default strings are written, if you want to encode
+        before writing specify an `encoding`.
+
+        Example usage::
+
+            Template('Hello {{ name }}!').stream(name='foo').dump('hello.html')
+        """
+        close = False
+
+        if isinstance(fp, str):
+            if encoding is None:
+                encoding = "utf-8"
+
+            real_fp: t.IO[bytes] = open(fp, "wb")
+            close = True
+        else:
+            real_fp = fp
+
+        try:
+            if encoding is not None:
+                iterable = (x.encode(encoding, errors) for x in self)  # type: ignore
+            else:
+                iterable = self  # type: ignore
+
+            if hasattr(real_fp, "writelines"):
+                real_fp.writelines(iterable)
+            else:
+                for item in iterable:
+                    real_fp.write(item)
+        finally:
+            if close:
+                real_fp.close()
+
+    def disable_buffering(self) -> None:
+        """Disable the output buffering."""
+        self._next = partial(next, self._gen)
+        self.buffered = False
+
+    def _buffered_generator(self, size: int) -> t.Iterator[str]:
+        buf: t.List[str] = []
+        c_size = 0
+        push = buf.append
+
+        while True:
+            try:
+                while c_size < size:
+                    c = next(self._gen)
+                    push(c)
+                    if c:
+                        c_size += 1
+            except StopIteration:
+                if not c_size:
+                    return
+            yield concat(buf)
+            del buf[:]
+            c_size = 0
+
+    def enable_buffering(self, size: int = 5) -> None:
+        """Enable buffering.  Buffer `size` items before yielding them."""
+        if size <= 1:
+            raise ValueError("buffer size too small")
+
+        self.buffered = True
+        self._next = partial(next, self._buffered_generator(size))
+
+    def __iter__(self) -> "TemplateStream":
+        return self
+
+    def __next__(self) -> str:
+        return self._next()  # type: ignore
+
+
+# hook in default template class.  if anyone reads this comment: ignore that
+# it's possible to use custom templates ;-)
+Environment.template_class = Template
diff --git a/lib/python3.11/site-packages/jinja2/exceptions.py b/lib/python3.11/site-packages/jinja2/exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..082ebe8f221d4e7e980e4d321c0a0c5da033b124
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/exceptions.py
@@ -0,0 +1,166 @@
+import typing as t
+
+if t.TYPE_CHECKING:
+    from .runtime import Undefined
+
+
+class TemplateError(Exception):
+    """Baseclass for all template errors."""
+
+    def __init__(self, message: t.Optional[str] = None) -> None:
+        super().__init__(message)
+
+    @property
+    def message(self) -> t.Optional[str]:
+        return self.args[0] if self.args else None
+
+
+class TemplateNotFound(IOError, LookupError, TemplateError):
+    """Raised if a template does not exist.
+
+    .. versionchanged:: 2.11
+        If the given name is :class:`Undefined` and no message was
+        provided, an :exc:`UndefinedError` is raised.
+    """
+
+    # Silence the Python warning about message being deprecated since
+    # it's not valid here.
+    message: t.Optional[str] = None
+
+    def __init__(
+        self,
+        name: t.Optional[t.Union[str, "Undefined"]],
+        message: t.Optional[str] = None,
+    ) -> None:
+        IOError.__init__(self, name)
+
+        if message is None:
+            from .runtime import Undefined
+
+            if isinstance(name, Undefined):
+                name._fail_with_undefined_error()
+
+            message = name
+
+        self.message = message
+        self.name = name
+        self.templates = [name]
+
+    def __str__(self) -> str:
+        return str(self.message)
+
+
+class TemplatesNotFound(TemplateNotFound):
+    """Like :class:`TemplateNotFound` but raised if multiple templates
+    are selected.  This is a subclass of :class:`TemplateNotFound`
+    exception, so just catching the base exception will catch both.
+
+    .. versionchanged:: 2.11
+        If a name in the list of names is :class:`Undefined`, a message
+        about it being undefined is shown rather than the empty string.
+
+    .. versionadded:: 2.2
+    """
+
+    def __init__(
+        self,
+        names: t.Sequence[t.Union[str, "Undefined"]] = (),
+        message: t.Optional[str] = None,
+    ) -> None:
+        if message is None:
+            from .runtime import Undefined
+
+            parts = []
+
+            for name in names:
+                if isinstance(name, Undefined):
+                    parts.append(name._undefined_message)
+                else:
+                    parts.append(name)
+
+            parts_str = ", ".join(map(str, parts))
+            message = f"none of the templates given were found: {parts_str}"
+
+        super().__init__(names[-1] if names else None, message)
+        self.templates = list(names)
+
+
+class TemplateSyntaxError(TemplateError):
+    """Raised to tell the user that there is a problem with the template."""
+
+    def __init__(
+        self,
+        message: str,
+        lineno: int,
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+    ) -> None:
+        super().__init__(message)
+        self.lineno = lineno
+        self.name = name
+        self.filename = filename
+        self.source: t.Optional[str] = None
+
+        # this is set to True if the debug.translate_syntax_error
+        # function translated the syntax error into a new traceback
+        self.translated = False
+
+    def __str__(self) -> str:
+        # for translated errors we only return the message
+        if self.translated:
+            return t.cast(str, self.message)
+
+        # otherwise attach some stuff
+        location = f"line {self.lineno}"
+        name = self.filename or self.name
+        if name:
+            location = f'File "{name}", {location}'
+        lines = [t.cast(str, self.message), "  " + location]
+
+        # if the source is set, add the line to the output
+        if self.source is not None:
+            try:
+                line = self.source.splitlines()[self.lineno - 1]
+            except IndexError:
+                pass
+            else:
+                lines.append("    " + line.strip())
+
+        return "\n".join(lines)
+
+    def __reduce__(self):  # type: ignore
+        # https://bugs.python.org/issue1692335 Exceptions that take
+        # multiple required arguments have problems with pickling.
+        # Without this, raises TypeError: __init__() missing 1 required
+        # positional argument: 'lineno'
+        return self.__class__, (self.message, self.lineno, self.name, self.filename)
+
+
+class TemplateAssertionError(TemplateSyntaxError):
+    """Like a template syntax error, but covers cases where something in the
+    template caused an error at compile time that wasn't necessarily caused
+    by a syntax error.  However it's a direct subclass of
+    :exc:`TemplateSyntaxError` and has the same attributes.
+    """
+
+
+class TemplateRuntimeError(TemplateError):
+    """A generic runtime error in the template engine.  Under some situations
+    Jinja may raise this exception.
+    """
+
+
+class UndefinedError(TemplateRuntimeError):
+    """Raised if a template tries to operate on :class:`Undefined`."""
+
+
+class SecurityError(TemplateRuntimeError):
+    """Raised if a template tries to do something insecure if the
+    sandbox is enabled.
+    """
+
+
+class FilterArgumentError(TemplateRuntimeError):
+    """This error is raised if a filter was called with inappropriate
+    arguments
+    """
diff --git a/lib/python3.11/site-packages/jinja2/ext.py b/lib/python3.11/site-packages/jinja2/ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7af8d45f06ad77832d09d32468d283b5cda2912
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/ext.py
@@ -0,0 +1,870 @@
+"""Extension API for adding custom tags and behavior."""
+
+import pprint
+import re
+import typing as t
+
+from markupsafe import Markup
+
+from . import defaults
+from . import nodes
+from .environment import Environment
+from .exceptions import TemplateAssertionError
+from .exceptions import TemplateSyntaxError
+from .runtime import concat  # type: ignore
+from .runtime import Context
+from .runtime import Undefined
+from .utils import import_string
+from .utils import pass_context
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .lexer import Token
+    from .lexer import TokenStream
+    from .parser import Parser
+
+    class _TranslationsBasic(te.Protocol):
+        def gettext(self, message: str) -> str: ...
+
+        def ngettext(self, singular: str, plural: str, n: int) -> str:
+            pass
+
+    class _TranslationsContext(_TranslationsBasic):
+        def pgettext(self, context: str, message: str) -> str: ...
+
+        def npgettext(
+            self, context: str, singular: str, plural: str, n: int
+        ) -> str: ...
+
+    _SupportedTranslations = t.Union[_TranslationsBasic, _TranslationsContext]
+
+
+# I18N functions available in Jinja templates. If the I18N library
+# provides ugettext, it will be assigned to gettext.
+GETTEXT_FUNCTIONS: t.Tuple[str, ...] = (
+    "_",
+    "gettext",
+    "ngettext",
+    "pgettext",
+    "npgettext",
+)
+_ws_re = re.compile(r"\s*\n\s*")
+
+
+class Extension:
+    """Extensions can be used to add extra functionality to the Jinja template
+    system at the parser level.  Custom extensions are bound to an environment
+    but may not store environment specific data on `self`.  The reason for
+    this is that an extension can be bound to another environment (for
+    overlays) by creating a copy and reassigning the `environment` attribute.
+
+    As extensions are created by the environment they cannot accept any
+    arguments for configuration.  One may want to work around that by using
+    a factory function, but that is not possible as extensions are identified
+    by their import name.  The correct way to configure the extension is
+    storing the configuration values on the environment.  Because this way the
+    environment ends up acting as central configuration storage the
+    attributes may clash which is why extensions have to ensure that the names
+    they choose for configuration are not too generic.  ``prefix`` for example
+    is a terrible name, ``fragment_cache_prefix`` on the other hand is a good
+    name as includes the name of the extension (fragment cache).
+    """
+
+    identifier: t.ClassVar[str]
+
+    def __init_subclass__(cls) -> None:
+        cls.identifier = f"{cls.__module__}.{cls.__name__}"
+
+    #: if this extension parses this is the list of tags it's listening to.
+    tags: t.Set[str] = set()
+
+    #: the priority of that extension.  This is especially useful for
+    #: extensions that preprocess values.  A lower value means higher
+    #: priority.
+    #:
+    #: .. versionadded:: 2.4
+    priority = 100
+
+    def __init__(self, environment: Environment) -> None:
+        self.environment = environment
+
+    def bind(self, environment: Environment) -> "te.Self":
+        """Create a copy of this extension bound to another environment."""
+        rv = object.__new__(self.__class__)
+        rv.__dict__.update(self.__dict__)
+        rv.environment = environment
+        return rv
+
+    def preprocess(
+        self, source: str, name: t.Optional[str], filename: t.Optional[str] = None
+    ) -> str:
+        """This method is called before the actual lexing and can be used to
+        preprocess the source.  The `filename` is optional.  The return value
+        must be the preprocessed source.
+        """
+        return source
+
+    def filter_stream(
+        self, stream: "TokenStream"
+    ) -> t.Union["TokenStream", t.Iterable["Token"]]:
+        """It's passed a :class:`~jinja2.lexer.TokenStream` that can be used
+        to filter tokens returned.  This method has to return an iterable of
+        :class:`~jinja2.lexer.Token`\\s, but it doesn't have to return a
+        :class:`~jinja2.lexer.TokenStream`.
+        """
+        return stream
+
+    def parse(self, parser: "Parser") -> t.Union[nodes.Node, t.List[nodes.Node]]:
+        """If any of the :attr:`tags` matched this method is called with the
+        parser as first argument.  The token the parser stream is pointing at
+        is the name token that matched.  This method has to return one or a
+        list of multiple nodes.
+        """
+        raise NotImplementedError()
+
+    def attr(
+        self, name: str, lineno: t.Optional[int] = None
+    ) -> nodes.ExtensionAttribute:
+        """Return an attribute node for the current extension.  This is useful
+        to pass constants on extensions to generated template code.
+
+        ::
+
+            self.attr('_my_attribute', lineno=lineno)
+        """
+        return nodes.ExtensionAttribute(self.identifier, name, lineno=lineno)
+
+    def call_method(
+        self,
+        name: str,
+        args: t.Optional[t.List[nodes.Expr]] = None,
+        kwargs: t.Optional[t.List[nodes.Keyword]] = None,
+        dyn_args: t.Optional[nodes.Expr] = None,
+        dyn_kwargs: t.Optional[nodes.Expr] = None,
+        lineno: t.Optional[int] = None,
+    ) -> nodes.Call:
+        """Call a method of the extension.  This is a shortcut for
+        :meth:`attr` + :class:`jinja2.nodes.Call`.
+        """
+        if args is None:
+            args = []
+        if kwargs is None:
+            kwargs = []
+        return nodes.Call(
+            self.attr(name, lineno=lineno),
+            args,
+            kwargs,
+            dyn_args,
+            dyn_kwargs,
+            lineno=lineno,
+        )
+
+
+@pass_context
+def _gettext_alias(
+    __context: Context, *args: t.Any, **kwargs: t.Any
+) -> t.Union[t.Any, Undefined]:
+    return __context.call(__context.resolve("gettext"), *args, **kwargs)
+
+
+def _make_new_gettext(func: t.Callable[[str], str]) -> t.Callable[..., str]:
+    @pass_context
+    def gettext(__context: Context, __string: str, **variables: t.Any) -> str:
+        rv = __context.call(func, __string)
+        if __context.eval_ctx.autoescape:
+            rv = Markup(rv)
+        # Always treat as a format string, even if there are no
+        # variables. This makes translation strings more consistent
+        # and predictable. This requires escaping
+        return rv % variables  # type: ignore
+
+    return gettext
+
+
+def _make_new_ngettext(func: t.Callable[[str, str, int], str]) -> t.Callable[..., str]:
+    @pass_context
+    def ngettext(
+        __context: Context,
+        __singular: str,
+        __plural: str,
+        __num: int,
+        **variables: t.Any,
+    ) -> str:
+        variables.setdefault("num", __num)
+        rv = __context.call(func, __singular, __plural, __num)
+        if __context.eval_ctx.autoescape:
+            rv = Markup(rv)
+        # Always treat as a format string, see gettext comment above.
+        return rv % variables  # type: ignore
+
+    return ngettext
+
+
+def _make_new_pgettext(func: t.Callable[[str, str], str]) -> t.Callable[..., str]:
+    @pass_context
+    def pgettext(
+        __context: Context, __string_ctx: str, __string: str, **variables: t.Any
+    ) -> str:
+        variables.setdefault("context", __string_ctx)
+        rv = __context.call(func, __string_ctx, __string)
+
+        if __context.eval_ctx.autoescape:
+            rv = Markup(rv)
+
+        # Always treat as a format string, see gettext comment above.
+        return rv % variables  # type: ignore
+
+    return pgettext
+
+
+def _make_new_npgettext(
+    func: t.Callable[[str, str, str, int], str],
+) -> t.Callable[..., str]:
+    @pass_context
+    def npgettext(
+        __context: Context,
+        __string_ctx: str,
+        __singular: str,
+        __plural: str,
+        __num: int,
+        **variables: t.Any,
+    ) -> str:
+        variables.setdefault("context", __string_ctx)
+        variables.setdefault("num", __num)
+        rv = __context.call(func, __string_ctx, __singular, __plural, __num)
+
+        if __context.eval_ctx.autoescape:
+            rv = Markup(rv)
+
+        # Always treat as a format string, see gettext comment above.
+        return rv % variables  # type: ignore
+
+    return npgettext
+
+
+class InternationalizationExtension(Extension):
+    """This extension adds gettext support to Jinja."""
+
+    tags = {"trans"}
+
+    # TODO: the i18n extension is currently reevaluating values in a few
+    # situations.  Take this example:
+    #   {% trans count=something() %}{{ count }} foo{% pluralize
+    #     %}{{ count }} fooss{% endtrans %}
+    # something is called twice here.  One time for the gettext value and
+    # the other time for the n-parameter of the ngettext function.
+
+    def __init__(self, environment: Environment) -> None:
+        super().__init__(environment)
+        environment.globals["_"] = _gettext_alias
+        environment.extend(
+            install_gettext_translations=self._install,
+            install_null_translations=self._install_null,
+            install_gettext_callables=self._install_callables,
+            uninstall_gettext_translations=self._uninstall,
+            extract_translations=self._extract,
+            newstyle_gettext=False,
+        )
+
+    def _install(
+        self, translations: "_SupportedTranslations", newstyle: t.Optional[bool] = None
+    ) -> None:
+        # ugettext and ungettext are preferred in case the I18N library
+        # is providing compatibility with older Python versions.
+        gettext = getattr(translations, "ugettext", None)
+        if gettext is None:
+            gettext = translations.gettext
+        ngettext = getattr(translations, "ungettext", None)
+        if ngettext is None:
+            ngettext = translations.ngettext
+
+        pgettext = getattr(translations, "pgettext", None)
+        npgettext = getattr(translations, "npgettext", None)
+        self._install_callables(
+            gettext, ngettext, newstyle=newstyle, pgettext=pgettext, npgettext=npgettext
+        )
+
+    def _install_null(self, newstyle: t.Optional[bool] = None) -> None:
+        import gettext
+
+        translations = gettext.NullTranslations()
+
+        if hasattr(translations, "pgettext"):
+            # Python < 3.8
+            pgettext = translations.pgettext
+        else:
+
+            def pgettext(c: str, s: str) -> str:  # type: ignore[misc]
+                return s
+
+        if hasattr(translations, "npgettext"):
+            npgettext = translations.npgettext
+        else:
+
+            def npgettext(c: str, s: str, p: str, n: int) -> str:  # type: ignore[misc]
+                return s if n == 1 else p
+
+        self._install_callables(
+            gettext=translations.gettext,
+            ngettext=translations.ngettext,
+            newstyle=newstyle,
+            pgettext=pgettext,
+            npgettext=npgettext,
+        )
+
+    def _install_callables(
+        self,
+        gettext: t.Callable[[str], str],
+        ngettext: t.Callable[[str, str, int], str],
+        newstyle: t.Optional[bool] = None,
+        pgettext: t.Optional[t.Callable[[str, str], str]] = None,
+        npgettext: t.Optional[t.Callable[[str, str, str, int], str]] = None,
+    ) -> None:
+        if newstyle is not None:
+            self.environment.newstyle_gettext = newstyle  # type: ignore
+        if self.environment.newstyle_gettext:  # type: ignore
+            gettext = _make_new_gettext(gettext)
+            ngettext = _make_new_ngettext(ngettext)
+
+            if pgettext is not None:
+                pgettext = _make_new_pgettext(pgettext)
+
+            if npgettext is not None:
+                npgettext = _make_new_npgettext(npgettext)
+
+        self.environment.globals.update(
+            gettext=gettext, ngettext=ngettext, pgettext=pgettext, npgettext=npgettext
+        )
+
+    def _uninstall(self, translations: "_SupportedTranslations") -> None:
+        for key in ("gettext", "ngettext", "pgettext", "npgettext"):
+            self.environment.globals.pop(key, None)
+
+    def _extract(
+        self,
+        source: t.Union[str, nodes.Template],
+        gettext_functions: t.Sequence[str] = GETTEXT_FUNCTIONS,
+    ) -> t.Iterator[
+        t.Tuple[int, str, t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]]]
+    ]:
+        if isinstance(source, str):
+            source = self.environment.parse(source)
+        return extract_from_ast(source, gettext_functions)
+
+    def parse(self, parser: "Parser") -> t.Union[nodes.Node, t.List[nodes.Node]]:
+        """Parse a translatable tag."""
+        lineno = next(parser.stream).lineno
+
+        context = None
+        context_token = parser.stream.next_if("string")
+
+        if context_token is not None:
+            context = context_token.value
+
+        # find all the variables referenced.  Additionally a variable can be
+        # defined in the body of the trans block too, but this is checked at
+        # a later state.
+        plural_expr: t.Optional[nodes.Expr] = None
+        plural_expr_assignment: t.Optional[nodes.Assign] = None
+        num_called_num = False
+        variables: t.Dict[str, nodes.Expr] = {}
+        trimmed = None
+        while parser.stream.current.type != "block_end":
+            if variables:
+                parser.stream.expect("comma")
+
+            # skip colon for python compatibility
+            if parser.stream.skip_if("colon"):
+                break
+
+            token = parser.stream.expect("name")
+            if token.value in variables:
+                parser.fail(
+                    f"translatable variable {token.value!r} defined twice.",
+                    token.lineno,
+                    exc=TemplateAssertionError,
+                )
+
+            # expressions
+            if parser.stream.current.type == "assign":
+                next(parser.stream)
+                variables[token.value] = var = parser.parse_expression()
+            elif trimmed is None and token.value in ("trimmed", "notrimmed"):
+                trimmed = token.value == "trimmed"
+                continue
+            else:
+                variables[token.value] = var = nodes.Name(token.value, "load")
+
+            if plural_expr is None:
+                if isinstance(var, nodes.Call):
+                    plural_expr = nodes.Name("_trans", "load")
+                    variables[token.value] = plural_expr
+                    plural_expr_assignment = nodes.Assign(
+                        nodes.Name("_trans", "store"), var
+                    )
+                else:
+                    plural_expr = var
+                num_called_num = token.value == "num"
+
+        parser.stream.expect("block_end")
+
+        plural = None
+        have_plural = False
+        referenced = set()
+
+        # now parse until endtrans or pluralize
+        singular_names, singular = self._parse_block(parser, True)
+        if singular_names:
+            referenced.update(singular_names)
+            if plural_expr is None:
+                plural_expr = nodes.Name(singular_names[0], "load")
+                num_called_num = singular_names[0] == "num"
+
+        # if we have a pluralize block, we parse that too
+        if parser.stream.current.test("name:pluralize"):
+            have_plural = True
+            next(parser.stream)
+            if parser.stream.current.type != "block_end":
+                token = parser.stream.expect("name")
+                if token.value not in variables:
+                    parser.fail(
+                        f"unknown variable {token.value!r} for pluralization",
+                        token.lineno,
+                        exc=TemplateAssertionError,
+                    )
+                plural_expr = variables[token.value]
+                num_called_num = token.value == "num"
+            parser.stream.expect("block_end")
+            plural_names, plural = self._parse_block(parser, False)
+            next(parser.stream)
+            referenced.update(plural_names)
+        else:
+            next(parser.stream)
+
+        # register free names as simple name expressions
+        for name in referenced:
+            if name not in variables:
+                variables[name] = nodes.Name(name, "load")
+
+        if not have_plural:
+            plural_expr = None
+        elif plural_expr is None:
+            parser.fail("pluralize without variables", lineno)
+
+        if trimmed is None:
+            trimmed = self.environment.policies["ext.i18n.trimmed"]
+        if trimmed:
+            singular = self._trim_whitespace(singular)
+            if plural:
+                plural = self._trim_whitespace(plural)
+
+        node = self._make_node(
+            singular,
+            plural,
+            context,
+            variables,
+            plural_expr,
+            bool(referenced),
+            num_called_num and have_plural,
+        )
+        node.set_lineno(lineno)
+        if plural_expr_assignment is not None:
+            return [plural_expr_assignment, node]
+        else:
+            return node
+
+    def _trim_whitespace(self, string: str, _ws_re: t.Pattern[str] = _ws_re) -> str:
+        return _ws_re.sub(" ", string.strip())
+
+    def _parse_block(
+        self, parser: "Parser", allow_pluralize: bool
+    ) -> t.Tuple[t.List[str], str]:
+        """Parse until the next block tag with a given name."""
+        referenced = []
+        buf = []
+
+        while True:
+            if parser.stream.current.type == "data":
+                buf.append(parser.stream.current.value.replace("%", "%%"))
+                next(parser.stream)
+            elif parser.stream.current.type == "variable_begin":
+                next(parser.stream)
+                name = parser.stream.expect("name").value
+                referenced.append(name)
+                buf.append(f"%({name})s")
+                parser.stream.expect("variable_end")
+            elif parser.stream.current.type == "block_begin":
+                next(parser.stream)
+                block_name = (
+                    parser.stream.current.value
+                    if parser.stream.current.type == "name"
+                    else None
+                )
+                if block_name == "endtrans":
+                    break
+                elif block_name == "pluralize":
+                    if allow_pluralize:
+                        break
+                    parser.fail(
+                        "a translatable section can have only one pluralize section"
+                    )
+                elif block_name == "trans":
+                    parser.fail(
+                        "trans blocks can't be nested; did you mean `endtrans`?"
+                    )
+                parser.fail(
+                    f"control structures in translatable sections are not allowed; "
+                    f"saw `{block_name}`"
+                )
+            elif parser.stream.eos:
+                parser.fail("unclosed translation block")
+            else:
+                raise RuntimeError("internal parser error")
+
+        return referenced, concat(buf)
+
+    def _make_node(
+        self,
+        singular: str,
+        plural: t.Optional[str],
+        context: t.Optional[str],
+        variables: t.Dict[str, nodes.Expr],
+        plural_expr: t.Optional[nodes.Expr],
+        vars_referenced: bool,
+        num_called_num: bool,
+    ) -> nodes.Output:
+        """Generates a useful node from the data provided."""
+        newstyle = self.environment.newstyle_gettext  # type: ignore
+        node: nodes.Expr
+
+        # no variables referenced?  no need to escape for old style
+        # gettext invocations only if there are vars.
+        if not vars_referenced and not newstyle:
+            singular = singular.replace("%%", "%")
+            if plural:
+                plural = plural.replace("%%", "%")
+
+        func_name = "gettext"
+        func_args: t.List[nodes.Expr] = [nodes.Const(singular)]
+
+        if context is not None:
+            func_args.insert(0, nodes.Const(context))
+            func_name = f"p{func_name}"
+
+        if plural_expr is not None:
+            func_name = f"n{func_name}"
+            func_args.extend((nodes.Const(plural), plural_expr))
+
+        node = nodes.Call(nodes.Name(func_name, "load"), func_args, [], None, None)
+
+        # in case newstyle gettext is used, the method is powerful
+        # enough to handle the variable expansion and autoescape
+        # handling itself
+        if newstyle:
+            for key, value in variables.items():
+                # the function adds that later anyways in case num was
+                # called num, so just skip it.
+                if num_called_num and key == "num":
+                    continue
+                node.kwargs.append(nodes.Keyword(key, value))
+
+        # otherwise do that here
+        else:
+            # mark the return value as safe if we are in an
+            # environment with autoescaping turned on
+            node = nodes.MarkSafeIfAutoescape(node)
+            if variables:
+                node = nodes.Mod(
+                    node,
+                    nodes.Dict(
+                        [
+                            nodes.Pair(nodes.Const(key), value)
+                            for key, value in variables.items()
+                        ]
+                    ),
+                )
+        return nodes.Output([node])
+
+
+class ExprStmtExtension(Extension):
+    """Adds a `do` tag to Jinja that works like the print statement just
+    that it doesn't print the return value.
+    """
+
+    tags = {"do"}
+
+    def parse(self, parser: "Parser") -> nodes.ExprStmt:
+        node = nodes.ExprStmt(lineno=next(parser.stream).lineno)
+        node.node = parser.parse_tuple()
+        return node
+
+
+class LoopControlExtension(Extension):
+    """Adds break and continue to the template engine."""
+
+    tags = {"break", "continue"}
+
+    def parse(self, parser: "Parser") -> t.Union[nodes.Break, nodes.Continue]:
+        token = next(parser.stream)
+        if token.value == "break":
+            return nodes.Break(lineno=token.lineno)
+        return nodes.Continue(lineno=token.lineno)
+
+
+class DebugExtension(Extension):
+    """A ``{% debug %}`` tag that dumps the available variables,
+    filters, and tests.
+
+    .. code-block:: html+jinja
+
+        <pre>{% debug %}</pre>
+
+    .. code-block:: text
+
+        {'context': {'cycler': <class 'jinja2.utils.Cycler'>,
+                     ...,
+                     'namespace': <class 'jinja2.utils.Namespace'>},
+         'filters': ['abs', 'attr', 'batch', 'capitalize', 'center', 'count', 'd',
+                     ..., 'urlencode', 'urlize', 'wordcount', 'wordwrap', 'xmlattr'],
+         'tests': ['!=', '<', '<=', '==', '>', '>=', 'callable', 'defined',
+                   ..., 'odd', 'sameas', 'sequence', 'string', 'undefined', 'upper']}
+
+    .. versionadded:: 2.11.0
+    """
+
+    tags = {"debug"}
+
+    def parse(self, parser: "Parser") -> nodes.Output:
+        lineno = parser.stream.expect("name:debug").lineno
+        context = nodes.ContextReference()
+        result = self.call_method("_render", [context], lineno=lineno)
+        return nodes.Output([result], lineno=lineno)
+
+    def _render(self, context: Context) -> str:
+        result = {
+            "context": context.get_all(),
+            "filters": sorted(self.environment.filters.keys()),
+            "tests": sorted(self.environment.tests.keys()),
+        }
+
+        # Set the depth since the intent is to show the top few names.
+        return pprint.pformat(result, depth=3, compact=True)
+
+
+def extract_from_ast(
+    ast: nodes.Template,
+    gettext_functions: t.Sequence[str] = GETTEXT_FUNCTIONS,
+    babel_style: bool = True,
+) -> t.Iterator[
+    t.Tuple[int, str, t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]]]
+]:
+    """Extract localizable strings from the given template node.  Per
+    default this function returns matches in babel style that means non string
+    parameters as well as keyword arguments are returned as `None`.  This
+    allows Babel to figure out what you really meant if you are using
+    gettext functions that allow keyword arguments for placeholder expansion.
+    If you don't want that behavior set the `babel_style` parameter to `False`
+    which causes only strings to be returned and parameters are always stored
+    in tuples.  As a consequence invalid gettext calls (calls without a single
+    string parameter or string parameters after non-string parameters) are
+    skipped.
+
+    This example explains the behavior:
+
+    >>> from jinja2 import Environment
+    >>> env = Environment()
+    >>> node = env.parse('{{ (_("foo"), _(), ngettext("foo", "bar", 42)) }}')
+    >>> list(extract_from_ast(node))
+    [(1, '_', 'foo'), (1, '_', ()), (1, 'ngettext', ('foo', 'bar', None))]
+    >>> list(extract_from_ast(node, babel_style=False))
+    [(1, '_', ('foo',)), (1, 'ngettext', ('foo', 'bar'))]
+
+    For every string found this function yields a ``(lineno, function,
+    message)`` tuple, where:
+
+    * ``lineno`` is the number of the line on which the string was found,
+    * ``function`` is the name of the ``gettext`` function used (if the
+      string was extracted from embedded Python code), and
+    *   ``message`` is the string, or a tuple of strings for functions
+         with multiple string arguments.
+
+    This extraction function operates on the AST and is because of that unable
+    to extract any comments.  For comment support you have to use the babel
+    extraction interface or extract comments yourself.
+    """
+    out: t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]]
+
+    for node in ast.find_all(nodes.Call):
+        if (
+            not isinstance(node.node, nodes.Name)
+            or node.node.name not in gettext_functions
+        ):
+            continue
+
+        strings: t.List[t.Optional[str]] = []
+
+        for arg in node.args:
+            if isinstance(arg, nodes.Const) and isinstance(arg.value, str):
+                strings.append(arg.value)
+            else:
+                strings.append(None)
+
+        for _ in node.kwargs:
+            strings.append(None)
+        if node.dyn_args is not None:
+            strings.append(None)
+        if node.dyn_kwargs is not None:
+            strings.append(None)
+
+        if not babel_style:
+            out = tuple(x for x in strings if x is not None)
+
+            if not out:
+                continue
+        else:
+            if len(strings) == 1:
+                out = strings[0]
+            else:
+                out = tuple(strings)
+
+        yield node.lineno, node.node.name, out
+
+
+class _CommentFinder:
+    """Helper class to find comments in a token stream.  Can only
+    find comments for gettext calls forwards.  Once the comment
+    from line 4 is found, a comment for line 1 will not return a
+    usable value.
+    """
+
+    def __init__(
+        self, tokens: t.Sequence[t.Tuple[int, str, str]], comment_tags: t.Sequence[str]
+    ) -> None:
+        self.tokens = tokens
+        self.comment_tags = comment_tags
+        self.offset = 0
+        self.last_lineno = 0
+
+    def find_backwards(self, offset: int) -> t.List[str]:
+        try:
+            for _, token_type, token_value in reversed(
+                self.tokens[self.offset : offset]
+            ):
+                if token_type in ("comment", "linecomment"):
+                    try:
+                        prefix, comment = token_value.split(None, 1)
+                    except ValueError:
+                        continue
+                    if prefix in self.comment_tags:
+                        return [comment.rstrip()]
+            return []
+        finally:
+            self.offset = offset
+
+    def find_comments(self, lineno: int) -> t.List[str]:
+        if not self.comment_tags or self.last_lineno > lineno:
+            return []
+        for idx, (token_lineno, _, _) in enumerate(self.tokens[self.offset :]):
+            if token_lineno > lineno:
+                return self.find_backwards(self.offset + idx)
+        return self.find_backwards(len(self.tokens))
+
+
+def babel_extract(
+    fileobj: t.BinaryIO,
+    keywords: t.Sequence[str],
+    comment_tags: t.Sequence[str],
+    options: t.Dict[str, t.Any],
+) -> t.Iterator[
+    t.Tuple[
+        int, str, t.Union[t.Optional[str], t.Tuple[t.Optional[str], ...]], t.List[str]
+    ]
+]:
+    """Babel extraction method for Jinja templates.
+
+    .. versionchanged:: 2.3
+       Basic support for translation comments was added.  If `comment_tags`
+       is now set to a list of keywords for extraction, the extractor will
+       try to find the best preceding comment that begins with one of the
+       keywords.  For best results, make sure to not have more than one
+       gettext call in one line of code and the matching comment in the
+       same line or the line before.
+
+    .. versionchanged:: 2.5.1
+       The `newstyle_gettext` flag can be set to `True` to enable newstyle
+       gettext calls.
+
+    .. versionchanged:: 2.7
+       A `silent` option can now be provided.  If set to `False` template
+       syntax errors are propagated instead of being ignored.
+
+    :param fileobj: the file-like object the messages should be extracted from
+    :param keywords: a list of keywords (i.e. function names) that should be
+                     recognized as translation functions
+    :param comment_tags: a list of translator tags to search for and include
+                         in the results.
+    :param options: a dictionary of additional options (optional)
+    :return: an iterator over ``(lineno, funcname, message, comments)`` tuples.
+             (comments will be empty currently)
+    """
+    extensions: t.Dict[t.Type[Extension], None] = {}
+
+    for extension_name in options.get("extensions", "").split(","):
+        extension_name = extension_name.strip()
+
+        if not extension_name:
+            continue
+
+        extensions[import_string(extension_name)] = None
+
+    if InternationalizationExtension not in extensions:
+        extensions[InternationalizationExtension] = None
+
+    def getbool(options: t.Mapping[str, str], key: str, default: bool = False) -> bool:
+        return options.get(key, str(default)).lower() in {"1", "on", "yes", "true"}
+
+    silent = getbool(options, "silent", True)
+    environment = Environment(
+        options.get("block_start_string", defaults.BLOCK_START_STRING),
+        options.get("block_end_string", defaults.BLOCK_END_STRING),
+        options.get("variable_start_string", defaults.VARIABLE_START_STRING),
+        options.get("variable_end_string", defaults.VARIABLE_END_STRING),
+        options.get("comment_start_string", defaults.COMMENT_START_STRING),
+        options.get("comment_end_string", defaults.COMMENT_END_STRING),
+        options.get("line_statement_prefix") or defaults.LINE_STATEMENT_PREFIX,
+        options.get("line_comment_prefix") or defaults.LINE_COMMENT_PREFIX,
+        getbool(options, "trim_blocks", defaults.TRIM_BLOCKS),
+        getbool(options, "lstrip_blocks", defaults.LSTRIP_BLOCKS),
+        defaults.NEWLINE_SEQUENCE,
+        getbool(options, "keep_trailing_newline", defaults.KEEP_TRAILING_NEWLINE),
+        tuple(extensions),
+        cache_size=0,
+        auto_reload=False,
+    )
+
+    if getbool(options, "trimmed"):
+        environment.policies["ext.i18n.trimmed"] = True
+    if getbool(options, "newstyle_gettext"):
+        environment.newstyle_gettext = True  # type: ignore
+
+    source = fileobj.read().decode(options.get("encoding", "utf-8"))
+    try:
+        node = environment.parse(source)
+        tokens = list(environment.lex(environment.preprocess(source)))
+    except TemplateSyntaxError:
+        if not silent:
+            raise
+        # skip templates with syntax errors
+        return
+
+    finder = _CommentFinder(tokens, comment_tags)
+    for lineno, func, message in extract_from_ast(node, keywords):
+        yield lineno, func, message, finder.find_comments(lineno)
+
+
+#: nicer import names
+i18n = InternationalizationExtension
+do = ExprStmtExtension
+loopcontrols = LoopControlExtension
+debug = DebugExtension
diff --git a/lib/python3.11/site-packages/jinja2/filters.py b/lib/python3.11/site-packages/jinja2/filters.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bcba4fbd3c660edb1166fcad6ef6fc9a4f6a1e5
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/filters.py
@@ -0,0 +1,1873 @@
+"""Built-in template filters used with the ``|`` operator."""
+
+import math
+import random
+import re
+import typing
+import typing as t
+from collections import abc
+from inspect import getattr_static
+from itertools import chain
+from itertools import groupby
+
+from markupsafe import escape
+from markupsafe import Markup
+from markupsafe import soft_str
+
+from .async_utils import async_variant
+from .async_utils import auto_aiter
+from .async_utils import auto_await
+from .async_utils import auto_to_list
+from .exceptions import FilterArgumentError
+from .runtime import Undefined
+from .utils import htmlsafe_json_dumps
+from .utils import pass_context
+from .utils import pass_environment
+from .utils import pass_eval_context
+from .utils import pformat
+from .utils import url_quote
+from .utils import urlize
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .environment import Environment
+    from .nodes import EvalContext
+    from .runtime import Context
+    from .sandbox import SandboxedEnvironment  # noqa: F401
+
+    class HasHTML(te.Protocol):
+        def __html__(self) -> str:
+            pass
+
+
+F = t.TypeVar("F", bound=t.Callable[..., t.Any])
+K = t.TypeVar("K")
+V = t.TypeVar("V")
+
+
+def ignore_case(value: V) -> V:
+    """For use as a postprocessor for :func:`make_attrgetter`. Converts strings
+    to lowercase and returns other types as-is."""
+    if isinstance(value, str):
+        return t.cast(V, value.lower())
+
+    return value
+
+
+def make_attrgetter(
+    environment: "Environment",
+    attribute: t.Optional[t.Union[str, int]],
+    postprocess: t.Optional[t.Callable[[t.Any], t.Any]] = None,
+    default: t.Optional[t.Any] = None,
+) -> t.Callable[[t.Any], t.Any]:
+    """Returns a callable that looks up the given attribute from a
+    passed object with the rules of the environment.  Dots are allowed
+    to access attributes of attributes.  Integer parts in paths are
+    looked up as integers.
+    """
+    parts = _prepare_attribute_parts(attribute)
+
+    def attrgetter(item: t.Any) -> t.Any:
+        for part in parts:
+            item = environment.getitem(item, part)
+
+            if default is not None and isinstance(item, Undefined):
+                item = default
+
+        if postprocess is not None:
+            item = postprocess(item)
+
+        return item
+
+    return attrgetter
+
+
+def make_multi_attrgetter(
+    environment: "Environment",
+    attribute: t.Optional[t.Union[str, int]],
+    postprocess: t.Optional[t.Callable[[t.Any], t.Any]] = None,
+) -> t.Callable[[t.Any], t.List[t.Any]]:
+    """Returns a callable that looks up the given comma separated
+    attributes from a passed object with the rules of the environment.
+    Dots are allowed to access attributes of each attribute.  Integer
+    parts in paths are looked up as integers.
+
+    The value returned by the returned callable is a list of extracted
+    attribute values.
+
+    Examples of attribute: "attr1,attr2", "attr1.inner1.0,attr2.inner2.0", etc.
+    """
+    if isinstance(attribute, str):
+        split: t.Sequence[t.Union[str, int, None]] = attribute.split(",")
+    else:
+        split = [attribute]
+
+    parts = [_prepare_attribute_parts(item) for item in split]
+
+    def attrgetter(item: t.Any) -> t.List[t.Any]:
+        items = [None] * len(parts)
+
+        for i, attribute_part in enumerate(parts):
+            item_i = item
+
+            for part in attribute_part:
+                item_i = environment.getitem(item_i, part)
+
+            if postprocess is not None:
+                item_i = postprocess(item_i)
+
+            items[i] = item_i
+
+        return items
+
+    return attrgetter
+
+
+def _prepare_attribute_parts(
+    attr: t.Optional[t.Union[str, int]],
+) -> t.List[t.Union[str, int]]:
+    if attr is None:
+        return []
+
+    if isinstance(attr, str):
+        return [int(x) if x.isdigit() else x for x in attr.split(".")]
+
+    return [attr]
+
+
+def do_forceescape(value: "t.Union[str, HasHTML]") -> Markup:
+    """Enforce HTML escaping.  This will probably double escape variables."""
+    if hasattr(value, "__html__"):
+        value = t.cast("HasHTML", value).__html__()
+
+    return escape(str(value))
+
+
+def do_urlencode(
+    value: t.Union[str, t.Mapping[str, t.Any], t.Iterable[t.Tuple[str, t.Any]]],
+) -> str:
+    """Quote data for use in a URL path or query using UTF-8.
+
+    Basic wrapper around :func:`urllib.parse.quote` when given a
+    string, or :func:`urllib.parse.urlencode` for a dict or iterable.
+
+    :param value: Data to quote. A string will be quoted directly. A
+        dict or iterable of ``(key, value)`` pairs will be joined as a
+        query string.
+
+    When given a string, "/" is not quoted. HTTP servers treat "/" and
+    "%2F" equivalently in paths. If you need quoted slashes, use the
+    ``|replace("/", "%2F")`` filter.
+
+    .. versionadded:: 2.7
+    """
+    if isinstance(value, str) or not isinstance(value, abc.Iterable):
+        return url_quote(value)
+
+    if isinstance(value, dict):
+        items: t.Iterable[t.Tuple[str, t.Any]] = value.items()
+    else:
+        items = value  # type: ignore
+
+    return "&".join(
+        f"{url_quote(k, for_qs=True)}={url_quote(v, for_qs=True)}" for k, v in items
+    )
+
+
+@pass_eval_context
+def do_replace(
+    eval_ctx: "EvalContext", s: str, old: str, new: str, count: t.Optional[int] = None
+) -> str:
+    """Return a copy of the value with all occurrences of a substring
+    replaced with a new one. The first argument is the substring
+    that should be replaced, the second is the replacement string.
+    If the optional third argument ``count`` is given, only the first
+    ``count`` occurrences are replaced:
+
+    .. sourcecode:: jinja
+
+        {{ "Hello World"|replace("Hello", "Goodbye") }}
+            -> Goodbye World
+
+        {{ "aaaaargh"|replace("a", "d'oh, ", 2) }}
+            -> d'oh, d'oh, aaargh
+    """
+    if count is None:
+        count = -1
+
+    if not eval_ctx.autoescape:
+        return str(s).replace(str(old), str(new), count)
+
+    if (
+        hasattr(old, "__html__")
+        or hasattr(new, "__html__")
+        and not hasattr(s, "__html__")
+    ):
+        s = escape(s)
+    else:
+        s = soft_str(s)
+
+    return s.replace(soft_str(old), soft_str(new), count)
+
+
+def do_upper(s: str) -> str:
+    """Convert a value to uppercase."""
+    return soft_str(s).upper()
+
+
+def do_lower(s: str) -> str:
+    """Convert a value to lowercase."""
+    return soft_str(s).lower()
+
+
+def do_items(value: t.Union[t.Mapping[K, V], Undefined]) -> t.Iterator[t.Tuple[K, V]]:
+    """Return an iterator over the ``(key, value)`` items of a mapping.
+
+    ``x|items`` is the same as ``x.items()``, except if ``x`` is
+    undefined an empty iterator is returned.
+
+    This filter is useful if you expect the template to be rendered with
+    an implementation of Jinja in another programming language that does
+    not have a ``.items()`` method on its mapping type.
+
+    .. code-block:: html+jinja
+
+        <dl>
+        {% for key, value in my_dict|items %}
+            <dt>{{ key }}
+            <dd>{{ value }}
+        {% endfor %}
+        </dl>
+
+    .. versionadded:: 3.1
+    """
+    if isinstance(value, Undefined):
+        return
+
+    if not isinstance(value, abc.Mapping):
+        raise TypeError("Can only get item pairs from a mapping.")
+
+    yield from value.items()
+
+
+# Check for characters that would move the parser state from key to value.
+# https://html.spec.whatwg.org/#attribute-name-state
+_attr_key_re = re.compile(r"[\s/>=]", flags=re.ASCII)
+
+
+@pass_eval_context
+def do_xmlattr(
+    eval_ctx: "EvalContext", d: t.Mapping[str, t.Any], autospace: bool = True
+) -> str:
+    """Create an SGML/XML attribute string based on the items in a dict.
+
+    **Values** that are neither ``none`` nor ``undefined`` are automatically
+    escaped, safely allowing untrusted user input.
+
+    User input should not be used as **keys** to this filter. If any key
+    contains a space, ``/`` solidus, ``>`` greater-than sign, or ``=`` equals
+    sign, this fails with a ``ValueError``. Regardless of this, user input
+    should never be used as keys to this filter, or must be separately validated
+    first.
+
+    .. sourcecode:: html+jinja
+
+        <ul{{ {'class': 'my_list', 'missing': none,
+                'id': 'list-%d'|format(variable)}|xmlattr }}>
+        ...
+        </ul>
+
+    Results in something like this:
+
+    .. sourcecode:: html
+
+        <ul class="my_list" id="list-42">
+        ...
+        </ul>
+
+    As you can see it automatically prepends a space in front of the item
+    if the filter returned something unless the second parameter is false.
+
+    .. versionchanged:: 3.1.4
+        Keys with ``/`` solidus, ``>`` greater-than sign, or ``=`` equals sign
+        are not allowed.
+
+    .. versionchanged:: 3.1.3
+        Keys with spaces are not allowed.
+    """
+    items = []
+
+    for key, value in d.items():
+        if value is None or isinstance(value, Undefined):
+            continue
+
+        if _attr_key_re.search(key) is not None:
+            raise ValueError(f"Invalid character in attribute name: {key!r}")
+
+        items.append(f'{escape(key)}="{escape(value)}"')
+
+    rv = " ".join(items)
+
+    if autospace and rv:
+        rv = " " + rv
+
+    if eval_ctx.autoescape:
+        rv = Markup(rv)
+
+    return rv
+
+
+def do_capitalize(s: str) -> str:
+    """Capitalize a value. The first character will be uppercase, all others
+    lowercase.
+    """
+    return soft_str(s).capitalize()
+
+
+_word_beginning_split_re = re.compile(r"([-\s({\[<]+)")
+
+
+def do_title(s: str) -> str:
+    """Return a titlecased version of the value. I.e. words will start with
+    uppercase letters, all remaining characters are lowercase.
+    """
+    return "".join(
+        [
+            item[0].upper() + item[1:].lower()
+            for item in _word_beginning_split_re.split(soft_str(s))
+            if item
+        ]
+    )
+
+
+def do_dictsort(
+    value: t.Mapping[K, V],
+    case_sensitive: bool = False,
+    by: 'te.Literal["key", "value"]' = "key",
+    reverse: bool = False,
+) -> t.List[t.Tuple[K, V]]:
+    """Sort a dict and yield (key, value) pairs. Python dicts may not
+    be in the order you want to display them in, so sort them first.
+
+    .. sourcecode:: jinja
+
+        {% for key, value in mydict|dictsort %}
+            sort the dict by key, case insensitive
+
+        {% for key, value in mydict|dictsort(reverse=true) %}
+            sort the dict by key, case insensitive, reverse order
+
+        {% for key, value in mydict|dictsort(true) %}
+            sort the dict by key, case sensitive
+
+        {% for key, value in mydict|dictsort(false, 'value') %}
+            sort the dict by value, case insensitive
+    """
+    if by == "key":
+        pos = 0
+    elif by == "value":
+        pos = 1
+    else:
+        raise FilterArgumentError('You can only sort by either "key" or "value"')
+
+    def sort_func(item: t.Tuple[t.Any, t.Any]) -> t.Any:
+        value = item[pos]
+
+        if not case_sensitive:
+            value = ignore_case(value)
+
+        return value
+
+    return sorted(value.items(), key=sort_func, reverse=reverse)
+
+
+@pass_environment
+def do_sort(
+    environment: "Environment",
+    value: "t.Iterable[V]",
+    reverse: bool = False,
+    case_sensitive: bool = False,
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> "t.List[V]":
+    """Sort an iterable using Python's :func:`sorted`.
+
+    .. sourcecode:: jinja
+
+        {% for city in cities|sort %}
+            ...
+        {% endfor %}
+
+    :param reverse: Sort descending instead of ascending.
+    :param case_sensitive: When sorting strings, sort upper and lower
+        case separately.
+    :param attribute: When sorting objects or dicts, an attribute or
+        key to sort by. Can use dot notation like ``"address.city"``.
+        Can be a list of attributes like ``"age,name"``.
+
+    The sort is stable, it does not change the relative order of
+    elements that compare equal. This makes it is possible to chain
+    sorts on different attributes and ordering.
+
+    .. sourcecode:: jinja
+
+        {% for user in users|sort(attribute="name")
+            |sort(reverse=true, attribute="age") %}
+            ...
+        {% endfor %}
+
+    As a shortcut to chaining when the direction is the same for all
+    attributes, pass a comma separate list of attributes.
+
+    .. sourcecode:: jinja
+
+        {% for user in users|sort(attribute="age,name") %}
+            ...
+        {% endfor %}
+
+    .. versionchanged:: 2.11.0
+        The ``attribute`` parameter can be a comma separated list of
+        attributes, e.g. ``"age,name"``.
+
+    .. versionchanged:: 2.6
+       The ``attribute`` parameter was added.
+    """
+    key_func = make_multi_attrgetter(
+        environment, attribute, postprocess=ignore_case if not case_sensitive else None
+    )
+    return sorted(value, key=key_func, reverse=reverse)
+
+
+@pass_environment
+def sync_do_unique(
+    environment: "Environment",
+    value: "t.Iterable[V]",
+    case_sensitive: bool = False,
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> "t.Iterator[V]":
+    """Returns a list of unique items from the given iterable.
+
+    .. sourcecode:: jinja
+
+        {{ ['foo', 'bar', 'foobar', 'FooBar']|unique|list }}
+            -> ['foo', 'bar', 'foobar']
+
+    The unique items are yielded in the same order as their first occurrence in
+    the iterable passed to the filter.
+
+    :param case_sensitive: Treat upper and lower case strings as distinct.
+    :param attribute: Filter objects with unique values for this attribute.
+    """
+    getter = make_attrgetter(
+        environment, attribute, postprocess=ignore_case if not case_sensitive else None
+    )
+    seen = set()
+
+    for item in value:
+        key = getter(item)
+
+        if key not in seen:
+            seen.add(key)
+            yield item
+
+
+@async_variant(sync_do_unique)  # type: ignore
+async def do_unique(
+    environment: "Environment",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    case_sensitive: bool = False,
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> "t.Iterator[V]":
+    return sync_do_unique(
+        environment, await auto_to_list(value), case_sensitive, attribute
+    )
+
+
+def _min_or_max(
+    environment: "Environment",
+    value: "t.Iterable[V]",
+    func: "t.Callable[..., V]",
+    case_sensitive: bool,
+    attribute: t.Optional[t.Union[str, int]],
+) -> "t.Union[V, Undefined]":
+    it = iter(value)
+
+    try:
+        first = next(it)
+    except StopIteration:
+        return environment.undefined("No aggregated item, sequence was empty.")
+
+    key_func = make_attrgetter(
+        environment, attribute, postprocess=ignore_case if not case_sensitive else None
+    )
+    return func(chain([first], it), key=key_func)
+
+
+@pass_environment
+def do_min(
+    environment: "Environment",
+    value: "t.Iterable[V]",
+    case_sensitive: bool = False,
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> "t.Union[V, Undefined]":
+    """Return the smallest item from the sequence.
+
+    .. sourcecode:: jinja
+
+        {{ [1, 2, 3]|min }}
+            -> 1
+
+    :param case_sensitive: Treat upper and lower case strings as distinct.
+    :param attribute: Get the object with the min value of this attribute.
+    """
+    return _min_or_max(environment, value, min, case_sensitive, attribute)
+
+
+@pass_environment
+def do_max(
+    environment: "Environment",
+    value: "t.Iterable[V]",
+    case_sensitive: bool = False,
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> "t.Union[V, Undefined]":
+    """Return the largest item from the sequence.
+
+    .. sourcecode:: jinja
+
+        {{ [1, 2, 3]|max }}
+            -> 3
+
+    :param case_sensitive: Treat upper and lower case strings as distinct.
+    :param attribute: Get the object with the max value of this attribute.
+    """
+    return _min_or_max(environment, value, max, case_sensitive, attribute)
+
+
+def do_default(
+    value: V,
+    default_value: V = "",  # type: ignore
+    boolean: bool = False,
+) -> V:
+    """If the value is undefined it will return the passed default value,
+    otherwise the value of the variable:
+
+    .. sourcecode:: jinja
+
+        {{ my_variable|default('my_variable is not defined') }}
+
+    This will output the value of ``my_variable`` if the variable was
+    defined, otherwise ``'my_variable is not defined'``. If you want
+    to use default with variables that evaluate to false you have to
+    set the second parameter to `true`:
+
+    .. sourcecode:: jinja
+
+        {{ ''|default('the string was empty', true) }}
+
+    .. versionchanged:: 2.11
+       It's now possible to configure the :class:`~jinja2.Environment` with
+       :class:`~jinja2.ChainableUndefined` to make the `default` filter work
+       on nested elements and attributes that may contain undefined values
+       in the chain without getting an :exc:`~jinja2.UndefinedError`.
+    """
+    if isinstance(value, Undefined) or (boolean and not value):
+        return default_value
+
+    return value
+
+
+@pass_eval_context
+def sync_do_join(
+    eval_ctx: "EvalContext",
+    value: t.Iterable[t.Any],
+    d: str = "",
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> str:
+    """Return a string which is the concatenation of the strings in the
+    sequence. The separator between elements is an empty string per
+    default, you can define it with the optional parameter:
+
+    .. sourcecode:: jinja
+
+        {{ [1, 2, 3]|join('|') }}
+            -> 1|2|3
+
+        {{ [1, 2, 3]|join }}
+            -> 123
+
+    It is also possible to join certain attributes of an object:
+
+    .. sourcecode:: jinja
+
+        {{ users|join(', ', attribute='username') }}
+
+    .. versionadded:: 2.6
+       The `attribute` parameter was added.
+    """
+    if attribute is not None:
+        value = map(make_attrgetter(eval_ctx.environment, attribute), value)
+
+    # no automatic escaping?  joining is a lot easier then
+    if not eval_ctx.autoescape:
+        return str(d).join(map(str, value))
+
+    # if the delimiter doesn't have an html representation we check
+    # if any of the items has.  If yes we do a coercion to Markup
+    if not hasattr(d, "__html__"):
+        value = list(value)
+        do_escape = False
+
+        for idx, item in enumerate(value):
+            if hasattr(item, "__html__"):
+                do_escape = True
+            else:
+                value[idx] = str(item)
+
+        if do_escape:
+            d = escape(d)
+        else:
+            d = str(d)
+
+        return d.join(value)
+
+    # no html involved, to normal joining
+    return soft_str(d).join(map(soft_str, value))
+
+
+@async_variant(sync_do_join)  # type: ignore
+async def do_join(
+    eval_ctx: "EvalContext",
+    value: t.Union[t.AsyncIterable[t.Any], t.Iterable[t.Any]],
+    d: str = "",
+    attribute: t.Optional[t.Union[str, int]] = None,
+) -> str:
+    return sync_do_join(eval_ctx, await auto_to_list(value), d, attribute)
+
+
+def do_center(value: str, width: int = 80) -> str:
+    """Centers the value in a field of a given width."""
+    return soft_str(value).center(width)
+
+
+@pass_environment
+def sync_do_first(
+    environment: "Environment", seq: "t.Iterable[V]"
+) -> "t.Union[V, Undefined]":
+    """Return the first item of a sequence."""
+    try:
+        return next(iter(seq))
+    except StopIteration:
+        return environment.undefined("No first item, sequence was empty.")
+
+
+@async_variant(sync_do_first)  # type: ignore
+async def do_first(
+    environment: "Environment", seq: "t.Union[t.AsyncIterable[V], t.Iterable[V]]"
+) -> "t.Union[V, Undefined]":
+    try:
+        return await auto_aiter(seq).__anext__()
+    except StopAsyncIteration:
+        return environment.undefined("No first item, sequence was empty.")
+
+
+@pass_environment
+def do_last(
+    environment: "Environment", seq: "t.Reversible[V]"
+) -> "t.Union[V, Undefined]":
+    """Return the last item of a sequence.
+
+    Note: Does not work with generators. You may want to explicitly
+    convert it to a list:
+
+    .. sourcecode:: jinja
+
+        {{ data | selectattr('name', '==', 'Jinja') | list | last }}
+    """
+    try:
+        return next(iter(reversed(seq)))
+    except StopIteration:
+        return environment.undefined("No last item, sequence was empty.")
+
+
+# No async do_last, it may not be safe in async mode.
+
+
+@pass_context
+def do_random(context: "Context", seq: "t.Sequence[V]") -> "t.Union[V, Undefined]":
+    """Return a random item from the sequence."""
+    try:
+        return random.choice(seq)
+    except IndexError:
+        return context.environment.undefined("No random item, sequence was empty.")
+
+
+def do_filesizeformat(value: t.Union[str, float, int], binary: bool = False) -> str:
+    """Format the value like a 'human-readable' file size (i.e. 13 kB,
+    4.1 MB, 102 Bytes, etc).  Per default decimal prefixes are used (Mega,
+    Giga, etc.), if the second parameter is set to `True` the binary
+    prefixes are used (Mebi, Gibi).
+    """
+    bytes = float(value)
+    base = 1024 if binary else 1000
+    prefixes = [
+        ("KiB" if binary else "kB"),
+        ("MiB" if binary else "MB"),
+        ("GiB" if binary else "GB"),
+        ("TiB" if binary else "TB"),
+        ("PiB" if binary else "PB"),
+        ("EiB" if binary else "EB"),
+        ("ZiB" if binary else "ZB"),
+        ("YiB" if binary else "YB"),
+    ]
+
+    if bytes == 1:
+        return "1 Byte"
+    elif bytes < base:
+        return f"{int(bytes)} Bytes"
+    else:
+        for i, prefix in enumerate(prefixes):
+            unit = base ** (i + 2)
+
+            if bytes < unit:
+                return f"{base * bytes / unit:.1f} {prefix}"
+
+        return f"{base * bytes / unit:.1f} {prefix}"
+
+
+def do_pprint(value: t.Any) -> str:
+    """Pretty print a variable. Useful for debugging."""
+    return pformat(value)
+
+
+_uri_scheme_re = re.compile(r"^([\w.+-]{2,}:(/){0,2})$")
+
+
+@pass_eval_context
+def do_urlize(
+    eval_ctx: "EvalContext",
+    value: str,
+    trim_url_limit: t.Optional[int] = None,
+    nofollow: bool = False,
+    target: t.Optional[str] = None,
+    rel: t.Optional[str] = None,
+    extra_schemes: t.Optional[t.Iterable[str]] = None,
+) -> str:
+    """Convert URLs in text into clickable links.
+
+    This may not recognize links in some situations. Usually, a more
+    comprehensive formatter, such as a Markdown library, is a better
+    choice.
+
+    Works on ``http://``, ``https://``, ``www.``, ``mailto:``, and email
+    addresses. Links with trailing punctuation (periods, commas, closing
+    parentheses) and leading punctuation (opening parentheses) are
+    recognized excluding the punctuation. Email addresses that include
+    header fields are not recognized (for example,
+    ``mailto:address@example.com?cc=copy@example.com``).
+
+    :param value: Original text containing URLs to link.
+    :param trim_url_limit: Shorten displayed URL values to this length.
+    :param nofollow: Add the ``rel=nofollow`` attribute to links.
+    :param target: Add the ``target`` attribute to links.
+    :param rel: Add the ``rel`` attribute to links.
+    :param extra_schemes: Recognize URLs that start with these schemes
+        in addition to the default behavior. Defaults to
+        ``env.policies["urlize.extra_schemes"]``, which defaults to no
+        extra schemes.
+
+    .. versionchanged:: 3.0
+        The ``extra_schemes`` parameter was added.
+
+    .. versionchanged:: 3.0
+        Generate ``https://`` links for URLs without a scheme.
+
+    .. versionchanged:: 3.0
+        The parsing rules were updated. Recognize email addresses with
+        or without the ``mailto:`` scheme. Validate IP addresses. Ignore
+        parentheses and brackets in more cases.
+
+    .. versionchanged:: 2.8
+       The ``target`` parameter was added.
+    """
+    policies = eval_ctx.environment.policies
+    rel_parts = set((rel or "").split())
+
+    if nofollow:
+        rel_parts.add("nofollow")
+
+    rel_parts.update((policies["urlize.rel"] or "").split())
+    rel = " ".join(sorted(rel_parts)) or None
+
+    if target is None:
+        target = policies["urlize.target"]
+
+    if extra_schemes is None:
+        extra_schemes = policies["urlize.extra_schemes"] or ()
+
+    for scheme in extra_schemes:
+        if _uri_scheme_re.fullmatch(scheme) is None:
+            raise FilterArgumentError(f"{scheme!r} is not a valid URI scheme prefix.")
+
+    rv = urlize(
+        value,
+        trim_url_limit=trim_url_limit,
+        rel=rel,
+        target=target,
+        extra_schemes=extra_schemes,
+    )
+
+    if eval_ctx.autoescape:
+        rv = Markup(rv)
+
+    return rv
+
+
+def do_indent(
+    s: str, width: t.Union[int, str] = 4, first: bool = False, blank: bool = False
+) -> str:
+    """Return a copy of the string with each line indented by 4 spaces. The
+    first line and blank lines are not indented by default.
+
+    :param width: Number of spaces, or a string, to indent by.
+    :param first: Don't skip indenting the first line.
+    :param blank: Don't skip indenting empty lines.
+
+    .. versionchanged:: 3.0
+        ``width`` can be a string.
+
+    .. versionchanged:: 2.10
+        Blank lines are not indented by default.
+
+        Rename the ``indentfirst`` argument to ``first``.
+    """
+    if isinstance(width, str):
+        indention = width
+    else:
+        indention = " " * width
+
+    newline = "\n"
+
+    if isinstance(s, Markup):
+        indention = Markup(indention)
+        newline = Markup(newline)
+
+    s += newline  # this quirk is necessary for splitlines method
+
+    if blank:
+        rv = (newline + indention).join(s.splitlines())
+    else:
+        lines = s.splitlines()
+        rv = lines.pop(0)
+
+        if lines:
+            rv += newline + newline.join(
+                indention + line if line else line for line in lines
+            )
+
+    if first:
+        rv = indention + rv
+
+    return rv
+
+
+@pass_environment
+def do_truncate(
+    env: "Environment",
+    s: str,
+    length: int = 255,
+    killwords: bool = False,
+    end: str = "...",
+    leeway: t.Optional[int] = None,
+) -> str:
+    """Return a truncated copy of the string. The length is specified
+    with the first parameter which defaults to ``255``. If the second
+    parameter is ``true`` the filter will cut the text at length. Otherwise
+    it will discard the last word. If the text was in fact
+    truncated it will append an ellipsis sign (``"..."``). If you want a
+    different ellipsis sign than ``"..."`` you can specify it using the
+    third parameter. Strings that only exceed the length by the tolerance
+    margin given in the fourth parameter will not be truncated.
+
+    .. sourcecode:: jinja
+
+        {{ "foo bar baz qux"|truncate(9) }}
+            -> "foo..."
+        {{ "foo bar baz qux"|truncate(9, True) }}
+            -> "foo ba..."
+        {{ "foo bar baz qux"|truncate(11) }}
+            -> "foo bar baz qux"
+        {{ "foo bar baz qux"|truncate(11, False, '...', 0) }}
+            -> "foo bar..."
+
+    The default leeway on newer Jinja versions is 5 and was 0 before but
+    can be reconfigured globally.
+    """
+    if leeway is None:
+        leeway = env.policies["truncate.leeway"]
+
+    assert length >= len(end), f"expected length >= {len(end)}, got {length}"
+    assert leeway >= 0, f"expected leeway >= 0, got {leeway}"
+
+    if len(s) <= length + leeway:
+        return s
+
+    if killwords:
+        return s[: length - len(end)] + end
+
+    result = s[: length - len(end)].rsplit(" ", 1)[0]
+    return result + end
+
+
+@pass_environment
+def do_wordwrap(
+    environment: "Environment",
+    s: str,
+    width: int = 79,
+    break_long_words: bool = True,
+    wrapstring: t.Optional[str] = None,
+    break_on_hyphens: bool = True,
+) -> str:
+    """Wrap a string to the given width. Existing newlines are treated
+    as paragraphs to be wrapped separately.
+
+    :param s: Original text to wrap.
+    :param width: Maximum length of wrapped lines.
+    :param break_long_words: If a word is longer than ``width``, break
+        it across lines.
+    :param break_on_hyphens: If a word contains hyphens, it may be split
+        across lines.
+    :param wrapstring: String to join each wrapped line. Defaults to
+        :attr:`Environment.newline_sequence`.
+
+    .. versionchanged:: 2.11
+        Existing newlines are treated as paragraphs wrapped separately.
+
+    .. versionchanged:: 2.11
+        Added the ``break_on_hyphens`` parameter.
+
+    .. versionchanged:: 2.7
+        Added the ``wrapstring`` parameter.
+    """
+    import textwrap
+
+    if wrapstring is None:
+        wrapstring = environment.newline_sequence
+
+    # textwrap.wrap doesn't consider existing newlines when wrapping.
+    # If the string has a newline before width, wrap will still insert
+    # a newline at width, resulting in a short line. Instead, split and
+    # wrap each paragraph individually.
+    return wrapstring.join(
+        [
+            wrapstring.join(
+                textwrap.wrap(
+                    line,
+                    width=width,
+                    expand_tabs=False,
+                    replace_whitespace=False,
+                    break_long_words=break_long_words,
+                    break_on_hyphens=break_on_hyphens,
+                )
+            )
+            for line in s.splitlines()
+        ]
+    )
+
+
+_word_re = re.compile(r"\w+")
+
+
+def do_wordcount(s: str) -> int:
+    """Count the words in that string."""
+    return len(_word_re.findall(soft_str(s)))
+
+
+def do_int(value: t.Any, default: int = 0, base: int = 10) -> int:
+    """Convert the value into an integer. If the
+    conversion doesn't work it will return ``0``. You can
+    override this default using the first parameter. You
+    can also override the default base (10) in the second
+    parameter, which handles input with prefixes such as
+    0b, 0o and 0x for bases 2, 8 and 16 respectively.
+    The base is ignored for decimal numbers and non-string values.
+    """
+    try:
+        if isinstance(value, str):
+            return int(value, base)
+
+        return int(value)
+    except (TypeError, ValueError):
+        # this quirk is necessary so that "42.23"|int gives 42.
+        try:
+            return int(float(value))
+        except (TypeError, ValueError, OverflowError):
+            return default
+
+
+def do_float(value: t.Any, default: float = 0.0) -> float:
+    """Convert the value into a floating point number. If the
+    conversion doesn't work it will return ``0.0``. You can
+    override this default using the first parameter.
+    """
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def do_format(value: str, *args: t.Any, **kwargs: t.Any) -> str:
+    """Apply the given values to a `printf-style`_ format string, like
+    ``string % values``.
+
+    .. sourcecode:: jinja
+
+        {{ "%s, %s!"|format(greeting, name) }}
+        Hello, World!
+
+    In most cases it should be more convenient and efficient to use the
+    ``%`` operator or :meth:`str.format`.
+
+    .. code-block:: text
+
+        {{ "%s, %s!" % (greeting, name) }}
+        {{ "{}, {}!".format(greeting, name) }}
+
+    .. _printf-style: https://docs.python.org/library/stdtypes.html
+        #printf-style-string-formatting
+    """
+    if args and kwargs:
+        raise FilterArgumentError(
+            "can't handle positional and keyword arguments at the same time"
+        )
+
+    return soft_str(value) % (kwargs or args)
+
+
+def do_trim(value: str, chars: t.Optional[str] = None) -> str:
+    """Strip leading and trailing characters, by default whitespace."""
+    return soft_str(value).strip(chars)
+
+
+def do_striptags(value: "t.Union[str, HasHTML]") -> str:
+    """Strip SGML/XML tags and replace adjacent whitespace by one space."""
+    if hasattr(value, "__html__"):
+        value = t.cast("HasHTML", value).__html__()
+
+    return Markup(str(value)).striptags()
+
+
+def sync_do_slice(
+    value: "t.Collection[V]", slices: int, fill_with: "t.Optional[V]" = None
+) -> "t.Iterator[t.List[V]]":
+    """Slice an iterator and return a list of lists containing
+    those items. Useful if you want to create a div containing
+    three ul tags that represent columns:
+
+    .. sourcecode:: html+jinja
+
+        <div class="columnwrapper">
+          {%- for column in items|slice(3) %}
+            <ul class="column-{{ loop.index }}">
+            {%- for item in column %}
+              <li>{{ item }}</li>
+            {%- endfor %}
+            </ul>
+          {%- endfor %}
+        </div>
+
+    If you pass it a second argument it's used to fill missing
+    values on the last iteration.
+    """
+    seq = list(value)
+    length = len(seq)
+    items_per_slice = length // slices
+    slices_with_extra = length % slices
+    offset = 0
+
+    for slice_number in range(slices):
+        start = offset + slice_number * items_per_slice
+
+        if slice_number < slices_with_extra:
+            offset += 1
+
+        end = offset + (slice_number + 1) * items_per_slice
+        tmp = seq[start:end]
+
+        if fill_with is not None and slice_number >= slices_with_extra:
+            tmp.append(fill_with)
+
+        yield tmp
+
+
+@async_variant(sync_do_slice)  # type: ignore
+async def do_slice(
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    slices: int,
+    fill_with: t.Optional[t.Any] = None,
+) -> "t.Iterator[t.List[V]]":
+    return sync_do_slice(await auto_to_list(value), slices, fill_with)
+
+
+def do_batch(
+    value: "t.Iterable[V]", linecount: int, fill_with: "t.Optional[V]" = None
+) -> "t.Iterator[t.List[V]]":
+    """
+    A filter that batches items. It works pretty much like `slice`
+    just the other way round. It returns a list of lists with the
+    given number of items. If you provide a second parameter this
+    is used to fill up missing items. See this example:
+
+    .. sourcecode:: html+jinja
+
+        <table>
+        {%- for row in items|batch(3, '&nbsp;') %}
+          <tr>
+          {%- for column in row %}
+            <td>{{ column }}</td>
+          {%- endfor %}
+          </tr>
+        {%- endfor %}
+        </table>
+    """
+    tmp: t.List[V] = []
+
+    for item in value:
+        if len(tmp) == linecount:
+            yield tmp
+            tmp = []
+
+        tmp.append(item)
+
+    if tmp:
+        if fill_with is not None and len(tmp) < linecount:
+            tmp += [fill_with] * (linecount - len(tmp))
+
+        yield tmp
+
+
+def do_round(
+    value: float,
+    precision: int = 0,
+    method: 'te.Literal["common", "ceil", "floor"]' = "common",
+) -> float:
+    """Round the number to a given precision. The first
+    parameter specifies the precision (default is ``0``), the
+    second the rounding method:
+
+    - ``'common'`` rounds either up or down
+    - ``'ceil'`` always rounds up
+    - ``'floor'`` always rounds down
+
+    If you don't specify a method ``'common'`` is used.
+
+    .. sourcecode:: jinja
+
+        {{ 42.55|round }}
+            -> 43.0
+        {{ 42.55|round(1, 'floor') }}
+            -> 42.5
+
+    Note that even if rounded to 0 precision, a float is returned.  If
+    you need a real integer, pipe it through `int`:
+
+    .. sourcecode:: jinja
+
+        {{ 42.55|round|int }}
+            -> 43
+    """
+    if method not in {"common", "ceil", "floor"}:
+        raise FilterArgumentError("method must be common, ceil or floor")
+
+    if method == "common":
+        return round(value, precision)
+
+    func = getattr(math, method)
+    return t.cast(float, func(value * (10**precision)) / (10**precision))
+
+
+class _GroupTuple(t.NamedTuple):
+    grouper: t.Any
+    list: t.List[t.Any]
+
+    # Use the regular tuple repr to hide this subclass if users print
+    # out the value during debugging.
+    def __repr__(self) -> str:
+        return tuple.__repr__(self)
+
+    def __str__(self) -> str:
+        return tuple.__str__(self)
+
+
+@pass_environment
+def sync_do_groupby(
+    environment: "Environment",
+    value: "t.Iterable[V]",
+    attribute: t.Union[str, int],
+    default: t.Optional[t.Any] = None,
+    case_sensitive: bool = False,
+) -> "t.List[_GroupTuple]":
+    """Group a sequence of objects by an attribute using Python's
+    :func:`itertools.groupby`. The attribute can use dot notation for
+    nested access, like ``"address.city"``. Unlike Python's ``groupby``,
+    the values are sorted first so only one group is returned for each
+    unique value.
+
+    For example, a list of ``User`` objects with a ``city`` attribute
+    can be rendered in groups. In this example, ``grouper`` refers to
+    the ``city`` value of the group.
+
+    .. sourcecode:: html+jinja
+
+        <ul>{% for city, items in users|groupby("city") %}
+          <li>{{ city }}
+            <ul>{% for user in items %}
+              <li>{{ user.name }}
+            {% endfor %}</ul>
+          </li>
+        {% endfor %}</ul>
+
+    ``groupby`` yields namedtuples of ``(grouper, list)``, which
+    can be used instead of the tuple unpacking above. ``grouper`` is the
+    value of the attribute, and ``list`` is the items with that value.
+
+    .. sourcecode:: html+jinja
+
+        <ul>{% for group in users|groupby("city") %}
+          <li>{{ group.grouper }}: {{ group.list|join(", ") }}
+        {% endfor %}</ul>
+
+    You can specify a ``default`` value to use if an object in the list
+    does not have the given attribute.
+
+    .. sourcecode:: jinja
+
+        <ul>{% for city, items in users|groupby("city", default="NY") %}
+          <li>{{ city }}: {{ items|map(attribute="name")|join(", ") }}</li>
+        {% endfor %}</ul>
+
+    Like the :func:`~jinja-filters.sort` filter, sorting and grouping is
+    case-insensitive by default. The ``key`` for each group will have
+    the case of the first item in that group of values. For example, if
+    a list of users has cities ``["CA", "NY", "ca"]``, the "CA" group
+    will have two values. This can be disabled by passing
+    ``case_sensitive=True``.
+
+    .. versionchanged:: 3.1
+        Added the ``case_sensitive`` parameter. Sorting and grouping is
+        case-insensitive by default, matching other filters that do
+        comparisons.
+
+    .. versionchanged:: 3.0
+        Added the ``default`` parameter.
+
+    .. versionchanged:: 2.6
+        The attribute supports dot notation for nested access.
+    """
+    expr = make_attrgetter(
+        environment,
+        attribute,
+        postprocess=ignore_case if not case_sensitive else None,
+        default=default,
+    )
+    out = [
+        _GroupTuple(key, list(values))
+        for key, values in groupby(sorted(value, key=expr), expr)
+    ]
+
+    if not case_sensitive:
+        # Return the real key from the first value instead of the lowercase key.
+        output_expr = make_attrgetter(environment, attribute, default=default)
+        out = [_GroupTuple(output_expr(values[0]), values) for _, values in out]
+
+    return out
+
+
+@async_variant(sync_do_groupby)  # type: ignore
+async def do_groupby(
+    environment: "Environment",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    attribute: t.Union[str, int],
+    default: t.Optional[t.Any] = None,
+    case_sensitive: bool = False,
+) -> "t.List[_GroupTuple]":
+    expr = make_attrgetter(
+        environment,
+        attribute,
+        postprocess=ignore_case if not case_sensitive else None,
+        default=default,
+    )
+    out = [
+        _GroupTuple(key, await auto_to_list(values))
+        for key, values in groupby(sorted(await auto_to_list(value), key=expr), expr)
+    ]
+
+    if not case_sensitive:
+        # Return the real key from the first value instead of the lowercase key.
+        output_expr = make_attrgetter(environment, attribute, default=default)
+        out = [_GroupTuple(output_expr(values[0]), values) for _, values in out]
+
+    return out
+
+
+@pass_environment
+def sync_do_sum(
+    environment: "Environment",
+    iterable: "t.Iterable[V]",
+    attribute: t.Optional[t.Union[str, int]] = None,
+    start: V = 0,  # type: ignore
+) -> V:
+    """Returns the sum of a sequence of numbers plus the value of parameter
+    'start' (which defaults to 0).  When the sequence is empty it returns
+    start.
+
+    It is also possible to sum up only certain attributes:
+
+    .. sourcecode:: jinja
+
+        Total: {{ items|sum(attribute='price') }}
+
+    .. versionchanged:: 2.6
+       The ``attribute`` parameter was added to allow summing up over
+       attributes.  Also the ``start`` parameter was moved on to the right.
+    """
+    if attribute is not None:
+        iterable = map(make_attrgetter(environment, attribute), iterable)
+
+    return sum(iterable, start)  # type: ignore[no-any-return, call-overload]
+
+
+@async_variant(sync_do_sum)  # type: ignore
+async def do_sum(
+    environment: "Environment",
+    iterable: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    attribute: t.Optional[t.Union[str, int]] = None,
+    start: V = 0,  # type: ignore
+) -> V:
+    rv = start
+
+    if attribute is not None:
+        func = make_attrgetter(environment, attribute)
+    else:
+
+        def func(x: V) -> V:
+            return x
+
+    async for item in auto_aiter(iterable):
+        rv += func(item)
+
+    return rv
+
+
+def sync_do_list(value: "t.Iterable[V]") -> "t.List[V]":
+    """Convert the value into a list.  If it was a string the returned list
+    will be a list of characters.
+    """
+    return list(value)
+
+
+@async_variant(sync_do_list)  # type: ignore
+async def do_list(value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]") -> "t.List[V]":
+    return await auto_to_list(value)
+
+
+def do_mark_safe(value: str) -> Markup:
+    """Mark the value as safe which means that in an environment with automatic
+    escaping enabled this variable will not be escaped.
+    """
+    return Markup(value)
+
+
+def do_mark_unsafe(value: str) -> str:
+    """Mark a value as unsafe.  This is the reverse operation for :func:`safe`."""
+    return str(value)
+
+
+@typing.overload
+def do_reverse(value: str) -> str: ...
+
+
+@typing.overload
+def do_reverse(value: "t.Iterable[V]") -> "t.Iterable[V]": ...
+
+
+def do_reverse(value: t.Union[str, t.Iterable[V]]) -> t.Union[str, t.Iterable[V]]:
+    """Reverse the object or return an iterator that iterates over it the other
+    way round.
+    """
+    if isinstance(value, str):
+        return value[::-1]
+
+    try:
+        return reversed(value)  # type: ignore
+    except TypeError:
+        try:
+            rv = list(value)
+            rv.reverse()
+            return rv
+        except TypeError as e:
+            raise FilterArgumentError("argument must be iterable") from e
+
+
+@pass_environment
+def do_attr(
+    environment: "Environment", obj: t.Any, name: str
+) -> t.Union[Undefined, t.Any]:
+    """Get an attribute of an object. ``foo|attr("bar")`` works like
+    ``foo.bar``, but returns undefined instead of falling back to ``foo["bar"]``
+    if the attribute doesn't exist.
+
+    See :ref:`Notes on subscriptions <notes-on-subscriptions>` for more details.
+    """
+    # Environment.getattr will fall back to obj[name] if obj.name doesn't exist.
+    # But we want to call env.getattr to get behavior such as sandboxing.
+    # Determine if the attr exists first, so we know the fallback won't trigger.
+    try:
+        # This avoids executing properties/descriptors, but misses __getattr__
+        # and __getattribute__ dynamic attrs.
+        getattr_static(obj, name)
+    except AttributeError:
+        # This finds dynamic attrs, and we know it's not a descriptor at this point.
+        if not hasattr(obj, name):
+            return environment.undefined(obj=obj, name=name)
+
+    return environment.getattr(obj, name)
+
+
+@typing.overload
+def sync_do_map(
+    context: "Context",
+    value: t.Iterable[t.Any],
+    name: str,
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> t.Iterable[t.Any]: ...
+
+
+@typing.overload
+def sync_do_map(
+    context: "Context",
+    value: t.Iterable[t.Any],
+    *,
+    attribute: str = ...,
+    default: t.Optional[t.Any] = None,
+) -> t.Iterable[t.Any]: ...
+
+
+@pass_context
+def sync_do_map(
+    context: "Context", value: t.Iterable[t.Any], *args: t.Any, **kwargs: t.Any
+) -> t.Iterable[t.Any]:
+    """Applies a filter on a sequence of objects or looks up an attribute.
+    This is useful when dealing with lists of objects but you are really
+    only interested in a certain value of it.
+
+    The basic usage is mapping on an attribute.  Imagine you have a list
+    of users but you are only interested in a list of usernames:
+
+    .. sourcecode:: jinja
+
+        Users on this page: {{ users|map(attribute='username')|join(', ') }}
+
+    You can specify a ``default`` value to use if an object in the list
+    does not have the given attribute.
+
+    .. sourcecode:: jinja
+
+        {{ users|map(attribute="username", default="Anonymous")|join(", ") }}
+
+    Alternatively you can let it invoke a filter by passing the name of the
+    filter and the arguments afterwards.  A good example would be applying a
+    text conversion filter on a sequence:
+
+    .. sourcecode:: jinja
+
+        Users on this page: {{ titles|map('lower')|join(', ') }}
+
+    Similar to a generator comprehension such as:
+
+    .. code-block:: python
+
+        (u.username for u in users)
+        (getattr(u, "username", "Anonymous") for u in users)
+        (do_lower(x) for x in titles)
+
+    .. versionchanged:: 2.11.0
+        Added the ``default`` parameter.
+
+    .. versionadded:: 2.7
+    """
+    if value:
+        func = prepare_map(context, args, kwargs)
+
+        for item in value:
+            yield func(item)
+
+
+@typing.overload
+def do_map(
+    context: "Context",
+    value: t.Union[t.AsyncIterable[t.Any], t.Iterable[t.Any]],
+    name: str,
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> t.Iterable[t.Any]: ...
+
+
+@typing.overload
+def do_map(
+    context: "Context",
+    value: t.Union[t.AsyncIterable[t.Any], t.Iterable[t.Any]],
+    *,
+    attribute: str = ...,
+    default: t.Optional[t.Any] = None,
+) -> t.Iterable[t.Any]: ...
+
+
+@async_variant(sync_do_map)  # type: ignore
+async def do_map(
+    context: "Context",
+    value: t.Union[t.AsyncIterable[t.Any], t.Iterable[t.Any]],
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> t.AsyncIterable[t.Any]:
+    if value:
+        func = prepare_map(context, args, kwargs)
+
+        async for item in auto_aiter(value):
+            yield await auto_await(func(item))
+
+
+@pass_context
+def sync_do_select(
+    context: "Context", value: "t.Iterable[V]", *args: t.Any, **kwargs: t.Any
+) -> "t.Iterator[V]":
+    """Filters a sequence of objects by applying a test to each object,
+    and only selecting the objects with the test succeeding.
+
+    If no test is specified, each object will be evaluated as a boolean.
+
+    Example usage:
+
+    .. sourcecode:: jinja
+
+        {{ numbers|select("odd") }}
+        {{ numbers|select("odd") }}
+        {{ numbers|select("divisibleby", 3) }}
+        {{ numbers|select("lessthan", 42) }}
+        {{ strings|select("equalto", "mystring") }}
+
+    Similar to a generator comprehension such as:
+
+    .. code-block:: python
+
+        (n for n in numbers if test_odd(n))
+        (n for n in numbers if test_divisibleby(n, 3))
+
+    .. versionadded:: 2.7
+    """
+    return select_or_reject(context, value, args, kwargs, lambda x: x, False)
+
+
+@async_variant(sync_do_select)  # type: ignore
+async def do_select(
+    context: "Context",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> "t.AsyncIterator[V]":
+    return async_select_or_reject(context, value, args, kwargs, lambda x: x, False)
+
+
+@pass_context
+def sync_do_reject(
+    context: "Context", value: "t.Iterable[V]", *args: t.Any, **kwargs: t.Any
+) -> "t.Iterator[V]":
+    """Filters a sequence of objects by applying a test to each object,
+    and rejecting the objects with the test succeeding.
+
+    If no test is specified, each object will be evaluated as a boolean.
+
+    Example usage:
+
+    .. sourcecode:: jinja
+
+        {{ numbers|reject("odd") }}
+
+    Similar to a generator comprehension such as:
+
+    .. code-block:: python
+
+        (n for n in numbers if not test_odd(n))
+
+    .. versionadded:: 2.7
+    """
+    return select_or_reject(context, value, args, kwargs, lambda x: not x, False)
+
+
+@async_variant(sync_do_reject)  # type: ignore
+async def do_reject(
+    context: "Context",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> "t.AsyncIterator[V]":
+    return async_select_or_reject(context, value, args, kwargs, lambda x: not x, False)
+
+
+@pass_context
+def sync_do_selectattr(
+    context: "Context", value: "t.Iterable[V]", *args: t.Any, **kwargs: t.Any
+) -> "t.Iterator[V]":
+    """Filters a sequence of objects by applying a test to the specified
+    attribute of each object, and only selecting the objects with the
+    test succeeding.
+
+    If no test is specified, the attribute's value will be evaluated as
+    a boolean.
+
+    Example usage:
+
+    .. sourcecode:: jinja
+
+        {{ users|selectattr("is_active") }}
+        {{ users|selectattr("email", "none") }}
+
+    Similar to a generator comprehension such as:
+
+    .. code-block:: python
+
+        (user for user in users if user.is_active)
+        (user for user in users if test_none(user.email))
+
+    .. versionadded:: 2.7
+    """
+    return select_or_reject(context, value, args, kwargs, lambda x: x, True)
+
+
+@async_variant(sync_do_selectattr)  # type: ignore
+async def do_selectattr(
+    context: "Context",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> "t.AsyncIterator[V]":
+    return async_select_or_reject(context, value, args, kwargs, lambda x: x, True)
+
+
+@pass_context
+def sync_do_rejectattr(
+    context: "Context", value: "t.Iterable[V]", *args: t.Any, **kwargs: t.Any
+) -> "t.Iterator[V]":
+    """Filters a sequence of objects by applying a test to the specified
+    attribute of each object, and rejecting the objects with the test
+    succeeding.
+
+    If no test is specified, the attribute's value will be evaluated as
+    a boolean.
+
+    .. sourcecode:: jinja
+
+        {{ users|rejectattr("is_active") }}
+        {{ users|rejectattr("email", "none") }}
+
+    Similar to a generator comprehension such as:
+
+    .. code-block:: python
+
+        (user for user in users if not user.is_active)
+        (user for user in users if not test_none(user.email))
+
+    .. versionadded:: 2.7
+    """
+    return select_or_reject(context, value, args, kwargs, lambda x: not x, True)
+
+
+@async_variant(sync_do_rejectattr)  # type: ignore
+async def do_rejectattr(
+    context: "Context",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    *args: t.Any,
+    **kwargs: t.Any,
+) -> "t.AsyncIterator[V]":
+    return async_select_or_reject(context, value, args, kwargs, lambda x: not x, True)
+
+
+@pass_eval_context
+def do_tojson(
+    eval_ctx: "EvalContext", value: t.Any, indent: t.Optional[int] = None
+) -> Markup:
+    """Serialize an object to a string of JSON, and mark it safe to
+    render in HTML. This filter is only for use in HTML documents.
+
+    The returned string is safe to render in HTML documents and
+    ``<script>`` tags. The exception is in HTML attributes that are
+    double quoted; either use single quotes or the ``|forceescape``
+    filter.
+
+    :param value: The object to serialize to JSON.
+    :param indent: The ``indent`` parameter passed to ``dumps``, for
+        pretty-printing the value.
+
+    .. versionadded:: 2.9
+    """
+    policies = eval_ctx.environment.policies
+    dumps = policies["json.dumps_function"]
+    kwargs = policies["json.dumps_kwargs"]
+
+    if indent is not None:
+        kwargs = kwargs.copy()
+        kwargs["indent"] = indent
+
+    return htmlsafe_json_dumps(value, dumps=dumps, **kwargs)
+
+
+def prepare_map(
+    context: "Context", args: t.Tuple[t.Any, ...], kwargs: t.Dict[str, t.Any]
+) -> t.Callable[[t.Any], t.Any]:
+    if not args and "attribute" in kwargs:
+        attribute = kwargs.pop("attribute")
+        default = kwargs.pop("default", None)
+
+        if kwargs:
+            raise FilterArgumentError(
+                f"Unexpected keyword argument {next(iter(kwargs))!r}"
+            )
+
+        func = make_attrgetter(context.environment, attribute, default=default)
+    else:
+        try:
+            name = args[0]
+            args = args[1:]
+        except LookupError:
+            raise FilterArgumentError("map requires a filter argument") from None
+
+        def func(item: t.Any) -> t.Any:
+            return context.environment.call_filter(
+                name, item, args, kwargs, context=context
+            )
+
+    return func
+
+
+def prepare_select_or_reject(
+    context: "Context",
+    args: t.Tuple[t.Any, ...],
+    kwargs: t.Dict[str, t.Any],
+    modfunc: t.Callable[[t.Any], t.Any],
+    lookup_attr: bool,
+) -> t.Callable[[t.Any], t.Any]:
+    if lookup_attr:
+        try:
+            attr = args[0]
+        except LookupError:
+            raise FilterArgumentError("Missing parameter for attribute name") from None
+
+        transfunc = make_attrgetter(context.environment, attr)
+        off = 1
+    else:
+        off = 0
+
+        def transfunc(x: V) -> V:
+            return x
+
+    try:
+        name = args[off]
+        args = args[1 + off :]
+
+        def func(item: t.Any) -> t.Any:
+            return context.environment.call_test(name, item, args, kwargs, context)
+
+    except LookupError:
+        func = bool  # type: ignore
+
+    return lambda item: modfunc(func(transfunc(item)))
+
+
+def select_or_reject(
+    context: "Context",
+    value: "t.Iterable[V]",
+    args: t.Tuple[t.Any, ...],
+    kwargs: t.Dict[str, t.Any],
+    modfunc: t.Callable[[t.Any], t.Any],
+    lookup_attr: bool,
+) -> "t.Iterator[V]":
+    if value:
+        func = prepare_select_or_reject(context, args, kwargs, modfunc, lookup_attr)
+
+        for item in value:
+            if func(item):
+                yield item
+
+
+async def async_select_or_reject(
+    context: "Context",
+    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
+    args: t.Tuple[t.Any, ...],
+    kwargs: t.Dict[str, t.Any],
+    modfunc: t.Callable[[t.Any], t.Any],
+    lookup_attr: bool,
+) -> "t.AsyncIterator[V]":
+    if value:
+        func = prepare_select_or_reject(context, args, kwargs, modfunc, lookup_attr)
+
+        async for item in auto_aiter(value):
+            if func(item):
+                yield item
+
+
+FILTERS = {
+    "abs": abs,
+    "attr": do_attr,
+    "batch": do_batch,
+    "capitalize": do_capitalize,
+    "center": do_center,
+    "count": len,
+    "d": do_default,
+    "default": do_default,
+    "dictsort": do_dictsort,
+    "e": escape,
+    "escape": escape,
+    "filesizeformat": do_filesizeformat,
+    "first": do_first,
+    "float": do_float,
+    "forceescape": do_forceescape,
+    "format": do_format,
+    "groupby": do_groupby,
+    "indent": do_indent,
+    "int": do_int,
+    "join": do_join,
+    "last": do_last,
+    "length": len,
+    "list": do_list,
+    "lower": do_lower,
+    "items": do_items,
+    "map": do_map,
+    "min": do_min,
+    "max": do_max,
+    "pprint": do_pprint,
+    "random": do_random,
+    "reject": do_reject,
+    "rejectattr": do_rejectattr,
+    "replace": do_replace,
+    "reverse": do_reverse,
+    "round": do_round,
+    "safe": do_mark_safe,
+    "select": do_select,
+    "selectattr": do_selectattr,
+    "slice": do_slice,
+    "sort": do_sort,
+    "string": soft_str,
+    "striptags": do_striptags,
+    "sum": do_sum,
+    "title": do_title,
+    "trim": do_trim,
+    "truncate": do_truncate,
+    "unique": do_unique,
+    "upper": do_upper,
+    "urlencode": do_urlencode,
+    "urlize": do_urlize,
+    "wordcount": do_wordcount,
+    "wordwrap": do_wordwrap,
+    "xmlattr": do_xmlattr,
+    "tojson": do_tojson,
+}
diff --git a/lib/python3.11/site-packages/jinja2/idtracking.py b/lib/python3.11/site-packages/jinja2/idtracking.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6dd8cd1110e138770652506a40a5c34101508cc
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/idtracking.py
@@ -0,0 +1,318 @@
+import typing as t
+
+from . import nodes
+from .visitor import NodeVisitor
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+VAR_LOAD_PARAMETER = "param"
+VAR_LOAD_RESOLVE = "resolve"
+VAR_LOAD_ALIAS = "alias"
+VAR_LOAD_UNDEFINED = "undefined"
+
+
+def find_symbols(
+    nodes: t.Iterable[nodes.Node], parent_symbols: t.Optional["Symbols"] = None
+) -> "Symbols":
+    sym = Symbols(parent=parent_symbols)
+    visitor = FrameSymbolVisitor(sym)
+    for node in nodes:
+        visitor.visit(node)
+    return sym
+
+
+def symbols_for_node(
+    node: nodes.Node, parent_symbols: t.Optional["Symbols"] = None
+) -> "Symbols":
+    sym = Symbols(parent=parent_symbols)
+    sym.analyze_node(node)
+    return sym
+
+
+class Symbols:
+    def __init__(
+        self, parent: t.Optional["Symbols"] = None, level: t.Optional[int] = None
+    ) -> None:
+        if level is None:
+            if parent is None:
+                level = 0
+            else:
+                level = parent.level + 1
+
+        self.level: int = level
+        self.parent = parent
+        self.refs: t.Dict[str, str] = {}
+        self.loads: t.Dict[str, t.Any] = {}
+        self.stores: t.Set[str] = set()
+
+    def analyze_node(self, node: nodes.Node, **kwargs: t.Any) -> None:
+        visitor = RootVisitor(self)
+        visitor.visit(node, **kwargs)
+
+    def _define_ref(
+        self, name: str, load: t.Optional[t.Tuple[str, t.Optional[str]]] = None
+    ) -> str:
+        ident = f"l_{self.level}_{name}"
+        self.refs[name] = ident
+        if load is not None:
+            self.loads[ident] = load
+        return ident
+
+    def find_load(self, target: str) -> t.Optional[t.Any]:
+        if target in self.loads:
+            return self.loads[target]
+
+        if self.parent is not None:
+            return self.parent.find_load(target)
+
+        return None
+
+    def find_ref(self, name: str) -> t.Optional[str]:
+        if name in self.refs:
+            return self.refs[name]
+
+        if self.parent is not None:
+            return self.parent.find_ref(name)
+
+        return None
+
+    def ref(self, name: str) -> str:
+        rv = self.find_ref(name)
+        if rv is None:
+            raise AssertionError(
+                "Tried to resolve a name to a reference that was"
+                f" unknown to the frame ({name!r})"
+            )
+        return rv
+
+    def copy(self) -> "te.Self":
+        rv = object.__new__(self.__class__)
+        rv.__dict__.update(self.__dict__)
+        rv.refs = self.refs.copy()
+        rv.loads = self.loads.copy()
+        rv.stores = self.stores.copy()
+        return rv
+
+    def store(self, name: str) -> None:
+        self.stores.add(name)
+
+        # If we have not see the name referenced yet, we need to figure
+        # out what to set it to.
+        if name not in self.refs:
+            # If there is a parent scope we check if the name has a
+            # reference there.  If it does it means we might have to alias
+            # to a variable there.
+            if self.parent is not None:
+                outer_ref = self.parent.find_ref(name)
+                if outer_ref is not None:
+                    self._define_ref(name, load=(VAR_LOAD_ALIAS, outer_ref))
+                    return
+
+            # Otherwise we can just set it to undefined.
+            self._define_ref(name, load=(VAR_LOAD_UNDEFINED, None))
+
+    def declare_parameter(self, name: str) -> str:
+        self.stores.add(name)
+        return self._define_ref(name, load=(VAR_LOAD_PARAMETER, None))
+
+    def load(self, name: str) -> None:
+        if self.find_ref(name) is None:
+            self._define_ref(name, load=(VAR_LOAD_RESOLVE, name))
+
+    def branch_update(self, branch_symbols: t.Sequence["Symbols"]) -> None:
+        stores: t.Set[str] = set()
+
+        for branch in branch_symbols:
+            stores.update(branch.stores)
+
+        stores.difference_update(self.stores)
+
+        for sym in branch_symbols:
+            self.refs.update(sym.refs)
+            self.loads.update(sym.loads)
+            self.stores.update(sym.stores)
+
+        for name in stores:
+            target = self.find_ref(name)
+            assert target is not None, "should not happen"
+
+            if self.parent is not None:
+                outer_target = self.parent.find_ref(name)
+                if outer_target is not None:
+                    self.loads[target] = (VAR_LOAD_ALIAS, outer_target)
+                    continue
+            self.loads[target] = (VAR_LOAD_RESOLVE, name)
+
+    def dump_stores(self) -> t.Dict[str, str]:
+        rv: t.Dict[str, str] = {}
+        node: t.Optional[Symbols] = self
+
+        while node is not None:
+            for name in sorted(node.stores):
+                if name not in rv:
+                    rv[name] = self.find_ref(name)  # type: ignore
+
+            node = node.parent
+
+        return rv
+
+    def dump_param_targets(self) -> t.Set[str]:
+        rv = set()
+        node: t.Optional[Symbols] = self
+
+        while node is not None:
+            for target, (instr, _) in self.loads.items():
+                if instr == VAR_LOAD_PARAMETER:
+                    rv.add(target)
+
+            node = node.parent
+
+        return rv
+
+
+class RootVisitor(NodeVisitor):
+    def __init__(self, symbols: "Symbols") -> None:
+        self.sym_visitor = FrameSymbolVisitor(symbols)
+
+    def _simple_visit(self, node: nodes.Node, **kwargs: t.Any) -> None:
+        for child in node.iter_child_nodes():
+            self.sym_visitor.visit(child)
+
+    visit_Template = _simple_visit
+    visit_Block = _simple_visit
+    visit_Macro = _simple_visit
+    visit_FilterBlock = _simple_visit
+    visit_Scope = _simple_visit
+    visit_If = _simple_visit
+    visit_ScopedEvalContextModifier = _simple_visit
+
+    def visit_AssignBlock(self, node: nodes.AssignBlock, **kwargs: t.Any) -> None:
+        for child in node.body:
+            self.sym_visitor.visit(child)
+
+    def visit_CallBlock(self, node: nodes.CallBlock, **kwargs: t.Any) -> None:
+        for child in node.iter_child_nodes(exclude=("call",)):
+            self.sym_visitor.visit(child)
+
+    def visit_OverlayScope(self, node: nodes.OverlayScope, **kwargs: t.Any) -> None:
+        for child in node.body:
+            self.sym_visitor.visit(child)
+
+    def visit_For(
+        self, node: nodes.For, for_branch: str = "body", **kwargs: t.Any
+    ) -> None:
+        if for_branch == "body":
+            self.sym_visitor.visit(node.target, store_as_param=True)
+            branch = node.body
+        elif for_branch == "else":
+            branch = node.else_
+        elif for_branch == "test":
+            self.sym_visitor.visit(node.target, store_as_param=True)
+            if node.test is not None:
+                self.sym_visitor.visit(node.test)
+            return
+        else:
+            raise RuntimeError("Unknown for branch")
+
+        if branch:
+            for item in branch:
+                self.sym_visitor.visit(item)
+
+    def visit_With(self, node: nodes.With, **kwargs: t.Any) -> None:
+        for target in node.targets:
+            self.sym_visitor.visit(target)
+        for child in node.body:
+            self.sym_visitor.visit(child)
+
+    def generic_visit(self, node: nodes.Node, *args: t.Any, **kwargs: t.Any) -> None:
+        raise NotImplementedError(f"Cannot find symbols for {type(node).__name__!r}")
+
+
+class FrameSymbolVisitor(NodeVisitor):
+    """A visitor for `Frame.inspect`."""
+
+    def __init__(self, symbols: "Symbols") -> None:
+        self.symbols = symbols
+
+    def visit_Name(
+        self, node: nodes.Name, store_as_param: bool = False, **kwargs: t.Any
+    ) -> None:
+        """All assignments to names go through this function."""
+        if store_as_param or node.ctx == "param":
+            self.symbols.declare_parameter(node.name)
+        elif node.ctx == "store":
+            self.symbols.store(node.name)
+        elif node.ctx == "load":
+            self.symbols.load(node.name)
+
+    def visit_NSRef(self, node: nodes.NSRef, **kwargs: t.Any) -> None:
+        self.symbols.load(node.name)
+
+    def visit_If(self, node: nodes.If, **kwargs: t.Any) -> None:
+        self.visit(node.test, **kwargs)
+        original_symbols = self.symbols
+
+        def inner_visit(nodes: t.Iterable[nodes.Node]) -> "Symbols":
+            self.symbols = rv = original_symbols.copy()
+
+            for subnode in nodes:
+                self.visit(subnode, **kwargs)
+
+            self.symbols = original_symbols
+            return rv
+
+        body_symbols = inner_visit(node.body)
+        elif_symbols = inner_visit(node.elif_)
+        else_symbols = inner_visit(node.else_ or ())
+        self.symbols.branch_update([body_symbols, elif_symbols, else_symbols])
+
+    def visit_Macro(self, node: nodes.Macro, **kwargs: t.Any) -> None:
+        self.symbols.store(node.name)
+
+    def visit_Import(self, node: nodes.Import, **kwargs: t.Any) -> None:
+        self.generic_visit(node, **kwargs)
+        self.symbols.store(node.target)
+
+    def visit_FromImport(self, node: nodes.FromImport, **kwargs: t.Any) -> None:
+        self.generic_visit(node, **kwargs)
+
+        for name in node.names:
+            if isinstance(name, tuple):
+                self.symbols.store(name[1])
+            else:
+                self.symbols.store(name)
+
+    def visit_Assign(self, node: nodes.Assign, **kwargs: t.Any) -> None:
+        """Visit assignments in the correct order."""
+        self.visit(node.node, **kwargs)
+        self.visit(node.target, **kwargs)
+
+    def visit_For(self, node: nodes.For, **kwargs: t.Any) -> None:
+        """Visiting stops at for blocks.  However the block sequence
+        is visited as part of the outer scope.
+        """
+        self.visit(node.iter, **kwargs)
+
+    def visit_CallBlock(self, node: nodes.CallBlock, **kwargs: t.Any) -> None:
+        self.visit(node.call, **kwargs)
+
+    def visit_FilterBlock(self, node: nodes.FilterBlock, **kwargs: t.Any) -> None:
+        self.visit(node.filter, **kwargs)
+
+    def visit_With(self, node: nodes.With, **kwargs: t.Any) -> None:
+        for target in node.values:
+            self.visit(target)
+
+    def visit_AssignBlock(self, node: nodes.AssignBlock, **kwargs: t.Any) -> None:
+        """Stop visiting at block assigns."""
+        self.visit(node.target, **kwargs)
+
+    def visit_Scope(self, node: nodes.Scope, **kwargs: t.Any) -> None:
+        """Stop visiting at scopes."""
+
+    def visit_Block(self, node: nodes.Block, **kwargs: t.Any) -> None:
+        """Stop visiting at blocks."""
+
+    def visit_OverlayScope(self, node: nodes.OverlayScope, **kwargs: t.Any) -> None:
+        """Do not visit into overlay scopes."""
diff --git a/lib/python3.11/site-packages/jinja2/lexer.py b/lib/python3.11/site-packages/jinja2/lexer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b1c969791f2ab4a263d6708d7c2d682f47f7387
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/lexer.py
@@ -0,0 +1,868 @@
+"""Implements a Jinja / Python combination lexer. The ``Lexer`` class
+is used to do some preprocessing. It filters out invalid operators like
+the bitshift operators we don't allow in templates. It separates
+template code and python code in expressions.
+"""
+
+import re
+import typing as t
+from ast import literal_eval
+from collections import deque
+from sys import intern
+
+from ._identifier import pattern as name_re
+from .exceptions import TemplateSyntaxError
+from .utils import LRUCache
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .environment import Environment
+
+# cache for the lexers. Exists in order to be able to have multiple
+# environments with the same lexer
+_lexer_cache: t.MutableMapping[t.Tuple, "Lexer"] = LRUCache(50)  # type: ignore
+
+# static regular expressions
+whitespace_re = re.compile(r"\s+")
+newline_re = re.compile(r"(\r\n|\r|\n)")
+string_re = re.compile(
+    r"('([^'\\]*(?:\\.[^'\\]*)*)'" r'|"([^"\\]*(?:\\.[^"\\]*)*)")', re.S
+)
+integer_re = re.compile(
+    r"""
+    (
+        0b(_?[0-1])+ # binary
+    |
+        0o(_?[0-7])+ # octal
+    |
+        0x(_?[\da-f])+ # hex
+    |
+        [1-9](_?\d)* # decimal
+    |
+        0(_?0)* # decimal zero
+    )
+    """,
+    re.IGNORECASE | re.VERBOSE,
+)
+float_re = re.compile(
+    r"""
+    (?<!\.)  # doesn't start with a .
+    (\d+_)*\d+  # digits, possibly _ separated
+    (
+        (\.(\d+_)*\d+)?  # optional fractional part
+        e[+\-]?(\d+_)*\d+  # exponent part
+    |
+        \.(\d+_)*\d+  # required fractional part
+    )
+    """,
+    re.IGNORECASE | re.VERBOSE,
+)
+
+# internal the tokens and keep references to them
+TOKEN_ADD = intern("add")
+TOKEN_ASSIGN = intern("assign")
+TOKEN_COLON = intern("colon")
+TOKEN_COMMA = intern("comma")
+TOKEN_DIV = intern("div")
+TOKEN_DOT = intern("dot")
+TOKEN_EQ = intern("eq")
+TOKEN_FLOORDIV = intern("floordiv")
+TOKEN_GT = intern("gt")
+TOKEN_GTEQ = intern("gteq")
+TOKEN_LBRACE = intern("lbrace")
+TOKEN_LBRACKET = intern("lbracket")
+TOKEN_LPAREN = intern("lparen")
+TOKEN_LT = intern("lt")
+TOKEN_LTEQ = intern("lteq")
+TOKEN_MOD = intern("mod")
+TOKEN_MUL = intern("mul")
+TOKEN_NE = intern("ne")
+TOKEN_PIPE = intern("pipe")
+TOKEN_POW = intern("pow")
+TOKEN_RBRACE = intern("rbrace")
+TOKEN_RBRACKET = intern("rbracket")
+TOKEN_RPAREN = intern("rparen")
+TOKEN_SEMICOLON = intern("semicolon")
+TOKEN_SUB = intern("sub")
+TOKEN_TILDE = intern("tilde")
+TOKEN_WHITESPACE = intern("whitespace")
+TOKEN_FLOAT = intern("float")
+TOKEN_INTEGER = intern("integer")
+TOKEN_NAME = intern("name")
+TOKEN_STRING = intern("string")
+TOKEN_OPERATOR = intern("operator")
+TOKEN_BLOCK_BEGIN = intern("block_begin")
+TOKEN_BLOCK_END = intern("block_end")
+TOKEN_VARIABLE_BEGIN = intern("variable_begin")
+TOKEN_VARIABLE_END = intern("variable_end")
+TOKEN_RAW_BEGIN = intern("raw_begin")
+TOKEN_RAW_END = intern("raw_end")
+TOKEN_COMMENT_BEGIN = intern("comment_begin")
+TOKEN_COMMENT_END = intern("comment_end")
+TOKEN_COMMENT = intern("comment")
+TOKEN_LINESTATEMENT_BEGIN = intern("linestatement_begin")
+TOKEN_LINESTATEMENT_END = intern("linestatement_end")
+TOKEN_LINECOMMENT_BEGIN = intern("linecomment_begin")
+TOKEN_LINECOMMENT_END = intern("linecomment_end")
+TOKEN_LINECOMMENT = intern("linecomment")
+TOKEN_DATA = intern("data")
+TOKEN_INITIAL = intern("initial")
+TOKEN_EOF = intern("eof")
+
+# bind operators to token types
+operators = {
+    "+": TOKEN_ADD,
+    "-": TOKEN_SUB,
+    "/": TOKEN_DIV,
+    "//": TOKEN_FLOORDIV,
+    "*": TOKEN_MUL,
+    "%": TOKEN_MOD,
+    "**": TOKEN_POW,
+    "~": TOKEN_TILDE,
+    "[": TOKEN_LBRACKET,
+    "]": TOKEN_RBRACKET,
+    "(": TOKEN_LPAREN,
+    ")": TOKEN_RPAREN,
+    "{": TOKEN_LBRACE,
+    "}": TOKEN_RBRACE,
+    "==": TOKEN_EQ,
+    "!=": TOKEN_NE,
+    ">": TOKEN_GT,
+    ">=": TOKEN_GTEQ,
+    "<": TOKEN_LT,
+    "<=": TOKEN_LTEQ,
+    "=": TOKEN_ASSIGN,
+    ".": TOKEN_DOT,
+    ":": TOKEN_COLON,
+    "|": TOKEN_PIPE,
+    ",": TOKEN_COMMA,
+    ";": TOKEN_SEMICOLON,
+}
+
+reverse_operators = {v: k for k, v in operators.items()}
+assert len(operators) == len(reverse_operators), "operators dropped"
+operator_re = re.compile(
+    f"({'|'.join(re.escape(x) for x in sorted(operators, key=lambda x: -len(x)))})"
+)
+
+ignored_tokens = frozenset(
+    [
+        TOKEN_COMMENT_BEGIN,
+        TOKEN_COMMENT,
+        TOKEN_COMMENT_END,
+        TOKEN_WHITESPACE,
+        TOKEN_LINECOMMENT_BEGIN,
+        TOKEN_LINECOMMENT_END,
+        TOKEN_LINECOMMENT,
+    ]
+)
+ignore_if_empty = frozenset(
+    [TOKEN_WHITESPACE, TOKEN_DATA, TOKEN_COMMENT, TOKEN_LINECOMMENT]
+)
+
+
+def _describe_token_type(token_type: str) -> str:
+    if token_type in reverse_operators:
+        return reverse_operators[token_type]
+
+    return {
+        TOKEN_COMMENT_BEGIN: "begin of comment",
+        TOKEN_COMMENT_END: "end of comment",
+        TOKEN_COMMENT: "comment",
+        TOKEN_LINECOMMENT: "comment",
+        TOKEN_BLOCK_BEGIN: "begin of statement block",
+        TOKEN_BLOCK_END: "end of statement block",
+        TOKEN_VARIABLE_BEGIN: "begin of print statement",
+        TOKEN_VARIABLE_END: "end of print statement",
+        TOKEN_LINESTATEMENT_BEGIN: "begin of line statement",
+        TOKEN_LINESTATEMENT_END: "end of line statement",
+        TOKEN_DATA: "template data / text",
+        TOKEN_EOF: "end of template",
+    }.get(token_type, token_type)
+
+
+def describe_token(token: "Token") -> str:
+    """Returns a description of the token."""
+    if token.type == TOKEN_NAME:
+        return token.value
+
+    return _describe_token_type(token.type)
+
+
+def describe_token_expr(expr: str) -> str:
+    """Like `describe_token` but for token expressions."""
+    if ":" in expr:
+        type, value = expr.split(":", 1)
+
+        if type == TOKEN_NAME:
+            return value
+    else:
+        type = expr
+
+    return _describe_token_type(type)
+
+
+def count_newlines(value: str) -> int:
+    """Count the number of newline characters in the string.  This is
+    useful for extensions that filter a stream.
+    """
+    return len(newline_re.findall(value))
+
+
+def compile_rules(environment: "Environment") -> t.List[t.Tuple[str, str]]:
+    """Compiles all the rules from the environment into a list of rules."""
+    e = re.escape
+    rules = [
+        (
+            len(environment.comment_start_string),
+            TOKEN_COMMENT_BEGIN,
+            e(environment.comment_start_string),
+        ),
+        (
+            len(environment.block_start_string),
+            TOKEN_BLOCK_BEGIN,
+            e(environment.block_start_string),
+        ),
+        (
+            len(environment.variable_start_string),
+            TOKEN_VARIABLE_BEGIN,
+            e(environment.variable_start_string),
+        ),
+    ]
+
+    if environment.line_statement_prefix is not None:
+        rules.append(
+            (
+                len(environment.line_statement_prefix),
+                TOKEN_LINESTATEMENT_BEGIN,
+                r"^[ \t\v]*" + e(environment.line_statement_prefix),
+            )
+        )
+    if environment.line_comment_prefix is not None:
+        rules.append(
+            (
+                len(environment.line_comment_prefix),
+                TOKEN_LINECOMMENT_BEGIN,
+                r"(?:^|(?<=\S))[^\S\r\n]*" + e(environment.line_comment_prefix),
+            )
+        )
+
+    return [x[1:] for x in sorted(rules, reverse=True)]
+
+
+class Failure:
+    """Class that raises a `TemplateSyntaxError` if called.
+    Used by the `Lexer` to specify known errors.
+    """
+
+    def __init__(
+        self, message: str, cls: t.Type[TemplateSyntaxError] = TemplateSyntaxError
+    ) -> None:
+        self.message = message
+        self.error_class = cls
+
+    def __call__(self, lineno: int, filename: t.Optional[str]) -> "te.NoReturn":
+        raise self.error_class(self.message, lineno, filename)
+
+
+class Token(t.NamedTuple):
+    lineno: int
+    type: str
+    value: str
+
+    def __str__(self) -> str:
+        return describe_token(self)
+
+    def test(self, expr: str) -> bool:
+        """Test a token against a token expression.  This can either be a
+        token type or ``'token_type:token_value'``.  This can only test
+        against string values and types.
+        """
+        # here we do a regular string equality check as test_any is usually
+        # passed an iterable of not interned strings.
+        if self.type == expr:
+            return True
+
+        if ":" in expr:
+            return expr.split(":", 1) == [self.type, self.value]
+
+        return False
+
+    def test_any(self, *iterable: str) -> bool:
+        """Test against multiple token expressions."""
+        return any(self.test(expr) for expr in iterable)
+
+
+class TokenStreamIterator:
+    """The iterator for tokenstreams.  Iterate over the stream
+    until the eof token is reached.
+    """
+
+    def __init__(self, stream: "TokenStream") -> None:
+        self.stream = stream
+
+    def __iter__(self) -> "TokenStreamIterator":
+        return self
+
+    def __next__(self) -> Token:
+        token = self.stream.current
+
+        if token.type is TOKEN_EOF:
+            self.stream.close()
+            raise StopIteration
+
+        next(self.stream)
+        return token
+
+
+class TokenStream:
+    """A token stream is an iterable that yields :class:`Token`\\s.  The
+    parser however does not iterate over it but calls :meth:`next` to go
+    one token ahead.  The current active token is stored as :attr:`current`.
+    """
+
+    def __init__(
+        self,
+        generator: t.Iterable[Token],
+        name: t.Optional[str],
+        filename: t.Optional[str],
+    ):
+        self._iter = iter(generator)
+        self._pushed: te.Deque[Token] = deque()
+        self.name = name
+        self.filename = filename
+        self.closed = False
+        self.current = Token(1, TOKEN_INITIAL, "")
+        next(self)
+
+    def __iter__(self) -> TokenStreamIterator:
+        return TokenStreamIterator(self)
+
+    def __bool__(self) -> bool:
+        return bool(self._pushed) or self.current.type is not TOKEN_EOF
+
+    @property
+    def eos(self) -> bool:
+        """Are we at the end of the stream?"""
+        return not self
+
+    def push(self, token: Token) -> None:
+        """Push a token back to the stream."""
+        self._pushed.append(token)
+
+    def look(self) -> Token:
+        """Look at the next token."""
+        old_token = next(self)
+        result = self.current
+        self.push(result)
+        self.current = old_token
+        return result
+
+    def skip(self, n: int = 1) -> None:
+        """Got n tokens ahead."""
+        for _ in range(n):
+            next(self)
+
+    def next_if(self, expr: str) -> t.Optional[Token]:
+        """Perform the token test and return the token if it matched.
+        Otherwise the return value is `None`.
+        """
+        if self.current.test(expr):
+            return next(self)
+
+        return None
+
+    def skip_if(self, expr: str) -> bool:
+        """Like :meth:`next_if` but only returns `True` or `False`."""
+        return self.next_if(expr) is not None
+
+    def __next__(self) -> Token:
+        """Go one token ahead and return the old one.
+
+        Use the built-in :func:`next` instead of calling this directly.
+        """
+        rv = self.current
+
+        if self._pushed:
+            self.current = self._pushed.popleft()
+        elif self.current.type is not TOKEN_EOF:
+            try:
+                self.current = next(self._iter)
+            except StopIteration:
+                self.close()
+
+        return rv
+
+    def close(self) -> None:
+        """Close the stream."""
+        self.current = Token(self.current.lineno, TOKEN_EOF, "")
+        self._iter = iter(())
+        self.closed = True
+
+    def expect(self, expr: str) -> Token:
+        """Expect a given token type and return it.  This accepts the same
+        argument as :meth:`jinja2.lexer.Token.test`.
+        """
+        if not self.current.test(expr):
+            expr = describe_token_expr(expr)
+
+            if self.current.type is TOKEN_EOF:
+                raise TemplateSyntaxError(
+                    f"unexpected end of template, expected {expr!r}.",
+                    self.current.lineno,
+                    self.name,
+                    self.filename,
+                )
+
+            raise TemplateSyntaxError(
+                f"expected token {expr!r}, got {describe_token(self.current)!r}",
+                self.current.lineno,
+                self.name,
+                self.filename,
+            )
+
+        return next(self)
+
+
+def get_lexer(environment: "Environment") -> "Lexer":
+    """Return a lexer which is probably cached."""
+    key = (
+        environment.block_start_string,
+        environment.block_end_string,
+        environment.variable_start_string,
+        environment.variable_end_string,
+        environment.comment_start_string,
+        environment.comment_end_string,
+        environment.line_statement_prefix,
+        environment.line_comment_prefix,
+        environment.trim_blocks,
+        environment.lstrip_blocks,
+        environment.newline_sequence,
+        environment.keep_trailing_newline,
+    )
+    lexer = _lexer_cache.get(key)
+
+    if lexer is None:
+        _lexer_cache[key] = lexer = Lexer(environment)
+
+    return lexer
+
+
+class OptionalLStrip(tuple):  # type: ignore[type-arg]
+    """A special tuple for marking a point in the state that can have
+    lstrip applied.
+    """
+
+    __slots__ = ()
+
+    # Even though it looks like a no-op, creating instances fails
+    # without this.
+    def __new__(cls, *members, **kwargs):  # type: ignore
+        return super().__new__(cls, members)
+
+
+class _Rule(t.NamedTuple):
+    pattern: t.Pattern[str]
+    tokens: t.Union[str, t.Tuple[str, ...], t.Tuple[Failure]]
+    command: t.Optional[str]
+
+
+class Lexer:
+    """Class that implements a lexer for a given environment. Automatically
+    created by the environment class, usually you don't have to do that.
+
+    Note that the lexer is not automatically bound to an environment.
+    Multiple environments can share the same lexer.
+    """
+
+    def __init__(self, environment: "Environment") -> None:
+        # shortcuts
+        e = re.escape
+
+        def c(x: str) -> t.Pattern[str]:
+            return re.compile(x, re.M | re.S)
+
+        # lexing rules for tags
+        tag_rules: t.List[_Rule] = [
+            _Rule(whitespace_re, TOKEN_WHITESPACE, None),
+            _Rule(float_re, TOKEN_FLOAT, None),
+            _Rule(integer_re, TOKEN_INTEGER, None),
+            _Rule(name_re, TOKEN_NAME, None),
+            _Rule(string_re, TOKEN_STRING, None),
+            _Rule(operator_re, TOKEN_OPERATOR, None),
+        ]
+
+        # assemble the root lexing rule. because "|" is ungreedy
+        # we have to sort by length so that the lexer continues working
+        # as expected when we have parsing rules like <% for block and
+        # <%= for variables. (if someone wants asp like syntax)
+        # variables are just part of the rules if variable processing
+        # is required.
+        root_tag_rules = compile_rules(environment)
+
+        block_start_re = e(environment.block_start_string)
+        block_end_re = e(environment.block_end_string)
+        comment_end_re = e(environment.comment_end_string)
+        variable_end_re = e(environment.variable_end_string)
+
+        # block suffix if trimming is enabled
+        block_suffix_re = "\\n?" if environment.trim_blocks else ""
+
+        self.lstrip_blocks = environment.lstrip_blocks
+
+        self.newline_sequence = environment.newline_sequence
+        self.keep_trailing_newline = environment.keep_trailing_newline
+
+        root_raw_re = (
+            rf"(?P<raw_begin>{block_start_re}(\-|\+|)\s*raw\s*"
+            rf"(?:\-{block_end_re}\s*|{block_end_re}))"
+        )
+        root_parts_re = "|".join(
+            [root_raw_re] + [rf"(?P<{n}>{r}(\-|\+|))" for n, r in root_tag_rules]
+        )
+
+        # global lexing rules
+        self.rules: t.Dict[str, t.List[_Rule]] = {
+            "root": [
+                # directives
+                _Rule(
+                    c(rf"(.*?)(?:{root_parts_re})"),
+                    OptionalLStrip(TOKEN_DATA, "#bygroup"),  # type: ignore
+                    "#bygroup",
+                ),
+                # data
+                _Rule(c(".+"), TOKEN_DATA, None),
+            ],
+            # comments
+            TOKEN_COMMENT_BEGIN: [
+                _Rule(
+                    c(
+                        rf"(.*?)((?:\+{comment_end_re}|\-{comment_end_re}\s*"
+                        rf"|{comment_end_re}{block_suffix_re}))"
+                    ),
+                    (TOKEN_COMMENT, TOKEN_COMMENT_END),
+                    "#pop",
+                ),
+                _Rule(c(r"(.)"), (Failure("Missing end of comment tag"),), None),
+            ],
+            # blocks
+            TOKEN_BLOCK_BEGIN: [
+                _Rule(
+                    c(
+                        rf"(?:\+{block_end_re}|\-{block_end_re}\s*"
+                        rf"|{block_end_re}{block_suffix_re})"
+                    ),
+                    TOKEN_BLOCK_END,
+                    "#pop",
+                ),
+            ]
+            + tag_rules,
+            # variables
+            TOKEN_VARIABLE_BEGIN: [
+                _Rule(
+                    c(rf"\-{variable_end_re}\s*|{variable_end_re}"),
+                    TOKEN_VARIABLE_END,
+                    "#pop",
+                )
+            ]
+            + tag_rules,
+            # raw block
+            TOKEN_RAW_BEGIN: [
+                _Rule(
+                    c(
+                        rf"(.*?)((?:{block_start_re}(\-|\+|))\s*endraw\s*"
+                        rf"(?:\+{block_end_re}|\-{block_end_re}\s*"
+                        rf"|{block_end_re}{block_suffix_re}))"
+                    ),
+                    OptionalLStrip(TOKEN_DATA, TOKEN_RAW_END),  # type: ignore
+                    "#pop",
+                ),
+                _Rule(c(r"(.)"), (Failure("Missing end of raw directive"),), None),
+            ],
+            # line statements
+            TOKEN_LINESTATEMENT_BEGIN: [
+                _Rule(c(r"\s*(\n|$)"), TOKEN_LINESTATEMENT_END, "#pop")
+            ]
+            + tag_rules,
+            # line comments
+            TOKEN_LINECOMMENT_BEGIN: [
+                _Rule(
+                    c(r"(.*?)()(?=\n|$)"),
+                    (TOKEN_LINECOMMENT, TOKEN_LINECOMMENT_END),
+                    "#pop",
+                )
+            ],
+        }
+
+    def _normalize_newlines(self, value: str) -> str:
+        """Replace all newlines with the configured sequence in strings
+        and template data.
+        """
+        return newline_re.sub(self.newline_sequence, value)
+
+    def tokenize(
+        self,
+        source: str,
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+        state: t.Optional[str] = None,
+    ) -> TokenStream:
+        """Calls tokeniter + tokenize and wraps it in a token stream."""
+        stream = self.tokeniter(source, name, filename, state)
+        return TokenStream(self.wrap(stream, name, filename), name, filename)
+
+    def wrap(
+        self,
+        stream: t.Iterable[t.Tuple[int, str, str]],
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+    ) -> t.Iterator[Token]:
+        """This is called with the stream as returned by `tokenize` and wraps
+        every token in a :class:`Token` and converts the value.
+        """
+        for lineno, token, value_str in stream:
+            if token in ignored_tokens:
+                continue
+
+            value: t.Any = value_str
+
+            if token == TOKEN_LINESTATEMENT_BEGIN:
+                token = TOKEN_BLOCK_BEGIN
+            elif token == TOKEN_LINESTATEMENT_END:
+                token = TOKEN_BLOCK_END
+            # we are not interested in those tokens in the parser
+            elif token in (TOKEN_RAW_BEGIN, TOKEN_RAW_END):
+                continue
+            elif token == TOKEN_DATA:
+                value = self._normalize_newlines(value_str)
+            elif token == "keyword":
+                token = value_str
+            elif token == TOKEN_NAME:
+                value = value_str
+
+                if not value.isidentifier():
+                    raise TemplateSyntaxError(
+                        "Invalid character in identifier", lineno, name, filename
+                    )
+            elif token == TOKEN_STRING:
+                # try to unescape string
+                try:
+                    value = (
+                        self._normalize_newlines(value_str[1:-1])
+                        .encode("ascii", "backslashreplace")
+                        .decode("unicode-escape")
+                    )
+                except Exception as e:
+                    msg = str(e).split(":")[-1].strip()
+                    raise TemplateSyntaxError(msg, lineno, name, filename) from e
+            elif token == TOKEN_INTEGER:
+                value = int(value_str.replace("_", ""), 0)
+            elif token == TOKEN_FLOAT:
+                # remove all "_" first to support more Python versions
+                value = literal_eval(value_str.replace("_", ""))
+            elif token == TOKEN_OPERATOR:
+                token = operators[value_str]
+
+            yield Token(lineno, token, value)
+
+    def tokeniter(
+        self,
+        source: str,
+        name: t.Optional[str],
+        filename: t.Optional[str] = None,
+        state: t.Optional[str] = None,
+    ) -> t.Iterator[t.Tuple[int, str, str]]:
+        """This method tokenizes the text and returns the tokens in a
+        generator. Use this method if you just want to tokenize a template.
+
+        .. versionchanged:: 3.0
+            Only ``\\n``, ``\\r\\n`` and ``\\r`` are treated as line
+            breaks.
+        """
+        lines = newline_re.split(source)[::2]
+
+        if not self.keep_trailing_newline and lines[-1] == "":
+            del lines[-1]
+
+        source = "\n".join(lines)
+        pos = 0
+        lineno = 1
+        stack = ["root"]
+
+        if state is not None and state != "root":
+            assert state in ("variable", "block"), "invalid state"
+            stack.append(state + "_begin")
+
+        statetokens = self.rules[stack[-1]]
+        source_length = len(source)
+        balancing_stack: t.List[str] = []
+        newlines_stripped = 0
+        line_starting = True
+
+        while True:
+            # tokenizer loop
+            for regex, tokens, new_state in statetokens:
+                m = regex.match(source, pos)
+
+                # if no match we try again with the next rule
+                if m is None:
+                    continue
+
+                # we only match blocks and variables if braces / parentheses
+                # are balanced. continue parsing with the lower rule which
+                # is the operator rule. do this only if the end tags look
+                # like operators
+                if balancing_stack and tokens in (
+                    TOKEN_VARIABLE_END,
+                    TOKEN_BLOCK_END,
+                    TOKEN_LINESTATEMENT_END,
+                ):
+                    continue
+
+                # tuples support more options
+                if isinstance(tokens, tuple):
+                    groups: t.Sequence[str] = m.groups()
+
+                    if isinstance(tokens, OptionalLStrip):
+                        # Rule supports lstrip. Match will look like
+                        # text, block type, whitespace control, type, control, ...
+                        text = groups[0]
+                        # Skipping the text and first type, every other group is the
+                        # whitespace control for each type. One of the groups will be
+                        # -, +, or empty string instead of None.
+                        strip_sign = next(g for g in groups[2::2] if g is not None)
+
+                        if strip_sign == "-":
+                            # Strip all whitespace between the text and the tag.
+                            stripped = text.rstrip()
+                            newlines_stripped = text[len(stripped) :].count("\n")
+                            groups = [stripped, *groups[1:]]
+                        elif (
+                            # Not marked for preserving whitespace.
+                            strip_sign != "+"
+                            # lstrip is enabled.
+                            and self.lstrip_blocks
+                            # Not a variable expression.
+                            and not m.groupdict().get(TOKEN_VARIABLE_BEGIN)
+                        ):
+                            # The start of text between the last newline and the tag.
+                            l_pos = text.rfind("\n") + 1
+
+                            if l_pos > 0 or line_starting:
+                                # If there's only whitespace between the newline and the
+                                # tag, strip it.
+                                if whitespace_re.fullmatch(text, l_pos):
+                                    groups = [text[:l_pos], *groups[1:]]
+
+                    for idx, token in enumerate(tokens):
+                        # failure group
+                        if isinstance(token, Failure):
+                            raise token(lineno, filename)
+                        # bygroup is a bit more complex, in that case we
+                        # yield for the current token the first named
+                        # group that matched
+                        elif token == "#bygroup":
+                            for key, value in m.groupdict().items():
+                                if value is not None:
+                                    yield lineno, key, value
+                                    lineno += value.count("\n")
+                                    break
+                            else:
+                                raise RuntimeError(
+                                    f"{regex!r} wanted to resolve the token dynamically"
+                                    " but no group matched"
+                                )
+                        # normal group
+                        else:
+                            data = groups[idx]
+
+                            if data or token not in ignore_if_empty:
+                                yield lineno, token, data  # type: ignore[misc]
+
+                            lineno += data.count("\n") + newlines_stripped
+                            newlines_stripped = 0
+
+                # strings as token just are yielded as it.
+                else:
+                    data = m.group()
+
+                    # update brace/parentheses balance
+                    if tokens == TOKEN_OPERATOR:
+                        if data == "{":
+                            balancing_stack.append("}")
+                        elif data == "(":
+                            balancing_stack.append(")")
+                        elif data == "[":
+                            balancing_stack.append("]")
+                        elif data in ("}", ")", "]"):
+                            if not balancing_stack:
+                                raise TemplateSyntaxError(
+                                    f"unexpected '{data}'", lineno, name, filename
+                                )
+
+                            expected_op = balancing_stack.pop()
+
+                            if expected_op != data:
+                                raise TemplateSyntaxError(
+                                    f"unexpected '{data}', expected '{expected_op}'",
+                                    lineno,
+                                    name,
+                                    filename,
+                                )
+
+                    # yield items
+                    if data or tokens not in ignore_if_empty:
+                        yield lineno, tokens, data
+
+                    lineno += data.count("\n")
+
+                line_starting = m.group()[-1:] == "\n"
+                # fetch new position into new variable so that we can check
+                # if there is a internal parsing error which would result
+                # in an infinite loop
+                pos2 = m.end()
+
+                # handle state changes
+                if new_state is not None:
+                    # remove the uppermost state
+                    if new_state == "#pop":
+                        stack.pop()
+                    # resolve the new state by group checking
+                    elif new_state == "#bygroup":
+                        for key, value in m.groupdict().items():
+                            if value is not None:
+                                stack.append(key)
+                                break
+                        else:
+                            raise RuntimeError(
+                                f"{regex!r} wanted to resolve the new state dynamically"
+                                f" but no group matched"
+                            )
+                    # direct state name given
+                    else:
+                        stack.append(new_state)
+
+                    statetokens = self.rules[stack[-1]]
+                # we are still at the same position and no stack change.
+                # this means a loop without break condition, avoid that and
+                # raise error
+                elif pos2 == pos:
+                    raise RuntimeError(
+                        f"{regex!r} yielded empty string without stack change"
+                    )
+
+                # publish new function and start again
+                pos = pos2
+                break
+            # if loop terminated without break we haven't found a single match
+            # either we are at the end of the file or we have a problem
+            else:
+                # end of text
+                if pos >= source_length:
+                    return
+
+                # something went wrong
+                raise TemplateSyntaxError(
+                    f"unexpected char {source[pos]!r} at {pos}", lineno, name, filename
+                )
diff --git a/lib/python3.11/site-packages/jinja2/loaders.py b/lib/python3.11/site-packages/jinja2/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..3913ee51e35d62211a9bca50d0f944ab4c4bc609
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/loaders.py
@@ -0,0 +1,693 @@
+"""API and implementations for loading templates from different data
+sources.
+"""
+
+import importlib.util
+import os
+import posixpath
+import sys
+import typing as t
+import weakref
+import zipimport
+from collections import abc
+from hashlib import sha1
+from importlib import import_module
+from types import ModuleType
+
+from .exceptions import TemplateNotFound
+from .utils import internalcode
+
+if t.TYPE_CHECKING:
+    from .environment import Environment
+    from .environment import Template
+
+
+def split_template_path(template: str) -> t.List[str]:
+    """Split a path into segments and perform a sanity check.  If it detects
+    '..' in the path it will raise a `TemplateNotFound` error.
+    """
+    pieces = []
+    for piece in template.split("/"):
+        if (
+            os.path.sep in piece
+            or (os.path.altsep and os.path.altsep in piece)
+            or piece == os.path.pardir
+        ):
+            raise TemplateNotFound(template)
+        elif piece and piece != ".":
+            pieces.append(piece)
+    return pieces
+
+
+class BaseLoader:
+    """Baseclass for all loaders.  Subclass this and override `get_source` to
+    implement a custom loading mechanism.  The environment provides a
+    `get_template` method that calls the loader's `load` method to get the
+    :class:`Template` object.
+
+    A very basic example for a loader that looks up templates on the file
+    system could look like this::
+
+        from jinja2 import BaseLoader, TemplateNotFound
+        from os.path import join, exists, getmtime
+
+        class MyLoader(BaseLoader):
+
+            def __init__(self, path):
+                self.path = path
+
+            def get_source(self, environment, template):
+                path = join(self.path, template)
+                if not exists(path):
+                    raise TemplateNotFound(template)
+                mtime = getmtime(path)
+                with open(path) as f:
+                    source = f.read()
+                return source, path, lambda: mtime == getmtime(path)
+    """
+
+    #: if set to `False` it indicates that the loader cannot provide access
+    #: to the source of templates.
+    #:
+    #: .. versionadded:: 2.4
+    has_source_access = True
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
+        """Get the template source, filename and reload helper for a template.
+        It's passed the environment and template name and has to return a
+        tuple in the form ``(source, filename, uptodate)`` or raise a
+        `TemplateNotFound` error if it can't locate the template.
+
+        The source part of the returned tuple must be the source of the
+        template as a string. The filename should be the name of the
+        file on the filesystem if it was loaded from there, otherwise
+        ``None``. The filename is used by Python for the tracebacks
+        if no loader extension is used.
+
+        The last item in the tuple is the `uptodate` function.  If auto
+        reloading is enabled it's always called to check if the template
+        changed.  No arguments are passed so the function must store the
+        old state somewhere (for example in a closure).  If it returns `False`
+        the template will be reloaded.
+        """
+        if not self.has_source_access:
+            raise RuntimeError(
+                f"{type(self).__name__} cannot provide access to the source"
+            )
+        raise TemplateNotFound(template)
+
+    def list_templates(self) -> t.List[str]:
+        """Iterates over all templates.  If the loader does not support that
+        it should raise a :exc:`TypeError` which is the default behavior.
+        """
+        raise TypeError("this loader cannot iterate over all templates")
+
+    @internalcode
+    def load(
+        self,
+        environment: "Environment",
+        name: str,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        """Loads a template.  This method looks up the template in the cache
+        or loads one by calling :meth:`get_source`.  Subclasses should not
+        override this method as loaders working on collections of other
+        loaders (such as :class:`PrefixLoader` or :class:`ChoiceLoader`)
+        will not call this method but `get_source` directly.
+        """
+        code = None
+        if globals is None:
+            globals = {}
+
+        # first we try to get the source for this template together
+        # with the filename and the uptodate function.
+        source, filename, uptodate = self.get_source(environment, name)
+
+        # try to load the code from the bytecode cache if there is a
+        # bytecode cache configured.
+        bcc = environment.bytecode_cache
+        if bcc is not None:
+            bucket = bcc.get_bucket(environment, name, filename, source)
+            code = bucket.code
+
+        # if we don't have code so far (not cached, no longer up to
+        # date) etc. we compile the template
+        if code is None:
+            code = environment.compile(source, name, filename)
+
+        # if the bytecode cache is available and the bucket doesn't
+        # have a code so far, we give the bucket the new code and put
+        # it back to the bytecode cache.
+        if bcc is not None and bucket.code is None:
+            bucket.code = code
+            bcc.set_bucket(bucket)
+
+        return environment.template_class.from_code(
+            environment, code, globals, uptodate
+        )
+
+
+class FileSystemLoader(BaseLoader):
+    """Load templates from a directory in the file system.
+
+    The path can be relative or absolute. Relative paths are relative to
+    the current working directory.
+
+    .. code-block:: python
+
+        loader = FileSystemLoader("templates")
+
+    A list of paths can be given. The directories will be searched in
+    order, stopping at the first matching template.
+
+    .. code-block:: python
+
+        loader = FileSystemLoader(["/override/templates", "/default/templates"])
+
+    :param searchpath: A path, or list of paths, to the directory that
+        contains the templates.
+    :param encoding: Use this encoding to read the text from template
+        files.
+    :param followlinks: Follow symbolic links in the path.
+
+    .. versionchanged:: 2.8
+        Added the ``followlinks`` parameter.
+    """
+
+    def __init__(
+        self,
+        searchpath: t.Union[
+            str, "os.PathLike[str]", t.Sequence[t.Union[str, "os.PathLike[str]"]]
+        ],
+        encoding: str = "utf-8",
+        followlinks: bool = False,
+    ) -> None:
+        if not isinstance(searchpath, abc.Iterable) or isinstance(searchpath, str):
+            searchpath = [searchpath]
+
+        self.searchpath = [os.fspath(p) for p in searchpath]
+        self.encoding = encoding
+        self.followlinks = followlinks
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, str, t.Callable[[], bool]]:
+        pieces = split_template_path(template)
+
+        for searchpath in self.searchpath:
+            # Use posixpath even on Windows to avoid "drive:" or UNC
+            # segments breaking out of the search directory.
+            filename = posixpath.join(searchpath, *pieces)
+
+            if os.path.isfile(filename):
+                break
+        else:
+            plural = "path" if len(self.searchpath) == 1 else "paths"
+            paths_str = ", ".join(repr(p) for p in self.searchpath)
+            raise TemplateNotFound(
+                template,
+                f"{template!r} not found in search {plural}: {paths_str}",
+            )
+
+        with open(filename, encoding=self.encoding) as f:
+            contents = f.read()
+
+        mtime = os.path.getmtime(filename)
+
+        def uptodate() -> bool:
+            try:
+                return os.path.getmtime(filename) == mtime
+            except OSError:
+                return False
+
+        # Use normpath to convert Windows altsep to sep.
+        return contents, os.path.normpath(filename), uptodate
+
+    def list_templates(self) -> t.List[str]:
+        found = set()
+        for searchpath in self.searchpath:
+            walk_dir = os.walk(searchpath, followlinks=self.followlinks)
+            for dirpath, _, filenames in walk_dir:
+                for filename in filenames:
+                    template = (
+                        os.path.join(dirpath, filename)[len(searchpath) :]
+                        .strip(os.path.sep)
+                        .replace(os.path.sep, "/")
+                    )
+                    if template[:2] == "./":
+                        template = template[2:]
+                    if template not in found:
+                        found.add(template)
+        return sorted(found)
+
+
+if sys.version_info >= (3, 13):
+
+    def _get_zipimporter_files(z: t.Any) -> t.Dict[str, object]:
+        try:
+            get_files = z._get_files
+        except AttributeError as e:
+            raise TypeError(
+                "This zip import does not have the required"
+                " metadata to list templates."
+            ) from e
+        return get_files()
+else:
+
+    def _get_zipimporter_files(z: t.Any) -> t.Dict[str, object]:
+        try:
+            files = z._files
+        except AttributeError as e:
+            raise TypeError(
+                "This zip import does not have the required"
+                " metadata to list templates."
+            ) from e
+        return files  # type: ignore[no-any-return]
+
+
+class PackageLoader(BaseLoader):
+    """Load templates from a directory in a Python package.
+
+    :param package_name: Import name of the package that contains the
+        template directory.
+    :param package_path: Directory within the imported package that
+        contains the templates.
+    :param encoding: Encoding of template files.
+
+    The following example looks up templates in the ``pages`` directory
+    within the ``project.ui`` package.
+
+    .. code-block:: python
+
+        loader = PackageLoader("project.ui", "pages")
+
+    Only packages installed as directories (standard pip behavior) or
+    zip/egg files (less common) are supported. The Python API for
+    introspecting data in packages is too limited to support other
+    installation methods the way this loader requires.
+
+    There is limited support for :pep:`420` namespace packages. The
+    template directory is assumed to only be in one namespace
+    contributor. Zip files contributing to a namespace are not
+    supported.
+
+    .. versionchanged:: 3.0
+        No longer uses ``setuptools`` as a dependency.
+
+    .. versionchanged:: 3.0
+        Limited PEP 420 namespace package support.
+    """
+
+    def __init__(
+        self,
+        package_name: str,
+        package_path: "str" = "templates",
+        encoding: str = "utf-8",
+    ) -> None:
+        package_path = os.path.normpath(package_path).rstrip(os.path.sep)
+
+        # normpath preserves ".", which isn't valid in zip paths.
+        if package_path == os.path.curdir:
+            package_path = ""
+        elif package_path[:2] == os.path.curdir + os.path.sep:
+            package_path = package_path[2:]
+
+        self.package_path = package_path
+        self.package_name = package_name
+        self.encoding = encoding
+
+        # Make sure the package exists. This also makes namespace
+        # packages work, otherwise get_loader returns None.
+        import_module(package_name)
+        spec = importlib.util.find_spec(package_name)
+        assert spec is not None, "An import spec was not found for the package."
+        loader = spec.loader
+        assert loader is not None, "A loader was not found for the package."
+        self._loader = loader
+        self._archive = None
+
+        if isinstance(loader, zipimport.zipimporter):
+            self._archive = loader.archive
+            pkgdir = next(iter(spec.submodule_search_locations))  # type: ignore
+            template_root = os.path.join(pkgdir, package_path).rstrip(os.path.sep)
+        else:
+            roots: t.List[str] = []
+
+            # One element for regular packages, multiple for namespace
+            # packages, or None for single module file.
+            if spec.submodule_search_locations:
+                roots.extend(spec.submodule_search_locations)
+            # A single module file, use the parent directory instead.
+            elif spec.origin is not None:
+                roots.append(os.path.dirname(spec.origin))
+
+            if not roots:
+                raise ValueError(
+                    f"The {package_name!r} package was not installed in a"
+                    " way that PackageLoader understands."
+                )
+
+            for root in roots:
+                root = os.path.join(root, package_path)
+
+                if os.path.isdir(root):
+                    template_root = root
+                    break
+            else:
+                raise ValueError(
+                    f"PackageLoader could not find a {package_path!r} directory"
+                    f" in the {package_name!r} package."
+                )
+
+        self._template_root = template_root
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, str, t.Optional[t.Callable[[], bool]]]:
+        # Use posixpath even on Windows to avoid "drive:" or UNC
+        # segments breaking out of the search directory. Use normpath to
+        # convert Windows altsep to sep.
+        p = os.path.normpath(
+            posixpath.join(self._template_root, *split_template_path(template))
+        )
+        up_to_date: t.Optional[t.Callable[[], bool]]
+
+        if self._archive is None:
+            # Package is a directory.
+            if not os.path.isfile(p):
+                raise TemplateNotFound(template)
+
+            with open(p, "rb") as f:
+                source = f.read()
+
+            mtime = os.path.getmtime(p)
+
+            def up_to_date() -> bool:
+                return os.path.isfile(p) and os.path.getmtime(p) == mtime
+
+        else:
+            # Package is a zip file.
+            try:
+                source = self._loader.get_data(p)  # type: ignore
+            except OSError as e:
+                raise TemplateNotFound(template) from e
+
+            # Could use the zip's mtime for all template mtimes, but
+            # would need to safely reload the module if it's out of
+            # date, so just report it as always current.
+            up_to_date = None
+
+        return source.decode(self.encoding), p, up_to_date
+
+    def list_templates(self) -> t.List[str]:
+        results: t.List[str] = []
+
+        if self._archive is None:
+            # Package is a directory.
+            offset = len(self._template_root)
+
+            for dirpath, _, filenames in os.walk(self._template_root):
+                dirpath = dirpath[offset:].lstrip(os.path.sep)
+                results.extend(
+                    os.path.join(dirpath, name).replace(os.path.sep, "/")
+                    for name in filenames
+                )
+        else:
+            files = _get_zipimporter_files(self._loader)
+
+            # Package is a zip file.
+            prefix = (
+                self._template_root[len(self._archive) :].lstrip(os.path.sep)
+                + os.path.sep
+            )
+            offset = len(prefix)
+
+            for name in files:
+                # Find names under the templates directory that aren't directories.
+                if name.startswith(prefix) and name[-1] != os.path.sep:
+                    results.append(name[offset:].replace(os.path.sep, "/"))
+
+        results.sort()
+        return results
+
+
+class DictLoader(BaseLoader):
+    """Loads a template from a Python dict mapping template names to
+    template source.  This loader is useful for unittesting:
+
+    >>> loader = DictLoader({'index.html': 'source here'})
+
+    Because auto reloading is rarely useful this is disabled by default.
+    """
+
+    def __init__(self, mapping: t.Mapping[str, str]) -> None:
+        self.mapping = mapping
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, None, t.Callable[[], bool]]:
+        if template in self.mapping:
+            source = self.mapping[template]
+            return source, None, lambda: source == self.mapping.get(template)
+        raise TemplateNotFound(template)
+
+    def list_templates(self) -> t.List[str]:
+        return sorted(self.mapping)
+
+
+class FunctionLoader(BaseLoader):
+    """A loader that is passed a function which does the loading.  The
+    function receives the name of the template and has to return either
+    a string with the template source, a tuple in the form ``(source,
+    filename, uptodatefunc)`` or `None` if the template does not exist.
+
+    >>> def load_template(name):
+    ...     if name == 'index.html':
+    ...         return '...'
+    ...
+    >>> loader = FunctionLoader(load_template)
+
+    The `uptodatefunc` is a function that is called if autoreload is enabled
+    and has to return `True` if the template is still up to date.  For more
+    details have a look at :meth:`BaseLoader.get_source` which has the same
+    return value.
+    """
+
+    def __init__(
+        self,
+        load_func: t.Callable[
+            [str],
+            t.Optional[
+                t.Union[
+                    str, t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]
+                ]
+            ],
+        ],
+    ) -> None:
+        self.load_func = load_func
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
+        rv = self.load_func(template)
+
+        if rv is None:
+            raise TemplateNotFound(template)
+
+        if isinstance(rv, str):
+            return rv, None, None
+
+        return rv
+
+
+class PrefixLoader(BaseLoader):
+    """A loader that is passed a dict of loaders where each loader is bound
+    to a prefix.  The prefix is delimited from the template by a slash per
+    default, which can be changed by setting the `delimiter` argument to
+    something else::
+
+        loader = PrefixLoader({
+            'app1':     PackageLoader('mypackage.app1'),
+            'app2':     PackageLoader('mypackage.app2')
+        })
+
+    By loading ``'app1/index.html'`` the file from the app1 package is loaded,
+    by loading ``'app2/index.html'`` the file from the second.
+    """
+
+    def __init__(
+        self, mapping: t.Mapping[str, BaseLoader], delimiter: str = "/"
+    ) -> None:
+        self.mapping = mapping
+        self.delimiter = delimiter
+
+    def get_loader(self, template: str) -> t.Tuple[BaseLoader, str]:
+        try:
+            prefix, name = template.split(self.delimiter, 1)
+            loader = self.mapping[prefix]
+        except (ValueError, KeyError) as e:
+            raise TemplateNotFound(template) from e
+        return loader, name
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
+        loader, name = self.get_loader(template)
+        try:
+            return loader.get_source(environment, name)
+        except TemplateNotFound as e:
+            # re-raise the exception with the correct filename here.
+            # (the one that includes the prefix)
+            raise TemplateNotFound(template) from e
+
+    @internalcode
+    def load(
+        self,
+        environment: "Environment",
+        name: str,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        loader, local_name = self.get_loader(name)
+        try:
+            return loader.load(environment, local_name, globals)
+        except TemplateNotFound as e:
+            # re-raise the exception with the correct filename here.
+            # (the one that includes the prefix)
+            raise TemplateNotFound(name) from e
+
+    def list_templates(self) -> t.List[str]:
+        result = []
+        for prefix, loader in self.mapping.items():
+            for template in loader.list_templates():
+                result.append(prefix + self.delimiter + template)
+        return result
+
+
+class ChoiceLoader(BaseLoader):
+    """This loader works like the `PrefixLoader` just that no prefix is
+    specified.  If a template could not be found by one loader the next one
+    is tried.
+
+    >>> loader = ChoiceLoader([
+    ...     FileSystemLoader('/path/to/user/templates'),
+    ...     FileSystemLoader('/path/to/system/templates')
+    ... ])
+
+    This is useful if you want to allow users to override builtin templates
+    from a different location.
+    """
+
+    def __init__(self, loaders: t.Sequence[BaseLoader]) -> None:
+        self.loaders = loaders
+
+    def get_source(
+        self, environment: "Environment", template: str
+    ) -> t.Tuple[str, t.Optional[str], t.Optional[t.Callable[[], bool]]]:
+        for loader in self.loaders:
+            try:
+                return loader.get_source(environment, template)
+            except TemplateNotFound:
+                pass
+        raise TemplateNotFound(template)
+
+    @internalcode
+    def load(
+        self,
+        environment: "Environment",
+        name: str,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        for loader in self.loaders:
+            try:
+                return loader.load(environment, name, globals)
+            except TemplateNotFound:
+                pass
+        raise TemplateNotFound(name)
+
+    def list_templates(self) -> t.List[str]:
+        found = set()
+        for loader in self.loaders:
+            found.update(loader.list_templates())
+        return sorted(found)
+
+
+class _TemplateModule(ModuleType):
+    """Like a normal module but with support for weak references"""
+
+
+class ModuleLoader(BaseLoader):
+    """This loader loads templates from precompiled templates.
+
+    Example usage:
+
+    >>> loader = ModuleLoader('/path/to/compiled/templates')
+
+    Templates can be precompiled with :meth:`Environment.compile_templates`.
+    """
+
+    has_source_access = False
+
+    def __init__(
+        self,
+        path: t.Union[
+            str, "os.PathLike[str]", t.Sequence[t.Union[str, "os.PathLike[str]"]]
+        ],
+    ) -> None:
+        package_name = f"_jinja2_module_templates_{id(self):x}"
+
+        # create a fake module that looks for the templates in the
+        # path given.
+        mod = _TemplateModule(package_name)
+
+        if not isinstance(path, abc.Iterable) or isinstance(path, str):
+            path = [path]
+
+        mod.__path__ = [os.fspath(p) for p in path]
+
+        sys.modules[package_name] = weakref.proxy(
+            mod, lambda x: sys.modules.pop(package_name, None)
+        )
+
+        # the only strong reference, the sys.modules entry is weak
+        # so that the garbage collector can remove it once the
+        # loader that created it goes out of business.
+        self.module = mod
+        self.package_name = package_name
+
+    @staticmethod
+    def get_template_key(name: str) -> str:
+        return "tmpl_" + sha1(name.encode("utf-8")).hexdigest()
+
+    @staticmethod
+    def get_module_filename(name: str) -> str:
+        return ModuleLoader.get_template_key(name) + ".py"
+
+    @internalcode
+    def load(
+        self,
+        environment: "Environment",
+        name: str,
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ) -> "Template":
+        key = self.get_template_key(name)
+        module = f"{self.package_name}.{key}"
+        mod = getattr(self.module, module, None)
+
+        if mod is None:
+            try:
+                mod = __import__(module, None, None, ["root"])
+            except ImportError as e:
+                raise TemplateNotFound(name) from e
+
+            # remove the entry from sys.modules, we only want the attribute
+            # on the module object we have stored on the loader.
+            sys.modules.pop(module, None)
+
+        if globals is None:
+            globals = {}
+
+        return environment.template_class.from_module_dict(
+            environment, mod.__dict__, globals
+        )
diff --git a/lib/python3.11/site-packages/jinja2/meta.py b/lib/python3.11/site-packages/jinja2/meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..298499e265253fb4cc5f365ea147043d0a83c6f9
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/meta.py
@@ -0,0 +1,112 @@
+"""Functions that expose information about templates that might be
+interesting for introspection.
+"""
+
+import typing as t
+
+from . import nodes
+from .compiler import CodeGenerator
+from .compiler import Frame
+
+if t.TYPE_CHECKING:
+    from .environment import Environment
+
+
+class TrackingCodeGenerator(CodeGenerator):
+    """We abuse the code generator for introspection."""
+
+    def __init__(self, environment: "Environment") -> None:
+        super().__init__(environment, "<introspection>", "<introspection>")
+        self.undeclared_identifiers: t.Set[str] = set()
+
+    def write(self, x: str) -> None:
+        """Don't write."""
+
+    def enter_frame(self, frame: Frame) -> None:
+        """Remember all undeclared identifiers."""
+        super().enter_frame(frame)
+
+        for _, (action, param) in frame.symbols.loads.items():
+            if action == "resolve" and param not in self.environment.globals:
+                self.undeclared_identifiers.add(param)
+
+
+def find_undeclared_variables(ast: nodes.Template) -> t.Set[str]:
+    """Returns a set of all variables in the AST that will be looked up from
+    the context at runtime.  Because at compile time it's not known which
+    variables will be used depending on the path the execution takes at
+    runtime, all variables are returned.
+
+    >>> from jinja2 import Environment, meta
+    >>> env = Environment()
+    >>> ast = env.parse('{% set foo = 42 %}{{ bar + foo }}')
+    >>> meta.find_undeclared_variables(ast) == {'bar'}
+    True
+
+    .. admonition:: Implementation
+
+       Internally the code generator is used for finding undeclared variables.
+       This is good to know because the code generator might raise a
+       :exc:`TemplateAssertionError` during compilation and as a matter of
+       fact this function can currently raise that exception as well.
+    """
+    codegen = TrackingCodeGenerator(ast.environment)  # type: ignore
+    codegen.visit(ast)
+    return codegen.undeclared_identifiers
+
+
+_ref_types = (nodes.Extends, nodes.FromImport, nodes.Import, nodes.Include)
+_RefType = t.Union[nodes.Extends, nodes.FromImport, nodes.Import, nodes.Include]
+
+
+def find_referenced_templates(ast: nodes.Template) -> t.Iterator[t.Optional[str]]:
+    """Finds all the referenced templates from the AST.  This will return an
+    iterator over all the hardcoded template extensions, inclusions and
+    imports.  If dynamic inheritance or inclusion is used, `None` will be
+    yielded.
+
+    >>> from jinja2 import Environment, meta
+    >>> env = Environment()
+    >>> ast = env.parse('{% extends "layout.html" %}{% include helper %}')
+    >>> list(meta.find_referenced_templates(ast))
+    ['layout.html', None]
+
+    This function is useful for dependency tracking.  For example if you want
+    to rebuild parts of the website after a layout template has changed.
+    """
+    template_name: t.Any
+
+    for node in ast.find_all(_ref_types):
+        template: nodes.Expr = node.template  # type: ignore
+
+        if not isinstance(template, nodes.Const):
+            # a tuple with some non consts in there
+            if isinstance(template, (nodes.Tuple, nodes.List)):
+                for template_name in template.items:
+                    # something const, only yield the strings and ignore
+                    # non-string consts that really just make no sense
+                    if isinstance(template_name, nodes.Const):
+                        if isinstance(template_name.value, str):
+                            yield template_name.value
+                    # something dynamic in there
+                    else:
+                        yield None
+            # something dynamic we don't know about here
+            else:
+                yield None
+            continue
+        # constant is a basestring, direct template name
+        if isinstance(template.value, str):
+            yield template.value
+        # a tuple or list (latter *should* not happen) made of consts,
+        # yield the consts that are strings.  We could warn here for
+        # non string values
+        elif isinstance(node, nodes.Include) and isinstance(
+            template.value, (tuple, list)
+        ):
+            for template_name in template.value:
+                if isinstance(template_name, str):
+                    yield template_name
+        # something else we don't care about, we could warn here
+        else:
+            yield None
diff --git a/lib/python3.11/site-packages/jinja2/nativetypes.py b/lib/python3.11/site-packages/jinja2/nativetypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..71db8cc31f45efee11eaad9d8807da389470cf15
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/nativetypes.py
@@ -0,0 +1,130 @@
+import typing as t
+from ast import literal_eval
+from ast import parse
+from itertools import chain
+from itertools import islice
+from types import GeneratorType
+
+from . import nodes
+from .compiler import CodeGenerator
+from .compiler import Frame
+from .compiler import has_safe_repr
+from .environment import Environment
+from .environment import Template
+
+
+def native_concat(values: t.Iterable[t.Any]) -> t.Optional[t.Any]:
+    """Return a native Python type from the list of compiled nodes. If
+    the result is a single node, its value is returned. Otherwise, the
+    nodes are concatenated as strings. If the result can be parsed with
+    :func:`ast.literal_eval`, the parsed value is returned. Otherwise,
+    the string is returned.
+
+    :param values: Iterable of outputs to concatenate.
+    """
+    head = list(islice(values, 2))
+
+    if not head:
+        return None
+
+    if len(head) == 1:
+        raw = head[0]
+        if not isinstance(raw, str):
+            return raw
+    else:
+        if isinstance(values, GeneratorType):
+            values = chain(head, values)
+        raw = "".join([str(v) for v in values])
+
+    try:
+        return literal_eval(
+            # In Python 3.10+ ast.literal_eval removes leading spaces/tabs
+            # from the given string. For backwards compatibility we need to
+            # parse the string ourselves without removing leading spaces/tabs.
+            parse(raw, mode="eval")
+        )
+    except (ValueError, SyntaxError, MemoryError):
+        return raw
+
+
+class NativeCodeGenerator(CodeGenerator):
+    """A code generator which renders Python types by not adding
+    ``str()`` around output nodes.
+    """
+
+    @staticmethod
+    def _default_finalize(value: t.Any) -> t.Any:
+        return value
+
+    def _output_const_repr(self, group: t.Iterable[t.Any]) -> str:
+        return repr("".join([str(v) for v in group]))
+
+    def _output_child_to_const(
+        self, node: nodes.Expr, frame: Frame, finalize: CodeGenerator._FinalizeInfo
+    ) -> t.Any:
+        const = node.as_const(frame.eval_ctx)
+
+        if not has_safe_repr(const):
+            raise nodes.Impossible()
+
+        if isinstance(node, nodes.TemplateData):
+            return const
+
+        return finalize.const(const)  # type: ignore
+
+    def _output_child_pre(
+        self, node: nodes.Expr, frame: Frame, finalize: CodeGenerator._FinalizeInfo
+    ) -> None:
+        if finalize.src is not None:
+            self.write(finalize.src)
+
+    def _output_child_post(
+        self, node: nodes.Expr, frame: Frame, finalize: CodeGenerator._FinalizeInfo
+    ) -> None:
+        if finalize.src is not None:
+            self.write(")")
+
+
+class NativeEnvironment(Environment):
+    """An environment that renders templates to native Python types."""
+
+    code_generator_class = NativeCodeGenerator
+    concat = staticmethod(native_concat)  # type: ignore
+
+
+class NativeTemplate(Template):
+    environment_class = NativeEnvironment
+
+    def render(self, *args: t.Any, **kwargs: t.Any) -> t.Any:
+        """Render the template to produce a native Python type. If the
+        result is a single node, its value is returned. Otherwise, the
+        nodes are concatenated as strings. If the result can be parsed
+        with :func:`ast.literal_eval`, the parsed value is returned.
+        Otherwise, the string is returned.
+        """
+        ctx = self.new_context(dict(*args, **kwargs))
+
+        try:
+            return self.environment_class.concat(  # type: ignore
+                self.root_render_func(ctx)
+            )
+        except Exception:
+            return self.environment.handle_exception()
+
+    async def render_async(self, *args: t.Any, **kwargs: t.Any) -> t.Any:
+        if not self.environment.is_async:
+            raise RuntimeError(
+                "The environment was not created with async mode enabled."
+            )
+
+        ctx = self.new_context(dict(*args, **kwargs))
+
+        try:
+            return self.environment_class.concat(  # type: ignore
+                [n async for n in self.root_render_func(ctx)]  # type: ignore
+            )
+        except Exception:
+            return self.environment.handle_exception()
+
+
+NativeEnvironment.template_class = NativeTemplate
diff --git a/lib/python3.11/site-packages/jinja2/nodes.py b/lib/python3.11/site-packages/jinja2/nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f93b90ec21e2302cc7149504f73b8d9cab472d9
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/nodes.py
@@ -0,0 +1,1206 @@
+"""AST nodes generated by the parser for the compiler. Also provides
+some node tree helper functions used by the parser and compiler in order
+to normalize nodes.
+"""
+
+import inspect
+import operator
+import typing as t
+from collections import deque
+
+from markupsafe import Markup
+
+from .utils import _PassArg
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .environment import Environment
+
+_NodeBound = t.TypeVar("_NodeBound", bound="Node")
+
+_binop_to_func: t.Dict[str, t.Callable[[t.Any, t.Any], t.Any]] = {
+    "*": operator.mul,
+    "/": operator.truediv,
+    "//": operator.floordiv,
+    "**": operator.pow,
+    "%": operator.mod,
+    "+": operator.add,
+    "-": operator.sub,
+}
+
+_uaop_to_func: t.Dict[str, t.Callable[[t.Any], t.Any]] = {
+    "not": operator.not_,
+    "+": operator.pos,
+    "-": operator.neg,
+}
+
+_cmpop_to_func: t.Dict[str, t.Callable[[t.Any, t.Any], t.Any]] = {
+    "eq": operator.eq,
+    "ne": operator.ne,
+    "gt": operator.gt,
+    "gteq": operator.ge,
+    "lt": operator.lt,
+    "lteq": operator.le,
+    "in": lambda a, b: a in b,
+    "notin": lambda a, b: a not in b,
+}
+
+
+class Impossible(Exception):
+    """Raised if the node could not perform a requested action."""
+
+
+class NodeType(type):
+    """A metaclass for nodes that handles the field and attribute
+    inheritance.  fields and attributes from the parent class are
+    automatically forwarded to the child."""
+
+    def __new__(mcs, name, bases, d):  # type: ignore
+        for attr in "fields", "attributes":
+            storage: t.List[t.Tuple[str, ...]] = []
+            storage.extend(getattr(bases[0] if bases else object, attr, ()))
+            storage.extend(d.get(attr, ()))
+            assert len(bases) <= 1, "multiple inheritance not allowed"
+            assert len(storage) == len(set(storage)), "layout conflict"
+            d[attr] = tuple(storage)
+        d.setdefault("abstract", False)
+        return type.__new__(mcs, name, bases, d)
+
+
+class EvalContext:
+    """Holds evaluation time information.  Custom attributes can be attached
+    to it in extensions.
+    """
+
+    def __init__(
+        self, environment: "Environment", template_name: t.Optional[str] = None
+    ) -> None:
+        self.environment = environment
+        if callable(environment.autoescape):
+            self.autoescape = environment.autoescape(template_name)
+        else:
+            self.autoescape = environment.autoescape
+        self.volatile = False
+
+    def save(self) -> t.Mapping[str, t.Any]:
+        return self.__dict__.copy()
+
+    def revert(self, old: t.Mapping[str, t.Any]) -> None:
+        self.__dict__.clear()
+        self.__dict__.update(old)
+
+
+def get_eval_context(node: "Node", ctx: t.Optional[EvalContext]) -> EvalContext:
+    if ctx is None:
+        if node.environment is None:
+            raise RuntimeError(
+                "if no eval context is passed, the node must have an"
+                " attached environment."
+            )
+        return EvalContext(node.environment)
+    return ctx
+
+
+class Node(metaclass=NodeType):
+    """Baseclass for all Jinja nodes.  There are a number of nodes available
+    of different types.  There are four major types:
+
+    -   :class:`Stmt`: statements
+    -   :class:`Expr`: expressions
+    -   :class:`Helper`: helper nodes
+    -   :class:`Template`: the outermost wrapper node
+
+    All nodes have fields and attributes.  Fields may be other nodes, lists,
+    or arbitrary values.  Fields are passed to the constructor as regular
+    positional arguments, attributes as keyword arguments.  Each node has
+    two attributes: `lineno` (the line number of the node) and `environment`.
+    The `environment` attribute is set at the end of the parsing process for
+    all nodes automatically.
+    """
+
+    fields: t.Tuple[str, ...] = ()
+    attributes: t.Tuple[str, ...] = ("lineno", "environment")
+    abstract = True
+
+    lineno: int
+    environment: t.Optional["Environment"]
+
+    def __init__(self, *fields: t.Any, **attributes: t.Any) -> None:
+        if self.abstract:
+            raise TypeError("abstract nodes are not instantiable")
+        if fields:
+            if len(fields) != len(self.fields):
+                if not self.fields:
+                    raise TypeError(f"{type(self).__name__!r} takes 0 arguments")
+                raise TypeError(
+                    f"{type(self).__name__!r} takes 0 or {len(self.fields)}"
+                    f" argument{'s' if len(self.fields) != 1 else ''}"
+                )
+            for name, arg in zip(self.fields, fields):
+                setattr(self, name, arg)
+        for attr in self.attributes:
+            setattr(self, attr, attributes.pop(attr, None))
+        if attributes:
+            raise TypeError(f"unknown attribute {next(iter(attributes))!r}")
+
+    def iter_fields(
+        self,
+        exclude: t.Optional[t.Container[str]] = None,
+        only: t.Optional[t.Container[str]] = None,
+    ) -> t.Iterator[t.Tuple[str, t.Any]]:
+        """This method iterates over all fields that are defined and yields
+        ``(key, value)`` tuples.  Per default all fields are returned, but
+        it's possible to limit that to some fields by providing the `only`
+        parameter or to exclude some using the `exclude` parameter.  Both
+        should be sets or tuples of field names.
+        """
+        for name in self.fields:
+            if (
+                (exclude is None and only is None)
+                or (exclude is not None and name not in exclude)
+                or (only is not None and name in only)
+            ):
+                try:
+                    yield name, getattr(self, name)
+                except AttributeError:
+                    pass
+
+    def iter_child_nodes(
+        self,
+        exclude: t.Optional[t.Container[str]] = None,
+        only: t.Optional[t.Container[str]] = None,
+    ) -> t.Iterator["Node"]:
+        """Iterates over all direct child nodes of the node.  This iterates
+        over all fields and yields the values of they are nodes.  If the value
+        of a field is a list all the nodes in that list are returned.
+        """
+        for _, item in self.iter_fields(exclude, only):
+            if isinstance(item, list):
+                for n in item:
+                    if isinstance(n, Node):
+                        yield n
+            elif isinstance(item, Node):
+                yield item
+
+    def find(self, node_type: t.Type[_NodeBound]) -> t.Optional[_NodeBound]:
+        """Find the first node of a given type.  If no such node exists the
+        return value is `None`.
+        """
+        for result in self.find_all(node_type):
+            return result
+
+        return None
+
+    def find_all(
+        self, node_type: t.Union[t.Type[_NodeBound], t.Tuple[t.Type[_NodeBound], ...]]
+    ) -> t.Iterator[_NodeBound]:
+        """Find all the nodes of a given type.  If the type is a tuple,
+        the check is performed for any of the tuple items.
+        """
+        for child in self.iter_child_nodes():
+            if isinstance(child, node_type):
+                yield child  # type: ignore
+            yield from child.find_all(node_type)
+
+    def set_ctx(self, ctx: str) -> "Node":
+        """Reset the context of a node and all child nodes.  Per default the
+        parser will all generate nodes that have a 'load' context as it's the
+        most common one.  This method is used in the parser to set assignment
+        targets and other nodes to a store context.
+        """
+        todo = deque([self])
+        while todo:
+            node = todo.popleft()
+            if "ctx" in node.fields:
+                node.ctx = ctx  # type: ignore
+            todo.extend(node.iter_child_nodes())
+        return self
+
+    def set_lineno(self, lineno: int, override: bool = False) -> "Node":
+        """Set the line numbers of the node and children."""
+        todo = deque([self])
+        while todo:
+            node = todo.popleft()
+            if "lineno" in node.attributes:
+                if node.lineno is None or override:
+                    node.lineno = lineno
+            todo.extend(node.iter_child_nodes())
+        return self
+
+    def set_environment(self, environment: "Environment") -> "Node":
+        """Set the environment for all nodes."""
+        todo = deque([self])
+        while todo:
+            node = todo.popleft()
+            node.environment = environment
+            todo.extend(node.iter_child_nodes())
+        return self
+
+    def __eq__(self, other: t.Any) -> bool:
+        if type(self) is not type(other):
+            return NotImplemented
+
+        return tuple(self.iter_fields()) == tuple(other.iter_fields())
+
+    __hash__ = object.__hash__
+
+    def __repr__(self) -> str:
+        args_str = ", ".join(f"{a}={getattr(self, a, None)!r}" for a in self.fields)
+        return f"{type(self).__name__}({args_str})"
+
+    def dump(self) -> str:
+        def _dump(node: t.Union[Node, t.Any]) -> None:
+            if not isinstance(node, Node):
+                buf.append(repr(node))
+                return
+
+            buf.append(f"nodes.{type(node).__name__}(")
+            if not node.fields:
+                buf.append(")")
+                return
+            for idx, field in enumerate(node.fields):
+                if idx:
+                    buf.append(", ")
+                value = getattr(node, field)
+                if isinstance(value, list):
+                    buf.append("[")
+                    for idx, item in enumerate(value):
+                        if idx:
+                            buf.append(", ")
+                        _dump(item)
+                    buf.append("]")
+                else:
+                    _dump(value)
+            buf.append(")")
+
+        buf: t.List[str] = []
+        _dump(self)
+        return "".join(buf)
+
+
+class Stmt(Node):
+    """Base node for all statements."""
+
+    abstract = True
+
+
+class Helper(Node):
+    """Nodes that exist in a specific context only."""
+
+    abstract = True
+
+
+class Template(Node):
+    """Node that represents a template.  This must be the outermost node that
+    is passed to the compiler.
+    """
+
+    fields = ("body",)
+    body: t.List[Node]
+
+
+class Output(Stmt):
+    """A node that holds multiple expressions which are then printed out.
+    This is used both for the `print` statement and the regular template data.
+    """
+
+    fields = ("nodes",)
+    nodes: t.List["Expr"]
+
+
+class Extends(Stmt):
+    """Represents an extends statement."""
+
+    fields = ("template",)
+    template: "Expr"
+
+
+class For(Stmt):
+    """The for loop.  `target` is the target for the iteration (usually a
+    :class:`Name` or :class:`Tuple`), `iter` the iterable.  `body` is a list
+    of nodes that are used as loop-body, and `else_` a list of nodes for the
+    `else` block.  If no else node exists it has to be an empty list.
+
+    For filtered nodes an expression can be stored as `test`, otherwise `None`.
+    """
+
+    fields = ("target", "iter", "body", "else_", "test", "recursive")
+    target: Node
+    iter: Node
+    body: t.List[Node]
+    else_: t.List[Node]
+    test: t.Optional[Node]
+    recursive: bool
+
+
+class If(Stmt):
+    """If `test` is true, `body` is rendered, else `else_`."""
+
+    fields = ("test", "body", "elif_", "else_")
+    test: Node
+    body: t.List[Node]
+    elif_: t.List["If"]
+    else_: t.List[Node]
+
+
+class Macro(Stmt):
+    """A macro definition.  `name` is the name of the macro, `args` a list of
+    arguments and `defaults` a list of defaults if there are any.  `body` is
+    a list of nodes for the macro body.
+    """
+
+    fields = ("name", "args", "defaults", "body")
+    name: str
+    args: t.List["Name"]
+    defaults: t.List["Expr"]
+    body: t.List[Node]
+
+
+class CallBlock(Stmt):
+    """Like a macro without a name but a call instead.  `call` is called with
+    the unnamed macro as `caller` argument this node holds.
+    """
+
+    fields = ("call", "args", "defaults", "body")
+    call: "Call"
+    args: t.List["Name"]
+    defaults: t.List["Expr"]
+    body: t.List[Node]
+
+
+class FilterBlock(Stmt):
+    """Node for filter sections."""
+
+    fields = ("body", "filter")
+    body: t.List[Node]
+    filter: "Filter"
+
+
+class With(Stmt):
+    """Specific node for with statements.  In older versions of Jinja the
+    with statement was implemented on the base of the `Scope` node instead.
+
+    .. versionadded:: 2.9.3
+    """
+
+    fields = ("targets", "values", "body")
+    targets: t.List["Expr"]
+    values: t.List["Expr"]
+    body: t.List[Node]
+
+
+class Block(Stmt):
+    """A node that represents a block.
+
+    .. versionchanged:: 3.0.0
+        the `required` field was added.
+    """
+
+    fields = ("name", "body", "scoped", "required")
+    name: str
+    body: t.List[Node]
+    scoped: bool
+    required: bool
+
+
+class Include(Stmt):
+    """A node that represents the include tag."""
+
+    fields = ("template", "with_context", "ignore_missing")
+    template: "Expr"
+    with_context: bool
+    ignore_missing: bool
+
+
+class Import(Stmt):
+    """A node that represents the import tag."""
+
+    fields = ("template", "target", "with_context")
+    template: "Expr"
+    target: str
+    with_context: bool
+
+
+class FromImport(Stmt):
+    """A node that represents the from import tag.  It's important to not
+    pass unsafe names to the name attribute.  The compiler translates the
+    attribute lookups directly into getattr calls and does *not* use the
+    subscript callback of the interface.  As exported variables may not
+    start with double underscores (which the parser asserts) this is not a
+    problem for regular Jinja code, but if this node is used in an extension
+    extra care must be taken.
+
+    The list of names may contain tuples if aliases are wanted.
+    """
+
+    fields = ("template", "names", "with_context")
+    template: "Expr"
+    names: t.List[t.Union[str, t.Tuple[str, str]]]
+    with_context: bool
+
+
+class ExprStmt(Stmt):
+    """A statement that evaluates an expression and discards the result."""
+
+    fields = ("node",)
+    node: Node
+
+
+class Assign(Stmt):
+    """Assigns an expression to a target."""
+
+    fields = ("target", "node")
+    target: "Expr"
+    node: Node
+
+
+class AssignBlock(Stmt):
+    """Assigns a block to a target."""
+
+    fields = ("target", "filter", "body")
+    target: "Expr"
+    filter: t.Optional["Filter"]
+    body: t.List[Node]
+
+
+class Expr(Node):
+    """Baseclass for all expressions."""
+
+    abstract = True
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        """Return the value of the expression as constant or raise
+        :exc:`Impossible` if this was not possible.
+
+        An :class:`EvalContext` can be provided, if none is given
+        a default context is created which requires the nodes to have
+        an attached environment.
+
+        .. versionchanged:: 2.4
+           the `eval_ctx` parameter was added.
+        """
+        raise Impossible()
+
+    def can_assign(self) -> bool:
+        """Check if it's possible to assign something to this node."""
+        return False
+
+
+class BinExpr(Expr):
+    """Baseclass for all binary expressions."""
+
+    fields = ("left", "right")
+    left: Expr
+    right: Expr
+    operator: str
+    abstract = True
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+
+        # intercepted operators cannot be folded at compile time
+        if (
+            eval_ctx.environment.sandboxed
+            and self.operator in eval_ctx.environment.intercepted_binops  # type: ignore
+        ):
+            raise Impossible()
+        f = _binop_to_func[self.operator]
+        try:
+            return f(self.left.as_const(eval_ctx), self.right.as_const(eval_ctx))
+        except Exception as e:
+            raise Impossible() from e
+
+
+class UnaryExpr(Expr):
+    """Baseclass for all unary expressions."""
+
+    fields = ("node",)
+    node: Expr
+    operator: str
+    abstract = True
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+
+        # intercepted operators cannot be folded at compile time
+        if (
+            eval_ctx.environment.sandboxed
+            and self.operator in eval_ctx.environment.intercepted_unops  # type: ignore
+        ):
+            raise Impossible()
+        f = _uaop_to_func[self.operator]
+        try:
+            return f(self.node.as_const(eval_ctx))
+        except Exception as e:
+            raise Impossible() from e
+
+
+class Name(Expr):
+    """Looks up a name or stores a value in a name.
+    The `ctx` of the node can be one of the following values:
+
+    -   `store`: store a value in the name
+    -   `load`: load that name
+    -   `param`: like `store` but if the name was defined as function parameter.
+    """
+
+    fields = ("name", "ctx")
+    name: str
+    ctx: str
+
+    def can_assign(self) -> bool:
+        return self.name not in {"true", "false", "none", "True", "False", "None"}
+
+
+class NSRef(Expr):
+    """Reference to a namespace value assignment"""
+
+    fields = ("name", "attr")
+    name: str
+    attr: str
+
+    def can_assign(self) -> bool:
+        # We don't need any special checks here; NSRef assignments have a
+        # runtime check to ensure the target is a namespace object which will
+        # have been checked already as it is created using a normal assignment
+        # which goes through a `Name` node.
+        return True
+
+
+class Literal(Expr):
+    """Baseclass for literals."""
+
+    abstract = True
+
+
+class Const(Literal):
+    """All constant values.  The parser will return this node for simple
+    constants such as ``42`` or ``"foo"`` but it can be used to store more
+    complex values such as lists too.  Only constants with a safe
+    representation (objects where ``eval(repr(x)) == x`` is true).
+    """
+
+    fields = ("value",)
+    value: t.Any
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        return self.value
+
+    @classmethod
+    def from_untrusted(
+        cls,
+        value: t.Any,
+        lineno: t.Optional[int] = None,
+        environment: "t.Optional[Environment]" = None,
+    ) -> "Const":
+        """Return a const object if the value is representable as
+        constant value in the generated code, otherwise it will raise
+        an `Impossible` exception.
+        """
+        from .compiler import has_safe_repr
+
+        if not has_safe_repr(value):
+            raise Impossible()
+        return cls(value, lineno=lineno, environment=environment)
+
+
+class TemplateData(Literal):
+    """A constant template string."""
+
+    fields = ("data",)
+    data: str
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> str:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        if eval_ctx.volatile:
+            raise Impossible()
+        if eval_ctx.autoescape:
+            return Markup(self.data)
+        return self.data
+
+
+class Tuple(Literal):
+    """For loop unpacking and some other things like multiple arguments
+    for subscripts.  Like for :class:`Name` `ctx` specifies if the tuple
+    is used for loading the names or storing.
+    """
+
+    fields = ("items", "ctx")
+    items: t.List[Expr]
+    ctx: str
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Tuple[t.Any, ...]:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return tuple(x.as_const(eval_ctx) for x in self.items)
+
+    def can_assign(self) -> bool:
+        for item in self.items:
+            if not item.can_assign():
+                return False
+        return True
+
+
+class List(Literal):
+    """Any list literal such as ``[1, 2, 3]``"""
+
+    fields = ("items",)
+    items: t.List[Expr]
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.List[t.Any]:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return [x.as_const(eval_ctx) for x in self.items]
+
+
+class Dict(Literal):
+    """Any dict literal such as ``{1: 2, 3: 4}``.  The items must be a list of
+    :class:`Pair` nodes.
+    """
+
+    fields = ("items",)
+    items: t.List["Pair"]
+
+    def as_const(
+        self, eval_ctx: t.Optional[EvalContext] = None
+    ) -> t.Dict[t.Any, t.Any]:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return dict(x.as_const(eval_ctx) for x in self.items)
+
+
+class Pair(Helper):
+    """A key, value pair for dicts."""
+
+    fields = ("key", "value")
+    key: Expr
+    value: Expr
+
+    def as_const(
+        self, eval_ctx: t.Optional[EvalContext] = None
+    ) -> t.Tuple[t.Any, t.Any]:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return self.key.as_const(eval_ctx), self.value.as_const(eval_ctx)
+
+
+class Keyword(Helper):
+    """A key, value pair for keyword arguments where key is a string."""
+
+    fields = ("key", "value")
+    key: str
+    value: Expr
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Tuple[str, t.Any]:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return self.key, self.value.as_const(eval_ctx)
+
+
+class CondExpr(Expr):
+    """A conditional expression (inline if expression).  (``{{
+    foo if bar else baz }}``)
+    """
+
+    fields = ("test", "expr1", "expr2")
+    test: Expr
+    expr1: Expr
+    expr2: t.Optional[Expr]
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        if self.test.as_const(eval_ctx):
+            return self.expr1.as_const(eval_ctx)
+
+        # if we evaluate to an undefined object, we better do that at runtime
+        if self.expr2 is None:
+            raise Impossible()
+
+        return self.expr2.as_const(eval_ctx)
+
+
+def args_as_const(
+    node: t.Union["_FilterTestCommon", "Call"], eval_ctx: t.Optional[EvalContext]
+) -> t.Tuple[t.List[t.Any], t.Dict[t.Any, t.Any]]:
+    args = [x.as_const(eval_ctx) for x in node.args]
+    kwargs = dict(x.as_const(eval_ctx) for x in node.kwargs)
+
+    if node.dyn_args is not None:
+        try:
+            args.extend(node.dyn_args.as_const(eval_ctx))
+        except Exception as e:
+            raise Impossible() from e
+
+    if node.dyn_kwargs is not None:
+        try:
+            kwargs.update(node.dyn_kwargs.as_const(eval_ctx))
+        except Exception as e:
+            raise Impossible() from e
+
+    return args, kwargs
+
+
+class _FilterTestCommon(Expr):
+    fields = ("node", "name", "args", "kwargs", "dyn_args", "dyn_kwargs")
+    node: Expr
+    name: str
+    args: t.List[Expr]
+    kwargs: t.List[Pair]
+    dyn_args: t.Optional[Expr]
+    dyn_kwargs: t.Optional[Expr]
+    abstract = True
+    _is_filter = True
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+
+        if eval_ctx.volatile:
+            raise Impossible()
+
+        if self._is_filter:
+            env_map = eval_ctx.environment.filters
+        else:
+            env_map = eval_ctx.environment.tests
+
+        func = env_map.get(self.name)
+        pass_arg = _PassArg.from_obj(func)  # type: ignore
+
+        if func is None or pass_arg is _PassArg.context:
+            raise Impossible()
+
+        if eval_ctx.environment.is_async and (
+            getattr(func, "jinja_async_variant", False) is True
+            or inspect.iscoroutinefunction(func)
+        ):
+            raise Impossible()
+
+        args, kwargs = args_as_const(self, eval_ctx)
+        args.insert(0, self.node.as_const(eval_ctx))
+
+        if pass_arg is _PassArg.eval_context:
+            args.insert(0, eval_ctx)
+        elif pass_arg is _PassArg.environment:
+            args.insert(0, eval_ctx.environment)
+
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            raise Impossible() from e
+
+
+class Filter(_FilterTestCommon):
+    """Apply a filter to an expression. ``name`` is the name of the
+    filter, the other fields are the same as :class:`Call`.
+
+    If ``node`` is ``None``, the filter is being used in a filter block
+    and is applied to the content of the block.
+    """
+
+    node: t.Optional[Expr]  # type: ignore
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        if self.node is None:
+            raise Impossible()
+
+        return super().as_const(eval_ctx=eval_ctx)
+
+
+class Test(_FilterTestCommon):
+    """Apply a test to an expression. ``name`` is the name of the test,
+    the other field are the same as :class:`Call`.
+
+    .. versionchanged:: 3.0
+        ``as_const`` shares the same logic for filters and tests. Tests
+        check for volatile, async, and ``@pass_context`` etc.
+        decorators.
+    """
+
+    _is_filter = False
+
+
+class Call(Expr):
+    """Calls an expression.  `args` is a list of arguments, `kwargs` a list
+    of keyword arguments (list of :class:`Keyword` nodes), and `dyn_args`
+    and `dyn_kwargs` has to be either `None` or a node that is used as
+    node for dynamic positional (``*args``) or keyword (``**kwargs``)
+    arguments.
+    """
+
+    fields = ("node", "args", "kwargs", "dyn_args", "dyn_kwargs")
+    node: Expr
+    args: t.List[Expr]
+    kwargs: t.List[Keyword]
+    dyn_args: t.Optional[Expr]
+    dyn_kwargs: t.Optional[Expr]
+
+
+class Getitem(Expr):
+    """Get an attribute or item from an expression and prefer the item."""
+
+    fields = ("node", "arg", "ctx")
+    node: Expr
+    arg: Expr
+    ctx: str
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        if self.ctx != "load":
+            raise Impossible()
+
+        eval_ctx = get_eval_context(self, eval_ctx)
+
+        try:
+            return eval_ctx.environment.getitem(
+                self.node.as_const(eval_ctx), self.arg.as_const(eval_ctx)
+            )
+        except Exception as e:
+            raise Impossible() from e
+
+
+class Getattr(Expr):
+    """Get an attribute or item from an expression that is a ascii-only
+    bytestring and prefer the attribute.
+    """
+
+    fields = ("node", "attr", "ctx")
+    node: Expr
+    attr: str
+    ctx: str
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        if self.ctx != "load":
+            raise Impossible()
+
+        eval_ctx = get_eval_context(self, eval_ctx)
+
+        try:
+            return eval_ctx.environment.getattr(self.node.as_const(eval_ctx), self.attr)
+        except Exception as e:
+            raise Impossible() from e
+
+
+class Slice(Expr):
+    """Represents a slice object.  This must only be used as argument for
+    :class:`Subscript`.
+    """
+
+    fields = ("start", "stop", "step")
+    start: t.Optional[Expr]
+    stop: t.Optional[Expr]
+    step: t.Optional[Expr]
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> slice:
+        eval_ctx = get_eval_context(self, eval_ctx)
+
+        def const(obj: t.Optional[Expr]) -> t.Optional[t.Any]:
+            if obj is None:
+                return None
+            return obj.as_const(eval_ctx)
+
+        return slice(const(self.start), const(self.stop), const(self.step))
+
+
+class Concat(Expr):
+    """Concatenates the list of expressions provided after converting
+    them to strings.
+    """
+
+    fields = ("nodes",)
+    nodes: t.List[Expr]
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> str:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return "".join(str(x.as_const(eval_ctx)) for x in self.nodes)
+
+
+class Compare(Expr):
+    """Compares an expression with some other expressions.  `ops` must be a
+    list of :class:`Operand`\\s.
+    """
+
+    fields = ("expr", "ops")
+    expr: Expr
+    ops: t.List["Operand"]
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        result = value = self.expr.as_const(eval_ctx)
+
+        try:
+            for op in self.ops:
+                new_value = op.expr.as_const(eval_ctx)
+                result = _cmpop_to_func[op.op](value, new_value)
+
+                if not result:
+                    return False
+
+                value = new_value
+        except Exception as e:
+            raise Impossible() from e
+
+        return result
+
+
+class Operand(Helper):
+    """Holds an operator and an expression."""
+
+    fields = ("op", "expr")
+    op: str
+    expr: Expr
+
+
+class Mul(BinExpr):
+    """Multiplies the left with the right node."""
+
+    operator = "*"
+
+
+class Div(BinExpr):
+    """Divides the left by the right node."""
+
+    operator = "/"
+
+
+class FloorDiv(BinExpr):
+    """Divides the left by the right node and converts the
+    result into an integer by truncating.
+    """
+
+    operator = "//"
+
+
+class Add(BinExpr):
+    """Add the left to the right node."""
+
+    operator = "+"
+
+
+class Sub(BinExpr):
+    """Subtract the right from the left node."""
+
+    operator = "-"
+
+
+class Mod(BinExpr):
+    """Left modulo right."""
+
+    operator = "%"
+
+
+class Pow(BinExpr):
+    """Left to the power of right."""
+
+    operator = "**"
+
+
+class And(BinExpr):
+    """Short circuited AND."""
+
+    operator = "and"
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return self.left.as_const(eval_ctx) and self.right.as_const(eval_ctx)
+
+
+class Or(BinExpr):
+    """Short circuited OR."""
+
+    operator = "or"
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return self.left.as_const(eval_ctx) or self.right.as_const(eval_ctx)
+
+
+class Not(UnaryExpr):
+    """Negate the expression."""
+
+    operator = "not"
+
+
+class Neg(UnaryExpr):
+    """Make the expression negative."""
+
+    operator = "-"
+
+
+class Pos(UnaryExpr):
+    """Make the expression positive (noop for most expressions)"""
+
+    operator = "+"
+
+
+# Helpers for extensions
+
+
+class EnvironmentAttribute(Expr):
+    """Loads an attribute from the environment object.  This is useful for
+    extensions that want to call a callback stored on the environment.
+    """
+
+    fields = ("name",)
+    name: str
+
+
+class ExtensionAttribute(Expr):
+    """Returns the attribute of an extension bound to the environment.
+    The identifier is the identifier of the :class:`Extension`.
+
+    This node is usually constructed by calling the
+    :meth:`~jinja2.ext.Extension.attr` method on an extension.
+    """
+
+    fields = ("identifier", "name")
+    identifier: str
+    name: str
+
+
+class ImportedName(Expr):
+    """If created with an import name the import name is returned on node
+    access.  For example ``ImportedName('cgi.escape')`` returns the `escape`
+    function from the cgi module on evaluation.  Imports are optimized by the
+    compiler so there is no need to assign them to local variables.
+    """
+
+    fields = ("importname",)
+    importname: str
+
+
+class InternalName(Expr):
+    """An internal name in the compiler.  You cannot create these nodes
+    yourself but the parser provides a
+    :meth:`~jinja2.parser.Parser.free_identifier` method that creates
+    a new identifier for you.  This identifier is not available from the
+    template and is not treated specially by the compiler.
+    """
+
+    fields = ("name",)
+    name: str
+
+    def __init__(self) -> None:
+        raise TypeError(
+            "Can't create internal names.  Use the "
+            "`free_identifier` method on a parser."
+        )
+
+
+class MarkSafe(Expr):
+    """Mark the wrapped expression as safe (wrap it as `Markup`)."""
+
+    fields = ("expr",)
+    expr: Expr
+
+    def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> Markup:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        return Markup(self.expr.as_const(eval_ctx))
+
+
+class MarkSafeIfAutoescape(Expr):
+    """Mark the wrapped expression as safe (wrap it as `Markup`) but
+    only if autoescaping is active.
+
+    .. versionadded:: 2.5
+    """
+
+    fields = ("expr",)
+    expr: Expr
+
+    def as_const(
+        self, eval_ctx: t.Optional[EvalContext] = None
+    ) -> t.Union[Markup, t.Any]:
+        eval_ctx = get_eval_context(self, eval_ctx)
+        if eval_ctx.volatile:
+            raise Impossible()
+        expr = self.expr.as_const(eval_ctx)
+        if eval_ctx.autoescape:
+            return Markup(expr)
+        return expr
+
+
+class ContextReference(Expr):
+    """Returns the current template context.  It can be used like a
+    :class:`Name` node, with a ``'load'`` ctx and will return the
+    current :class:`~jinja2.runtime.Context` object.
+
+    Here an example that assigns the current template name to a
+    variable named `foo`::
+
+        Assign(Name('foo', ctx='store'),
+               Getattr(ContextReference(), 'name'))
+
+    This is basically equivalent to using the
+    :func:`~jinja2.pass_context` decorator when using the high-level
+    API, which causes a reference to the context to be passed as the
+    first argument to a function.
+    """
+
+
+class DerivedContextReference(Expr):
+    """Return the current template context including locals. Behaves
+    exactly like :class:`ContextReference`, but includes local
+    variables, such as from a ``for`` loop.
+
+    .. versionadded:: 2.11
+    """
+
+
+class Continue(Stmt):
+    """Continue a loop."""
+
+
+class Break(Stmt):
+    """Break a loop."""
+
+
+class Scope(Stmt):
+    """An artificial scope."""
+
+    fields = ("body",)
+    body: t.List[Node]
+
+
+class OverlayScope(Stmt):
+    """An overlay scope for extensions.  This is a largely unoptimized scope
+    that however can be used to introduce completely arbitrary variables into
+    a sub scope from a dictionary or dictionary like object.  The `context`
+    field has to evaluate to a dictionary object.
+
+    Example usage::
+
+        OverlayScope(context=self.call_method('get_context'),
+                     body=[...])
+
+    .. versionadded:: 2.10
+    """
+
+    fields = ("context", "body")
+    context: Expr
+    body: t.List[Node]
+
+
+class EvalContextModifier(Stmt):
+    """Modifies the eval context.  For each option that should be modified,
+    a :class:`Keyword` has to be added to the :attr:`options` list.
+
+    Example to change the `autoescape` setting::
+
+        EvalContextModifier(options=[Keyword('autoescape', Const(True))])
+    """
+
+    fields = ("options",)
+    options: t.List[Keyword]
+
+
+class ScopedEvalContextModifier(EvalContextModifier):
+    """Modifies the eval context and reverts it later.  Works exactly like
+    :class:`EvalContextModifier` but will only modify the
+    :class:`~jinja2.nodes.EvalContext` for nodes in the :attr:`body`.
+    """
+
+    fields = ("body",)
+    body: t.List[Node]
+
+
+# make sure nobody creates custom nodes
+def _failing_new(*args: t.Any, **kwargs: t.Any) -> "te.NoReturn":
+    raise TypeError("can't create custom node types")
+
+
+NodeType.__new__ = staticmethod(_failing_new)  # type: ignore
+del _failing_new
diff --git a/lib/python3.11/site-packages/jinja2/optimizer.py b/lib/python3.11/site-packages/jinja2/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..32d1c717b3859530a6addf6204afd3c724b529ee
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/optimizer.py
@@ -0,0 +1,48 @@
+"""The optimizer tries to constant fold expressions and modify the AST
+in place so that it should be faster to evaluate.
+
+Because the AST does not contain all the scoping information and the
+compiler has to find that out, we cannot do all the optimizations we
+want. For example, loop unrolling doesn't work because unrolled loops
+would have a different scope. The solution would be a second syntax tree
+that stored the scoping rules.
+"""
+
+import typing as t
+
+from . import nodes
+from .visitor import NodeTransformer
+
+if t.TYPE_CHECKING:
+    from .environment import Environment
+
+
+def optimize(node: nodes.Node, environment: "Environment") -> nodes.Node:
+    """The context hint can be used to perform an static optimization
+    based on the context given."""
+    optimizer = Optimizer(environment)
+    return t.cast(nodes.Node, optimizer.visit(node))
+
+
+class Optimizer(NodeTransformer):
+    def __init__(self, environment: "t.Optional[Environment]") -> None:
+        self.environment = environment
+
+    def generic_visit(
+        self, node: nodes.Node, *args: t.Any, **kwargs: t.Any
+    ) -> nodes.Node:
+        node = super().generic_visit(node, *args, **kwargs)
+
+        # Do constant folding. Some other nodes besides Expr have
+        # as_const, but folding them causes errors later on.
+        if isinstance(node, nodes.Expr):
+            try:
+                return nodes.Const.from_untrusted(
+                    node.as_const(args[0] if args else None),
+                    lineno=node.lineno,
+                    environment=self.environment,
+                )
+            except nodes.Impossible:
+                pass
+
+        return node
diff --git a/lib/python3.11/site-packages/jinja2/parser.py b/lib/python3.11/site-packages/jinja2/parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4117754aaf94ae525d183fb3c36fae1dc8afd62
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/parser.py
@@ -0,0 +1,1049 @@
+"""Parse tokens from the lexer into nodes for the compiler."""
+
+import typing
+import typing as t
+
+from . import nodes
+from .exceptions import TemplateAssertionError
+from .exceptions import TemplateSyntaxError
+from .lexer import describe_token
+from .lexer import describe_token_expr
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    from .environment import Environment
+
+_ImportInclude = t.TypeVar("_ImportInclude", nodes.Import, nodes.Include)
+_MacroCall = t.TypeVar("_MacroCall", nodes.Macro, nodes.CallBlock)
+
+_statement_keywords = frozenset(
+    [
+        "for",
+        "if",
+        "block",
+        "extends",
+        "print",
+        "macro",
+        "include",
+        "from",
+        "import",
+        "set",
+        "with",
+        "autoescape",
+    ]
+)
+_compare_operators = frozenset(["eq", "ne", "lt", "lteq", "gt", "gteq"])
+
+_math_nodes: t.Dict[str, t.Type[nodes.Expr]] = {
+    "add": nodes.Add,
+    "sub": nodes.Sub,
+    "mul": nodes.Mul,
+    "div": nodes.Div,
+    "floordiv": nodes.FloorDiv,
+    "mod": nodes.Mod,
+}
+
+
+class Parser:
+    """This is the central parsing class Jinja uses.  It's passed to
+    extensions and can be used to parse expressions or statements.
+    """
+
+    def __init__(
+        self,
+        environment: "Environment",
+        source: str,
+        name: t.Optional[str] = None,
+        filename: t.Optional[str] = None,
+        state: t.Optional[str] = None,
+    ) -> None:
+        self.environment = environment
+        self.stream = environment._tokenize(source, name, filename, state)
+        self.name = name
+        self.filename = filename
+        self.closed = False
+        self.extensions: t.Dict[
+            str, t.Callable[[Parser], t.Union[nodes.Node, t.List[nodes.Node]]]
+        ] = {}
+        for extension in environment.iter_extensions():
+            for tag in extension.tags:
+                self.extensions[tag] = extension.parse
+        self._last_identifier = 0
+        self._tag_stack: t.List[str] = []
+        self._end_token_stack: t.List[t.Tuple[str, ...]] = []
+
+    def fail(
+        self,
+        msg: str,
+        lineno: t.Optional[int] = None,
+        exc: t.Type[TemplateSyntaxError] = TemplateSyntaxError,
+    ) -> "te.NoReturn":
+        """Convenience method that raises `exc` with the message, passed
+        line number or last line number as well as the current name and
+        filename.
+        """
+        if lineno is None:
+            lineno = self.stream.current.lineno
+        raise exc(msg, lineno, self.name, self.filename)
+
+    def _fail_ut_eof(
+        self,
+        name: t.Optional[str],
+        end_token_stack: t.List[t.Tuple[str, ...]],
+        lineno: t.Optional[int],
+    ) -> "te.NoReturn":
+        expected: t.Set[str] = set()
+        for exprs in end_token_stack:
+            expected.update(map(describe_token_expr, exprs))
+        if end_token_stack:
+            currently_looking: t.Optional[str] = " or ".join(
+                map(repr, map(describe_token_expr, end_token_stack[-1]))
+            )
+        else:
+            currently_looking = None
+
+        if name is None:
+            message = ["Unexpected end of template."]
+        else:
+            message = [f"Encountered unknown tag {name!r}."]
+
+        if currently_looking:
+            if name is not None and name in expected:
+                message.append(
+                    "You probably made a nesting mistake. Jinja is expecting this tag,"
+                    f" but currently looking for {currently_looking}."
+                )
+            else:
+                message.append(
+                    f"Jinja was looking for the following tags: {currently_looking}."
+                )
+
+        if self._tag_stack:
+            message.append(
+                "The innermost block that needs to be closed is"
+                f" {self._tag_stack[-1]!r}."
+            )
+
+        self.fail(" ".join(message), lineno)
+
+    def fail_unknown_tag(
+        self, name: str, lineno: t.Optional[int] = None
+    ) -> "te.NoReturn":
+        """Called if the parser encounters an unknown tag.  Tries to fail
+        with a human readable error message that could help to identify
+        the problem.
+        """
+        self._fail_ut_eof(name, self._end_token_stack, lineno)
+
+    def fail_eof(
+        self,
+        end_tokens: t.Optional[t.Tuple[str, ...]] = None,
+        lineno: t.Optional[int] = None,
+    ) -> "te.NoReturn":
+        """Like fail_unknown_tag but for end of template situations."""
+        stack = list(self._end_token_stack)
+        if end_tokens is not None:
+            stack.append(end_tokens)
+        self._fail_ut_eof(None, stack, lineno)
+
+    def is_tuple_end(
+        self, extra_end_rules: t.Optional[t.Tuple[str, ...]] = None
+    ) -> bool:
+        """Are we at the end of a tuple?"""
+        if self.stream.current.type in ("variable_end", "block_end", "rparen"):
+            return True
+        elif extra_end_rules is not None:
+            return self.stream.current.test_any(extra_end_rules)  # type: ignore
+        return False
+
+    def free_identifier(self, lineno: t.Optional[int] = None) -> nodes.InternalName:
+        """Return a new free identifier as :class:`~jinja2.nodes.InternalName`."""
+        self._last_identifier += 1
+        rv = object.__new__(nodes.InternalName)
+        nodes.Node.__init__(rv, f"fi{self._last_identifier}", lineno=lineno)
+        return rv
+
+    def parse_statement(self) -> t.Union[nodes.Node, t.List[nodes.Node]]:
+        """Parse a single statement."""
+        token = self.stream.current
+        if token.type != "name":
+            self.fail("tag name expected", token.lineno)
+        self._tag_stack.append(token.value)
+        pop_tag = True
+        try:
+            if token.value in _statement_keywords:
+                f = getattr(self, f"parse_{self.stream.current.value}")
+                return f()  # type: ignore
+            if token.value == "call":
+                return self.parse_call_block()
+            if token.value == "filter":
+                return self.parse_filter_block()
+            ext = self.extensions.get(token.value)
+            if ext is not None:
+                return ext(self)
+
+            # did not work out, remove the token we pushed by accident
+            # from the stack so that the unknown tag fail function can
+            # produce a proper error message.
+            self._tag_stack.pop()
+            pop_tag = False
+            self.fail_unknown_tag(token.value, token.lineno)
+        finally:
+            if pop_tag:
+                self._tag_stack.pop()
+
+    def parse_statements(
+        self, end_tokens: t.Tuple[str, ...], drop_needle: bool = False
+    ) -> t.List[nodes.Node]:
+        """Parse multiple statements into a list until one of the end tokens
+        is reached.  This is used to parse the body of statements as it also
+        parses template data if appropriate.  The parser checks first if the
+        current token is a colon and skips it if there is one.  Then it checks
+        for the block end and parses until if one of the `end_tokens` is
+        reached.  Per default the active token in the stream at the end of
+        the call is the matched end token.  If this is not wanted `drop_needle`
+        can be set to `True` and the end token is removed.
+        """
+        # the first token may be a colon for python compatibility
+        self.stream.skip_if("colon")
+
+        # in the future it would be possible to add whole code sections
+        # by adding some sort of end of statement token and parsing those here.
+        self.stream.expect("block_end")
+        result = self.subparse(end_tokens)
+
+        # we reached the end of the template too early, the subparser
+        # does not check for this, so we do that now
+        if self.stream.current.type == "eof":
+            self.fail_eof(end_tokens)
+
+        if drop_needle:
+            next(self.stream)
+        return result
+
+    def parse_set(self) -> t.Union[nodes.Assign, nodes.AssignBlock]:
+        """Parse an assign statement."""
+        lineno = next(self.stream).lineno
+        target = self.parse_assign_target(with_namespace=True)
+        if self.stream.skip_if("assign"):
+            expr = self.parse_tuple()
+            return nodes.Assign(target, expr, lineno=lineno)
+        filter_node = self.parse_filter(None)
+        body = self.parse_statements(("name:endset",), drop_needle=True)
+        return nodes.AssignBlock(target, filter_node, body, lineno=lineno)
+
+    def parse_for(self) -> nodes.For:
+        """Parse a for loop."""
+        lineno = self.stream.expect("name:for").lineno
+        target = self.parse_assign_target(extra_end_rules=("name:in",))
+        self.stream.expect("name:in")
+        iter = self.parse_tuple(
+            with_condexpr=False, extra_end_rules=("name:recursive",)
+        )
+        test = None
+        if self.stream.skip_if("name:if"):
+            test = self.parse_expression()
+        recursive = self.stream.skip_if("name:recursive")
+        body = self.parse_statements(("name:endfor", "name:else"))
+        if next(self.stream).value == "endfor":
+            else_ = []
+        else:
+            else_ = self.parse_statements(("name:endfor",), drop_needle=True)
+        return nodes.For(target, iter, body, else_, test, recursive, lineno=lineno)
+
+    def parse_if(self) -> nodes.If:
+        """Parse an if construct."""
+        node = result = nodes.If(lineno=self.stream.expect("name:if").lineno)
+        while True:
+            node.test = self.parse_tuple(with_condexpr=False)
+            node.body = self.parse_statements(("name:elif", "name:else", "name:endif"))
+            node.elif_ = []
+            node.else_ = []
+            token = next(self.stream)
+            if token.test("name:elif"):
+                node = nodes.If(lineno=self.stream.current.lineno)
+                result.elif_.append(node)
+                continue
+            elif token.test("name:else"):
+                result.else_ = self.parse_statements(("name:endif",), drop_needle=True)
+            break
+        return result
+
+    def parse_with(self) -> nodes.With:
+        node = nodes.With(lineno=next(self.stream).lineno)
+        targets: t.List[nodes.Expr] = []
+        values: t.List[nodes.Expr] = []
+        while self.stream.current.type != "block_end":
+            if targets:
+                self.stream.expect("comma")
+            target = self.parse_assign_target()
+            target.set_ctx("param")
+            targets.append(target)
+            self.stream.expect("assign")
+            values.append(self.parse_expression())
+        node.targets = targets
+        node.values = values
+        node.body = self.parse_statements(("name:endwith",), drop_needle=True)
+        return node
+
+    def parse_autoescape(self) -> nodes.Scope:
+        node = nodes.ScopedEvalContextModifier(lineno=next(self.stream).lineno)
+        node.options = [nodes.Keyword("autoescape", self.parse_expression())]
+        node.body = self.parse_statements(("name:endautoescape",), drop_needle=True)
+        return nodes.Scope([node])
+
+    def parse_block(self) -> nodes.Block:
+        node = nodes.Block(lineno=next(self.stream).lineno)
+        node.name = self.stream.expect("name").value
+        node.scoped = self.stream.skip_if("name:scoped")
+        node.required = self.stream.skip_if("name:required")
+
+        # common problem people encounter when switching from django
+        # to jinja.  we do not support hyphens in block names, so let's
+        # raise a nicer error message in that case.
+        if self.stream.current.type == "sub":
+            self.fail(
+                "Block names in Jinja have to be valid Python identifiers and may not"
+                " contain hyphens, use an underscore instead."
+            )
+
+        node.body = self.parse_statements(("name:endblock",), drop_needle=True)
+
+        # enforce that required blocks only contain whitespace or comments
+        # by asserting that the body, if not empty, is just TemplateData nodes
+        # with whitespace data
+        if node.required:
+            for body_node in node.body:
+                if not isinstance(body_node, nodes.Output) or any(
+                    not isinstance(output_node, nodes.TemplateData)
+                    or not output_node.data.isspace()
+                    for output_node in body_node.nodes
+                ):
+                    self.fail("Required blocks can only contain comments or whitespace")
+
+        self.stream.skip_if("name:" + node.name)
+        return node
+
+    def parse_extends(self) -> nodes.Extends:
+        node = nodes.Extends(lineno=next(self.stream).lineno)
+        node.template = self.parse_expression()
+        return node
+
+    def parse_import_context(
+        self, node: _ImportInclude, default: bool
+    ) -> _ImportInclude:
+        if self.stream.current.test_any(
+            "name:with", "name:without"
+        ) and self.stream.look().test("name:context"):
+            node.with_context = next(self.stream).value == "with"
+            self.stream.skip()
+        else:
+            node.with_context = default
+        return node
+
+    def parse_include(self) -> nodes.Include:
+        node = nodes.Include(lineno=next(self.stream).lineno)
+        node.template = self.parse_expression()
+        if self.stream.current.test("name:ignore") and self.stream.look().test(
+            "name:missing"
+        ):
+            node.ignore_missing = True
+            self.stream.skip(2)
+        else:
+            node.ignore_missing = False
+        return self.parse_import_context(node, True)
+
+    def parse_import(self) -> nodes.Import:
+        node = nodes.Import(lineno=next(self.stream).lineno)
+        node.template = self.parse_expression()
+        self.stream.expect("name:as")
+        node.target = self.parse_assign_target(name_only=True).name
+        return self.parse_import_context(node, False)
+
+    def parse_from(self) -> nodes.FromImport:
+        node = nodes.FromImport(lineno=next(self.stream).lineno)
+        node.template = self.parse_expression()
+        self.stream.expect("name:import")
+        node.names = []
+
+        def parse_context() -> bool:
+            if self.stream.current.value in {
+                "with",
+                "without",
+            } and self.stream.look().test("name:context"):
+                node.with_context = next(self.stream).value == "with"
+                self.stream.skip()
+                return True
+            return False
+
+        while True:
+            if node.names:
+                self.stream.expect("comma")
+            if self.stream.current.type == "name":
+                if parse_context():
+                    break
+                target = self.parse_assign_target(name_only=True)
+                if target.name.startswith("_"):
+                    self.fail(
+                        "names starting with an underline can not be imported",
+                        target.lineno,
+                        exc=TemplateAssertionError,
+                    )
+                if self.stream.skip_if("name:as"):
+                    alias = self.parse_assign_target(name_only=True)
+                    node.names.append((target.name, alias.name))
+                else:
+                    node.names.append(target.name)
+                if parse_context() or self.stream.current.type != "comma":
+                    break
+            else:
+                self.stream.expect("name")
+        if not hasattr(node, "with_context"):
+            node.with_context = False
+        return node
+
+    def parse_signature(self, node: _MacroCall) -> None:
+        args = node.args = []
+        defaults = node.defaults = []
+        self.stream.expect("lparen")
+        while self.stream.current.type != "rparen":
+            if args:
+                self.stream.expect("comma")
+            arg = self.parse_assign_target(name_only=True)
+            arg.set_ctx("param")
+            if self.stream.skip_if("assign"):
+                defaults.append(self.parse_expression())
+            elif defaults:
+                self.fail("non-default argument follows default argument")
+            args.append(arg)
+        self.stream.expect("rparen")
+
+    def parse_call_block(self) -> nodes.CallBlock:
+        node = nodes.CallBlock(lineno=next(self.stream).lineno)
+        if self.stream.current.type == "lparen":
+            self.parse_signature(node)
+        else:
+            node.args = []
+            node.defaults = []
+
+        call_node = self.parse_expression()
+        if not isinstance(call_node, nodes.Call):
+            self.fail("expected call", node.lineno)
+        node.call = call_node
+        node.body = self.parse_statements(("name:endcall",), drop_needle=True)
+        return node
+
+    def parse_filter_block(self) -> nodes.FilterBlock:
+        node = nodes.FilterBlock(lineno=next(self.stream).lineno)
+        node.filter = self.parse_filter(None, start_inline=True)  # type: ignore
+        node.body = self.parse_statements(("name:endfilter",), drop_needle=True)
+        return node
+
+    def parse_macro(self) -> nodes.Macro:
+        node = nodes.Macro(lineno=next(self.stream).lineno)
+        node.name = self.parse_assign_target(name_only=True).name
+        self.parse_signature(node)
+        node.body = self.parse_statements(("name:endmacro",), drop_needle=True)
+        return node
+
+    def parse_print(self) -> nodes.Output:
+        node = nodes.Output(lineno=next(self.stream).lineno)
+        node.nodes = []
+        while self.stream.current.type != "block_end":
+            if node.nodes:
+                self.stream.expect("comma")
+            node.nodes.append(self.parse_expression())
+        return node
+
+    @typing.overload
+    def parse_assign_target(
+        self, with_tuple: bool = ..., name_only: "te.Literal[True]" = ...
+    ) -> nodes.Name: ...
+
+    @typing.overload
+    def parse_assign_target(
+        self,
+        with_tuple: bool = True,
+        name_only: bool = False,
+        extra_end_rules: t.Optional[t.Tuple[str, ...]] = None,
+        with_namespace: bool = False,
+    ) -> t.Union[nodes.NSRef, nodes.Name, nodes.Tuple]: ...
+
+    def parse_assign_target(
+        self,
+        with_tuple: bool = True,
+        name_only: bool = False,
+        extra_end_rules: t.Optional[t.Tuple[str, ...]] = None,
+        with_namespace: bool = False,
+    ) -> t.Union[nodes.NSRef, nodes.Name, nodes.Tuple]:
+        """Parse an assignment target.  As Jinja allows assignments to
+        tuples, this function can parse all allowed assignment targets.  Per
+        default assignments to tuples are parsed, that can be disable however
+        by setting `with_tuple` to `False`.  If only assignments to names are
+        wanted `name_only` can be set to `True`.  The `extra_end_rules`
+        parameter is forwarded to the tuple parsing function.  If
+        `with_namespace` is enabled, a namespace assignment may be parsed.
+        """
+        target: nodes.Expr
+
+        if name_only:
+            token = self.stream.expect("name")
+            target = nodes.Name(token.value, "store", lineno=token.lineno)
+        else:
+            if with_tuple:
+                target = self.parse_tuple(
+                    simplified=True,
+                    extra_end_rules=extra_end_rules,
+                    with_namespace=with_namespace,
+                )
+            else:
+                target = self.parse_primary(with_namespace=with_namespace)
+
+            target.set_ctx("store")
+
+        if not target.can_assign():
+            self.fail(
+                f"can't assign to {type(target).__name__.lower()!r}", target.lineno
+            )
+
+        return target  # type: ignore
+
+    def parse_expression(self, with_condexpr: bool = True) -> nodes.Expr:
+        """Parse an expression.  Per default all expressions are parsed, if
+        the optional `with_condexpr` parameter is set to `False` conditional
+        expressions are not parsed.
+        """
+        if with_condexpr:
+            return self.parse_condexpr()
+        return self.parse_or()
+
+    def parse_condexpr(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        expr1 = self.parse_or()
+        expr3: t.Optional[nodes.Expr]
+
+        while self.stream.skip_if("name:if"):
+            expr2 = self.parse_or()
+            if self.stream.skip_if("name:else"):
+                expr3 = self.parse_condexpr()
+            else:
+                expr3 = None
+            expr1 = nodes.CondExpr(expr2, expr1, expr3, lineno=lineno)
+            lineno = self.stream.current.lineno
+        return expr1
+
+    def parse_or(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        left = self.parse_and()
+        while self.stream.skip_if("name:or"):
+            right = self.parse_and()
+            left = nodes.Or(left, right, lineno=lineno)
+            lineno = self.stream.current.lineno
+        return left
+
+    def parse_and(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        left = self.parse_not()
+        while self.stream.skip_if("name:and"):
+            right = self.parse_not()
+            left = nodes.And(left, right, lineno=lineno)
+            lineno = self.stream.current.lineno
+        return left
+
+    def parse_not(self) -> nodes.Expr:
+        if self.stream.current.test("name:not"):
+            lineno = next(self.stream).lineno
+            return nodes.Not(self.parse_not(), lineno=lineno)
+        return self.parse_compare()
+
+    def parse_compare(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        expr = self.parse_math1()
+        ops = []
+        while True:
+            token_type = self.stream.current.type
+            if token_type in _compare_operators:
+                next(self.stream)
+                ops.append(nodes.Operand(token_type, self.parse_math1()))
+            elif self.stream.skip_if("name:in"):
+                ops.append(nodes.Operand("in", self.parse_math1()))
+            elif self.stream.current.test("name:not") and self.stream.look().test(
+                "name:in"
+            ):
+                self.stream.skip(2)
+                ops.append(nodes.Operand("notin", self.parse_math1()))
+            else:
+                break
+            lineno = self.stream.current.lineno
+        if not ops:
+            return expr
+        return nodes.Compare(expr, ops, lineno=lineno)
+
+    def parse_math1(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        left = self.parse_concat()
+        while self.stream.current.type in ("add", "sub"):
+            cls = _math_nodes[self.stream.current.type]
+            next(self.stream)
+            right = self.parse_concat()
+            left = cls(left, right, lineno=lineno)
+            lineno = self.stream.current.lineno
+        return left
+
+    def parse_concat(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        args = [self.parse_math2()]
+        while self.stream.current.type == "tilde":
+            next(self.stream)
+            args.append(self.parse_math2())
+        if len(args) == 1:
+            return args[0]
+        return nodes.Concat(args, lineno=lineno)
+
+    def parse_math2(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        left = self.parse_pow()
+        while self.stream.current.type in ("mul", "div", "floordiv", "mod"):
+            cls = _math_nodes[self.stream.current.type]
+            next(self.stream)
+            right = self.parse_pow()
+            left = cls(left, right, lineno=lineno)
+            lineno = self.stream.current.lineno
+        return left
+
+    def parse_pow(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        left = self.parse_unary()
+        while self.stream.current.type == "pow":
+            next(self.stream)
+            right = self.parse_unary()
+            left = nodes.Pow(left, right, lineno=lineno)
+            lineno = self.stream.current.lineno
+        return left
+
+    def parse_unary(self, with_filter: bool = True) -> nodes.Expr:
+        token_type = self.stream.current.type
+        lineno = self.stream.current.lineno
+        node: nodes.Expr
+
+        if token_type == "sub":
+            next(self.stream)
+            node = nodes.Neg(self.parse_unary(False), lineno=lineno)
+        elif token_type == "add":
+            next(self.stream)
+            node = nodes.Pos(self.parse_unary(False), lineno=lineno)
+        else:
+            node = self.parse_primary()
+        node = self.parse_postfix(node)
+        if with_filter:
+            node = self.parse_filter_expr(node)
+        return node
+
+    def parse_primary(self, with_namespace: bool = False) -> nodes.Expr:
+        """Parse a name or literal value. If ``with_namespace`` is enabled, also
+        parse namespace attr refs, for use in assignments."""
+        token = self.stream.current
+        node: nodes.Expr
+        if token.type == "name":
+            next(self.stream)
+            if token.value in ("true", "false", "True", "False"):
+                node = nodes.Const(token.value in ("true", "True"), lineno=token.lineno)
+            elif token.value in ("none", "None"):
+                node = nodes.Const(None, lineno=token.lineno)
+            elif with_namespace and self.stream.current.type == "dot":
+                # If namespace attributes are allowed at this point, and the next
+                # token is a dot, produce a namespace reference.
+                next(self.stream)
+                attr = self.stream.expect("name")
+                node = nodes.NSRef(token.value, attr.value, lineno=token.lineno)
+            else:
+                node = nodes.Name(token.value, "load", lineno=token.lineno)
+        elif token.type == "string":
+            next(self.stream)
+            buf = [token.value]
+            lineno = token.lineno
+            while self.stream.current.type == "string":
+                buf.append(self.stream.current.value)
+                next(self.stream)
+            node = nodes.Const("".join(buf), lineno=lineno)
+        elif token.type in ("integer", "float"):
+            next(self.stream)
+            node = nodes.Const(token.value, lineno=token.lineno)
+        elif token.type == "lparen":
+            next(self.stream)
+            node = self.parse_tuple(explicit_parentheses=True)
+            self.stream.expect("rparen")
+        elif token.type == "lbracket":
+            node = self.parse_list()
+        elif token.type == "lbrace":
+            node = self.parse_dict()
+        else:
+            self.fail(f"unexpected {describe_token(token)!r}", token.lineno)
+        return node
+
+    def parse_tuple(
+        self,
+        simplified: bool = False,
+        with_condexpr: bool = True,
+        extra_end_rules: t.Optional[t.Tuple[str, ...]] = None,
+        explicit_parentheses: bool = False,
+        with_namespace: bool = False,
+    ) -> t.Union[nodes.Tuple, nodes.Expr]:
+        """Works like `parse_expression` but if multiple expressions are
+        delimited by a comma a :class:`~jinja2.nodes.Tuple` node is created.
+        This method could also return a regular expression instead of a tuple
+        if no commas where found.
+
+        The default parsing mode is a full tuple.  If `simplified` is `True`
+        only names and literals are parsed; ``with_namespace`` allows namespace
+        attr refs as well. The `no_condexpr` parameter is forwarded to
+        :meth:`parse_expression`.
+
+        Because tuples do not require delimiters and may end in a bogus comma
+        an extra hint is needed that marks the end of a tuple.  For example
+        for loops support tuples between `for` and `in`.  In that case the
+        `extra_end_rules` is set to ``['name:in']``.
+
+        `explicit_parentheses` is true if the parsing was triggered by an
+        expression in parentheses.  This is used to figure out if an empty
+        tuple is a valid expression or not.
+        """
+        lineno = self.stream.current.lineno
+        if simplified:
+
+            def parse() -> nodes.Expr:
+                return self.parse_primary(with_namespace=with_namespace)
+
+        else:
+
+            def parse() -> nodes.Expr:
+                return self.parse_expression(with_condexpr=with_condexpr)
+
+        args: t.List[nodes.Expr] = []
+        is_tuple = False
+
+        while True:
+            if args:
+                self.stream.expect("comma")
+            if self.is_tuple_end(extra_end_rules):
+                break
+            args.append(parse())
+            if self.stream.current.type == "comma":
+                is_tuple = True
+            else:
+                break
+            lineno = self.stream.current.lineno
+
+        if not is_tuple:
+            if args:
+                return args[0]
+
+            # if we don't have explicit parentheses, an empty tuple is
+            # not a valid expression.  This would mean nothing (literally
+            # nothing) in the spot of an expression would be an empty
+            # tuple.
+            if not explicit_parentheses:
+                self.fail(
+                    "Expected an expression,"
+                    f" got {describe_token(self.stream.current)!r}"
+                )
+
+        return nodes.Tuple(args, "load", lineno=lineno)
+
+    def parse_list(self) -> nodes.List:
+        token = self.stream.expect("lbracket")
+        items: t.List[nodes.Expr] = []
+        while self.stream.current.type != "rbracket":
+            if items:
+                self.stream.expect("comma")
+            if self.stream.current.type == "rbracket":
+                break
+            items.append(self.parse_expression())
+        self.stream.expect("rbracket")
+        return nodes.List(items, lineno=token.lineno)
+
+    def parse_dict(self) -> nodes.Dict:
+        token = self.stream.expect("lbrace")
+        items: t.List[nodes.Pair] = []
+        while self.stream.current.type != "rbrace":
+            if items:
+                self.stream.expect("comma")
+            if self.stream.current.type == "rbrace":
+                break
+            key = self.parse_expression()
+            self.stream.expect("colon")
+            value = self.parse_expression()
+            items.append(nodes.Pair(key, value, lineno=key.lineno))
+        self.stream.expect("rbrace")
+        return nodes.Dict(items, lineno=token.lineno)
+
+    def parse_postfix(self, node: nodes.Expr) -> nodes.Expr:
+        while True:
+            token_type = self.stream.current.type
+            if token_type == "dot" or token_type == "lbracket":
+                node = self.parse_subscript(node)
+            # calls are valid both after postfix expressions (getattr
+            # and getitem) as well as filters and tests
+            elif token_type == "lparen":
+                node = self.parse_call(node)
+            else:
+                break
+        return node
+
+    def parse_filter_expr(self, node: nodes.Expr) -> nodes.Expr:
+        while True:
+            token_type = self.stream.current.type
+            if token_type == "pipe":
+                node = self.parse_filter(node)  # type: ignore
+            elif token_type == "name" and self.stream.current.value == "is":
+                node = self.parse_test(node)
+            # calls are valid both after postfix expressions (getattr
+            # and getitem) as well as filters and tests
+            elif token_type == "lparen":
+                node = self.parse_call(node)
+            else:
+                break
+        return node
+
+    def parse_subscript(
+        self, node: nodes.Expr
+    ) -> t.Union[nodes.Getattr, nodes.Getitem]:
+        token = next(self.stream)
+        arg: nodes.Expr
+
+        if token.type == "dot":
+            attr_token = self.stream.current
+            next(self.stream)
+            if attr_token.type == "name":
+                return nodes.Getattr(
+                    node, attr_token.value, "load", lineno=token.lineno
+                )
+            elif attr_token.type != "integer":
+                self.fail("expected name or number", attr_token.lineno)
+            arg = nodes.Const(attr_token.value, lineno=attr_token.lineno)
+            return nodes.Getitem(node, arg, "load", lineno=token.lineno)
+        if token.type == "lbracket":
+            args: t.List[nodes.Expr] = []
+            while self.stream.current.type != "rbracket":
+                if args:
+                    self.stream.expect("comma")
+                args.append(self.parse_subscribed())
+            self.stream.expect("rbracket")
+            if len(args) == 1:
+                arg = args[0]
+            else:
+                arg = nodes.Tuple(args, "load", lineno=token.lineno)
+            return nodes.Getitem(node, arg, "load", lineno=token.lineno)
+        self.fail("expected subscript expression", token.lineno)
+
+    def parse_subscribed(self) -> nodes.Expr:
+        lineno = self.stream.current.lineno
+        args: t.List[t.Optional[nodes.Expr]]
+
+        if self.stream.current.type == "colon":
+            next(self.stream)
+            args = [None]
+        else:
+            node = self.parse_expression()
+            if self.stream.current.type != "colon":
+                return node
+            next(self.stream)
+            args = [node]
+
+        if self.stream.current.type == "colon":
+            args.append(None)
+        elif self.stream.current.type not in ("rbracket", "comma"):
+            args.append(self.parse_expression())
+        else:
+            args.append(None)
+
+        if self.stream.current.type == "colon":
+            next(self.stream)
+            if self.stream.current.type not in ("rbracket", "comma"):
+                args.append(self.parse_expression())
+            else:
+                args.append(None)
+        else:
+            args.append(None)
+
+        return nodes.Slice(lineno=lineno, *args)  # noqa: B026
+
+    def parse_call_args(
+        self,
+    ) -> t.Tuple[
+        t.List[nodes.Expr],
+        t.List[nodes.Keyword],
+        t.Optional[nodes.Expr],
+        t.Optional[nodes.Expr],
+    ]:
+        token = self.stream.expect("lparen")
+        args = []
+        kwargs = []
+        dyn_args = None
+        dyn_kwargs = None
+        require_comma = False
+
+        def ensure(expr: bool) -> None:
+            if not expr:
+                self.fail("invalid syntax for function call expression", token.lineno)
+
+        while self.stream.current.type != "rparen":
+            if require_comma:
+                self.stream.expect("comma")
+
+                # support for trailing comma
+                if self.stream.current.type == "rparen":
+                    break
+
+            if self.stream.current.type == "mul":
+                ensure(dyn_args is None and dyn_kwargs is None)
+                next(self.stream)
+                dyn_args = self.parse_expression()
+            elif self.stream.current.type == "pow":
+                ensure(dyn_kwargs is None)
+                next(self.stream)
+                dyn_kwargs = self.parse_expression()
+            else:
+                if (
+                    self.stream.current.type == "name"
+                    and self.stream.look().type == "assign"
+                ):
+                    # Parsing a kwarg
+                    ensure(dyn_kwargs is None)
+                    key = self.stream.current.value
+                    self.stream.skip(2)
+                    value = self.parse_expression()
+                    kwargs.append(nodes.Keyword(key, value, lineno=value.lineno))
+                else:
+                    # Parsing an arg
+                    ensure(dyn_args is None and dyn_kwargs is None and not kwargs)
+                    args.append(self.parse_expression())
+
+            require_comma = True
+
+        self.stream.expect("rparen")
+        return args, kwargs, dyn_args, dyn_kwargs
+
+    def parse_call(self, node: nodes.Expr) -> nodes.Call:
+        # The lparen will be expected in parse_call_args, but the lineno
+        # needs to be recorded before the stream is advanced.
+        token = self.stream.current
+        args, kwargs, dyn_args, dyn_kwargs = self.parse_call_args()
+        return nodes.Call(node, args, kwargs, dyn_args, dyn_kwargs, lineno=token.lineno)
+
+    def parse_filter(
+        self, node: t.Optional[nodes.Expr], start_inline: bool = False
+    ) -> t.Optional[nodes.Expr]:
+        while self.stream.current.type == "pipe" or start_inline:
+            if not start_inline:
+                next(self.stream)
+            token = self.stream.expect("name")
+            name = token.value
+            while self.stream.current.type == "dot":
+                next(self.stream)
+                name += "." + self.stream.expect("name").value
+            if self.stream.current.type == "lparen":
+                args, kwargs, dyn_args, dyn_kwargs = self.parse_call_args()
+            else:
+                args = []
+                kwargs = []
+                dyn_args = dyn_kwargs = None
+            node = nodes.Filter(
+                node, name, args, kwargs, dyn_args, dyn_kwargs, lineno=token.lineno
+            )
+            start_inline = False
+        return node
+
+    def parse_test(self, node: nodes.Expr) -> nodes.Expr:
+        token = next(self.stream)
+        if self.stream.current.test("name:not"):
+            next(self.stream)
+            negated = True
+        else:
+            negated = False
+        name = self.stream.expect("name").value
+        while self.stream.current.type == "dot":
+            next(self.stream)
+            name += "." + self.stream.expect("name").value
+        dyn_args = dyn_kwargs = None
+        kwargs: t.List[nodes.Keyword] = []
+        if self.stream.current.type == "lparen":
+            args, kwargs, dyn_args, dyn_kwargs = self.parse_call_args()
+        elif self.stream.current.type in {
+            "name",
+            "string",
+            "integer",
+            "float",
+            "lparen",
+            "lbracket",
+            "lbrace",
+        } and not self.stream.current.test_any("name:else", "name:or", "name:and"):
+            if self.stream.current.test("name:is"):
+                self.fail("You cannot chain multiple tests with is")
+            arg_node = self.parse_primary()
+            arg_node = self.parse_postfix(arg_node)
+            args = [arg_node]
+        else:
+            args = []
+        node = nodes.Test(
+            node, name, args, kwargs, dyn_args, dyn_kwargs, lineno=token.lineno
+        )
+        if negated:
+            node = nodes.Not(node, lineno=token.lineno)
+        return node
+
+    def subparse(
+        self, end_tokens: t.Optional[t.Tuple[str, ...]] = None
+    ) -> t.List[nodes.Node]:
+        body: t.List[nodes.Node] = []
+        data_buffer: t.List[nodes.Node] = []
+        add_data = data_buffer.append
+
+        if end_tokens is not None:
+            self._end_token_stack.append(end_tokens)
+
+        def flush_data() -> None:
+            if data_buffer:
+                lineno = data_buffer[0].lineno
+                body.append(nodes.Output(data_buffer[:], lineno=lineno))
+                del data_buffer[:]
+
+        try:
+            while self.stream:
+                token = self.stream.current
+                if token.type == "data":
+                    if token.value:
+                        add_data(nodes.TemplateData(token.value, lineno=token.lineno))
+                    next(self.stream)
+                elif token.type == "variable_begin":
+                    next(self.stream)
+                    add_data(self.parse_tuple(with_condexpr=True))
+                    self.stream.expect("variable_end")
+                elif token.type == "block_begin":
+                    flush_data()
+                    next(self.stream)
+                    if end_tokens is not None and self.stream.current.test_any(
+                        *end_tokens
+                    ):
+                        return body
+                    rv = self.parse_statement()
+                    if isinstance(rv, list):
+                        body.extend(rv)
+                    else:
+                        body.append(rv)
+                    self.stream.expect("block_end")
+                else:
+                    raise AssertionError("internal parsing error")
+
+            flush_data()
+        finally:
+            if end_tokens is not None:
+                self._end_token_stack.pop()
+        return body
+
+    def parse(self) -> nodes.Template:
+        """Parse the whole template into a `Template` node."""
+        result = nodes.Template(self.subparse(), lineno=1)
+        result.set_environment(self.environment)
+        return result
diff --git a/lib/python3.11/site-packages/jinja2/py.typed b/lib/python3.11/site-packages/jinja2/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/jinja2/runtime.py b/lib/python3.11/site-packages/jinja2/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..09119e2ae550e7001a64be0c7150837fb39af26d
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/runtime.py
@@ -0,0 +1,1062 @@
+"""The runtime functions and state used by compiled templates."""
+
+import functools
+import sys
+import typing as t
+from collections import abc
+from itertools import chain
+
+from markupsafe import escape  # noqa: F401
+from markupsafe import Markup
+from markupsafe import soft_str
+
+from .async_utils import auto_aiter
+from .async_utils import auto_await  # noqa: F401
+from .exceptions import TemplateNotFound  # noqa: F401
+from .exceptions import TemplateRuntimeError  # noqa: F401
+from .exceptions import UndefinedError
+from .nodes import EvalContext
+from .utils import _PassArg
+from .utils import concat
+from .utils import internalcode
+from .utils import missing
+from .utils import Namespace  # noqa: F401
+from .utils import object_type_repr
+from .utils import pass_eval_context
+
+V = t.TypeVar("V")
+F = t.TypeVar("F", bound=t.Callable[..., t.Any])
+
+if t.TYPE_CHECKING:
+    import logging
+
+    import typing_extensions as te
+
+    from .environment import Environment
+
+    class LoopRenderFunc(te.Protocol):
+        def __call__(
+            self,
+            reciter: t.Iterable[V],
+            loop_render_func: "LoopRenderFunc",
+            depth: int = 0,
+        ) -> str: ...
+
+
+# these variables are exported to the template runtime
+exported = [
+    "LoopContext",
+    "TemplateReference",
+    "Macro",
+    "Markup",
+    "TemplateRuntimeError",
+    "missing",
+    "escape",
+    "markup_join",
+    "str_join",
+    "identity",
+    "TemplateNotFound",
+    "Namespace",
+    "Undefined",
+    "internalcode",
+]
+async_exported = [
+    "AsyncLoopContext",
+    "auto_aiter",
+    "auto_await",
+]
+
+
+def identity(x: V) -> V:
+    """Returns its argument. Useful for certain things in the
+    environment.
+    """
+    return x
+
+
+def markup_join(seq: t.Iterable[t.Any]) -> str:
+    """Concatenation that escapes if necessary and converts to string."""
+    buf = []
+    iterator = map(soft_str, seq)
+    for arg in iterator:
+        buf.append(arg)
+        if hasattr(arg, "__html__"):
+            return Markup("").join(chain(buf, iterator))
+    return concat(buf)
+
+
+def str_join(seq: t.Iterable[t.Any]) -> str:
+    """Simple args to string conversion and concatenation."""
+    return concat(map(str, seq))
+
+
+def new_context(
+    environment: "Environment",
+    template_name: t.Optional[str],
+    blocks: t.Dict[str, t.Callable[["Context"], t.Iterator[str]]],
+    vars: t.Optional[t.Dict[str, t.Any]] = None,
+    shared: bool = False,
+    globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    locals: t.Optional[t.Mapping[str, t.Any]] = None,
+) -> "Context":
+    """Internal helper for context creation."""
+    if vars is None:
+        vars = {}
+    if shared:
+        parent = vars
+    else:
+        parent = dict(globals or (), **vars)
+    if locals:
+        # if the parent is shared a copy should be created because
+        # we don't want to modify the dict passed
+        if shared:
+            parent = dict(parent)
+        for key, value in locals.items():
+            if value is not missing:
+                parent[key] = value
+    return environment.context_class(
+        environment, parent, template_name, blocks, globals=globals
+    )
+
+
+class TemplateReference:
+    """The `self` in templates."""
+
+    def __init__(self, context: "Context") -> None:
+        self.__context = context
+
+    def __getitem__(self, name: str) -> t.Any:
+        blocks = self.__context.blocks[name]
+        return BlockReference(name, self.__context, blocks, 0)
+
+    def __repr__(self) -> str:
+        return f"<{type(self).__name__} {self.__context.name!r}>"
+
+
+def _dict_method_all(dict_method: F) -> F:
+    @functools.wraps(dict_method)
+    def f_all(self: "Context") -> t.Any:
+        return dict_method(self.get_all())
+
+    return t.cast(F, f_all)
+
+
+@abc.Mapping.register
+class Context:
+    """The template context holds the variables of a template.  It stores the
+    values passed to the template and also the names the template exports.
+    Creating instances is neither supported nor useful as it's created
+    automatically at various stages of the template evaluation and should not
+    be created by hand.
+
+    The context is immutable.  Modifications on :attr:`parent` **must not**
+    happen and modifications on :attr:`vars` are allowed from generated
+    template code only.  Template filters and global functions marked as
+    :func:`pass_context` get the active context passed as first argument
+    and are allowed to access the context read-only.
+
+    The template context supports read only dict operations (`get`,
+    `keys`, `values`, `items`, `iterkeys`, `itervalues`, `iteritems`,
+    `__getitem__`, `__contains__`).  Additionally there is a :meth:`resolve`
+    method that doesn't fail with a `KeyError` but returns an
+    :class:`Undefined` object for missing variables.
+    """
+
+    def __init__(
+        self,
+        environment: "Environment",
+        parent: t.Dict[str, t.Any],
+        name: t.Optional[str],
+        blocks: t.Dict[str, t.Callable[["Context"], t.Iterator[str]]],
+        globals: t.Optional[t.MutableMapping[str, t.Any]] = None,
+    ):
+        self.parent = parent
+        self.vars: t.Dict[str, t.Any] = {}
+        self.environment: Environment = environment
+        self.eval_ctx = EvalContext(self.environment, name)
+        self.exported_vars: t.Set[str] = set()
+        self.name = name
+        self.globals_keys = set() if globals is None else set(globals)
+
+        # create the initial mapping of blocks.  Whenever template inheritance
+        # takes place the runtime will update this mapping with the new blocks
+        # from the template.
+        self.blocks = {k: [v] for k, v in blocks.items()}
+
+    def super(
+        self, name: str, current: t.Callable[["Context"], t.Iterator[str]]
+    ) -> t.Union["BlockReference", "Undefined"]:
+        """Render a parent block."""
+        try:
+            blocks = self.blocks[name]
+            index = blocks.index(current) + 1
+            blocks[index]
+        except LookupError:
+            return self.environment.undefined(
+                f"there is no parent block called {name!r}.", name="super"
+            )
+        return BlockReference(name, self, blocks, index)
+
+    def get(self, key: str, default: t.Any = None) -> t.Any:
+        """Look up a variable by name, or return a default if the key is
+        not found.
+
+        :param key: The variable name to look up.
+        :param default: The value to return if the key is not found.
+        """
+        try:
+            return self[key]
+        except KeyError:
+            return default
+
+    def resolve(self, key: str) -> t.Union[t.Any, "Undefined"]:
+        """Look up a variable by name, or return an :class:`Undefined`
+        object if the key is not found.
+
+        If you need to add custom behavior, override
+        :meth:`resolve_or_missing`, not this method. The various lookup
+        functions use that method, not this one.
+
+        :param key: The variable name to look up.
+        """
+        rv = self.resolve_or_missing(key)
+
+        if rv is missing:
+            return self.environment.undefined(name=key)
+
+        return rv
+
+    def resolve_or_missing(self, key: str) -> t.Any:
+        """Look up a variable by name, or return a ``missing`` sentinel
+        if the key is not found.
+
+        Override this method to add custom lookup behavior.
+        :meth:`resolve`, :meth:`get`, and :meth:`__getitem__` use this
+        method. Don't call this method directly.
+
+        :param key: The variable name to look up.
+        """
+        if key in self.vars:
+            return self.vars[key]
+
+        if key in self.parent:
+            return self.parent[key]
+
+        return missing
+
+    def get_exported(self) -> t.Dict[str, t.Any]:
+        """Get a new dict with the exported variables."""
+        return {k: self.vars[k] for k in self.exported_vars}
+
+    def get_all(self) -> t.Dict[str, t.Any]:
+        """Return the complete context as dict including the exported
+        variables.  For optimizations reasons this might not return an
+        actual copy so be careful with using it.
+        """
+        if not self.vars:
+            return self.parent
+        if not self.parent:
+            return self.vars
+        return dict(self.parent, **self.vars)
+
+    @internalcode
+    def call(
+        __self,
+        __obj: t.Callable[..., t.Any],
+        *args: t.Any,
+        **kwargs: t.Any,  # noqa: B902
+    ) -> t.Union[t.Any, "Undefined"]:
+        """Call the callable with the arguments and keyword arguments
+        provided but inject the active context or environment as first
+        argument if the callable has :func:`pass_context` or
+        :func:`pass_environment`.
+        """
+        if __debug__:
+            __traceback_hide__ = True  # noqa
+
+        # Allow callable classes to take a context
+        if (
+            hasattr(__obj, "__call__")  # noqa: B004
+            and _PassArg.from_obj(__obj.__call__) is not None
+        ):
+            __obj = __obj.__call__
+
+        pass_arg = _PassArg.from_obj(__obj)
+
+        if pass_arg is _PassArg.context:
+            # the active context should have access to variables set in
+            # loops and blocks without mutating the context itself
+            if kwargs.get("_loop_vars"):
+                __self = __self.derived(kwargs["_loop_vars"])
+            if kwargs.get("_block_vars"):
+                __self = __self.derived(kwargs["_block_vars"])
+            args = (__self,) + args
+        elif pass_arg is _PassArg.eval_context:
+            args = (__self.eval_ctx,) + args
+        elif pass_arg is _PassArg.environment:
+            args = (__self.environment,) + args
+
+        kwargs.pop("_block_vars", None)
+        kwargs.pop("_loop_vars", None)
+
+        try:
+            return __obj(*args, **kwargs)
+        except StopIteration:
+            return __self.environment.undefined(
+                "value was undefined because a callable raised a"
+                " StopIteration exception"
+            )
+
+    def derived(self, locals: t.Optional[t.Dict[str, t.Any]] = None) -> "Context":
+        """Internal helper function to create a derived context.  This is
+        used in situations where the system needs a new context in the same
+        template that is independent.
+        """
+        context = new_context(
+            self.environment, self.name, {}, self.get_all(), True, None, locals
+        )
+        context.eval_ctx = self.eval_ctx
+        context.blocks.update((k, list(v)) for k, v in self.blocks.items())
+        return context
+
+    keys = _dict_method_all(dict.keys)
+    values = _dict_method_all(dict.values)
+    items = _dict_method_all(dict.items)
+
+    def __contains__(self, name: str) -> bool:
+        return name in self.vars or name in self.parent
+
+    def __getitem__(self, key: str) -> t.Any:
+        """Look up a variable by name with ``[]`` syntax, or raise a
+        ``KeyError`` if the key is not found.
+        """
+        item = self.resolve_or_missing(key)
+
+        if item is missing:
+            raise KeyError(key)
+
+        return item
+
+    def __repr__(self) -> str:
+        return f"<{type(self).__name__} {self.get_all()!r} of {self.name!r}>"
+
+
+class BlockReference:
+    """One block on a template reference."""
+
+    def __init__(
+        self,
+        name: str,
+        context: "Context",
+        stack: t.List[t.Callable[["Context"], t.Iterator[str]]],
+        depth: int,
+    ) -> None:
+        self.name = name
+        self._context = context
+        self._stack = stack
+        self._depth = depth
+
+    @property
+    def super(self) -> t.Union["BlockReference", "Undefined"]:
+        """Super the block."""
+        if self._depth + 1 >= len(self._stack):
+            return self._context.environment.undefined(
+                f"there is no parent block called {self.name!r}.", name="super"
+            )
+        return BlockReference(self.name, self._context, self._stack, self._depth + 1)
+
+    @internalcode
+    async def _async_call(self) -> str:
+        rv = self._context.environment.concat(  # type: ignore
+            [x async for x in self._stack[self._depth](self._context)]  # type: ignore
+        )
+
+        if self._context.eval_ctx.autoescape:
+            return Markup(rv)
+
+        return rv
+
+    @internalcode
+    def __call__(self) -> str:
+        if self._context.environment.is_async:
+            return self._async_call()  # type: ignore
+
+        rv = self._context.environment.concat(  # type: ignore
+            self._stack[self._depth](self._context)
+        )
+
+        if self._context.eval_ctx.autoescape:
+            return Markup(rv)
+
+        return rv
+
+
+class LoopContext:
+    """A wrapper iterable for dynamic ``for`` loops, with information
+    about the loop and iteration.
+    """
+
+    #: Current iteration of the loop, starting at 0.
+    index0 = -1
+
+    _length: t.Optional[int] = None
+    _after: t.Any = missing
+    _current: t.Any = missing
+    _before: t.Any = missing
+    _last_changed_value: t.Any = missing
+
+    def __init__(
+        self,
+        iterable: t.Iterable[V],
+        undefined: t.Type["Undefined"],
+        recurse: t.Optional["LoopRenderFunc"] = None,
+        depth0: int = 0,
+    ) -> None:
+        """
+        :param iterable: Iterable to wrap.
+        :param undefined: :class:`Undefined` class to use for next and
+            previous items.
+        :param recurse: The function to render the loop body when the
+            loop is marked recursive.
+        :param depth0: Incremented when looping recursively.
+        """
+        self._iterable = iterable
+        self._iterator = self._to_iterator(iterable)
+        self._undefined = undefined
+        self._recurse = recurse
+        #: How many levels deep a recursive loop currently is, starting at 0.
+        self.depth0 = depth0
+
+    @staticmethod
+    def _to_iterator(iterable: t.Iterable[V]) -> t.Iterator[V]:
+        return iter(iterable)
+
+    @property
+    def length(self) -> int:
+        """Length of the iterable.
+
+        If the iterable is a generator or otherwise does not have a
+        size, it is eagerly evaluated to get a size.
+        """
+        if self._length is not None:
+            return self._length
+
+        try:
+            self._length = len(self._iterable)  # type: ignore
+        except TypeError:
+            iterable = list(self._iterator)
+            self._iterator = self._to_iterator(iterable)
+            self._length = len(iterable) + self.index + (self._after is not missing)
+
+        return self._length
+
+    def __len__(self) -> int:
+        return self.length
+
+    @property
+    def depth(self) -> int:
+        """How many levels deep a recursive loop currently is, starting at 1."""
+        return self.depth0 + 1
+
+    @property
+    def index(self) -> int:
+        """Current iteration of the loop, starting at 1."""
+        return self.index0 + 1
+
+    @property
+    def revindex0(self) -> int:
+        """Number of iterations from the end of the loop, ending at 0.
+
+        Requires calculating :attr:`length`.
+        """
+        return self.length - self.index
+
+    @property
+    def revindex(self) -> int:
+        """Number of iterations from the end of the loop, ending at 1.
+
+        Requires calculating :attr:`length`.
+        """
+        return self.length - self.index0
+
+    @property
+    def first(self) -> bool:
+        """Whether this is the first iteration of the loop."""
+        return self.index0 == 0
+
+    def _peek_next(self) -> t.Any:
+        """Return the next element in the iterable, or :data:`missing`
+        if the iterable is exhausted. Only peeks one item ahead, caching
+        the result in :attr:`_last` for use in subsequent checks. The
+        cache is reset when :meth:`__next__` is called.
+        """
+        if self._after is not missing:
+            return self._after
+
+        self._after = next(self._iterator, missing)
+        return self._after
+
+    @property
+    def last(self) -> bool:
+        """Whether this is the last iteration of the loop.
+
+        Causes the iterable to advance early. See
+        :func:`itertools.groupby` for issues this can cause.
+        The :func:`groupby` filter avoids that issue.
+        """
+        return self._peek_next() is missing
+
+    @property
+    def previtem(self) -> t.Union[t.Any, "Undefined"]:
+        """The item in the previous iteration. Undefined during the
+        first iteration.
+        """
+        if self.first:
+            return self._undefined("there is no previous item")
+
+        return self._before
+
+    @property
+    def nextitem(self) -> t.Union[t.Any, "Undefined"]:
+        """The item in the next iteration. Undefined during the last
+        iteration.
+
+        Causes the iterable to advance early. See
+        :func:`itertools.groupby` for issues this can cause.
+        The :func:`jinja-filters.groupby` filter avoids that issue.
+        """
+        rv = self._peek_next()
+
+        if rv is missing:
+            return self._undefined("there is no next item")
+
+        return rv
+
+    def cycle(self, *args: V) -> V:
+        """Return a value from the given args, cycling through based on
+        the current :attr:`index0`.
+
+        :param args: One or more values to cycle through.
+        """
+        if not args:
+            raise TypeError("no items for cycling given")
+
+        return args[self.index0 % len(args)]
+
+    def changed(self, *value: t.Any) -> bool:
+        """Return ``True`` if previously called with a different value
+        (including when called for the first time).
+
+        :param value: One or more values to compare to the last call.
+        """
+        if self._last_changed_value != value:
+            self._last_changed_value = value
+            return True
+
+        return False
+
+    def __iter__(self) -> "LoopContext":
+        return self
+
+    def __next__(self) -> t.Tuple[t.Any, "LoopContext"]:
+        if self._after is not missing:
+            rv = self._after
+            self._after = missing
+        else:
+            rv = next(self._iterator)
+
+        self.index0 += 1
+        self._before = self._current
+        self._current = rv
+        return rv, self
+
+    @internalcode
+    def __call__(self, iterable: t.Iterable[V]) -> str:
+        """When iterating over nested data, render the body of the loop
+        recursively with the given inner iterable data.
+
+        The loop must have the ``recursive`` marker for this to work.
+        """
+        if self._recurse is None:
+            raise TypeError(
+                "The loop must have the 'recursive' marker to be called recursively."
+            )
+
+        return self._recurse(iterable, self._recurse, depth=self.depth)
+
+    def __repr__(self) -> str:
+        return f"<{type(self).__name__} {self.index}/{self.length}>"
+
+
+class AsyncLoopContext(LoopContext):
+    _iterator: t.AsyncIterator[t.Any]  # type: ignore
+
+    @staticmethod
+    def _to_iterator(  # type: ignore
+        iterable: t.Union[t.Iterable[V], t.AsyncIterable[V]],
+    ) -> t.AsyncIterator[V]:
+        return auto_aiter(iterable)
+
+    @property
+    async def length(self) -> int:  # type: ignore
+        if self._length is not None:
+            return self._length
+
+        try:
+            self._length = len(self._iterable)  # type: ignore
+        except TypeError:
+            iterable = [x async for x in self._iterator]
+            self._iterator = self._to_iterator(iterable)
+            self._length = len(iterable) + self.index + (self._after is not missing)
+
+        return self._length
+
+    @property
+    async def revindex0(self) -> int:  # type: ignore
+        return await self.length - self.index
+
+    @property
+    async def revindex(self) -> int:  # type: ignore
+        return await self.length - self.index0
+
+    async def _peek_next(self) -> t.Any:
+        if self._after is not missing:
+            return self._after
+
+        try:
+            self._after = await self._iterator.__anext__()
+        except StopAsyncIteration:
+            self._after = missing
+
+        return self._after
+
+    @property
+    async def last(self) -> bool:  # type: ignore
+        return await self._peek_next() is missing
+
+    @property
+    async def nextitem(self) -> t.Union[t.Any, "Undefined"]:
+        rv = await self._peek_next()
+
+        if rv is missing:
+            return self._undefined("there is no next item")
+
+        return rv
+
+    def __aiter__(self) -> "AsyncLoopContext":
+        return self
+
+    async def __anext__(self) -> t.Tuple[t.Any, "AsyncLoopContext"]:
+        if self._after is not missing:
+            rv = self._after
+            self._after = missing
+        else:
+            rv = await self._iterator.__anext__()
+
+        self.index0 += 1
+        self._before = self._current
+        self._current = rv
+        return rv, self
+
+
+class Macro:
+    """Wraps a macro function."""
+
+    def __init__(
+        self,
+        environment: "Environment",
+        func: t.Callable[..., str],
+        name: str,
+        arguments: t.List[str],
+        catch_kwargs: bool,
+        catch_varargs: bool,
+        caller: bool,
+        default_autoescape: t.Optional[bool] = None,
+    ):
+        self._environment = environment
+        self._func = func
+        self._argument_count = len(arguments)
+        self.name = name
+        self.arguments = arguments
+        self.catch_kwargs = catch_kwargs
+        self.catch_varargs = catch_varargs
+        self.caller = caller
+        self.explicit_caller = "caller" in arguments
+
+        if default_autoescape is None:
+            if callable(environment.autoescape):
+                default_autoescape = environment.autoescape(None)
+            else:
+                default_autoescape = environment.autoescape
+
+        self._default_autoescape = default_autoescape
+
+    @internalcode
+    @pass_eval_context
+    def __call__(self, *args: t.Any, **kwargs: t.Any) -> str:
+        # This requires a bit of explanation,  In the past we used to
+        # decide largely based on compile-time information if a macro is
+        # safe or unsafe.  While there was a volatile mode it was largely
+        # unused for deciding on escaping.  This turns out to be
+        # problematic for macros because whether a macro is safe depends not
+        # on the escape mode when it was defined, but rather when it was used.
+        #
+        # Because however we export macros from the module system and
+        # there are historic callers that do not pass an eval context (and
+        # will continue to not pass one), we need to perform an instance
+        # check here.
+        #
+        # This is considered safe because an eval context is not a valid
+        # argument to callables otherwise anyway.  Worst case here is
+        # that if no eval context is passed we fall back to the compile
+        # time autoescape flag.
+        if args and isinstance(args[0], EvalContext):
+            autoescape = args[0].autoescape
+            args = args[1:]
+        else:
+            autoescape = self._default_autoescape
+
+        # try to consume the positional arguments
+        arguments = list(args[: self._argument_count])
+        off = len(arguments)
+
+        # For information why this is necessary refer to the handling
+        # of caller in the `macro_body` handler in the compiler.
+        found_caller = False
+
+        # if the number of arguments consumed is not the number of
+        # arguments expected we start filling in keyword arguments
+        # and defaults.
+        if off != self._argument_count:
+            for name in self.arguments[len(arguments) :]:
+                try:
+                    value = kwargs.pop(name)
+                except KeyError:
+                    value = missing
+                if name == "caller":
+                    found_caller = True
+                arguments.append(value)
+        else:
+            found_caller = self.explicit_caller
+
+        # it's important that the order of these arguments does not change
+        # if not also changed in the compiler's `function_scoping` method.
+        # the order is caller, keyword arguments, positional arguments!
+        if self.caller and not found_caller:
+            caller = kwargs.pop("caller", None)
+            if caller is None:
+                caller = self._environment.undefined("No caller defined", name="caller")
+            arguments.append(caller)
+
+        if self.catch_kwargs:
+            arguments.append(kwargs)
+        elif kwargs:
+            if "caller" in kwargs:
+                raise TypeError(
+                    f"macro {self.name!r} was invoked with two values for the special"
+                    " caller argument. This is most likely a bug."
+                )
+            raise TypeError(
+                f"macro {self.name!r} takes no keyword argument {next(iter(kwargs))!r}"
+            )
+        if self.catch_varargs:
+            arguments.append(args[self._argument_count :])
+        elif len(args) > self._argument_count:
+            raise TypeError(
+                f"macro {self.name!r} takes not more than"
+                f" {len(self.arguments)} argument(s)"
+            )
+
+        return self._invoke(arguments, autoescape)
+
+    async def _async_invoke(self, arguments: t.List[t.Any], autoescape: bool) -> str:
+        rv = await self._func(*arguments)  # type: ignore
+
+        if autoescape:
+            return Markup(rv)
+
+        return rv  # type: ignore
+
+    def _invoke(self, arguments: t.List[t.Any], autoescape: bool) -> str:
+        if self._environment.is_async:
+            return self._async_invoke(arguments, autoescape)  # type: ignore
+
+        rv = self._func(*arguments)
+
+        if autoescape:
+            rv = Markup(rv)
+
+        return rv
+
+    def __repr__(self) -> str:
+        name = "anonymous" if self.name is None else repr(self.name)
+        return f"<{type(self).__name__} {name}>"
+
+
+class Undefined:
+    """The default undefined type. This can be printed, iterated, and treated as
+    a boolean. Any other operation will raise an :exc:`UndefinedError`.
+
+    >>> foo = Undefined(name='foo')
+    >>> str(foo)
+    ''
+    >>> not foo
+    True
+    >>> foo + 42
+    Traceback (most recent call last):
+      ...
+    jinja2.exceptions.UndefinedError: 'foo' is undefined
+    """
+
+    __slots__ = (
+        "_undefined_hint",
+        "_undefined_obj",
+        "_undefined_name",
+        "_undefined_exception",
+    )
+
+    def __init__(
+        self,
+        hint: t.Optional[str] = None,
+        obj: t.Any = missing,
+        name: t.Optional[str] = None,
+        exc: t.Type[TemplateRuntimeError] = UndefinedError,
+    ) -> None:
+        self._undefined_hint = hint
+        self._undefined_obj = obj
+        self._undefined_name = name
+        self._undefined_exception = exc
+
+    @property
+    def _undefined_message(self) -> str:
+        """Build a message about the undefined value based on how it was
+        accessed.
+        """
+        if self._undefined_hint:
+            return self._undefined_hint
+
+        if self._undefined_obj is missing:
+            return f"{self._undefined_name!r} is undefined"
+
+        if not isinstance(self._undefined_name, str):
+            return (
+                f"{object_type_repr(self._undefined_obj)} has no"
+                f" element {self._undefined_name!r}"
+            )
+
+        return (
+            f"{object_type_repr(self._undefined_obj)!r} has no"
+            f" attribute {self._undefined_name!r}"
+        )
+
+    @internalcode
+    def _fail_with_undefined_error(
+        self, *args: t.Any, **kwargs: t.Any
+    ) -> "te.NoReturn":
+        """Raise an :exc:`UndefinedError` when operations are performed
+        on the undefined value.
+        """
+        raise self._undefined_exception(self._undefined_message)
+
+    @internalcode
+    def __getattr__(self, name: str) -> t.Any:
+        # Raise AttributeError on requests for names that appear to be unimplemented
+        # dunder methods to keep Python's internal protocol probing behaviors working
+        # properly in cases where another exception type could cause unexpected or
+        # difficult-to-diagnose failures.
+        if name[:2] == "__" and name[-2:] == "__":
+            raise AttributeError(name)
+
+        return self._fail_with_undefined_error()
+
+    __add__ = __radd__ = __sub__ = __rsub__ = _fail_with_undefined_error
+    __mul__ = __rmul__ = __div__ = __rdiv__ = _fail_with_undefined_error
+    __truediv__ = __rtruediv__ = _fail_with_undefined_error
+    __floordiv__ = __rfloordiv__ = _fail_with_undefined_error
+    __mod__ = __rmod__ = _fail_with_undefined_error
+    __pos__ = __neg__ = _fail_with_undefined_error
+    __call__ = __getitem__ = _fail_with_undefined_error
+    __lt__ = __le__ = __gt__ = __ge__ = _fail_with_undefined_error
+    __int__ = __float__ = __complex__ = _fail_with_undefined_error
+    __pow__ = __rpow__ = _fail_with_undefined_error
+
+    def __eq__(self, other: t.Any) -> bool:
+        return type(self) is type(other)
+
+    def __ne__(self, other: t.Any) -> bool:
+        return not self.__eq__(other)
+
+    def __hash__(self) -> int:
+        return id(type(self))
+
+    def __str__(self) -> str:
+        return ""
+
+    def __len__(self) -> int:
+        return 0
+
+    def __iter__(self) -> t.Iterator[t.Any]:
+        yield from ()
+
+    async def __aiter__(self) -> t.AsyncIterator[t.Any]:
+        for _ in ():
+            yield
+
+    def __bool__(self) -> bool:
+        return False
+
+    def __repr__(self) -> str:
+        return "Undefined"
+
+
+def make_logging_undefined(
+    logger: t.Optional["logging.Logger"] = None, base: t.Type[Undefined] = Undefined
+) -> t.Type[Undefined]:
+    """Given a logger object this returns a new undefined class that will
+    log certain failures.  It will log iterations and printing.  If no
+    logger is given a default logger is created.
+
+    Example::
+
+        logger = logging.getLogger(__name__)
+        LoggingUndefined = make_logging_undefined(
+            logger=logger,
+            base=Undefined
+        )
+
+    .. versionadded:: 2.8
+
+    :param logger: the logger to use.  If not provided, a default logger
+                   is created.
+    :param base: the base class to add logging functionality to.  This
+                 defaults to :class:`Undefined`.
+    """
+    if logger is None:
+        import logging
+
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.StreamHandler(sys.stderr))
+
+    def _log_message(undef: Undefined) -> None:
+        logger.warning("Template variable warning: %s", undef._undefined_message)
+
+    class LoggingUndefined(base):  # type: ignore
+        __slots__ = ()
+
+        def _fail_with_undefined_error(  # type: ignore
+            self, *args: t.Any, **kwargs: t.Any
+        ) -> "te.NoReturn":
+            try:
+                super()._fail_with_undefined_error(*args, **kwargs)
+            except self._undefined_exception as e:
+                logger.error("Template variable error: %s", e)  # type: ignore
+                raise e
+
+        def __str__(self) -> str:
+            _log_message(self)
+            return super().__str__()  # type: ignore
+
+        def __iter__(self) -> t.Iterator[t.Any]:
+            _log_message(self)
+            return super().__iter__()  # type: ignore
+
+        def __bool__(self) -> bool:
+            _log_message(self)
+            return super().__bool__()  # type: ignore
+
+    return LoggingUndefined
+
+
+class ChainableUndefined(Undefined):
+    """An undefined that is chainable, where both ``__getattr__`` and
+    ``__getitem__`` return itself rather than raising an
+    :exc:`UndefinedError`.
+
+    >>> foo = ChainableUndefined(name='foo')
+    >>> str(foo.bar['baz'])
+    ''
+    >>> foo.bar['baz'] + 42
+    Traceback (most recent call last):
+      ...
+    jinja2.exceptions.UndefinedError: 'foo' is undefined
+
+    .. versionadded:: 2.11.0
+    """
+
+    __slots__ = ()
+
+    def __html__(self) -> str:
+        return str(self)
+
+    def __getattr__(self, name: str) -> "ChainableUndefined":
+        # Raise AttributeError on requests for names that appear to be unimplemented
+        # dunder methods to avoid confusing Python with truthy non-method objects that
+        # do not implement the protocol being probed for. e.g., copy.copy(Undefined())
+        # fails spectacularly if getattr(Undefined(), '__setstate__') returns an
+        # Undefined object instead of raising AttributeError to signal that it does not
+        # support that style of object initialization.
+        if name[:2] == "__" and name[-2:] == "__":
+            raise AttributeError(name)
+
+        return self
+
+    def __getitem__(self, _name: str) -> "ChainableUndefined":  # type: ignore[override]
+        return self
+
+
+class DebugUndefined(Undefined):
+    """An undefined that returns the debug info when printed.
+
+    >>> foo = DebugUndefined(name='foo')
+    >>> str(foo)
+    '{{ foo }}'
+    >>> not foo
+    True
+    >>> foo + 42
+    Traceback (most recent call last):
+      ...
+    jinja2.exceptions.UndefinedError: 'foo' is undefined
+    """
+
+    __slots__ = ()
+
+    def __str__(self) -> str:
+        if self._undefined_hint:
+            message = f"undefined value printed: {self._undefined_hint}"
+
+        elif self._undefined_obj is missing:
+            message = self._undefined_name  # type: ignore
+
+        else:
+            message = (
+                f"no such element: {object_type_repr(self._undefined_obj)}"
+                f"[{self._undefined_name!r}]"
+            )
+
+        return f"{{{{ {message} }}}}"
+
+
+class StrictUndefined(Undefined):
+    """An undefined that barks on print and iteration as well as boolean
+    tests and all kinds of comparisons.  In other words: you can do nothing
+    with it except checking if it's defined using the `defined` test.
+
+    >>> foo = StrictUndefined(name='foo')
+    >>> str(foo)
+    Traceback (most recent call last):
+      ...
+    jinja2.exceptions.UndefinedError: 'foo' is undefined
+    >>> not foo
+    Traceback (most recent call last):
+      ...
+    jinja2.exceptions.UndefinedError: 'foo' is undefined
+    >>> foo + 42
+    Traceback (most recent call last):
+      ...
+    jinja2.exceptions.UndefinedError: 'foo' is undefined
+    """
+
+    __slots__ = ()
+    __iter__ = __str__ = __len__ = Undefined._fail_with_undefined_error
+    __eq__ = __ne__ = __bool__ = __hash__ = Undefined._fail_with_undefined_error
+    __contains__ = Undefined._fail_with_undefined_error
diff --git a/lib/python3.11/site-packages/jinja2/sandbox.py b/lib/python3.11/site-packages/jinja2/sandbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c9dae22f1c2c19f976edeb6478bcf53c6207cb1
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/sandbox.py
@@ -0,0 +1,436 @@
+"""A sandbox layer that ensures unsafe operations cannot be performed.
+Useful when the template itself comes from an untrusted source.
+"""
+
+import operator
+import types
+import typing as t
+from _string import formatter_field_name_split  # type: ignore
+from collections import abc
+from collections import deque
+from functools import update_wrapper
+from string import Formatter
+
+from markupsafe import EscapeFormatter
+from markupsafe import Markup
+
+from .environment import Environment
+from .exceptions import SecurityError
+from .runtime import Context
+from .runtime import Undefined
+
+F = t.TypeVar("F", bound=t.Callable[..., t.Any])
+
+#: maximum number of items a range may produce
+MAX_RANGE = 100000
+
+#: Unsafe function attributes.
+UNSAFE_FUNCTION_ATTRIBUTES: t.Set[str] = set()
+
+#: Unsafe method attributes. Function attributes are unsafe for methods too.
+UNSAFE_METHOD_ATTRIBUTES: t.Set[str] = set()
+
+#: unsafe generator attributes.
+UNSAFE_GENERATOR_ATTRIBUTES = {"gi_frame", "gi_code"}
+
+#: unsafe attributes on coroutines
+UNSAFE_COROUTINE_ATTRIBUTES = {"cr_frame", "cr_code"}
+
+#: unsafe attributes on async generators
+UNSAFE_ASYNC_GENERATOR_ATTRIBUTES = {"ag_code", "ag_frame"}
+
+_mutable_spec: t.Tuple[t.Tuple[t.Type[t.Any], t.FrozenSet[str]], ...] = (
+    (
+        abc.MutableSet,
+        frozenset(
+            [
+                "add",
+                "clear",
+                "difference_update",
+                "discard",
+                "pop",
+                "remove",
+                "symmetric_difference_update",
+                "update",
+            ]
+        ),
+    ),
+    (
+        abc.MutableMapping,
+        frozenset(["clear", "pop", "popitem", "setdefault", "update"]),
+    ),
+    (
+        abc.MutableSequence,
+        frozenset(
+            ["append", "clear", "pop", "reverse", "insert", "sort", "extend", "remove"]
+        ),
+    ),
+    (
+        deque,
+        frozenset(
+            [
+                "append",
+                "appendleft",
+                "clear",
+                "extend",
+                "extendleft",
+                "pop",
+                "popleft",
+                "remove",
+                "rotate",
+            ]
+        ),
+    ),
+)
+
+
+def safe_range(*args: int) -> range:
+    """A range that can't generate ranges with a length of more than
+    MAX_RANGE items.
+    """
+    rng = range(*args)
+
+    if len(rng) > MAX_RANGE:
+        raise OverflowError(
+            "Range too big. The sandbox blocks ranges larger than"
+            f" MAX_RANGE ({MAX_RANGE})."
+        )
+
+    return rng
+
+
+def unsafe(f: F) -> F:
+    """Marks a function or method as unsafe.
+
+    .. code-block: python
+
+        @unsafe
+        def delete(self):
+            pass
+    """
+    f.unsafe_callable = True  # type: ignore
+    return f
+
+
+def is_internal_attribute(obj: t.Any, attr: str) -> bool:
+    """Test if the attribute given is an internal python attribute.  For
+    example this function returns `True` for the `func_code` attribute of
+    python objects.  This is useful if the environment method
+    :meth:`~SandboxedEnvironment.is_safe_attribute` is overridden.
+
+    >>> from jinja2.sandbox import is_internal_attribute
+    >>> is_internal_attribute(str, "mro")
+    True
+    >>> is_internal_attribute(str, "upper")
+    False
+    """
+    if isinstance(obj, types.FunctionType):
+        if attr in UNSAFE_FUNCTION_ATTRIBUTES:
+            return True
+    elif isinstance(obj, types.MethodType):
+        if attr in UNSAFE_FUNCTION_ATTRIBUTES or attr in UNSAFE_METHOD_ATTRIBUTES:
+            return True
+    elif isinstance(obj, type):
+        if attr == "mro":
+            return True
+    elif isinstance(obj, (types.CodeType, types.TracebackType, types.FrameType)):
+        return True
+    elif isinstance(obj, types.GeneratorType):
+        if attr in UNSAFE_GENERATOR_ATTRIBUTES:
+            return True
+    elif hasattr(types, "CoroutineType") and isinstance(obj, types.CoroutineType):
+        if attr in UNSAFE_COROUTINE_ATTRIBUTES:
+            return True
+    elif hasattr(types, "AsyncGeneratorType") and isinstance(
+        obj, types.AsyncGeneratorType
+    ):
+        if attr in UNSAFE_ASYNC_GENERATOR_ATTRIBUTES:
+            return True
+    return attr.startswith("__")
+
+
+def modifies_known_mutable(obj: t.Any, attr: str) -> bool:
+    """This function checks if an attribute on a builtin mutable object
+    (list, dict, set or deque) or the corresponding ABCs would modify it
+    if called.
+
+    >>> modifies_known_mutable({}, "clear")
+    True
+    >>> modifies_known_mutable({}, "keys")
+    False
+    >>> modifies_known_mutable([], "append")
+    True
+    >>> modifies_known_mutable([], "index")
+    False
+
+    If called with an unsupported object, ``False`` is returned.
+
+    >>> modifies_known_mutable("foo", "upper")
+    False
+    """
+    for typespec, unsafe in _mutable_spec:
+        if isinstance(obj, typespec):
+            return attr in unsafe
+    return False
+
+
+class SandboxedEnvironment(Environment):
+    """The sandboxed environment.  It works like the regular environment but
+    tells the compiler to generate sandboxed code.  Additionally subclasses of
+    this environment may override the methods that tell the runtime what
+    attributes or functions are safe to access.
+
+    If the template tries to access insecure code a :exc:`SecurityError` is
+    raised.  However also other exceptions may occur during the rendering so
+    the caller has to ensure that all exceptions are caught.
+    """
+
+    sandboxed = True
+
+    #: default callback table for the binary operators.  A copy of this is
+    #: available on each instance of a sandboxed environment as
+    #: :attr:`binop_table`
+    default_binop_table: t.Dict[str, t.Callable[[t.Any, t.Any], t.Any]] = {
+        "+": operator.add,
+        "-": operator.sub,
+        "*": operator.mul,
+        "/": operator.truediv,
+        "//": operator.floordiv,
+        "**": operator.pow,
+        "%": operator.mod,
+    }
+
+    #: default callback table for the unary operators.  A copy of this is
+    #: available on each instance of a sandboxed environment as
+    #: :attr:`unop_table`
+    default_unop_table: t.Dict[str, t.Callable[[t.Any], t.Any]] = {
+        "+": operator.pos,
+        "-": operator.neg,
+    }
+
+    #: a set of binary operators that should be intercepted.  Each operator
+    #: that is added to this set (empty by default) is delegated to the
+    #: :meth:`call_binop` method that will perform the operator.  The default
+    #: operator callback is specified by :attr:`binop_table`.
+    #:
+    #: The following binary operators are interceptable:
+    #: ``//``, ``%``, ``+``, ``*``, ``-``, ``/``, and ``**``
+    #:
+    #: The default operation form the operator table corresponds to the
+    #: builtin function.  Intercepted calls are always slower than the native
+    #: operator call, so make sure only to intercept the ones you are
+    #: interested in.
+    #:
+    #: .. versionadded:: 2.6
+    intercepted_binops: t.FrozenSet[str] = frozenset()
+
+    #: a set of unary operators that should be intercepted.  Each operator
+    #: that is added to this set (empty by default) is delegated to the
+    #: :meth:`call_unop` method that will perform the operator.  The default
+    #: operator callback is specified by :attr:`unop_table`.
+    #:
+    #: The following unary operators are interceptable: ``+``, ``-``
+    #:
+    #: The default operation form the operator table corresponds to the
+    #: builtin function.  Intercepted calls are always slower than the native
+    #: operator call, so make sure only to intercept the ones you are
+    #: interested in.
+    #:
+    #: .. versionadded:: 2.6
+    intercepted_unops: t.FrozenSet[str] = frozenset()
+
+    def __init__(self, *args: t.Any, **kwargs: t.Any) -> None:
+        super().__init__(*args, **kwargs)
+        self.globals["range"] = safe_range
+        self.binop_table = self.default_binop_table.copy()
+        self.unop_table = self.default_unop_table.copy()
+
+    def is_safe_attribute(self, obj: t.Any, attr: str, value: t.Any) -> bool:
+        """The sandboxed environment will call this method to check if the
+        attribute of an object is safe to access.  Per default all attributes
+        starting with an underscore are considered private as well as the
+        special attributes of internal python objects as returned by the
+        :func:`is_internal_attribute` function.
+        """
+        return not (attr.startswith("_") or is_internal_attribute(obj, attr))
+
+    def is_safe_callable(self, obj: t.Any) -> bool:
+        """Check if an object is safely callable. By default callables
+        are considered safe unless decorated with :func:`unsafe`.
+
+        This also recognizes the Django convention of setting
+        ``func.alters_data = True``.
+        """
+        return not (
+            getattr(obj, "unsafe_callable", False) or getattr(obj, "alters_data", False)
+        )
+
+    def call_binop(
+        self, context: Context, operator: str, left: t.Any, right: t.Any
+    ) -> t.Any:
+        """For intercepted binary operator calls (:meth:`intercepted_binops`)
+        this function is executed instead of the builtin operator.  This can
+        be used to fine tune the behavior of certain operators.
+
+        .. versionadded:: 2.6
+        """
+        return self.binop_table[operator](left, right)
+
+    def call_unop(self, context: Context, operator: str, arg: t.Any) -> t.Any:
+        """For intercepted unary operator calls (:meth:`intercepted_unops`)
+        this function is executed instead of the builtin operator.  This can
+        be used to fine tune the behavior of certain operators.
+
+        .. versionadded:: 2.6
+        """
+        return self.unop_table[operator](arg)
+
+    def getitem(
+        self, obj: t.Any, argument: t.Union[str, t.Any]
+    ) -> t.Union[t.Any, Undefined]:
+        """Subscribe an object from sandboxed code."""
+        try:
+            return obj[argument]
+        except (TypeError, LookupError):
+            if isinstance(argument, str):
+                try:
+                    attr = str(argument)
+                except Exception:
+                    pass
+                else:
+                    try:
+                        value = getattr(obj, attr)
+                    except AttributeError:
+                        pass
+                    else:
+                        fmt = self.wrap_str_format(value)
+                        if fmt is not None:
+                            return fmt
+                        if self.is_safe_attribute(obj, argument, value):
+                            return value
+                        return self.unsafe_undefined(obj, argument)
+        return self.undefined(obj=obj, name=argument)
+
+    def getattr(self, obj: t.Any, attribute: str) -> t.Union[t.Any, Undefined]:
+        """Subscribe an object from sandboxed code and prefer the
+        attribute.  The attribute passed *must* be a bytestring.
+        """
+        try:
+            value = getattr(obj, attribute)
+        except AttributeError:
+            try:
+                return obj[attribute]
+            except (TypeError, LookupError):
+                pass
+        else:
+            fmt = self.wrap_str_format(value)
+            if fmt is not None:
+                return fmt
+            if self.is_safe_attribute(obj, attribute, value):
+                return value
+            return self.unsafe_undefined(obj, attribute)
+        return self.undefined(obj=obj, name=attribute)
+
+    def unsafe_undefined(self, obj: t.Any, attribute: str) -> Undefined:
+        """Return an undefined object for unsafe attributes."""
+        return self.undefined(
+            f"access to attribute {attribute!r} of"
+            f" {type(obj).__name__!r} object is unsafe.",
+            name=attribute,
+            obj=obj,
+            exc=SecurityError,
+        )
+
+    def wrap_str_format(self, value: t.Any) -> t.Optional[t.Callable[..., str]]:
+        """If the given value is a ``str.format`` or ``str.format_map`` method,
+        return a new function than handles sandboxing. This is done at access
+        rather than in :meth:`call`, so that calls made without ``call`` are
+        also sandboxed.
+        """
+        if not isinstance(
+            value, (types.MethodType, types.BuiltinMethodType)
+        ) or value.__name__ not in ("format", "format_map"):
+            return None
+
+        f_self: t.Any = value.__self__
+
+        if not isinstance(f_self, str):
+            return None
+
+        str_type: t.Type[str] = type(f_self)
+        is_format_map = value.__name__ == "format_map"
+        formatter: SandboxedFormatter
+
+        if isinstance(f_self, Markup):
+            formatter = SandboxedEscapeFormatter(self, escape=f_self.escape)
+        else:
+            formatter = SandboxedFormatter(self)
+
+        vformat = formatter.vformat
+
+        def wrapper(*args: t.Any, **kwargs: t.Any) -> str:
+            if is_format_map:
+                if kwargs:
+                    raise TypeError("format_map() takes no keyword arguments")
+
+                if len(args) != 1:
+                    raise TypeError(
+                        f"format_map() takes exactly one argument ({len(args)} given)"
+                    )
+
+                kwargs = args[0]
+                args = ()
+
+            return str_type(vformat(f_self, args, kwargs))
+
+        return update_wrapper(wrapper, value)
+
+    def call(
+        __self,  # noqa: B902
+        __context: Context,
+        __obj: t.Any,
+        *args: t.Any,
+        **kwargs: t.Any,
+    ) -> t.Any:
+        """Call an object from sandboxed code."""
+
+        # the double prefixes are to avoid double keyword argument
+        # errors when proxying the call.
+        if not __self.is_safe_callable(__obj):
+            raise SecurityError(f"{__obj!r} is not safely callable")
+        return __context.call(__obj, *args, **kwargs)
+
+
+class ImmutableSandboxedEnvironment(SandboxedEnvironment):
+    """Works exactly like the regular `SandboxedEnvironment` but does not
+    permit modifications on the builtin mutable objects `list`, `set`, and
+    `dict` by using the :func:`modifies_known_mutable` function.
+    """
+
+    def is_safe_attribute(self, obj: t.Any, attr: str, value: t.Any) -> bool:
+        if not super().is_safe_attribute(obj, attr, value):
+            return False
+
+        return not modifies_known_mutable(obj, attr)
+
+
+class SandboxedFormatter(Formatter):
+    def __init__(self, env: Environment, **kwargs: t.Any) -> None:
+        self._env = env
+        super().__init__(**kwargs)
+
+    def get_field(
+        self, field_name: str, args: t.Sequence[t.Any], kwargs: t.Mapping[str, t.Any]
+    ) -> t.Tuple[t.Any, str]:
+        first, rest = formatter_field_name_split(field_name)
+        obj = self.get_value(first, args, kwargs)
+        for is_attr, i in rest:
+            if is_attr:
+                obj = self._env.getattr(obj, i)
+            else:
+                obj = self._env.getitem(obj, i)
+        return obj, first
+
+
+class SandboxedEscapeFormatter(SandboxedFormatter, EscapeFormatter):
+    pass
diff --git a/lib/python3.11/site-packages/jinja2/tests.py b/lib/python3.11/site-packages/jinja2/tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a59e37032d51db5e8f75914334e6650f26ce4e9
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/tests.py
@@ -0,0 +1,256 @@
+"""Built-in template tests used with the ``is`` operator."""
+
+import operator
+import typing as t
+from collections import abc
+from numbers import Number
+
+from .runtime import Undefined
+from .utils import pass_environment
+
+if t.TYPE_CHECKING:
+    from .environment import Environment
+
+
+def test_odd(value: int) -> bool:
+    """Return true if the variable is odd."""
+    return value % 2 == 1
+
+
+def test_even(value: int) -> bool:
+    """Return true if the variable is even."""
+    return value % 2 == 0
+
+
+def test_divisibleby(value: int, num: int) -> bool:
+    """Check if a variable is divisible by a number."""
+    return value % num == 0
+
+
+def test_defined(value: t.Any) -> bool:
+    """Return true if the variable is defined:
+
+    .. sourcecode:: jinja
+
+        {% if variable is defined %}
+            value of variable: {{ variable }}
+        {% else %}
+            variable is not defined
+        {% endif %}
+
+    See the :func:`default` filter for a simple way to set undefined
+    variables.
+    """
+    return not isinstance(value, Undefined)
+
+
+def test_undefined(value: t.Any) -> bool:
+    """Like :func:`defined` but the other way round."""
+    return isinstance(value, Undefined)
+
+
+@pass_environment
+def test_filter(env: "Environment", value: str) -> bool:
+    """Check if a filter exists by name. Useful if a filter may be
+    optionally available.
+
+    .. code-block:: jinja
+
+        {% if 'markdown' is filter %}
+            {{ value | markdown }}
+        {% else %}
+            {{ value }}
+        {% endif %}
+
+    .. versionadded:: 3.0
+    """
+    return value in env.filters
+
+
+@pass_environment
+def test_test(env: "Environment", value: str) -> bool:
+    """Check if a test exists by name. Useful if a test may be
+    optionally available.
+
+    .. code-block:: jinja
+
+        {% if 'loud' is test %}
+            {% if value is loud %}
+                {{ value|upper }}
+            {% else %}
+                {{ value|lower }}
+            {% endif %}
+        {% else %}
+            {{ value }}
+        {% endif %}
+
+    .. versionadded:: 3.0
+    """
+    return value in env.tests
+
+
+def test_none(value: t.Any) -> bool:
+    """Return true if the variable is none."""
+    return value is None
+
+
+def test_boolean(value: t.Any) -> bool:
+    """Return true if the object is a boolean value.
+
+    .. versionadded:: 2.11
+    """
+    return value is True or value is False
+
+
+def test_false(value: t.Any) -> bool:
+    """Return true if the object is False.
+
+    .. versionadded:: 2.11
+    """
+    return value is False
+
+
+def test_true(value: t.Any) -> bool:
+    """Return true if the object is True.
+
+    .. versionadded:: 2.11
+    """
+    return value is True
+
+
+# NOTE: The existing 'number' test matches booleans and floats
+def test_integer(value: t.Any) -> bool:
+    """Return true if the object is an integer.
+
+    .. versionadded:: 2.11
+    """
+    return isinstance(value, int) and value is not True and value is not False
+
+
+# NOTE: The existing 'number' test matches booleans and integers
+def test_float(value: t.Any) -> bool:
+    """Return true if the object is a float.
+
+    .. versionadded:: 2.11
+    """
+    return isinstance(value, float)
+
+
+def test_lower(value: str) -> bool:
+    """Return true if the variable is lowercased."""
+    return str(value).islower()
+
+
+def test_upper(value: str) -> bool:
+    """Return true if the variable is uppercased."""
+    return str(value).isupper()
+
+
+def test_string(value: t.Any) -> bool:
+    """Return true if the object is a string."""
+    return isinstance(value, str)
+
+
+def test_mapping(value: t.Any) -> bool:
+    """Return true if the object is a mapping (dict etc.).
+
+    .. versionadded:: 2.6
+    """
+    return isinstance(value, abc.Mapping)
+
+
+def test_number(value: t.Any) -> bool:
+    """Return true if the variable is a number."""
+    return isinstance(value, Number)
+
+
+def test_sequence(value: t.Any) -> bool:
+    """Return true if the variable is a sequence. Sequences are variables
+    that are iterable.
+    """
+    try:
+        len(value)
+        value.__getitem__  # noqa B018
+    except Exception:
+        return False
+
+    return True
+
+
+def test_sameas(value: t.Any, other: t.Any) -> bool:
+    """Check if an object points to the same memory address than another
+    object:
+
+    .. sourcecode:: jinja
+
+        {% if foo.attribute is sameas false %}
+            the foo attribute really is the `False` singleton
+        {% endif %}
+    """
+    return value is other
+
+
+def test_iterable(value: t.Any) -> bool:
+    """Check if it's possible to iterate over an object."""
+    try:
+        iter(value)
+    except TypeError:
+        return False
+
+    return True
+
+
+def test_escaped(value: t.Any) -> bool:
+    """Check if the value is escaped."""
+    return hasattr(value, "__html__")
+
+
+def test_in(value: t.Any, seq: t.Container[t.Any]) -> bool:
+    """Check if value is in seq.
+
+    .. versionadded:: 2.10
+    """
+    return value in seq
+
+
+TESTS = {
+    "odd": test_odd,
+    "even": test_even,
+    "divisibleby": test_divisibleby,
+    "defined": test_defined,
+    "undefined": test_undefined,
+    "filter": test_filter,
+    "test": test_test,
+    "none": test_none,
+    "boolean": test_boolean,
+    "false": test_false,
+    "true": test_true,
+    "integer": test_integer,
+    "float": test_float,
+    "lower": test_lower,
+    "upper": test_upper,
+    "string": test_string,
+    "mapping": test_mapping,
+    "number": test_number,
+    "sequence": test_sequence,
+    "iterable": test_iterable,
+    "callable": callable,
+    "sameas": test_sameas,
+    "escaped": test_escaped,
+    "in": test_in,
+    "==": operator.eq,
+    "eq": operator.eq,
+    "equalto": operator.eq,
+    "!=": operator.ne,
+    "ne": operator.ne,
+    ">": operator.gt,
+    "gt": operator.gt,
+    "greaterthan": operator.gt,
+    "ge": operator.ge,
+    ">=": operator.ge,
+    "<": operator.lt,
+    "lt": operator.lt,
+    "lessthan": operator.lt,
+    "<=": operator.le,
+    "le": operator.le,
+}
diff --git a/lib/python3.11/site-packages/jinja2/utils.py b/lib/python3.11/site-packages/jinja2/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c922629a926c83c55476bd339f6dc32e48a9aec
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/utils.py
@@ -0,0 +1,766 @@
+import enum
+import json
+import os
+import re
+import typing as t
+from collections import abc
+from collections import deque
+from random import choice
+from random import randrange
+from threading import Lock
+from types import CodeType
+from urllib.parse import quote_from_bytes
+
+import markupsafe
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+F = t.TypeVar("F", bound=t.Callable[..., t.Any])
+
+
+class _MissingType:
+    def __repr__(self) -> str:
+        return "missing"
+
+    def __reduce__(self) -> str:
+        return "missing"
+
+
+missing: t.Any = _MissingType()
+"""Special singleton representing missing values for the runtime."""
+
+internal_code: t.MutableSet[CodeType] = set()
+
+concat = "".join
+
+
+def pass_context(f: F) -> F:
+    """Pass the :class:`~jinja2.runtime.Context` as the first argument
+    to the decorated function when called while rendering a template.
+
+    Can be used on functions, filters, and tests.
+
+    If only ``Context.eval_context`` is needed, use
+    :func:`pass_eval_context`. If only ``Context.environment`` is
+    needed, use :func:`pass_environment`.
+
+    .. versionadded:: 3.0.0
+        Replaces ``contextfunction`` and ``contextfilter``.
+    """
+    f.jinja_pass_arg = _PassArg.context  # type: ignore
+    return f
+
+
+def pass_eval_context(f: F) -> F:
+    """Pass the :class:`~jinja2.nodes.EvalContext` as the first argument
+    to the decorated function when called while rendering a template.
+    See :ref:`eval-context`.
+
+    Can be used on functions, filters, and tests.
+
+    If only ``EvalContext.environment`` is needed, use
+    :func:`pass_environment`.
+
+    .. versionadded:: 3.0.0
+        Replaces ``evalcontextfunction`` and ``evalcontextfilter``.
+    """
+    f.jinja_pass_arg = _PassArg.eval_context  # type: ignore
+    return f
+
+
+def pass_environment(f: F) -> F:
+    """Pass the :class:`~jinja2.Environment` as the first argument to
+    the decorated function when called while rendering a template.
+
+    Can be used on functions, filters, and tests.
+
+    .. versionadded:: 3.0.0
+        Replaces ``environmentfunction`` and ``environmentfilter``.
+    """
+    f.jinja_pass_arg = _PassArg.environment  # type: ignore
+    return f
+
+
+class _PassArg(enum.Enum):
+    context = enum.auto()
+    eval_context = enum.auto()
+    environment = enum.auto()
+
+    @classmethod
+    def from_obj(cls, obj: F) -> t.Optional["_PassArg"]:
+        if hasattr(obj, "jinja_pass_arg"):
+            return obj.jinja_pass_arg  # type: ignore
+
+        return None
+
+
+def internalcode(f: F) -> F:
+    """Marks the function as internally used"""
+    internal_code.add(f.__code__)
+    return f
+
+
+def is_undefined(obj: t.Any) -> bool:
+    """Check if the object passed is undefined.  This does nothing more than
+    performing an instance check against :class:`Undefined` but looks nicer.
+    This can be used for custom filters or tests that want to react to
+    undefined variables.  For example a custom default filter can look like
+    this::
+
+        def default(var, default=''):
+            if is_undefined(var):
+                return default
+            return var
+    """
+    from .runtime import Undefined
+
+    return isinstance(obj, Undefined)
+
+
+def consume(iterable: t.Iterable[t.Any]) -> None:
+    """Consumes an iterable without doing anything with it."""
+    for _ in iterable:
+        pass
+
+
+def clear_caches() -> None:
+    """Jinja keeps internal caches for environments and lexers.  These are
+    used so that Jinja doesn't have to recreate environments and lexers all
+    the time.  Normally you don't have to care about that but if you are
+    measuring memory consumption you may want to clean the caches.
+    """
+    from .environment import get_spontaneous_environment
+    from .lexer import _lexer_cache
+
+    get_spontaneous_environment.cache_clear()
+    _lexer_cache.clear()
+
+
+def import_string(import_name: str, silent: bool = False) -> t.Any:
+    """Imports an object based on a string.  This is useful if you want to
+    use import paths as endpoints or something similar.  An import path can
+    be specified either in dotted notation (``xml.sax.saxutils.escape``)
+    or with a colon as object delimiter (``xml.sax.saxutils:escape``).
+
+    If the `silent` is True the return value will be `None` if the import
+    fails.
+
+    :return: imported object
+    """
+    try:
+        if ":" in import_name:
+            module, obj = import_name.split(":", 1)
+        elif "." in import_name:
+            module, _, obj = import_name.rpartition(".")
+        else:
+            return __import__(import_name)
+        return getattr(__import__(module, None, None, [obj]), obj)
+    except (ImportError, AttributeError):
+        if not silent:
+            raise
+
+
+def open_if_exists(filename: str, mode: str = "rb") -> t.Optional[t.IO[t.Any]]:
+    """Returns a file descriptor for the filename if that file exists,
+    otherwise ``None``.
+    """
+    if not os.path.isfile(filename):
+        return None
+
+    return open(filename, mode)
+
+
+def object_type_repr(obj: t.Any) -> str:
+    """Returns the name of the object's type.  For some recognized
+    singletons the name of the object is returned instead. (For
+    example for `None` and `Ellipsis`).
+    """
+    if obj is None:
+        return "None"
+    elif obj is Ellipsis:
+        return "Ellipsis"
+
+    cls = type(obj)
+
+    if cls.__module__ == "builtins":
+        return f"{cls.__name__} object"
+
+    return f"{cls.__module__}.{cls.__name__} object"
+
+
+def pformat(obj: t.Any) -> str:
+    """Format an object using :func:`pprint.pformat`."""
+    from pprint import pformat
+
+    return pformat(obj)
+
+
+_http_re = re.compile(
+    r"""
+    ^
+    (
+        (https?://|www\.)  # scheme or www
+        (([\w%-]+\.)+)?  # subdomain
+        (
+            [a-z]{2,63}  # basic tld
+        |
+            xn--[\w%]{2,59}  # idna tld
+        )
+    |
+        ([\w%-]{2,63}\.)+  # basic domain
+        (com|net|int|edu|gov|org|info|mil)  # basic tld
+    |
+        (https?://)  # scheme
+        (
+            (([\d]{1,3})(\.[\d]{1,3}){3})  # IPv4
+        |
+            (\[([\da-f]{0,4}:){2}([\da-f]{0,4}:?){1,6}])  # IPv6
+        )
+    )
+    (?::[\d]{1,5})?  # port
+    (?:[/?#]\S*)?  # path, query, and fragment
+    $
+    """,
+    re.IGNORECASE | re.VERBOSE,
+)
+_email_re = re.compile(r"^\S+@\w[\w.-]*\.\w+$")
+
+
+def urlize(
+    text: str,
+    trim_url_limit: t.Optional[int] = None,
+    rel: t.Optional[str] = None,
+    target: t.Optional[str] = None,
+    extra_schemes: t.Optional[t.Iterable[str]] = None,
+) -> str:
+    """Convert URLs in text into clickable links.
+
+    This may not recognize links in some situations. Usually, a more
+    comprehensive formatter, such as a Markdown library, is a better
+    choice.
+
+    Works on ``http://``, ``https://``, ``www.``, ``mailto:``, and email
+    addresses. Links with trailing punctuation (periods, commas, closing
+    parentheses) and leading punctuation (opening parentheses) are
+    recognized excluding the punctuation. Email addresses that include
+    header fields are not recognized (for example,
+    ``mailto:address@example.com?cc=copy@example.com``).
+
+    :param text: Original text containing URLs to link.
+    :param trim_url_limit: Shorten displayed URL values to this length.
+    :param target: Add the ``target`` attribute to links.
+    :param rel: Add the ``rel`` attribute to links.
+    :param extra_schemes: Recognize URLs that start with these schemes
+        in addition to the default behavior.
+
+    .. versionchanged:: 3.0
+        The ``extra_schemes`` parameter was added.
+
+    .. versionchanged:: 3.0
+        Generate ``https://`` links for URLs without a scheme.
+
+    .. versionchanged:: 3.0
+        The parsing rules were updated. Recognize email addresses with
+        or without the ``mailto:`` scheme. Validate IP addresses. Ignore
+        parentheses and brackets in more cases.
+    """
+    if trim_url_limit is not None:
+
+        def trim_url(x: str) -> str:
+            if len(x) > trim_url_limit:
+                return f"{x[:trim_url_limit]}..."
+
+            return x
+
+    else:
+
+        def trim_url(x: str) -> str:
+            return x
+
+    words = re.split(r"(\s+)", str(markupsafe.escape(text)))
+    rel_attr = f' rel="{markupsafe.escape(rel)}"' if rel else ""
+    target_attr = f' target="{markupsafe.escape(target)}"' if target else ""
+
+    for i, word in enumerate(words):
+        head, middle, tail = "", word, ""
+        match = re.match(r"^([(<]|&lt;)+", middle)
+
+        if match:
+            head = match.group()
+            middle = middle[match.end() :]
+
+        # Unlike lead, which is anchored to the start of the string,
+        # need to check that the string ends with any of the characters
+        # before trying to match all of them, to avoid backtracking.
+        if middle.endswith((")", ">", ".", ",", "\n", "&gt;")):
+            match = re.search(r"([)>.,\n]|&gt;)+$", middle)
+
+            if match:
+                tail = match.group()
+                middle = middle[: match.start()]
+
+        # Prefer balancing parentheses in URLs instead of ignoring a
+        # trailing character.
+        for start_char, end_char in ("(", ")"), ("<", ">"), ("&lt;", "&gt;"):
+            start_count = middle.count(start_char)
+
+            if start_count <= middle.count(end_char):
+                # Balanced, or lighter on the left
+                continue
+
+            # Move as many as possible from the tail to balance
+            for _ in range(min(start_count, tail.count(end_char))):
+                end_index = tail.index(end_char) + len(end_char)
+                # Move anything in the tail before the end char too
+                middle += tail[:end_index]
+                tail = tail[end_index:]
+
+        if _http_re.match(middle):
+            if middle.startswith("https://") or middle.startswith("http://"):
+                middle = (
+                    f'<a href="{middle}"{rel_attr}{target_attr}>{trim_url(middle)}</a>'
+                )
+            else:
+                middle = (
+                    f'<a href="https://{middle}"{rel_attr}{target_attr}>'
+                    f"{trim_url(middle)}</a>"
+                )
+
+        elif middle.startswith("mailto:") and _email_re.match(middle[7:]):
+            middle = f'<a href="{middle}">{middle[7:]}</a>'
+
+        elif (
+            "@" in middle
+            and not middle.startswith("www.")
+            # ignore values like `@a@b`
+            and not middle.startswith("@")
+            and ":" not in middle
+            and _email_re.match(middle)
+        ):
+            middle = f'<a href="mailto:{middle}">{middle}</a>'
+
+        elif extra_schemes is not None:
+            for scheme in extra_schemes:
+                if middle != scheme and middle.startswith(scheme):
+                    middle = f'<a href="{middle}"{rel_attr}{target_attr}>{middle}</a>'
+
+        words[i] = f"{head}{middle}{tail}"
+
+    return "".join(words)
+
+
+def generate_lorem_ipsum(
+    n: int = 5, html: bool = True, min: int = 20, max: int = 100
+) -> str:
+    """Generate some lorem ipsum for the template."""
+    from .constants import LOREM_IPSUM_WORDS
+
+    words = LOREM_IPSUM_WORDS.split()
+    result = []
+
+    for _ in range(n):
+        next_capitalized = True
+        last_comma = last_fullstop = 0
+        word = None
+        last = None
+        p = []
+
+        # each paragraph contains out of 20 to 100 words.
+        for idx, _ in enumerate(range(randrange(min, max))):
+            while True:
+                word = choice(words)
+                if word != last:
+                    last = word
+                    break
+            if next_capitalized:
+                word = word.capitalize()
+                next_capitalized = False
+            # add commas
+            if idx - randrange(3, 8) > last_comma:
+                last_comma = idx
+                last_fullstop += 2
+                word += ","
+            # add end of sentences
+            if idx - randrange(10, 20) > last_fullstop:
+                last_comma = last_fullstop = idx
+                word += "."
+                next_capitalized = True
+            p.append(word)
+
+        # ensure that the paragraph ends with a dot.
+        p_str = " ".join(p)
+
+        if p_str.endswith(","):
+            p_str = p_str[:-1] + "."
+        elif not p_str.endswith("."):
+            p_str += "."
+
+        result.append(p_str)
+
+    if not html:
+        return "\n\n".join(result)
+    return markupsafe.Markup(
+        "\n".join(f"<p>{markupsafe.escape(x)}</p>" for x in result)
+    )
+
+
+def url_quote(obj: t.Any, charset: str = "utf-8", for_qs: bool = False) -> str:
+    """Quote a string for use in a URL using the given charset.
+
+    :param obj: String or bytes to quote. Other types are converted to
+        string then encoded to bytes using the given charset.
+    :param charset: Encode text to bytes using this charset.
+    :param for_qs: Quote "/" and use "+" for spaces.
+    """
+    if not isinstance(obj, bytes):
+        if not isinstance(obj, str):
+            obj = str(obj)
+
+        obj = obj.encode(charset)
+
+    safe = b"" if for_qs else b"/"
+    rv = quote_from_bytes(obj, safe)
+
+    if for_qs:
+        rv = rv.replace("%20", "+")
+
+    return rv
+
+
+@abc.MutableMapping.register
+class LRUCache:
+    """A simple LRU Cache implementation."""
+
+    # this is fast for small capacities (something below 1000) but doesn't
+    # scale.  But as long as it's only used as storage for templates this
+    # won't do any harm.
+
+    def __init__(self, capacity: int) -> None:
+        self.capacity = capacity
+        self._mapping: t.Dict[t.Any, t.Any] = {}
+        self._queue: te.Deque[t.Any] = deque()
+        self._postinit()
+
+    def _postinit(self) -> None:
+        # alias all queue methods for faster lookup
+        self._popleft = self._queue.popleft
+        self._pop = self._queue.pop
+        self._remove = self._queue.remove
+        self._wlock = Lock()
+        self._append = self._queue.append
+
+    def __getstate__(self) -> t.Mapping[str, t.Any]:
+        return {
+            "capacity": self.capacity,
+            "_mapping": self._mapping,
+            "_queue": self._queue,
+        }
+
+    def __setstate__(self, d: t.Mapping[str, t.Any]) -> None:
+        self.__dict__.update(d)
+        self._postinit()
+
+    def __getnewargs__(self) -> t.Tuple[t.Any, ...]:
+        return (self.capacity,)
+
+    def copy(self) -> "te.Self":
+        """Return a shallow copy of the instance."""
+        rv = self.__class__(self.capacity)
+        rv._mapping.update(self._mapping)
+        rv._queue.extend(self._queue)
+        return rv
+
+    def get(self, key: t.Any, default: t.Any = None) -> t.Any:
+        """Return an item from the cache dict or `default`"""
+        try:
+            return self[key]
+        except KeyError:
+            return default
+
+    def setdefault(self, key: t.Any, default: t.Any = None) -> t.Any:
+        """Set `default` if the key is not in the cache otherwise
+        leave unchanged. Return the value of this key.
+        """
+        try:
+            return self[key]
+        except KeyError:
+            self[key] = default
+            return default
+
+    def clear(self) -> None:
+        """Clear the cache."""
+        with self._wlock:
+            self._mapping.clear()
+            self._queue.clear()
+
+    def __contains__(self, key: t.Any) -> bool:
+        """Check if a key exists in this cache."""
+        return key in self._mapping
+
+    def __len__(self) -> int:
+        """Return the current size of the cache."""
+        return len(self._mapping)
+
+    def __repr__(self) -> str:
+        return f"<{type(self).__name__} {self._mapping!r}>"
+
+    def __getitem__(self, key: t.Any) -> t.Any:
+        """Get an item from the cache. Moves the item up so that it has the
+        highest priority then.
+
+        Raise a `KeyError` if it does not exist.
+        """
+        with self._wlock:
+            rv = self._mapping[key]
+
+            if self._queue[-1] != key:
+                try:
+                    self._remove(key)
+                except ValueError:
+                    # if something removed the key from the container
+                    # when we read, ignore the ValueError that we would
+                    # get otherwise.
+                    pass
+
+                self._append(key)
+
+            return rv
+
+    def __setitem__(self, key: t.Any, value: t.Any) -> None:
+        """Sets the value for an item. Moves the item up so that it
+        has the highest priority then.
+        """
+        with self._wlock:
+            if key in self._mapping:
+                self._remove(key)
+            elif len(self._mapping) == self.capacity:
+                del self._mapping[self._popleft()]
+
+            self._append(key)
+            self._mapping[key] = value
+
+    def __delitem__(self, key: t.Any) -> None:
+        """Remove an item from the cache dict.
+        Raise a `KeyError` if it does not exist.
+        """
+        with self._wlock:
+            del self._mapping[key]
+
+            try:
+                self._remove(key)
+            except ValueError:
+                pass
+
+    def items(self) -> t.Iterable[t.Tuple[t.Any, t.Any]]:
+        """Return a list of items."""
+        result = [(key, self._mapping[key]) for key in list(self._queue)]
+        result.reverse()
+        return result
+
+    def values(self) -> t.Iterable[t.Any]:
+        """Return a list of all values."""
+        return [x[1] for x in self.items()]
+
+    def keys(self) -> t.Iterable[t.Any]:
+        """Return a list of all keys ordered by most recent usage."""
+        return list(self)
+
+    def __iter__(self) -> t.Iterator[t.Any]:
+        return reversed(tuple(self._queue))
+
+    def __reversed__(self) -> t.Iterator[t.Any]:
+        """Iterate over the keys in the cache dict, oldest items
+        coming first.
+        """
+        return iter(tuple(self._queue))
+
+    __copy__ = copy
+
+
+def select_autoescape(
+    enabled_extensions: t.Collection[str] = ("html", "htm", "xml"),
+    disabled_extensions: t.Collection[str] = (),
+    default_for_string: bool = True,
+    default: bool = False,
+) -> t.Callable[[t.Optional[str]], bool]:
+    """Intelligently sets the initial value of autoescaping based on the
+    filename of the template.  This is the recommended way to configure
+    autoescaping if you do not want to write a custom function yourself.
+
+    If you want to enable it for all templates created from strings or
+    for all templates with `.html` and `.xml` extensions::
+
+        from jinja2 import Environment, select_autoescape
+        env = Environment(autoescape=select_autoescape(
+            enabled_extensions=('html', 'xml'),
+            default_for_string=True,
+        ))
+
+    Example configuration to turn it on at all times except if the template
+    ends with `.txt`::
+
+        from jinja2 import Environment, select_autoescape
+        env = Environment(autoescape=select_autoescape(
+            disabled_extensions=('txt',),
+            default_for_string=True,
+            default=True,
+        ))
+
+    The `enabled_extensions` is an iterable of all the extensions that
+    autoescaping should be enabled for.  Likewise `disabled_extensions` is
+    a list of all templates it should be disabled for.  If a template is
+    loaded from a string then the default from `default_for_string` is used.
+    If nothing matches then the initial value of autoescaping is set to the
+    value of `default`.
+
+    For security reasons this function operates case insensitive.
+
+    .. versionadded:: 2.9
+    """
+    enabled_patterns = tuple(f".{x.lstrip('.').lower()}" for x in enabled_extensions)
+    disabled_patterns = tuple(f".{x.lstrip('.').lower()}" for x in disabled_extensions)
+
+    def autoescape(template_name: t.Optional[str]) -> bool:
+        if template_name is None:
+            return default_for_string
+        template_name = template_name.lower()
+        if template_name.endswith(enabled_patterns):
+            return True
+        if template_name.endswith(disabled_patterns):
+            return False
+        return default
+
+    return autoescape
+
+
+def htmlsafe_json_dumps(
+    obj: t.Any, dumps: t.Optional[t.Callable[..., str]] = None, **kwargs: t.Any
+) -> markupsafe.Markup:
+    """Serialize an object to a string of JSON with :func:`json.dumps`,
+    then replace HTML-unsafe characters with Unicode escapes and mark
+    the result safe with :class:`~markupsafe.Markup`.
+
+    This is available in templates as the ``|tojson`` filter.
+
+    The following characters are escaped: ``<``, ``>``, ``&``, ``'``.
+
+    The returned string is safe to render in HTML documents and
+    ``<script>`` tags. The exception is in HTML attributes that are
+    double quoted; either use single quotes or the ``|forceescape``
+    filter.
+
+    :param obj: The object to serialize to JSON.
+    :param dumps: The ``dumps`` function to use. Defaults to
+        ``env.policies["json.dumps_function"]``, which defaults to
+        :func:`json.dumps`.
+    :param kwargs: Extra arguments to pass to ``dumps``. Merged onto
+        ``env.policies["json.dumps_kwargs"]``.
+
+    .. versionchanged:: 3.0
+        The ``dumper`` parameter is renamed to ``dumps``.
+
+    .. versionadded:: 2.9
+    """
+    if dumps is None:
+        dumps = json.dumps
+
+    return markupsafe.Markup(
+        dumps(obj, **kwargs)
+        .replace("<", "\\u003c")
+        .replace(">", "\\u003e")
+        .replace("&", "\\u0026")
+        .replace("'", "\\u0027")
+    )
+
+
+class Cycler:
+    """Cycle through values by yield them one at a time, then restarting
+    once the end is reached. Available as ``cycler`` in templates.
+
+    Similar to ``loop.cycle``, but can be used outside loops or across
+    multiple loops. For example, render a list of folders and files in a
+    list, alternating giving them "odd" and "even" classes.
+
+    .. code-block:: html+jinja
+
+        {% set row_class = cycler("odd", "even") %}
+        <ul class="browser">
+        {% for folder in folders %}
+          <li class="folder {{ row_class.next() }}">{{ folder }}
+        {% endfor %}
+        {% for file in files %}
+          <li class="file {{ row_class.next() }}">{{ file }}
+        {% endfor %}
+        </ul>
+
+    :param items: Each positional argument will be yielded in the order
+        given for each cycle.
+
+    .. versionadded:: 2.1
+    """
+
+    def __init__(self, *items: t.Any) -> None:
+        if not items:
+            raise RuntimeError("at least one item has to be provided")
+        self.items = items
+        self.pos = 0
+
+    def reset(self) -> None:
+        """Resets the current item to the first item."""
+        self.pos = 0
+
+    @property
+    def current(self) -> t.Any:
+        """Return the current item. Equivalent to the item that will be
+        returned next time :meth:`next` is called.
+        """
+        return self.items[self.pos]
+
+    def next(self) -> t.Any:
+        """Return the current item, then advance :attr:`current` to the
+        next item.
+        """
+        rv = self.current
+        self.pos = (self.pos + 1) % len(self.items)
+        return rv
+
+    __next__ = next
+
+
+class Joiner:
+    """A joining helper for templates."""
+
+    def __init__(self, sep: str = ", ") -> None:
+        self.sep = sep
+        self.used = False
+
+    def __call__(self) -> str:
+        if not self.used:
+            self.used = True
+            return ""
+        return self.sep
+
+
+class Namespace:
+    """A namespace object that can hold arbitrary attributes.  It may be
+    initialized from a dictionary or with keyword arguments."""
+
+    def __init__(*args: t.Any, **kwargs: t.Any) -> None:  # noqa: B902
+        self, args = args[0], args[1:]
+        self.__attrs = dict(*args, **kwargs)
+
+    def __getattribute__(self, name: str) -> t.Any:
+        # __class__ is needed for the awaitable check in async mode
+        if name in {"_Namespace__attrs", "__class__"}:
+            return object.__getattribute__(self, name)
+        try:
+            return self.__attrs[name]
+        except KeyError:
+            raise AttributeError(name) from None
+
+    def __setitem__(self, name: str, value: t.Any) -> None:
+        self.__attrs[name] = value
+
+    def __repr__(self) -> str:
+        return f"<Namespace {self.__attrs!r}>"
diff --git a/lib/python3.11/site-packages/jinja2/visitor.py b/lib/python3.11/site-packages/jinja2/visitor.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b8e180601232b345e9fe13a0b2f9aa484761cdc
--- /dev/null
+++ b/lib/python3.11/site-packages/jinja2/visitor.py
@@ -0,0 +1,92 @@
+"""API for traversing the AST nodes. Implemented by the compiler and
+meta introspection.
+"""
+
+import typing as t
+
+from .nodes import Node
+
+if t.TYPE_CHECKING:
+    import typing_extensions as te
+
+    class VisitCallable(te.Protocol):
+        def __call__(self, node: Node, *args: t.Any, **kwargs: t.Any) -> t.Any: ...
+
+
+class NodeVisitor:
+    """Walks the abstract syntax tree and call visitor functions for every
+    node found.  The visitor functions may return values which will be
+    forwarded by the `visit` method.
+
+    Per default the visitor functions for the nodes are ``'visit_'`` +
+    class name of the node.  So a `TryFinally` node visit function would
+    be `visit_TryFinally`.  This behavior can be changed by overriding
+    the `get_visitor` function.  If no visitor function exists for a node
+    (return value `None`) the `generic_visit` visitor is used instead.
+    """
+
+    def get_visitor(self, node: Node) -> "t.Optional[VisitCallable]":
+        """Return the visitor function for this node or `None` if no visitor
+        exists for this node.  In that case the generic visit function is
+        used instead.
+        """
+        return getattr(self, f"visit_{type(node).__name__}", None)
+
+    def visit(self, node: Node, *args: t.Any, **kwargs: t.Any) -> t.Any:
+        """Visit a node."""
+        f = self.get_visitor(node)
+
+        if f is not None:
+            return f(node, *args, **kwargs)
+
+        return self.generic_visit(node, *args, **kwargs)
+
+    def generic_visit(self, node: Node, *args: t.Any, **kwargs: t.Any) -> t.Any:
+        """Called if no explicit visitor function exists for a node."""
+        for child_node in node.iter_child_nodes():
+            self.visit(child_node, *args, **kwargs)
+
+
+class NodeTransformer(NodeVisitor):
+    """Walks the abstract syntax tree and allows modifications of nodes.
+
+    The `NodeTransformer` will walk the AST and use the return value of the
+    visitor functions to replace or remove the old node.  If the return
+    value of the visitor function is `None` the node will be removed
+    from the previous location otherwise it's replaced with the return
+    value.  The return value may be the original node in which case no
+    replacement takes place.
+    """
+
+    def generic_visit(self, node: Node, *args: t.Any, **kwargs: t.Any) -> Node:
+        for field, old_value in node.iter_fields():
+            if isinstance(old_value, list):
+                new_values = []
+                for value in old_value:
+                    if isinstance(value, Node):
+                        value = self.visit(value, *args, **kwargs)
+                        if value is None:
+                            continue
+                        elif not isinstance(value, Node):
+                            new_values.extend(value)
+                            continue
+                    new_values.append(value)
+                old_value[:] = new_values
+            elif isinstance(old_value, Node):
+                new_node = self.visit(old_value, *args, **kwargs)
+                if new_node is None:
+                    delattr(node, field)
+                else:
+                    setattr(node, field, new_node)
+        return node
+
+    def visit_list(self, node: Node, *args: t.Any, **kwargs: t.Any) -> t.List[Node]:
+        """As transformers may return lists in some places this method
+        can be used to enforce a list as return value.
+        """
+        rv = self.visit(node, *args, **kwargs)
+
+        if not isinstance(rv, list):
+            return [rv]
+
+        return rv
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e04f74ee026194c84ea4ceef09d73cff2d25cd70
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/asteroidal.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/asteroidal.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cb44c4f41c62428999b4b7e52ff64d07b215834d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/asteroidal.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/boundary.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/boundary.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..72ffb1a5aab79c9aa2c0f341d23da972337845e2
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/boundary.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/bridges.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/bridges.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f9110af60c3d9223005bb25b01ec7c1e4de90b17
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/bridges.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/broadcasting.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/broadcasting.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bc681d2eba721068ab80cc49198698684fb8a9f9
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/broadcasting.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/chains.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/chains.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ed39e030e4ed9caaadbd6fd135a389b71af3d511
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/chains.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/chordal.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/chordal.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9a13e946695ef9008658ce4b195f118c858857eb
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/chordal.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/clique.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/clique.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ff2a5971fa3447953892bfccdb30ac4bb18d7f9b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/clique.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cluster.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cluster.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ab4cc11e06901d004711d65edbd983a7e4beb403
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cluster.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/communicability_alg.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/communicability_alg.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d6d156d93e04b22b7f75c5db7b8921706e0108ae
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/communicability_alg.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/core.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/core.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ee3ff56da51f815ad4bda6d806c79f04ae846b3a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/core.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/covering.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/covering.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7d5d4cc7dcffdc327f10ff006f9f8629cb7c694e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/covering.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cuts.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cuts.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..59fe4e1fdd75c4aa07cd929fde0f98d1c59460dc
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cuts.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cycles.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cycles.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..73915420cbf525e0bb27edab2f4bbecf889c5c3f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/cycles.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/d_separation.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/d_separation.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..64d41e4b691a726fabe49c6f42852b3ab1c8a2ef
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/d_separation.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dag.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dag.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c01c11ba2b933fb5cd08989c0b718396d70ff43f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dag.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/distance_measures.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/distance_measures.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6cff4c1506f927c4c378812dbf9ec2808fe5307f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/distance_measures.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/distance_regular.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/distance_regular.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..348c71c07e40e1f6d44f0481b83b7576d87bb320
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/distance_regular.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dominance.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dominance.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..809afca09fa10cd58d8171dbb6e899b31f68de7f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dominance.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dominating.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dominating.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..23d5498819c61a0263e7c23a517a11394cc04aa8
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/dominating.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/efficiency_measures.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/efficiency_measures.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4048d24b6cd8e27f75f7d3e2c25ef832685a6517
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/efficiency_measures.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/euler.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/euler.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4ea7aff515e1297d954c375af06d7d6cda8ab247
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/euler.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/graph_hashing.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/graph_hashing.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dd47ede168ff70d5b2b69818793bc2805e1ee486
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/graph_hashing.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/graphical.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/graphical.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ec93cda00ed062b5fdc28372afae1cc83c025e01
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/graphical.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/hierarchy.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/hierarchy.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..20af4a32fbbf591a4915c2a857fec46f0419cc32
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/hierarchy.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/hybrid.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/hybrid.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..496f0e63bb88573f88f6fd3e809f57f9295e7223
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/hybrid.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/isolate.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/isolate.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ebbfa9206d6bbea9728457518392702f48617ccc
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/isolate.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/link_prediction.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/link_prediction.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8a459d8c7454100d98d794f4f83de77bbec292f6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/link_prediction.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/lowest_common_ancestors.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/lowest_common_ancestors.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2c041b35d4b039154e57dd342876b0e7ff9dc024
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/lowest_common_ancestors.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/matching.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/matching.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9f094e539b410dd27d14785a998610fdc6b392c3
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/matching.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/mis.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/mis.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d09cb1d56270652b863ac7ceba47a8a37a76afce
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/mis.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/moral.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/moral.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a9fa3942144153e18d9fd8ca7cdc10d1896c8925
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/moral.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/node_classification.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/node_classification.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0bce8143602e6f09642d8104ba2c4e5c8d85fadd
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/node_classification.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/non_randomness.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/non_randomness.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..67e2088b1f84addf0c2662b1e9c101b097fedc21
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/non_randomness.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/perfect_graph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/perfect_graph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ee8399d1a88a96ac026684cf6b018f2fce15791f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/perfect_graph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/planar_drawing.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/planar_drawing.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..435645030c9f7550510814cace8a0288cd9b2c8d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/planar_drawing.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/planarity.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/planarity.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..db2a31429cda469165ca17562e3cbcb2485fc7ef
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/planarity.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/polynomials.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/polynomials.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..518a625978d6e27a8e9ef1cefdb5729aa1a2b455
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/polynomials.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/reciprocity.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/reciprocity.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..94075b348001fb43677b238bd7f7e143a052209e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/reciprocity.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/regular.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/regular.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0a49c5087eeb452c12b6aa483b367d4dfc2218db
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/regular.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/richclub.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/richclub.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fdfbc9ea0e044d19b7595e16be5ec21c8b535227
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/richclub.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/similarity.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/similarity.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f715e16af8407ca5a611c23e1799419dae085e98
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/similarity.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/simple_paths.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/simple_paths.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3ac263d9710a9a5e6eeae5bd8fd15b4686e06c0b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/simple_paths.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/smallworld.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/smallworld.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e0f6fd2d75a65fc5da6dd4e214193b69d8717ac9
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/smallworld.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/smetric.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/smetric.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4f54f75d9695770fafa48a5d407783be4e9d08b5
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/smetric.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/sparsifiers.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/sparsifiers.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7b25f1ec4089f921aa29311affe8f8b52ea5c233
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/sparsifiers.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/structuralholes.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/structuralholes.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..398395f203e3293dfad4a0ade4a300d1fc106492
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/structuralholes.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/summarization.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/summarization.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..262fb2c2b2f907e291c9eb9700922efae2f14510
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/summarization.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/swap.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/swap.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2fc18a0a9b7de37ea2a232a84ed7f7fe6cea2ef6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/swap.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/threshold.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/threshold.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..de736099883ac6ac45549cc393de06261c440991
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/threshold.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/time_dependent.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/time_dependent.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a88c4a5486cff81656730c6a1c97ae6279f73466
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/time_dependent.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/tournament.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/tournament.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..09aa2df0e8ff57fa1d2cf8c7768861c3f814efc6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/tournament.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/triads.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/triads.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..025798e6ee0b272bb8d72a07d82f481a67d79ef8
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/triads.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/vitality.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/vitality.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a9b8a46878a127540e26fa9d3e9dcacb0eccb9e3
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/vitality.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/voronoi.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/voronoi.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7e52395d12ea843a19b3753264ccd903d3df90f3
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/voronoi.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/walks.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/walks.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..70b70628a2a1cefcae5f18ef3bed7d9af58c1682
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/walks.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/__pycache__/wiener.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/wiener.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..99ae2cd0b966940af03d187b710f623a0bc0694b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/__pycache__/wiener.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1acc850ba25d3b80d0bee82d361772f7650c31e1
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/all.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/all.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f626c9c315995ac1a8cd7f7fa0dceecda1cf3f6f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/all.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/binary.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/binary.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a09ba19dc5181a52dd6221d7517555c435287871
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/binary.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/product.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/product.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6df41629ee4c58829f52015514ebbb181aa49a41
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/product.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/unary.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/unary.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cb8686e4c63bbba185283f7b42a959dfc2aa289d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/__pycache__/unary.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/tests/__pycache__/test_product.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/tests/__pycache__/test_product.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a9d8db28ef3456a1d084058371939c895cf2d51c
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/tests/__pycache__/test_product.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/operators/tests/__pycache__/test_unary.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/operators/tests/__pycache__/test_unary.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..25c8f615d6a55276335cbd787d2cb9c3498232d2
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/operators/tests/__pycache__/test_unary.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__init__.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb0d91cecc902f6390cb8309c017cb1558f7753f
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__init__.py
@@ -0,0 +1,5 @@
+from networkx.algorithms.shortest_paths.generic import *
+from networkx.algorithms.shortest_paths.unweighted import *
+from networkx.algorithms.shortest_paths.weighted import *
+from networkx.algorithms.shortest_paths.astar import *
+from networkx.algorithms.shortest_paths.dense import *
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1e5aa04b0dd36cf5fad33a5f316805135a35f008
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/astar.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/astar.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4f2cf7e2afe77fb39f2ed2e302e3bcb603d008d3
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/astar.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/dense.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/dense.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..38ce12f9d57c24ba0f661a2462dfa135a903863b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/dense.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/generic.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/generic.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3d69d2baf246e8e9b558d5bd72e49cea86501306
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/generic.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/unweighted.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/unweighted.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f3f518aec2e1347b7363371bd95ef83fe04d830f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/unweighted.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/weighted.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/weighted.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..179ecbb5090a35cb2b8bab95ca47cddf055211d2
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/__pycache__/weighted.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/astar.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/astar.py
new file mode 100644
index 0000000000000000000000000000000000000000..118229716cdfd5fe3a45619010437ec0df502d3d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/astar.py
@@ -0,0 +1,239 @@
+"""Shortest paths and path lengths using the A* ("A star") algorithm."""
+
+from heapq import heappop, heappush
+from itertools import count
+
+import networkx as nx
+from networkx.algorithms.shortest_paths.weighted import _weight_function
+
+__all__ = ["astar_path", "astar_path_length"]
+
+
+@nx._dispatchable(edge_attrs="weight", preserve_node_attrs="heuristic")
+def astar_path(G, source, target, heuristic=None, weight="weight", *, cutoff=None):
+    """Returns a list of nodes in a shortest path between source and target
+    using the A* ("A-star") algorithm.
+
+    There may be more than one shortest path.  This returns only one.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+
+    heuristic : function
+       A function to evaluate the estimate of the distance
+       from the a node to the target.  The function takes
+       two nodes arguments and must return a number.
+       If the heuristic is inadmissible (if it might
+       overestimate the cost of reaching the goal from a node),
+       the result may not be a shortest path.
+       The algorithm does not support updating heuristic
+       values for the same node due to caching the first
+       heuristic calculation per node.
+
+    weight : string or function
+       If this is a string, then edge weights will be accessed via the
+       edge attribute with this key (that is, the weight of the edge
+       joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+       such edge attribute exists, the weight of the edge is assumed to
+       be one.
+       If this is a function, the weight of an edge is the value
+       returned by the function. The function must accept exactly three
+       positional arguments: the two endpoints of an edge and the
+       dictionary of edge attributes for that edge. The function must
+       return a number or None to indicate a hidden edge.
+
+    cutoff : float, optional
+       If this is provided, the search will be bounded to this value. I.e. if
+       the evaluation function surpasses this value for a node n, the node will not
+       be expanded further and will be ignored. More formally, let h'(n) be the
+       heuristic function, and g(n) be the cost of reaching n from the source node. Then,
+       if g(n) + h'(n) > cutoff, the node will not be explored further.
+       Note that if the heuristic is inadmissible, it is possible that paths
+       are ignored even though they satisfy the cutoff.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> print(nx.astar_path(G, 0, 4))
+    [0, 1, 2, 3, 4]
+    >>> G = nx.grid_graph(dim=[3, 3])  # nodes are two-tuples (x,y)
+    >>> nx.set_edge_attributes(G, {e: e[1][0] * 2 for e in G.edges()}, "cost")
+    >>> def dist(a, b):
+    ...     (x1, y1) = a
+    ...     (x2, y2) = b
+    ...     return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
+    >>> print(nx.astar_path(G, (0, 0), (2, 2), heuristic=dist, weight="cost"))
+    [(0, 0), (0, 1), (0, 2), (1, 2), (2, 2)]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    See Also
+    --------
+    shortest_path, dijkstra_path
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} is not in G")
+
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} is not in G")
+
+    if heuristic is None:
+        # The default heuristic is h=0 - same as Dijkstra's algorithm
+        def heuristic(u, v):
+            return 0
+
+    weight = _weight_function(G, weight)
+
+    G_succ = G._adj  # For speed-up (and works for both directed and undirected graphs)
+
+    # The queue stores priority, node, cost to reach, and parent.
+    # Uses Python heapq to keep in priority order.
+    # Add a counter to the queue to prevent the underlying heap from
+    # attempting to compare the nodes themselves. The hash breaks ties in the
+    # priority and is guaranteed unique for all nodes in the graph.
+    c = count()
+    queue = [(0, next(c), source, 0, None)]
+
+    # Maps enqueued nodes to distance of discovered paths and the
+    # computed heuristics to target. We avoid computing the heuristics
+    # more than once and inserting the node into the queue too many times.
+    enqueued = {}
+    # Maps explored nodes to parent closest to the source.
+    explored = {}
+
+    while queue:
+        # Pop the smallest item from queue.
+        _, __, curnode, dist, parent = heappop(queue)
+
+        if curnode == target:
+            path = [curnode]
+            node = parent
+            while node is not None:
+                path.append(node)
+                node = explored[node]
+            path.reverse()
+            return path
+
+        if curnode in explored:
+            # Do not override the parent of starting node
+            if explored[curnode] is None:
+                continue
+
+            # Skip bad paths that were enqueued before finding a better one
+            qcost, h = enqueued[curnode]
+            if qcost < dist:
+                continue
+
+        explored[curnode] = parent
+
+        for neighbor, w in G_succ[curnode].items():
+            cost = weight(curnode, neighbor, w)
+            if cost is None:
+                continue
+            ncost = dist + cost
+            if neighbor in enqueued:
+                qcost, h = enqueued[neighbor]
+                # if qcost <= ncost, a less costly path from the
+                # neighbor to the source was already determined.
+                # Therefore, we won't attempt to push this neighbor
+                # to the queue
+                if qcost <= ncost:
+                    continue
+            else:
+                h = heuristic(neighbor, target)
+
+            if cutoff and ncost + h > cutoff:
+                continue
+
+            enqueued[neighbor] = ncost, h
+            heappush(queue, (ncost + h, next(c), neighbor, ncost, curnode))
+
+    raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+
+
+@nx._dispatchable(edge_attrs="weight", preserve_node_attrs="heuristic")
+def astar_path_length(
+    G, source, target, heuristic=None, weight="weight", *, cutoff=None
+):
+    """Returns the length of the shortest path between source and target using
+    the A* ("A-star") algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+
+    heuristic : function
+       A function to evaluate the estimate of the distance
+       from the a node to the target.  The function takes
+       two nodes arguments and must return a number.
+       If the heuristic is inadmissible (if it might
+       overestimate the cost of reaching the goal from a node),
+       the result may not be a shortest path.
+       The algorithm does not support updating heuristic
+       values for the same node due to caching the first
+       heuristic calculation per node.
+
+    weight : string or function
+       If this is a string, then edge weights will be accessed via the
+       edge attribute with this key (that is, the weight of the edge
+       joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+       such edge attribute exists, the weight of the edge is assumed to
+       be one.
+       If this is a function, the weight of an edge is the value
+       returned by the function. The function must accept exactly three
+       positional arguments: the two endpoints of an edge and the
+       dictionary of edge attributes for that edge. The function must
+       return a number or None to indicate a hidden edge.
+
+    cutoff : float, optional
+       If this is provided, the search will be bounded to this value. I.e. if
+       the evaluation function surpasses this value for a node n, the node will not
+       be expanded further and will be ignored. More formally, let h'(n) be the
+       heuristic function, and g(n) be the cost of reaching n from the source node. Then,
+       if g(n) + h'(n) > cutoff, the node will not be explored further.
+       Note that if the heuristic is inadmissible, it is possible that paths
+       are ignored even though they satisfy the cutoff.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    See Also
+    --------
+    astar_path
+
+    """
+    if source not in G or target not in G:
+        msg = f"Either source {source} or target {target} is not in G"
+        raise nx.NodeNotFound(msg)
+
+    weight = _weight_function(G, weight)
+    path = astar_path(G, source, target, heuristic, weight, cutoff=cutoff)
+    return sum(weight(u, v, G[u][v]) for u, v in zip(path[:-1], path[1:]))
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/dense.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/dense.py
new file mode 100644
index 0000000000000000000000000000000000000000..d259d8c2710dfeb0c299d8ec8e46677b1f27606d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/dense.py
@@ -0,0 +1,264 @@
+"""Floyd-Warshall algorithm for shortest paths."""
+
+import networkx as nx
+
+__all__ = [
+    "floyd_warshall",
+    "floyd_warshall_predecessor_and_distance",
+    "reconstruct_path",
+    "floyd_warshall_numpy",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def floyd_warshall_numpy(G, nodelist=None, weight="weight"):
+    """Find all-pairs shortest path lengths using Floyd's algorithm.
+
+    This algorithm for finding shortest paths takes advantage of
+    matrix representations of a graph and works well for dense
+    graphs where all-pairs shortest path lengths are desired.
+    The results are returned as a NumPy array, distance[i, j],
+    where i and j are the indexes of two nodes in nodelist.
+    The entry distance[i, j] is the distance along a shortest
+    path from i to j. If no path exists the distance is Inf.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nodelist : list, optional (default=G.nodes)
+       The rows and columns are ordered by the nodes in nodelist.
+       If nodelist is None then the ordering is produced by G.nodes.
+       Nodelist should include all nodes in G.
+
+    weight: string, optional (default='weight')
+       Edge data key corresponding to the edge weight.
+
+    Returns
+    -------
+    distance : 2D numpy.ndarray
+        A numpy array of shortest path distances between nodes.
+        If there is no path between two nodes the value is Inf.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     [(0, 1, 5), (1, 2, 2), (2, 3, -3), (1, 3, 10), (3, 2, 8)]
+    ... )
+    >>> nx.floyd_warshall_numpy(G)
+    array([[ 0.,  5.,  7.,  4.],
+           [inf,  0.,  2., -1.],
+           [inf, inf,  0., -3.],
+           [inf, inf,  8.,  0.]])
+
+    Notes
+    -----
+    Floyd's algorithm is appropriate for finding shortest paths in
+    dense graphs or graphs with negative weights when Dijkstra's
+    algorithm fails. This algorithm can still fail if there are negative
+    cycles. It has running time $O(n^3)$ with running space of $O(n^2)$.
+
+    Raises
+    ------
+    NetworkXError
+        If nodelist is not a list of the nodes in G.
+    """
+    import numpy as np
+
+    if nodelist is not None:
+        if not (len(nodelist) == len(G) == len(set(nodelist))):
+            raise nx.NetworkXError(
+                "nodelist must contain every node in G with no repeats."
+                "If you wanted a subgraph of G use G.subgraph(nodelist)"
+            )
+
+    # To handle cases when an edge has weight=0, we must make sure that
+    # nonedges are not given the value 0 as well.
+    A = nx.to_numpy_array(
+        G, nodelist, multigraph_weight=min, weight=weight, nonedge=np.inf
+    )
+    n, m = A.shape
+    np.fill_diagonal(A, 0)  # diagonal elements should be zero
+    for i in range(n):
+        # The second term has the same shape as A due to broadcasting
+        A = np.minimum(A, A[i, :][np.newaxis, :] + A[:, i][:, np.newaxis])
+    return A
+
+
+@nx._dispatchable(edge_attrs="weight")
+def floyd_warshall_predecessor_and_distance(G, weight="weight"):
+    """Find all-pairs shortest path lengths using Floyd's algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight: string, optional (default= 'weight')
+       Edge data key corresponding to the edge weight.
+
+    Returns
+    -------
+    predecessor,distance : dictionaries
+       Dictionaries, keyed by source and target, of predecessors and distances
+       in the shortest path.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     [
+    ...         ("s", "u", 10),
+    ...         ("s", "x", 5),
+    ...         ("u", "v", 1),
+    ...         ("u", "x", 2),
+    ...         ("v", "y", 1),
+    ...         ("x", "u", 3),
+    ...         ("x", "v", 5),
+    ...         ("x", "y", 2),
+    ...         ("y", "s", 7),
+    ...         ("y", "v", 6),
+    ...     ]
+    ... )
+    >>> predecessors, _ = nx.floyd_warshall_predecessor_and_distance(G)
+    >>> print(nx.reconstruct_path("s", "v", predecessors))
+    ['s', 'x', 'u', 'v']
+
+    Notes
+    -----
+    Floyd's algorithm is appropriate for finding shortest paths
+    in dense graphs or graphs with negative weights when Dijkstra's algorithm
+    fails.  This algorithm can still fail if there are negative cycles.
+    It has running time $O(n^3)$ with running space of $O(n^2)$.
+
+    See Also
+    --------
+    floyd_warshall
+    floyd_warshall_numpy
+    all_pairs_shortest_path
+    all_pairs_shortest_path_length
+    """
+    from collections import defaultdict
+
+    # dictionary-of-dictionaries representation for dist and pred
+    # use some defaultdict magick here
+    # for dist the default is the floating point inf value
+    dist = defaultdict(lambda: defaultdict(lambda: float("inf")))
+    for u in G:
+        dist[u][u] = 0
+    pred = defaultdict(dict)
+    # initialize path distance dictionary to be the adjacency matrix
+    # also set the distance to self to 0 (zero diagonal)
+    undirected = not G.is_directed()
+    for u, v, d in G.edges(data=True):
+        e_weight = d.get(weight, 1.0)
+        dist[u][v] = min(e_weight, dist[u][v])
+        pred[u][v] = u
+        if undirected:
+            dist[v][u] = min(e_weight, dist[v][u])
+            pred[v][u] = v
+    for w in G:
+        dist_w = dist[w]  # save recomputation
+        for u in G:
+            dist_u = dist[u]  # save recomputation
+            for v in G:
+                d = dist_u[w] + dist_w[v]
+                if dist_u[v] > d:
+                    dist_u[v] = d
+                    pred[u][v] = pred[w][v]
+    return dict(pred), dict(dist)
+
+
+@nx._dispatchable(graphs=None)
+def reconstruct_path(source, target, predecessors):
+    """Reconstruct a path from source to target using the predecessors
+    dict as returned by floyd_warshall_predecessor_and_distance
+
+    Parameters
+    ----------
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+
+    predecessors: dictionary
+       Dictionary, keyed by source and target, of predecessors in the
+       shortest path, as returned by floyd_warshall_predecessor_and_distance
+
+    Returns
+    -------
+    path : list
+       A list of nodes containing the shortest path from source to target
+
+       If source and target are the same, an empty list is returned
+
+    Notes
+    -----
+    This function is meant to give more applicability to the
+    floyd_warshall_predecessor_and_distance function
+
+    See Also
+    --------
+    floyd_warshall_predecessor_and_distance
+    """
+    if source == target:
+        return []
+    prev = predecessors[source]
+    curr = prev[target]
+    path = [target, curr]
+    while curr != source:
+        curr = prev[curr]
+        path.append(curr)
+    return list(reversed(path))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def floyd_warshall(G, weight="weight"):
+    """Find all-pairs shortest path lengths using Floyd's algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight: string, optional (default= 'weight')
+       Edge data key corresponding to the edge weight.
+
+
+    Returns
+    -------
+    distance : dict
+       A dictionary,  keyed by source and target, of shortest paths distances
+       between nodes.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     [(0, 1, 5), (1, 2, 2), (2, 3, -3), (1, 3, 10), (3, 2, 8)]
+    ... )
+    >>> fw = nx.floyd_warshall(G, weight="weight")
+    >>> results = {a: dict(b) for a, b in fw.items()}
+    >>> pprint(results)
+    {0: {0: 0, 1: 5, 2: 7, 3: 4},
+     1: {0: inf, 1: 0, 2: 2, 3: -1},
+     2: {0: inf, 1: inf, 2: 0, 3: -3},
+     3: {0: inf, 1: inf, 2: 8, 3: 0}}
+
+    Notes
+    -----
+    Floyd's algorithm is appropriate for finding shortest paths
+    in dense graphs or graphs with negative weights when Dijkstra's algorithm
+    fails.  This algorithm can still fail if there are negative cycles.
+    It has running time $O(n^3)$ with running space of $O(n^2)$.
+
+    See Also
+    --------
+    floyd_warshall_predecessor_and_distance
+    floyd_warshall_numpy
+    all_pairs_shortest_path
+    all_pairs_shortest_path_length
+    """
+    # could make this its own function to reduce memory costs
+    return floyd_warshall_predecessor_and_distance(G, weight=weight)[1]
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/generic.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fd0eafdb1b30de7cc501144807f4c77ac8a7a08
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/generic.py
@@ -0,0 +1,716 @@
+"""
+Compute the shortest paths and path lengths between nodes in the graph.
+
+These algorithms work with undirected and directed graphs.
+
+"""
+
+import networkx as nx
+
+__all__ = [
+    "shortest_path",
+    "all_shortest_paths",
+    "single_source_all_shortest_paths",
+    "all_pairs_all_shortest_paths",
+    "shortest_path_length",
+    "average_shortest_path_length",
+    "has_path",
+]
+
+
+@nx._dispatchable
+def has_path(G, source, target):
+    """Returns *True* if *G* has a path from *source* to *target*.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+    """
+    try:
+        nx.shortest_path(G, source, target)
+    except nx.NetworkXNoPath:
+        return False
+    return True
+
+
+@nx._dispatchable(edge_attrs="weight")
+def shortest_path(G, source=None, target=None, weight=None, method="dijkstra"):
+    """Compute shortest paths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node, optional
+        Starting node for path. If not specified, compute shortest
+        paths for each possible starting node.
+
+    target : node, optional
+        Ending node for path. If not specified, compute shortest
+        paths to all possible nodes.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+        The algorithm to use to compute the path.
+        Supported options: 'dijkstra', 'bellman-ford'.
+        Other inputs produce a ValueError.
+        If `weight` is None, unweighted graph methods are used, and this
+        suggestion is ignored.
+
+    Returns
+    -------
+    path: list or dictionary or iterator
+        All returned paths include both the source and target in the path.
+
+        If the source and target are both specified, return a single list
+        of nodes in a shortest path from the source to the target.
+
+        If only the source is specified, return a dictionary keyed by
+        targets with a list of nodes in a shortest path from the source
+        to one of the targets.
+
+        If only the target is specified, return a dictionary keyed by
+        sources with a list of nodes in a shortest path from one of the
+        sources to the target.
+
+        If neither the source nor target are specified, return an iterator
+        over (source, dictionary) where dictionary is keyed by target to
+        list of nodes in a shortest path from the source to the target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    ValueError
+        If `method` is not among the supported options.
+
+    NetworkXNoPath
+       If `source` and `target` are specified but no path exists between them.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> print(nx.shortest_path(G, source=0, target=4))
+    [0, 1, 2, 3, 4]
+    >>> p = nx.shortest_path(G, source=0)  # target not specified
+    >>> p[3]  # shortest path from source=0 to target=3
+    [0, 1, 2, 3]
+    >>> p = nx.shortest_path(G, target=4)  # source not specified
+    >>> p[1]  # shortest path from source=1 to target=4
+    [1, 2, 3, 4]
+    >>> p = dict(nx.shortest_path(G))  # source, target not specified
+    >>> p[2][4]  # shortest path from source=2 to target=4
+    [2, 3, 4]
+
+    Notes
+    -----
+    There may be more than one shortest path between a source and target.
+    This returns only one of them.
+
+    See Also
+    --------
+    all_pairs_shortest_path
+    all_pairs_dijkstra_path
+    all_pairs_bellman_ford_path
+    single_source_shortest_path
+    single_source_dijkstra_path
+    single_source_bellman_ford_path
+    """
+    if method not in ("dijkstra", "bellman-ford"):
+        # so we don't need to check in each branch later
+        raise ValueError(f"method not supported: {method}")
+    method = "unweighted" if weight is None else method
+    if source is None:
+        if target is None:
+            # Find paths between all pairs. Iterator of dicts.
+            if method == "unweighted":
+                paths = nx.all_pairs_shortest_path(G)
+            elif method == "dijkstra":
+                paths = nx.all_pairs_dijkstra_path(G, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.all_pairs_bellman_ford_path(G, weight=weight)
+        else:
+            # Find paths from all nodes co-accessible to the target.
+            if G.is_directed():
+                G = G.reverse(copy=False)
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path(G, target)
+            elif method == "dijkstra":
+                paths = nx.single_source_dijkstra_path(G, target, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.single_source_bellman_ford_path(G, target, weight=weight)
+            # Now flip the paths so they go from a source to the target.
+            for target in paths:
+                paths[target] = list(reversed(paths[target]))
+    else:
+        if target is None:
+            # Find paths to all nodes accessible from the source.
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path(G, source)
+            elif method == "dijkstra":
+                paths = nx.single_source_dijkstra_path(G, source, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.single_source_bellman_ford_path(G, source, weight=weight)
+        else:
+            # Find shortest source-target path.
+            if method == "unweighted":
+                paths = nx.bidirectional_shortest_path(G, source, target)
+            elif method == "dijkstra":
+                _, paths = nx.bidirectional_dijkstra(G, source, target, weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.bellman_ford_path(G, source, target, weight)
+    return paths
+
+
+@nx._dispatchable(edge_attrs="weight")
+def shortest_path_length(G, source=None, target=None, weight=None, method="dijkstra"):
+    """Compute shortest path lengths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node, optional
+        Starting node for path.
+        If not specified, compute shortest path lengths using all nodes as
+        source nodes.
+
+    target : node, optional
+        Ending node for path.
+        If not specified, compute shortest path lengths using all nodes as
+        target nodes.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+        The algorithm to use to compute the path length.
+        Supported options: 'dijkstra', 'bellman-ford'.
+        Other inputs produce a ValueError.
+        If `weight` is None, unweighted graph methods are used, and this
+        suggestion is ignored.
+
+    Returns
+    -------
+    length: number or iterator
+        If the source and target are both specified, return the length of
+        the shortest path from the source to the target.
+
+        If only the source is specified, return a dict keyed by target
+        to the shortest path length from the source to that target.
+
+        If only the target is specified, return a dict keyed by source
+        to the shortest path length from that source to the target.
+
+        If neither the source nor target are specified, return an iterator
+        over (source, dictionary) where dictionary is keyed by target to
+        shortest path length from source to that target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.shortest_path_length(G, source=0, target=4)
+    4
+    >>> p = nx.shortest_path_length(G, source=0)  # target not specified
+    >>> p[4]
+    4
+    >>> p = nx.shortest_path_length(G, target=4)  # source not specified
+    >>> p[0]
+    4
+    >>> p = dict(nx.shortest_path_length(G))  # source,target not specified
+    >>> p[0][4]
+    4
+
+    Notes
+    -----
+    The length of the path is always 1 less than the number of nodes involved
+    in the path since the length measures the number of edges followed.
+
+    For digraphs this returns the shortest directed path length. To find path
+    lengths in the reverse direction use G.reverse(copy=False) first to flip
+    the edge orientation.
+
+    See Also
+    --------
+    all_pairs_shortest_path_length
+    all_pairs_dijkstra_path_length
+    all_pairs_bellman_ford_path_length
+    single_source_shortest_path_length
+    single_source_dijkstra_path_length
+    single_source_bellman_ford_path_length
+    """
+    if method not in ("dijkstra", "bellman-ford"):
+        # so we don't need to check in each branch later
+        raise ValueError(f"method not supported: {method}")
+    method = "unweighted" if weight is None else method
+    if source is None:
+        if target is None:
+            # Find paths between all pairs.
+            if method == "unweighted":
+                paths = nx.all_pairs_shortest_path_length(G)
+            elif method == "dijkstra":
+                paths = nx.all_pairs_dijkstra_path_length(G, weight=weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.all_pairs_bellman_ford_path_length(G, weight=weight)
+        else:
+            # Find paths from all nodes co-accessible to the target.
+            if G.is_directed():
+                G = G.reverse(copy=False)
+            if method == "unweighted":
+                path_length = nx.single_source_shortest_path_length
+                paths = path_length(G, target)
+            elif method == "dijkstra":
+                path_length = nx.single_source_dijkstra_path_length
+                paths = path_length(G, target, weight=weight)
+            else:  # method == 'bellman-ford':
+                path_length = nx.single_source_bellman_ford_path_length
+                paths = path_length(G, target, weight=weight)
+    else:
+        if target is None:
+            # Find paths to all nodes accessible from the source.
+            if method == "unweighted":
+                paths = nx.single_source_shortest_path_length(G, source)
+            elif method == "dijkstra":
+                path_length = nx.single_source_dijkstra_path_length
+                paths = path_length(G, source, weight=weight)
+            else:  # method == 'bellman-ford':
+                path_length = nx.single_source_bellman_ford_path_length
+                paths = path_length(G, source, weight=weight)
+        else:
+            # Find shortest source-target path.
+            if method == "unweighted":
+                p = nx.bidirectional_shortest_path(G, source, target)
+                paths = len(p) - 1
+            elif method == "dijkstra":
+                paths = nx.dijkstra_path_length(G, source, target, weight)
+            else:  # method == 'bellman-ford':
+                paths = nx.bellman_ford_path_length(G, source, target, weight)
+    return paths
+
+
+@nx._dispatchable(edge_attrs="weight")
+def average_shortest_path_length(G, weight=None, method=None):
+    r"""Returns the average shortest path length.
+
+    The average shortest path length is
+
+    .. math::
+
+       a =\sum_{\substack{s,t \in V \\ s\neq t}} \frac{d(s, t)}{n(n-1)}
+
+    where `V` is the set of nodes in `G`,
+    `d(s, t)` is the shortest path from `s` to `t`,
+    and `n` is the number of nodes in `G`.
+
+    .. versionchanged:: 3.0
+       An exception is raised for directed graphs that are not strongly
+       connected.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'unweighted' or 'dijkstra')
+        The algorithm to use to compute the path lengths.
+        Supported options are 'unweighted', 'dijkstra', 'bellman-ford',
+        'floyd-warshall' and 'floyd-warshall-numpy'.
+        Other method values produce a ValueError.
+        The default method is 'unweighted' if `weight` is None,
+        otherwise the default method is 'dijkstra'.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` is the null graph (that is, the graph on zero nodes).
+
+    NetworkXError
+        If `G` is not connected (or not strongly connected, in the case
+        of a directed graph).
+
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.average_shortest_path_length(G)
+    2.0
+
+    For disconnected graphs, you can compute the average shortest path
+    length for each component
+
+    >>> G = nx.Graph([(1, 2), (3, 4)])
+    >>> for C in (G.subgraph(c).copy() for c in nx.connected_components(G)):
+    ...     print(nx.average_shortest_path_length(C))
+    1.0
+    1.0
+
+    """
+    single_source_methods = ["unweighted", "dijkstra", "bellman-ford"]
+    all_pairs_methods = ["floyd-warshall", "floyd-warshall-numpy"]
+    supported_methods = single_source_methods + all_pairs_methods
+
+    if method is None:
+        method = "unweighted" if weight is None else "dijkstra"
+    if method not in supported_methods:
+        raise ValueError(f"method not supported: {method}")
+
+    n = len(G)
+    # For the special case of the null graph, raise an exception, since
+    # there are no paths in the null graph.
+    if n == 0:
+        msg = (
+            "the null graph has no paths, thus there is no average shortest path length"
+        )
+        raise nx.NetworkXPointlessConcept(msg)
+    # For the special case of the trivial graph, return zero immediately.
+    if n == 1:
+        return 0
+    # Shortest path length is undefined if the graph is not strongly connected.
+    if G.is_directed() and not nx.is_strongly_connected(G):
+        raise nx.NetworkXError("Graph is not strongly connected.")
+    # Shortest path length is undefined if the graph is not connected.
+    if not G.is_directed() and not nx.is_connected(G):
+        raise nx.NetworkXError("Graph is not connected.")
+
+    # Compute all-pairs shortest paths.
+    def path_length(v):
+        if method == "unweighted":
+            return nx.single_source_shortest_path_length(G, v)
+        elif method == "dijkstra":
+            return nx.single_source_dijkstra_path_length(G, v, weight=weight)
+        elif method == "bellman-ford":
+            return nx.single_source_bellman_ford_path_length(G, v, weight=weight)
+
+    if method in single_source_methods:
+        # Sum the distances for each (ordered) pair of source and target node.
+        s = sum(l for u in G for l in path_length(u).values())
+    else:
+        if method == "floyd-warshall":
+            all_pairs = nx.floyd_warshall(G, weight=weight)
+            s = sum(sum(t.values()) for t in all_pairs.values())
+        elif method == "floyd-warshall-numpy":
+            s = float(nx.floyd_warshall_numpy(G, weight=weight).sum())
+    return s / (n * (n - 1))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_shortest_paths(G, source, target, weight=None, method="dijkstra"):
+    """Compute all shortest simple paths in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path.
+
+    target : node
+       Ending node for path.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of lists
+        A generator of all paths between source and target.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    NetworkXNoPath
+        If `target` cannot be reached from `source`.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2])
+    >>> nx.add_path(G, [0, 10, 2])
+    >>> print([p for p in nx.all_shortest_paths(G, source=0, target=2)])
+    [[0, 1, 2], [0, 10, 2]]
+
+    Notes
+    -----
+    There may be many shortest paths between the source and target.  If G
+    contains zero-weight cycles, this function will not produce all shortest
+    paths because doing so would produce infinitely many paths of unbounded
+    length -- instead, we only produce the shortest simple paths.
+
+    See Also
+    --------
+    shortest_path
+    single_source_shortest_path
+    all_pairs_shortest_path
+    """
+    method = "unweighted" if weight is None else method
+    if method == "unweighted":
+        pred = nx.predecessor(G, source)
+    elif method == "dijkstra":
+        pred, dist = nx.dijkstra_predecessor_and_distance(G, source, weight=weight)
+    elif method == "bellman-ford":
+        pred, dist = nx.bellman_ford_predecessor_and_distance(G, source, weight=weight)
+    else:
+        raise ValueError(f"method not supported: {method}")
+
+    return _build_paths_from_predecessors({source}, target, pred)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_all_shortest_paths(G, source, weight=None, method="dijkstra"):
+    """Compute all shortest simple paths from the given source in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path.
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of dictionary
+        A generator of all paths between source and all nodes in the graph.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 0])
+    >>> dict(nx.single_source_all_shortest_paths(G, source=0))
+    {0: [[0]], 1: [[0, 1]], 3: [[0, 3]], 2: [[0, 1, 2], [0, 3, 2]]}
+
+    Notes
+    -----
+    There may be many shortest paths between the source and target.  If G
+    contains zero-weight cycles, this function will not produce all shortest
+    paths because doing so would produce infinitely many paths of unbounded
+    length -- instead, we only produce the shortest simple paths.
+
+    See Also
+    --------
+    shortest_path
+    all_shortest_paths
+    single_source_shortest_path
+    all_pairs_shortest_path
+    all_pairs_all_shortest_paths
+    """
+    method = "unweighted" if weight is None else method
+    if method == "unweighted":
+        pred = nx.predecessor(G, source)
+    elif method == "dijkstra":
+        pred, dist = nx.dijkstra_predecessor_and_distance(G, source, weight=weight)
+    elif method == "bellman-ford":
+        pred, dist = nx.bellman_ford_predecessor_and_distance(G, source, weight=weight)
+    else:
+        raise ValueError(f"method not supported: {method}")
+    for n in pred:
+        yield n, list(_build_paths_from_predecessors({source}, n, pred))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_pairs_all_shortest_paths(G, weight=None, method="dijkstra"):
+    """Compute all shortest paths between all nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : None, string or function, optional (default = None)
+        If None, every edge has weight/distance/cost 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly
+        three positional arguments: the two endpoints of an edge and
+        the dictionary of edge attributes for that edge.
+        The function must return a number.
+
+    method : string, optional (default = 'dijkstra')
+       The algorithm to use to compute the path lengths.
+       Supported options: 'dijkstra', 'bellman-ford'.
+       Other inputs produce a ValueError.
+       If `weight` is None, unweighted graph methods are used, and this
+       suggestion is ignored.
+
+    Returns
+    -------
+    paths : generator of dictionary
+        Dictionary of arrays, keyed by source and target, of all shortest paths.
+
+    Raises
+    ------
+    ValueError
+        If `method` is not among the supported options.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> dict(nx.all_pairs_all_shortest_paths(G))[0][2]
+    [[0, 1, 2], [0, 3, 2]]
+    >>> dict(nx.all_pairs_all_shortest_paths(G))[0][3]
+    [[0, 3]]
+
+    Notes
+    -----
+    There may be multiple shortest paths with equal lengths. Unlike
+    all_pairs_shortest_path, this method returns all shortest paths.
+
+    See Also
+    --------
+    all_pairs_shortest_path
+    single_source_all_shortest_paths
+    """
+    for n in G:
+        yield (
+            n,
+            dict(single_source_all_shortest_paths(G, n, weight=weight, method=method)),
+        )
+
+
+def _build_paths_from_predecessors(sources, target, pred):
+    """Compute all simple paths to target, given the predecessors found in
+    pred, terminating when any source in sources is found.
+
+    Parameters
+    ----------
+    sources : set
+       Starting nodes for path.
+
+    target : node
+       Ending node for path.
+
+    pred : dict
+       A dictionary of predecessor lists, keyed by node
+
+    Returns
+    -------
+    paths : generator of lists
+        A generator of all paths between source and target.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If `target` cannot be reached from `source`.
+
+    Notes
+    -----
+    There may be many paths between the sources and target.  If there are
+    cycles among the predecessors, this function will not produce all
+    possible paths because doing so would produce infinitely many paths
+    of unbounded length -- instead, we only produce simple paths.
+
+    See Also
+    --------
+    shortest_path
+    single_source_shortest_path
+    all_pairs_shortest_path
+    all_shortest_paths
+    bellman_ford_path
+    """
+    if target not in pred:
+        raise nx.NetworkXNoPath(f"Target {target} cannot be reached from given sources")
+
+    seen = {target}
+    stack = [[target, 0]]
+    top = 0
+    while top >= 0:
+        node, i = stack[top]
+        if node in sources:
+            yield [p for p, n in reversed(stack[: top + 1])]
+        if len(pred[node]) > i:
+            stack[top][1] = i + 1
+            next = pred[node][i]
+            if next in seen:
+                continue
+            else:
+                seen.add(next)
+            top += 1
+            if top == len(stack):
+                stack.append([next, 0])
+            else:
+                stack[top][:] = [next, 0]
+        else:
+            seen.discard(node)
+            top -= 1
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__init__.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..76abd2b05fa76d05072d0fefdba0d71f978e3fe7
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_astar.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_astar.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e1f971f64a653fad3dcb7a92e765235242665811
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_astar.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_dense.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_dense.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..039124187c0fcf04f2a06f5100bdae1c66d1f452
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_dense.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_dense_numpy.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_dense_numpy.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..415f0491279b0e4e0c133b2b0206e02fd86942e6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_dense_numpy.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_generic.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_generic.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4b823e63da703e1cf6f909b41baaf1da52031ec9
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_generic.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_unweighted.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_unweighted.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1db4e0acdd5d499c5173c439a2dc6138796eaf7e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_unweighted.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_weighted.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_weighted.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b51c81a08b57f21bb418210b6eff1ad144e15c5d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/__pycache__/test_weighted.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_astar.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_astar.py
new file mode 100644
index 0000000000000000000000000000000000000000..3abf2114528cd7708b7a36baf13cb3172eb342fa
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_astar.py
@@ -0,0 +1,254 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import pairwise
+
+
+class TestAStar:
+    @classmethod
+    def setup_class(cls):
+        edges = [
+            ("s", "u", 10),
+            ("s", "x", 5),
+            ("u", "v", 1),
+            ("u", "x", 2),
+            ("v", "y", 1),
+            ("x", "u", 3),
+            ("x", "v", 5),
+            ("x", "y", 2),
+            ("y", "s", 7),
+            ("y", "v", 6),
+        ]
+        cls.XG = nx.DiGraph()
+        cls.XG.add_weighted_edges_from(edges)
+
+    def test_multiple_optimal_paths(self):
+        """Tests that A* algorithm finds any of multiple optimal paths"""
+        heuristic_values = {"a": 1.35, "b": 1.18, "c": 0.67, "d": 0}
+
+        def h(u, v):
+            return heuristic_values[u]
+
+        graph = nx.Graph()
+        points = ["a", "b", "c", "d"]
+        edges = [("a", "b", 0.18), ("a", "c", 0.68), ("b", "c", 0.50), ("c", "d", 0.67)]
+
+        graph.add_nodes_from(points)
+        graph.add_weighted_edges_from(edges)
+
+        path1 = ["a", "c", "d"]
+        path2 = ["a", "b", "c", "d"]
+        assert nx.astar_path(graph, "a", "d", h) in (path1, path2)
+
+    def test_astar_directed(self):
+        assert nx.astar_path(self.XG, "s", "v") == ["s", "x", "u", "v"]
+        assert nx.astar_path_length(self.XG, "s", "v") == 9
+
+    def test_astar_directed_weight_function(self):
+        def w1(u, v, d):
+            return d["weight"]
+
+        assert nx.astar_path(self.XG, "x", "u", weight=w1) == ["x", "u"]
+        assert nx.astar_path_length(self.XG, "x", "u", weight=w1) == 3
+        assert nx.astar_path(self.XG, "s", "v", weight=w1) == ["s", "x", "u", "v"]
+        assert nx.astar_path_length(self.XG, "s", "v", weight=w1) == 9
+
+        def w2(u, v, d):
+            return None if (u, v) == ("x", "u") else d["weight"]
+
+        assert nx.astar_path(self.XG, "x", "u", weight=w2) == ["x", "y", "s", "u"]
+        assert nx.astar_path_length(self.XG, "x", "u", weight=w2) == 19
+        assert nx.astar_path(self.XG, "s", "v", weight=w2) == ["s", "x", "v"]
+        assert nx.astar_path_length(self.XG, "s", "v", weight=w2) == 10
+
+        def w3(u, v, d):
+            return d["weight"] + 10
+
+        assert nx.astar_path(self.XG, "x", "u", weight=w3) == ["x", "u"]
+        assert nx.astar_path_length(self.XG, "x", "u", weight=w3) == 13
+        assert nx.astar_path(self.XG, "s", "v", weight=w3) == ["s", "x", "v"]
+        assert nx.astar_path_length(self.XG, "s", "v", weight=w3) == 30
+
+    def test_astar_multigraph(self):
+        G = nx.MultiDiGraph(self.XG)
+        G.add_weighted_edges_from((u, v, 1000) for (u, v) in list(G.edges()))
+        assert nx.astar_path(G, "s", "v") == ["s", "x", "u", "v"]
+        assert nx.astar_path_length(G, "s", "v") == 9
+
+    def test_astar_undirected(self):
+        GG = self.XG.to_undirected()
+        # make sure we get lower weight
+        # to_undirected might choose either edge with weight 2 or weight 3
+        GG["u"]["x"]["weight"] = 2
+        GG["y"]["v"]["weight"] = 2
+        assert nx.astar_path(GG, "s", "v") == ["s", "x", "u", "v"]
+        assert nx.astar_path_length(GG, "s", "v") == 8
+
+    def test_astar_directed2(self):
+        XG2 = nx.DiGraph()
+        edges = [
+            (1, 4, 1),
+            (4, 5, 1),
+            (5, 6, 1),
+            (6, 3, 1),
+            (1, 3, 50),
+            (1, 2, 100),
+            (2, 3, 100),
+        ]
+        XG2.add_weighted_edges_from(edges)
+        assert nx.astar_path(XG2, 1, 3) == [1, 4, 5, 6, 3]
+
+    def test_astar_undirected2(self):
+        XG3 = nx.Graph()
+        edges = [(0, 1, 2), (1, 2, 12), (2, 3, 1), (3, 4, 5), (4, 5, 1), (5, 0, 10)]
+        XG3.add_weighted_edges_from(edges)
+        assert nx.astar_path(XG3, 0, 3) == [0, 1, 2, 3]
+        assert nx.astar_path_length(XG3, 0, 3) == 15
+
+    def test_astar_undirected3(self):
+        XG4 = nx.Graph()
+        edges = [
+            (0, 1, 2),
+            (1, 2, 2),
+            (2, 3, 1),
+            (3, 4, 1),
+            (4, 5, 1),
+            (5, 6, 1),
+            (6, 7, 1),
+            (7, 0, 1),
+        ]
+        XG4.add_weighted_edges_from(edges)
+        assert nx.astar_path(XG4, 0, 2) == [0, 1, 2]
+        assert nx.astar_path_length(XG4, 0, 2) == 4
+
+    """ Tests that A* finds correct path when multiple paths exist
+        and the best one is not expanded first (GH issue #3464)
+    """
+
+    def test_astar_directed3(self):
+        heuristic_values = {"n5": 36, "n2": 4, "n1": 0, "n0": 0}
+
+        def h(u, v):
+            return heuristic_values[u]
+
+        edges = [("n5", "n1", 11), ("n5", "n2", 9), ("n2", "n1", 1), ("n1", "n0", 32)]
+        graph = nx.DiGraph()
+        graph.add_weighted_edges_from(edges)
+        answer = ["n5", "n2", "n1", "n0"]
+        assert nx.astar_path(graph, "n5", "n0", h) == answer
+
+    """ Tests that parent is not wrongly overridden when a node
+        is re-explored multiple times.
+    """
+
+    def test_astar_directed4(self):
+        edges = [
+            ("a", "b", 1),
+            ("a", "c", 1),
+            ("b", "d", 2),
+            ("c", "d", 1),
+            ("d", "e", 1),
+        ]
+        graph = nx.DiGraph()
+        graph.add_weighted_edges_from(edges)
+        assert nx.astar_path(graph, "a", "e") == ["a", "c", "d", "e"]
+
+    # >>> MXG4=NX.MultiGraph(XG4)
+    # >>> MXG4.add_edge(0,1,3)
+    # >>> NX.dijkstra_path(MXG4,0,2)
+    # [0, 1, 2]
+
+    def test_astar_w1(self):
+        G = nx.DiGraph()
+        G.add_edges_from(
+            [
+                ("s", "u"),
+                ("s", "x"),
+                ("u", "v"),
+                ("u", "x"),
+                ("v", "y"),
+                ("x", "u"),
+                ("x", "w"),
+                ("w", "v"),
+                ("x", "y"),
+                ("y", "s"),
+                ("y", "v"),
+            ]
+        )
+        assert nx.astar_path(G, "s", "v") == ["s", "u", "v"]
+        assert nx.astar_path_length(G, "s", "v") == 2
+
+    def test_astar_nopath(self):
+        with pytest.raises(nx.NodeNotFound):
+            nx.astar_path(self.XG, "s", "moon")
+
+    def test_astar_cutoff(self):
+        with pytest.raises(nx.NetworkXNoPath):
+            # optimal path_length in XG is 9
+            nx.astar_path(self.XG, "s", "v", cutoff=8.0)
+        with pytest.raises(nx.NetworkXNoPath):
+            nx.astar_path_length(self.XG, "s", "v", cutoff=8.0)
+
+    def test_astar_admissible_heuristic_with_cutoff(self):
+        heuristic_values = {"s": 36, "y": 4, "x": 0, "u": 0, "v": 0}
+
+        def h(u, v):
+            return heuristic_values[u]
+
+        assert nx.astar_path_length(self.XG, "s", "v") == 9
+        assert nx.astar_path_length(self.XG, "s", "v", heuristic=h) == 9
+        assert nx.astar_path_length(self.XG, "s", "v", heuristic=h, cutoff=12) == 9
+        assert nx.astar_path_length(self.XG, "s", "v", heuristic=h, cutoff=9) == 9
+        with pytest.raises(nx.NetworkXNoPath):
+            nx.astar_path_length(self.XG, "s", "v", heuristic=h, cutoff=8)
+
+    def test_astar_inadmissible_heuristic_with_cutoff(self):
+        heuristic_values = {"s": 36, "y": 14, "x": 10, "u": 10, "v": 0}
+
+        def h(u, v):
+            return heuristic_values[u]
+
+        # optimal path_length in XG is 9. This heuristic gives over-estimate.
+        assert nx.astar_path_length(self.XG, "s", "v", heuristic=h) == 10
+        assert nx.astar_path_length(self.XG, "s", "v", heuristic=h, cutoff=15) == 10
+        with pytest.raises(nx.NetworkXNoPath):
+            nx.astar_path_length(self.XG, "s", "v", heuristic=h, cutoff=9)
+        with pytest.raises(nx.NetworkXNoPath):
+            nx.astar_path_length(self.XG, "s", "v", heuristic=h, cutoff=12)
+
+    def test_astar_cutoff2(self):
+        assert nx.astar_path(self.XG, "s", "v", cutoff=10.0) == ["s", "x", "u", "v"]
+        assert nx.astar_path_length(self.XG, "s", "v") == 9
+
+    def test_cycle(self):
+        C = nx.cycle_graph(7)
+        assert nx.astar_path(C, 0, 3) == [0, 1, 2, 3]
+        assert nx.dijkstra_path(C, 0, 4) == [0, 6, 5, 4]
+
+    def test_unorderable_nodes(self):
+        """Tests that A* accommodates nodes that are not orderable.
+
+        For more information, see issue #554.
+
+        """
+        # Create the cycle graph on four nodes, with nodes represented
+        # as (unorderable) Python objects.
+        nodes = [object() for n in range(4)]
+        G = nx.Graph()
+        G.add_edges_from(pairwise(nodes, cyclic=True))
+        path = nx.astar_path(G, nodes[0], nodes[2])
+        assert len(path) == 3
+
+    def test_astar_NetworkXNoPath(self):
+        """Tests that exception is raised when there exists no
+        path between source and target"""
+        G = nx.gnp_random_graph(10, 0.2, seed=10)
+        with pytest.raises(nx.NetworkXNoPath):
+            nx.astar_path(G, 4, 9)
+
+    def test_astar_NodeNotFound(self):
+        """Tests that exception is raised when either
+        source or target is not in graph"""
+        G = nx.gnp_random_graph(10, 0.2, seed=10)
+        with pytest.raises(nx.NodeNotFound):
+            nx.astar_path_length(G, 11, 9)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_dense.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_dense.py
new file mode 100644
index 0000000000000000000000000000000000000000..6923bfef856c83bd3e65573b97fe96ff16cdbc71
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_dense.py
@@ -0,0 +1,212 @@
+import pytest
+
+import networkx as nx
+
+
+class TestFloyd:
+    @classmethod
+    def setup_class(cls):
+        pass
+
+    def test_floyd_warshall_predecessor_and_distance(self):
+        XG = nx.DiGraph()
+        XG.add_weighted_edges_from(
+            [
+                ("s", "u", 10),
+                ("s", "x", 5),
+                ("u", "v", 1),
+                ("u", "x", 2),
+                ("v", "y", 1),
+                ("x", "u", 3),
+                ("x", "v", 5),
+                ("x", "y", 2),
+                ("y", "s", 7),
+                ("y", "v", 6),
+            ]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(XG)
+        assert dist["s"]["v"] == 9
+        assert path["s"]["v"] == "u"
+        assert dist == {
+            "y": {"y": 0, "x": 12, "s": 7, "u": 15, "v": 6},
+            "x": {"y": 2, "x": 0, "s": 9, "u": 3, "v": 4},
+            "s": {"y": 7, "x": 5, "s": 0, "u": 8, "v": 9},
+            "u": {"y": 2, "x": 2, "s": 9, "u": 0, "v": 1},
+            "v": {"y": 1, "x": 13, "s": 8, "u": 16, "v": 0},
+        }
+
+        GG = XG.to_undirected()
+        # make sure we get lower weight
+        # to_undirected might choose either edge with weight 2 or weight 3
+        GG["u"]["x"]["weight"] = 2
+        path, dist = nx.floyd_warshall_predecessor_and_distance(GG)
+        assert dist["s"]["v"] == 8
+        # skip this test, could be alternate path s-u-v
+        #        assert_equal(path['s']['v'],'y')
+
+        G = nx.DiGraph()  # no weights
+        G.add_edges_from(
+            [
+                ("s", "u"),
+                ("s", "x"),
+                ("u", "v"),
+                ("u", "x"),
+                ("v", "y"),
+                ("x", "u"),
+                ("x", "v"),
+                ("x", "y"),
+                ("y", "s"),
+                ("y", "v"),
+            ]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(G)
+        assert dist["s"]["v"] == 2
+        # skip this test, could be alternate path s-u-v
+        # assert_equal(path['s']['v'],'x')
+
+        # alternate interface
+        dist = nx.floyd_warshall(G)
+        assert dist["s"]["v"] == 2
+
+        # floyd_warshall_predecessor_and_distance returns
+        # dicts-of-defautdicts
+        # make sure we don't get empty dictionary
+        XG = nx.DiGraph()
+        XG.add_weighted_edges_from(
+            [("v", "x", 5.0), ("y", "x", 5.0), ("v", "y", 6.0), ("x", "u", 2.0)]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(XG)
+        inf = float("inf")
+        assert dist == {
+            "v": {"v": 0, "x": 5.0, "y": 6.0, "u": 7.0},
+            "x": {"x": 0, "u": 2.0, "v": inf, "y": inf},
+            "y": {"y": 0, "x": 5.0, "v": inf, "u": 7.0},
+            "u": {"u": 0, "v": inf, "x": inf, "y": inf},
+        }
+        assert path == {
+            "v": {"x": "v", "y": "v", "u": "x"},
+            "x": {"u": "x"},
+            "y": {"x": "y", "u": "x"},
+        }
+
+    def test_reconstruct_path(self):
+        with pytest.raises(KeyError):
+            XG = nx.DiGraph()
+            XG.add_weighted_edges_from(
+                [
+                    ("s", "u", 10),
+                    ("s", "x", 5),
+                    ("u", "v", 1),
+                    ("u", "x", 2),
+                    ("v", "y", 1),
+                    ("x", "u", 3),
+                    ("x", "v", 5),
+                    ("x", "y", 2),
+                    ("y", "s", 7),
+                    ("y", "v", 6),
+                ]
+            )
+            predecessors, _ = nx.floyd_warshall_predecessor_and_distance(XG)
+
+            path = nx.reconstruct_path("s", "v", predecessors)
+            assert path == ["s", "x", "u", "v"]
+
+            path = nx.reconstruct_path("s", "s", predecessors)
+            assert path == []
+
+            # this part raises the keyError
+            nx.reconstruct_path("1", "2", predecessors)
+
+    def test_cycle(self):
+        path, dist = nx.floyd_warshall_predecessor_and_distance(nx.cycle_graph(7))
+        assert dist[0][3] == 3
+        assert path[0][3] == 2
+        assert dist[0][4] == 3
+
+    def test_weighted(self):
+        XG3 = nx.Graph()
+        XG3.add_weighted_edges_from(
+            [[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(XG3)
+        assert dist[0][3] == 15
+        assert path[0][3] == 2
+
+    def test_weighted2(self):
+        XG4 = nx.Graph()
+        XG4.add_weighted_edges_from(
+            [
+                [0, 1, 2],
+                [1, 2, 2],
+                [2, 3, 1],
+                [3, 4, 1],
+                [4, 5, 1],
+                [5, 6, 1],
+                [6, 7, 1],
+                [7, 0, 1],
+            ]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(XG4)
+        assert dist[0][2] == 4
+        assert path[0][2] == 1
+
+    def test_weight_parameter(self):
+        XG4 = nx.Graph()
+        XG4.add_edges_from(
+            [
+                (0, 1, {"heavy": 2}),
+                (1, 2, {"heavy": 2}),
+                (2, 3, {"heavy": 1}),
+                (3, 4, {"heavy": 1}),
+                (4, 5, {"heavy": 1}),
+                (5, 6, {"heavy": 1}),
+                (6, 7, {"heavy": 1}),
+                (7, 0, {"heavy": 1}),
+            ]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(XG4, weight="heavy")
+        assert dist[0][2] == 4
+        assert path[0][2] == 1
+
+    def test_zero_distance(self):
+        XG = nx.DiGraph()
+        XG.add_weighted_edges_from(
+            [
+                ("s", "u", 10),
+                ("s", "x", 5),
+                ("u", "v", 1),
+                ("u", "x", 2),
+                ("v", "y", 1),
+                ("x", "u", 3),
+                ("x", "v", 5),
+                ("x", "y", 2),
+                ("y", "s", 7),
+                ("y", "v", 6),
+            ]
+        )
+        path, dist = nx.floyd_warshall_predecessor_and_distance(XG)
+
+        for u in XG:
+            assert dist[u][u] == 0
+
+        GG = XG.to_undirected()
+        # make sure we get lower weight
+        # to_undirected might choose either edge with weight 2 or weight 3
+        GG["u"]["x"]["weight"] = 2
+        path, dist = nx.floyd_warshall_predecessor_and_distance(GG)
+
+        for u in GG:
+            dist[u][u] = 0
+
+    def test_zero_weight(self):
+        G = nx.DiGraph()
+        edges = [(1, 2, -2), (2, 3, -4), (1, 5, 1), (5, 4, 0), (4, 3, -5), (2, 5, -7)]
+        G.add_weighted_edges_from(edges)
+        dist = nx.floyd_warshall(G)
+        assert dist[1][3] == -14
+
+        G = nx.MultiDiGraph()
+        edges.append((2, 5, -7))
+        G.add_weighted_edges_from(edges)
+        dist = nx.floyd_warshall(G)
+        assert dist[1][3] == -14
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_dense_numpy.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_dense_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eb95a5280bd74d8b88cfc3049f020e7ee187a51
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_dense_numpy.py
@@ -0,0 +1,88 @@
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+
+
+def test_cycle_numpy():
+    dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
+    assert dist[0, 3] == 3
+    assert dist[0, 4] == 3
+
+
+def test_weighted_numpy_three_edges():
+    XG3 = nx.Graph()
+    XG3.add_weighted_edges_from(
+        [[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]]
+    )
+    dist = nx.floyd_warshall_numpy(XG3)
+    assert dist[0, 3] == 15
+
+
+def test_weighted_numpy_two_edges():
+    XG4 = nx.Graph()
+    XG4.add_weighted_edges_from(
+        [
+            [0, 1, 2],
+            [1, 2, 2],
+            [2, 3, 1],
+            [3, 4, 1],
+            [4, 5, 1],
+            [5, 6, 1],
+            [6, 7, 1],
+            [7, 0, 1],
+        ]
+    )
+    dist = nx.floyd_warshall_numpy(XG4)
+    assert dist[0, 2] == 4
+
+
+def test_weight_parameter_numpy():
+    XG4 = nx.Graph()
+    XG4.add_edges_from(
+        [
+            (0, 1, {"heavy": 2}),
+            (1, 2, {"heavy": 2}),
+            (2, 3, {"heavy": 1}),
+            (3, 4, {"heavy": 1}),
+            (4, 5, {"heavy": 1}),
+            (5, 6, {"heavy": 1}),
+            (6, 7, {"heavy": 1}),
+            (7, 0, {"heavy": 1}),
+        ]
+    )
+    dist = nx.floyd_warshall_numpy(XG4, weight="heavy")
+    assert dist[0, 2] == 4
+
+
+def test_directed_cycle_numpy():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [0, 1, 2, 3])
+    pred, dist = nx.floyd_warshall_predecessor_and_distance(G)
+    D = nx.utils.dict_to_numpy_array(dist)
+    np.testing.assert_equal(nx.floyd_warshall_numpy(G), D)
+
+
+def test_zero_weight():
+    G = nx.DiGraph()
+    edges = [(1, 2, -2), (2, 3, -4), (1, 5, 1), (5, 4, 0), (4, 3, -5), (2, 5, -7)]
+    G.add_weighted_edges_from(edges)
+    dist = nx.floyd_warshall_numpy(G)
+    assert int(np.min(dist)) == -14
+
+    G = nx.MultiDiGraph()
+    edges.append((2, 5, -7))
+    G.add_weighted_edges_from(edges)
+    dist = nx.floyd_warshall_numpy(G)
+    assert int(np.min(dist)) == -14
+
+
+def test_nodelist():
+    G = nx.path_graph(7)
+    dist = nx.floyd_warshall_numpy(G, nodelist=[3, 5, 4, 6, 2, 1, 0])
+    assert dist[0, 3] == 3
+    assert dist[0, 1] == 2
+    assert dist[6, 2] == 4
+    pytest.raises(nx.NetworkXError, nx.floyd_warshall_numpy, G, [1, 3])
+    pytest.raises(nx.NetworkXError, nx.floyd_warshall_numpy, G, list(range(9)))
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_generic.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..09f6953d4bce83d79db2f424c5645c65cb6e2234
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_generic.py
@@ -0,0 +1,511 @@
+import pytest
+
+import networkx as nx
+
+
+def validate_grid_path(r, c, s, t, p):
+    assert isinstance(p, list)
+    assert p[0] == s
+    assert p[-1] == t
+    s = ((s - 1) // c, (s - 1) % c)
+    t = ((t - 1) // c, (t - 1) % c)
+    assert len(p) == abs(t[0] - s[0]) + abs(t[1] - s[1]) + 1
+    p = [((u - 1) // c, (u - 1) % c) for u in p]
+    for u in p:
+        assert 0 <= u[0] < r
+        assert 0 <= u[1] < c
+    for u, v in zip(p[:-1], p[1:]):
+        assert (abs(v[0] - u[0]), abs(v[1] - u[1])) in [(0, 1), (1, 0)]
+
+
+class TestGenericPath:
+    @classmethod
+    def setup_class(cls):
+        from networkx import convert_node_labels_to_integers as cnlti
+
+        cls.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+        cls.cycle = nx.cycle_graph(7)
+        cls.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
+        cls.neg_weights = nx.DiGraph()
+        cls.neg_weights.add_edge(0, 1, weight=1)
+        cls.neg_weights.add_edge(0, 2, weight=3)
+        cls.neg_weights.add_edge(1, 3, weight=1)
+        cls.neg_weights.add_edge(2, 3, weight=-2)
+
+    def test_sentinel_trick_all_algorithms(self):
+        def reconstruct_path(pred, source, target):
+            path = [target]
+            while path[-1] != source:
+                path.append(pred[path[-1]])
+            return list(reversed(path))
+
+        # Build the test graph inline
+        G = nx.Graph()
+        G.add_edge("A", "B", weight=1)
+        G.add_edge("B", "C", weight=1)
+        G.add_edge("C", "D", weight=1)
+        G.add_edge("D", "E", weight=1)
+
+        source = "A"
+        targets = {"C", "D", "E"}
+        expected_target = "C"  # A-B-C is closest
+
+        sentinel = "_sentinel_"
+        G.add_node(sentinel)
+        for t in targets:
+            G.add_edge(t, sentinel, weight=0)
+
+        # shortest_path: Dijkstra (default)
+        path = nx.shortest_path(G, source=source, target=sentinel, weight="weight")
+        assert path[-2] == expected_target
+
+        # shortest_path: Bellman-Ford
+        path = nx.shortest_path(
+            G, source=source, target=sentinel, weight="weight", method="bellman-ford"
+        )
+        assert path[-2] == expected_target
+
+        # shortest_path: Unweighted (BFS)
+        path = nx.shortest_path(G, source=source, target=sentinel, weight=None)
+        assert path[-2] == expected_target
+
+        # bidirectional_dijkstra
+        _, path = nx.bidirectional_dijkstra(G, source, sentinel, weight="weight")
+        assert path[-2] == expected_target
+
+        # goldberg_radzik
+        pred, _ = nx.goldberg_radzik(G, source, weight="weight")
+        path = reconstruct_path(pred, source, sentinel)
+        assert path[-2] == expected_target
+
+        # astar_path with zero heuristic
+        path = nx.astar_path(
+            G, source, sentinel, heuristic=lambda u, v: 0, weight="weight"
+        )
+        assert path[-2] == expected_target
+
+        # johnson (all-pairs shortest paths)
+        paths = nx.johnson(G, weight="weight")
+        assert paths[source][sentinel][-2] == expected_target
+
+        # floyd_warshall_predecessor_and_distance
+        pred, _ = nx.floyd_warshall_predecessor_and_distance(G, weight="weight")
+        path = reconstruct_path(pred[source], source, sentinel)
+        assert path[-2] == expected_target
+
+    def test_shortest_path(self):
+        assert nx.shortest_path(self.cycle, 0, 3) == [0, 1, 2, 3]
+        assert nx.shortest_path(self.cycle, 0, 4) == [0, 6, 5, 4]
+        validate_grid_path(4, 4, 1, 12, nx.shortest_path(self.grid, 1, 12))
+        assert nx.shortest_path(self.directed_cycle, 0, 3) == [0, 1, 2, 3]
+        # now with weights
+        assert nx.shortest_path(self.cycle, 0, 3, weight="weight") == [0, 1, 2, 3]
+        assert nx.shortest_path(self.cycle, 0, 4, weight="weight") == [0, 6, 5, 4]
+        validate_grid_path(
+            4, 4, 1, 12, nx.shortest_path(self.grid, 1, 12, weight="weight")
+        )
+        assert nx.shortest_path(self.directed_cycle, 0, 3, weight="weight") == [
+            0,
+            1,
+            2,
+            3,
+        ]
+        # weights and method specified
+        assert nx.shortest_path(
+            self.directed_cycle, 0, 3, weight="weight", method="dijkstra"
+        ) == [0, 1, 2, 3]
+        assert nx.shortest_path(
+            self.directed_cycle, 0, 3, weight="weight", method="bellman-ford"
+        ) == [0, 1, 2, 3]
+        # when Dijkstra's will probably (depending on precise implementation)
+        # incorrectly return [0, 1, 3] instead
+        assert nx.shortest_path(
+            self.neg_weights, 0, 3, weight="weight", method="bellman-ford"
+        ) == [0, 2, 3]
+        # confirm bad method rejection
+        pytest.raises(ValueError, nx.shortest_path, self.cycle, method="SPAM")
+        # confirm absent source rejection
+        pytest.raises(nx.NodeNotFound, nx.shortest_path, self.cycle, 8)
+
+    def test_shortest_path_target(self):
+        answer = {0: [0, 1], 1: [1], 2: [2, 1]}
+        sp = nx.shortest_path(nx.path_graph(3), target=1)
+        assert sp == answer
+        # with weights
+        sp = nx.shortest_path(nx.path_graph(3), target=1, weight="weight")
+        assert sp == answer
+        # weights and method specified
+        sp = nx.shortest_path(
+            nx.path_graph(3), target=1, weight="weight", method="dijkstra"
+        )
+        assert sp == answer
+        sp = nx.shortest_path(
+            nx.path_graph(3), target=1, weight="weight", method="bellman-ford"
+        )
+        assert sp == answer
+
+    def test_shortest_path_length(self):
+        assert nx.shortest_path_length(self.cycle, 0, 3) == 3
+        assert nx.shortest_path_length(self.grid, 1, 12) == 5
+        assert nx.shortest_path_length(self.directed_cycle, 0, 4) == 4
+        # now with weights
+        assert nx.shortest_path_length(self.cycle, 0, 3, weight="weight") == 3
+        assert nx.shortest_path_length(self.grid, 1, 12, weight="weight") == 5
+        assert nx.shortest_path_length(self.directed_cycle, 0, 4, weight="weight") == 4
+        # weights and method specified
+        assert (
+            nx.shortest_path_length(
+                self.cycle, 0, 3, weight="weight", method="dijkstra"
+            )
+            == 3
+        )
+        assert (
+            nx.shortest_path_length(
+                self.cycle, 0, 3, weight="weight", method="bellman-ford"
+            )
+            == 3
+        )
+        # confirm bad method rejection
+        pytest.raises(ValueError, nx.shortest_path_length, self.cycle, method="SPAM")
+        # confirm absent source rejection
+        pytest.raises(nx.NodeNotFound, nx.shortest_path_length, self.cycle, 8)
+
+    def test_shortest_path_length_target(self):
+        answer = {0: 1, 1: 0, 2: 1}
+        sp = nx.shortest_path_length(nx.path_graph(3), target=1)
+        assert sp == answer
+        # with weights
+        sp = nx.shortest_path_length(nx.path_graph(3), target=1, weight="weight")
+        assert sp == answer
+        # weights and method specified
+        sp = nx.shortest_path_length(
+            nx.path_graph(3), target=1, weight="weight", method="dijkstra"
+        )
+        assert sp == answer
+        sp = nx.shortest_path_length(
+            nx.path_graph(3), target=1, weight="weight", method="bellman-ford"
+        )
+        assert sp == answer
+
+    def test_single_source_shortest_path(self):
+        p = nx.shortest_path(self.cycle, 0)
+        assert p[3] == [0, 1, 2, 3]
+        assert p == nx.single_source_shortest_path(self.cycle, 0)
+        p = nx.shortest_path(self.grid, 1)
+        validate_grid_path(4, 4, 1, 12, p[12])
+        # now with weights
+        p = nx.shortest_path(self.cycle, 0, weight="weight")
+        assert p[3] == [0, 1, 2, 3]
+        assert p == nx.single_source_dijkstra_path(self.cycle, 0)
+        p = nx.shortest_path(self.grid, 1, weight="weight")
+        validate_grid_path(4, 4, 1, 12, p[12])
+        # weights and method specified
+        p = nx.shortest_path(self.cycle, 0, method="dijkstra", weight="weight")
+        assert p[3] == [0, 1, 2, 3]
+        assert p == nx.single_source_shortest_path(self.cycle, 0)
+        p = nx.shortest_path(self.cycle, 0, method="bellman-ford", weight="weight")
+        assert p[3] == [0, 1, 2, 3]
+        assert p == nx.single_source_shortest_path(self.cycle, 0)
+
+    def test_single_source_shortest_path_length(self):
+        ans = nx.shortest_path_length(self.cycle, 0)
+        assert ans == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == nx.single_source_shortest_path_length(self.cycle, 0)
+        ans = nx.shortest_path_length(self.grid, 1)
+        assert ans[16] == 6
+        # now with weights
+        ans = nx.shortest_path_length(self.cycle, 0, weight="weight")
+        assert ans == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == nx.single_source_dijkstra_path_length(self.cycle, 0)
+        ans = nx.shortest_path_length(self.grid, 1, weight="weight")
+        assert ans[16] == 6
+        # weights and method specified
+        ans = dict(
+            nx.shortest_path_length(self.cycle, 0, weight="weight", method="dijkstra")
+        )
+        assert ans == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == nx.single_source_dijkstra_path_length(self.cycle, 0)
+        ans = dict(
+            nx.shortest_path_length(
+                self.cycle, 0, weight="weight", method="bellman-ford"
+            )
+        )
+        assert ans == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == nx.single_source_bellman_ford_path_length(self.cycle, 0)
+
+    def test_single_source_all_shortest_paths(self):
+        cycle_ans = {0: [[0]], 1: [[0, 1]], 2: [[0, 1, 2], [0, 3, 2]], 3: [[0, 3]]}
+        ans = dict(nx.single_source_all_shortest_paths(nx.cycle_graph(4), 0))
+        assert sorted(ans[2]) == cycle_ans[2]
+        ans = dict(nx.single_source_all_shortest_paths(self.grid, 1))
+        grid_ans = [
+            [1, 2, 3, 7, 11],
+            [1, 2, 6, 7, 11],
+            [1, 2, 6, 10, 11],
+            [1, 5, 6, 7, 11],
+            [1, 5, 6, 10, 11],
+            [1, 5, 9, 10, 11],
+        ]
+        assert sorted(ans[11]) == grid_ans
+        ans = dict(
+            nx.single_source_all_shortest_paths(nx.cycle_graph(4), 0, weight="weight")
+        )
+        assert sorted(ans[2]) == cycle_ans[2]
+        ans = dict(
+            nx.single_source_all_shortest_paths(
+                nx.cycle_graph(4), 0, method="bellman-ford", weight="weight"
+            )
+        )
+        assert sorted(ans[2]) == cycle_ans[2]
+        ans = dict(nx.single_source_all_shortest_paths(self.grid, 1, weight="weight"))
+        assert sorted(ans[11]) == grid_ans
+        ans = dict(
+            nx.single_source_all_shortest_paths(
+                self.grid, 1, method="bellman-ford", weight="weight"
+            )
+        )
+        assert sorted(ans[11]) == grid_ans
+        G = nx.cycle_graph(4)
+        G.add_node(4)
+        ans = dict(nx.single_source_all_shortest_paths(G, 0))
+        assert sorted(ans[2]) == [[0, 1, 2], [0, 3, 2]]
+        ans = dict(nx.single_source_all_shortest_paths(G, 4))
+        assert sorted(ans[4]) == [[4]]
+
+    def test_all_pairs_shortest_path(self):
+        # shortest_path w/o source and target returns a generator instead of
+        # a dict beginning in version 3.5. Only the first call needed changed here.
+        p = dict(nx.shortest_path(self.cycle))
+        assert p[0][3] == [0, 1, 2, 3]
+        assert p == dict(nx.all_pairs_shortest_path(self.cycle))
+        p = dict(nx.shortest_path(self.grid))
+        validate_grid_path(4, 4, 1, 12, p[1][12])
+        # now with weights
+        p = dict(nx.shortest_path(self.cycle, weight="weight"))
+        assert p[0][3] == [0, 1, 2, 3]
+        assert p == dict(nx.all_pairs_dijkstra_path(self.cycle))
+        p = dict(nx.shortest_path(self.grid, weight="weight"))
+        validate_grid_path(4, 4, 1, 12, p[1][12])
+        # weights and method specified
+        p = dict(nx.shortest_path(self.cycle, weight="weight", method="dijkstra"))
+        assert p[0][3] == [0, 1, 2, 3]
+        assert p == dict(nx.all_pairs_dijkstra_path(self.cycle))
+        p = dict(nx.shortest_path(self.cycle, weight="weight", method="bellman-ford"))
+        assert p[0][3] == [0, 1, 2, 3]
+        assert p == dict(nx.all_pairs_bellman_ford_path(self.cycle))
+
+    def test_all_pairs_shortest_path_length(self):
+        ans = dict(nx.shortest_path_length(self.cycle))
+        assert ans[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == dict(nx.all_pairs_shortest_path_length(self.cycle))
+        ans = dict(nx.shortest_path_length(self.grid))
+        assert ans[1][16] == 6
+        # now with weights
+        ans = dict(nx.shortest_path_length(self.cycle, weight="weight"))
+        assert ans[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == dict(nx.all_pairs_dijkstra_path_length(self.cycle))
+        ans = dict(nx.shortest_path_length(self.grid, weight="weight"))
+        assert ans[1][16] == 6
+        # weights and method specified
+        ans = dict(
+            nx.shortest_path_length(self.cycle, weight="weight", method="dijkstra")
+        )
+        assert ans[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == dict(nx.all_pairs_dijkstra_path_length(self.cycle))
+        ans = dict(
+            nx.shortest_path_length(self.cycle, weight="weight", method="bellman-ford")
+        )
+        assert ans[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert ans == dict(nx.all_pairs_bellman_ford_path_length(self.cycle))
+
+    def test_all_pairs_all_shortest_paths(self):
+        ans = dict(nx.all_pairs_all_shortest_paths(nx.cycle_graph(4)))
+        assert sorted(ans[1][3]) == [[1, 0, 3], [1, 2, 3]]
+        ans = dict(nx.all_pairs_all_shortest_paths(nx.cycle_graph(4)), weight="weight")
+        assert sorted(ans[1][3]) == [[1, 0, 3], [1, 2, 3]]
+        ans = dict(
+            nx.all_pairs_all_shortest_paths(nx.cycle_graph(4)),
+            method="bellman-ford",
+            weight="weight",
+        )
+        assert sorted(ans[1][3]) == [[1, 0, 3], [1, 2, 3]]
+        G = nx.cycle_graph(4)
+        G.add_node(4)
+        ans = dict(nx.all_pairs_all_shortest_paths(G))
+        assert sorted(ans[4][4]) == [[4]]
+
+    def test_has_path(self):
+        G = nx.Graph()
+        nx.add_path(G, range(3))
+        nx.add_path(G, range(3, 5))
+        assert nx.has_path(G, 0, 2)
+        assert not nx.has_path(G, 0, 4)
+
+    def test_has_path_singleton(self):
+        G = nx.empty_graph(1)
+        assert nx.has_path(G, 0, 0)
+
+    def test_all_shortest_paths(self):
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2, 3])
+        nx.add_path(G, [0, 10, 20, 3])
+        assert [[0, 1, 2, 3], [0, 10, 20, 3]] == sorted(nx.all_shortest_paths(G, 0, 3))
+        # with weights
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2, 3])
+        nx.add_path(G, [0, 10, 20, 3])
+        assert [[0, 1, 2, 3], [0, 10, 20, 3]] == sorted(
+            nx.all_shortest_paths(G, 0, 3, weight="weight")
+        )
+        # weights and method specified
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2, 3])
+        nx.add_path(G, [0, 10, 20, 3])
+        assert [[0, 1, 2, 3], [0, 10, 20, 3]] == sorted(
+            nx.all_shortest_paths(G, 0, 3, weight="weight", method="dijkstra")
+        )
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2, 3])
+        nx.add_path(G, [0, 10, 20, 3])
+        assert [[0, 1, 2, 3], [0, 10, 20, 3]] == sorted(
+            nx.all_shortest_paths(G, 0, 3, weight="weight", method="bellman-ford")
+        )
+
+    def test_all_shortest_paths_raise(self):
+        with pytest.raises(nx.NetworkXNoPath):
+            G = nx.path_graph(4)
+            G.add_node(4)
+            list(nx.all_shortest_paths(G, 0, 4))
+
+    def test_bad_method(self):
+        with pytest.raises(ValueError):
+            G = nx.path_graph(2)
+            list(nx.all_shortest_paths(G, 0, 1, weight="weight", method="SPAM"))
+
+    def test_single_source_all_shortest_paths_bad_method(self):
+        with pytest.raises(ValueError):
+            G = nx.path_graph(2)
+            dict(
+                nx.single_source_all_shortest_paths(
+                    G, 0, weight="weight", method="SPAM"
+                )
+            )
+
+    def test_all_shortest_paths_zero_weight_edge(self):
+        g = nx.Graph()
+        nx.add_path(g, [0, 1, 3])
+        nx.add_path(g, [0, 1, 2, 3])
+        g.edges[1, 2]["weight"] = 0
+        paths30d = list(
+            nx.all_shortest_paths(g, 3, 0, weight="weight", method="dijkstra")
+        )
+        paths03d = list(
+            nx.all_shortest_paths(g, 0, 3, weight="weight", method="dijkstra")
+        )
+        paths30b = list(
+            nx.all_shortest_paths(g, 3, 0, weight="weight", method="bellman-ford")
+        )
+        paths03b = list(
+            nx.all_shortest_paths(g, 0, 3, weight="weight", method="bellman-ford")
+        )
+        assert sorted(paths03d) == sorted(p[::-1] for p in paths30d)
+        assert sorted(paths03d) == sorted(p[::-1] for p in paths30b)
+        assert sorted(paths03b) == sorted(p[::-1] for p in paths30b)
+
+
+class TestAverageShortestPathLength:
+    def test_cycle_graph(self):
+        ans = nx.average_shortest_path_length(nx.cycle_graph(7))
+        assert ans == pytest.approx(2, abs=1e-7)
+
+    def test_path_graph(self):
+        ans = nx.average_shortest_path_length(nx.path_graph(5))
+        assert ans == pytest.approx(2, abs=1e-7)
+
+    def test_weighted(self):
+        G = nx.Graph()
+        nx.add_cycle(G, range(7), weight=2)
+        ans = nx.average_shortest_path_length(G, weight="weight")
+        assert ans == pytest.approx(4, abs=1e-7)
+        G = nx.Graph()
+        nx.add_path(G, range(5), weight=2)
+        ans = nx.average_shortest_path_length(G, weight="weight")
+        assert ans == pytest.approx(4, abs=1e-7)
+
+    def test_specified_methods(self):
+        G = nx.Graph()
+        nx.add_cycle(G, range(7), weight=2)
+        ans = nx.average_shortest_path_length(G, weight="weight", method="dijkstra")
+        assert ans == pytest.approx(4, abs=1e-7)
+        ans = nx.average_shortest_path_length(G, weight="weight", method="bellman-ford")
+        assert ans == pytest.approx(4, abs=1e-7)
+        ans = nx.average_shortest_path_length(
+            G, weight="weight", method="floyd-warshall"
+        )
+        assert ans == pytest.approx(4, abs=1e-7)
+
+        G = nx.Graph()
+        nx.add_path(G, range(5), weight=2)
+        ans = nx.average_shortest_path_length(G, weight="weight", method="dijkstra")
+        assert ans == pytest.approx(4, abs=1e-7)
+        ans = nx.average_shortest_path_length(G, weight="weight", method="bellman-ford")
+        assert ans == pytest.approx(4, abs=1e-7)
+        ans = nx.average_shortest_path_length(
+            G, weight="weight", method="floyd-warshall"
+        )
+        assert ans == pytest.approx(4, abs=1e-7)
+
+    def test_directed_not_strongly_connected(self):
+        G = nx.DiGraph([(0, 1)])
+        with pytest.raises(nx.NetworkXError, match="Graph is not strongly connected"):
+            nx.average_shortest_path_length(G)
+
+    def test_undirected_not_connected(self):
+        g = nx.Graph()
+        g.add_nodes_from(range(3))
+        g.add_edge(0, 1)
+        pytest.raises(nx.NetworkXError, nx.average_shortest_path_length, g)
+
+    def test_trivial_graph(self):
+        """Tests that the trivial graph has average path length zero,
+        since there is exactly one path of length zero in the trivial
+        graph.
+
+        For more information, see issue #1960.
+
+        """
+        G = nx.trivial_graph()
+        assert nx.average_shortest_path_length(G) == 0
+
+    def test_null_graph(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.average_shortest_path_length(nx.null_graph())
+
+    def test_bad_method(self):
+        with pytest.raises(ValueError):
+            G = nx.path_graph(2)
+            nx.average_shortest_path_length(G, weight="weight", method="SPAM")
+
+
+class TestAverageShortestPathLengthNumpy:
+    @classmethod
+    def setup_class(cls):
+        global np
+        import pytest
+
+        np = pytest.importorskip("numpy")
+
+    def test_specified_methods_numpy(self):
+        G = nx.Graph()
+        nx.add_cycle(G, range(7), weight=2)
+        ans = nx.average_shortest_path_length(
+            G, weight="weight", method="floyd-warshall-numpy"
+        )
+        np.testing.assert_almost_equal(ans, 4)
+
+        G = nx.Graph()
+        nx.add_path(G, range(5), weight=2)
+        ans = nx.average_shortest_path_length(
+            G, weight="weight", method="floyd-warshall-numpy"
+        )
+        np.testing.assert_almost_equal(ans, 4)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_unweighted.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_unweighted.py
new file mode 100644
index 0000000000000000000000000000000000000000..4adbd84de7fca27e58ba2c09edf54b92a0fbdf40
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_unweighted.py
@@ -0,0 +1,149 @@
+import pytest
+
+import networkx as nx
+
+
+def validate_grid_path(r, c, s, t, p):
+    assert isinstance(p, list)
+    assert p[0] == s
+    assert p[-1] == t
+    s = ((s - 1) // c, (s - 1) % c)
+    t = ((t - 1) // c, (t - 1) % c)
+    assert len(p) == abs(t[0] - s[0]) + abs(t[1] - s[1]) + 1
+    p = [((u - 1) // c, (u - 1) % c) for u in p]
+    for u in p:
+        assert 0 <= u[0] < r
+        assert 0 <= u[1] < c
+    for u, v in zip(p[:-1], p[1:]):
+        assert (abs(v[0] - u[0]), abs(v[1] - u[1])) in [(0, 1), (1, 0)]
+
+
+class TestUnweightedPath:
+    @classmethod
+    def setup_class(cls):
+        from networkx import convert_node_labels_to_integers as cnlti
+
+        cls.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+        cls.cycle = nx.cycle_graph(7)
+        cls.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
+
+    def test_bidirectional_shortest_path(self):
+        assert nx.bidirectional_shortest_path(self.cycle, 0, 3) == [0, 1, 2, 3]
+        assert nx.bidirectional_shortest_path(self.cycle, 0, 4) == [0, 6, 5, 4]
+        validate_grid_path(
+            4, 4, 1, 12, nx.bidirectional_shortest_path(self.grid, 1, 12)
+        )
+        assert nx.bidirectional_shortest_path(self.directed_cycle, 0, 3) == [0, 1, 2, 3]
+        # test source = target
+        assert nx.bidirectional_shortest_path(self.cycle, 3, 3) == [3]
+
+    @pytest.mark.parametrize(
+        ("src", "tgt"),
+        (
+            (8, 3),  # source not in graph
+            (3, 8),  # target not in graph
+            (8, 10),  # neither source nor target in graph
+            (8, 8),  # src == tgt, neither in graph - tests order of input checks
+        ),
+    )
+    def test_bidirectional_shortest_path_src_tgt_not_in_graph(self, src, tgt):
+        with pytest.raises(
+            nx.NodeNotFound,
+            match=f"(Source {src}|Target {tgt}) is not in G",
+        ):
+            nx.bidirectional_shortest_path(self.cycle, src, tgt)
+
+    def test_shortest_path_length(self):
+        assert nx.shortest_path_length(self.cycle, 0, 3) == 3
+        assert nx.shortest_path_length(self.grid, 1, 12) == 5
+        assert nx.shortest_path_length(self.directed_cycle, 0, 4) == 4
+        # now with weights
+        assert nx.shortest_path_length(self.cycle, 0, 3, weight=True) == 3
+        assert nx.shortest_path_length(self.grid, 1, 12, weight=True) == 5
+        assert nx.shortest_path_length(self.directed_cycle, 0, 4, weight=True) == 4
+
+    def test_single_source_shortest_path(self):
+        p = nx.single_source_shortest_path(self.directed_cycle, 3)
+        assert p[0] == [3, 4, 5, 6, 0]
+        p = nx.single_source_shortest_path(self.cycle, 0)
+        assert p[3] == [0, 1, 2, 3]
+        p = nx.single_source_shortest_path(self.cycle, 0, cutoff=0)
+        assert p == {0: [0]}
+
+    def test_single_source_shortest_path_length(self):
+        pl = nx.single_source_shortest_path_length
+        lengths = {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert dict(pl(self.cycle, 0)) == lengths
+        lengths = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}
+        assert dict(pl(self.directed_cycle, 0)) == lengths
+
+    def test_single_target_shortest_path(self):
+        p = nx.single_target_shortest_path(self.directed_cycle, 0)
+        assert p[3] == [3, 4, 5, 6, 0]
+        p = nx.single_target_shortest_path(self.cycle, 0)
+        assert p[3] == [3, 2, 1, 0]
+        p = nx.single_target_shortest_path(self.cycle, 0, cutoff=0)
+        assert p == {0: [0]}
+        # test missing targets
+        target = 8
+        with pytest.raises(nx.NodeNotFound, match=f"Target {target} not in G"):
+            nx.single_target_shortest_path(self.cycle, target)
+
+    def test_single_target_shortest_path_length(self):
+        pl = nx.single_target_shortest_path_length
+        lengths = {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert pl(self.cycle, 0) == lengths
+        lengths = {0: 0, 1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1}
+        assert pl(self.directed_cycle, 0) == lengths
+        # test missing targets
+        target = 8
+        with pytest.raises(nx.NodeNotFound, match=f"Target {target} is not in G"):
+            nx.single_target_shortest_path_length(self.cycle, target)
+
+    def test_all_pairs_shortest_path(self):
+        p = dict(nx.all_pairs_shortest_path(self.cycle))
+        assert p[0][3] == [0, 1, 2, 3]
+        p = dict(nx.all_pairs_shortest_path(self.grid))
+        validate_grid_path(4, 4, 1, 12, p[1][12])
+
+    def test_all_pairs_shortest_path_length(self):
+        l = dict(nx.all_pairs_shortest_path_length(self.cycle))
+        assert l[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        l = dict(nx.all_pairs_shortest_path_length(self.grid))
+        assert l[1][16] == 6
+
+    def test_predecessor_path(self):
+        G = nx.path_graph(4)
+        assert nx.predecessor(G, 0) == {0: [], 1: [0], 2: [1], 3: [2]}
+        assert nx.predecessor(G, 0, 3) == [2]
+
+    def test_predecessor_cycle(self):
+        G = nx.cycle_graph(4)
+        pred = nx.predecessor(G, 0)
+        assert pred[0] == []
+        assert pred[1] == [0]
+        assert pred[2] in [[1, 3], [3, 1]]
+        assert pred[3] == [0]
+
+    def test_predecessor_cutoff(self):
+        G = nx.path_graph(4)
+        p = nx.predecessor(G, 0, 3)
+        assert 4 not in p
+
+    def test_predecessor_target(self):
+        G = nx.path_graph(4)
+        p = nx.predecessor(G, 0, 3)
+        assert p == [2]
+        p = nx.predecessor(G, 0, 3, cutoff=2)
+        assert p == []
+        p, s = nx.predecessor(G, 0, 3, return_seen=True)
+        assert p == [2]
+        assert s == 3
+        p, s = nx.predecessor(G, 0, 3, cutoff=2, return_seen=True)
+        assert p == []
+        assert s == -1
+
+    def test_predecessor_missing_source(self):
+        source = 8
+        with pytest.raises(nx.NodeNotFound, match=f"Source {source} not in G"):
+            nx.predecessor(self.cycle, source)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_weighted.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_weighted.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a6a98a13b0eae03a82ef594e15368eb744c8497
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/tests/test_weighted.py
@@ -0,0 +1,983 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import pairwise
+
+
+def validate_path(G, s, t, soln_len, path, weight="weight"):
+    assert path[0] == s
+    assert path[-1] == t
+
+    if callable(weight):
+        weight_f = weight
+    else:
+        if G.is_multigraph():
+
+            def weight_f(u, v, d):
+                return min(e.get(weight, 1) for e in d.values())
+
+        else:
+
+            def weight_f(u, v, d):
+                return d.get(weight, 1)
+
+    computed = sum(weight_f(u, v, G[u][v]) for u, v in pairwise(path))
+    assert soln_len == computed
+
+
+def validate_length_path(G, s, t, soln_len, length, path, weight="weight"):
+    assert soln_len == length
+    validate_path(G, s, t, length, path, weight=weight)
+
+
+class WeightedTestBase:
+    """Base class for test classes that test functions for computing
+    shortest paths in weighted graphs.
+
+    """
+
+    def setup_method(self):
+        """Creates some graphs for use in the unit tests."""
+        cnlti = nx.convert_node_labels_to_integers
+        self.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+        self.cycle = nx.cycle_graph(7)
+        self.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
+        self.XG = nx.DiGraph()
+        self.XG.add_weighted_edges_from(
+            [
+                ("s", "u", 10),
+                ("s", "x", 5),
+                ("u", "v", 1),
+                ("u", "x", 2),
+                ("v", "y", 1),
+                ("x", "u", 3),
+                ("x", "v", 5),
+                ("x", "y", 2),
+                ("y", "s", 7),
+                ("y", "v", 6),
+            ]
+        )
+        self.MXG = nx.MultiDiGraph(self.XG)
+        self.MXG.add_edge("s", "u", weight=15)
+        self.XG2 = nx.DiGraph()
+        self.XG2.add_weighted_edges_from(
+            [
+                [1, 4, 1],
+                [4, 5, 1],
+                [5, 6, 1],
+                [6, 3, 1],
+                [1, 3, 50],
+                [1, 2, 100],
+                [2, 3, 100],
+            ]
+        )
+
+        self.XG3 = nx.Graph()
+        self.XG3.add_weighted_edges_from(
+            [[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]]
+        )
+
+        self.XG4 = nx.Graph()
+        self.XG4.add_weighted_edges_from(
+            [
+                [0, 1, 2],
+                [1, 2, 2],
+                [2, 3, 1],
+                [3, 4, 1],
+                [4, 5, 1],
+                [5, 6, 1],
+                [6, 7, 1],
+                [7, 0, 1],
+            ]
+        )
+        self.MXG4 = nx.MultiGraph(self.XG4)
+        self.MXG4.add_edge(0, 1, weight=3)
+        self.G = nx.DiGraph()  # no weights
+        self.G.add_edges_from(
+            [
+                ("s", "u"),
+                ("s", "x"),
+                ("u", "v"),
+                ("u", "x"),
+                ("v", "y"),
+                ("x", "u"),
+                ("x", "v"),
+                ("x", "y"),
+                ("y", "s"),
+                ("y", "v"),
+            ]
+        )
+
+
+class TestWeightedPath(WeightedTestBase):
+    def test_dijkstra(self):
+        (D, P) = nx.single_source_dijkstra(self.XG, "s")
+        validate_path(self.XG, "s", "v", 9, P["v"])
+        assert D["v"] == 9
+
+        validate_path(
+            self.XG, "s", "v", 9, nx.single_source_dijkstra_path(self.XG, "s")["v"]
+        )
+        assert nx.single_source_dijkstra_path_length(self.XG, "s")["v"] == 9
+
+        validate_path(
+            self.XG, "s", "v", 9, nx.single_source_dijkstra(self.XG, "s")[1]["v"]
+        )
+        validate_path(
+            self.MXG, "s", "v", 9, nx.single_source_dijkstra_path(self.MXG, "s")["v"]
+        )
+
+        GG = self.XG.to_undirected()
+        # make sure we get lower weight
+        # to_undirected might choose either edge with weight 2 or weight 3
+        GG["u"]["x"]["weight"] = 2
+        (D, P) = nx.single_source_dijkstra(GG, "s")
+        validate_path(GG, "s", "v", 8, P["v"])
+        assert D["v"] == 8  # uses lower weight of 2 on u<->x edge
+        validate_path(GG, "s", "v", 8, nx.dijkstra_path(GG, "s", "v"))
+        assert nx.dijkstra_path_length(GG, "s", "v") == 8
+
+        validate_path(self.XG2, 1, 3, 4, nx.dijkstra_path(self.XG2, 1, 3))
+        validate_path(self.XG3, 0, 3, 15, nx.dijkstra_path(self.XG3, 0, 3))
+        assert nx.dijkstra_path_length(self.XG3, 0, 3) == 15
+        validate_path(self.XG4, 0, 2, 4, nx.dijkstra_path(self.XG4, 0, 2))
+        assert nx.dijkstra_path_length(self.XG4, 0, 2) == 4
+        validate_path(self.MXG4, 0, 2, 4, nx.dijkstra_path(self.MXG4, 0, 2))
+        validate_path(
+            self.G, "s", "v", 2, nx.single_source_dijkstra(self.G, "s", "v")[1]
+        )
+        validate_path(
+            self.G, "s", "v", 2, nx.single_source_dijkstra(self.G, "s")[1]["v"]
+        )
+
+        validate_path(self.G, "s", "v", 2, nx.dijkstra_path(self.G, "s", "v"))
+        assert nx.dijkstra_path_length(self.G, "s", "v") == 2
+
+        # NetworkXError: node s not reachable from moon
+        pytest.raises(nx.NetworkXNoPath, nx.dijkstra_path, self.G, "s", "moon")
+        pytest.raises(nx.NetworkXNoPath, nx.dijkstra_path_length, self.G, "s", "moon")
+
+        validate_path(self.cycle, 0, 3, 3, nx.dijkstra_path(self.cycle, 0, 3))
+        validate_path(self.cycle, 0, 4, 3, nx.dijkstra_path(self.cycle, 0, 4))
+
+        assert nx.single_source_dijkstra(self.cycle, 0, 0) == (0, [0])
+
+    def test_bidirectional_dijkstra(self):
+        validate_length_path(
+            self.XG, "s", "v", 9, *nx.bidirectional_dijkstra(self.XG, "s", "v")
+        )
+        validate_length_path(
+            self.G, "s", "v", 2, *nx.bidirectional_dijkstra(self.G, "s", "v")
+        )
+        validate_length_path(
+            self.cycle, 0, 3, 3, *nx.bidirectional_dijkstra(self.cycle, 0, 3)
+        )
+        validate_length_path(
+            self.cycle, 0, 4, 3, *nx.bidirectional_dijkstra(self.cycle, 0, 4)
+        )
+        validate_length_path(
+            self.XG3, 0, 3, 15, *nx.bidirectional_dijkstra(self.XG3, 0, 3)
+        )
+        validate_length_path(
+            self.XG4, 0, 2, 4, *nx.bidirectional_dijkstra(self.XG4, 0, 2)
+        )
+
+        # need more tests here
+        P = nx.single_source_dijkstra_path(self.XG, "s")["v"]
+        validate_path(
+            self.XG,
+            "s",
+            "v",
+            sum(self.XG[u][v]["weight"] for u, v in zip(P[:-1], P[1:])),
+            nx.dijkstra_path(self.XG, "s", "v"),
+        )
+
+        # check absent source
+        G = nx.path_graph(2)
+        pytest.raises(nx.NodeNotFound, nx.bidirectional_dijkstra, G, 3, 0)
+
+    def test_weight_functions(self):
+        def heuristic(*z):
+            return sum(val**2 for val in z)
+
+        def getpath(pred, v, s):
+            return [v] if v == s else getpath(pred, pred[v], s) + [v]
+
+        def goldberg_radzik(g, s, t, weight="weight"):
+            pred, dist = nx.goldberg_radzik(g, s, weight=weight)
+            dist = dist[t]
+            return dist, getpath(pred, t, s)
+
+        def astar(g, s, t, weight="weight"):
+            path = nx.astar_path(g, s, t, heuristic, weight=weight)
+            dist = nx.astar_path_length(g, s, t, heuristic, weight=weight)
+            return dist, path
+
+        def vlp(G, s, t, l, F, w):
+            res = F(G, s, t, weight=w)
+            validate_length_path(G, s, t, l, *res, weight=w)
+
+        G = self.cycle
+        s = 6
+        t = 4
+        path = [6] + list(range(t + 1))
+
+        def weight(u, v, _):
+            return 1 + v**2
+
+        length = sum(weight(u, v, None) for u, v in pairwise(path))
+        vlp(G, s, t, length, nx.bidirectional_dijkstra, weight)
+        vlp(G, s, t, length, nx.single_source_dijkstra, weight)
+        vlp(G, s, t, length, nx.single_source_bellman_ford, weight)
+        vlp(G, s, t, length, goldberg_radzik, weight)
+        vlp(G, s, t, length, astar, weight)
+
+        def weight(u, v, _):
+            return 2 ** (u * v)
+
+        length = sum(weight(u, v, None) for u, v in pairwise(path))
+        vlp(G, s, t, length, nx.bidirectional_dijkstra, weight)
+        vlp(G, s, t, length, nx.single_source_dijkstra, weight)
+        vlp(G, s, t, length, nx.single_source_bellman_ford, weight)
+        vlp(G, s, t, length, goldberg_radzik, weight)
+        vlp(G, s, t, length, astar, weight)
+
+    def test_bidirectional_dijkstra_no_path(self):
+        with pytest.raises(nx.NetworkXNoPath):
+            G = nx.Graph()
+            nx.add_path(G, [1, 2, 3])
+            nx.add_path(G, [4, 5, 6])
+            path = nx.bidirectional_dijkstra(G, 1, 6)
+
+    @pytest.mark.parametrize(
+        "fn",
+        (
+            nx.dijkstra_path,
+            nx.dijkstra_path_length,
+            nx.single_source_dijkstra_path,
+            nx.single_source_dijkstra_path_length,
+            nx.single_source_dijkstra,
+            nx.dijkstra_predecessor_and_distance,
+        ),
+    )
+    def test_absent_source(self, fn):
+        G = nx.path_graph(2)
+        with pytest.raises(nx.NodeNotFound):
+            fn(G, 3, 0)
+        # Test when source == target, which is handled specially by some functions
+        with pytest.raises(nx.NodeNotFound):
+            fn(G, 3, 3)
+
+    def test_dijkstra_predecessor1(self):
+        G = nx.path_graph(4)
+        assert nx.dijkstra_predecessor_and_distance(G, 0) == (
+            {0: [], 1: [0], 2: [1], 3: [2]},
+            {0: 0, 1: 1, 2: 2, 3: 3},
+        )
+
+    def test_dijkstra_predecessor2(self):
+        # 4-cycle
+        G = nx.Graph([(0, 1), (1, 2), (2, 3), (3, 0)])
+        pred, dist = nx.dijkstra_predecessor_and_distance(G, (0))
+        assert pred[0] == []
+        assert pred[1] == [0]
+        assert pred[2] in [[1, 3], [3, 1]]
+        assert pred[3] == [0]
+        assert dist == {0: 0, 1: 1, 2: 2, 3: 1}
+
+    def test_dijkstra_predecessor3(self):
+        XG = nx.DiGraph()
+        XG.add_weighted_edges_from(
+            [
+                ("s", "u", 10),
+                ("s", "x", 5),
+                ("u", "v", 1),
+                ("u", "x", 2),
+                ("v", "y", 1),
+                ("x", "u", 3),
+                ("x", "v", 5),
+                ("x", "y", 2),
+                ("y", "s", 7),
+                ("y", "v", 6),
+            ]
+        )
+        (P, D) = nx.dijkstra_predecessor_and_distance(XG, "s")
+        assert P["v"] == ["u"]
+        assert D["v"] == 9
+        (P, D) = nx.dijkstra_predecessor_and_distance(XG, "s", cutoff=8)
+        assert "v" not in D
+
+    def test_single_source_dijkstra_path_length(self):
+        pl = nx.single_source_dijkstra_path_length
+        assert dict(pl(self.MXG4, 0))[2] == 4
+        spl = pl(self.MXG4, 0, cutoff=2)
+        assert 2 not in spl
+
+    def test_bidirectional_dijkstra_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge("a", "b", weight=10)
+        G.add_edge("a", "b", weight=100)
+        dp = nx.bidirectional_dijkstra(G, "a", "b")
+        assert dp == (10, ["a", "b"])
+
+    def test_dijkstra_pred_distance_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge("a", "b", key="short", foo=5, weight=100)
+        G.add_edge("a", "b", key="long", bar=1, weight=110)
+        p, d = nx.dijkstra_predecessor_and_distance(G, "a")
+        assert p == {"a": [], "b": ["a"]}
+        assert d == {"a": 0, "b": 100}
+
+    def test_negative_edge_cycle(self):
+        G = nx.cycle_graph(5, create_using=nx.DiGraph())
+        assert not nx.negative_edge_cycle(G)
+        G.add_edge(8, 9, weight=-7)
+        G.add_edge(9, 8, weight=3)
+        graph_size = len(G)
+        assert nx.negative_edge_cycle(G)
+        assert graph_size == len(G)
+        pytest.raises(ValueError, nx.single_source_dijkstra_path_length, G, 8)
+        pytest.raises(ValueError, nx.single_source_dijkstra, G, 8)
+        pytest.raises(ValueError, nx.dijkstra_predecessor_and_distance, G, 8)
+        G.add_edge(9, 10)
+        pytest.raises(ValueError, nx.bidirectional_dijkstra, G, 8, 10)
+        G = nx.MultiDiGraph()
+        G.add_edge(2, 2, weight=-1)
+        assert nx.negative_edge_cycle(G)
+
+    def test_negative_edge_cycle_empty(self):
+        G = nx.DiGraph()
+        assert not nx.negative_edge_cycle(G)
+
+    def test_negative_edge_cycle_custom_weight_key(self):
+        d = nx.DiGraph()
+        d.add_edge("a", "b", w=-2)
+        d.add_edge("b", "a", w=-1)
+        assert nx.negative_edge_cycle(d, weight="w")
+
+    def test_weight_function(self):
+        """Tests that a callable weight is interpreted as a weight
+        function instead of an edge attribute.
+
+        """
+        # Create a triangle in which the edge from node 0 to node 2 has
+        # a large weight and the other two edges have a small weight.
+        G = nx.complete_graph(3)
+        G.adj[0][2]["weight"] = 10
+        G.adj[0][1]["weight"] = 1
+        G.adj[1][2]["weight"] = 1
+
+        # The weight function will take the multiplicative inverse of
+        # the weights on the edges. This way, weights that were large
+        # before now become small and vice versa.
+
+        def weight(u, v, d):
+            return 1 / d["weight"]
+
+        # The shortest path from 0 to 2 using the actual weights on the
+        # edges should be [0, 1, 2].
+        distance, path = nx.single_source_dijkstra(G, 0, 2)
+        assert distance == 2
+        assert path == [0, 1, 2]
+        # However, with the above weight function, the shortest path
+        # should be [0, 2], since that has a very small weight.
+        distance, path = nx.single_source_dijkstra(G, 0, 2, weight=weight)
+        assert distance == 1 / 10
+        assert path == [0, 2]
+
+    def test_all_pairs_dijkstra_path(self):
+        cycle = nx.cycle_graph(7)
+        p = dict(nx.all_pairs_dijkstra_path(cycle))
+        assert p[0][3] == [0, 1, 2, 3]
+
+        cycle[1][2]["weight"] = 10
+        p = dict(nx.all_pairs_dijkstra_path(cycle))
+        assert p[0][3] == [0, 6, 5, 4, 3]
+
+    def test_all_pairs_dijkstra_path_length(self):
+        cycle = nx.cycle_graph(7)
+        pl = dict(nx.all_pairs_dijkstra_path_length(cycle))
+        assert pl[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+
+        cycle[1][2]["weight"] = 10
+        pl = dict(nx.all_pairs_dijkstra_path_length(cycle))
+        assert pl[0] == {0: 0, 1: 1, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1}
+
+    def test_all_pairs_dijkstra(self):
+        cycle = nx.cycle_graph(7)
+        out = dict(nx.all_pairs_dijkstra(cycle))
+        assert out[0][0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1}
+        assert out[0][1][3] == [0, 1, 2, 3]
+
+        cycle[1][2]["weight"] = 10
+        out = dict(nx.all_pairs_dijkstra(cycle))
+        assert out[0][0] == {0: 0, 1: 1, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1}
+        assert out[0][1][3] == [0, 6, 5, 4, 3]
+
+
+class TestDijkstraPathLength:
+    """Unit tests for the :func:`networkx.dijkstra_path_length`
+    function.
+
+    """
+
+    def test_weight_function(self):
+        """Tests for computing the length of the shortest path using
+        Dijkstra's algorithm with a user-defined weight function.
+
+        """
+        # Create a triangle in which the edge from node 0 to node 2 has
+        # a large weight and the other two edges have a small weight.
+        G = nx.complete_graph(3)
+        G.adj[0][2]["weight"] = 10
+        G.adj[0][1]["weight"] = 1
+        G.adj[1][2]["weight"] = 1
+
+        # The weight function will take the multiplicative inverse of
+        # the weights on the edges. This way, weights that were large
+        # before now become small and vice versa.
+
+        def weight(u, v, d):
+            return 1 / d["weight"]
+
+        # The shortest path from 0 to 2 using the actual weights on the
+        # edges should be [0, 1, 2]. However, with the above weight
+        # function, the shortest path should be [0, 2], since that has a
+        # very small weight.
+        length = nx.dijkstra_path_length(G, 0, 2, weight=weight)
+        assert length == 1 / 10
+
+
+class TestMultiSourceDijkstra:
+    """Unit tests for the multi-source dialect of Dijkstra's shortest
+    path algorithms.
+
+    """
+
+    def test_no_sources(self):
+        with pytest.raises(ValueError):
+            nx.multi_source_dijkstra(nx.Graph(), {})
+
+    def test_path_no_sources(self):
+        with pytest.raises(ValueError):
+            nx.multi_source_dijkstra_path(nx.Graph(), {})
+
+    def test_path_length_no_sources(self):
+        with pytest.raises(ValueError):
+            nx.multi_source_dijkstra_path_length(nx.Graph(), {})
+
+    @pytest.mark.parametrize(
+        "fn",
+        (
+            nx.multi_source_dijkstra_path,
+            nx.multi_source_dijkstra_path_length,
+            nx.multi_source_dijkstra,
+        ),
+    )
+    def test_absent_source(self, fn):
+        G = nx.path_graph(2)
+        with pytest.raises(nx.NodeNotFound):
+            fn(G, [3], 0)
+        with pytest.raises(nx.NodeNotFound):
+            fn(G, [3], 3)
+
+    def test_two_sources(self):
+        edges = [(0, 1, 1), (1, 2, 1), (2, 3, 10), (3, 4, 1)]
+        G = nx.Graph()
+        G.add_weighted_edges_from(edges)
+        sources = {0, 4}
+        distances, paths = nx.multi_source_dijkstra(G, sources)
+        expected_distances = {0: 0, 1: 1, 2: 2, 3: 1, 4: 0}
+        expected_paths = {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [4, 3], 4: [4]}
+        assert distances == expected_distances
+        assert paths == expected_paths
+
+    def test_simple_paths(self):
+        G = nx.path_graph(4)
+        lengths = nx.multi_source_dijkstra_path_length(G, [0])
+        assert lengths == {n: n for n in G}
+        paths = nx.multi_source_dijkstra_path(G, [0])
+        assert paths == {n: list(range(n + 1)) for n in G}
+
+
+class TestBellmanFordAndGoldbergRadzik(WeightedTestBase):
+    def test_single_node_graph(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+        assert nx.single_source_bellman_ford_path(G, 0) == {0: [0]}
+        assert nx.single_source_bellman_ford_path_length(G, 0) == {0: 0}
+        assert nx.single_source_bellman_ford(G, 0) == ({0: 0}, {0: [0]})
+        assert nx.bellman_ford_predecessor_and_distance(G, 0) == ({0: []}, {0: 0})
+        assert nx.goldberg_radzik(G, 0) == ({0: None}, {0: 0})
+
+    def test_absent_source_bellman_ford(self):
+        # the check is in _bellman_ford; this provides regression testing
+        # against later changes to "client" Bellman-Ford functions
+        G = nx.path_graph(2)
+        for fn in (
+            nx.bellman_ford_predecessor_and_distance,
+            nx.bellman_ford_path,
+            nx.bellman_ford_path_length,
+            nx.single_source_bellman_ford_path,
+            nx.single_source_bellman_ford_path_length,
+            nx.single_source_bellman_ford,
+        ):
+            pytest.raises(nx.NodeNotFound, fn, G, 3, 0)
+            pytest.raises(nx.NodeNotFound, fn, G, 3, 3)
+
+    def test_absent_source_goldberg_radzik(self):
+        with pytest.raises(nx.NodeNotFound):
+            G = nx.path_graph(2)
+            nx.goldberg_radzik(G, 3, 0)
+
+    def test_negative_cycle_heuristic(self):
+        G = nx.DiGraph()
+        G.add_edge(0, 1, weight=-1)
+        G.add_edge(1, 2, weight=-1)
+        G.add_edge(2, 3, weight=-1)
+        G.add_edge(3, 0, weight=3)
+        assert not nx.negative_edge_cycle(G, heuristic=True)
+        G.add_edge(2, 0, weight=1.999)
+        assert nx.negative_edge_cycle(G, heuristic=True)
+        G.edges[2, 0]["weight"] = 2
+        assert not nx.negative_edge_cycle(G, heuristic=True)
+
+    def test_negative_cycle_consistency(self):
+        import random
+
+        unif = random.uniform
+        for random_seed in range(2):  # range(20):
+            random.seed(random_seed)
+            for density in [0.1, 0.9]:  # .3, .7, .9]:
+                for N in [1, 10, 20]:  # range(1, 60 - int(30 * density)):
+                    for max_cost in [1, 90]:  # [1, 10, 40, 90]:
+                        G = nx.binomial_graph(N, density, seed=4, directed=True)
+                        edges = ((u, v, unif(-1, max_cost)) for u, v in G.edges)
+                        G.add_weighted_edges_from(edges)
+
+                        no_heuristic = nx.negative_edge_cycle(G, heuristic=False)
+                        with_heuristic = nx.negative_edge_cycle(G, heuristic=True)
+                        assert no_heuristic == with_heuristic
+
+    def test_negative_cycle(self):
+        G = nx.cycle_graph(5, create_using=nx.DiGraph())
+        G.add_edge(1, 2, weight=-7)
+        for i in range(5):
+            pytest.raises(
+                nx.NetworkXUnbounded, nx.single_source_bellman_ford_path, G, i
+            )
+            pytest.raises(
+                nx.NetworkXUnbounded, nx.single_source_bellman_ford_path_length, G, i
+            )
+            pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford, G, i)
+            pytest.raises(
+                nx.NetworkXUnbounded, nx.bellman_ford_predecessor_and_distance, G, i
+            )
+            pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, i)
+        G = nx.cycle_graph(5)  # undirected Graph
+        G.add_edge(1, 2, weight=-3)
+        for i in range(5):
+            pytest.raises(
+                nx.NetworkXUnbounded, nx.single_source_bellman_ford_path, G, i
+            )
+            pytest.raises(
+                nx.NetworkXUnbounded, nx.single_source_bellman_ford_path_length, G, i
+            )
+            pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford, G, i)
+            pytest.raises(
+                nx.NetworkXUnbounded, nx.bellman_ford_predecessor_and_distance, G, i
+            )
+            pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, i)
+        G = nx.DiGraph([(1, 1, {"weight": -1})])
+        pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford_path, G, 1)
+        pytest.raises(
+            nx.NetworkXUnbounded, nx.single_source_bellman_ford_path_length, G, 1
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford, G, 1)
+        pytest.raises(
+            nx.NetworkXUnbounded, nx.bellman_ford_predecessor_and_distance, G, 1
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, 1)
+        G = nx.MultiDiGraph([(1, 1, {"weight": -1})])
+        pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford_path, G, 1)
+        pytest.raises(
+            nx.NetworkXUnbounded, nx.single_source_bellman_ford_path_length, G, 1
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford, G, 1)
+        pytest.raises(
+            nx.NetworkXUnbounded, nx.bellman_ford_predecessor_and_distance, G, 1
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, 1)
+
+    def test_zero_cycle(self):
+        G = nx.cycle_graph(5, create_using=nx.DiGraph())
+        G.add_edge(2, 3, weight=-4)
+        # check that zero cycle doesn't raise
+        nx.goldberg_radzik(G, 1)
+        nx.bellman_ford_predecessor_and_distance(G, 1)
+
+        G.add_edge(2, 3, weight=-4.0001)
+        # check that negative cycle does raise
+        pytest.raises(
+            nx.NetworkXUnbounded, nx.bellman_ford_predecessor_and_distance, G, 1
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, 1)
+
+    def test_find_negative_cycle_longer_cycle(self):
+        G = nx.cycle_graph(5, create_using=nx.DiGraph())
+        nx.add_cycle(G, [3, 5, 6, 7, 8, 9])
+        G.add_edge(1, 2, weight=-30)
+        assert nx.find_negative_cycle(G, 1) == [0, 1, 2, 3, 4, 0]
+        assert nx.find_negative_cycle(G, 7) == [2, 3, 4, 0, 1, 2]
+
+    def test_find_negative_cycle_no_cycle(self):
+        G = nx.path_graph(5, create_using=nx.DiGraph())
+        pytest.raises(nx.NetworkXError, nx.find_negative_cycle, G, 3)
+
+    def test_find_negative_cycle_single_edge(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=-1)
+        assert nx.find_negative_cycle(G, 1) == [1, 0, 1]
+
+    def test_negative_weight(self):
+        G = nx.cycle_graph(5, create_using=nx.DiGraph())
+        G.add_edge(1, 2, weight=-3)
+        assert nx.single_source_bellman_ford_path(G, 0) == {
+            0: [0],
+            1: [0, 1],
+            2: [0, 1, 2],
+            3: [0, 1, 2, 3],
+            4: [0, 1, 2, 3, 4],
+        }
+        assert nx.single_source_bellman_ford_path_length(G, 0) == {
+            0: 0,
+            1: 1,
+            2: -2,
+            3: -1,
+            4: 0,
+        }
+        assert nx.single_source_bellman_ford(G, 0) == (
+            {0: 0, 1: 1, 2: -2, 3: -1, 4: 0},
+            {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]},
+        )
+        assert nx.bellman_ford_predecessor_and_distance(G, 0) == (
+            {0: [], 1: [0], 2: [1], 3: [2], 4: [3]},
+            {0: 0, 1: 1, 2: -2, 3: -1, 4: 0},
+        )
+        assert nx.goldberg_radzik(G, 0) == (
+            {0: None, 1: 0, 2: 1, 3: 2, 4: 3},
+            {0: 0, 1: 1, 2: -2, 3: -1, 4: 0},
+        )
+
+    def test_not_connected(self):
+        G = nx.complete_graph(6)
+        G.add_edge(10, 11)
+        G.add_edge(10, 12)
+        assert nx.single_source_bellman_ford_path(G, 0) == {
+            0: [0],
+            1: [0, 1],
+            2: [0, 2],
+            3: [0, 3],
+            4: [0, 4],
+            5: [0, 5],
+        }
+        assert nx.single_source_bellman_ford_path_length(G, 0) == {
+            0: 0,
+            1: 1,
+            2: 1,
+            3: 1,
+            4: 1,
+            5: 1,
+        }
+        assert nx.single_source_bellman_ford(G, 0) == (
+            {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
+            {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]},
+        )
+        assert nx.bellman_ford_predecessor_and_distance(G, 0) == (
+            {0: [], 1: [0], 2: [0], 3: [0], 4: [0], 5: [0]},
+            {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
+        )
+        assert nx.goldberg_radzik(G, 0) == (
+            {0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
+            {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
+        )
+
+        # not connected, with a component not containing the source that
+        # contains a negative cycle.
+        G = nx.complete_graph(6)
+        G.add_edges_from(
+            [
+                ("A", "B", {"load": 3}),
+                ("B", "C", {"load": -10}),
+                ("C", "A", {"load": 2}),
+            ]
+        )
+        assert nx.single_source_bellman_ford_path(G, 0, weight="load") == {
+            0: [0],
+            1: [0, 1],
+            2: [0, 2],
+            3: [0, 3],
+            4: [0, 4],
+            5: [0, 5],
+        }
+        assert nx.single_source_bellman_ford_path_length(G, 0, weight="load") == {
+            0: 0,
+            1: 1,
+            2: 1,
+            3: 1,
+            4: 1,
+            5: 1,
+        }
+        assert nx.single_source_bellman_ford(G, 0, weight="load") == (
+            {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
+            {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]},
+        )
+        assert nx.bellman_ford_predecessor_and_distance(G, 0, weight="load") == (
+            {0: [], 1: [0], 2: [0], 3: [0], 4: [0], 5: [0]},
+            {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
+        )
+        assert nx.goldberg_radzik(G, 0, weight="load") == (
+            {0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
+            {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
+        )
+
+    def test_multigraph(self):
+        assert nx.bellman_ford_path(self.MXG, "s", "v") == ["s", "x", "u", "v"]
+        assert nx.bellman_ford_path_length(self.MXG, "s", "v") == 9
+        assert nx.single_source_bellman_ford_path(self.MXG, "s")["v"] == [
+            "s",
+            "x",
+            "u",
+            "v",
+        ]
+        assert nx.single_source_bellman_ford_path_length(self.MXG, "s")["v"] == 9
+        D, P = nx.single_source_bellman_ford(self.MXG, "s", target="v")
+        assert D == 9
+        assert P == ["s", "x", "u", "v"]
+        P, D = nx.bellman_ford_predecessor_and_distance(self.MXG, "s")
+        assert P["v"] == ["u"]
+        assert D["v"] == 9
+        P, D = nx.goldberg_radzik(self.MXG, "s")
+        assert P["v"] == "u"
+        assert D["v"] == 9
+        assert nx.bellman_ford_path(self.MXG4, 0, 2) == [0, 1, 2]
+        assert nx.bellman_ford_path_length(self.MXG4, 0, 2) == 4
+        assert nx.single_source_bellman_ford_path(self.MXG4, 0)[2] == [0, 1, 2]
+        assert nx.single_source_bellman_ford_path_length(self.MXG4, 0)[2] == 4
+        D, P = nx.single_source_bellman_ford(self.MXG4, 0, target=2)
+        assert D == 4
+        assert P == [0, 1, 2]
+        P, D = nx.bellman_ford_predecessor_and_distance(self.MXG4, 0)
+        assert P[2] == [1]
+        assert D[2] == 4
+        P, D = nx.goldberg_radzik(self.MXG4, 0)
+        assert P[2] == 1
+        assert D[2] == 4
+
+    def test_others(self):
+        assert nx.bellman_ford_path(self.XG, "s", "v") == ["s", "x", "u", "v"]
+        assert nx.bellman_ford_path_length(self.XG, "s", "v") == 9
+        assert nx.single_source_bellman_ford_path(self.XG, "s")["v"] == [
+            "s",
+            "x",
+            "u",
+            "v",
+        ]
+        assert nx.single_source_bellman_ford_path_length(self.XG, "s")["v"] == 9
+        D, P = nx.single_source_bellman_ford(self.XG, "s", target="v")
+        assert D == 9
+        assert P == ["s", "x", "u", "v"]
+        (P, D) = nx.bellman_ford_predecessor_and_distance(self.XG, "s")
+        assert P["v"] == ["u"]
+        assert D["v"] == 9
+        (P, D) = nx.goldberg_radzik(self.XG, "s")
+        assert P["v"] == "u"
+        assert D["v"] == 9
+
+    def test_path_graph(self):
+        G = nx.path_graph(4)
+        assert nx.single_source_bellman_ford_path(G, 0) == {
+            0: [0],
+            1: [0, 1],
+            2: [0, 1, 2],
+            3: [0, 1, 2, 3],
+        }
+        assert nx.single_source_bellman_ford_path_length(G, 0) == {
+            0: 0,
+            1: 1,
+            2: 2,
+            3: 3,
+        }
+        assert nx.single_source_bellman_ford(G, 0) == (
+            {0: 0, 1: 1, 2: 2, 3: 3},
+            {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3]},
+        )
+        assert nx.bellman_ford_predecessor_and_distance(G, 0) == (
+            {0: [], 1: [0], 2: [1], 3: [2]},
+            {0: 0, 1: 1, 2: 2, 3: 3},
+        )
+        assert nx.goldberg_radzik(G, 0) == (
+            {0: None, 1: 0, 2: 1, 3: 2},
+            {0: 0, 1: 1, 2: 2, 3: 3},
+        )
+        assert nx.single_source_bellman_ford_path(G, 3) == {
+            0: [3, 2, 1, 0],
+            1: [3, 2, 1],
+            2: [3, 2],
+            3: [3],
+        }
+        assert nx.single_source_bellman_ford_path_length(G, 3) == {
+            0: 3,
+            1: 2,
+            2: 1,
+            3: 0,
+        }
+        assert nx.single_source_bellman_ford(G, 3) == (
+            {0: 3, 1: 2, 2: 1, 3: 0},
+            {0: [3, 2, 1, 0], 1: [3, 2, 1], 2: [3, 2], 3: [3]},
+        )
+        assert nx.bellman_ford_predecessor_and_distance(G, 3) == (
+            {0: [1], 1: [2], 2: [3], 3: []},
+            {0: 3, 1: 2, 2: 1, 3: 0},
+        )
+        assert nx.goldberg_radzik(G, 3) == (
+            {0: 1, 1: 2, 2: 3, 3: None},
+            {0: 3, 1: 2, 2: 1, 3: 0},
+        )
+
+    def test_4_cycle(self):
+        # 4-cycle
+        G = nx.Graph([(0, 1), (1, 2), (2, 3), (3, 0)])
+        dist, path = nx.single_source_bellman_ford(G, 0)
+        assert dist == {0: 0, 1: 1, 2: 2, 3: 1}
+        assert path[0] == [0]
+        assert path[1] == [0, 1]
+        assert path[2] in [[0, 1, 2], [0, 3, 2]]
+        assert path[3] == [0, 3]
+
+        pred, dist = nx.bellman_ford_predecessor_and_distance(G, 0)
+        assert pred[0] == []
+        assert pred[1] == [0]
+        assert pred[2] in [[1, 3], [3, 1]]
+        assert pred[3] == [0]
+        assert dist == {0: 0, 1: 1, 2: 2, 3: 1}
+
+        pred, dist = nx.goldberg_radzik(G, 0)
+        assert pred[0] is None
+        assert pred[1] == 0
+        assert pred[2] in [1, 3]
+        assert pred[3] == 0
+        assert dist == {0: 0, 1: 1, 2: 2, 3: 1}
+
+    def test_negative_weight_bf_path(self):
+        G = nx.DiGraph()
+        G.add_nodes_from("abcd")
+        G.add_edge("a", "d", weight=0)
+        G.add_edge("a", "b", weight=1)
+        G.add_edge("b", "c", weight=-3)
+        G.add_edge("c", "d", weight=1)
+
+        assert nx.bellman_ford_path(G, "a", "d") == ["a", "b", "c", "d"]
+        assert nx.bellman_ford_path_length(G, "a", "d") == -1
+
+    def test_zero_cycle_smoke(self):
+        D = nx.DiGraph()
+        D.add_weighted_edges_from([(0, 1, 1), (1, 2, 1), (2, 3, 1), (3, 1, -2)])
+
+        nx.bellman_ford_path(D, 1, 3)
+        nx.dijkstra_path(D, 1, 3)
+        nx.bidirectional_dijkstra(D, 1, 3)
+        # FIXME nx.goldberg_radzik(D, 1)
+
+    def test_skip_visited_unweighted(self):
+        """Check that `goldberg_radzik` correctly skips visited nodes in `topo_sort`.
+
+        This doesn't reliably get tested by other tests because iterating over
+        the `relabeled` set is not deterministic.
+        """
+        G = nx.Graph([(0, 4), (0, 5), (1, 3), (1, 4), (2, 3), (2, 5), (3, 5), (3, 6)])
+
+        _, dist = nx.goldberg_radzik(G, 4)
+        assert dist == {0: 1, 1: 1, 2: 3, 3: 2, 4: 0, 5: 2, 6: 3}
+
+
+class TestJohnsonAlgorithm(WeightedTestBase):
+    def test_single_node_graph(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+        assert nx.johnson(G) == {0: {0: [0]}}
+
+    def test_negative_cycle(self):
+        G = nx.DiGraph()
+        G.add_weighted_edges_from(
+            [
+                ("0", "3", 3),
+                ("0", "1", -5),
+                ("1", "0", -5),
+                ("0", "2", 2),
+                ("1", "2", 4),
+                ("2", "3", 1),
+            ]
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.johnson, G)
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                ("0", "3", 3),
+                ("0", "1", -5),
+                ("1", "0", -5),
+                ("0", "2", 2),
+                ("1", "2", 4),
+                ("2", "3", 1),
+            ]
+        )
+        pytest.raises(nx.NetworkXUnbounded, nx.johnson, G)
+
+    def test_negative_weights(self):
+        G = nx.DiGraph()
+        G.add_weighted_edges_from(
+            [("0", "3", 3), ("0", "1", -5), ("0", "2", 2), ("1", "2", 4), ("2", "3", 1)]
+        )
+        paths = nx.johnson(G)
+        assert paths == {
+            "1": {"1": ["1"], "3": ["1", "2", "3"], "2": ["1", "2"]},
+            "0": {
+                "1": ["0", "1"],
+                "0": ["0"],
+                "3": ["0", "1", "2", "3"],
+                "2": ["0", "1", "2"],
+            },
+            "3": {"3": ["3"]},
+            "2": {"3": ["2", "3"], "2": ["2"]},
+        }
+
+    def test_unweighted_graph(self):
+        G = nx.Graph()
+        G.add_edges_from([(1, 0), (2, 1)])
+        H = G.copy()
+        nx.set_edge_attributes(H, values=1, name="weight")
+        assert nx.johnson(G) == nx.johnson(H)
+
+    def test_partially_weighted_graph_with_negative_edges(self):
+        G = nx.DiGraph()
+        G.add_edges_from([(0, 1), (1, 2), (2, 0), (1, 0)])
+        G[1][0]["weight"] = -2
+        G[0][1]["weight"] = 3
+        G[1][2]["weight"] = -4
+
+        H = G.copy()
+        H[2][0]["weight"] = 1
+
+        I = G.copy()
+        I[2][0]["weight"] = 8
+
+        assert nx.johnson(G) == nx.johnson(H)
+        assert nx.johnson(G) != nx.johnson(I)
+
+    def test_graphs(self):
+        validate_path(self.XG, "s", "v", 9, nx.johnson(self.XG)["s"]["v"])
+        validate_path(self.MXG, "s", "v", 9, nx.johnson(self.MXG)["s"]["v"])
+        validate_path(self.XG2, 1, 3, 4, nx.johnson(self.XG2)[1][3])
+        validate_path(self.XG3, 0, 3, 15, nx.johnson(self.XG3)[0][3])
+        validate_path(self.XG4, 0, 2, 4, nx.johnson(self.XG4)[0][2])
+        validate_path(self.MXG4, 0, 2, 4, nx.johnson(self.MXG4)[0][2])
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/unweighted.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/unweighted.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dffeb51825dca4c04ed09fffc48fc93a3d7e63d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/unweighted.py
@@ -0,0 +1,625 @@
+"""
+Shortest path algorithms for unweighted graphs.
+"""
+
+import operator
+
+import networkx as nx
+
+__all__ = [
+    "bidirectional_shortest_path",
+    "single_source_shortest_path",
+    "single_source_shortest_path_length",
+    "single_target_shortest_path",
+    "single_target_shortest_path_length",
+    "all_pairs_shortest_path",
+    "all_pairs_shortest_path_length",
+    "predecessor",
+]
+
+
+@nx._dispatchable
+def single_source_shortest_path_length(G, source, cutoff=None):
+    """Compute the shortest path lengths from `source` to all reachable nodes in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only target nodes where the shortest path to
+        this node from the source node contains <= ``cutoff + 1`` nodes will be
+        included in the returned results.
+
+    Returns
+    -------
+    lengths : dict
+        Dict keyed by node to shortest path length from source node.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = nx.single_source_shortest_path_length(G, 0)
+    >>> length[4]
+    4
+    >>> for node in sorted(length):
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 3
+    4: 4
+
+    Only include paths with length less than or equal to the `cutoff` keyword
+    argument:
+
+    >>> length = nx.single_source_shortest_path_length(G, 0, cutoff=2)
+    >>> for node in sorted(length):
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+
+    See Also
+    --------
+    :any:`shortest_path_length` :
+       Shortest path length with specifiable source, target, and weight.
+    :any:`single_source_dijkstra_path_length` :
+       Shortest weighted path length from source with Dijkstra algorithm.
+    :any:`single_source_bellman_ford_path_length` :
+       Shortest weighted path length from source with Bellman-Ford algorithm.
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} is not in G")
+    if cutoff is None:
+        cutoff = float("inf")
+    nextlevel = [source]
+    return dict(_single_shortest_path_length(G._adj, nextlevel, cutoff))
+
+
+def _single_shortest_path_length(adj, firstlevel, cutoff):
+    """Yields (node, level) in a breadth first search
+
+    Shortest Path Length helper function
+    Parameters
+    ----------
+        adj : dict
+            Adjacency dict or view
+        firstlevel : list
+            starting nodes, e.g. [source] or [target]
+        cutoff : int or float
+            level at which we stop the process
+    """
+    seen = set(firstlevel)
+    nextlevel = firstlevel
+    level = 0
+    n = len(adj)
+    for v in nextlevel:
+        yield (v, level)
+    while nextlevel and cutoff > level:
+        level += 1
+        thislevel = nextlevel
+        nextlevel = []
+        for v in thislevel:
+            for w in adj[v]:
+                if w not in seen:
+                    seen.add(w)
+                    nextlevel.append(w)
+                    yield (w, level)
+            if len(seen) == n:
+                return
+
+
+@nx._dispatchable
+def single_target_shortest_path_length(G, target, cutoff=None):
+    """Compute the shortest path lengths to target from all reachable nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    target : node
+       Target node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only source nodes where the shortest path
+        from this node to the target node contains <= ``cutoff + 1`` nodes will
+        be included in the returned results.
+
+    Returns
+    -------
+    lengths : dictionary
+        Dictionary, keyed by source, of shortest path lengths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> length = nx.single_target_shortest_path_length(G, 4)
+    >>> length[0]
+    4
+    >>> for node in sorted(length):
+    ...     print(f"{node}: {length[node]}")
+    0: 4
+    1: 3
+    2: 2
+    3: 1
+    4: 0
+
+    Only include paths with length less than or equal to the `cutoff` keyword
+    argument:
+
+    >>> length = nx.single_target_shortest_path_length(G, 4, cutoff=2)
+    >>> for node in sorted(length):
+    ...     print(f"{node}: {length[node]}")
+    2: 2
+    3: 1
+    4: 0
+
+    See Also
+    --------
+    single_source_shortest_path_length, shortest_path_length
+    """
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} is not in G")
+    if cutoff is None:
+        cutoff = float("inf")
+    # handle either directed or undirected
+    adj = G._pred if G.is_directed() else G._adj
+    nextlevel = [target]
+    return dict(_single_shortest_path_length(adj, nextlevel, cutoff))
+
+
+@nx._dispatchable
+def all_pairs_shortest_path_length(G, cutoff=None):
+    """Computes the shortest path lengths between all nodes in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer, optional
+        Depth at which to stop the search. Only paths of length at most
+        `cutoff` (i.e. paths containing <= ``cutoff + 1`` nodes) are returned.
+
+    Returns
+    -------
+    lengths : iterator
+        (source, dictionary) iterator with dictionary keyed by target and
+        shortest path length as the key value.
+
+    Notes
+    -----
+    The iterator returned only has reachable node pairs.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = dict(nx.all_pairs_shortest_path_length(G))
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"1 - {node}: {length[1][node]}")
+    1 - 0: 1
+    1 - 1: 0
+    1 - 2: 1
+    1 - 3: 2
+    1 - 4: 3
+    >>> length[3][2]
+    1
+    >>> length[2][2]
+    0
+
+    Only include paths with length less than or equal to the `cutoff` keyword
+    argument:
+
+    >>> path_lengths = dict(nx.all_pairs_shortest_path_length(G, cutoff=2))
+    >>> path_lengths[1]  # node 4 is too far away to appear
+    {1: 0, 0: 1, 2: 1, 3: 2}
+    """
+    length = single_source_shortest_path_length
+    # TODO This can be trivially parallelized.
+    for n in G:
+        yield (n, length(G, n, cutoff=cutoff))
+
+
+@nx._dispatchable
+def bidirectional_shortest_path(G, source, target):
+    """Returns a list of nodes in a shortest path between source and target.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+       starting node for path
+
+    target : node label
+       ending node for path
+
+    Returns
+    -------
+    path: list
+       List of nodes in a path from source to target.
+
+    Raises
+    ------
+    NetworkXNoPath
+       If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 0, 4, 5, 6, 7, 4])
+    >>> nx.bidirectional_shortest_path(G, 2, 6)
+    [2, 1, 0, 4, 5, 6]
+
+    See Also
+    --------
+    shortest_path
+
+    Notes
+    -----
+    This algorithm is used by shortest_path(G, source, target).
+    """
+
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} is not in G")
+
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} is not in G")
+
+    # call helper to do the real work
+    results = _bidirectional_pred_succ(G, source, target)
+    pred, succ, w = results
+
+    # build path from pred+w+succ
+    path = []
+    # from source to w
+    while w is not None:
+        path.append(w)
+        w = pred[w]
+    path.reverse()
+    # from w to target
+    w = succ[path[-1]]
+    while w is not None:
+        path.append(w)
+        w = succ[w]
+
+    return path
+
+
+def _bidirectional_pred_succ(G, source, target):
+    """Bidirectional shortest path helper.
+
+    Returns (pred, succ, w) where
+    pred is a dictionary of predecessors from w to the source, and
+    succ is a dictionary of successors from w to the target.
+    """
+    # does BFS from both source and target and meets in the middle
+    if target == source:
+        return ({target: None}, {source: None}, source)
+
+    # handle either directed or undirected
+    if G.is_directed():
+        Gpred = G.pred
+        Gsucc = G.succ
+    else:
+        Gpred = G.adj
+        Gsucc = G.adj
+
+    # predecessor and successors in search
+    pred = {source: None}
+    succ = {target: None}
+
+    # initialize fringes, start with forward
+    forward_fringe = [source]
+    reverse_fringe = [target]
+
+    while forward_fringe and reverse_fringe:
+        if len(forward_fringe) <= len(reverse_fringe):
+            this_level = forward_fringe
+            forward_fringe = []
+            for v in this_level:
+                for w in Gsucc[v]:
+                    if w not in pred:
+                        forward_fringe.append(w)
+                        pred[w] = v
+                    if w in succ:  # path found
+                        return pred, succ, w
+        else:
+            this_level = reverse_fringe
+            reverse_fringe = []
+            for v in this_level:
+                for w in Gpred[v]:
+                    if w not in succ:
+                        succ[w] = v
+                        reverse_fringe.append(w)
+                    if w in pred:  # found path
+                        return pred, succ, w
+
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
+
+
+@nx._dispatchable
+def single_source_shortest_path(G, source, cutoff=None):
+    """Compute shortest path between source
+    and all other nodes reachable from source.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+       Starting node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only target nodes where the shortest path to
+        this node from the source node contains <= ``cutoff + 1`` nodes will be
+        included in the returned results.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary, keyed by target, of shortest paths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.single_source_shortest_path(G, 0)
+    {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]}
+
+    Only include paths with length less than or equal to the `cutoff` keyword
+    argument:
+
+    >>> nx.single_source_shortest_path(G, 0, cutoff=2)
+    {0: [0], 1: [0, 1], 2: [0, 1, 2]}
+
+    Notes
+    -----
+    The shortest path is not necessarily unique. So there can be multiple
+    paths between the source and each target node, all of which have the
+    same 'shortest' length. For each target node, this function returns
+    only one of those paths.
+
+    See Also
+    --------
+    shortest_path
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} not in G")
+    if cutoff is None:
+        cutoff = float("inf")
+    nextlevel = [source]  # list of nodes to check at next level
+    paths = {source: [source]}  # paths dictionary  (paths to key from source)
+    return dict(_single_shortest_path(G._adj, nextlevel, paths, cutoff, operator.add))
+
+
+def _single_shortest_path(adj, firstlevel, paths, cutoff, join):
+    """Returns shortest paths
+
+    Shortest Path helper function
+    Parameters
+    ----------
+        adj : dict
+            Adjacency dict or view
+        firstlevel : list
+            starting nodes, e.g. [source] or [target]
+        paths : dict
+            paths for starting nodes, e.g. {source: [source]}
+        cutoff : int or float
+            level at which we stop the process
+        join : function
+            function to construct a path from two partial paths. Requires two
+            list inputs `p1` and `p2`, and returns a list. Usually returns
+            `p1 + p2` (forward from source) or `p2 + p1` (backward from target)
+    """
+    level = 0
+    nextlevel = firstlevel
+    n = len(adj)
+    while nextlevel and cutoff > level:
+        thislevel = nextlevel
+        nextlevel = []
+        for v in thislevel:
+            for w in adj[v]:
+                if w not in paths:
+                    paths[w] = join(paths[v], [w])
+                    nextlevel.append(w)
+            if len(paths) == n:
+                return paths
+        level += 1
+    return paths
+
+
+@nx._dispatchable
+def single_target_shortest_path(G, target, cutoff=None):
+    """Compute shortest path to target from all nodes that reach target.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    target : node label
+       Target node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only source nodes where the shortest path
+        from this node to the target node contains <= ``cutoff + 1`` nodes will
+        be included in the returned results.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary, keyed by source, of shortest paths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> nx.single_target_shortest_path(G, 4)
+    {4: [4], 3: [3, 4], 2: [2, 3, 4], 1: [1, 2, 3, 4], 0: [0, 1, 2, 3, 4]}
+
+    Only include paths with length less than or equal to the `cutoff` keyword
+    argument:
+
+    >>> nx.single_target_shortest_path(G, 4, cutoff=2)
+    {4: [4], 3: [3, 4], 2: [2, 3, 4]}
+
+    Notes
+    -----
+    The shortest path is not necessarily unique. So there can be multiple
+    paths between the source and each target node, all of which have the
+    same 'shortest' length. For each target node, this function returns
+    only one of those paths.
+
+    See Also
+    --------
+    shortest_path, single_source_shortest_path
+    """
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} not in G")
+
+    def join(p1, p2):
+        return p2 + p1
+
+    # handle undirected graphs
+    adj = G._pred if G.is_directed() else G._adj
+    if cutoff is None:
+        cutoff = float("inf")
+    nextlevel = [target]  # list of nodes to check at next level
+    paths = {target: [target]}  # paths dictionary  (paths to key from source)
+    return dict(_single_shortest_path(adj, nextlevel, paths, cutoff, join))
+
+
+@nx._dispatchable
+def all_pairs_shortest_path(G, cutoff=None):
+    """Compute shortest paths between all nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer, optional
+        Depth at which to stop the search. Only paths containing at most
+        ``cutoff + 1`` nodes are returned.
+
+    Returns
+    -------
+    paths : iterator
+        Dictionary, keyed by source and target, of shortest paths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = dict(nx.all_pairs_shortest_path(G))
+    >>> print(path[0])
+    {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]}
+
+    Only include paths with length less than or equal to the `cutoff` keyword
+    argument:
+
+    >>> path = dict(nx.all_pairs_shortest_path(G, cutoff=2))
+    >>> print(path[0])
+    {0: [0], 1: [0, 1], 2: [0, 1, 2]}
+
+    Notes
+    -----
+    There may be multiple shortest paths with the same length between
+    two nodes. For each pair, this function returns only one of those paths.
+
+    See Also
+    --------
+    floyd_warshall
+    all_pairs_all_shortest_paths
+
+    """
+    # TODO This can be trivially parallelized.
+    for n in G:
+        yield (n, single_source_shortest_path(G, n, cutoff=cutoff))
+
+
+@nx._dispatchable
+def predecessor(G, source, target=None, cutoff=None, return_seen=None):
+    """Returns dict of predecessors for the path from source to all nodes in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+       Starting node for path
+
+    target : node label, optional
+       Ending node for path. If provided only predecessors between
+       source and target are returned
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= `cutoff` are
+        returned.
+
+    return_seen : bool, optional (default=None)
+        Whether to return a dictionary, keyed by node, of the level (number of
+        hops) to reach the node (as seen during breadth-first-search).
+
+    Returns
+    -------
+    pred : dictionary
+        Dictionary, keyed by node, of predecessors in the shortest path.
+
+
+    (pred, seen): tuple of dictionaries
+        If `return_seen` argument is set to `True`, then a tuple of dictionaries
+        is returned. The first element is the dictionary, keyed by node, of
+        predecessors in the shortest path. The second element is the dictionary,
+        keyed by node, of the level (number of hops) to reach the node (as seen
+        during breadth-first-search).
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> list(G)
+    [0, 1, 2, 3]
+    >>> nx.predecessor(G, 0)
+    {0: [], 1: [0], 2: [1], 3: [2]}
+    >>> nx.predecessor(G, 0, cutoff=2)
+    {0: [], 1: [0], 2: [1]}
+    >>> nx.predecessor(G, 0, return_seen=True)
+    ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3})
+
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} not in G")
+
+    level = 0  # the current level
+    nextlevel = [source]  # list of nodes to check at next level
+    seen = {source: level}  # level (number of hops) when seen in BFS
+    pred = {source: []}  # predecessor dictionary
+    while nextlevel:
+        level = level + 1
+        thislevel = nextlevel
+        nextlevel = []
+        for v in thislevel:
+            for w in G[v]:
+                if w not in seen:
+                    pred[w] = [v]
+                    seen[w] = level
+                    nextlevel.append(w)
+                elif seen[w] == level:  # add v to predecessor list if it
+                    pred[w].append(v)  # is at the correct level
+        if cutoff and cutoff <= level:
+            break
+
+    if target is not None:
+        if return_seen:
+            if target not in pred:
+                return ([], -1)  # No predecessor
+            return (pred[target], seen[target])
+        else:
+            if target not in pred:
+                return []  # No predecessor
+            return pred[target]
+    else:
+        if return_seen:
+            return (pred, seen)
+        else:
+            return pred
diff --git a/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/weighted.py b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/weighted.py
new file mode 100644
index 0000000000000000000000000000000000000000..a75398be79a83bee9bb1ecc6f6ce8fd62bca5ee4
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/shortest_paths/weighted.py
@@ -0,0 +1,2542 @@
+"""
+Shortest path algorithms for weighted graphs.
+"""
+
+from collections import deque
+from heapq import heappop, heappush
+from itertools import count, islice
+
+import networkx as nx
+from networkx.algorithms.shortest_paths.generic import _build_paths_from_predecessors
+
+__all__ = [
+    "dijkstra_path",
+    "dijkstra_path_length",
+    "bidirectional_dijkstra",
+    "single_source_dijkstra",
+    "single_source_dijkstra_path",
+    "single_source_dijkstra_path_length",
+    "multi_source_dijkstra",
+    "multi_source_dijkstra_path",
+    "multi_source_dijkstra_path_length",
+    "all_pairs_dijkstra",
+    "all_pairs_dijkstra_path",
+    "all_pairs_dijkstra_path_length",
+    "dijkstra_predecessor_and_distance",
+    "bellman_ford_path",
+    "bellman_ford_path_length",
+    "single_source_bellman_ford",
+    "single_source_bellman_ford_path",
+    "single_source_bellman_ford_path_length",
+    "all_pairs_bellman_ford_path",
+    "all_pairs_bellman_ford_path_length",
+    "bellman_ford_predecessor_and_distance",
+    "negative_edge_cycle",
+    "find_negative_cycle",
+    "goldberg_radzik",
+    "johnson",
+]
+
+
+def _weight_function(G, weight):
+    """Returns a function that returns the weight of an edge.
+
+    The returned function is specifically suitable for input to
+    functions :func:`_dijkstra` and :func:`_bellman_ford_relaxation`.
+
+    Parameters
+    ----------
+    G : NetworkX graph.
+
+    weight : string or function
+        If it is callable, `weight` itself is returned. If it is a string,
+        it is assumed to be the name of the edge attribute that represents
+        the weight of an edge. In that case, a function is returned that
+        gets the edge weight according to the specified edge attribute.
+
+    Returns
+    -------
+    function
+        This function returns a callable that accepts exactly three inputs:
+        a node, an node adjacent to the first one, and the edge attribute
+        dictionary for the eedge joining those nodes. That function returns
+        a number representing the weight of an edge.
+
+    If `G` is a multigraph, and `weight` is not callable, the
+    minimum edge weight over all parallel edges is returned. If any edge
+    does not have an attribute with key `weight`, it is assumed to
+    have weight one.
+
+    """
+    if callable(weight):
+        return weight
+    # If the weight keyword argument is not callable, we assume it is a
+    # string representing the edge attribute containing the weight of
+    # the edge.
+    if G.is_multigraph():
+        return lambda u, v, d: min(attr.get(weight, 1) for attr in d.values())
+    return lambda u, v, data: data.get(weight, 1)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def dijkstra_path(G, source, target, weight="weight"):
+    """Returns the shortest weighted path from source to target in G.
+
+    Uses Dijkstra's Method to compute the shortest weighted path
+    between two nodes in a graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node
+
+    target : node
+        Ending node
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    path : list
+        List of nodes in a shortest path.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> print(nx.dijkstra_path(G, 0, 4))
+    [0, 1, 2, 3, 4]
+
+    Find edges of shortest path in Multigraph
+
+    >>> G = nx.MultiDiGraph()
+    >>> G.add_weighted_edges_from([(1, 2, 0.75), (1, 2, 0.5), (2, 3, 0.5), (1, 3, 1.5)])
+    >>> nodes = nx.dijkstra_path(G, 1, 3)
+    >>> edges = nx.utils.pairwise(nodes)
+    >>> list(
+    ...     (u, v, min(G[u][v], key=lambda k: G[u][v][k].get("weight", 1)))
+    ...     for u, v in edges
+    ... )
+    [(1, 2, 1), (2, 3, 0)]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    The weight function can be used to include node weights.
+
+    >>> def func(u, v, d):
+    ...     node_u_wt = G.nodes[u].get("node_weight", 1)
+    ...     node_v_wt = G.nodes[v].get("node_weight", 1)
+    ...     edge_wt = d.get("weight", 1)
+    ...     return node_u_wt / 2 + node_v_wt / 2 + edge_wt
+
+    In this example we take the average of start and end node
+    weights of an edge and add it to the weight of the edge.
+
+    The function :func:`single_source_dijkstra` computes both
+    path and length-of-path if you need both, use that.
+
+    See Also
+    --------
+    bidirectional_dijkstra
+    bellman_ford_path
+    single_source_dijkstra
+    """
+    (length, path) = single_source_dijkstra(G, source, target=target, weight=weight)
+    return path
+
+
+@nx._dispatchable(edge_attrs="weight")
+def dijkstra_path_length(G, source, target, weight="weight"):
+    """Returns the shortest weighted path length in G from source to target.
+
+    Uses Dijkstra's Method to compute the shortest weighted path length
+    between two nodes in a graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        starting node for path
+
+    target : node label
+        ending node for path
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    length : number
+        Shortest path length.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.dijkstra_path_length(G, 0, 4)
+    4
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    The function :func:`single_source_dijkstra` computes both
+    path and length-of-path if you need both, use that.
+
+    See Also
+    --------
+    bidirectional_dijkstra
+    bellman_ford_path_length
+    single_source_dijkstra
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Node {source} not found in graph")
+    if source == target:
+        return 0
+    weight = _weight_function(G, weight)
+    length = _dijkstra(G, source, weight, target=target)
+    try:
+        return length[target]
+    except KeyError as err:
+        raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}") from err
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_dijkstra_path(G, source, cutoff=None, weight="weight"):
+    """Find shortest weighted paths in G from a source node.
+
+    Compute shortest path between source and all other reachable
+    nodes for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node for path.
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary of shortest path lengths keyed by target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = nx.single_source_dijkstra_path(G, 0)
+    >>> path[4]
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    See Also
+    --------
+    single_source_dijkstra, single_source_bellman_ford
+
+    """
+    return multi_source_dijkstra_path(G, {source}, cutoff=cutoff, weight=weight)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_dijkstra_path_length(G, source, cutoff=None, weight="weight"):
+    """Find shortest weighted path lengths in G from a source node.
+
+    Compute the shortest path length between source and all other
+    reachable nodes for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        Starting node for path
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    length : dict
+        Dict keyed by node to shortest path length from source.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = nx.single_source_dijkstra_path_length(G, 0)
+    >>> length[4]
+    4
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 3
+    4: 4
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    See Also
+    --------
+    single_source_dijkstra, single_source_bellman_ford_path_length
+
+    """
+    return multi_source_dijkstra_path_length(G, {source}, cutoff=cutoff, weight=weight)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_dijkstra(G, source, target=None, cutoff=None, weight="weight"):
+    """Find shortest weighted paths and lengths from a source node.
+
+    Compute the shortest path length between source and all other
+    reachable nodes for a weighted graph.
+
+    Uses Dijkstra's algorithm to compute shortest paths and lengths
+    between a source and all other reachable nodes in a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        Starting node for path
+
+    target : node label, optional
+        Ending node for path
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    distance, path : pair of dictionaries, or numeric and list.
+        If target is None, paths and lengths to all nodes are computed.
+        The return value is a tuple of two dictionaries keyed by target nodes.
+        The first dictionary stores distance to each target node.
+        The second stores the path to each target node.
+        If target is not None, returns a tuple (distance, path), where
+        distance is the distance from source to target and path is a list
+        representing the path from source to target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length, path = nx.single_source_dijkstra(G, 0)
+    >>> length[4]
+    4
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 3
+    4: 4
+    >>> path[4]
+    [0, 1, 2, 3, 4]
+    >>> length, path = nx.single_source_dijkstra(G, 0, 1)
+    >>> length
+    1
+    >>> path
+    [0, 1]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    Based on the Python cookbook recipe (119466) at
+    https://code.activestate.com/recipes/119466/
+
+    This algorithm is not guaranteed to work if edge weights
+    are negative or are floating point numbers
+    (overflows and roundoff errors can cause problems).
+
+    See Also
+    --------
+    single_source_dijkstra_path
+    single_source_dijkstra_path_length
+    single_source_bellman_ford
+    """
+    return multi_source_dijkstra(
+        G, {source}, cutoff=cutoff, target=target, weight=weight
+    )
+
+
+@nx._dispatchable(edge_attrs="weight")
+def multi_source_dijkstra_path(G, sources, cutoff=None, weight="weight"):
+    """Find shortest weighted paths in G from a given set of source
+    nodes.
+
+    Compute shortest path between any of the source nodes and all other
+    reachable nodes for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    sources : non-empty set of nodes
+        Starting nodes for paths. If this is just a set containing a
+        single node, then all paths computed by this function will start
+        from that node. If there are two or more nodes in the set, the
+        computed paths may begin from any one of the start nodes.
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary of shortest paths keyed by target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = nx.multi_source_dijkstra_path(G, {0, 4})
+    >>> path[1]
+    [0, 1]
+    >>> path[3]
+    [4, 3]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    Raises
+    ------
+    ValueError
+        If `sources` is empty.
+    NodeNotFound
+        If any of `sources` is not in `G`.
+
+    See Also
+    --------
+    multi_source_dijkstra, multi_source_bellman_ford
+
+    """
+    length, path = multi_source_dijkstra(G, sources, cutoff=cutoff, weight=weight)
+    return path
+
+
+@nx._dispatchable(edge_attrs="weight")
+def multi_source_dijkstra_path_length(G, sources, cutoff=None, weight="weight"):
+    """Find shortest weighted path lengths in G from a given set of
+    source nodes.
+
+    Compute the shortest path length between any of the source nodes and
+    all other reachable nodes for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    sources : non-empty set of nodes
+        Starting nodes for paths. If this is just a set containing a
+        single node, then all paths computed by this function will start
+        from that node. If there are two or more nodes in the set, the
+        computed paths may begin from any one of the start nodes.
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    length : dict
+        Dict keyed by node to shortest path length to nearest source.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = nx.multi_source_dijkstra_path_length(G, {0, 4})
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 1
+    4: 0
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    Raises
+    ------
+    ValueError
+        If `sources` is empty.
+    NodeNotFound
+        If any of `sources` is not in `G`.
+
+    See Also
+    --------
+    multi_source_dijkstra
+
+    """
+    if not sources:
+        raise ValueError("sources must not be empty")
+    for s in sources:
+        if s not in G:
+            raise nx.NodeNotFound(f"Node {s} not found in graph")
+    weight = _weight_function(G, weight)
+    return _dijkstra_multisource(G, sources, weight, cutoff=cutoff)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def multi_source_dijkstra(G, sources, target=None, cutoff=None, weight="weight"):
+    """Find shortest weighted paths and lengths from a given set of
+    source nodes.
+
+    Uses Dijkstra's algorithm to compute the shortest paths and lengths
+    between one of the source nodes and the given `target`, or all other
+    reachable nodes if not specified, for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    sources : non-empty set of nodes
+        Starting nodes for paths. If this is just a set containing a
+        single node, then all paths computed by this function will start
+        from that node. If there are two or more nodes in the set, the
+        computed paths may begin from any one of the start nodes.
+
+    target : node label, optional
+        Ending node for path
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    distance, path : pair of dictionaries, or numeric and list
+        If target is None, returns a tuple of two dictionaries keyed by node.
+        The first dictionary stores distance from one of the source nodes.
+        The second stores the path from one of the sources to that node.
+        If target is not None, returns a tuple of (distance, path) where
+        distance is the distance from source to target and path is a list
+        representing the path from source to target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length, path = nx.multi_source_dijkstra(G, {0, 4})
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 1
+    4: 0
+    >>> path[1]
+    [0, 1]
+    >>> path[3]
+    [4, 3]
+
+    >>> length, path = nx.multi_source_dijkstra(G, {0, 4}, 1)
+    >>> length
+    1
+    >>> path
+    [0, 1]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    Based on the Python cookbook recipe (119466) at
+    https://code.activestate.com/recipes/119466/
+
+    This algorithm is not guaranteed to work if edge weights
+    are negative or are floating point numbers
+    (overflows and roundoff errors can cause problems).
+
+    Raises
+    ------
+    ValueError
+        If `sources` is empty.
+    NodeNotFound
+        If any of `sources` is not in `G`.
+
+    See Also
+    --------
+    multi_source_dijkstra_path
+    multi_source_dijkstra_path_length
+
+    """
+    if not sources:
+        raise ValueError("sources must not be empty")
+    for s in sources:
+        if s not in G:
+            raise nx.NodeNotFound(f"Node {s} not found in graph")
+    if target in sources:
+        return (0, [target])
+    weight = _weight_function(G, weight)
+    paths = {source: [source] for source in sources}  # dictionary of paths
+    dist = _dijkstra_multisource(
+        G, sources, weight, paths=paths, cutoff=cutoff, target=target
+    )
+    if target is None:
+        return (dist, paths)
+    try:
+        return (dist[target], paths[target])
+    except KeyError as err:
+        raise nx.NetworkXNoPath(f"No path to {target}.") from err
+
+
+def _dijkstra(G, source, weight, pred=None, paths=None, cutoff=None, target=None):
+    """Uses Dijkstra's algorithm to find shortest weighted paths from a
+    single source.
+
+    This is a convenience function for :func:`_dijkstra_multisource`
+    with all the arguments the same, except the keyword argument
+    `sources` set to ``[source]``.
+
+    """
+    return _dijkstra_multisource(
+        G, [source], weight, pred=pred, paths=paths, cutoff=cutoff, target=target
+    )
+
+
+def _dijkstra_multisource(
+    G, sources, weight, pred=None, paths=None, cutoff=None, target=None
+):
+    """Uses Dijkstra's algorithm to find shortest weighted paths
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    sources : non-empty iterable of nodes
+        Starting nodes for paths. If this is just an iterable containing
+        a single node, then all paths computed by this function will
+        start from that node. If there are two or more nodes in this
+        iterable, the computed paths may begin from any one of the start
+        nodes.
+
+    weight: function
+        Function with (u, v, data) input that returns that edge's weight
+        or None to indicate a hidden edge
+
+    pred: dict of lists, optional(default=None)
+        dict to store a list of predecessors keyed by that node
+        If None, predecessors are not stored.
+
+    paths: dict, optional (default=None)
+        dict to store the path list from source to each node, keyed by node.
+        If None, paths are not stored.
+
+    target : node label, optional
+        Ending node for path. Search is halted when target is found.
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    Returns
+    -------
+    distance : dictionary
+        A mapping from node to shortest distance to that node from one
+        of the source nodes.
+
+    Raises
+    ------
+    NodeNotFound
+        If any of `sources` is not in `G`.
+
+    Notes
+    -----
+    The optional predecessor and path dictionaries can be accessed by
+    the caller through the original pred and paths objects passed
+    as arguments. No need to explicitly return pred or paths.
+
+    """
+    # If `paths` is specified, we use a temporary internal dictionary (`pred_dict`) to
+    # store predecessors, used to reconstruct paths. However, if the caller
+    # passed in a `pred` dictionary, we must compute *all* predecessors, since the caller
+    # expects the full predecessor structure.
+    pred_dict = pred if paths is None or pred is not None else {}
+
+    G_succ = G._adj  # For speed-up (and works for both directed and undirected graphs)
+
+    dist = {}  # dictionary of final distances
+    seen = {}
+    # fringe is heapq with 3-tuples (distance,c,node)
+    # use the count c to avoid comparing nodes (may not be able to)
+    c = count()
+    fringe = []
+    for source in sources:
+        seen[source] = 0
+        heappush(fringe, (0, next(c), source))
+    number_of_sources = len(seen)
+    while fringe:
+        (dist_v, _, v) = heappop(fringe)
+        if v in dist:
+            continue  # already searched this node.
+        dist[v] = dist_v
+        if v == target:
+            break
+        for u, e in G_succ[v].items():
+            cost = weight(v, u, e)
+            if cost is None:
+                continue
+            vu_dist = dist_v + cost
+            if cutoff is not None and vu_dist > cutoff:
+                continue
+            if u in dist:
+                u_dist = dist[u]
+                if vu_dist < u_dist:
+                    raise ValueError("Contradictory paths found:", "negative weights?")
+                elif pred is not None and vu_dist == u_dist:
+                    # Found another shortest path to u with equal distance (including zero-weight edges).
+                    # We must store *all* predecessors because `pred` was provided by the caller.
+                    pred_dict[u].append(v)
+            elif u not in seen or vu_dist < seen[u]:
+                seen[u] = vu_dist
+                heappush(fringe, (vu_dist, next(c), u))
+                if pred_dict is not None:
+                    pred_dict[u] = [v]
+            elif pred is not None and vu_dist == seen[u]:
+                # Found another shortest path to u
+                # We must store *all* predecessors because `pred` was provided by the caller.
+                pred_dict[u].append(v)
+
+    if paths is not None:
+        # Reconstruct the path from source to target using the predecessor dictionary.
+        if target is None:
+            # Since `dist` is in increasing distance order, each predecessor's path is
+            # already computed by the time we process `v`. We skip the first
+            # `number_of_sources` entries because sources already have their paths defined.
+            for v in islice(dist, number_of_sources, None):
+                # `v` must be in `pred_dict`: any node with a distance (and not a source)
+                # has a predecessor.
+                paths[v] = paths[pred_dict[v][0]] + [v]
+        else:
+            # Caller requested the path to a specific target node.
+            path = paths[target] = [target]
+            while (current_preds := pred_dict.get(path[-1])) is not None:
+                path.append(current_preds[0])
+            # The path was built in reverse order, so reverse it at the end.
+            path.reverse()
+
+    # The optional predecessor and path dictionaries can be accessed
+    # by the caller via the pred and paths objects passed as arguments.
+    return dist
+
+
+@nx._dispatchable(edge_attrs="weight")
+def dijkstra_predecessor_and_distance(G, source, cutoff=None, weight="weight"):
+    """Compute weighted shortest path length and predecessors.
+
+    Uses Dijkstra's Method to obtain the shortest weighted paths
+    and return dictionaries of predecessors for each node and
+    distance for each node from the `source`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        Starting node for path
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    pred, distance : dictionaries
+        Returns two dictionaries representing a list of predecessors
+        of a node and the distance to each node.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The list of predecessors contains more than one element only when
+    there are more than one shortest paths to the key node.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> pred, dist = nx.dijkstra_predecessor_and_distance(G, 0)
+    >>> sorted(pred.items())
+    [(0, []), (1, [0]), (2, [1]), (3, [2]), (4, [3])]
+    >>> sorted(dist.items())
+    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
+
+    >>> pred, dist = nx.dijkstra_predecessor_and_distance(G, 0, 1)
+    >>> sorted(pred.items())
+    [(0, []), (1, [0])]
+    >>> sorted(dist.items())
+    [(0, 0), (1, 1)]
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Node {source} is not found in the graph")
+    weight = _weight_function(G, weight)
+    pred = {source: []}  # dictionary of predecessors
+    return (pred, _dijkstra(G, source, weight, pred=pred, cutoff=cutoff))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_pairs_dijkstra(G, cutoff=None, weight="weight"):
+    """Find shortest weighted paths and lengths between all nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edge[u][v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Yields
+    ------
+    (node, (distance, path)) : (node obj, (dict, dict))
+        Each source node has two associated dicts. The first holds distance
+        keyed by target and the second holds paths keyed by target.
+        (See single_source_dijkstra for the source/target node terminology.)
+        If desired you can apply `dict()` to this function to create a dict
+        keyed by source node to the two dicts.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> len_path = dict(nx.all_pairs_dijkstra(G))
+    >>> len_path[3][0][1]
+    2
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"3 - {node}: {len_path[3][0][node]}")
+    3 - 0: 3
+    3 - 1: 2
+    3 - 2: 1
+    3 - 3: 0
+    3 - 4: 1
+    >>> len_path[3][1][1]
+    [3, 2, 1]
+    >>> for n, (dist, path) in nx.all_pairs_dijkstra(G):
+    ...     print(path[1])
+    [0, 1]
+    [1]
+    [2, 1]
+    [3, 2, 1]
+    [4, 3, 2, 1]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The yielded dicts only have keys for reachable nodes.
+    """
+    for n in G:
+        dist, path = single_source_dijkstra(G, n, cutoff=cutoff, weight=weight)
+        yield (n, (dist, path))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_pairs_dijkstra_path_length(G, cutoff=None, weight="weight"):
+    """Compute shortest path lengths between all nodes in a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    distance : iterator
+        (source, dictionary) iterator with dictionary keyed by target and
+        shortest path length as the key value.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = dict(nx.all_pairs_dijkstra_path_length(G))
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"1 - {node}: {length[1][node]}")
+    1 - 0: 1
+    1 - 1: 0
+    1 - 2: 1
+    1 - 3: 2
+    1 - 4: 3
+    >>> length[3][2]
+    1
+    >>> length[2][2]
+    0
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The dictionary returned only has keys for reachable node pairs.
+    """
+    length = single_source_dijkstra_path_length
+    for n in G:
+        yield (n, length(G, n, cutoff=cutoff, weight=weight))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_pairs_dijkstra_path(G, cutoff=None, weight="weight"):
+    """Compute shortest paths between all nodes in a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer or float, optional
+        Length (sum of edge weights) at which the search is stopped.
+        If cutoff is provided, only return paths with summed weight <= cutoff.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    paths : iterator
+        (source, dictionary) iterator with dictionary keyed by target and
+        shortest path as the key value.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = dict(nx.all_pairs_dijkstra_path(G))
+    >>> path[0][4]
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    floyd_warshall, all_pairs_bellman_ford_path
+
+    """
+    path = single_source_dijkstra_path
+    # TODO This can be trivially parallelized.
+    for n in G:
+        yield (n, path(G, n, cutoff=cutoff, weight=weight))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def bellman_ford_predecessor_and_distance(
+    G, source, target=None, weight="weight", heuristic=False
+):
+    """Compute shortest path lengths and predecessors on shortest paths
+    in weighted graphs.
+
+    The algorithm has a running time of $O(mn)$ where $n$ is the number of
+    nodes and $m$ is the number of edges.  It is slower than Dijkstra but
+    can handle negative edge weights.
+
+    If a negative cycle is detected, you can use :func:`find_negative_cycle`
+    to return the cycle and examine it. Shortest paths are not defined when
+    a negative cycle exists because once reached, the path can cycle forever
+    to build up arbitrarily low weights.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The algorithm works for all types of graphs, including directed
+        graphs and multigraphs.
+
+    source: node label
+        Starting node for path
+
+    target : node label, optional
+        Ending node for path
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    heuristic : bool
+        Determines whether to use a heuristic to early detect negative
+        cycles at a hopefully negligible cost.
+
+    Returns
+    -------
+    pred, dist : dictionaries
+        Returns two dictionaries keyed by node to predecessor in the
+        path and to the distance from the source respectively.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXUnbounded
+        If the (di)graph contains a negative (di)cycle, the
+        algorithm raises an exception to indicate the presence of the
+        negative (di)cycle.  Note: any negative weight edge in an
+        undirected graph is a negative cycle.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> pred, dist = nx.bellman_ford_predecessor_and_distance(G, 0)
+    >>> sorted(pred.items())
+    [(0, []), (1, [0]), (2, [1]), (3, [2]), (4, [3])]
+    >>> sorted(dist.items())
+    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
+
+    >>> pred, dist = nx.bellman_ford_predecessor_and_distance(G, 0, 1)
+    >>> sorted(pred.items())
+    [(0, []), (1, [0]), (2, [1]), (3, [2]), (4, [3])]
+    >>> sorted(dist.items())
+    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
+
+    >>> G = nx.cycle_graph(5, create_using=nx.DiGraph())
+    >>> G[1][2]["weight"] = -7
+    >>> nx.bellman_ford_predecessor_and_distance(G, 0)
+    Traceback (most recent call last):
+        ...
+    networkx.exception.NetworkXUnbounded: Negative cycle detected.
+
+    See Also
+    --------
+    find_negative_cycle
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The dictionaries returned only have keys for nodes reachable from
+    the source.
+
+    In the case where the (di)graph is not connected, if a component
+    not containing the source contains a negative (di)cycle, it
+    will not be detected.
+
+    In NetworkX v2.1 and prior, the source node had predecessor `[None]`.
+    In NetworkX v2.2 this changed to the source node having predecessor `[]`
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Node {source} is not found in the graph")
+    weight = _weight_function(G, weight)
+    if G.is_multigraph():
+        if any(
+            weight(u, v, {k: d}) < 0
+            for u, v, k, d in nx.selfloop_edges(G, keys=True, data=True)
+        ):
+            raise nx.NetworkXUnbounded("Negative cycle detected.")
+    else:
+        if any(weight(u, v, d) < 0 for u, v, d in nx.selfloop_edges(G, data=True)):
+            raise nx.NetworkXUnbounded("Negative cycle detected.")
+
+    dist = {source: 0}
+    pred = {source: []}
+
+    if len(G) == 1:
+        return pred, dist
+
+    weight = _weight_function(G, weight)
+
+    dist = _bellman_ford(
+        G, [source], weight, pred=pred, dist=dist, target=target, heuristic=heuristic
+    )
+    return (pred, dist)
+
+
+def _bellman_ford(
+    G,
+    source,
+    weight,
+    pred=None,
+    paths=None,
+    dist=None,
+    target=None,
+    heuristic=True,
+):
+    """Calls relaxation loop for Bellman–Ford algorithm and builds paths
+
+    This is an implementation of the SPFA variant.
+    See https://en.wikipedia.org/wiki/Shortest_Path_Faster_Algorithm
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source: list
+        List of source nodes. The shortest path from any of the source
+        nodes will be found if multiple sources are provided.
+
+    weight : function
+        The weight of an edge is the value returned by the function. The
+        function must accept exactly three positional arguments: the two
+        endpoints of an edge and the dictionary of edge attributes for
+        that edge. The function must return a number.
+
+    pred: dict of lists, optional (default=None)
+        dict to store a list of predecessors keyed by that node
+        If None, predecessors are not stored
+
+    paths: dict, optional (default=None)
+        dict to store the path list from source to each node, keyed by node
+        If None, paths are not stored
+
+    dist: dict, optional (default=None)
+        dict to store distance from source to the keyed node
+        If None, returned dist dict contents default to 0 for every node in the
+        source list
+
+    target: node label, optional
+        Ending node for path. Path lengths to other destinations may (and
+        probably will) be incorrect.
+
+    heuristic : bool
+        Determines whether to use a heuristic to early detect negative
+        cycles at a hopefully negligible cost.
+
+    Returns
+    -------
+    dist : dict
+        Returns a dict keyed by node to the distance from the source.
+        Dicts for paths and pred are in the mutated input dicts by those names.
+
+    Raises
+    ------
+    NodeNotFound
+        If any of `source` is not in `G`.
+
+    NetworkXUnbounded
+        If the (di)graph contains a negative (di)cycle, the
+        algorithm raises an exception to indicate the presence of the
+        negative (di)cycle.  Note: any negative weight edge in an
+        undirected graph is a negative cycle
+    """
+    if pred is None:
+        pred = {v: [] for v in source}
+
+    if dist is None:
+        dist = {v: 0 for v in source}
+
+    negative_cycle_found = _inner_bellman_ford(
+        G,
+        source,
+        weight,
+        pred,
+        dist,
+        heuristic,
+    )
+    if negative_cycle_found is not None:
+        raise nx.NetworkXUnbounded("Negative cycle detected.")
+
+    if paths is not None:
+        sources = set(source)
+        dsts = [target] if target is not None else pred
+        for dst in dsts:
+            gen = _build_paths_from_predecessors(sources, dst, pred)
+            paths[dst] = next(gen)
+
+    return dist
+
+
+def _inner_bellman_ford(
+    G,
+    sources,
+    weight,
+    pred,
+    dist=None,
+    heuristic=True,
+):
+    """Inner Relaxation loop for Bellman–Ford algorithm.
+
+    This is an implementation of the SPFA variant.
+    See https://en.wikipedia.org/wiki/Shortest_Path_Faster_Algorithm
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source: list
+        List of source nodes. The shortest path from any of the source
+        nodes will be found if multiple sources are provided.
+
+    weight : function
+        The weight of an edge is the value returned by the function. The
+        function must accept exactly three positional arguments: the two
+        endpoints of an edge and the dictionary of edge attributes for
+        that edge. The function must return a number.
+
+    pred: dict of lists
+        dict to store a list of predecessors keyed by that node
+
+    dist: dict, optional (default=None)
+        dict to store distance from source to the keyed node
+        If None, returned dist dict contents default to 0 for every node in the
+        source list
+
+    heuristic : bool
+        Determines whether to use a heuristic to early detect negative
+        cycles at a hopefully negligible cost.
+
+    Returns
+    -------
+    node or None
+        Return a node `v` where processing discovered a negative cycle.
+        If no negative cycle found, return None.
+
+    Raises
+    ------
+    NodeNotFound
+        If any of `source` is not in `G`.
+    """
+    for s in sources:
+        if s not in G:
+            raise nx.NodeNotFound(f"Source {s} not in G")
+
+    if pred is None:
+        pred = {v: [] for v in sources}
+
+    if dist is None:
+        dist = {v: 0 for v in sources}
+
+    # Heuristic Storage setup. Note: use None because nodes cannot be None
+    nonexistent_edge = (None, None)
+    pred_edge = {v: None for v in sources}
+    recent_update = {v: nonexistent_edge for v in sources}
+
+    G_succ = G._adj  # For speed-up (and works for both directed and undirected graphs)
+    inf = float("inf")
+    n = len(G)
+
+    count = {}
+    q = deque(sources)
+    in_q = set(sources)
+    while q:
+        u = q.popleft()
+        in_q.remove(u)
+
+        # Skip relaxations if any of the predecessors of u is in the queue.
+        if all(pred_u not in in_q for pred_u in pred[u]):
+            dist_u = dist[u]
+            for v, e in G_succ[u].items():
+                dist_v = dist_u + weight(u, v, e)
+
+                if dist_v < dist.get(v, inf):
+                    # In this conditional branch we are updating the path with v.
+                    # If it happens that some earlier update also added node v
+                    # that implies the existence of a negative cycle since
+                    # after the update node v would lie on the update path twice.
+                    # The update path is stored up to one of the source nodes,
+                    # therefore u is always in the dict recent_update
+                    if heuristic:
+                        if v in recent_update[u]:
+                            # Negative cycle found!
+                            pred[v].append(u)
+                            return v
+
+                        # Transfer the recent update info from u to v if the
+                        # same source node is the head of the update path.
+                        # If the source node is responsible for the cost update,
+                        # then clear the history and use it instead.
+                        if v in pred_edge and pred_edge[v] == u:
+                            recent_update[v] = recent_update[u]
+                        else:
+                            recent_update[v] = (u, v)
+
+                    if v not in in_q:
+                        q.append(v)
+                        in_q.add(v)
+                        count_v = count.get(v, 0) + 1
+                        if count_v == n:
+                            # Negative cycle found!
+                            return v
+
+                        count[v] = count_v
+                    dist[v] = dist_v
+                    pred[v] = [u]
+                    pred_edge[v] = u
+
+                elif dist.get(v) is not None and dist_v == dist.get(v):
+                    pred[v].append(u)
+
+    # successfully found shortest_path. No negative cycles found.
+    return None
+
+
+@nx._dispatchable(edge_attrs="weight")
+def bellman_ford_path(G, source, target, weight="weight"):
+    """Returns the shortest path from source to target in a weighted graph G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node
+
+    target : node
+        Ending node
+
+    weight : string or function (default="weight")
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    path : list
+        List of nodes in a shortest path.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.bellman_ford_path(G, 0, 4)
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    dijkstra_path, bellman_ford_path_length
+    """
+    length, path = single_source_bellman_ford(G, source, target=target, weight=weight)
+    return path
+
+
+@nx._dispatchable(edge_attrs="weight")
+def bellman_ford_path_length(G, source, target, weight="weight"):
+    """Returns the shortest path length from source to target
+    in a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        starting node for path
+
+    target : node label
+        ending node for path
+
+    weight : string or function (default="weight")
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    length : number
+        Shortest path length.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.bellman_ford_path_length(G, 0, 4)
+    4
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    dijkstra_path_length, bellman_ford_path
+    """
+    if source == target:
+        if source not in G:
+            raise nx.NodeNotFound(f"Node {source} not found in graph")
+        return 0
+
+    weight = _weight_function(G, weight)
+
+    length = _bellman_ford(G, [source], weight, target=target)
+
+    try:
+        return length[target]
+    except KeyError as err:
+        raise nx.NetworkXNoPath(f"node {target} not reachable from {source}") from err
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_bellman_ford_path(G, source, weight="weight"):
+    """Compute shortest path between source and all other reachable
+    nodes for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node for path.
+
+    weight : string or function (default="weight")
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary of shortest path lengths keyed by target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = nx.single_source_bellman_ford_path(G, 0)
+    >>> path[4]
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    single_source_dijkstra, single_source_bellman_ford
+
+    """
+    (length, path) = single_source_bellman_ford(G, source, weight=weight)
+    return path
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_bellman_ford_path_length(G, source, weight="weight"):
+    """Compute the shortest path length between source and all other
+    reachable nodes for a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        Starting node for path
+
+    weight : string or function (default="weight")
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    length : dictionary
+        Dictionary of shortest path length keyed by target
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = nx.single_source_bellman_ford_path_length(G, 0)
+    >>> length[4]
+    4
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 3
+    4: 4
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    single_source_dijkstra, single_source_bellman_ford
+
+    """
+    weight = _weight_function(G, weight)
+    return _bellman_ford(G, [source], weight)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def single_source_bellman_ford(G, source, target=None, weight="weight"):
+    """Compute shortest paths and lengths in a weighted graph G.
+
+    Uses Bellman-Ford algorithm for shortest paths.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+        Starting node for path
+
+    target : node label, optional
+        Ending node for path
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    distance, path : pair of dictionaries, or numeric and list
+        If target is None, returns a tuple of two dictionaries keyed by node.
+        The first dictionary stores distance from one of the source nodes.
+        The second stores the path from one of the sources to that node.
+        If target is not None, returns a tuple of (distance, path) where
+        distance is the distance from source to target and path is a list
+        representing the path from source to target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length, path = nx.single_source_bellman_ford(G, 0)
+    >>> length[4]
+    4
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 3
+    4: 4
+    >>> path[4]
+    [0, 1, 2, 3, 4]
+    >>> length, path = nx.single_source_bellman_ford(G, 0, 1)
+    >>> length
+    1
+    >>> path
+    [0, 1]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    single_source_dijkstra
+    single_source_bellman_ford_path
+    single_source_bellman_ford_path_length
+    """
+    if source == target:
+        if source not in G:
+            raise nx.NodeNotFound(f"Node {source} is not found in the graph")
+        return (0, [source])
+
+    weight = _weight_function(G, weight)
+
+    paths = {source: [source]}  # dictionary of paths
+    dist = _bellman_ford(G, [source], weight, paths=paths, target=target)
+    if target is None:
+        return (dist, paths)
+    try:
+        return (dist[target], paths[target])
+    except KeyError as err:
+        msg = f"Node {target} not reachable from {source}"
+        raise nx.NetworkXNoPath(msg) from err
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_pairs_bellman_ford_path_length(G, weight="weight"):
+    """Compute shortest path lengths between all nodes in a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or function (default="weight")
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    distance : iterator
+        (source, dictionary) iterator with dictionary keyed by target and
+        shortest path length as the key value.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = dict(nx.all_pairs_bellman_ford_path_length(G))
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"1 - {node}: {length[1][node]}")
+    1 - 0: 1
+    1 - 1: 0
+    1 - 2: 1
+    1 - 3: 2
+    1 - 4: 3
+    >>> length[3][2]
+    1
+    >>> length[2][2]
+    0
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The dictionary returned only has keys for reachable node pairs.
+    """
+    length = single_source_bellman_ford_path_length
+    for n in G:
+        yield (n, dict(length(G, n, weight=weight)))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def all_pairs_bellman_ford_path(G, weight="weight"):
+    """Compute shortest paths between all nodes in a weighted graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or function (default="weight")
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    paths : iterator
+        (source, dictionary) iterator with dictionary keyed by target and
+        shortest path as the key value.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = dict(nx.all_pairs_bellman_ford_path(G))
+    >>> path[0][4]
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    See Also
+    --------
+    floyd_warshall, all_pairs_dijkstra_path
+
+    """
+    path = single_source_bellman_ford_path
+    for n in G:
+        yield (n, path(G, n, weight=weight))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def goldberg_radzik(G, source, weight="weight"):
+    """Compute shortest path lengths and predecessors on shortest paths
+    in weighted graphs.
+
+    The algorithm has a running time of $O(mn)$ where $n$ is the number of
+    nodes and $m$ is the number of edges.  It is slower than Dijkstra but
+    can handle negative edge weights.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The algorithm works for all types of graphs, including directed
+        graphs and multigraphs.
+
+    source: node label
+        Starting node for path
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    pred, dist : dictionaries
+        Returns two dictionaries keyed by node to predecessor in the
+        path and to the distance from the source respectively.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` is not in `G`.
+
+    NetworkXUnbounded
+        If the (di)graph contains a negative (di)cycle, the
+        algorithm raises an exception to indicate the presence of the
+        negative (di)cycle.  Note: any negative weight edge in an
+        undirected graph is a negative cycle.
+
+        As of NetworkX v3.2, a zero weight cycle is no longer
+        incorrectly reported as a negative weight cycle.
+
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> pred, dist = nx.goldberg_radzik(G, 0)
+    >>> sorted(pred.items())
+    [(0, None), (1, 0), (2, 1), (3, 2), (4, 3)]
+    >>> sorted(dist.items())
+    [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
+
+    >>> G = nx.cycle_graph(5, create_using=nx.DiGraph())
+    >>> G[1][2]["weight"] = -7
+    >>> nx.goldberg_radzik(G, 0)
+    Traceback (most recent call last):
+        ...
+    networkx.exception.NetworkXUnbounded: Negative cycle detected.
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The dictionaries returned only have keys for nodes reachable from
+    the source.
+
+    In the case where the (di)graph is not connected, if a component
+    not containing the source contains a negative (di)cycle, it
+    will not be detected.
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Node {source} is not found in the graph")
+    weight = _weight_function(G, weight)
+    if G.is_multigraph():
+        if any(
+            weight(u, v, {k: d}) < 0
+            for u, v, k, d in nx.selfloop_edges(G, keys=True, data=True)
+        ):
+            raise nx.NetworkXUnbounded("Negative cycle detected.")
+    else:
+        if any(weight(u, v, d) < 0 for u, v, d in nx.selfloop_edges(G, data=True)):
+            raise nx.NetworkXUnbounded("Negative cycle detected.")
+
+    if len(G) == 1:
+        return {source: None}, {source: 0}
+
+    G_succ = G._adj  # For speed-up (and works for both directed and undirected graphs)
+
+    inf = float("inf")
+    d = {u: inf for u in G}
+    d[source] = 0
+    pred = {source: None}
+
+    def topo_sort(relabeled):
+        """Topologically sort nodes relabeled in the previous round and detect
+        negative cycles.
+        """
+        # List of nodes to scan in this round. Denoted by A in Goldberg and
+        # Radzik's paper.
+        to_scan = []
+        # In the DFS in the loop below, neg_count records for each node the
+        # number of edges of negative reduced costs on the path from a DFS root
+        # to the node in the DFS forest. The reduced cost of an edge (u, v) is
+        # defined as d[u] + weight[u][v] - d[v].
+        #
+        # neg_count also doubles as the DFS visit marker array.
+        neg_count = {}
+        for u in relabeled - neg_count.keys():
+            d_u = d[u]
+            # Skip nodes without out-edges of negative reduced costs.
+            if all(d_u + weight(u, v, e) >= d[v] for v, e in G_succ[u].items()):
+                continue
+            # Nonrecursive DFS that inserts nodes reachable from u via edges of
+            # nonpositive reduced costs into to_scan in (reverse) topological
+            # order.
+            stack = [(u, iter(G_succ[u].items()))]
+            in_stack = {u}
+            neg_count[u] = 0
+            while stack:
+                u, it = stack[-1]
+                try:
+                    v, e = next(it)
+                except StopIteration:
+                    to_scan.append(u)
+                    stack.pop()
+                    in_stack.remove(u)
+                    continue
+                t = d[u] + weight(u, v, e)
+                d_v = d[v]
+                if t < d_v:
+                    d[v] = t
+                    pred[v] = u
+                    if v not in neg_count:
+                        neg_count[v] = neg_count[u] + 1
+                        stack.append((v, iter(G_succ[v].items())))
+                        in_stack.add(v)
+                    elif v in in_stack and neg_count[u] + 1 > neg_count[v]:
+                        # (u, v) is a back edge, and the cycle formed by the
+                        # path v to u and (u, v) contains at least one edge of
+                        # negative reduced cost. The cycle must be of negative
+                        # cost.
+                        raise nx.NetworkXUnbounded("Negative cycle detected.")
+        to_scan.reverse()
+        return to_scan
+
+    def relax(to_scan):
+        """Relax out-edges of relabeled nodes."""
+        relabeled = set()
+        # Scan nodes in to_scan in topological order and relax incident
+        # out-edges. Add the relabeled nodes to labeled.
+        for u in to_scan:
+            d_u = d[u]
+            for v, e in G_succ[u].items():
+                w_e = weight(u, v, e)
+                if d_u + w_e < d[v]:
+                    d[v] = d_u + w_e
+                    pred[v] = u
+                    relabeled.add(v)
+        return relabeled
+
+    # Set of nodes relabeled in the last round of scan operations. Denoted by B
+    # in Goldberg and Radzik's paper.
+    relabeled = {source}
+
+    while relabeled:
+        to_scan = topo_sort(relabeled)
+        relabeled = relax(to_scan)
+
+    d = {u: d[u] for u in pred}
+    return pred, d
+
+
+@nx._dispatchable(edge_attrs="weight")
+def negative_edge_cycle(G, weight="weight", heuristic=True):
+    """Returns True if there exists a negative edge cycle anywhere in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    heuristic : bool
+        Determines whether to use a heuristic to early detect negative
+        cycles at a negligible cost. In case of graphs with a negative cycle,
+        the performance of detection increases by at least an order of magnitude.
+
+    Returns
+    -------
+    negative_cycle : bool
+        True if a negative edge cycle exists, otherwise False.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(5, create_using=nx.DiGraph())
+    >>> print(nx.negative_edge_cycle(G))
+    False
+    >>> G[1][2]["weight"] = -7
+    >>> print(nx.negative_edge_cycle(G))
+    True
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    This algorithm uses bellman_ford_predecessor_and_distance() but finds
+    negative cycles on any component by first adding a new node connected to
+    every node, and starting bellman_ford_predecessor_and_distance on that
+    node.  It then removes that extra node.
+    """
+    if G.size() == 0:
+        return False
+
+    # find unused node to use temporarily
+    newnode = -1
+    while newnode in G:
+        newnode -= 1
+    # connect it to all nodes
+    G.add_edges_from([(newnode, n) for n in G])
+
+    try:
+        bellman_ford_predecessor_and_distance(
+            G, newnode, weight=weight, heuristic=heuristic
+        )
+    except nx.NetworkXUnbounded:
+        return True
+    finally:
+        G.remove_node(newnode)
+    return False
+
+
+@nx._dispatchable(edge_attrs="weight")
+def find_negative_cycle(G, source, weight="weight"):
+    """Returns a cycle with negative total weight if it exists.
+
+    Bellman-Ford is used to find shortest_paths. That algorithm
+    stops if there exists a negative cycle. This algorithm
+    picks up from there and returns the found negative cycle.
+
+    The cycle consists of a list of nodes in the cycle order. The last
+    node equals the first to make it a cycle.
+    You can look up the edge weights in the original graph. In the case
+    of multigraphs the relevant edge is the minimal weight edge between
+    the nodes in the 2-tuple.
+
+    If the graph has no negative cycle, a NetworkXError is raised.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source: node label
+        The search for the negative cycle will start from this node.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     [(0, 1, 2), (1, 2, 2), (2, 0, 1), (1, 4, 2), (4, 0, -5)]
+    ... )
+    >>> nx.find_negative_cycle(G, 0)
+    [4, 0, 1, 4]
+
+    Returns
+    -------
+    cycle : list
+        A list of nodes in the order of the cycle found. The last node
+        equals the first to indicate a cycle.
+
+    Raises
+    ------
+    NetworkXError
+        If no negative cycle is found.
+    """
+    weight = _weight_function(G, weight)
+    pred = {source: []}
+
+    v = _inner_bellman_ford(G, [source], weight, pred=pred)
+    if v is None:
+        raise nx.NetworkXError("No negative cycles detected.")
+
+    # negative cycle detected... find it
+    neg_cycle = []
+    stack = [(v, list(pred[v]))]
+    seen = {v}
+    while stack:
+        node, preds = stack[-1]
+        if v in preds:
+            # found the cycle
+            neg_cycle.extend([node, v])
+            neg_cycle = list(reversed(neg_cycle))
+            return neg_cycle
+
+        if preds:
+            nbr = preds.pop()
+            if nbr not in seen:
+                stack.append((nbr, list(pred[nbr])))
+                neg_cycle.append(node)
+                seen.add(nbr)
+        else:
+            stack.pop()
+            if neg_cycle:
+                neg_cycle.pop()
+            else:
+                if v in G[v] and weight(G, v, v) < 0:
+                    return [v, v]
+                # should not reach here
+                raise nx.NetworkXError("Negative cycle is detected but not found")
+    # should not get here...
+    msg = "negative cycle detected but not identified"
+    raise nx.NetworkXUnbounded(msg)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def bidirectional_dijkstra(G, source, target, weight="weight"):
+    r"""Dijkstra's algorithm for shortest paths using bidirectional search.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node.
+
+    target : node
+        Ending node.
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    Returns
+    -------
+    length, path : number and list
+        length is the distance from source to target.
+        path is a list of nodes on a path from source to target.
+
+    Raises
+    ------
+    NodeNotFound
+        If `source` or `target` is not in `G`.
+
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length, path = nx.bidirectional_dijkstra(G, 0, 4)
+    >>> print(length)
+    4
+    >>> print(path)
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    In practice  bidirectional Dijkstra is much more than twice as fast as
+    ordinary Dijkstra.
+
+    Ordinary Dijkstra expands nodes in a sphere-like manner from the
+    source. The radius of this sphere will eventually be the length
+    of the shortest path. Bidirectional Dijkstra will expand nodes
+    from both the source and the target, making two spheres of half
+    this radius. Volume of the first sphere is `\pi*r*r` while the
+    others are `2*\pi*r/2*r/2`, making up half the volume.
+
+    This algorithm is not guaranteed to work if edge weights
+    are negative or are floating point numbers
+    (overflows and roundoff errors can cause problems).
+
+    See Also
+    --------
+    shortest_path
+    shortest_path_length
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} is not in G")
+
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} is not in G")
+
+    if source == target:
+        return (0, [source])
+
+    weight = _weight_function(G, weight)
+    # Init:  [Forward, Backward]
+    dists = [{}, {}]  # dictionary of final distances
+    preds = [{source: None}, {target: None}]  # dictionary of preds
+
+    def path(curr, direction):
+        ret = []
+        while curr is not None:
+            ret.append(curr)
+            curr = preds[direction][curr]
+        return list(reversed(ret)) if direction == 0 else ret
+
+    fringe = [[], []]  # heap of (distance, node) for choosing node to expand
+    seen = [{source: 0}, {target: 0}]  # dict of distances to seen nodes
+    c = count()
+    # initialize fringe heap
+    heappush(fringe[0], (0, next(c), source))
+    heappush(fringe[1], (0, next(c), target))
+    # neighbors for extracting correct neighbor information
+    if G.is_directed():
+        neighbors = [G._succ, G._pred]
+    else:
+        neighbors = [G._adj, G._adj]
+    # variables to hold shortest discovered path
+    finaldist = None
+    meetnode = None
+    direction = 1
+    while fringe[0] and fringe[1]:
+        # choose direction
+        # direction == 0 is forward direction and direction == 1 is back
+        direction = 1 - direction
+        # extract closest to expand
+        (dist, _, v) = heappop(fringe[direction])
+        if v in dists[direction]:
+            # Shortest path to v has already been found
+            continue
+        # update distance
+        dists[direction][v] = dist  # equal to seen[direction][v]
+        if v in dists[1 - direction]:
+            # if we have scanned v in both directions we are done
+            # we have now discovered the shortest path
+            return (finaldist, path(meetnode, 0) + path(preds[1][meetnode], 1))
+
+        for w, d in neighbors[direction][v].items():
+            # weight(v, w, d) for forward and weight(w, v, d) for back direction
+            cost = weight(v, w, d) if direction == 0 else weight(w, v, d)
+            if cost is None:
+                continue
+            vwLength = dist + cost
+            if w in dists[direction]:
+                if vwLength < dists[direction][w]:
+                    raise ValueError("Contradictory paths found: negative weights?")
+            elif w not in seen[direction] or vwLength < seen[direction][w]:
+                # relaxing
+                seen[direction][w] = vwLength
+                heappush(fringe[direction], (vwLength, next(c), w))
+                preds[direction][w] = v
+                if w in seen[1 - direction]:
+                    # see if this path is better than the already
+                    # discovered shortest path
+                    finaldist_w = vwLength + seen[1 - direction][w]
+                    if finaldist is None or finaldist > finaldist_w:
+                        finaldist, meetnode = finaldist_w, w
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
+
+
+@nx._dispatchable(edge_attrs="weight")
+def johnson(G, weight="weight"):
+    r"""Uses Johnson's Algorithm to compute shortest paths.
+
+    Johnson's Algorithm finds a shortest path between each pair of
+    nodes in a weighted graph even if negative weights are present.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or function
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+    Returns
+    -------
+    distance : dictionary
+        Dictionary, keyed by source and target, of shortest paths.
+
+    Examples
+    --------
+    >>> graph = nx.DiGraph()
+    >>> graph.add_weighted_edges_from(
+    ...     [("0", "3", 3), ("0", "1", -5), ("0", "2", 2), ("1", "2", 4), ("2", "3", 1)]
+    ... )
+    >>> paths = nx.johnson(graph, weight="weight")
+    >>> paths["0"]["2"]
+    ['0', '1', '2']
+
+    Notes
+    -----
+    Johnson's algorithm is suitable even for graphs with negative weights. It
+    works by using the Bellman–Ford algorithm to compute a transformation of
+    the input graph that removes all negative weights, allowing Dijkstra's
+    algorithm to be used on the transformed graph.
+
+    The time complexity of this algorithm is $O(n^2 \log n + n m)$,
+    where $n$ is the number of nodes and $m$ the number of edges in the
+    graph. For dense graphs, this may be faster than the Floyd–Warshall
+    algorithm.
+
+    See Also
+    --------
+    floyd_warshall_predecessor_and_distance
+    floyd_warshall_numpy
+    all_pairs_shortest_path
+    all_pairs_shortest_path_length
+    all_pairs_dijkstra_path
+    bellman_ford_predecessor_and_distance
+    all_pairs_bellman_ford_path
+    all_pairs_bellman_ford_path_length
+
+    """
+    dist = {v: 0 for v in G}
+    pred = {v: [] for v in G}
+    weight = _weight_function(G, weight)
+
+    # Calculate distance of shortest paths
+    dist_bellman = _bellman_ford(G, list(G), weight, pred=pred, dist=dist)
+
+    # Update the weight function to take into account the Bellman--Ford
+    # relaxation distances.
+    def new_weight(u, v, d):
+        return weight(u, v, d) + dist_bellman[u] - dist_bellman[v]
+
+    def dist_path(v):
+        paths = {v: [v]}
+        _dijkstra(G, v, new_weight, paths=paths)
+        return paths
+
+    return {v: dist_path(v) for v in G}
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/__init__.py b/lib/python3.11/site-packages/networkx/algorithms/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_asteroidal.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_asteroidal.py
new file mode 100644
index 0000000000000000000000000000000000000000..67131b2d05026317b496d06e6b382836c8c26367
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_asteroidal.py
@@ -0,0 +1,23 @@
+import networkx as nx
+
+
+def test_is_at_free():
+    is_at_free = nx.asteroidal.is_at_free
+
+    cycle = nx.cycle_graph(6)
+    assert not is_at_free(cycle)
+
+    path = nx.path_graph(6)
+    assert is_at_free(path)
+
+    small_graph = nx.complete_graph(2)
+    assert is_at_free(small_graph)
+
+    petersen = nx.petersen_graph()
+    assert not is_at_free(petersen)
+
+    clique = nx.complete_graph(6)
+    assert is_at_free(clique)
+
+    line_clique = nx.line_graph(clique)
+    assert not is_at_free(line_clique)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_boundary.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_boundary.py
new file mode 100644
index 0000000000000000000000000000000000000000..856be465556941fe6f2bfc2c8bab6d4b508cf999
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_boundary.py
@@ -0,0 +1,154 @@
+"""Unit tests for the :mod:`networkx.algorithms.boundary` module."""
+
+from itertools import combinations
+
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.utils import edges_equal
+
+
+class TestNodeBoundary:
+    """Unit tests for the :func:`~networkx.node_boundary` function."""
+
+    def test_null_graph(self):
+        """Tests that the null graph has empty node boundaries."""
+        null = nx.null_graph()
+        assert nx.node_boundary(null, []) == set()
+        assert nx.node_boundary(null, [], []) == set()
+        assert nx.node_boundary(null, [1, 2, 3]) == set()
+        assert nx.node_boundary(null, [1, 2, 3], [4, 5, 6]) == set()
+        assert nx.node_boundary(null, [1, 2, 3], [3, 4, 5]) == set()
+
+    def test_path_graph(self):
+        P10 = cnlti(nx.path_graph(10), first_label=1)
+        assert nx.node_boundary(P10, []) == set()
+        assert nx.node_boundary(P10, [], []) == set()
+        assert nx.node_boundary(P10, [1, 2, 3]) == {4}
+        assert nx.node_boundary(P10, [4, 5, 6]) == {3, 7}
+        assert nx.node_boundary(P10, [3, 4, 5, 6, 7]) == {2, 8}
+        assert nx.node_boundary(P10, [8, 9, 10]) == {7}
+        assert nx.node_boundary(P10, [4, 5, 6], [9, 10]) == set()
+
+    def test_complete_graph(self):
+        K10 = cnlti(nx.complete_graph(10), first_label=1)
+        assert nx.node_boundary(K10, []) == set()
+        assert nx.node_boundary(K10, [], []) == set()
+        assert nx.node_boundary(K10, [1, 2, 3]) == {4, 5, 6, 7, 8, 9, 10}
+        assert nx.node_boundary(K10, [4, 5, 6]) == {1, 2, 3, 7, 8, 9, 10}
+        assert nx.node_boundary(K10, [3, 4, 5, 6, 7]) == {1, 2, 8, 9, 10}
+        assert nx.node_boundary(K10, [4, 5, 6], []) == set()
+        assert nx.node_boundary(K10, K10) == set()
+        assert nx.node_boundary(K10, [1, 2, 3], [3, 4, 5]) == {4, 5}
+
+    def test_petersen(self):
+        """Check boundaries in the petersen graph
+
+        cheeger(G,k)=min(|bdy(S)|/|S| for |S|=k, 0<k<=|V(G)|/2)
+
+        """
+
+        def cheeger(G, k):
+            return min(len(nx.node_boundary(G, nn)) / k for nn in combinations(G, k))
+
+        P = nx.petersen_graph()
+        assert cheeger(P, 1) == pytest.approx(3.00, abs=1e-2)
+        assert cheeger(P, 2) == pytest.approx(2.00, abs=1e-2)
+        assert cheeger(P, 3) == pytest.approx(1.67, abs=1e-2)
+        assert cheeger(P, 4) == pytest.approx(1.00, abs=1e-2)
+        assert cheeger(P, 5) == pytest.approx(0.80, abs=1e-2)
+
+    def test_directed(self):
+        """Tests the node boundary of a directed graph."""
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)])
+        S = {0, 1}
+        boundary = nx.node_boundary(G, S)
+        expected = {2}
+        assert boundary == expected
+
+    def test_multigraph(self):
+        """Tests the node boundary of a multigraph."""
+        G = nx.MultiGraph(list(nx.cycle_graph(5).edges()) * 2)
+        S = {0, 1}
+        boundary = nx.node_boundary(G, S)
+        expected = {2, 4}
+        assert boundary == expected
+
+    def test_multidigraph(self):
+        """Tests the edge boundary of a multidigraph."""
+        edges = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)]
+        G = nx.MultiDiGraph(edges * 2)
+        S = {0, 1}
+        boundary = nx.node_boundary(G, S)
+        expected = {2}
+        assert boundary == expected
+
+
+class TestEdgeBoundary:
+    """Unit tests for the :func:`~networkx.edge_boundary` function."""
+
+    def test_null_graph(self):
+        null = nx.null_graph()
+        assert list(nx.edge_boundary(null, [])) == []
+        assert list(nx.edge_boundary(null, [], [])) == []
+        assert list(nx.edge_boundary(null, [1, 2, 3])) == []
+        assert list(nx.edge_boundary(null, [1, 2, 3], [4, 5, 6])) == []
+        assert list(nx.edge_boundary(null, [1, 2, 3], [3, 4, 5])) == []
+
+    def test_path_graph(self):
+        P10 = cnlti(nx.path_graph(10), first_label=1)
+        assert list(nx.edge_boundary(P10, [])) == []
+        assert list(nx.edge_boundary(P10, [], [])) == []
+        assert list(nx.edge_boundary(P10, [1, 2, 3])) == [(3, 4)]
+        assert sorted(nx.edge_boundary(P10, [4, 5, 6])) == [(4, 3), (6, 7)]
+        assert sorted(nx.edge_boundary(P10, [3, 4, 5, 6, 7])) == [(3, 2), (7, 8)]
+        assert list(nx.edge_boundary(P10, [8, 9, 10])) == [(8, 7)]
+        assert sorted(nx.edge_boundary(P10, [4, 5, 6], [9, 10])) == []
+        assert list(nx.edge_boundary(P10, [1, 2, 3], [3, 4, 5])) == [(2, 3), (3, 4)]
+
+    def test_complete_graph(self):
+        K10 = cnlti(nx.complete_graph(10), first_label=1)
+
+        def ilen(iterable):
+            return sum(1 for i in iterable)
+
+        assert list(nx.edge_boundary(K10, [])) == []
+        assert list(nx.edge_boundary(K10, [], [])) == []
+        assert ilen(nx.edge_boundary(K10, [1, 2, 3])) == 21
+        assert ilen(nx.edge_boundary(K10, [4, 5, 6, 7])) == 24
+        assert ilen(nx.edge_boundary(K10, [3, 4, 5, 6, 7])) == 25
+        assert ilen(nx.edge_boundary(K10, [8, 9, 10])) == 21
+        assert edges_equal(
+            nx.edge_boundary(K10, [4, 5, 6], [9, 10]),
+            [(4, 9), (4, 10), (5, 9), (5, 10), (6, 9), (6, 10)],
+        )
+        assert edges_equal(
+            nx.edge_boundary(K10, [1, 2, 3], [3, 4, 5]),
+            [(1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5)],
+        )
+
+    def test_directed(self):
+        """Tests the edge boundary of a directed graph."""
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)])
+        S = {0, 1}
+        boundary = list(nx.edge_boundary(G, S))
+        expected = [(1, 2)]
+        assert boundary == expected
+
+    def test_multigraph(self):
+        """Tests the edge boundary of a multigraph."""
+        G = nx.MultiGraph(list(nx.cycle_graph(5).edges()) * 2)
+        S = {0, 1}
+        boundary = list(nx.edge_boundary(G, S))
+        expected = [(0, 4), (0, 4), (1, 2), (1, 2)]
+        assert boundary == expected
+
+    def test_multidigraph(self):
+        """Tests the edge boundary of a multidigraph."""
+        edges = [(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)]
+        G = nx.MultiDiGraph(edges * 2)
+        S = {0, 1}
+        boundary = list(nx.edge_boundary(G, S))
+        expected = [(1, 2), (1, 2)]
+        assert boundary == expected
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_bridges.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_bridges.py
new file mode 100644
index 0000000000000000000000000000000000000000..b47f5860083a0bbb80a04d480d87316476c92fce
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_bridges.py
@@ -0,0 +1,144 @@
+"""Unit tests for bridge-finding algorithms."""
+
+import pytest
+
+import networkx as nx
+
+
+class TestBridges:
+    """Unit tests for the bridge-finding function."""
+
+    def test_single_bridge(self):
+        edges = [
+            # DFS tree edges.
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (3, 5),
+            (5, 6),
+            (6, 7),
+            (7, 8),
+            (5, 9),
+            (9, 10),
+            # Nontree edges.
+            (1, 3),
+            (1, 4),
+            (2, 5),
+            (5, 10),
+            (6, 8),
+        ]
+        G = nx.Graph(edges)
+        source = 1
+        bridges = list(nx.bridges(G, source))
+        assert bridges == [(5, 6)]
+
+    def test_barbell_graph(self):
+        # The (3, 0) barbell graph has two triangles joined by a single edge.
+        G = nx.barbell_graph(3, 0)
+        source = 0
+        bridges = list(nx.bridges(G, source))
+        assert bridges == [(2, 3)]
+
+    def test_multiedge_bridge(self):
+        edges = [
+            (0, 1),
+            (0, 2),
+            (1, 2),
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (3, 4),
+        ]
+        G = nx.MultiGraph(edges)
+        assert list(nx.bridges(G)) == [(2, 3)]
+
+
+class TestHasBridges:
+    """Unit tests for the has bridges function."""
+
+    def test_single_bridge(self):
+        edges = [
+            # DFS tree edges.
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (3, 5),
+            (5, 6),  # The only bridge edge
+            (6, 7),
+            (7, 8),
+            (5, 9),
+            (9, 10),
+            # Nontree edges.
+            (1, 3),
+            (1, 4),
+            (2, 5),
+            (5, 10),
+            (6, 8),
+        ]
+        G = nx.Graph(edges)
+        assert nx.has_bridges(G)  # Default root
+        assert nx.has_bridges(G, root=1)  # arbitrary root in G
+
+    def test_has_bridges_raises_root_not_in_G(self):
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3])
+        with pytest.raises(nx.NodeNotFound):
+            nx.has_bridges(G, root=6)
+
+    def test_multiedge_bridge(self):
+        edges = [
+            (0, 1),
+            (0, 2),
+            (1, 2),
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (3, 4),
+        ]
+        G = nx.MultiGraph(edges)
+        assert nx.has_bridges(G)
+        # Make every edge a multiedge
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        assert not nx.has_bridges(G)
+
+    def test_bridges_multiple_components(self):
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2])  # One connected component
+        nx.add_path(G, [4, 5, 6])  # Another connected component
+        assert list(nx.bridges(G, root=4)) == [(4, 5), (5, 6)]
+
+
+class TestLocalBridges:
+    """Unit tests for the local_bridge function."""
+
+    @classmethod
+    def setup_class(cls):
+        cls.BB = nx.barbell_graph(4, 0)
+        cls.square = nx.cycle_graph(4)
+        cls.tri = nx.cycle_graph(3)
+
+    def test_nospan(self):
+        expected = {(3, 4), (4, 3)}
+        assert next(nx.local_bridges(self.BB, with_span=False)) in expected
+        assert set(nx.local_bridges(self.square, with_span=False)) == self.square.edges
+        assert list(nx.local_bridges(self.tri, with_span=False)) == []
+
+    def test_no_weight(self):
+        inf = float("inf")
+        expected = {(3, 4, inf), (4, 3, inf)}
+        assert next(nx.local_bridges(self.BB)) in expected
+        expected = {(u, v, 3) for u, v in self.square.edges}
+        assert set(nx.local_bridges(self.square)) == expected
+        assert list(nx.local_bridges(self.tri)) == []
+
+    def test_weight(self):
+        inf = float("inf")
+        G = self.square.copy()
+
+        G.edges[1, 2]["weight"] = 2
+        expected = {(u, v, 5 - wt) for u, v, wt in G.edges(data="weight", default=1)}
+        assert set(nx.local_bridges(G, weight="weight")) == expected
+
+        expected = {(u, v, 6) for u, v in G.edges}
+        lb = nx.local_bridges(G, weight=lambda u, v, d: 2)
+        assert set(lb) == expected
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_broadcasting.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_broadcasting.py
new file mode 100644
index 0000000000000000000000000000000000000000..64f484b3caf21d33a202a00eb4b4c3f62f42c559
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_broadcasting.py
@@ -0,0 +1,109 @@
+"""Unit tests for the broadcasting module."""
+
+import math
+
+import pytest
+
+import networkx as nx
+
+
+def test_example_tree_broadcast():
+    """
+    Test the BROADCAST algorithm on the example in the paper titled: "Information Dissemination in Trees"
+    """
+    edge_list = [
+        (0, 1),
+        (1, 2),
+        (2, 7),
+        (3, 4),
+        (5, 4),
+        (4, 7),
+        (6, 7),
+        (7, 9),
+        (8, 9),
+        (9, 13),
+        (13, 14),
+        (14, 15),
+        (14, 16),
+        (14, 17),
+        (13, 11),
+        (11, 10),
+        (11, 12),
+        (13, 18),
+        (18, 19),
+        (18, 20),
+    ]
+    G = nx.Graph(edge_list)
+    b_T, b_C = nx.tree_broadcast_center(G)
+    assert b_T == 6
+    assert b_C == {13, 9}
+    # test broadcast time from specific vertex
+    assert nx.tree_broadcast_time(G, 17) == 8
+    assert nx.tree_broadcast_time(G, 3) == 9
+    # test broadcast time of entire tree
+    assert nx.tree_broadcast_time(G) == 10
+
+
+@pytest.mark.parametrize("n", range(2, 12))
+def test_path_broadcast(n):
+    G = nx.path_graph(n)
+    b_T, b_C = nx.tree_broadcast_center(G)
+    assert b_T == math.ceil(n / 2)
+    assert b_C == {
+        math.ceil(n / 2),
+        n // 2,
+        math.ceil(n / 2 - 1),
+        n // 2 - 1,
+    }
+    assert nx.tree_broadcast_time(G) == n - 1
+
+
+def test_empty_graph_broadcast():
+    H = nx.empty_graph(1)
+    b_T, b_C = nx.tree_broadcast_center(H)
+    assert b_T == 0
+    assert b_C == {0}
+    assert nx.tree_broadcast_time(H) == 0
+
+
+@pytest.mark.parametrize("n", range(4, 12))
+def test_star_broadcast(n):
+    G = nx.star_graph(n)
+    b_T, b_C = nx.tree_broadcast_center(G)
+    assert b_T == n
+    assert b_C == set(G.nodes())
+    assert nx.tree_broadcast_time(G) == b_T
+
+
+@pytest.mark.parametrize("n", range(2, 8))
+def test_binomial_tree_broadcast(n):
+    G = nx.binomial_tree(n)
+    b_T, b_C = nx.tree_broadcast_center(G)
+    assert b_T == n
+    assert b_C == {0, 2 ** (n - 1)}
+    assert nx.tree_broadcast_time(G) == 2 * n - 1
+
+
+@pytest.mark.parametrize("fn", [nx.tree_broadcast_center, nx.tree_broadcast_time])
+@pytest.mark.parametrize("graph_type", [nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph])
+def test_raises_graph_type(fn, graph_type):
+    """Check that broadcast functions properly raise for directed and multigraph types."""
+    G = nx.path_graph(5, create_using=graph_type)
+    with pytest.raises(nx.NetworkXNotImplemented, match=r"not implemented for"):
+        fn(G)
+
+
+@pytest.mark.parametrize("fn", [nx.tree_broadcast_center, nx.tree_broadcast_time])
+@pytest.mark.parametrize("gen", [nx.empty_graph, nx.cycle_graph])
+def test_raises_not_tree(fn, gen):
+    """Check that broadcast functions properly raise for nontree graphs."""
+    G = gen(5)
+    with pytest.raises(nx.NotATree, match=r"not a tree"):
+        fn(G)
+
+
+def test_raises_node_not_in_G():
+    """Check that `tree_broadcast_time` properly raises for invalid nodes."""
+    G = nx.path_graph(5)
+    with pytest.raises(nx.NodeNotFound, match=r"node.*not in G"):
+        nx.tree_broadcast_time(G, node=10)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_chains.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_chains.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0c8c8448080c75a25c1952b46a02db4c03fefb8
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_chains.py
@@ -0,0 +1,136 @@
+"""Unit tests for the chain decomposition functions."""
+
+from itertools import cycle, islice
+
+import pytest
+
+import networkx as nx
+
+
+def cycles(seq):
+    """Yields cyclic permutations of the given sequence.
+
+    For example::
+
+        >>> list(cycles("abc"))
+        [('a', 'b', 'c'), ('b', 'c', 'a'), ('c', 'a', 'b')]
+
+    """
+    n = len(seq)
+    cycled_seq = cycle(seq)
+    for x in seq:
+        yield tuple(islice(cycled_seq, n))
+        next(cycled_seq)
+
+
+def cyclic_equals(seq1, seq2):
+    """Decide whether two sequences are equal up to cyclic permutations.
+
+    For example::
+
+        >>> cyclic_equals("xyz", "zxy")
+        True
+        >>> cyclic_equals("xyz", "zyx")
+        False
+
+    """
+    # Cast seq2 to a tuple since `cycles()` yields tuples.
+    seq2 = tuple(seq2)
+    return any(x == tuple(seq2) for x in cycles(seq1))
+
+
+class TestChainDecomposition:
+    """Unit tests for the chain decomposition function."""
+
+    def assertContainsChain(self, chain, expected):
+        # A cycle could be expressed in two different orientations, one
+        # forward and one backward, so we need to check for cyclic
+        # equality in both orientations.
+        reversed_chain = list(reversed([tuple(reversed(e)) for e in chain]))
+        for candidate in expected:
+            if cyclic_equals(chain, candidate):
+                break
+            if cyclic_equals(reversed_chain, candidate):
+                break
+        else:
+            self.fail("chain not found")
+
+    def test_decomposition(self):
+        edges = [
+            # DFS tree edges.
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (3, 5),
+            (5, 6),
+            (6, 7),
+            (7, 8),
+            (5, 9),
+            (9, 10),
+            # Nontree edges.
+            (1, 3),
+            (1, 4),
+            (2, 5),
+            (5, 10),
+            (6, 8),
+        ]
+        G = nx.Graph(edges)
+        expected = [
+            [(1, 3), (3, 2), (2, 1)],
+            [(1, 4), (4, 3)],
+            [(2, 5), (5, 3)],
+            [(5, 10), (10, 9), (9, 5)],
+            [(6, 8), (8, 7), (7, 6)],
+        ]
+        chains = list(nx.chain_decomposition(G, root=1))
+        assert len(chains) == len(expected)
+
+    def test_barbell_graph(self):
+        # The (3, 0) barbell graph has two triangles joined by a single edge.
+        G = nx.barbell_graph(3, 0)
+        chains = list(nx.chain_decomposition(G, root=0))
+        expected = [[(0, 1), (1, 2), (2, 0)], [(3, 4), (4, 5), (5, 3)]]
+        assert len(chains) == len(expected)
+        for chain in chains:
+            self.assertContainsChain(chain, expected)
+
+    def test_disconnected_graph(self):
+        """Test for a graph with multiple connected components."""
+        G = nx.barbell_graph(3, 0)
+        H = nx.barbell_graph(3, 0)
+        mapping = dict(zip(range(6), "abcdef"))
+        nx.relabel_nodes(H, mapping, copy=False)
+        G = nx.union(G, H)
+        chains = list(nx.chain_decomposition(G))
+        expected = [
+            [(0, 1), (1, 2), (2, 0)],
+            [(3, 4), (4, 5), (5, 3)],
+            [("a", "b"), ("b", "c"), ("c", "a")],
+            [("d", "e"), ("e", "f"), ("f", "d")],
+        ]
+        assert len(chains) == len(expected)
+        for chain in chains:
+            self.assertContainsChain(chain, expected)
+
+    def test_disconnected_graph_root_node(self):
+        """Test for a single component of a disconnected graph."""
+        G = nx.barbell_graph(3, 0)
+        H = nx.barbell_graph(3, 0)
+        mapping = dict(zip(range(6), "abcdef"))
+        nx.relabel_nodes(H, mapping, copy=False)
+        G = nx.union(G, H)
+        chains = list(nx.chain_decomposition(G, root="a"))
+        expected = [
+            [("a", "b"), ("b", "c"), ("c", "a")],
+            [("d", "e"), ("e", "f"), ("f", "d")],
+        ]
+        assert len(chains) == len(expected)
+        for chain in chains:
+            self.assertContainsChain(chain, expected)
+
+    def test_chain_decomposition_root_not_in_G(self):
+        """Test chain decomposition when root is not in graph"""
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3])
+        with pytest.raises(nx.NodeNotFound):
+            nx.has_bridges(G, root=6)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_chordal.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_chordal.py
new file mode 100644
index 0000000000000000000000000000000000000000..148b22f2632d722522483b556f11285a8e823126
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_chordal.py
@@ -0,0 +1,129 @@
+import pytest
+
+import networkx as nx
+
+
+class TestMCS:
+    @classmethod
+    def setup_class(cls):
+        # simple graph
+        connected_chordal_G = nx.Graph()
+        connected_chordal_G.add_edges_from(
+            [
+                (1, 2),
+                (1, 3),
+                (2, 3),
+                (2, 4),
+                (3, 4),
+                (3, 5),
+                (3, 6),
+                (4, 5),
+                (4, 6),
+                (5, 6),
+            ]
+        )
+        cls.connected_chordal_G = connected_chordal_G
+
+        chordal_G = nx.Graph()
+        chordal_G.add_edges_from(
+            [
+                (1, 2),
+                (1, 3),
+                (2, 3),
+                (2, 4),
+                (3, 4),
+                (3, 5),
+                (3, 6),
+                (4, 5),
+                (4, 6),
+                (5, 6),
+                (7, 8),
+            ]
+        )
+        chordal_G.add_node(9)
+        cls.chordal_G = chordal_G
+
+        non_chordal_G = nx.Graph()
+        non_chordal_G.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5), (3, 4), (3, 5)])
+        cls.non_chordal_G = non_chordal_G
+
+        self_loop_G = nx.Graph()
+        self_loop_G.add_edges_from([(1, 1)])
+        cls.self_loop_G = self_loop_G
+
+    @pytest.mark.parametrize("G", (nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()))
+    def test_is_chordal_not_implemented(self, G):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.is_chordal(G)
+
+    def test_is_chordal(self):
+        assert not nx.is_chordal(self.non_chordal_G)
+        assert nx.is_chordal(self.chordal_G)
+        assert nx.is_chordal(self.connected_chordal_G)
+        assert nx.is_chordal(nx.Graph())
+        assert nx.is_chordal(nx.complete_graph(3))
+        assert nx.is_chordal(nx.cycle_graph(3))
+        assert not nx.is_chordal(nx.cycle_graph(5))
+        assert nx.is_chordal(self.self_loop_G)
+
+    def test_induced_nodes(self):
+        G = nx.generators.classic.path_graph(10)
+        Induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
+        assert Induced_nodes == {1, 2, 3, 4, 5, 6, 7, 8, 9}
+        pytest.raises(
+            nx.NetworkXTreewidthBoundExceeded, nx.find_induced_nodes, G, 1, 9, 1
+        )
+        Induced_nodes = nx.find_induced_nodes(self.chordal_G, 1, 6)
+        assert Induced_nodes == {1, 2, 4, 6}
+        pytest.raises(nx.NetworkXError, nx.find_induced_nodes, self.non_chordal_G, 1, 5)
+
+    def test_graph_treewidth(self):
+        with pytest.raises(nx.NetworkXError, match="Input graph is not chordal"):
+            nx.chordal_graph_treewidth(self.non_chordal_G)
+
+    def test_chordal_find_cliques(self):
+        cliques = {
+            frozenset([9]),
+            frozenset([7, 8]),
+            frozenset([1, 2, 3]),
+            frozenset([2, 3, 4]),
+            frozenset([3, 4, 5, 6]),
+        }
+        assert set(nx.chordal_graph_cliques(self.chordal_G)) == cliques
+        with pytest.raises(nx.NetworkXError, match="Input graph is not chordal"):
+            set(nx.chordal_graph_cliques(self.non_chordal_G))
+        with pytest.raises(nx.NetworkXError, match="Input graph is not chordal"):
+            set(nx.chordal_graph_cliques(self.self_loop_G))
+
+    def test_chordal_find_cliques_path(self):
+        G = nx.path_graph(10)
+        cliqueset = nx.chordal_graph_cliques(G)
+        for u, v in G.edges():
+            assert frozenset([u, v]) in cliqueset or frozenset([v, u]) in cliqueset
+
+    def test_chordal_find_cliquesCC(self):
+        cliques = {frozenset([1, 2, 3]), frozenset([2, 3, 4]), frozenset([3, 4, 5, 6])}
+        cgc = nx.chordal_graph_cliques
+        assert set(cgc(self.connected_chordal_G)) == cliques
+
+    def test_complete_to_chordal_graph(self):
+        fgrg = nx.fast_gnp_random_graph
+        test_graphs = [
+            nx.barbell_graph(6, 2),
+            nx.cycle_graph(15),
+            nx.wheel_graph(20),
+            nx.grid_graph([10, 4]),
+            nx.ladder_graph(15),
+            nx.star_graph(5),
+            nx.bull_graph(),
+            fgrg(20, 0.3, seed=1),
+        ]
+        for G in test_graphs:
+            H, a = nx.complete_to_chordal_graph(G)
+            assert nx.is_chordal(H)
+            assert len(a) == H.number_of_nodes()
+            if nx.is_chordal(G):
+                assert G.number_of_edges() == H.number_of_edges()
+                assert set(a.values()) == {0}
+            else:
+                assert len(set(a.values())) == H.number_of_nodes()
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_clique.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_clique.py
new file mode 100644
index 0000000000000000000000000000000000000000..118b3093cf3fd12ffe458983b37549faf78c83c3
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_clique.py
@@ -0,0 +1,300 @@
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+
+
+class TestCliques:
+    def setup_method(self):
+        z = [3, 4, 3, 4, 2, 4, 2, 1, 1, 1, 1]
+        self.G = cnlti(nx.generators.havel_hakimi_graph(z), first_label=1)
+        self.cl = list(nx.find_cliques(self.G))
+        H = nx.complete_graph(6)
+        H = nx.relabel_nodes(H, {i: i + 1 for i in range(6)})
+        H.remove_edges_from([(2, 6), (2, 5), (2, 4), (1, 3), (5, 3)])
+        self.H = H
+
+    def test_find_cliques1(self):
+        cl = list(nx.find_cliques(self.G))
+        rcl = nx.find_cliques_recursive(self.G)
+        expected = [[2, 6, 1, 3], [2, 6, 4], [5, 4, 7], [8, 9], [10, 11]]
+        assert sorted(map(sorted, cl)) == sorted(map(sorted, rcl))
+        assert sorted(map(sorted, cl)) == sorted(map(sorted, expected))
+
+    def test_selfloops(self):
+        self.G.add_edge(1, 1)
+        cl = list(nx.find_cliques(self.G))
+        rcl = list(nx.find_cliques_recursive(self.G))
+        assert set(map(frozenset, cl)) == set(map(frozenset, rcl))
+        answer = [{2, 6, 1, 3}, {2, 6, 4}, {5, 4, 7}, {8, 9}, {10, 11}]
+        assert len(answer) == len(cl)
+        assert all(set(c) in answer for c in cl)
+
+    def test_find_cliques2(self):
+        hcl = list(nx.find_cliques(self.H))
+        assert sorted(map(sorted, hcl)) == [[1, 2], [1, 4, 5, 6], [2, 3], [3, 4, 6]]
+
+    def test_find_cliques3(self):
+        # all cliques are [[2, 6, 1, 3], [2, 6, 4], [5, 4, 7], [8, 9], [10, 11]]
+
+        cl = list(nx.find_cliques(self.G, [2]))
+        rcl = nx.find_cliques_recursive(self.G, [2])
+        expected = [[2, 6, 1, 3], [2, 6, 4]]
+        assert sorted(map(sorted, rcl)) == sorted(map(sorted, expected))
+        assert sorted(map(sorted, cl)) == sorted(map(sorted, expected))
+
+        cl = list(nx.find_cliques(self.G, [2, 3]))
+        rcl = nx.find_cliques_recursive(self.G, [2, 3])
+        expected = [[2, 6, 1, 3]]
+        assert sorted(map(sorted, rcl)) == sorted(map(sorted, expected))
+        assert sorted(map(sorted, cl)) == sorted(map(sorted, expected))
+
+        cl = list(nx.find_cliques(self.G, [2, 6, 4]))
+        rcl = nx.find_cliques_recursive(self.G, [2, 6, 4])
+        expected = [[2, 6, 4]]
+        assert sorted(map(sorted, rcl)) == sorted(map(sorted, expected))
+        assert sorted(map(sorted, cl)) == sorted(map(sorted, expected))
+
+        cl = list(nx.find_cliques(self.G, [2, 6, 4]))
+        rcl = nx.find_cliques_recursive(self.G, [2, 6, 4])
+        expected = [[2, 6, 4]]
+        assert sorted(map(sorted, rcl)) == sorted(map(sorted, expected))
+        assert sorted(map(sorted, cl)) == sorted(map(sorted, expected))
+
+        with pytest.raises(ValueError):
+            list(nx.find_cliques(self.G, [2, 6, 4, 1]))
+
+        with pytest.raises(ValueError):
+            list(nx.find_cliques_recursive(self.G, [2, 6, 4, 1]))
+
+    def test_find_cliques_directed(self):
+        G = nx.path_graph(4, create_using=nx.DiGraph)
+        msg = "not implemented for directed"
+        with pytest.raises(nx.NetworkXNotImplemented, match=msg):
+            list(nx.find_cliques(G))
+
+        with pytest.raises(nx.NetworkXNotImplemented, match=msg):
+            list(nx.find_cliques_recursive(G))
+
+    def test_number_of_cliques(self):
+        G = self.G
+        assert nx.number_of_cliques(G, 1) == 1
+        assert list(nx.number_of_cliques(G, [1]).values()) == [1]
+        assert list(nx.number_of_cliques(G, [1, 2]).values()) == [1, 2]
+        assert nx.number_of_cliques(G, [1, 2]) == {1: 1, 2: 2}
+        assert nx.number_of_cliques(G, 2) == 2
+        assert nx.number_of_cliques(G) == {
+            1: 1,
+            2: 2,
+            3: 1,
+            4: 2,
+            5: 1,
+            6: 2,
+            7: 1,
+            8: 1,
+            9: 1,
+            10: 1,
+            11: 1,
+        }
+        assert nx.number_of_cliques(G, nodes=list(G)) == {
+            1: 1,
+            2: 2,
+            3: 1,
+            4: 2,
+            5: 1,
+            6: 2,
+            7: 1,
+            8: 1,
+            9: 1,
+            10: 1,
+            11: 1,
+        }
+        assert nx.number_of_cliques(G, nodes=[2, 3, 4]) == {2: 2, 3: 1, 4: 2}
+        assert nx.number_of_cliques(G, cliques=self.cl) == {
+            1: 1,
+            2: 2,
+            3: 1,
+            4: 2,
+            5: 1,
+            6: 2,
+            7: 1,
+            8: 1,
+            9: 1,
+            10: 1,
+            11: 1,
+        }
+        assert nx.number_of_cliques(G, list(G), cliques=self.cl) == {
+            1: 1,
+            2: 2,
+            3: 1,
+            4: 2,
+            5: 1,
+            6: 2,
+            7: 1,
+            8: 1,
+            9: 1,
+            10: 1,
+            11: 1,
+        }
+
+    def test_node_clique_number(self):
+        G = self.G
+        assert nx.node_clique_number(G, 1) == 4
+        assert list(nx.node_clique_number(G, [1]).values()) == [4]
+        assert list(nx.node_clique_number(G, [1, 2]).values()) == [4, 4]
+        assert nx.node_clique_number(G, [1, 2]) == {1: 4, 2: 4}
+        assert nx.node_clique_number(G, 1) == 4
+        assert nx.node_clique_number(G) == {
+            1: 4,
+            2: 4,
+            3: 4,
+            4: 3,
+            5: 3,
+            6: 4,
+            7: 3,
+            8: 2,
+            9: 2,
+            10: 2,
+            11: 2,
+        }
+        assert nx.node_clique_number(G, cliques=self.cl) == {
+            1: 4,
+            2: 4,
+            3: 4,
+            4: 3,
+            5: 3,
+            6: 4,
+            7: 3,
+            8: 2,
+            9: 2,
+            10: 2,
+            11: 2,
+        }
+        assert nx.node_clique_number(G, [1, 2], cliques=self.cl) == {1: 4, 2: 4}
+        assert nx.node_clique_number(G, 1, cliques=self.cl) == 4
+
+    def test_make_clique_bipartite(self):
+        G = self.G
+        B = nx.make_clique_bipartite(G)
+        assert sorted(B) == [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+        # Project onto the nodes of the original graph.
+        H = nx.projected_graph(B, range(1, 12))
+        assert H.adj == G.adj
+        # Project onto the nodes representing the cliques.
+        H1 = nx.projected_graph(B, range(-5, 0))
+        # Relabel the negative numbers as positive ones.
+        H1 = nx.relabel_nodes(H1, {-v: v for v in range(1, 6)})
+        assert sorted(H1) == [1, 2, 3, 4, 5]
+
+    def test_make_max_clique_graph(self):
+        """Tests that the maximal clique graph is the same as the bipartite
+        clique graph after being projected onto the nodes representing the
+        cliques.
+
+        """
+        G = self.G
+        B = nx.make_clique_bipartite(G)
+        # Project onto the nodes representing the cliques.
+        H1 = nx.projected_graph(B, range(-5, 0))
+        # Relabel the negative numbers as nonnegative ones, starting at
+        # 0.
+        H1 = nx.relabel_nodes(H1, {-v: v - 1 for v in range(1, 6)})
+        H2 = nx.make_max_clique_graph(G)
+        assert H1.adj == H2.adj
+
+    def test_directed(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            next(nx.find_cliques(nx.DiGraph()))
+
+    def test_find_cliques_trivial(self):
+        G = nx.Graph()
+        assert sorted(nx.find_cliques(G)) == []
+        assert sorted(nx.find_cliques_recursive(G)) == []
+
+    def test_make_max_clique_graph_create_using(self):
+        G = nx.Graph([(1, 2), (3, 1), (4, 1), (5, 6)])
+        E = nx.Graph([(0, 1), (0, 2), (1, 2)])
+        E.add_node(3)
+        assert nx.is_isomorphic(nx.make_max_clique_graph(G, create_using=nx.Graph), E)
+
+
+class TestEnumerateAllCliques:
+    def test_paper_figure_4(self):
+        # Same graph as given in Fig. 4 of paper enumerate_all_cliques is
+        # based on.
+        # http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1559964&isnumber=33129
+        G = nx.Graph()
+        edges_fig_4 = [
+            ("a", "b"),
+            ("a", "c"),
+            ("a", "d"),
+            ("a", "e"),
+            ("b", "c"),
+            ("b", "d"),
+            ("b", "e"),
+            ("c", "d"),
+            ("c", "e"),
+            ("d", "e"),
+            ("f", "b"),
+            ("f", "c"),
+            ("f", "g"),
+            ("g", "f"),
+            ("g", "c"),
+            ("g", "d"),
+            ("g", "e"),
+        ]
+        G.add_edges_from(edges_fig_4)
+
+        cliques = list(nx.enumerate_all_cliques(G))
+        clique_sizes = list(map(len, cliques))
+        assert sorted(clique_sizes) == clique_sizes
+
+        expected_cliques = [
+            ["a"],
+            ["b"],
+            ["c"],
+            ["d"],
+            ["e"],
+            ["f"],
+            ["g"],
+            ["a", "b"],
+            ["a", "b", "d"],
+            ["a", "b", "d", "e"],
+            ["a", "b", "e"],
+            ["a", "c"],
+            ["a", "c", "d"],
+            ["a", "c", "d", "e"],
+            ["a", "c", "e"],
+            ["a", "d"],
+            ["a", "d", "e"],
+            ["a", "e"],
+            ["b", "c"],
+            ["b", "c", "d"],
+            ["b", "c", "d", "e"],
+            ["b", "c", "e"],
+            ["b", "c", "f"],
+            ["b", "d"],
+            ["b", "d", "e"],
+            ["b", "e"],
+            ["b", "f"],
+            ["c", "d"],
+            ["c", "d", "e"],
+            ["c", "d", "e", "g"],
+            ["c", "d", "g"],
+            ["c", "e"],
+            ["c", "e", "g"],
+            ["c", "f"],
+            ["c", "f", "g"],
+            ["c", "g"],
+            ["d", "e"],
+            ["d", "e", "g"],
+            ["d", "g"],
+            ["e", "g"],
+            ["f", "g"],
+            ["a", "b", "c"],
+            ["a", "b", "c", "d"],
+            ["a", "b", "c", "d", "e"],
+            ["a", "b", "c", "e"],
+        ]
+
+        assert sorted(map(sorted, cliques)) == sorted(map(sorted, expected_cliques))
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_cluster.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4741b420af781cec73c86e917af6ad68fa091a7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_cluster.py
@@ -0,0 +1,678 @@
+import pytest
+
+import networkx as nx
+
+
+def test_square_clustering_adjacent_squares():
+    G = nx.Graph([(1, 2), (1, 3), (2, 4), (3, 4), (3, 5), (4, 6), (5, 6)])
+    # Corner nodes: C_4 == 0.5, central face nodes: C_4 = 1 / 3
+    expected = {1: 0.5, 2: 0.5, 3: 1 / 3, 4: 1 / 3, 5: 0.5, 6: 0.5}
+    assert nx.square_clustering(G) == expected
+
+
+def test_square_clustering_2d_grid():
+    G = nx.grid_2d_graph(3, 3)
+    # Central node: 4 squares out of 20 potential
+    expected = {
+        (0, 0): 1 / 3,
+        (0, 1): 0.25,
+        (0, 2): 1 / 3,
+        (1, 0): 0.25,
+        (1, 1): 0.2,
+        (1, 2): 0.25,
+        (2, 0): 1 / 3,
+        (2, 1): 0.25,
+        (2, 2): 1 / 3,
+    }
+    assert nx.square_clustering(G) == expected
+
+
+def test_square_clustering_multiple_squares_non_complete():
+    """An example where all nodes are part of all squares, but not every node
+    is connected to every other."""
+    G = nx.Graph([(0, 1), (0, 2), (1, 3), (2, 3), (1, 4), (2, 4), (1, 5), (2, 5)])
+    expected = {n: 1 for n in G}
+    assert nx.square_clustering(G) == expected
+
+
+class TestTriangles:
+    def test_empty(self):
+        G = nx.Graph()
+        assert list(nx.triangles(G).values()) == []
+
+    def test_path(self):
+        G = nx.path_graph(10)
+        assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+        assert nx.triangles(G) == {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+            5: 0,
+            6: 0,
+            7: 0,
+            8: 0,
+            9: 0,
+        }
+
+    def test_cubical(self):
+        G = nx.cubical_graph()
+        assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0]
+        assert nx.triangles(G, 1) == 0
+        assert list(nx.triangles(G, [1, 2]).values()) == [0, 0]
+        assert nx.triangles(G, 1) == 0
+        assert nx.triangles(G, [1, 2]) == {1: 0, 2: 0}
+
+    def test_k5(self):
+        G = nx.complete_graph(5)
+        assert list(nx.triangles(G).values()) == [6, 6, 6, 6, 6]
+        assert sum(nx.triangles(G).values()) / 3 == 10
+        assert nx.triangles(G, 1) == 6
+        G.remove_edge(1, 2)
+        assert list(nx.triangles(G).values()) == [5, 3, 3, 5, 5]
+        assert nx.triangles(G, 1) == 3
+        G.add_edge(3, 3)  # ignore self-edges
+        assert list(nx.triangles(G).values()) == [5, 3, 3, 5, 5]
+        assert nx.triangles(G, 3) == 5
+
+
+def test_all_triangles_non_integer_nodes():
+    G = nx.Graph()
+    G.add_edges_from(
+        [
+            ("a", "b"),
+            ("b", "c"),
+            ("c", "a"),  # triangle: a-b-c
+        ]
+    )
+    expected = {frozenset({"a", "b", "c"})}
+    assert {frozenset(t) for t in nx.all_triangles(G)} == expected
+
+
+def test_all_triangles_overlapping():
+    G = nx.Graph()
+    G.add_edges_from(
+        [
+            (0, 1),
+            (1, 2),
+            (2, 0),  # triangle: 0-1-2
+            (0, 2),
+            (2, 3),
+            (3, 0),  # triangle: 0-2-3
+        ]
+    )
+    expected = {frozenset({0, 1, 2}), frozenset({0, 2, 3})}
+    assert {frozenset(t) for t in nx.all_triangles(G)} == expected
+
+
+def test_all_triangles_subset():
+    G = nx.Graph()
+    G.add_edges_from(
+        [
+            (0, 1),
+            (1, 2),
+            (2, 0),  # triangle: 0-1-2
+            (2, 3),
+            (3, 4),
+            (4, 2),  # triangle: 2-3-4
+        ]
+    )
+    assert {frozenset(t) for t in nx.all_triangles(G, nbunch=[0, 1])} == {
+        frozenset({0, 1, 2})
+    }
+
+
+def test_all_triangles_subset_empty():
+    G = nx.Graph()
+    G.add_edges_from(
+        [
+            (0, 1),
+            (1, 2),
+            (2, 0),  # triangle: 0-1-2
+            (2, 3),
+            (3, 4),
+            (4, 2),  # triangle: 2-3-4
+            (5, 2),
+        ]
+    )
+    assert list(nx.all_triangles(G, nbunch=[5])) == []
+
+
+def test_all_triangles_no_triangles():
+    G = nx.path_graph(4)
+    assert list(nx.all_triangles(G)) == []
+
+
+def test_all_triangles_complete_graph_exact():
+    G = nx.complete_graph(4)
+
+    expected = {
+        frozenset({0, 1, 2}),
+        frozenset({0, 1, 3}),
+        frozenset({0, 2, 3}),
+        frozenset({1, 2, 3}),
+    }
+
+    assert {frozenset(t) for t in nx.all_triangles(G)} == expected
+
+
+def test_all_triangles_directed_graph():
+    G = nx.DiGraph()
+    G.add_edges_from([(0, 1), (1, 2), (2, 0)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        list(nx.all_triangles(G))
+
+
+@pytest.mark.parametrize("graph_type", [nx.Graph, nx.MultiGraph])
+def test_all_triangles_multiedges(graph_type):
+    G = graph_type()
+    G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 2)])
+    assert {frozenset(t) for t in nx.all_triangles(G)} == {frozenset({0, 1, 2})}
+
+
+class TestDirectedClustering:
+    def test_clustering(self):
+        G = nx.DiGraph()
+        assert list(nx.clustering(G).values()) == []
+        assert nx.clustering(G) == {}
+
+    def test_path(self):
+        G = nx.path_graph(10, create_using=nx.DiGraph())
+        assert list(nx.clustering(G).values()) == [
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ]
+        assert nx.clustering(G) == {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+            5: 0,
+            6: 0,
+            7: 0,
+            8: 0,
+            9: 0,
+        }
+        assert nx.clustering(G, 0) == 0
+
+    def test_k5(self):
+        G = nx.complete_graph(5, create_using=nx.DiGraph())
+        assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1]
+        assert nx.average_clustering(G) == 1
+        G.remove_edge(1, 2)
+        assert list(nx.clustering(G).values()) == [
+            11 / 12,
+            1,
+            1,
+            11 / 12,
+            11 / 12,
+        ]
+        assert nx.clustering(G, [1, 4]) == {1: 1, 4: 11 / 12}
+        G.remove_edge(2, 1)
+        assert list(nx.clustering(G).values()) == [
+            5 / 6,
+            1,
+            1,
+            5 / 6,
+            5 / 6,
+        ]
+        assert nx.clustering(G, [1, 4]) == {1: 1, 4: 0.83333333333333337}
+        assert nx.clustering(G, 4) == 5 / 6
+
+    def test_triangle_and_edge(self):
+        G = nx.cycle_graph(3, create_using=nx.DiGraph())
+        G.add_edge(0, 4)
+        assert nx.clustering(G)[0] == 1 / 6
+
+
+class TestDirectedWeightedClustering:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+
+    def test_clustering(self):
+        G = nx.DiGraph()
+        assert list(nx.clustering(G, weight="weight").values()) == []
+        assert nx.clustering(G) == {}
+
+    def test_path(self):
+        G = nx.path_graph(10, create_using=nx.DiGraph())
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ]
+        assert nx.clustering(G, weight="weight") == {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+            5: 0,
+            6: 0,
+            7: 0,
+            8: 0,
+            9: 0,
+        }
+
+    def test_k5(self):
+        G = nx.complete_graph(5, create_using=nx.DiGraph())
+        assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1]
+        assert nx.average_clustering(G, weight="weight") == 1
+        G.remove_edge(1, 2)
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            11 / 12,
+            1,
+            1,
+            11 / 12,
+            11 / 12,
+        ]
+        assert nx.clustering(G, [1, 4], weight="weight") == {1: 1, 4: 11 / 12}
+        G.remove_edge(2, 1)
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            5 / 6,
+            1,
+            1,
+            5 / 6,
+            5 / 6,
+        ]
+        assert nx.clustering(G, [1, 4], weight="weight") == {
+            1: 1,
+            4: 0.83333333333333337,
+        }
+
+    def test_triangle_and_edge(self):
+        G = nx.cycle_graph(3, create_using=nx.DiGraph())
+        G.add_edge(0, 4, weight=2)
+        assert nx.clustering(G)[0] == 1 / 6
+        # Relaxed comparisons to allow graphblas-algorithms to pass tests
+        np.testing.assert_allclose(nx.clustering(G, weight="weight")[0], 1 / 12)
+        np.testing.assert_allclose(nx.clustering(G, 0, weight="weight"), 1 / 12)
+
+
+class TestWeightedClustering:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+
+    def test_clustering(self):
+        G = nx.Graph()
+        assert list(nx.clustering(G, weight="weight").values()) == []
+        assert nx.clustering(G) == {}
+
+    def test_path(self):
+        G = nx.path_graph(10)
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ]
+        assert nx.clustering(G, weight="weight") == {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+            5: 0,
+            6: 0,
+            7: 0,
+            8: 0,
+            9: 0,
+        }
+
+    def test_cubical(self):
+        G = nx.cubical_graph()
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ]
+        assert nx.clustering(G, 1) == 0
+        assert list(nx.clustering(G, [1, 2], weight="weight").values()) == [0, 0]
+        assert nx.clustering(G, 1, weight="weight") == 0
+        assert nx.clustering(G, [1, 2], weight="weight") == {1: 0, 2: 0}
+
+    def test_k5(self):
+        G = nx.complete_graph(5)
+        assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1]
+        assert nx.average_clustering(G, weight="weight") == 1
+        G.remove_edge(1, 2)
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            5 / 6,
+            1,
+            1,
+            5 / 6,
+            5 / 6,
+        ]
+        assert nx.clustering(G, [1, 4], weight="weight") == {
+            1: 1,
+            4: 0.83333333333333337,
+        }
+
+    def test_triangle_and_edge(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 4, weight=2)
+        assert nx.clustering(G)[0] == 1 / 3
+        np.testing.assert_allclose(nx.clustering(G, weight="weight")[0], 1 / 6)
+        np.testing.assert_allclose(nx.clustering(G, 0, weight="weight"), 1 / 6)
+
+    def test_triangle_and_signed_edge(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 1, weight=-1)
+        G.add_edge(3, 0, weight=0)
+        assert nx.clustering(G)[0] == 1 / 3
+        assert nx.clustering(G, weight="weight")[0] == -1 / 3
+
+
+class TestClustering:
+    @classmethod
+    def setup_class(cls):
+        pytest.importorskip("numpy")
+
+    def test_clustering(self):
+        G = nx.Graph()
+        assert list(nx.clustering(G).values()) == []
+        assert nx.clustering(G) == {}
+
+    def test_path(self):
+        G = nx.path_graph(10)
+        assert list(nx.clustering(G).values()) == [
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ]
+        assert nx.clustering(G) == {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+            5: 0,
+            6: 0,
+            7: 0,
+            8: 0,
+            9: 0,
+        }
+
+    def test_cubical(self):
+        G = nx.cubical_graph()
+        assert list(nx.clustering(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0]
+        assert nx.clustering(G, 1) == 0
+        assert list(nx.clustering(G, [1, 2]).values()) == [0, 0]
+        assert nx.clustering(G, 1) == 0
+        assert nx.clustering(G, [1, 2]) == {1: 0, 2: 0}
+
+    def test_k5(self):
+        G = nx.complete_graph(5)
+        assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1]
+        assert nx.average_clustering(G) == 1
+        G.remove_edge(1, 2)
+        assert list(nx.clustering(G).values()) == [
+            5 / 6,
+            1,
+            1,
+            5 / 6,
+            5 / 6,
+        ]
+        assert nx.clustering(G, [1, 4]) == {1: 1, 4: 0.83333333333333337}
+
+    def test_k5_signed(self):
+        G = nx.complete_graph(5)
+        assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1]
+        assert nx.average_clustering(G) == 1
+        G.remove_edge(1, 2)
+        G.add_edge(0, 1, weight=-1)
+        assert list(nx.clustering(G, weight="weight").values()) == [
+            1 / 6,
+            -1 / 3,
+            1,
+            3 / 6,
+            3 / 6,
+        ]
+
+
+class TestTransitivity:
+    def test_transitivity(self):
+        G = nx.Graph()
+        assert nx.transitivity(G) == 0
+
+    def test_path(self):
+        G = nx.path_graph(10)
+        assert nx.transitivity(G) == 0
+
+    def test_cubical(self):
+        G = nx.cubical_graph()
+        assert nx.transitivity(G) == 0
+
+    def test_k5(self):
+        G = nx.complete_graph(5)
+        assert nx.transitivity(G) == 1
+        G.remove_edge(1, 2)
+        assert nx.transitivity(G) == 0.875
+
+
+class TestSquareClustering:
+    def test_clustering(self):
+        G = nx.Graph()
+        assert list(nx.square_clustering(G).values()) == []
+        assert nx.square_clustering(G) == {}
+
+    def test_path(self):
+        G = nx.path_graph(10)
+        assert list(nx.square_clustering(G).values()) == [
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ]
+        assert nx.square_clustering(G) == {
+            0: 0,
+            1: 0,
+            2: 0,
+            3: 0,
+            4: 0,
+            5: 0,
+            6: 0,
+            7: 0,
+            8: 0,
+            9: 0,
+        }
+
+    def test_cubical(self):
+        G = nx.cubical_graph()
+        assert list(nx.square_clustering(G).values()) == [
+            1 / 3,
+            1 / 3,
+            1 / 3,
+            1 / 3,
+            1 / 3,
+            1 / 3,
+            1 / 3,
+            1 / 3,
+        ]
+        assert list(nx.square_clustering(G, [1, 2]).values()) == [1 / 3, 1 / 3]
+        assert nx.square_clustering(G, [1])[1] == 1 / 3
+        assert nx.square_clustering(G, 1) == 1 / 3
+        assert nx.square_clustering(G, [1, 2]) == {1: 1 / 3, 2: 1 / 3}
+
+    def test_k5(self):
+        G = nx.complete_graph(5)
+        assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1]
+
+    def test_bipartite_k5(self):
+        G = nx.complete_bipartite_graph(5, 5)
+        assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+    def test_lind_square_clustering(self):
+        """Test C4 for figure 1 Lind et al (2005)"""
+        G = nx.Graph(
+            [
+                (1, 2),
+                (1, 3),
+                (1, 6),
+                (1, 7),
+                (2, 4),
+                (2, 5),
+                (3, 4),
+                (3, 5),
+                (6, 7),
+                (7, 8),
+                (6, 8),
+                (7, 9),
+                (7, 10),
+                (6, 11),
+                (6, 12),
+                (2, 13),
+                (2, 14),
+                (3, 15),
+                (3, 16),
+            ]
+        )
+        G1 = G.subgraph([1, 2, 3, 4, 5, 13, 14, 15, 16])
+        G2 = G.subgraph([1, 6, 7, 8, 9, 10, 11, 12])
+        assert nx.square_clustering(G, [1])[1] == 3 / 43
+        assert nx.square_clustering(G1, [1])[1] == 2 / 6
+        assert nx.square_clustering(G2, [1])[1] == 1 / 5
+
+    def test_peng_square_clustering(self):
+        """Test eq2 for figure 1 Peng et al (2008)"""
+        # Example graph from figure 1b
+        G = nx.Graph([(1, 2), (1, 3), (2, 4), (3, 4), (3, 5), (3, 6)])
+        # From table 1, row 2
+        expected = {1: 1 / 3, 2: 1, 3: 0.2, 4: 1 / 3, 5: 0, 6: 0}
+        assert nx.square_clustering(G) == expected
+
+    def test_self_loops_square_clustering(self):
+        G = nx.path_graph(5)
+        assert nx.square_clustering(G) == {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}
+        G.add_edges_from([(0, 0), (1, 1), (2, 2)])
+        assert nx.square_clustering(G) == {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}
+
+
+class TestAverageClustering:
+    @classmethod
+    def setup_class(cls):
+        pytest.importorskip("numpy")
+
+    def test_empty(self):
+        G = nx.Graph()
+        with pytest.raises(ZeroDivisionError):
+            nx.average_clustering(G)
+
+    def test_average_clustering(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(2, 3)
+        assert nx.average_clustering(G) == (1 + 1 + 1 / 3) / 4
+        assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3) / 4
+        assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3) / 3
+        assert nx.average_clustering(G, [1, 2, 3]) == (1 + 1 / 3) / 3
+        assert nx.average_clustering(G, [1, 2, 3], count_zeros=True) == (1 + 1 / 3) / 3
+        assert nx.average_clustering(G, [1, 2, 3], count_zeros=False) == (1 + 1 / 3) / 2
+
+    def test_average_clustering_signed(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(2, 3)
+        G.add_edge(0, 1, weight=-1)
+        assert nx.average_clustering(G, weight="weight") == (-1 - 1 - 1 / 3) / 4
+        assert (
+            nx.average_clustering(G, weight="weight", count_zeros=True)
+            == (-1 - 1 - 1 / 3) / 4
+        )
+        assert (
+            nx.average_clustering(G, weight="weight", count_zeros=False)
+            == (-1 - 1 - 1 / 3) / 3
+        )
+
+
+class TestDirectedAverageClustering:
+    @classmethod
+    def setup_class(cls):
+        pytest.importorskip("numpy")
+
+    def test_empty(self):
+        G = nx.DiGraph()
+        with pytest.raises(ZeroDivisionError):
+            nx.average_clustering(G)
+
+    def test_average_clustering(self):
+        G = nx.cycle_graph(3, create_using=nx.DiGraph())
+        G.add_edge(2, 3)
+        assert nx.average_clustering(G) == (1 + 1 + 1 / 3) / 8
+        assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3) / 8
+        assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3) / 6
+        assert nx.average_clustering(G, [1, 2, 3]) == (1 + 1 / 3) / 6
+        assert nx.average_clustering(G, [1, 2, 3], count_zeros=True) == (1 + 1 / 3) / 6
+        assert nx.average_clustering(G, [1, 2, 3], count_zeros=False) == (1 + 1 / 3) / 4
+
+
+class TestGeneralizedDegree:
+    def test_generalized_degree(self):
+        G = nx.Graph()
+        assert nx.generalized_degree(G) == {}
+
+    def test_path(self):
+        G = nx.path_graph(5)
+        assert nx.generalized_degree(G, 0) == {0: 1}
+        assert nx.generalized_degree(G, 1) == {0: 2}
+
+    def test_cubical(self):
+        G = nx.cubical_graph()
+        assert nx.generalized_degree(G, 0) == {0: 3}
+
+    def test_k5(self):
+        G = nx.complete_graph(5)
+        assert nx.generalized_degree(G, 0) == {3: 4}
+        G.remove_edge(0, 1)
+        assert nx.generalized_degree(G, 0) == {2: 3}
+        assert nx.generalized_degree(G, [1, 2]) == {1: {2: 3}, 2: {2: 2, 3: 2}}
+        assert nx.generalized_degree(G) == {
+            0: {2: 3},
+            1: {2: 3},
+            2: {2: 2, 3: 2},
+            3: {2: 2, 3: 2},
+            4: {2: 2, 3: 2},
+        }
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_communicability.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_communicability.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f447094548415c089710b9b62ac4d73a27efeb5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_communicability.py
@@ -0,0 +1,80 @@
+from collections import defaultdict
+
+import pytest
+
+pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.algorithms.communicability_alg import communicability, communicability_exp
+
+
+class TestCommunicability:
+    def test_communicability(self):
+        answer = {
+            0: {0: 1.5430806348152435, 1: 1.1752011936438012},
+            1: {0: 1.1752011936438012, 1: 1.5430806348152435},
+        }
+        #        answer={(0, 0): 1.5430806348152435,
+        #                (0, 1): 1.1752011936438012,
+        #                (1, 0): 1.1752011936438012,
+        #                (1, 1): 1.5430806348152435}
+
+        result = communicability(nx.path_graph(2))
+        for k1, val in result.items():
+            for k2 in val:
+                assert answer[k1][k2] == pytest.approx(result[k1][k2], abs=1e-7)
+
+    def test_communicability2(self):
+        answer_orig = {
+            ("1", "1"): 1.6445956054135658,
+            ("1", "Albert"): 0.7430186221096251,
+            ("1", "Aric"): 0.7430186221096251,
+            ("1", "Dan"): 1.6208126320442937,
+            ("1", "Franck"): 0.42639707170035257,
+            ("Albert", "1"): 0.7430186221096251,
+            ("Albert", "Albert"): 2.4368257358712189,
+            ("Albert", "Aric"): 1.4368257358712191,
+            ("Albert", "Dan"): 2.0472097037446453,
+            ("Albert", "Franck"): 1.8340111678944691,
+            ("Aric", "1"): 0.7430186221096251,
+            ("Aric", "Albert"): 1.4368257358712191,
+            ("Aric", "Aric"): 2.4368257358712193,
+            ("Aric", "Dan"): 2.0472097037446457,
+            ("Aric", "Franck"): 1.8340111678944691,
+            ("Dan", "1"): 1.6208126320442937,
+            ("Dan", "Albert"): 2.0472097037446453,
+            ("Dan", "Aric"): 2.0472097037446457,
+            ("Dan", "Dan"): 3.1306328496328168,
+            ("Dan", "Franck"): 1.4860372442192515,
+            ("Franck", "1"): 0.42639707170035257,
+            ("Franck", "Albert"): 1.8340111678944691,
+            ("Franck", "Aric"): 1.8340111678944691,
+            ("Franck", "Dan"): 1.4860372442192515,
+            ("Franck", "Franck"): 2.3876142275231915,
+        }
+
+        answer = defaultdict(dict)
+        for (k1, k2), v in answer_orig.items():
+            answer[k1][k2] = v
+
+        G1 = nx.Graph(
+            [
+                ("Franck", "Aric"),
+                ("Aric", "Dan"),
+                ("Dan", "Albert"),
+                ("Albert", "Franck"),
+                ("Dan", "1"),
+                ("Franck", "Albert"),
+            ]
+        )
+
+        result = communicability(G1)
+        for k1, val in result.items():
+            for k2 in val:
+                assert answer[k1][k2] == pytest.approx(result[k1][k2], abs=1e-7)
+
+        result = communicability_exp(G1)
+        for k1, val in result.items():
+            for k2 in val:
+                assert answer[k1][k2] == pytest.approx(result[k1][k2], abs=1e-7)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_core.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cbaf759be2ae91cd053629f73353e33bd3a5ee5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_core.py
@@ -0,0 +1,266 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import nodes_equal
+
+
+class TestCore:
+    @classmethod
+    def setup_class(cls):
+        # G is the example graph in Figure 1 from Batagelj and
+        # Zaversnik's paper titled An O(m) Algorithm for Cores
+        # Decomposition of Networks, 2003,
+        # http://arXiv.org/abs/cs/0310049.  With nodes labeled as
+        # shown, the 3-core is given by nodes 1-8, the 2-core by nodes
+        # 9-16, the 1-core by nodes 17-20 and node 21 is in the
+        # 0-core.
+        t1 = nx.convert_node_labels_to_integers(nx.tetrahedral_graph(), 1)
+        t2 = nx.convert_node_labels_to_integers(t1, 5)
+        G = nx.union(t1, t2)
+        G.add_edges_from(
+            [
+                (3, 7),
+                (2, 11),
+                (11, 5),
+                (11, 12),
+                (5, 12),
+                (12, 19),
+                (12, 18),
+                (3, 9),
+                (7, 9),
+                (7, 10),
+                (9, 10),
+                (9, 20),
+                (17, 13),
+                (13, 14),
+                (14, 15),
+                (15, 16),
+                (16, 13),
+            ]
+        )
+        G.add_node(21)
+        cls.G = G
+
+        # Create the graph H resulting from the degree sequence
+        # [0, 1, 2, 2, 2, 2, 3] when using the Havel-Hakimi algorithm.
+
+        degseq = [0, 1, 2, 2, 2, 2, 3]
+        H = nx.havel_hakimi_graph(degseq)
+        mapping = {6: 0, 0: 1, 4: 3, 5: 6, 3: 4, 1: 2, 2: 5}
+        cls.H = nx.relabel_nodes(H, mapping)
+
+    def test_trivial(self):
+        """Empty graph"""
+        G = nx.Graph()
+        assert nx.core_number(G) == {}
+
+    def test_core_number(self):
+        core = nx.core_number(self.G)
+        nodes_by_core = [sorted(n for n in core if core[n] == val) for val in range(4)]
+        assert nodes_equal(nodes_by_core[0], [21])
+        assert nodes_equal(nodes_by_core[1], [17, 18, 19, 20])
+        assert nodes_equal(nodes_by_core[2], [9, 10, 11, 12, 13, 14, 15, 16])
+        assert nodes_equal(nodes_by_core[3], [1, 2, 3, 4, 5, 6, 7, 8])
+
+    def test_core_number2(self):
+        core = nx.core_number(self.H)
+        nodes_by_core = [sorted(n for n in core if core[n] == val) for val in range(3)]
+        assert nodes_equal(nodes_by_core[0], [0])
+        assert nodes_equal(nodes_by_core[1], [1, 3])
+        assert nodes_equal(nodes_by_core[2], [2, 4, 5, 6])
+
+    def test_core_number_multigraph(self):
+        G = nx.complete_graph(3)
+        G = nx.MultiGraph(G)
+        G.add_edge(1, 2)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for multigraph type"
+        ):
+            nx.core_number(G)
+
+    def test_core_number_self_loop(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 0)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="Input graph has self loops"
+        ):
+            nx.core_number(G)
+
+    def test_directed_core_number(self):
+        """core number had a bug for directed graphs found in issue #1959"""
+        # small example where too timid edge removal can make cn[2] = 3
+        G = nx.DiGraph()
+        edges = [(1, 2), (2, 1), (2, 3), (2, 4), (3, 4), (4, 3)]
+        G.add_edges_from(edges)
+        assert nx.core_number(G) == {1: 2, 2: 2, 3: 2, 4: 2}
+        # small example where too aggressive edge removal can make cn[2] = 2
+        more_edges = [(1, 5), (3, 5), (4, 5), (3, 6), (4, 6), (5, 6)]
+        G.add_edges_from(more_edges)
+        assert nx.core_number(G) == {1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3}
+
+    def test_main_core(self):
+        main_core_subgraph = nx.k_core(self.H)
+        assert sorted(main_core_subgraph.nodes()) == [2, 4, 5, 6]
+
+    def test_k_core(self):
+        # k=0
+        k_core_subgraph = nx.k_core(self.H, k=0)
+        assert sorted(k_core_subgraph.nodes()) == sorted(self.H.nodes())
+        # k=1
+        k_core_subgraph = nx.k_core(self.H, k=1)
+        assert sorted(k_core_subgraph.nodes()) == [1, 2, 3, 4, 5, 6]
+        # k = 2
+        k_core_subgraph = nx.k_core(self.H, k=2)
+        assert sorted(k_core_subgraph.nodes()) == [2, 4, 5, 6]
+
+    def test_k_core_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.k_core(H, k=0, core_number=core_number)
+
+    def test_main_crust(self):
+        main_crust_subgraph = nx.k_crust(self.H)
+        assert sorted(main_crust_subgraph.nodes()) == [0, 1, 3]
+
+    def test_k_crust(self):
+        # k = 0
+        k_crust_subgraph = nx.k_crust(self.H, k=2)
+        assert sorted(k_crust_subgraph.nodes()) == sorted(self.H.nodes())
+        # k=1
+        k_crust_subgraph = nx.k_crust(self.H, k=1)
+        assert sorted(k_crust_subgraph.nodes()) == [0, 1, 3]
+        # k=2
+        k_crust_subgraph = nx.k_crust(self.H, k=0)
+        assert sorted(k_crust_subgraph.nodes()) == [0]
+
+    def test_k_crust_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.k_crust(H, k=0, core_number=core_number)
+
+    def test_main_shell(self):
+        main_shell_subgraph = nx.k_shell(self.H)
+        assert sorted(main_shell_subgraph.nodes()) == [2, 4, 5, 6]
+
+    def test_k_shell(self):
+        # k=0
+        k_shell_subgraph = nx.k_shell(self.H, k=2)
+        assert sorted(k_shell_subgraph.nodes()) == [2, 4, 5, 6]
+        # k=1
+        k_shell_subgraph = nx.k_shell(self.H, k=1)
+        assert sorted(k_shell_subgraph.nodes()) == [1, 3]
+        # k=2
+        k_shell_subgraph = nx.k_shell(self.H, k=0)
+        assert sorted(k_shell_subgraph.nodes()) == [0]
+
+    def test_k_shell_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.k_shell(H, k=0, core_number=core_number)
+
+    def test_k_corona(self):
+        # k=0
+        k_corona_subgraph = nx.k_corona(self.H, k=2)
+        assert sorted(k_corona_subgraph.nodes()) == [2, 4, 5, 6]
+        # k=1
+        k_corona_subgraph = nx.k_corona(self.H, k=1)
+        assert sorted(k_corona_subgraph.nodes()) == [1]
+        # k=2
+        k_corona_subgraph = nx.k_corona(self.H, k=0)
+        assert sorted(k_corona_subgraph.nodes()) == [0]
+
+    def test_k_corona_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.k_corona(H, k=0, core_number=core_number)
+
+    def test_k_truss(self):
+        # k=-1
+        k_truss_subgraph = nx.k_truss(self.G, -1)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=0
+        k_truss_subgraph = nx.k_truss(self.G, 0)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=1
+        k_truss_subgraph = nx.k_truss(self.G, 1)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=2
+        k_truss_subgraph = nx.k_truss(self.G, 2)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=3
+        k_truss_subgraph = nx.k_truss(self.G, 3)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 13))
+
+        k_truss_subgraph = nx.k_truss(self.G, 4)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 9))
+
+        k_truss_subgraph = nx.k_truss(self.G, 5)
+        assert sorted(k_truss_subgraph.nodes()) == []
+
+    def test_k_truss_digraph(self):
+        G = nx.complete_graph(3)
+        G = nx.DiGraph(G)
+        G.add_edge(2, 1)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for directed type"
+        ):
+            nx.k_truss(G, k=1)
+
+    def test_k_truss_multigraph(self):
+        G = nx.complete_graph(3)
+        G = nx.MultiGraph(G)
+        G.add_edge(1, 2)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for multigraph type"
+        ):
+            nx.k_truss(G, k=1)
+
+    def test_k_truss_self_loop(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 0)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="Input graph has self loops"
+        ):
+            nx.k_truss(G, k=1)
+
+    def test_onion_layers(self):
+        layers = nx.onion_layers(self.G)
+        nodes_by_layer = [
+            sorted(n for n in layers if layers[n] == val) for val in range(1, 7)
+        ]
+        assert nodes_equal(nodes_by_layer[0], [21])
+        assert nodes_equal(nodes_by_layer[1], [17, 18, 19, 20])
+        assert nodes_equal(nodes_by_layer[2], [10, 12, 13, 14, 15, 16])
+        assert nodes_equal(nodes_by_layer[3], [9, 11])
+        assert nodes_equal(nodes_by_layer[4], [1, 2, 4, 5, 6, 8])
+        assert nodes_equal(nodes_by_layer[5], [3, 7])
+
+    def test_onion_digraph(self):
+        G = nx.complete_graph(3)
+        G = nx.DiGraph(G)
+        G.add_edge(2, 1)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for directed type"
+        ):
+            nx.onion_layers(G)
+
+    def test_onion_multigraph(self):
+        G = nx.complete_graph(3)
+        G = nx.MultiGraph(G)
+        G.add_edge(1, 2)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for multigraph type"
+        ):
+            nx.onion_layers(G)
+
+    def test_onion_self_loop(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 0)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="Input graph contains self loops"
+        ):
+            nx.onion_layers(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_covering.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_covering.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f97a866b0e09c199c2edb9f40f20986caa8fbc
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_covering.py
@@ -0,0 +1,85 @@
+import pytest
+
+import networkx as nx
+
+
+class TestMinEdgeCover:
+    """Tests for :func:`networkx.algorithms.min_edge_cover`"""
+
+    def test_empty_graph(self):
+        G = nx.Graph()
+        assert nx.min_edge_cover(G) == set()
+
+    def test_graph_with_loop(self):
+        G = nx.Graph()
+        G.add_edge(0, 0)
+        assert nx.min_edge_cover(G) == {(0, 0)}
+
+    def test_graph_with_isolated_v(self):
+        G = nx.Graph()
+        G.add_node(1)
+        with pytest.raises(
+            nx.NetworkXException,
+            match="Graph has a node with no edge incident on it, so no edge cover exists.",
+        ):
+            nx.min_edge_cover(G)
+
+    def test_graph_single_edge(self):
+        G = nx.Graph([(0, 1)])
+        assert nx.min_edge_cover(G) in ({(0, 1)}, {(1, 0)})
+
+    def test_graph_two_edge_path(self):
+        G = nx.path_graph(3)
+        min_cover = nx.min_edge_cover(G)
+        assert len(min_cover) == 2
+        for u, v in G.edges:
+            assert (u, v) in min_cover or (v, u) in min_cover
+
+    def test_bipartite_explicit(self):
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3, 4], bipartite=0)
+        G.add_nodes_from(["a", "b", "c"], bipartite=1)
+        G.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])
+        # Use bipartite method by prescribing the algorithm
+        min_cover = nx.min_edge_cover(
+            G, nx.algorithms.bipartite.matching.eppstein_matching
+        )
+        assert nx.is_edge_cover(G, min_cover)
+        assert len(min_cover) == 8
+        # Use the default method which is not specialized for bipartite
+        min_cover2 = nx.min_edge_cover(G)
+        assert nx.is_edge_cover(G, min_cover2)
+        assert len(min_cover2) == 4
+
+    def test_complete_graph_even(self):
+        G = nx.complete_graph(10)
+        min_cover = nx.min_edge_cover(G)
+        assert nx.is_edge_cover(G, min_cover)
+        assert len(min_cover) == 5
+
+    def test_complete_graph_odd(self):
+        G = nx.complete_graph(11)
+        min_cover = nx.min_edge_cover(G)
+        assert nx.is_edge_cover(G, min_cover)
+        assert len(min_cover) == 6
+
+
+class TestIsEdgeCover:
+    """Tests for :func:`networkx.algorithms.is_edge_cover`"""
+
+    def test_empty_graph(self):
+        G = nx.Graph()
+        assert nx.is_edge_cover(G, set())
+
+    def test_graph_with_loop(self):
+        G = nx.Graph()
+        G.add_edge(1, 1)
+        assert nx.is_edge_cover(G, {(1, 1)})
+
+    def test_graph_single_edge(self):
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        assert nx.is_edge_cover(G, {(0, 0), (1, 1)})
+        assert nx.is_edge_cover(G, {(0, 1), (1, 0)})
+        assert nx.is_edge_cover(G, {(0, 1)})
+        assert not nx.is_edge_cover(G, {(0, 0)})
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_cuts.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_cuts.py
new file mode 100644
index 0000000000000000000000000000000000000000..923efa502acc623650f36ff41e72884e5e508bc9
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_cuts.py
@@ -0,0 +1,171 @@
+"""Unit tests for the :mod:`networkx.algorithms.cuts` module."""
+
+import networkx as nx
+
+
+class TestCutSize:
+    """Unit tests for the :func:`~networkx.cut_size` function."""
+
+    def test_symmetric(self):
+        """Tests that the cut size is symmetric."""
+        G = nx.barbell_graph(3, 0)
+        S = {0, 1, 4}
+        T = {2, 3, 5}
+        assert nx.cut_size(G, S, T) == 4
+        assert nx.cut_size(G, T, S) == 4
+
+    def test_single_edge(self):
+        """Tests for a cut of a single edge."""
+        G = nx.barbell_graph(3, 0)
+        S = {0, 1, 2}
+        T = {3, 4, 5}
+        assert nx.cut_size(G, S, T) == 1
+        assert nx.cut_size(G, T, S) == 1
+
+    def test_directed(self):
+        """Tests that each directed edge is counted once in the cut."""
+        G = nx.barbell_graph(3, 0).to_directed()
+        S = {0, 1, 2}
+        T = {3, 4, 5}
+        assert nx.cut_size(G, S, T) == 2
+        assert nx.cut_size(G, T, S) == 2
+
+    def test_directed_symmetric(self):
+        """Tests that a cut in a directed graph is symmetric."""
+        G = nx.barbell_graph(3, 0).to_directed()
+        S = {0, 1, 4}
+        T = {2, 3, 5}
+        assert nx.cut_size(G, S, T) == 8
+        assert nx.cut_size(G, T, S) == 8
+
+    def test_multigraph(self):
+        """Tests that parallel edges are each counted for a cut."""
+        G = nx.MultiGraph(["ab", "ab"])
+        assert nx.cut_size(G, {"a"}, {"b"}) == 2
+
+
+class TestVolume:
+    """Unit tests for the :func:`~networkx.volume` function."""
+
+    def test_graph(self):
+        G = nx.cycle_graph(4)
+        assert nx.volume(G, {0, 1}) == 4
+
+    def test_digraph(self):
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0)])
+        assert nx.volume(G, {0, 1}) == 2
+
+    def test_multigraph(self):
+        edges = list(nx.cycle_graph(4).edges())
+        G = nx.MultiGraph(edges * 2)
+        assert nx.volume(G, {0, 1}) == 8
+
+    def test_multidigraph(self):
+        edges = [(0, 1), (1, 2), (2, 3), (3, 0)]
+        G = nx.MultiDiGraph(edges * 2)
+        assert nx.volume(G, {0, 1}) == 4
+
+    def test_barbell(self):
+        G = nx.barbell_graph(3, 0)
+        assert nx.volume(G, {0, 1, 2}) == 7
+        assert nx.volume(G, {3, 4, 5}) == 7
+
+
+class TestNormalizedCutSize:
+    """Unit tests for the :func:`~networkx.normalized_cut_size` function."""
+
+    def test_graph(self):
+        G = nx.path_graph(4)
+        S = {1, 2}
+        T = set(G) - S
+        size = nx.normalized_cut_size(G, S, T)
+        # The cut looks like this: o-{-o--o-}-o
+        expected = 2 * ((1 / 4) + (1 / 2))
+        assert expected == size
+        # Test with no input T
+        assert expected == nx.normalized_cut_size(G, S)
+
+    def test_directed(self):
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+        S = {1, 2}
+        T = set(G) - S
+        size = nx.normalized_cut_size(G, S, T)
+        # The cut looks like this: o-{->o-->o-}->o
+        expected = 2 * ((1 / 2) + (1 / 1))
+        assert expected == size
+        # Test with no input T
+        assert expected == nx.normalized_cut_size(G, S)
+
+
+class TestConductance:
+    """Unit tests for the :func:`~networkx.conductance` function."""
+
+    def test_graph(self):
+        G = nx.barbell_graph(5, 0)
+        # Consider the singleton sets containing the "bridge" nodes.
+        # There is only one cut edge, and each set has volume five.
+        S = {4}
+        T = {5}
+        conductance = nx.conductance(G, S, T)
+        expected = 1 / 5
+        assert expected == conductance
+        # Test with no input T
+        G2 = nx.barbell_graph(3, 0)
+        # There is only one cut edge, and each set has volume seven.
+        S2 = {0, 1, 2}
+        assert nx.conductance(G2, S2) == 1 / 7
+
+
+class TestEdgeExpansion:
+    """Unit tests for the :func:`~networkx.edge_expansion` function."""
+
+    def test_graph(self):
+        G = nx.barbell_graph(5, 0)
+        S = set(range(5))
+        T = set(G) - S
+        expansion = nx.edge_expansion(G, S, T)
+        expected = 1 / 5
+        assert expected == expansion
+        # Test with no input T
+        assert expected == nx.edge_expansion(G, S)
+
+
+class TestNodeExpansion:
+    """Unit tests for the :func:`~networkx.node_expansion` function."""
+
+    def test_graph(self):
+        G = nx.path_graph(8)
+        S = {3, 4, 5}
+        expansion = nx.node_expansion(G, S)
+        # The neighborhood of S has cardinality five, and S has
+        # cardinality three.
+        expected = 5 / 3
+        assert expected == expansion
+
+
+class TestBoundaryExpansion:
+    """Unit tests for the :func:`~networkx.boundary_expansion` function."""
+
+    def test_graph(self):
+        G = nx.complete_graph(10)
+        S = set(range(4))
+        expansion = nx.boundary_expansion(G, S)
+        # The node boundary of S has cardinality six, and S has
+        # cardinality three.
+        expected = 6 / 4
+        assert expected == expansion
+
+
+class TestMixingExpansion:
+    """Unit tests for the :func:`~networkx.mixing_expansion` function."""
+
+    def test_graph(self):
+        G = nx.barbell_graph(5, 0)
+        S = set(range(5))
+        T = set(G) - S
+        expansion = nx.mixing_expansion(G, S, T)
+        # There is one cut edge, and the total number of edges in the
+        # graph is twice the total number of edges in a clique of size
+        # five, plus one more for the bridge.
+        expected = 1 / (2 * (5 * 4 + 1))
+        assert expected == expansion
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_cycles.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_cycles.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b43929aae00be120f7fb2cd2780cd7d4ad20b03
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_cycles.py
@@ -0,0 +1,984 @@
+import random
+from itertools import chain, islice, tee
+from math import inf
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.traversal.edgedfs import FORWARD, REVERSE
+
+
+def check_independent(basis):
+    if len(basis) == 0:
+        return
+
+    np = pytest.importorskip("numpy")
+    sp = pytest.importorskip("scipy")  # Required by incidence_matrix
+
+    H = nx.Graph()
+    for b in basis:
+        nx.add_cycle(H, b)
+    inc = nx.incidence_matrix(H, oriented=True)
+    rank = np.linalg.matrix_rank(inc.toarray(), tol=None, hermitian=False)
+    assert inc.shape[1] - rank == len(basis)
+
+
+class TestCycles:
+    @classmethod
+    def setup_class(cls):
+        G = nx.Graph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+        nx.add_cycle(G, [0, 3, 4, 5])
+        nx.add_cycle(G, [0, 1, 6, 7, 8])
+        G.add_edge(8, 9)
+        cls.G = G
+
+    def is_cyclic_permutation(self, a, b):
+        n = len(a)
+        if len(b) != n:
+            return False
+        l = a + a
+        return any(l[i : i + n] == b for i in range(n))
+
+    def test_cycle_basis(self):
+        G = self.G
+        cy = nx.cycle_basis(G, 0)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5]]
+        cy = nx.cycle_basis(G, 1)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5]]
+        cy = nx.cycle_basis(G, 9)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5]]
+        # test disconnected graphs
+        nx.add_cycle(G, "ABC")
+        cy = nx.cycle_basis(G, 9)
+        sort_cy = sorted(sorted(c) for c in cy[:-1]) + [sorted(cy[-1])]
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5], ["A", "B", "C"]]
+
+    def test_cycle_basis2(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.DiGraph()
+            cy = nx.cycle_basis(G, 0)
+
+    def test_cycle_basis3(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.MultiGraph()
+            cy = nx.cycle_basis(G, 0)
+
+    def test_cycle_basis_ordered(self):
+        # see gh-6654 replace sets with (ordered) dicts
+        G = nx.cycle_graph(5)
+        G.update(nx.cycle_graph(range(3, 8)))
+        cbG = nx.cycle_basis(G)
+
+        perm = {1: 0, 0: 1}  # switch 0 and 1
+        H = nx.relabel_nodes(G, perm)
+        cbH = [[perm.get(n, n) for n in cyc] for cyc in nx.cycle_basis(H)]
+        assert cbG == cbH
+
+    def test_cycle_basis_self_loop(self):
+        """Tests the function for graphs with self loops"""
+        G = nx.Graph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+        nx.add_cycle(G, [0, 0, 6, 2])
+        cy = nx.cycle_basis(G)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0], [0, 1, 2], [0, 2, 3], [0, 2, 6]]
+
+    def test_simple_cycles(self):
+        edges = [(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)]
+        G = nx.DiGraph(edges)
+        cc = sorted(nx.simple_cycles(G))
+        ca = [[0], [0, 1, 2], [0, 2], [1, 2], [2]]
+        assert len(cc) == len(ca)
+        for c in cc:
+            assert any(self.is_cyclic_permutation(c, rc) for rc in ca)
+
+    def test_simple_cycles_singleton(self):
+        G = nx.Graph([(0, 0)])  # self-loop
+        assert list(nx.simple_cycles(G)) == [[0]]
+
+    def test_unsortable(self):
+        # this test ensures that graphs whose nodes without an intrinsic
+        # ordering do not cause issues
+        G = nx.DiGraph()
+        nx.add_cycle(G, ["a", 1])
+        c = list(nx.simple_cycles(G))
+        assert len(c) == 1
+
+    def test_simple_cycles_small(self):
+        G = nx.DiGraph()
+        nx.add_cycle(G, [1, 2, 3])
+        c = sorted(nx.simple_cycles(G))
+        assert len(c) == 1
+        assert self.is_cyclic_permutation(c[0], [1, 2, 3])
+        nx.add_cycle(G, [10, 20, 30])
+        cc = sorted(nx.simple_cycles(G))
+        assert len(cc) == 2
+        ca = [[1, 2, 3], [10, 20, 30]]
+        for c in cc:
+            assert any(self.is_cyclic_permutation(c, rc) for rc in ca)
+
+    def test_simple_cycles_empty(self):
+        G = nx.DiGraph()
+        assert list(nx.simple_cycles(G)) == []
+
+    def worst_case_graph(self, k):
+        # see figure 1 in Johnson's paper
+        # this graph has exactly 3k simple cycles
+        G = nx.DiGraph()
+        for n in range(2, k + 2):
+            G.add_edge(1, n)
+            G.add_edge(n, k + 2)
+        G.add_edge(2 * k + 1, 1)
+        for n in range(k + 2, 2 * k + 2):
+            G.add_edge(n, 2 * k + 2)
+            G.add_edge(n, n + 1)
+        G.add_edge(2 * k + 3, k + 2)
+        for n in range(2 * k + 3, 3 * k + 3):
+            G.add_edge(2 * k + 2, n)
+            G.add_edge(n, 3 * k + 3)
+        G.add_edge(3 * k + 3, 2 * k + 2)
+        return G
+
+    def test_worst_case_graph(self):
+        # see figure 1 in Johnson's paper
+        for k in range(3, 10):
+            G = self.worst_case_graph(k)
+            l = len(list(nx.simple_cycles(G)))
+            assert l == 3 * k
+
+    def test_recursive_simple_and_not(self):
+        for k in range(2, 10):
+            G = self.worst_case_graph(k)
+            cc = sorted(nx.simple_cycles(G))
+            rcc = sorted(nx.recursive_simple_cycles(G))
+            assert len(cc) == len(rcc)
+            for c in cc:
+                assert any(self.is_cyclic_permutation(c, r) for r in rcc)
+            for rc in rcc:
+                assert any(self.is_cyclic_permutation(rc, c) for c in cc)
+
+    def test_simple_graph_with_reported_bug(self):
+        G = nx.DiGraph()
+        edges = [
+            (0, 2),
+            (0, 3),
+            (1, 0),
+            (1, 3),
+            (2, 1),
+            (2, 4),
+            (3, 2),
+            (3, 4),
+            (4, 0),
+            (4, 1),
+            (4, 5),
+            (5, 0),
+            (5, 1),
+            (5, 2),
+            (5, 3),
+        ]
+        G.add_edges_from(edges)
+        cc = sorted(nx.simple_cycles(G))
+        assert len(cc) == 26
+        rcc = sorted(nx.recursive_simple_cycles(G))
+        assert len(cc) == len(rcc)
+        for c in cc:
+            assert any(self.is_cyclic_permutation(c, rc) for rc in rcc)
+        for rc in rcc:
+            assert any(self.is_cyclic_permutation(rc, c) for c in cc)
+
+
+def pairwise(iterable):
+    a, b = tee(iterable)
+    next(b, None)
+    return zip(a, b)
+
+
+def cycle_edges(c):
+    return pairwise(chain(c, islice(c, 1)))
+
+
+def directed_cycle_edgeset(c):
+    return frozenset(cycle_edges(c))
+
+
+def undirected_cycle_edgeset(c):
+    if len(c) == 1:
+        return frozenset(cycle_edges(c))
+    return frozenset(map(frozenset, cycle_edges(c)))
+
+
+def multigraph_cycle_edgeset(c):
+    if len(c) <= 2:
+        return frozenset(cycle_edges(c))
+    else:
+        return frozenset(map(frozenset, cycle_edges(c)))
+
+
+class TestCycleEnumeration:
+    @staticmethod
+    def K(n):
+        return nx.complete_graph(n)
+
+    @staticmethod
+    def D(n):
+        return nx.complete_graph(n).to_directed()
+
+    @staticmethod
+    def edgeset_function(g):
+        if g.is_directed():
+            return directed_cycle_edgeset
+        elif g.is_multigraph():
+            return multigraph_cycle_edgeset
+        else:
+            return undirected_cycle_edgeset
+
+    def check_cycle(self, g, c, es, cache, source, original_c, length_bound, chordless):
+        if length_bound is not None and len(c) > length_bound:
+            raise RuntimeError(
+                f"computed cycle {original_c} exceeds length bound {length_bound}"
+            )
+        if source == "computed":
+            if es in cache:
+                raise RuntimeError(
+                    f"computed cycle {original_c} has already been found!"
+                )
+            else:
+                cache[es] = tuple(original_c)
+        else:
+            if es in cache:
+                cache.pop(es)
+            else:
+                raise RuntimeError(f"expected cycle {original_c} was not computed")
+
+        if not all(g.has_edge(*e) for e in es):
+            raise RuntimeError(
+                f"{source} claimed cycle {original_c} is not a cycle of g"
+            )
+        if chordless and len(g.subgraph(c).edges) > len(c):
+            raise RuntimeError(f"{source} cycle {original_c} is not chordless")
+
+    def check_cycle_algorithm(
+        self,
+        g,
+        expected_cycles,
+        length_bound=None,
+        chordless=False,
+        algorithm=None,
+    ):
+        if algorithm is None:
+            algorithm = nx.chordless_cycles if chordless else nx.simple_cycles
+
+        # note: we shuffle the labels of g to rule out accidentally-correct
+        # behavior which occurred during the development of chordless cycle
+        # enumeration algorithms
+
+        relabel = list(range(len(g)))
+        rng = random.Random(42)
+        rng.shuffle(relabel)
+        label = dict(zip(g, relabel))
+        unlabel = dict(zip(relabel, g))
+        h = nx.relabel_nodes(g, label, copy=True)
+
+        edgeset = self.edgeset_function(h)
+
+        params = {}
+        if length_bound is not None:
+            params["length_bound"] = length_bound
+
+        cycle_cache = {}
+        for c in algorithm(h, **params):
+            original_c = [unlabel[x] for x in c]
+            es = edgeset(c)
+            self.check_cycle(
+                h, c, es, cycle_cache, "computed", original_c, length_bound, chordless
+            )
+
+        if isinstance(expected_cycles, int):
+            if len(cycle_cache) != expected_cycles:
+                raise RuntimeError(
+                    f"expected {expected_cycles} cycles, got {len(cycle_cache)}"
+                )
+            return
+        for original_c in expected_cycles:
+            c = [label[x] for x in original_c]
+            es = edgeset(c)
+            self.check_cycle(
+                h, c, es, cycle_cache, "expected", original_c, length_bound, chordless
+            )
+
+        if len(cycle_cache):
+            for c in cycle_cache.values():
+                raise RuntimeError(
+                    f"computed cycle {c} is valid but not in the expected cycle set!"
+                )
+
+    def check_cycle_enumeration_integer_sequence(
+        self,
+        g_family,
+        cycle_counts,
+        length_bound=None,
+        chordless=False,
+        algorithm=None,
+    ):
+        for g, num_cycles in zip(g_family, cycle_counts):
+            self.check_cycle_algorithm(
+                g,
+                num_cycles,
+                length_bound=length_bound,
+                chordless=chordless,
+                algorithm=algorithm,
+            )
+
+    def test_directed_chordless_cycle_digons(self):
+        g = nx.DiGraph()
+        nx.add_cycle(g, range(5))
+        nx.add_cycle(g, range(5)[::-1])
+        g.add_edge(0, 0)
+        expected_cycles = [(0,), (1, 2), (2, 3), (3, 4)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True, length_bound=2)
+
+        expected_cycles = [c for c in expected_cycles if len(c) < 2]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True, length_bound=1)
+
+    def test_chordless_cycles_multigraph_self_loops(self):
+        G = nx.MultiGraph([(1, 1), (2, 2), (1, 2), (1, 2)])
+        expected_cycles = [[1], [2]]
+        self.check_cycle_algorithm(G, expected_cycles, chordless=True)
+
+        G.add_edges_from([(2, 3), (3, 4), (3, 4), (1, 3)])
+        expected_cycles = [[1], [2], [3, 4]]
+        self.check_cycle_algorithm(G, expected_cycles, chordless=True)
+
+    def test_directed_chordless_cycle_undirected(self):
+        g = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 5), (5, 0), (5, 1), (0, 2)])
+        expected_cycles = [(0, 2, 3, 4, 5), (1, 2, 3, 4, 5)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        g = nx.DiGraph()
+        nx.add_cycle(g, range(5))
+        nx.add_cycle(g, range(4, 9))
+        g.add_edge(7, 3)
+        expected_cycles = [(0, 1, 2, 3, 4), (3, 4, 5, 6, 7), (4, 5, 6, 7, 8)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        g.add_edge(3, 7)
+        expected_cycles = [(0, 1, 2, 3, 4), (3, 7), (4, 5, 6, 7, 8)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        expected_cycles = [(3, 7)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True, length_bound=4)
+
+        g.remove_edge(7, 3)
+        expected_cycles = [(0, 1, 2, 3, 4), (4, 5, 6, 7, 8)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        g = nx.DiGraph((i, j) for i in range(10) for j in range(i))
+        expected_cycles = []
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+    def test_chordless_cycles_directed(self):
+        G = nx.DiGraph()
+        nx.add_cycle(G, range(5))
+        nx.add_cycle(G, range(4, 12))
+        expected = [[*range(5)], [*range(4, 12)]]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        G.add_edge(7, 3)
+        expected.append([*range(3, 8)])
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        G.add_edge(3, 7)
+        expected[-1] = [7, 3]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        expected.pop()
+        G.remove_edge(7, 3)
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+    def test_directed_chordless_cycle_diclique(self):
+        g_family = [self.D(n) for n in range(10)]
+        expected_cycles = [(n * n - n) // 2 for n in range(10)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected_cycles, chordless=True
+        )
+
+        expected_cycles = [(n * n - n) // 2 for n in range(10)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected_cycles, length_bound=2
+        )
+
+    def test_directed_chordless_loop_blockade(self):
+        g = nx.DiGraph((i, i) for i in range(10))
+        nx.add_cycle(g, range(10))
+        expected_cycles = [(i,) for i in range(10)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        self.check_cycle_algorithm(g, expected_cycles, length_bound=1)
+
+        g = nx.MultiDiGraph(g)
+        g.add_edges_from((i, i) for i in range(0, 10, 2))
+        expected_cycles = [(i,) for i in range(1, 10, 2)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+    def test_simple_cycles_notable_clique_sequences(self):
+        # A000292: Number of labeled graphs on n+3 nodes that are triangles.
+        g_family = [self.K(n) for n in range(2, 12)]
+        expected = [0, 1, 4, 10, 20, 35, 56, 84, 120, 165, 220]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=3
+        )
+
+        def triangles(g, **kwargs):
+            yield from (c for c in nx.simple_cycles(g, **kwargs) if len(c) == 3)
+
+        # directed complete graphs have twice as many triangles thanks to reversal
+        g_family = [self.D(n) for n in range(2, 12)]
+        expected = [2 * e for e in expected]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=3, algorithm=triangles
+        )
+
+        def four_cycles(g, **kwargs):
+            yield from (c for c in nx.simple_cycles(g, **kwargs) if len(c) == 4)
+
+        # A050534: the number of 4-cycles in the complete graph K_{n+1}
+        expected = [0, 0, 0, 3, 15, 45, 105, 210, 378, 630, 990]
+        g_family = [self.K(n) for n in range(1, 12)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=4, algorithm=four_cycles
+        )
+
+        # directed complete graphs have twice as many 4-cycles thanks to reversal
+        expected = [2 * e for e in expected]
+        g_family = [self.D(n) for n in range(1, 15)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=4, algorithm=four_cycles
+        )
+
+        # A006231: the number of elementary circuits in a complete directed graph with n nodes
+        expected = [0, 1, 5, 20, 84, 409, 2365]
+        g_family = [self.D(n) for n in range(1, 8)]
+        self.check_cycle_enumeration_integer_sequence(g_family, expected)
+
+        # A002807: Number of cycles in the complete graph on n nodes K_{n}.
+        expected = [0, 0, 0, 1, 7, 37, 197, 1172]
+        g_family = [self.K(n) for n in range(8)]
+        self.check_cycle_enumeration_integer_sequence(g_family, expected)
+
+    def test_directed_chordless_cycle_parallel_multiedges(self):
+        g = nx.MultiGraph()
+
+        nx.add_cycle(g, range(5))
+        expected = [[*range(5)]]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        expected = [*cycle_edges(range(5))]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        expected = []
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        g = nx.MultiDiGraph()
+
+        nx.add_cycle(g, range(5))
+        expected = [[*range(5)]]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        self.check_cycle_algorithm(g, [], chordless=True)
+
+        nx.add_cycle(g, range(5))
+        self.check_cycle_algorithm(g, [], chordless=True)
+
+        g = nx.MultiDiGraph()
+
+        nx.add_cycle(g, range(5))
+        nx.add_cycle(g, range(5)[::-1])
+        expected = [*cycle_edges(range(5))]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        self.check_cycle_algorithm(g, [], chordless=True)
+
+    def test_chordless_cycles_graph(self):
+        G = nx.Graph()
+        nx.add_cycle(G, range(5))
+        nx.add_cycle(G, range(4, 12))
+        expected = [[*range(5)], [*range(4, 12)]]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        G.add_edge(7, 3)
+        expected.append([*range(3, 8)])
+        expected.append([4, 3, 7, 8, 9, 10, 11])
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+    def test_chordless_cycles_giant_hamiltonian(self):
+        # ... o - e - o - e - o ... # o = odd, e = even
+        # ... ---/ \-----/ \--- ... # <-- "long" edges
+        #
+        # each long edge belongs to exactly one triangle, and one giant cycle
+        # of length n/2.  The remaining edges each belong to a triangle
+
+        n = 1000
+        assert n % 2 == 0
+        G = nx.Graph()
+        for v in range(n):
+            if not v % 2:
+                G.add_edge(v, (v + 2) % n)
+            G.add_edge(v, (v + 1) % n)
+
+        expected = [[*range(0, n, 2)]] + [
+            [x % n for x in range(i, i + 3)] for i in range(0, n, 2)
+        ]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 3], length_bound=3, chordless=True
+        )
+
+        # ... o -> e -> o -> e -> o ... # o = odd, e = even
+        # ... <---/ \---<---/ \---< ... # <-- "long" edges
+        #
+        # this time, we orient the short and long edges in opposition
+        # the cycle structure of this graph is the same, but we need to reverse
+        # the long one in our representation.  Also, we need to drop the size
+        # because our partitioning algorithm uses strongly connected components
+        # instead of separating graphs by their strong articulation points
+
+        n = 100
+        assert n % 2 == 0
+        G = nx.DiGraph()
+        for v in range(n):
+            G.add_edge(v, (v + 1) % n)
+            if not v % 2:
+                G.add_edge((v + 2) % n, v)
+
+        expected = [[*range(n - 2, -2, -2)]] + [
+            [x % n for x in range(i, i + 3)] for i in range(0, n, 2)
+        ]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 3], length_bound=3, chordless=True
+        )
+
+    def test_simple_cycles_acyclic_tournament(self):
+        n = 10
+        G = nx.DiGraph((x, y) for x in range(n) for y in range(x))
+        self.check_cycle_algorithm(G, [])
+        self.check_cycle_algorithm(G, [], chordless=True)
+
+        for k in range(n + 1):
+            self.check_cycle_algorithm(G, [], length_bound=k)
+            self.check_cycle_algorithm(G, [], length_bound=k, chordless=True)
+
+    def test_simple_cycles_graph(self):
+        testG = nx.cycle_graph(8)
+        cyc1 = tuple(range(8))
+        self.check_cycle_algorithm(testG, [cyc1])
+
+        testG.add_edge(4, -1)
+        nx.add_path(testG, [3, -2, -3, -4])
+        self.check_cycle_algorithm(testG, [cyc1])
+
+        testG.update(nx.cycle_graph(range(8, 16)))
+        cyc2 = tuple(range(8, 16))
+        self.check_cycle_algorithm(testG, [cyc1, cyc2])
+
+        testG.update(nx.cycle_graph(range(4, 12)))
+        cyc3 = tuple(range(4, 12))
+        expected = {
+            (0, 1, 2, 3, 4, 5, 6, 7),  # cyc1
+            (8, 9, 10, 11, 12, 13, 14, 15),  # cyc2
+            (4, 5, 6, 7, 8, 9, 10, 11),  # cyc3
+            (4, 5, 6, 7, 8, 15, 14, 13, 12, 11),  # cyc2 + cyc3
+            (0, 1, 2, 3, 4, 11, 10, 9, 8, 7),  # cyc1 + cyc3
+            (0, 1, 2, 3, 4, 11, 12, 13, 14, 15, 8, 7),  # cyc1 + cyc2 + cyc3
+        }
+        self.check_cycle_algorithm(testG, expected)
+        assert len(expected) == (2**3 - 1) - 1  # 1 disjoint comb: cyc1 + cyc2
+
+        # Basis size = 5 (2 loops overlapping gives 5 small loops
+        #        E
+        #       / \         Note: A-F = 10-15
+        #    1-2-3-4-5
+        #    / |   |  \   cyc1=012DAB -- left
+        #   0  D   F  6   cyc2=234E   -- top
+        #   \  |   |  /   cyc3=45678F -- right
+        #    B-A-9-8-7    cyc4=89AC   -- bottom
+        #       \ /       cyc5=234F89AD -- middle
+        #        C
+        #
+        # combinations of 5 basis elements: 2^5 - 1  (one includes no cycles)
+        #
+        # disjoint combs: (11 total) not simple cycles
+        #   Any pair not including cyc5 => choose(4, 2) = 6
+        #   Any triple not including cyc5 => choose(4, 3) = 4
+        #   Any quad not including cyc5 => choose(4, 4) = 1
+        #
+        # we expect 31 - 11 = 20 simple cycles
+        #
+        testG = nx.cycle_graph(12)
+        testG.update(nx.cycle_graph([12, 10, 13, 2, 14, 4, 15, 8]).edges)
+        expected = (2**5 - 1) - 11  # 11 disjoint combinations
+        self.check_cycle_algorithm(testG, expected)
+
+    def test_simple_cycles_bounded(self):
+        # iteratively construct a cluster of nested cycles running in the same direction
+        # there should be one cycle of every length
+        d = nx.DiGraph()
+        expected = []
+        for n in range(10):
+            nx.add_cycle(d, range(n))
+            expected.append(n)
+            for k, e in enumerate(expected):
+                self.check_cycle_algorithm(d, e, length_bound=k)
+
+        # iteratively construct a path of undirected cycles, connected at articulation
+        # points.  there should be one cycle of every length except 2: no digons
+        g = nx.Graph()
+        top = 0
+        expected = []
+        for n in range(10):
+            expected.append(n if n < 2 else n - 1)
+            if n == 2:
+                # no digons in undirected graphs
+                continue
+            nx.add_cycle(g, range(top, top + n))
+            top += n
+            for k, e in enumerate(expected):
+                self.check_cycle_algorithm(g, e, length_bound=k)
+
+    def test_simple_cycles_bound_corner_cases(self):
+        G = nx.cycle_graph(4)
+        DG = nx.cycle_graph(4, create_using=nx.DiGraph)
+        assert list(nx.simple_cycles(G, length_bound=0)) == []
+        assert list(nx.simple_cycles(DG, length_bound=0)) == []
+        assert list(nx.chordless_cycles(G, length_bound=0)) == []
+        assert list(nx.chordless_cycles(DG, length_bound=0)) == []
+
+    def test_simple_cycles_bound_error(self):
+        with pytest.raises(ValueError):
+            G = nx.DiGraph()
+            for c in nx.simple_cycles(G, -1):
+                assert False
+
+        with pytest.raises(ValueError):
+            G = nx.Graph()
+            for c in nx.simple_cycles(G, -1):
+                assert False
+
+        with pytest.raises(ValueError):
+            G = nx.Graph()
+            for c in nx.chordless_cycles(G, -1):
+                assert False
+
+        with pytest.raises(ValueError):
+            G = nx.DiGraph()
+            for c in nx.chordless_cycles(G, -1):
+                assert False
+
+    def test_chordless_cycles_clique(self):
+        g_family = [self.K(n) for n in range(2, 15)]
+        expected = [0, 1, 4, 10, 20, 35, 56, 84, 120, 165, 220, 286, 364]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, chordless=True
+        )
+
+        # directed cliques have as many digons as undirected graphs have edges
+        expected = [(n * n - n) // 2 for n in range(15)]
+        g_family = [self.D(n) for n in range(15)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, chordless=True
+        )
+
+
+# These tests might fail with hash randomization since they depend on
+# edge_dfs. For more information, see the comments in:
+#    networkx/algorithms/traversal/tests/test_edgedfs.py
+
+
+class TestFindCycle:
+    @classmethod
+    def setup_class(cls):
+        cls.nodes = [0, 1, 2, 3]
+        cls.edges = [(-1, 0), (0, 1), (1, 0), (1, 0), (2, 1), (3, 1)]
+
+    def test_graph_nocycle(self):
+        G = nx.Graph(self.edges)
+        pytest.raises(nx.exception.NetworkXNoCycle, nx.find_cycle, G, self.nodes)
+
+    def test_graph_cycle(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(2, 0)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1), (1, 2), (2, 0)]
+        assert x == x_
+
+    def test_graph_orientation_none(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(2, 0)
+        x = list(nx.find_cycle(G, self.nodes, orientation=None))
+        x_ = [(0, 1), (1, 2), (2, 0)]
+        assert x == x_
+
+    def test_graph_orientation_original(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(2, 0)
+        x = list(nx.find_cycle(G, self.nodes, orientation="original"))
+        x_ = [(0, 1, FORWARD), (1, 2, FORWARD), (2, 0, FORWARD)]
+        assert x == x_
+
+    def test_digraph(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1), (1, 0)]
+        assert x == x_
+
+    def test_digraph_orientation_none(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation=None))
+        x_ = [(0, 1), (1, 0)]
+        assert x == x_
+
+    def test_digraph_orientation_original(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="original"))
+        x_ = [(0, 1, FORWARD), (1, 0, FORWARD)]
+        assert x == x_
+
+    def test_multigraph(self):
+        G = nx.MultiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 1)]  # or (1, 0, 2)
+        # Hash randomization...could be any edge.
+        assert x[0] == x_[0]
+        assert x[1][:2] == x_[1][:2]
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 0)]  # (1, 0, 1)
+        assert x[0] == x_[0]
+        assert x[1][:2] == x_[1][:2]
+
+    def test_digraph_ignore(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="ignore"))
+        x_ = [(0, 1, FORWARD), (1, 0, FORWARD)]
+        assert x == x_
+
+    def test_digraph_reverse(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="reverse"))
+        x_ = [(1, 0, REVERSE), (0, 1, REVERSE)]
+        assert x == x_
+
+    def test_multidigraph_ignore(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="ignore"))
+        x_ = [(0, 1, 0, FORWARD), (1, 0, 0, FORWARD)]  # or (1, 0, 1, 1)
+        assert x[0] == x_[0]
+        assert x[1][:2] == x_[1][:2]
+        assert x[1][3] == x_[1][3]
+
+    def test_multidigraph_ignore2(self):
+        # Loop traversed an edge while ignoring its orientation.
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (1, 2)])
+        x = list(nx.find_cycle(G, [0, 1, 2], orientation="ignore"))
+        x_ = [(1, 2, 0, FORWARD), (1, 2, 1, REVERSE)]
+        assert x == x_
+
+    def test_multidigraph_original(self):
+        # Node 2 doesn't need to be searched again from visited from 4.
+        # The goal here is to cover the case when 2 to be researched from 4,
+        # when 4 is visited from the first time (so we must make sure that 4
+        # is not visited from 2, and hence, we respect the edge orientation).
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (2, 3), (4, 2)])
+        pytest.raises(
+            nx.exception.NetworkXNoCycle,
+            nx.find_cycle,
+            G,
+            [0, 1, 2, 3, 4],
+            orientation="original",
+        )
+
+    def test_dag(self):
+        G = nx.DiGraph([(0, 1), (0, 2), (1, 2)])
+        pytest.raises(
+            nx.exception.NetworkXNoCycle, nx.find_cycle, G, orientation="original"
+        )
+        x = list(nx.find_cycle(G, orientation="ignore"))
+        assert x == [(0, 1, FORWARD), (1, 2, FORWARD), (0, 2, REVERSE)]
+
+    def test_prev_explored(self):
+        # https://github.com/networkx/networkx/issues/2323
+
+        G = nx.DiGraph()
+        G.add_edges_from([(1, 0), (2, 0), (1, 2), (2, 1)])
+        pytest.raises(nx.NetworkXNoCycle, nx.find_cycle, G, source=0)
+        x = list(nx.find_cycle(G, 1))
+        x_ = [(1, 2), (2, 1)]
+        assert x == x_
+
+        x = list(nx.find_cycle(G, 2))
+        x_ = [(2, 1), (1, 2)]
+        assert x == x_
+
+        x = list(nx.find_cycle(G))
+        x_ = [(1, 2), (2, 1)]
+        assert x == x_
+
+    def test_no_cycle(self):
+        # https://github.com/networkx/networkx/issues/2439
+
+        G = nx.DiGraph()
+        G.add_edges_from([(1, 2), (2, 0), (3, 1), (3, 2)])
+        pytest.raises(nx.NetworkXNoCycle, nx.find_cycle, G, source=0)
+        pytest.raises(nx.NetworkXNoCycle, nx.find_cycle, G)
+
+
+def assert_basis_equal(a, b):
+    assert sorted(a) == sorted(b)
+
+
+class TestMinimumCycleBasis:
+    @classmethod
+    def setup_class(cls):
+        T = nx.Graph()
+        nx.add_cycle(T, [1, 2, 3, 4], weight=1)
+        T.add_edge(2, 4, weight=5)
+        cls.diamond_graph = T
+
+    def test_unweighted_diamond(self):
+        mcb = nx.minimum_cycle_basis(self.diamond_graph)
+        assert_basis_equal(mcb, [[2, 4, 1], [3, 4, 2]])
+
+    def test_weighted_diamond(self):
+        mcb = nx.minimum_cycle_basis(self.diamond_graph, weight="weight")
+        assert_basis_equal(mcb, [[2, 4, 1], [4, 3, 2, 1]])
+
+    def test_dimensionality(self):
+        # checks |MCB|=|E|-|V|+|NC|
+        ntrial = 10
+        for seed in range(1234, 1234 + ntrial):
+            rg = nx.erdos_renyi_graph(10, 0.3, seed=seed)
+            nnodes = rg.number_of_nodes()
+            nedges = rg.number_of_edges()
+            ncomp = nx.number_connected_components(rg)
+
+            mcb = nx.minimum_cycle_basis(rg)
+            assert len(mcb) == nedges - nnodes + ncomp
+            check_independent(mcb)
+
+    def test_complete_graph(self):
+        cg = nx.complete_graph(5)
+        mcb = nx.minimum_cycle_basis(cg)
+        assert all(len(cycle) == 3 for cycle in mcb)
+        check_independent(mcb)
+
+    def test_tree_graph(self):
+        tg = nx.balanced_tree(3, 3)
+        assert not nx.minimum_cycle_basis(tg)
+
+    def test_petersen_graph(self):
+        G = nx.petersen_graph()
+        mcb = list(nx.minimum_cycle_basis(G))
+        expected = [
+            [4, 9, 7, 5, 0],
+            [1, 2, 3, 4, 0],
+            [1, 6, 8, 5, 0],
+            [4, 3, 8, 5, 0],
+            [1, 6, 9, 4, 0],
+            [1, 2, 7, 5, 0],
+        ]
+        assert len(mcb) == len(expected)
+        assert all(c in expected for c in mcb)
+
+        # check that order of the nodes is a path
+        for c in mcb:
+            assert all(G.has_edge(u, v) for u, v in nx.utils.pairwise(c, cyclic=True))
+        # check independence of the basis
+        check_independent(mcb)
+
+    def test_gh6787_variable_weighted_complete_graph(self):
+        N = 8
+        cg = nx.complete_graph(N)
+        cg.add_weighted_edges_from([(u, v, 9) for u, v in cg.edges])
+        cg.add_weighted_edges_from([(u, v, 1) for u, v in nx.cycle_graph(N).edges])
+        mcb = nx.minimum_cycle_basis(cg, weight="weight")
+        check_independent(mcb)
+
+    def test_gh6787_and_edge_attribute_names(self):
+        G = nx.cycle_graph(4)
+        G.add_weighted_edges_from([(0, 2, 10), (1, 3, 10)], weight="dist")
+        expected = [[1, 3, 0], [3, 2, 1, 0], [1, 2, 0]]
+        mcb = list(nx.minimum_cycle_basis(G, weight="dist"))
+        assert len(mcb) == len(expected)
+        assert all(c in expected for c in mcb)
+
+        # test not using a weight with weight attributes
+        expected = [[1, 3, 0], [1, 2, 0], [3, 2, 0]]
+        mcb = list(nx.minimum_cycle_basis(G))
+        assert len(mcb) == len(expected)
+        assert all(c in expected for c in mcb)
+
+
+class TestGirth:
+    @pytest.mark.parametrize(
+        ("G", "expected"),
+        (
+            (nx.chvatal_graph(), 4),
+            (nx.tutte_graph(), 4),
+            (nx.petersen_graph(), 5),
+            (nx.heawood_graph(), 6),
+            (nx.pappus_graph(), 6),
+            (nx.random_labeled_tree(10, seed=42), inf),
+            (nx.empty_graph(10), inf),
+            (nx.Graph(chain(cycle_edges(range(5)), cycle_edges(range(6, 10)))), 4),
+            (
+                nx.Graph(
+                    [
+                        (0, 6),
+                        (0, 8),
+                        (0, 9),
+                        (1, 8),
+                        (2, 8),
+                        (2, 9),
+                        (4, 9),
+                        (5, 9),
+                        (6, 8),
+                        (6, 9),
+                        (7, 8),
+                    ]
+                ),
+                3,
+            ),
+        ),
+    )
+    def test_girth(self, G, expected):
+        assert nx.girth(G) == expected
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_d_separation.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_d_separation.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7608295afa2e8e20116e5e3cc0b655b0f2a23d6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_d_separation.py
@@ -0,0 +1,340 @@
+from itertools import combinations
+
+import pytest
+
+import networkx as nx
+
+
+def path_graph():
+    """Return a path graph of length three."""
+    G = nx.path_graph(3, create_using=nx.DiGraph)
+    G.graph["name"] = "path"
+    nx.freeze(G)
+    return G
+
+
+def fork_graph():
+    """Return a three node fork graph."""
+    G = nx.DiGraph(name="fork")
+    G.add_edges_from([(0, 1), (0, 2)])
+    nx.freeze(G)
+    return G
+
+
+def collider_graph():
+    """Return a collider/v-structure graph with three nodes."""
+    G = nx.DiGraph(name="collider")
+    G.add_edges_from([(0, 2), (1, 2)])
+    nx.freeze(G)
+    return G
+
+
+def naive_bayes_graph():
+    """Return a simply Naive Bayes PGM graph."""
+    G = nx.DiGraph(name="naive_bayes")
+    G.add_edges_from([(0, 1), (0, 2), (0, 3), (0, 4)])
+    nx.freeze(G)
+    return G
+
+
+def asia_graph():
+    """Return the 'Asia' PGM graph."""
+    G = nx.DiGraph(name="asia")
+    G.add_edges_from(
+        [
+            ("asia", "tuberculosis"),
+            ("smoking", "cancer"),
+            ("smoking", "bronchitis"),
+            ("tuberculosis", "either"),
+            ("cancer", "either"),
+            ("either", "xray"),
+            ("either", "dyspnea"),
+            ("bronchitis", "dyspnea"),
+        ]
+    )
+    nx.freeze(G)
+    return G
+
+
+@pytest.fixture(name="path_graph")
+def path_graph_fixture():
+    return path_graph()
+
+
+@pytest.fixture(name="fork_graph")
+def fork_graph_fixture():
+    return fork_graph()
+
+
+@pytest.fixture(name="collider_graph")
+def collider_graph_fixture():
+    return collider_graph()
+
+
+@pytest.fixture(name="naive_bayes_graph")
+def naive_bayes_graph_fixture():
+    return naive_bayes_graph()
+
+
+@pytest.fixture(name="asia_graph")
+def asia_graph_fixture():
+    return asia_graph()
+
+
+@pytest.fixture()
+def large_collider_graph():
+    edge_list = [("A", "B"), ("C", "B"), ("B", "D"), ("D", "E"), ("B", "F"), ("G", "E")]
+    G = nx.DiGraph(edge_list)
+    return G
+
+
+@pytest.fixture()
+def chain_and_fork_graph():
+    edge_list = [("A", "B"), ("B", "C"), ("B", "D"), ("D", "C")]
+    G = nx.DiGraph(edge_list)
+    return G
+
+
+@pytest.fixture()
+def no_separating_set_graph():
+    edge_list = [("A", "B")]
+    G = nx.DiGraph(edge_list)
+    return G
+
+
+@pytest.fixture()
+def large_no_separating_set_graph():
+    edge_list = [("A", "B"), ("C", "A"), ("C", "B")]
+    G = nx.DiGraph(edge_list)
+    return G
+
+
+@pytest.fixture()
+def collider_trek_graph():
+    edge_list = [("A", "B"), ("C", "B"), ("C", "D")]
+    G = nx.DiGraph(edge_list)
+    return G
+
+
+@pytest.mark.parametrize(
+    "graph",
+    [path_graph(), fork_graph(), collider_graph(), naive_bayes_graph(), asia_graph()],
+)
+def test_markov_condition(graph):
+    """Test that the Markov condition holds for each PGM graph."""
+    for node in graph.nodes:
+        parents = set(graph.predecessors(node))
+        non_descendants = graph.nodes - nx.descendants(graph, node) - {node} - parents
+        assert nx.is_d_separator(graph, {node}, non_descendants, parents)
+
+
+def test_path_graph_dsep(path_graph):
+    """Example-based test of d-separation for path_graph."""
+    assert nx.is_d_separator(path_graph, {0}, {2}, {1})
+    assert not nx.is_d_separator(path_graph, {0}, {2}, set())
+
+
+def test_fork_graph_dsep(fork_graph):
+    """Example-based test of d-separation for fork_graph."""
+    assert nx.is_d_separator(fork_graph, {1}, {2}, {0})
+    assert not nx.is_d_separator(fork_graph, {1}, {2}, set())
+
+
+def test_collider_graph_dsep(collider_graph):
+    """Example-based test of d-separation for collider_graph."""
+    assert nx.is_d_separator(collider_graph, {0}, {1}, set())
+    assert not nx.is_d_separator(collider_graph, {0}, {1}, {2})
+
+
+def test_naive_bayes_dsep(naive_bayes_graph):
+    """Example-based test of d-separation for naive_bayes_graph."""
+    for u, v in combinations(range(1, 5), 2):
+        assert nx.is_d_separator(naive_bayes_graph, {u}, {v}, {0})
+        assert not nx.is_d_separator(naive_bayes_graph, {u}, {v}, set())
+
+
+def test_asia_graph_dsep(asia_graph):
+    """Example-based test of d-separation for asia_graph."""
+    assert nx.is_d_separator(
+        asia_graph, {"asia", "smoking"}, {"dyspnea", "xray"}, {"bronchitis", "either"}
+    )
+    assert nx.is_d_separator(
+        asia_graph, {"tuberculosis", "cancer"}, {"bronchitis"}, {"smoking", "xray"}
+    )
+
+
+def test_undirected_graphs_are_not_supported():
+    """
+    Test that undirected graphs are not supported.
+
+    d-separation and its related algorithms do not apply in
+    the case of undirected graphs.
+    """
+    g = nx.path_graph(3, nx.Graph)
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.is_d_separator(g, {0}, {1}, {2})
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.is_minimal_d_separator(g, {0}, {1}, {2})
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.find_minimal_d_separator(g, {0}, {1})
+
+
+def test_cyclic_graphs_raise_error():
+    """
+    Test that cycle graphs should cause erroring.
+
+    This is because PGMs assume a directed acyclic graph.
+    """
+    g = nx.cycle_graph(3, nx.DiGraph)
+    with pytest.raises(nx.NetworkXError):
+        nx.is_d_separator(g, {0}, {1}, {2})
+    with pytest.raises(nx.NetworkXError):
+        nx.find_minimal_d_separator(g, {0}, {1})
+    with pytest.raises(nx.NetworkXError):
+        nx.is_minimal_d_separator(g, {0}, {1}, {2})
+
+
+def test_invalid_nodes_raise_error(asia_graph):
+    """
+    Test that graphs that have invalid nodes passed in raise errors.
+    """
+    # Check both set and node arguments
+    with pytest.raises(nx.NodeNotFound):
+        nx.is_d_separator(asia_graph, {0}, {1}, {2})
+    with pytest.raises(nx.NodeNotFound):
+        nx.is_d_separator(asia_graph, 0, 1, 2)
+    with pytest.raises(nx.NodeNotFound):
+        nx.is_minimal_d_separator(asia_graph, {0}, {1}, {2})
+    with pytest.raises(nx.NodeNotFound):
+        nx.is_minimal_d_separator(asia_graph, 0, 1, 2)
+    with pytest.raises(nx.NodeNotFound):
+        nx.find_minimal_d_separator(asia_graph, {0}, {1})
+    with pytest.raises(nx.NodeNotFound):
+        nx.find_minimal_d_separator(asia_graph, 0, 1)
+
+
+def test_nondisjoint_node_sets_raise_error(collider_graph):
+    """
+    Test that error is raised when node sets aren't disjoint.
+    """
+    with pytest.raises(nx.NetworkXError):
+        nx.is_d_separator(collider_graph, 0, 1, 0)
+    with pytest.raises(nx.NetworkXError):
+        nx.is_d_separator(collider_graph, 0, 2, 0)
+    with pytest.raises(nx.NetworkXError):
+        nx.is_d_separator(collider_graph, 0, 0, 1)
+    with pytest.raises(nx.NetworkXError):
+        nx.is_d_separator(collider_graph, 1, 0, 0)
+    with pytest.raises(nx.NetworkXError):
+        nx.find_minimal_d_separator(collider_graph, 0, 0)
+    with pytest.raises(nx.NetworkXError):
+        nx.find_minimal_d_separator(collider_graph, 0, 1, included=0)
+    with pytest.raises(nx.NetworkXError):
+        nx.find_minimal_d_separator(collider_graph, 1, 0, included=0)
+    with pytest.raises(nx.NetworkXError):
+        nx.is_minimal_d_separator(collider_graph, 0, 0, set())
+    with pytest.raises(nx.NetworkXError):
+        nx.is_minimal_d_separator(collider_graph, 0, 1, set(), included=0)
+    with pytest.raises(nx.NetworkXError):
+        nx.is_minimal_d_separator(collider_graph, 1, 0, set(), included=0)
+
+
+def test_is_minimal_d_separator(
+    large_collider_graph,
+    chain_and_fork_graph,
+    no_separating_set_graph,
+    large_no_separating_set_graph,
+    collider_trek_graph,
+):
+    # Case 1:
+    # create a graph A -> B <- C
+    # B -> D -> E;
+    # B -> F;
+    # G -> E;
+    assert not nx.is_d_separator(large_collider_graph, {"B"}, {"E"}, set())
+
+    # minimal set of the corresponding graph
+    # for B and E should be (D,)
+    Zmin = nx.find_minimal_d_separator(large_collider_graph, "B", "E")
+    # check that the minimal d-separator is a d-separating set
+    assert nx.is_d_separator(large_collider_graph, "B", "E", Zmin)
+    # the minimal separating set should also pass the test for minimality
+    assert nx.is_minimal_d_separator(large_collider_graph, "B", "E", Zmin)
+    # function should also work with set arguments
+    assert nx.is_minimal_d_separator(large_collider_graph, {"A", "B"}, {"G", "E"}, Zmin)
+    assert Zmin == {"D"}
+
+    # Case 2:
+    # create a graph A -> B -> C
+    # B -> D -> C;
+    assert not nx.is_d_separator(chain_and_fork_graph, {"A"}, {"C"}, set())
+    Zmin = nx.find_minimal_d_separator(chain_and_fork_graph, "A", "C")
+
+    # the minimal separating set should pass the test for minimality
+    assert nx.is_minimal_d_separator(chain_and_fork_graph, "A", "C", Zmin)
+    assert Zmin == {"B"}
+    Znotmin = Zmin.union({"D"})
+    assert not nx.is_minimal_d_separator(chain_and_fork_graph, "A", "C", Znotmin)
+
+    # Case 3:
+    # create a graph A -> B
+
+    # there is no m-separating set between A and B at all, so
+    # no minimal m-separating set can exist
+    assert not nx.is_d_separator(no_separating_set_graph, {"A"}, {"B"}, set())
+    assert nx.find_minimal_d_separator(no_separating_set_graph, "A", "B") is None
+
+    # Case 4:
+    # create a graph A -> B with A <- C -> B
+
+    # there is no m-separating set between A and B at all, so
+    # no minimal m-separating set can exist
+    # however, the algorithm will initially propose C as a
+    # minimal (but invalid) separating set
+    assert not nx.is_d_separator(large_no_separating_set_graph, {"A"}, {"B"}, {"C"})
+    assert nx.find_minimal_d_separator(large_no_separating_set_graph, "A", "B") is None
+
+    # Test `included` and `excluded` args
+    # create graph A -> B <- C -> D
+    assert nx.find_minimal_d_separator(collider_trek_graph, "A", "D", included="B") == {
+        "B",
+        "C",
+    }
+    assert (
+        nx.find_minimal_d_separator(
+            collider_trek_graph, "A", "D", included="B", restricted="B"
+        )
+        is None
+    )
+
+
+def test_is_minimal_d_separator_checks_dsep():
+    """Test that is_minimal_d_separator checks for d-separation as well."""
+    g = nx.DiGraph()
+    g.add_edges_from(
+        [
+            ("A", "B"),
+            ("A", "E"),
+            ("B", "C"),
+            ("B", "D"),
+            ("D", "C"),
+            ("D", "F"),
+            ("E", "D"),
+            ("E", "F"),
+        ]
+    )
+
+    assert not nx.is_d_separator(g, {"C"}, {"F"}, {"D"})
+
+    # since {'D'} and {} are not d-separators, we return false
+    assert not nx.is_minimal_d_separator(g, "C", "F", {"D"})
+    assert not nx.is_minimal_d_separator(g, "C", "F", set())
+
+
+def test__reachable(large_collider_graph):
+    reachable = nx.algorithms.d_separation._reachable
+    g = large_collider_graph
+    x = {"F", "D"}
+    ancestors = {"A", "B", "C", "D", "F"}
+    assert reachable(g, x, ancestors, {"B"}) == {"B", "F", "D"}
+    assert reachable(g, x, ancestors, set()) == ancestors
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_dag.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_dag.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c76be8119c44096703dbd276a3275a4c33bc5b6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_dag.py
@@ -0,0 +1,835 @@
+from collections import deque
+from itertools import combinations, permutations
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, pairwise
+
+
+# Recipe from the itertools documentation.
+def _consume(iterator):
+    "Consume the iterator entirely."
+    # Feed the entire iterator into a zero-length deque.
+    deque(iterator, maxlen=0)
+
+
+class TestDagLongestPath:
+    """Unit tests computing the longest path in a directed acyclic graph."""
+
+    def test_empty(self):
+        G = nx.DiGraph()
+        assert nx.dag_longest_path(G) == []
+
+    def test_unweighted1(self):
+        edges = [(1, 2), (2, 3), (2, 4), (3, 5), (5, 6), (3, 7)]
+        G = nx.DiGraph(edges)
+        assert nx.dag_longest_path(G) == [1, 2, 3, 5, 6]
+
+    def test_unweighted2(self):
+        edges = [(1, 2), (2, 3), (3, 4), (4, 5), (1, 3), (1, 5), (3, 5)]
+        G = nx.DiGraph(edges)
+        assert nx.dag_longest_path(G) == [1, 2, 3, 4, 5]
+
+    def test_weighted(self):
+        G = nx.DiGraph()
+        edges = [(1, 2, -5), (2, 3, 1), (3, 4, 1), (4, 5, 0), (3, 5, 4), (1, 6, 2)]
+        G.add_weighted_edges_from(edges)
+        assert nx.dag_longest_path(G) == [2, 3, 5]
+
+    def test_undirected_not_implemented(self):
+        G = nx.Graph()
+        pytest.raises(nx.NetworkXNotImplemented, nx.dag_longest_path, G)
+
+    def test_unorderable_nodes(self):
+        """Tests that computing the longest path does not depend on
+        nodes being orderable.
+
+        For more information, see issue #1989.
+
+        """
+        # Create the directed path graph on four nodes in a diamond shape,
+        # with nodes represented as (unorderable) Python objects.
+        nodes = [object() for n in range(4)]
+        G = nx.DiGraph()
+        G.add_edge(nodes[0], nodes[1])
+        G.add_edge(nodes[0], nodes[2])
+        G.add_edge(nodes[2], nodes[3])
+        G.add_edge(nodes[1], nodes[3])
+
+        # this will raise NotImplementedError when nodes need to be ordered
+        nx.dag_longest_path(G)
+
+    def test_multigraph_unweighted(self):
+        edges = [(1, 2), (2, 3), (2, 3), (3, 4), (4, 5), (1, 3), (1, 5), (3, 5)]
+        G = nx.MultiDiGraph(edges)
+        assert nx.dag_longest_path(G) == [1, 2, 3, 4, 5]
+
+    def test_multigraph_weighted(self):
+        G = nx.MultiDiGraph()
+        edges = [
+            (1, 2, 2),
+            (2, 3, 2),
+            (1, 3, 1),
+            (1, 3, 5),
+            (1, 3, 2),
+        ]
+        G.add_weighted_edges_from(edges)
+        assert nx.dag_longest_path(G) == [1, 3]
+
+    def test_multigraph_weighted_default_weight(self):
+        G = nx.MultiDiGraph([(1, 2), (2, 3)])  # Unweighted edges
+        G.add_weighted_edges_from([(1, 3, 1), (1, 3, 5), (1, 3, 2)])
+
+        # Default value for default weight is 1
+        assert nx.dag_longest_path(G) == [1, 3]
+        assert nx.dag_longest_path(G, default_weight=3) == [1, 2, 3]
+
+
+class TestDagLongestPathLength:
+    """Unit tests for computing the length of a longest path in a
+    directed acyclic graph.
+
+    """
+
+    def test_unweighted(self):
+        edges = [(1, 2), (2, 3), (2, 4), (3, 5), (5, 6), (5, 7)]
+        G = nx.DiGraph(edges)
+        assert nx.dag_longest_path_length(G) == 4
+
+        edges = [(1, 2), (2, 3), (3, 4), (4, 5), (1, 3), (1, 5), (3, 5)]
+        G = nx.DiGraph(edges)
+        assert nx.dag_longest_path_length(G) == 4
+
+        # test degenerate graphs
+        G = nx.DiGraph()
+        G.add_node(1)
+        assert nx.dag_longest_path_length(G) == 0
+
+    def test_undirected_not_implemented(self):
+        G = nx.Graph()
+        pytest.raises(nx.NetworkXNotImplemented, nx.dag_longest_path_length, G)
+
+    def test_weighted(self):
+        edges = [(1, 2, -5), (2, 3, 1), (3, 4, 1), (4, 5, 0), (3, 5, 4), (1, 6, 2)]
+        G = nx.DiGraph()
+        G.add_weighted_edges_from(edges)
+        assert nx.dag_longest_path_length(G) == 5
+
+    def test_multigraph_unweighted(self):
+        edges = [(1, 2), (2, 3), (2, 3), (3, 4), (4, 5), (1, 3), (1, 5), (3, 5)]
+        G = nx.MultiDiGraph(edges)
+        assert nx.dag_longest_path_length(G) == 4
+
+    def test_multigraph_weighted(self):
+        G = nx.MultiDiGraph()
+        edges = [
+            (1, 2, 2),
+            (2, 3, 2),
+            (1, 3, 1),
+            (1, 3, 5),
+            (1, 3, 2),
+        ]
+        G.add_weighted_edges_from(edges)
+        assert nx.dag_longest_path_length(G) == 5
+
+
+class TestDAG:
+    @classmethod
+    def setup_class(cls):
+        pass
+
+    def test_topological_sort1(self):
+        DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])
+
+        for algorithm in [nx.topological_sort, nx.lexicographical_topological_sort]:
+            assert tuple(algorithm(DG)) == (1, 2, 3)
+
+        DG.add_edge(3, 2)
+
+        for algorithm in [nx.topological_sort, nx.lexicographical_topological_sort]:
+            pytest.raises(nx.NetworkXUnfeasible, _consume, algorithm(DG))
+
+        DG.remove_edge(2, 3)
+
+        for algorithm in [nx.topological_sort, nx.lexicographical_topological_sort]:
+            assert tuple(algorithm(DG)) == (1, 3, 2)
+
+        DG.remove_edge(3, 2)
+
+        assert tuple(nx.topological_sort(DG)) in {(1, 2, 3), (1, 3, 2)}
+        assert tuple(nx.lexicographical_topological_sort(DG)) == (1, 2, 3)
+
+    def test_is_directed_acyclic_graph(self):
+        G = nx.generators.complete_graph(2)
+        assert not nx.is_directed_acyclic_graph(G)
+        assert not nx.is_directed_acyclic_graph(G.to_directed())
+        assert not nx.is_directed_acyclic_graph(nx.Graph([(3, 4), (4, 5)]))
+        assert nx.is_directed_acyclic_graph(nx.DiGraph([(3, 4), (4, 5)]))
+
+    def test_topological_sort2(self):
+        DG = nx.DiGraph(
+            {
+                1: [2],
+                2: [3],
+                3: [4],
+                4: [5],
+                5: [1],
+                11: [12],
+                12: [13],
+                13: [14],
+                14: [15],
+            }
+        )
+        pytest.raises(nx.NetworkXUnfeasible, _consume, nx.topological_sort(DG))
+
+        assert not nx.is_directed_acyclic_graph(DG)
+
+        DG.remove_edge(1, 2)
+        _consume(nx.topological_sort(DG))
+        assert nx.is_directed_acyclic_graph(DG)
+
+    def test_topological_sort3(self):
+        DG = nx.DiGraph()
+        DG.add_edges_from([(1, i) for i in range(2, 5)])
+        DG.add_edges_from([(2, i) for i in range(5, 9)])
+        DG.add_edges_from([(6, i) for i in range(9, 12)])
+        DG.add_edges_from([(4, i) for i in range(12, 15)])
+
+        def validate(order):
+            assert isinstance(order, list)
+            assert set(order) == set(DG)
+            for u, v in combinations(order, 2):
+                assert not nx.has_path(DG, v, u)
+
+        validate(list(nx.topological_sort(DG)))
+
+        DG.add_edge(14, 1)
+        pytest.raises(nx.NetworkXUnfeasible, _consume, nx.topological_sort(DG))
+
+    def test_topological_sort4(self):
+        G = nx.Graph()
+        G.add_edge(1, 2)
+        # Only directed graphs can be topologically sorted.
+        pytest.raises(nx.NetworkXError, _consume, nx.topological_sort(G))
+
+    def test_topological_sort5(self):
+        G = nx.DiGraph()
+        G.add_edge(0, 1)
+        assert list(nx.topological_sort(G)) == [0, 1]
+
+    def test_topological_sort6(self):
+        for algorithm in [nx.topological_sort, nx.lexicographical_topological_sort]:
+
+            def runtime_error():
+                DG = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+                first = True
+                for x in algorithm(DG):
+                    if first:
+                        first = False
+                        DG.add_edge(5 - x, 5)
+
+            def unfeasible_error():
+                DG = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+                first = True
+                for x in algorithm(DG):
+                    if first:
+                        first = False
+                        DG.remove_node(4)
+
+            def runtime_error2():
+                DG = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+                first = True
+                for x in algorithm(DG):
+                    if first:
+                        first = False
+                        DG.remove_node(2)
+
+            pytest.raises(RuntimeError, runtime_error)
+            pytest.raises(RuntimeError, runtime_error2)
+            pytest.raises(nx.NetworkXUnfeasible, unfeasible_error)
+
+    def test_all_topological_sorts_1(self):
+        DG = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 5)])
+        assert list(nx.all_topological_sorts(DG)) == [[1, 2, 3, 4, 5]]
+
+    def test_all_topological_sorts_2(self):
+        DG = nx.DiGraph([(1, 3), (2, 1), (2, 4), (4, 3), (4, 5)])
+        assert sorted(nx.all_topological_sorts(DG)) == [
+            [2, 1, 4, 3, 5],
+            [2, 1, 4, 5, 3],
+            [2, 4, 1, 3, 5],
+            [2, 4, 1, 5, 3],
+            [2, 4, 5, 1, 3],
+        ]
+
+    def test_all_topological_sorts_3(self):
+        def unfeasible():
+            DG = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 2), (4, 5)])
+            # convert to list to execute generator
+            list(nx.all_topological_sorts(DG))
+
+        def not_implemented():
+            G = nx.Graph([(1, 2), (2, 3)])
+            # convert to list to execute generator
+            list(nx.all_topological_sorts(G))
+
+        def not_implemented_2():
+            G = nx.MultiGraph([(1, 2), (1, 2), (2, 3)])
+            list(nx.all_topological_sorts(G))
+
+        pytest.raises(nx.NetworkXUnfeasible, unfeasible)
+        pytest.raises(nx.NetworkXNotImplemented, not_implemented)
+        pytest.raises(nx.NetworkXNotImplemented, not_implemented_2)
+
+    def test_all_topological_sorts_4(self):
+        DG = nx.DiGraph()
+        for i in range(7):
+            DG.add_node(i)
+        assert sorted(map(list, permutations(DG.nodes))) == sorted(
+            nx.all_topological_sorts(DG)
+        )
+
+    def test_all_topological_sorts_multigraph_1(self):
+        DG = nx.MultiDiGraph([(1, 2), (1, 2), (2, 3), (3, 4), (3, 5), (3, 5), (3, 5)])
+        assert sorted(nx.all_topological_sorts(DG)) == sorted(
+            [[1, 2, 3, 4, 5], [1, 2, 3, 5, 4]]
+        )
+
+    def test_all_topological_sorts_multigraph_2(self):
+        N = 9
+        edges = []
+        for i in range(1, N):
+            edges.extend([(i, i + 1)] * i)
+        DG = nx.MultiDiGraph(edges)
+        assert list(nx.all_topological_sorts(DG)) == [list(range(1, N + 1))]
+
+    def test_ancestors(self):
+        G = nx.DiGraph()
+        ancestors = nx.algorithms.dag.ancestors
+        G.add_edges_from([(1, 2), (1, 3), (4, 2), (4, 3), (4, 5), (2, 6), (5, 6)])
+        assert ancestors(G, 6) == {1, 2, 4, 5}
+        assert ancestors(G, 3) == {1, 4}
+        assert ancestors(G, 1) == set()
+        pytest.raises(nx.NetworkXError, ancestors, G, 8)
+
+    def test_descendants(self):
+        G = nx.DiGraph()
+        descendants = nx.algorithms.dag.descendants
+        G.add_edges_from([(1, 2), (1, 3), (4, 2), (4, 3), (4, 5), (2, 6), (5, 6)])
+        assert descendants(G, 1) == {2, 3, 6}
+        assert descendants(G, 4) == {2, 3, 5, 6}
+        assert descendants(G, 3) == set()
+        pytest.raises(nx.NetworkXError, descendants, G, 8)
+
+    def test_transitive_closure(self):
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        assert edges_equal(nx.transitive_closure(G).edges(), solution, directed=True)
+        G = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
+        assert edges_equal(nx.transitive_closure(G).edges(), solution, directed=True)
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+        solution = [(1, 2), (2, 1), (2, 3), (3, 2), (1, 3), (3, 1)]
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(
+            sorted(nx.transitive_closure(G).edges()), soln, directed=True
+        )
+
+        G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        assert edges_equal(sorted(nx.transitive_closure(G).edges()), solution)
+
+        G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        assert edges_equal(sorted(nx.transitive_closure(G).edges()), solution)
+
+        G = nx.MultiDiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        assert edges_equal(
+            sorted(nx.transitive_closure(G).edges()), solution, directed=True
+        )
+
+        # test if edge data is copied
+        G = nx.DiGraph([(1, 2, {"a": 3}), (2, 3, {"b": 0}), (3, 4)])
+        H = nx.transitive_closure(G)
+        for u, v in G.edges():
+            assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
+
+        k = 10
+        G = nx.DiGraph((i, i + 1, {"f": "b", "weight": i}) for i in range(k))
+        H = nx.transitive_closure(G)
+        for u, v in G.edges():
+            assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
+
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.transitive_closure(G, reflexive="wrong input")
+
+    def test_reflexive_transitive_closure(self):
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(nx.transitive_closure(G).edges(), solution, directed=True)
+        assert edges_equal(
+            nx.transitive_closure(G, False).edges(), solution, directed=True
+        )
+        assert edges_equal(nx.transitive_closure(G, True).edges(), soln, directed=True)
+        assert edges_equal(
+            nx.transitive_closure(G, None).edges(), solution, directed=True
+        )
+
+        G = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(nx.transitive_closure(G).edges(), solution, directed=True)
+        assert edges_equal(
+            nx.transitive_closure(G, False).edges(), solution, directed=True
+        )
+        assert edges_equal(nx.transitive_closure(G, True).edges(), soln, directed=True)
+        assert edges_equal(
+            nx.transitive_closure(G, None).edges(), solution, directed=True
+        )
+
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+        solution = sorted([(1, 2), (2, 1), (2, 3), (3, 2), (1, 3), (3, 1)])
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(
+            sorted(nx.transitive_closure(G).edges()), soln, directed=True
+        )
+        assert edges_equal(
+            sorted(nx.transitive_closure(G, False).edges()), soln, directed=True
+        )
+        assert edges_equal(
+            sorted(nx.transitive_closure(G, None).edges()), solution, directed=True
+        )
+        assert edges_equal(
+            sorted(nx.transitive_closure(G, True).edges()), soln, directed=True
+        )
+
+        G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(nx.transitive_closure(G).edges(), solution)
+        assert edges_equal(nx.transitive_closure(G, False).edges(), solution)
+        assert edges_equal(nx.transitive_closure(G, True).edges(), soln)
+        assert edges_equal(nx.transitive_closure(G, None).edges(), solution)
+
+        G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(nx.transitive_closure(G).edges(), solution)
+        assert edges_equal(nx.transitive_closure(G, False).edges(), solution)
+        assert edges_equal(nx.transitive_closure(G, True).edges(), soln)
+        assert edges_equal(nx.transitive_closure(G, None).edges(), solution)
+
+        G = nx.MultiDiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        soln = sorted(solution + [(n, n) for n in G])
+        assert edges_equal(nx.transitive_closure(G).edges(), solution, directed=True)
+        assert edges_equal(
+            nx.transitive_closure(G, False).edges(), solution, directed=True
+        )
+        assert edges_equal(nx.transitive_closure(G, True).edges(), soln, directed=True)
+        assert edges_equal(
+            nx.transitive_closure(G, None).edges(), solution, directed=True
+        )
+
+    def test_transitive_closure_dag(self):
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+        transitive_closure = nx.algorithms.dag.transitive_closure_dag
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        assert edges_equal(transitive_closure(G).edges(), solution, directed=True)
+        G = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
+        solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
+        assert edges_equal(transitive_closure(G).edges(), solution, directed=True)
+        G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        pytest.raises(nx.NetworkXNotImplemented, transitive_closure, G)
+
+        # test if edge data is copied
+        G = nx.DiGraph([(1, 2, {"a": 3}), (2, 3, {"b": 0}), (3, 4)])
+        H = transitive_closure(G)
+        for u, v in G.edges():
+            assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
+
+        k = 10
+        G = nx.DiGraph((i, i + 1, {"foo": "bar", "weight": i}) for i in range(k))
+        H = transitive_closure(G)
+        for u, v in G.edges():
+            assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
+
+    def test_transitive_reduction(self):
+        G = nx.DiGraph([(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)])
+        transitive_reduction = nx.algorithms.dag.transitive_reduction
+        solution = [(1, 2), (2, 3), (3, 4)]
+        assert edges_equal(transitive_reduction(G).edges(), solution, directed=True)
+        G = nx.DiGraph([(1, 2), (1, 3), (1, 4), (2, 3), (2, 4)])
+        transitive_reduction = nx.algorithms.dag.transitive_reduction
+        solution = [(1, 2), (2, 3), (2, 4)]
+        assert edges_equal(transitive_reduction(G).edges(), solution, directed=True)
+        G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        pytest.raises(nx.NetworkXNotImplemented, transitive_reduction, G)
+
+    def _check_antichains(self, solution, result):
+        sol = [frozenset(a) for a in solution]
+        res = [frozenset(a) for a in result]
+        assert set(sol) == set(res)
+
+    def test_antichains(self):
+        antichains = nx.algorithms.dag.antichains
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+        solution = [[], [4], [3], [2], [1]]
+        self._check_antichains(list(antichains(G)), solution)
+        G = nx.DiGraph([(1, 2), (2, 3), (2, 4), (3, 5), (5, 6), (5, 7)])
+        solution = [
+            [],
+            [4],
+            [7],
+            [7, 4],
+            [6],
+            [6, 4],
+            [6, 7],
+            [6, 7, 4],
+            [5],
+            [5, 4],
+            [3],
+            [3, 4],
+            [2],
+            [1],
+        ]
+        self._check_antichains(list(antichains(G)), solution)
+        G = nx.DiGraph([(1, 2), (1, 3), (3, 4), (3, 5), (5, 6)])
+        solution = [
+            [],
+            [6],
+            [5],
+            [4],
+            [4, 6],
+            [4, 5],
+            [3],
+            [2],
+            [2, 6],
+            [2, 5],
+            [2, 4],
+            [2, 4, 6],
+            [2, 4, 5],
+            [2, 3],
+            [1],
+        ]
+        self._check_antichains(list(antichains(G)), solution)
+        G = nx.DiGraph({0: [1, 2], 1: [4], 2: [3], 3: [4]})
+        solution = [[], [4], [3], [2], [1], [1, 3], [1, 2], [0]]
+        self._check_antichains(list(antichains(G)), solution)
+        G = nx.DiGraph()
+        self._check_antichains(list(antichains(G)), [[]])
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2])
+        solution = [[], [0], [1], [1, 0], [2], [2, 0], [2, 1], [2, 1, 0]]
+        self._check_antichains(list(antichains(G)), solution)
+
+        def f(x):
+            return list(antichains(x))
+
+        G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        pytest.raises(nx.NetworkXNotImplemented, f, G)
+        G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+        pytest.raises(nx.NetworkXUnfeasible, f, G)
+
+    def test_lexicographical_topological_sort(self):
+        G = nx.DiGraph([(1, 2), (2, 3), (1, 4), (1, 5), (2, 6)])
+        assert list(nx.lexicographical_topological_sort(G)) == [1, 2, 3, 4, 5, 6]
+        assert list(nx.lexicographical_topological_sort(G, key=lambda x: x)) == [
+            1,
+            2,
+            3,
+            4,
+            5,
+            6,
+        ]
+        assert list(nx.lexicographical_topological_sort(G, key=lambda x: -x)) == [
+            1,
+            5,
+            4,
+            2,
+            6,
+            3,
+        ]
+
+    def test_lexicographical_topological_sort2(self):
+        """
+        Check the case of two or more nodes with same key value.
+        Want to avoid exception raised due to comparing nodes directly.
+        See Issue #3493
+        """
+
+        class Test_Node:
+            def __init__(self, n):
+                self.label = n
+                self.priority = 1
+
+            def __repr__(self):
+                return f"Node({self.label})"
+
+        def sorting_key(node):
+            return node.priority
+
+        test_nodes = [Test_Node(n) for n in range(4)]
+        G = nx.DiGraph()
+        edges = [(0, 1), (0, 2), (0, 3), (2, 3)]
+        G.add_edges_from((test_nodes[a], test_nodes[b]) for a, b in edges)
+
+        sorting = list(nx.lexicographical_topological_sort(G, key=sorting_key))
+        assert sorting == test_nodes
+
+
+def test_topological_generations():
+    G = nx.DiGraph(
+        {1: [2, 3], 2: [4, 5], 3: [7], 4: [], 5: [6, 7], 6: [], 7: []}
+    ).reverse()
+    # order within each generation is inconsequential
+    generations = [sorted(gen) for gen in nx.topological_generations(G)]
+    expected = [[4, 6, 7], [3, 5], [2], [1]]
+    assert generations == expected
+
+    MG = nx.MultiDiGraph(G.edges)
+    MG.add_edge(2, 1)
+    generations = [sorted(gen) for gen in nx.topological_generations(MG)]
+    assert generations == expected
+
+
+def test_topological_generations_empty():
+    G = nx.DiGraph()
+    assert list(nx.topological_generations(G)) == []
+
+
+def test_topological_generations_cycle():
+    G = nx.DiGraph([[2, 1], [3, 1], [1, 2]])
+    with pytest.raises(nx.NetworkXUnfeasible):
+        list(nx.topological_generations(G))
+
+
+def test_is_aperiodic_cycle():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [1, 2, 3, 4])
+    assert not nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_cycle2():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [1, 2, 3, 4])
+    nx.add_cycle(G, [3, 4, 5, 6, 7])
+    assert nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_cycle3():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [1, 2, 3, 4])
+    nx.add_cycle(G, [3, 4, 5, 6])
+    assert not nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_cycle4():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [1, 2, 3, 4])
+    G.add_edge(1, 3)
+    assert nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_selfloop():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [1, 2, 3, 4])
+    G.add_edge(1, 1)
+    assert nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_null_graph_raises():
+    G = nx.DiGraph()
+    pytest.raises(nx.NetworkXPointlessConcept, nx.is_aperiodic, G)
+
+
+def test_is_aperiodic_undirected_raises():
+    G = nx.Graph([(1, 2), (2, 3), (3, 1)])
+    pytest.raises(nx.NetworkXError, nx.is_aperiodic, G)
+
+
+def test_is_aperiodic_disconnected_raises():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [0, 1, 2])
+    G.add_edge(3, 3)
+    pytest.raises(nx.NetworkXError, nx.is_aperiodic, G)
+
+
+def test_is_aperiodic_weakly_connected_raises():
+    G = nx.DiGraph([(1, 2), (2, 3)])
+    pytest.raises(nx.NetworkXError, nx.is_aperiodic, G)
+
+
+def test_is_aperiodic_empty_graph():
+    G = nx.empty_graph(create_using=nx.DiGraph)
+    with pytest.raises(nx.NetworkXPointlessConcept, match="Graph has no nodes."):
+        nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_bipartite():
+    # Bipartite graph
+    G = nx.DiGraph(nx.davis_southern_women_graph())
+    assert not nx.is_aperiodic(G)
+
+
+def test_is_aperiodic_single_node():
+    G = nx.DiGraph()
+    G.add_node(0)
+    assert not nx.is_aperiodic(G)
+    G.add_edge(0, 0)
+    assert nx.is_aperiodic(G)
+
+
+class TestDagToBranching:
+    """Unit tests for the :func:`networkx.dag_to_branching` function."""
+
+    def test_single_root(self):
+        """Tests that a directed acyclic graph with a single degree
+        zero node produces an arborescence.
+
+        """
+        G = nx.DiGraph([(0, 1), (0, 2), (1, 3), (2, 3)])
+        B = nx.dag_to_branching(G)
+        expected = nx.DiGraph([(0, 1), (1, 3), (0, 2), (2, 4)])
+        assert nx.is_arborescence(B)
+        assert nx.is_isomorphic(B, expected)
+
+    def test_multiple_roots(self):
+        """Tests that a directed acyclic graph with multiple degree zero
+        nodes creates an arborescence with multiple (weakly) connected
+        components.
+
+        """
+        G = nx.DiGraph([(0, 1), (0, 2), (1, 3), (2, 3), (5, 2)])
+        B = nx.dag_to_branching(G)
+        expected = nx.DiGraph([(0, 1), (1, 3), (0, 2), (2, 4), (5, 6), (6, 7)])
+        assert nx.is_branching(B)
+        assert not nx.is_arborescence(B)
+        assert nx.is_isomorphic(B, expected)
+
+    # # Attributes are not copied by this function. If they were, this would
+    # # be a good test to uncomment.
+    # def test_copy_attributes(self):
+    #     """Tests that node attributes are copied in the branching."""
+    #     G = nx.DiGraph([(0, 1), (0, 2), (1, 3), (2, 3)])
+    #     for v in G:
+    #         G.node[v]['label'] = str(v)
+    #     B = nx.dag_to_branching(G)
+    #     # Determine the root node of the branching.
+    #     root = next(v for v, d in B.in_degree() if d == 0)
+    #     assert_equal(B.node[root]['label'], '0')
+    #     children = B[root]
+    #     # Get the left and right children, nodes 1 and 2, respectively.
+    #     left, right = sorted(children, key=lambda v: B.node[v]['label'])
+    #     assert_equal(B.node[left]['label'], '1')
+    #     assert_equal(B.node[right]['label'], '2')
+    #     # Get the left grandchild.
+    #     children = B[left]
+    #     assert_equal(len(children), 1)
+    #     left_grandchild = arbitrary_element(children)
+    #     assert_equal(B.node[left_grandchild]['label'], '3')
+    #     # Get the right grandchild.
+    #     children = B[right]
+    #     assert_equal(len(children), 1)
+    #     right_grandchild = arbitrary_element(children)
+    #     assert_equal(B.node[right_grandchild]['label'], '3')
+
+    def test_already_arborescence(self):
+        """Tests that a directed acyclic graph that is already an
+        arborescence produces an isomorphic arborescence as output.
+
+        """
+        A = nx.balanced_tree(2, 2, create_using=nx.DiGraph())
+        B = nx.dag_to_branching(A)
+        assert nx.is_isomorphic(A, B)
+
+    def test_already_branching(self):
+        """Tests that a directed acyclic graph that is already a
+        branching produces an isomorphic branching as output.
+
+        """
+        T1 = nx.balanced_tree(2, 2, create_using=nx.DiGraph())
+        T2 = nx.balanced_tree(2, 2, create_using=nx.DiGraph())
+        G = nx.disjoint_union(T1, T2)
+        B = nx.dag_to_branching(G)
+        assert nx.is_isomorphic(G, B)
+
+    def test_not_acyclic(self):
+        """Tests that a non-acyclic graph causes an exception."""
+        with pytest.raises(nx.HasACycle):
+            G = nx.DiGraph(pairwise("abc", cyclic=True))
+            nx.dag_to_branching(G)
+
+    def test_undirected(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.dag_to_branching(nx.Graph())
+
+    def test_multigraph(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.dag_to_branching(nx.MultiGraph())
+
+    def test_multidigraph(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.dag_to_branching(nx.MultiDiGraph())
+
+
+def test_ancestors_descendants_undirected():
+    """Regression test to ensure ancestors and descendants work as expected on
+    undirected graphs."""
+    G = nx.path_graph(5)
+    nx.ancestors(G, 2) == nx.descendants(G, 2) == {0, 1, 3, 4}
+
+
+def test_v_structures_raise():
+    G = nx.Graph()
+    with pytest.raises(nx.NetworkXNotImplemented, match="for undirected type"):
+        nx.dag.v_structures(G)
+
+
+@pytest.mark.parametrize(
+    ("edgelist", "expected"),
+    (
+        (
+            [(0, 1), (0, 2), (3, 2)],
+            {(0, 2, 3)},
+        ),
+        (
+            [("A", "B"), ("C", "B"), ("D", "G"), ("D", "E"), ("G", "E")],
+            {("A", "B", "C")},
+        ),
+        ([(0, 1), (2, 1), (0, 2)], set()),  # adjacent parents case: see gh-7385
+    ),
+)
+def test_v_structures(edgelist, expected):
+    G = nx.DiGraph(edgelist)
+    v_structs = set(nx.dag.v_structures(G))
+    assert v_structs == expected
+
+
+def test_colliders_raise():
+    G = nx.Graph()
+    with pytest.raises(nx.NetworkXNotImplemented, match="for undirected type"):
+        nx.dag.colliders(G)
+
+
+@pytest.mark.parametrize(
+    ("edgelist", "expected"),
+    (
+        (
+            [(0, 1), (0, 2), (3, 2)],
+            {(0, 2, 3)},
+        ),
+        (
+            [("A", "B"), ("C", "B"), ("D", "G"), ("D", "E"), ("G", "E")],
+            {("A", "B", "C"), ("D", "E", "G")},
+        ),
+    ),
+)
+def test_colliders(edgelist, expected):
+    G = nx.DiGraph(edgelist)
+    colliders = set(nx.dag.colliders(G))
+    assert colliders == expected
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_distance_measures.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_distance_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..1668fefdf4bb68985bc58de5658e149adeef32bd
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_distance_measures.py
@@ -0,0 +1,831 @@
+import itertools
+import math
+from random import Random
+
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.algorithms.distance_measures import _extrema_bounding
+
+
+def test__extrema_bounding_invalid_compute_kwarg():
+    G = nx.path_graph(3)
+    with pytest.raises(ValueError, match="compute must be one of"):
+        _extrema_bounding(G, compute="spam")
+
+
+class TestDistance:
+    def setup_method(self):
+        self.G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+
+    @pytest.mark.parametrize("seed", list(range(10)))
+    @pytest.mark.parametrize("n", list(range(10, 20)))
+    @pytest.mark.parametrize("prob", [x / 10 for x in range(0, 10, 2)])
+    def test_use_bounds_on_off_consistency(self, seed, n, prob):
+        """Test for consistency of distance metrics when using usebounds=True.
+
+        We validate consistency for `networkx.diameter`, `networkx.radius`, `networkx.periphery`
+        and `networkx.center` when passing `usebounds=True`. Expectation is that method
+        returns the same result whether we pass usebounds=True or not.
+
+        For this we generate random connected graphs and validate method returns the same.
+        """
+        metrics = [nx.diameter, nx.radius, nx.periphery, nx.center]
+        max_weight = [5, 10, 1000]
+        rng = Random(seed)
+        # we compose it with a random tree to ensure graph is connected
+        G = nx.compose(
+            nx.random_labeled_tree(n, seed=rng),
+            nx.erdos_renyi_graph(n, prob, seed=rng),
+        )
+        for metric in metrics:
+            # checking unweighted case
+            assert metric(G) == metric(G, usebounds=True)
+            for w in max_weight:
+                for u, v in G.edges():
+                    G[u][v]["w"] = rng.randint(0, w)
+                # checking weighted case
+                assert metric(G, weight="w") == metric(G, weight="w", usebounds=True)
+
+    def test_eccentricity(self):
+        assert nx.eccentricity(self.G, 1) == 6
+        e = nx.eccentricity(self.G)
+        assert e[1] == 6
+
+        sp = dict(nx.shortest_path_length(self.G))
+        e = nx.eccentricity(self.G, sp=sp)
+        assert e[1] == 6
+
+        e = nx.eccentricity(self.G, v=1)
+        assert e == 6
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1])
+        assert e[1] == 6
+        e = nx.eccentricity(self.G, v=[1, 2])
+        assert e[1] == 6
+
+        # test against graph with one node
+        G = nx.path_graph(1)
+        e = nx.eccentricity(G)
+        assert e[0] == 0
+        e = nx.eccentricity(G, v=0)
+        assert e == 0
+        pytest.raises(nx.NetworkXError, nx.eccentricity, G, 1)
+
+        # test against empty graph
+        G = nx.empty_graph()
+        e = nx.eccentricity(G)
+        assert e == {}
+
+    def test_diameter(self):
+        assert nx.diameter(self.G) == 6
+
+    def test_harmonic_diameter(self):
+        assert nx.harmonic_diameter(self.G) == pytest.approx(2.0477815699658715)
+        assert nx.harmonic_diameter(nx.star_graph(3)) == pytest.approx(1.333333)
+
+    def test_harmonic_diameter_empty(self):
+        assert math.isnan(nx.harmonic_diameter(nx.empty_graph()))
+
+    def test_harmonic_diameter_single_node(self):
+        assert math.isnan(nx.harmonic_diameter(nx.empty_graph(1)))
+
+    def test_harmonic_diameter_discrete(self):
+        assert math.isinf(nx.harmonic_diameter(nx.empty_graph(3)))
+
+    def test_harmonic_diameter_not_strongly_connected(self):
+        DG = nx.DiGraph()
+        DG.add_edge(0, 1)
+        assert nx.harmonic_diameter(DG) == 2
+
+    def test_harmonic_diameter_weighted_paths(self):
+        G = nx.star_graph(3)
+        # check defaults
+        G.add_weighted_edges_from([(*e, 1) for i, e in enumerate(G.edges)], "weight")
+        assert nx.harmonic_diameter(G) == pytest.approx(1.333333)
+        assert nx.harmonic_diameter(G, weight="weight") == pytest.approx(1.333333)
+
+        # check impact of weights and alternate weight name
+        G.add_weighted_edges_from([(*e, i) for i, e in enumerate(G.edges)], "dist")
+        assert nx.harmonic_diameter(G, weight="dist") == pytest.approx(1.8)
+
+    def test_radius(self):
+        assert nx.radius(self.G) == 4
+
+    def test_periphery(self):
+        assert set(nx.periphery(self.G)) == {1, 4, 13, 16}
+
+    def test_center_simple_tree(self):
+        G = nx.Graph([(1, 2), (1, 3), (2, 4), (2, 5)])
+        assert nx.center(G) == [1, 2]
+
+    @pytest.mark.parametrize("r", range(2, 5))
+    @pytest.mark.parametrize("h", range(1, 5))
+    def test_center_balanced_tree(self, r, h):
+        G = nx.balanced_tree(r, h)
+        assert nx.center(G) == [0]
+
+    def test_center(self):
+        assert set(nx.center(self.G)) == {6, 7, 10, 11}
+
+    @pytest.mark.parametrize("n", [1, 2, 99, 100])
+    def test_center_path_graphs(self, n):
+        G = nx.path_graph(n)
+        expected = {(n - 1) // 2, math.ceil((n - 1) / 2)}
+        assert set(nx.center(G)) == expected
+
+    def test_bound_diameter(self):
+        assert nx.diameter(self.G, usebounds=True) == 6
+
+    def test_bound_radius(self):
+        assert nx.radius(self.G, usebounds=True) == 4
+
+    def test_bound_periphery(self):
+        result = {1, 4, 13, 16}
+        assert set(nx.periphery(self.G, usebounds=True)) == result
+
+    def test_bound_center(self):
+        result = {6, 7, 10, 11}
+        assert set(nx.center(self.G, usebounds=True)) == result
+
+    def test_radius_exception(self):
+        G = nx.Graph()
+        G.add_edge(1, 2)
+        G.add_edge(3, 4)
+        pytest.raises(nx.NetworkXError, nx.diameter, G)
+
+    def test_eccentricity_infinite(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph([(1, 2), (3, 4)])
+            e = nx.eccentricity(G)
+
+    def test_eccentricity_undirected_not_connected(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph([(1, 2), (3, 4)])
+            e = nx.eccentricity(G, sp=1)
+
+    def test_eccentricity_directed_weakly_connected(self):
+        with pytest.raises(nx.NetworkXError):
+            DG = nx.DiGraph([(1, 2), (1, 3)])
+            nx.eccentricity(DG)
+
+
+class TestWeightedDistance:
+    def setup_method(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=0.6, cost=0.6, high_cost=6)
+        G.add_edge(0, 2, weight=0.2, cost=0.2, high_cost=2)
+        G.add_edge(2, 3, weight=0.1, cost=0.1, high_cost=1)
+        G.add_edge(2, 4, weight=0.7, cost=0.7, high_cost=7)
+        G.add_edge(2, 5, weight=0.9, cost=0.9, high_cost=9)
+        G.add_edge(1, 5, weight=0.3, cost=0.3, high_cost=3)
+        self.G = G
+        self.weight_fn = lambda v, u, e: 2
+
+    def test_eccentricity_weight_None(self):
+        assert nx.eccentricity(self.G, 1, weight=None) == 3
+        e = nx.eccentricity(self.G, weight=None)
+        assert e[1] == 3
+
+        e = nx.eccentricity(self.G, v=1, weight=None)
+        assert e == 3
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1], weight=None)
+        assert e[1] == 3
+        e = nx.eccentricity(self.G, v=[1, 2], weight=None)
+        assert e[1] == 3
+
+    def test_eccentricity_weight_attr(self):
+        assert nx.eccentricity(self.G, 1, weight="weight") == 1.5
+        e = nx.eccentricity(self.G, weight="weight")
+        assert (
+            e
+            == nx.eccentricity(self.G, weight="cost")
+            != nx.eccentricity(self.G, weight="high_cost")
+        )
+        assert e[1] == 1.5
+
+        e = nx.eccentricity(self.G, v=1, weight="weight")
+        assert e == 1.5
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1], weight="weight")
+        assert e[1] == 1.5
+        e = nx.eccentricity(self.G, v=[1, 2], weight="weight")
+        assert e[1] == 1.5
+
+    def test_eccentricity_weight_fn(self):
+        assert nx.eccentricity(self.G, 1, weight=self.weight_fn) == 6
+        e = nx.eccentricity(self.G, weight=self.weight_fn)
+        assert e[1] == 6
+
+        e = nx.eccentricity(self.G, v=1, weight=self.weight_fn)
+        assert e == 6
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1], weight=self.weight_fn)
+        assert e[1] == 6
+        e = nx.eccentricity(self.G, v=[1, 2], weight=self.weight_fn)
+        assert e[1] == 6
+
+    def test_diameter_weight_None(self):
+        assert nx.diameter(self.G, weight=None) == 3
+
+    def test_diameter_weight_attr(self):
+        assert (
+            nx.diameter(self.G, weight="weight")
+            == nx.diameter(self.G, weight="cost")
+            == 1.6
+            != nx.diameter(self.G, weight="high_cost")
+        )
+
+    def test_diameter_weight_fn(self):
+        assert nx.diameter(self.G, weight=self.weight_fn) == 6
+
+    def test_radius_weight_None(self):
+        assert pytest.approx(nx.radius(self.G, weight=None)) == 2
+
+    def test_radius_weight_attr(self):
+        assert (
+            pytest.approx(nx.radius(self.G, weight="weight"))
+            == pytest.approx(nx.radius(self.G, weight="cost"))
+            == 0.9
+            != nx.radius(self.G, weight="high_cost")
+        )
+
+    def test_radius_weight_fn(self):
+        assert nx.radius(self.G, weight=self.weight_fn) == 4
+
+    def test_periphery_weight_None(self):
+        for v in set(nx.periphery(self.G, weight=None)):
+            assert nx.eccentricity(self.G, v, weight=None) == nx.diameter(
+                self.G, weight=None
+            )
+
+    def test_periphery_weight_attr(self):
+        periphery = set(nx.periphery(self.G, weight="weight"))
+        assert (
+            periphery
+            == set(nx.periphery(self.G, weight="cost"))
+            == set(nx.periphery(self.G, weight="high_cost"))
+        )
+        for v in periphery:
+            assert (
+                nx.eccentricity(self.G, v, weight="high_cost")
+                != nx.eccentricity(self.G, v, weight="weight")
+                == nx.eccentricity(self.G, v, weight="cost")
+                == nx.diameter(self.G, weight="weight")
+                == nx.diameter(self.G, weight="cost")
+                != nx.diameter(self.G, weight="high_cost")
+            )
+            assert nx.eccentricity(self.G, v, weight="high_cost") == nx.diameter(
+                self.G, weight="high_cost"
+            )
+
+    def test_periphery_weight_fn(self):
+        for v in set(nx.periphery(self.G, weight=self.weight_fn)):
+            assert nx.eccentricity(self.G, v, weight=self.weight_fn) == nx.diameter(
+                self.G, weight=self.weight_fn
+            )
+
+    def test_center_weight_None(self):
+        for v in set(nx.center(self.G, weight=None)):
+            assert pytest.approx(nx.eccentricity(self.G, v, weight=None)) == nx.radius(
+                self.G, weight=None
+            )
+
+    def test_center_weight_attr(self):
+        center = set(nx.center(self.G, weight="weight"))
+        assert (
+            center
+            == set(nx.center(self.G, weight="cost"))
+            != set(nx.center(self.G, weight="high_cost"))
+        )
+        for v in center:
+            assert (
+                nx.eccentricity(self.G, v, weight="high_cost")
+                != pytest.approx(nx.eccentricity(self.G, v, weight="weight"))
+                == pytest.approx(nx.eccentricity(self.G, v, weight="cost"))
+                == nx.radius(self.G, weight="weight")
+                == nx.radius(self.G, weight="cost")
+                != nx.radius(self.G, weight="high_cost")
+            )
+            assert nx.eccentricity(self.G, v, weight="high_cost") == nx.radius(
+                self.G, weight="high_cost"
+            )
+
+    def test_center_weight_fn(self):
+        for v in set(nx.center(self.G, weight=self.weight_fn)):
+            assert nx.eccentricity(self.G, v, weight=self.weight_fn) == nx.radius(
+                self.G, weight=self.weight_fn
+            )
+
+    def test_bound_diameter_weight_None(self):
+        assert nx.diameter(self.G, usebounds=True, weight=None) == 3
+
+    def test_bound_diameter_weight_attr(self):
+        assert (
+            nx.diameter(self.G, usebounds=True, weight="high_cost")
+            != nx.diameter(self.G, usebounds=True, weight="weight")
+            == nx.diameter(self.G, usebounds=True, weight="cost")
+            == 1.6
+            != nx.diameter(self.G, usebounds=True, weight="high_cost")
+        )
+        assert nx.diameter(self.G, usebounds=True, weight="high_cost") == nx.diameter(
+            self.G, usebounds=True, weight="high_cost"
+        )
+
+    def test_bound_diameter_weight_fn(self):
+        assert nx.diameter(self.G, usebounds=True, weight=self.weight_fn) == 6
+
+    def test_bound_radius_weight_None(self):
+        assert pytest.approx(nx.radius(self.G, usebounds=True, weight=None)) == 2
+
+    def test_bound_radius_weight_attr(self):
+        assert (
+            nx.radius(self.G, usebounds=True, weight="high_cost")
+            != pytest.approx(nx.radius(self.G, usebounds=True, weight="weight"))
+            == pytest.approx(nx.radius(self.G, usebounds=True, weight="cost"))
+            == 0.9
+            != nx.radius(self.G, usebounds=True, weight="high_cost")
+        )
+        assert nx.radius(self.G, usebounds=True, weight="high_cost") == nx.radius(
+            self.G, usebounds=True, weight="high_cost"
+        )
+
+    def test_bound_radius_weight_fn(self):
+        assert nx.radius(self.G, usebounds=True, weight=self.weight_fn) == 4
+
+    def test_bound_periphery_weight_None(self):
+        result = {1, 3, 4}
+        assert set(nx.periphery(self.G, usebounds=True, weight=None)) == result
+
+    def test_bound_periphery_weight_attr(self):
+        result = {4, 5}
+        assert (
+            set(nx.periphery(self.G, usebounds=True, weight="weight"))
+            == set(nx.periphery(self.G, usebounds=True, weight="cost"))
+            == result
+        )
+
+    def test_bound_periphery_weight_fn(self):
+        result = {1, 3, 4}
+        assert (
+            set(nx.periphery(self.G, usebounds=True, weight=self.weight_fn)) == result
+        )
+
+    def test_bound_center_weight_None(self):
+        result = {0, 2, 5}
+        assert set(nx.center(self.G, usebounds=True, weight=None)) == result
+
+    def test_bound_center_weight_attr(self):
+        result = {0}
+        assert (
+            set(nx.center(self.G, usebounds=True, weight="weight"))
+            == set(nx.center(self.G, usebounds=True, weight="cost"))
+            == result
+        )
+
+    def test_bound_center_weight_fn(self):
+        result = {0, 2, 5}
+        assert set(nx.center(self.G, usebounds=True, weight=self.weight_fn)) == result
+
+
+class TestResistanceDistance:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def setup_method(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(2, 3, weight=4)
+        G.add_edge(3, 4, weight=1)
+        G.add_edge(1, 4, weight=3)
+        self.G = G
+
+    def test_resistance_distance_directed_graph(self):
+        G = nx.DiGraph()
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.resistance_distance(G)
+
+    def test_resistance_distance_empty(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.resistance_distance(G)
+
+    def test_resistance_distance_not_connected(self):
+        with pytest.raises(nx.NetworkXError):
+            self.G.add_node(5)
+            nx.resistance_distance(self.G, 1, 5)
+
+    def test_resistance_distance_nodeA_not_in_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.resistance_distance(self.G, 9, 1)
+
+    def test_resistance_distance_nodeB_not_in_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.resistance_distance(self.G, 1, 9)
+
+    def test_resistance_distance(self):
+        rd = nx.resistance_distance(self.G, 1, 3, "weight", True)
+        test_data = 1 / (1 / (2 + 4) + 1 / (1 + 3))
+        assert round(rd, 5) == round(test_data, 5)
+
+    def test_resistance_distance_noinv(self):
+        rd = nx.resistance_distance(self.G, 1, 3, "weight", False)
+        test_data = 1 / (1 / (1 / 2 + 1 / 4) + 1 / (1 / 1 + 1 / 3))
+        assert round(rd, 5) == round(test_data, 5)
+
+    def test_resistance_distance_no_weight(self):
+        rd = nx.resistance_distance(self.G, 1, 3)
+        assert round(rd, 5) == 1
+
+    def test_resistance_distance_neg_weight(self):
+        self.G[2][3]["weight"] = -4
+        rd = nx.resistance_distance(self.G, 1, 3, "weight", True)
+        test_data = 1 / (1 / (2 + -4) + 1 / (1 + 3))
+        assert round(rd, 5) == round(test_data, 5)
+
+    def test_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(2, 3, weight=4)
+        G.add_edge(3, 4, weight=1)
+        G.add_edge(1, 4, weight=3)
+        rd = nx.resistance_distance(G, 1, 3, "weight", True)
+        assert np.isclose(rd, 1 / (1 / (2 + 4) + 1 / (1 + 3)))
+
+    def test_resistance_distance_div0(self):
+        with pytest.raises(ZeroDivisionError):
+            self.G[1][2]["weight"] = 0
+            nx.resistance_distance(self.G, 1, 3, "weight")
+
+    def test_resistance_distance_same_node(self):
+        assert nx.resistance_distance(self.G, 1, 1) == 0
+
+    def test_resistance_distance_only_nodeA(self):
+        rd = nx.resistance_distance(self.G, nodeA=1)
+        test_data = {}
+        test_data[1] = 0
+        test_data[2] = 0.75
+        test_data[3] = 1
+        test_data[4] = 0.75
+        assert isinstance(rd, dict)
+        assert sorted(rd.keys()) == sorted(test_data.keys())
+        for key in rd:
+            assert np.isclose(rd[key], test_data[key])
+
+    def test_resistance_distance_only_nodeB(self):
+        rd = nx.resistance_distance(self.G, nodeB=1)
+        test_data = {}
+        test_data[1] = 0
+        test_data[2] = 0.75
+        test_data[3] = 1
+        test_data[4] = 0.75
+        assert isinstance(rd, dict)
+        assert sorted(rd.keys()) == sorted(test_data.keys())
+        for key in rd:
+            assert np.isclose(rd[key], test_data[key])
+
+    def test_resistance_distance_all(self):
+        rd = nx.resistance_distance(self.G)
+        assert isinstance(rd, dict)
+        assert round(rd[1][3], 5) == 1
+
+
+class TestEffectiveGraphResistance:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def setup_method(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=1)
+        G.add_edge(2, 3, weight=4)
+        self.G = G
+
+    def test_effective_graph_resistance_directed_graph(self):
+        G = nx.DiGraph()
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.effective_graph_resistance(G)
+
+    def test_effective_graph_resistance_empty(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.effective_graph_resistance(G)
+
+    def test_effective_graph_resistance_not_connected(self):
+        G = nx.Graph([(1, 2), (3, 4)])
+        RG = nx.effective_graph_resistance(G)
+        assert np.isinf(RG)
+
+    def test_effective_graph_resistance(self):
+        RG = nx.effective_graph_resistance(self.G, "weight", True)
+        rd12 = 1 / (1 / (1 + 4) + 1 / 2)
+        rd13 = 1 / (1 / (1 + 2) + 1 / 4)
+        rd23 = 1 / (1 / (2 + 4) + 1 / 1)
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_noinv(self):
+        RG = nx.effective_graph_resistance(self.G, "weight", False)
+        rd12 = 1 / (1 / (1 / 1 + 1 / 4) + 1 / (1 / 2))
+        rd13 = 1 / (1 / (1 / 1 + 1 / 2) + 1 / (1 / 4))
+        rd23 = 1 / (1 / (1 / 2 + 1 / 4) + 1 / (1 / 1))
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_no_weight(self):
+        RG = nx.effective_graph_resistance(self.G)
+        assert np.isclose(RG, 2)
+
+    def test_effective_graph_resistance_neg_weight(self):
+        self.G[2][3]["weight"] = -4
+        RG = nx.effective_graph_resistance(self.G, "weight", True)
+        rd12 = 1 / (1 / (1 + -4) + 1 / 2)
+        rd13 = 1 / (1 / (1 + 2) + 1 / (-4))
+        rd23 = 1 / (1 / (2 + -4) + 1 / 1)
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=1)
+        G.add_edge(2, 3, weight=1)
+        G.add_edge(2, 3, weight=3)
+        RG = nx.effective_graph_resistance(G, "weight", True)
+        edge23 = 1 / (1 / 1 + 1 / 3)
+        rd12 = 1 / (1 / (1 + edge23) + 1 / 2)
+        rd13 = 1 / (1 / (1 + 2) + 1 / edge23)
+        rd23 = 1 / (1 / (2 + edge23) + 1 / 1)
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_div0(self):
+        with pytest.raises(ZeroDivisionError):
+            self.G[1][2]["weight"] = 0
+            nx.effective_graph_resistance(self.G, "weight")
+
+    def test_effective_graph_resistance_complete_graph(self):
+        N = 10
+        G = nx.complete_graph(N)
+        RG = nx.effective_graph_resistance(G)
+        assert np.isclose(RG, N - 1)
+
+    def test_effective_graph_resistance_path_graph(self):
+        N = 10
+        G = nx.path_graph(N)
+        RG = nx.effective_graph_resistance(G)
+        assert np.isclose(RG, (N - 1) * N * (N + 1) // 6)
+
+
+class TestBarycenter:
+    """Test :func:`networkx.algorithms.distance_measures.barycenter`."""
+
+    def barycenter_as_subgraph(self, g, **kwargs):
+        """Return the subgraph induced on the barycenter of g"""
+        b = nx.barycenter(g, **kwargs)
+        assert isinstance(b, list)
+        assert set(b) <= set(g)
+        return g.subgraph(b)
+
+    def test_must_be_connected(self):
+        pytest.raises(nx.NetworkXNoPath, nx.barycenter, nx.empty_graph(5))
+
+    def test_sp_kwarg(self):
+        # Complete graph K_5. Normally it works...
+        K_5 = nx.complete_graph(5)
+        sp = dict(nx.shortest_path_length(K_5))
+        assert nx.barycenter(K_5, sp=sp) == list(K_5)
+
+        # ...but not with the weight argument
+        for u, v, data in K_5.edges.data():
+            data["weight"] = 1
+        pytest.raises(ValueError, nx.barycenter, K_5, sp=sp, weight="weight")
+
+        # ...and a corrupted sp can make it seem like K_5 is disconnected
+        del sp[0][1]
+        pytest.raises(nx.NetworkXNoPath, nx.barycenter, K_5, sp=sp)
+
+    def test_trees(self):
+        """The barycenter of a tree is a single vertex or an edge.
+
+        See [West01]_, p. 78.
+        """
+        prng = Random(0xDEADBEEF)
+        for i in range(50):
+            RT = nx.random_labeled_tree(prng.randint(1, 75), seed=prng)
+            b = self.barycenter_as_subgraph(RT)
+            if len(b) == 2:
+                assert b.size() == 1
+            else:
+                assert len(b) == 1
+                assert b.size() == 0
+
+    def test_this_one_specific_tree(self):
+        """Test the tree pictured at the bottom of [West01]_, p. 78."""
+        g = nx.Graph(
+            {
+                "a": ["b"],
+                "b": ["a", "x"],
+                "x": ["b", "y"],
+                "y": ["x", "z"],
+                "z": ["y", 0, 1, 2, 3, 4],
+                0: ["z"],
+                1: ["z"],
+                2: ["z"],
+                3: ["z"],
+                4: ["z"],
+            }
+        )
+        b = self.barycenter_as_subgraph(g, attr="barycentricity")
+        assert list(b) == ["z"]
+        assert not b.edges
+        expected_barycentricity = {
+            0: 23,
+            1: 23,
+            2: 23,
+            3: 23,
+            4: 23,
+            "a": 35,
+            "b": 27,
+            "x": 21,
+            "y": 17,
+            "z": 15,
+        }
+        for node, barycentricity in expected_barycentricity.items():
+            assert g.nodes[node]["barycentricity"] == barycentricity
+
+        # Doubling weights should do nothing but double the barycentricities
+        for edge in g.edges:
+            g.edges[edge]["weight"] = 2
+        b = self.barycenter_as_subgraph(g, weight="weight", attr="barycentricity2")
+        assert list(b) == ["z"]
+        assert not b.edges
+        for node, barycentricity in expected_barycentricity.items():
+            assert g.nodes[node]["barycentricity2"] == barycentricity * 2
+
+
+class TestKemenyConstant:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def setup_method(self):
+        G = nx.Graph()
+        w12 = 2
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        self.G = G
+
+    def test_kemeny_constant_directed(self):
+        G = nx.DiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(1, 3)
+        G.add_edge(2, 3)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.kemeny_constant(G)
+
+    def test_kemeny_constant_not_connected(self):
+        self.G.add_node(5)
+        with pytest.raises(nx.NetworkXError):
+            nx.kemeny_constant(self.G)
+
+    def test_kemeny_constant_no_nodes(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.kemeny_constant(G)
+
+    def test_kemeny_constant_negative_weight(self):
+        G = nx.Graph()
+        w12 = 2
+        w13 = 3
+        w23 = -10
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        with pytest.raises(nx.NetworkXError):
+            nx.kemeny_constant(G, weight="weight")
+
+    def test_kemeny_constant(self):
+        K = nx.kemeny_constant(self.G, weight="weight")
+        w12 = 2
+        w13 = 3
+        w23 = 4
+        test_data = (
+            3
+            / 2
+            * (w12 + w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                w12**2 * (w13 + w23)
+                + w13**2 * (w12 + w23)
+                + w23**2 * (w12 + w13)
+                + 3 * w12 * w13 * w23
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_no_weight(self):
+        K = nx.kemeny_constant(self.G)
+        assert np.isclose(K, 4 / 3)
+
+    def test_kemeny_constant_multigraph(self):
+        G = nx.MultiGraph()
+        w12_1 = 2
+        w12_2 = 1
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 2, weight=w12_1)
+        G.add_edge(1, 2, weight=w12_2)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        K = nx.kemeny_constant(G, weight="weight")
+        w12 = w12_1 + w12_2
+        test_data = (
+            3
+            / 2
+            * (w12 + w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                w12**2 * (w13 + w23)
+                + w13**2 * (w12 + w23)
+                + w23**2 * (w12 + w13)
+                + 3 * w12 * w13 * w23
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_weight0(self):
+        G = nx.Graph()
+        w12 = 0
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        K = nx.kemeny_constant(G, weight="weight")
+        test_data = (
+            3
+            / 2
+            * (w12 + w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                w12**2 * (w13 + w23)
+                + w13**2 * (w12 + w23)
+                + w23**2 * (w12 + w13)
+                + 3 * w12 * w13 * w23
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_selfloop(self):
+        G = nx.Graph()
+        w11 = 1
+        w12 = 2
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 1, weight=w11)
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        K = nx.kemeny_constant(G, weight="weight")
+        test_data = (
+            (2 * w11 + 3 * w12 + 3 * w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                (w12 * w13 + w12 * w23 + w13 * w23)
+                * (w11 + 2 * w12 + 2 * w13 + 2 * w23)
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_complete_bipartite_graph(self):
+        # Theorem 1 in https://www.sciencedirect.com/science/article/pii/S0166218X20302912
+        n1 = 5
+        n2 = 4
+        G = nx.complete_bipartite_graph(n1, n2)
+        K = nx.kemeny_constant(G)
+        assert np.isclose(K, n1 + n2 - 3 / 2)
+
+    def test_kemeny_constant_path_graph(self):
+        # Theorem 2 in https://www.sciencedirect.com/science/article/pii/S0166218X20302912
+        n = 10
+        G = nx.path_graph(n)
+        K = nx.kemeny_constant(G)
+        assert np.isclose(K, n**2 / 3 - 2 * n / 3 + 1 / 2)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_distance_regular.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_distance_regular.py
new file mode 100644
index 0000000000000000000000000000000000000000..545fb6dee6a915230971cf4b5a141e47adc2cc15
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_distance_regular.py
@@ -0,0 +1,85 @@
+import pytest
+
+import networkx as nx
+from networkx import is_strongly_regular
+
+
+@pytest.mark.parametrize(
+    "f", (nx.is_distance_regular, nx.intersection_array, nx.is_strongly_regular)
+)
+@pytest.mark.parametrize("graph_constructor", (nx.DiGraph, nx.MultiGraph))
+def test_raises_on_directed_and_multigraphs(f, graph_constructor):
+    G = graph_constructor([(0, 1), (1, 2)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        f(G)
+
+
+class TestDistanceRegular:
+    def test_is_distance_regular(self):
+        assert nx.is_distance_regular(nx.icosahedral_graph())
+        assert nx.is_distance_regular(nx.petersen_graph())
+        assert nx.is_distance_regular(nx.cubical_graph())
+        assert nx.is_distance_regular(nx.complete_bipartite_graph(3, 3))
+        assert nx.is_distance_regular(nx.tetrahedral_graph())
+        assert nx.is_distance_regular(nx.dodecahedral_graph())
+        assert nx.is_distance_regular(nx.pappus_graph())
+        assert nx.is_distance_regular(nx.heawood_graph())
+        assert nx.is_distance_regular(nx.cycle_graph(3))
+        # no distance regular
+        assert not nx.is_distance_regular(nx.path_graph(4))
+
+    def test_not_connected(self):
+        G = nx.cycle_graph(4)
+        nx.add_cycle(G, [5, 6, 7])
+        assert not nx.is_distance_regular(G)
+
+    def test_global_parameters(self):
+        b, c = nx.intersection_array(nx.cycle_graph(5))
+        g = nx.global_parameters(b, c)
+        assert list(g) == [(0, 0, 2), (1, 0, 1), (1, 1, 0)]
+        b, c = nx.intersection_array(nx.cycle_graph(3))
+        g = nx.global_parameters(b, c)
+        assert list(g) == [(0, 0, 2), (1, 1, 0)]
+
+    def test_intersection_array(self):
+        b, c = nx.intersection_array(nx.cycle_graph(5))
+        assert b == [2, 1]
+        assert c == [1, 1]
+        b, c = nx.intersection_array(nx.dodecahedral_graph())
+        assert b == [3, 2, 1, 1, 1]
+        assert c == [1, 1, 1, 2, 3]
+        b, c = nx.intersection_array(nx.icosahedral_graph())
+        assert b == [5, 2, 1]
+        assert c == [1, 2, 5]
+
+
+@pytest.mark.parametrize("f", (nx.is_distance_regular, nx.is_strongly_regular))
+def test_empty_graph_raises(f):
+    G = nx.Graph()
+    with pytest.raises(nx.NetworkXPointlessConcept, match="Graph has no nodes"):
+        f(G)
+
+
+class TestStronglyRegular:
+    """Unit tests for the :func:`~networkx.is_strongly_regular`
+    function.
+
+    """
+
+    def test_cycle_graph(self):
+        """Tests that the cycle graph on five vertices is strongly
+        regular.
+
+        """
+        G = nx.cycle_graph(5)
+        assert is_strongly_regular(G)
+
+    def test_petersen_graph(self):
+        """Tests that the Petersen graph is strongly regular."""
+        G = nx.petersen_graph()
+        assert is_strongly_regular(G)
+
+    def test_path_graph(self):
+        """Tests that the path graph is not strongly regular."""
+        G = nx.path_graph(4)
+        assert not is_strongly_regular(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_dominance.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_dominance.py
new file mode 100644
index 0000000000000000000000000000000000000000..480b3a5c841af11b807ba533ae9f809eb1e52672
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_dominance.py
@@ -0,0 +1,299 @@
+import pytest
+
+import networkx as nx
+
+
+class TestImmediateDominators:
+    @pytest.mark.parametrize("G", [nx.Graph(), nx.MultiGraph()])
+    def test_raises_undirected(self, G):
+        """Check that `immediate_dominators` raises for undirected graphs."""
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match=r"not implemented for undirected"
+        ):
+            nx.immediate_dominators(G, 0)
+
+    def test_raises_node(self):
+        """Check that `immediate_dominators` raises when `start` is not in the graph."""
+        G = nx.empty_graph(1, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError, match=r"not in G"):
+            nx.immediate_dominators(G, 1)
+
+    def test_singleton(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+        assert nx.immediate_dominators(G, 0) == {}
+        G.add_edge(0, 0)
+        assert nx.immediate_dominators(G, 0) == {}
+
+    @pytest.mark.parametrize("gen", [nx.path_graph, nx.cycle_graph])
+    @pytest.mark.parametrize("n", [5, 10, 20])
+    def test_path_and_cycle(self, gen, n):
+        """Check `immediate_dominators` is correct for path and cycle graphs."""
+        G = gen(n, create_using=nx.DiGraph())
+        idom = nx.immediate_dominators(G, 0)
+        assert idom == {i: i - 1 for i in range(1, n)}
+
+    def test_unreachable(self):
+        n = 5
+        G = nx.path_graph(n, create_using=nx.DiGraph())
+        idom = nx.immediate_dominators(G, 1)
+        assert idom == {i: i - 1 for i in range(2, n)}
+
+    @pytest.mark.parametrize(
+        ["edgelist", "start"],
+        [
+            ([(1, 2), (2, 1), (3, 2), (4, 1), (5, 3), (5, 4)], 5),
+            (
+                [
+                    (1, 2),
+                    (2, 1),
+                    (2, 3),
+                    (3, 2),
+                    (4, 2),
+                    (4, 3),
+                    (5, 1),
+                    (6, 4),
+                    (6, 5),
+                ],
+                6,
+            ),
+        ],
+    )
+    def test_irreducible(self, edgelist, start):
+        """
+        Check `immediate_dominators` on irreducible reference graphs.
+
+        Graphs taken from figures 2 and 4 of "A simple, fast dominance algorithm." (2006).
+        https://hdl.handle.net/1911/96345
+        """
+        G = nx.DiGraph(edgelist)
+        idom = nx.immediate_dominators(G, start)
+        assert idom == dict.fromkeys(range(1, start), start)
+
+    def test_domrel_png(self):
+        # Graph taken from https://commons.wikipedia.org/wiki/File:Domrel.png
+        edges = [(1, 2), (2, 3), (2, 4), (2, 6), (3, 5), (4, 5), (5, 2)]
+        G = nx.DiGraph(edges)
+        result = nx.immediate_dominators(G, 1)
+        assert result == {2: 1, 3: 2, 4: 2, 5: 2, 6: 2}
+        # Test postdominance.
+        result = nx.immediate_dominators(G.reverse(copy=False), 6)
+        assert result == {1: 2, 2: 6, 3: 5, 4: 5, 5: 2}
+
+    def test_boost_example(self):
+        # Graph taken from Figure 1 of
+        # http://www.boost.org/doc/libs/1_56_0/libs/graph/doc/lengauer_tarjan_dominator.htm
+        edges = [(0, 1), (1, 2), (1, 3), (2, 7), (3, 4), (4, 5), (4, 6), (5, 7), (6, 4)]
+        G = nx.DiGraph(edges)
+        result = nx.immediate_dominators(G, 0)
+        assert result == {1: 0, 2: 1, 3: 1, 4: 3, 5: 4, 6: 4, 7: 1}
+        # Test postdominance.
+        result = nx.immediate_dominators(G.reverse(copy=False), 7)
+        assert result == {0: 1, 1: 7, 2: 7, 3: 4, 4: 5, 5: 7, 6: 4}
+
+
+class TestDominanceFrontiers:
+    @pytest.mark.parametrize("G", [nx.Graph(), nx.MultiGraph()])
+    def test_raises_undirected(self, G):
+        """Check that `dominance_frontiers` raises for undirected graphs."""
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match=r"not implemented for undirected"
+        ):
+            nx.dominance_frontiers(G, 0)
+
+    def test_raises_node(self):
+        """Check that `dominance_frontiers` raises when `start` is not in the graph."""
+        G = nx.empty_graph(1, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError, match=r"not in G"):
+            nx.dominance_frontiers(G, 1)
+
+    def test_singleton(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+        assert nx.dominance_frontiers(G, 0) == {0: set()}
+        G.add_edge(0, 0)
+        assert nx.dominance_frontiers(G, 0) == {0: {0}}
+
+    @pytest.mark.parametrize("gen, df", [(nx.path_graph, set()), (nx.cycle_graph, {0})])
+    @pytest.mark.parametrize("n", [5, 10, 20])
+    def test_path_and_cycle(self, gen, df, n):
+        """Check that `dominance_frontiers` is correct for path and cycle graphs."""
+        G = gen(n, create_using=nx.DiGraph())
+        assert nx.dominance_frontiers(G, 0) == dict.fromkeys(range(n), df)
+
+    def test_unreachable(self):
+        n = 5
+        G = nx.path_graph(n, create_using=nx.DiGraph())
+        assert nx.dominance_frontiers(G, 1) == dict.fromkeys(range(1, n), set())
+
+    def test_irreducible1(self):
+        """
+        Graph taken from figure 2 of "A simple, fast dominance algorithm." (2006).
+        https://hdl.handle.net/1911/96345
+        """
+        edges = [(1, 2), (2, 1), (3, 2), (4, 1), (5, 3), (5, 4)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 5) == {
+            1: {2},
+            2: {1},
+            3: {2},
+            4: {1},
+            5: set(),
+        }
+
+    def test_irreducible2(self):
+        """
+        Graph taken from figure 4 of "A simple, fast dominance algorithm." (2006).
+        https://hdl.handle.net/1911/96345
+        """
+        edges = [(1, 2), (2, 1), (2, 3), (3, 2), (4, 2), (4, 3), (5, 1), (6, 4), (6, 5)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 6) == {
+            1: {2},
+            2: {1, 3},
+            3: {2},
+            4: {2, 3},
+            5: {1},
+            6: set(),
+        }
+
+    def test_domrel_png(self):
+        # Graph taken from https://commons.wikipedia.org/wiki/File:Domrel.png
+        edges = [(1, 2), (2, 3), (2, 4), (2, 6), (3, 5), (4, 5), (5, 2)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 1) == {
+            1: set(),
+            2: {2},
+            3: {5},
+            4: {5},
+            5: {2},
+            6: set(),
+        }
+        # Test postdominance.
+        result = nx.dominance_frontiers(G.reverse(copy=False), 6)
+        assert result == {1: set(), 2: {2}, 3: {2}, 4: {2}, 5: {2}, 6: set()}
+
+    def test_boost_example(self):
+        # Graph taken from Figure 1 of
+        # http://www.boost.org/doc/libs/1_56_0/libs/graph/doc/lengauer_tarjan_dominator.htm
+        edges = [(0, 1), (1, 2), (1, 3), (2, 7), (3, 4), (4, 5), (4, 6), (5, 7), (6, 4)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 0) == {
+            0: set(),
+            1: set(),
+            2: {7},
+            3: {7},
+            4: {4, 7},
+            5: {7},
+            6: {4},
+            7: set(),
+        }
+        # Test postdominance.
+        result = nx.dominance_frontiers(G.reverse(copy=False), 7)
+        expected = {
+            0: set(),
+            1: set(),
+            2: {1},
+            3: {1},
+            4: {1, 4},
+            5: {1},
+            6: {4},
+            7: set(),
+        }
+        assert result == expected
+
+    def test_discard_issue(self):
+        # https://github.com/networkx/networkx/issues/2071
+        g = nx.DiGraph()
+        g.add_edges_from(
+            [
+                ("b0", "b1"),
+                ("b1", "b2"),
+                ("b2", "b3"),
+                ("b3", "b1"),
+                ("b1", "b5"),
+                ("b5", "b6"),
+                ("b5", "b8"),
+                ("b6", "b7"),
+                ("b8", "b7"),
+                ("b7", "b3"),
+                ("b3", "b4"),
+            ]
+        )
+        df = nx.dominance_frontiers(g, "b0")
+        assert df == {
+            "b4": set(),
+            "b5": {"b3"},
+            "b6": {"b7"},
+            "b7": {"b3"},
+            "b0": set(),
+            "b1": {"b1"},
+            "b2": {"b3"},
+            "b3": {"b1"},
+            "b8": {"b7"},
+        }
+
+    def test_loop(self):
+        g = nx.DiGraph()
+        g.add_edges_from([("a", "b"), ("b", "c"), ("b", "a")])
+        df = nx.dominance_frontiers(g, "a")
+        assert df == {"a": {"a"}, "b": {"a"}, "c": set()}
+
+    def test_missing_immediate_doms(self):
+        # see https://github.com/networkx/networkx/issues/2070
+        g = nx.DiGraph()
+        edges = [
+            ("entry_1", "b1"),
+            ("b1", "b2"),
+            ("b2", "b3"),
+            ("b3", "exit"),
+            ("entry_2", "b3"),
+        ]
+
+        # entry_1
+        #   |
+        #   b1
+        #   |
+        #   b2  entry_2
+        #    |  /
+        #    b3
+        #    |
+        #   exit
+
+        g.add_edges_from(edges)
+        # formerly raised KeyError on entry_2 when parsing b3
+        # because entry_2 does not have immediate doms (no path)
+        nx.dominance_frontiers(g, "entry_1")
+
+    def test_loops_larger(self):
+        # from
+        # http://ecee.colorado.edu/~waite/Darmstadt/motion.html
+        g = nx.DiGraph()
+        edges = [
+            ("entry", "exit"),
+            ("entry", "1"),
+            ("1", "2"),
+            ("2", "3"),
+            ("3", "4"),
+            ("4", "5"),
+            ("5", "6"),
+            ("6", "exit"),
+            ("6", "2"),
+            ("5", "3"),
+            ("4", "4"),
+        ]
+
+        g.add_edges_from(edges)
+        df = nx.dominance_frontiers(g, "entry")
+        answer = {
+            "entry": set(),
+            "1": {"exit"},
+            "2": {"exit", "2"},
+            "3": {"exit", "3", "2"},
+            "4": {"exit", "4", "3", "2"},
+            "5": {"exit", "3", "2"},
+            "6": {"exit", "2"},
+            "exit": set(),
+        }
+        for n in df:
+            assert set(df[n]) == set(answer[n])
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_dominating.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_dominating.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f51777c72c7d4b9cc22e77a6aa6f470200b66a7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_dominating.py
@@ -0,0 +1,115 @@
+import pytest
+
+import networkx as nx
+
+
+def test_dominating_set():
+    G = nx.gnp_random_graph(100, 0.1)
+    D = nx.dominating_set(G)
+    assert nx.is_dominating_set(G, D)
+    D = nx.dominating_set(G, start_with=0)
+    assert nx.is_dominating_set(G, D)
+
+
+def test_complete():
+    """In complete graphs each node is a dominating set.
+    Thus the dominating set has to be of cardinality 1.
+    """
+    K4 = nx.complete_graph(4)
+    assert len(nx.dominating_set(K4)) == 1
+    K5 = nx.complete_graph(5)
+    assert len(nx.dominating_set(K5)) == 1
+
+
+def test_raise_dominating_set():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.path_graph(4)
+        D = nx.dominating_set(G, start_with=10)
+
+
+def test_is_dominating_set():
+    G = nx.path_graph(4)
+    d = {1, 3}
+    assert nx.is_dominating_set(G, d)
+    d = {0, 2}
+    assert nx.is_dominating_set(G, d)
+    d = {1}
+    assert not nx.is_dominating_set(G, d)
+
+
+def test_wikipedia_is_dominating_set():
+    """Example from https://en.wikipedia.org/wiki/Dominating_set"""
+    G = nx.cycle_graph(4)
+    G.add_edges_from([(0, 4), (1, 4), (2, 5)])
+    assert nx.is_dominating_set(G, {4, 3, 5})
+    assert nx.is_dominating_set(G, {0, 2})
+    assert nx.is_dominating_set(G, {1, 2})
+
+
+def test_is_connected_dominating_set():
+    G = nx.path_graph(4)
+    D = {1, 2}
+    assert nx.is_connected_dominating_set(G, D)
+    D = {1, 3}
+    assert not nx.is_connected_dominating_set(G, D)
+    D = {2, 3}
+    assert nx.is_connected(nx.subgraph(G, D))
+    assert not nx.is_connected_dominating_set(G, D)
+
+
+def test_null_graph_connected_dominating_set():
+    G = nx.Graph()
+    assert 0 == len(nx.connected_dominating_set(G))
+
+
+def test_single_node_graph_connected_dominating_set():
+    G = nx.Graph()
+    G.add_node(1)
+    CD = nx.connected_dominating_set(G)
+    assert nx.is_connected_dominating_set(G, CD)
+
+
+def test_raise_disconnected_graph_connected_dominating_set():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.Graph()
+        G.add_node(1)
+        G.add_node(2)
+        nx.connected_dominating_set(G)
+
+
+def test_complete_graph_connected_dominating_set():
+    K5 = nx.complete_graph(5)
+    assert 1 == len(nx.connected_dominating_set(K5))
+    K7 = nx.complete_graph(7)
+    assert 1 == len(nx.connected_dominating_set(K7))
+
+
+def test_docstring_example_connected_dominating_set():
+    G = nx.Graph(
+        [
+            (1, 2),
+            (1, 3),
+            (1, 4),
+            (1, 5),
+            (1, 6),
+            (2, 7),
+            (3, 8),
+            (4, 9),
+            (5, 10),
+            (6, 11),
+            (7, 12),
+            (8, 12),
+            (9, 12),
+            (10, 12),
+            (11, 12),
+        ]
+    )
+    assert {1, 2, 3, 4, 5, 6, 7} == nx.connected_dominating_set(G)
+
+
+@pytest.mark.parametrize("seed", [1, 13, 29])
+@pytest.mark.parametrize("n,k,p", [(10, 3, 0.2), (100, 10, 0.7), (1000, 50, 0.5)])
+def test_connected_watts_strogatz_graph_connected_dominating_set(n, k, p, seed):
+    G = nx.connected_watts_strogatz_graph(n, k, p, seed=seed)
+    D = nx.connected_dominating_set(G)
+    assert nx.is_connected_dominating_set(G, D)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_efficiency.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_efficiency.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a2e7d0463b3a0abeb8395df4ab870456faa64b7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_efficiency.py
@@ -0,0 +1,58 @@
+"""Unit tests for the :mod:`networkx.algorithms.efficiency` module."""
+
+import networkx as nx
+
+
+class TestEfficiency:
+    def setup_method(self):
+        # G1 is a disconnected graph
+        self.G1 = nx.Graph()
+        self.G1.add_nodes_from([1, 2, 3])
+        # G2 is a cycle graph
+        self.G2 = nx.cycle_graph(4)
+        # G3 is the triangle graph with one additional edge
+        self.G3 = nx.lollipop_graph(3, 1)
+
+    def test_efficiency_disconnected_nodes(self):
+        """
+        When nodes are disconnected, efficiency is 0
+        """
+        assert nx.efficiency(self.G1, 1, 2) == 0
+
+    def test_local_efficiency_disconnected_graph(self):
+        """
+        In a disconnected graph the efficiency is 0
+        """
+        assert nx.local_efficiency(self.G1) == 0
+
+    def test_efficiency(self):
+        assert nx.efficiency(self.G2, 0, 1) == 1
+        assert nx.efficiency(self.G2, 0, 2) == 1 / 2
+
+    def test_global_efficiency(self):
+        assert nx.global_efficiency(self.G2) == 5 / 6
+
+    def test_global_efficiency_complete_graph(self):
+        """
+        Tests that the average global efficiency of the complete graph is one.
+        """
+        for n in range(2, 10):
+            G = nx.complete_graph(n)
+            assert nx.global_efficiency(G) == 1
+
+    def test_local_efficiency_complete_graph(self):
+        """
+        Test that the local efficiency for a complete graph with at least 3
+        nodes should be one. For a graph with only 2 nodes, the induced
+        subgraph has no edges.
+        """
+        for n in range(3, 10):
+            G = nx.complete_graph(n)
+            assert nx.local_efficiency(G) == 1
+
+    def test_using_ego_graph(self):
+        """
+        Test that the ego graph is used when computing local efficiency.
+        For more information, see GitHub issue #2710.
+        """
+        assert nx.local_efficiency(self.G3) == 7 / 12
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_euler.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_euler.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5871f09b5a309df2bb00d9945ca9cf662e6f656
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_euler.py
@@ -0,0 +1,314 @@
+import collections
+
+import pytest
+
+import networkx as nx
+
+
+@pytest.mark.parametrize("f", (nx.is_eulerian, nx.is_semieulerian))
+def test_empty_graph_raises(f):
+    G = nx.Graph()
+    with pytest.raises(nx.NetworkXPointlessConcept, match="Connectivity is undefined"):
+        f(G)
+
+
+class TestIsEulerian:
+    def test_is_eulerian(self):
+        assert nx.is_eulerian(nx.complete_graph(5))
+        assert nx.is_eulerian(nx.complete_graph(7))
+        assert nx.is_eulerian(nx.hypercube_graph(4))
+        assert nx.is_eulerian(nx.hypercube_graph(6))
+
+        assert not nx.is_eulerian(nx.complete_graph(4))
+        assert not nx.is_eulerian(nx.complete_graph(6))
+        assert not nx.is_eulerian(nx.hypercube_graph(3))
+        assert not nx.is_eulerian(nx.hypercube_graph(5))
+
+        assert not nx.is_eulerian(nx.petersen_graph())
+        assert not nx.is_eulerian(nx.path_graph(4))
+
+    def test_is_eulerian2(self):
+        # not connected
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3])
+        assert not nx.is_eulerian(G)
+        # not strongly connected
+        G = nx.DiGraph()
+        G.add_nodes_from([1, 2, 3])
+        assert not nx.is_eulerian(G)
+        G = nx.MultiDiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(2, 3)
+        G.add_edge(2, 3)
+        G.add_edge(3, 1)
+        assert not nx.is_eulerian(G)
+
+
+class TestEulerianCircuit:
+    def test_eulerian_circuit_cycle(self):
+        G = nx.cycle_graph(4)
+
+        edges = list(nx.eulerian_circuit(G, source=0))
+        nodes = [u for u, v in edges]
+        assert nodes == [0, 3, 2, 1]
+        assert edges == [(0, 3), (3, 2), (2, 1), (1, 0)]
+
+        edges = list(nx.eulerian_circuit(G, source=1))
+        nodes = [u for u, v in edges]
+        assert nodes == [1, 2, 3, 0]
+        assert edges == [(1, 2), (2, 3), (3, 0), (0, 1)]
+
+        G = nx.complete_graph(3)
+
+        edges = list(nx.eulerian_circuit(G, source=0))
+        nodes = [u for u, v in edges]
+        assert nodes == [0, 2, 1]
+        assert edges == [(0, 2), (2, 1), (1, 0)]
+
+        edges = list(nx.eulerian_circuit(G, source=1))
+        nodes = [u for u, v in edges]
+        assert nodes == [1, 2, 0]
+        assert edges == [(1, 2), (2, 0), (0, 1)]
+
+    def test_eulerian_circuit_digraph(self):
+        G = nx.DiGraph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+
+        edges = list(nx.eulerian_circuit(G, source=0))
+        nodes = [u for u, v in edges]
+        assert nodes == [0, 1, 2, 3]
+        assert edges == [(0, 1), (1, 2), (2, 3), (3, 0)]
+
+        edges = list(nx.eulerian_circuit(G, source=1))
+        nodes = [u for u, v in edges]
+        assert nodes == [1, 2, 3, 0]
+        assert edges == [(1, 2), (2, 3), (3, 0), (0, 1)]
+
+    def test_multigraph(self):
+        G = nx.MultiGraph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+        G.add_edge(1, 2)
+        G.add_edge(1, 2)
+        edges = list(nx.eulerian_circuit(G, source=0))
+        nodes = [u for u, v in edges]
+        assert nodes == [0, 3, 2, 1, 2, 1]
+        assert edges == [(0, 3), (3, 2), (2, 1), (1, 2), (2, 1), (1, 0)]
+
+    def test_multigraph_with_keys(self):
+        G = nx.MultiGraph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+        G.add_edge(1, 2)
+        G.add_edge(1, 2)
+        edges = list(nx.eulerian_circuit(G, source=0, keys=True))
+        nodes = [u for u, v, k in edges]
+        assert nodes == [0, 3, 2, 1, 2, 1]
+        assert edges[:2] == [(0, 3, 0), (3, 2, 0)]
+        assert collections.Counter(edges[2:5]) == collections.Counter(
+            [(2, 1, 0), (1, 2, 1), (2, 1, 2)]
+        )
+        assert edges[5:] == [(1, 0, 0)]
+
+    def test_not_eulerian(self):
+        with pytest.raises(nx.NetworkXError):
+            f = list(nx.eulerian_circuit(nx.complete_graph(4)))
+
+
+class TestIsSemiEulerian:
+    def test_is_semieulerian(self):
+        # Test graphs with Eulerian paths but no cycles return True.
+        assert nx.is_semieulerian(nx.path_graph(4))
+        G = nx.path_graph(6, create_using=nx.DiGraph)
+        assert nx.is_semieulerian(G)
+
+        # Test graphs with Eulerian cycles return False.
+        assert not nx.is_semieulerian(nx.complete_graph(5))
+        assert not nx.is_semieulerian(nx.complete_graph(7))
+        assert not nx.is_semieulerian(nx.hypercube_graph(4))
+        assert not nx.is_semieulerian(nx.hypercube_graph(6))
+
+
+class TestHasEulerianPath:
+    def test_has_eulerian_path_cyclic(self):
+        # Test graphs with Eulerian cycles return True.
+        assert nx.has_eulerian_path(nx.complete_graph(5))
+        assert nx.has_eulerian_path(nx.complete_graph(7))
+        assert nx.has_eulerian_path(nx.hypercube_graph(4))
+        assert nx.has_eulerian_path(nx.hypercube_graph(6))
+
+    def test_has_eulerian_path_non_cyclic(self):
+        # Test graphs with Eulerian paths but no cycles return True.
+        assert nx.has_eulerian_path(nx.path_graph(4))
+        G = nx.path_graph(6, create_using=nx.DiGraph)
+        assert nx.has_eulerian_path(G)
+
+    def test_has_eulerian_path_directed_graph(self):
+        # Test directed graphs and returns False
+        G = nx.DiGraph()
+        G.add_edges_from([(0, 1), (1, 2), (0, 2)])
+        assert not nx.has_eulerian_path(G)
+
+        # Test directed graphs without isolated node returns True
+        G = nx.DiGraph()
+        G.add_edges_from([(0, 1), (1, 2), (2, 0)])
+        assert nx.has_eulerian_path(G)
+
+        # Test directed graphs with isolated node returns False
+        G.add_node(3)
+        assert not nx.has_eulerian_path(G)
+
+    @pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+    def test_has_eulerian_path_not_weakly_connected(self, G):
+        G.add_edges_from([(0, 1), (2, 3), (3, 2)])
+        assert not nx.has_eulerian_path(G)
+
+    @pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+    def test_has_eulerian_path_unbalancedins_more_than_one(self, G):
+        G.add_edges_from([(0, 1), (2, 3)])
+        assert not nx.has_eulerian_path(G)
+
+
+class TestFindPathStart:
+    def testfind_path_start(self):
+        find_path_start = nx.algorithms.euler._find_path_start
+        # Test digraphs return correct starting node.
+        G = nx.path_graph(6, create_using=nx.DiGraph)
+        assert find_path_start(G) == 0
+        edges = [(0, 1), (1, 2), (2, 0), (4, 0)]
+        assert find_path_start(nx.DiGraph(edges)) == 4
+
+        # Test graph with no Eulerian path return None.
+        edges = [(0, 1), (1, 2), (2, 3), (2, 4)]
+        assert find_path_start(nx.DiGraph(edges)) is None
+
+
+class TestEulerianPath:
+    def test_eulerian_path(self):
+        x = [(4, 0), (0, 1), (1, 2), (2, 0)]
+        for e1, e2 in zip(x, nx.eulerian_path(nx.DiGraph(x))):
+            assert e1 == e2
+
+    def test_eulerian_path_straight_link(self):
+        G = nx.DiGraph()
+        result = [(1, 2), (2, 3), (3, 4), (4, 5)]
+        G.add_edges_from(result)
+        assert result == list(nx.eulerian_path(G))
+        assert result == list(nx.eulerian_path(G, source=1))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=3))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=4))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=5))
+
+    def test_eulerian_path_multigraph(self):
+        G = nx.MultiDiGraph()
+        result = [(2, 1), (1, 2), (2, 1), (1, 2), (2, 3), (3, 4), (4, 3)]
+        G.add_edges_from(result)
+        assert result == list(nx.eulerian_path(G))
+        assert result == list(nx.eulerian_path(G, source=2))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=3))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=4))
+
+    def test_eulerian_path_eulerian_circuit(self):
+        G = nx.DiGraph()
+        result = [(1, 2), (2, 3), (3, 4), (4, 1)]
+        result2 = [(2, 3), (3, 4), (4, 1), (1, 2)]
+        result3 = [(3, 4), (4, 1), (1, 2), (2, 3)]
+        G.add_edges_from(result)
+        assert result == list(nx.eulerian_path(G))
+        assert result == list(nx.eulerian_path(G, source=1))
+        assert result2 == list(nx.eulerian_path(G, source=2))
+        assert result3 == list(nx.eulerian_path(G, source=3))
+
+    def test_eulerian_path_undirected(self):
+        G = nx.Graph()
+        result = [(1, 2), (2, 3), (3, 4), (4, 5)]
+        result2 = [(5, 4), (4, 3), (3, 2), (2, 1)]
+        G.add_edges_from(result)
+        assert list(nx.eulerian_path(G)) in (result, result2)
+        assert result == list(nx.eulerian_path(G, source=1))
+        assert result2 == list(nx.eulerian_path(G, source=5))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=3))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=2))
+
+    def test_eulerian_path_multigraph_undirected(self):
+        G = nx.MultiGraph()
+        result = [(2, 1), (1, 2), (2, 1), (1, 2), (2, 3), (3, 4)]
+        G.add_edges_from(result)
+        assert result == list(nx.eulerian_path(G))
+        assert result == list(nx.eulerian_path(G, source=2))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=3))
+        with pytest.raises(nx.NetworkXError):
+            list(nx.eulerian_path(G, source=1))
+
+    @pytest.mark.parametrize(
+        ("graph_type", "result"),
+        (
+            (nx.MultiGraph, [(0, 1, 0), (1, 0, 1)]),
+            (nx.MultiDiGraph, [(0, 1, 0), (1, 0, 0)]),
+        ),
+    )
+    def test_eulerian_with_keys(self, graph_type, result):
+        G = graph_type([(0, 1), (1, 0)])
+        answer = nx.eulerian_path(G, keys=True)
+        assert list(answer) == result
+
+
+class TestEulerize:
+    def test_disconnected(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.from_edgelist([(0, 1), (2, 3)])
+            nx.eulerize(G)
+
+    def test_null_graph(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.eulerize(nx.Graph())
+
+    def test_null_multigraph(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.eulerize(nx.MultiGraph())
+
+    def test_on_empty_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.eulerize(nx.empty_graph(3))
+
+    def test_on_eulerian(self):
+        G = nx.cycle_graph(3)
+        H = nx.eulerize(G)
+        assert nx.is_isomorphic(G, H)
+
+    def test_on_eulerian_multigraph(self):
+        G = nx.MultiGraph(nx.cycle_graph(3))
+        G.add_edge(0, 1)
+        H = nx.eulerize(G)
+        assert nx.is_eulerian(H)
+
+    def test_on_complete_graph(self):
+        G = nx.complete_graph(4)
+        assert nx.is_eulerian(nx.eulerize(G))
+        assert nx.is_eulerian(nx.eulerize(nx.MultiGraph(G)))
+
+    def test_on_non_eulerian_graph(self):
+        G = nx.cycle_graph(18)
+        G.add_edge(0, 18)
+        G.add_edge(18, 19)
+        G.add_edge(17, 19)
+        G.add_edge(4, 20)
+        G.add_edge(20, 21)
+        G.add_edge(21, 22)
+        G.add_edge(22, 23)
+        G.add_edge(23, 24)
+        G.add_edge(24, 25)
+        G.add_edge(25, 26)
+        G.add_edge(26, 27)
+        G.add_edge(27, 28)
+        G.add_edge(28, 13)
+        assert not nx.is_eulerian(G)
+        G = nx.eulerize(G)
+        assert nx.is_eulerian(G)
+        assert nx.number_of_edges(G) == 39
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_graph_hashing.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_graph_hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c90c8ff128a02143c48322853bc2dcaa5f6fffc
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_graph_hashing.py
@@ -0,0 +1,872 @@
+import copy
+
+import pytest
+
+import networkx as nx
+
+# Unit tests relevant for both functions in this module.
+
+
+def test_positive_iters():
+    G1 = nx.empty_graph()
+    with pytest.raises(
+        ValueError,
+        match="The WL algorithm requires that `iterations` be positive",
+    ):
+        nx.weisfeiler_lehman_graph_hash(G1, iterations=-3)
+    with pytest.raises(
+        ValueError,
+        match="The WL algorithm requires that `iterations` be positive",
+    ):
+        nx.weisfeiler_lehman_subgraph_hashes(G1, iterations=-3)
+    with pytest.raises(
+        ValueError,
+        match="The WL algorithm requires that `iterations` be positive",
+    ):
+        nx.weisfeiler_lehman_graph_hash(G1, iterations=0)
+    with pytest.raises(
+        ValueError,
+        match="The WL algorithm requires that `iterations` be positive",
+    ):
+        nx.weisfeiler_lehman_subgraph_hashes(G1, iterations=0)
+
+
+# Unit tests for the :func:`~networkx.weisfeiler_lehman_graph_hash` function
+
+
+def test_empty_graph_hash():
+    """
+    empty graphs should give hashes regardless of other params
+    """
+    G1 = nx.empty_graph()
+    G2 = nx.empty_graph()
+
+    h1 = nx.weisfeiler_lehman_graph_hash(G1)
+    h2 = nx.weisfeiler_lehman_graph_hash(G2)
+    h3 = nx.weisfeiler_lehman_graph_hash(G2, edge_attr="edge_attr1")
+    h4 = nx.weisfeiler_lehman_graph_hash(G2, node_attr="node_attr1")
+    h5 = nx.weisfeiler_lehman_graph_hash(
+        G2, edge_attr="edge_attr1", node_attr="node_attr1"
+    )
+    h6 = nx.weisfeiler_lehman_graph_hash(G2, iterations=10)
+
+    assert h1 == h2
+    assert h1 == h3
+    assert h1 == h4
+    assert h1 == h5
+    assert h1 == h6
+
+
+def test_directed():
+    """
+    A directed graph with no bi-directional edges should yield different a graph hash
+    to the same graph taken as undirected if there are no hash collisions.
+    """
+    r = 10
+    for i in range(r):
+        G_directed = nx.gn_graph(10 + r, seed=100 + i)
+        G_undirected = nx.to_undirected(G_directed)
+
+        h_directed = nx.weisfeiler_lehman_graph_hash(G_directed)
+        h_undirected = nx.weisfeiler_lehman_graph_hash(G_undirected)
+
+        assert h_directed != h_undirected
+
+
+def test_reversed():
+    """
+    A directed graph with no bi-directional edges should yield different a graph hash
+    to the same graph taken with edge directions reversed if there are no hash
+    collisions. Here we test a cycle graph which is the minimal counterexample
+    """
+    G = nx.cycle_graph(5, create_using=nx.DiGraph)
+    nx.set_node_attributes(G, {n: str(n) for n in G.nodes()}, name="label")
+
+    G_reversed = G.reverse()
+
+    h = nx.weisfeiler_lehman_graph_hash(G, node_attr="label")
+    h_reversed = nx.weisfeiler_lehman_graph_hash(G_reversed, node_attr="label")
+
+    assert h != h_reversed
+
+
+def test_isomorphic():
+    """
+    graph hashes should be invariant to node-relabeling (when the output is reindexed
+    by the same mapping)
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=200 + i)
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g1_hash = nx.weisfeiler_lehman_graph_hash(G1)
+        g2_hash = nx.weisfeiler_lehman_graph_hash(G2)
+
+        assert g1_hash == g2_hash
+
+
+def test_isomorphic_edge_attr():
+    """
+    Isomorphic graphs with differing edge attributes should yield different graph
+    hashes if the 'edge_attr' argument is supplied and populated in the graph,
+    and there are no hash collisions.
+    The output should still be invariant to node-relabeling
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=300 + i)
+
+        for a, b in G1.edges:
+            G1[a][b]["edge_attr1"] = f"{a}-{b}-1"
+            G1[a][b]["edge_attr2"] = f"{a}-{b}-2"
+
+        g1_hash_with_edge_attr1 = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="edge_attr1"
+        )
+        g1_hash_with_edge_attr2 = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="edge_attr2"
+        )
+        g1_hash_no_edge_attr = nx.weisfeiler_lehman_graph_hash(G1, edge_attr=None)
+
+        assert g1_hash_with_edge_attr1 != g1_hash_no_edge_attr
+        assert g1_hash_with_edge_attr2 != g1_hash_no_edge_attr
+        assert g1_hash_with_edge_attr1 != g1_hash_with_edge_attr2
+
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g2_hash_with_edge_attr1 = nx.weisfeiler_lehman_graph_hash(
+            G2, edge_attr="edge_attr1"
+        )
+        g2_hash_with_edge_attr2 = nx.weisfeiler_lehman_graph_hash(
+            G2, edge_attr="edge_attr2"
+        )
+
+        assert g1_hash_with_edge_attr1 == g2_hash_with_edge_attr1
+        assert g1_hash_with_edge_attr2 == g2_hash_with_edge_attr2
+
+
+def test_missing_edge_attr():
+    """
+    If the 'edge_attr' argument is supplied but is missing from an edge in the graph,
+    we should raise a KeyError
+    """
+    G = nx.Graph()
+    G.add_edges_from([(1, 2, {"edge_attr1": "a"}), (1, 3, {})])
+    pytest.raises(KeyError, nx.weisfeiler_lehman_graph_hash, G, edge_attr="edge_attr1")
+
+
+def test_isomorphic_node_attr():
+    """
+    Isomorphic graphs with differing node attributes should yield different graph
+    hashes if the 'node_attr' argument is supplied and populated in the graph, and
+    there are no hash collisions.
+    The output should still be invariant to node-relabeling
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=400 + i)
+
+        for u in G1.nodes():
+            G1.nodes[u]["node_attr1"] = f"{u}-1"
+            G1.nodes[u]["node_attr2"] = f"{u}-2"
+
+        g1_hash_with_node_attr1 = nx.weisfeiler_lehman_graph_hash(
+            G1, node_attr="node_attr1"
+        )
+        g1_hash_with_node_attr2 = nx.weisfeiler_lehman_graph_hash(
+            G1, node_attr="node_attr2"
+        )
+        g1_hash_no_node_attr = nx.weisfeiler_lehman_graph_hash(G1, node_attr=None)
+
+        assert g1_hash_with_node_attr1 != g1_hash_no_node_attr
+        assert g1_hash_with_node_attr2 != g1_hash_no_node_attr
+        assert g1_hash_with_node_attr1 != g1_hash_with_node_attr2
+
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g2_hash_with_node_attr1 = nx.weisfeiler_lehman_graph_hash(
+            G2, node_attr="node_attr1"
+        )
+        g2_hash_with_node_attr2 = nx.weisfeiler_lehman_graph_hash(
+            G2, node_attr="node_attr2"
+        )
+
+        assert g1_hash_with_node_attr1 == g2_hash_with_node_attr1
+        assert g1_hash_with_node_attr2 == g2_hash_with_node_attr2
+
+
+def test_missing_node_attr():
+    """
+    If the 'node_attr' argument is supplied but is missing from a node in the graph,
+    we should raise a KeyError
+    """
+    G = nx.Graph()
+    G.add_nodes_from([(1, {"node_attr1": "a"}), (2, {})])
+    G.add_edges_from([(1, 2), (2, 3), (3, 1), (1, 4)])
+    pytest.raises(KeyError, nx.weisfeiler_lehman_graph_hash, G, node_attr="node_attr1")
+
+
+def test_isomorphic_edge_attr_and_node_attr():
+    """
+    Isomorphic graphs with differing node attributes should yield different graph
+    hashes if the 'node_attr' and 'edge_attr' argument is supplied and populated in
+    the graph, and there are no hash collisions.
+    The output should still be invariant to node-relabeling
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=500 + i)
+
+        for u in G1.nodes():
+            G1.nodes[u]["node_attr1"] = f"{u}-1"
+            G1.nodes[u]["node_attr2"] = f"{u}-2"
+
+        for a, b in G1.edges:
+            G1[a][b]["edge_attr1"] = f"{a}-{b}-1"
+            G1[a][b]["edge_attr2"] = f"{a}-{b}-2"
+
+        g1_hash_edge1_node1 = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="edge_attr1", node_attr="node_attr1"
+        )
+        g1_hash_edge2_node2 = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="edge_attr2", node_attr="node_attr2"
+        )
+        g1_hash_edge1_node2 = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="edge_attr1", node_attr="node_attr2"
+        )
+        g1_hash_no_attr = nx.weisfeiler_lehman_graph_hash(G1)
+
+        assert g1_hash_edge1_node1 != g1_hash_no_attr
+        assert g1_hash_edge2_node2 != g1_hash_no_attr
+        assert g1_hash_edge1_node1 != g1_hash_edge2_node2
+        assert g1_hash_edge1_node2 != g1_hash_edge2_node2
+        assert g1_hash_edge1_node2 != g1_hash_edge1_node1
+
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g2_hash_edge1_node1 = nx.weisfeiler_lehman_graph_hash(
+            G2, edge_attr="edge_attr1", node_attr="node_attr1"
+        )
+        g2_hash_edge2_node2 = nx.weisfeiler_lehman_graph_hash(
+            G2, edge_attr="edge_attr2", node_attr="node_attr2"
+        )
+
+        assert g1_hash_edge1_node1 == g2_hash_edge1_node1
+        assert g1_hash_edge2_node2 == g2_hash_edge2_node2
+
+
+def test_digest_size():
+    """
+    The hash string lengths should be as expected for a variety of graphs and
+    digest sizes
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G = nx.erdos_renyi_graph(n, p * i, seed=1000 + i)
+
+        h16 = nx.weisfeiler_lehman_graph_hash(G)
+        h32 = nx.weisfeiler_lehman_graph_hash(G, digest_size=32)
+
+        assert h16 != h32
+        assert len(h16) == 16 * 2
+        assert len(h32) == 32 * 2
+
+
+def test_directed_bugs():
+    """
+    These were bugs for directed graphs as discussed in issue #7806
+    """
+    Ga = nx.DiGraph()
+    Gb = nx.DiGraph()
+    Ga.add_nodes_from([1, 2, 3, 4])
+    Gb.add_nodes_from([1, 2, 3, 4])
+    Ga.add_edges_from([(1, 2), (3, 2)])
+    Gb.add_edges_from([(1, 2), (3, 4)])
+    Ga_hash = nx.weisfeiler_lehman_graph_hash(Ga)
+    Gb_hash = nx.weisfeiler_lehman_graph_hash(Gb)
+    assert Ga_hash != Gb_hash
+
+    Tree1 = nx.DiGraph()
+    Tree1.add_edges_from([(0, 4), (1, 5), (2, 6), (3, 7)])
+    Tree1.add_edges_from([(4, 8), (5, 8), (6, 9), (7, 9)])
+    Tree1.add_edges_from([(8, 10), (9, 10)])
+    nx.set_node_attributes(
+        Tree1, {10: "s", 8: "a", 9: "a", 4: "b", 5: "b", 6: "b", 7: "b"}, "weight"
+    )
+    Tree2 = copy.deepcopy(Tree1)
+    nx.set_node_attributes(Tree1, {0: "d", 1: "c", 2: "d", 3: "c"}, "weight")
+    nx.set_node_attributes(Tree2, {0: "d", 1: "d", 2: "c", 3: "c"}, "weight")
+    Tree1_hash_short = nx.weisfeiler_lehman_graph_hash(
+        Tree1, iterations=1, node_attr="weight"
+    )
+    Tree2_hash_short = nx.weisfeiler_lehman_graph_hash(
+        Tree2, iterations=1, node_attr="weight"
+    )
+    assert Tree1_hash_short == Tree2_hash_short
+    Tree1_hash = nx.weisfeiler_lehman_graph_hash(
+        Tree1, node_attr="weight"
+    )  # Default is 3 iterations
+    Tree2_hash = nx.weisfeiler_lehman_graph_hash(Tree2, node_attr="weight")
+    assert Tree1_hash != Tree2_hash
+
+
+def test_trivial_labels_isomorphism():
+    """
+    Trivial labelling of the graph should not change isomorphism verdicts.
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=500 + i)
+        G2 = nx.erdos_renyi_graph(n, p * i, seed=42 + i)
+        G1_hash = nx.weisfeiler_lehman_graph_hash(G1)
+        G2_hash = nx.weisfeiler_lehman_graph_hash(G2)
+        equal = G1_hash == G2_hash
+
+        nx.set_node_attributes(G1, values=1, name="weight")
+        nx.set_node_attributes(G2, values=1, name="weight")
+        G1_hash_node = nx.weisfeiler_lehman_graph_hash(G1, node_attr="weight")
+        G2_hash_node = nx.weisfeiler_lehman_graph_hash(G2, node_attr="weight")
+        equal_node = G1_hash_node == G2_hash_node
+
+        nx.set_edge_attributes(G1, values="a", name="e_weight")
+        nx.set_edge_attributes(G2, values="a", name="e_weight")
+        G1_hash_edge = nx.weisfeiler_lehman_graph_hash(G1, edge_attr="e_weight")
+        G2_hash_edge = nx.weisfeiler_lehman_graph_hash(G2, edge_attr="e_weight")
+        equal_edge = G1_hash_edge == G2_hash_edge
+
+        G1_hash_both = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="e_weight", node_attr="weight"
+        )
+        G2_hash_both = nx.weisfeiler_lehman_graph_hash(
+            G2, edge_attr="e_weight", node_attr="weight"
+        )
+        equal_both = G1_hash_both == G2_hash_both
+
+        assert equal == equal_node
+        assert equal_node == equal_edge
+        assert equal_edge == equal_both
+
+
+def test_trivial_labels_isomorphism_directed():
+    """
+    Trivial labelling of the graph should not change isomorphism verdicts on digraphs.
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, directed=True, seed=500 + i)
+        G2 = nx.erdos_renyi_graph(n, p * i, directed=True, seed=42 + i)
+        G1_hash = nx.weisfeiler_lehman_graph_hash(G1)
+        G2_hash = nx.weisfeiler_lehman_graph_hash(G2)
+        equal = G1_hash == G2_hash
+
+        nx.set_node_attributes(G1, values=1, name="weight")
+        nx.set_node_attributes(G2, values=1, name="weight")
+        G1_hash_node = nx.weisfeiler_lehman_graph_hash(G1, node_attr="weight")
+        G2_hash_node = nx.weisfeiler_lehman_graph_hash(G2, node_attr="weight")
+        equal_node = G1_hash_node == G2_hash_node
+
+        nx.set_edge_attributes(G1, values="a", name="e_weight")
+        nx.set_edge_attributes(G2, values="a", name="e_weight")
+        G1_hash_edge = nx.weisfeiler_lehman_graph_hash(G1, edge_attr="e_weight")
+        G2_hash_edge = nx.weisfeiler_lehman_graph_hash(G2, edge_attr="e_weight")
+        equal_edge = G1_hash_edge == G2_hash_edge
+
+        G1_hash_both = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="e_weight", node_attr="weight"
+        )
+        G2_hash_both = nx.weisfeiler_lehman_graph_hash(
+            G2, edge_attr="e_weight", node_attr="weight"
+        )
+        equal_both = G1_hash_both == G2_hash_both
+
+        assert equal == equal_node
+        assert equal_node == equal_edge
+        assert equal_edge == equal_both
+
+    # Specific case that was found to be a bug in issue #7806
+    # Without weights worked
+    Ga = nx.DiGraph()
+    Ga.add_nodes_from([1, 2, 3, 4])
+    Gb = copy.deepcopy(Ga)
+    Ga.add_edges_from([(1, 2), (3, 2)])
+    Gb.add_edges_from([(1, 2), (3, 4)])
+    Ga_hash = nx.weisfeiler_lehman_graph_hash(Ga)
+    Gb_hash = nx.weisfeiler_lehman_graph_hash(Gb)
+    assert Ga_hash != Gb_hash
+
+    # Now with trivial weights
+    nx.set_node_attributes(Ga, values=1, name="weight")
+    nx.set_node_attributes(Gb, values=1, name="weight")
+    Ga_hash = nx.weisfeiler_lehman_graph_hash(Ga, node_attr="weight")
+    Gb_hash = nx.weisfeiler_lehman_graph_hash(Gb, node_attr="weight")
+    assert Ga_hash != Gb_hash
+
+
+def test_trivial_labels_hashes():
+    """
+    Test that 'empty' labelling of nodes or edges shouldn't have a different impact
+    on the calculated hash. Note that we cannot assume that trivial weights have no
+    impact at all. Without (trivial) weights, a node will start with hashing its
+    degree. This step is omitted when there are weights.
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=500 + i)
+        nx.set_node_attributes(G1, values="", name="weight")
+        first = nx.weisfeiler_lehman_graph_hash(G1, node_attr="weight")
+        nx.set_edge_attributes(G1, values="", name="e_weight")
+        second = nx.weisfeiler_lehman_graph_hash(G1, edge_attr="e_weight")
+        assert first == second
+        third = nx.weisfeiler_lehman_graph_hash(
+            G1, edge_attr="e_weight", node_attr="weight"
+        )
+        assert second == third
+
+
+# Unit tests for the :func:`~networkx.weisfeiler_lehman_subgraph_hashes` function
+
+
+def is_subiteration(a, b):
+    """
+    returns True if that each hash sequence in 'a' is a prefix for
+    the corresponding sequence indexed by the same node in 'b'.
+    """
+    return all(b[node][: len(hashes)] == hashes for node, hashes in a.items())
+
+
+def hexdigest_sizes_correct(a, digest_size):
+    """
+    returns True if all hex digest sizes are the expected length in a
+    node:subgraph-hashes dictionary. Hex digest string length == 2 * bytes digest
+    length since each pair of hex digits encodes 1 byte
+    (https://docs.python.org/3/library/hashlib.html)
+    """
+    hexdigest_size = digest_size * 2
+
+    def list_digest_sizes_correct(l):
+        return all(len(x) == hexdigest_size for x in l)
+
+    return all(list_digest_sizes_correct(hashes) for hashes in a.values())
+
+
+def test_empty_graph_subgraph_hash():
+    """ "
+    empty graphs should give empty dict subgraph hashes regardless of other params
+    """
+    G = nx.empty_graph()
+
+    subgraph_hashes1 = nx.weisfeiler_lehman_subgraph_hashes(G)
+    subgraph_hashes2 = nx.weisfeiler_lehman_subgraph_hashes(G, edge_attr="edge_attr")
+    subgraph_hashes3 = nx.weisfeiler_lehman_subgraph_hashes(G, node_attr="edge_attr")
+    subgraph_hashes4 = nx.weisfeiler_lehman_subgraph_hashes(G, iterations=2)
+    subgraph_hashes5 = nx.weisfeiler_lehman_subgraph_hashes(G, digest_size=64)
+
+    assert subgraph_hashes1 == {}
+    assert subgraph_hashes2 == {}
+    assert subgraph_hashes3 == {}
+    assert subgraph_hashes4 == {}
+    assert subgraph_hashes5 == {}
+
+
+def test_directed_subgraph_hash():
+    """
+    A directed graph with no bi-directional edges should yield different subgraph
+    hashes to the same graph taken as undirected, if all hashes don't collide.
+    """
+    r = 10
+    for i in range(r):
+        G_directed = nx.gn_graph(10 + r, seed=100 + i)
+        G_undirected = nx.to_undirected(G_directed)
+
+        directed_subgraph_hashes = nx.weisfeiler_lehman_subgraph_hashes(G_directed)
+        undirected_subgraph_hashes = nx.weisfeiler_lehman_subgraph_hashes(G_undirected)
+
+        assert directed_subgraph_hashes != undirected_subgraph_hashes
+
+
+def test_reversed_subgraph_hash():
+    """
+    A directed graph with no bi-directional edges should yield different subgraph
+    hashes to the same graph taken with edge directions reversed if there are no
+    hash collisions. Here we test a cycle graph which is the minimal counterexample
+    """
+    G = nx.cycle_graph(5, create_using=nx.DiGraph)
+    nx.set_node_attributes(G, {n: str(n) for n in G.nodes()}, name="label")
+
+    G_reversed = G.reverse()
+
+    h = nx.weisfeiler_lehman_subgraph_hashes(G, node_attr="label")
+    h_reversed = nx.weisfeiler_lehman_subgraph_hashes(G_reversed, node_attr="label")
+
+    assert h != h_reversed
+
+
+def test_isomorphic_subgraph_hash():
+    """
+    the subgraph hashes should be invariant to node-relabeling when the output is
+    reindexed by the same mapping and all hashes don't collide.
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=200 + i)
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g1_subgraph_hashes = nx.weisfeiler_lehman_subgraph_hashes(G1)
+        g2_subgraph_hashes = nx.weisfeiler_lehman_subgraph_hashes(G2)
+
+        assert g1_subgraph_hashes == {-1 * k: v for k, v in g2_subgraph_hashes.items()}
+
+
+def test_isomorphic_edge_attr_subgraph_hash():
+    """
+    Isomorphic graphs with differing edge attributes should yield different subgraph
+    hashes if the 'edge_attr' argument is supplied and populated in the graph, and
+    all hashes don't collide.
+    The output should still be invariant to node-relabeling
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=300 + i)
+
+        for a, b in G1.edges:
+            G1[a][b]["edge_attr1"] = f"{a}-{b}-1"
+            G1[a][b]["edge_attr2"] = f"{a}-{b}-2"
+
+        g1_hash_with_edge_attr1 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, edge_attr="edge_attr1"
+        )
+        g1_hash_with_edge_attr2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, edge_attr="edge_attr2"
+        )
+        g1_hash_no_edge_attr = nx.weisfeiler_lehman_subgraph_hashes(G1, edge_attr=None)
+
+        assert g1_hash_with_edge_attr1 != g1_hash_no_edge_attr
+        assert g1_hash_with_edge_attr2 != g1_hash_no_edge_attr
+        assert g1_hash_with_edge_attr1 != g1_hash_with_edge_attr2
+
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g2_hash_with_edge_attr1 = nx.weisfeiler_lehman_subgraph_hashes(
+            G2, edge_attr="edge_attr1"
+        )
+        g2_hash_with_edge_attr2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G2, edge_attr="edge_attr2"
+        )
+
+        assert g1_hash_with_edge_attr1 == {
+            -1 * k: v for k, v in g2_hash_with_edge_attr1.items()
+        }
+        assert g1_hash_with_edge_attr2 == {
+            -1 * k: v for k, v in g2_hash_with_edge_attr2.items()
+        }
+
+
+def test_missing_edge_attr_subgraph_hash():
+    """
+    If the 'edge_attr' argument is supplied but is missing from an edge in the graph,
+    we should raise a KeyError
+    """
+    G = nx.Graph()
+    G.add_edges_from([(1, 2, {"edge_attr1": "a"}), (1, 3, {})])
+    pytest.raises(
+        KeyError, nx.weisfeiler_lehman_subgraph_hashes, G, edge_attr="edge_attr1"
+    )
+
+
+def test_isomorphic_node_attr_subgraph_hash():
+    """
+    Isomorphic graphs with differing node attributes should yield different subgraph
+    hashes if the 'node_attr' argument is supplied and populated in the graph, and
+    all hashes don't collide.
+    The output should still be invariant to node-relabeling
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=400 + i)
+
+        for u in G1.nodes():
+            G1.nodes[u]["node_attr1"] = f"{u}-1"
+            G1.nodes[u]["node_attr2"] = f"{u}-2"
+
+        g1_hash_with_node_attr1 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, node_attr="node_attr1"
+        )
+        g1_hash_with_node_attr2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, node_attr="node_attr2"
+        )
+        g1_hash_no_node_attr = nx.weisfeiler_lehman_subgraph_hashes(G1, node_attr=None)
+
+        assert g1_hash_with_node_attr1 != g1_hash_no_node_attr
+        assert g1_hash_with_node_attr2 != g1_hash_no_node_attr
+        assert g1_hash_with_node_attr1 != g1_hash_with_node_attr2
+
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g2_hash_with_node_attr1 = nx.weisfeiler_lehman_subgraph_hashes(
+            G2, node_attr="node_attr1"
+        )
+        g2_hash_with_node_attr2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G2, node_attr="node_attr2"
+        )
+
+        assert g1_hash_with_node_attr1 == {
+            -1 * k: v for k, v in g2_hash_with_node_attr1.items()
+        }
+        assert g1_hash_with_node_attr2 == {
+            -1 * k: v for k, v in g2_hash_with_node_attr2.items()
+        }
+
+
+def test_missing_node_attr_subgraph_hash():
+    """
+    If the 'node_attr' argument is supplied but is missing from a node in the graph,
+    we should raise a KeyError
+    """
+    G = nx.Graph()
+    G.add_nodes_from([(1, {"node_attr1": "a"}), (2, {})])
+    G.add_edges_from([(1, 2), (2, 3), (3, 1), (1, 4)])
+    pytest.raises(
+        KeyError, nx.weisfeiler_lehman_subgraph_hashes, G, node_attr="node_attr1"
+    )
+
+
+def test_isomorphic_edge_attr_and_node_attr_subgraph_hash():
+    """
+    Isomorphic graphs with differing node attributes should yield different subgraph
+    hashes if the 'node_attr' and 'edge_attr' argument is supplied and populated in
+    the graph, and all hashes don't collide
+    The output should still be invariant to node-relabeling
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G1 = nx.erdos_renyi_graph(n, p * i, seed=500 + i)
+
+        for u in G1.nodes():
+            G1.nodes[u]["node_attr1"] = f"{u}-1"
+            G1.nodes[u]["node_attr2"] = f"{u}-2"
+
+        for a, b in G1.edges:
+            G1[a][b]["edge_attr1"] = f"{a}-{b}-1"
+            G1[a][b]["edge_attr2"] = f"{a}-{b}-2"
+
+        g1_hash_edge1_node1 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, edge_attr="edge_attr1", node_attr="node_attr1"
+        )
+        g1_hash_edge2_node2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, edge_attr="edge_attr2", node_attr="node_attr2"
+        )
+        g1_hash_edge1_node2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G1, edge_attr="edge_attr1", node_attr="node_attr2"
+        )
+        g1_hash_no_attr = nx.weisfeiler_lehman_subgraph_hashes(G1)
+
+        assert g1_hash_edge1_node1 != g1_hash_no_attr
+        assert g1_hash_edge2_node2 != g1_hash_no_attr
+        assert g1_hash_edge1_node1 != g1_hash_edge2_node2
+        assert g1_hash_edge1_node2 != g1_hash_edge2_node2
+        assert g1_hash_edge1_node2 != g1_hash_edge1_node1
+
+        G2 = nx.relabel_nodes(G1, {u: -1 * u for u in G1.nodes()})
+
+        g2_hash_edge1_node1 = nx.weisfeiler_lehman_subgraph_hashes(
+            G2, edge_attr="edge_attr1", node_attr="node_attr1"
+        )
+        g2_hash_edge2_node2 = nx.weisfeiler_lehman_subgraph_hashes(
+            G2, edge_attr="edge_attr2", node_attr="node_attr2"
+        )
+
+        assert g1_hash_edge1_node1 == {
+            -1 * k: v for k, v in g2_hash_edge1_node1.items()
+        }
+        assert g1_hash_edge2_node2 == {
+            -1 * k: v for k, v in g2_hash_edge2_node2.items()
+        }
+
+
+def test_iteration_depth():
+    """
+    All nodes should have the correct number of subgraph hashes in the output when
+    using degree as initial node labels.
+    Subsequent iteration depths for the same graph should be additive for each node
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G = nx.erdos_renyi_graph(n, p * i, seed=600 + i)
+
+        depth3 = nx.weisfeiler_lehman_subgraph_hashes(G, iterations=3)
+        depth4 = nx.weisfeiler_lehman_subgraph_hashes(G, iterations=4)
+        depth5 = nx.weisfeiler_lehman_subgraph_hashes(G, iterations=5)
+
+        assert all(len(hashes) == 3 for hashes in depth3.values())
+        assert all(len(hashes) == 4 for hashes in depth4.values())
+        assert all(len(hashes) == 5 for hashes in depth5.values())
+
+        assert is_subiteration(depth3, depth4)
+        assert is_subiteration(depth4, depth5)
+        assert is_subiteration(depth3, depth5)
+
+
+def test_iteration_depth_edge_attr():
+    """
+    All nodes should have the correct number of subgraph hashes in the output when
+    setting initial node labels empty and using an edge attribute when aggregating
+    neighborhoods.
+    Subsequent iteration depths for the same graph should be additive for each node
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G = nx.erdos_renyi_graph(n, p * i, seed=700 + i)
+
+        for a, b in G.edges:
+            G[a][b]["edge_attr1"] = f"{a}-{b}-1"
+
+        depth3 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, edge_attr="edge_attr1", iterations=3
+        )
+        depth4 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, edge_attr="edge_attr1", iterations=4
+        )
+        depth5 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, edge_attr="edge_attr1", iterations=5
+        )
+
+        assert all(len(hashes) == 3 for hashes in depth3.values())
+        assert all(len(hashes) == 4 for hashes in depth4.values())
+        assert all(len(hashes) == 5 for hashes in depth5.values())
+
+        assert is_subiteration(depth3, depth4)
+        assert is_subiteration(depth4, depth5)
+        assert is_subiteration(depth3, depth5)
+
+
+def test_iteration_depth_node_attr():
+    """
+    All nodes should have the correct number of subgraph hashes in the output when
+    setting initial node labels to an attribute.
+    Subsequent iteration depths for the same graph should be additive for each node
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G = nx.erdos_renyi_graph(n, p * i, seed=800 + i)
+
+        for u in G.nodes():
+            G.nodes[u]["node_attr1"] = f"{u}-1"
+
+        depth3 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, node_attr="node_attr1", iterations=3
+        )
+        depth4 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, node_attr="node_attr1", iterations=4
+        )
+        depth5 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, node_attr="node_attr1", iterations=5
+        )
+
+        assert all(len(hashes) == 3 for hashes in depth3.values())
+        assert all(len(hashes) == 4 for hashes in depth4.values())
+        assert all(len(hashes) == 5 for hashes in depth5.values())
+
+        assert is_subiteration(depth3, depth4)
+        assert is_subiteration(depth4, depth5)
+        assert is_subiteration(depth3, depth5)
+
+
+def test_iteration_depth_node_edge_attr():
+    """
+    All nodes should have the correct number of subgraph hashes in the output when
+    setting initial node labels to an attribute and also using an edge attribute when
+    aggregating neighborhoods.
+    Subsequent iteration depths for the same graph should be additive for each node
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G = nx.erdos_renyi_graph(n, p * i, seed=900 + i)
+
+        for u in G.nodes():
+            G.nodes[u]["node_attr1"] = f"{u}-1"
+
+        for a, b in G.edges:
+            G[a][b]["edge_attr1"] = f"{a}-{b}-1"
+
+        depth3 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, edge_attr="edge_attr1", node_attr="node_attr1", iterations=3
+        )
+        depth4 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, edge_attr="edge_attr1", node_attr="node_attr1", iterations=4
+        )
+        depth5 = nx.weisfeiler_lehman_subgraph_hashes(
+            G, edge_attr="edge_attr1", node_attr="node_attr1", iterations=5
+        )
+
+        assert all(len(hashes) == 3 for hashes in depth3.values())
+        assert all(len(hashes) == 4 for hashes in depth4.values())
+        assert all(len(hashes) == 5 for hashes in depth5.values())
+
+        assert is_subiteration(depth3, depth4)
+        assert is_subiteration(depth4, depth5)
+        assert is_subiteration(depth3, depth5)
+
+
+def test_digest_size_subgraph_hash():
+    """
+    The hash string lengths should be as expected for a variety of graphs and
+    digest sizes
+    """
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(1, r + 1):
+        G = nx.erdos_renyi_graph(n, p * i, seed=1000 + i)
+
+        digest_size16_hashes = nx.weisfeiler_lehman_subgraph_hashes(G)
+        digest_size32_hashes = nx.weisfeiler_lehman_subgraph_hashes(G, digest_size=32)
+
+        assert digest_size16_hashes != digest_size32_hashes
+
+        assert hexdigest_sizes_correct(digest_size16_hashes, 16)
+        assert hexdigest_sizes_correct(digest_size32_hashes, 32)
+
+
+def test_initial_node_labels_subgraph_hash():
+    """
+    Including the hashed initial label prepends an extra hash to the lists
+    """
+    G = nx.path_graph(5)
+    nx.set_node_attributes(G, {i: int(0 < i < 4) for i in G}, "label")
+    # initial node labels:
+    # 0--1--1--1--0
+
+    without_initial_label = nx.weisfeiler_lehman_subgraph_hashes(G, node_attr="label")
+    assert all(len(v) == 3 for v in without_initial_label.values())
+    # 3 different 1 hop nhds
+    assert len({v[0] for v in without_initial_label.values()}) == 3
+
+    with_initial_label = nx.weisfeiler_lehman_subgraph_hashes(
+        G, node_attr="label", include_initial_labels=True
+    )
+    assert all(len(v) == 4 for v in with_initial_label.values())
+    # 2 different initial labels
+    assert len({v[0] for v in with_initial_label.values()}) == 2
+
+    # check hashes match otherwise
+    for u in G:
+        for a, b in zip(
+            with_initial_label[u][1:], without_initial_label[u], strict=True
+        ):
+            assert a == b
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_graphical.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_graphical.py
new file mode 100644
index 0000000000000000000000000000000000000000..99f766f799d8573e80d905482f4b685a2d16bcc0
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_graphical.py
@@ -0,0 +1,163 @@
+import pytest
+
+import networkx as nx
+
+
+def test_valid_degree_sequence1():
+    n = 100
+    p = 0.3
+    for i in range(10):
+        G = nx.erdos_renyi_graph(n, p)
+        deg = (d for n, d in G.degree())
+        assert nx.is_graphical(deg, method="eg")
+        assert nx.is_graphical(deg, method="hh")
+
+
+def test_valid_degree_sequence2():
+    n = 100
+    for i in range(10):
+        G = nx.barabasi_albert_graph(n, 1)
+        deg = (d for n, d in G.degree())
+        assert nx.is_graphical(deg, method="eg")
+        assert nx.is_graphical(deg, method="hh")
+
+
+def test_string_input():
+    pytest.raises(nx.NetworkXException, nx.is_graphical, [], "foo")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ["red"], "hh")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ["red"], "eg")
+
+
+def test_non_integer_input():
+    pytest.raises(nx.NetworkXException, nx.is_graphical, [72.5], "eg")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, [72.5], "hh")
+
+
+def test_negative_input():
+    assert not nx.is_graphical([-1], "hh")
+    assert not nx.is_graphical([-1], "eg")
+
+
+class TestAtlas:
+    @classmethod
+    def setup_class(cls):
+        global atlas
+        from networkx.generators import atlas
+
+        cls.GAG = atlas.graph_atlas_g()
+
+    def test_atlas(self):
+        for graph in self.GAG:
+            deg = (d for n, d in graph.degree())
+            assert nx.is_graphical(deg, method="eg")
+            assert nx.is_graphical(deg, method="hh")
+
+
+def test_small_graph_true():
+    z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    assert nx.is_graphical(z, method="hh")
+    assert nx.is_graphical(z, method="eg")
+    z = [10, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2]
+    assert nx.is_graphical(z, method="hh")
+    assert nx.is_graphical(z, method="eg")
+    z = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    assert nx.is_graphical(z, method="hh")
+    assert nx.is_graphical(z, method="eg")
+
+
+def test_small_graph_false():
+    z = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    assert not nx.is_graphical(z, method="hh")
+    assert not nx.is_graphical(z, method="eg")
+    z = [6, 5, 4, 4, 2, 1, 1, 1]
+    assert not nx.is_graphical(z, method="hh")
+    assert not nx.is_graphical(z, method="eg")
+    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    assert not nx.is_graphical(z, method="hh")
+    assert not nx.is_graphical(z, method="eg")
+
+
+def test_directed_degree_sequence():
+    # Test a range of valid directed degree sequences
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(r):
+        G = nx.erdos_renyi_graph(n, p * (i + 1), None, True)
+        din = (d for n, d in G.in_degree())
+        dout = (d for n, d in G.out_degree())
+        assert nx.is_digraphical(din, dout)
+
+
+def test_small_directed_sequences():
+    dout = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    din = [3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 1]
+    assert nx.is_digraphical(din, dout)
+    # Test nongraphical directed sequence
+    dout = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    din = [103, 102, 102, 102, 102, 102, 102, 102, 102, 102]
+    assert not nx.is_digraphical(din, dout)
+    # Test digraphical small sequence
+    dout = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 1, 1]
+    assert nx.is_digraphical(din, dout)
+    # Test nonmatching sum
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1]
+    assert not nx.is_digraphical(din, dout)
+    # Test for negative integer in sequence
+    din = [2, 2, 2, -2, 2, 2, 2, 2, 1, 1, 4]
+    assert not nx.is_digraphical(din, dout)
+    # Test for noninteger
+    din = dout = [1, 1, 1.1, 1]
+    assert not nx.is_digraphical(din, dout)
+    din = dout = [1, 1, "rer", 1]
+    assert not nx.is_digraphical(din, dout)
+
+
+def test_multi_sequence():
+    # Test nongraphical multi sequence
+    seq = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1]
+    assert not nx.is_multigraphical(seq)
+    # Test small graphical multi sequence
+    seq = [6, 5, 4, 4, 2, 1, 1, 1]
+    assert nx.is_multigraphical(seq)
+    # Test for negative integer in sequence
+    seq = [6, 5, 4, -4, 2, 1, 1, 1]
+    assert not nx.is_multigraphical(seq)
+    # Test for sequence with odd sum
+    seq = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    assert not nx.is_multigraphical(seq)
+    # Test for noninteger
+    seq = [1, 1, 1.1, 1]
+    assert not nx.is_multigraphical(seq)
+    seq = [1, 1, "rer", 1]
+    assert not nx.is_multigraphical(seq)
+
+
+def test_pseudo_sequence():
+    # Test small valid pseudo sequence
+    seq = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1]
+    assert nx.is_pseudographical(seq)
+    # Test for sequence with odd sum
+    seq = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    assert not nx.is_pseudographical(seq)
+    # Test for negative integer in sequence
+    seq = [1000, 3, 3, 3, 3, 2, 2, -2, 1, 1]
+    assert not nx.is_pseudographical(seq)
+    # Test for noninteger
+    seq = [1, 1, 1.1, 1]
+    assert not nx.is_pseudographical(seq)
+    seq = [1, 1, "rer", 1]
+    assert not nx.is_pseudographical(seq)
+
+
+def test_numpy_degree_sequence():
+    np = pytest.importorskip("numpy")
+    ds = np.array([1, 2, 2, 2, 1], dtype=np.int64)
+    assert nx.is_graphical(ds, "eg")
+    assert nx.is_graphical(ds, "hh")
+    ds = np.array([1, 2, 2, 2, 1], dtype=np.float64)
+    assert nx.is_graphical(ds, "eg")
+    assert nx.is_graphical(ds, "hh")
+    ds = np.array([1.1, 2, 2, 2, 1], dtype=np.float64)
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ds, "eg")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ds, "hh")
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_hierarchy.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_hierarchy.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa6a67b8b7f048719aa189b8365ef8e4c65951c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_hierarchy.py
@@ -0,0 +1,46 @@
+import pytest
+
+import networkx as nx
+
+
+def test_hierarchy_undirected():
+    G = nx.cycle_graph(5)
+    pytest.raises(nx.NetworkXError, nx.flow_hierarchy, G)
+
+
+def test_hierarchy_cycle():
+    G = nx.cycle_graph(5, create_using=nx.DiGraph())
+    assert nx.flow_hierarchy(G) == 0.0
+
+
+def test_hierarchy_tree():
+    G = nx.full_rary_tree(2, 16, create_using=nx.DiGraph())
+    assert nx.flow_hierarchy(G) == 1.0
+
+
+def test_hierarchy_1():
+    G = nx.DiGraph()
+    G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 1), (3, 4), (0, 4)])
+    assert nx.flow_hierarchy(G) == 0.5
+
+
+def test_hierarchy_weight():
+    G = nx.DiGraph()
+    G.add_edges_from(
+        [
+            (0, 1, {"weight": 0.3}),
+            (1, 2, {"weight": 0.1}),
+            (2, 3, {"weight": 0.1}),
+            (3, 1, {"weight": 0.1}),
+            (3, 4, {"weight": 0.3}),
+            (0, 4, {"weight": 0.3}),
+        ]
+    )
+    assert nx.flow_hierarchy(G, weight="weight") == 0.75
+
+
+@pytest.mark.parametrize("n", (0, 1, 3))
+def test_hierarchy_empty_graph(n):
+    G = nx.empty_graph(n, create_using=nx.DiGraph)
+    with pytest.raises(nx.NetworkXError, match=".*not applicable to empty graphs"):
+        nx.flow_hierarchy(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_hybrid.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_hybrid.py
new file mode 100644
index 0000000000000000000000000000000000000000..6af0016498549caed58772e304c93113a8b693d9
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_hybrid.py
@@ -0,0 +1,24 @@
+import networkx as nx
+
+
+def test_2d_grid_graph():
+    # FC article claims 2d grid graph of size n is (3,3)-connected
+    # and (5,9)-connected, but I don't think it is (5,9)-connected
+    G = nx.grid_2d_graph(8, 8, periodic=True)
+    assert nx.is_kl_connected(G, 3, 3)
+    assert not nx.is_kl_connected(G, 5, 9)
+    (H, graphOK) = nx.kl_connected_subgraph(G, 5, 9, same_as_graph=True)
+    assert not graphOK
+
+
+def test_small_graph():
+    G = nx.Graph()
+    G.add_edge(1, 2)
+    G.add_edge(1, 3)
+    G.add_edge(2, 3)
+    assert nx.is_kl_connected(G, 2, 2)
+    H = nx.kl_connected_subgraph(G, 2, 2)
+    (H, graphOK) = nx.kl_connected_subgraph(
+        G, 2, 2, low_memory=True, same_as_graph=True
+    )
+    assert graphOK
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_isolate.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_isolate.py
new file mode 100644
index 0000000000000000000000000000000000000000..d29b306d2b13c2457905c41218e5c60793b309ba
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_isolate.py
@@ -0,0 +1,26 @@
+"""Unit tests for the :mod:`networkx.algorithms.isolates` module."""
+
+import networkx as nx
+
+
+def test_is_isolate():
+    G = nx.Graph()
+    G.add_edge(0, 1)
+    G.add_node(2)
+    assert not nx.is_isolate(G, 0)
+    assert not nx.is_isolate(G, 1)
+    assert nx.is_isolate(G, 2)
+
+
+def test_isolates():
+    G = nx.Graph()
+    G.add_edge(0, 1)
+    G.add_nodes_from([2, 3])
+    assert sorted(nx.isolates(G)) == [2, 3]
+
+
+def test_number_of_isolates():
+    G = nx.Graph()
+    G.add_edge(0, 1)
+    G.add_nodes_from([2, 3])
+    assert nx.number_of_isolates(G) == 2
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_link_prediction.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_link_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..0220d9cd24dc5f1eceb8937e3274a21415529349
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_link_prediction.py
@@ -0,0 +1,615 @@
+import math
+from functools import partial
+
+import pytest
+
+import networkx as nx
+
+
+def _test_func(G, ebunch, expected, predict_func, **kwargs):
+    result = predict_func(G, ebunch, **kwargs)
+    exp_dict = {tuple(sorted([u, v])): score for u, v, score in expected}
+    res_dict = {tuple(sorted([u, v])): score for u, v, score in result}
+
+    assert len(exp_dict) == len(res_dict)
+    for p in exp_dict:
+        assert exp_dict[p] == pytest.approx(res_dict[p], abs=1e-7)
+
+
+class TestResourceAllocationIndex:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.resource_allocation_index)
+        cls.test = staticmethod(partial(_test_func, predict_func=cls.func))
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        self.test(G, [(0, 1)], [(0, 1, 0.75)])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        self.test(G, [(0, 2)], [(0, 2, 0.5)])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        self.test(G, [(1, 2)], [(1, 2, 0.25)])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_equal_nodes(self):
+        G = nx.complete_graph(4)
+        self.test(G, [(0, 0)], [(0, 0, 1)])
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        self.test(G, None, [(0, 3, 0.5), (1, 2, 0.5), (1, 3, 0)])
+
+
+class TestJaccardCoefficient:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.jaccard_coefficient)
+        cls.test = staticmethod(partial(_test_func, predict_func=cls.func))
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        self.test(G, [(0, 1)], [(0, 1, 0.6)])
+
+    def test_P4(self):
+        G = nx.path_graph(4)
+        self.test(G, [(0, 2)], [(0, 2, 0.5)])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (2, 3)])
+        self.test(G, [(0, 2)], [(0, 2, 0)])
+
+    def test_isolated_nodes(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        self.test(G, None, [(0, 3, 0.5), (1, 2, 0.5), (1, 3, 0)])
+
+
+class TestAdamicAdarIndex:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.adamic_adar_index)
+        cls.test = staticmethod(partial(_test_func, predict_func=cls.func))
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        self.test(G, [(0, 1)], [(0, 1, 3 / math.log(4))])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        self.test(G, [(0, 2)], [(0, 2, 1 / math.log(2))])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        self.test(G, [(1, 2)], [(1, 2, 1 / math.log(4))])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = graph_type([(0, 1), (1, 2)])
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_equal_nodes(self):
+        G = nx.complete_graph(4)
+        self.test(G, [(0, 0)], [(0, 0, 3 / math.log(3))])
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        self.test(
+            G, None, [(0, 3, 1 / math.log(2)), (1, 2, 1 / math.log(2)), (1, 3, 0)]
+        )
+
+
+class TestCommonNeighborCentrality:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.common_neighbor_centrality)
+        cls.test = staticmethod(partial(_test_func, predict_func=cls.func))
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        self.test(G, [(0, 1)], [(0, 1, 3.0)], alpha=1)
+        self.test(G, [(0, 1)], [(0, 1, 5.0)], alpha=0)
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        self.test(G, [(0, 2)], [(0, 2, 1.25)], alpha=0.5)
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        self.test(G, [(1, 2)], [(1, 2, 1.75)], alpha=0.5)
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_u_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(1, 3), (2, 3)])
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 1)])
+
+    def test_node_v_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_equal_nodes(self):
+        G = nx.complete_graph(4)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            self.test(G, [(0, 0)], [])
+
+    def test_equal_nodes_with_alpha_one_raises_error(self):
+        G = nx.complete_graph(4)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            self.test(G, [(0, 0)], [], alpha=1.0)
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        self.test(G, None, [(0, 3, 1.5), (1, 2, 1.5), (1, 3, 2 / 3)], alpha=0.5)
+
+
+class TestPreferentialAttachment:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.preferential_attachment)
+        cls.test = staticmethod(partial(_test_func, predict_func=cls.func))
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        self.test(G, [(0, 1)], [(0, 1, 16)])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        self.test(G, [(0, 1)], [(0, 1, 2)])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        self.test(G, [(0, 2)], [(0, 2, 4)])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_zero_degrees(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        self.test(G, None, [(0, 3, 2), (1, 2, 2), (1, 3, 1)])
+
+
+class TestCNSoundarajanHopcroft:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.cn_soundarajan_hopcroft)
+        cls.test = staticmethod(
+            partial(_test_func, predict_func=cls.func, community="community")
+        )
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 1
+        self.test(G, [(0, 1)], [(0, 1, 5)])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        self.test(G, [(0, 2)], [(0, 2, 1)])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        G.nodes[0]["community"] = 1
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 1
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 0
+        self.test(G, [(1, 2)], [(1, 2, 2)])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        G.add_nodes_from([0, 1, 2], community=0)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_equal_nodes(self):
+        G = nx.complete_graph(3)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        self.test(G, [(0, 0)], [(0, 0, 4)])
+
+    def test_different_community(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 1
+        self.test(G, [(0, 3)], [(0, 3, 2)])
+
+    def test_no_community_information(self):
+        G = nx.complete_graph(5)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            list(self.func(G, [(0, 1)]))
+
+    def test_insufficient_community_information(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[3]["community"] = 0
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            list(self.func(G, [(0, 3)]))
+
+    def test_sufficient_community_information(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 0
+        self.test(G, [(1, 4)], [(1, 4, 4)])
+
+    def test_custom_community_attribute_name(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["cmty"] = 0
+        G.nodes[1]["cmty"] = 0
+        G.nodes[2]["cmty"] = 0
+        G.nodes[3]["cmty"] = 1
+        self.test(G, [(0, 3)], [(0, 3, 2)], community="cmty")
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        self.test(G, None, [(0, 3, 2), (1, 2, 1), (1, 3, 0)])
+
+
+class TestRAIndexSoundarajanHopcroft:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.ra_index_soundarajan_hopcroft)
+        cls.test = staticmethod(
+            partial(_test_func, predict_func=cls.func, community="community")
+        )
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 1
+        self.test(G, [(0, 1)], [(0, 1, 0.5)])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        self.test(G, [(0, 2)], [(0, 2, 0)])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        G.nodes[0]["community"] = 1
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 1
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 0
+        self.test(G, [(1, 2)], [(1, 2, 0.25)])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        G.add_nodes_from([0, 1, 2], community=0)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_equal_nodes(self):
+        G = nx.complete_graph(3)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        self.test(G, [(0, 0)], [(0, 0, 1)])
+
+    def test_different_community(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 1
+        self.test(G, [(0, 3)], [(0, 3, 0)])
+
+    def test_no_community_information(self):
+        G = nx.complete_graph(5)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            list(self.func(G, [(0, 1)]))
+
+    def test_insufficient_community_information(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[3]["community"] = 0
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            list(self.func(G, [(0, 3)]))
+
+    def test_sufficient_community_information(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 0
+        self.test(G, [(1, 4)], [(1, 4, 1)])
+
+    def test_custom_community_attribute_name(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["cmty"] = 0
+        G.nodes[1]["cmty"] = 0
+        G.nodes[2]["cmty"] = 0
+        G.nodes[3]["cmty"] = 1
+        self.test(G, [(0, 3)], [(0, 3, 0)], community="cmty")
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        self.test(G, None, [(0, 3, 0.5), (1, 2, 0), (1, 3, 0)])
+
+
+class TestWithinInterCluster:
+    @classmethod
+    def setup_class(cls):
+        cls.delta = 0.001
+        cls.func = staticmethod(nx.within_inter_cluster)
+        cls.test = staticmethod(
+            partial(
+                _test_func,
+                predict_func=cls.func,
+                delta=cls.delta,
+                community="community",
+            )
+        )
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 1
+        self.test(G, [(0, 1)], [(0, 1, 2 / (1 + self.delta))])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        self.test(G, [(0, 2)], [(0, 2, 0)])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        G.nodes[0]["community"] = 1
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 1
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 0
+        self.test(G, [(1, 2)], [(1, 2, 1 / self.delta)])
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph))
+    def test_notimplemented(self, graph_type):
+        G = graph_type([(0, 1), (1, 2)])
+        G.add_nodes_from([0, 1, 2], community=0)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            self.func(G, [(0, 2)])
+
+    def test_node_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        with pytest.raises(nx.NodeNotFound):
+            self.func(G, [(0, 4)])
+
+    def test_no_common_neighbor(self):
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        self.test(G, [(0, 1)], [(0, 1, 0)])
+
+    def test_equal_nodes(self):
+        G = nx.complete_graph(3)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        self.test(G, [(0, 0)], [(0, 0, 2 / self.delta)])
+
+    def test_different_community(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 1
+        self.test(G, [(0, 3)], [(0, 3, 0)])
+
+    def test_no_inter_cluster_common_neighbor(self):
+        G = nx.complete_graph(4)
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        self.test(G, [(0, 3)], [(0, 3, 2 / self.delta)])
+
+    def test_no_community_information(self):
+        G = nx.complete_graph(5)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            list(self.func(G, [(0, 1)]))
+
+    def test_insufficient_community_information(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 0
+        G.nodes[3]["community"] = 0
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            list(self.func(G, [(0, 3)]))
+
+    def test_sufficient_community_information(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])
+        G.nodes[1]["community"] = 0
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        G.nodes[4]["community"] = 0
+        self.test(G, [(1, 4)], [(1, 4, 2 / self.delta)])
+
+    def test_invalid_delta(self):
+        G = nx.complete_graph(3)
+        G.add_nodes_from([0, 1, 2], community=0)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            self.func(G, [(0, 1)], 0)
+        with pytest.raises(nx.NetworkXAlgorithmError):
+            self.func(G, [(0, 1)], -0.5)
+
+    def test_custom_community_attribute_name(self):
+        G = nx.complete_graph(4)
+        G.nodes[0]["cmty"] = 0
+        G.nodes[1]["cmty"] = 0
+        G.nodes[2]["cmty"] = 0
+        G.nodes[3]["cmty"] = 0
+        self.test(G, [(0, 3)], [(0, 3, 2 / self.delta)], community="cmty")
+
+    def test_all_nonexistent_edges(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (2, 3)])
+        G.nodes[0]["community"] = 0
+        G.nodes[1]["community"] = 1
+        G.nodes[2]["community"] = 0
+        G.nodes[3]["community"] = 0
+        self.test(G, None, [(0, 3, 1 / self.delta), (1, 2, 0), (1, 3, 0)])
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_lowest_common_ancestors.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_lowest_common_ancestors.py
new file mode 100644
index 0000000000000000000000000000000000000000..639a31fd5f306e2df432cf03d45154f5ee3ea48d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_lowest_common_ancestors.py
@@ -0,0 +1,459 @@
+from itertools import chain, combinations, product
+
+import pytest
+
+import networkx as nx
+
+tree_all_pairs_lca = nx.tree_all_pairs_lowest_common_ancestor
+all_pairs_lca = nx.all_pairs_lowest_common_ancestor
+
+
+def get_pair(dictionary, n1, n2):
+    if (n1, n2) in dictionary:
+        return dictionary[n1, n2]
+    else:
+        return dictionary[n2, n1]
+
+
+class TestTreeLCA:
+    @classmethod
+    def setup_class(cls):
+        cls.DG = nx.DiGraph()
+        edges = [(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)]
+        cls.DG.add_edges_from(edges)
+        cls.ans = dict(tree_all_pairs_lca(cls.DG, 0))
+        gold = {(n, n): n for n in cls.DG}
+        gold.update({(0, i): 0 for i in range(1, 7)})
+        gold.update(
+            {
+                (1, 2): 0,
+                (1, 3): 1,
+                (1, 4): 1,
+                (1, 5): 0,
+                (1, 6): 0,
+                (2, 3): 0,
+                (2, 4): 0,
+                (2, 5): 2,
+                (2, 6): 2,
+                (3, 4): 1,
+                (3, 5): 0,
+                (3, 6): 0,
+                (4, 5): 0,
+                (4, 6): 0,
+                (5, 6): 2,
+            }
+        )
+
+        cls.gold = gold
+
+    @staticmethod
+    def assert_has_same_pairs(d1, d2):
+        for a, b in ((min(pair), max(pair)) for pair in chain(d1, d2)):
+            assert get_pair(d1, a, b) == get_pair(d2, a, b)
+
+    def test_tree_all_pairs_lca_default_root(self):
+        assert dict(tree_all_pairs_lca(self.DG)) == self.ans
+
+    def test_tree_all_pairs_lca_return_subset(self):
+        test_pairs = [(0, 1), (0, 1), (1, 0)]
+        ans = dict(tree_all_pairs_lca(self.DG, 0, test_pairs))
+        assert (0, 1) in ans and (1, 0) in ans
+        assert len(ans) == 2
+
+    def test_tree_all_pairs_lca(self):
+        all_pairs = chain(combinations(self.DG, 2), ((node, node) for node in self.DG))
+
+        ans = dict(tree_all_pairs_lca(self.DG, 0, all_pairs))
+        self.assert_has_same_pairs(ans, self.ans)
+
+    def test_tree_all_pairs_gold_example(self):
+        ans = dict(tree_all_pairs_lca(self.DG))
+        self.assert_has_same_pairs(self.gold, ans)
+
+    def test_tree_all_pairs_lca_invalid_input(self):
+        empty_digraph = tree_all_pairs_lca(nx.DiGraph())
+        pytest.raises(nx.NetworkXPointlessConcept, list, empty_digraph)
+
+        bad_pairs_digraph = tree_all_pairs_lca(self.DG, pairs=[(-1, -2)])
+        pytest.raises(nx.NodeNotFound, list, bad_pairs_digraph)
+
+    def test_tree_all_pairs_lca_subtrees(self):
+        ans = dict(tree_all_pairs_lca(self.DG, 1))
+        gold = {
+            pair: lca
+            for (pair, lca) in self.gold.items()
+            if all(n in (1, 3, 4) for n in pair)
+        }
+        self.assert_has_same_pairs(gold, ans)
+
+    def test_tree_all_pairs_lca_disconnected_nodes(self):
+        G = nx.DiGraph()
+        G.add_node(1)
+        assert {(1, 1): 1} == dict(tree_all_pairs_lca(G))
+
+        G.add_node(0)
+        assert {(1, 1): 1} == dict(tree_all_pairs_lca(G, 1))
+        assert {(0, 0): 0} == dict(tree_all_pairs_lca(G, 0))
+
+        pytest.raises(nx.NetworkXError, list, tree_all_pairs_lca(G))
+
+    def test_tree_all_pairs_lca_error_if_input_not_tree(self):
+        # Cycle
+        G = nx.DiGraph([(1, 2), (2, 1)])
+        pytest.raises(nx.NetworkXError, list, tree_all_pairs_lca(G))
+        # DAG
+        G = nx.DiGraph([(0, 2), (1, 2)])
+        pytest.raises(nx.NetworkXError, list, tree_all_pairs_lca(G))
+
+    def test_tree_all_pairs_lca_generator(self):
+        pairs = iter([(0, 1), (0, 1), (1, 0)])
+        some_pairs = dict(tree_all_pairs_lca(self.DG, 0, pairs))
+        assert (0, 1) in some_pairs and (1, 0) in some_pairs
+        assert len(some_pairs) == 2
+
+    def test_tree_all_pairs_lca_nonexisting_pairs_exception(self):
+        lca = tree_all_pairs_lca(self.DG, 0, [(-1, -1)])
+        pytest.raises(nx.NodeNotFound, list, lca)
+        # check if node is None
+        lca = tree_all_pairs_lca(self.DG, None, [(-1, -1)])
+        pytest.raises(nx.NodeNotFound, list, lca)
+
+    def test_tree_all_pairs_lca_routine_bails_on_DAGs(self):
+        G = nx.DiGraph([(3, 4), (5, 4)])
+        pytest.raises(nx.NetworkXError, list, tree_all_pairs_lca(G))
+
+    def test_tree_all_pairs_lca_not_implemented(self):
+        NNI = nx.NetworkXNotImplemented
+        G = nx.Graph([(0, 1)])
+        with pytest.raises(NNI):
+            next(tree_all_pairs_lca(G))
+        with pytest.raises(NNI):
+            next(all_pairs_lca(G))
+        pytest.raises(NNI, nx.lowest_common_ancestor, G, 0, 1)
+        G = nx.MultiGraph([(0, 1)])
+        with pytest.raises(NNI):
+            next(tree_all_pairs_lca(G))
+        with pytest.raises(NNI):
+            next(all_pairs_lca(G))
+        pytest.raises(NNI, nx.lowest_common_ancestor, G, 0, 1)
+
+    def test_tree_all_pairs_lca_trees_without_LCAs(self):
+        G = nx.DiGraph()
+        G.add_node(3)
+        ans = list(tree_all_pairs_lca(G))
+        assert ans == [((3, 3), 3)]
+
+
+class TestMultiTreeLCA(TestTreeLCA):
+    @classmethod
+    def setup_class(cls):
+        cls.DG = nx.MultiDiGraph()
+        edges = [(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)]
+        cls.DG.add_edges_from(edges)
+        cls.ans = dict(tree_all_pairs_lca(cls.DG, 0))
+        # add multiedges
+        cls.DG.add_edges_from(edges)
+
+        gold = {(n, n): n for n in cls.DG}
+        gold.update({(0, i): 0 for i in range(1, 7)})
+        gold.update(
+            {
+                (1, 2): 0,
+                (1, 3): 1,
+                (1, 4): 1,
+                (1, 5): 0,
+                (1, 6): 0,
+                (2, 3): 0,
+                (2, 4): 0,
+                (2, 5): 2,
+                (2, 6): 2,
+                (3, 4): 1,
+                (3, 5): 0,
+                (3, 6): 0,
+                (4, 5): 0,
+                (4, 6): 0,
+                (5, 6): 2,
+            }
+        )
+
+        cls.gold = gold
+
+
+class TestDAGLCA:
+    @classmethod
+    def setup_class(cls):
+        cls.DG = nx.DiGraph()
+        nx.add_path(cls.DG, (0, 1, 2, 3))
+        nx.add_path(cls.DG, (0, 4, 3))
+        nx.add_path(cls.DG, (0, 5, 6, 8, 3))
+        nx.add_path(cls.DG, (5, 7, 8))
+        cls.DG.add_edge(6, 2)
+        cls.DG.add_edge(7, 2)
+
+        cls.root_distance = nx.shortest_path_length(cls.DG, source=0)
+
+        cls.gold = {
+            (1, 1): 1,
+            (1, 2): 1,
+            (1, 3): 1,
+            (1, 4): 0,
+            (1, 5): 0,
+            (1, 6): 0,
+            (1, 7): 0,
+            (1, 8): 0,
+            (2, 2): 2,
+            (2, 3): 2,
+            (2, 4): 0,
+            (2, 5): 5,
+            (2, 6): 6,
+            (2, 7): 7,
+            (2, 8): 7,
+            (3, 3): 3,
+            (3, 4): 4,
+            (3, 5): 5,
+            (3, 6): 6,
+            (3, 7): 7,
+            (3, 8): 8,
+            (4, 4): 4,
+            (4, 5): 0,
+            (4, 6): 0,
+            (4, 7): 0,
+            (4, 8): 0,
+            (5, 5): 5,
+            (5, 6): 5,
+            (5, 7): 5,
+            (5, 8): 5,
+            (6, 6): 6,
+            (6, 7): 5,
+            (6, 8): 6,
+            (7, 7): 7,
+            (7, 8): 7,
+            (8, 8): 8,
+        }
+        cls.gold.update(((0, n), 0) for n in cls.DG)
+
+    def assert_lca_dicts_same(self, d1, d2, G=None):
+        """Checks if d1 and d2 contain the same pairs and
+        have a node at the same distance from root for each.
+        If G is None use self.DG."""
+        if G is None:
+            G = self.DG
+            root_distance = self.root_distance
+        else:
+            roots = [n for n, deg in G.in_degree if deg == 0]
+            assert len(roots) == 1
+            root_distance = nx.shortest_path_length(G, source=roots[0])
+
+        for a, b in ((min(pair), max(pair)) for pair in chain(d1, d2)):
+            assert (
+                root_distance[get_pair(d1, a, b)] == root_distance[get_pair(d2, a, b)]
+            )
+
+    def test_all_pairs_lca_gold_example(self):
+        self.assert_lca_dicts_same(dict(all_pairs_lca(self.DG)), self.gold)
+
+    def test_all_pairs_lca_all_pairs_given(self):
+        all_pairs = list(product(self.DG.nodes(), self.DG.nodes()))
+        ans = all_pairs_lca(self.DG, pairs=all_pairs)
+        self.assert_lca_dicts_same(dict(ans), self.gold)
+
+    def test_all_pairs_lca_generator(self):
+        all_pairs = product(self.DG.nodes(), self.DG.nodes())
+        ans = all_pairs_lca(self.DG, pairs=all_pairs)
+        self.assert_lca_dicts_same(dict(ans), self.gold)
+
+    def test_all_pairs_lca_input_graph_with_two_roots(self):
+        G = self.DG.copy()
+        G.add_edge(9, 10)
+        G.add_edge(9, 4)
+        gold = self.gold.copy()
+        gold[9, 9] = 9
+        gold[9, 10] = 9
+        gold[9, 4] = 9
+        gold[9, 3] = 9
+        gold[10, 4] = 9
+        gold[10, 3] = 9
+        gold[10, 10] = 10
+
+        testing = dict(all_pairs_lca(G))
+
+        G.add_edge(-1, 9)
+        G.add_edge(-1, 0)
+        self.assert_lca_dicts_same(testing, gold, G)
+
+    def test_all_pairs_lca_nonexisting_pairs_exception(self):
+        pytest.raises(nx.NodeNotFound, all_pairs_lca, self.DG, [(-1, -1)])
+
+    def test_all_pairs_lca_pairs_without_lca(self):
+        G = self.DG.copy()
+        G.add_node(-1)
+        gen = all_pairs_lca(G, [(-1, -1), (-1, 0)])
+        assert dict(gen) == {(-1, -1): -1}
+
+    def test_all_pairs_lca_null_graph(self):
+        pytest.raises(nx.NetworkXPointlessConcept, all_pairs_lca, nx.DiGraph())
+
+    def test_all_pairs_lca_non_dags(self):
+        pytest.raises(nx.NetworkXError, all_pairs_lca, nx.DiGraph([(3, 4), (4, 3)]))
+
+    def test_all_pairs_lca_nonempty_graph_without_lca(self):
+        G = nx.DiGraph()
+        G.add_node(3)
+        ans = list(all_pairs_lca(G))
+        assert ans == [((3, 3), 3)]
+
+    def test_all_pairs_lca_bug_gh4942(self):
+        G = nx.DiGraph([(0, 2), (1, 2), (2, 3)])
+        ans = list(all_pairs_lca(G))
+        assert len(ans) == 9
+
+    def test_all_pairs_lca_default_kwarg(self):
+        G = nx.DiGraph([(0, 1), (2, 1)])
+        sentinel = object()
+        assert nx.lowest_common_ancestor(G, 0, 2, default=sentinel) is sentinel
+
+    def test_all_pairs_lca_identity(self):
+        G = nx.DiGraph()
+        G.add_node(3)
+        assert nx.lowest_common_ancestor(G, 3, 3) == 3
+
+    def test_all_pairs_lca_issue_4574(self):
+        G = nx.DiGraph()
+        G.add_nodes_from(range(17))
+        G.add_edges_from(
+            [
+                (2, 0),
+                (1, 2),
+                (3, 2),
+                (5, 2),
+                (8, 2),
+                (11, 2),
+                (4, 5),
+                (6, 5),
+                (7, 8),
+                (10, 8),
+                (13, 11),
+                (14, 11),
+                (15, 11),
+                (9, 10),
+                (12, 13),
+                (16, 15),
+            ]
+        )
+
+        assert nx.lowest_common_ancestor(G, 7, 9) is None
+
+    def test_all_pairs_lca_one_pair_gh4942(self):
+        G = nx.DiGraph()
+        # Note: order edge addition is critical to the test
+        G.add_edge(0, 1)
+        G.add_edge(2, 0)
+        G.add_edge(2, 3)
+        G.add_edge(4, 0)
+        G.add_edge(5, 2)
+
+        assert nx.lowest_common_ancestor(G, 1, 3) == 2
+
+
+class TestMultiDiGraph_DAGLCA(TestDAGLCA):
+    @classmethod
+    def setup_class(cls):
+        cls.DG = nx.MultiDiGraph()
+        nx.add_path(cls.DG, (0, 1, 2, 3))
+        # add multiedges
+        nx.add_path(cls.DG, (0, 1, 2, 3))
+        nx.add_path(cls.DG, (0, 4, 3))
+        nx.add_path(cls.DG, (0, 5, 6, 8, 3))
+        nx.add_path(cls.DG, (5, 7, 8))
+        cls.DG.add_edge(6, 2)
+        cls.DG.add_edge(7, 2)
+
+        cls.root_distance = nx.shortest_path_length(cls.DG, source=0)
+
+        cls.gold = {
+            (1, 1): 1,
+            (1, 2): 1,
+            (1, 3): 1,
+            (1, 4): 0,
+            (1, 5): 0,
+            (1, 6): 0,
+            (1, 7): 0,
+            (1, 8): 0,
+            (2, 2): 2,
+            (2, 3): 2,
+            (2, 4): 0,
+            (2, 5): 5,
+            (2, 6): 6,
+            (2, 7): 7,
+            (2, 8): 7,
+            (3, 3): 3,
+            (3, 4): 4,
+            (3, 5): 5,
+            (3, 6): 6,
+            (3, 7): 7,
+            (3, 8): 8,
+            (4, 4): 4,
+            (4, 5): 0,
+            (4, 6): 0,
+            (4, 7): 0,
+            (4, 8): 0,
+            (5, 5): 5,
+            (5, 6): 5,
+            (5, 7): 5,
+            (5, 8): 5,
+            (6, 6): 6,
+            (6, 7): 5,
+            (6, 8): 6,
+            (7, 7): 7,
+            (7, 8): 7,
+            (8, 8): 8,
+        }
+        cls.gold.update(((0, n), 0) for n in cls.DG)
+
+
+def test_all_pairs_lca_self_ancestors():
+    """Self-ancestors should always be the node itself, i.e. lca of (0, 0) is 0.
+    See gh-4458."""
+    # DAG for test - note order of node/edge addition is relevant
+    G = nx.DiGraph()
+    G.add_nodes_from(range(5))
+    G.add_edges_from([(1, 0), (2, 0), (3, 2), (4, 1), (4, 3)])
+
+    ap_lca = nx.all_pairs_lowest_common_ancestor
+    assert all(u == v == a for (u, v), a in ap_lca(G) if u == v)
+    MG = nx.MultiDiGraph(G)
+    assert all(u == v == a for (u, v), a in ap_lca(MG) if u == v)
+    MG.add_edges_from([(1, 0), (2, 0)])
+    assert all(u == v == a for (u, v), a in ap_lca(MG) if u == v)
+
+
+def test_lca_on_null_graph():
+    G = nx.null_graph(create_using=nx.DiGraph)
+    with pytest.raises(
+        nx.NetworkXPointlessConcept, match="LCA meaningless on null graphs"
+    ):
+        nx.lowest_common_ancestor(G, 0, 0)
+
+
+def test_lca_on_cycle_graph():
+    G = nx.cycle_graph(6, create_using=nx.DiGraph)
+    with pytest.raises(
+        nx.NetworkXError, match="LCA only defined on directed acyclic graphs"
+    ):
+        nx.lowest_common_ancestor(G, 0, 3)
+
+
+def test_lca_multiple_valid_solutions():
+    G = nx.DiGraph()
+    G.add_nodes_from(range(4))
+    G.add_edges_from([(2, 0), (3, 0), (2, 1), (3, 1)])
+    assert nx.lowest_common_ancestor(G, 0, 1) in {2, 3}
+
+
+def test_lca_dont_rely_on_single_successor():
+    # Nodes 0 and 1 have nodes 2 and 3 as immediate ancestors,
+    # and node 2 also has node 3 as an immediate ancestor.
+    G = nx.DiGraph()
+    G.add_nodes_from(range(4))
+    G.add_edges_from([(2, 0), (2, 1), (3, 1), (3, 0), (3, 2)])
+    assert nx.lowest_common_ancestor(G, 0, 1) == 2
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_matching.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c98900669edf2cd1edbe37471b2cb1b56ae466e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_matching.py
@@ -0,0 +1,556 @@
+import math
+from itertools import permutations
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal
+
+
+@pytest.mark.parametrize(
+    "fn", (nx.is_matching, nx.is_maximal_matching, nx.is_perfect_matching)
+)
+@pytest.mark.parametrize(
+    "edgeset",
+    (
+        {(0, 5)},  # Single edge, node not in G
+        {(5, 0)},  # for both edge orders
+        {(0, 5), (2, 3)},  # node not in G, but other edge is valid matching
+        {(5, 5), (2, 3)},  # Self-loop hits node not in G validation first
+    ),
+)
+def test_is_matching_node_not_in_G(fn, edgeset):
+    """All is_*matching functions have consistent exception message for node
+    not in G."""
+    G = nx.path_graph(4)
+    with pytest.raises(nx.NetworkXError, match="matching.*with node not in G"):
+        fn(G, edgeset)
+
+
+@pytest.mark.parametrize(
+    "fn", (nx.is_matching, nx.is_maximal_matching, nx.is_perfect_matching)
+)
+@pytest.mark.parametrize(
+    "edgeset",
+    (
+        {(0, 1, 2), (2, 3)},  # 3-tuple
+        {(0,), (2, 3)},  # 1-tuple
+    ),
+)
+def test_is_matching_invalid_edge(fn, edgeset):
+    """All is_*matching functions have consistent exception message for invalid
+    edges in matching."""
+    G = nx.path_graph(4)
+    with pytest.raises(nx.NetworkXError, match=".*non-2-tuple edge.*"):
+        fn(G, edgeset)
+
+
+@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.DiGraph, nx.MultiDiGraph))
+@pytest.mark.parametrize(
+    "fn", (nx.max_weight_matching, nx.min_weight_matching, nx.maximal_matching)
+)
+def test_wrong_graph_type(fn, graph_type):
+    G = graph_type()
+    with pytest.raises(nx.NetworkXNotImplemented):
+        fn(G)
+
+
+class TestMaxWeightMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.max_weight_matching` function.
+
+    """
+
+    def test_trivial1(self):
+        """Empty graph"""
+        G = nx.Graph()
+        assert nx.max_weight_matching(G) == set()
+        assert nx.min_weight_matching(G) == set()
+
+    def test_selfloop(self):
+        G = nx.Graph()
+        G.add_edge(0, 0, weight=100)
+        assert nx.max_weight_matching(G) == set()
+        assert nx.min_weight_matching(G) == set()
+
+    def test_single_edge(self):
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        assert edges_equal(nx.max_weight_matching(G), {(0, 1)})
+        assert edges_equal(nx.min_weight_matching(G), {(0, 1)})
+
+    def test_two_path(self):
+        G = nx.Graph()
+        G.add_edge("one", "two", weight=10)
+        G.add_edge("two", "three", weight=11)
+        assert edges_equal(nx.max_weight_matching(G), {("two", "three")})
+        assert edges_equal(nx.min_weight_matching(G), {("one", "two")})
+
+    def test_path(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=5)
+        G.add_edge(2, 3, weight=11)
+        G.add_edge(3, 4, weight=5)
+        assert edges_equal(nx.max_weight_matching(G), {(2, 3)})
+        assert edges_equal(nx.max_weight_matching(G, weight=None), {(1, 2), (3, 4)})
+        assert edges_equal(nx.min_weight_matching(G), {(1, 2), (3, 4)})
+        assert edges_equal(nx.min_weight_matching(G, weight=None), {(1, 2), (3, 4)})
+
+    def test_square(self):
+        G = nx.Graph()
+        G.add_edge(1, 4, weight=2)
+        G.add_edge(2, 3, weight=2)
+        G.add_edge(1, 2, weight=1)
+        G.add_edge(3, 4, weight=4)
+        assert edges_equal(nx.max_weight_matching(G), {(1, 2), (3, 4)})
+        assert edges_equal(nx.min_weight_matching(G), {(1, 4), (2, 3)})
+
+    def test_edge_attribute_name(self):
+        G = nx.Graph()
+        G.add_edge("one", "two", weight=10, abcd=11)
+        G.add_edge("two", "three", weight=11, abcd=10)
+        assert edges_equal(nx.max_weight_matching(G, weight="abcd"), {("one", "two")})
+        assert edges_equal(nx.min_weight_matching(G, weight="abcd"), {("two", "three")})
+
+    def test_floating_point_weights(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=math.pi)
+        G.add_edge(2, 3, weight=math.exp(1))
+        G.add_edge(1, 3, weight=3.0)
+        G.add_edge(1, 4, weight=math.sqrt(2.0))
+        assert edges_equal(nx.max_weight_matching(G), {(1, 4), (2, 3)})
+        assert edges_equal(nx.min_weight_matching(G), {(1, 4), (2, 3)})
+
+    def test_negative_weights(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=-2)
+        G.add_edge(2, 3, weight=1)
+        G.add_edge(2, 4, weight=-1)
+        G.add_edge(3, 4, weight=-6)
+        assert edges_equal(nx.max_weight_matching(G), {(1, 2)})
+        assert edges_equal(
+            nx.max_weight_matching(G, maxcardinality=True), {(1, 3), (2, 4)}
+        )
+        assert edges_equal(nx.min_weight_matching(G), {(1, 2), (3, 4)})
+
+    def test_s_blossom(self):
+        """Create S-blossom and use it for augmentation:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from([(1, 2, 8), (1, 3, 9), (2, 3, 10), (3, 4, 7)])
+        answer = {(1, 2), (3, 4)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+        G.add_weighted_edges_from([(1, 6, 5), (4, 5, 6)])
+        answer = {(1, 6), (2, 3), (4, 5)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_s_t_blossom(self):
+        """Create S-blossom, relabel as T-blossom, use for augmentation:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [(1, 2, 9), (1, 3, 8), (2, 3, 10), (1, 4, 5), (4, 5, 4), (1, 6, 3)]
+        )
+        answer = {(1, 6), (2, 3), (4, 5)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+        G.add_edge(4, 5, weight=3)
+        G.add_edge(1, 6, weight=4)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+        G.remove_edge(1, 6)
+        G.add_edge(3, 6, weight=4)
+        answer = {(1, 2), (3, 6), (4, 5)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nested_s_blossom(self):
+        """Create nested S-blossom, use for augmentation:"""
+
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 9),
+                (1, 3, 9),
+                (2, 3, 10),
+                (2, 4, 8),
+                (3, 5, 8),
+                (4, 5, 10),
+                (5, 6, 6),
+            ]
+        )
+        expected_edgeset = {(1, 3), (2, 4), (5, 6)}
+        expected = {frozenset(e) for e in expected_edgeset}
+        answer = {frozenset(e) for e in nx.max_weight_matching(G)}
+        assert answer == expected
+        answer = {frozenset(e) for e in nx.min_weight_matching(G)}
+        assert answer == expected
+
+    def test_nested_s_blossom_relabel(self):
+        """Create S-blossom, relabel as S, include in nested S-blossom:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 10),
+                (1, 7, 10),
+                (2, 3, 12),
+                (3, 4, 20),
+                (3, 5, 20),
+                (4, 5, 25),
+                (5, 6, 10),
+                (6, 7, 10),
+                (7, 8, 8),
+            ]
+        )
+        answer = {(1, 2), (3, 4), (5, 6), (7, 8)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nested_s_blossom_expand(self):
+        """Create nested S-blossom, augment, expand recursively:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 8),
+                (1, 3, 8),
+                (2, 3, 10),
+                (2, 4, 12),
+                (3, 5, 12),
+                (4, 5, 14),
+                (4, 6, 12),
+                (5, 7, 12),
+                (6, 7, 14),
+                (7, 8, 12),
+            ]
+        )
+        answer = {(1, 2), (3, 5), (4, 6), (7, 8)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_s_blossom_relabel_expand(self):
+        """Create S-blossom, relabel as T, expand:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 23),
+                (1, 5, 22),
+                (1, 6, 15),
+                (2, 3, 25),
+                (3, 4, 22),
+                (4, 5, 25),
+                (4, 8, 14),
+                (5, 7, 13),
+            ]
+        )
+        answer = {(1, 6), (2, 3), (4, 8), (5, 7)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nested_s_blossom_relabel_expand(self):
+        """Create nested S-blossom, relabel as T, expand:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 19),
+                (1, 3, 20),
+                (1, 8, 8),
+                (2, 3, 25),
+                (2, 4, 18),
+                (3, 5, 18),
+                (4, 5, 13),
+                (4, 7, 7),
+                (5, 6, 7),
+            ]
+        )
+        answer = {(1, 8), (2, 3), (4, 7), (5, 6)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom1(self):
+        """Create blossom, relabel as T in more than one way, expand,
+        augment:
+        """
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 5, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 50),
+                (1, 6, 30),
+                (3, 9, 35),
+                (4, 8, 35),
+                (5, 7, 26),
+                (9, 10, 5),
+            ]
+        )
+        answer = {(1, 6), (2, 3), (4, 8), (5, 7), (9, 10)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom2(self):
+        """Again but slightly different:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 5, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 50),
+                (1, 6, 30),
+                (3, 9, 35),
+                (4, 8, 26),
+                (5, 7, 40),
+                (9, 10, 5),
+            ]
+        )
+        answer = {(1, 6), (2, 3), (4, 8), (5, 7), (9, 10)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom_least_slack(self):
+        """Create blossom, relabel as T, expand such that a new
+        least-slack S-to-free dge is produced, augment:
+        """
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 5, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 50),
+                (1, 6, 30),
+                (3, 9, 35),
+                (4, 8, 28),
+                (5, 7, 26),
+                (9, 10, 5),
+            ]
+        )
+        answer = {(1, 6), (2, 3), (4, 8), (5, 7), (9, 10)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom_augmenting(self):
+        """Create nested blossom, relabel as T in more than one way"""
+        # expand outer blossom such that inner blossom ends up on an
+        # augmenting path:
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 7, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 95),
+                (4, 6, 94),
+                (5, 6, 94),
+                (6, 7, 50),
+                (1, 8, 30),
+                (3, 11, 35),
+                (5, 9, 36),
+                (7, 10, 26),
+                (11, 12, 5),
+            ]
+        )
+        answer = {(1, 8), (2, 3), (4, 6), (5, 9), (7, 10), (11, 12)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom_expand_recursively(self):
+        """Create nested S-blossom, relabel as S, expand recursively:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 40),
+                (1, 3, 40),
+                (2, 3, 60),
+                (2, 4, 55),
+                (3, 5, 55),
+                (4, 5, 50),
+                (1, 8, 15),
+                (5, 7, 30),
+                (7, 6, 10),
+                (8, 10, 10),
+                (4, 9, 30),
+            ]
+        )
+        answer = {(1, 2), (3, 5), (4, 9), (6, 7), (8, 10)}
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_min_weight_matching_max_cardinality(self):
+        G = nx.Graph()
+        G.add_weighted_edges_from([(1, 2, 1000), (2, 3, 2), (3, 4, 3000)])
+        # The minimum-weight maximal matching is {(2, 3)}; the minimum-weight
+        # maximum-cardinality matching is {(1, 2), (3, 4)}. See gh-8062.
+        answer = {(1, 2), (3, 4)}
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+
+class TestIsMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.is_matching` function.
+
+    """
+
+    def test_dict(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {0: 1, 1: 0, 2: 3, 3: 2})
+
+    def test_empty_matching(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, set())
+
+    def test_single_edge(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {(1, 2)})
+
+    def test_edge_order(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {(0, 1), (2, 3)})
+        assert nx.is_matching(G, {(1, 0), (2, 3)})
+        assert nx.is_matching(G, {(0, 1), (3, 2)})
+        assert nx.is_matching(G, {(1, 0), (3, 2)})
+
+    def test_valid_matching(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {(0, 1), (2, 3)})
+
+    def test_selfloops(self):
+        G = nx.path_graph(4)
+        # selfloop edge not in G
+        assert not nx.is_matching(G, {(0, 0), (1, 2), (2, 3)})
+        # selfloop edge in G
+        G.add_edge(0, 0)
+        assert not nx.is_matching(G, {(0, 0), (1, 2)})
+
+    def test_invalid_matching(self):
+        G = nx.path_graph(4)
+        assert not nx.is_matching(G, {(0, 1), (1, 2), (2, 3)})
+
+    def test_invalid_edge(self):
+        G = nx.path_graph(4)
+        assert not nx.is_matching(G, {(0, 3), (1, 2)})
+
+        G = nx.DiGraph(G.edges)
+        assert nx.is_matching(G, {(0, 1)})
+        assert not nx.is_matching(G, {(1, 0)})
+
+
+class TestIsMaximalMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.is_maximal_matching` function.
+
+    """
+
+    def test_dict(self):
+        G = nx.path_graph(4)
+        assert nx.is_maximal_matching(G, {0: 1, 1: 0, 2: 3, 3: 2})
+
+    def test_valid(self):
+        G = nx.path_graph(4)
+        assert nx.is_maximal_matching(G, {(0, 1), (2, 3)})
+
+    def test_not_matching(self):
+        G = nx.path_graph(4)
+        assert not nx.is_maximal_matching(G, {(0, 1), (1, 2), (2, 3)})
+        assert not nx.is_maximal_matching(G, {(0, 3)})
+        G.add_edge(0, 0)
+        assert not nx.is_maximal_matching(G, {(0, 0)})
+
+    def test_not_maximal(self):
+        G = nx.path_graph(4)
+        assert not nx.is_maximal_matching(G, {(0, 1)})
+
+
+class TestIsPerfectMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.is_perfect_matching` function.
+
+    """
+
+    def test_dict(self):
+        G = nx.path_graph(4)
+        assert nx.is_perfect_matching(G, {0: 1, 1: 0, 2: 3, 3: 2})
+
+    def test_valid(self):
+        G = nx.path_graph(4)
+        assert nx.is_perfect_matching(G, {(0, 1), (2, 3)})
+
+    def test_valid_not_path(self):
+        G = nx.cycle_graph(4)
+        G.add_edge(0, 4)
+        G.add_edge(1, 4)
+        G.add_edge(5, 2)
+
+        assert nx.is_perfect_matching(G, {(1, 4), (0, 3), (5, 2)})
+
+    def test_selfloops(self):
+        G = nx.path_graph(4)
+        # selfloop edge not in G
+        assert not nx.is_perfect_matching(G, {(0, 0), (1, 2), (2, 3)})
+        # selfloop edge in G
+        G.add_edge(0, 0)
+        assert not nx.is_perfect_matching(G, {(0, 0), (1, 2)})
+
+    def test_not_matching(self):
+        G = nx.path_graph(4)
+        assert not nx.is_perfect_matching(G, {(0, 3)})
+        assert not nx.is_perfect_matching(G, {(0, 1), (1, 2), (2, 3)})
+
+    def test_maximal_but_not_perfect(self):
+        G = nx.cycle_graph(4)
+        G.add_edge(0, 4)
+        G.add_edge(1, 4)
+
+        assert not nx.is_perfect_matching(G, {(1, 4), (0, 3)})
+
+
+class TestMaximalMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.maximal_matching`.
+
+    """
+
+    def test_valid_matching(self):
+        edges = [(1, 2), (1, 5), (2, 3), (2, 5), (3, 4), (3, 6), (5, 6)]
+        G = nx.Graph(edges)
+        matching = nx.maximal_matching(G)
+        assert nx.is_maximal_matching(G, matching)
+
+    def test_single_edge_matching(self):
+        # In the star graph, any maximal matching has just one edge.
+        G = nx.star_graph(5)
+        matching = nx.maximal_matching(G)
+        assert 1 == len(matching)
+        assert nx.is_maximal_matching(G, matching)
+
+    def test_self_loops(self):
+        # Create the path graph with two self-loops.
+        G = nx.path_graph(3)
+        G.add_edges_from([(0, 0), (1, 1)])
+        matching = nx.maximal_matching(G)
+        assert len(matching) == 1
+        # The matching should never include self-loops.
+        assert not any(u == v for u, v in matching)
+        assert nx.is_maximal_matching(G, matching)
+
+    def test_ordering(self):
+        """Tests that a maximal matching is computed correctly
+        regardless of the order in which nodes are added to the graph.
+
+        """
+        for nodes in permutations(range(3)):
+            G = nx.Graph()
+            G.add_nodes_from(nodes)
+            G.add_edges_from([(0, 1), (0, 2)])
+            matching = nx.maximal_matching(G)
+            assert len(matching) == 1
+            assert nx.is_maximal_matching(G, matching)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_max_weight_clique.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_max_weight_clique.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cd8584ebd5c2ab04741234018a976472a92ef91
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_max_weight_clique.py
@@ -0,0 +1,179 @@
+"""Maximum weight clique test suite."""
+
+import pytest
+
+import networkx as nx
+
+
+class TestMaximumWeightClique:
+    def test_basic_cases(self):
+        def check_basic_case(graph_func, expected_weight, weight_accessor):
+            graph = graph_func()
+            clique, weight = nx.algorithms.max_weight_clique(graph, weight_accessor)
+            assert verify_clique(
+                graph, clique, weight, expected_weight, weight_accessor
+            )
+
+        for graph_func, (expected_weight, expected_size) in TEST_CASES.items():
+            check_basic_case(graph_func, expected_weight, "weight")
+            check_basic_case(graph_func, expected_size, None)
+
+    def test_key_error(self):
+        graph = two_node_graph()
+        with pytest.raises(KeyError):
+            nx.algorithms.max_weight_clique(graph, "nonexistent-key")
+
+    def test_error_on_non_integer_weight(self):
+        graph = two_node_graph()
+        graph.nodes[2]["weight"] = 1.5
+        with pytest.raises(ValueError):
+            nx.algorithms.max_weight_clique(graph)
+
+    def test_unaffected_by_self_loops(self):
+        graph = two_node_graph()
+        graph.add_edge(1, 1)
+        graph.add_edge(2, 2)
+        clique, weight = nx.algorithms.max_weight_clique(graph, "weight")
+        assert verify_clique(graph, clique, weight, 30, "weight")
+        graph = three_node_independent_set()
+        graph.add_edge(1, 1)
+        clique, weight = nx.algorithms.max_weight_clique(graph, "weight")
+        assert verify_clique(graph, clique, weight, 20, "weight")
+
+    def test_30_node_prob(self):
+        G = nx.Graph()
+        G.add_nodes_from(range(1, 31))
+        for i in range(1, 31):
+            G.nodes[i]["weight"] = i + 1
+        # fmt: off
+        G.add_edges_from(
+            [
+                (1, 12), (1, 13), (1, 15), (1, 16), (1, 18), (1, 19), (1, 20),
+                (1, 23), (1, 26), (1, 28), (1, 29), (1, 30), (2, 3), (2, 4),
+                (2, 5), (2, 8), (2, 9), (2, 10), (2, 14), (2, 17), (2, 18),
+                (2, 21), (2, 22), (2, 23), (2, 27), (3, 9), (3, 15), (3, 21),
+                (3, 22), (3, 23), (3, 24), (3, 27), (3, 28), (3, 29), (4, 5),
+                (4, 6), (4, 8), (4, 21), (4, 22), (4, 23), (4, 26), (4, 28),
+                (4, 30), (5, 6), (5, 8), (5, 9), (5, 13), (5, 14), (5, 15),
+                (5, 16), (5, 20), (5, 21), (5, 22), (5, 25), (5, 28), (5, 29),
+                (6, 7), (6, 8), (6, 13), (6, 17), (6, 18), (6, 19), (6, 24),
+                (6, 26), (6, 27), (6, 28), (6, 29), (7, 12), (7, 14), (7, 15),
+                (7, 16), (7, 17), (7, 20), (7, 25), (7, 27), (7, 29), (7, 30),
+                (8, 10), (8, 15), (8, 16), (8, 18), (8, 20), (8, 22), (8, 24),
+                (8, 26), (8, 27), (8, 28), (8, 30), (9, 11), (9, 12), (9, 13),
+                (9, 14), (9, 15), (9, 16), (9, 19), (9, 20), (9, 21), (9, 24),
+                (9, 30), (10, 12), (10, 15), (10, 18), (10, 19), (10, 20),
+                (10, 22), (10, 23), (10, 24), (10, 26), (10, 27), (10, 29),
+                (10, 30), (11, 13), (11, 15), (11, 16), (11, 17), (11, 18),
+                (11, 19), (11, 20), (11, 22), (11, 29), (11, 30), (12, 14),
+                (12, 17), (12, 18), (12, 19), (12, 20), (12, 21), (12, 23),
+                (12, 25), (12, 26), (12, 30), (13, 20), (13, 22), (13, 23),
+                (13, 24), (13, 30), (14, 16), (14, 20), (14, 21), (14, 22),
+                (14, 23), (14, 25), (14, 26), (14, 27), (14, 29), (14, 30),
+                (15, 17), (15, 18), (15, 20), (15, 21), (15, 26), (15, 27),
+                (15, 28), (16, 17), (16, 18), (16, 19), (16, 20), (16, 21),
+                (16, 29), (16, 30), (17, 18), (17, 21), (17, 22), (17, 25),
+                (17, 27), (17, 28), (17, 30), (18, 19), (18, 20), (18, 21),
+                (18, 22), (18, 23), (18, 24), (19, 20), (19, 22), (19, 23),
+                (19, 24), (19, 25), (19, 27), (19, 30), (20, 21), (20, 23),
+                (20, 24), (20, 26), (20, 28), (20, 29), (21, 23), (21, 26),
+                (21, 27), (21, 29), (22, 24), (22, 25), (22, 26), (22, 29),
+                (23, 25), (23, 30), (24, 25), (24, 26), (25, 27), (25, 29),
+                (26, 27), (26, 28), (26, 30), (28, 29), (29, 30),
+            ]
+        )
+        # fmt: on
+        clique, weight = nx.algorithms.max_weight_clique(G)
+        assert verify_clique(G, clique, weight, 111, "weight")
+
+
+#  ############################  Utility functions ############################
+def verify_clique(
+    graph, clique, reported_clique_weight, expected_clique_weight, weight_accessor
+):
+    for node1 in clique:
+        for node2 in clique:
+            if node1 == node2:
+                continue
+            if not graph.has_edge(node1, node2):
+                return False
+
+    if weight_accessor is None:
+        clique_weight = len(clique)
+    else:
+        clique_weight = sum(graph.nodes[v]["weight"] for v in clique)
+
+    if clique_weight != expected_clique_weight:
+        return False
+    if clique_weight != reported_clique_weight:
+        return False
+
+    return True
+
+
+#  ############################  Graph Generation ############################
+
+
+def empty_graph():
+    return nx.Graph()
+
+
+def one_node_graph():
+    graph = nx.Graph()
+    graph.add_nodes_from([1])
+    graph.nodes[1]["weight"] = 10
+    return graph
+
+
+def two_node_graph():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2])
+    graph.add_edges_from([(1, 2)])
+    graph.nodes[1]["weight"] = 10
+    graph.nodes[2]["weight"] = 20
+    return graph
+
+
+def three_node_clique():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3])
+    graph.add_edges_from([(1, 2), (1, 3), (2, 3)])
+    graph.nodes[1]["weight"] = 10
+    graph.nodes[2]["weight"] = 20
+    graph.nodes[3]["weight"] = 5
+    return graph
+
+
+def three_node_independent_set():
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3])
+    graph.nodes[1]["weight"] = 10
+    graph.nodes[2]["weight"] = 20
+    graph.nodes[3]["weight"] = 5
+    return graph
+
+
+def disconnected():
+    graph = nx.Graph()
+    graph.add_edges_from([(1, 2), (2, 3), (4, 5), (5, 6)])
+    graph.nodes[1]["weight"] = 10
+    graph.nodes[2]["weight"] = 20
+    graph.nodes[3]["weight"] = 5
+    graph.nodes[4]["weight"] = 100
+    graph.nodes[5]["weight"] = 200
+    graph.nodes[6]["weight"] = 50
+    return graph
+
+
+# --------------------------------------------------------------------------
+# Basic tests for all strategies
+# For each basic graph function, specify expected weight of max weight clique
+# and expected size of maximum clique
+TEST_CASES = {
+    empty_graph: (0, 0),
+    one_node_graph: (10, 1),
+    two_node_graph: (30, 2),
+    three_node_clique: (35, 3),
+    three_node_independent_set: (20, 1),
+    disconnected: (300, 2),
+}
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_mis.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_mis.py
new file mode 100644
index 0000000000000000000000000000000000000000..02be02d4c33f233d27d2838e5e3d361c4212c40b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_mis.py
@@ -0,0 +1,62 @@
+"""
+Tests for maximal (not maximum) independent sets.
+
+"""
+
+import random
+
+import pytest
+
+import networkx as nx
+
+
+def test_random_seed():
+    G = nx.empty_graph(5)
+    assert nx.maximal_independent_set(G, seed=1) == [1, 0, 3, 2, 4]
+
+
+@pytest.mark.parametrize("graph", [nx.complete_graph(5), nx.complete_graph(55)])
+def test_K5(graph):
+    """Maximal independent set for complete graphs"""
+    assert all(nx.maximal_independent_set(graph, [n]) == [n] for n in graph)
+
+
+def test_exceptions():
+    """Bad input should raise exception."""
+    G = nx.florentine_families_graph()
+    pytest.raises(nx.NetworkXUnfeasible, nx.maximal_independent_set, G, ["Smith"])
+    pytest.raises(
+        nx.NetworkXUnfeasible, nx.maximal_independent_set, G, ["Salviati", "Pazzi"]
+    )
+    # MaximalIndependentSet is not implemented for directed graphs
+    pytest.raises(nx.NetworkXNotImplemented, nx.maximal_independent_set, nx.DiGraph(G))
+
+
+def test_florentine_family():
+    G = nx.florentine_families_graph()
+    indep = nx.maximal_independent_set(G, ["Medici", "Bischeri"])
+    assert set(indep) == {
+        "Medici",
+        "Bischeri",
+        "Castellani",
+        "Pazzi",
+        "Ginori",
+        "Lamberteschi",
+    }
+
+
+def test_bipartite():
+    G = nx.complete_bipartite_graph(12, 34)
+    indep = nx.maximal_independent_set(G, [4, 5, 9, 10])
+    assert sorted(indep) == list(range(12))
+
+
+def test_random_graphs():
+    """Generate 5 random graphs of different types and sizes and
+    make sure that all sets are independent and maximal."""
+    for i in range(0, 50, 10):
+        G = nx.erdos_renyi_graph(i * 10 + 1, random.random())
+        IS = nx.maximal_independent_set(G)
+        assert G.subgraph(IS).number_of_edges() == 0
+        nbrs_of_MIS = set.union(*(set(G.neighbors(v)) for v in IS))
+        assert all(v in nbrs_of_MIS for v in set(G.nodes()).difference(IS))
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_moral.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_moral.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc98c9729a95897857013ae22333e3b8c17202fb
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_moral.py
@@ -0,0 +1,15 @@
+import networkx as nx
+from networkx.algorithms.moral import moral_graph
+
+
+def test_get_moral_graph():
+    graph = nx.DiGraph()
+    graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
+    graph.add_edges_from([(1, 2), (3, 2), (4, 1), (4, 5), (6, 5), (7, 5)])
+    H = moral_graph(graph)
+    assert not H.is_directed()
+    assert H.has_edge(1, 3)
+    assert H.has_edge(4, 6)
+    assert H.has_edge(6, 7)
+    assert H.has_edge(4, 7)
+    assert not H.has_edge(1, 5)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_node_classification.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_node_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e1fc79d48ae830625c3528f52e805d2e0d183ad
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_node_classification.py
@@ -0,0 +1,140 @@
+import pytest
+
+pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.algorithms import node_classification
+
+
+class TestHarmonicFunction:
+    def test_path_graph(self):
+        G = nx.path_graph(4)
+        label_name = "label"
+        G.nodes[0][label_name] = "A"
+        G.nodes[3][label_name] = "B"
+        predicted = node_classification.harmonic_function(G, label_name=label_name)
+        assert predicted[0] == "A"
+        assert predicted[1] == "A"
+        assert predicted[2] == "B"
+        assert predicted[3] == "B"
+
+    def test_no_labels(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.path_graph(4)
+            node_classification.harmonic_function(G)
+
+    def test_no_nodes(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph()
+            node_classification.harmonic_function(G)
+
+    def test_no_edges(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph()
+            G.add_node(1)
+            G.add_node(2)
+            node_classification.harmonic_function(G)
+
+    def test_digraph(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.DiGraph()
+            G.add_edge(0, 1)
+            G.add_edge(1, 2)
+            G.add_edge(2, 3)
+            label_name = "label"
+            G.nodes[0][label_name] = "A"
+            G.nodes[3][label_name] = "B"
+            node_classification.harmonic_function(G)
+
+    def test_one_labeled_node(self):
+        G = nx.path_graph(4)
+        label_name = "label"
+        G.nodes[0][label_name] = "A"
+        predicted = node_classification.harmonic_function(G, label_name=label_name)
+        assert predicted[0] == "A"
+        assert predicted[1] == "A"
+        assert predicted[2] == "A"
+        assert predicted[3] == "A"
+
+    def test_nodes_all_labeled(self):
+        G = nx.karate_club_graph()
+        label_name = "club"
+        predicted = node_classification.harmonic_function(G, label_name=label_name)
+        for i in range(len(G)):
+            assert predicted[i] == G.nodes[i][label_name]
+
+    def test_labeled_nodes_are_not_changed(self):
+        G = nx.karate_club_graph()
+        label_name = "club"
+        label_removed = {0, 1, 2, 3, 4, 5, 6, 7}
+        for i in label_removed:
+            del G.nodes[i][label_name]
+        predicted = node_classification.harmonic_function(G, label_name=label_name)
+        label_not_removed = set(range(len(G))) - label_removed
+        for i in label_not_removed:
+            assert predicted[i] == G.nodes[i][label_name]
+
+
+class TestLocalAndGlobalConsistency:
+    def test_path_graph(self):
+        G = nx.path_graph(4)
+        label_name = "label"
+        G.nodes[0][label_name] = "A"
+        G.nodes[3][label_name] = "B"
+        predicted = node_classification.local_and_global_consistency(
+            G, label_name=label_name
+        )
+        assert predicted[0] == "A"
+        assert predicted[1] == "A"
+        assert predicted[2] == "B"
+        assert predicted[3] == "B"
+
+    def test_no_labels(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.path_graph(4)
+            node_classification.local_and_global_consistency(G)
+
+    def test_no_nodes(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph()
+            node_classification.local_and_global_consistency(G)
+
+    def test_no_edges(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph()
+            G.add_node(1)
+            G.add_node(2)
+            node_classification.local_and_global_consistency(G)
+
+    def test_digraph(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.DiGraph()
+            G.add_edge(0, 1)
+            G.add_edge(1, 2)
+            G.add_edge(2, 3)
+            label_name = "label"
+            G.nodes[0][label_name] = "A"
+            G.nodes[3][label_name] = "B"
+            node_classification.harmonic_function(G)
+
+    def test_one_labeled_node(self):
+        G = nx.path_graph(4)
+        label_name = "label"
+        G.nodes[0][label_name] = "A"
+        predicted = node_classification.local_and_global_consistency(
+            G, label_name=label_name
+        )
+        assert predicted[0] == "A"
+        assert predicted[1] == "A"
+        assert predicted[2] == "A"
+        assert predicted[3] == "A"
+
+    def test_nodes_all_labeled(self):
+        G = nx.karate_club_graph()
+        label_name = "club"
+        predicted = node_classification.local_and_global_consistency(
+            G, alpha=0, label_name=label_name
+        )
+        for i in range(len(G)):
+            assert predicted[i] == G.nodes[i][label_name]
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_non_randomness.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_non_randomness.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdbcdaf15445f17fb22a7e3ec737a9f1a774b51c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_non_randomness.py
@@ -0,0 +1,60 @@
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+
+
+@pytest.mark.parametrize(
+    "k, weight, expected",
+    [
+        (None, None, 7.21),  # infers 3 communities
+        (2, None, 11.7),
+        (None, "weight", 25.45),
+        (2, "weight", 38.8),
+    ],
+)
+def test_non_randomness(k, weight, expected):
+    G = nx.karate_club_graph()
+    np.testing.assert_almost_equal(
+        nx.non_randomness(G, k, weight)[0], expected, decimal=2
+    )
+
+
+def test_non_connected():
+    G = nx.Graph([(1, 2)])
+    G.add_node(3)
+    with pytest.raises(nx.NetworkXException, match="Non connected"):
+        nx.non_randomness(G)
+
+
+def test_self_loops():
+    G = nx.Graph()
+    G.add_edge(1, 2)
+    G.add_edge(1, 1)
+    with pytest.raises(nx.NetworkXError, match="Graph must not contain self-loops"):
+        nx.non_randomness(G)
+
+
+def test_empty_graph():
+    G = nx.empty_graph(1)
+    with pytest.raises(nx.NetworkXError, match=".*not applicable to empty graphs"):
+        nx.non_randomness(G)
+
+
+@pytest.mark.parametrize("k", [-1, 0, 2, 5])
+def test_value_error(k):
+    """
+    Check that invalid values of k raise (must be between 1 and n - 1, inclusive,
+    and such that the probability is between 0 and 1, exclusive).
+    """
+    G = nx.path_graph(5)
+    with pytest.raises(ValueError, match=r"invalid number of communities"):
+        nx.non_randomness(G, k=k)
+
+
+@pytest.mark.parametrize("G", [nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()])
+def test_not_implemented(G):
+    """Check that non-randomness is not implemented for directed or multigraphs."""
+    with pytest.raises(nx.NetworkXNotImplemented, match=r"not implemented for"):
+        nx.non_randomness(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_perfect_graph.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_perfect_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..51c12589fb713176d1a06b272c63e095d04ad2af
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_perfect_graph.py
@@ -0,0 +1,27 @@
+import networkx as nx
+
+
+def test_chordal_graph():
+    G = nx.complete_graph(5)
+    assert nx.is_perfect_graph(G)
+
+
+def test_odd_cycle():
+    G = nx.cycle_graph(5)  # Induced odd cycle
+    assert not nx.is_perfect_graph(G)
+
+
+def test_even_cycle():
+    G = nx.cycle_graph(6)  # Even cycle is perfect
+    assert nx.is_perfect_graph(G)
+
+
+def test_complement_of_odd_cycle():
+    G = nx.cycle_graph(7)
+    GC = nx.complement(G)
+    assert not nx.is_perfect_graph(GC)
+
+
+def test_disconnected_union_of_cliques():
+    G = nx.disjoint_union(nx.complete_graph(3), nx.complete_graph(4))
+    assert nx.is_perfect_graph(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_planar_drawing.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_planar_drawing.py
new file mode 100644
index 0000000000000000000000000000000000000000..b59d5c17331d6aac71e688b3dfde46991ea2ef97
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_planar_drawing.py
@@ -0,0 +1,274 @@
+import math
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.planar_drawing import triangulate_embedding
+
+
+def test_graph1():
+    embedding_data = {0: [1, 2, 3], 1: [2, 0], 2: [3, 0, 1], 3: [2, 0]}
+    check_embedding_data(embedding_data)
+
+
+def test_graph2():
+    embedding_data = {
+        0: [8, 6],
+        1: [2, 6, 9],
+        2: [8, 1, 7, 9, 6, 4],
+        3: [9],
+        4: [2],
+        5: [6, 8],
+        6: [9, 1, 0, 5, 2],
+        7: [9, 2],
+        8: [0, 2, 5],
+        9: [1, 6, 2, 7, 3],
+    }
+    check_embedding_data(embedding_data)
+
+
+def test_circle_graph():
+    embedding_data = {
+        0: [1, 9],
+        1: [0, 2],
+        2: [1, 3],
+        3: [2, 4],
+        4: [3, 5],
+        5: [4, 6],
+        6: [5, 7],
+        7: [6, 8],
+        8: [7, 9],
+        9: [8, 0],
+    }
+    check_embedding_data(embedding_data)
+
+
+def test_grid_graph():
+    embedding_data = {
+        (0, 1): [(0, 0), (1, 1), (0, 2)],
+        (1, 2): [(1, 1), (2, 2), (0, 2)],
+        (0, 0): [(0, 1), (1, 0)],
+        (2, 1): [(2, 0), (2, 2), (1, 1)],
+        (1, 1): [(2, 1), (1, 2), (0, 1), (1, 0)],
+        (2, 0): [(1, 0), (2, 1)],
+        (2, 2): [(1, 2), (2, 1)],
+        (1, 0): [(0, 0), (2, 0), (1, 1)],
+        (0, 2): [(1, 2), (0, 1)],
+    }
+    check_embedding_data(embedding_data)
+
+
+def test_one_node_graph():
+    embedding_data = {0: []}
+    check_embedding_data(embedding_data)
+
+
+def test_two_node_graph():
+    embedding_data = {0: [1], 1: [0]}
+    check_embedding_data(embedding_data)
+
+
+def test_three_node_graph():
+    embedding_data = {0: [1, 2], 1: [0, 2], 2: [0, 1]}
+    check_embedding_data(embedding_data)
+
+
+def test_multiple_component_graph1():
+    embedding_data = {0: [], 1: []}
+    check_embedding_data(embedding_data)
+
+
+def test_multiple_component_graph2():
+    embedding_data = {0: [1, 2], 1: [0, 2], 2: [0, 1], 3: [4, 5], 4: [3, 5], 5: [3, 4]}
+    check_embedding_data(embedding_data)
+
+
+def test_invalid_half_edge():
+    with pytest.raises(nx.NetworkXException):
+        embedding_data = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2, 4], 4: [1, 2, 3]}
+        embedding = nx.PlanarEmbedding()
+        embedding.set_data(embedding_data)
+        nx.combinatorial_embedding_to_pos(embedding)
+
+
+def test_triangulate_embedding1():
+    embedding = nx.PlanarEmbedding()
+    embedding.add_node(1)
+    expected_embedding = {1: []}
+    check_triangulation(embedding, expected_embedding)
+
+
+def test_triangulate_embedding2():
+    embedding = nx.PlanarEmbedding()
+    embedding.connect_components(1, 2)
+    expected_embedding = {1: [2], 2: [1]}
+    check_triangulation(embedding, expected_embedding)
+
+
+def check_triangulation(embedding, expected_embedding):
+    res_embedding, _ = triangulate_embedding(embedding, True)
+    assert res_embedding.get_data() == expected_embedding, (
+        "Expected embedding incorrect"
+    )
+    res_embedding, _ = triangulate_embedding(embedding, False)
+    assert res_embedding.get_data() == expected_embedding, (
+        "Expected embedding incorrect"
+    )
+
+
+def check_embedding_data(embedding_data):
+    """Checks that the planar embedding of the input is correct"""
+    embedding = nx.PlanarEmbedding()
+    embedding.set_data(embedding_data)
+    pos_fully = nx.combinatorial_embedding_to_pos(embedding, False)
+    msg = "Planar drawing does not conform to the embedding (fully triangulation)"
+    assert planar_drawing_conforms_to_embedding(embedding, pos_fully), msg
+    check_edge_intersections(embedding, pos_fully)
+    pos_internally = nx.combinatorial_embedding_to_pos(embedding, True)
+    msg = "Planar drawing does not conform to the embedding (internal triangulation)"
+    assert planar_drawing_conforms_to_embedding(embedding, pos_internally), msg
+    check_edge_intersections(embedding, pos_internally)
+
+
+def is_close(a, b, rel_tol=1e-09, abs_tol=0.0):
+    # Check if float numbers are basically equal, for python >=3.5 there is
+    # function for that in the standard library
+    return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
+
+
+def point_in_between(a, b, p):
+    # checks if p is on the line between a and b
+    x1, y1 = a
+    x2, y2 = b
+    px, py = p
+    dist_1_2 = math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
+    dist_1_p = math.sqrt((x1 - px) ** 2 + (y1 - py) ** 2)
+    dist_2_p = math.sqrt((x2 - px) ** 2 + (y2 - py) ** 2)
+    return is_close(dist_1_p + dist_2_p, dist_1_2)
+
+
+def check_edge_intersections(G, pos):
+    """Check all edges in G for intersections.
+
+    Raises an exception if an intersection is found.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    pos : dict
+        Maps every node to a tuple (x, y) representing its position
+
+    """
+    for a, b in G.edges():
+        for c, d in G.edges():
+            # Check if end points are different
+            if a != c and b != d and b != c and a != d:
+                x1, y1 = pos[a]
+                x2, y2 = pos[b]
+                x3, y3 = pos[c]
+                x4, y4 = pos[d]
+                determinant = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+                if determinant != 0:  # the lines are not parallel
+                    # calculate intersection point, see:
+                    # https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection
+                    px = (x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (
+                        x3 * y4 - y3 * x4
+                    ) / determinant
+                    py = (x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (
+                        x3 * y4 - y3 * x4
+                    ) / determinant
+
+                    # Check if intersection lies between the points
+                    if point_in_between(pos[a], pos[b], (px, py)) and point_in_between(
+                        pos[c], pos[d], (px, py)
+                    ):
+                        msg = f"There is an intersection at {px},{py}"
+                        raise nx.NetworkXException(msg)
+
+                #  Check overlap
+                msg = "A node lies on a edge connecting two other nodes"
+                if (
+                    point_in_between(pos[a], pos[b], pos[c])
+                    or point_in_between(pos[a], pos[b], pos[d])
+                    or point_in_between(pos[c], pos[d], pos[a])
+                    or point_in_between(pos[c], pos[d], pos[b])
+                ):
+                    raise nx.NetworkXException(msg)
+    # No edge intersection found
+
+
+class Vector:
+    """Compare vectors by their angle without loss of precision
+
+    All vectors in direction [0, 1] are the smallest.
+    The vectors grow in clockwise direction.
+    """
+
+    __slots__ = ["x", "y", "node", "quadrant"]
+
+    def __init__(self, x, y, node):
+        self.x = x
+        self.y = y
+        self.node = node
+        if self.x >= 0 and self.y > 0:
+            self.quadrant = 1
+        elif self.x > 0 and self.y <= 0:
+            self.quadrant = 2
+        elif self.x <= 0 and self.y < 0:
+            self.quadrant = 3
+        else:
+            self.quadrant = 4
+
+    def __eq__(self, other):
+        return self.quadrant == other.quadrant and self.x * other.y == self.y * other.x
+
+    def __lt__(self, other):
+        if self.quadrant < other.quadrant:
+            return True
+        elif self.quadrant > other.quadrant:
+            return False
+        else:
+            return self.x * other.y < self.y * other.x
+
+    def __ne__(self, other):
+        return self != other
+
+    def __le__(self, other):
+        return not other < self
+
+    def __gt__(self, other):
+        return other < self
+
+    def __ge__(self, other):
+        return not self < other
+
+
+def planar_drawing_conforms_to_embedding(embedding, pos):
+    """Checks if pos conforms to the planar embedding
+
+    Returns true iff the neighbors are actually oriented in the orientation
+    specified of the embedding
+    """
+    for v in embedding:
+        nbr_vectors = []
+        v_pos = pos[v]
+        for nbr in embedding[v]:
+            new_vector = Vector(pos[nbr][0] - v_pos[0], pos[nbr][1] - v_pos[1], nbr)
+            nbr_vectors.append(new_vector)
+        # Sort neighbors according to their phi angle
+        nbr_vectors.sort()
+        for idx, nbr_vector in enumerate(nbr_vectors):
+            cw_vector = nbr_vectors[(idx + 1) % len(nbr_vectors)]
+            ccw_vector = nbr_vectors[idx - 1]
+            if (
+                embedding[v][nbr_vector.node]["cw"] != cw_vector.node
+                or embedding[v][nbr_vector.node]["ccw"] != ccw_vector.node
+            ):
+                return False
+            if cw_vector.node != nbr_vector.node and cw_vector == nbr_vector:
+                # Lines overlap
+                return False
+            if ccw_vector.node != nbr_vector.node and ccw_vector == nbr_vector:
+                # Lines overlap
+                return False
+    return True
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_planarity.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_planarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..af2d9a981d0120f97dce4a3a32bef457431c2e3d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_planarity.py
@@ -0,0 +1,556 @@
+import pytest
+
+import networkx as nx
+from networkx.algorithms.planarity import (
+    check_planarity_recursive,
+    get_counterexample,
+    get_counterexample_recursive,
+)
+
+
+class TestLRPlanarity:
+    """Nose Unit tests for the :mod:`networkx.algorithms.planarity` module.
+
+    Tests three things:
+    1. Check that the result is correct
+        (returns planar if and only if the graph is actually planar)
+    2. In case a counter example is returned: Check if it is correct
+    3. In case an embedding is returned: Check if its actually an embedding
+    """
+
+    @staticmethod
+    def check_graph(G, is_planar=None):
+        """Raises an exception if the lr_planarity check returns a wrong result
+
+        Parameters
+        ----------
+        G : NetworkX graph
+        is_planar : bool
+            The expected result of the planarity check.
+            If set to None only counter example or embedding are verified.
+
+        """
+
+        # obtain results of planarity check
+        is_planar_lr, result = nx.check_planarity(G, True)
+        is_planar_lr_rec, result_rec = check_planarity_recursive(G, True)
+
+        if is_planar is not None:
+            # set a message for the assert
+            if is_planar:
+                msg = "Wrong planarity check result. Should be planar."
+            else:
+                msg = "Wrong planarity check result. Should be non-planar."
+
+            # check if the result is as expected
+            assert is_planar == is_planar_lr, msg
+            assert is_planar == is_planar_lr_rec, msg
+
+        if is_planar_lr:
+            # check embedding
+            check_embedding(G, result)
+            check_embedding(G, result_rec)
+        else:
+            # check counter example
+            check_counterexample(G, result)
+            check_counterexample(G, result_rec)
+
+    def test_simple_planar_graph(self):
+        e = [
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (4, 6),
+            (6, 7),
+            (7, 1),
+            (1, 5),
+            (5, 2),
+            (2, 4),
+            (4, 5),
+            (5, 7),
+        ]
+        self.check_graph(nx.Graph(e), is_planar=True)
+
+    def test_planar_with_selfloop(self):
+        e = [
+            (1, 1),
+            (2, 2),
+            (3, 3),
+            (4, 4),
+            (5, 5),
+            (1, 2),
+            (1, 3),
+            (1, 5),
+            (2, 5),
+            (2, 4),
+            (3, 4),
+            (3, 5),
+            (4, 5),
+        ]
+        self.check_graph(nx.Graph(e), is_planar=True)
+
+    def test_k3_3(self):
+        self.check_graph(nx.complete_bipartite_graph(3, 3), is_planar=False)
+
+    def test_k5(self):
+        self.check_graph(nx.complete_graph(5), is_planar=False)
+
+    def test_multiple_components_planar(self):
+        e = [(1, 2), (2, 3), (3, 1), (4, 5), (5, 6), (6, 4)]
+        self.check_graph(nx.Graph(e), is_planar=True)
+
+    def test_multiple_components_non_planar(self):
+        G = nx.complete_graph(5)
+        # add another planar component to the non planar component
+        # G stays non planar
+        G.add_edges_from([(6, 7), (7, 8), (8, 6)])
+        self.check_graph(G, is_planar=False)
+
+    def test_non_planar_with_selfloop(self):
+        G = nx.complete_graph(5)
+        # add self loops
+        for i in range(5):
+            G.add_edge(i, i)
+        self.check_graph(G, is_planar=False)
+
+    def test_non_planar1(self):
+        # tests a graph that has no subgraph directly isomorph to K5 or K3_3
+        e = [
+            (1, 5),
+            (1, 6),
+            (1, 7),
+            (2, 6),
+            (2, 3),
+            (3, 5),
+            (3, 7),
+            (4, 5),
+            (4, 6),
+            (4, 7),
+        ]
+        self.check_graph(nx.Graph(e), is_planar=False)
+
+    def test_loop(self):
+        # test a graph with a selfloop
+        e = [(1, 2), (2, 2)]
+        G = nx.Graph(e)
+        self.check_graph(G, is_planar=True)
+
+    def test_comp(self):
+        # test multiple component graph
+        e = [(1, 2), (3, 4)]
+        G = nx.Graph(e)
+        G.remove_edge(1, 2)
+        self.check_graph(G, is_planar=True)
+
+    def test_goldner_harary(self):
+        # test goldner-harary graph (a maximal planar graph)
+        e = [
+            (1, 2),
+            (1, 3),
+            (1, 4),
+            (1, 5),
+            (1, 7),
+            (1, 8),
+            (1, 10),
+            (1, 11),
+            (2, 3),
+            (2, 4),
+            (2, 6),
+            (2, 7),
+            (2, 9),
+            (2, 10),
+            (2, 11),
+            (3, 4),
+            (4, 5),
+            (4, 6),
+            (4, 7),
+            (5, 7),
+            (6, 7),
+            (7, 8),
+            (7, 9),
+            (7, 10),
+            (8, 10),
+            (9, 10),
+            (10, 11),
+        ]
+        G = nx.Graph(e)
+        self.check_graph(G, is_planar=True)
+
+    def test_planar_multigraph(self):
+        G = nx.MultiGraph([(1, 2), (1, 2), (1, 2), (1, 2), (2, 3), (3, 1)])
+        self.check_graph(G, is_planar=True)
+
+    def test_non_planar_multigraph(self):
+        G = nx.MultiGraph(nx.complete_graph(5))
+        G.add_edges_from([(1, 2)] * 5)
+        self.check_graph(G, is_planar=False)
+
+    def test_planar_digraph(self):
+        G = nx.DiGraph([(1, 2), (2, 3), (2, 4), (4, 1), (4, 2), (1, 4), (3, 2)])
+        self.check_graph(G, is_planar=True)
+
+    def test_non_planar_digraph(self):
+        G = nx.DiGraph(nx.complete_graph(5))
+        G.remove_edge(1, 2)
+        G.remove_edge(4, 1)
+        self.check_graph(G, is_planar=False)
+
+    def test_single_component(self):
+        # Test a graph with only a single node
+        G = nx.Graph()
+        G.add_node(1)
+        self.check_graph(G, is_planar=True)
+
+    def test_graph1(self):
+        G = nx.Graph(
+            [
+                (3, 10),
+                (2, 13),
+                (1, 13),
+                (7, 11),
+                (0, 8),
+                (8, 13),
+                (0, 2),
+                (0, 7),
+                (0, 10),
+                (1, 7),
+            ]
+        )
+        self.check_graph(G, is_planar=True)
+
+    def test_graph2(self):
+        G = nx.Graph(
+            [
+                (1, 2),
+                (4, 13),
+                (0, 13),
+                (4, 5),
+                (7, 10),
+                (1, 7),
+                (0, 3),
+                (2, 6),
+                (5, 6),
+                (7, 13),
+                (4, 8),
+                (0, 8),
+                (0, 9),
+                (2, 13),
+                (6, 7),
+                (3, 6),
+                (2, 8),
+            ]
+        )
+        self.check_graph(G, is_planar=False)
+
+    def test_graph3(self):
+        G = nx.Graph(
+            [
+                (0, 7),
+                (3, 11),
+                (3, 4),
+                (8, 9),
+                (4, 11),
+                (1, 7),
+                (1, 13),
+                (1, 11),
+                (3, 5),
+                (5, 7),
+                (1, 3),
+                (0, 4),
+                (5, 11),
+                (5, 13),
+            ]
+        )
+        self.check_graph(G, is_planar=False)
+
+    def test_counterexample_planar(self):
+        with pytest.raises(nx.NetworkXException):
+            # Try to get a counterexample of a planar graph
+            G = nx.Graph()
+            G.add_node(1)
+            get_counterexample(G)
+
+    def test_counterexample_planar_recursive(self):
+        with pytest.raises(nx.NetworkXException):
+            # Try to get a counterexample of a planar graph
+            G = nx.Graph()
+            G.add_node(1)
+            get_counterexample_recursive(G)
+
+    def test_edge_removal_from_planar_embedding(self):
+        # PlanarEmbedding.check_structure() must succeed after edge removal
+        edges = ((0, 1), (1, 2), (2, 3), (3, 4), (4, 0), (0, 2), (0, 3))
+        G = nx.Graph(edges)
+        cert, P = nx.check_planarity(G)
+        assert cert is True
+        P.remove_edge(0, 2)
+        self.check_graph(P, is_planar=True)
+        P.add_half_edge_ccw(1, 3, 2)
+        P.add_half_edge_cw(3, 1, 2)
+        self.check_graph(P, is_planar=True)
+        P.remove_edges_from(((0, 3), (1, 3)))
+        self.check_graph(P, is_planar=True)
+
+    @pytest.mark.parametrize("graph_type", (nx.Graph, nx.MultiGraph))
+    def test_graph_planar_embedding_to_undirected(self, graph_type):
+        G = graph_type([(0, 1), (0, 1), (1, 2), (2, 3), (3, 0), (0, 2)])
+        is_planar, P = nx.check_planarity(G)
+        assert is_planar
+        U = P.to_undirected()
+        assert isinstance(U, nx.Graph)
+        assert all((d == {} for _, _, d in U.edges(data=True)))
+
+    @pytest.mark.parametrize(
+        "reciprocal, as_view", [(True, True), (True, False), (False, True)]
+    )
+    def test_planar_embedding_to_undirected_invalid_parameters(
+        self, reciprocal, as_view
+    ):
+        G = nx.Graph([(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)])
+        is_planar, P = nx.check_planarity(G)
+        assert is_planar
+        with pytest.raises(ValueError, match="is not supported for PlanarEmbedding."):
+            P.to_undirected(reciprocal=reciprocal, as_view=as_view)
+
+
+def check_embedding(G, embedding):
+    """Raises an exception if the combinatorial embedding is not correct
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    embedding : a dict mapping nodes to a list of edges
+        This specifies the ordering of the outgoing edges from a node for
+        a combinatorial embedding
+
+    Notes
+    -----
+    Checks the following things:
+        - The type of the embedding is correct
+        - The nodes and edges match the original graph
+        - Every half edge has its matching opposite half edge
+        - No intersections of edges (checked by Euler's formula)
+    """
+
+    if not isinstance(embedding, nx.PlanarEmbedding):
+        raise nx.NetworkXException("Bad embedding. Not of type nx.PlanarEmbedding")
+
+    # Check structure
+    embedding.check_structure()
+
+    # Check that graphs are equivalent
+
+    assert set(G.nodes) == set(embedding.nodes), (
+        "Bad embedding. Nodes don't match the original graph."
+    )
+
+    # Check that the edges are equal
+    g_edges = set()
+    for edge in G.edges:
+        if edge[0] != edge[1]:
+            g_edges.add((edge[0], edge[1]))
+            g_edges.add((edge[1], edge[0]))
+    assert g_edges == set(embedding.edges), (
+        "Bad embedding. Edges don't match the original graph."
+    )
+
+
+def check_counterexample(G, sub_graph):
+    """Raises an exception if the counterexample is wrong.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    subdivision_nodes : set
+        A set of nodes inducing a subgraph as a counterexample
+    """
+    # 1. Create the sub graph
+    sub_graph = nx.Graph(sub_graph)
+
+    # 2. Remove self loops
+    for u in sub_graph:
+        if sub_graph.has_edge(u, u):
+            sub_graph.remove_edge(u, u)
+
+    # keep track of nodes we might need to contract
+    contract = list(sub_graph)
+
+    # 3. Contract Edges
+    while len(contract) > 0:
+        contract_node = contract.pop()
+        if contract_node not in sub_graph:
+            # Node was already contracted
+            continue
+        degree = sub_graph.degree[contract_node]
+        # Check if we can remove the node
+        if degree == 2:
+            # Get the two neighbors
+            neighbors = iter(sub_graph[contract_node])
+            u = next(neighbors)
+            v = next(neighbors)
+            # Save nodes for later
+            contract.append(u)
+            contract.append(v)
+            # Contract edge
+            sub_graph.remove_node(contract_node)
+            sub_graph.add_edge(u, v)
+
+    # 4. Check for isomorphism with K5 or K3_3 graphs
+    if len(sub_graph) == 5:
+        if not nx.is_isomorphic(nx.complete_graph(5), sub_graph):
+            raise nx.NetworkXException("Bad counter example.")
+    elif len(sub_graph) == 6:
+        if not nx.is_isomorphic(nx.complete_bipartite_graph(3, 3), sub_graph):
+            raise nx.NetworkXException("Bad counter example.")
+    else:
+        raise nx.NetworkXException("Bad counter example.")
+
+
+class TestPlanarEmbeddingClass:
+    def test_add_half_edge(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.add_half_edge(0, 1)
+        with pytest.raises(
+            nx.NetworkXException, match="Invalid clockwise reference node."
+        ):
+            embedding.add_half_edge(0, 2, cw=3)
+        with pytest.raises(
+            nx.NetworkXException, match="Invalid counterclockwise reference node."
+        ):
+            embedding.add_half_edge(0, 2, ccw=3)
+        with pytest.raises(
+            nx.NetworkXException, match="Only one of cw/ccw can be specified."
+        ):
+            embedding.add_half_edge(0, 2, cw=1, ccw=1)
+        with pytest.raises(
+            nx.NetworkXException,
+            match=(
+                r"Node already has out-half-edge\(s\), either"
+                " cw or ccw reference node required."
+            ),
+        ):
+            embedding.add_half_edge(0, 2)
+        # these should work
+        embedding.add_half_edge(0, 2, cw=1)
+        embedding.add_half_edge(0, 3, ccw=1)
+        assert sorted(embedding.edges(data=True)) == [
+            (0, 1, {"ccw": 2, "cw": 3}),
+            (0, 2, {"cw": 1, "ccw": 3}),
+            (0, 3, {"cw": 2, "ccw": 1}),
+        ]
+
+    def test_get_data(self):
+        embedding = self.get_star_embedding(4)
+        data = embedding.get_data()
+        data_cmp = {0: [3, 2, 1], 1: [0], 2: [0], 3: [0]}
+        assert data == data_cmp
+
+    def test_edge_removal(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.set_data(
+            {
+                1: [2, 5, 7],
+                2: [1, 3, 4, 5],
+                3: [2, 4],
+                4: [3, 6, 5, 2],
+                5: [7, 1, 2, 4],
+                6: [4, 7],
+                7: [6, 1, 5],
+            }
+        )
+        # remove_edges_from() calls remove_edge(), so both are tested here
+        embedding.remove_edges_from(((5, 4), (1, 5)))
+        embedding.check_structure()
+        embedding_expected = nx.PlanarEmbedding()
+        embedding_expected.set_data(
+            {
+                1: [2, 7],
+                2: [1, 3, 4, 5],
+                3: [2, 4],
+                4: [3, 6, 2],
+                5: [7, 2],
+                6: [4, 7],
+                7: [6, 1, 5],
+            }
+        )
+        assert nx.utils.graphs_equal(embedding, embedding_expected)
+
+    def test_missing_edge_orientation(self):
+        embedding = nx.PlanarEmbedding({1: {2: {}}, 2: {1: {}}})
+        with pytest.raises(nx.NetworkXException):
+            # Invalid structure because the orientation of the edge was not set
+            embedding.check_structure()
+
+    def test_invalid_edge_orientation(self):
+        embedding = nx.PlanarEmbedding(
+            {
+                1: {2: {"cw": 2, "ccw": 2}},
+                2: {1: {"cw": 1, "ccw": 1}},
+                1: {3: {}},
+                3: {1: {}},
+            }
+        )
+        with pytest.raises(nx.NetworkXException):
+            embedding.check_structure()
+
+    def test_missing_half_edge(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.add_half_edge(1, 2)
+        with pytest.raises(nx.NetworkXException):
+            # Invalid structure because other half edge is missing
+            embedding.check_structure()
+
+    def test_not_fulfilling_euler_formula(self):
+        embedding = nx.PlanarEmbedding()
+        for i in range(5):
+            ref = None
+            for j in range(5):
+                if i != j:
+                    embedding.add_half_edge(i, j, cw=ref)
+                    ref = j
+        with pytest.raises(nx.NetworkXException):
+            embedding.check_structure()
+
+    def test_missing_reference(self):
+        embedding = nx.PlanarEmbedding()
+        with pytest.raises(nx.NetworkXException, match="Invalid reference node."):
+            embedding.add_half_edge(1, 2, ccw=3)
+
+    def test_connect_components(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.connect_components(1, 2)
+
+    def test_successful_face_traversal(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.add_half_edge(1, 2)
+        embedding.add_half_edge(2, 1)
+        face = embedding.traverse_face(1, 2)
+        assert face == [1, 2]
+
+    def test_unsuccessful_face_traversal(self):
+        embedding = nx.PlanarEmbedding(
+            {1: {2: {"cw": 3, "ccw": 2}}, 2: {1: {"cw": 3, "ccw": 1}}}
+        )
+        with pytest.raises(nx.NetworkXException):
+            embedding.traverse_face(1, 2)
+
+    def test_forbidden_methods(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.add_node(42)  # no exception
+        embedding.add_nodes_from([(23, 24)])  # no exception
+        with pytest.raises(NotImplementedError):
+            embedding.add_edge(1, 3)
+        with pytest.raises(NotImplementedError):
+            embedding.add_edges_from([(0, 2), (1, 4)])
+        with pytest.raises(NotImplementedError):
+            embedding.add_weighted_edges_from([(0, 2, 350), (1, 4, 125)])
+
+    @staticmethod
+    def get_star_embedding(n):
+        embedding = nx.PlanarEmbedding()
+        ref = None
+        for i in range(1, n):
+            embedding.add_half_edge(0, i, cw=ref)
+            ref = i
+            embedding.add_half_edge(i, 0)
+        return embedding
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_polynomials.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_polynomials.py
new file mode 100644
index 0000000000000000000000000000000000000000..a81d6a69551ead74d3335fda408111a0b580bf6a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_polynomials.py
@@ -0,0 +1,57 @@
+"""Unit tests for the :mod:`networkx.algorithms.polynomials` module."""
+
+import pytest
+
+import networkx as nx
+
+sympy = pytest.importorskip("sympy")
+
+
+# Mapping of input graphs to a string representation of their tutte polynomials
+_test_tutte_graphs = {
+    nx.complete_graph(1): "1",
+    nx.complete_graph(4): "x**3 + 3*x**2 + 4*x*y + 2*x + y**3 + 3*y**2 + 2*y",
+    nx.cycle_graph(5): "x**4 + x**3 + x**2 + x + y",
+    nx.diamond_graph(): "x**3 + 2*x**2 + 2*x*y + x + y**2 + y",
+}
+
+_test_chromatic_graphs = {
+    nx.complete_graph(1): "x",
+    nx.complete_graph(4): "x**4 - 6*x**3 + 11*x**2 - 6*x",
+    nx.cycle_graph(5): "x**5 - 5*x**4 + 10*x**3 - 10*x**2 + 4*x",
+    nx.diamond_graph(): "x**4 - 5*x**3 + 8*x**2 - 4*x",
+    nx.path_graph(5): "x**5 - 4*x**4 + 6*x**3 - 4*x**2 + x",
+}
+
+
+@pytest.mark.parametrize(("G", "expected"), _test_tutte_graphs.items())
+def test_tutte_polynomial(G, expected):
+    assert nx.tutte_polynomial(G).equals(expected)
+
+
+@pytest.mark.parametrize("G", _test_tutte_graphs.keys())
+def test_tutte_polynomial_disjoint(G):
+    """Tutte polynomial factors into the Tutte polynomials of its components.
+    Verify this property with the disjoint union of two copies of the input graph.
+    """
+    t_g = nx.tutte_polynomial(G)
+    H = nx.disjoint_union(G, G)
+    t_h = nx.tutte_polynomial(H)
+    assert sympy.simplify(t_g * t_g).equals(t_h)
+
+
+@pytest.mark.parametrize(("G", "expected"), _test_chromatic_graphs.items())
+def test_chromatic_polynomial(G, expected):
+    assert nx.chromatic_polynomial(G).equals(expected)
+
+
+@pytest.mark.parametrize("G", _test_chromatic_graphs.keys())
+def test_chromatic_polynomial_disjoint(G):
+    """Chromatic polynomial factors into the Chromatic polynomials of its
+    components. Verify this property with the disjoint union of two copies of
+    the input graph.
+    """
+    x_g = nx.chromatic_polynomial(G)
+    H = nx.disjoint_union(G, G)
+    x_h = nx.chromatic_polynomial(H)
+    assert sympy.simplify(x_g * x_g).equals(x_h)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_reciprocity.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_reciprocity.py
new file mode 100644
index 0000000000000000000000000000000000000000..e713bc4303f9bfea1199f01d8369c6bdab1a221f
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_reciprocity.py
@@ -0,0 +1,37 @@
+import pytest
+
+import networkx as nx
+
+
+class TestReciprocity:
+    # test overall reciprocity by passing whole graph
+    def test_reciprocity_digraph(self):
+        DG = nx.DiGraph([(1, 2), (2, 1)])
+        reciprocity = nx.reciprocity(DG)
+        assert reciprocity == 1.0
+
+    # test empty graph's overall reciprocity which will throw an error
+    def test_overall_reciprocity_empty_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            DG = nx.DiGraph()
+            nx.overall_reciprocity(DG)
+
+    # test for reciprocity for a list of nodes
+    def test_reciprocity_graph_nodes(self):
+        DG = nx.DiGraph([(1, 2), (2, 3), (3, 2)])
+        reciprocity = nx.reciprocity(DG, [1, 2])
+        expected_reciprocity = {1: 0.0, 2: 0.6666666666666666}
+        assert reciprocity == expected_reciprocity
+
+    # test for reciprocity for a single node
+    def test_reciprocity_graph_node(self):
+        DG = nx.DiGraph([(1, 2), (2, 3), (3, 2)])
+        reciprocity = nx.reciprocity(DG, 2)
+        assert reciprocity == 0.6666666666666666
+
+    # test for reciprocity for an isolated node
+    def test_reciprocity_graph_isolated_nodes(self):
+        with pytest.raises(nx.NetworkXError):
+            DG = nx.DiGraph([(1, 2)])
+            DG.add_node(4)
+            nx.reciprocity(DG, 4)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_regular.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_regular.py
new file mode 100644
index 0000000000000000000000000000000000000000..b652089a65c927256efda2bae2f4510d7aead739
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_regular.py
@@ -0,0 +1,88 @@
+import pytest
+
+import networkx as nx
+import networkx.algorithms.regular as reg
+import networkx.generators as gen
+
+
+class TestKFactor:
+    @pytest.mark.parametrize("n", [3, 4, 5])
+    def test_k_factor_cycle(self, n):
+        g = nx.cycle_graph(n)
+        kf = nx.k_factor(g, 2)
+        assert g.edges == kf.edges
+        assert g.nodes == kf.nodes
+
+    @pytest.mark.parametrize("k", range(3))
+    def test_k_factor_grid(self, k):
+        g = nx.grid_2d_graph(4, 4)
+        kf = nx.k_factor(g, k)
+        assert g.nodes == kf.nodes
+        assert all(g.has_edge(*e) for e in kf.edges)
+        assert nx.is_k_regular(kf, k)
+
+    @pytest.mark.parametrize("k", range(6))
+    def test_k_factor_complete(self, k):
+        g = nx.complete_graph(6)
+        kf = nx.k_factor(g, k)
+        assert g.nodes == kf.nodes
+        assert all(g.has_edge(*e) for e in kf.edges)
+        assert nx.is_k_regular(kf, k)
+
+    def test_k_factor_degree(self):
+        g = nx.grid_2d_graph(4, 4)
+        with pytest.raises(nx.NetworkXUnfeasible, match=r"degree less than"):
+            nx.k_factor(g, 3)
+
+    def test_k_factor_no_matching(self):
+        g = nx.hexagonal_lattice_graph(4, 4)
+        # Perfect matching doesn't exist for 4,4 hexagonal lattice graph
+        with pytest.raises(nx.NetworkXUnfeasible, match=r"no perfect matching"):
+            nx.k_factor(g, 2)
+
+    @pytest.mark.parametrize("graph_type", [nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph])
+    def test_k_factor_not_implemented(self, graph_type):
+        with pytest.raises(nx.NetworkXNotImplemented, match=r"not implemented for"):
+            nx.k_factor(graph_type(), 2)
+
+
+class TestIsRegular:
+    @pytest.mark.parametrize(
+        "graph,expected",
+        [
+            (nx.cycle_graph(4), True),
+            (nx.complete_graph(5), True),
+            (nx.path_graph(5), False),
+            (nx.lollipop_graph(5, 5), False),
+            (nx.cycle_graph(3, create_using=nx.DiGraph), True),
+            (nx.Graph([(0, 1)]), True),
+            (nx.DiGraph([(0, 1)]), False),
+            (nx.MultiGraph([(0, 1), (0, 1)]), True),
+            (nx.MultiDiGraph([(0, 1), (0, 1)]), False),
+        ],
+    )
+    def test_is_regular(self, graph, expected):
+        assert reg.is_regular(graph) == expected
+
+    def test_is_regular_empty_graph_raises(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXPointlessConcept, match="Graph has no nodes"):
+            nx.is_regular(G)
+
+
+class TestIsKRegular:
+    def test_is_k_regular1(self):
+        g = gen.cycle_graph(4)
+        assert reg.is_k_regular(g, 2)
+        assert not reg.is_k_regular(g, 3)
+
+    def test_is_k_regular2(self):
+        g = gen.complete_graph(5)
+        assert reg.is_k_regular(g, 4)
+        assert not reg.is_k_regular(g, 3)
+        assert not reg.is_k_regular(g, 6)
+
+    def test_is_k_regular3(self):
+        g = gen.lollipop_graph(5, 5)
+        assert not reg.is_k_regular(g, 5)
+        assert not reg.is_k_regular(g, 6)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_richclub.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_richclub.py
new file mode 100644
index 0000000000000000000000000000000000000000..21721577fed219aebcfe9a4388b25503ad252140
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_richclub.py
@@ -0,0 +1,149 @@
+import pytest
+
+import networkx as nx
+
+
+def test_richclub():
+    G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5)])
+    rc = nx.richclub.rich_club_coefficient(G, normalized=False)
+    assert rc == {0: 12.0 / 30, 1: 8.0 / 12}
+
+    # test single value
+    rc0 = nx.richclub.rich_club_coefficient(G, normalized=False)[0]
+    assert rc0 == 12.0 / 30.0
+
+
+def test_richclub_seed():
+    G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5)])
+    rcNorm = nx.richclub.rich_club_coefficient(G, Q=2, seed=1)
+    assert rcNorm == {0: 1.0, 1: 1.0}
+
+
+def test_richclub_normalized():
+    G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5)])
+    rcNorm = nx.richclub.rich_club_coefficient(G, Q=2, seed=42)
+    assert rcNorm == {0: 1.0, 1: 1.0}
+
+
+def test_richclub2():
+    T = nx.balanced_tree(2, 10)
+    rc = nx.richclub.rich_club_coefficient(T, normalized=False)
+    assert rc == {
+        0: 4092 / (2047 * 2046.0),
+        1: (2044.0 / (1023 * 1022)),
+        2: (2040.0 / (1022 * 1021)),
+    }
+
+
+def test_richclub3():
+    # tests edgecase
+    G = nx.karate_club_graph()
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {
+        0: 156.0 / 1122,
+        1: 154.0 / 1056,
+        2: 110.0 / 462,
+        3: 78.0 / 240,
+        4: 44.0 / 90,
+        5: 22.0 / 42,
+        6: 10.0 / 20,
+        7: 10.0 / 20,
+        8: 10.0 / 20,
+        9: 6.0 / 12,
+        10: 2.0 / 6,
+        11: 2.0 / 6,
+        12: 0.0,
+        13: 0.0,
+        14: 0.0,
+        15: 0.0,
+    }
+
+
+def test_richclub4():
+    G = nx.Graph()
+    G.add_edges_from(
+        [(0, 1), (0, 2), (0, 3), (0, 4), (4, 5), (5, 9), (6, 9), (7, 9), (8, 9)]
+    )
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {0: 18 / 90.0, 1: 6 / 12.0, 2: 0.0, 3: 0.0}
+
+
+def test_richclub_exception():
+    with pytest.raises(nx.NetworkXNotImplemented):
+        G = nx.DiGraph()
+        nx.rich_club_coefficient(G)
+
+
+def test_rich_club_exception2():
+    with pytest.raises(nx.NetworkXNotImplemented):
+        G = nx.MultiGraph()
+        nx.rich_club_coefficient(G)
+
+
+def test_rich_club_selfloop():
+    G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    G.add_edge(1, 1)  # self loop
+    G.add_edge(1, 2)
+    with pytest.raises(
+        Exception,
+        match="rich_club_coefficient is not implemented for graphs with self loops.",
+    ):
+        nx.rich_club_coefficient(G)
+
+
+def test_rich_club_leq_3_nodes_unnormalized():
+    # edgeless graphs upto 3 nodes
+    G = nx.Graph()
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {}
+
+    for i in range(3):
+        G.add_node(i)
+        rc = nx.rich_club_coefficient(G, normalized=False)
+        assert rc == {}
+
+    # 2 nodes, single edge
+    G = nx.Graph()
+    G.add_edge(0, 1)
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {0: 1}
+
+    # 3 nodes, single edge
+    G = nx.Graph()
+    G.add_nodes_from([0, 1, 2])
+    G.add_edge(0, 1)
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {0: 1}
+
+    # 3 nodes, 2 edges
+    G.add_edge(1, 2)
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {0: 2 / 3}
+
+    # 3 nodes, 3 edges
+    G.add_edge(0, 2)
+    rc = nx.rich_club_coefficient(G, normalized=False)
+    assert rc == {0: 1, 1: 1}
+
+
+def test_rich_club_leq_3_nodes_normalized():
+    G = nx.Graph()
+    with pytest.raises(
+        nx.exception.NetworkXError,
+        match="Graph has fewer than four nodes",
+    ):
+        rc = nx.rich_club_coefficient(G, normalized=True)
+
+    for i in range(3):
+        G.add_node(i)
+        with pytest.raises(
+            nx.exception.NetworkXError,
+            match="Graph has fewer than four nodes",
+        ):
+            rc = nx.rich_club_coefficient(G, normalized=True)
+
+
+# def test_richclub2_normalized():
+#    T = nx.balanced_tree(2,10)
+#    rcNorm = nx.richclub.rich_club_coefficient(T,Q=2)
+#    assert_true(rcNorm[0] ==1.0 and rcNorm[1] < 0.9 and rcNorm[2] < 0.9)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_similarity.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ee0a1fd10bb25d57af5d355ec3ff91b2140fd0e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_similarity.py
@@ -0,0 +1,1158 @@
+import pytest
+
+import networkx as nx
+from networkx.algorithms.similarity import (
+    graph_edit_distance,
+    optimal_edit_paths,
+    optimize_graph_edit_distance,
+)
+from networkx.generators.classic import (
+    circular_ladder_graph,
+    cycle_graph,
+    path_graph,
+    wheel_graph,
+)
+
+
+@pytest.mark.parametrize("source", (10, "foo"))
+def test_generate_random_paths_source_not_in_G(source):
+    pytest.importorskip("numpy")
+    G = nx.complete_graph(5)
+    # No exception at generator construction time
+    path_gen = nx.generate_random_paths(G, sample_size=3, source=source)
+    with pytest.raises(nx.NodeNotFound, match="Initial node.*not in G"):
+        next(path_gen)
+
+
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
+def test_generate_random_paths_with_isolated_nodes():
+    pytest.importorskip("numpy")
+    G = nx.Graph()
+    G.add_nodes_from([0, 1, 2])
+    G.add_edge(0, 1)
+
+    # Connected source node
+    paths = list(nx.generate_random_paths(G, 2, path_length=2, source=0, seed=42))
+    assert len(paths) == 2
+    assert all(len(path) == 3 for path in paths)
+    assert all(path[0] == 0 for path in paths)
+
+    # Isolated source node
+    path_gen = nx.generate_random_paths(G, 2, path_length=2, source=2, seed=42)
+    with pytest.raises(ValueError, match="probabilities contain NaN"):
+        list(path_gen)
+
+    # Random source that might pick isolated node
+    path_gen = nx.generate_random_paths(G, 2, path_length=2, seed=42)
+    with pytest.raises(ValueError, match="probabilities contain NaN"):
+        list(path_gen)
+
+
+def nmatch(n1, n2):
+    return n1 == n2
+
+
+def ematch(e1, e2):
+    return e1 == e2
+
+
+def getCanonical():
+    G = nx.Graph()
+    G.add_node("A", label="A")
+    G.add_node("B", label="B")
+    G.add_node("C", label="C")
+    G.add_node("D", label="D")
+    G.add_edge("A", "B", label="a-b")
+    G.add_edge("B", "C", label="b-c")
+    G.add_edge("B", "D", label="b-d")
+    return G
+
+
+class TestSimilarity:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        pytest.importorskip("scipy")
+
+    def test_graph_edit_distance_roots_and_timeout(self):
+        G0 = nx.star_graph(5)
+        G1 = G0.copy()
+        pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2])
+        pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2, 3, 4])
+        pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 3))
+        pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(3, 9))
+        pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 9))
+        assert graph_edit_distance(G0, G1, roots=(1, 2)) == 0
+        assert graph_edit_distance(G0, G1, roots=(0, 1)) == 8
+        assert graph_edit_distance(G0, G1, roots=(1, 2), timeout=5) == 0
+        assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=5) == 8
+        assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=0.0001) is None
+        # test raise on 0 timeout
+        pytest.raises(nx.NetworkXError, graph_edit_distance, G0, G1, timeout=0)
+
+    def test_graph_edit_distance(self):
+        G0 = nx.Graph()
+        G1 = path_graph(6)
+        G2 = cycle_graph(6)
+        G3 = wheel_graph(7)
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 11
+        assert graph_edit_distance(G1, G0) == 11
+        assert graph_edit_distance(G0, G2) == 12
+        assert graph_edit_distance(G2, G0) == 12
+        assert graph_edit_distance(G0, G3) == 19
+        assert graph_edit_distance(G3, G0) == 19
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 1
+        assert graph_edit_distance(G2, G1) == 1
+        assert graph_edit_distance(G1, G3) == 8
+        assert graph_edit_distance(G3, G1) == 8
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 7
+        assert graph_edit_distance(G3, G2) == 7
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_graph_edit_distance_node_match(self):
+        G1 = cycle_graph(5)
+        G2 = cycle_graph(5)
+        for n, attr in G1.nodes.items():
+            attr["color"] = "red" if n % 2 == 0 else "blue"
+        for n, attr in G2.nodes.items():
+            attr["color"] = "red" if n % 2 == 1 else "blue"
+        assert graph_edit_distance(G1, G2) == 0
+        assert (
+            graph_edit_distance(
+                G1, G2, node_match=lambda n1, n2: n1["color"] == n2["color"]
+            )
+            == 1
+        )
+
+    def test_graph_edit_distance_edge_match(self):
+        G1 = path_graph(6)
+        G2 = path_graph(6)
+        for e, attr in G1.edges.items():
+            attr["color"] = "red" if min(e) % 2 == 0 else "blue"
+        for e, attr in G2.edges.items():
+            attr["color"] = "red" if min(e) // 3 == 0 else "blue"
+        assert graph_edit_distance(G1, G2) == 0
+        assert (
+            graph_edit_distance(
+                G1, G2, edge_match=lambda e1, e2: e1["color"] == e2["color"]
+            )
+            == 2
+        )
+
+    def test_graph_edit_distance_node_cost(self):
+        G1 = path_graph(6)
+        G2 = path_graph(6)
+        for n, attr in G1.nodes.items():
+            attr["color"] = "red" if n % 2 == 0 else "blue"
+        for n, attr in G2.nodes.items():
+            attr["color"] = "red" if n % 2 == 1 else "blue"
+
+        def node_subst_cost(uattr, vattr):
+            if uattr["color"] == vattr["color"]:
+                return 1
+            else:
+                return 10
+
+        def node_del_cost(attr):
+            if attr["color"] == "blue":
+                return 20
+            else:
+                return 50
+
+        def node_ins_cost(attr):
+            if attr["color"] == "blue":
+                return 40
+            else:
+                return 100
+
+        assert (
+            graph_edit_distance(
+                G1,
+                G2,
+                node_subst_cost=node_subst_cost,
+                node_del_cost=node_del_cost,
+                node_ins_cost=node_ins_cost,
+            )
+            == 6
+        )
+
+    def test_graph_edit_distance_edge_cost(self):
+        G1 = path_graph(6)
+        G2 = path_graph(6)
+        for e, attr in G1.edges.items():
+            attr["color"] = "red" if min(e) % 2 == 0 else "blue"
+        for e, attr in G2.edges.items():
+            attr["color"] = "red" if min(e) // 3 == 0 else "blue"
+
+        def edge_subst_cost(gattr, hattr):
+            if gattr["color"] == hattr["color"]:
+                return 0.01
+            else:
+                return 0.1
+
+        def edge_del_cost(attr):
+            if attr["color"] == "blue":
+                return 0.2
+            else:
+                return 0.5
+
+        def edge_ins_cost(attr):
+            if attr["color"] == "blue":
+                return 0.4
+            else:
+                return 1.0
+
+        assert (
+            graph_edit_distance(
+                G1,
+                G2,
+                edge_subst_cost=edge_subst_cost,
+                edge_del_cost=edge_del_cost,
+                edge_ins_cost=edge_ins_cost,
+            )
+            == 0.23
+        )
+
+    def test_graph_edit_distance_upper_bound(self):
+        G1 = circular_ladder_graph(2)
+        G2 = circular_ladder_graph(6)
+        assert graph_edit_distance(G1, G2, upper_bound=5) is None
+        assert graph_edit_distance(G1, G2, upper_bound=24) == 22
+        assert graph_edit_distance(G1, G2) == 22
+
+    def test_optimal_edit_paths(self):
+        G1 = path_graph(3)
+        G2 = cycle_graph(3)
+        paths, cost = optimal_edit_paths(G1, G2)
+        assert cost == 1
+        assert len(paths) == 6
+
+        def canonical(vertex_path, edge_path):
+            return (
+                tuple(sorted(vertex_path)),
+                tuple(sorted(edge_path, key=lambda x: (None in x, x))),
+            )
+
+        expected_paths = [
+            (
+                [(0, 0), (1, 1), (2, 2)],
+                [((0, 1), (0, 1)), ((1, 2), (1, 2)), (None, (0, 2))],
+            ),
+            (
+                [(0, 0), (1, 2), (2, 1)],
+                [((0, 1), (0, 2)), ((1, 2), (1, 2)), (None, (0, 1))],
+            ),
+            (
+                [(0, 1), (1, 0), (2, 2)],
+                [((0, 1), (0, 1)), ((1, 2), (0, 2)), (None, (1, 2))],
+            ),
+            (
+                [(0, 1), (1, 2), (2, 0)],
+                [((0, 1), (1, 2)), ((1, 2), (0, 2)), (None, (0, 1))],
+            ),
+            (
+                [(0, 2), (1, 0), (2, 1)],
+                [((0, 1), (0, 2)), ((1, 2), (0, 1)), (None, (1, 2))],
+            ),
+            (
+                [(0, 2), (1, 1), (2, 0)],
+                [((0, 1), (1, 2)), ((1, 2), (0, 1)), (None, (0, 2))],
+            ),
+        ]
+        assert {canonical(*p) for p in paths} == {canonical(*p) for p in expected_paths}
+
+    def test_optimize_graph_edit_distance(self):
+        G1 = circular_ladder_graph(2)
+        G2 = circular_ladder_graph(6)
+        bestcost = 1000
+        for cost in optimize_graph_edit_distance(G1, G2):
+            assert cost < bestcost
+            bestcost = cost
+        assert bestcost == 22
+
+    # def test_graph_edit_distance_bigger(self):
+    #     G1 = circular_ladder_graph(12)
+    #     G2 = circular_ladder_graph(16)
+    #     assert_equal(graph_edit_distance(G1, G2), 22)
+
+    def test_selfloops(self):
+        G0 = nx.Graph()
+        G1 = nx.Graph()
+        G1.add_edges_from((("A", "A"), ("A", "B")))
+        G2 = nx.Graph()
+        G2.add_edges_from((("A", "B"), ("B", "B")))
+        G3 = nx.Graph()
+        G3.add_edges_from((("A", "A"), ("A", "B"), ("B", "B")))
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 4
+        assert graph_edit_distance(G1, G0) == 4
+        assert graph_edit_distance(G0, G2) == 4
+        assert graph_edit_distance(G2, G0) == 4
+        assert graph_edit_distance(G0, G3) == 5
+        assert graph_edit_distance(G3, G0) == 5
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 0
+        assert graph_edit_distance(G2, G1) == 0
+        assert graph_edit_distance(G1, G3) == 1
+        assert graph_edit_distance(G3, G1) == 1
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 1
+        assert graph_edit_distance(G3, G2) == 1
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_digraph(self):
+        G0 = nx.DiGraph()
+        G1 = nx.DiGraph()
+        G1.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("D", "A")))
+        G2 = nx.DiGraph()
+        G2.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("A", "D")))
+        G3 = nx.DiGraph()
+        G3.add_edges_from((("A", "B"), ("A", "C"), ("B", "D"), ("C", "D")))
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 8
+        assert graph_edit_distance(G1, G0) == 8
+        assert graph_edit_distance(G0, G2) == 8
+        assert graph_edit_distance(G2, G0) == 8
+        assert graph_edit_distance(G0, G3) == 8
+        assert graph_edit_distance(G3, G0) == 8
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 2
+        assert graph_edit_distance(G2, G1) == 2
+        assert graph_edit_distance(G1, G3) == 4
+        assert graph_edit_distance(G3, G1) == 4
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 2
+        assert graph_edit_distance(G3, G2) == 2
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_multigraph(self):
+        G0 = nx.MultiGraph()
+        G1 = nx.MultiGraph()
+        G1.add_edges_from((("A", "B"), ("B", "C"), ("A", "C")))
+        G2 = nx.MultiGraph()
+        G2.add_edges_from((("A", "B"), ("B", "C"), ("B", "C"), ("A", "C")))
+        G3 = nx.MultiGraph()
+        G3.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"), ("A", "C"), ("A", "C")))
+
+        assert graph_edit_distance(G0, G0) == 0
+        assert graph_edit_distance(G0, G1) == 6
+        assert graph_edit_distance(G1, G0) == 6
+        assert graph_edit_distance(G0, G2) == 7
+        assert graph_edit_distance(G2, G0) == 7
+        assert graph_edit_distance(G0, G3) == 8
+        assert graph_edit_distance(G3, G0) == 8
+
+        assert graph_edit_distance(G1, G1) == 0
+        assert graph_edit_distance(G1, G2) == 1
+        assert graph_edit_distance(G2, G1) == 1
+        assert graph_edit_distance(G1, G3) == 2
+        assert graph_edit_distance(G3, G1) == 2
+
+        assert graph_edit_distance(G2, G2) == 0
+        assert graph_edit_distance(G2, G3) == 1
+        assert graph_edit_distance(G3, G2) == 1
+
+        assert graph_edit_distance(G3, G3) == 0
+
+    def test_multidigraph(self):
+        G1 = nx.MultiDiGraph()
+        G1.add_edges_from(
+            (
+                ("hardware", "kernel"),
+                ("kernel", "hardware"),
+                ("kernel", "userspace"),
+                ("userspace", "kernel"),
+            )
+        )
+        G2 = nx.MultiDiGraph()
+        G2.add_edges_from(
+            (
+                ("winter", "spring"),
+                ("spring", "summer"),
+                ("summer", "autumn"),
+                ("autumn", "winter"),
+            )
+        )
+
+        assert graph_edit_distance(G1, G2) == 5
+        assert graph_edit_distance(G2, G1) == 5
+
+    # by https://github.com/jfbeaumont
+    def testCopy(self):
+        G = nx.Graph()
+        G.add_node("A", label="A")
+        G.add_node("B", label="B")
+        G.add_edge("A", "B", label="a-b")
+        assert (
+            graph_edit_distance(G, G.copy(), node_match=nmatch, edge_match=ematch) == 0
+        )
+
+    def testSame(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="a-b")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 0
+
+    def testOneEdgeLabelDiff(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="bad")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneNodeLabelDiff(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="Z")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="a-b")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneExtraNode(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_node("C", label="C")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneExtraEdge(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_node("C", label="C")
+        G1.add_node("C", label="C")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("A", "C", label="a-c")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    def testOneExtraNodeAndEdge(self):
+        G1 = nx.Graph()
+        G1.add_node("A", label="A")
+        G1.add_node("B", label="B")
+        G1.add_edge("A", "B", label="a-b")
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("A", "C", label="a-c")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2
+
+    def testGraph1(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("D", label="D")
+        G2.add_node("E", label="E")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "D", label="b-d")
+        G2.add_edge("D", "E", label="d-e")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 3
+
+    def testGraph2(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_node("E", label="E")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("C", "D", label="c-d")
+        G2.add_edge("C", "E", label="c-e")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 4
+
+    def testGraph3(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_node("E", label="E")
+        G2.add_node("F", label="F")
+        G2.add_node("G", label="G")
+        G2.add_edge("A", "C", label="a-c")
+        G2.add_edge("A", "D", label="a-d")
+        G2.add_edge("D", "E", label="d-e")
+        G2.add_edge("D", "F", label="d-f")
+        G2.add_edge("D", "G", label="d-g")
+        G2.add_edge("E", "B", label="e-b")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 12
+
+    def testGraph4(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("C", "D", label="c-d")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2
+
+    def testGraph4_a(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("A", "D", label="a-d")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2
+
+    def testGraph4_b(self):
+        G1 = getCanonical()
+        G2 = nx.Graph()
+        G2.add_node("A", label="A")
+        G2.add_node("B", label="B")
+        G2.add_node("C", label="C")
+        G2.add_node("D", label="D")
+        G2.add_edge("A", "B", label="a-b")
+        G2.add_edge("B", "C", label="b-c")
+        G2.add_edge("B", "D", label="bad")
+        assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1
+
+    # note: nx.simrank_similarity_numpy not included because returns np.array
+    simrank_algs = [
+        nx.simrank_similarity,
+        nx.algorithms.similarity._simrank_similarity_python,
+    ]
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_no_source_no_target(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        expected = {
+            0: {
+                0: 1,
+                1: 0.3951219505902448,
+                2: 0.5707317069281646,
+                3: 0.5707317069281646,
+                4: 0.3951219505902449,
+            },
+            1: {
+                0: 0.3951219505902448,
+                1: 1,
+                2: 0.3951219505902449,
+                3: 0.5707317069281646,
+                4: 0.5707317069281646,
+            },
+            2: {
+                0: 0.5707317069281646,
+                1: 0.3951219505902449,
+                2: 1,
+                3: 0.3951219505902449,
+                4: 0.5707317069281646,
+            },
+            3: {
+                0: 0.5707317069281646,
+                1: 0.5707317069281646,
+                2: 0.3951219505902449,
+                3: 1,
+                4: 0.3951219505902449,
+            },
+            4: {
+                0: 0.3951219505902449,
+                1: 0.5707317069281646,
+                2: 0.5707317069281646,
+                3: 0.3951219505902449,
+                4: 1,
+            },
+        }
+        actual = simrank_similarity(G)
+        for k, v in expected.items():
+            assert v == pytest.approx(actual[k], abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+
+        expected = {
+            0: {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443},
+            1: {0: 0.0, 1: 1, 2: 0.4135512472705618, 3: 0.0, 4: 0.10586911930126384},
+            2: {
+                0: 0.1323363991265798,
+                1: 0.4135512472705618,
+                2: 1,
+                3: 0.04234764772050554,
+                4: 0.08822426608438655,
+            },
+            3: {0: 0.0, 1: 0.0, 2: 0.04234764772050554, 3: 1, 4: 0.3308409978164495},
+            4: {
+                0: 0.03387811817640443,
+                1: 0.10586911930126384,
+                2: 0.08822426608438655,
+                3: 0.3308409978164495,
+                4: 1,
+            },
+        }
+        # Use the importance_factor from the paper to get the same numbers.
+        actual = simrank_similarity(G, importance_factor=0.8)
+        for k, v in expected.items():
+            assert v == pytest.approx(actual[k], abs=1e-2)
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_source_no_target(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        expected = {
+            0: 1,
+            1: 0.3951219505902448,
+            2: 0.5707317069281646,
+            3: 0.5707317069281646,
+            4: 0.3951219505902449,
+        }
+        actual = simrank_similarity(G, source=0)
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+
+        expected = {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443}
+        # Use the importance_factor from the paper to get the same numbers.
+        actual = simrank_similarity(G, importance_factor=0.8, source=0)
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_noninteger_nodes(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        G = nx.relabel_nodes(G, dict(enumerate("abcde")))
+        expected = {
+            "a": 1,
+            "b": 0.3951219505902448,
+            "c": 0.5707317069281646,
+            "d": 0.5707317069281646,
+            "e": 0.3951219505902449,
+        }
+        actual = simrank_similarity(G, source="a")
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+        node_labels = dict(enumerate(nx.get_node_attributes(G, "label").values()))
+        G = nx.relabel_nodes(G, node_labels)
+
+        expected = {
+            "Univ": 1,
+            "ProfA": 0.0,
+            "ProfB": 0.1323363991265798,
+            "StudentA": 0.0,
+            "StudentB": 0.03387811817640443,
+        }
+        # Use the importance_factor from the paper to get the same numbers.
+        actual = simrank_similarity(G, importance_factor=0.8, source="Univ")
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+    @pytest.mark.parametrize("simrank_similarity", simrank_algs)
+    def test_simrank_source_and_target(self, simrank_similarity):
+        G = nx.cycle_graph(5)
+        expected = 1
+        actual = simrank_similarity(G, source=0, target=0)
+        assert expected == pytest.approx(actual, abs=1e-2)
+
+        # For a DiGraph test, use the first graph from the paper cited in
+        # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126
+        G = nx.DiGraph()
+        G.add_node(0, label="Univ")
+        G.add_node(1, label="ProfA")
+        G.add_node(2, label="ProfB")
+        G.add_node(3, label="StudentA")
+        G.add_node(4, label="StudentB")
+        G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)])
+
+        expected = 0.1323363991265798
+        # Use the importance_factor from the paper to get the same numbers.
+        # Use the pair (0,2) because (0,0) and (0,1) have trivial results.
+        actual = simrank_similarity(G, importance_factor=0.8, source=0, target=2)
+        assert expected == pytest.approx(actual, abs=1e-5)
+
+    @pytest.mark.parametrize("alg", simrank_algs)
+    def test_simrank_max_iterations(self, alg):
+        G = nx.cycle_graph(5)
+        pytest.raises(nx.ExceededMaxIterations, alg, G, max_iterations=10)
+
+    def test_simrank_source_not_found(self):
+        G = nx.cycle_graph(5)
+        with pytest.raises(nx.NodeNotFound, match="Source node 10 not in G"):
+            nx.simrank_similarity(G, source=10)
+
+    def test_simrank_target_not_found(self):
+        G = nx.cycle_graph(5)
+        with pytest.raises(nx.NodeNotFound, match="Target node 10 not in G"):
+            nx.simrank_similarity(G, target=10)
+
+    def test_simrank_between_versions(self):
+        G = nx.cycle_graph(5)
+        # _python tolerance 1e-4
+        expected_python_tol4 = {
+            0: 1,
+            1: 0.394512499239852,
+            2: 0.5703550452791322,
+            3: 0.5703550452791323,
+            4: 0.394512499239852,
+        }
+        # _numpy tolerance 1e-4
+        expected_numpy_tol4 = {
+            0: 1.0,
+            1: 0.3947180735764555,
+            2: 0.570482097206368,
+            3: 0.570482097206368,
+            4: 0.3947180735764555,
+        }
+        actual = nx.simrank_similarity(G, source=0)
+        assert expected_numpy_tol4 == pytest.approx(actual, abs=1e-7)
+        # versions differ at 1e-4 level but equal at 1e-3
+        assert expected_python_tol4 != pytest.approx(actual, abs=1e-4)
+        assert expected_python_tol4 == pytest.approx(actual, abs=1e-3)
+
+        actual = nx.similarity._simrank_similarity_python(G, source=0)
+        assert expected_python_tol4 == pytest.approx(actual, abs=1e-7)
+        # versions differ at 1e-4 level but equal at 1e-3
+        assert expected_numpy_tol4 != pytest.approx(actual, abs=1e-4)
+        assert expected_numpy_tol4 == pytest.approx(actual, abs=1e-3)
+
+    def test_simrank_numpy_no_source_no_target(self):
+        G = nx.cycle_graph(5)
+        expected = np.array(
+            [
+                [
+                    1.0,
+                    0.3947180735764555,
+                    0.570482097206368,
+                    0.570482097206368,
+                    0.3947180735764555,
+                ],
+                [
+                    0.3947180735764555,
+                    1.0,
+                    0.3947180735764555,
+                    0.570482097206368,
+                    0.570482097206368,
+                ],
+                [
+                    0.570482097206368,
+                    0.3947180735764555,
+                    1.0,
+                    0.3947180735764555,
+                    0.570482097206368,
+                ],
+                [
+                    0.570482097206368,
+                    0.570482097206368,
+                    0.3947180735764555,
+                    1.0,
+                    0.3947180735764555,
+                ],
+                [
+                    0.3947180735764555,
+                    0.570482097206368,
+                    0.570482097206368,
+                    0.3947180735764555,
+                    1.0,
+                ],
+            ]
+        )
+        actual = nx.similarity._simrank_similarity_numpy(G)
+        np.testing.assert_allclose(expected, actual, atol=1e-7)
+
+    def test_simrank_numpy_source_no_target(self):
+        G = nx.cycle_graph(5)
+        expected = np.array(
+            [
+                1.0,
+                0.3947180735764555,
+                0.570482097206368,
+                0.570482097206368,
+                0.3947180735764555,
+            ]
+        )
+        actual = nx.similarity._simrank_similarity_numpy(G, source=0)
+        np.testing.assert_allclose(expected, actual, atol=1e-7)
+
+    def test_simrank_numpy_source_and_target(self):
+        G = nx.cycle_graph(5)
+        expected = 1.0
+        actual = nx.similarity._simrank_similarity_numpy(G, source=0, target=0)
+        np.testing.assert_allclose(expected, actual, atol=1e-7)
+
+    def test_panther_similarity_unweighted(self):
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        G.add_edge(0, 2)
+        G.add_edge(0, 3)
+        G.add_edge(1, 2)
+        G.add_edge(2, 4)
+        expected = {3: 0.5, 2: 0.5, 1: 0.5, 4: 0.125}
+        sim = nx.panther_similarity(G, 0, path_length=2, seed=42)
+        assert sim == expected
+
+    def test_panther_similarity_weighted(self):
+        G = nx.Graph()
+        G.add_edge("v1", "v2", w=5)
+        G.add_edge("v1", "v3", w=1)
+        G.add_edge("v1", "v4", w=2)
+        G.add_edge("v2", "v3", w=0.1)
+        G.add_edge("v3", "v5", w=1)
+        expected = {"v3": 0.75, "v4": 0.5, "v2": 0.5, "v5": 0.25}
+        sim = nx.panther_similarity(G, "v1", path_length=2, weight="w", seed=42)
+        assert sim == expected
+
+    def test_panther_similarity_source_not_found(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (2, 4)])
+        with pytest.raises(nx.NodeNotFound, match="Source node 10 not in G"):
+            nx.panther_similarity(G, source=10)
+
+    def test_panther_similarity_isolated(self):
+        G = nx.Graph()
+        G.add_nodes_from(range(5))
+        with pytest.raises(
+            nx.NetworkXUnfeasible,
+            match="Panther similarity is not defined for the isolated source node 1.",
+        ):
+            nx.panther_similarity(G, source=1)
+
+    @pytest.mark.parametrize("num_paths", (1, 3, 10))
+    @pytest.mark.parametrize("source", (0, 1))
+    def test_generate_random_paths_with_start(self, num_paths, source):
+        G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (2, 4)])
+        index_map = {}
+
+        path_gen = nx.generate_random_paths(
+            G,
+            num_paths,
+            path_length=2,
+            index_map=index_map,
+            source=source,
+        )
+        paths = list(path_gen)
+
+        # There should be num_paths paths
+        assert len(paths) == num_paths
+        # And they should all start with `source`
+        assert all(p[0] == source for p in paths)
+        # The index_map for the `source` node should contain the indices for
+        # all of the generated paths.
+        assert sorted(index_map[source]) == list(range(num_paths))
+
+    def test_generate_random_paths_unweighted(self):
+        index_map = {}
+        num_paths = 10
+        path_length = 2
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        G.add_edge(0, 2)
+        G.add_edge(0, 3)
+        G.add_edge(1, 2)
+        G.add_edge(2, 4)
+        paths = nx.generate_random_paths(
+            G, num_paths, path_length=path_length, index_map=index_map, seed=42
+        )
+        expected_paths = [
+            [3, 0, 3],
+            [4, 2, 1],
+            [2, 1, 0],
+            [2, 0, 3],
+            [3, 0, 1],
+            [3, 0, 1],
+            [4, 2, 0],
+            [2, 1, 0],
+            [3, 0, 2],
+            [2, 1, 2],
+        ]
+        expected_map = {
+            0: {0, 2, 3, 4, 5, 6, 7, 8},
+            1: {1, 2, 4, 5, 7, 9},
+            2: {1, 2, 3, 6, 7, 8, 9},
+            3: {0, 3, 4, 5, 8},
+            4: {1, 6},
+        }
+
+        assert expected_paths == list(paths)
+        assert expected_map == index_map
+
+    def test_generate_random_paths_weighted(self):
+        index_map = {}
+        num_paths = 10
+        path_length = 6
+        G = nx.Graph()
+        G.add_edge("a", "b", weight=0.6)
+        G.add_edge("a", "c", weight=0.2)
+        G.add_edge("c", "d", weight=0.1)
+        G.add_edge("c", "e", weight=0.7)
+        G.add_edge("c", "f", weight=0.9)
+        G.add_edge("a", "d", weight=0.3)
+        paths = nx.generate_random_paths(
+            G, num_paths, path_length=path_length, index_map=index_map, seed=42
+        )
+
+        expected_paths = [
+            ["d", "c", "f", "c", "d", "a", "b"],
+            ["e", "c", "f", "c", "f", "c", "e"],
+            ["d", "a", "b", "a", "b", "a", "c"],
+            ["b", "a", "d", "a", "b", "a", "b"],
+            ["d", "a", "b", "a", "b", "a", "d"],
+            ["d", "a", "b", "a", "b", "a", "c"],
+            ["d", "a", "b", "a", "b", "a", "b"],
+            ["f", "c", "f", "c", "f", "c", "e"],
+            ["d", "a", "d", "a", "b", "a", "b"],
+            ["e", "c", "f", "c", "e", "c", "d"],
+        ]
+        expected_map = {
+            "d": {0, 2, 3, 4, 5, 6, 8, 9},
+            "c": {0, 1, 2, 5, 7, 9},
+            "f": {0, 1, 9, 7},
+            "a": {0, 2, 3, 4, 5, 6, 8},
+            "b": {0, 2, 3, 4, 5, 6, 8},
+            "e": {1, 9, 7},
+        }
+
+        assert expected_paths == list(paths)
+        assert expected_map == index_map
+
+    def test_one_node_one_loop_and_empty_graph(self):
+        G1 = nx.DiGraph([(0, 0)])
+        G2 = nx.DiGraph()
+        assert nx.graph_edit_distance(G1, G2) == 2
+
+    def test_one_node_two_loops_and_empty_graph(self):
+        G1 = nx.MultiDiGraph([(0, 0), (0, 0)])
+        assert nx.graph_edit_distance(G1, nx.DiGraph()) == 3
+        assert nx.graph_edit_distance(G1, nx.MultiDiGraph()) == 3
+
+    def test_two_directed_loops(self):
+        G = nx.DiGraph([(0, 0), (1, 1)])
+        assert nx.graph_edit_distance(G, nx.DiGraph()) == 4
+
+    def test_symmetry_with_custom_matching(self):
+        """G2 has edge (a,b) and G3 has edge (a,a) but node order for G2 is (a,b)
+        while for G3 it is (b,a)"""
+
+        a, b = "A", "B"
+        G2 = nx.Graph()
+        G2.add_nodes_from((a, b))
+        G2.add_edges_from([(a, b)])
+        G3 = nx.Graph()
+        G3.add_nodes_from((b, a))
+        G3.add_edges_from([(a, a)])
+        for G in (G2, G3):
+            for n in G:
+                G.nodes[n]["attr"] = n
+            for e in G.edges:
+                G.edges[e]["attr"] = e
+
+        def user_match(x, y):
+            return x == y
+
+        assert (
+            nx.graph_edit_distance(G2, G3, node_match=user_match, edge_match=user_match)
+            == 1
+        )
+        assert (
+            nx.graph_edit_distance(G3, G2, node_match=user_match, edge_match=user_match)
+            == 1
+        )
+
+    def test_panther_vector_similarity_basic(self):
+        """Basic test for panther_vector_similarity function."""
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        G.add_edge(0, 2)
+        G.add_edge(0, 3)
+        G.add_edge(1, 2)
+        G.add_edge(2, 4)
+
+        sim = nx.panther_vector_similarity(G, 0, D=3, k=4, path_length=2, seed=42)
+
+        assert len(sim) > 0
+        assert 0 not in sim  # Source node should not be included
+        assert all(node in [1, 2, 3, 4] for node in sim)  # Only valid nodes
+        assert all(0 <= score <= 1 for score in sim.values())  # Valid scores
+
+    def test_panther_vector_similarity_unweighted(self):
+        """Test panther_vector_similarity with unweighted graph."""
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        G.add_edge(0, 2)
+        G.add_edge(0, 3)
+        G.add_edge(1, 2)
+        G.add_edge(2, 4)
+
+        sim = nx.panther_vector_similarity(G, 0, D=3, k=4, path_length=2, seed=42)
+
+        assert len(sim) == 4
+        assert 0 not in sim
+        assert all(node in sim for node in [1, 2, 3, 4])
+        assert all(0 <= score <= 1 for score in sim.values())
+
+    def test_panther_vector_similarity_weighted(self):
+        """Test panther_vector_similarity with weighted graph."""
+        G = nx.Graph()
+        G.add_edge("v1", "v2", weight=5)
+        G.add_edge("v1", "v3", weight=1)
+        G.add_edge("v1", "v4", weight=2)
+        G.add_edge("v2", "v3", weight=0.1)
+        G.add_edge("v3", "v5", weight=1)
+
+        sim = nx.panther_vector_similarity(
+            G, "v1", D=3, k=4, path_length=2, weight="weight", seed=42
+        )
+
+        assert len(sim) == 4
+        assert "v1" not in sim
+        assert all(0 <= score <= 1 for score in sim.values())
+        assert all(node in sim for node in ["v2", "v3", "v4"])
+
+    def test_panther_vector_similarity_source_not_found(self):
+        """Test panther_vector_similarity with non-existent source node."""
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (2, 4)])
+
+        with pytest.raises(nx.NodeNotFound):
+            nx.panther_vector_similarity(G, source=10)
+
+    def test_panther_vector_similarity_isolated(self):
+        """Test panther_vector_similarity with isolated source node."""
+        G = nx.Graph()
+        G.add_nodes_from(range(5))
+        G.add_edge(0, 1)
+
+        with pytest.raises(nx.NetworkXUnfeasible):
+            nx.panther_vector_similarity(G, source=2)
+
+    def test_panther_vector_similarity_too_large_D(self):
+        """Test raises when D > number of nodes."""
+        G = nx.star_graph(3)
+
+        with pytest.raises(nx.NetworkXUnfeasible):
+            nx.panther_vector_similarity(G, 0, D=5, k=3)
+
+    def test_panther_vector_similarity_too_large_k(self):
+        """Test raises when k > number of nodes."""
+        G = nx.star_graph(3)
+
+        with pytest.raises(nx.NetworkXUnfeasible):
+            nx.panther_vector_similarity(G, 0, k=5)
+
+    def test_panther_vector_similarity_small_graph(self):
+        """Test panther_vector_similarity with a very small graph."""
+        G = nx.Graph()
+        G.add_edge(0, 1)
+
+        sim = nx.panther_vector_similarity(G, 0, D=2, k=2, seed=42)
+
+        assert len(sim) == 1
+        assert 1 in sim
+        assert sim[1] > 0
+
+    def test_panther_vector_similarity_deterministic(self):
+        """Test that results are deterministic with fixed seed."""
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (2, 4)])
+
+        sim1 = nx.panther_vector_similarity(G, 0, D=3, path_length=2, seed=42)
+
+        sim2 = nx.panther_vector_similarity(G, 0, D=3, path_length=2, seed=42)
+
+        assert sim1 == sim2
+
+    def test_panther_similarity_string_nodes(self):
+        """Test panther_similarity with string node names."""
+        pytest.importorskip("numpy")
+        G = nx.Graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("A", "D"), ("B", "C")])
+
+        sim = nx.panther_similarity(G, "A", k=2, path_length=2, seed=42)
+
+        assert "A" not in sim  # Source node should not be included
+        assert all(isinstance(node, str) for node in sim)  # Nodes should remain strings
+
+    def test_panther_vector_similarity_string_nodes(self):
+        """Test panther_vector_similarity with string node names."""
+        pytest.importorskip("numpy")
+        G = nx.Graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("A", "D"), ("B", "C")])
+
+        sim = nx.panther_vector_similarity(G, "A", D=3, k=2, path_length=2, seed=42)
+
+        assert "A" not in sim  # Source node should not be included
+        assert all(isinstance(node, str) for node in sim)  # Nodes should remain strings
+
+    def test_panther_similarity_k_parameter_returns_k_results(self):
+        pytest.importorskip("numpy")
+        G = nx.star_graph(100)
+
+        for k_val in [1, 2, 3, 4, 5, 10]:
+            result_panther = nx.panther_similarity(G, source=1, k=k_val, seed=42)
+            assert len(result_panther) == k_val, (
+                f"panther_similarity k={k_val} returned {len(result_panther)} results"
+            )
+            assert 1 not in result_panther, "Source node should not be in results"
+
+            result_vector = nx.panther_vector_similarity(G, source=1, k=k_val, seed=42)
+            assert len(result_vector) == k_val, (
+                f"panther_vector_similarity k={k_val} returned {len(result_vector)} results"
+            )
+            assert 1 not in result_vector, "Source node should not be in results"
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_simple_paths.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_simple_paths.py
new file mode 100644
index 0000000000000000000000000000000000000000..7855bbad27b896750faa932a74062aa2bc8ca143
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_simple_paths.py
@@ -0,0 +1,803 @@
+import random
+
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.algorithms.simple_paths import (
+    _bidirectional_dijkstra,
+    _bidirectional_shortest_path,
+)
+from networkx.utils import arbitrary_element, pairwise
+
+
+class TestIsSimplePath:
+    """Unit tests for the
+    :func:`networkx.algorithms.simple_paths.is_simple_path` function.
+
+    """
+
+    def test_empty_list(self):
+        """Tests that the empty list is not a valid path, since there
+        should be a one-to-one correspondence between paths as lists of
+        nodes and paths as lists of edges.
+
+        """
+        G = nx.trivial_graph()
+        assert not nx.is_simple_path(G, [])
+
+    def test_trivial_path(self):
+        """Tests that the trivial path, a path of length one, is
+        considered a simple path in a graph.
+
+        """
+        G = nx.trivial_graph()
+        assert nx.is_simple_path(G, [0])
+
+    def test_trivial_nonpath(self):
+        """Tests that a list whose sole element is an object not in the
+        graph is not considered a simple path.
+
+        """
+        G = nx.trivial_graph()
+        assert not nx.is_simple_path(G, ["not a node"])
+
+    def test_simple_path(self):
+        G = nx.path_graph(2)
+        assert nx.is_simple_path(G, [0, 1])
+
+    def test_non_simple_path(self):
+        G = nx.path_graph(2)
+        assert not nx.is_simple_path(G, [0, 1, 0])
+
+    def test_cycle(self):
+        G = nx.cycle_graph(3)
+        assert not nx.is_simple_path(G, [0, 1, 2, 0])
+
+    def test_missing_node(self):
+        G = nx.path_graph(2)
+        assert not nx.is_simple_path(G, [0, 2])
+
+    def test_missing_starting_node(self):
+        G = nx.path_graph(2)
+        assert not nx.is_simple_path(G, [2, 0])
+
+    def test_directed_path(self):
+        G = nx.DiGraph([(0, 1), (1, 2)])
+        assert nx.is_simple_path(G, [0, 1, 2])
+
+    def test_directed_non_path(self):
+        G = nx.DiGraph([(0, 1), (1, 2)])
+        assert not nx.is_simple_path(G, [2, 1, 0])
+
+    def test_directed_cycle(self):
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+        assert not nx.is_simple_path(G, [0, 1, 2, 0])
+
+    def test_multigraph(self):
+        G = nx.MultiGraph([(0, 1), (0, 1)])
+        assert nx.is_simple_path(G, [0, 1])
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 1), (1, 0), (1, 0)])
+        assert nx.is_simple_path(G, [0, 1])
+
+
+# Tests for all_simple_paths
+def test_all_simple_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3)}
+
+
+def test_all_simple_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_digraph_all_simple_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_all_simple_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_digraph_all_simple_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_all_simple_paths_with_two_targets_in_line_emits_two_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {(0, 1, 2), (0, 1, 2, 3)}
+
+
+def test_all_simple_paths_ignores_cycle():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {(0, 1, 3)}
+
+
+def test_all_simple_paths_with_two_targets_inside_cycle_emits_two_paths():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {(0, 1, 2), (0, 1, 3)}
+
+
+def test_all_simple_paths_source_target():
+    G = nx.path_graph(4)
+    assert list(nx.all_simple_paths(G, 1, 1)) == [[1]]
+
+
+def test_all_simple_paths_cutoff():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_paths(G, 0, 1, cutoff=1)
+    assert {tuple(p) for p in paths} == {(0, 1)}
+    paths = nx.all_simple_paths(G, 0, 1, cutoff=2)
+    assert {tuple(p) for p in paths} == {(0, 1), (0, 2, 1), (0, 3, 1)}
+
+
+def test_all_simple_paths_on_non_trivial_graph():
+    """you may need to draw this graph to make sure it is reasonable"""
+    G = nx.path_graph(5, create_using=nx.DiGraph())
+    G.add_edges_from([(0, 5), (1, 5), (1, 3), (5, 4), (4, 2), (4, 3)])
+    paths = nx.all_simple_paths(G, 1, [2, 3])
+    assert {tuple(p) for p in paths} == {
+        (1, 2),
+        (1, 3, 4, 2),
+        (1, 5, 4, 2),
+        (1, 3),
+        (1, 2, 3),
+        (1, 5, 4, 3),
+        (1, 5, 4, 2, 3),
+    }
+    paths = nx.all_simple_paths(G, 1, [2, 3], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        (1, 2),
+        (1, 3, 4, 2),
+        (1, 5, 4, 2),
+        (1, 3),
+        (1, 2, 3),
+        (1, 5, 4, 3),
+    }
+    paths = nx.all_simple_paths(G, 1, [2, 3], cutoff=2)
+    assert {tuple(p) for p in paths} == {(1, 2), (1, 3), (1, 2, 3)}
+
+
+def test_all_simple_paths_multigraph():
+    G = nx.MultiGraph([(1, 2), (1, 2)])
+    assert list(nx.all_simple_paths(G, 1, 1)) == [[1]]
+    nx.add_path(G, [3, 1, 10, 2])
+    paths = list(nx.all_simple_paths(G, 1, 2))
+    assert len(paths) == 3
+    assert {tuple(p) for p in paths} == {(1, 2), (1, 2), (1, 10, 2)}
+
+
+def test_all_simple_paths_multigraph_with_cutoff():
+    G = nx.MultiGraph([(1, 2), (1, 2), (1, 10), (10, 2)])
+    paths = list(nx.all_simple_paths(G, 1, 2, cutoff=1))
+    assert len(paths) == 2
+    assert {tuple(p) for p in paths} == {(1, 2), (1, 2)}
+
+    # See GitHub issue #6732.
+    G = nx.MultiGraph([(0, 1), (0, 2)])
+    assert list(nx.all_simple_paths(G, 0, {1, 2}, cutoff=1)) == [[0, 1], [0, 2]]
+
+
+def test_all_simple_paths_directed():
+    G = nx.DiGraph()
+    nx.add_path(G, [1, 2, 3])
+    nx.add_path(G, [3, 2, 1])
+    paths = nx.all_simple_paths(G, 1, 3)
+    assert {tuple(p) for p in paths} == {(1, 2, 3)}
+
+
+def test_all_simple_paths_empty():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_paths(G, 0, 3, cutoff=2)
+    assert list(paths) == []
+
+
+def test_all_simple_paths_corner_cases():
+    assert list(nx.all_simple_paths(nx.empty_graph(2), 0, 0)) == [[0]]
+    assert list(nx.all_simple_paths(nx.empty_graph(2), 0, 1)) == []
+    assert list(nx.all_simple_paths(nx.path_graph(9), 0, 8, 0)) == []
+
+
+def test_all_simple_paths_source_in_targets():
+    # See GitHub issue #6690.
+    G = nx.path_graph(3)
+    assert list(nx.all_simple_paths(G, 0, {0, 1, 2})) == [[0], [0, 1], [0, 1, 2]]
+
+
+def hamiltonian_path(G, source):
+    source = arbitrary_element(G)
+    neighbors = set(G[source]) - {source}
+    n = len(G)
+    for target in neighbors:
+        for path in nx.all_simple_paths(G, source, target):
+            if len(path) == n:
+                yield path
+
+
+def test_hamiltonian_path():
+    from itertools import permutations
+
+    G = nx.complete_graph(4)
+    paths = [list(p) for p in hamiltonian_path(G, 0)]
+    exact = [[0] + list(p) for p in permutations([1, 2, 3], 3)]
+    assert sorted(paths) == sorted(exact)
+
+
+def test_cutoff_zero():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_paths(G, 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+    paths = nx.all_simple_paths(nx.MultiGraph(G), 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+
+
+def test_source_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_paths(nx.MultiGraph(G), 0, 3))
+
+
+def test_target_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_paths(nx.MultiGraph(G), 1, 4))
+
+
+# Tests for all_simple_edge_paths
+def test_all_simple_edge_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_empty_path():
+    G = nx.empty_graph(1)
+    assert list(nx.all_simple_edge_paths(G, 0, 0)) == [[]]
+
+
+def test_all_simple_edge_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_digraph_all_simple_edge_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_all_simple_edge_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_digraph_all_simple_edge_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_all_simple_edge_paths_with_two_targets_in_line_emits_two_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 2)), ((0, 1), (1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_ignores_cycle():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_edge_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 3))}
+
+
+def test_all_simple_edge_paths_with_two_targets_inside_cycle_emits_two_paths():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_edge_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 2)), ((0, 1), (1, 3))}
+
+
+def test_all_simple_edge_paths_source_target():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 1, 1)
+    assert list(paths) == [[]]
+
+
+def test_all_simple_edge_paths_cutoff():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 1, cutoff=1)
+    assert {tuple(p) for p in paths} == {((0, 1),)}
+    paths = nx.all_simple_edge_paths(G, 0, 1, cutoff=2)
+    assert {tuple(p) for p in paths} == {((0, 1),), ((0, 2), (2, 1)), ((0, 3), (3, 1))}
+
+
+def test_all_simple_edge_paths_on_non_trivial_graph():
+    """you may need to draw this graph to make sure it is reasonable"""
+    G = nx.path_graph(5, create_using=nx.DiGraph())
+    G.add_edges_from([(0, 5), (1, 5), (1, 3), (5, 4), (4, 2), (4, 3)])
+    paths = nx.all_simple_edge_paths(G, 1, [2, 3])
+    assert {tuple(p) for p in paths} == {
+        ((1, 2),),
+        ((1, 3), (3, 4), (4, 2)),
+        ((1, 5), (5, 4), (4, 2)),
+        ((1, 3),),
+        ((1, 2), (2, 3)),
+        ((1, 5), (5, 4), (4, 3)),
+        ((1, 5), (5, 4), (4, 2), (2, 3)),
+    }
+    paths = nx.all_simple_edge_paths(G, 1, [2, 3], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        ((1, 2),),
+        ((1, 3), (3, 4), (4, 2)),
+        ((1, 5), (5, 4), (4, 2)),
+        ((1, 3),),
+        ((1, 2), (2, 3)),
+        ((1, 5), (5, 4), (4, 3)),
+    }
+    paths = nx.all_simple_edge_paths(G, 1, [2, 3], cutoff=2)
+    assert {tuple(p) for p in paths} == {((1, 2),), ((1, 3),), ((1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_multigraph():
+    G = nx.MultiGraph([(1, 2), (1, 2)])
+    paths = nx.all_simple_edge_paths(G, 1, 1)
+    assert list(paths) == [[]]
+    nx.add_path(G, [3, 1, 10, 2])
+    paths = list(nx.all_simple_edge_paths(G, 1, 2))
+    assert len(paths) == 3
+    assert {tuple(p) for p in paths} == {
+        ((1, 2, 0),),
+        ((1, 2, 1),),
+        ((1, 10, 0), (10, 2, 0)),
+    }
+
+
+def test_all_simple_edge_paths_multigraph_with_cutoff():
+    G = nx.MultiGraph([(1, 2), (1, 2), (1, 10), (10, 2)])
+    paths = list(nx.all_simple_edge_paths(G, 1, 2, cutoff=1))
+    assert len(paths) == 2
+    assert {tuple(p) for p in paths} == {((1, 2, 0),), ((1, 2, 1),)}
+
+
+def test_all_simple_edge_paths_directed():
+    G = nx.DiGraph()
+    nx.add_path(G, [1, 2, 3])
+    nx.add_path(G, [3, 2, 1])
+    paths = nx.all_simple_edge_paths(G, 1, 3)
+    assert {tuple(p) for p in paths} == {((1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_empty():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 3, cutoff=2)
+    assert list(paths) == []
+
+
+def test_all_simple_edge_paths_corner_cases():
+    assert list(nx.all_simple_edge_paths(nx.empty_graph(2), 0, 0)) == [[]]
+    assert list(nx.all_simple_edge_paths(nx.empty_graph(2), 0, 1)) == []
+    assert list(nx.all_simple_edge_paths(nx.path_graph(9), 0, 8, 0)) == []
+
+
+def test_all_simple_edge_paths_ignores_self_loop():
+    G = nx.Graph([(0, 0), (0, 1), (1, 1), (1, 2)])
+    assert list(nx.all_simple_edge_paths(G, 0, 2)) == [[(0, 1), (1, 2)]]
+
+
+def hamiltonian_edge_path(G, source):
+    source = arbitrary_element(G)
+    neighbors = set(G[source]) - {source}
+    n = len(G)
+    for target in neighbors:
+        for path in nx.all_simple_edge_paths(G, source, target):
+            if len(path) == n - 1:
+                yield path
+
+
+def test_hamiltonian__edge_path():
+    from itertools import permutations
+
+    G = nx.complete_graph(4)
+    paths = hamiltonian_edge_path(G, 0)
+    exact = [list(pairwise([0] + list(p))) for p in permutations([1, 2, 3], 3)]
+    assert sorted(exact) == sorted(paths)
+
+
+def test_edge_cutoff_zero():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+    paths = nx.all_simple_edge_paths(nx.MultiGraph(G), 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+
+
+def test_edge_source_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_edge_paths(nx.MultiGraph(G), 0, 3))
+
+
+def test_edge_target_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_edge_paths(nx.MultiGraph(G), 1, 4))
+
+
+# Tests for shortest_simple_paths
+def test_shortest_simple_paths():
+    G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+    paths = nx.shortest_simple_paths(G, 1, 12)
+    assert next(paths) == [1, 2, 3, 4, 8, 12]
+    assert next(paths) == [1, 5, 6, 7, 8, 12]
+    assert [len(path) for path in nx.shortest_simple_paths(G, 1, 12)] == sorted(
+        len(path) for path in nx.all_simple_paths(G, 1, 12)
+    )
+
+
+def test_shortest_simple_paths_singleton_path():
+    G = nx.empty_graph(3)
+    assert list(nx.shortest_simple_paths(G, 0, 0)) == [[0]]
+
+
+def test_shortest_simple_paths_directed():
+    G = nx.cycle_graph(7, create_using=nx.DiGraph())
+    paths = nx.shortest_simple_paths(G, 0, 3)
+    assert list(paths) == [[0, 1, 2, 3]]
+
+
+def test_shortest_simple_paths_directed_with_weight_function():
+    def cost(u, v, x):
+        return 1
+
+    G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+    paths = nx.shortest_simple_paths(G, 1, 12)
+    assert next(paths) == [1, 2, 3, 4, 8, 12]
+    assert next(paths) == [1, 5, 6, 7, 8, 12]
+    assert [
+        len(path) for path in nx.shortest_simple_paths(G, 1, 12, weight=cost)
+    ] == sorted(len(path) for path in nx.all_simple_paths(G, 1, 12))
+
+
+def test_shortest_simple_paths_with_weight_function():
+    def cost(u, v, x):
+        return 1
+
+    G = nx.cycle_graph(7, create_using=nx.DiGraph())
+    paths = nx.shortest_simple_paths(G, 0, 3, weight=cost)
+    assert list(paths) == [[0, 1, 2, 3]]
+
+
+def test_shortest_simple_paths_with_none_weight_function():
+    def cost(u, v, x):
+        delta = abs(u - v)
+        # ignore interior edges
+        return 1 if (delta == 1 or delta == 4) else None
+
+    G = nx.complete_graph(5)
+    paths = nx.shortest_simple_paths(G, 0, 2, weight=cost)
+    assert list(paths) == [[0, 1, 2], [0, 4, 3, 2]]
+
+
+def test_Greg_Bernstein():
+    g1 = nx.Graph()
+    g1.add_nodes_from(["N0", "N1", "N2", "N3", "N4"])
+    g1.add_edge("N4", "N1", weight=10.0, capacity=50, name="L5")
+    g1.add_edge("N4", "N0", weight=7.0, capacity=40, name="L4")
+    g1.add_edge("N0", "N1", weight=10.0, capacity=45, name="L1")
+    g1.add_edge("N3", "N0", weight=10.0, capacity=50, name="L0")
+    g1.add_edge("N2", "N3", weight=12.0, capacity=30, name="L2")
+    g1.add_edge("N1", "N2", weight=15.0, capacity=42, name="L3")
+    solution = [["N1", "N0", "N3"], ["N1", "N2", "N3"], ["N1", "N4", "N0", "N3"]]
+    result = list(nx.shortest_simple_paths(g1, "N1", "N3", weight="weight"))
+    assert result == solution
+
+
+def test_weighted_shortest_simple_path():
+    def cost_func(path):
+        return sum(G.adj[u][v]["weight"] for (u, v) in zip(path, path[1:]))
+
+    G = nx.complete_graph(5)
+    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
+    nx.set_edge_attributes(G, weight, "weight")
+    cost = 0
+    for path in nx.shortest_simple_paths(G, 0, 3, weight="weight"):
+        this_cost = cost_func(path)
+        assert cost <= this_cost
+        cost = this_cost
+
+
+def test_directed_weighted_shortest_simple_path():
+    def cost_func(path):
+        return sum(G.adj[u][v]["weight"] for (u, v) in zip(path, path[1:]))
+
+    G = nx.complete_graph(5)
+    G = G.to_directed()
+    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
+    nx.set_edge_attributes(G, weight, "weight")
+    cost = 0
+    for path in nx.shortest_simple_paths(G, 0, 3, weight="weight"):
+        this_cost = cost_func(path)
+        assert cost <= this_cost
+        cost = this_cost
+
+
+def test_weighted_shortest_simple_path_issue2427():
+    G = nx.Graph()
+    G.add_edge("IN", "OUT", weight=2)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=2)
+    G.add_edge("B", "OUT", weight=2)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "OUT"],
+        ["IN", "B", "OUT"],
+    ]
+    G = nx.Graph()
+    G.add_edge("IN", "OUT", weight=10)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=1)
+    G.add_edge("B", "OUT", weight=1)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "B", "OUT"],
+        ["IN", "OUT"],
+    ]
+
+
+def test_directed_weighted_shortest_simple_path_issue2427():
+    G = nx.DiGraph()
+    G.add_edge("IN", "OUT", weight=2)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=2)
+    G.add_edge("B", "OUT", weight=2)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "OUT"],
+        ["IN", "B", "OUT"],
+    ]
+    G = nx.DiGraph()
+    G.add_edge("IN", "OUT", weight=10)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=1)
+    G.add_edge("B", "OUT", weight=1)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "B", "OUT"],
+        ["IN", "OUT"],
+    ]
+
+
+def test_weight_name():
+    G = nx.cycle_graph(7)
+    nx.set_edge_attributes(G, 1, "weight")
+    nx.set_edge_attributes(G, 1, "foo")
+    G.adj[1][2]["foo"] = 7
+    paths = list(nx.shortest_simple_paths(G, 0, 3, weight="foo"))
+    solution = [[0, 6, 5, 4, 3], [0, 1, 2, 3]]
+    assert paths == solution
+
+
+def test_ssp_source_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.shortest_simple_paths(G, 0, 3))
+
+
+def test_ssp_target_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.shortest_simple_paths(G, 1, 4))
+
+
+def test_ssp_multigraph():
+    with pytest.raises(nx.NetworkXNotImplemented):
+        G = nx.MultiGraph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.shortest_simple_paths(G, 1, 4))
+
+
+def test_ssp_source_missing2():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2])
+        nx.add_path(G, [3, 4, 5])
+        list(nx.shortest_simple_paths(G, 0, 3))
+
+
+def test_bidirectional_shortest_path_restricted_cycle():
+    cycle = nx.cycle_graph(7)
+    length, path = _bidirectional_shortest_path(cycle, 0, 3)
+    assert path == [0, 1, 2, 3]
+    length, path = _bidirectional_shortest_path(cycle, 0, 3, ignore_nodes=[1])
+    assert path == [0, 6, 5, 4, 3]
+
+
+def test_bidirectional_shortest_path_restricted_wheel():
+    wheel = nx.wheel_graph(6)
+    length, path = _bidirectional_shortest_path(wheel, 1, 3)
+    assert path in [[1, 0, 3], [1, 2, 3]]
+    length, path = _bidirectional_shortest_path(wheel, 1, 3, ignore_nodes=[0])
+    assert path == [1, 2, 3]
+    length, path = _bidirectional_shortest_path(wheel, 1, 3, ignore_nodes=[0, 2])
+    assert path == [1, 5, 4, 3]
+    length, path = _bidirectional_shortest_path(
+        wheel, 1, 3, ignore_edges=[(1, 0), (5, 0), (2, 3)]
+    )
+    assert path in [[1, 2, 0, 3], [1, 5, 4, 3]]
+
+
+def test_bidirectional_shortest_path_restricted_directed_cycle():
+    directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
+    length, path = _bidirectional_shortest_path(directed_cycle, 0, 3)
+    assert path == [0, 1, 2, 3]
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_shortest_path,
+        directed_cycle,
+        0,
+        3,
+        ignore_nodes=[1],
+    )
+    length, path = _bidirectional_shortest_path(
+        directed_cycle, 0, 3, ignore_edges=[(2, 1)]
+    )
+    assert path == [0, 1, 2, 3]
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_shortest_path,
+        directed_cycle,
+        0,
+        3,
+        ignore_edges=[(1, 2)],
+    )
+
+
+def test_bidirectional_shortest_path_ignore():
+    G = nx.Graph()
+    nx.add_path(G, [1, 2])
+    nx.add_path(G, [1, 3])
+    nx.add_path(G, [1, 4])
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_shortest_path, G, 1, 2, ignore_nodes=[1]
+    )
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_shortest_path, G, 1, 2, ignore_nodes=[2]
+    )
+    G = nx.Graph()
+    nx.add_path(G, [1, 3])
+    nx.add_path(G, [1, 4])
+    nx.add_path(G, [3, 2])
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_shortest_path, G, 1, 2, ignore_nodes=[1, 2]
+    )
+
+
+def validate_path(G, s, t, soln_len, path):
+    assert path[0] == s
+    assert path[-1] == t
+    assert soln_len == sum(
+        G[u][v].get("weight", 1) for u, v in zip(path[:-1], path[1:])
+    )
+
+
+def validate_length_path(G, s, t, soln_len, length, path):
+    assert soln_len == length
+    validate_path(G, s, t, length, path)
+
+
+def test_bidirectional_dijkstra_restricted():
+    XG = nx.DiGraph()
+    XG.add_weighted_edges_from(
+        [
+            ("s", "u", 10),
+            ("s", "x", 5),
+            ("u", "v", 1),
+            ("u", "x", 2),
+            ("v", "y", 1),
+            ("x", "u", 3),
+            ("x", "v", 5),
+            ("x", "y", 2),
+            ("y", "s", 7),
+            ("y", "v", 6),
+        ]
+    )
+
+    XG3 = nx.Graph()
+    XG3.add_weighted_edges_from(
+        [[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]]
+    )
+    validate_length_path(XG, "s", "v", 9, *_bidirectional_dijkstra(XG, "s", "v"))
+    validate_length_path(
+        XG, "s", "v", 10, *_bidirectional_dijkstra(XG, "s", "v", ignore_nodes=["u"])
+    )
+    validate_length_path(
+        XG,
+        "s",
+        "v",
+        11,
+        *_bidirectional_dijkstra(XG, "s", "v", ignore_edges=[("s", "x")]),
+    )
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_dijkstra,
+        XG,
+        "s",
+        "v",
+        ignore_nodes=["u"],
+        ignore_edges=[("s", "x")],
+    )
+    validate_length_path(XG3, 0, 3, 15, *_bidirectional_dijkstra(XG3, 0, 3))
+    validate_length_path(
+        XG3, 0, 3, 16, *_bidirectional_dijkstra(XG3, 0, 3, ignore_nodes=[1])
+    )
+    validate_length_path(
+        XG3, 0, 3, 16, *_bidirectional_dijkstra(XG3, 0, 3, ignore_edges=[(2, 3)])
+    )
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_dijkstra,
+        XG3,
+        0,
+        3,
+        ignore_nodes=[1],
+        ignore_edges=[(5, 4)],
+    )
+
+
+def test_bidirectional_dijkstra_no_path():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        nx.add_path(G, [4, 5, 6])
+        _bidirectional_dijkstra(G, 1, 6)
+
+
+def test_bidirectional_dijkstra_ignore():
+    G = nx.Graph()
+    nx.add_path(G, [1, 2, 10])
+    nx.add_path(G, [1, 3, 10])
+    pytest.raises(nx.NetworkXNoPath, _bidirectional_dijkstra, G, 1, 2, ignore_nodes=[1])
+    pytest.raises(nx.NetworkXNoPath, _bidirectional_dijkstra, G, 1, 2, ignore_nodes=[2])
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_dijkstra, G, 1, 2, ignore_nodes=[1, 2]
+    )
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tests/test_smallworld.py b/lib/python3.11/site-packages/networkx/algorithms/tests/test_smallworld.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8e454293b4325a5c7557480182b1e1271a1118e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tests/test_smallworld.py
@@ -0,0 +1,76 @@
+import pytest
+
+pytest.importorskip("numpy")
+
+import networkx as nx
+from networkx import lattice_reference, omega, random_reference, sigma
+
+rng = 42
+
+
+def test_random_reference():
+    G = nx.connected_watts_strogatz_graph(50, 6, 0.1, seed=rng)
+    Gr = random_reference(G, niter=1, seed=rng)
+    C = nx.average_clustering(G)
+    Cr = nx.average_clustering(Gr)
+    assert C > Cr
+
+    with pytest.raises(nx.NetworkXError):
+        next(random_reference(nx.Graph()))
+    with pytest.raises(nx.NetworkXNotImplemented):
+        next(random_reference(nx.DiGraph()))
+
+    H = nx.Graph(((0, 1), (2, 3)))
+    Hl = random_reference(H, niter=1, seed=rng)
+
+
+def test_lattice_reference():
+    G = nx.connected_watts_strogatz_graph(50, 6, 1, seed=rng)
+    Gl = lattice_reference(G, niter=1, seed=rng)
+    L = nx.average_shortest_path_length(G)
+    Ll = nx.average_shortest_path_length(Gl)
+    assert Ll > L
+
+    pytest.raises(nx.NetworkXError, lattice_reference, nx.Graph())
+    pytest.raises(nx.NetworkXNotImplemented, lattice_reference, nx.DiGraph())
+
+    H = nx.Graph(((0, 1), (2, 3)))
+    Hl = lattice_reference(H, niter=1)
+
+
+def test_sigma():
+    Gs = nx.connected_watts_strogatz_graph(50, 6, 0.1, seed=rng)
+    Gr = nx.connected_watts_strogatz_graph(50, 6, 1, seed=rng)
+    sigmas = sigma(Gs, niter=1, nrand=2, seed=rng)
+    sigmar = sigma(Gr, niter=1, nrand=2, seed=rng)
+    assert sigmar < sigmas
+
+
+def test_omega():
+    Gl = nx.connected_watts_strogatz_graph(50, 6, 0, seed=rng)
+    Gr = nx.connected_watts_strogatz_graph(50, 6, 1, seed=rng)
+    Gs = nx.connected_watts_strogatz_graph(50, 6, 0.1, seed=rng)
+    omegal = omega(Gl, niter=1, nrand=1, seed=rng)
+    omegar = omega(Gr, niter=1, nrand=1, seed=rng)
+    omegas = omega(Gs, niter=1, nrand=1, seed=rng)
+    assert omegal < omegas and omegas < omegar
+
+    # Test that omega lies within the [-1, 1] bounds
+    G_barbell = nx.barbell_graph(5, 1)
+    G_karate = nx.karate_club_graph()
+
+    omega_barbell = nx.omega(G_barbell)
+    omega_karate = nx.omega(G_karate, nrand=2)
+
+    omegas = (omegal, omegar, omegas, omega_barbell, omega_karate)
+
+    for o in omegas:
+        assert -1 <= o <= 1
+
+
+@pytest.mark.parametrize("f", (nx.random_reference, nx.lattice_reference))
+def test_graph_no_edges(f):
+    G = nx.Graph()
+    G.add_nodes_from([0, 1, 2, 3])
+    with pytest.raises(nx.NetworkXError, match="Graph has fewer that 2 edges"):
+        f(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..26c7eef33429c8676d3ad0b54a5dfa620e8e4789
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/branchings.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/branchings.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..45ae1e5c8e80551b83b9498b02b78f7f9f3ef3ea
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/branchings.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/coding.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/coding.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c52b6ab619b4b1a32cce072b47f56368dc7ab064
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/coding.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/decomposition.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/decomposition.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..033d25e35c449d22e8b7aab1d62f63e521b6de1d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/decomposition.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/distance_measures.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/distance_measures.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8d4d8979f405512ed7232b51485dc344ce875fb6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/distance_measures.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/mst.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/mst.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bcd4e9e1f7d6b8be94f9d418b672cb18fd5c3d6b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/mst.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/operations.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/operations.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c3fc2ac668263fe6550db286a310117c9b928ea9
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/operations.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/recognition.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/recognition.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ad9a939468d3bec88e3700d040162867221e9245
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/__pycache__/recognition.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/mst.py b/lib/python3.11/site-packages/networkx/algorithms/tree/mst.py
new file mode 100644
index 0000000000000000000000000000000000000000..5886c6ef8a5e951550187917534e445c5a943cf5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/mst.py
@@ -0,0 +1,1281 @@
+"""
+Algorithms for calculating min/max spanning trees/forests.
+
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from heapq import heappop, heappush
+from itertools import count
+from math import isnan
+from operator import itemgetter
+from queue import PriorityQueue
+
+import networkx as nx
+from networkx.utils import UnionFind, not_implemented_for, py_random_state
+
+__all__ = [
+    "minimum_spanning_edges",
+    "maximum_spanning_edges",
+    "minimum_spanning_tree",
+    "maximum_spanning_tree",
+    "number_of_spanning_trees",
+    "random_spanning_tree",
+    "partition_spanning_tree",
+    "EdgePartition",
+    "SpanningTreeIterator",
+]
+
+
+class EdgePartition(Enum):
+    """
+    An enum to store the state of an edge partition. The enum is written to the
+    edges of a graph before being pasted to `kruskal_mst_edges`. Options are:
+
+    - EdgePartition.OPEN
+    - EdgePartition.INCLUDED
+    - EdgePartition.EXCLUDED
+    """
+
+    OPEN = 0
+    INCLUDED = 1
+    EXCLUDED = 2
+
+
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight", preserve_edge_attrs="data")
+def boruvka_mst_edges(
+    G, minimum=True, weight="weight", keys=False, data=True, ignore_nan=False
+):
+    """Iterate over edges of a Borůvka's algorithm min/max spanning tree.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The edges of `G` must have distinct weights,
+        otherwise the edges may not form a tree.
+
+    minimum : bool (default: True)
+        Find the minimum (True) or maximum (False) spanning tree.
+
+    weight : string (default: 'weight')
+        The name of the edge attribute holding the edge weights.
+
+    keys : bool (default: True)
+        This argument is ignored since this function is not
+        implemented for multigraphs; it exists only for consistency
+        with the other minimum spanning tree functions.
+
+    data : bool (default: True)
+        Flag for whether to yield edge attribute dicts.
+        If True, yield edges `(u, v, d)`, where `d` is the attribute dict.
+        If False, yield edges `(u, v)`.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+    """
+    # Initialize a forest, assuming initially that it is the discrete
+    # partition of the nodes of the graph.
+    forest = UnionFind(G)
+
+    def best_edge(component):
+        """Returns the optimum (minimum or maximum) edge on the edge
+        boundary of the given set of nodes.
+
+        A return value of ``None`` indicates an empty boundary.
+
+        """
+        sign = 1 if minimum else -1
+        minwt = float("inf")
+        boundary = None
+        for e in nx.edge_boundary(G, component, data=True):
+            wt = e[-1].get(weight, 1) * sign
+            if isnan(wt):
+                if ignore_nan:
+                    continue
+                msg = f"NaN found as an edge weight. Edge {e}"
+                raise ValueError(msg)
+            if wt < minwt:
+                minwt = wt
+                boundary = e
+        return boundary
+
+    # Determine the optimum edge in the edge boundary of each component
+    # in the forest.
+    best_edges = (best_edge(component) for component in forest.to_sets())
+    best_edges = [edge for edge in best_edges if edge is not None]
+    # If each entry was ``None``, that means the graph was disconnected,
+    # so we are done generating the forest.
+    while best_edges:
+        # Determine the optimum edge in the edge boundary of each
+        # component in the forest.
+        #
+        # This must be a sequence, not an iterator. In this list, the
+        # same edge may appear twice, in different orientations (but
+        # that's okay, since a union operation will be called on the
+        # endpoints the first time it is seen, but not the second time).
+        #
+        # Any ``None`` indicates that the edge boundary for that
+        # component was empty, so that part of the forest has been
+        # completed.
+        #
+        # TODO This can be parallelized, both in the outer loop over
+        # each component in the forest and in the computation of the
+        # minimum. (Same goes for the identical lines outside the loop.)
+        best_edges = (best_edge(component) for component in forest.to_sets())
+        best_edges = [edge for edge in best_edges if edge is not None]
+        # Join trees in the forest using the best edges, and yield that
+        # edge, since it is part of the spanning tree.
+        #
+        # TODO This loop can be parallelized, to an extent (the union
+        # operation must be atomic).
+        for u, v, d in best_edges:
+            if forest[u] != forest[v]:
+                if data:
+                    yield u, v, d
+                else:
+                    yield u, v
+                forest.union(u, v)
+
+
+@nx._dispatchable(
+    edge_attrs={"weight": None, "partition": None}, preserve_edge_attrs="data"
+)
+def kruskal_mst_edges(
+    G, minimum, weight="weight", keys=True, data=True, ignore_nan=False, partition=None
+):
+    """
+    Iterate over edge of a Kruskal's algorithm min/max spanning tree.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph holding the tree of interest.
+
+    minimum : bool (default: True)
+        Find the minimum (True) or maximum (False) spanning tree.
+
+    weight : string (default: 'weight')
+        The name of the edge attribute holding the edge weights.
+
+    keys : bool (default: True)
+        If `G` is a multigraph, `keys` controls whether edge keys ar yielded.
+        Otherwise `keys` is ignored.
+
+    data : bool (default: True)
+        Flag for whether to yield edge attribute dicts.
+        If True, yield edges `(u, v, d)`, where `d` is the attribute dict.
+        If False, yield edges `(u, v)`.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+    partition : string (default: None)
+        The name of the edge attribute holding the partition data, if it exists.
+        Partition data is written to the edges using the `EdgePartition` enum.
+        If a partition exists, all included edges and none of the excluded edges
+        will appear in the final tree. Open edges may or may not be used.
+
+    Yields
+    ------
+    edge tuple
+        The edges as discovered by Kruskal's method. Each edge can
+        take the following forms: `(u, v)`, `(u, v, d)` or `(u, v, k, d)`
+        depending on the `key` and `data` parameters
+    """
+    subtrees = UnionFind()
+    if G.is_multigraph():
+        edges = G.edges(keys=True, data=True)
+    else:
+        edges = G.edges(data=True)
+
+    # Sort the edges of the graph with respect to the partition data.
+    # Edges are returned in the following order:
+
+    # * Included edges
+    # * Open edges from smallest to largest weight
+    # * Excluded edges
+    included_edges = []
+    open_edges = []
+    for e in edges:
+        d = e[-1]
+        wt = d.get(weight, 1)
+        if isnan(wt):
+            if ignore_nan:
+                continue
+            raise ValueError(f"NaN found as an edge weight. Edge {e}")
+
+        edge = (wt,) + e
+        if d.get(partition) == EdgePartition.INCLUDED:
+            included_edges.append(edge)
+        elif d.get(partition) == EdgePartition.EXCLUDED:
+            continue
+        else:
+            open_edges.append(edge)
+
+    if minimum:
+        sorted_open_edges = sorted(open_edges, key=itemgetter(0))
+    else:
+        sorted_open_edges = sorted(open_edges, key=itemgetter(0), reverse=True)
+
+    # Condense the lists into one
+    included_edges.extend(sorted_open_edges)
+    sorted_edges = included_edges
+    del open_edges, sorted_open_edges, included_edges
+
+    # Multigraphs need to handle edge keys in addition to edge data.
+    if G.is_multigraph():
+        for wt, u, v, k, d in sorted_edges:
+            if subtrees[u] != subtrees[v]:
+                if keys:
+                    if data:
+                        yield u, v, k, d
+                    else:
+                        yield u, v, k
+                else:
+                    if data:
+                        yield u, v, d
+                    else:
+                        yield u, v
+                subtrees.union(u, v)
+    else:
+        for wt, u, v, d in sorted_edges:
+            if subtrees[u] != subtrees[v]:
+                if data:
+                    yield u, v, d
+                else:
+                    yield u, v
+                subtrees.union(u, v)
+
+
+@nx._dispatchable(edge_attrs="weight", preserve_edge_attrs="data")
+def prim_mst_edges(G, minimum, weight="weight", keys=True, data=True, ignore_nan=False):
+    """Iterate over edges of Prim's algorithm min/max spanning tree.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph holding the tree of interest.
+
+    minimum : bool (default: True)
+        Find the minimum (True) or maximum (False) spanning tree.
+
+    weight : string (default: 'weight')
+        The name of the edge attribute holding the edge weights.
+
+    keys : bool (default: True)
+        If `G` is a multigraph, `keys` controls whether edge keys ar yielded.
+        Otherwise `keys` is ignored.
+
+    data : bool (default: True)
+        Flag for whether to yield edge attribute dicts.
+        If True, yield edges `(u, v, d)`, where `d` is the attribute dict.
+        If False, yield edges `(u, v)`.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+    """
+    is_multigraph = G.is_multigraph()
+
+    nodes = set(G)
+    c = count()
+
+    sign = 1 if minimum else -1
+
+    while nodes:
+        u = nodes.pop()
+        frontier = []
+        visited = {u}
+        if is_multigraph:
+            for v, keydict in G.adj[u].items():
+                for k, d in keydict.items():
+                    wt = d.get(weight, 1) * sign
+                    if isnan(wt):
+                        if ignore_nan:
+                            continue
+                        msg = f"NaN found as an edge weight. Edge {(u, v, k, d)}"
+                        raise ValueError(msg)
+                    heappush(frontier, (wt, next(c), u, v, k, d))
+        else:
+            for v, d in G.adj[u].items():
+                wt = d.get(weight, 1) * sign
+                if isnan(wt):
+                    if ignore_nan:
+                        continue
+                    msg = f"NaN found as an edge weight. Edge {(u, v, d)}"
+                    raise ValueError(msg)
+                heappush(frontier, (wt, next(c), u, v, d))
+        while nodes and frontier:
+            if is_multigraph:
+                W, _, u, v, k, d = heappop(frontier)
+            else:
+                W, _, u, v, d = heappop(frontier)
+            if v in visited or v not in nodes:
+                continue
+            # Multigraphs need to handle edge keys in addition to edge data.
+            if is_multigraph and keys:
+                if data:
+                    yield u, v, k, d
+                else:
+                    yield u, v, k
+            else:
+                if data:
+                    yield u, v, d
+                else:
+                    yield u, v
+            # update frontier
+            visited.add(v)
+            nodes.discard(v)
+            if is_multigraph:
+                for w, keydict in G.adj[v].items():
+                    if w in visited:
+                        continue
+                    for k2, d2 in keydict.items():
+                        new_weight = d2.get(weight, 1) * sign
+                        if isnan(new_weight):
+                            if ignore_nan:
+                                continue
+                            msg = f"NaN found as an edge weight. Edge {(v, w, k2, d2)}"
+                            raise ValueError(msg)
+                        heappush(frontier, (new_weight, next(c), v, w, k2, d2))
+            else:
+                for w, d2 in G.adj[v].items():
+                    if w in visited:
+                        continue
+                    new_weight = d2.get(weight, 1) * sign
+                    if isnan(new_weight):
+                        if ignore_nan:
+                            continue
+                        msg = f"NaN found as an edge weight. Edge {(v, w, d2)}"
+                        raise ValueError(msg)
+                    heappush(frontier, (new_weight, next(c), v, w, d2))
+
+
+ALGORITHMS = {
+    "boruvka": boruvka_mst_edges,
+    "borůvka": boruvka_mst_edges,
+    "kruskal": kruskal_mst_edges,
+    "prim": prim_mst_edges,
+}
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight", preserve_edge_attrs="data")
+def minimum_spanning_edges(
+    G, algorithm="kruskal", weight="weight", keys=True, data=True, ignore_nan=False
+):
+    """Generate edges in a minimum spanning forest of an undirected
+    weighted graph.
+
+    A minimum spanning tree is a subgraph of the graph (a tree)
+    with the minimum sum of edge weights.  A spanning forest is a
+    union of the spanning trees for each connected component of the graph.
+
+    Parameters
+    ----------
+    G : undirected Graph
+       An undirected graph. If `G` is connected, then the algorithm finds a
+       spanning tree. Otherwise, a spanning forest is found.
+
+    algorithm : string
+       The algorithm to use when finding a minimum spanning tree. Valid
+       choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'.
+
+    weight : string
+       Edge data key to use for weight (default 'weight').
+
+    keys : bool
+       Whether to yield edge key in multigraphs in addition to the edge.
+       If `G` is not a multigraph, this is ignored.
+
+    data : bool, optional
+       If True yield the edge data along with the edge.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+    Returns
+    -------
+    edges : iterator
+       An iterator over edges in a maximum spanning tree of `G`.
+       Edges connecting nodes `u` and `v` are represented as tuples:
+       `(u, v, k, d)` or `(u, v, k)` or `(u, v, d)` or `(u, v)`
+
+       If `G` is a multigraph, `keys` indicates whether the edge key `k` will
+       be reported in the third position in the edge tuple. `data` indicates
+       whether the edge datadict `d` will appear at the end of the edge tuple.
+
+       If `G` is not a multigraph, the tuples are `(u, v, d)` if `data` is True
+       or `(u, v)` if `data` is False.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import tree
+
+    Find minimum spanning edges by Kruskal's algorithm
+
+    >>> G = nx.cycle_graph(4)
+    >>> G.add_edge(0, 3, weight=2)
+    >>> mst = tree.minimum_spanning_edges(G, algorithm="kruskal", data=False)
+    >>> edgelist = list(mst)
+    >>> sorted(sorted(e) for e in edgelist)
+    [[0, 1], [1, 2], [2, 3]]
+
+    Find minimum spanning edges by Prim's algorithm
+
+    >>> G = nx.cycle_graph(4)
+    >>> G.add_edge(0, 3, weight=2)
+    >>> mst = tree.minimum_spanning_edges(G, algorithm="prim", data=False)
+    >>> edgelist = list(mst)
+    >>> sorted(sorted(e) for e in edgelist)
+    [[0, 1], [1, 2], [2, 3]]
+
+    Notes
+    -----
+    For Borůvka's algorithm, each edge must have a weight attribute, and
+    each edge weight must be distinct.
+
+    For the other algorithms, if the graph edges do not have a weight
+    attribute a default weight of 1 will be used.
+
+    Modified code from David Eppstein, April 2006
+    http://www.ics.uci.edu/~eppstein/PADS/
+
+    """
+    try:
+        algo = ALGORITHMS[algorithm]
+    except KeyError as err:
+        msg = f"{algorithm} is not a valid choice for an algorithm."
+        raise ValueError(msg) from err
+
+    return algo(
+        G, minimum=True, weight=weight, keys=keys, data=data, ignore_nan=ignore_nan
+    )
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight", preserve_edge_attrs="data")
+def maximum_spanning_edges(
+    G, algorithm="kruskal", weight="weight", keys=True, data=True, ignore_nan=False
+):
+    """Generate edges in a maximum spanning forest of an undirected
+    weighted graph.
+
+    A maximum spanning tree is a subgraph of the graph (a tree)
+    with the maximum possible sum of edge weights.  A spanning forest is a
+    union of the spanning trees for each connected component of the graph.
+
+    Parameters
+    ----------
+    G : undirected Graph
+       An undirected graph. If `G` is connected, then the algorithm finds a
+       spanning tree. Otherwise, a spanning forest is found.
+
+    algorithm : string
+       The algorithm to use when finding a maximum spanning tree. Valid
+       choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'.
+
+    weight : string
+       Edge data key to use for weight (default 'weight').
+
+    keys : bool
+       Whether to yield edge key in multigraphs in addition to the edge.
+       If `G` is not a multigraph, this is ignored.
+
+    data : bool, optional
+       If True yield the edge data along with the edge.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+    Returns
+    -------
+    edges : iterator
+       An iterator over edges in a maximum spanning tree of `G`.
+       Edges connecting nodes `u` and `v` are represented as tuples:
+       `(u, v, k, d)` or `(u, v, k)` or `(u, v, d)` or `(u, v)`
+
+       If `G` is a multigraph, `keys` indicates whether the edge key `k` will
+       be reported in the third position in the edge tuple. `data` indicates
+       whether the edge datadict `d` will appear at the end of the edge tuple.
+
+       If `G` is not a multigraph, the tuples are `(u, v, d)` if `data` is True
+       or `(u, v)` if `data` is False.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import tree
+
+    Find maximum spanning edges by Kruskal's algorithm
+
+    >>> G = nx.cycle_graph(4)
+    >>> G.add_edge(0, 3, weight=2)
+    >>> mst = tree.maximum_spanning_edges(G, algorithm="kruskal", data=False)
+    >>> edgelist = list(mst)
+    >>> sorted(sorted(e) for e in edgelist)
+    [[0, 1], [0, 3], [1, 2]]
+
+    Find maximum spanning edges by Prim's algorithm
+
+    >>> G = nx.cycle_graph(4)
+    >>> G.add_edge(0, 3, weight=2)  # assign weight 2 to edge 0-3
+    >>> mst = tree.maximum_spanning_edges(G, algorithm="prim", data=False)
+    >>> edgelist = list(mst)
+    >>> sorted(sorted(e) for e in edgelist)
+    [[0, 1], [0, 3], [2, 3]]
+
+    Notes
+    -----
+    For Borůvka's algorithm, each edge must have a weight attribute, and
+    each edge weight must be distinct.
+
+    For the other algorithms, if the graph edges do not have a weight
+    attribute a default weight of 1 will be used.
+
+    Modified code from David Eppstein, April 2006
+    http://www.ics.uci.edu/~eppstein/PADS/
+    """
+    try:
+        algo = ALGORITHMS[algorithm]
+    except KeyError as err:
+        msg = f"{algorithm} is not a valid choice for an algorithm."
+        raise ValueError(msg) from err
+
+    return algo(
+        G, minimum=False, weight=weight, keys=keys, data=data, ignore_nan=ignore_nan
+    )
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def minimum_spanning_tree(G, weight="weight", algorithm="kruskal", ignore_nan=False):
+    """Returns a minimum spanning tree or forest on an undirected graph `G`.
+
+    Parameters
+    ----------
+    G : undirected graph
+        An undirected graph. If `G` is connected, then the algorithm finds a
+        spanning tree. Otherwise, a spanning forest is found.
+
+    weight : str
+       Data key to use for edge weights.
+
+    algorithm : string
+       The algorithm to use when finding a minimum spanning tree. Valid
+       choices are 'kruskal', 'prim', or 'boruvka'. The default is
+       'kruskal'.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+    Returns
+    -------
+    G : NetworkX Graph
+       A minimum spanning tree or forest.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> G.add_edge(0, 3, weight=2)
+    >>> T = nx.minimum_spanning_tree(G)
+    >>> sorted(T.edges(data=True))
+    [(0, 1, {}), (1, 2, {}), (2, 3, {})]
+
+
+    Notes
+    -----
+    For Borůvka's algorithm, each edge must have a weight attribute, and
+    each edge weight must be distinct.
+
+    For the other algorithms, if the graph edges do not have a weight
+    attribute a default weight of 1 will be used.
+
+    There may be more than one tree with the same minimum or maximum weight.
+    See :mod:`networkx.tree.recognition` for more detailed definitions.
+
+    Isolated nodes with self-loops are in the tree as edgeless isolated nodes.
+
+    """
+    edges = minimum_spanning_edges(
+        G, algorithm, weight, keys=True, data=True, ignore_nan=ignore_nan
+    )
+    T = G.__class__()  # Same graph class as G
+    T.graph.update(G.graph)
+    T.add_nodes_from(G.nodes.items())
+    T.add_edges_from(edges)
+    return T
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def partition_spanning_tree(
+    G, minimum=True, weight="weight", partition="partition", ignore_nan=False
+):
+    """
+    Find a spanning tree while respecting a partition of edges.
+
+    Edges can be flagged as either `INCLUDED` which are required to be in the
+    returned tree, `EXCLUDED`, which cannot be in the returned tree and `OPEN`.
+
+    This is used in the SpanningTreeIterator to create new partitions following
+    the algorithm of Sörensen and Janssens [1]_.
+
+    Parameters
+    ----------
+    G : undirected graph
+        An undirected graph.
+
+    minimum : bool (default: True)
+        Determines whether the returned tree is the minimum spanning tree of
+        the partition of the maximum one.
+
+    weight : str
+        Data key to use for edge weights.
+
+    partition : str
+        The key for the edge attribute containing the partition
+        data on the graph. Edges can be included, excluded or open using the
+        `EdgePartition` enum.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+
+    Returns
+    -------
+    G : NetworkX Graph
+        A minimum spanning tree using all of the included edges in the graph and
+        none of the excluded edges.
+
+    References
+    ----------
+    .. [1] G.K. Janssens, K. Sörensen, An algorithm to generate all spanning
+           trees in order of increasing cost, Pesquisa Operacional, 2005-08,
+           Vol. 25 (2), p. 219-229,
+           https://www.scielo.br/j/pope/a/XHswBwRwJyrfL88dmMwYNWp/?lang=en
+    """
+    edges = kruskal_mst_edges(
+        G,
+        minimum,
+        weight,
+        keys=True,
+        data=True,
+        ignore_nan=ignore_nan,
+        partition=partition,
+    )
+    T = G.__class__()  # Same graph class as G
+    T.graph.update(G.graph)
+    T.add_nodes_from(G.nodes.items())
+    T.add_edges_from(edges)
+    return T
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def maximum_spanning_tree(G, weight="weight", algorithm="kruskal", ignore_nan=False):
+    """Returns a maximum spanning tree or forest on an undirected graph `G`.
+
+    Parameters
+    ----------
+    G : undirected graph
+        An undirected graph. If `G` is connected, then the algorithm finds a
+        spanning tree. Otherwise, a spanning forest is found.
+
+    weight : str
+       Data key to use for edge weights.
+
+    algorithm : string
+       The algorithm to use when finding a maximum spanning tree. Valid
+       choices are 'kruskal', 'prim', or 'boruvka'. The default is
+       'kruskal'.
+
+    ignore_nan : bool (default: False)
+        If a NaN is found as an edge weight normally an exception is raised.
+        If `ignore_nan is True` then that edge is ignored instead.
+
+
+    Returns
+    -------
+    G : NetworkX Graph
+       A maximum spanning tree or forest.
+
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> G.add_edge(0, 3, weight=2)
+    >>> T = nx.maximum_spanning_tree(G)
+    >>> sorted(T.edges(data=True))
+    [(0, 1, {}), (0, 3, {'weight': 2}), (1, 2, {})]
+
+
+    Notes
+    -----
+    For Borůvka's algorithm, each edge must have a weight attribute, and
+    each edge weight must be distinct.
+
+    For the other algorithms, if the graph edges do not have a weight
+    attribute a default weight of 1 will be used.
+
+    There may be more than one tree with the same minimum or maximum weight.
+    See :mod:`networkx.tree.recognition` for more detailed definitions.
+
+    Isolated nodes with self-loops are in the tree as edgeless isolated nodes.
+
+    """
+    edges = maximum_spanning_edges(
+        G, algorithm, weight, keys=True, data=True, ignore_nan=ignore_nan
+    )
+    edges = list(edges)
+    T = G.__class__()  # Same graph class as G
+    T.graph.update(G.graph)
+    T.add_nodes_from(G.nodes.items())
+    T.add_edges_from(edges)
+    return T
+
+
+@py_random_state(3)
+@nx._dispatchable(preserve_edge_attrs=True, returns_graph=True)
+def random_spanning_tree(G, weight=None, *, multiplicative=True, seed=None):
+    """
+    Sample a random spanning tree using the edges weights of `G`.
+
+    This function supports two different methods for determining the
+    probability of the graph. If ``multiplicative=True``, the probability
+    is based on the product of edge weights, and if ``multiplicative=False``
+    it is based on the sum of the edge weight. However, since it is
+    easier to determine the total weight of all spanning trees for the
+    multiplicative version, that is significantly faster and should be used if
+    possible. Additionally, setting `weight` to `None` will cause a spanning tree
+    to be selected with uniform probability.
+
+    The function uses algorithm A8 in [1]_ .
+
+    Parameters
+    ----------
+    G : nx.Graph
+        An undirected version of the original graph.
+
+    weight : string
+        The edge key for the edge attribute holding edge weight.
+
+    multiplicative : bool, default=True
+        If `True`, the probability of each tree is the product of its edge weight
+        over the sum of the product of all the spanning trees in the graph. If
+        `False`, the probability is the sum of its edge weight over the sum of
+        the sum of weights for all spanning trees in the graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    nx.Graph
+        A spanning tree using the distribution defined by the weight of the tree.
+
+    References
+    ----------
+    .. [1] V. Kulkarni, Generating random combinatorial objects, Journal of
+       Algorithms, 11 (1990), pp. 185–207
+    """
+
+    def find_node(merged_nodes, node):
+        """
+        We can think of clusters of contracted nodes as having one
+        representative in the graph. Each node which is not in merged_nodes
+        is still its own representative. Since a representative can be later
+        contracted, we need to recursively search though the dict to find
+        the final representative, but once we know it we can use path
+        compression to speed up the access of the representative for next time.
+
+        This cannot be replaced by the standard NetworkX union_find since that
+        data structure will merge nodes with less representing nodes into the
+        one with more representing nodes but this function requires we merge
+        them using the order that contract_edges contracts using.
+
+        Parameters
+        ----------
+        merged_nodes : dict
+            The dict storing the mapping from node to representative
+        node
+            The node whose representative we seek
+
+        Returns
+        -------
+        The representative of the `node`
+        """
+        if node not in merged_nodes:
+            return node
+        else:
+            rep = find_node(merged_nodes, merged_nodes[node])
+            merged_nodes[node] = rep
+            return rep
+
+    def prepare_graph():
+        """
+        For the graph `G`, remove all edges not in the set `V` and then
+        contract all edges in the set `U`.
+
+        Returns
+        -------
+        A copy of `G` which has had all edges not in `V` removed and all edges
+        in `U` contracted.
+        """
+
+        # The result is a MultiGraph version of G so that parallel edges are
+        # allowed during edge contraction
+        result = nx.MultiGraph(incoming_graph_data=G)
+
+        # Remove all edges not in V
+        edges_to_remove = set(result.edges()).difference(V)
+        result.remove_edges_from(edges_to_remove)
+
+        # Contract all edges in U
+        #
+        # Imagine that you have two edges to contract and they share an
+        # endpoint like this:
+        #                        [0] ----- [1] ----- [2]
+        # If we contract (0, 1) first, the contraction function will always
+        # delete the second node it is passed so the resulting graph would be
+        #                             [0] ----- [2]
+        # and edge (1, 2) no longer exists but (0, 2) would need to be contracted
+        # in its place now. That is why I use the below dict as a merge-find
+        # data structure with path compression to track how the nodes are merged.
+        merged_nodes = {}
+
+        for u, v in U:
+            u_rep = find_node(merged_nodes, u)
+            v_rep = find_node(merged_nodes, v)
+            # We cannot contract a node with itself
+            if u_rep == v_rep:
+                continue
+            nx.contracted_nodes(result, u_rep, v_rep, self_loops=False, copy=False)
+            merged_nodes[v_rep] = u_rep
+
+        return merged_nodes, result
+
+    def spanning_tree_total_weight(G, weight):
+        """
+        Find the sum of weights of the spanning trees of `G` using the
+        appropriate `method`.
+
+        This is easy if the chosen method is 'multiplicative', since we can
+        use Kirchhoff's Tree Matrix Theorem directly. However, with the
+        'additive' method, this process is slightly more complex and less
+        computationally efficient as we have to find the number of spanning
+        trees which contain each possible edge in the graph.
+
+        Parameters
+        ----------
+        G : NetworkX Graph
+            The graph to find the total weight of all spanning trees on.
+
+        weight : string
+            The key for the weight edge attribute of the graph.
+
+        Returns
+        -------
+        float
+            The sum of either the multiplicative or additive weight for all
+            spanning trees in the graph.
+        """
+        if multiplicative:
+            return number_of_spanning_trees(G, weight=weight)
+        else:
+            # There are two cases for the total spanning tree additive weight.
+            # 1. There is one edge in the graph. Then the only spanning tree is
+            #    that edge itself, which will have a total weight of that edge
+            #    itself.
+            if G.number_of_edges() == 1:
+                return G.edges(data=weight).__iter__().__next__()[2]
+            # 2. There are no edges or two or more edges in the graph. Then, we find the
+            #    total weight of the spanning trees using the formula in the
+            #    reference paper: take the weight of each edge and multiply it by
+            #    the number of spanning trees which include that edge. This
+            #    can be accomplished by contracting the edge and finding the
+            #    multiplicative total spanning tree weight if the weight of each edge
+            #    is assumed to be 1, which is conveniently built into networkx already,
+            #    by calling number_of_spanning_trees with weight=None.
+            #    Note that with no edges the returned value is just zero.
+            else:
+                total = 0
+                for u, v, w in G.edges(data=weight):
+                    total += w * nx.number_of_spanning_trees(
+                        nx.contracted_edge(G, edge=(u, v), self_loops=False),
+                        weight=None,
+                    )
+                return total
+
+    if G.number_of_nodes() < 2:
+        # no edges in the spanning tree
+        return nx.empty_graph(G.nodes)
+
+    U = set()
+    st_cached_value = 0
+    V = set(G.edges())
+    shuffled_edges = list(G.edges())
+    seed.shuffle(shuffled_edges)
+
+    for u, v in shuffled_edges:
+        e_weight = G[u][v][weight] if weight is not None else 1
+        node_map, prepared_G = prepare_graph()
+        G_total_tree_weight = spanning_tree_total_weight(prepared_G, weight)
+        # Add the edge to U so that we can compute the total tree weight
+        # assuming we include that edge
+        # Now, if (u, v) cannot exist in G because it is fully contracted out
+        # of existence, then it by definition cannot influence G_e's Kirchhoff
+        # value. But, we also cannot pick it.
+        rep_edge = (find_node(node_map, u), find_node(node_map, v))
+        # Check to see if the 'representative edge' for the current edge is
+        # in prepared_G. If so, then we can pick it.
+        if rep_edge in prepared_G.edges:
+            prepared_G_e = nx.contracted_edge(
+                prepared_G, edge=rep_edge, self_loops=False
+            )
+            G_e_total_tree_weight = spanning_tree_total_weight(prepared_G_e, weight)
+            if multiplicative:
+                threshold = e_weight * G_e_total_tree_weight / G_total_tree_weight
+            else:
+                numerator = (st_cached_value + e_weight) * nx.number_of_spanning_trees(
+                    prepared_G_e
+                ) + G_e_total_tree_weight
+                denominator = (
+                    st_cached_value * nx.number_of_spanning_trees(prepared_G)
+                    + G_total_tree_weight
+                )
+                threshold = numerator / denominator
+        else:
+            threshold = 0.0
+        z = seed.uniform(0.0, 1.0)
+        if z > threshold:
+            # Remove the edge from V since we did not pick it.
+            V.remove((u, v))
+        else:
+            # Add the edge to U since we picked it.
+            st_cached_value += e_weight
+            U.add((u, v))
+        # If we decide to keep an edge, it may complete the spanning tree.
+        if len(U) == G.number_of_nodes() - 1:
+            spanning_tree = nx.Graph()
+            spanning_tree.add_edges_from(U)
+            return spanning_tree
+    raise Exception(f"Something went wrong! Only {len(U)} edges in the spanning tree!")
+
+
+class SpanningTreeIterator:
+    """
+    Iterate over all spanning trees of a graph in either increasing or
+    decreasing cost.
+
+    Notes
+    -----
+    This iterator uses the partition scheme from [1]_ (included edges,
+    excluded edges and open edges) as well as a modified Kruskal's Algorithm
+    to generate minimum spanning trees which respect the partition of edges.
+    For spanning trees with the same weight, ties are broken arbitrarily.
+
+    References
+    ----------
+    .. [1] G.K. Janssens, K. Sörensen, An algorithm to generate all spanning
+           trees in order of increasing cost, Pesquisa Operacional, 2005-08,
+           Vol. 25 (2), p. 219-229,
+           https://www.scielo.br/j/pope/a/XHswBwRwJyrfL88dmMwYNWp/?lang=en
+    """
+
+    @dataclass(order=True)
+    class Partition:
+        """
+        This dataclass represents a partition and stores a dict with the edge
+        data and the weight of the minimum spanning tree of the partition dict.
+        """
+
+        mst_weight: float
+        partition_dict: dict = field(compare=False)
+
+        def __copy__(self):
+            return SpanningTreeIterator.Partition(
+                self.mst_weight, self.partition_dict.copy()
+            )
+
+    def __init__(self, G, weight="weight", minimum=True, ignore_nan=False):
+        """
+        Initialize the iterator
+
+        Parameters
+        ----------
+        G : nx.Graph
+            The directed graph which we need to iterate trees over
+
+        weight : String, default = "weight"
+            The edge attribute used to store the weight of the edge
+
+        minimum : bool, default = True
+            Return the trees in increasing order while true and decreasing order
+            while false.
+
+        ignore_nan : bool, default = False
+            If a NaN is found as an edge weight normally an exception is raised.
+            If `ignore_nan is True` then that edge is ignored instead.
+        """
+        self.G = G.copy()
+        self.G.__networkx_cache__ = None  # Disable caching
+        self.weight = weight
+        self.minimum = minimum
+        self.ignore_nan = ignore_nan
+        # Randomly create a key for an edge attribute to hold the partition data
+        self.partition_key = (
+            "SpanningTreeIterators super secret partition attribute name"
+        )
+
+    def __iter__(self):
+        """
+        Returns
+        -------
+        SpanningTreeIterator
+            The iterator object for this graph
+        """
+        self.partition_queue = PriorityQueue()
+        self._clear_partition(self.G)
+        mst_weight = partition_spanning_tree(
+            self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan
+        ).size(weight=self.weight)
+
+        self.partition_queue.put(
+            self.Partition(mst_weight if self.minimum else -mst_weight, {})
+        )
+
+        return self
+
+    def __next__(self):
+        """
+        Returns
+        -------
+        (multi)Graph
+            The spanning tree of next greatest weight, which ties broken
+            arbitrarily.
+        """
+        if self.partition_queue.empty():
+            del self.G, self.partition_queue
+            raise StopIteration
+
+        partition = self.partition_queue.get()
+        self._write_partition(partition)
+        next_tree = partition_spanning_tree(
+            self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan
+        )
+        self._partition(partition, next_tree)
+
+        self._clear_partition(next_tree)
+        return next_tree
+
+    def _partition(self, partition, partition_tree):
+        """
+        Create new partitions based of the minimum spanning tree of the
+        current minimum partition.
+
+        Parameters
+        ----------
+        partition : Partition
+            The Partition instance used to generate the current minimum spanning
+            tree.
+        partition_tree : nx.Graph
+            The minimum spanning tree of the input partition.
+        """
+        # create two new partitions with the data from the input partition dict
+        p1 = self.Partition(0, partition.partition_dict.copy())
+        p2 = self.Partition(0, partition.partition_dict.copy())
+        for e in partition_tree.edges:
+            # determine if the edge was open or included
+            if e not in partition.partition_dict:
+                # This is an open edge
+                p1.partition_dict[e] = EdgePartition.EXCLUDED
+                p2.partition_dict[e] = EdgePartition.INCLUDED
+
+                self._write_partition(p1)
+                p1_mst = partition_spanning_tree(
+                    self.G,
+                    self.minimum,
+                    self.weight,
+                    self.partition_key,
+                    self.ignore_nan,
+                )
+                p1_mst_weight = p1_mst.size(weight=self.weight)
+                if nx.is_connected(p1_mst):
+                    p1.mst_weight = p1_mst_weight if self.minimum else -p1_mst_weight
+                    self.partition_queue.put(p1.__copy__())
+                p1.partition_dict = p2.partition_dict.copy()
+
+    def _write_partition(self, partition):
+        """
+        Writes the desired partition into the graph to calculate the minimum
+        spanning tree.
+
+        Parameters
+        ----------
+        partition : Partition
+            A Partition dataclass describing a partition on the edges of the
+            graph.
+        """
+
+        partition_dict = partition.partition_dict
+        partition_key = self.partition_key
+        G = self.G
+
+        edges = (
+            G.edges(keys=True, data=True) if G.is_multigraph() else G.edges(data=True)
+        )
+        for *e, d in edges:
+            d[partition_key] = partition_dict.get(tuple(e), EdgePartition.OPEN)
+
+    def _clear_partition(self, G):
+        """
+        Removes partition data from the graph
+        """
+        partition_key = self.partition_key
+        edges = (
+            G.edges(keys=True, data=True) if G.is_multigraph() else G.edges(data=True)
+        )
+        for *e, d in edges:
+            if partition_key in d:
+                del d[partition_key]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def number_of_spanning_trees(G, *, root=None, weight=None):
+    """Returns the number of spanning trees in `G`.
+
+    A spanning tree for an undirected graph is a tree that connects
+    all nodes in the graph. For a directed graph, the analog of a
+    spanning tree is called a (spanning) arborescence. The arborescence
+    includes a unique directed path from the `root` node to each other node.
+    The graph must be weakly connected, and the root must be a node
+    that includes all nodes as successors [3]_. Note that to avoid
+    discussing sink-roots and reverse-arborescences, we have reversed
+    the edge orientation from [3]_ and use the in-degree laplacian.
+
+    This function (when `weight` is `None`) returns the number of
+    spanning trees for an undirected graph and the number of
+    arborescences from a single root node for a directed graph.
+    When `weight` is the name of an edge attribute which holds the
+    weight value of each edge, the function returns the sum over
+    all trees of the multiplicative weight of each tree. That is,
+    the weight of the tree is the product of its edge weights.
+
+    Kirchoff's Tree Matrix Theorem states that any cofactor of the
+    Laplacian matrix of a graph is the number of spanning trees in the
+    graph. (Here we use cofactors for a diagonal entry so that the
+    cofactor becomes the determinant of the matrix with one row
+    and its matching column removed.) For a weighted Laplacian matrix,
+    the cofactor is the sum across all spanning trees of the
+    multiplicative weight of each tree. That is, the weight of each
+    tree is the product of its edge weights. The theorem is also
+    known as Kirchhoff's theorem [1]_ and the Matrix-Tree theorem [2]_.
+
+    For directed graphs, a similar theorem (Tutte's Theorem) holds with
+    the cofactor chosen to be the one with row and column removed that
+    correspond to the root. The cofactor is the number of arborescences
+    with the specified node as root. And the weighted version gives the
+    sum of the arborescence weights with root `root`. The arborescence
+    weight is the product of its edge weights.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    root : node
+       A node in the directed graph `G` that has all nodes as descendants.
+       (This is ignored for undirected graphs.)
+
+    weight : string or None, optional (default=None)
+        The name of the edge attribute holding the edge weight.
+        If `None`, then each edge is assumed to have a weight of 1.
+
+    Returns
+    -------
+    Number
+        Undirected graphs:
+            The number of spanning trees of the graph `G`.
+            Or the sum of all spanning tree weights of the graph `G`
+            where the weight of a tree is the product of its edge weights.
+        Directed graphs:
+            The number of arborescences of `G` rooted at node `root`.
+            Or the sum of all arborescence weights of the graph `G` with
+            specified root where the weight of an arborescence is the product
+            of its edge weights.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` does not contain any nodes.
+
+    NetworkXError
+        If the graph `G` is directed and the root node
+        is not specified or is not in G.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> round(nx.number_of_spanning_trees(G))
+    125
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=2)
+    >>> G.add_edge(1, 3, weight=1)
+    >>> G.add_edge(2, 3, weight=1)
+    >>> round(nx.number_of_spanning_trees(G, weight="weight"))
+    5
+
+    Notes
+    -----
+    Self-loops are excluded. Multi-edges are contracted in one edge
+    equal to the sum of the weights.
+
+    References
+    ----------
+    .. [1] Wikipedia
+       "Kirchhoff's theorem."
+       https://en.wikipedia.org/wiki/Kirchhoff%27s_theorem
+    .. [2] Kirchhoff, G. R.
+        Über die Auflösung der Gleichungen, auf welche man
+        bei der Untersuchung der linearen Vertheilung
+        Galvanischer Ströme geführt wird
+        Annalen der Physik und Chemie, vol. 72, pp. 497-508, 1847.
+    .. [3] Margoliash, J.
+        "Matrix-Tree Theorem for Directed Graphs"
+        https://www.math.uchicago.edu/~may/VIGRE/VIGRE2010/REUPapers/Margoliash.pdf
+    """
+    import numpy as np
+
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("Graph G must contain at least one node.")
+
+    # undirected G
+    if not nx.is_directed(G):
+        if not nx.is_connected(G):
+            return 0
+        G_laplacian = nx.laplacian_matrix(G, weight=weight).toarray()
+        return float(np.linalg.det(G_laplacian[1:, 1:]))
+
+    # directed G
+    if root is None:
+        raise nx.NetworkXError("Input `root` must be provided when G is directed")
+    if root not in G:
+        raise nx.NetworkXError("The node root is not in the graph G.")
+    if not nx.is_weakly_connected(G):
+        return 0
+
+    # Compute directed Laplacian matrix
+    nodelist = [root] + [n for n in G if n != root]
+    A = nx.adjacency_matrix(G, nodelist=nodelist, weight=weight)
+    D = np.diag(A.sum(axis=0))
+    G_laplacian = D - A
+
+    # Compute number of spanning trees
+    return float(np.linalg.det(G_laplacian[1:, 1:]))
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/operations.py b/lib/python3.11/site-packages/networkx/algorithms/tree/operations.py
new file mode 100644
index 0000000000000000000000000000000000000000..a75770a5968eaff5f5975d23a6af4557b52e6e7e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/operations.py
@@ -0,0 +1,106 @@
+"""Operations on trees."""
+
+from functools import partial
+from itertools import accumulate, chain
+
+import networkx as nx
+
+__all__ = ["join_trees"]
+
+
+# Argument types don't match dispatching, but allow manual selection of backend
+@nx._dispatchable(graphs=None, returns_graph=True)
+def join_trees(rooted_trees, *, label_attribute=None, first_label=0):
+    """Returns a new rooted tree made by joining `rooted_trees`
+
+    Constructs a new tree by joining each tree in `rooted_trees`.
+    A new root node is added and connected to each of the roots
+    of the input trees. While copying the nodes from the trees,
+    relabeling to integers occurs. If the `label_attribute` is provided,
+    the old node labels will be stored in the new tree under this attribute.
+
+    Parameters
+    ----------
+    rooted_trees : list
+        A list of pairs in which each left element is a NetworkX graph
+        object representing a tree and each right element is the root
+        node of that tree. The nodes of these trees will be relabeled to
+        integers.
+
+    label_attribute : str
+        If provided, the old node labels will be stored in the new tree
+        under this node attribute. If not provided, the original labels
+        of the nodes in the input trees are not stored.
+
+    first_label : int, optional (default=0)
+        Specifies the label for the new root node. If provided, the root node of the joined tree
+        will have this label. If not provided, the root node will default to a label of 0.
+
+    Returns
+    -------
+    NetworkX graph
+        The rooted tree resulting from joining the provided `rooted_trees`. The new tree has a root node
+        labeled as specified by `first_label` (defaulting to 0 if not provided). Subtrees from the input
+        `rooted_trees` are attached to this new root node. Each non-root node, if the `label_attribute`
+        is provided, has an attribute that indicates the original label of the node in the input tree.
+
+    Notes
+    -----
+    Trees are stored in NetworkX as NetworkX Graphs. There is no specific
+    enforcement of the fact that these are trees. Testing for each tree
+    can be done using :func:`networkx.is_tree`.
+
+    Graph, edge, and node attributes are propagated from the given
+    rooted trees to the created tree. If there are any overlapping graph
+    attributes, those from later trees will overwrite those from earlier
+    trees in the tuple of positional arguments.
+
+    Examples
+    --------
+    Join two full balanced binary trees of height *h* to get a full
+    balanced binary tree of depth *h* + 1::
+
+        >>> h = 4
+        >>> left = nx.balanced_tree(2, h)
+        >>> right = nx.balanced_tree(2, h)
+        >>> joined_tree = nx.join_trees([(left, 0), (right, 0)])
+        >>> nx.is_isomorphic(joined_tree, nx.balanced_tree(2, h + 1))
+        True
+
+    """
+    if not rooted_trees:
+        return nx.empty_graph(1)
+
+    # Unzip the zipped list of (tree, root) pairs.
+    trees, roots = zip(*rooted_trees)
+
+    # The join of the trees has the same type as the type of the first tree.
+    R = type(trees[0])()
+
+    lengths = (len(tree) for tree in trees[:-1])
+    first_labels = list(accumulate(lengths, initial=first_label + 1))
+
+    new_roots = []
+    for tree, root, first_node in zip(trees, roots, first_labels):
+        new_root = first_node + list(tree.nodes()).index(root)
+        new_roots.append(new_root)
+
+    # Relabel the nodes so that their union is the integers starting at first_label.
+    relabel = partial(
+        nx.convert_node_labels_to_integers, label_attribute=label_attribute
+    )
+    new_trees = [
+        relabel(tree, first_label=first_label)
+        for tree, first_label in zip(trees, first_labels)
+    ]
+
+    # Add all sets of nodes and edges, attributes
+    for tree in new_trees:
+        R.update(tree)
+
+    # Finally, join the subtrees at the root. We know first_label is unused by
+    # the way we relabeled the subtrees.
+    R.add_node(first_label)
+    R.add_edges_from((first_label, root) for root in new_roots)
+
+    return R
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/recognition.py b/lib/python3.11/site-packages/networkx/algorithms/tree/recognition.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9eae98707a6889213ff8b93887c481ba59215a0
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/recognition.py
@@ -0,0 +1,273 @@
+"""
+Recognition Tests
+=================
+
+A *forest* is an acyclic, undirected graph, and a *tree* is a connected forest.
+Depending on the subfield, there are various conventions for generalizing these
+definitions to directed graphs.
+
+In one convention, directed variants of forest and tree are defined in an
+identical manner, except that the direction of the edges is ignored. In effect,
+each directed edge is treated as a single undirected edge. Then, additional
+restrictions are imposed to define *branchings* and *arborescences*.
+
+In another convention, directed variants of forest and tree correspond to
+the previous convention's branchings and arborescences, respectively. Then two
+new terms, *polyforest* and *polytree*, are defined to correspond to the other
+convention's forest and tree.
+
+Summarizing::
+
+   +-----------------------------+
+   | Convention A | Convention B |
+   +=============================+
+   | forest       | polyforest   |
+   | tree         | polytree     |
+   | branching    | forest       |
+   | arborescence | tree         |
+   +-----------------------------+
+
+Each convention has its reasons. The first convention emphasizes definitional
+similarity in that directed forests and trees are only concerned with
+acyclicity and do not have an in-degree constraint, just as their undirected
+counterparts do not. The second convention emphasizes functional similarity
+in the sense that the directed analog of a spanning tree is a spanning
+arborescence. That is, take any spanning tree and choose one node as the root.
+Then every edge is assigned a direction such there is a directed path from the
+root to every other node. The result is a spanning arborescence.
+
+NetworkX follows convention "A". Explicitly, these are:
+
+undirected forest
+   An undirected graph with no undirected cycles.
+
+undirected tree
+   A connected, undirected forest.
+
+directed forest
+   A directed graph with no undirected cycles. Equivalently, the underlying
+   graph structure (which ignores edge orientations) is an undirected forest.
+   In convention B, this is known as a polyforest.
+
+directed tree
+   A weakly connected, directed forest. Equivalently, the underlying graph
+   structure (which ignores edge orientations) is an undirected tree. In
+   convention B, this is known as a polytree.
+
+branching
+   A directed forest with each node having, at most, one parent. So the maximum
+   in-degree is equal to 1. In convention B, this is known as a forest.
+
+arborescence
+   A directed tree with each node having, at most, one parent. So the maximum
+   in-degree is equal to 1. In convention B, this is known as a tree.
+
+For trees and arborescences, the adjective "spanning" may be added to designate
+that the graph, when considered as a forest/branching, consists of a single
+tree/arborescence that includes all nodes in the graph. It is true, by
+definition, that every tree/arborescence is spanning with respect to the nodes
+that define the tree/arborescence and so, it might seem redundant to introduce
+the notion of "spanning". However, the nodes may represent a subset of
+nodes from a larger graph, and it is in this context that the term "spanning"
+becomes a useful notion.
+
+"""
+
+import networkx as nx
+
+__all__ = ["is_arborescence", "is_branching", "is_forest", "is_tree"]
+
+
+@nx.utils.not_implemented_for("undirected")
+@nx._dispatchable
+def is_arborescence(G):
+    """
+    Returns True if `G` is an arborescence.
+
+    An arborescence is a directed tree with maximum in-degree equal to 1.
+
+    Parameters
+    ----------
+    G : graph
+        The graph to test.
+
+    Returns
+    -------
+    b : bool
+        A boolean that is True if `G` is an arborescence.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (0, 2), (2, 3), (3, 4)])
+    >>> nx.is_arborescence(G)
+    True
+    >>> G.remove_edge(0, 1)
+    >>> G.add_edge(1, 2)  # maximum in-degree is 2
+    >>> nx.is_arborescence(G)
+    False
+
+    Notes
+    -----
+    In another convention, an arborescence is known as a *tree*.
+
+    See Also
+    --------
+    is_tree
+
+    """
+    return is_tree(G) and max(d for n, d in G.in_degree()) <= 1
+
+
+@nx.utils.not_implemented_for("undirected")
+@nx._dispatchable
+def is_branching(G):
+    """
+    Returns True if `G` is a branching.
+
+    A branching is a directed forest with maximum in-degree equal to 1.
+
+    Parameters
+    ----------
+    G : directed graph
+        The directed graph to test.
+
+    Returns
+    -------
+    b : bool
+        A boolean that is True if `G` is a branching.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 4)])
+    >>> nx.is_branching(G)
+    True
+    >>> G.remove_edge(2, 3)
+    >>> G.add_edge(3, 1)  # maximum in-degree is 2
+    >>> nx.is_branching(G)
+    False
+
+    Notes
+    -----
+    In another convention, a branching is also known as a *forest*.
+
+    See Also
+    --------
+    is_forest
+
+    """
+    return is_forest(G) and max(d for n, d in G.in_degree()) <= 1
+
+
+@nx._dispatchable
+def is_forest(G):
+    """
+    Returns True if `G` is a forest.
+
+    A forest is a graph with no undirected cycles.
+
+    For directed graphs, `G` is a forest if the underlying graph is a forest.
+    The underlying graph is obtained by treating each directed edge as a single
+    undirected edge in a multigraph.
+
+    Parameters
+    ----------
+    G : graph
+        The graph to test.
+
+    Returns
+    -------
+    b : bool
+        A boolean that is True if `G` is a forest.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` is empty.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5)])
+    >>> nx.is_forest(G)
+    True
+    >>> G.add_edge(4, 1)
+    >>> nx.is_forest(G)
+    False
+
+    Notes
+    -----
+    In another convention, a directed forest is known as a *polyforest* and
+    then *forest* corresponds to a *branching*.
+
+    See Also
+    --------
+    is_branching
+
+    """
+    if len(G) == 0:
+        raise nx.exception.NetworkXPointlessConcept("G has no nodes.")
+
+    if G.is_directed():
+        components = (G.subgraph(c) for c in nx.weakly_connected_components(G))
+    else:
+        components = (G.subgraph(c) for c in nx.connected_components(G))
+
+    return all(len(c) - 1 == c.number_of_edges() for c in components)
+
+
+@nx._dispatchable
+def is_tree(G):
+    """
+    Returns True if `G` is a tree.
+
+    A tree is a connected graph with no undirected cycles.
+
+    For directed graphs, `G` is a tree if the underlying graph is a tree. The
+    underlying graph is obtained by treating each directed edge as a single
+    undirected edge in a multigraph.
+
+    Parameters
+    ----------
+    G : graph
+        The graph to test.
+
+    Returns
+    -------
+    b : bool
+        A boolean that is True if `G` is a tree.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` is empty.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5)])
+    >>> nx.is_tree(G)  # n-1 edges
+    True
+    >>> G.add_edge(3, 4)
+    >>> nx.is_tree(G)  # n edges
+    False
+
+    Notes
+    -----
+    In another convention, a directed tree is known as a *polytree* and then
+    *tree* corresponds to an *arborescence*.
+
+    See Also
+    --------
+    is_arborescence
+
+    """
+    if len(G) == 0:
+        raise nx.exception.NetworkXPointlessConcept("G has no nodes.")
+
+    if G.is_directed():
+        is_connected = nx.is_weakly_connected
+    else:
+        is_connected = nx.is_connected
+
+    # A connected graph with no cycles has n-1 edges.
+    return len(G) - 1 == G.number_of_edges() and is_connected(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__init__.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c5b0b8769e38d824f067a647735fdd4db8e8163d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_branchings.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_branchings.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0ae94a2750b2e78df1be987ce2bdf15185050512
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_branchings.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_coding.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_coding.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..43026c57e8ef135420e9ec2321efea8d648853fd
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_coding.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_decomposition.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_decomposition.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..27232c8fa22b94ebb68247cf1f47771e94fb3a85
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_decomposition.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_distance_measures.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_distance_measures.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7d7caecc186872de59d42d142b2c2d631ac16654
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_distance_measures.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_mst.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_mst.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5d0a5b6d5bac1ecb66d7fcd15d9b144f8fc01fce
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_mst.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_operations.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_operations.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cb985828dd544d6308c5446f31468e2a8fd62bce
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_operations.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_recognition.cpython-311.pyc b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_recognition.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a5ed6050b9d34b976125ec34fed91d988627a5bb
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/__pycache__/test_recognition.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_branchings.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_branchings.py
new file mode 100644
index 0000000000000000000000000000000000000000..9be976abdeb0298f576b1c72208d4379aea56c9d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_branchings.py
@@ -0,0 +1,624 @@
+import math
+from operator import itemgetter
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.tree import branchings, recognition
+
+np = pytest.importorskip("numpy")
+
+#
+# Explicitly discussed examples from Edmonds paper.
+#
+
+# Used in Figures A-F.
+#
+# fmt: off
+G_array = np.array([
+    # 0   1   2   3   4   5   6   7   8
+    [0, 0, 12, 0, 12, 0, 0, 0, 0],  # 0
+    [4, 0, 0, 0, 0, 13, 0, 0, 0],  # 1
+    [0, 17, 0, 21, 0, 12, 0, 0, 0],  # 2
+    [5, 0, 0, 0, 17, 0, 18, 0, 0],  # 3
+    [0, 0, 0, 0, 0, 0, 0, 12, 0],  # 4
+    [0, 0, 0, 0, 0, 0, 14, 0, 12],  # 5
+    [0, 0, 21, 0, 0, 0, 0, 0, 15],  # 6
+    [0, 0, 0, 19, 0, 0, 15, 0, 0],  # 7
+    [0, 0, 0, 0, 0, 0, 0, 18, 0],  # 8
+], dtype=int)
+
+# Two copies of the graph from the original paper as disconnected components
+G_big_array = np.zeros(np.array(G_array.shape) * 2, dtype=int)
+G_big_array[:G_array.shape[0], :G_array.shape[1]] = G_array
+G_big_array[G_array.shape[0]:, G_array.shape[1]:] = G_array
+
+# fmt: on
+
+
+def G1():
+    G = nx.from_numpy_array(G_array, create_using=nx.MultiDiGraph)
+    return G
+
+
+def G2():
+    # Now we shift all the weights by -10.
+    # Should not affect optimal arborescence, but does affect optimal branching.
+    Garr = G_array.copy()
+    Garr[np.nonzero(Garr)] -= 10
+    G = nx.from_numpy_array(Garr, create_using=nx.MultiDiGraph)
+    return G
+
+
+# An optimal branching for G1 that is also a spanning arborescence. So it is
+# also an optimal spanning arborescence.
+#
+optimal_arborescence_1 = [
+    (0, 2, 12),
+    (2, 1, 17),
+    (2, 3, 21),
+    (1, 5, 13),
+    (3, 4, 17),
+    (3, 6, 18),
+    (6, 8, 15),
+    (8, 7, 18),
+]
+
+# For G2, the optimal branching of G1 (with shifted weights) is no longer
+# an optimal branching, but it is still an optimal spanning arborescence
+# (just with shifted weights). An optimal branching for G2 is similar to what
+# appears in figure G (this is greedy_subopt_branching_1a below), but with the
+# edge (3, 0, 5), which is now (3, 0, -5), removed. Thus, the optimal branching
+# is not a spanning arborescence. The code finds optimal_branching_2a.
+# An alternative and equivalent branching is optimal_branching_2b. We would
+# need to modify the code to iterate through all equivalent optimal branchings.
+#
+# These are maximal branchings or arborescences.
+optimal_branching_2a = [
+    (5, 6, 4),
+    (6, 2, 11),
+    (6, 8, 5),
+    (8, 7, 8),
+    (2, 1, 7),
+    (2, 3, 11),
+    (3, 4, 7),
+]
+optimal_branching_2b = [
+    (8, 7, 8),
+    (7, 3, 9),
+    (3, 4, 7),
+    (3, 6, 8),
+    (6, 2, 11),
+    (2, 1, 7),
+    (1, 5, 3),
+]
+optimal_arborescence_2 = [
+    (0, 2, 2),
+    (2, 1, 7),
+    (2, 3, 11),
+    (1, 5, 3),
+    (3, 4, 7),
+    (3, 6, 8),
+    (6, 8, 5),
+    (8, 7, 8),
+]
+
+# Two suboptimal maximal branchings on G1 obtained from a greedy algorithm.
+# 1a matches what is shown in Figure G in Edmonds's paper.
+greedy_subopt_branching_1a = [
+    (5, 6, 14),
+    (6, 2, 21),
+    (6, 8, 15),
+    (8, 7, 18),
+    (2, 1, 17),
+    (2, 3, 21),
+    (3, 0, 5),
+    (3, 4, 17),
+]
+greedy_subopt_branching_1b = [
+    (8, 7, 18),
+    (7, 6, 15),
+    (6, 2, 21),
+    (2, 1, 17),
+    (2, 3, 21),
+    (1, 5, 13),
+    (3, 0, 5),
+    (3, 4, 17),
+]
+
+
+def build_branching(edges, double=False):
+    G = nx.DiGraph()
+    for u, v, weight in edges:
+        G.add_edge(u, v, weight=weight)
+        if double:
+            G.add_edge(u + 9, v + 9, weight=weight)
+    return G
+
+
+def sorted_edges(G, attr="weight", default=1):
+    edges = [(u, v, data.get(attr, default)) for (u, v, data) in G.edges(data=True)]
+    edges = sorted(edges, key=lambda x: (x[2], x[1], x[0]))
+    return edges
+
+
+def assert_equal_branchings(G1, G2, attr="weight", default=1):
+    edges1 = list(G1.edges(data=True))
+    edges2 = list(G2.edges(data=True))
+    assert len(edges1) == len(edges2)
+
+    # Grab the weights only.
+    e1 = sorted_edges(G1, attr, default)
+    e2 = sorted_edges(G2, attr, default)
+
+    for a, b in zip(e1, e2):
+        assert a[:2] == b[:2]
+        np.testing.assert_almost_equal(a[2], b[2])
+
+
+################
+
+
+def test_optimal_branching1():
+    G = build_branching(optimal_arborescence_1)
+    assert recognition.is_arborescence(G), True
+    assert branchings.branching_weight(G) == 131
+
+
+def test_optimal_branching2a():
+    G = build_branching(optimal_branching_2a)
+    assert recognition.is_arborescence(G), True
+    assert branchings.branching_weight(G) == 53
+
+
+def test_optimal_branching2b():
+    G = build_branching(optimal_branching_2b)
+    assert recognition.is_arborescence(G), True
+    assert branchings.branching_weight(G) == 53
+
+
+def test_optimal_arborescence2():
+    G = build_branching(optimal_arborescence_2)
+    assert recognition.is_arborescence(G), True
+    assert branchings.branching_weight(G) == 51
+
+
+def test_greedy_suboptimal_branching1a():
+    G = build_branching(greedy_subopt_branching_1a)
+    assert recognition.is_arborescence(G), True
+    assert branchings.branching_weight(G) == 128
+
+
+def test_greedy_suboptimal_branching1b():
+    G = build_branching(greedy_subopt_branching_1b)
+    assert recognition.is_arborescence(G), True
+    assert branchings.branching_weight(G) == 127
+
+
+def test_greedy_max1():
+    # Standard test.
+    #
+    G = G1()
+    B = branchings.greedy_branching(G)
+    # There are only two possible greedy branchings. The sorting is such
+    # that it should equal the second suboptimal branching: 1b.
+    B_ = build_branching(greedy_subopt_branching_1b)
+    assert_equal_branchings(B, B_)
+
+
+def test_greedy_branching_kwarg_kind():
+    G = G1()
+    with pytest.raises(nx.NetworkXException, match="Unknown value for `kind`."):
+        B = branchings.greedy_branching(G, kind="lol")
+
+
+def test_greedy_branching_for_unsortable_nodes():
+    G = nx.DiGraph()
+    G.add_weighted_edges_from([((2, 3), 5, 1), (3, "a", 1), (2, 4, 5)])
+    edges = [(u, v, data.get("weight", 1)) for (u, v, data) in G.edges(data=True)]
+    with pytest.raises(TypeError):
+        edges.sort(key=itemgetter(2, 0, 1), reverse=True)
+    B = branchings.greedy_branching(G, kind="max").edges(data=True)
+    assert list(B) == [
+        ((2, 3), 5, {"weight": 1}),
+        (3, "a", {"weight": 1}),
+        (2, 4, {"weight": 5}),
+    ]
+
+
+def test_greedy_max2():
+    # Different default weight.
+    #
+    G = G1()
+    del G[1][0][0]["weight"]
+    B = branchings.greedy_branching(G, default=6)
+    # Chosen so that edge (3,0,5) is not selected and (1,0,6) is instead.
+
+    edges = [
+        (1, 0, 6),
+        (1, 5, 13),
+        (7, 6, 15),
+        (2, 1, 17),
+        (3, 4, 17),
+        (8, 7, 18),
+        (2, 3, 21),
+        (6, 2, 21),
+    ]
+    B_ = build_branching(edges)
+    assert_equal_branchings(B, B_)
+
+
+def test_greedy_max3():
+    # All equal weights.
+    #
+    G = G1()
+    B = branchings.greedy_branching(G, attr=None)
+
+    # This is mostly arbitrary...the output was generated by running the algo.
+    edges = [
+        (2, 1, 1),
+        (3, 0, 1),
+        (3, 4, 1),
+        (5, 8, 1),
+        (6, 2, 1),
+        (7, 3, 1),
+        (7, 6, 1),
+        (8, 7, 1),
+    ]
+    B_ = build_branching(edges)
+    assert_equal_branchings(B, B_, default=1)
+
+
+def test_greedy_min():
+    G = G1()
+    B = branchings.greedy_branching(G, kind="min")
+
+    edges = [
+        (1, 0, 4),
+        (0, 2, 12),
+        (0, 4, 12),
+        (2, 5, 12),
+        (4, 7, 12),
+        (5, 8, 12),
+        (5, 6, 14),
+        (7, 3, 19),
+    ]
+    B_ = build_branching(edges)
+    assert_equal_branchings(B, B_)
+
+
+def test_edmonds1_maxbranch():
+    G = G1()
+    x = branchings.maximum_branching(G)
+    x_ = build_branching(optimal_arborescence_1)
+    assert_equal_branchings(x, x_)
+
+
+def test_edmonds1_maxarbor():
+    G = G1()
+    x = branchings.maximum_spanning_arborescence(G)
+    x_ = build_branching(optimal_arborescence_1)
+    assert_equal_branchings(x, x_)
+
+
+def test_edmonds1_minimal_branching():
+    # graph will have something like a minimum arborescence but no spanning one
+    G = nx.from_numpy_array(G_big_array, create_using=nx.DiGraph)
+    B = branchings.minimal_branching(G)
+    edges = [
+        (3, 0, 5),
+        (0, 2, 12),
+        (0, 4, 12),
+        (2, 5, 12),
+        (4, 7, 12),
+        (5, 8, 12),
+        (5, 6, 14),
+        (2, 1, 17),
+    ]
+    B_ = build_branching(edges, double=True)
+    assert_equal_branchings(B, B_)
+
+
+def test_edmonds2_maxbranch():
+    G = G2()
+    x = branchings.maximum_branching(G)
+    x_ = build_branching(optimal_branching_2a)
+    assert_equal_branchings(x, x_)
+
+
+def test_edmonds2_maxarbor():
+    G = G2()
+    x = branchings.maximum_spanning_arborescence(G)
+    x_ = build_branching(optimal_arborescence_2)
+    assert_equal_branchings(x, x_)
+
+
+def test_edmonds2_minarbor():
+    G = G1()
+    x = branchings.minimum_spanning_arborescence(G)
+    # This was obtained from algorithm. Need to verify it independently.
+    # Branch weight is: 96
+    edges = [
+        (3, 0, 5),
+        (0, 2, 12),
+        (0, 4, 12),
+        (2, 5, 12),
+        (4, 7, 12),
+        (5, 8, 12),
+        (5, 6, 14),
+        (2, 1, 17),
+    ]
+    x_ = build_branching(edges)
+    assert_equal_branchings(x, x_)
+
+
+def test_edmonds3_minbranch1():
+    G = G1()
+    x = branchings.minimum_branching(G)
+    edges = []
+    x_ = build_branching(edges)
+    assert_equal_branchings(x, x_)
+
+
+def test_edmonds3_minbranch2():
+    G = G1()
+    G.add_edge(8, 9, weight=-10)
+    x = branchings.minimum_branching(G)
+    edges = [(8, 9, -10)]
+    x_ = build_branching(edges)
+    assert_equal_branchings(x, x_)
+
+
+# Need more tests
+
+
+def test_mst():
+    # Make sure we get the same results for undirected graphs.
+    # Example from: https://en.wikipedia.org/wiki/Kruskal's_algorithm
+    G = nx.Graph()
+    edgelist = [
+        (0, 3, [("weight", 5)]),
+        (0, 1, [("weight", 7)]),
+        (1, 3, [("weight", 9)]),
+        (1, 2, [("weight", 8)]),
+        (1, 4, [("weight", 7)]),
+        (3, 4, [("weight", 15)]),
+        (3, 5, [("weight", 6)]),
+        (2, 4, [("weight", 5)]),
+        (4, 5, [("weight", 8)]),
+        (4, 6, [("weight", 9)]),
+        (5, 6, [("weight", 11)]),
+    ]
+    G.add_edges_from(edgelist)
+    G = G.to_directed()
+    x = branchings.minimum_spanning_arborescence(G)
+
+    edges = [
+        ({0, 1}, 7),
+        ({0, 3}, 5),
+        ({3, 5}, 6),
+        ({1, 4}, 7),
+        ({4, 2}, 5),
+        ({4, 6}, 9),
+    ]
+
+    assert x.number_of_edges() == len(edges)
+    for u, v, d in x.edges(data=True):
+        assert ({u, v}, d["weight"]) in edges
+
+
+def test_mixed_nodetypes():
+    # Smoke test to make sure no TypeError is raised for mixed node types.
+    G = nx.Graph()
+    edgelist = [(0, 3, [("weight", 5)]), (0, "1", [("weight", 5)])]
+    G.add_edges_from(edgelist)
+    G = G.to_directed()
+    x = branchings.minimum_spanning_arborescence(G)
+
+
+def test_edmonds1_minbranch():
+    # Using -G_array and min should give the same as optimal_arborescence_1,
+    # but with all edges negative.
+    edges = [(u, v, -w) for (u, v, w) in optimal_arborescence_1]
+
+    G = nx.from_numpy_array(-G_array, create_using=nx.DiGraph)
+
+    # Quickly make sure max branching is empty.
+    x = branchings.maximum_branching(G)
+    x_ = build_branching([])
+    assert_equal_branchings(x, x_)
+
+    # Now test the min branching.
+    x = branchings.minimum_branching(G)
+    x_ = build_branching(edges)
+    assert_equal_branchings(x, x_)
+
+
+def test_edge_attribute_preservation_normal_graph():
+    # Test that edge attributes are preserved when finding an optimum graph
+    # using the Edmonds class for normal graphs.
+    G = nx.Graph()
+
+    edgelist = [
+        (0, 1, [("weight", 5), ("otherattr", 1), ("otherattr2", 3)]),
+        (0, 2, [("weight", 5), ("otherattr", 2), ("otherattr2", 2)]),
+        (1, 2, [("weight", 6), ("otherattr", 3), ("otherattr2", 1)]),
+    ]
+    G.add_edges_from(edgelist)
+
+    B = branchings.maximum_branching(G, preserve_attrs=True)
+
+    assert B[0][1]["otherattr"] == 1
+    assert B[0][1]["otherattr2"] == 3
+
+
+def test_edge_attribute_preservation_multigraph():
+    # Test that edge attributes are preserved when finding an optimum graph
+    # using the Edmonds class for multigraphs.
+    G = nx.MultiGraph()
+
+    edgelist = [
+        (0, 1, [("weight", 5), ("otherattr", 1), ("otherattr2", 3)]),
+        (0, 2, [("weight", 5), ("otherattr", 2), ("otherattr2", 2)]),
+        (1, 2, [("weight", 6), ("otherattr", 3), ("otherattr2", 1)]),
+    ]
+    G.add_edges_from(edgelist * 2)  # Make sure we have duplicate edge paths
+
+    B = branchings.maximum_branching(G, preserve_attrs=True)
+
+    assert B[0][1][0]["otherattr"] == 1
+    assert B[0][1][0]["otherattr2"] == 3
+
+
+def test_edge_attribute_discard():
+    # Test that edge attributes are discarded if we do not specify to keep them
+    G = nx.Graph()
+
+    edgelist = [
+        (0, 1, [("weight", 5), ("otherattr", 1), ("otherattr2", 3)]),
+        (0, 2, [("weight", 5), ("otherattr", 2), ("otherattr2", 2)]),
+        (1, 2, [("weight", 6), ("otherattr", 3), ("otherattr2", 1)]),
+    ]
+    G.add_edges_from(edgelist)
+
+    B = branchings.maximum_branching(G, preserve_attrs=False)
+
+    edge_dict = B[0][1]
+    with pytest.raises(KeyError):
+        _ = edge_dict["otherattr"]
+
+
+def test_partition_spanning_arborescence():
+    """
+    Test that we can generate minimum spanning arborescences which respect the
+    given partition.
+    """
+    G = nx.from_numpy_array(G_array, create_using=nx.DiGraph)
+    G[3][0]["partition"] = nx.EdgePartition.EXCLUDED
+    G[2][3]["partition"] = nx.EdgePartition.INCLUDED
+    G[7][3]["partition"] = nx.EdgePartition.EXCLUDED
+    G[0][2]["partition"] = nx.EdgePartition.EXCLUDED
+    G[6][2]["partition"] = nx.EdgePartition.INCLUDED
+
+    actual_edges = [
+        (0, 4, 12),
+        (1, 0, 4),
+        (1, 5, 13),
+        (2, 3, 21),
+        (4, 7, 12),
+        (5, 6, 14),
+        (5, 8, 12),
+        (6, 2, 21),
+    ]
+
+    B = branchings.minimum_spanning_arborescence(G, partition="partition")
+    assert_equal_branchings(build_branching(actual_edges), B)
+
+
+def test_arborescence_iterator_min():
+    """
+    Tests the arborescence iterator.
+
+    A brute force method found 680 arborescences in this graph.
+    This test will not verify all of them individually, but will check two
+    things
+
+    * The iterator returns 680 arborescences
+    * The weight of the arborescences is non-strictly increasing
+
+    for more information please visit
+    https://mjschwenne.github.io/2021/06/10/implementing-the-iterators.html
+    """
+    G = nx.from_numpy_array(G_array, create_using=nx.DiGraph)
+
+    arborescence_count = 0
+    arborescence_weight = -math.inf
+    for B in branchings.ArborescenceIterator(G):
+        arborescence_count += 1
+        new_arborescence_weight = B.size(weight="weight")
+        assert new_arborescence_weight >= arborescence_weight
+        arborescence_weight = new_arborescence_weight
+
+    assert arborescence_count == 680
+
+
+def test_arborescence_iterator_max():
+    """
+    Tests the arborescence iterator.
+
+    A brute force method found 680 arborescences in this graph.
+    This test will not verify all of them individually, but will check two
+    things
+
+    * The iterator returns 680 arborescences
+    * The weight of the arborescences is non-strictly decreasing
+
+    for more information please visit
+    https://mjschwenne.github.io/2021/06/10/implementing-the-iterators.html
+    """
+    G = nx.from_numpy_array(G_array, create_using=nx.DiGraph)
+
+    arborescence_count = 0
+    arborescence_weight = math.inf
+    for B in branchings.ArborescenceIterator(G, minimum=False):
+        arborescence_count += 1
+        new_arborescence_weight = B.size(weight="weight")
+        assert new_arborescence_weight <= arborescence_weight
+        arborescence_weight = new_arborescence_weight
+
+    assert arborescence_count == 680
+
+
+def test_arborescence_iterator_initial_partition():
+    """
+    Tests the arborescence iterator with three included edges and three excluded
+    in the initial partition.
+
+    A brute force method similar to the one used in the above tests found that
+    there are 16 arborescences which contain the included edges and not the
+    excluded edges.
+    """
+    G = nx.from_numpy_array(G_array, create_using=nx.DiGraph)
+    included_edges = [(1, 0), (5, 6), (8, 7)]
+    excluded_edges = [(0, 2), (3, 6), (1, 5)]
+
+    arborescence_count = 0
+    arborescence_weight = -math.inf
+    for B in branchings.ArborescenceIterator(
+        G, init_partition=(included_edges, excluded_edges)
+    ):
+        arborescence_count += 1
+        new_arborescence_weight = B.size(weight="weight")
+        assert new_arborescence_weight >= arborescence_weight
+        arborescence_weight = new_arborescence_weight
+        for e in included_edges:
+            assert e in B.edges
+        for e in excluded_edges:
+            assert e not in B.edges
+    assert arborescence_count == 16
+
+
+def test_branchings_with_default_weights():
+    """
+    Tests that various branching algorithms work on graphs without weights.
+    For more information, see issue #7279.
+    """
+    graph = nx.erdos_renyi_graph(10, p=0.2, directed=True, seed=123)
+
+    assert all("weight" not in d for (u, v, d) in graph.edges(data=True)), (
+        "test is for graphs without a weight attribute"
+    )
+
+    # Calling these functions will modify graph inplace to add weights
+    # copy the graph to avoid this.
+    nx.minimum_spanning_arborescence(graph.copy())
+    nx.maximum_spanning_arborescence(graph.copy())
+    nx.minimum_branching(graph.copy())
+    nx.maximum_branching(graph.copy())
+    nx.algorithms.tree.minimal_branching(graph.copy())
+    nx.algorithms.tree.branching_weight(graph.copy())
+    nx.algorithms.tree.greedy_branching(graph.copy())
+
+    assert all("weight" not in d for (u, v, d) in graph.edges(data=True)), (
+        "The above calls should not modify the initial graph in-place"
+    )
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_coding.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_coding.py
new file mode 100644
index 0000000000000000000000000000000000000000..26bd4083f52a0cc90b94c6de6d47b2c44e70a079
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_coding.py
@@ -0,0 +1,114 @@
+"""Unit tests for the :mod:`~networkx.algorithms.tree.coding` module."""
+
+from itertools import product
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestPruferSequence:
+    """Unit tests for the Prüfer sequence encoding and decoding
+    functions.
+
+    """
+
+    def test_nontree(self):
+        with pytest.raises(nx.NotATree):
+            G = nx.cycle_graph(3)
+            nx.to_prufer_sequence(G)
+
+    def test_null_graph(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.to_prufer_sequence(nx.null_graph())
+
+    def test_trivial_graph(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.to_prufer_sequence(nx.trivial_graph())
+
+    def test_bad_integer_labels(self):
+        with pytest.raises(KeyError):
+            T = nx.Graph(nx.utils.pairwise("abc"))
+            nx.to_prufer_sequence(T)
+
+    def test_encoding(self):
+        """Tests for encoding a tree as a Prüfer sequence using the
+        iterative strategy.
+
+        """
+        # Example from Wikipedia.
+        tree = nx.Graph([(0, 3), (1, 3), (2, 3), (3, 4), (4, 5)])
+        sequence = nx.to_prufer_sequence(tree)
+        assert sequence == [3, 3, 3, 4]
+
+    def test_decoding(self):
+        """Tests for decoding a tree from a Prüfer sequence."""
+        # Example from Wikipedia.
+        sequence = [3, 3, 3, 4]
+        tree = nx.from_prufer_sequence(sequence)
+        assert nodes_equal(list(tree), list(range(6)))
+        edges = [(0, 3), (1, 3), (2, 3), (3, 4), (4, 5)]
+        assert edges_equal(list(tree.edges()), edges)
+
+    def test_decoding2(self):
+        # Example from "An Optimal Algorithm for Prufer Codes".
+        sequence = [2, 4, 0, 1, 3, 3]
+        tree = nx.from_prufer_sequence(sequence)
+        assert nodes_equal(list(tree), list(range(8)))
+        edges = [(0, 1), (0, 4), (1, 3), (2, 4), (2, 5), (3, 6), (3, 7)]
+        assert edges_equal(list(tree.edges()), edges)
+
+    def test_inverse(self):
+        """Tests that the encoding and decoding functions are inverses."""
+        for T in nx.nonisomorphic_trees(4):
+            T2 = nx.from_prufer_sequence(nx.to_prufer_sequence(T))
+            assert nodes_equal(list(T), list(T2))
+            assert edges_equal(list(T.edges()), list(T2.edges()))
+
+        for seq in product(range(4), repeat=2):
+            seq2 = nx.to_prufer_sequence(nx.from_prufer_sequence(seq))
+            assert list(seq) == seq2
+
+
+class TestNestedTuple:
+    """Unit tests for the nested tuple encoding and decoding functions."""
+
+    def test_nontree(self):
+        with pytest.raises(nx.NotATree):
+            G = nx.cycle_graph(3)
+            nx.to_nested_tuple(G, 0)
+
+    def test_unknown_root(self):
+        with pytest.raises(nx.NodeNotFound):
+            G = nx.path_graph(2)
+            nx.to_nested_tuple(G, "bogus")
+
+    def test_encoding(self):
+        T = nx.full_rary_tree(2, 2**3 - 1)
+        expected = (((), ()), ((), ()))
+        actual = nx.to_nested_tuple(T, 0)
+        assert nodes_equal(expected, actual)
+
+    def test_canonical_form(self):
+        T = nx.Graph()
+        T.add_edges_from([(0, 1), (0, 2), (0, 3)])
+        T.add_edges_from([(1, 4), (1, 5)])
+        T.add_edges_from([(3, 6), (3, 7)])
+        root = 0
+        actual = nx.to_nested_tuple(T, root, canonical_form=True)
+        expected = ((), ((), ()), ((), ()))
+        assert actual == expected
+
+    def test_decoding(self):
+        balanced = (((), ()), ((), ()))
+        expected = nx.full_rary_tree(2, 2**3 - 1)
+        actual = nx.from_nested_tuple(balanced)
+        assert nx.is_isomorphic(expected, actual)
+
+    def test_sensible_relabeling(self):
+        balanced = (((), ()), ((), ()))
+        T = nx.from_nested_tuple(balanced, sensible_relabeling=True)
+        edges = [(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)]
+        assert nodes_equal(list(T), list(range(2**3 - 1)))
+        assert edges_equal(list(T.edges()), edges)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_decomposition.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_decomposition.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c376053794537611f46c038ed074eb92b1ba676
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_decomposition.py
@@ -0,0 +1,79 @@
+import networkx as nx
+from networkx.algorithms.tree.decomposition import junction_tree
+
+
+def test_junction_tree_directed_confounders():
+    B = nx.DiGraph()
+    B.add_edges_from([("A", "C"), ("B", "C"), ("C", "D"), ("C", "E")])
+
+    G = junction_tree(B)
+    J = nx.Graph()
+    J.add_edges_from(
+        [
+            (("C", "E"), ("C",)),
+            (("C",), ("A", "B", "C")),
+            (("A", "B", "C"), ("C",)),
+            (("C",), ("C", "D")),
+        ]
+    )
+
+    assert nx.is_isomorphic(G, J)
+
+
+def test_junction_tree_directed_unconnected_nodes():
+    B = nx.DiGraph()
+    B.add_nodes_from([("A", "B", "C", "D")])
+    G = junction_tree(B)
+
+    J = nx.Graph()
+    J.add_nodes_from([("A", "B", "C", "D")])
+
+    assert nx.is_isomorphic(G, J)
+
+
+def test_junction_tree_directed_cascade():
+    B = nx.DiGraph()
+    B.add_edges_from([("A", "B"), ("B", "C"), ("C", "D")])
+    G = junction_tree(B)
+
+    J = nx.Graph()
+    J.add_edges_from(
+        [
+            (("A", "B"), ("B",)),
+            (("B",), ("B", "C")),
+            (("B", "C"), ("C",)),
+            (("C",), ("C", "D")),
+        ]
+    )
+    assert nx.is_isomorphic(G, J)
+
+
+def test_junction_tree_directed_unconnected_edges():
+    B = nx.DiGraph()
+    B.add_edges_from([("A", "B"), ("C", "D"), ("E", "F")])
+    G = junction_tree(B)
+
+    J = nx.Graph()
+    J.add_nodes_from([("A", "B"), ("C", "D"), ("E", "F")])
+
+    assert nx.is_isomorphic(G, J)
+
+
+def test_junction_tree_undirected():
+    B = nx.Graph()
+    B.add_edges_from([("A", "C"), ("A", "D"), ("B", "C"), ("C", "E")])
+    G = junction_tree(B)
+
+    J = nx.Graph()
+    J.add_edges_from(
+        [
+            (("A", "D"), ("A",)),
+            (("A",), ("A", "C")),
+            (("A", "C"), ("C",)),
+            (("C",), ("B", "C")),
+            (("B", "C"), ("C",)),
+            (("C",), ("C", "E")),
+        ]
+    )
+
+    assert nx.is_isomorphic(G, J)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_distance_measures.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_distance_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..c595d35954c22965fd2aa5272c62dd22af65018f
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_distance_measures.py
@@ -0,0 +1,99 @@
+import math
+
+import pytest
+
+import networkx as nx
+
+
+class TestCenter:
+    @pytest.mark.parametrize("graph_type", (nx.Graph, nx.MultiGraph))
+    def test_center_simple_tree(self, graph_type):
+        G = nx.Graph([(1, 2), (1, 3), (2, 4), (2, 5)], create_using=graph_type)
+        assert set(nx.tree.center(G)) == {1, 2}
+
+    @pytest.mark.parametrize("r", range(2, 5))
+    @pytest.mark.parametrize("h", range(1, 5))
+    def test_center_balanced_tree(self, r, h):
+        G = nx.balanced_tree(r, h)
+        assert nx.tree.center(G) == [0]
+
+    @pytest.mark.parametrize("n", [1, 2, 99, 100])
+    def test_center_path_graph(self, n):
+        G = nx.path_graph(n)
+        expected = {(n - 1) // 2, math.ceil((n - 1) / 2)}
+        assert set(nx.tree.center(G)) == expected
+
+    @pytest.mark.parametrize("n", [0, 2, 3, 5, 99, 100])
+    def test_center_star_graph(self, n):
+        G = nx.star_graph(n)
+        assert nx.tree.center(G) == [0]
+
+    @pytest.mark.parametrize(
+        "G",
+        (
+            nx.cycle_graph(5),
+            nx.complete_graph(5),
+            nx.Graph([(0, 1), (2, 3)]),
+            nx.empty_graph(2),
+            nx.Graph(),
+            nx.MultiGraph([(0, 1), (0, 1)]),
+            nx.Graph([(0, 1), (1, 2), (3, 4)]),
+            nx.Graph([(0, 1), (1, 2), (3, 4), (4, 5)]),
+            pytest.param(
+                nx.Graph([(0, 0)]),
+                marks=pytest.mark.xfail(reason="no check for self-loops"),
+            ),
+        ),
+    )
+    def test_center_non_tree(self, G):
+        with pytest.raises(nx.NotATree, match=r"not a tree"):
+            nx.tree.center(G)
+
+    @pytest.mark.parametrize("graph_type", (nx.DiGraph, nx.MultiDiGraph))
+    def test_center_directed(self, graph_type):
+        G = nx.path_graph(4, create_using=graph_type)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match=r"not implemented for directed"
+        ):
+            nx.tree.center(G)
+
+
+class TestDistance:
+    @pytest.mark.parametrize("n", [1, 2, 99, 100])
+    def test_tree_centroid_path_graphs(self, n):
+        G = nx.path_graph(n)
+        expected = {(n - 1) // 2, math.ceil((n - 1) / 2)}
+        assert set(nx.tree.centroid(G)) == expected
+
+    @pytest.mark.parametrize("r", range(2, 5))
+    @pytest.mark.parametrize("h", range(1, 5))
+    def test_tree_centroid_balanced_tree(self, r, h):
+        G = nx.balanced_tree(r, h)
+        assert nx.tree.centroid(G) == [0]
+
+    def test_tree_centroid_multiple_centroids(self):
+        G = nx.full_rary_tree(2, 8)
+        assert nx.tree.centroid(G) == [0, 1]
+
+    def test_tree_centroid_different_from_graph_center(self):
+        G = nx.star_graph(6)
+        nx.add_path(G, [6, 7, 8, 9, 10])
+        # nx.center(G) would be [7]
+        assert nx.tree.centroid(G) == [0]
+
+    def test_tree_centroid_not_a_tree(self):
+        G = nx.cycle_graph(3)
+        with pytest.raises(nx.NotATree, match=r"not a tree"):
+            nx.tree.centroid(G)
+
+    @pytest.mark.parametrize("G", [nx.DiGraph([(0, 1)]), nx.MultiDiGraph([(0, 1)])])
+    def test_tree_centroid_direct_raises(self, G):
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match=r"not implemented for directed type"
+        ):
+            nx.tree.centroid(G)
+
+    def test_tree_centroid_empty(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXPointlessConcept, match=r"has no nodes"):
+            nx.tree.centroid(G)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_mst.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_mst.py
new file mode 100644
index 0000000000000000000000000000000000000000..66ed8652e332fab22813c73b0ea419fd1ca6136a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_mst.py
@@ -0,0 +1,934 @@
+"""Unit tests for the :mod:`networkx.algorithms.tree.mst` module."""
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+def test_unknown_algorithm():
+    with pytest.raises(ValueError):
+        nx.minimum_spanning_tree(nx.Graph(), algorithm="random")
+    with pytest.raises(
+        ValueError, match="random is not a valid choice for an algorithm."
+    ):
+        nx.maximum_spanning_edges(nx.Graph(), algorithm="random")
+
+
+class MinimumSpanningTreeTestBase:
+    """Base class for test classes for minimum spanning tree algorithms.
+    This class contains some common tests that will be inherited by
+    subclasses. Each subclass must have a class attribute
+    :data:`algorithm` that is a string representing the algorithm to
+    run, as described under the ``algorithm`` keyword argument for the
+    :func:`networkx.minimum_spanning_edges` function.  Subclasses can
+    then implement any algorithm-specific tests.
+    """
+
+    def setup_method(self, method):
+        """Creates an example graph and stores the expected minimum and
+        maximum spanning tree edges.
+        """
+        # This stores the class attribute `algorithm` in an instance attribute.
+        self.algo = self.algorithm
+        # This example graph comes from Wikipedia:
+        # https://en.wikipedia.org/wiki/Kruskal's_algorithm
+        edges = [
+            (0, 1, 7),
+            (0, 3, 5),
+            (1, 2, 8),
+            (1, 3, 9),
+            (1, 4, 7),
+            (2, 4, 5),
+            (3, 4, 15),
+            (3, 5, 6),
+            (4, 5, 8),
+            (4, 6, 9),
+            (5, 6, 11),
+        ]
+        self.G = nx.Graph()
+        self.G.add_weighted_edges_from(edges)
+        self.minimum_spanning_edgelist = [
+            (0, 1, {"weight": 7}),
+            (0, 3, {"weight": 5}),
+            (1, 4, {"weight": 7}),
+            (2, 4, {"weight": 5}),
+            (3, 5, {"weight": 6}),
+            (4, 6, {"weight": 9}),
+        ]
+        self.maximum_spanning_edgelist = [
+            (0, 1, {"weight": 7}),
+            (1, 2, {"weight": 8}),
+            (1, 3, {"weight": 9}),
+            (3, 4, {"weight": 15}),
+            (4, 6, {"weight": 9}),
+            (5, 6, {"weight": 11}),
+        ]
+
+    def test_minimum_edges(self):
+        edges = nx.minimum_spanning_edges(self.G, algorithm=self.algo)
+        # Edges from the spanning edges functions don't come in sorted
+        # orientation, so we need to sort each edge individually.
+        actual = sorted((min(u, v), max(u, v), d) for u, v, d in edges)
+        assert edges_equal(actual, self.minimum_spanning_edgelist)
+
+    def test_maximum_edges(self):
+        edges = nx.maximum_spanning_edges(self.G, algorithm=self.algo)
+        # Edges from the spanning edges functions don't come in sorted
+        # orientation, so we need to sort each edge individually.
+        actual = sorted((min(u, v), max(u, v), d) for u, v, d in edges)
+        assert edges_equal(actual, self.maximum_spanning_edgelist)
+
+    def test_without_data(self):
+        edges = nx.minimum_spanning_edges(self.G, algorithm=self.algo, data=False)
+        # Edges from the spanning edges functions don't come in sorted
+        # orientation, so we need to sort each edge individually.
+        actual = sorted((min(u, v), max(u, v)) for u, v in edges)
+        expected = [(u, v) for u, v, d in self.minimum_spanning_edgelist]
+        assert edges_equal(actual, expected)
+
+    def test_nan_weights(self):
+        # Edge weights NaN never appear in the spanning tree. see #2164
+        G = self.G
+        G.add_edge(0, 12, weight=float("nan"))
+        edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, data=False, ignore_nan=True
+        )
+        actual = sorted((min(u, v), max(u, v)) for u, v in edges)
+        expected = [(u, v) for u, v, d in self.minimum_spanning_edgelist]
+        assert edges_equal(actual, expected)
+        # Now test for raising exception
+        edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, data=False, ignore_nan=False
+        )
+        with pytest.raises(ValueError):
+            list(edges)
+        # test default for ignore_nan as False
+        edges = nx.minimum_spanning_edges(G, algorithm=self.algo, data=False)
+        with pytest.raises(ValueError):
+            list(edges)
+
+    def test_nan_weights_MultiGraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(0, 12, weight=float("nan"))
+        edges = nx.minimum_spanning_edges(
+            G, algorithm="prim", data=False, ignore_nan=False
+        )
+        with pytest.raises(ValueError):
+            list(edges)
+        # test default for ignore_nan as False
+        edges = nx.minimum_spanning_edges(G, algorithm="prim", data=False)
+        with pytest.raises(ValueError):
+            list(edges)
+
+    def test_nan_weights_order(self):
+        # now try again with a nan edge at the beginning of G.nodes
+        edges = [
+            (0, 1, 7),
+            (0, 3, 5),
+            (1, 2, 8),
+            (1, 3, 9),
+            (1, 4, 7),
+            (2, 4, 5),
+            (3, 4, 15),
+            (3, 5, 6),
+            (4, 5, 8),
+            (4, 6, 9),
+            (5, 6, 11),
+        ]
+        G = nx.Graph()
+        G.add_weighted_edges_from([(u + 1, v + 1, wt) for u, v, wt in edges])
+        G.add_edge(0, 7, weight=float("nan"))
+        edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, data=False, ignore_nan=True
+        )
+        actual = sorted((min(u, v), max(u, v)) for u, v in edges)
+        shift = [(u + 1, v + 1) for u, v, d in self.minimum_spanning_edgelist]
+        assert edges_equal(actual, shift)
+
+    def test_isolated_node(self):
+        # now try again with an isolated node
+        edges = [
+            (0, 1, 7),
+            (0, 3, 5),
+            (1, 2, 8),
+            (1, 3, 9),
+            (1, 4, 7),
+            (2, 4, 5),
+            (3, 4, 15),
+            (3, 5, 6),
+            (4, 5, 8),
+            (4, 6, 9),
+            (5, 6, 11),
+        ]
+        G = nx.Graph()
+        G.add_weighted_edges_from([(u + 1, v + 1, wt) for u, v, wt in edges])
+        G.add_node(0)
+        edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, data=False, ignore_nan=True
+        )
+        actual = sorted((min(u, v), max(u, v)) for u, v in edges)
+        shift = [(u + 1, v + 1) for u, v, d in self.minimum_spanning_edgelist]
+        assert edges_equal(actual, shift)
+
+    def test_minimum_tree(self):
+        T = nx.minimum_spanning_tree(self.G, algorithm=self.algo)
+        actual = sorted(T.edges(data=True))
+        assert edges_equal(actual, self.minimum_spanning_edgelist)
+
+    def test_maximum_tree(self):
+        T = nx.maximum_spanning_tree(self.G, algorithm=self.algo)
+        actual = sorted(T.edges(data=True))
+        assert edges_equal(actual, self.maximum_spanning_edgelist)
+
+    def test_disconnected(self):
+        G = nx.Graph([(0, 1, {"weight": 1}), (2, 3, {"weight": 2})])
+        T = nx.minimum_spanning_tree(G, algorithm=self.algo)
+        assert nodes_equal(list(T), list(range(4)))
+        assert edges_equal(list(T.edges()), [(0, 1), (2, 3)])
+
+    def test_empty_graph(self):
+        G = nx.empty_graph(3)
+        T = nx.minimum_spanning_tree(G, algorithm=self.algo)
+        assert nodes_equal(sorted(T), list(range(3)))
+        assert T.number_of_edges() == 0
+
+    def test_attributes(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=1, color="red", distance=7)
+        G.add_edge(2, 3, weight=1, color="green", distance=2)
+        G.add_edge(1, 3, weight=10, color="blue", distance=1)
+        G.graph["foo"] = "bar"
+        T = nx.minimum_spanning_tree(G, algorithm=self.algo)
+        assert T.graph == G.graph
+        assert nodes_equal(T, G)
+        for u, v in T.edges():
+            assert T.adj[u][v] == G.adj[u][v]
+
+    def test_weight_attribute(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=1, distance=7)
+        G.add_edge(0, 2, weight=30, distance=1)
+        G.add_edge(1, 2, weight=1, distance=1)
+        G.add_node(3)
+        T = nx.minimum_spanning_tree(G, algorithm=self.algo, weight="distance")
+        assert nodes_equal(sorted(T), list(range(4)))
+        assert edges_equal(sorted(T.edges()), [(0, 2), (1, 2)])
+        T = nx.maximum_spanning_tree(G, algorithm=self.algo, weight="distance")
+        assert nodes_equal(sorted(T), list(range(4)))
+        assert edges_equal(sorted(T.edges()), [(0, 1), (0, 2)])
+
+    def test_minimum_spanning_edges_directed_raises(self):
+        DG = nx.DiGraph()
+        DG.add_edge(0, 1, weight=1)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            list(nx.minimum_spanning_edges(DG, algorithm=self.algo))
+        with pytest.raises(nx.NetworkXNotImplemented):
+            list(nx.maximum_spanning_edges(DG, algorithm=self.algo))
+
+
+class TestBoruvka(MinimumSpanningTreeTestBase):
+    """Unit tests for computing a minimum (or maximum) spanning tree
+    using Borůvka's algorithm.
+    """
+
+    algorithm = "boruvka"
+
+    def test_unicode_name(self):
+        """Tests that using a Unicode string can correctly indicate
+        Borůvka's algorithm.
+        """
+        edges = nx.minimum_spanning_edges(self.G, algorithm="borůvka")
+        # Edges from the spanning edges functions don't come in sorted
+        # orientation, so we need to sort each edge individually.
+        actual = sorted((min(u, v), max(u, v), d) for u, v, d in edges)
+        assert edges_equal(actual, self.minimum_spanning_edgelist)
+
+    def test_minimum_spanning_edges_multigraph_raises(self):
+        MG = nx.MultiGraph()
+        MG.add_edge(0, 1, weight=1)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            list(nx.minimum_spanning_edges(MG, algorithm=self.algo))
+        with pytest.raises(nx.NetworkXNotImplemented):
+            list(nx.maximum_spanning_edges(MG, algorithm=self.algo))
+
+
+class MultigraphMSTTestBase(MinimumSpanningTreeTestBase):
+    # Abstract class
+
+    def test_multigraph_keys_min(self):
+        """Tests that the minimum spanning edges of a multigraph
+        preserves edge keys.
+        """
+        G = nx.MultiGraph()
+        G.add_edge(0, 1, key="a", weight=2)
+        G.add_edge(0, 1, key="b", weight=1)
+        min_edges = nx.minimum_spanning_edges
+        mst_edges = min_edges(G, algorithm=self.algo, data=False)
+        assert edges_equal([(0, 1, "b")], list(mst_edges))
+
+    def test_multigraph_keys_max(self):
+        """Tests that the maximum spanning edges of a multigraph
+        preserves edge keys.
+        """
+        G = nx.MultiGraph()
+        G.add_edge(0, 1, key="a", weight=2)
+        G.add_edge(0, 1, key="b", weight=1)
+        max_edges = nx.maximum_spanning_edges
+        mst_edges = max_edges(G, algorithm=self.algo, data=False)
+        assert edges_equal([(0, 1, "a")], list(mst_edges))
+
+
+class TestKruskal(MultigraphMSTTestBase):
+    """Unit tests for computing a minimum (or maximum) spanning tree
+    using Kruskal's algorithm.
+    """
+
+    algorithm = "kruskal"
+
+    def test_key_data_bool(self):
+        """Tests that the keys and data values are included in
+        MST edges based on whether keys and data parameters are
+        true or false"""
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, key=1, weight=2)
+        G.add_edge(1, 2, key=2, weight=3)
+        G.add_edge(3, 2, key=1, weight=2)
+        G.add_edge(3, 1, key=1, weight=4)
+
+        # keys are included and data is not included
+        mst_edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, keys=True, data=False
+        )
+        assert edges_equal([(1, 2, 1), (2, 3, 1)], list(mst_edges))
+
+        # keys are not included and data is included
+        mst_edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, keys=False, data=True
+        )
+        assert edges_equal(
+            [(1, 2, {"weight": 2}), (2, 3, {"weight": 2})], list(mst_edges)
+        )
+
+        # both keys and data are not included
+        mst_edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, keys=False, data=False
+        )
+        assert edges_equal([(1, 2), (2, 3)], list(mst_edges))
+
+        # both keys and data are included
+        mst_edges = nx.minimum_spanning_edges(
+            G, algorithm=self.algo, keys=True, data=True
+        )
+        assert edges_equal(
+            [(1, 2, 1, {"weight": 2}), (2, 3, 1, {"weight": 2})], list(mst_edges)
+        )
+
+
+class TestPrim(MultigraphMSTTestBase):
+    """Unit tests for computing a minimum (or maximum) spanning tree
+    using Prim's algorithm.
+    """
+
+    algorithm = "prim"
+
+    def test_prim_mst_edges_simple_graph(self):
+        H = nx.Graph()
+        H.add_edge(1, 2, key=2, weight=3)
+        H.add_edge(3, 2, key=1, weight=2)
+        H.add_edge(3, 1, key=1, weight=4)
+
+        mst_edges = nx.minimum_spanning_edges(H, algorithm=self.algo, ignore_nan=True)
+        assert edges_equal(
+            [(1, 2, {"key": 2, "weight": 3}), (2, 3, {"key": 1, "weight": 2})],
+            list(mst_edges),
+        )
+
+    def test_ignore_nan(self):
+        """Tests that the edges with NaN weights are ignored or
+        raise an Error based on ignore_nan is true or false"""
+        H = nx.MultiGraph()
+        H.add_edge(1, 2, key=1, weight=float("nan"))
+        H.add_edge(1, 2, key=2, weight=3)
+        H.add_edge(3, 2, key=1, weight=2)
+        H.add_edge(3, 1, key=1, weight=4)
+
+        # NaN weight edges are ignored when ignore_nan=True
+        mst_edges = nx.minimum_spanning_edges(H, algorithm=self.algo, ignore_nan=True)
+        assert edges_equal(
+            [(1, 2, 2, {"weight": 3}), (2, 3, 1, {"weight": 2})], list(mst_edges)
+        )
+
+        # NaN weight edges raise Error when ignore_nan=False
+        with pytest.raises(ValueError):
+            list(nx.minimum_spanning_edges(H, algorithm=self.algo, ignore_nan=False))
+
+    def test_multigraph_keys_tree(self):
+        G = nx.MultiGraph()
+        G.add_edge(0, 1, key="a", weight=2)
+        G.add_edge(0, 1, key="b", weight=1)
+        T = nx.minimum_spanning_tree(G, algorithm=self.algo)
+        assert edges_equal([(0, 1, 1)], list(T.edges(data="weight")))
+
+    def test_multigraph_keys_tree_max(self):
+        G = nx.MultiGraph()
+        G.add_edge(0, 1, key="a", weight=2)
+        G.add_edge(0, 1, key="b", weight=1)
+        T = nx.maximum_spanning_tree(G, algorithm=self.algo)
+        assert edges_equal([(0, 1, 2)], list(T.edges(data="weight")))
+
+
+class TestSpanningTreeIterator:
+    """
+    Tests the spanning tree iterator on the example graph in the 2005 Sörensen
+    and Janssens paper An Algorithm to Generate all Spanning Trees of a Graph in
+    Order of Increasing Cost
+    """
+
+    def setup_method(self):
+        # Original Graph
+        edges = [(0, 1, 5), (1, 2, 4), (1, 4, 6), (2, 3, 5), (2, 4, 7), (3, 4, 3)]
+        self.G = nx.Graph()
+        self.G.add_weighted_edges_from(edges)
+        # List of lists of spanning trees in increasing order
+        self.spanning_trees = [
+            # 1, MST, cost = 17
+            [
+                (0, 1, {"weight": 5}),
+                (1, 2, {"weight": 4}),
+                (2, 3, {"weight": 5}),
+                (3, 4, {"weight": 3}),
+            ],
+            # 2, cost = 18
+            [
+                (0, 1, {"weight": 5}),
+                (1, 2, {"weight": 4}),
+                (1, 4, {"weight": 6}),
+                (3, 4, {"weight": 3}),
+            ],
+            # 3, cost = 19
+            [
+                (0, 1, {"weight": 5}),
+                (1, 4, {"weight": 6}),
+                (2, 3, {"weight": 5}),
+                (3, 4, {"weight": 3}),
+            ],
+            # 4, cost = 19
+            [
+                (0, 1, {"weight": 5}),
+                (1, 2, {"weight": 4}),
+                (2, 4, {"weight": 7}),
+                (3, 4, {"weight": 3}),
+            ],
+            # 5, cost = 20
+            [
+                (0, 1, {"weight": 5}),
+                (1, 2, {"weight": 4}),
+                (1, 4, {"weight": 6}),
+                (2, 3, {"weight": 5}),
+            ],
+            # 6, cost = 21
+            [
+                (0, 1, {"weight": 5}),
+                (1, 4, {"weight": 6}),
+                (2, 4, {"weight": 7}),
+                (3, 4, {"weight": 3}),
+            ],
+            # 7, cost = 21
+            [
+                (0, 1, {"weight": 5}),
+                (1, 2, {"weight": 4}),
+                (2, 3, {"weight": 5}),
+                (2, 4, {"weight": 7}),
+            ],
+            # 8, cost = 23
+            [
+                (0, 1, {"weight": 5}),
+                (1, 4, {"weight": 6}),
+                (2, 3, {"weight": 5}),
+                (2, 4, {"weight": 7}),
+            ],
+        ]
+
+    def test_minimum_spanning_tree_iterator(self):
+        """
+        Tests that the spanning trees are correctly returned in increasing order
+        """
+        tree_index = 0
+        for tree in nx.SpanningTreeIterator(self.G):
+            actual = sorted(tree.edges(data=True))
+            assert edges_equal(actual, self.spanning_trees[tree_index])
+            tree_index += 1
+
+    def test_maximum_spanning_tree_iterator(self):
+        """
+        Tests that the spanning trees are correctly returned in decreasing order
+        """
+        tree_index = 7
+        for tree in nx.SpanningTreeIterator(self.G, minimum=False):
+            actual = sorted(tree.edges(data=True))
+            assert edges_equal(actual, self.spanning_trees[tree_index])
+            tree_index -= 1
+
+
+class TestSpanningTreeMultiGraphIterator:
+    """
+    Uses the same graph as the above class but with an added edge of twice the weight.
+    """
+
+    def setup_method(self):
+        # New graph
+        edges = [
+            (0, 1, 5),
+            (0, 1, 10),
+            (1, 2, 4),
+            (1, 2, 8),
+            (1, 4, 6),
+            (1, 4, 12),
+            (2, 3, 5),
+            (2, 3, 10),
+            (2, 4, 7),
+            (2, 4, 14),
+            (3, 4, 3),
+            (3, 4, 6),
+        ]
+        self.G = nx.MultiGraph()
+        self.G.add_weighted_edges_from(edges)
+
+        # There are 128 trees. I'd rather not list all 128 here, and computing them
+        # on such a small graph actually doesn't take that long.
+        from itertools import combinations
+
+        self.spanning_trees = []
+        for e in combinations(self.G.edges, 4):
+            tree = self.G.edge_subgraph(e)
+            if nx.is_tree(tree):
+                self.spanning_trees.append(sorted(tree.edges(keys=True, data=True)))
+
+    def test_minimum_spanning_tree_iterator_multigraph(self):
+        """
+        Tests that the spanning trees are correctly returned in increasing order
+        """
+        tree_index = 0
+        last_weight = 0
+        for tree in nx.SpanningTreeIterator(self.G):
+            actual = sorted(tree.edges(keys=True, data=True))
+            weight = sum([e[3]["weight"] for e in actual])
+            assert actual in self.spanning_trees
+            assert weight >= last_weight
+            tree_index += 1
+
+    def test_maximum_spanning_tree_iterator_multigraph(self):
+        """
+        Tests that the spanning trees are correctly returned in decreasing order
+        """
+        tree_index = 127
+        # Maximum weight tree is 46
+        last_weight = 50
+        for tree in nx.SpanningTreeIterator(self.G, minimum=False):
+            actual = sorted(tree.edges(keys=True, data=True))
+            weight = sum([e[3]["weight"] for e in actual])
+            assert actual in self.spanning_trees
+            assert weight <= last_weight
+            tree_index -= 1
+
+
+def test_random_spanning_tree_multiplicative_small():
+    """
+    Using a fixed seed, sample one tree for repeatability.
+    """
+    from math import exp
+
+    pytest.importorskip("scipy")
+
+    gamma = {
+        (0, 1): -0.6383,
+        (0, 2): -0.6827,
+        (0, 5): 0,
+        (1, 2): -1.0781,
+        (1, 4): 0,
+        (2, 3): 0,
+        (5, 3): -0.2820,
+        (5, 4): -0.3327,
+        (4, 3): -0.9927,
+    }
+
+    # The undirected support of gamma
+    G = nx.Graph()
+    for u, v in gamma:
+        G.add_edge(u, v, lambda_key=exp(gamma[(u, v)]))
+
+    solution_edges = [(2, 3), (3, 4), (0, 5), (5, 4), (4, 1)]
+    solution = nx.Graph()
+    solution.add_edges_from(solution_edges)
+
+    sampled_tree = nx.random_spanning_tree(G, "lambda_key", seed=42)
+
+    assert nx.utils.edges_equal(solution.edges, sampled_tree.edges)
+
+
+@pytest.mark.slow
+def test_random_spanning_tree_multiplicative_large():
+    """
+    Sample many trees from the distribution created in the last test
+    """
+    from math import exp
+    from random import Random
+
+    pytest.importorskip("numpy")
+    stats = pytest.importorskip("scipy.stats")
+
+    gamma = {
+        (0, 1): -0.6383,
+        (0, 2): -0.6827,
+        (0, 5): 0,
+        (1, 2): -1.0781,
+        (1, 4): 0,
+        (2, 3): 0,
+        (5, 3): -0.2820,
+        (5, 4): -0.3327,
+        (4, 3): -0.9927,
+    }
+
+    # The undirected support of gamma
+    G = nx.Graph()
+    for u, v in gamma:
+        G.add_edge(u, v, lambda_key=exp(gamma[(u, v)]))
+
+    # Find the multiplicative weight for each tree.
+    total_weight = 0
+    tree_expected = {}
+    for t in nx.SpanningTreeIterator(G):
+        # Find the multiplicative weight of the spanning tree
+        weight = 1
+        for u, v, d in t.edges(data="lambda_key"):
+            weight *= d
+        tree_expected[t] = weight
+        total_weight += weight
+
+    # Assert that every tree has an entry in the expected distribution
+    assert len(tree_expected) == 75
+
+    # Set the sample size and then calculate the expected number of times we
+    # expect to see each tree. This test uses a near minimum sample size where
+    # the most unlikely tree has an expected frequency of 5.15.
+    # (Minimum required is 5)
+    #
+    # Here we also initialize the tree_actual dict so that we know the keys
+    # match between the two. We will later take advantage of the fact that since
+    # python 3.7 dict order is guaranteed so the expected and actual data will
+    # have the same order.
+    sample_size = 1200
+    tree_actual = {}
+    for t in tree_expected:
+        tree_expected[t] = (tree_expected[t] / total_weight) * sample_size
+        tree_actual[t] = 0
+
+    # Sample the spanning trees
+    #
+    # Assert that they are actually trees and record which of the 75 trees we
+    # have sampled.
+    #
+    # For repeatability, we want to take advantage of the decorators in NetworkX
+    # to randomly sample the same sample each time. However, if we pass in a
+    # constant seed to sample_spanning_tree we will get the same tree each time.
+    # Instead, we can create our own random number generator with a fixed seed
+    # and pass those into sample_spanning_tree.
+    rng = Random(37)
+    for _ in range(sample_size):
+        sampled_tree = nx.random_spanning_tree(G, "lambda_key", seed=rng)
+        assert nx.is_tree(sampled_tree)
+
+        for t in tree_expected:
+            if nx.utils.edges_equal(t.edges, sampled_tree.edges):
+                tree_actual[t] += 1
+                break
+
+    # Conduct a Chi squared test to see if the actual distribution matches the
+    # expected one at an alpha = 0.05 significance level.
+    #
+    # H_0: The distribution of trees in tree_actual matches the normalized product
+    # of the edge weights in the tree.
+    #
+    # H_a: The distribution of trees in tree_actual follows some other
+    # distribution of spanning trees.
+    _, p = stats.chisquare(list(tree_actual.values()), list(tree_expected.values()))
+
+    # Assert that p is greater than the significance level so that we do not
+    # reject the null hypothesis
+    assert not p < 0.05
+
+
+def test_random_spanning_tree_additive_small():
+    """
+    Sample a single spanning tree from the additive method.
+    """
+    pytest.importorskip("scipy")
+
+    edges = {
+        (0, 1): 1,
+        (0, 2): 1,
+        (0, 5): 3,
+        (1, 2): 2,
+        (1, 4): 3,
+        (2, 3): 3,
+        (5, 3): 4,
+        (5, 4): 5,
+        (4, 3): 4,
+    }
+
+    # Build the graph
+    G = nx.Graph()
+    for u, v in edges:
+        G.add_edge(u, v, weight=edges[(u, v)])
+
+    solution_edges = [(0, 2), (1, 2), (2, 3), (3, 4), (3, 5)]
+    solution = nx.Graph()
+    solution.add_edges_from(solution_edges)
+
+    sampled_tree = nx.random_spanning_tree(
+        G, weight="weight", multiplicative=False, seed=37
+    )
+
+    assert nx.utils.edges_equal(solution.edges, sampled_tree.edges)
+
+
+@pytest.mark.slow
+def test_random_spanning_tree_additive_large():
+    """
+    Sample many spanning trees from the additive method.
+    """
+    from random import Random
+
+    pytest.importorskip("numpy")
+    stats = pytest.importorskip("scipy.stats")
+
+    edges = {
+        (0, 1): 1,
+        (0, 2): 1,
+        (0, 5): 3,
+        (1, 2): 2,
+        (1, 4): 3,
+        (2, 3): 3,
+        (5, 3): 4,
+        (5, 4): 5,
+        (4, 3): 4,
+    }
+
+    # Build the graph
+    G = nx.Graph()
+    for u, v in edges:
+        G.add_edge(u, v, weight=edges[(u, v)])
+
+    # Find the additive weight for each tree.
+    total_weight = 0
+    tree_expected = {}
+    for t in nx.SpanningTreeIterator(G):
+        # Find the multiplicative weight of the spanning tree
+        weight = 0
+        for u, v, d in t.edges(data="weight"):
+            weight += d
+        tree_expected[t] = weight
+        total_weight += weight
+
+    # Assert that every tree has an entry in the expected distribution
+    assert len(tree_expected) == 75
+
+    # Set the sample size and then calculate the expected number of times we
+    # expect to see each tree. This test uses a near minimum sample size where
+    # the most unlikely tree has an expected frequency of 5.07.
+    # (Minimum required is 5)
+    #
+    # Here we also initialize the tree_actual dict so that we know the keys
+    # match between the two. We will later take advantage of the fact that since
+    # python 3.7 dict order is guaranteed so the expected and actual data will
+    # have the same order.
+    sample_size = 500
+    tree_actual = {}
+    for t in tree_expected:
+        tree_expected[t] = (tree_expected[t] / total_weight) * sample_size
+        tree_actual[t] = 0
+
+    # Sample the spanning trees
+    #
+    # Assert that they are actually trees and record which of the 75 trees we
+    # have sampled.
+    #
+    # For repeatability, we want to take advantage of the decorators in NetworkX
+    # to randomly sample the same sample each time. However, if we pass in a
+    # constant seed to sample_spanning_tree we will get the same tree each time.
+    # Instead, we can create our own random number generator with a fixed seed
+    # and pass those into sample_spanning_tree.
+    rng = Random(37)
+    for _ in range(sample_size):
+        sampled_tree = nx.random_spanning_tree(
+            G, "weight", multiplicative=False, seed=rng
+        )
+        assert nx.is_tree(sampled_tree)
+
+        for t in tree_expected:
+            if nx.utils.edges_equal(t.edges, sampled_tree.edges):
+                tree_actual[t] += 1
+                break
+
+    # Conduct a Chi squared test to see if the actual distribution matches the
+    # expected one at an alpha = 0.05 significance level.
+    #
+    # H_0: The distribution of trees in tree_actual matches the normalized product
+    # of the edge weights in the tree.
+    #
+    # H_a: The distribution of trees in tree_actual follows some other
+    # distribution of spanning trees.
+    _, p = stats.chisquare(list(tree_actual.values()), list(tree_expected.values()))
+
+    # Assert that p is greater than the significance level so that we do not
+    # reject the null hypothesis
+    assert not p < 0.05
+
+
+def test_random_spanning_tree_empty_graph():
+    G = nx.Graph()
+    rst = nx.tree.random_spanning_tree(G)
+    assert len(rst.nodes) == 0
+    assert len(rst.edges) == 0
+
+
+def test_random_spanning_tree_single_node_graph():
+    G = nx.Graph()
+    G.add_node(0)
+    rst = nx.tree.random_spanning_tree(G)
+    assert len(rst.nodes) == 1
+    assert len(rst.edges) == 0
+
+
+def test_random_spanning_tree_single_node_loop():
+    G = nx.Graph()
+    G.add_node(0)
+    G.add_edge(0, 0)
+    rst = nx.tree.random_spanning_tree(G)
+    assert len(rst.nodes) == 1
+    assert len(rst.edges) == 0
+
+
+class TestNumberSpanningTrees:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def test_nst_disconnected(self):
+        G = nx.empty_graph(2)
+        assert np.isclose(nx.number_of_spanning_trees(G), 0)
+
+    def test_nst_no_nodes(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.number_of_spanning_trees(G)
+
+    def test_nst_weight(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=1)
+        G.add_edge(1, 3, weight=1)
+        G.add_edge(2, 3, weight=2)
+        # weights are ignored
+        assert np.isclose(nx.number_of_spanning_trees(G), 3)
+        # including weight
+        assert np.isclose(nx.number_of_spanning_trees(G, weight="weight"), 5)
+
+    def test_nst_negative_weight(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=1)
+        G.add_edge(1, 3, weight=-1)
+        G.add_edge(2, 3, weight=-2)
+        # weights are ignored
+        assert np.isclose(nx.number_of_spanning_trees(G), 3)
+        # including weight
+        assert np.isclose(nx.number_of_spanning_trees(G, weight="weight"), -1)
+
+    def test_nst_selfloop(self):
+        # self-loops are ignored
+        G = nx.complete_graph(3)
+        G.add_edge(1, 1)
+        assert np.isclose(nx.number_of_spanning_trees(G), 3)
+
+    def test_nst_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(1, 2)
+        G.add_edge(1, 3)
+        G.add_edge(2, 3)
+        assert np.isclose(nx.number_of_spanning_trees(G), 5)
+
+    def test_nst_complete_graph(self):
+        # this is known as Cayley's formula
+        N = 5
+        G = nx.complete_graph(N)
+        assert np.isclose(nx.number_of_spanning_trees(G), N ** (N - 2))
+
+    def test_nst_path_graph(self):
+        G = nx.path_graph(5)
+        assert np.isclose(nx.number_of_spanning_trees(G), 1)
+
+    def test_nst_cycle_graph(self):
+        G = nx.cycle_graph(5)
+        assert np.isclose(nx.number_of_spanning_trees(G), 5)
+
+    def test_nst_directed_noroot(self):
+        G = nx.empty_graph(3, create_using=nx.MultiDiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.number_of_spanning_trees(G)
+
+    def test_nst_directed_root_not_exist(self):
+        G = nx.empty_graph(3, create_using=nx.MultiDiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.number_of_spanning_trees(G, root=42)
+
+    def test_nst_directed_not_weak_connected(self):
+        G = nx.DiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(3, 4)
+        assert np.isclose(nx.number_of_spanning_trees(G, root=1), 0)
+
+    def test_nst_directed_cycle_graph(self):
+        G = nx.DiGraph()
+        G = nx.cycle_graph(7, G)
+        assert np.isclose(nx.number_of_spanning_trees(G, root=0), 1)
+
+    def test_nst_directed_complete_graph(self):
+        G = nx.DiGraph()
+        G = nx.complete_graph(7, G)
+        assert np.isclose(nx.number_of_spanning_trees(G, root=0), 7**5)
+
+    def test_nst_directed_multi(self):
+        G = nx.MultiDiGraph()
+        G = nx.cycle_graph(3, G)
+        G.add_edge(1, 2)
+        assert np.isclose(nx.number_of_spanning_trees(G, root=0), 2)
+
+    def test_nst_directed_selfloop(self):
+        G = nx.MultiDiGraph()
+        G = nx.cycle_graph(3, G)
+        G.add_edge(1, 1)
+        assert np.isclose(nx.number_of_spanning_trees(G, root=0), 1)
+
+    def test_nst_directed_weak_connected(self):
+        G = nx.MultiDiGraph()
+        G = nx.cycle_graph(3, G)
+        G.remove_edge(1, 2)
+        assert np.isclose(nx.number_of_spanning_trees(G, root=0), 0)
+
+    def test_nst_directed_weighted(self):
+        # from root=1:
+        # arborescence 1: 1->2, 1->3, weight=2*1
+        # arborescence 2: 1->2, 2->3, weight=2*3
+        G = nx.DiGraph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=1)
+        G.add_edge(2, 3, weight=3)
+        Nst = nx.number_of_spanning_trees(G, root=1, weight="weight")
+        assert np.isclose(Nst, 8)
+        Nst = nx.number_of_spanning_trees(G, root=2, weight="weight")
+        assert np.isclose(Nst, 0)
+        Nst = nx.number_of_spanning_trees(G, root=3, weight="weight")
+        assert np.isclose(Nst, 0)
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_operations.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_operations.py
new file mode 100644
index 0000000000000000000000000000000000000000..284d94e2e5059de267b5ea47f6012a42c6ac4639
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_operations.py
@@ -0,0 +1,53 @@
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+def _check_custom_label_attribute(input_trees, res_tree, label_attribute):
+    res_attr_dict = nx.get_node_attributes(res_tree, label_attribute)
+    res_attr_set = set(res_attr_dict.values())
+    input_label = (tree for tree, root in input_trees)
+    input_label_set = set(chain.from_iterable(input_label))
+    return res_attr_set == input_label_set
+
+
+def test_empty_sequence():
+    """Joining the empty sequence results in the tree with one node."""
+    T = nx.join_trees([])
+    assert len(T) == 1
+    assert T.number_of_edges() == 0
+
+
+def test_single():
+    """Joining just one tree yields a tree with one more node."""
+    T = nx.empty_graph(1)
+    trees = [(T, 0)]
+    actual_with_label = nx.join_trees(trees, label_attribute="custom_label")
+    expected = nx.path_graph(2)
+    assert nodes_equal(list(expected), list(actual_with_label))
+    assert edges_equal(list(expected.edges()), list(actual_with_label.edges()))
+
+
+def test_basic():
+    """Joining multiple subtrees at a root node."""
+    trees = [(nx.full_rary_tree(2, 2**2 - 1), 0) for i in range(2)]
+    expected = nx.full_rary_tree(2, 2**3 - 1)
+    actual = nx.join_trees(trees, label_attribute="old_labels")
+    assert nx.is_isomorphic(actual, expected)
+    assert _check_custom_label_attribute(trees, actual, "old_labels")
+
+    actual_without_label = nx.join_trees(trees)
+    assert nx.is_isomorphic(actual_without_label, expected)
+    # check that no labels were stored
+    assert all(not data for _, data in actual_without_label.nodes(data=True))
+
+
+def test_first_label():
+    """Test the functionality of the first_label argument."""
+    T1 = nx.path_graph(3)
+    T2 = nx.path_graph(2)
+    actual = nx.join_trees([(T1, 0), (T2, 0)], first_label=10)
+    expected_nodes = set(range(10, 16))
+    assert set(actual.nodes()) == expected_nodes
+    assert set(actual.neighbors(10)) == {11, 14}
diff --git a/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_recognition.py b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_recognition.py
new file mode 100644
index 0000000000000000000000000000000000000000..105f5a89e9b10d37d1cc140880a66bc860d2e9f8
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/algorithms/tree/tests/test_recognition.py
@@ -0,0 +1,174 @@
+import pytest
+
+import networkx as nx
+
+
+class TestTreeRecognition:
+    graph = nx.Graph
+    multigraph = nx.MultiGraph
+
+    @classmethod
+    def setup_class(cls):
+        cls.T1 = cls.graph()
+
+        cls.T2 = cls.graph()
+        cls.T2.add_node(1)
+
+        cls.T3 = cls.graph()
+        cls.T3.add_nodes_from(range(5))
+        edges = [(i, i + 1) for i in range(4)]
+        cls.T3.add_edges_from(edges)
+
+        cls.T5 = cls.multigraph()
+        cls.T5.add_nodes_from(range(5))
+        edges = [(i, i + 1) for i in range(4)]
+        cls.T5.add_edges_from(edges)
+
+        cls.T6 = cls.graph()
+        cls.T6.add_nodes_from([6, 7])
+        cls.T6.add_edge(6, 7)
+
+        cls.F1 = nx.compose(cls.T6, cls.T3)
+
+        cls.N4 = cls.graph()
+        cls.N4.add_node(1)
+        cls.N4.add_edge(1, 1)
+
+        cls.N5 = cls.graph()
+        cls.N5.add_nodes_from(range(5))
+
+        cls.N6 = cls.graph()
+        cls.N6.add_nodes_from(range(3))
+        cls.N6.add_edges_from([(0, 1), (1, 2), (2, 0)])
+
+        cls.NF1 = nx.compose(cls.T6, cls.N6)
+
+    def test_null_tree(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.is_tree(self.graph())
+
+    def test_null_tree2(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.is_tree(self.multigraph())
+
+    def test_null_forest(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.is_forest(self.graph())
+
+    def test_null_forest2(self):
+        with pytest.raises(nx.NetworkXPointlessConcept):
+            nx.is_forest(self.multigraph())
+
+    def test_is_tree(self):
+        assert nx.is_tree(self.T2)
+        assert nx.is_tree(self.T3)
+        assert nx.is_tree(self.T5)
+
+    def test_is_not_tree(self):
+        assert not nx.is_tree(self.N4)
+        assert not nx.is_tree(self.N5)
+        assert not nx.is_tree(self.N6)
+
+    def test_is_forest(self):
+        assert nx.is_forest(self.T2)
+        assert nx.is_forest(self.T3)
+        assert nx.is_forest(self.T5)
+        assert nx.is_forest(self.F1)
+        assert nx.is_forest(self.N5)
+
+    def test_is_not_forest(self):
+        assert not nx.is_forest(self.N4)
+        assert not nx.is_forest(self.N6)
+        assert not nx.is_forest(self.NF1)
+
+
+class TestDirectedTreeRecognition(TestTreeRecognition):
+    graph = nx.DiGraph
+    multigraph = nx.MultiDiGraph
+
+
+def test_disconnected_graph():
+    # https://github.com/networkx/networkx/issues/1144
+    G = nx.Graph()
+    G.add_edges_from([(0, 1), (1, 2), (2, 0), (3, 4)])
+    assert not nx.is_tree(G)
+
+    G = nx.DiGraph()
+    G.add_edges_from([(0, 1), (1, 2), (2, 0), (3, 4)])
+    assert not nx.is_tree(G)
+
+
+def test_dag_nontree():
+    G = nx.DiGraph()
+    G.add_edges_from([(0, 1), (0, 2), (1, 2)])
+    assert not nx.is_tree(G)
+    assert nx.is_directed_acyclic_graph(G)
+
+
+def test_multicycle():
+    G = nx.MultiDiGraph()
+    G.add_edges_from([(0, 1), (0, 1)])
+    assert not nx.is_tree(G)
+    assert nx.is_directed_acyclic_graph(G)
+
+
+def test_emptybranch():
+    G = nx.DiGraph()
+    G.add_nodes_from(range(10))
+    assert nx.is_branching(G)
+    assert not nx.is_arborescence(G)
+
+
+def test_is_branching_empty_graph_raises():
+    G = nx.DiGraph()
+    with pytest.raises(nx.NetworkXPointlessConcept, match="G has no nodes."):
+        nx.is_branching(G)
+
+
+def test_path():
+    G = nx.DiGraph()
+    nx.add_path(G, range(5))
+    assert nx.is_branching(G)
+    assert nx.is_arborescence(G)
+
+
+def test_notbranching1():
+    # Acyclic violation.
+    G = nx.MultiDiGraph()
+    G.add_nodes_from(range(10))
+    G.add_edges_from([(0, 1), (1, 0)])
+    assert not nx.is_branching(G)
+    assert not nx.is_arborescence(G)
+
+
+def test_notbranching2():
+    # In-degree violation.
+    G = nx.MultiDiGraph()
+    G.add_nodes_from(range(10))
+    G.add_edges_from([(0, 1), (0, 2), (3, 2)])
+    assert not nx.is_branching(G)
+    assert not nx.is_arborescence(G)
+
+
+def test_notarborescence1():
+    # Not an arborescence due to not spanning.
+    G = nx.MultiDiGraph()
+    G.add_nodes_from(range(10))
+    G.add_edges_from([(0, 1), (0, 2), (1, 3), (5, 6)])
+    assert nx.is_branching(G)
+    assert not nx.is_arborescence(G)
+
+
+def test_notarborescence2():
+    # Not an arborescence due to in-degree violation.
+    G = nx.MultiDiGraph()
+    nx.add_path(G, range(5))
+    G.add_edge(6, 4)
+    assert not nx.is_branching(G)
+    assert not nx.is_arborescence(G)
+
+
+def test_is_arborescense_empty_graph_raises():
+    G = nx.DiGraph()
+    with pytest.raises(nx.NetworkXPointlessConcept, match="G has no nodes."):
+        nx.is_arborescence(G)
diff --git a/lib/python3.11/site-packages/networkx/classes/__init__.py b/lib/python3.11/site-packages/networkx/classes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..721fa8b4767233bc2b624f6b2ce4d10533a4d66c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/__init__.py
@@ -0,0 +1,13 @@
+from .graph import Graph
+from .digraph import DiGraph
+from .multigraph import MultiGraph
+from .multidigraph import MultiDiGraph
+
+from .function import *
+from .graphviews import subgraph_view, reverse_view
+
+from networkx.classes import filters
+
+from networkx.classes import coreviews
+from networkx.classes import graphviews
+from networkx.classes import reportviews
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..00ed654df90349c4acdb9e38c60ac38bbdd715d8
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/coreviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/coreviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dc494eb41f64da9cbbfbbdbe2683ca0589b4c40a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/coreviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/digraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/digraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..09b9813e58a847e1a4eebb45b4baacc6440adcaf
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/digraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/filters.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/filters.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5c0b7417e81b4a9765b62f37099f9367c6f9ad2d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/filters.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/function.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/function.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..29602795ef6cb12379552db895c5ebbbbe1bccb0
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/function.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/graph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/graph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ab034ac94d224af55bf77fa72313a9200bdb5767
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/graph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/graphviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/graphviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fcf53457212c169211dbdcf0a8b970c1d01ce121
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/graphviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/multidigraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/multidigraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c77a6f4c19dfc9939bdcfc7d6ae1fb26fd4141dc
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/multidigraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/multigraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/multigraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..92cfd6d2a429aff5d26f78b96e87645b77f9c416
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/multigraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/__pycache__/reportviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/__pycache__/reportviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8261853830ff20a4fd89b708f074bfadbd04ae46
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/__pycache__/reportviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/coreviews.py b/lib/python3.11/site-packages/networkx/classes/coreviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..4769ffa71ab823c154e6f7b990f0cb07299090a6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/coreviews.py
@@ -0,0 +1,435 @@
+"""Views of core data structures such as nested Mappings (e.g. dict-of-dicts).
+These ``Views`` often restrict element access, with either the entire view or
+layers of nested mappings being read-only.
+"""
+
+from collections.abc import Mapping
+
+__all__ = [
+    "AtlasView",
+    "AdjacencyView",
+    "MultiAdjacencyView",
+    "UnionAtlas",
+    "UnionAdjacency",
+    "UnionMultiInner",
+    "UnionMultiAdjacency",
+    "FilterAtlas",
+    "FilterAdjacency",
+    "FilterMultiInner",
+    "FilterMultiAdjacency",
+]
+
+
+class AtlasView(Mapping):
+    """An AtlasView is a Read-only Mapping of Mappings.
+
+    It is a View into a dict-of-dict data structure.
+    The inner level of dict is read-write. But the
+    outer level is read-only.
+
+    See Also
+    ========
+    AdjacencyView: View into dict-of-dict-of-dict
+    MultiAdjacencyView: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ("_atlas",)
+
+    def __getstate__(self):
+        return {"_atlas": self._atlas}
+
+    def __setstate__(self, state):
+        self._atlas = state["_atlas"]
+
+    def __init__(self, d):
+        self._atlas = d
+
+    def __len__(self):
+        return len(self._atlas)
+
+    def __iter__(self):
+        return iter(self._atlas)
+
+    def __getitem__(self, key):
+        return self._atlas[key]
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._atlas}
+
+    def __str__(self):
+        return str(self._atlas)  # {nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._atlas!r})"
+
+
+class AdjacencyView(AtlasView):
+    """An AdjacencyView is a Read-only Map of Maps of Maps.
+
+    It is a View into a dict-of-dict-of-dict data structure.
+    The inner level of dict is read-write. But the
+    outer levels are read-only.
+
+    See Also
+    ========
+    AtlasView: View into dict-of-dict
+    MultiAdjacencyView: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses AtlasView slots names _atlas
+
+    def __getitem__(self, name):
+        return AtlasView(self._atlas[name])
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._atlas}
+
+
+class MultiAdjacencyView(AdjacencyView):
+    """An MultiAdjacencyView is a Read-only Map of Maps of Maps of Maps.
+
+    It is a View into a dict-of-dict-of-dict-of-dict data structure.
+    The inner level of dict is read-write. But the
+    outer levels are read-only.
+
+    See Also
+    ========
+    AtlasView: View into dict-of-dict
+    AdjacencyView: View into dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses AtlasView slots names _atlas
+
+    def __getitem__(self, name):
+        return AdjacencyView(self._atlas[name])
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._atlas}
+
+
+class UnionAtlas(Mapping):
+    """A read-only union of two atlases (dict-of-dict).
+
+    The two dict-of-dicts represent the inner dict of
+    an Adjacency:  `G.succ[node]` and `G.pred[node]`.
+    The inner level of dict of both hold attribute key:value
+    pairs and is read-write. But the outer level is read-only.
+
+    See Also
+    ========
+    UnionAdjacency: View into dict-of-dict-of-dict
+    UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ("_succ", "_pred")
+
+    def __getstate__(self):
+        return {"_succ": self._succ, "_pred": self._pred}
+
+    def __setstate__(self, state):
+        self._succ = state["_succ"]
+        self._pred = state["_pred"]
+
+    def __init__(self, succ, pred):
+        self._succ = succ
+        self._pred = pred
+
+    def __len__(self):
+        return len(self._succ.keys() | self._pred.keys())
+
+    def __iter__(self):
+        return iter(set(self._succ.keys()) | set(self._pred.keys()))
+
+    def __getitem__(self, key):
+        try:
+            return self._succ[key]
+        except KeyError:
+            return self._pred[key]
+
+    def copy(self):
+        result = {nbr: dd.copy() for nbr, dd in self._succ.items()}
+        for nbr, dd in self._pred.items():
+            if nbr in result:
+                result[nbr].update(dd)
+            else:
+                result[nbr] = dd.copy()
+        return result
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._succ!r}, {self._pred!r})"
+
+
+class UnionAdjacency(Mapping):
+    """A read-only union of dict Adjacencies as a Map of Maps of Maps.
+
+    The two input dict-of-dict-of-dicts represent the union of
+    `G.succ` and `G.pred`. Return values are UnionAtlas
+    The inner level of dict is read-write. But the
+    middle and outer levels are read-only.
+
+    succ : a dict-of-dict-of-dict {node: nbrdict}
+    pred : a dict-of-dict-of-dict {node: nbrdict}
+    The keys for the two dicts should be the same
+
+    See Also
+    ========
+    UnionAtlas: View into dict-of-dict
+    UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ("_succ", "_pred")
+
+    def __getstate__(self):
+        return {"_succ": self._succ, "_pred": self._pred}
+
+    def __setstate__(self, state):
+        self._succ = state["_succ"]
+        self._pred = state["_pred"]
+
+    def __init__(self, succ, pred):
+        # keys must be the same for two input dicts
+        assert len(set(succ.keys()) ^ set(pred.keys())) == 0
+        self._succ = succ
+        self._pred = pred
+
+    def __len__(self):
+        return len(self._succ)  # length of each dict should be the same
+
+    def __iter__(self):
+        return iter(self._succ)
+
+    def __getitem__(self, nbr):
+        return UnionAtlas(self._succ[nbr], self._pred[nbr])
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._succ}
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._succ!r}, {self._pred!r})"
+
+
+class UnionMultiInner(UnionAtlas):
+    """A read-only union of two inner dicts of MultiAdjacencies.
+
+    The two input dict-of-dict-of-dicts represent the union of
+    `G.succ[node]` and `G.pred[node]` for MultiDiGraphs.
+    Return values are UnionAtlas.
+    The inner level of dict is read-write. But the outer levels are read-only.
+
+    See Also
+    ========
+    UnionAtlas: View into dict-of-dict
+    UnionAdjacency:  View into dict-of-dict-of-dict
+    UnionMultiAdjacency:  View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses UnionAtlas slots names _succ, _pred
+
+    def __getitem__(self, node):
+        in_succ = node in self._succ
+        in_pred = node in self._pred
+        if in_succ:
+            if in_pred:
+                return UnionAtlas(self._succ[node], self._pred[node])
+            return UnionAtlas(self._succ[node], {})
+        return UnionAtlas({}, self._pred[node])
+
+    def copy(self):
+        nodes = set(self._succ.keys()) | set(self._pred.keys())
+        return {n: self[n].copy() for n in nodes}
+
+
+class UnionMultiAdjacency(UnionAdjacency):
+    """A read-only union of two dict MultiAdjacencies.
+
+    The two input dict-of-dict-of-dict-of-dicts represent the union of
+    `G.succ` and `G.pred` for MultiDiGraphs. Return values are UnionAdjacency.
+    The inner level of dict is read-write. But the outer levels are read-only.
+
+    See Also
+    ========
+    UnionAtlas:  View into dict-of-dict
+    UnionMultiInner:  View into dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses UnionAdjacency slots names _succ, _pred
+
+    def __getitem__(self, node):
+        return UnionMultiInner(self._succ[node], self._pred[node])
+
+
+class FilterAtlas(Mapping):  # nodedict, nbrdict, keydict
+    """A read-only Mapping of Mappings with filtering criteria for nodes.
+
+    It is a view into a dict-of-dict data structure, and it selects only
+    nodes that meet the criteria defined by ``NODE_OK``.
+
+    See Also
+    ========
+    FilterAdjacency
+    FilterMultiInner
+    FilterMultiAdjacency
+    """
+
+    def __init__(self, d, NODE_OK):
+        self._atlas = d
+        self.NODE_OK = NODE_OK
+
+    def __len__(self):
+        # check whether NODE_OK stores the number of nodes as `length`
+        # or the nodes themselves as a set `nodes`. If not, count the nodes.
+        if hasattr(self.NODE_OK, "length"):
+            return self.NODE_OK.length
+        if hasattr(self.NODE_OK, "nodes"):
+            return len(self.NODE_OK.nodes & self._atlas.keys())
+        return sum(1 for n in self._atlas if self.NODE_OK(n))
+
+    def __iter__(self):
+        try:  # check that NODE_OK has attr 'nodes'
+            node_ok_shorter = 2 * len(self.NODE_OK.nodes) < len(self._atlas)
+        except AttributeError:
+            node_ok_shorter = False
+        if node_ok_shorter:
+            return (n for n in self.NODE_OK.nodes if n in self._atlas)
+        return (n for n in self._atlas if self.NODE_OK(n))
+
+    def __getitem__(self, key):
+        if key in self._atlas and self.NODE_OK(key):
+            return self._atlas[key]
+        raise KeyError(f"Key {key} not found")
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._atlas!r}, {self.NODE_OK!r})"
+
+
+class FilterAdjacency(Mapping):  # edgedict
+    """A read-only Mapping of Mappings with filtering criteria for nodes and edges.
+
+    It is a view into a dict-of-dict-of-dict data structure, and it selects nodes
+    and edges that satisfy specific criteria defined by ``NODE_OK`` and ``EDGE_OK``,
+    respectively.
+
+    See Also
+    ========
+    FilterAtlas
+    FilterMultiInner
+    FilterMultiAdjacency
+    """
+
+    def __init__(self, d, NODE_OK, EDGE_OK):
+        self._atlas = d
+        self.NODE_OK = NODE_OK
+        self.EDGE_OK = EDGE_OK
+
+    def __len__(self):
+        # check whether NODE_OK stores the number of nodes as `length`
+        # or the nodes themselves as a set `nodes`. If not, count the nodes.
+        if hasattr(self.NODE_OK, "length"):
+            return self.NODE_OK.length
+        if hasattr(self.NODE_OK, "nodes"):
+            return len(self.NODE_OK.nodes & self._atlas.keys())
+        return sum(1 for n in self._atlas if self.NODE_OK(n))
+
+    def __iter__(self):
+        try:  # check that NODE_OK has attr 'nodes'
+            node_ok_shorter = 2 * len(self.NODE_OK.nodes) < len(self._atlas)
+        except AttributeError:
+            node_ok_shorter = False
+        if node_ok_shorter:
+            return (n for n in self.NODE_OK.nodes if n in self._atlas)
+        return (n for n in self._atlas if self.NODE_OK(n))
+
+    def __getitem__(self, node):
+        if node in self._atlas and self.NODE_OK(node):
+
+            def new_node_ok(nbr):
+                return self.NODE_OK(nbr) and self.EDGE_OK(node, nbr)
+
+            return FilterAtlas(self._atlas[node], new_node_ok)
+        raise KeyError(f"Key {node} not found")
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        return f"{name}({self._atlas!r}, {self.NODE_OK!r}, {self.EDGE_OK!r})"
+
+
+class FilterMultiInner(FilterAdjacency):  # muliedge_seconddict
+    """A read-only Mapping of Mappings with filtering criteria for nodes and edges.
+
+    It is a view into a dict-of-dict-of-dict-of-dict data structure, and it selects nodes
+    and edges that meet specific criteria defined by ``NODE_OK`` and ``EDGE_OK``.
+
+    See Also
+    ========
+    FilterAtlas
+    FilterAdjacency
+    FilterMultiAdjacency
+    """
+
+    def __iter__(self):
+        try:  # check that NODE_OK has attr 'nodes'
+            node_ok_shorter = 2 * len(self.NODE_OK.nodes) < len(self._atlas)
+        except AttributeError:
+            node_ok_shorter = False
+        if node_ok_shorter:
+            my_nodes = (n for n in self.NODE_OK.nodes if n in self._atlas)
+        else:
+            my_nodes = (n for n in self._atlas if self.NODE_OK(n))
+        for n in my_nodes:
+            some_keys_ok = False
+            for key in self._atlas[n]:
+                if self.EDGE_OK(n, key):
+                    some_keys_ok = True
+                    break
+            if some_keys_ok is True:
+                yield n
+
+    def __getitem__(self, nbr):
+        if (
+            nbr in self._atlas
+            and self.NODE_OK(nbr)
+            and any(self.EDGE_OK(nbr, key) for key in self._atlas[nbr])
+        ):
+
+            def new_node_ok(key):
+                return self.EDGE_OK(nbr, key)
+
+            return FilterAtlas(self._atlas[nbr], new_node_ok)
+        raise KeyError(f"Key {nbr} not found")
+
+
+class FilterMultiAdjacency(FilterAdjacency):  # multiedgedict
+    """A read-only Mapping of Mappings with filtering criteria
+    for nodes and edges.
+
+    It is a view into a dict-of-dict-of-dict-of-dict data structure,
+    and it selects nodes and edges that satisfy specific criteria
+    defined by ``NODE_OK`` and ``EDGE_OK``, respectively.
+
+    See Also
+    ========
+    FilterAtlas
+    FilterAdjacency
+    FilterMultiInner
+    """
+
+    def __getitem__(self, node):
+        if node in self._atlas and self.NODE_OK(node):
+
+            def edge_ok(nbr, key):
+                return self.NODE_OK(nbr) and self.EDGE_OK(node, nbr, key)
+
+            return FilterMultiInner(self._atlas[node], self.NODE_OK, edge_ok)
+        raise KeyError(f"Key {node} not found")
diff --git a/lib/python3.11/site-packages/networkx/classes/digraph.py b/lib/python3.11/site-packages/networkx/classes/digraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae35128e26814315277ac4f0500abc53f36bbbb1
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/digraph.py
@@ -0,0 +1,1363 @@
+"""Base class for directed graphs."""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import convert
+from networkx.classes.coreviews import AdjacencyView
+from networkx.classes.graph import Graph
+from networkx.classes.reportviews import (
+    DiDegreeView,
+    InDegreeView,
+    InEdgeView,
+    OutDegreeView,
+    OutEdgeView,
+)
+from networkx.exception import NetworkXError
+
+__all__ = ["DiGraph"]
+
+
+class _CachedPropertyResetterAdjAndSucc:
+    """Data Descriptor class that syncs and resets cached properties adj and succ
+
+    The cached properties `adj` and `succ` are reset whenever `_adj` or `_succ`
+    are set to new objects. In addition, the attributes `_succ` and `_adj`
+    are synced so these two names point to the same object.
+
+    Warning: most of the time, when ``G._adj`` is set, ``G._pred`` should also
+    be set to maintain a valid data structure. They share datadicts.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached properties "succ" and "adj" whenever the
+    underlying instance attributes "_succ" or "_adj" are set to a new object.
+    It only affects the set process of the obj._adj and obj._succ attribute.
+    All get/del operations act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_adj"] = value
+        od["_succ"] = value
+        # reset cached properties
+        props = [
+            "adj",
+            "succ",
+            "edges",
+            "out_edges",
+            "degree",
+            "out_degree",
+            "in_degree",
+        ]
+        for prop in props:
+            if prop in od:
+                del od[prop]
+
+
+class _CachedPropertyResetterPred:
+    """Data Descriptor class for _pred that resets ``pred`` cached_property when needed
+
+    This assumes that the ``cached_property`` ``G.pred`` should be reset whenever
+    ``G._pred`` is set to a new value.
+
+    Warning: most of the time, when ``G._pred`` is set, ``G._adj`` should also
+    be set to maintain a valid data structure. They share datadicts.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached property "pred" whenever the underlying
+    instance attribute "_pred" is set to a new object. It only affects
+    the set process of the obj._pred attribute. All get/del operations
+    act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_pred"] = value
+        # reset cached properties
+        props = ["pred", "in_edges", "degree", "out_degree", "in_degree"]
+        for prop in props:
+            if prop in od:
+                del od[prop]
+
+
+class DiGraph(Graph):
+    """
+    Base class for directed graphs.
+
+    A DiGraph stores nodes and edges with optional data, or attributes.
+
+    DiGraphs hold directed edges.  Self loops are allowed but multiple
+    (parallel) edges are not.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes. By convention `None` is not used as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
+        sparse matrix, or PyGraphviz graph.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    Graph
+    MultiGraph
+    MultiDiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.DiGraph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  There are no errors when adding
+    nodes or edges that already exist.
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.DiGraph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> G.add_edge(1, 2, weight=4.7)
+    >>> G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2]["weight"] = 4.7
+    >>> G.edges[1, 2]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges[1, 2]` a
+    read-only dict-like structure. However, you can assign to attributes
+    in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
+    data attributes: `G.edges[1, 2]['weight'] = 4`
+    (For multigraphs: `MG.edges[u, v, key][name] = value`).
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, eattr in nbrsdict.items():
+    ...         if "weight" in eattr:
+    ...             # Do something useful with the edges
+    ...             pass
+
+    But the edges reporting object is often more convenient:
+
+    >>> for u, v, weight in G.edges(data="weight"):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using object-attributes and methods.
+    Reporting usually provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The Graph class uses a dict-of-dict-of-dict data structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information and holds
+    edge data keyed by neighbor.  The inner dict (edge_attr_dict) represents
+    the edge data and holds edge attribute values keyed by attribute names.
+
+    Each of these three dicts can be replaced in a subclass by a user defined
+    dict-like object. In general, the dict-like features should be
+    maintained but extra features can be added. To replace one of the
+    dicts create a new graph class by changing the class(!) variable
+    holding the factory for that dict-like structure. The variable names are
+    node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
+    adjlist_outer_dict_factory, edge_attr_dict_factory and graph_attr_dict_factory.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, optional (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds edge data keyed by neighbor.
+        It should require no arguments and return a dict-like object
+
+    edge_attr_dict_factory : function, optional (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherited without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    _adj = _CachedPropertyResetterAdjAndSucc()  # type: ignore[assignment]
+    _succ = _adj  # type: ignore[has-type]
+    _pred = _CachedPropertyResetterPred()
+
+    # This __new__ method just does what Python itself does automatically.
+    # We include it here as part of the dispatchable/backend interface.
+    # If your goal is to understand how the graph classes work, you can ignore
+    # this method, even when subclassing the base classes. If you are subclassing
+    # in order to provide a backend that allows class instantiation, this method
+    # can be overridden to return your own backend graph class.
+    @nx._dispatchable(name="digraph__new__", graphs=None, returns_graph=True)
+    def __new__(cls, *args, **kwargs):
+        return object.__new__(cls)
+
+    def __init__(self, incoming_graph_data=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph (optional, default: None)
+            Data to initialize graph.  If None (default) an empty
+            graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G = nx.Graph(name="my graph")
+        >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.Graph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.Graph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        self.graph = self.graph_attr_dict_factory()  # dictionary for graph attributes
+        self._node = self.node_dict_factory()  # dictionary for node attr
+        # We store two adjacency lists:
+        # the predecessors of node n are stored in the dict self._pred
+        # the successors of node n are stored in the dict self._succ=self._adj
+        self._adj = self.adjlist_outer_dict_factory()  # empty adjacency dict successor
+        self._pred = self.adjlist_outer_dict_factory()  # predecessor
+        # Note: self._succ = self._adj  # successor
+
+        self.__networkx_cache__ = {}
+        # attempt to load graph with data
+        if incoming_graph_data is not None:
+            convert.to_networkx_graph(incoming_graph_data, create_using=self)
+        # load graph attributes (must be after convert)
+        attr.pop("backend", None)  # Ignore explicit `backend="networkx"`
+        self.graph.update(attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.adj[3][2]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return AdjacencyView(self._succ)
+
+    @cached_property
+    def succ(self):
+        """Graph adjacency object holding the successors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.succ[3][2]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.succ behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.succ[n].items():`.  A data-view not provided
+        by dicts also exists: `for nbr, foovalue in G.succ[node].data('foo'):`
+        and a default can be set via a `default` argument to the `data` method.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` is identical to `G.succ`.
+        """
+        return AdjacencyView(self._succ)
+
+    @cached_property
+    def pred(self):
+        """Graph adjacency object holding the predecessors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.pred[2][3]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.pred behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.pred[n].items():`.  A data-view not provided
+        by dicts also exists: `for nbr, foovalue in G.pred[node].data('foo'):`
+        A default can be set via a `default` argument to the `data` method.
+        """
+        return AdjacencyView(self._pred)
+
+    def add_node(self, node_for_adding, **attr):
+        """Add a single node `node_for_adding` and update node attributes.
+
+        Parameters
+        ----------
+        node_for_adding : node
+            A node can be any hashable Python object except None.
+        attr : keyword arguments, optional
+            Set or change node attributes using key=value.
+
+        See Also
+        --------
+        add_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_node(1)
+        >>> G.add_node("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_node(K3)
+        >>> G.number_of_nodes()
+        3
+
+        Use keywords set/change node attributes:
+
+        >>> G.add_node(1, size=10)
+        >>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
+
+        Notes
+        -----
+        A hashable object is one that can be used as a key in a Python
+        dictionary. This includes strings, numbers, tuples of strings
+        and numbers, etc.
+
+        On many platforms hashable items also include mutables such as
+        NetworkX Graphs, though one should be careful that the hash
+        doesn't change on mutables.
+        """
+        if node_for_adding not in self._succ:
+            if node_for_adding is None:
+                raise ValueError("None cannot be a node")
+            self._succ[node_for_adding] = self.adjlist_inner_dict_factory()
+            self._pred[node_for_adding] = self.adjlist_inner_dict_factory()
+            attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
+            attr_dict.update(attr)
+        else:  # update attr even if node already exists
+            self._node[node_for_adding].update(attr)
+        nx._clear_cache(self)
+
+    def add_nodes_from(self, nodes_for_adding, **attr):
+        """Add multiple nodes.
+
+        Parameters
+        ----------
+        nodes_for_adding : iterable container
+            A container of nodes (list, dict, set, etc.).
+            OR
+            A container of (node, attribute dict) tuples.
+            Node attributes are updated using the attribute dict.
+        attr : keyword arguments, optional (default= no attributes)
+            Update attributes for all nodes in nodes.
+            Node attributes specified in nodes as a tuple take
+            precedence over attributes specified via keyword arguments.
+
+        See Also
+        --------
+        add_node
+
+        Notes
+        -----
+        When adding nodes from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.add_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_nodes_from("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_nodes_from(K3)
+        >>> sorted(G.nodes(), key=str)
+        [0, 1, 2, 'H', 'e', 'l', 'o']
+
+        Use keywords to update specific node attributes for every node.
+
+        >>> G.add_nodes_from([1, 2], size=10)
+        >>> G.add_nodes_from([3, 4], weight=0.4)
+
+        Use (node, attrdict) tuples to update attributes for specific nodes.
+
+        >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
+        >>> G.nodes[1]["size"]
+        11
+        >>> H = nx.Graph()
+        >>> H.add_nodes_from(G.nodes(data=True))
+        >>> H.nodes[1]["size"]
+        11
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_nodes_from(n + 1 for n in G.nodes)
+        >>> # correct way
+        >>> G.add_nodes_from(list(n + 1 for n in G.nodes))
+        """
+        for n in nodes_for_adding:
+            try:
+                newnode = n not in self._node
+                newdict = attr
+            except TypeError:
+                n, ndict = n
+                newnode = n not in self._node
+                newdict = attr.copy()
+                newdict.update(ndict)
+            if newnode:
+                if n is None:
+                    raise ValueError("None cannot be a node")
+                self._succ[n] = self.adjlist_inner_dict_factory()
+                self._pred[n] = self.adjlist_inner_dict_factory()
+                self._node[n] = self.node_attr_dict_factory()
+            self._node[n].update(newdict)
+        nx._clear_cache(self)
+
+    def remove_node(self, n):
+        """Remove node n.
+
+        Removes the node n and all adjacent edges.
+        Attempting to remove a nonexistent node will raise an exception.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        remove_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> list(G.edges)
+        [(0, 1), (1, 2)]
+        >>> G.remove_node(1)
+        >>> list(G.edges)
+        []
+
+        """
+        try:
+            nbrs = self._succ[n]
+            del self._node[n]
+        except KeyError as err:  # NetworkXError if n not in self
+            raise NetworkXError(f"The node {n} is not in the digraph.") from err
+        for u in nbrs:
+            del self._pred[u][n]  # remove all edges n-u in digraph
+        del self._succ[n]  # remove node from succ
+        for u in self._pred[n]:
+            del self._succ[u][n]  # remove all edges n-u in digraph
+        del self._pred[n]  # remove node from pred
+        nx._clear_cache(self)
+
+    def remove_nodes_from(self, nodes):
+        """Remove multiple nodes.
+
+        Parameters
+        ----------
+        nodes : iterable container
+            A container of nodes (list, dict, set, etc.).  If a node
+            in the container is not in the graph it is silently ignored.
+
+        See Also
+        --------
+        remove_node
+
+        Notes
+        -----
+        When removing nodes from an iterator over the graph you are changing,
+        a `RuntimeError` will be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.remove_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = list(G.nodes)
+        >>> e
+        [0, 1, 2]
+        >>> G.remove_nodes_from(e)
+        >>> list(G.nodes)
+        []
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])
+        >>> # this command will fail, as the graph's dict is modified during iteration
+        >>> # G.remove_nodes_from(n for n in G.nodes if n < 2)
+        >>> # this command will work, since the dictionary underlying graph is not modified
+        >>> G.remove_nodes_from(list(n for n in G.nodes if n < 2))
+        """
+        for n in nodes:
+            try:
+                succs = self._succ[n]
+                del self._node[n]
+                for u in succs:
+                    del self._pred[u][n]  # remove all edges n-u in digraph
+                del self._succ[n]  # now remove node
+                for u in self._pred[n]:
+                    del self._succ[u][n]  # remove all edges n-u in digraph
+                del self._pred[n]  # now remove node
+            except KeyError:
+                pass  # silent failure on remove
+        nx._clear_cache(self)
+
+    def add_edge(self, u_of_edge, v_of_edge, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_of_edge, v_of_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        Adding an edge that already exists updates the edge data.
+
+        Many NetworkX algorithms designed for weighted graphs use
+        an edge attribute (by default `weight`) to hold a numerical value.
+
+        Examples
+        --------
+        The following all add the edge e=(1, 2) to graph G:
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = (1, 2)
+        >>> G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+
+        Associate data to edges using keywords:
+
+        >>> G.add_edge(1, 2, weight=3)
+        >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> G.add_edge(1, 2)
+        >>> G[1][2].update({0: 5})
+        >>> G.edges[1, 2].update({0: 5})
+        """
+        u, v = u_of_edge, v_of_edge
+        # add nodes
+        if u not in self._succ:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._succ[u] = self.adjlist_inner_dict_factory()
+            self._pred[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._succ:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._succ[v] = self.adjlist_inner_dict_factory()
+            self._pred[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        # add the edge
+        datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+        datadict.update(attr)
+        self._succ[u][v] = datadict
+        self._pred[v][u] = datadict
+        nx._clear_cache(self)
+
+    def add_edges_from(self, ebunch_to_add, **attr):
+        """Add all the edges in ebunch_to_add.
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the container will be added to the
+            graph. The edges must be given as 2-tuples (u, v) or
+            3-tuples (u, v, d) where d is a dictionary containing edge data.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_weighted_edges_from : convenient way to add weighted edges
+
+        Notes
+        -----
+        Adding the same edge twice has no effect but any edge data
+        will be updated when each duplicate edge is added.
+
+        Edge attributes specified in an ebunch take precedence over
+        attributes specified via keyword arguments.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
+        >>> e = zip(range(0, 3), range(1, 4))
+        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
+
+        Associate data to edges
+
+        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
+        >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_edges_from(((5, n) for n in G.nodes))
+        >>> # right way - note that there will be no self-edge for node 5
+        >>> G.add_edges_from(list((5, n) for n in G.nodes))
+        """
+        for e in ebunch_to_add:
+            ne = len(e)
+            if ne == 3:
+                u, v, dd = e
+            elif ne == 2:
+                u, v = e
+                dd = {}
+            else:
+                raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
+            if u not in self._succ:
+                if u is None:
+                    raise ValueError("None cannot be a node")
+                self._succ[u] = self.adjlist_inner_dict_factory()
+                self._pred[u] = self.adjlist_inner_dict_factory()
+                self._node[u] = self.node_attr_dict_factory()
+            if v not in self._succ:
+                if v is None:
+                    raise ValueError("None cannot be a node")
+                self._succ[v] = self.adjlist_inner_dict_factory()
+                self._pred[v] = self.adjlist_inner_dict_factory()
+                self._node[v] = self.node_attr_dict_factory()
+            datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            datadict.update(dd)
+            self._succ[u][v] = datadict
+            self._pred[v][u] = datadict
+        nx._clear_cache(self)
+
+    def remove_edge(self, u, v):
+        """Remove the edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove the edge between nodes u and v.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, etc
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+        >>> e = (2, 3, {"weight": 7})  # an edge with attribute data
+        >>> G.remove_edge(*e[:2])  # select first part of edge tuple
+        """
+        try:
+            del self._succ[u][v]
+            del self._pred[v][u]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} not in graph.") from err
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each edge given in the list or container will be removed
+            from the graph. The edges can be:
+
+                - 2-tuples (u, v) edge between u and v.
+                - 3-tuples (u, v, k) where k is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+        """
+        for e in ebunch:
+            u, v = e[:2]  # ignore edge data
+            if u in self._succ and v in self._succ[u]:
+                del self._succ[u][v]
+                del self._pred[v][u]
+        nx._clear_cache(self)
+
+    def has_successor(self, u, v):
+        """Returns True if node u has successor v.
+
+        This is true if graph has the edge u->v.
+        """
+        return u in self._succ and v in self._succ[u]
+
+    def has_predecessor(self, u, v):
+        """Returns True if node u has predecessor v.
+
+        This is true if graph has the edge u<-v.
+        """
+        return u in self._pred and v in self._pred[u]
+
+    def successors(self, n):
+        """Returns an iterator over successor nodes of n.
+
+        A successor of n is a node m such that there exists a directed
+        edge from n to m.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        predecessors
+
+        Notes
+        -----
+        neighbors() and successors() are the same.
+        """
+        try:
+            return iter(self._succ[n])
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the digraph.") from err
+
+    # digraph definitions
+    neighbors = successors
+
+    def predecessors(self, n):
+        """Returns an iterator over predecessor nodes of n.
+
+        A predecessor of n is a node m such that there exists a directed
+        edge from m to n.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        successors
+        """
+        try:
+            return iter(self._pred[n])
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the digraph.") from err
+
+    @cached_property
+    def edges(self):
+        """An OutEdgeView of the DiGraph as G.edges or G.edges().
+
+        edges(self, nbunch=None, data=False, default=None)
+
+        The OutEdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, `G.edges[u, v]['color']` provides the value of the color
+        attribute for edge `(u, v)` while
+        `for (u, v, c) in G.edges.data('color', default='red'):`
+        iterates through all the edges yielding the color attribute
+        with default `'red'` if no color attribute exists.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : OutEdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, d) tuples of edges, but can also be used for
+            attribute lookup as `edges[u, v]['foo']`.
+
+        See Also
+        --------
+        in_edges, out_edges
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()  # or MultiDiGraph, etc
+        >>> nx.add_path(G, [0, 1, 2])
+        >>> G.add_edge(2, 3, weight=5)
+        >>> [e for e in G.edges]
+        [(0, 1), (1, 2), (2, 3)]
+        >>> G.edges.data()  # default data is {} (empty dict)
+        OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
+        >>> G.edges.data("weight", default=1)
+        OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
+        >>> G.edges([0, 2])  # only edges originating from these nodes
+        OutEdgeDataView([(0, 1), (2, 3)])
+        >>> G.edges(0)  # only edges from node 0
+        OutEdgeDataView([(0, 1)])
+
+        """
+        return OutEdgeView(self)
+
+    # alias out_edges to edges
+    @cached_property
+    def out_edges(self):
+        return OutEdgeView(self)
+
+    out_edges.__doc__ = edges.__doc__
+
+    @cached_property
+    def in_edges(self):
+        """A view of the in edges of the graph as G.in_edges or G.in_edges().
+
+        in_edges(self, nbunch=None, data=False, default=None):
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        in_edges : InEdgeView or InEdgeDataView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, d) tuples of edges, but can also be used for
+            attribute lookup as `edges[u, v]['foo']`.
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()
+        >>> G.add_edge(1, 2, color="blue")
+        >>> G.in_edges()
+        InEdgeView([(1, 2)])
+        >>> G.in_edges(nbunch=2)
+        InEdgeDataView([(1, 2)])
+
+        See Also
+        --------
+        edges
+        """
+        return InEdgeView(self)
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        DiDegreeView or int
+            If multiple nodes are requested (the default), returns a `DiDegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        See Also
+        --------
+        in_degree, out_degree
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()  # or MultiDiGraph
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1, 2]))
+        [(0, 1), (1, 2), (2, 2)]
+
+        """
+        return DiDegreeView(self)
+
+    @cached_property
+    def in_degree(self):
+        """An InDegreeView for (node, in_degree) or in_degree for single node.
+
+        The node in_degree is the number of edges pointing to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iteration over (node, in_degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            In-degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, in-degree).
+
+        See Also
+        --------
+        degree, out_degree
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.in_degree(0)  # node 0 with degree 0
+        0
+        >>> list(G.in_degree([0, 1, 2]))
+        [(0, 0), (1, 1), (2, 1)]
+
+        """
+        return InDegreeView(self)
+
+    @cached_property
+    def out_degree(self):
+        """An OutDegreeView for (node, out_degree)
+
+        The node out_degree is the number of edges pointing out of the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator over (node, out_degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            Out-degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, out-degree).
+
+        See Also
+        --------
+        degree, in_degree
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.out_degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.out_degree([0, 1, 2]))
+        [(0, 1), (1, 1), (2, 1)]
+
+        """
+        return OutDegreeView(self)
+
+    def clear(self):
+        """Remove all nodes and edges from the graph.
+
+        This also removes the name, and all graph, node, and edge attributes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear()
+        >>> list(G.nodes)
+        []
+        >>> list(G.edges)
+        []
+
+        """
+        self._succ.clear()
+        self._pred.clear()
+        self._node.clear()
+        self.graph.clear()
+        nx._clear_cache(self)
+
+    def clear_edges(self):
+        """Remove all edges from the graph without altering nodes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear_edges()
+        >>> list(G.nodes)
+        [0, 1, 2, 3]
+        >>> list(G.edges)
+        []
+
+        """
+        for predecessor_dict in self._pred.values():
+            predecessor_dict.clear()
+        for successor_dict in self._succ.values():
+            successor_dict.clear()
+        nx._clear_cache(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return False
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return True
+
+    def to_undirected(self, reciprocal=False, as_view=False):
+        """Returns an undirected representation of the digraph.
+
+        Parameters
+        ----------
+        reciprocal : bool (optional)
+          If True only keep edges that appear in both directions
+          in the original digraph.
+        as_view : bool (optional, default=False)
+          If True return an undirected view of the original directed graph.
+
+        Returns
+        -------
+        G : Graph
+            An undirected graph with the same name and nodes and
+            with edge (u, v, data) if either (u, v, data) or (v, u, data)
+            is in the digraph.  If both edges exist in digraph and
+            their edge data is different, only one edge is created
+            with an arbitrary choice of which edge data to use.
+            You must check and correct for this manually if desired.
+
+        See Also
+        --------
+        Graph, copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        If edges in both directions (u, v) and (v, u) exist in the
+        graph, attributes for the new undirected edge will be a combination of
+        the attributes of the directed edges.  The edge data is updated
+        in the (arbitrary) order that the edges are encountered.  For
+        more customized control of the edge attributes use add_edge().
+
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar G=DiGraph(D) which returns a
+        shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed DiGraph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        Graph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(2)  # or MultiGraph, etc
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        if reciprocal is True:
+            G.add_edges_from(
+                (u, v, deepcopy(d))
+                for u, nbrs in self._adj.items()
+                for v, d in nbrs.items()
+                if v in self._pred[u]
+            )
+        else:
+            G.add_edges_from(
+                (u, v, deepcopy(d))
+                for u, nbrs in self._adj.items()
+                for v, d in nbrs.items()
+            )
+        return G
+
+    def reverse(self, copy=True):
+        """Returns the reverse of the graph.
+
+        The reverse is a graph with the same nodes and edges
+        but with the directions of the edges reversed.
+
+        Parameters
+        ----------
+        copy : bool optional (default=True)
+            If True, return a new DiGraph holding the reversed edges.
+            If False, the reverse graph is created using a view of
+            the original graph.
+        """
+        if copy:
+            H = self.__class__()
+            H.graph.update(deepcopy(self.graph))
+            H.add_nodes_from((n, deepcopy(d)) for n, d in self.nodes.items())
+            H.add_edges_from((v, u, deepcopy(d)) for u, v, d in self.edges(data=True))
+            return H
+        return nx.reverse_view(self)
diff --git a/lib/python3.11/site-packages/networkx/classes/filters.py b/lib/python3.11/site-packages/networkx/classes/filters.py
new file mode 100644
index 0000000000000000000000000000000000000000..e989e22bb6d7e79b6eab34103edd263d82694fd4
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/filters.py
@@ -0,0 +1,95 @@
+"""Filter factories to hide or show sets of nodes and edges.
+
+These filters return the function used when creating `SubGraph`.
+"""
+
+__all__ = [
+    "no_filter",
+    "hide_nodes",
+    "hide_edges",
+    "hide_multiedges",
+    "hide_diedges",
+    "hide_multidiedges",
+    "show_nodes",
+    "show_edges",
+    "show_multiedges",
+    "show_diedges",
+    "show_multidiedges",
+]
+
+
+def no_filter(*items):
+    """Returns a filter function that always evaluates to True."""
+    return True
+
+
+def hide_nodes(nodes):
+    """Returns a filter function that hides specific nodes."""
+    nodes = set(nodes)
+    return lambda node: node not in nodes
+
+
+def hide_diedges(edges):
+    """Returns a filter function that hides specific directed edges."""
+    edges = {(u, v) for u, v in edges}
+    return lambda u, v: (u, v) not in edges
+
+
+def hide_edges(edges):
+    """Returns a filter function that hides specific undirected edges."""
+    alledges = set(edges) | {(v, u) for (u, v) in edges}
+    return lambda u, v: (u, v) not in alledges
+
+
+def hide_multidiedges(edges):
+    """Returns a filter function that hides specific multi-directed edges."""
+    edges = {(u, v, k) for u, v, k in edges}
+    return lambda u, v, k: (u, v, k) not in edges
+
+
+def hide_multiedges(edges):
+    """Returns a filter function that hides specific multi-undirected edges."""
+    alledges = set(edges) | {(v, u, k) for (u, v, k) in edges}
+    return lambda u, v, k: (u, v, k) not in alledges
+
+
+# write show_nodes as a class to make SubGraph pickleable
+class show_nodes:
+    """Filter class to show specific nodes.
+
+    Attach the set of nodes as an attribute to speed up this commonly used filter
+
+    Note that another allowed attribute for filters is to store the number of nodes
+    on the filter as attribute `length` (used in `__len__`). It is a user
+    responsibility to ensure this attribute is accurate if present.
+    """
+
+    def __init__(self, nodes):
+        self.nodes = set(nodes)
+
+    def __call__(self, node):
+        return node in self.nodes
+
+
+def show_diedges(edges):
+    """Returns a filter function that shows specific directed edges."""
+    edges = {(u, v) for u, v in edges}
+    return lambda u, v: (u, v) in edges
+
+
+def show_edges(edges):
+    """Returns a filter function that shows specific undirected edges."""
+    alledges = set(edges) | {(v, u) for (u, v) in edges}
+    return lambda u, v: (u, v) in alledges
+
+
+def show_multidiedges(edges):
+    """Returns a filter function that shows specific multi-directed edges."""
+    edges = {(u, v, k) for u, v, k in edges}
+    return lambda u, v, k: (u, v, k) in edges
+
+
+def show_multiedges(edges):
+    """Returns a filter function that shows specific multi-undirected edges."""
+    alledges = set(edges) | {(v, u, k) for (u, v, k) in edges}
+    return lambda u, v, k: (u, v, k) in alledges
diff --git a/lib/python3.11/site-packages/networkx/classes/function.py b/lib/python3.11/site-packages/networkx/classes/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..31f088ede87f01b2815514f1914f67e222ff66b6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/function.py
@@ -0,0 +1,1549 @@
+"""Functional interface to graph methods and assorted utilities."""
+
+from collections import Counter
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import not_implemented_for, pairwise
+
+__all__ = [
+    "nodes",
+    "edges",
+    "degree",
+    "degree_histogram",
+    "neighbors",
+    "number_of_nodes",
+    "number_of_edges",
+    "density",
+    "is_directed",
+    "freeze",
+    "is_frozen",
+    "subgraph",
+    "induced_subgraph",
+    "edge_subgraph",
+    "restricted_view",
+    "to_directed",
+    "to_undirected",
+    "add_star",
+    "add_path",
+    "add_cycle",
+    "create_empty_copy",
+    "set_node_attributes",
+    "get_node_attributes",
+    "remove_node_attributes",
+    "set_edge_attributes",
+    "get_edge_attributes",
+    "remove_edge_attributes",
+    "all_neighbors",
+    "non_neighbors",
+    "non_edges",
+    "common_neighbors",
+    "is_weighted",
+    "is_negatively_weighted",
+    "is_empty",
+    "selfloop_edges",
+    "nodes_with_selfloops",
+    "number_of_selfloops",
+    "path_weight",
+    "is_path",
+    "describe",
+]
+
+
+def nodes(G):
+    """Returns a NodeView over the graph nodes.
+
+    This function wraps the :func:`G.nodes <networkx.Graph.nodes>` property.
+    """
+    return G.nodes()
+
+
+def edges(G, nbunch=None):
+    """Returns an edge view of edges incident to nodes in nbunch.
+
+    Return all edges if nbunch is unspecified or nbunch=None.
+
+    For digraphs, edges=out_edges
+
+    This function wraps the :func:`G.edges <networkx.Graph.edges>` property.
+    """
+    return G.edges(nbunch)
+
+
+def degree(G, nbunch=None, weight=None):
+    """Returns a degree view of single node or of nbunch of nodes.
+    If nbunch is omitted, then return degrees of *all* nodes.
+
+    This function wraps the :func:`G.degree <networkx.Graph.degree>` property.
+    """
+    return G.degree(nbunch, weight)
+
+
+def neighbors(G, n):
+    """Returns an iterator over all neighbors of node n.
+
+    This function wraps the :func:`G.neighbors <networkx.Graph.neighbors>` function.
+    """
+    return G.neighbors(n)
+
+
+def number_of_nodes(G):
+    """Returns the number of nodes in the graph.
+
+    This function wraps the :func:`G.number_of_nodes <networkx.Graph.number_of_nodes>` function.
+    """
+    return G.number_of_nodes()
+
+
+def number_of_edges(G):
+    """Returns the number of edges in the graph.
+
+    This function wraps the :func:`G.number_of_edges <networkx.Graph.number_of_edges>` function.
+    """
+    return G.number_of_edges()
+
+
+def density(G):
+    r"""Returns the density of a graph.
+
+    The density for undirected graphs is
+
+    .. math::
+
+       d = \frac{2m}{n(n-1)},
+
+    and for directed graphs is
+
+    .. math::
+
+       d = \frac{m}{n(n-1)},
+
+    where `n` is the number of nodes and `m`  is the number of edges in `G`.
+
+    Notes
+    -----
+    The density is 0 for a graph without edges and 1 for a complete graph.
+    The density of multigraphs can be higher than 1.
+
+    Self loops are counted in the total number of edges so graphs with self
+    loops can have density higher than 1.
+    """
+    n = number_of_nodes(G)
+    m = number_of_edges(G)
+    if m == 0 or n <= 1:
+        return 0
+    d = m / (n * (n - 1))
+    if not G.is_directed():
+        d *= 2
+    return d
+
+
+def degree_histogram(G):
+    """Returns a list of the frequency of each degree value.
+
+    Parameters
+    ----------
+    G : Networkx graph
+       A graph
+
+    Returns
+    -------
+    hist : list
+       A list of frequencies of degrees.
+       The degree values are the index in the list.
+
+    Notes
+    -----
+    Note: the bins are width one, hence len(list) can be large
+    (Order(number_of_edges))
+    """
+    counts = Counter(d for n, d in G.degree())
+    return [counts.get(i, 0) for i in range(max(counts) + 1 if counts else 0)]
+
+
+def is_directed(G):
+    """Return True if graph is directed."""
+    return G.is_directed()
+
+
+def frozen(*args, **kwargs):
+    """Dummy method for raising errors when trying to modify frozen graphs"""
+    raise nx.NetworkXError("Frozen graph can't be modified")
+
+
+def freeze(G):
+    """Modify graph to prevent further change by adding or removing
+    nodes or edges.
+
+    Node and edge data can still be modified.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G = nx.freeze(G)
+    >>> try:
+    ...     G.add_edge(4, 5)
+    ... except nx.NetworkXError as err:
+    ...     print(str(err))
+    Frozen graph can't be modified
+
+    Notes
+    -----
+    To "unfreeze" a graph you must make a copy by creating a new graph object:
+
+    >>> graph = nx.path_graph(4)
+    >>> frozen_graph = nx.freeze(graph)
+    >>> unfrozen_graph = nx.Graph(frozen_graph)
+    >>> nx.is_frozen(unfrozen_graph)
+    False
+
+    See Also
+    --------
+    is_frozen
+    """
+    G.add_node = frozen
+    G.add_nodes_from = frozen
+    G.remove_node = frozen
+    G.remove_nodes_from = frozen
+    G.add_edge = frozen
+    G.add_edges_from = frozen
+    G.add_weighted_edges_from = frozen
+    G.remove_edge = frozen
+    G.remove_edges_from = frozen
+    G.clear = frozen
+    G.clear_edges = frozen
+    G.frozen = True
+    return G
+
+
+def is_frozen(G):
+    """Returns True if graph is frozen.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    See Also
+    --------
+    freeze
+    """
+    try:
+        return G.frozen
+    except AttributeError:
+        return False
+
+
+def add_star(G_to_add_to, nodes_for_star, **attr):
+    """Add a star to Graph G_to_add_to.
+
+    The first node in `nodes_for_star` is the middle of the star.
+    It is connected to all other nodes.
+
+    Parameters
+    ----------
+    G_to_add_to : graph
+        A NetworkX graph
+    nodes_for_star : iterable container
+        A container of nodes.
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to every edge in star.
+
+    See Also
+    --------
+    add_path, add_cycle
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_star(G, [0, 1, 2, 3])
+    >>> nx.add_star(G, [10, 11, 12], weight=2)
+    """
+    nlist = iter(nodes_for_star)
+    try:
+        v = next(nlist)
+    except StopIteration:
+        return
+    G_to_add_to.add_node(v)
+    edges = ((v, n) for n in nlist)
+    G_to_add_to.add_edges_from(edges, **attr)
+
+
+def add_path(G_to_add_to, nodes_for_path, **attr):
+    """Add a path to the Graph G_to_add_to.
+
+    Parameters
+    ----------
+    G_to_add_to : graph
+        A NetworkX graph
+    nodes_for_path : iterable container
+        A container of nodes.  A path will be constructed from
+        the nodes (in order) and added to the graph.
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to every edge in path.
+
+    See Also
+    --------
+    add_star, add_cycle
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3])
+    >>> nx.add_path(G, [10, 11, 12], weight=7)
+    """
+    nlist = iter(nodes_for_path)
+    try:
+        first_node = next(nlist)
+    except StopIteration:
+        return
+    G_to_add_to.add_node(first_node)
+    G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist)), **attr)
+
+
+def add_cycle(G_to_add_to, nodes_for_cycle, **attr):
+    """Add a cycle to the Graph G_to_add_to.
+
+    Parameters
+    ----------
+    G_to_add_to : graph
+        A NetworkX graph
+    nodes_for_cycle: iterable container
+        A container of nodes.  A cycle will be constructed from
+        the nodes (in order) and added to the graph.
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to every edge in cycle.
+
+    See Also
+    --------
+    add_path, add_star
+
+    Examples
+    --------
+    >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> nx.add_cycle(G, [0, 1, 2, 3])
+    >>> nx.add_cycle(G, [10, 11, 12], weight=7)
+    """
+    nlist = iter(nodes_for_cycle)
+    try:
+        first_node = next(nlist)
+    except StopIteration:
+        return
+    G_to_add_to.add_node(first_node)
+    G_to_add_to.add_edges_from(
+        pairwise(chain((first_node,), nlist), cyclic=True), **attr
+    )
+
+
+def subgraph(G, nbunch):
+    """Returns the subgraph induced on nodes in nbunch.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nbunch : list, iterable
+       A container of nodes that will be iterated through once (thus
+       it should be an iterator or be iterable).  Each element of the
+       container should be a valid node type: any hashable type except
+       None.  If nbunch is None, return all edges data in the graph.
+       Nodes in nbunch that are not in the graph will be (quietly)
+       ignored.
+
+    Notes
+    -----
+    subgraph(G) calls G.subgraph()
+    """
+    return G.subgraph(nbunch)
+
+
+def induced_subgraph(G, nbunch):
+    """Returns a SubGraph view of `G` showing only nodes in nbunch.
+
+    The induced subgraph of a graph on a set of nodes N is the
+    graph with nodes N and edges from G which have both ends in N.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    nbunch : node, container of nodes or None (for all nodes)
+
+    Returns
+    -------
+    subgraph : SubGraph View
+        A read-only view of the subgraph in `G` induced by the nodes.
+        Changes to the graph `G` will be reflected in the view.
+
+    Notes
+    -----
+    To create a mutable subgraph with its own copies of nodes
+    edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
+
+    For an inplace reduction of a graph to a subgraph you can remove nodes:
+    `G.remove_nodes_from(n in G if n not in set(nbunch))`
+
+    If you are going to compute subgraphs of your subgraphs you could
+    end up with a chain of views that can be very slow once the chain
+    has about 15 views in it. If they are all induced subgraphs, you
+    can short-cut the chain by making them all subgraphs of the original
+    graph. The graph class method `G.subgraph` does this when `G` is
+    a subgraph. In contrast, this function allows you to choose to build
+    chains or not, as you wish. The returned subgraph is a view on `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> H = nx.induced_subgraph(G, [0, 1, 3])
+    >>> list(H.edges)
+    [(0, 1)]
+    >>> list(H.nodes)
+    [0, 1, 3]
+    """
+    induced_nodes = nx.filters.show_nodes(G.nbunch_iter(nbunch))
+    return nx.subgraph_view(G, filter_node=induced_nodes)
+
+
+def edge_subgraph(G, edges):
+    """Returns a view of the subgraph induced by the specified edges.
+
+    The induced subgraph contains each edge in `edges` and each
+    node incident to any of those edges.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    edges : iterable
+        An iterable of edges. Edges not present in `G` are ignored.
+
+    Returns
+    -------
+    subgraph : SubGraph View
+        A read-only edge-induced subgraph of `G`.
+        Changes to `G` are reflected in the view.
+
+    Notes
+    -----
+    To create a mutable subgraph with its own copies of nodes
+    edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
+
+    If you create a subgraph of a subgraph recursively you can end up
+    with a chain of subgraphs that becomes very slow with about 15
+    nested subgraph views. Luckily the edge_subgraph filter nests
+    nicely so you can use the original graph as G in this function
+    to avoid chains. We do not rule out chains programmatically so
+    that odd cases like an `edge_subgraph` of a `restricted_view`
+    can be created.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> H = G.edge_subgraph([(0, 1), (3, 4)])
+    >>> list(H.nodes)
+    [0, 1, 3, 4]
+    >>> list(H.edges)
+    [(0, 1), (3, 4)]
+    """
+    nxf = nx.filters
+    edges = set(edges)
+    nodes = set()
+    for e in edges:
+        nodes.update(e[:2])
+    induced_nodes = nxf.show_nodes(nodes)
+    if G.is_multigraph():
+        if G.is_directed():
+            induced_edges = nxf.show_multidiedges(edges)
+        else:
+            induced_edges = nxf.show_multiedges(edges)
+    else:
+        if G.is_directed():
+            induced_edges = nxf.show_diedges(edges)
+        else:
+            induced_edges = nxf.show_edges(edges)
+    return nx.subgraph_view(G, filter_node=induced_nodes, filter_edge=induced_edges)
+
+
+def restricted_view(G, nodes, edges):
+    """Returns a view of `G` with hidden nodes and edges.
+
+    The resulting subgraph filters out node `nodes` and edges `edges`.
+    Filtered out nodes also filter out any of their edges.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    nodes : iterable
+        An iterable of nodes. Nodes not present in `G` are ignored.
+    edges : iterable
+        An iterable of edges. Edges not present in `G` are ignored.
+
+    Returns
+    -------
+    subgraph : SubGraph View
+        A read-only restricted view of `G` filtering out nodes and edges.
+        Changes to `G` are reflected in the view.
+
+    Notes
+    -----
+    To create a mutable subgraph with its own copies of nodes
+    edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
+
+    If you create a subgraph of a subgraph recursively you may end up
+    with a chain of subgraph views. Such chains can get quite slow
+    for lengths near 15. To avoid long chains, try to make your subgraph
+    based on the original graph.  We do not rule out chains programmatically
+    so that odd cases like an `edge_subgraph` of a `restricted_view`
+    can be created.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> H = nx.restricted_view(G, [0], [(1, 2), (3, 4)])
+    >>> list(H.nodes)
+    [1, 2, 3, 4]
+    >>> list(H.edges)
+    [(2, 3)]
+    """
+    nxf = nx.filters
+    hide_nodes = nxf.hide_nodes(nodes)
+    if G.is_multigraph():
+        if G.is_directed():
+            hide_edges = nxf.hide_multidiedges(edges)
+        else:
+            hide_edges = nxf.hide_multiedges(edges)
+    else:
+        if G.is_directed():
+            hide_edges = nxf.hide_diedges(edges)
+        else:
+            hide_edges = nxf.hide_edges(edges)
+    return nx.subgraph_view(G, filter_node=hide_nodes, filter_edge=hide_edges)
+
+
+def to_directed(graph):
+    """Returns a directed view of the graph `graph`.
+
+    Identical to graph.to_directed(as_view=True)
+    Note that graph.to_directed defaults to `as_view=False`
+    while this function always provides a view.
+    """
+    return graph.to_directed(as_view=True)
+
+
+def to_undirected(graph):
+    """Returns an undirected view of the graph `graph`.
+
+    Identical to graph.to_undirected(as_view=True)
+    Note that graph.to_undirected defaults to `as_view=False`
+    while this function always provides a view.
+    """
+    return graph.to_undirected(as_view=True)
+
+
+def create_empty_copy(G, with_data=True):
+    """Returns a copy of the graph G with all of the edges removed.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    with_data :  bool (default=True)
+       Propagate Graph and Nodes data to the new graph.
+
+    See Also
+    --------
+    empty_graph
+
+    """
+    H = G.__class__()
+    H.add_nodes_from(G.nodes(data=with_data))
+    if with_data:
+        H.graph.update(G.graph)
+    return H
+
+
+@nx._dispatchable(preserve_node_attrs=True, mutates_input=True)
+def set_node_attributes(G, values, name=None):
+    """Sets node attributes from a given value or dictionary of values.
+
+    .. Warning:: The call order of arguments `values` and `name`
+        switched between v1.x & v2.x.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    values : scalar value, dict-like
+        What the node attribute should be set to.  If `values` is
+        not a dictionary, then it is treated as a single attribute value
+        that is then applied to every node in `G`.  This means that if
+        you provide a mutable object, like a list, updates to that object
+        will be reflected in the node attribute for every node.
+        The attribute name will be `name`.
+
+        If `values` is a dict or a dict of dict, it should be keyed
+        by node to either an attribute value or a dict of attribute key/value
+        pairs used to update the node's attributes.
+
+    name : string (optional, default=None)
+        Name of the node attribute to set if values is a scalar.
+
+    Examples
+    --------
+    After computing some property of the nodes of a graph, you may want
+    to assign a node attribute to store the value of that property for
+    each node::
+
+        >>> G = nx.path_graph(3)
+        >>> bb = nx.betweenness_centrality(G)
+        >>> isinstance(bb, dict)
+        True
+        >>> nx.set_node_attributes(G, bb, "betweenness")
+        >>> G.nodes[1]["betweenness"]
+        1.0
+
+    If you provide a list as the second argument, updates to the list
+    will be reflected in the node attribute for each node::
+
+        >>> G = nx.path_graph(3)
+        >>> labels = []
+        >>> nx.set_node_attributes(G, labels, "labels")
+        >>> labels.append("foo")
+        >>> G.nodes[0]["labels"]
+        ['foo']
+        >>> G.nodes[1]["labels"]
+        ['foo']
+        >>> G.nodes[2]["labels"]
+        ['foo']
+
+    If you provide a dictionary of dictionaries as the second argument,
+    the outer dictionary is assumed to be keyed by node to an inner
+    dictionary of node attributes for that node::
+
+        >>> G = nx.path_graph(3)
+        >>> attrs = {0: {"attr1": 20, "attr2": "nothing"}, 1: {"attr2": 3}}
+        >>> nx.set_node_attributes(G, attrs)
+        >>> G.nodes[0]["attr1"]
+        20
+        >>> G.nodes[0]["attr2"]
+        'nothing'
+        >>> G.nodes[1]["attr2"]
+        3
+        >>> G.nodes[2]
+        {}
+
+    Note that if the dictionary contains nodes that are not in `G`, the
+    values are silently ignored::
+
+        >>> G = nx.Graph()
+        >>> G.add_node(0)
+        >>> nx.set_node_attributes(G, {0: "red", 1: "blue"}, name="color")
+        >>> G.nodes[0]["color"]
+        'red'
+        >>> 1 in G.nodes
+        False
+
+    """
+    # Set node attributes based on type of `values`
+    if name is not None:  # `values` must not be a dict of dict
+        try:  # `values` is a dict
+            for n, v in values.items():
+                try:
+                    G.nodes[n][name] = values[n]
+                except KeyError:
+                    pass
+        except AttributeError:  # `values` is a constant
+            for n in G:
+                G.nodes[n][name] = values
+    else:  # `values` must be dict of dict
+        for n, d in values.items():
+            try:
+                G.nodes[n].update(d)
+            except KeyError:
+                pass
+    nx._clear_cache(G)
+
+
+@nx._dispatchable(node_attrs={"name": "default"})
+def get_node_attributes(G, name, default=None):
+    """Get node attributes from graph
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    name : string
+       Attribute name
+
+    default: object (default=None)
+       Default value of the node attribute if there is no value set for that
+       node in graph. If `None` then nodes without this attribute are not
+       included in the returned dict.
+
+    Returns
+    -------
+    Dictionary of attributes keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([1, 2, 3], color="red")
+    >>> color = nx.get_node_attributes(G, "color")
+    >>> color[1]
+    'red'
+    >>> G.add_node(4)
+    >>> color = nx.get_node_attributes(G, "color", default="yellow")
+    >>> color[4]
+    'yellow'
+    """
+    if default is not None:
+        return {n: d.get(name, default) for n, d in G.nodes.items()}
+    return {n: d[name] for n, d in G.nodes.items() if name in d}
+
+
+@nx._dispatchable(preserve_node_attrs=True, mutates_input=True)
+def remove_node_attributes(G, *attr_names, nbunch=None):
+    """Remove node attributes from all nodes in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    *attr_names : List of Strings
+        The attribute names to remove from the graph.
+
+    nbunch : List of Nodes
+        Remove the node attributes only from the nodes in this list.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([1, 2, 3], color="blue")
+    >>> nx.get_node_attributes(G, "color")
+    {1: 'blue', 2: 'blue', 3: 'blue'}
+    >>> nx.remove_node_attributes(G, "color")
+    >>> nx.get_node_attributes(G, "color")
+    {}
+    """
+
+    if nbunch is None:
+        nbunch = G.nodes()
+
+    for attr in attr_names:
+        for n, d in G.nodes(data=True):
+            if n in nbunch:
+                try:
+                    del d[attr]
+                except KeyError:
+                    pass
+
+
+@nx._dispatchable(preserve_edge_attrs=True, mutates_input=True)
+def set_edge_attributes(G, values, name=None):
+    """Sets edge attributes from a given value or dictionary of values.
+
+    .. Warning:: The call order of arguments `values` and `name`
+        switched between v1.x & v2.x.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    values : scalar value, dict-like
+        What the edge attribute should be set to.  If `values` is
+        not a dictionary, then it is treated as a single attribute value
+        that is then applied to every edge in `G`.  This means that if
+        you provide a mutable object, like a list, updates to that object
+        will be reflected in the edge attribute for each edge.  The attribute
+        name will be `name`.
+
+        If `values` is a dict or a dict of dict, it should be keyed
+        by edge tuple to either an attribute value or a dict of attribute
+        key/value pairs used to update the edge's attributes.
+        For multigraphs, the edge tuples must be of the form ``(u, v, key)``,
+        where `u` and `v` are nodes and `key` is the edge key.
+        For non-multigraphs, the keys must be tuples of the form ``(u, v)``.
+
+    name : string (optional, default=None)
+        Name of the edge attribute to set if values is a scalar.
+
+    Examples
+    --------
+    After computing some property of the edges of a graph, you may want
+    to assign a edge attribute to store the value of that property for
+    each edge::
+
+        >>> G = nx.path_graph(3)
+        >>> bb = nx.edge_betweenness_centrality(G, normalized=False)
+        >>> nx.set_edge_attributes(G, bb, "betweenness")
+        >>> G.edges[1, 2]["betweenness"]
+        2.0
+
+    If you provide a list as the second argument, updates to the list
+    will be reflected in the edge attribute for each edge::
+
+        >>> labels = []
+        >>> nx.set_edge_attributes(G, labels, "labels")
+        >>> labels.append("foo")
+        >>> G.edges[0, 1]["labels"]
+        ['foo']
+        >>> G.edges[1, 2]["labels"]
+        ['foo']
+
+    If you provide a dictionary of dictionaries as the second argument,
+    the entire dictionary will be used to update edge attributes::
+
+        >>> G = nx.path_graph(3)
+        >>> attrs = {(0, 1): {"attr1": 20, "attr2": "nothing"}, (1, 2): {"attr2": 3}}
+        >>> nx.set_edge_attributes(G, attrs)
+        >>> G[0][1]["attr1"]
+        20
+        >>> G[0][1]["attr2"]
+        'nothing'
+        >>> G[1][2]["attr2"]
+        3
+
+    The attributes of one Graph can be used to set those of another.
+
+        >>> H = nx.path_graph(3)
+        >>> nx.set_edge_attributes(H, G.edges)
+
+    Note that if the dict contains edges that are not in `G`, they are
+    silently ignored::
+
+        >>> G = nx.Graph([(0, 1)])
+        >>> nx.set_edge_attributes(G, {(1, 2): {"weight": 2.0}})
+        >>> (1, 2) in G.edges()
+        False
+
+    For multigraphs, the `values` dict is expected to be keyed by 3-tuples
+    including the edge key::
+
+        >>> MG = nx.MultiGraph()
+        >>> edges = [(0, 1), (0, 1)]
+        >>> MG.add_edges_from(edges)  # Returns list of edge keys
+        [0, 1]
+        >>> attributes = {(0, 1, 0): {"cost": 21}, (0, 1, 1): {"cost": 7}}
+        >>> nx.set_edge_attributes(MG, attributes)
+        >>> MG[0][1][0]["cost"]
+        21
+        >>> MG[0][1][1]["cost"]
+        7
+
+    If MultiGraph attributes are desired for a Graph, you must convert the 3-tuple
+    multiedge to a 2-tuple edge and the last multiedge's attribute value will
+    overwrite the previous values. Continuing from the previous case we get::
+
+        >>> H = nx.path_graph([0, 1, 2])
+        >>> nx.set_edge_attributes(H, {(u, v): ed for u, v, ed in MG.edges.data()})
+        >>> nx.get_edge_attributes(H, "cost")
+        {(0, 1): 7}
+
+    """
+    if name is not None:
+        # `values` does not contain attribute names
+        try:
+            # if `values` is a dict using `.items()` => {edge: value}
+            if G.is_multigraph():
+                for (u, v, key), value in values.items():
+                    try:
+                        G._adj[u][v][key][name] = value
+                    except KeyError:
+                        pass
+            else:
+                for (u, v), value in values.items():
+                    try:
+                        G._adj[u][v][name] = value
+                    except KeyError:
+                        pass
+        except AttributeError:
+            # treat `values` as a constant
+            for u, v, data in G.edges(data=True):
+                data[name] = values
+    else:
+        # `values` consists of doct-of-dict {edge: {attr: value}} shape
+        if G.is_multigraph():
+            for (u, v, key), d in values.items():
+                try:
+                    G._adj[u][v][key].update(d)
+                except KeyError:
+                    pass
+        else:
+            for (u, v), d in values.items():
+                try:
+                    G._adj[u][v].update(d)
+                except KeyError:
+                    pass
+    nx._clear_cache(G)
+
+
+@nx._dispatchable(edge_attrs={"name": "default"})
+def get_edge_attributes(G, name, default=None):
+    """Get edge attributes from graph
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    name : string
+       Attribute name
+
+    default: object (default=None)
+       Default value of the edge attribute if there is no value set for that
+       edge in graph. If `None` then edges without this attribute are not
+       included in the returned dict.
+
+    Returns
+    -------
+    Dictionary of attributes keyed by edge. For (di)graphs, the keys are
+    2-tuples of the form: (u, v). For multi(di)graphs, the keys are 3-tuples of
+    the form: (u, v, key).
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [1, 2, 3], color="red")
+    >>> color = nx.get_edge_attributes(G, "color")
+    >>> color[(1, 2)]
+    'red'
+    >>> G.add_edge(3, 4)
+    >>> color = nx.get_edge_attributes(G, "color", default="yellow")
+    >>> color[(3, 4)]
+    'yellow'
+    """
+    if G.is_multigraph():
+        edges = G.edges(keys=True, data=True)
+    else:
+        edges = G.edges(data=True)
+    if default is not None:
+        return {x[:-1]: x[-1].get(name, default) for x in edges}
+    return {x[:-1]: x[-1][name] for x in edges if name in x[-1]}
+
+
+@nx._dispatchable(preserve_edge_attrs=True, mutates_input=True)
+def remove_edge_attributes(G, *attr_names, ebunch=None):
+    """Remove edge attributes from all edges in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    *attr_names : List of Strings
+        The attribute names to remove from the graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> nx.set_edge_attributes(G, {(u, v): u + v for u, v in G.edges()}, name="weight")
+    >>> nx.get_edge_attributes(G, "weight")
+    {(0, 1): 1, (1, 2): 3}
+    >>> remove_edge_attributes(G, "weight")
+    >>> nx.get_edge_attributes(G, "weight")
+    {}
+    """
+    if ebunch is None:
+        ebunch = G.edges(keys=True) if G.is_multigraph() else G.edges()
+
+    for attr in attr_names:
+        edges = (
+            G.edges(keys=True, data=True) if G.is_multigraph() else G.edges(data=True)
+        )
+        for *e, d in edges:
+            if tuple(e) in ebunch:
+                try:
+                    del d[attr]
+                except KeyError:
+                    pass
+
+
+def all_neighbors(graph, node):
+    """Returns all of the neighbors of a node in the graph.
+
+    If the graph is directed returns predecessors as well as successors.
+
+    Parameters
+    ----------
+    graph : NetworkX graph
+        Graph to find neighbors.
+    node : node
+        The node whose neighbors will be returned.
+
+    Returns
+    -------
+    neighbors : iterator
+        Iterator of neighbors
+
+    Raises
+    ------
+    NetworkXError
+        If `node` is not in the graph.
+
+    Examples
+    --------
+    For undirected graphs, this function is equivalent to ``G.neighbors(node)``.
+
+    >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> list(nx.all_neighbors(G, 1))
+    [0, 2]
+
+    For directed graphs, this function returns both predecessors and successors,
+    which may include duplicates if a node is both a predecessor and successor
+    (e.g., in bidirectional edges or self-loops).
+
+    >>> DG = nx.DiGraph([(0, 1), (1, 2), (2, 1)])
+    >>> list(nx.all_neighbors(DG, 1))
+    [0, 2, 2]
+
+    Notes
+    -----
+    This function iterates over all neighbors (both predecessors and successors).
+
+    See Also
+    --------
+    Graph.neighbors : Returns successors for both Graph and DiGraph
+    DiGraph.predecessors : Returns predecessors for directed graphs only
+    DiGraph.successors : Returns successors for directed graphs only
+    """
+    if graph.is_directed():
+        values = chain(graph.predecessors(node), graph.successors(node))
+    else:
+        values = graph.neighbors(node)
+    return values
+
+
+def non_neighbors(graph, node):
+    """Returns the non-neighbors of the node in the graph.
+
+    Parameters
+    ----------
+    graph : NetworkX graph
+        Graph to find neighbors.
+
+    node : node
+        The node whose neighbors will be returned.
+
+    Returns
+    -------
+    non_neighbors : set
+        Set of nodes in the graph that are not neighbors of the node.
+    """
+    return graph._adj.keys() - graph._adj[node].keys() - {node}
+
+
+def non_edges(graph):
+    """Returns the nonexistent edges in the graph.
+
+    Parameters
+    ----------
+    graph : NetworkX graph.
+        Graph to find nonexistent edges.
+
+    Returns
+    -------
+    non_edges : iterator
+        Iterator of edges that are not in the graph.
+    """
+    if graph.is_directed():
+        for u in graph:
+            for v in non_neighbors(graph, u):
+                yield (u, v)
+    else:
+        nodes = set(graph)
+        while nodes:
+            u = nodes.pop()
+            for v in nodes - set(graph[u]):
+                yield (u, v)
+
+
+@not_implemented_for("directed")
+def common_neighbors(G, u, v):
+    """Returns the common neighbors of two nodes in a graph.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    u, v : nodes
+        Nodes in the graph.
+
+    Returns
+    -------
+    cnbors : set
+        Set of common neighbors of u and v in the graph.
+
+    Raises
+    ------
+    NetworkXError
+        If u or v is not a node in the graph.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> sorted(nx.common_neighbors(G, 0, 1))
+    [2, 3, 4]
+    """
+    if u not in G:
+        raise nx.NetworkXError("u is not in the graph.")
+    if v not in G:
+        raise nx.NetworkXError("v is not in the graph.")
+
+    return G._adj[u].keys() & G._adj[v].keys() - {u, v}
+
+
+@nx._dispatchable(preserve_edge_attrs=True)
+def is_weighted(G, edge=None, weight="weight"):
+    """Returns True if `G` has weighted edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    edge : tuple, optional
+        A 2-tuple specifying the only edge in `G` that will be tested. If
+        None, then every edge in `G` is tested.
+
+    weight: string, optional
+        The attribute name used to query for edge weights.
+
+    Returns
+    -------
+    bool
+        A boolean signifying if `G`, or the specified edge, is weighted.
+
+    Raises
+    ------
+    NetworkXError
+        If the specified edge does not exist.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.is_weighted(G)
+    False
+    >>> nx.is_weighted(G, (2, 3))
+    False
+
+    >>> G = nx.DiGraph()
+    >>> G.add_edge(1, 2, weight=1)
+    >>> nx.is_weighted(G)
+    True
+
+    """
+    if edge is not None:
+        data = G.get_edge_data(*edge)
+        if data is None:
+            msg = f"Edge {edge!r} does not exist."
+            raise nx.NetworkXError(msg)
+        return weight in data
+
+    if is_empty(G):
+        # Special handling required since: all([]) == True
+        return False
+
+    return all(weight in data for u, v, data in G.edges(data=True))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def is_negatively_weighted(G, edge=None, weight="weight"):
+    """Returns True if `G` has negatively weighted edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    edge : tuple, optional
+        A 2-tuple specifying the only edge in `G` that will be tested. If
+        None, then every edge in `G` is tested.
+
+    weight: string, optional
+        The attribute name used to query for edge weights.
+
+    Returns
+    -------
+    bool
+        A boolean signifying if `G`, or the specified edge, is negatively
+        weighted.
+
+    Raises
+    ------
+    NetworkXError
+        If the specified edge does not exist.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edges_from([(1, 3), (2, 4), (2, 6)])
+    >>> G.add_edge(1, 2, weight=4)
+    >>> nx.is_negatively_weighted(G, (1, 2))
+    False
+    >>> G[2][4]["weight"] = -2
+    >>> nx.is_negatively_weighted(G)
+    True
+    >>> G = nx.DiGraph()
+    >>> edges = [("0", "3", 3), ("0", "1", -5), ("1", "0", -2)]
+    >>> G.add_weighted_edges_from(edges)
+    >>> nx.is_negatively_weighted(G)
+    True
+
+    """
+    if edge is not None:
+        data = G.get_edge_data(*edge)
+        if data is None:
+            msg = f"Edge {edge!r} does not exist."
+            raise nx.NetworkXError(msg)
+        return weight in data and data[weight] < 0
+
+    return any(weight in data and data[weight] < 0 for u, v, data in G.edges(data=True))
+
+
+@nx._dispatchable
+def is_empty(G):
+    """Returns True if `G` has no edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    Returns
+    -------
+    bool
+        True if `G` has no edges, and False otherwise.
+
+    Notes
+    -----
+    An empty graph can have nodes but not edges. The empty graph with zero
+    nodes is known as the null graph. This is an $O(n)$ operation where n
+    is the number of nodes in the graph.
+
+    """
+    return not any(G._adj.values())
+
+
+def nodes_with_selfloops(G):
+    """Returns an iterator over nodes with self loops.
+
+    A node with a self loop has an edge with both ends adjacent
+    to that node.
+
+    Returns
+    -------
+    nodelist : iterator
+        A iterator over nodes with self loops.
+
+    See Also
+    --------
+    selfloop_edges, number_of_selfloops
+
+    Examples
+    --------
+    >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> G.add_edge(1, 1)
+    >>> G.add_edge(1, 2)
+    >>> list(nx.nodes_with_selfloops(G))
+    [1]
+
+    """
+    return (n for n, nbrs in G._adj.items() if n in nbrs)
+
+
+def selfloop_edges(G, data=False, keys=False, default=None):
+    """Returns an iterator over selfloop edges.
+
+    A selfloop edge has the same node at both ends.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+    data : string or bool, optional (default=False)
+        Return selfloop edges as two tuples (u, v) (data=False)
+        or three-tuples (u, v, datadict) (data=True)
+        or three-tuples (u, v, datavalue) (data='attrname')
+    keys : bool, optional (default=False)
+        If True, return edge keys with each edge.
+    default : value, optional (default=None)
+        Value used for edges that don't have the requested attribute.
+        Only relevant if data is not True or False.
+
+    Returns
+    -------
+    edgeiter : iterator over edge tuples
+        An iterator over all selfloop edges.
+
+    See Also
+    --------
+    nodes_with_selfloops, number_of_selfloops
+
+    Examples
+    --------
+    >>> G = nx.MultiGraph()  # or Graph, DiGraph, MultiDiGraph, etc
+    >>> ekey = G.add_edge(1, 1)
+    >>> ekey = G.add_edge(1, 2)
+    >>> list(nx.selfloop_edges(G))
+    [(1, 1)]
+    >>> list(nx.selfloop_edges(G, data=True))
+    [(1, 1, {})]
+    >>> list(nx.selfloop_edges(G, keys=True))
+    [(1, 1, 0)]
+    >>> list(nx.selfloop_edges(G, keys=True, data=True))
+    [(1, 1, 0, {})]
+    """
+    if data is True:
+        if G.is_multigraph():
+            if keys is True:
+                return (
+                    (n, n, k, d)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for k, d in nbrs[n].items()
+                )
+            else:
+                return (
+                    (n, n, d)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for d in nbrs[n].values()
+                )
+        else:
+            return ((n, n, nbrs[n]) for n, nbrs in G._adj.items() if n in nbrs)
+    elif data is not False:
+        if G.is_multigraph():
+            if keys is True:
+                return (
+                    (n, n, k, d.get(data, default))
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for k, d in nbrs[n].items()
+                )
+            else:
+                return (
+                    (n, n, d.get(data, default))
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for d in nbrs[n].values()
+                )
+        else:
+            return (
+                (n, n, nbrs[n].get(data, default))
+                for n, nbrs in G._adj.items()
+                if n in nbrs
+            )
+    else:
+        if G.is_multigraph():
+            if keys is True:
+                return (
+                    (n, n, k)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for k in nbrs[n]
+                )
+            else:
+                return (
+                    (n, n)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for i in range(len(nbrs[n]))  # for easy edge removal (#4068)
+                )
+        else:
+            return ((n, n) for n, nbrs in G._adj.items() if n in nbrs)
+
+
+@nx._dispatchable
+def number_of_selfloops(G):
+    """Returns the number of selfloop edges.
+
+    A selfloop edge has the same node at both ends.
+
+    Returns
+    -------
+    nloops : int
+        The number of selfloops.
+
+    See Also
+    --------
+    nodes_with_selfloops, selfloop_edges
+
+    Examples
+    --------
+    >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> G.add_edge(1, 1)
+    >>> G.add_edge(1, 2)
+    >>> nx.number_of_selfloops(G)
+    1
+    """
+    return sum(1 for _ in nx.selfloop_edges(G))
+
+
+def is_path(G, path):
+    """Returns whether or not the specified path exists.
+
+    For it to return True, every node on the path must exist and
+    each consecutive pair must be connected via one or more edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    path : list
+        A list of nodes which defines the path to traverse
+
+    Returns
+    -------
+    bool
+        True if `path` is a valid path in `G`
+
+    """
+    try:
+        return all(nbr in G._adj[node] for node, nbr in nx.utils.pairwise(path))
+    except (KeyError, TypeError):
+        return False
+
+
+def path_weight(G, path, weight):
+    """Returns total cost associated with specified path and weight
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    path: list
+        A list of node labels which defines the path to traverse
+
+    weight: string
+        A string indicating which edge attribute to use for path cost
+
+    Returns
+    -------
+    cost: int or float
+        An integer or a float representing the total cost with respect to the
+        specified weight of the specified path
+
+    Raises
+    ------
+    NetworkXNoPath
+        If the specified edge does not exist.
+    """
+    multigraph = G.is_multigraph()
+    cost = 0
+
+    if not nx.is_path(G, path):
+        raise nx.NetworkXNoPath("path does not exist")
+    for node, nbr in nx.utils.pairwise(path):
+        if multigraph:
+            cost += min(v[weight] for v in G._adj[node][nbr].values())
+        else:
+            cost += G._adj[node][nbr][weight]
+    return cost
+
+
+def describe(G, describe_hook=None):
+    """Prints a description of the graph G.
+
+    By default, the description includes some basic properties of the graph.
+    You can also provide additional functions to compute and include
+    more properties in the description.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    describe_hook: callable, optional (default=None)
+        A function that takes a graph as input and returns a
+        dictionary of additional properties to include in the description.
+        The keys of the dictionary are the property names, and the values
+        are the corresponding property values.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.describe(G)
+    Number of nodes                : 5
+    Number of edges                : 4
+    Directed                       : False
+    Multigraph                     : False
+    Tree                           : True
+    Bipartite                      : True
+    Average degree (min, max)      : 1.60 (1, 2)
+    Number of connected components : 1
+
+    >>> def augment_description(G):
+    ...     return {"Average Shortest Path Length": nx.average_shortest_path_length(G)}
+    >>> nx.describe(G, describe_hook=augment_description)
+    Number of nodes                : 5
+    Number of edges                : 4
+    Directed                       : False
+    Multigraph                     : False
+    Tree                           : True
+    Bipartite                      : True
+    Average degree (min, max)      : 1.60 (1, 2)
+    Number of connected components : 1
+    Average Shortest Path Length   : 2.0
+
+    >>> G.name = "Path Graph of 5 nodes"
+    >>> nx.describe(G)
+    Name of Graph                  : Path Graph of 5 nodes
+    Number of nodes                : 5
+    Number of edges                : 4
+    Directed                       : False
+    Multigraph                     : False
+    Tree                           : True
+    Bipartite                      : True
+    Average degree (min, max)      : 1.60 (1, 2)
+    Number of connected components : 1
+
+    """
+    info_dict = _create_describe_info_dict(G)
+
+    if describe_hook is not None:
+        additional_info = describe_hook(G)
+        info_dict.update(additional_info)
+
+    max_key_len = max(len(k) for k in info_dict)
+    for key, val in info_dict.items():
+        print(f"{key:<{max_key_len}} : {val}")
+
+
+def _create_describe_info_dict(G):
+    info = {}
+    if G.name != "":
+        info["Name of Graph"] = G.name
+    info.update(
+        {
+            "Number of nodes": len(G),
+            "Number of edges": G.number_of_edges(),
+            "Directed": G.is_directed(),
+            "Multigraph": G.is_multigraph(),
+            "Tree": nx.is_tree(G),
+            "Bipartite": nx.is_bipartite(G),
+        }
+    )
+    if len(G) == 0:
+        return info
+
+    degree_values = dict(nx.degree(G)).values()
+    avg_degree = sum(degree_values) / len(G)
+    max_degree, min_degree = max(degree_values), min(degree_values)
+    info["Average degree (min, max)"] = f"{avg_degree:.2f} ({min_degree}, {max_degree})"
+
+    if G.is_directed():
+        info["Number of strongly connected components"] = (
+            nx.number_strongly_connected_components(G)
+        )
+        info["Number of weakly connected components"] = (
+            nx.number_weakly_connected_components(G)
+        )
+    else:
+        info["Number of connected components"] = nx.number_connected_components(G)
+    return info
diff --git a/lib/python3.11/site-packages/networkx/classes/graph.py b/lib/python3.11/site-packages/networkx/classes/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..0eb184f4c5bfdd2a49890a480c2be08b4c0190d4
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/graph.py
@@ -0,0 +1,2082 @@
+"""Base class for undirected graphs.
+
+The Graph class allows any hashable object as a node
+and can associate key/value attribute pairs with each undirected edge.
+
+Self-loops are allowed but multiple edges are not (see MultiGraph).
+
+For directed graphs see DiGraph and MultiDiGraph.
+"""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import convert
+from networkx.classes.coreviews import AdjacencyView
+from networkx.classes.reportviews import DegreeView, EdgeView, NodeView
+from networkx.exception import NetworkXError
+
+__all__ = ["Graph"]
+
+
+class _CachedPropertyResetterAdj:
+    """Data Descriptor class for _adj that resets ``adj`` cached_property when needed
+
+    This assumes that the ``cached_property`` ``G.adj`` should be reset whenever
+    ``G._adj`` is set to a new value.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached property "adj" whenever the underlying
+    instance attribute "_adj" is set to a new object. It only affects
+    the set process of the obj._adj attribute. All get/del operations
+    act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_adj"] = value
+        # reset cached properties
+        props = ["adj", "edges", "degree"]
+        for prop in props:
+            if prop in od:
+                del od[prop]
+
+
+class _CachedPropertyResetterNode:
+    """Data Descriptor class for _node that resets ``nodes`` cached_property when needed
+
+    This assumes that the ``cached_property`` ``G.node`` should be reset whenever
+    ``G._node`` is set to a new value.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached property "nodes" whenever the underlying
+    instance attribute "_node" is set to a new object. It only affects
+    the set process of the obj._adj attribute. All get/del operations
+    act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_node"] = value
+        # reset cached properties
+        if "nodes" in od:
+            del od["nodes"]
+
+
+class Graph:
+    """
+    Base class for undirected graphs.
+
+    A Graph stores nodes and edges with optional data, or attributes.
+
+    Graphs hold undirected edges.  Self loops are allowed but multiple
+    (parallel) edges are not.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes, except that `None` is not allowed as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
+        sparse matrix, or PyGraphviz graph.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    DiGraph
+    MultiGraph
+    MultiDiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.Graph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  There are no errors when adding
+    nodes or edges that already exist.
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.Graph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714  # node must exist already to use G.nodes
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> G.add_edge(1, 2, weight=4.7)
+    >>> G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2]["weight"] = 4.7
+    >>> G.edges[1, 2]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges` a
+    read-only dict-like structure. However, you can assign to attributes
+    in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
+    data attributes: `G.edges[1, 2]['weight'] = 4`
+    (For multigraphs: `MG.edges[u, v, key][name] = value`).
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, eattr in nbrsdict.items():
+    ...         if "weight" in eattr:
+    ...             # Do something useful with the edges
+    ...             pass
+
+    But the edges() method is often more convenient:
+
+    >>> for u, v, weight in G.edges.data("weight"):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using object-attributes and methods.
+    Reporting typically provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The Graph class uses a dict-of-dict-of-dict data structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information and holds
+    edge data keyed by neighbor.  The inner dict (edge_attr_dict) represents
+    the edge data and holds edge attribute values keyed by attribute names.
+
+    Each of these three dicts can be replaced in a subclass by a user defined
+    dict-like object. In general, the dict-like features should be
+    maintained but extra features can be added. To replace one of the
+    dicts create a new graph class by changing the class(!) variable
+    holding the factory for that dict-like structure.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds edge data keyed by neighbor.
+        It should require no arguments and return a dict-like object
+
+    edge_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherit without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    __networkx_backend__ = "networkx"
+
+    _adj = _CachedPropertyResetterAdj()
+    _node = _CachedPropertyResetterNode()
+
+    node_dict_factory = dict
+    node_attr_dict_factory = dict
+    adjlist_outer_dict_factory = dict
+    adjlist_inner_dict_factory = dict
+    edge_attr_dict_factory = dict
+    graph_attr_dict_factory = dict
+
+    def to_directed_class(self):
+        """Returns the class to use for empty directed copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return nx.DiGraph
+
+    def to_undirected_class(self):
+        """Returns the class to use for empty undirected copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return Graph
+
+    # This __new__ method just does what Python itself does automatically.
+    # We include it here as part of the dispatchable/backend interface.
+    # If your goal is to understand how the graph classes work, you can ignore
+    # this method, even when subclassing the base classes. If you are subclassing
+    # in order to provide a backend that allows class instantiation, this method
+    # can be overridden to return your own backend graph class.
+    @nx._dispatchable(name="graph__new__", graphs=None, returns_graph=True)
+    def __new__(cls, *args, **kwargs):
+        return object.__new__(cls)
+
+    def __init__(self, incoming_graph_data=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph (optional, default: None)
+            Data to initialize graph. If None (default) an empty
+            graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G = nx.Graph(name="my graph")
+        >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.Graph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.Graph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        self.graph = self.graph_attr_dict_factory()  # dictionary for graph attributes
+        self._node = self.node_dict_factory()  # empty node attribute dict
+        self._adj = self.adjlist_outer_dict_factory()  # empty adjacency dict
+        self.__networkx_cache__ = {}
+        # attempt to load graph with data
+        if incoming_graph_data is not None:
+            convert.to_networkx_graph(incoming_graph_data, create_using=self)
+        # load graph attributes (must be after convert)
+        attr.pop("backend", None)  # Ignore explicit `backend="networkx"`
+        self.graph.update(attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.adj[3][2]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return AdjacencyView(self._adj)
+
+    @property
+    def name(self):
+        """String identifier of the graph.
+
+        This graph attribute appears in the attribute dict G.graph
+        keyed by the string `"name"`. as well as an attribute (technically
+        a property) `G.name`. This is entirely user controlled.
+        """
+        return self.graph.get("name", "")
+
+    @name.setter
+    def name(self, s):
+        self.graph["name"] = s
+        nx._clear_cache(self)
+
+    def __str__(self):
+        """Returns a short summary of the graph.
+
+        Returns
+        -------
+        info : string
+            Graph information including the graph name (if any), graph type, and the
+            number of nodes and edges.
+
+        Examples
+        --------
+        >>> G = nx.Graph(name="foo")
+        >>> str(G)
+        "Graph named 'foo' with 0 nodes and 0 edges"
+
+        >>> G = nx.path_graph(3)
+        >>> str(G)
+        'Graph with 3 nodes and 2 edges'
+
+        """
+        return "".join(
+            [
+                type(self).__name__,
+                f" named {self.name!r}" if self.name else "",
+                f" with {self.number_of_nodes()} nodes and {self.number_of_edges()} edges",
+            ]
+        )
+
+    def __iter__(self):
+        """Iterate over the nodes. Use: 'for n in G'.
+
+        Returns
+        -------
+        niter : iterator
+            An iterator over all nodes in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> [n for n in G]
+        [0, 1, 2, 3]
+        >>> list(G)
+        [0, 1, 2, 3]
+        """
+        return iter(self._node)
+
+    def __contains__(self, n):
+        """Returns True if n is a node, False otherwise. Use: 'n in G'.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> 1 in G
+        True
+        """
+        try:
+            return n in self._node
+        except TypeError:
+            return False
+
+    def __len__(self):
+        """Returns the number of nodes in the graph. Use: 'len(G)'.
+
+        Returns
+        -------
+        nnodes : int
+            The number of nodes in the graph.
+
+        See Also
+        --------
+        number_of_nodes: identical method
+        order: identical method
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> len(G)
+        4
+
+        """
+        return len(self._node)
+
+    def __getitem__(self, n):
+        """Returns a dict of neighbors of node n.  Use: 'G[n]'.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph.
+
+        Returns
+        -------
+        adj_dict : dictionary
+           The adjacency dictionary for nodes connected to n.
+
+        Notes
+        -----
+        G[n] is the same as G.adj[n] and similar to G.neighbors(n)
+        (which is an iterator over G.adj[n])
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G[0]
+        AtlasView({1: {}})
+        """
+        return self.adj[n]
+
+    def add_node(self, node_for_adding, **attr):
+        """Add a single node `node_for_adding` and update node attributes.
+
+        Parameters
+        ----------
+        node_for_adding : node
+            A node can be any hashable Python object except None.
+        attr : keyword arguments, optional
+            Set or change node attributes using key=value.
+
+        See Also
+        --------
+        add_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_node(1)
+        >>> G.add_node("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_node(K3)
+        >>> G.number_of_nodes()
+        3
+
+        Use keywords set/change node attributes:
+
+        >>> G.add_node(1, size=10)
+        >>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
+
+        Notes
+        -----
+        A hashable object is one that can be used as a key in a Python
+        dictionary. This includes strings, numbers, tuples of strings
+        and numbers, etc.
+
+        On many platforms hashable items also include mutables such as
+        NetworkX Graphs, though one should be careful that the hash
+        doesn't change on mutables.
+        """
+        if node_for_adding not in self._node:
+            if node_for_adding is None:
+                raise ValueError("None cannot be a node")
+            self._adj[node_for_adding] = self.adjlist_inner_dict_factory()
+            attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
+            attr_dict.update(attr)
+        else:  # update attr even if node already exists
+            self._node[node_for_adding].update(attr)
+        nx._clear_cache(self)
+
+    def add_nodes_from(self, nodes_for_adding, **attr):
+        """Add multiple nodes.
+
+        Parameters
+        ----------
+        nodes_for_adding : iterable container
+            A container of nodes (list, dict, set, etc.).
+            OR
+            A container of (node, attribute dict) tuples.
+            Node attributes are updated using the attribute dict.
+        attr : keyword arguments, optional (default= no attributes)
+            Update attributes for all nodes in nodes.
+            Node attributes specified in nodes as a tuple take
+            precedence over attributes specified via keyword arguments.
+
+        See Also
+        --------
+        add_node
+
+        Notes
+        -----
+        When adding nodes from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.add_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_nodes_from("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_nodes_from(K3)
+        >>> sorted(G.nodes(), key=str)
+        [0, 1, 2, 'H', 'e', 'l', 'o']
+
+        Use keywords to update specific node attributes for every node.
+
+        >>> G.add_nodes_from([1, 2], size=10)
+        >>> G.add_nodes_from([3, 4], weight=0.4)
+
+        Use (node, attrdict) tuples to update attributes for specific nodes.
+
+        >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
+        >>> G.nodes[1]["size"]
+        11
+        >>> H = nx.Graph()
+        >>> H.add_nodes_from(G.nodes(data=True))
+        >>> H.nodes[1]["size"]
+        11
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.Graph([(0, 1), (1, 2), (3, 4)])
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_nodes_from(n + 1 for n in G.nodes)
+        >>> # correct way
+        >>> G.add_nodes_from(list(n + 1 for n in G.nodes))
+        """
+        for n in nodes_for_adding:
+            try:
+                newnode = n not in self._node
+                newdict = attr
+            except TypeError:
+                n, ndict = n
+                newnode = n not in self._node
+                newdict = attr.copy()
+                newdict.update(ndict)
+            if newnode:
+                if n is None:
+                    raise ValueError("None cannot be a node")
+                self._adj[n] = self.adjlist_inner_dict_factory()
+                self._node[n] = self.node_attr_dict_factory()
+            self._node[n].update(newdict)
+        nx._clear_cache(self)
+
+    def remove_node(self, n):
+        """Remove node n.
+
+        Removes the node n and all adjacent edges.
+        Attempting to remove a nonexistent node will raise an exception.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        remove_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> list(G.edges)
+        [(0, 1), (1, 2)]
+        >>> G.remove_node(1)
+        >>> list(G.edges)
+        []
+
+        """
+        adj = self._adj
+        try:
+            nbrs = list(adj[n])  # list handles self-loops (allows mutation)
+            del self._node[n]
+        except KeyError as err:  # NetworkXError if n not in self
+            raise NetworkXError(f"The node {n} is not in the graph.") from err
+        for u in nbrs:
+            del adj[u][n]  # remove all edges n-u in graph
+        del adj[n]  # now remove node
+        nx._clear_cache(self)
+
+    def remove_nodes_from(self, nodes):
+        """Remove multiple nodes.
+
+        Parameters
+        ----------
+        nodes : iterable container
+            A container of nodes (list, dict, set, etc.).  If a node
+            in the container is not in the graph it is silently
+            ignored.
+
+        See Also
+        --------
+        remove_node
+
+        Notes
+        -----
+        When removing nodes from an iterator over the graph you are changing,
+        a `RuntimeError` will be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.remove_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = list(G.nodes)
+        >>> e
+        [0, 1, 2]
+        >>> G.remove_nodes_from(e)
+        >>> list(G.nodes)
+        []
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.Graph([(0, 1), (1, 2), (3, 4)])
+        >>> # this command will fail, as the graph's dict is modified during iteration
+        >>> # G.remove_nodes_from(n for n in G.nodes if n < 2)
+        >>> # this command will work, since the dictionary underlying graph is not modified
+        >>> G.remove_nodes_from(list(n for n in G.nodes if n < 2))
+        """
+        adj = self._adj
+        for n in nodes:
+            try:
+                del self._node[n]
+                for u in list(adj[n]):  # list handles self-loops
+                    del adj[u][n]  # (allows mutation of dict in loop)
+                del adj[n]
+            except KeyError:
+                pass
+        nx._clear_cache(self)
+
+    @cached_property
+    def nodes(self):
+        """A NodeView of the Graph as G.nodes or G.nodes().
+
+        Can be used as `G.nodes` for data lookup and for set-like operations.
+        Can also be used as `G.nodes(data='color', default=None)` to return a
+        NodeDataView which reports specific node data but no set operations.
+        It presents a dict-like interface as well with `G.nodes.items()`
+        iterating over `(node, nodedata)` 2-tuples and `G.nodes[3]['foo']`
+        providing the value of the `foo` attribute for node `3`. In addition,
+        a view `G.nodes.data('foo')` provides a dict-like interface to the
+        `foo` attribute of each node. `G.nodes.data('foo', default=1)`
+        provides a default for nodes that do not have attribute `foo`.
+
+        Parameters
+        ----------
+        data : string or bool, optional (default=False)
+            The node attribute returned in 2-tuple (n, ddict[data]).
+            If True, return entire node attribute dict as (n, ddict).
+            If False, return just the nodes n.
+
+        default : value, optional (default=None)
+            Value used for nodes that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        NodeView
+            Allows set-like operations over the nodes as well as node
+            attribute dict lookup and calling to get a NodeDataView.
+            A NodeDataView iterates over `(n, data)` and has no set operations.
+            A NodeView iterates over `n` and includes set operations.
+
+            When called, if data is False, an iterator over nodes.
+            Otherwise an iterator of 2-tuples (node, attribute value)
+            where the attribute is specified in `data`.
+            If data is True then the attribute becomes the
+            entire data dictionary.
+
+        Notes
+        -----
+        If your node data is not needed, it is simpler and equivalent
+        to use the expression ``for n in G``, or ``list(G)``.
+
+        Examples
+        --------
+        There are two simple ways of getting a list of all nodes in the graph:
+
+        >>> G = nx.path_graph(3)
+        >>> list(G.nodes)
+        [0, 1, 2]
+        >>> list(G)
+        [0, 1, 2]
+
+        To get the node data along with the nodes:
+
+        >>> G.add_node(1, time="5pm")
+        >>> G.nodes[0]["foo"] = "bar"
+        >>> list(G.nodes(data=True))
+        [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
+        >>> list(G.nodes.data())
+        [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
+
+        >>> list(G.nodes(data="foo"))
+        [(0, 'bar'), (1, None), (2, None)]
+        >>> list(G.nodes.data("foo"))
+        [(0, 'bar'), (1, None), (2, None)]
+
+        >>> list(G.nodes(data="time"))
+        [(0, None), (1, '5pm'), (2, None)]
+        >>> list(G.nodes.data("time"))
+        [(0, None), (1, '5pm'), (2, None)]
+
+        >>> list(G.nodes(data="time", default="Not Available"))
+        [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
+        >>> list(G.nodes.data("time", default="Not Available"))
+        [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
+
+        If some of your nodes have an attribute and the rest are assumed
+        to have a default attribute value you can create a dictionary
+        from node/attribute pairs using the `default` keyword argument
+        to guarantee the value is never None::
+
+            >>> G = nx.Graph()
+            >>> G.add_node(0)
+            >>> G.add_node(1, weight=2)
+            >>> G.add_node(2, weight=3)
+            >>> dict(G.nodes(data="weight", default=1))
+            {0: 1, 1: 2, 2: 3}
+
+        """
+        return NodeView(self)
+
+    def number_of_nodes(self):
+        """Returns the number of nodes in the graph.
+
+        Returns
+        -------
+        nnodes : int
+            The number of nodes in the graph.
+
+        See Also
+        --------
+        order: identical method
+        __len__: identical method
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.number_of_nodes()
+        3
+        """
+        return len(self._node)
+
+    def order(self):
+        """Returns the number of nodes in the graph.
+
+        Returns
+        -------
+        nnodes : int
+            The number of nodes in the graph.
+
+        See Also
+        --------
+        number_of_nodes: identical method
+        __len__: identical method
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.order()
+        3
+        """
+        return len(self._node)
+
+    def has_node(self, n):
+        """Returns True if the graph contains the node n.
+
+        Identical to `n in G`
+
+        Parameters
+        ----------
+        n : node
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.has_node(0)
+        True
+
+        It is more readable and simpler to use
+
+        >>> 0 in G
+        True
+
+        """
+        try:
+            return n in self._node
+        except TypeError:
+            return False
+
+    def add_edge(self, u_of_edge, v_of_edge, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_of_edge, v_of_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        Adding an edge that already exists updates the edge data.
+
+        Many NetworkX algorithms designed for weighted graphs use
+        an edge attribute (by default `weight`) to hold a numerical value.
+
+        Examples
+        --------
+        The following all add the edge e=(1, 2) to graph G:
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = (1, 2)
+        >>> G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+
+        Associate data to edges using keywords:
+
+        >>> G.add_edge(1, 2, weight=3)
+        >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> G.add_edge(1, 2)
+        >>> G[1][2].update({0: 5})
+        >>> G.edges[1, 2].update({0: 5})
+        """
+        u, v = u_of_edge, v_of_edge
+        # add nodes
+        if u not in self._node:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._adj[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._node:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._adj[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        # add the edge
+        datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+        datadict.update(attr)
+        self._adj[u][v] = datadict
+        self._adj[v][u] = datadict
+        nx._clear_cache(self)
+
+    def add_edges_from(self, ebunch_to_add, **attr):
+        """Add all the edges in ebunch_to_add.
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the container will be added to the
+            graph. The edges must be given as 2-tuples (u, v) or
+            3-tuples (u, v, d) where d is a dictionary containing edge data.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_weighted_edges_from : convenient way to add weighted edges
+
+        Notes
+        -----
+        Adding the same edge twice has no effect but any edge data
+        will be updated when each duplicate edge is added.
+
+        Edge attributes specified in an ebunch take precedence over
+        attributes specified via keyword arguments.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
+        >>> e = zip(range(0, 3), range(1, 4))
+        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
+
+        Associate data to edges
+
+        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
+        >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_edges_from(((5, n) for n in G.nodes))
+        >>> # correct way - note that there will be no self-edge for node 5
+        >>> G.add_edges_from(list((5, n) for n in G.nodes))
+        """
+        for e in ebunch_to_add:
+            ne = len(e)
+            if ne == 3:
+                u, v, dd = e
+            elif ne == 2:
+                u, v = e
+                dd = {}  # doesn't need edge_attr_dict_factory
+            else:
+                raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
+            if u not in self._node:
+                if u is None:
+                    raise ValueError("None cannot be a node")
+                self._adj[u] = self.adjlist_inner_dict_factory()
+                self._node[u] = self.node_attr_dict_factory()
+            if v not in self._node:
+                if v is None:
+                    raise ValueError("None cannot be a node")
+                self._adj[v] = self.adjlist_inner_dict_factory()
+                self._node[v] = self.node_attr_dict_factory()
+            datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            datadict.update(dd)
+            self._adj[u][v] = datadict
+            self._adj[v][u] = datadict
+        nx._clear_cache(self)
+
+    def add_weighted_edges_from(self, ebunch_to_add, weight="weight", **attr):
+        """Add weighted edges in `ebunch_to_add` with specified weight attr
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the list or container will be added
+            to the graph. The edges must be given as 3-tuples (u, v, w)
+            where w is a number.
+        weight : string, optional (default= 'weight')
+            The attribute name for the edge weights to be added.
+        attr : keyword arguments, optional (default= no attributes)
+            Edge attributes to add/update for all edges.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_edges_from : add multiple edges
+
+        Notes
+        -----
+        Adding the same edge twice for Graph/DiGraph simply updates
+        the edge data. For MultiGraph/MultiDiGraph, duplicate edges
+        are stored.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_weighted_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])
+
+        Evaluate an iterator over edges before passing it
+
+        >>> G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        >>> weight = 0.1
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_weighted_edges_from(((5, n, weight) for n in G.nodes))
+        >>> # correct way - note that there will be no self-edge for node 5
+        >>> G.add_weighted_edges_from(list((5, n, weight) for n in G.nodes))
+        """
+        self.add_edges_from(((u, v, {weight: d}) for u, v, d in ebunch_to_add), **attr)
+        nx._clear_cache(self)
+
+    def remove_edge(self, u, v):
+        """Remove the edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove the edge between nodes u and v.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, etc
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+        >>> e = (2, 3, {"weight": 7})  # an edge with attribute data
+        >>> G.remove_edge(*e[:2])  # select first part of edge tuple
+        """
+        try:
+            del self._adj[u][v]
+            if u != v:  # self-loop needs only one entry removed
+                del self._adj[v][u]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} is not in the graph") from err
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each edge given in the list or container will be removed
+            from the graph. The edges can be:
+
+                - 2-tuples (u, v) edge between u and v.
+                - 3-tuples (u, v, k) where k is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+        """
+        adj = self._adj
+        for e in ebunch:
+            u, v = e[:2]  # ignore edge data if present
+            if u in adj and v in adj[u]:
+                del adj[u][v]
+                if u != v:  # self loop needs only one entry removed
+                    del adj[v][u]
+        nx._clear_cache(self)
+
+    def update(self, edges=None, nodes=None):
+        """Update the graph using nodes/edges/graphs as input.
+
+        Like dict.update, this method takes a graph as input, adding the
+        graph's nodes and edges to this graph. It can also take two inputs:
+        edges and nodes. Finally it can take either edges or nodes.
+        To specify only nodes the keyword `nodes` must be used.
+
+        The collections of edges and nodes are treated similarly to
+        the add_edges_from/add_nodes_from methods. When iterated, they
+        should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
+
+        Parameters
+        ----------
+        edges : Graph object, collection of edges, or None
+            The first parameter can be a graph or some edges. If it has
+            attributes `nodes` and `edges`, then it is taken to be a
+            Graph-like object and those attributes are used as collections
+            of nodes and edges to be added to the graph.
+            If the first parameter does not have those attributes, it is
+            treated as a collection of edges and added to the graph.
+            If the first argument is None, no edges are added.
+        nodes : collection of nodes, or None
+            The second parameter is treated as a collection of nodes
+            to be added to the graph unless it is None.
+            If `edges is None` and `nodes is None` an exception is raised.
+            If the first parameter is a Graph, then `nodes` is ignored.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(5)
+        >>> G.update(nx.complete_graph(range(4, 10)))
+        >>> from itertools import combinations
+        >>> edges = (
+        ...     (u, v, {"power": u * v})
+        ...     for u, v in combinations(range(10, 20), 2)
+        ...     if u * v < 225
+        ... )
+        >>> nodes = [1000]  # for singleton, use a container
+        >>> G.update(edges, nodes)
+
+        Notes
+        -----
+        It you want to update the graph using an adjacency structure
+        it is straightforward to obtain the edges/nodes from adjacency.
+        The following examples provide common cases, your adjacency may
+        be slightly different and require tweaks of these examples::
+
+        >>> # dict-of-set/list/tuple
+        >>> adj = {1: {2, 3}, 2: {1, 3}, 3: {1, 2}}
+        >>> e = [(u, v) for u, nbrs in adj.items() for v in nbrs]
+        >>> G.update(edges=e, nodes=adj)
+
+        >>> DG = nx.DiGraph()
+        >>> # dict-of-dict-of-attribute
+        >>> adj = {1: {2: 1.3, 3: 0.7}, 2: {1: 1.4}, 3: {1: 0.7}}
+        >>> e = [
+        ...     (u, v, {"weight": d})
+        ...     for u, nbrs in adj.items()
+        ...     for v, d in nbrs.items()
+        ... ]
+        >>> DG.update(edges=e, nodes=adj)
+
+        >>> # dict-of-dict-of-dict
+        >>> adj = {1: {2: {"weight": 1.3}, 3: {"color": 0.7, "weight": 1.2}}}
+        >>> e = [
+        ...     (u, v, {"weight": d})
+        ...     for u, nbrs in adj.items()
+        ...     for v, d in nbrs.items()
+        ... ]
+        >>> DG.update(edges=e, nodes=adj)
+
+        >>> # predecessor adjacency (dict-of-set)
+        >>> pred = {1: {2, 3}, 2: {3}, 3: {3}}
+        >>> e = [(v, u) for u, nbrs in pred.items() for v in nbrs]
+
+        >>> # MultiGraph dict-of-dict-of-dict-of-attribute
+        >>> MDG = nx.MultiDiGraph()
+        >>> adj = {
+        ...     1: {2: {0: {"weight": 1.3}, 1: {"weight": 1.2}}},
+        ...     3: {2: {0: {"weight": 0.7}}},
+        ... }
+        >>> e = [
+        ...     (u, v, ekey, d)
+        ...     for u, nbrs in adj.items()
+        ...     for v, keydict in nbrs.items()
+        ...     for ekey, d in keydict.items()
+        ... ]
+        >>> MDG.update(edges=e)
+
+        See Also
+        --------
+        add_edges_from: add multiple edges to a graph
+        add_nodes_from: add multiple nodes to a graph
+        """
+        if edges is not None:
+            if nodes is not None:
+                self.add_nodes_from(nodes)
+                self.add_edges_from(edges)
+            else:
+                # check if edges is a Graph object
+                try:
+                    graph_nodes = edges.nodes
+                    graph_edges = edges.edges
+                except AttributeError:
+                    # edge not Graph-like
+                    self.add_edges_from(edges)
+                else:  # edges is Graph-like
+                    self.add_nodes_from(graph_nodes.data())
+                    self.add_edges_from(graph_edges.data())
+                    self.graph.update(edges.graph)
+        elif nodes is not None:
+            self.add_nodes_from(nodes)
+        else:
+            raise NetworkXError("update needs nodes or edges input")
+
+    def has_edge(self, u, v):
+        """Returns True if the edge (u, v) is in the graph.
+
+        This is the same as `v in G[u]` without KeyError exceptions.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+
+        Returns
+        -------
+        edge_ind : bool
+            True if edge is in the graph, False otherwise.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.has_edge(0, 1)  # using two nodes
+        True
+        >>> e = (0, 1)
+        >>> G.has_edge(*e)  #  e is a 2-tuple (u, v)
+        True
+        >>> e = (0, 1, {"weight": 7})
+        >>> G.has_edge(*e[:2])  # e is a 3-tuple (u, v, data_dictionary)
+        True
+
+        The following syntax are equivalent:
+
+        >>> G.has_edge(0, 1)
+        True
+        >>> 1 in G[0]  # though this gives KeyError if 0 not in G
+        True
+
+        """
+        try:
+            return v in self._adj[u]
+        except KeyError:
+            return False
+
+    def neighbors(self, n):
+        """Returns an iterator over all neighbors of node n.
+
+        This is identical to `iter(G[n])`
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Returns
+        -------
+        neighbors : iterator
+            An iterator over all neighbors of node n
+
+        Raises
+        ------
+        NetworkXError
+            If the node n is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> [n for n in G.neighbors(0)]
+        [1]
+
+        Notes
+        -----
+        Alternate ways to access the neighbors are ``G.adj[n]`` or ``G[n]``:
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edge("a", "b", weight=7)
+        >>> G["a"]
+        AtlasView({'b': {'weight': 7}})
+        >>> G = nx.path_graph(4)
+        >>> [n for n in G[0]]
+        [1]
+        """
+        try:
+            return iter(self._adj[n])
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the graph.") from err
+
+    @cached_property
+    def edges(self):
+        """An EdgeView of the Graph as G.edges or G.edges().
+
+        edges(self, nbunch=None, data=False, default=None)
+
+        The EdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, `G.edges[u, v]['color']` provides the value of the color
+        attribute for edge `(u, v)` while
+        `for (u, v, c) in G.edges.data('color', default='red'):`
+        iterates through all the edges yielding the color attribute
+        with default `'red'` if no color attribute exists.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : EdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, d) tuples of edges, but can also be used for
+            attribute lookup as `edges[u, v]['foo']`.
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or MultiGraph, etc
+        >>> G.add_edge(2, 3, weight=5)
+        >>> [e for e in G.edges]
+        [(0, 1), (1, 2), (2, 3)]
+        >>> G.edges.data()  # default data is {} (empty dict)
+        EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
+        >>> G.edges.data("weight", default=1)
+        EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
+        >>> G.edges([0, 3])  # only edges from these nodes
+        EdgeDataView([(0, 1), (3, 2)])
+        >>> G.edges(0)  # only edges from node 0
+        EdgeDataView([(0, 1)])
+        """
+        return EdgeView(self)
+
+    def get_edge_data(self, u, v, default=None):
+        """Returns the attribute dictionary associated with edge (u, v).
+
+        This is identical to `G[u][v]` except the default is returned
+        instead of an exception if the edge doesn't exist.
+
+        Parameters
+        ----------
+        u, v : nodes
+        default:  any Python object (default=None)
+            Value to return if the edge (u, v) is not found.
+
+        Returns
+        -------
+        edge_dict : dictionary
+            The edge attribute dictionary.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G[0][1]
+        {}
+
+        Warning: Assigning to `G[u][v]` is not permitted.
+        But it is safe to assign attributes `G[u][v]['foo']`
+
+        >>> G[0][1]["weight"] = 7
+        >>> G[0][1]["weight"]
+        7
+        >>> G[1][0]["weight"]
+        7
+
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.get_edge_data(0, 1)  # default edge data is {}
+        {}
+        >>> e = (0, 1)
+        >>> G.get_edge_data(*e)  # tuple form
+        {}
+        >>> G.get_edge_data("a", "b", default=0)  # edge not in graph, return 0
+        0
+        """
+        try:
+            return self._adj[u][v]
+        except KeyError:
+            return default
+
+    def adjacency(self):
+        """Returns an iterator over (node, adjacency dict) tuples for all nodes.
+
+        For directed graphs, only outgoing neighbors/adjacencies are included.
+
+        Returns
+        -------
+        adj_iter : iterator
+           An iterator over (node, adjacency dictionary) for all nodes in
+           the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> [(n, nbrdict) for n, nbrdict in G.adjacency()]
+        [(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
+
+        """
+        return iter(self._adj.items())
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        DegreeView or int
+            If multiple nodes are requested (the default), returns a `DegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.degree[0]  # node 0 has degree 1
+        1
+        >>> list(G.degree([0, 1, 2]))
+        [(0, 1), (1, 2), (2, 2)]
+        """
+        return DegreeView(self)
+
+    def clear(self):
+        """Remove all nodes and edges from the graph.
+
+        This also removes the name, and all graph, node, and edge attributes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear()
+        >>> list(G.nodes)
+        []
+        >>> list(G.edges)
+        []
+
+        """
+        self._adj.clear()
+        self._node.clear()
+        self.graph.clear()
+        nx._clear_cache(self)
+
+    def clear_edges(self):
+        """Remove all edges from the graph without altering nodes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear_edges()
+        >>> list(G.nodes)
+        [0, 1, 2, 3]
+        >>> list(G.edges)
+        []
+        """
+        for nbr_dict in self._adj.values():
+            nbr_dict.clear()
+        nx._clear_cache(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return False
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return False
+
+    def copy(self, as_view=False):
+        """Returns a copy of the graph.
+
+        The copy method by default returns an independent shallow copy
+        of the graph and attributes. That is, if an attribute is a
+        container, that container is shared by the original an the copy.
+        Use Python's `copy.deepcopy` for new containers.
+
+        If `as_view` is True then a view is returned instead of a copy.
+
+        Notes
+        -----
+        All copies reproduce the graph structure, but data attributes
+        may be handled in different ways. There are four types of copies
+        of a graph that people might want.
+
+        Deepcopy -- A "deepcopy" copies the graph structure as well as
+        all data attributes and any objects they might contain.
+        The entire graph object is new so that changes in the copy
+        do not affect the original object. (see Python's copy.deepcopy)
+
+        Data Reference (Shallow) -- For a shallow copy the graph structure
+        is copied but the edge, node and graph attribute dicts are
+        references to those in the original graph. This saves
+        time and memory but could cause confusion if you change an attribute
+        in one graph and it changes the attribute in the other.
+        NetworkX does not provide this level of shallow copy.
+
+        Independent Shallow -- This copy creates new independent attribute
+        dicts and then does a shallow copy of the attributes. That is, any
+        attributes that are containers are shared between the new graph
+        and the original. This is exactly what `dict.copy()` provides.
+        You can obtain this style copy using:
+
+            >>> G = nx.path_graph(5)
+            >>> H = G.copy()
+            >>> H = G.copy(as_view=False)
+            >>> H = nx.Graph(G)
+            >>> H = G.__class__(G)
+
+        Fresh Data -- For fresh data, the graph structure is copied while
+        new empty data attribute dicts are created. The resulting graph
+        is independent of the original and it has no edge, node or graph
+        attributes. Fresh copies are not enabled. Instead use:
+
+            >>> H = G.__class__()
+            >>> H.add_nodes_from(G)
+            >>> H.add_edges_from(G.edges)
+
+        View -- Inspired by dict-views, graph-views act like read-only
+        versions of the original graph, providing a copy of the original
+        structure without requiring any memory for copying the information.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Parameters
+        ----------
+        as_view : bool, optional (default=False)
+            If True, the returned graph-view provides a read-only view
+            of the original graph without actually copying any data.
+
+        Returns
+        -------
+        G : Graph
+            A copy of the graph.
+
+        See Also
+        --------
+        to_directed: return a directed copy of the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> H = G.copy()
+
+        """
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self)
+        G = self.__class__()
+        G.graph.update(self.graph)
+        G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, datadict.copy())
+            for u, nbrs in self._adj.items()
+            for v, datadict in nbrs.items()
+        )
+        return G
+
+    def to_directed(self, as_view=False):
+        """Returns a directed representation of the graph.
+
+        Returns
+        -------
+        G : DiGraph
+            A directed graph with the same name, same nodes, and with
+            each edge (u, v, data) replaced by two directed edges
+            (u, v, data) and (v, u, data).
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar D=DiGraph(G) which returns a
+        shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed Graph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        DiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or MultiGraph, etc
+        >>> G.add_edge(0, 1)
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+
+        If already directed, return a (deep) copy
+
+        >>> G = nx.DiGraph()  # or MultiDiGraph, etc
+        >>> G.add_edge(0, 1)
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_directed_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, deepcopy(data))
+            for u, nbrs in self._adj.items()
+            for v, data in nbrs.items()
+        )
+        return G
+
+    def to_undirected(self, as_view=False):
+        """Returns an undirected copy of the graph.
+
+        Parameters
+        ----------
+        as_view : bool (optional, default=False)
+          If True return a view of the original undirected graph.
+
+        Returns
+        -------
+        G : Graph/MultiGraph
+            A deepcopy of the graph.
+
+        See Also
+        --------
+        Graph, copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar `G = nx.DiGraph(D)` which returns a
+        shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed DiGraph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        Graph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(2)  # or MultiGraph, etc
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, deepcopy(d))
+            for u, nbrs in self._adj.items()
+            for v, d in nbrs.items()
+        )
+        return G
+
+    def subgraph(self, nodes):
+        """Returns a SubGraph view of the subgraph induced on `nodes`.
+
+        The induced subgraph of the graph contains the nodes in `nodes`
+        and the edges between those nodes.
+
+        Parameters
+        ----------
+        nodes : list, iterable
+            A container of nodes which will be iterated through once.
+
+        Returns
+        -------
+        G : SubGraph View
+            A subgraph view of the graph. The graph structure cannot be
+            changed but node/edge attributes can and are shared with the
+            original graph.
+
+        Notes
+        -----
+        The graph, edge and node attributes are shared with the original graph.
+        Changes to the graph structure is ruled out by the view, but changes
+        to attributes are reflected in the original graph.
+
+        To create a subgraph with its own copy of the edge/node attributes use:
+        G.subgraph(nodes).copy()
+
+        For an inplace reduction of a graph to a subgraph you can remove nodes:
+        G.remove_nodes_from([n for n in G if n not in set(nodes)])
+
+        Subgraph views are sometimes NOT what you want. In most cases where
+        you want to do more than simply look at the induced edges, it makes
+        more sense to just create the subgraph as its own graph with code like:
+
+        ::
+
+            # Create a subgraph SG based on a (possibly multigraph) G
+            SG = G.__class__()
+            SG.add_nodes_from((n, G.nodes[n]) for n in largest_wcc)
+            if SG.is_multigraph():
+                SG.add_edges_from(
+                    (n, nbr, key, d)
+                    for n, nbrs in G.adj.items()
+                    if n in largest_wcc
+                    for nbr, keydict in nbrs.items()
+                    if nbr in largest_wcc
+                    for key, d in keydict.items()
+                )
+            else:
+                SG.add_edges_from(
+                    (n, nbr, d)
+                    for n, nbrs in G.adj.items()
+                    if n in largest_wcc
+                    for nbr, d in nbrs.items()
+                    if nbr in largest_wcc
+                )
+            SG.graph.update(G.graph)
+
+        Subgraphs are not guaranteed to preserve the order of nodes or edges
+        as they appear in the original graph. For example:
+
+        >>> G = nx.Graph()
+        >>> G.add_nodes_from(reversed(range(10)))
+        >>> list(G)
+        [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+        >>> list(G.subgraph([1, 3, 2]))
+        [1, 2, 3]
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> H = G.subgraph([0, 1, 2])
+        >>> list(H.edges)
+        [(0, 1), (1, 2)]
+        """
+        induced_nodes = nx.filters.show_nodes(self.nbunch_iter(nodes))
+        # if already a subgraph, don't make a chain
+        subgraph = nx.subgraph_view
+        if hasattr(self, "_NODE_OK"):
+            return subgraph(
+                self._graph, filter_node=induced_nodes, filter_edge=self._EDGE_OK
+            )
+        return subgraph(self, filter_node=induced_nodes)
+
+    def edge_subgraph(self, edges):
+        """Returns the subgraph induced by the specified edges.
+
+        The induced subgraph contains each edge in `edges` and each
+        node incident to any one of those edges.
+
+        Parameters
+        ----------
+        edges : iterable
+            An iterable of edges in this graph.
+
+        Returns
+        -------
+        G : Graph
+            An edge-induced subgraph of this graph with the same edge
+            attributes.
+
+        Notes
+        -----
+        The graph, edge, and node attributes in the returned subgraph
+        view are references to the corresponding attributes in the original
+        graph. The view is read-only.
+
+        To create a full graph version of the subgraph with its own copy
+        of the edge or node attributes, use::
+
+            G.edge_subgraph(edges).copy()
+
+        Examples
+        --------
+        >>> G = nx.path_graph(5)
+        >>> H = G.edge_subgraph([(0, 1), (3, 4)])
+        >>> list(H.nodes)
+        [0, 1, 3, 4]
+        >>> list(H.edges)
+        [(0, 1), (3, 4)]
+
+        """
+        return nx.edge_subgraph(self, edges)
+
+    def size(self, weight=None):
+        """Returns the number of edges or total of all edge weights.
+
+        Parameters
+        ----------
+        weight : string or None, optional (default=None)
+            The edge attribute that holds the numerical value used
+            as a weight. If None, then each edge has weight 1.
+
+        Returns
+        -------
+        size : numeric
+            The number of edges or
+            (if weight keyword is provided) the total weight sum.
+
+            If weight is None, returns an int. Otherwise a float
+            (or more general numeric if the weights are more general).
+
+        See Also
+        --------
+        number_of_edges
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.size()
+        3
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edge("a", "b", weight=2)
+        >>> G.add_edge("b", "c", weight=4)
+        >>> G.size()
+        2
+        >>> G.size(weight="weight")
+        6.0
+        """
+        s = sum(d for v, d in self.degree(weight=weight))
+        # If `weight` is None, the sum of the degrees is guaranteed to be
+        # even, so we can perform integer division and hence return an
+        # integer. Otherwise, the sum of the weighted degrees is not
+        # guaranteed to be an integer, so we perform "real" division.
+        return s // 2 if weight is None else s / 2
+
+    def number_of_edges(self, u=None, v=None):
+        """Returns the number of edges between two nodes.
+
+        Parameters
+        ----------
+        u, v : nodes, optional (default=all edges)
+            If u and v are specified, return the number of edges between
+            u and v. Otherwise return the total number of all edges.
+
+        Returns
+        -------
+        nedges : int
+            The number of edges in the graph.  If nodes `u` and `v` are
+            specified return the number of edges between those nodes. If
+            the graph is directed, this only returns the number of edges
+            from `u` to `v`.
+
+        See Also
+        --------
+        size
+
+        Examples
+        --------
+        For undirected graphs, this method counts the total number of
+        edges in the graph:
+
+        >>> G = nx.path_graph(4)
+        >>> G.number_of_edges()
+        3
+
+        If you specify two nodes, this counts the total number of edges
+        joining the two nodes:
+
+        >>> G.number_of_edges(0, 1)
+        1
+
+        For directed graphs, this method can count the total number of
+        directed edges from `u` to `v`:
+
+        >>> G = nx.DiGraph()
+        >>> G.add_edge(0, 1)
+        >>> G.add_edge(1, 0)
+        >>> G.number_of_edges(0, 1)
+        1
+
+        """
+        if u is None:
+            return int(self.size())
+        if v in self._adj[u]:
+            return 1
+        return 0
+
+    def nbunch_iter(self, nbunch=None):
+        """Returns an iterator over nodes contained in nbunch that are
+        also in the graph.
+
+        The nodes in an iterable nbunch are checked for membership in the graph
+        and if not are silently ignored.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        Returns
+        -------
+        niter : iterator
+            An iterator over nodes in nbunch that are also in the graph.
+            If nbunch is None, iterate over all nodes in the graph.
+
+        Raises
+        ------
+        NetworkXError
+            If nbunch is not a node or sequence of nodes.
+            If a node in nbunch is not hashable.
+
+        See Also
+        --------
+        Graph.__iter__
+
+        Notes
+        -----
+        When nbunch is an iterator, the returned iterator yields values
+        directly from nbunch, becoming exhausted when nbunch is exhausted.
+
+        To test whether nbunch is a single node, one can use
+        "if nbunch in self:", even after processing with this routine.
+
+        If nbunch is not a node or a (possibly empty) sequence/iterator
+        or None, a :exc:`NetworkXError` is raised.  Also, if any object in
+        nbunch is not hashable, a :exc:`NetworkXError` is raised.
+        """
+        if nbunch is None:  # include all nodes via iterator
+            bunch = iter(self._adj)
+        elif nbunch in self:  # if nbunch is a single node
+            bunch = iter([nbunch])
+        else:  # if nbunch is a sequence of nodes
+
+            def bunch_iter(nlist, adj):
+                try:
+                    for n in nlist:
+                        if n in adj:
+                            yield n
+                except TypeError as err:
+                    exc, message = err, err.args[0]
+                    # capture error for non-sequence/iterator nbunch.
+                    if "iter" in message:
+                        exc = NetworkXError(
+                            "nbunch is not a node or a sequence of nodes."
+                        )
+                    # capture single nodes that are not in the graph.
+                    if "object is not iterable" in message:
+                        exc = NetworkXError(f"Node {nbunch} is not in the graph.")
+                    # capture error for unhashable node.
+                    if "hashable" in message:
+                        exc = NetworkXError(
+                            f"Node {n} in sequence nbunch is not a valid node."
+                        )
+                    raise exc
+
+            bunch = bunch_iter(nbunch, self._adj)
+        return bunch
diff --git a/lib/python3.11/site-packages/networkx/classes/graphviews.py b/lib/python3.11/site-packages/networkx/classes/graphviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b09df649ef48fa484d27e51d86cce1e10d593a7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/graphviews.py
@@ -0,0 +1,269 @@
+"""View of Graphs as SubGraph, Reverse, Directed, Undirected.
+
+In some algorithms it is convenient to temporarily morph
+a graph to exclude some nodes or edges. It should be better
+to do that via a view than to remove and then re-add.
+In other algorithms it is convenient to temporarily morph
+a graph to reverse directed edges, or treat a directed graph
+as undirected, etc. This module provides those graph views.
+
+The resulting views are essentially read-only graphs that
+report data from the original graph object. We provide an
+attribute G._graph which points to the underlying graph object.
+
+Note: Since graphviews look like graphs, one can end up with
+view-of-view-of-view chains. Be careful with chains because
+they become very slow with about 15 nested views.
+For the common simple case of node induced subgraphs created
+from the graph class, we short-cut the chain by returning a
+subgraph of the original graph directly rather than a subgraph
+of a subgraph. We are careful not to disrupt any edge filter in
+the middle subgraph. In general, determining how to short-cut
+the chain is tricky and much harder with restricted_views than
+with induced subgraphs.
+Often it is easiest to use .copy() to avoid chains.
+"""
+
+import networkx as nx
+from networkx.classes.coreviews import (
+    FilterAdjacency,
+    FilterAtlas,
+    FilterMultiAdjacency,
+    UnionAdjacency,
+    UnionMultiAdjacency,
+)
+from networkx.classes.filters import no_filter
+from networkx.exception import NetworkXError
+from networkx.utils import not_implemented_for
+
+__all__ = ["generic_graph_view", "subgraph_view", "reverse_view"]
+
+
+def generic_graph_view(G, create_using=None):
+    """Returns a read-only view of `G`.
+
+    The graph `G` and its attributes are not copied but viewed through the new graph object
+    of the same class as `G` (or of the class specified in `create_using`).
+
+    Parameters
+    ----------
+    G : graph
+        A directed/undirected graph/multigraph.
+
+    create_using : NetworkX graph constructor, optional (default=None)
+       Graph type to create. If graph instance, then cleared before populated.
+       If `None`, then the appropriate Graph type is inferred from `G`.
+
+    Returns
+    -------
+    newG : graph
+        A view of the input graph `G` and its attributes as viewed through
+        the `create_using` class.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is a multigraph (or multidigraph) but `create_using` is not, or vice versa.
+
+    Notes
+    -----
+    The returned graph view is read-only (cannot modify the graph).
+    Yet the view reflects any changes in `G`. The intent is to mimic dict views.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=0.3)
+    >>> G.add_edge(2, 3, weight=0.5)
+    >>> G.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3}), (2, 3, {'weight': 0.5})])
+
+    The view exposes the attributes from the original graph.
+
+    >>> viewG = nx.graphviews.generic_graph_view(G)
+    >>> viewG.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3}), (2, 3, {'weight': 0.5})])
+
+    Changes to `G` are reflected in `viewG`.
+
+    >>> G.remove_edge(2, 3)
+    >>> G.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3})])
+
+    >>> viewG.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3})])
+
+    We can change the graph type with the `create_using` parameter.
+
+    >>> type(G)
+    <class 'networkx.classes.graph.Graph'>
+    >>> viewDG = nx.graphviews.generic_graph_view(G, create_using=nx.DiGraph)
+    >>> type(viewDG)
+    <class 'networkx.classes.digraph.DiGraph'>
+    """
+    if create_using is None:
+        newG = G.__class__()
+    else:
+        newG = nx.empty_graph(0, create_using)
+    if G.is_multigraph() != newG.is_multigraph():
+        raise NetworkXError("Multigraph for G must agree with create_using")
+    newG = nx.freeze(newG)
+
+    # create view by assigning attributes from G
+    newG._graph = G
+    newG.graph = G.graph
+
+    newG._node = G._node
+    if newG.is_directed():
+        if G.is_directed():
+            newG._succ = G._succ
+            newG._pred = G._pred
+            # newG._adj is synced with _succ
+        else:
+            newG._succ = G._adj
+            newG._pred = G._adj
+            # newG._adj is synced with _succ
+    elif G.is_directed():
+        if G.is_multigraph():
+            newG._adj = UnionMultiAdjacency(G._succ, G._pred)
+        else:
+            newG._adj = UnionAdjacency(G._succ, G._pred)
+    else:
+        newG._adj = G._adj
+    return newG
+
+
+def subgraph_view(G, *, filter_node=no_filter, filter_edge=no_filter):
+    """View of `G` applying a filter on nodes and edges.
+
+    `subgraph_view` provides a read-only view of the input graph that excludes
+    nodes and edges based on the outcome of two filter functions `filter_node`
+    and `filter_edge`.
+
+    The `filter_node` function takes one argument --- the node --- and returns
+    `True` if the node should be included in the subgraph, and `False` if it
+    should not be included.
+
+    The `filter_edge` function takes two (or three arguments if `G` is a
+    multi-graph) --- the nodes describing an edge, plus the edge-key if
+    parallel edges are possible --- and returns `True` if the edge should be
+    included in the subgraph, and `False` if it should not be included.
+
+    Both node and edge filter functions are called on graph elements as they
+    are queried, meaning there is no up-front cost to creating the view.
+
+    Parameters
+    ----------
+    G : networkx.Graph
+        A directed/undirected graph/multigraph
+
+    filter_node : callable, optional
+        A function taking a node as input, which returns `True` if the node
+        should appear in the view.
+
+    filter_edge : callable, optional
+        A function taking as input the two nodes describing an edge (plus the
+        edge-key if `G` is a multi-graph), which returns `True` if the edge
+        should appear in the view.
+
+    Returns
+    -------
+    graph : networkx.Graph
+        A read-only graph view of the input graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(6)
+
+    Filter functions operate on the node, and return `True` if the node should
+    appear in the view:
+
+    >>> def filter_node(n1):
+    ...     return n1 != 5
+    >>> view = nx.subgraph_view(G, filter_node=filter_node)
+    >>> view.nodes()
+    NodeView((0, 1, 2, 3, 4))
+
+    We can use a closure pattern to filter graph elements based on additional
+    data --- for example, filtering on edge data attached to the graph:
+
+    >>> G[3][4]["cross_me"] = False
+    >>> def filter_edge(n1, n2):
+    ...     return G[n1][n2].get("cross_me", True)
+    >>> view = nx.subgraph_view(G, filter_edge=filter_edge)
+    >>> view.edges()
+    EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])
+
+    >>> view = nx.subgraph_view(
+    ...     G,
+    ...     filter_node=filter_node,
+    ...     filter_edge=filter_edge,
+    ... )
+    >>> view.nodes()
+    NodeView((0, 1, 2, 3, 4))
+    >>> view.edges()
+    EdgeView([(0, 1), (1, 2), (2, 3)])
+    """
+    newG = nx.freeze(G.__class__())
+    newG._NODE_OK = filter_node
+    newG._EDGE_OK = filter_edge
+
+    # create view by assigning attributes from G
+    newG._graph = G
+    newG.graph = G.graph
+
+    newG._node = FilterAtlas(G._node, filter_node)
+    if G.is_multigraph():
+        Adj = FilterMultiAdjacency
+
+        def reverse_edge(u, v, k=None):
+            return filter_edge(v, u, k)
+
+    else:
+        Adj = FilterAdjacency
+
+        def reverse_edge(u, v, k=None):
+            return filter_edge(v, u)
+
+    if G.is_directed():
+        newG._succ = Adj(G._succ, filter_node, filter_edge)
+        newG._pred = Adj(G._pred, filter_node, reverse_edge)
+        # newG._adj is synced with _succ
+    else:
+        newG._adj = Adj(G._adj, filter_node, filter_edge)
+    return newG
+
+
+@not_implemented_for("undirected")
+def reverse_view(G):
+    """View of `G` with edge directions reversed
+
+    `reverse_view` returns a read-only view of the input graph where
+    edge directions are reversed.
+
+    Identical to digraph.reverse(copy=False)
+
+    Parameters
+    ----------
+    G : networkx.DiGraph
+
+    Returns
+    -------
+    graph : networkx.DiGraph
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_edge(1, 2)
+    >>> G.add_edge(2, 3)
+    >>> G.edges()
+    OutEdgeView([(1, 2), (2, 3)])
+
+    >>> view = nx.reverse_view(G)
+    >>> view.edges()
+    OutEdgeView([(2, 1), (3, 2)])
+    """
+    newG = generic_graph_view(G)
+    newG._succ, newG._pred = G._pred, G._succ
+    # newG._adj is synced with _succ
+    return newG
diff --git a/lib/python3.11/site-packages/networkx/classes/multidigraph.py b/lib/python3.11/site-packages/networkx/classes/multidigraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c987037e2a47dd80045042893139f0aad226f5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/multidigraph.py
@@ -0,0 +1,977 @@
+"""Base class for MultiDiGraph."""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import convert
+from networkx.classes.coreviews import MultiAdjacencyView
+from networkx.classes.digraph import DiGraph
+from networkx.classes.multigraph import MultiGraph
+from networkx.classes.reportviews import (
+    DiMultiDegreeView,
+    InMultiDegreeView,
+    InMultiEdgeView,
+    OutMultiDegreeView,
+    OutMultiEdgeView,
+)
+from networkx.exception import NetworkXError
+
+__all__ = ["MultiDiGraph"]
+
+
+class MultiDiGraph(MultiGraph, DiGraph):
+    """A directed graph class that can store multiedges.
+
+    Multiedges are multiple edges between two nodes.  Each edge
+    can hold optional data or attributes.
+
+    A MultiDiGraph holds directed edges.  Self loops are allowed.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes. By convention `None` is not used as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
+        sparse matrix, or PyGraphviz graph.
+
+    multigraph_input : bool or None (default None)
+        Note: Only used when `incoming_graph_data` is a dict.
+        If True, `incoming_graph_data` is assumed to be a
+        dict-of-dict-of-dict-of-dict structure keyed by
+        node to neighbor to edge keys to edge data for multi-edges.
+        A NetworkXError is raised if this is not the case.
+        If False, :func:`to_networkx_graph` is used to try to determine
+        the dict's graph data structure as either a dict-of-dict-of-dict
+        keyed by node to neighbor to edge data, or a dict-of-iterable
+        keyed by node to neighbors.
+        If None, the treatment for True is tried, but if it fails,
+        the treatment for False is tried.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    Graph
+    DiGraph
+    MultiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.MultiDiGraph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> key = G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> keys = G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> keys = G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  If an edge already exists, an additional
+    edge is created and stored using a key to identify the edge.
+    By default the key is the lowest unused integer.
+
+    >>> keys = G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])
+    >>> G[4]
+    AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}})
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.MultiDiGraph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> key = G.add_edge(1, 2, weight=4.7)
+    >>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2][0]["weight"] = 4.7
+    >>> G.edges[1, 2, 0]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges[1,
+    2, 0]` a read-only dict-like structure. However, you can assign to
+    attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
+    to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`
+    (for multigraphs the edge key is required: `MG.edges[u, v,
+    key][name] = value`).
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+    >>> G[1]  # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
+    AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are available as an adjacency-view `G.adj` object or via
+    the method `G.adjacency()`.
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, keydict in nbrsdict.items():
+    ...         for key, eattr in keydict.items():
+    ...             if "weight" in eattr:
+    ...                 # Do something useful with the edges
+    ...                 pass
+
+    But the edges() method is often more convenient:
+
+    >>> for u, v, keys, weight in G.edges(data="weight", keys=True):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using methods and object-attributes.
+    Reporting usually provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information
+    and holds edge_key dicts keyed by neighbor. The edge_key dict holds
+    each edge_attr dict keyed by edge key. The inner dict
+    (edge_attr_dict) represents the edge data and holds edge attribute
+    values keyed by attribute names.
+
+    Each of these four dicts in the dict-of-dict-of-dict-of-dict
+    structure can be replaced by a user defined dict-like object.
+    In general, the dict-like features should be maintained but
+    extra features can be added. To replace one of the dicts create
+    a new graph class by changing the class(!) variable holding the
+    factory for that dict-like structure. The variable names are
+    node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
+    adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
+    and graph_attr_dict_factory.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds multiedge key dicts keyed by neighbor.
+        It should require no arguments and return a dict-like object.
+
+    edge_key_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge key dict
+        which holds edge data keyed by edge key.
+        It should require no arguments and return a dict-like object.
+
+    edge_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherited without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    # node_dict_factory = dict    # already assigned in Graph
+    # adjlist_outer_dict_factory = dict
+    # adjlist_inner_dict_factory = dict
+    edge_key_dict_factory = dict
+    # edge_attr_dict_factory = dict
+
+    # This __new__ method just does what Python itself does automatically.
+    # We include it here as part of the dispatchable/backend interface.
+    # If your goal is to understand how the graph classes work, you can ignore
+    # this method, even when subclassing the base classes. If you are subclassing
+    # in order to provide a backend that allows class instantiation, this method
+    # can be overridden to return your own backend graph class.
+    @nx._dispatchable(name="multidigraph__new__", graphs=None, returns_graph=True)
+    def __new__(cls, *args, **kwargs):
+        return object.__new__(cls)
+
+    def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph
+            Data to initialize graph.  If incoming_graph_data=None (default)
+            an empty graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        multigraph_input : bool or None (default None)
+            Note: Only used when `incoming_graph_data` is a dict.
+            If True, `incoming_graph_data` is assumed to be a
+            dict-of-dict-of-dict-of-dict structure keyed by
+            node to neighbor to edge keys to edge data for multi-edges.
+            A NetworkXError is raised if this is not the case.
+            If False, :func:`to_networkx_graph` is used to try to determine
+            the dict's graph data structure as either a dict-of-dict-of-dict
+            keyed by node to neighbor to edge data, or a dict-of-iterable
+            keyed by node to neighbors.
+            If None, the treatment for True is tried, but if it fails,
+            the treatment for False is tried.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G = nx.Graph(name="my graph")
+        >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.Graph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.Graph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        attr.pop("backend", None)  # Ignore explicit `backend="networkx"`
+        # multigraph_input can be None/True/False. So check "is not False"
+        if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
+            DiGraph.__init__(self)
+            try:
+                convert.from_dict_of_dicts(
+                    incoming_graph_data, create_using=self, multigraph_input=True
+                )
+                self.graph.update(attr)
+            except Exception as err:
+                if multigraph_input is True:
+                    raise nx.NetworkXError(
+                        f"converting multigraph_input raised:\n{type(err)}: {err}"
+                    )
+                DiGraph.__init__(self, incoming_graph_data, **attr)
+        else:
+            DiGraph.__init__(self, incoming_graph_data, **attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return MultiAdjacencyView(self._succ)
+
+    @cached_property
+    def succ(self):
+        """Graph adjacency object holding the successors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.succ` is identical to `G.adj`.
+        """
+        return MultiAdjacencyView(self._succ)
+
+    @cached_property
+    def pred(self):
+        """Graph adjacency object holding the predecessors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+        """
+        return MultiAdjacencyView(self._pred)
+
+    def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_for_edge, v_for_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        key : hashable identifier, optional (default=lowest unused integer)
+            Used to distinguish multiedges between a pair of nodes.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        Returns
+        -------
+        The edge key assigned to the edge.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        To replace/update edge data, use the optional key argument
+        to identify a unique edge.  Otherwise a new edge will be created.
+
+        NetworkX algorithms designed for weighted graphs cannot use
+        multigraphs directly because it is not clear how to handle
+        multiedge weights.  Convert to Graph using edge attribute
+        'weight' to enable weighted graph algorithms.
+
+        Default keys are generated using the method `new_edge_key()`.
+        This method can be overridden by subclassing the base class and
+        providing a custom `new_edge_key()` method.
+
+        Examples
+        --------
+        The following all add the edge e=(1, 2) to graph G:
+
+        >>> G = nx.MultiDiGraph()
+        >>> e = (1, 2)
+        >>> key = G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        1
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+        [2]
+
+        Associate data to edges using keywords:
+
+        >>> key = G.add_edge(1, 2, weight=3)
+        >>> key = G.add_edge(1, 2, key=0, weight=4)  # update data for key=0
+        >>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> ekey = G.add_edge(1, 2)
+        >>> G[1][2][0].update({0: 5})
+        >>> G.edges[1, 2, 0].update({0: 5})
+        """
+        u, v = u_for_edge, v_for_edge
+        # add nodes
+        if u not in self._succ:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._succ[u] = self.adjlist_inner_dict_factory()
+            self._pred[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._succ:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._succ[v] = self.adjlist_inner_dict_factory()
+            self._pred[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        if key is None:
+            key = self.new_edge_key(u, v)
+        if v in self._succ[u]:
+            keydict = self._adj[u][v]
+            datadict = keydict.get(key, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            keydict[key] = datadict
+        else:
+            # selfloops work this way without special treatment
+            datadict = self.edge_attr_dict_factory()
+            datadict.update(attr)
+            keydict = self.edge_key_dict_factory()
+            keydict[key] = datadict
+            self._succ[u][v] = keydict
+            self._pred[v][u] = keydict
+        nx._clear_cache(self)
+        return key
+
+    def remove_edge(self, u, v, key=None):
+        """Remove an edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove an edge between nodes u and v.
+        key : hashable identifier, optional (default=None)
+            Used to distinguish multiple edges between a pair of nodes.
+            If None, remove a single edge between u and v. If there are
+            multiple edges, removes the last edge added in terms of
+            insertion order.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v, or
+            if there is no edge with the specified key.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+
+        For multiple edges
+
+        >>> G = nx.MultiDiGraph()
+        >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)])  # key_list returned
+        [0, 1, 2]
+
+        When ``key=None`` (the default), edges are removed in the opposite
+        order that they were added:
+
+        >>> G.remove_edge(1, 2)
+        >>> G.edges(keys=True)
+        OutMultiEdgeView([(1, 2, 0), (1, 2, 1)])
+
+        For edges with keys
+
+        >>> G = nx.MultiDiGraph()
+        >>> G.add_edge(1, 2, key="first")
+        'first'
+        >>> G.add_edge(1, 2, key="second")
+        'second'
+        >>> G.remove_edge(1, 2, key="first")
+        >>> G.edges(keys=True)
+        OutMultiEdgeView([(1, 2, 'second')])
+
+        """
+        try:
+            d = self._adj[u][v]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
+        # remove the edge with specified data
+        if key is None:
+            d.popitem()
+        else:
+            try:
+                del d[key]
+            except KeyError as err:
+                msg = f"The edge {u}-{v} with key {key} is not in the graph."
+                raise NetworkXError(msg) from err
+        if len(d) == 0:
+            # remove the key entries if last edge
+            del self._succ[u][v]
+            del self._pred[v][u]
+        nx._clear_cache(self)
+
+    @cached_property
+    def edges(self):
+        """An OutMultiEdgeView of the Graph as G.edges or G.edges().
+
+        edges(self, nbunch=None, data=False, keys=False, default=None)
+
+        The OutMultiEdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, ``G.edges[u, v, k]['color']`` provides the value of the color
+        attribute for the edge from ``u`` to ``v`` with key ``k`` while
+        ``for (u, v, k, c) in G.edges(data='color', default='red', keys=True):``
+        iterates through all the edges yielding the color attribute with
+        default `'red'` if no color attribute exists.
+
+        Edges are returned as tuples with optional data and keys
+        in the order (node, neighbor, key, data). If ``keys=True`` is not
+        provided, the tuples will just be (node, neighbor, data), but
+        multiple tuples with the same node and neighbor will be
+        generated when multiple edges between two nodes exist.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        keys : bool, optional (default=False)
+            If True, return edge keys with each edge, creating (u, v, k,
+            d) tuples when data is also requested (the default) and (u,
+            v, k) tuples when data is not requested.
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : OutMultiEdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+            used for attribute lookup as ``edges[u, v, k]['foo']``.
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2])
+        >>> key = G.add_edge(2, 3, weight=5)
+        >>> key2 = G.add_edge(1, 2)  # second edge between these nodes
+        >>> [e for e in G.edges()]
+        [(0, 1), (1, 2), (1, 2), (2, 3)]
+        >>> list(G.edges(data=True))  # default data is {} (empty dict)
+        [(0, 1, {}), (1, 2, {}), (1, 2, {}), (2, 3, {'weight': 5})]
+        >>> list(G.edges(data="weight", default=1))
+        [(0, 1, 1), (1, 2, 1), (1, 2, 1), (2, 3, 5)]
+        >>> list(G.edges(keys=True))  # default keys are integers
+        [(0, 1, 0), (1, 2, 0), (1, 2, 1), (2, 3, 0)]
+        >>> list(G.edges(data=True, keys=True))
+        [(0, 1, 0, {}), (1, 2, 0, {}), (1, 2, 1, {}), (2, 3, 0, {'weight': 5})]
+        >>> list(G.edges(data="weight", default=1, keys=True))
+        [(0, 1, 0, 1), (1, 2, 0, 1), (1, 2, 1, 1), (2, 3, 0, 5)]
+        >>> list(G.edges([0, 2]))
+        [(0, 1), (2, 3)]
+        >>> list(G.edges(0))
+        [(0, 1)]
+        >>> list(G.edges(1))
+        [(1, 2), (1, 2)]
+
+        See Also
+        --------
+        in_edges, out_edges
+        """
+        return OutMultiEdgeView(self)
+
+    # alias out_edges to edges
+    @cached_property
+    def out_edges(self):
+        return OutMultiEdgeView(self)
+
+    out_edges.__doc__ = edges.__doc__
+
+    @cached_property
+    def in_edges(self):
+        """A view of the in edges of the graph as G.in_edges or G.in_edges().
+
+        in_edges(self, nbunch=None, data=False, keys=False, default=None)
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        keys : bool, optional (default=False)
+            If True, return edge keys with each edge, creating 3-tuples
+            (u, v, k) or with data, 4-tuples (u, v, k, d).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        in_edges : InMultiEdgeView or InMultiEdgeDataView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+            used for attribute lookup as `edges[u, v, k]['foo']`.
+
+        See Also
+        --------
+        edges
+        """
+        return InMultiEdgeView(self)
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        DiMultiDegreeView or int
+            If multiple nodes are requested (the default), returns a `DiMultiDegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        See Also
+        --------
+        out_degree, in_degree
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1, 2]))
+        [(0, 1), (1, 2), (2, 2)]
+        >>> G.add_edge(0, 1)  # parallel edge
+        1
+        >>> list(G.degree([0, 1, 2]))  # parallel edges are counted
+        [(0, 2), (1, 3), (2, 2)]
+
+        """
+        return DiMultiDegreeView(self)
+
+    @cached_property
+    def in_degree(self):
+        """A DegreeView for (node, in_degree) or in_degree for single node.
+
+        The node in-degree is the number of edges pointing into the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            Degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, in-degree).
+
+        See Also
+        --------
+        degree, out_degree
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.in_degree(0)  # node 0 with degree 0
+        0
+        >>> list(G.in_degree([0, 1, 2]))
+        [(0, 0), (1, 1), (2, 1)]
+        >>> G.add_edge(0, 1)  # parallel edge
+        1
+        >>> list(G.in_degree([0, 1, 2]))  # parallel edges counted
+        [(0, 0), (1, 2), (2, 1)]
+
+        """
+        return InMultiDegreeView(self)
+
+    @cached_property
+    def out_degree(self):
+        """Returns an iterator for (node, out-degree) or out-degree for single node.
+
+        out_degree(self, nbunch=None, weight=None)
+
+        The node out-degree is the number of edges pointing out of the node.
+        This function returns the out-degree for a single node or an iterator
+        for a bunch of nodes or if nothing is passed as argument.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            Degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, out-degree).
+
+        See Also
+        --------
+        degree, in_degree
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.out_degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.out_degree([0, 1, 2]))
+        [(0, 1), (1, 1), (2, 1)]
+        >>> G.add_edge(0, 1)  # parallel edge
+        1
+        >>> list(G.out_degree([0, 1, 2]))  # counts parallel edges
+        [(0, 2), (1, 1), (2, 1)]
+
+        """
+        return OutMultiDegreeView(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return True
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return True
+
+    def to_undirected(self, reciprocal=False, as_view=False):
+        """Returns an undirected representation of the digraph.
+
+        Parameters
+        ----------
+        reciprocal : bool (optional)
+          If True only keep edges that appear in both directions
+          in the original digraph.
+        as_view : bool (optional, default=False)
+          If True return an undirected view of the original directed graph.
+
+        Returns
+        -------
+        G : MultiGraph
+            An undirected graph with the same name and nodes and
+            with edge (u, v, data) if either (u, v, data) or (v, u, data)
+            is in the digraph.  If both edges exist in digraph and
+            their edge data is different, only one edge is created
+            with an arbitrary choice of which edge data to use.
+            You must check and correct for this manually if desired.
+
+        See Also
+        --------
+        MultiGraph, copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar D=MultiDiGraph(G) which
+        returns a shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed MultiDiGraph to use dict-like
+        objects in the data structure, those changes do not transfer
+        to the MultiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(2)  # or MultiGraph, etc
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        if reciprocal is True:
+            G.add_edges_from(
+                (u, v, key, deepcopy(data))
+                for u, nbrs in self._adj.items()
+                for v, keydict in nbrs.items()
+                for key, data in keydict.items()
+                if v in self._pred[u] and key in self._pred[u][v]
+            )
+        else:
+            G.add_edges_from(
+                (u, v, key, deepcopy(data))
+                for u, nbrs in self._adj.items()
+                for v, keydict in nbrs.items()
+                for key, data in keydict.items()
+            )
+        return G
+
+    def reverse(self, copy=True):
+        """Returns the reverse of the graph.
+
+        The reverse is a graph with the same nodes and edges
+        but with the directions of the edges reversed.
+
+        Parameters
+        ----------
+        copy : bool optional (default=True)
+            If True, return a new DiGraph holding the reversed edges.
+            If False, the reverse graph is created using a view of
+            the original graph.
+        """
+        if copy:
+            H = self.__class__()
+            H.graph.update(deepcopy(self.graph))
+            H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+            H.add_edges_from(
+                (v, u, k, deepcopy(d))
+                for u, v, k, d in self.edges(keys=True, data=True)
+            )
+            return H
+        return nx.reverse_view(self)
diff --git a/lib/python3.11/site-packages/networkx/classes/multigraph.py b/lib/python3.11/site-packages/networkx/classes/multigraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..a942f2e04ccea8a877e40d9b03cd8484844cb147
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/multigraph.py
@@ -0,0 +1,1294 @@
+"""Base class for MultiGraph."""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import NetworkXError, convert
+from networkx.classes.coreviews import MultiAdjacencyView
+from networkx.classes.graph import Graph
+from networkx.classes.reportviews import MultiDegreeView, MultiEdgeView
+
+__all__ = ["MultiGraph"]
+
+
+class MultiGraph(Graph):
+    """
+    An undirected graph class that can store multiedges.
+
+    Multiedges are multiple edges between two nodes.  Each edge
+    can hold optional data or attributes.
+
+    A MultiGraph holds undirected edges.  Self loops are allowed.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes. By convention `None` is not used as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes, in a MultiGraph each edge has a key to
+    distinguish between multiple edges that have the same source and
+    destination nodes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array,
+        SciPy sparse array, or PyGraphviz graph.
+
+    multigraph_input : bool or None (default None)
+        Note: Only used when `incoming_graph_data` is a dict.
+        If True, `incoming_graph_data` is assumed to be a
+        dict-of-dict-of-dict-of-dict structure keyed by
+        node to neighbor to edge keys to edge data for multi-edges.
+        A NetworkXError is raised if this is not the case.
+        If False, :func:`to_networkx_graph` is used to try to determine
+        the dict's graph data structure as either a dict-of-dict-of-dict
+        keyed by node to neighbor to edge data, or a dict-of-iterable
+        keyed by node to neighbors.
+        If None, the treatment for True is tried, but if it fails,
+        the treatment for False is tried.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    Graph
+    DiGraph
+    MultiDiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.MultiGraph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> key = G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> keys = G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> keys = G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  If an edge already exists, an additional
+    edge is created and stored using a key to identify the edge.
+    By default the key is the lowest unused integer.
+
+    >>> keys = G.add_edges_from([(4, 5, {"route": 28}), (4, 5, {"route": 37})])
+    >>> G[4]
+    AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 2: {'route': 37}}})
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.MultiGraph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> key = G.add_edge(1, 2, weight=4.7)
+    >>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2][0]["weight"] = 4.7
+    >>> G.edges[1, 2, 0]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges[1,
+    2, 0]` a read-only dict-like structure. However, you can assign to
+    attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
+    to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`.
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+    >>> G[1]  # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
+    AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`.
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, keydict in nbrsdict.items():
+    ...         for key, eattr in keydict.items():
+    ...             if "weight" in eattr:
+    ...                 # Do something useful with the edges
+    ...                 pass
+
+    But the edges() method is often more convenient:
+
+    >>> for u, v, keys, weight in G.edges(data="weight", keys=True):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using methods and object-attributes.
+    Reporting usually provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information
+    and holds edge_key dicts keyed by neighbor. The edge_key dict holds
+    each edge_attr dict keyed by edge key. The inner dict
+    (edge_attr_dict) represents the edge data and holds edge attribute
+    values keyed by attribute names.
+
+    Each of these four dicts in the dict-of-dict-of-dict-of-dict
+    structure can be replaced by a user defined dict-like object.
+    In general, the dict-like features should be maintained but
+    extra features can be added. To replace one of the dicts create
+    a new graph class by changing the class(!) variable holding the
+    factory for that dict-like structure. The variable names are
+    node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
+    adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
+    and graph_attr_dict_factory.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds multiedge key dicts keyed by neighbor.
+        It should require no arguments and return a dict-like object.
+
+    edge_key_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge key dict
+        which holds edge data keyed by edge key.
+        It should require no arguments and return a dict-like object.
+
+    edge_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherited without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    # node_dict_factory = dict    # already assigned in Graph
+    # adjlist_outer_dict_factory = dict
+    # adjlist_inner_dict_factory = dict
+    edge_key_dict_factory = dict
+    # edge_attr_dict_factory = dict
+
+    def to_directed_class(self):
+        """Returns the class to use for empty directed copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return nx.MultiDiGraph
+
+    def to_undirected_class(self):
+        """Returns the class to use for empty undirected copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return MultiGraph
+
+    # This __new__ method just does what Python itself does automatically.
+    # We include it here as part of the dispatchable/backend interface.
+    # If your goal is to understand how the graph classes work, you can ignore
+    # this method, even when subclassing the base classes. If you are subclassing
+    # in order to provide a backend that allows class instantiation, this method
+    # can be overridden to return your own backend graph class.
+    @nx._dispatchable(name="multigraph__new__", graphs=None, returns_graph=True)
+    def __new__(cls, *args, **kwargs):
+        return object.__new__(cls)
+
+    def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph
+            Data to initialize graph.  If incoming_graph_data=None (default)
+            an empty graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        multigraph_input : bool or None (default None)
+            Note: Only used when `incoming_graph_data` is a dict.
+            If True, `incoming_graph_data` is assumed to be a
+            dict-of-dict-of-dict-of-dict structure keyed by
+            node to neighbor to edge keys to edge data for multi-edges.
+            A NetworkXError is raised if this is not the case.
+            If False, :func:`to_networkx_graph` is used to try to determine
+            the dict's graph data structure as either a dict-of-dict-of-dict
+            keyed by node to neighbor to edge data, or a dict-of-iterable
+            keyed by node to neighbors.
+            If None, the treatment for True is tried, but if it fails,
+            the treatment for False is tried.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> G = nx.MultiGraph(name="my graph")
+        >>> e = [(1, 2), (1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.MultiGraph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.MultiGraph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        attr.pop("backend", None)  # Ignore explicit `backend="networkx"`
+        # multigraph_input can be None/True/False. So check "is not False"
+        if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
+            Graph.__init__(self)
+            try:
+                convert.from_dict_of_dicts(
+                    incoming_graph_data, create_using=self, multigraph_input=True
+                )
+                self.graph.update(attr)
+            except Exception as err:
+                if multigraph_input is True:
+                    raise nx.NetworkXError(
+                        f"converting multigraph_input raised:\n{type(err)}: {err}"
+                    )
+                Graph.__init__(self, incoming_graph_data, **attr)
+        else:
+            Graph.__init__(self, incoming_graph_data, **attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-data-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, edgesdict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+
+        Examples
+        --------
+        >>> e = [(1, 2), (1, 2), (1, 3), (3, 4)]  # list of edges
+        >>> G = nx.MultiGraph(e)
+        >>> G.edges[1, 2, 0]["weight"] = 3
+        >>> result = set()
+        >>> for edgekey, data in G[1][2].items():
+        ...     result.add(data.get("weight", 1))
+        >>> result
+        {1, 3}
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return MultiAdjacencyView(self._adj)
+
+    def new_edge_key(self, u, v):
+        """Returns an unused key for edges between nodes `u` and `v`.
+
+        The nodes `u` and `v` do not need to be already in the graph.
+
+        Notes
+        -----
+        In the standard MultiGraph class the new key is the number of existing
+        edges between `u` and `v` (increased if necessary to ensure unused).
+        The first edge will have key 0, then 1, etc. If an edge is removed
+        further new_edge_keys may not be in this order.
+
+        Parameters
+        ----------
+        u, v : nodes
+
+        Returns
+        -------
+        key : int
+        """
+        try:
+            keydict = self._adj[u][v]
+        except KeyError:
+            return 0
+        key = len(keydict)
+        while key in keydict:
+            key += 1
+        return key
+
+    def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_for_edge, v_for_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        key : hashable identifier, optional (default=lowest unused integer)
+            Used to distinguish multiedges between a pair of nodes.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        Returns
+        -------
+        The edge key assigned to the edge.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        To replace/update edge data, use the optional key argument
+        to identify a unique edge.  Otherwise a new edge will be created.
+
+        NetworkX algorithms designed for weighted graphs cannot use
+        multigraphs directly because it is not clear how to handle
+        multiedge weights.  Convert to Graph using edge attribute
+        'weight' to enable weighted graph algorithms.
+
+        Default keys are generated using the method `new_edge_key()`.
+        This method can be overridden by subclassing the base class and
+        providing a custom `new_edge_key()` method.
+
+        Examples
+        --------
+        The following each add an additional edge e=(1, 2) to graph G:
+
+        >>> G = nx.MultiGraph()
+        >>> e = (1, 2)
+        >>> ekey = G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        1
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+        [2]
+
+        Associate data to edges using keywords:
+
+        >>> ekey = G.add_edge(1, 2, weight=3)
+        >>> ekey = G.add_edge(1, 2, key=0, weight=4)  # update data for key=0
+        >>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> ekey = G.add_edge(1, 2)
+        >>> G[1][2][0].update({0: 5})
+        >>> G.edges[1, 2, 0].update({0: 5})
+        """
+        u, v = u_for_edge, v_for_edge
+        # add nodes
+        if u not in self._adj:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._adj[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._adj:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._adj[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        if key is None:
+            key = self.new_edge_key(u, v)
+        if v in self._adj[u]:
+            keydict = self._adj[u][v]
+            datadict = keydict.get(key, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            keydict[key] = datadict
+        else:
+            # selfloops work this way without special treatment
+            datadict = self.edge_attr_dict_factory()
+            datadict.update(attr)
+            keydict = self.edge_key_dict_factory()
+            keydict[key] = datadict
+            self._adj[u][v] = keydict
+            self._adj[v][u] = keydict
+        nx._clear_cache(self)
+        return key
+
+    def add_edges_from(self, ebunch_to_add, **attr):
+        """Add all the edges in ebunch_to_add.
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the container will be added to the
+            graph. The edges can be:
+
+                - 2-tuples (u, v) or
+                - 3-tuples (u, v, d) for an edge data dict d, or
+                - 3-tuples (u, v, k) for not iterable key k, or
+                - 4-tuples (u, v, k, d) for an edge with data and key k
+
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        Returns
+        -------
+        A list of edge keys assigned to the edges in `ebunch`.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_weighted_edges_from : convenient way to add weighted edges
+
+        Notes
+        -----
+        Adding the same edge twice has no effect but any edge data
+        will be updated when each duplicate edge is added.
+
+        Edge attributes specified in an ebunch take precedence over
+        attributes specified via keyword arguments.
+
+        Default keys are generated using the method ``new_edge_key()``.
+        This method can be overridden by subclassing the base class and
+        providing a custom ``new_edge_key()`` method.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
+        >>> e = zip(range(0, 3), range(1, 4))
+        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
+
+        Associate data to edges
+
+        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
+        >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_edges_from(((5, n) for n in G.nodes))
+        >>> # right way - note that there will be no self-edge for node 5
+        >>> assigned_keys = G.add_edges_from(list((5, n) for n in G.nodes))
+        """
+        keylist = []
+        for e in ebunch_to_add:
+            ne = len(e)
+            if ne == 4:
+                u, v, key, dd = e
+            elif ne == 3:
+                u, v, dd = e
+                key = None
+            elif ne == 2:
+                u, v = e
+                dd = {}
+                key = None
+            else:
+                msg = f"Edge tuple {e} must be a 2-tuple, 3-tuple or 4-tuple."
+                raise NetworkXError(msg)
+            ddd = {}
+            ddd.update(attr)
+            try:
+                ddd.update(dd)
+            except (TypeError, ValueError):
+                if ne != 3:
+                    raise
+                key = dd  # ne == 3 with 3rd value not dict, must be a key
+            key = self.add_edge(u, v, key)
+            self[u][v][key].update(ddd)
+            keylist.append(key)
+        nx._clear_cache(self)
+        return keylist
+
+    def remove_edge(self, u, v, key=None):
+        """Remove an edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove an edge between nodes u and v.
+        key : hashable identifier, optional (default=None)
+            Used to distinguish multiple edges between a pair of nodes.
+            If None, remove a single edge between u and v. If there are
+            multiple edges, removes the last edge added in terms of
+            insertion order.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v, or
+            if there is no edge with the specified key.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+
+        For multiple edges
+
+        >>> G = nx.MultiGraph()  # or MultiDiGraph, etc
+        >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)])  # key_list returned
+        [0, 1, 2]
+
+        When ``key=None`` (the default), edges are removed in the opposite
+        order that they were added:
+
+        >>> G.remove_edge(1, 2)
+        >>> G.edges(keys=True)
+        MultiEdgeView([(1, 2, 0), (1, 2, 1)])
+        >>> G.remove_edge(2, 1)  # edges are not directed
+        >>> G.edges(keys=True)
+        MultiEdgeView([(1, 2, 0)])
+
+        For edges with keys
+
+        >>> G = nx.MultiGraph()
+        >>> G.add_edge(1, 2, key="first")
+        'first'
+        >>> G.add_edge(1, 2, key="second")
+        'second'
+        >>> G.remove_edge(1, 2, key="first")
+        >>> G.edges(keys=True)
+        MultiEdgeView([(1, 2, 'second')])
+
+        """
+        try:
+            d = self._adj[u][v]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
+        # remove the edge with specified data
+        if key is None:
+            d.popitem()
+        else:
+            try:
+                del d[key]
+            except KeyError as err:
+                msg = f"The edge {u}-{v} with key {key} is not in the graph."
+                raise NetworkXError(msg) from err
+        if len(d) == 0:
+            # remove the key entries if last edge
+            del self._adj[u][v]
+            if u != v:  # check for selfloop
+                del self._adj[v][u]
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each edge given in the list or container will be removed
+            from the graph. The edges can be:
+
+                - 2-tuples (u, v) A single edge between u and v is removed.
+                - 3-tuples (u, v, key) The edge identified by key is removed.
+                - 4-tuples (u, v, key, data) where data is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+
+        Removing multiple copies of edges
+
+        >>> G = nx.MultiGraph()
+        >>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)])
+        >>> G.remove_edges_from([(1, 2), (2, 1)])  # edges aren't directed
+        >>> list(G.edges())
+        [(1, 2)]
+        >>> G.remove_edges_from([(1, 2), (1, 2)])  # silently ignore extra copy
+        >>> list(G.edges)  # now empty graph
+        []
+
+        When the edge is a 2-tuple ``(u, v)`` but there are multiple edges between
+        u and v in the graph, the most recent edge (in terms of insertion
+        order) is removed.
+
+        >>> G = nx.MultiGraph()
+        >>> for key in ("x", "y", "a"):
+        ...     k = G.add_edge(0, 1, key=key)
+        >>> G.edges(keys=True)
+        MultiEdgeView([(0, 1, 'x'), (0, 1, 'y'), (0, 1, 'a')])
+        >>> G.remove_edges_from([(0, 1)])
+        >>> G.edges(keys=True)
+        MultiEdgeView([(0, 1, 'x'), (0, 1, 'y')])
+
+        """
+        for e in ebunch:
+            try:
+                self.remove_edge(*e[:3])
+            except NetworkXError:
+                pass
+        nx._clear_cache(self)
+
+    def has_edge(self, u, v, key=None):
+        """Returns True if the graph has an edge between nodes u and v.
+
+        This is the same as `v in G[u] or key in G[u][v]`
+        without KeyError exceptions.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Nodes can be, for example, strings or numbers.
+
+        key : hashable identifier, optional (default=None)
+            If specified return True only if the edge with
+            key is found.
+
+        Returns
+        -------
+        edge_ind : bool
+            True if edge is in the graph, False otherwise.
+
+        Examples
+        --------
+        Can be called either using two nodes u, v, an edge tuple (u, v),
+        or an edge tuple (u, v, key).
+
+        >>> G = nx.MultiGraph()  # or MultiDiGraph
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.has_edge(0, 1)  # using two nodes
+        True
+        >>> e = (0, 1)
+        >>> G.has_edge(*e)  #  e is a 2-tuple (u, v)
+        True
+        >>> G.add_edge(0, 1, key="a")
+        'a'
+        >>> G.has_edge(0, 1, key="a")  # specify key
+        True
+        >>> G.has_edge(1, 0, key="a")  # edges aren't directed
+        True
+        >>> e = (0, 1, "a")
+        >>> G.has_edge(*e)  # e is a 3-tuple (u, v, 'a')
+        True
+
+        The following syntax are equivalent:
+
+        >>> G.has_edge(0, 1)
+        True
+        >>> 1 in G[0]  # though this gives :exc:`KeyError` if 0 not in G
+        True
+        >>> 0 in G[1]  # other order; also gives :exc:`KeyError` if 0 not in G
+        True
+
+        """
+        try:
+            if key is None:
+                return v in self._adj[u]
+            else:
+                return key in self._adj[u][v]
+        except KeyError:
+            return False
+
+    @cached_property
+    def edges(self):
+        """Returns an iterator over the edges.
+
+        edges(self, nbunch=None, data=False, keys=False, default=None)
+
+        The MultiEdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, ``G.edges[u, v, k]['color']`` provides the value of the color
+        attribute for the edge from ``u`` to ``v`` with key ``k`` while
+        ``for (u, v, k, c) in G.edges(data='color', keys=True, default="red"):``
+        iterates through all the edges yielding the color attribute with
+        default `'red'` if no color attribute exists.
+
+        Edges are returned as tuples with optional data and keys
+        in the order (node, neighbor, key, data). If ``keys=True`` is not
+        provided, the tuples will just be (node, neighbor, data), but
+        multiple tuples with the same node and neighbor will be generated
+        when multiple edges exist between two nodes.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        keys : bool, optional (default=False)
+            If True, return edge keys with each edge, creating (u, v, k)
+            tuples or (u, v, k, d) tuples if data is also requested.
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : MultiEdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+            used for attribute lookup as ``edges[u, v, k]['foo']``.
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> nx.add_path(G, [0, 1, 2])
+        >>> key = G.add_edge(2, 3, weight=5)
+        >>> key2 = G.add_edge(2, 1, weight=2)  # multi-edge
+        >>> [e for e in G.edges()]
+        [(0, 1), (1, 2), (1, 2), (2, 3)]
+        >>> G.edges.data()  # default data is {} (empty dict)
+        MultiEdgeDataView([(0, 1, {}), (1, 2, {}), (1, 2, {'weight': 2}), (2, 3, {'weight': 5})])
+        >>> G.edges.data("weight", default=1)
+        MultiEdgeDataView([(0, 1, 1), (1, 2, 1), (1, 2, 2), (2, 3, 5)])
+        >>> G.edges(keys=True)  # default keys are integers
+        MultiEdgeView([(0, 1, 0), (1, 2, 0), (1, 2, 1), (2, 3, 0)])
+        >>> G.edges.data(keys=True)
+        MultiEdgeDataView([(0, 1, 0, {}), (1, 2, 0, {}), (1, 2, 1, {'weight': 2}), (2, 3, 0, {'weight': 5})])
+        >>> G.edges.data("weight", default=1, keys=True)
+        MultiEdgeDataView([(0, 1, 0, 1), (1, 2, 0, 1), (1, 2, 1, 2), (2, 3, 0, 5)])
+        >>> G.edges([0, 3])  # Note ordering of tuples from listed sources
+        MultiEdgeDataView([(0, 1), (3, 2)])
+        >>> G.edges([0, 3, 2, 1])  # Note ordering of tuples
+        MultiEdgeDataView([(0, 1), (3, 2), (2, 1), (2, 1)])
+        >>> G.edges(0)
+        MultiEdgeDataView([(0, 1)])
+        """
+        return MultiEdgeView(self)
+
+    def get_edge_data(self, u, v, key=None, default=None):
+        """Returns the attribute dictionary associated with edge (u, v,
+        key).
+
+        If a key is not provided, returns a dictionary mapping edge keys
+        to attribute dictionaries for each edge between u and v.
+
+        This is identical to `G[u][v][key]` except the default is returned
+        instead of an exception is the edge doesn't exist.
+
+        Parameters
+        ----------
+        u, v : nodes
+
+        default :  any Python object (default=None)
+            Value to return if the specific edge (u, v, key) is not
+            found, OR if there are no edges between u and v and no key
+            is specified.
+
+        key : hashable identifier, optional (default=None)
+            Return data only for the edge with specified key, as an
+            attribute dictionary (rather than a dictionary mapping keys
+            to attribute dictionaries).
+
+        Returns
+        -------
+        edge_dict : dictionary
+            The edge attribute dictionary, OR a dictionary mapping edge
+            keys to attribute dictionaries for each of those edges if no
+            specific key is provided (even if there's only one edge
+            between u and v).
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()  # or MultiDiGraph
+        >>> key = G.add_edge(0, 1, key="a", weight=7)
+        >>> G[0][1]["a"]  # key='a'
+        {'weight': 7}
+        >>> G.edges[0, 1, "a"]  # key='a'
+        {'weight': 7}
+
+        Warning: we protect the graph data structure by making
+        `G.edges` and `G[1][2]` read-only dict-like structures.
+        However, you can assign values to attributes in e.g.
+        `G.edges[1, 2, 'a']` or `G[1][2]['a']` using an additional
+        bracket as shown next. You need to specify all edge info
+        to assign to the edge data associated with an edge.
+
+        >>> G[0][1]["a"]["weight"] = 10
+        >>> G.edges[0, 1, "a"]["weight"] = 10
+        >>> G[0][1]["a"]["weight"]
+        10
+        >>> G.edges[1, 0, "a"]["weight"]
+        10
+
+        >>> G = nx.MultiGraph()  # or MultiDiGraph
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.edges[0, 1, 0]["weight"] = 5
+        >>> G.get_edge_data(0, 1)
+        {0: {'weight': 5}}
+        >>> e = (0, 1)
+        >>> G.get_edge_data(*e)  # tuple form
+        {0: {'weight': 5}}
+        >>> G.get_edge_data(3, 0)  # edge not in graph, returns None
+        >>> G.get_edge_data(3, 0, default=0)  # edge not in graph, return default
+        0
+        >>> G.get_edge_data(1, 0, 0)  # specific key gives back
+        {'weight': 5}
+        """
+        try:
+            if key is None:
+                return self._adj[u][v]
+            else:
+                return self._adj[u][v][key]
+        except KeyError:
+            return default
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        MultiDegreeView or int
+            If multiple nodes are requested (the default), returns a `MultiDegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1]))
+        [(0, 1), (1, 2)]
+
+        """
+        return MultiDegreeView(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return True
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return False
+
+    def copy(self, as_view=False):
+        """Returns a copy of the graph.
+
+        The copy method by default returns an independent shallow copy
+        of the graph and attributes. That is, if an attribute is a
+        container, that container is shared by the original an the copy.
+        Use Python's `copy.deepcopy` for new containers.
+
+        If `as_view` is True then a view is returned instead of a copy.
+
+        Notes
+        -----
+        All copies reproduce the graph structure, but data attributes
+        may be handled in different ways. There are four types of copies
+        of a graph that people might want.
+
+        Deepcopy -- A "deepcopy" copies the graph structure as well as
+        all data attributes and any objects they might contain.
+        The entire graph object is new so that changes in the copy
+        do not affect the original object. (see Python's copy.deepcopy)
+
+        Data Reference (Shallow) -- For a shallow copy the graph structure
+        is copied but the edge, node and graph attribute dicts are
+        references to those in the original graph. This saves
+        time and memory but could cause confusion if you change an attribute
+        in one graph and it changes the attribute in the other.
+        NetworkX does not provide this level of shallow copy.
+
+        Independent Shallow -- This copy creates new independent attribute
+        dicts and then does a shallow copy of the attributes. That is, any
+        attributes that are containers are shared between the new graph
+        and the original. This is exactly what `dict.copy()` provides.
+        You can obtain this style copy using:
+
+            >>> G = nx.path_graph(5)
+            >>> H = G.copy()
+            >>> H = G.copy(as_view=False)
+            >>> H = nx.Graph(G)
+            >>> H = G.__class__(G)
+
+        Fresh Data -- For fresh data, the graph structure is copied while
+        new empty data attribute dicts are created. The resulting graph
+        is independent of the original and it has no edge, node or graph
+        attributes. Fresh copies are not enabled. Instead use:
+
+            >>> H = G.__class__()
+            >>> H.add_nodes_from(G)
+            >>> H.add_edges_from(G.edges)
+
+        View -- Inspired by dict-views, graph-views act like read-only
+        versions of the original graph, providing a copy of the original
+        structure without requiring any memory for copying the information.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Parameters
+        ----------
+        as_view : bool, optional (default=False)
+            If True, the returned graph-view provides a read-only view
+            of the original graph without actually copying any data.
+
+        Returns
+        -------
+        G : Graph
+            A copy of the graph.
+
+        See Also
+        --------
+        to_directed: return a directed copy of the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> H = G.copy()
+
+        """
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self)
+        G = self.__class__()
+        G.graph.update(self.graph)
+        G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, key, datadict.copy())
+            for u, nbrs in self._adj.items()
+            for v, keydict in nbrs.items()
+            for key, datadict in keydict.items()
+        )
+        return G
+
+    def to_directed(self, as_view=False):
+        """Returns a directed representation of the graph.
+
+        Returns
+        -------
+        G : MultiDiGraph
+            A directed graph with the same name, same nodes, and with
+            each edge (u, v, k, data) replaced by two directed edges
+            (u, v, k, data) and (v, u, k, data).
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar D=MultiDiGraph(G) which
+        returns a shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed MultiGraph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        MultiDiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> G.add_edge(0, 1)
+        0
+        >>> G.add_edge(0, 1)
+        1
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1)]
+
+        If already directed, return a (deep) copy
+
+        >>> G = nx.MultiDiGraph()
+        >>> G.add_edge(0, 1)
+        0
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1, 0)]
+        """
+        graph_class = self.to_directed_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, key, deepcopy(datadict))
+            for u, nbrs in self.adj.items()
+            for v, keydict in nbrs.items()
+            for key, datadict in keydict.items()
+        )
+        return G
+
+    def to_undirected(self, as_view=False):
+        """Returns an undirected copy of the graph.
+
+        Returns
+        -------
+        G : Graph/MultiGraph
+            A deepcopy of the graph.
+
+        See Also
+        --------
+        copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar `G = nx.MultiGraph(D)`
+        which returns a shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed MultiGraph to use dict-like
+        objects in the data structure, those changes do not transfer
+        to the MultiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph([(0, 1), (0, 1), (1, 2)])
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 2, 0), (2, 1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1, 0), (0, 1, 1), (1, 2, 0)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, key, deepcopy(datadict))
+            for u, nbrs in self._adj.items()
+            for v, keydict in nbrs.items()
+            for key, datadict in keydict.items()
+        )
+        return G
+
+    def number_of_edges(self, u=None, v=None):
+        """Returns the number of edges between two nodes.
+
+        Parameters
+        ----------
+        u, v : nodes, optional (Default=all edges)
+            If u and v are specified, return the number of edges between
+            u and v. Otherwise return the total number of all edges.
+
+        Returns
+        -------
+        nedges : int
+            The number of edges in the graph.  If nodes `u` and `v` are
+            specified return the number of edges between those nodes. If
+            the graph is directed, this only returns the number of edges
+            from `u` to `v`.
+
+        See Also
+        --------
+        size
+
+        Examples
+        --------
+        For undirected multigraphs, this method counts the total number
+        of edges in the graph::
+
+            >>> G = nx.MultiGraph()
+            >>> G.add_edges_from([(0, 1), (0, 1), (1, 2)])
+            [0, 1, 0]
+            >>> G.number_of_edges()
+            3
+
+        If you specify two nodes, this counts the total number of edges
+        joining the two nodes::
+
+            >>> G.number_of_edges(0, 1)
+            2
+
+        For directed multigraphs, this method can count the total number
+        of directed edges from `u` to `v`::
+
+            >>> G = nx.MultiDiGraph()
+            >>> G.add_edges_from([(0, 1), (0, 1), (1, 0)])
+            [0, 1, 0]
+            >>> G.number_of_edges(0, 1)
+            2
+            >>> G.number_of_edges(1, 0)
+            1
+
+        """
+        if u is None:
+            return self.size()
+        try:
+            edgedata = self._adj[u][v]
+        except KeyError:
+            return 0  # no such edge
+        return len(edgedata)
diff --git a/lib/python3.11/site-packages/networkx/classes/reportviews.py b/lib/python3.11/site-packages/networkx/classes/reportviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..789662de19600ec2a7922db612c525dfb75695ea
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/reportviews.py
@@ -0,0 +1,1447 @@
+"""
+View Classes provide node, edge and degree "views" of a graph.
+
+Views for nodes, edges and degree are provided for all base graph classes.
+A view means a read-only object that is quick to create, automatically
+updated when the graph changes, and provides basic access like `n in V`,
+`for n in V`, `V[n]` and sometimes set operations.
+
+The views are read-only iterable containers that are updated as the
+graph is updated. As with dicts, the graph should not be updated
+while iterating through the view. Views can be iterated multiple times.
+
+Edge and Node views also allow data attribute lookup.
+The resulting attribute dict is writable as `G.edges[3, 4]['color']='red'`
+Degree views allow lookup of degree values for single nodes.
+Weighted degree is supported with the `weight` argument.
+
+NodeView
+========
+
+    `V = G.nodes` (or `V = G.nodes()`) allows `len(V)`, `n in V`, set
+    operations e.g. "G.nodes & H.nodes", and `dd = G.nodes[n]`, where
+    `dd` is the node data dict. Iteration is over the nodes by default.
+
+NodeDataView
+============
+
+    To iterate over (node, data) pairs, use arguments to `G.nodes()`
+    to create a DataView e.g. `DV = G.nodes(data='color', default='red')`.
+    The DataView iterates as `for n, color in DV` and allows
+    `(n, 'red') in DV`. Using `DV = G.nodes(data=True)`, the DataViews
+    use the full datadict in writeable form also allowing contain testing as
+    `(n, {'color': 'red'}) in VD`. DataViews allow set operations when
+    data attributes are hashable.
+
+DegreeView
+==========
+
+    `V = G.degree` allows iteration over (node, degree) pairs as well
+    as lookup: `deg=V[n]`. There are many flavors of DegreeView
+    for In/Out/Directed/Multi. For Directed Graphs, `G.degree`
+    counts both in and out going edges. `G.out_degree` and
+    `G.in_degree` count only specific directions.
+    Weighted degree using edge data attributes is provide via
+    `V = G.degree(weight='attr_name')` where any string with the
+    attribute name can be used. `weight=None` is the default.
+    No set operations are implemented for degrees, use NodeView.
+
+    The argument `nbunch` restricts iteration to nodes in nbunch.
+    The DegreeView can still lookup any node even if nbunch is specified.
+
+EdgeView
+========
+
+    `V = G.edges` or `V = G.edges()` allows iteration over edges as well as
+    `e in V`, set operations and edge data lookup `dd = G.edges[2, 3]`.
+    Iteration is over 2-tuples `(u, v)` for Graph/DiGraph. For multigraphs
+    edges 3-tuples `(u, v, key)` are the default but 2-tuples can be obtained
+    via `V = G.edges(keys=False)`.
+
+    Set operations for directed graphs treat the edges as a set of 2-tuples.
+    For undirected graphs, 2-tuples are not a unique representation of edges.
+    So long as the set being compared to contains unique representations
+    of its edges, the set operations will act as expected. If the other
+    set contains both `(0, 1)` and `(1, 0)` however, the result of set
+    operations may contain both representations of the same edge.
+
+EdgeDataView
+============
+
+    Edge data can be reported using an EdgeDataView typically created
+    by calling an EdgeView: `DV = G.edges(data='weight', default=1)`.
+    The EdgeDataView allows iteration over edge tuples, membership checking
+    but no set operations.
+
+    Iteration depends on `data` and `default` and for multigraph `keys`
+    If `data is False` (the default) then iterate over 2-tuples `(u, v)`.
+    If `data is True` iterate over 3-tuples `(u, v, datadict)`.
+    Otherwise iterate over `(u, v, datadict.get(data, default))`.
+    For Multigraphs, if `keys is True`, replace `u, v` with `u, v, key`
+    to create 3-tuples and 4-tuples.
+
+    The argument `nbunch` restricts edges to those incident to nodes in nbunch.
+"""
+
+from abc import ABC
+from collections.abc import Mapping, Set
+
+import networkx as nx
+
+__all__ = [
+    "NodeView",
+    "NodeDataView",
+    "EdgeView",
+    "OutEdgeView",
+    "InEdgeView",
+    "EdgeDataView",
+    "OutEdgeDataView",
+    "InEdgeDataView",
+    "MultiEdgeView",
+    "OutMultiEdgeView",
+    "InMultiEdgeView",
+    "MultiEdgeDataView",
+    "OutMultiEdgeDataView",
+    "InMultiEdgeDataView",
+    "DegreeView",
+    "DiDegreeView",
+    "InDegreeView",
+    "OutDegreeView",
+    "MultiDegreeView",
+    "DiMultiDegreeView",
+    "InMultiDegreeView",
+    "OutMultiDegreeView",
+]
+
+
+# NodeViews
+class NodeView(Mapping, Set):
+    """A NodeView class to act as G.nodes for a NetworkX Graph
+
+    Set operations act on the nodes without considering data.
+    Iteration is over nodes. Node data can be looked up like a dict.
+    Use NodeDataView to iterate over node data or to specify a data
+    attribute for lookup. NodeDataView is created by calling the NodeView.
+
+    Parameters
+    ----------
+    graph : NetworkX graph-like class
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> NV = G.nodes()
+    >>> 2 in NV
+    True
+    >>> for n in NV:
+    ...     print(n)
+    0
+    1
+    2
+    >>> assert NV & {1, 2, 3} == {1, 2}
+
+    >>> G.add_node(2, color="blue")
+    >>> NV[2]
+    {'color': 'blue'}
+    >>> G.add_node(8, color="red")
+    >>> NDV = G.nodes(data=True)
+    >>> (2, NV[2]) in NDV
+    True
+    >>> for n, dd in NDV:
+    ...     print((n, dd.get("color", "aqua")))
+    (0, 'aqua')
+    (1, 'aqua')
+    (2, 'blue')
+    (8, 'red')
+    >>> NDV[2] == NV[2]
+    True
+
+    >>> NVdata = G.nodes(data="color", default="aqua")
+    >>> (2, NVdata[2]) in NVdata
+    True
+    >>> for n, dd in NVdata:
+    ...     print((n, dd))
+    (0, 'aqua')
+    (1, 'aqua')
+    (2, 'blue')
+    (8, 'red')
+    >>> NVdata[2] == NV[2]  # NVdata gets 'color', NV gets datadict
+    False
+    """
+
+    __slots__ = ("_nodes",)
+
+    def __getstate__(self):
+        return {"_nodes": self._nodes}
+
+    def __setstate__(self, state):
+        self._nodes = state["_nodes"]
+
+    def __init__(self, graph):
+        self._nodes = graph._node
+
+    # Mapping methods
+    def __len__(self):
+        return len(self._nodes)
+
+    def __iter__(self):
+        return iter(self._nodes)
+
+    def __getitem__(self, n):
+        if isinstance(n, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.nodes)[{n.start}:{n.stop}:{n.step}]"
+            )
+        return self._nodes[n]
+
+    # Set methods
+    def __contains__(self, n):
+        return n in self._nodes
+
+    @classmethod
+    def _from_iterable(cls, it):
+        return set(it)
+
+    # DataView method
+    def __call__(self, data=False, default=None):
+        if data is False:
+            return self
+        return NodeDataView(self._nodes, data, default)
+
+    def data(self, data=True, default=None):
+        """
+        Return a read-only view of node data.
+
+        Parameters
+        ----------
+        data : bool or node data key, default=True
+            If ``data=True`` (the default), return a `NodeDataView` object that
+            maps each node to *all* of its attributes. `data` may also be an
+            arbitrary key, in which case the `NodeDataView` maps each node to
+            the value for the keyed attribute. In this case, if a node does
+            not have the `data` attribute, the `default` value is used.
+        default : object, default=None
+            The value used when a node does not have a specific attribute.
+
+        Returns
+        -------
+        NodeDataView
+            The layout of the returned NodeDataView depends on the value of the
+            `data` parameter.
+
+        Notes
+        -----
+        If ``data=False``, returns a `NodeView` object without data.
+
+        See Also
+        --------
+        NodeDataView
+
+        Examples
+        --------
+        >>> G = nx.Graph()
+        >>> G.add_nodes_from(
+        ...     [
+        ...         (0, {"color": "red", "weight": 10}),
+        ...         (1, {"color": "blue"}),
+        ...         (2, {"color": "yellow", "weight": 2}),
+        ...     ]
+        ... )
+
+        Accessing node data with ``data=True`` (the default) returns a
+        NodeDataView mapping each node to all of its attributes:
+
+        >>> G.nodes.data()
+        NodeDataView({0: {'color': 'red', 'weight': 10}, 1: {'color': 'blue'}, 2: {'color': 'yellow', 'weight': 2}})
+
+        If `data` represents  a key in the node attribute dict, a NodeDataView mapping
+        the nodes to the value for that specific key is returned:
+
+        >>> G.nodes.data("color")
+        NodeDataView({0: 'red', 1: 'blue', 2: 'yellow'}, data='color')
+
+        If a specific key is not found in an attribute dict, the value specified
+        by `default` is returned:
+
+        >>> G.nodes.data("weight", default=-999)
+        NodeDataView({0: 10, 1: -999, 2: 2}, data='weight')
+
+        Note that there is no check that the `data` key is in any of the
+        node attribute dictionaries:
+
+        >>> G.nodes.data("height")
+        NodeDataView({0: None, 1: None, 2: None}, data='height')
+        """
+        if data is False:
+            return self
+        return NodeDataView(self._nodes, data, default)
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({tuple(self)})"
+
+
+class NodeDataView(Set):
+    """A DataView class for nodes of a NetworkX Graph
+
+    The main use for this class is to iterate through node-data pairs.
+    The data can be the entire data-dictionary for each node, or it
+    can be a specific attribute (with default) for each node.
+    Set operations are enabled with NodeDataView, but don't work in
+    cases where the data is not hashable. Use with caution.
+    Typically, set operations on nodes use NodeView, not NodeDataView.
+    That is, they use `G.nodes` instead of `G.nodes(data='foo')`.
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    data : bool or string (default=False)
+    default : object (default=None)
+    """
+
+    __slots__ = ("_nodes", "_data", "_default")
+
+    def __getstate__(self):
+        return {"_nodes": self._nodes, "_data": self._data, "_default": self._default}
+
+    def __setstate__(self, state):
+        self._nodes = state["_nodes"]
+        self._data = state["_data"]
+        self._default = state["_default"]
+
+    def __init__(self, nodedict, data=False, default=None):
+        self._nodes = nodedict
+        self._data = data
+        self._default = default
+
+    @classmethod
+    def _from_iterable(cls, it):
+        try:
+            return set(it)
+        except TypeError as err:
+            if "unhashable" in str(err):
+                msg = " : Could be b/c data=True or your values are unhashable"
+                raise TypeError(str(err) + msg) from err
+            raise
+
+    def __len__(self):
+        return len(self._nodes)
+
+    def __iter__(self):
+        data = self._data
+        if data is False:
+            return iter(self._nodes)
+        if data is True:
+            return iter(self._nodes.items())
+        return (
+            (n, dd[data] if data in dd else self._default)
+            for n, dd in self._nodes.items()
+        )
+
+    def __contains__(self, n):
+        try:
+            node_in = n in self._nodes
+        except TypeError:
+            n, d = n
+            return n in self._nodes and self[n] == d
+        if node_in is True:
+            return node_in
+        try:
+            n, d = n
+        except (TypeError, ValueError):
+            return False
+        return n in self._nodes and self[n] == d
+
+    def __getitem__(self, n):
+        if isinstance(n, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.nodes.data())[{n.start}:{n.stop}:{n.step}]"
+            )
+        ddict = self._nodes[n]
+        data = self._data
+        if data is False or data is True:
+            return ddict
+        return ddict[data] if data in ddict else self._default
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        if self._data is False:
+            return f"{name}({tuple(self)})"
+        if self._data is True:
+            return f"{name}({dict(self)})"
+        return f"{name}({dict(self)}, data={self._data!r})"
+
+
+# DegreeViews
+class DiDegreeView:
+    """A View class for degree of nodes in a NetworkX Graph
+
+    The functionality is like dict.items() with (node, degree) pairs.
+    Additional functionality includes read-only lookup of node degree,
+    and calling with optional features nbunch (for only a subset of nodes)
+    and weight (use edge weights to compute degree).
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    nbunch : node, container of nodes, or None meaning all nodes (default=None)
+    weight : bool or string (default=None)
+
+    Notes
+    -----
+    DegreeView can still lookup any node even if nbunch is specified.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> DV = G.degree()
+    >>> assert DV[2] == 1
+    >>> assert sum(deg for n, deg in DV) == 4
+
+    >>> DVweight = G.degree(weight="span")
+    >>> G.add_edge(1, 2, span=34)
+    >>> DVweight[2]
+    34
+    >>> DVweight[0]  #  default edge weight is 1
+    1
+    >>> sum(span for n, span in DVweight)  # sum weighted degrees
+    70
+
+    >>> DVnbunch = G.degree(nbunch=(1, 2))
+    >>> assert len(list(DVnbunch)) == 2  # iteration over nbunch only
+    """
+
+    def __init__(self, G, nbunch=None, weight=None):
+        self._graph = G
+        self._succ = G._succ if hasattr(G, "_succ") else G._adj
+        self._pred = G._pred if hasattr(G, "_pred") else G._adj
+        self._nodes = self._succ if nbunch is None else list(G.nbunch_iter(nbunch))
+        self._weight = weight
+
+    def __call__(self, nbunch=None, weight=None):
+        if nbunch is None:
+            if weight == self._weight:
+                return self
+            return self.__class__(self._graph, None, weight)
+        try:
+            if nbunch in self._nodes:
+                if weight == self._weight:
+                    return self[nbunch]
+                return self.__class__(self._graph, None, weight)[nbunch]
+        except TypeError:
+            pass
+        return self.__class__(self._graph, nbunch, weight)
+
+    def __getitem__(self, n):
+        weight = self._weight
+        succs = self._succ[n]
+        preds = self._pred[n]
+        if weight is None:
+            return len(succs) + len(preds)
+        return sum(dd.get(weight, 1) for dd in succs.values()) + sum(
+            dd.get(weight, 1) for dd in preds.values()
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                yield (n, len(succs) + len(preds))
+        else:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                deg = sum(dd.get(weight, 1) for dd in succs.values()) + sum(
+                    dd.get(weight, 1) for dd in preds.values()
+                )
+                yield (n, deg)
+
+    def __len__(self):
+        return len(self._nodes)
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({dict(self)})"
+
+
+class DegreeView(DiDegreeView):
+    """A DegreeView class to act as G.degree for a NetworkX Graph
+
+    Typical usage focuses on iteration over `(node, degree)` pairs.
+    The degree is by default the number of edges incident to the node.
+    Optional argument `weight` enables weighted degree using the edge
+    attribute named in the `weight` argument.  Reporting and iteration
+    can also be restricted to a subset of nodes using `nbunch`.
+
+    Additional functionality include node lookup so that `G.degree[n]`
+    reported the (possibly weighted) degree of node `n`. Calling the
+    view creates a view with different arguments `nbunch` or `weight`.
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    nbunch : node, container of nodes, or None meaning all nodes (default=None)
+    weight : string or None (default=None)
+
+    Notes
+    -----
+    DegreeView can still lookup any node even if nbunch is specified.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> DV = G.degree()
+    >>> assert DV[2] == 1
+    >>> assert G.degree[2] == 1
+    >>> assert sum(deg for n, deg in DV) == 4
+
+    >>> DVweight = G.degree(weight="span")
+    >>> G.add_edge(1, 2, span=34)
+    >>> DVweight[2]
+    34
+    >>> DVweight[0]  #  default edge weight is 1
+    1
+    >>> sum(span for n, span in DVweight)  # sum weighted degrees
+    70
+
+    >>> DVnbunch = G.degree(nbunch=(1, 2))
+    >>> assert len(list(DVnbunch)) == 2  # iteration over nbunch only
+    """
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if weight is None:
+            return len(nbrs) + (n in nbrs)
+        return sum(dd.get(weight, 1) for dd in nbrs.values()) + (
+            n in nbrs and nbrs[n].get(weight, 1)
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                yield (n, len(nbrs) + (n in nbrs))
+        else:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(dd.get(weight, 1) for dd in nbrs.values()) + (
+                    n in nbrs and nbrs[n].get(weight, 1)
+                )
+                yield (n, deg)
+
+
+class OutDegreeView(DiDegreeView):
+    """A DegreeView class to report out_degree for a DiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if self._weight is None:
+            return len(nbrs)
+        return sum(dd.get(self._weight, 1) for dd in nbrs.values())
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                succs = self._succ[n]
+                yield (n, len(succs))
+        else:
+            for n in self._nodes:
+                succs = self._succ[n]
+                deg = sum(dd.get(weight, 1) for dd in succs.values())
+                yield (n, deg)
+
+
+class InDegreeView(DiDegreeView):
+    """A DegreeView class to report in_degree for a DiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._pred[n]
+        if weight is None:
+            return len(nbrs)
+        return sum(dd.get(weight, 1) for dd in nbrs.values())
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                preds = self._pred[n]
+                yield (n, len(preds))
+        else:
+            for n in self._nodes:
+                preds = self._pred[n]
+                deg = sum(dd.get(weight, 1) for dd in preds.values())
+                yield (n, deg)
+
+
+class MultiDegreeView(DiDegreeView):
+    """A DegreeView class for undirected multigraphs; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if weight is None:
+            return sum(len(keys) for keys in nbrs.values()) + (
+                n in nbrs and len(nbrs[n])
+            )
+        # edge weighted graph - degree is sum of nbr edge weights
+        deg = sum(
+            d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
+        )
+        if n in nbrs:
+            deg += sum(d.get(weight, 1) for d in nbrs[n].values())
+        return deg
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(len(keys) for keys in nbrs.values()) + (
+                    n in nbrs and len(nbrs[n])
+                )
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in nbrs.values()
+                    for d in key_dict.values()
+                )
+                if n in nbrs:
+                    deg += sum(d.get(weight, 1) for d in nbrs[n].values())
+                yield (n, deg)
+
+
+class DiMultiDegreeView(DiDegreeView):
+    """A DegreeView class for MultiDiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        succs = self._succ[n]
+        preds = self._pred[n]
+        if weight is None:
+            return sum(len(keys) for keys in succs.values()) + sum(
+                len(keys) for keys in preds.values()
+            )
+        # edge weighted graph - degree is sum of nbr edge weights
+        deg = sum(
+            d.get(weight, 1) for key_dict in succs.values() for d in key_dict.values()
+        ) + sum(
+            d.get(weight, 1) for key_dict in preds.values() for d in key_dict.values()
+        )
+        return deg
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                deg = sum(len(keys) for keys in succs.values()) + sum(
+                    len(keys) for keys in preds.values()
+                )
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in succs.values()
+                    for d in key_dict.values()
+                ) + sum(
+                    d.get(weight, 1)
+                    for key_dict in preds.values()
+                    for d in key_dict.values()
+                )
+                yield (n, deg)
+
+
+class InMultiDegreeView(DiDegreeView):
+    """A DegreeView class for inward degree of MultiDiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._pred[n]
+        if weight is None:
+            return sum(len(data) for data in nbrs.values())
+        # edge weighted graph - degree is sum of nbr edge weights
+        return sum(
+            d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._pred[n]
+                deg = sum(len(data) for data in nbrs.values())
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                nbrs = self._pred[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in nbrs.values()
+                    for d in key_dict.values()
+                )
+                yield (n, deg)
+
+
+class OutMultiDegreeView(DiDegreeView):
+    """A DegreeView class for outward degree of MultiDiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if weight is None:
+            return sum(len(data) for data in nbrs.values())
+        # edge weighted graph - degree is sum of nbr edge weights
+        return sum(
+            d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(len(data) for data in nbrs.values())
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in nbrs.values()
+                    for d in key_dict.values()
+                )
+                yield (n, deg)
+
+
+# A base class for all edge views. Ensures all edge view and edge data view
+# objects/classes are captured by `isinstance(obj, EdgeViewABC)` and
+# `issubclass(cls, EdgeViewABC)` respectively
+class EdgeViewABC(ABC):
+    pass
+
+
+# EdgeDataViews
+class OutEdgeDataView(EdgeViewABC):
+    """EdgeDataView for outward edges of DiGraph; See EdgeDataView"""
+
+    __slots__ = (
+        "_viewer",
+        "_nbunch",
+        "_data",
+        "_default",
+        "_adjdict",
+        "_nodes_nbrs",
+        "_report",
+    )
+
+    def __getstate__(self):
+        return {
+            "viewer": self._viewer,
+            "nbunch": self._nbunch,
+            "data": self._data,
+            "default": self._default,
+        }
+
+    def __setstate__(self, state):
+        self.__init__(**state)
+
+    def __init__(self, viewer, nbunch=None, data=False, *, default=None):
+        self._viewer = viewer
+        adjdict = self._adjdict = viewer._adjdict
+        if nbunch is None:
+            self._nodes_nbrs = adjdict.items
+        else:
+            # dict retains order of nodes but acts like a set
+            nbunch = dict.fromkeys(viewer._graph.nbunch_iter(nbunch))
+            self._nodes_nbrs = lambda: [(n, adjdict[n]) for n in nbunch]
+        self._nbunch = nbunch
+        self._data = data
+        self._default = default
+        # Set _report based on data and default
+        if data is True:
+            self._report = lambda n, nbr, dd: (n, nbr, dd)
+        elif data is False:
+            self._report = lambda n, nbr, dd: (n, nbr)
+        else:  # data is attribute name
+            self._report = (
+                lambda n, nbr, dd: (n, nbr, dd[data])
+                if data in dd
+                else (n, nbr, default)
+            )
+
+    def __len__(self):
+        return sum(len(nbrs) for n, nbrs in self._nodes_nbrs())
+
+    def __iter__(self):
+        return (
+            self._report(n, nbr, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, dd in nbrs.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch:
+            return False  # this edge doesn't start in nbunch
+        try:
+            ddict = self._adjdict[u][v]
+        except KeyError:
+            return False
+        return e == self._report(u, v, ddict)
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({list(self)})"
+
+
+class EdgeDataView(OutEdgeDataView):
+    """A EdgeDataView class for edges of Graph
+
+    This view is primarily used to iterate over the edges reporting
+    edges as node-tuples with edge data optionally reported. The
+    argument `nbunch` allows restriction to edges incident to nodes
+    in that container/singleton. The default (nbunch=None)
+    reports all edges. The arguments `data` and `default` control
+    what edge data is reported. The default `data is False` reports
+    only node-tuples for each edge. If `data is True` the entire edge
+    data dict is returned. Otherwise `data` is assumed to hold the name
+    of the edge attribute to report with default `default` if  that
+    edge attribute is not present.
+
+    Parameters
+    ----------
+    nbunch : container of nodes, node or None (default None)
+    data : False, True or string (default False)
+    default : default value (default None)
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> G.add_edge(1, 2, foo="bar")
+    >>> list(G.edges(data="foo", default="biz"))
+    [(0, 1, 'biz'), (1, 2, 'bar')]
+    >>> assert (0, 1, "biz") in G.edges(data="foo", default="biz")
+    """
+
+    __slots__ = ()
+
+    def __len__(self):
+        return sum(1 for e in self)
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, dd in nbrs.items():
+                if nbr not in seen:
+                    yield self._report(n, nbr, dd)
+            seen[n] = 1
+        del seen
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch and v not in self._nbunch:
+            return False  # this edge doesn't start and it doesn't end in nbunch
+        try:
+            ddict = self._adjdict[u][v]
+        except KeyError:
+            return False
+        return e == self._report(u, v, ddict)
+
+
+class InEdgeDataView(OutEdgeDataView):
+    """An EdgeDataView class for outward edges of DiGraph; See EdgeDataView"""
+
+    __slots__ = ()
+
+    def __iter__(self):
+        return (
+            self._report(nbr, n, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, dd in nbrs.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and v not in self._nbunch:
+            return False  # this edge doesn't end in nbunch
+        try:
+            ddict = self._adjdict[v][u]
+        except KeyError:
+            return False
+        return e == self._report(u, v, ddict)
+
+
+class OutMultiEdgeDataView(OutEdgeDataView):
+    """An EdgeDataView for outward edges of MultiDiGraph; See EdgeDataView"""
+
+    __slots__ = ("keys",)
+
+    def __getstate__(self):
+        return {
+            "viewer": self._viewer,
+            "nbunch": self._nbunch,
+            "keys": self.keys,
+            "data": self._data,
+            "default": self._default,
+        }
+
+    def __setstate__(self, state):
+        self.__init__(**state)
+
+    def __init__(self, viewer, nbunch=None, data=False, *, default=None, keys=False):
+        self._viewer = viewer
+        adjdict = self._adjdict = viewer._adjdict
+        self.keys = keys
+        if nbunch is None:
+            self._nodes_nbrs = adjdict.items
+        else:
+            # dict retains order of nodes but acts like a set
+            nbunch = dict.fromkeys(viewer._graph.nbunch_iter(nbunch))
+            self._nodes_nbrs = lambda: [(n, adjdict[n]) for n in nbunch]
+        self._nbunch = nbunch
+        self._data = data
+        self._default = default
+        # Set _report based on data and default
+        if data is True:
+            if keys is True:
+                self._report = lambda n, nbr, k, dd: (n, nbr, k, dd)
+            else:
+                self._report = lambda n, nbr, k, dd: (n, nbr, dd)
+        elif data is False:
+            if keys is True:
+                self._report = lambda n, nbr, k, dd: (n, nbr, k)
+            else:
+                self._report = lambda n, nbr, k, dd: (n, nbr)
+        else:  # data is attribute name
+            if keys is True:
+                self._report = (
+                    lambda n, nbr, k, dd: (n, nbr, k, dd[data])
+                    if data in dd
+                    else (n, nbr, k, default)
+                )
+            else:
+                self._report = (
+                    lambda n, nbr, k, dd: (n, nbr, dd[data])
+                    if data in dd
+                    else (n, nbr, default)
+                )
+
+    def __len__(self):
+        return sum(1 for e in self)
+
+    def __iter__(self):
+        return (
+            self._report(n, nbr, k, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, kd in nbrs.items()
+            for k, dd in kd.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch:
+            return False  # this edge doesn't start in nbunch
+        try:
+            kdict = self._adjdict[u][v]
+        except KeyError:
+            return False
+        if self.keys is True:
+            k = e[2]
+            try:
+                dd = kdict[k]
+            except KeyError:
+                return False
+            return e == self._report(u, v, k, dd)
+        return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
+
+
+class MultiEdgeDataView(OutMultiEdgeDataView):
+    """An EdgeDataView class for edges of MultiGraph; See EdgeDataView"""
+
+    __slots__ = ()
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kd in nbrs.items():
+                if nbr not in seen:
+                    for k, dd in kd.items():
+                        yield self._report(n, nbr, k, dd)
+            seen[n] = 1
+        del seen
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch and v not in self._nbunch:
+            return False  # this edge doesn't start and doesn't end in nbunch
+        try:
+            kdict = self._adjdict[u][v]
+        except KeyError:
+            try:
+                kdict = self._adjdict[v][u]
+            except KeyError:
+                return False
+        if self.keys is True:
+            k = e[2]
+            try:
+                dd = kdict[k]
+            except KeyError:
+                return False
+            return e == self._report(u, v, k, dd)
+        return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
+
+
+class InMultiEdgeDataView(OutMultiEdgeDataView):
+    """An EdgeDataView for inward edges of MultiDiGraph; See EdgeDataView"""
+
+    __slots__ = ()
+
+    def __iter__(self):
+        return (
+            self._report(nbr, n, k, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, kd in nbrs.items()
+            for k, dd in kd.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and v not in self._nbunch:
+            return False  # this edge doesn't end in nbunch
+        try:
+            kdict = self._adjdict[v][u]
+        except KeyError:
+            return False
+        if self.keys is True:
+            k = e[2]
+            dd = kdict[k]
+            return e == self._report(u, v, k, dd)
+        return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
+
+
+# EdgeViews    have set operations and no data reported
+class OutEdgeView(Set, Mapping, EdgeViewABC):
+    """A EdgeView class for outward edges of a DiGraph"""
+
+    __slots__ = ("_adjdict", "_graph", "_nodes_nbrs")
+
+    def __getstate__(self):
+        return {"_graph": self._graph, "_adjdict": self._adjdict}
+
+    def __setstate__(self, state):
+        self._graph = state["_graph"]
+        self._adjdict = state["_adjdict"]
+        self._nodes_nbrs = self._adjdict.items
+
+    @classmethod
+    def _from_iterable(cls, it):
+        return set(it)
+
+    dataview = OutEdgeDataView
+
+    def __init__(self, G):
+        self._graph = G
+        self._adjdict = G._succ if hasattr(G, "succ") else G._adj
+        self._nodes_nbrs = self._adjdict.items
+
+    # Set methods
+    def __len__(self):
+        return sum(len(nbrs) for n, nbrs in self._nodes_nbrs())
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr in nbrs:
+                yield (n, nbr)
+
+    def __contains__(self, e):
+        try:
+            u, v = e
+            return v in self._adjdict[u]
+        except KeyError:
+            return False
+
+    # Mapping Methods
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v = e
+        try:
+            return self._adjdict[u][v]
+        except KeyError as ex:  # Customize msg to indicate exception origin
+            raise KeyError(f"The edge {e} is not in the graph.")
+
+    # EdgeDataView methods
+    def __call__(self, nbunch=None, data=False, *, default=None):
+        if nbunch is None and data is False:
+            return self
+        return self.dataview(self, nbunch, data, default=default)
+
+    def data(self, data=True, default=None, nbunch=None):
+        """
+        Return a read-only view of edge data.
+
+        Parameters
+        ----------
+        data : bool or edge attribute key
+            If ``data=True``, then the data view maps each edge to a dictionary
+            containing all of its attributes. If `data` is a key in the edge
+            dictionary, then the data view maps each edge to its value for
+            the keyed attribute. In this case, if the edge doesn't have the
+            attribute, the `default` value is returned.
+        default : object, default=None
+            The value used when an edge does not have a specific attribute
+        nbunch : container of nodes, optional (default=None)
+            Allows restriction to edges only involving certain nodes. All edges
+            are considered by default.
+
+        Returns
+        -------
+        dataview
+            Returns an `EdgeDataView` for undirected Graphs, `OutEdgeDataView`
+            for DiGraphs, `MultiEdgeDataView` for MultiGraphs and
+            `OutMultiEdgeDataView` for MultiDiGraphs.
+
+        Notes
+        -----
+        If ``data=False``, returns an `EdgeView` without any edge data.
+
+        See Also
+        --------
+        EdgeDataView
+        OutEdgeDataView
+        MultiEdgeDataView
+        OutMultiEdgeDataView
+
+        Examples
+        --------
+        >>> G = nx.Graph()
+        >>> G.add_edges_from(
+        ...     [
+        ...         (0, 1, {"dist": 3, "capacity": 20}),
+        ...         (1, 2, {"dist": 4}),
+        ...         (2, 0, {"dist": 5}),
+        ...     ]
+        ... )
+
+        Accessing edge data with ``data=True`` (the default) returns an
+        edge data view object listing each edge with all of its attributes:
+
+        >>> G.edges.data()
+        EdgeDataView([(0, 1, {'dist': 3, 'capacity': 20}), (0, 2, {'dist': 5}), (1, 2, {'dist': 4})])
+
+        If `data` represents a key in the edge attribute dict, a dataview listing
+        each edge with its value for that specific key is returned:
+
+        >>> G.edges.data("dist")
+        EdgeDataView([(0, 1, 3), (0, 2, 5), (1, 2, 4)])
+
+        `nbunch` can be used to limit the edges:
+
+        >>> G.edges.data("dist", nbunch=[0])
+        EdgeDataView([(0, 1, 3), (0, 2, 5)])
+
+        If a specific key is not found in an edge attribute dict, the value
+        specified by `default` is used:
+
+        >>> G.edges.data("capacity")
+        EdgeDataView([(0, 1, 20), (0, 2, None), (1, 2, None)])
+
+        Note that there is no check that the `data` key is present in any of
+        the edge attribute dictionaries:
+
+        >>> G.edges.data("speed")
+        EdgeDataView([(0, 1, None), (0, 2, None), (1, 2, None)])
+        """
+        if nbunch is None and data is False:
+            return self
+        return self.dataview(self, nbunch, data, default=default)
+
+    # String Methods
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({list(self)})"
+
+
+class EdgeView(OutEdgeView):
+    """A EdgeView class for edges of a Graph
+
+    This densely packed View allows iteration over edges, data lookup
+    like a dict and set operations on edges represented by node-tuples.
+    In addition, edge data can be controlled by calling this object
+    possibly creating an EdgeDataView. Typically edges are iterated over
+    and reported as `(u, v)` node tuples or `(u, v, key)` node/key tuples
+    for multigraphs. Those edge representations can also be using to
+    lookup the data dict for any edge. Set operations also are available
+    where those tuples are the elements of the set.
+    Calling this object with optional arguments `data`, `default` and `keys`
+    controls the form of the tuple (see EdgeDataView). Optional argument
+    `nbunch` allows restriction to edges only involving certain nodes.
+
+    If `data is False` (the default) then iterate over 2-tuples `(u, v)`.
+    If `data is True` iterate over 3-tuples `(u, v, datadict)`.
+    Otherwise iterate over `(u, v, datadict.get(data, default))`.
+    For Multigraphs, if `keys is True`, replace `u, v` with `u, v, key` above.
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    nbunch : (default= all nodes in graph) only report edges with these nodes
+    keys : (only for MultiGraph. default=False) report edge key in tuple
+    data : bool or string (default=False) see above
+    default : object (default=None)
+
+    Examples
+    ========
+    >>> G = nx.path_graph(4)
+    >>> EV = G.edges()
+    >>> (2, 3) in EV
+    True
+    >>> for u, v in EV:
+    ...     print((u, v))
+    (0, 1)
+    (1, 2)
+    (2, 3)
+    >>> assert EV & {(1, 2), (3, 4)} == {(1, 2)}
+
+    >>> EVdata = G.edges(data="color", default="aqua")
+    >>> G.add_edge(2, 3, color="blue")
+    >>> assert (2, 3, "blue") in EVdata
+    >>> for u, v, c in EVdata:
+    ...     print(f"({u}, {v}) has color: {c}")
+    (0, 1) has color: aqua
+    (1, 2) has color: aqua
+    (2, 3) has color: blue
+
+    >>> EVnbunch = G.edges(nbunch=2)
+    >>> assert (2, 3) in EVnbunch
+    >>> assert (0, 1) not in EVnbunch
+    >>> for u, v in EVnbunch:
+    ...     assert u == 2 or v == 2
+
+    >>> MG = nx.path_graph(4, create_using=nx.MultiGraph)
+    >>> EVmulti = MG.edges(keys=True)
+    >>> (2, 3, 0) in EVmulti
+    True
+    >>> (2, 3) in EVmulti  # 2-tuples work even when keys is True
+    True
+    >>> key = MG.add_edge(2, 3)
+    >>> for u, v, k in EVmulti:
+    ...     print((u, v, k))
+    (0, 1, 0)
+    (1, 2, 0)
+    (2, 3, 0)
+    (2, 3, 1)
+    """
+
+    __slots__ = ()
+
+    dataview = EdgeDataView
+
+    def __len__(self):
+        num_nbrs = (len(nbrs) + (n in nbrs) for n, nbrs in self._nodes_nbrs())
+        return sum(num_nbrs) // 2
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr in list(nbrs):
+                if nbr not in seen:
+                    yield (n, nbr)
+            seen[n] = 1
+        del seen
+
+    def __contains__(self, e):
+        try:
+            u, v = e[:2]
+            return v in self._adjdict[u] or u in self._adjdict[v]
+        except (KeyError, ValueError):
+            return False
+
+
+class InEdgeView(OutEdgeView):
+    """A EdgeView class for inward edges of a DiGraph"""
+
+    __slots__ = ()
+
+    def __setstate__(self, state):
+        self._graph = state["_graph"]
+        self._adjdict = state["_adjdict"]
+        self._nodes_nbrs = self._adjdict.items
+
+    dataview = InEdgeDataView
+
+    def __init__(self, G):
+        self._graph = G
+        self._adjdict = G._pred if hasattr(G, "pred") else G._adj
+        self._nodes_nbrs = self._adjdict.items
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr in nbrs:
+                yield (nbr, n)
+
+    def __contains__(self, e):
+        try:
+            u, v = e
+            return u in self._adjdict[v]
+        except KeyError:
+            return False
+
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.in_edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v = e
+        return self._adjdict[v][u]
+
+
+class OutMultiEdgeView(OutEdgeView):
+    """A EdgeView class for outward edges of a MultiDiGraph"""
+
+    __slots__ = ()
+
+    dataview = OutMultiEdgeDataView
+
+    def __len__(self):
+        return sum(
+            len(kdict) for n, nbrs in self._nodes_nbrs() for nbr, kdict in nbrs.items()
+        )
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kdict in nbrs.items():
+                for key in kdict:
+                    yield (n, nbr, key)
+
+    def __contains__(self, e):
+        N = len(e)
+        if N == 3:
+            u, v, k = e
+        elif N == 2:
+            u, v = e
+            k = 0
+        else:
+            raise ValueError("MultiEdge must have length 2 or 3")
+        try:
+            return k in self._adjdict[u][v]
+        except KeyError:
+            return False
+
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v, k = e
+        return self._adjdict[u][v][k]
+
+    def __call__(self, nbunch=None, data=False, *, default=None, keys=False):
+        if nbunch is None and data is False and keys is True:
+            return self
+        return self.dataview(self, nbunch, data, default=default, keys=keys)
+
+    def data(self, data=True, default=None, nbunch=None, keys=False):
+        if nbunch is None and data is False and keys is True:
+            return self
+        return self.dataview(self, nbunch, data, default=default, keys=keys)
+
+
+class MultiEdgeView(OutMultiEdgeView):
+    """A EdgeView class for edges of a MultiGraph"""
+
+    __slots__ = ()
+
+    dataview = MultiEdgeDataView
+
+    def __len__(self):
+        return sum(1 for e in self)
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kd in nbrs.items():
+                if nbr not in seen:
+                    for k, dd in kd.items():
+                        yield (n, nbr, k)
+            seen[n] = 1
+        del seen
+
+
+class InMultiEdgeView(OutMultiEdgeView):
+    """A EdgeView class for inward edges of a MultiDiGraph"""
+
+    __slots__ = ()
+
+    def __setstate__(self, state):
+        self._graph = state["_graph"]
+        self._adjdict = state["_adjdict"]
+        self._nodes_nbrs = self._adjdict.items
+
+    dataview = InMultiEdgeDataView
+
+    def __init__(self, G):
+        self._graph = G
+        self._adjdict = G._pred if hasattr(G, "pred") else G._adj
+        self._nodes_nbrs = self._adjdict.items
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kdict in nbrs.items():
+                for key in kdict:
+                    yield (nbr, n, key)
+
+    def __contains__(self, e):
+        N = len(e)
+        if N == 3:
+            u, v, k = e
+        elif N == 2:
+            u, v = e
+            k = 0
+        else:
+            raise ValueError("MultiEdge must have length 2 or 3")
+        try:
+            return k in self._adjdict[v][u]
+        except KeyError:
+            return False
+
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.in_edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v, k = e
+        return self._adjdict[v][u][k]
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__init__.py b/lib/python3.11/site-packages/networkx/classes/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cee1e40c10692f68cdfca3bbd0b6198f749fab99
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/dispatch_interface.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/dispatch_interface.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..554f0a98c72a60be04263dd1bbc083e3b34e772e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/dispatch_interface.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/historical_tests.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/historical_tests.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bd16673e6009bb65f17b1d662bb6a94feb84c332
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/historical_tests.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_coreviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_coreviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c0ef77b4c17fdcbe4cd290595228ee67863e019c
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_coreviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_digraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_digraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8dd536391de09a8751d22f7881c470581cb9efd7
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_digraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_digraph_historical.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_digraph_historical.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d8b995ae8e3075292df865c172b5d5e21a73417b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_digraph_historical.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_filters.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_filters.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7dc3fb1bfd1abf03788b91431cfc7b340a6905e5
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_filters.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_function.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_function.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..51b4455c75490be3c41a5917c5e48cd54f2180c1
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_function.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..048ac61ad27ead4175b8fd90219b1a27deaf8424
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graph_historical.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graph_historical.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4ae855560dced79d511c8db42ddd299a6a67ec78
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graph_historical.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graphviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graphviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..91d515d2b9b2764022f717ff536bc9b828ca0c19
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_graphviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_multidigraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_multidigraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6f6cd81ba1868d3d5b78d8c3cccad659b7da7b9f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_multidigraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_multigraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_multigraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0bf3235886f57135dd3aa3fae75211dfd3952ada
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_multigraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_reportviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_reportviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..96cc3f62185779c352175b67864e95ebd5925e95
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_reportviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_special.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_special.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ce67fc9197de4c787f0fe0607a0687c59db0701a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_special.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_subgraphviews.cpython-311.pyc b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_subgraphviews.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b2074a09f3b7d0ee64f406988159177b09df7c87
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/classes/tests/__pycache__/test_subgraphviews.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/dispatch_interface.py b/lib/python3.11/site-packages/networkx/classes/tests/dispatch_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfc0b5f6bfabffcab0530e31222242ae144b506d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/dispatch_interface.py
@@ -0,0 +1,192 @@
+# This file contains utilities for testing the dispatching feature
+
+# A full test of all dispatchable algorithms is performed by
+# modifying the pytest invocation and setting an environment variable
+# NETWORKX_TEST_BACKEND=nx_loopback pytest
+# This is comprehensive, but only tests the `test_override_dispatch`
+# function in networkx.classes.backends.
+
+# To test the `_dispatchable` function directly, several tests scattered throughout
+# NetworkX have been augmented to test normal and dispatch mode.
+# Searching for `dispatch_interface` should locate the specific tests.
+
+import networkx as nx
+from networkx import DiGraph, Graph, MultiDiGraph, MultiGraph, PlanarEmbedding
+from networkx.classes.reportviews import NodeView
+
+
+class LoopbackGraph(Graph):
+    __networkx_backend__ = "nx_loopback"
+
+
+class LoopbackDiGraph(DiGraph):
+    __networkx_backend__ = "nx_loopback"
+
+
+class LoopbackMultiGraph(MultiGraph):
+    __networkx_backend__ = "nx_loopback"
+
+
+class LoopbackMultiDiGraph(MultiDiGraph):
+    __networkx_backend__ = "nx_loopback"
+
+
+class LoopbackPlanarEmbedding(PlanarEmbedding):
+    __networkx_backend__ = "nx_loopback"
+
+
+def convert(graph):
+    if isinstance(graph, PlanarEmbedding):
+        return LoopbackPlanarEmbedding(graph)
+    if isinstance(graph, MultiDiGraph):
+        return LoopbackMultiDiGraph(graph)
+    if isinstance(graph, MultiGraph):
+        return LoopbackMultiGraph(graph)
+    if isinstance(graph, DiGraph):
+        return LoopbackDiGraph(graph)
+    if isinstance(graph, Graph):
+        return LoopbackGraph(graph)
+    raise TypeError(f"Unsupported type of graph: {type(graph)}")
+
+
+class LoopbackBackendInterface:
+    def __getattr__(self, item):
+        try:
+            return nx.utils.backends._registered_algorithms[item].orig_func
+        except KeyError:
+            raise AttributeError(item) from None
+
+    @staticmethod
+    def graph__new__(cls, incoming_graph_data=None, **attr):
+        # LoopbackGraph.__init__ will be called next since the returned
+        # object is an instance of an nx.Graph. For more details, see:
+        # https://docs.python.org/3/reference/datamodel.html#object.__new__
+        return object.__new__(LoopbackGraph)
+
+    @staticmethod
+    def convert_from_nx(
+        graph,
+        *,
+        edge_attrs=None,
+        node_attrs=None,
+        preserve_edge_attrs=None,
+        preserve_node_attrs=None,
+        preserve_graph_attrs=None,
+        name=None,
+        graph_name=None,
+    ):
+        if name in {
+            # Raise if input graph changes. See test_dag.py::test_topological_sort6
+            "lexicographical_topological_sort",
+            "topological_generations",
+            "topological_sort",
+            # Would be nice to some day avoid these cutoffs of full testing
+        }:
+            return graph
+        if isinstance(graph, NodeView):
+            # Convert to a Graph with only nodes (no edges)
+            new_graph = Graph()
+            new_graph.add_nodes_from(graph.items())
+            graph = new_graph
+            G = LoopbackGraph()
+        elif not isinstance(graph, Graph):
+            raise TypeError(
+                f"Bad type for graph argument {graph_name} in {name}: {type(graph)}"
+            )
+        elif graph.__class__ in {Graph, LoopbackGraph}:
+            G = LoopbackGraph()
+        elif graph.__class__ in {DiGraph, LoopbackDiGraph}:
+            G = LoopbackDiGraph()
+        elif graph.__class__ in {MultiGraph, LoopbackMultiGraph}:
+            G = LoopbackMultiGraph()
+        elif graph.__class__ in {MultiDiGraph, LoopbackMultiDiGraph}:
+            G = LoopbackMultiDiGraph()
+        elif graph.__class__ in {PlanarEmbedding, LoopbackPlanarEmbedding}:
+            G = LoopbackDiGraph()  # or LoopbackPlanarEmbedding
+        else:
+            # Would be nice to handle these better some day
+            # nx.algorithms.approximation.kcomponents._AntiGraph
+            # nx.classes.tests.test_multidigraph.MultiDiGraphSubClass
+            # nx.classes.tests.test_multigraph.MultiGraphSubClass
+            G = graph.__class__()
+
+        if preserve_graph_attrs:
+            G.graph.update(graph.graph)
+
+        # add nodes
+        G.add_nodes_from(graph)
+        if preserve_node_attrs:
+            for n, dd in G._node.items():
+                dd.update(graph.nodes[n])
+        elif node_attrs:
+            for n, dd in G._node.items():
+                dd.update(
+                    (attr, graph._node[n].get(attr, default))
+                    for attr, default in node_attrs.items()
+                    if default is not None or attr in graph._node[n]
+                )
+
+        # tools to build datadict and keydict
+        if preserve_edge_attrs:
+
+            def G_new_datadict(old_dd):
+                return G.edge_attr_dict_factory(old_dd)
+        elif edge_attrs:
+
+            def G_new_datadict(old_dd):
+                return G.edge_attr_dict_factory(
+                    (attr, old_dd.get(attr, default))
+                    for attr, default in edge_attrs.items()
+                    if default is not None or attr in old_dd
+                )
+        else:
+
+            def G_new_datadict(old_dd):
+                return G.edge_attr_dict_factory()
+
+        if G.is_multigraph():
+
+            def G_new_inner(keydict):
+                kd = G.adjlist_inner_dict_factory(
+                    (k, G_new_datadict(dd)) for k, dd in keydict.items()
+                )
+                return kd
+        else:
+            G_new_inner = G_new_datadict
+
+        # add edges keeping the same order in _adj and _pred
+        G_adj = G._adj
+        if G.is_directed():
+            for n, nbrs in graph._adj.items():
+                G_adj[n].update((nbr, G_new_inner(dd)) for nbr, dd in nbrs.items())
+            # ensure same datadict for pred and adj; and pred order of graph._pred
+            G_pred = G._pred
+            for n, nbrs in graph._pred.items():
+                G_pred[n].update((nbr, G_adj[nbr][n]) for nbr in nbrs)
+        else:  # undirected
+            for n, nbrs in graph._adj.items():
+                # ensure same datadict for both ways; and adj order of graph._adj
+                G_adj[n].update(
+                    (nbr, G_adj[nbr][n] if n in G_adj[nbr] else G_new_inner(dd))
+                    for nbr, dd in nbrs.items()
+                )
+
+        return G
+
+    @staticmethod
+    def convert_to_nx(obj, *, name=None):
+        return obj
+
+    @staticmethod
+    def on_start_tests(items):
+        # Verify that items can be xfailed
+        for item in items:
+            assert hasattr(item, "add_marker")
+
+    def can_run(self, name, args, kwargs):
+        # It is unnecessary to define this function if algorithms are fully supported.
+        # We include it for illustration purposes.
+        return hasattr(self, name)
+
+
+backend_interface = LoopbackBackendInterface()
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/historical_tests.py b/lib/python3.11/site-packages/networkx/classes/tests/historical_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b70a2e985b64477ea3d26b89ccb21c8cb4dc6c5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/historical_tests.py
@@ -0,0 +1,476 @@
+"""Original NetworkX graph tests"""
+
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.utils import edges_equal, nodes_equal
+
+
+class HistoricalTests:
+    @classmethod
+    def setup_class(cls):
+        cls.null = nx.null_graph()
+        cls.P1 = cnlti(nx.path_graph(1), first_label=1)
+        cls.P3 = cnlti(nx.path_graph(3), first_label=1)
+        cls.P10 = cnlti(nx.path_graph(10), first_label=1)
+        cls.K1 = cnlti(nx.complete_graph(1), first_label=1)
+        cls.K3 = cnlti(nx.complete_graph(3), first_label=1)
+        cls.K4 = cnlti(nx.complete_graph(4), first_label=1)
+        cls.K5 = cnlti(nx.complete_graph(5), first_label=1)
+        cls.K10 = cnlti(nx.complete_graph(10), first_label=1)
+        cls.G = nx.Graph
+
+    def test_name(self):
+        G = self.G(name="test")
+        assert G.name == "test"
+        H = self.G()
+        assert H.name == ""
+
+    # Nodes
+
+    def test_add_remove_node(self):
+        G = self.G()
+        G.add_node("A")
+        assert G.has_node("A")
+        G.remove_node("A")
+        assert not G.has_node("A")
+
+    def test_nonhashable_node(self):
+        # Test if a non-hashable object is in the Graph.  A python dict will
+        # raise a TypeError, but for a Graph class a simple  False should be
+        # returned (see Graph __contains__). If it cannot be a node then it is
+        # not a node.
+        G = self.G()
+        assert not G.has_node(["A"])
+        assert not G.has_node({"A": 1})
+
+    def test_add_nodes_from(self):
+        G = self.G()
+        G.add_nodes_from(list("ABCDEFGHIJKL"))
+        assert G.has_node("L")
+        G.remove_nodes_from(["H", "I", "J", "K", "L"])
+        G.add_nodes_from([1, 2, 3, 4])
+        assert sorted(G.nodes(), key=str) == [
+            1,
+            2,
+            3,
+            4,
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+        ]
+        # test __iter__
+        assert sorted(G, key=str) == [1, 2, 3, 4, "A", "B", "C", "D", "E", "F", "G"]
+
+    def test_contains(self):
+        G = self.G()
+        G.add_node("A")
+        assert "A" in G
+        assert [] not in G  # never raise a Key or TypeError in this test
+        assert {1: 1} not in G
+
+    def test_add_remove(self):
+        # Test add_node and remove_node acting for various nbunch
+        G = self.G()
+        G.add_node("m")
+        assert G.has_node("m")
+        G.add_node("m")  # no complaints
+        pytest.raises(nx.NetworkXError, G.remove_node, "j")
+        G.remove_node("m")
+        assert list(G) == []
+
+    def test_nbunch_is_list(self):
+        G = self.G()
+        G.add_nodes_from(list("ABCD"))
+        G.add_nodes_from(self.P3)  # add nbunch of nodes (nbunch=Graph)
+        assert sorted(G.nodes(), key=str) == [1, 2, 3, "A", "B", "C", "D"]
+        G.remove_nodes_from(self.P3)  # remove nbunch of nodes (nbunch=Graph)
+        assert sorted(G.nodes(), key=str) == ["A", "B", "C", "D"]
+
+    def test_nbunch_is_set(self):
+        G = self.G()
+        nbunch = set("ABCDEFGHIJKL")
+        G.add_nodes_from(nbunch)
+        assert G.has_node("L")
+
+    def test_nbunch_dict(self):
+        # nbunch is a dict with nodes as keys
+        G = self.G()
+        nbunch = set("ABCDEFGHIJKL")
+        G.add_nodes_from(nbunch)
+        nbunch = {"I": "foo", "J": 2, "K": True, "L": "spam"}
+        G.remove_nodes_from(nbunch)
+        assert sorted(G.nodes(), key=str), ["A", "B", "C", "D", "E", "F", "G", "H"]
+
+    def test_nbunch_iterator(self):
+        G = self.G()
+        G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
+        n_iter = self.P3.nodes()
+        G.add_nodes_from(n_iter)
+        assert sorted(G.nodes(), key=str) == [
+            1,
+            2,
+            3,
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+        ]
+        n_iter = self.P3.nodes()  # rebuild same iterator
+        G.remove_nodes_from(n_iter)  # remove nbunch of nodes (nbunch=iterator)
+        assert sorted(G.nodes(), key=str) == ["A", "B", "C", "D", "E", "F", "G", "H"]
+
+    def test_nbunch_graph(self):
+        G = self.G()
+        G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
+        nbunch = self.K3
+        G.add_nodes_from(nbunch)
+        assert sorted(G.nodes(), key=str), [
+            1,
+            2,
+            3,
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+        ]
+
+    # Edges
+
+    def test_add_edge(self):
+        G = self.G()
+        pytest.raises(TypeError, G.add_edge, "A")
+
+        G.add_edge("A", "B")  # testing add_edge()
+        G.add_edge("A", "B")  # should fail silently
+        assert G.has_edge("A", "B")
+        assert not G.has_edge("A", "C")
+        assert G.has_edge(*("A", "B"))
+        if G.is_directed():
+            assert not G.has_edge("B", "A")
+        else:
+            # G is undirected, so B->A is an edge
+            assert G.has_edge("B", "A")
+
+        G.add_edge("A", "C")  # test directedness
+        G.add_edge("C", "A")
+        G.remove_edge("C", "A")
+        if G.is_directed():
+            assert G.has_edge("A", "C")
+        else:
+            assert not G.has_edge("A", "C")
+        assert not G.has_edge("C", "A")
+
+    def test_self_loop(self):
+        G = self.G()
+        G.add_edge("A", "A")  # test self loops
+        assert G.has_edge("A", "A")
+        G.remove_edge("A", "A")
+        G.add_edge("X", "X")
+        assert G.has_node("X")
+        G.remove_node("X")
+        G.add_edge("A", "Z")  # should add the node silently
+        assert G.has_node("Z")
+
+    def test_add_edges_from(self):
+        G = self.G()
+        G.add_edges_from([("B", "C")])  # test add_edges_from()
+        assert G.has_edge("B", "C")
+        if G.is_directed():
+            assert not G.has_edge("C", "B")
+        else:
+            assert G.has_edge("C", "B")  # undirected
+
+        G.add_edges_from([("D", "F"), ("B", "D")])
+        assert G.has_edge("D", "F")
+        assert G.has_edge("B", "D")
+
+        if G.is_directed():
+            assert not G.has_edge("D", "B")
+        else:
+            assert G.has_edge("D", "B")  # undirected
+
+    def test_add_edges_from2(self):
+        G = self.G()
+        # after failing silently, should add 2nd edge
+        G.add_edges_from([tuple("IJ"), list("KK"), tuple("JK")])
+        assert G.has_edge(*("I", "J"))
+        assert G.has_edge(*("K", "K"))
+        assert G.has_edge(*("J", "K"))
+        if G.is_directed():
+            assert not G.has_edge(*("K", "J"))
+        else:
+            assert G.has_edge(*("K", "J"))
+
+    def test_add_edges_from3(self):
+        G = self.G()
+        G.add_edges_from(zip(list("ACD"), list("CDE")))
+        assert G.has_edge("D", "E")
+        assert not G.has_edge("E", "C")
+
+    def test_remove_edge(self):
+        G = self.G()
+        G.add_nodes_from([1, 2, 3, "A", "B", "C", "D", "E", "F", "G", "H"])
+
+        G.add_edges_from(zip(list("MNOP"), list("NOPM")))
+        assert G.has_edge("O", "P")
+        assert G.has_edge("P", "M")
+        G.remove_node("P")  # tests remove_node()'s handling of edges.
+        assert not G.has_edge("P", "M")
+        pytest.raises(TypeError, G.remove_edge, "M")
+
+        G.add_edge("N", "M")
+        assert G.has_edge("M", "N")
+        G.remove_edge("M", "N")
+        assert not G.has_edge("M", "N")
+
+        # self loop fails silently
+        G.remove_edges_from([list("HI"), list("DF"), tuple("KK"), tuple("JK")])
+        assert not G.has_edge("H", "I")
+        assert not G.has_edge("J", "K")
+        G.remove_edges_from([list("IJ"), list("KK"), list("JK")])
+        assert not G.has_edge("I", "J")
+        G.remove_nodes_from(set("ZEFHIMNO"))
+        G.add_edge("J", "K")
+
+    def test_edges_nbunch(self):
+        # Test G.edges(nbunch) with various forms of nbunch
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        # node not in nbunch should be quietly ignored
+        pytest.raises(nx.NetworkXError, G.edges, 6)
+        assert list(G.edges("Z")) == []  # iterable non-node
+        # nbunch can be an empty list
+        assert list(G.edges([])) == []
+        if G.is_directed():
+            elist = [("A", "B"), ("A", "C"), ("B", "D")]
+        else:
+            elist = [("A", "B"), ("A", "C"), ("B", "C"), ("B", "D")]
+        # nbunch can be a list
+        assert edges_equal(list(G.edges(["A", "B"])), elist, directed=G.is_directed())
+        # nbunch can be a set
+        assert edges_equal(G.edges({"A", "B"}), elist, directed=G.is_directed())
+        # nbunch can be a graph
+        G1 = self.G()
+        G1.add_nodes_from("AB")
+        assert edges_equal(G.edges(G1), elist, directed=G.is_directed())
+        # nbunch can be a dict with nodes as keys
+        ndict = {"A": "thing1", "B": "thing2"}
+        assert edges_equal(G.edges(ndict), elist, directed=G.is_directed())
+        # nbunch can be a single node
+        assert edges_equal(list(G.edges("A")), [("A", "B"), ("A", "C")])
+        assert nodes_equal(sorted(G), ["A", "B", "C", "D"])
+
+        # nbunch can be nothing (whole graph)
+        assert edges_equal(
+            list(G.edges()),
+            [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")],
+        )
+
+    def test_degree(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        assert G.degree("A") == 2
+
+        # degree of single node in iterable container must return dict
+        assert list(G.degree(["A"])) == [("A", 2)]
+        assert sorted(d for n, d in G.degree(["A", "B"])) == [2, 3]
+        assert sorted(d for n, d in G.degree()) == [2, 2, 3, 3]
+
+    def test_degree2(self):
+        H = self.G()
+        H.add_edges_from([(1, 24), (1, 2)])
+        assert sorted(d for n, d in H.degree([1, 24])) == [1, 2]
+
+    def test_degree_graph(self):
+        P3 = nx.path_graph(3)
+        P5 = nx.path_graph(5)
+        # silently ignore nodes not in P3
+        assert dict(d for n, d in P3.degree(["A", "B"])) == {}
+        # nbunch can be a graph
+        assert sorted(d for n, d in P5.degree(P3)) == [1, 2, 2]
+        # nbunch can be a graph that's way too big
+        assert sorted(d for n, d in P3.degree(P5)) == [1, 1, 2]
+        assert list(P5.degree([])) == []
+        assert dict(P5.degree([])) == {}
+
+    def test_null(self):
+        null = nx.null_graph()
+        assert list(null.degree()) == []
+        assert dict(null.degree()) == {}
+
+    def test_order_size(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        assert G.order() == 4
+        assert G.size() == 5
+        assert G.number_of_edges() == 5
+        assert G.number_of_edges("A", "B") == 1
+        assert G.number_of_edges("A", "D") == 0
+
+    def test_copy(self):
+        G = self.G()
+        H = G.copy()  # copy
+        assert H.adj == G.adj
+        assert H.name == G.name
+        assert H is not G
+
+    def test_subgraph(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        SG = G.subgraph(["A", "B", "D"])
+        assert nodes_equal(list(SG), ["A", "B", "D"])
+        assert edges_equal(list(SG.edges()), [("A", "B"), ("B", "D")])
+
+    def test_to_directed(self):
+        G = self.G()
+        if not G.is_directed():
+            G.add_edges_from(
+                [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")]
+            )
+
+            DG = G.to_directed()
+            assert DG is not G  # directed copy or copy
+
+            assert DG.is_directed()
+            assert DG.name == G.name
+            assert DG.adj == G.adj
+            assert sorted(DG.out_edges(list("AB"))) == [
+                ("A", "B"),
+                ("A", "C"),
+                ("B", "A"),
+                ("B", "C"),
+                ("B", "D"),
+            ]
+            DG.remove_edge("A", "B")
+            assert DG.has_edge("B", "A")  # this removes B-A but not  A-B
+            assert not DG.has_edge("A", "B")
+
+    def test_to_undirected(self):
+        G = self.G()
+        if G.is_directed():
+            G.add_edges_from(
+                [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")]
+            )
+            UG = G.to_undirected()  # to_undirected
+            assert UG is not G
+            assert not UG.is_directed()
+            assert G.is_directed()
+            assert UG.name == G.name
+            assert UG.adj != G.adj
+            assert sorted(UG.edges(list("AB"))) == [
+                ("A", "B"),
+                ("A", "C"),
+                ("B", "C"),
+                ("B", "D"),
+            ]
+            assert sorted(UG.edges(["A", "B"])) == [
+                ("A", "B"),
+                ("A", "C"),
+                ("B", "C"),
+                ("B", "D"),
+            ]
+            UG.remove_edge("A", "B")
+            assert not UG.has_edge("B", "A")
+            assert not UG.has_edge("A", "B")
+
+    def test_neighbors(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        G.add_nodes_from("GJK")
+        assert sorted(G["A"]) == ["B", "C"]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        assert sorted(G.neighbors("G")) == []
+        pytest.raises(nx.NetworkXError, G.neighbors, "j")
+
+    def test_iterators(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        G.add_nodes_from("GJK")
+        assert sorted(G.nodes()) == ["A", "B", "C", "D", "G", "J", "K"]
+        assert edges_equal(
+            G.edges(),
+            [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")],
+        )
+
+        assert sorted(v for k, v in G.degree()) == [0, 0, 0, 2, 2, 3, 3]
+        assert sorted(G.degree(), key=str) == [
+            ("A", 2),
+            ("B", 3),
+            ("C", 3),
+            ("D", 2),
+            ("G", 0),
+            ("J", 0),
+            ("K", 0),
+        ]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        pytest.raises(nx.NetworkXError, G.neighbors, "X")
+        G.clear()
+        assert nx.number_of_nodes(G) == 0
+        assert nx.number_of_edges(G) == 0
+
+    def test_null_subgraph(self):
+        # Subgraph of a null graph is a null graph
+        nullgraph = nx.null_graph()
+        G = nx.null_graph()
+        H = G.subgraph([])
+        assert nx.is_isomorphic(H, nullgraph)
+
+    def test_empty_subgraph(self):
+        # Subgraph of an empty graph is an empty graph. test 1
+        nullgraph = nx.null_graph()
+        E5 = nx.empty_graph(5)
+        E10 = nx.empty_graph(10)
+        H = E10.subgraph([])
+        assert nx.is_isomorphic(H, nullgraph)
+        H = E10.subgraph([1, 2, 3, 4, 5])
+        assert nx.is_isomorphic(H, E5)
+
+    def test_complete_subgraph(self):
+        # Subgraph of a complete graph is a complete graph
+        K1 = nx.complete_graph(1)
+        K3 = nx.complete_graph(3)
+        K5 = nx.complete_graph(5)
+        H = K5.subgraph([1, 2, 3])
+        assert nx.is_isomorphic(H, K3)
+
+    def test_subgraph_nbunch(self):
+        nullgraph = nx.null_graph()
+        K1 = nx.complete_graph(1)
+        K3 = nx.complete_graph(3)
+        K5 = nx.complete_graph(5)
+        # Test G.subgraph(nbunch), where nbunch is a single node
+        H = K5.subgraph(1)
+        assert nx.is_isomorphic(H, K1)
+        # Test G.subgraph(nbunch), where nbunch is a set
+        H = K5.subgraph({1})
+        assert nx.is_isomorphic(H, K1)
+        # Test G.subgraph(nbunch), where nbunch is an iterator
+        H = K5.subgraph(iter(K3))
+        assert nx.is_isomorphic(H, K3)
+        # Test G.subgraph(nbunch), where nbunch is another graph
+        H = K5.subgraph(K3)
+        assert nx.is_isomorphic(H, K3)
+        H = K5.subgraph([9])
+        assert nx.is_isomorphic(H, nullgraph)
+
+    def test_node_tuple_issue(self):
+        H = self.G()
+        # Test error handling of tuple as a node
+        pytest.raises(nx.NetworkXError, H.remove_node, (1, 2))
+        H.remove_nodes_from([(1, 2)])  # no error
+        pytest.raises(nx.NetworkXError, H.neighbors, (1, 2))
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_coreviews.py b/lib/python3.11/site-packages/networkx/classes/tests/test_coreviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..24de7f2f1115b864682b261daa256eff0deef696
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_coreviews.py
@@ -0,0 +1,362 @@
+import pickle
+
+import pytest
+
+import networkx as nx
+
+
+class TestAtlasView:
+    # node->data
+    def setup_method(self):
+        self.d = {0: {"color": "blue", "weight": 1.2}, 1: {}, 2: {"color": 1}}
+        self.av = nx.classes.coreviews.AtlasView(self.d)
+
+    def test_pickle(self):
+        view = self.av
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+        pview = pickle.loads(pickle.dumps(view))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.av) == len(self.d)
+
+    def test_iter(self):
+        assert list(self.av) == list(self.d)
+
+    def test_getitem(self):
+        assert self.av[1] is self.d[1]
+        assert self.av[2]["color"] == 1
+        pytest.raises(KeyError, self.av.__getitem__, 3)
+
+    def test_copy(self):
+        avcopy = self.av.copy()
+        assert avcopy[0] == self.av[0]
+        assert avcopy == self.av
+        assert avcopy[0] is not self.av[0]
+        assert avcopy is not self.av
+        avcopy[5] = {}
+        assert avcopy != self.av
+
+        avcopy[0]["ht"] = 4
+        assert avcopy[0] != self.av[0]
+        self.av[0]["ht"] = 4
+        assert avcopy[0] == self.av[0]
+        del self.av[0]["ht"]
+
+        assert not hasattr(self.av, "__setitem__")
+
+    def test_items(self):
+        assert sorted(self.av.items()) == sorted(self.d.items())
+
+    def test_str(self):
+        out = str(self.d)
+        assert str(self.av) == out
+
+    def test_repr(self):
+        out = "AtlasView(" + str(self.d) + ")"
+        assert repr(self.av) == out
+
+
+class TestAdjacencyView:
+    # node->nbr->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.nd = {0: dd, 1: {}, 2: {"color": 1}}
+        self.adj = {3: self.nd, 0: {3: dd}, 1: {}, 2: {3: {"color": 1}}}
+        self.adjview = nx.classes.coreviews.AdjacencyView(self.adj)
+
+    def test_pickle(self):
+        view = self.adjview
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.adjview) == len(self.adj)
+
+    def test_iter(self):
+        assert list(self.adjview) == list(self.adj)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.adj[1]
+        assert self.adjview[3][0] is self.adjview[0][3]
+        assert self.adjview[2][3]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+
+    def test_items(self):
+        view_items = sorted((n, dict(d)) for n, d in self.adjview.items())
+        assert view_items == sorted(self.adj.items())
+
+    def test_str(self):
+        out = str(dict(self.adj))
+        assert str(self.adjview) == out
+
+    def test_repr(self):
+        out = self.adjview.__class__.__name__ + "(" + str(self.adj) + ")"
+        assert repr(self.adjview) == out
+
+
+class TestMultiAdjacencyView(TestAdjacencyView):
+    # node->nbr->key->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.kd = {0: dd, 1: {}, 2: {"color": 1}}
+        self.nd = {3: self.kd, 0: {3: dd}, 1: {0: {}}, 2: {3: {"color": 1}}}
+        self.adj = {3: self.nd, 0: {3: {3: dd}}, 1: {}, 2: {3: {8: {}}}}
+        self.adjview = nx.classes.coreviews.MultiAdjacencyView(self.adj)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.adj[1]
+        assert self.adjview[3][0][3] is self.adjview[0][3][3]
+        assert self.adjview[3][2][3]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3][8]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3][8]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3][8]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+
+
+class TestUnionAtlas:
+    # node->data
+    def setup_method(self):
+        self.s = {0: {"color": "blue", "weight": 1.2}, 1: {}, 2: {"color": 1}}
+        self.p = {3: {"color": "blue", "weight": 1.2}, 4: {}, 2: {"watch": 2}}
+        self.av = nx.classes.coreviews.UnionAtlas(self.s, self.p)
+
+    def test_pickle(self):
+        view = self.av
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.av) == len(self.s.keys() | self.p.keys()) == 5
+
+    def test_iter(self):
+        assert set(self.av) == set(self.s) | set(self.p)
+
+    def test_getitem(self):
+        assert self.av[0] is self.s[0]
+        assert self.av[4] is self.p[4]
+        assert self.av[2]["color"] == 1
+        pytest.raises(KeyError, self.av[2].__getitem__, "watch")
+        pytest.raises(KeyError, self.av.__getitem__, 8)
+
+    def test_copy(self):
+        avcopy = self.av.copy()
+        assert avcopy[0] == self.av[0]
+        assert avcopy[0] is not self.av[0]
+        assert avcopy is not self.av
+        avcopy[5] = {}
+        assert avcopy != self.av
+
+        avcopy[0]["ht"] = 4
+        assert avcopy[0] != self.av[0]
+        self.av[0]["ht"] = 4
+        assert avcopy[0] == self.av[0]
+        del self.av[0]["ht"]
+
+        assert not hasattr(self.av, "__setitem__")
+
+    def test_items(self):
+        expected = dict(self.p.items())
+        expected.update(self.s)
+        assert sorted(self.av.items()) == sorted(expected.items())
+
+    def test_str(self):
+        out = str(dict(self.av))
+        assert str(self.av) == out
+
+    def test_repr(self):
+        out = f"{self.av.__class__.__name__}({self.s}, {self.p})"
+        assert repr(self.av) == out
+
+
+class TestUnionAdjacency:
+    # node->nbr->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.nd = {0: dd, 1: {}, 2: {"color": 1}}
+        self.s = {3: self.nd, 0: {}, 1: {}, 2: {3: {"color": 1}}}
+        self.p = {3: {}, 0: {3: dd}, 1: {0: {}}, 2: {1: {"color": 1}}}
+        self.adjview = nx.classes.coreviews.UnionAdjacency(self.s, self.p)
+
+    def test_pickle(self):
+        view = self.adjview
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.adjview) == len(self.s)
+
+    def test_iter(self):
+        assert sorted(self.adjview) == sorted(self.s)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.s[1]
+        assert self.adjview[3][0] is self.adjview[0][3]
+        assert self.adjview[2][3]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+
+    def test_str(self):
+        out = str(dict(self.adjview))
+        assert str(self.adjview) == out
+
+    def test_repr(self):
+        clsname = self.adjview.__class__.__name__
+        out = f"{clsname}({self.s}, {self.p})"
+        assert repr(self.adjview) == out
+
+
+class TestUnionMultiInner(TestUnionAdjacency):
+    # nbr->key->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.kd = {7: {}, "ekey": {}, 9: {"color": 1}}
+        self.s = {3: self.kd, 0: {7: dd}, 1: {}, 2: {"key": {"color": 1}}}
+        self.p = {3: {}, 0: {3: dd}, 1: {}, 2: {1: {"span": 2}}}
+        self.adjview = nx.classes.coreviews.UnionMultiInner(self.s, self.p)
+
+    def test_len(self):
+        assert len(self.adjview) == len(self.s.keys() | self.p.keys()) == 4
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.s[1]
+        assert self.adjview[0][7] is self.adjview[0][3]
+        assert self.adjview[2]["key"]["color"] == 1
+        assert self.adjview[2][1]["span"] == 2
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+        pytest.raises(KeyError, self.adjview[1].__getitem__, "key")
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][1]["width"] = 8
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][1]["width"] = 8
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][1]["width"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+        assert hasattr(avcopy, "__setitem__")
+
+
+class TestUnionMultiAdjacency(TestUnionAdjacency):
+    # node->nbr->key->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.kd = {7: {}, 8: {}, 9: {"color": 1}}
+        self.nd = {3: self.kd, 0: {9: dd}, 1: {8: {}}, 2: {9: {"color": 1}}}
+        self.s = {3: self.nd, 0: {3: {7: dd}}, 1: {}, 2: {3: {8: {}}}}
+        self.p = {3: {}, 0: {3: {9: dd}}, 1: {}, 2: {1: {8: {}}}}
+        self.adjview = nx.classes.coreviews.UnionMultiAdjacency(self.s, self.p)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.s[1]
+        assert self.adjview[3][0][9] is self.adjview[0][3][9]
+        assert self.adjview[3][2][9]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3][8]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3][8]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3][8]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+        assert hasattr(avcopy, "__setitem__")
+
+
+class TestFilteredGraphs:
+    def setup_method(self):
+        self.Graphs = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+
+    def test_hide_show_nodes(self):
+        SubGraph = nx.subgraph_view
+        for Graph in self.Graphs:
+            G = nx.path_graph(4, Graph)
+            SG = G.subgraph([2, 3])
+            RG = SubGraph(G, filter_node=nx.filters.hide_nodes([0, 1]))
+            assert SG.nodes == RG.nodes
+            assert SG.edges == RG.edges
+            SGC = SG.copy()
+            RGC = RG.copy()
+            assert SGC.nodes == RGC.nodes
+            assert SGC.edges == RGC.edges
+
+    def test_str_repr(self):
+        SubGraph = nx.subgraph_view
+        for Graph in self.Graphs:
+            G = nx.path_graph(4, Graph)
+            SG = G.subgraph([2, 3])
+            RG = SubGraph(G, filter_node=nx.filters.hide_nodes([0, 1]))
+            str(SG.adj)
+            str(RG.adj)
+            repr(SG.adj)
+            repr(RG.adj)
+            str(SG.adj[2])
+            str(RG.adj[2])
+            repr(SG.adj[2])
+            repr(RG.adj[2])
+
+    def test_copy(self):
+        SubGraph = nx.subgraph_view
+        for Graph in self.Graphs:
+            G = nx.path_graph(4, Graph)
+            SG = G.subgraph([2, 3])
+            RG = SubGraph(G, filter_node=nx.filters.hide_nodes([0, 1]))
+            RsG = SubGraph(G, filter_node=nx.filters.show_nodes([2, 3]))
+            assert G.adj.copy() == G.adj
+            assert G.adj[2].copy() == G.adj[2]
+            assert SG.adj.copy() == SG.adj
+            assert SG.adj[2].copy() == SG.adj[2]
+            assert RG.adj.copy() == RG.adj
+            assert RG.adj[2].copy() == RG.adj[2]
+            assert RsG.adj.copy() == RsG.adj
+            assert RsG.adj[2].copy() == RsG.adj[2]
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_digraph.py b/lib/python3.11/site-packages/networkx/classes/tests/test_digraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9972f9a5f1ab101b9f6f2f9a1584ddafccd2ff3
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_digraph.py
@@ -0,0 +1,331 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import nodes_equal
+
+from .test_graph import BaseAttrGraphTester, BaseGraphTester
+from .test_graph import TestEdgeSubgraph as _TestGraphEdgeSubgraph
+from .test_graph import TestGraph as _TestGraph
+
+
+class BaseDiGraphTester(BaseGraphTester):
+    def test_has_successor(self):
+        G = self.K3
+        assert G.has_successor(0, 1)
+        assert not G.has_successor(0, -1)
+
+    def test_successors(self):
+        G = self.K3
+        assert sorted(G.successors(0)) == [1, 2]
+        with pytest.raises(nx.NetworkXError):
+            G.successors(-1)
+
+    def test_has_predecessor(self):
+        G = self.K3
+        assert G.has_predecessor(0, 1)
+        assert not G.has_predecessor(0, -1)
+
+    def test_predecessors(self):
+        G = self.K3
+        assert sorted(G.predecessors(0)) == [1, 2]
+        with pytest.raises(nx.NetworkXError):
+            G.predecessors(-1)
+
+    def test_edges(self):
+        G = self.K3
+        assert sorted(G.edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.edges(0)) == [(0, 1), (0, 2)]
+        assert sorted(G.edges([0, 1])) == [(0, 1), (0, 2), (1, 0), (1, 2)]
+        with pytest.raises(nx.NetworkXError):
+            G.edges(-1)
+
+    def test_out_edges(self):
+        G = self.K3
+        assert sorted(G.out_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)]
+        with pytest.raises(nx.NetworkXError):
+            G.out_edges(-1)
+
+    def test_out_edges_dir(self):
+        G = self.P3
+        assert sorted(G.out_edges()) == [(0, 1), (1, 2)]
+        assert sorted(G.out_edges(0)) == [(0, 1)]
+        assert sorted(G.out_edges(2)) == []
+
+    def test_out_edges_data(self):
+        G = nx.DiGraph([(0, 1, {"data": 0}), (1, 0, {})])
+        assert sorted(G.out_edges(data=True)) == [(0, 1, {"data": 0}), (1, 0, {})]
+        assert sorted(G.out_edges(0, data=True)) == [(0, 1, {"data": 0})]
+        assert sorted(G.out_edges(data="data")) == [(0, 1, 0), (1, 0, None)]
+        assert sorted(G.out_edges(0, data="data")) == [(0, 1, 0)]
+
+    def test_in_edges_dir(self):
+        G = self.P3
+        assert sorted(G.in_edges()) == [(0, 1), (1, 2)]
+        assert sorted(G.in_edges(0)) == []
+        assert sorted(G.in_edges(2)) == [(1, 2)]
+
+    def test_in_edges_data(self):
+        G = nx.DiGraph([(0, 1, {"data": 0}), (1, 0, {})])
+        assert sorted(G.in_edges(data=True)) == [(0, 1, {"data": 0}), (1, 0, {})]
+        assert sorted(G.in_edges(1, data=True)) == [(0, 1, {"data": 0})]
+        assert sorted(G.in_edges(data="data")) == [(0, 1, 0), (1, 0, None)]
+        assert sorted(G.in_edges(1, data="data")) == [(0, 1, 0)]
+
+    def test_degree(self):
+        G = self.K3
+        assert sorted(G.degree()) == [(0, 4), (1, 4), (2, 4)]
+        assert dict(G.degree()) == {0: 4, 1: 4, 2: 4}
+        assert G.degree(0) == 4
+        assert list(G.degree(iter([0]))) == [(0, 4)]  # run through iterator
+
+    def test_in_degree(self):
+        G = self.K3
+        assert sorted(G.in_degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.in_degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.in_degree(0) == 2
+        assert list(G.in_degree(iter([0]))) == [(0, 2)]  # run through iterator
+
+    def test_out_degree(self):
+        G = self.K3
+        assert sorted(G.out_degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.out_degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.out_degree(0) == 2
+        assert list(G.out_degree(iter([0]))) == [(0, 2)]
+
+    def test_size(self):
+        G = self.K3
+        assert G.size() == 6
+        assert G.number_of_edges() == 6
+
+    def test_to_undirected_reciprocal(self):
+        G = self.Graph()
+        G.add_edge(1, 2)
+        assert G.to_undirected().has_edge(1, 2)
+        assert not G.to_undirected(reciprocal=True).has_edge(1, 2)
+        G.add_edge(2, 1)
+        assert G.to_undirected(reciprocal=True).has_edge(1, 2)
+
+    def test_reverse_copy(self):
+        G = nx.DiGraph([(0, 1), (1, 2)])
+        R = G.reverse()
+        assert sorted(R.edges()) == [(1, 0), (2, 1)]
+        R.remove_edge(1, 0)
+        assert sorted(R.edges()) == [(2, 1)]
+        assert sorted(G.edges()) == [(0, 1), (1, 2)]
+
+    def test_reverse_nocopy(self):
+        G = nx.DiGraph([(0, 1), (1, 2)])
+        R = G.reverse(copy=False)
+        assert sorted(R.edges()) == [(1, 0), (2, 1)]
+        with pytest.raises(nx.NetworkXError):
+            R.remove_edge(1, 0)
+
+    def test_reverse_hashable(self):
+        class Foo:
+            pass
+
+        x = Foo()
+        y = Foo()
+        G = nx.DiGraph()
+        G.add_edge(x, y)
+        assert nodes_equal(G.nodes(), G.reverse().nodes())
+        assert [(y, x)] == list(G.reverse().edges())
+
+    def test_di_cache_reset(self):
+        G = self.K3.copy()
+        old_succ = G.succ
+        assert id(G.succ) == id(old_succ)
+        old_adj = G.adj
+        assert id(G.adj) == id(old_adj)
+
+        G._succ = {}
+        assert id(G.succ) != id(old_succ)
+        assert id(G.adj) != id(old_adj)
+
+        old_pred = G.pred
+        assert id(G.pred) == id(old_pred)
+        G._pred = {}
+        assert id(G.pred) != id(old_pred)
+
+    def test_di_attributes_cached(self):
+        G = self.K3.copy()
+        assert id(G.in_edges) == id(G.in_edges)
+        assert id(G.out_edges) == id(G.out_edges)
+        assert id(G.in_degree) == id(G.in_degree)
+        assert id(G.out_degree) == id(G.out_degree)
+        assert id(G.succ) == id(G.succ)
+        assert id(G.pred) == id(G.pred)
+
+
+class BaseAttrDiGraphTester(BaseDiGraphTester, BaseAttrGraphTester):
+    def test_edges_data(self):
+        G = self.K3
+        all_edges = [
+            (0, 1, {}),
+            (0, 2, {}),
+            (1, 0, {}),
+            (1, 2, {}),
+            (2, 0, {}),
+            (2, 1, {}),
+        ]
+        assert sorted(G.edges(data=True)) == all_edges
+        assert sorted(G.edges(0, data=True)) == all_edges[:2]
+        assert sorted(G.edges([0, 1], data=True)) == all_edges[:4]
+        with pytest.raises(nx.NetworkXError):
+            G.edges(-1, True)
+
+    def test_in_degree_weighted(self):
+        G = self.K3.copy()
+        G.add_edge(0, 1, weight=0.3, other=1.2)
+        assert sorted(G.in_degree(weight="weight")) == [(0, 2), (1, 1.3), (2, 2)]
+        assert dict(G.in_degree(weight="weight")) == {0: 2, 1: 1.3, 2: 2}
+        assert G.in_degree(1, weight="weight") == 1.3
+        assert sorted(G.in_degree(weight="other")) == [(0, 2), (1, 2.2), (2, 2)]
+        assert dict(G.in_degree(weight="other")) == {0: 2, 1: 2.2, 2: 2}
+        assert G.in_degree(1, weight="other") == 2.2
+        assert list(G.in_degree(iter([1]), weight="other")) == [(1, 2.2)]
+
+    def test_out_degree_weighted(self):
+        G = self.K3.copy()
+        G.add_edge(0, 1, weight=0.3, other=1.2)
+        assert sorted(G.out_degree(weight="weight")) == [(0, 1.3), (1, 2), (2, 2)]
+        assert dict(G.out_degree(weight="weight")) == {0: 1.3, 1: 2, 2: 2}
+        assert G.out_degree(0, weight="weight") == 1.3
+        assert sorted(G.out_degree(weight="other")) == [(0, 2.2), (1, 2), (2, 2)]
+        assert dict(G.out_degree(weight="other")) == {0: 2.2, 1: 2, 2: 2}
+        assert G.out_degree(0, weight="other") == 2.2
+        assert list(G.out_degree(iter([0]), weight="other")) == [(0, 2.2)]
+
+
+class TestDiGraph(BaseAttrDiGraphTester, _TestGraph):
+    """Tests specific to dict-of-dict-of-dict digraph data structure"""
+
+    def setup_method(self):
+        self.Graph = nx.DiGraph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3, ed4, ed5, ed6 = ({}, {}, {}, {}, {}, {})
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed3, 2: ed4}, 2: {0: ed5, 1: ed6}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = self.k3adj  # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {1: ed3, 2: ed5}, 1: {0: ed1, 2: ed6}, 2: {0: ed2, 1: ed4}}
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+        ed1, ed2 = ({}, {})
+        self.P3 = self.Graph()
+        self.P3._succ = {0: {1: ed1}, 1: {2: ed2}, 2: {}}
+        self.P3._pred = {0: {}, 1: {0: ed1}, 2: {1: ed2}}
+        # P3._adj is synced with P3._succ
+        self.P3._node = {}
+        self.P3._node[0] = {}
+        self.P3._node[1] = {}
+        self.P3._node[2] = {}
+
+    def test_data_input(self):
+        G = self.Graph({1: [2], 2: [1]}, name="test")
+        assert G.name == "test"
+        assert sorted(G.adj.items()) == [(1, {2: {}}), (2, {1: {}})]
+        assert sorted(G.succ.items()) == [(1, {2: {}}), (2, {1: {}})]
+        assert sorted(G.pred.items()) == [(1, {2: {}}), (2, {1: {}})]
+
+    def test_add_edge(self):
+        G = self.Graph()
+        G.add_edge(0, 1)
+        assert G.adj == {0: {1: {}}, 1: {}}
+        assert G.succ == {0: {1: {}}, 1: {}}
+        assert G.pred == {0: {}, 1: {0: {}}}
+        G = self.Graph()
+        G.add_edge(*(0, 1))
+        assert G.adj == {0: {1: {}}, 1: {}}
+        assert G.succ == {0: {1: {}}, 1: {}}
+        assert G.pred == {0: {}, 1: {0: {}}}
+        with pytest.raises(ValueError, match="None cannot be a node"):
+            G.add_edge(None, 3)
+
+    def test_add_edges_from(self):
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 2, {"data": 3})], data=2)
+        assert G.adj == {0: {1: {"data": 2}, 2: {"data": 3}}, 1: {}, 2: {}}
+        assert G.succ == {0: {1: {"data": 2}, 2: {"data": 3}}, 1: {}, 2: {}}
+        assert G.pred == {0: {}, 1: {0: {"data": 2}}, 2: {0: {"data": 3}}}
+
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0,)])  # too few in tuple
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0, 1, 2, 3)])  # too many in tuple
+        with pytest.raises(TypeError):
+            G.add_edges_from([0])  # not a tuple
+        with pytest.raises(ValueError, match="None cannot be a node"):
+            G.add_edges_from([(None, 3), (3, 2)])
+
+    def test_remove_edge(self):
+        G = self.K3.copy()
+        G.remove_edge(0, 1)
+        assert G.succ == {0: {2: {}}, 1: {0: {}, 2: {}}, 2: {0: {}, 1: {}}}
+        assert G.pred == {0: {1: {}, 2: {}}, 1: {2: {}}, 2: {0: {}, 1: {}}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_edge(-1, 0)
+
+    def test_remove_edges_from(self):
+        G = self.K3.copy()
+        G.remove_edges_from([(0, 1)])
+        assert G.succ == {0: {2: {}}, 1: {0: {}, 2: {}}, 2: {0: {}, 1: {}}}
+        assert G.pred == {0: {1: {}, 2: {}}, 1: {2: {}}, 2: {0: {}, 1: {}}}
+        G.remove_edges_from([(0, 0)])  # silent fail
+
+    def test_clear(self):
+        G = self.K3
+        G.graph["name"] = "K3"
+        G.clear()
+        assert list(G.nodes) == []
+        assert G.succ == {}
+        assert G.pred == {}
+        assert G.graph == {}
+
+    def test_clear_edges(self):
+        G = self.K3
+        G.graph["name"] = "K3"
+        nodes = list(G.nodes)
+        G.clear_edges()
+        assert list(G.nodes) == nodes
+        expected = {0: {}, 1: {}, 2: {}}
+        assert G.succ == expected
+        assert G.pred == expected
+        assert list(G.edges) == []
+        assert G.graph["name"] == "K3"
+
+
+class TestEdgeSubgraph(_TestGraphEdgeSubgraph):
+    """Unit tests for the :meth:`DiGraph.edge_subgraph` method."""
+
+    def setup_method(self):
+        # Create a doubly-linked path graph on five nodes.
+        G = nx.DiGraph(nx.path_graph(5))
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.edges[0, 1]["name"] = "edge01"
+        G.edges[3, 4]["name"] = "edge34"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by the first and last edges.
+        self.G = G
+        self.H = G.edge_subgraph([(0, 1), (3, 4)])
+
+    def test_pred_succ(self):
+        """Test that nodes are added to predecessors and successors.
+
+        For more information, see GitHub issue #2370.
+
+        """
+        G = nx.DiGraph()
+        G.add_edge(0, 1)
+        H = G.edge_subgraph([(0, 1)])
+        assert list(H.predecessors(0)) == []
+        assert list(H.successors(0)) == [1]
+        assert list(H.predecessors(1)) == [0]
+        assert list(H.successors(1)) == []
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_digraph_historical.py b/lib/python3.11/site-packages/networkx/classes/tests/test_digraph_historical.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff9f9e9cc4e698a70a6123452fde20069a09f0a4
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_digraph_historical.py
@@ -0,0 +1,110 @@
+"""Original NetworkX graph tests"""
+
+import pytest
+
+import networkx as nx
+
+from .historical_tests import HistoricalTests
+
+
+class TestDiGraphHistorical(HistoricalTests):
+    @classmethod
+    def setup_class(cls):
+        HistoricalTests.setup_class()
+        cls.G = nx.DiGraph
+
+    def test_in_degree(self):
+        G = self.G()
+        G.add_nodes_from("GJK")
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("B", "C"), ("C", "D")])
+
+        assert sorted(d for n, d in G.in_degree()) == [0, 0, 0, 0, 1, 2, 2]
+        assert dict(G.in_degree()) == {
+            "A": 0,
+            "C": 2,
+            "B": 1,
+            "D": 2,
+            "G": 0,
+            "K": 0,
+            "J": 0,
+        }
+
+    def test_out_degree(self):
+        G = self.G()
+        G.add_nodes_from("GJK")
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("B", "C"), ("C", "D")])
+        assert sorted(v for k, v in G.in_degree()) == [0, 0, 0, 0, 1, 2, 2]
+        assert dict(G.out_degree()) == {
+            "A": 2,
+            "C": 1,
+            "B": 2,
+            "D": 0,
+            "G": 0,
+            "K": 0,
+            "J": 0,
+        }
+
+    def test_degree_digraph(self):
+        H = nx.DiGraph()
+        H.add_edges_from([(1, 24), (1, 2)])
+        assert sorted(d for n, d in H.in_degree([1, 24])) == [0, 1]
+        assert sorted(d for n, d in H.out_degree([1, 24])) == [0, 2]
+        assert sorted(d for n, d in H.degree([1, 24])) == [1, 2]
+
+    def test_neighbors(self):
+        G = self.G()
+        G.add_nodes_from("GJK")
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("B", "C"), ("C", "D")])
+
+        assert sorted(G.neighbors("C")) == ["D"]
+        assert sorted(G["C"]) == ["D"]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        pytest.raises(nx.NetworkXError, G.neighbors, "j")
+        pytest.raises(nx.NetworkXError, G.neighbors, "j")
+
+    def test_successors(self):
+        G = self.G()
+        G.add_nodes_from("GJK")
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("B", "C"), ("C", "D")])
+        assert sorted(G.successors("A")) == ["B", "C"]
+        assert sorted(G.successors("A")) == ["B", "C"]
+        assert sorted(G.successors("G")) == []
+        assert sorted(G.successors("D")) == []
+        assert sorted(G.successors("G")) == []
+        pytest.raises(nx.NetworkXError, G.successors, "j")
+        pytest.raises(nx.NetworkXError, G.successors, "j")
+
+    def test_predecessors(self):
+        G = self.G()
+        G.add_nodes_from("GJK")
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("B", "C"), ("C", "D")])
+        assert sorted(G.predecessors("C")) == ["A", "B"]
+        assert sorted(G.predecessors("C")) == ["A", "B"]
+        assert sorted(G.predecessors("G")) == []
+        assert sorted(G.predecessors("A")) == []
+        assert sorted(G.predecessors("G")) == []
+        assert sorted(G.predecessors("A")) == []
+        assert sorted(G.successors("D")) == []
+
+        pytest.raises(nx.NetworkXError, G.predecessors, "j")
+        pytest.raises(nx.NetworkXError, G.predecessors, "j")
+
+    def test_reverse(self):
+        G = nx.complete_graph(10)
+        H = G.to_directed()
+        HR = H.reverse()
+        assert nx.is_isomorphic(H, HR)
+        assert sorted(H.edges()) == sorted(HR.edges())
+
+    def test_reverse2(self):
+        H = nx.DiGraph()
+        foo = [H.add_edge(u, u + 1) for u in range(5)]
+        HR = H.reverse()
+        for u in range(5):
+            assert HR.has_edge(u + 1, u)
+
+    def test_reverse3(self):
+        H = nx.DiGraph()
+        H.add_nodes_from([1, 2, 3, 4])
+        HR = H.reverse()
+        assert sorted(HR.nodes()) == [1, 2, 3, 4]
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_filters.py b/lib/python3.11/site-packages/networkx/classes/tests/test_filters.py
new file mode 100644
index 0000000000000000000000000000000000000000..2da59117cad0d72d5830b53c8d19c6e0ca988d54
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_filters.py
@@ -0,0 +1,177 @@
+import pytest
+
+import networkx as nx
+
+
+class TestFilterFactory:
+    def test_no_filter(self):
+        nf = nx.filters.no_filter
+        assert nf()
+        assert nf(1)
+        assert nf(2, 1)
+
+    def test_hide_nodes(self):
+        f = nx.classes.filters.hide_nodes([1, 2, 3])
+        assert not f(1)
+        assert not f(2)
+        assert not f(3)
+        assert f(4)
+        assert f(0)
+        assert f("a")
+        pytest.raises(TypeError, f, 1, 2)
+        pytest.raises(TypeError, f)
+
+    def test_show_nodes(self):
+        f = nx.classes.filters.show_nodes([1, 2, 3])
+        assert f(1)
+        assert f(2)
+        assert f(3)
+        assert not f(4)
+        assert not f(0)
+        assert not f("a")
+        pytest.raises(TypeError, f, 1, 2)
+        pytest.raises(TypeError, f)
+
+    def test_hide_edges(self):
+        factory = nx.classes.filters.hide_edges
+        f = factory([(1, 2), (3, 4)])
+        assert not f(1, 2)
+        assert not f(3, 4)
+        assert not f(4, 3)
+        assert f(2, 3)
+        assert f(0, -1)
+        assert f("a", "b")
+        pytest.raises(TypeError, f, 1, 2, 3)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2, 3)])
+
+    def test_show_edges(self):
+        factory = nx.classes.filters.show_edges
+        f = factory([(1, 2), (3, 4)])
+        assert f(1, 2)
+        assert f(3, 4)
+        assert f(4, 3)
+        assert not f(2, 3)
+        assert not f(0, -1)
+        assert not f("a", "b")
+        pytest.raises(TypeError, f, 1, 2, 3)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2, 3)])
+
+    def test_hide_diedges(self):
+        factory = nx.classes.filters.hide_diedges
+        f = factory([(1, 2), (3, 4)])
+        assert not f(1, 2)
+        assert not f(3, 4)
+        assert f(4, 3)
+        assert f(2, 3)
+        assert f(0, -1)
+        assert f("a", "b")
+        pytest.raises(TypeError, f, 1, 2, 3)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2, 3)])
+
+    def test_show_diedges(self):
+        factory = nx.classes.filters.show_diedges
+        f = factory([(1, 2), (3, 4)])
+        assert f(1, 2)
+        assert f(3, 4)
+        assert not f(4, 3)
+        assert not f(2, 3)
+        assert not f(0, -1)
+        assert not f("a", "b")
+        pytest.raises(TypeError, f, 1, 2, 3)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2, 3)])
+
+    def test_hide_multiedges(self):
+        factory = nx.classes.filters.hide_multiedges
+        f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)])
+        assert not f(1, 2, 0)
+        assert not f(1, 2, 1)
+        assert f(1, 2, 2)
+        assert f(3, 4, 0)
+        assert not f(3, 4, 1)
+        assert not f(4, 3, 1)
+        assert f(4, 3, 0)
+        assert f(2, 3, 0)
+        assert f(0, -1, 0)
+        assert f("a", "b", 0)
+        pytest.raises(TypeError, f, 1, 2, 3, 4)
+        pytest.raises(TypeError, f, 1, 2)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2)])
+        pytest.raises(ValueError, factory, [(1, 2, 3, 4)])
+
+    def test_show_multiedges(self):
+        factory = nx.classes.filters.show_multiedges
+        f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)])
+        assert f(1, 2, 0)
+        assert f(1, 2, 1)
+        assert not f(1, 2, 2)
+        assert not f(3, 4, 0)
+        assert f(3, 4, 1)
+        assert f(4, 3, 1)
+        assert not f(4, 3, 0)
+        assert not f(2, 3, 0)
+        assert not f(0, -1, 0)
+        assert not f("a", "b", 0)
+        pytest.raises(TypeError, f, 1, 2, 3, 4)
+        pytest.raises(TypeError, f, 1, 2)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2)])
+        pytest.raises(ValueError, factory, [(1, 2, 3, 4)])
+
+    def test_hide_multidiedges(self):
+        factory = nx.classes.filters.hide_multidiedges
+        f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)])
+        assert not f(1, 2, 0)
+        assert not f(1, 2, 1)
+        assert f(1, 2, 2)
+        assert f(3, 4, 0)
+        assert not f(3, 4, 1)
+        assert f(4, 3, 1)
+        assert f(4, 3, 0)
+        assert f(2, 3, 0)
+        assert f(0, -1, 0)
+        assert f("a", "b", 0)
+        pytest.raises(TypeError, f, 1, 2, 3, 4)
+        pytest.raises(TypeError, f, 1, 2)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2)])
+        pytest.raises(ValueError, factory, [(1, 2, 3, 4)])
+
+    def test_show_multidiedges(self):
+        factory = nx.classes.filters.show_multidiedges
+        f = factory([(1, 2, 0), (3, 4, 1), (1, 2, 1)])
+        assert f(1, 2, 0)
+        assert f(1, 2, 1)
+        assert not f(1, 2, 2)
+        assert not f(3, 4, 0)
+        assert f(3, 4, 1)
+        assert not f(4, 3, 1)
+        assert not f(4, 3, 0)
+        assert not f(2, 3, 0)
+        assert not f(0, -1, 0)
+        assert not f("a", "b", 0)
+        pytest.raises(TypeError, f, 1, 2, 3, 4)
+        pytest.raises(TypeError, f, 1, 2)
+        pytest.raises(TypeError, f, 1)
+        pytest.raises(TypeError, f)
+        pytest.raises(TypeError, factory, [1, 2, 3])
+        pytest.raises(ValueError, factory, [(1, 2)])
+        pytest.raises(ValueError, factory, [(1, 2, 3, 4)])
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_function.py b/lib/python3.11/site-packages/networkx/classes/tests/test_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d875c84c0de403ad0f4294913ad04f4c5c8220c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_function.py
@@ -0,0 +1,1045 @@
+import random
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+def test_degree_histogram_empty():
+    G = nx.Graph()
+    assert nx.degree_histogram(G) == []
+
+
+class TestFunction:
+    def setup_method(self):
+        self.G = nx.Graph({0: [1, 2, 3], 1: [1, 2, 0], 4: []}, name="Test")
+        self.Gdegree = {0: 3, 1: 2, 2: 2, 3: 1, 4: 0}
+        self.Gnodes = list(range(5))
+        self.Gedges = [(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)]
+        self.DG = nx.DiGraph({0: [1, 2, 3], 1: [1, 2, 0], 4: []})
+        self.DGin_degree = {0: 1, 1: 2, 2: 2, 3: 1, 4: 0}
+        self.DGout_degree = {0: 3, 1: 3, 2: 0, 3: 0, 4: 0}
+        self.DGnodes = list(range(5))
+        self.DGedges = [(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)]
+
+    def test_describe_info_dict(self):
+        info_dict = nx.classes.function._create_describe_info_dict(self.G)
+        assert info_dict["Name of Graph"] == "Test"
+        assert not info_dict["Directed"]
+        assert not info_dict["Multigraph"]
+        assert info_dict["Number of nodes"] == 5
+        assert info_dict["Number of edges"] == 5
+        assert info_dict["Average degree (min, max)"] == "2.00 (0, 4)"
+        assert info_dict["Number of connected components"] == 2
+
+    def test_nodes(self):
+        assert nodes_equal(self.G.nodes(), list(nx.nodes(self.G)))
+        assert nodes_equal(self.DG.nodes(), list(nx.nodes(self.DG)))
+
+    def test_edges(self):
+        assert edges_equal(self.G.edges(), list(nx.edges(self.G)))
+        assert sorted(self.DG.edges()) == sorted(nx.edges(self.DG))
+        assert edges_equal(
+            self.G.edges(nbunch=[0, 1, 3]), list(nx.edges(self.G, nbunch=[0, 1, 3]))
+        )
+        assert sorted(self.DG.edges(nbunch=[0, 1, 3])) == sorted(
+            nx.edges(self.DG, nbunch=[0, 1, 3])
+        )
+
+    def test_degree(self):
+        assert edges_equal(self.G.degree(), list(nx.degree(self.G)))
+        assert sorted(self.DG.degree()) == sorted(nx.degree(self.DG))
+        assert edges_equal(
+            self.G.degree(nbunch=[0, 1]), list(nx.degree(self.G, nbunch=[0, 1]))
+        )
+        assert sorted(self.DG.degree(nbunch=[0, 1])) == sorted(
+            nx.degree(self.DG, nbunch=[0, 1])
+        )
+        assert edges_equal(
+            self.G.degree(weight="weight"), list(nx.degree(self.G, weight="weight"))
+        )
+        assert sorted(self.DG.degree(weight="weight")) == sorted(
+            nx.degree(self.DG, weight="weight")
+        )
+
+    def test_neighbors(self):
+        assert list(self.G.neighbors(1)) == list(nx.neighbors(self.G, 1))
+        assert list(self.DG.neighbors(1)) == list(nx.neighbors(self.DG, 1))
+
+    def test_number_of_nodes(self):
+        assert self.G.number_of_nodes() == nx.number_of_nodes(self.G)
+        assert self.DG.number_of_nodes() == nx.number_of_nodes(self.DG)
+
+    def test_number_of_edges(self):
+        assert self.G.number_of_edges() == nx.number_of_edges(self.G)
+        assert self.DG.number_of_edges() == nx.number_of_edges(self.DG)
+
+    def test_is_directed(self):
+        assert self.G.is_directed() == nx.is_directed(self.G)
+        assert self.DG.is_directed() == nx.is_directed(self.DG)
+
+    def test_add_star(self):
+        G = self.G.copy()
+        nlist = [12, 13, 14, 15]
+        nx.add_star(G, nlist)
+        assert edges_equal(G.edges(nlist), [(12, 13), (12, 14), (12, 15)])
+
+        G = self.G.copy()
+        nx.add_star(G, nlist, weight=2.0)
+        assert edges_equal(
+            G.edges(nlist, data=True),
+            [
+                (12, 13, {"weight": 2.0}),
+                (12, 14, {"weight": 2.0}),
+                (12, 15, {"weight": 2.0}),
+            ],
+        )
+
+        G = self.G.copy()
+        nlist = [12]
+        nx.add_star(G, nlist)
+        assert nodes_equal(G, list(self.G) + nlist)
+
+        G = self.G.copy()
+        nlist = []
+        nx.add_star(G, nlist)
+        assert nodes_equal(G.nodes, self.Gnodes)
+        assert edges_equal(G.edges, self.G.edges)
+
+    def test_add_path(self):
+        G = self.G.copy()
+        nlist = [12, 13, 14, 15]
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges(nlist), [(12, 13), (13, 14), (14, 15)])
+        G = self.G.copy()
+        nx.add_path(G, nlist, weight=2.0)
+        assert edges_equal(
+            G.edges(nlist, data=True),
+            [
+                (12, 13, {"weight": 2.0}),
+                (13, 14, {"weight": 2.0}),
+                (14, 15, {"weight": 2.0}),
+            ],
+        )
+
+        G = self.G.copy()
+        nlist = ["node"]
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges(nlist), [])
+        assert nodes_equal(G, list(self.G) + ["node"])
+
+        G = self.G.copy()
+        nlist = iter(["node"])
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges(["node"]), [])
+        assert nodes_equal(G, list(self.G) + ["node"])
+
+        G = self.G.copy()
+        nlist = [12]
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges(nlist), [])
+        assert nodes_equal(G, list(self.G) + [12])
+
+        G = self.G.copy()
+        nlist = iter([12])
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges([12]), [])
+        assert nodes_equal(G, list(self.G) + [12])
+
+        G = self.G.copy()
+        nlist = []
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges, self.G.edges)
+        assert nodes_equal(G, list(self.G))
+
+        G = self.G.copy()
+        nlist = iter([])
+        nx.add_path(G, nlist)
+        assert edges_equal(G.edges, self.G.edges)
+        assert nodes_equal(G, list(self.G))
+
+    def test_add_cycle(self):
+        G = self.G.copy()
+        nlist = [12, 13, 14, 15]
+        oklists = [
+            [(12, 13), (12, 15), (13, 14), (14, 15)],
+            [(12, 13), (13, 14), (14, 15), (15, 12)],
+        ]
+        nx.add_cycle(G, nlist)
+        assert sorted(G.edges(nlist)) in oklists
+        G = self.G.copy()
+        oklists = [
+            [
+                (12, 13, {"weight": 1.0}),
+                (12, 15, {"weight": 1.0}),
+                (13, 14, {"weight": 1.0}),
+                (14, 15, {"weight": 1.0}),
+            ],
+            [
+                (12, 13, {"weight": 1.0}),
+                (13, 14, {"weight": 1.0}),
+                (14, 15, {"weight": 1.0}),
+                (15, 12, {"weight": 1.0}),
+            ],
+        ]
+        nx.add_cycle(G, nlist, weight=1.0)
+        assert sorted(G.edges(nlist, data=True)) in oklists
+
+        G = self.G.copy()
+        nlist = [12]
+        nx.add_cycle(G, nlist)
+        assert nodes_equal(G, list(self.G) + nlist)
+
+        G = self.G.copy()
+        nlist = []
+        nx.add_cycle(G, nlist)
+        assert nodes_equal(G.nodes, self.Gnodes)
+        assert edges_equal(G.edges, self.G.edges)
+
+    def test_subgraph(self):
+        assert (
+            self.G.subgraph([0, 1, 2, 4]).adj == nx.subgraph(self.G, [0, 1, 2, 4]).adj
+        )
+        assert (
+            self.DG.subgraph([0, 1, 2, 4]).adj == nx.subgraph(self.DG, [0, 1, 2, 4]).adj
+        )
+        assert (
+            self.G.subgraph([0, 1, 2, 4]).adj
+            == nx.induced_subgraph(self.G, [0, 1, 2, 4]).adj
+        )
+        assert (
+            self.DG.subgraph([0, 1, 2, 4]).adj
+            == nx.induced_subgraph(self.DG, [0, 1, 2, 4]).adj
+        )
+        # subgraph-subgraph chain is allowed in function interface
+        H = nx.induced_subgraph(self.G.subgraph([0, 1, 2, 4]), [0, 1, 4])
+        assert H._graph is not self.G
+        assert H.adj == self.G.subgraph([0, 1, 4]).adj
+
+    def test_edge_subgraph(self):
+        assert (
+            self.G.edge_subgraph([(1, 2), (0, 3)]).adj
+            == nx.edge_subgraph(self.G, [(1, 2), (0, 3)]).adj
+        )
+        assert (
+            self.DG.edge_subgraph([(1, 2), (0, 3)]).adj
+            == nx.edge_subgraph(self.DG, [(1, 2), (0, 3)]).adj
+        )
+
+    def test_create_empty_copy(self):
+        G = nx.create_empty_copy(self.G, with_data=False)
+        assert nodes_equal(G, list(self.G))
+        assert G.graph == {}
+        assert G._node == {}.fromkeys(self.G.nodes(), {})
+        assert G._adj == {}.fromkeys(self.G.nodes(), {})
+        G = nx.create_empty_copy(self.G)
+        assert nodes_equal(G, list(self.G))
+        assert G.graph == self.G.graph
+        assert G._node == self.G._node
+        assert G._adj == {}.fromkeys(self.G.nodes(), {})
+
+    def test_degree_histogram(self):
+        assert nx.degree_histogram(self.G) == [1, 1, 1, 1, 1]
+
+    def test_density(self):
+        assert nx.density(self.G) == 0.5
+        assert nx.density(self.DG) == 0.3
+        G = nx.Graph()
+        G.add_node(1)
+        assert nx.density(G) == 0.0
+
+    def test_density_selfloop(self):
+        G = nx.Graph()
+        G.add_edge(1, 1)
+        assert nx.density(G) == 0.0
+        G.add_edge(1, 2)
+        assert nx.density(G) == 2.0
+
+    def test_freeze(self):
+        G = nx.freeze(self.G)
+        assert G.frozen
+        pytest.raises(nx.NetworkXError, G.add_node, 1)
+        pytest.raises(nx.NetworkXError, G.add_nodes_from, [1])
+        pytest.raises(nx.NetworkXError, G.remove_node, 1)
+        pytest.raises(nx.NetworkXError, G.remove_nodes_from, [1])
+        pytest.raises(nx.NetworkXError, G.add_edge, 1, 2)
+        pytest.raises(nx.NetworkXError, G.add_edges_from, [(1, 2)])
+        pytest.raises(nx.NetworkXError, G.remove_edge, 1, 2)
+        pytest.raises(nx.NetworkXError, G.remove_edges_from, [(1, 2)])
+        pytest.raises(nx.NetworkXError, G.clear_edges)
+        pytest.raises(nx.NetworkXError, G.clear)
+
+    def test_is_frozen(self):
+        assert not nx.is_frozen(self.G)
+        G = nx.freeze(self.G)
+        assert G.frozen == nx.is_frozen(self.G)
+        assert G.frozen
+
+    def test_node_attributes_are_still_mutable_on_frozen_graph(self):
+        G = nx.freeze(nx.path_graph(3))
+        node = G.nodes[0]
+        node["node_attribute"] = True
+        assert node["node_attribute"] is True
+
+    def test_edge_attributes_are_still_mutable_on_frozen_graph(self):
+        G = nx.freeze(nx.path_graph(3))
+        edge = G.edges[(0, 1)]
+        edge["edge_attribute"] = True
+        assert edge["edge_attribute"] is True
+
+    def test_neighbors_complete_graph(self):
+        graph = nx.complete_graph(100)
+        pop = random.sample(list(graph), 1)
+        nbors = list(nx.neighbors(graph, pop[0]))
+        # should be all the other vertices in the graph
+        assert len(nbors) == len(graph) - 1
+
+        graph = nx.path_graph(100)
+        node = random.sample(list(graph), 1)[0]
+        nbors = list(nx.neighbors(graph, node))
+        # should be all the other vertices in the graph
+        if node != 0 and node != 99:
+            assert len(nbors) == 2
+        else:
+            assert len(nbors) == 1
+
+        # create a star graph with 99 outer nodes
+        graph = nx.star_graph(99)
+        nbors = list(nx.neighbors(graph, 0))
+        assert len(nbors) == 99
+
+    def test_non_neighbors(self):
+        graph = nx.complete_graph(100)
+        pop = random.sample(list(graph), 1)
+        nbors = nx.non_neighbors(graph, pop[0])
+        # should be all the other vertices in the graph
+        assert len(nbors) == 0
+
+        graph = nx.path_graph(100)
+        node = random.sample(list(graph), 1)[0]
+        nbors = nx.non_neighbors(graph, node)
+        # should be all the other vertices in the graph
+        if node != 0 and node != 99:
+            assert len(nbors) == 97
+        else:
+            assert len(nbors) == 98
+
+        # create a star graph with 99 outer nodes
+        graph = nx.star_graph(99)
+        nbors = nx.non_neighbors(graph, 0)
+        assert len(nbors) == 0
+
+        # disconnected graph
+        graph = nx.Graph()
+        graph.add_nodes_from(range(10))
+        nbors = nx.non_neighbors(graph, 0)
+        assert len(nbors) == 9
+
+    def test_non_edges(self):
+        # All possible edges exist
+        graph = nx.complete_graph(5)
+        nedges = list(nx.non_edges(graph))
+        assert len(nedges) == 0
+
+        graph = nx.path_graph(4)
+        expected = [(0, 2), (0, 3), (1, 3)]
+        nedges = list(nx.non_edges(graph))
+        for u, v in expected:
+            assert (u, v) in nedges or (v, u) in nedges
+
+        graph = nx.star_graph(4)
+        expected = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        nedges = list(nx.non_edges(graph))
+        for u, v in expected:
+            assert (u, v) in nedges or (v, u) in nedges
+
+        # Directed graphs
+        graph = nx.DiGraph()
+        graph.add_edges_from([(0, 2), (2, 0), (2, 1)])
+        expected = [(0, 1), (1, 0), (1, 2)]
+        nedges = list(nx.non_edges(graph))
+        for e in expected:
+            assert e in nedges
+
+    def test_is_weighted(self):
+        G = nx.Graph()
+        assert not nx.is_weighted(G)
+
+        G = nx.path_graph(4)
+        assert not nx.is_weighted(G)
+        assert not nx.is_weighted(G, (2, 3))
+
+        G.add_node(4)
+        G.add_edge(3, 4, weight=4)
+        assert not nx.is_weighted(G)
+        assert nx.is_weighted(G, (3, 4))
+
+        G = nx.DiGraph()
+        G.add_weighted_edges_from(
+            [
+                ("0", "3", 3),
+                ("0", "1", -5),
+                ("1", "0", -5),
+                ("0", "2", 2),
+                ("1", "2", 4),
+                ("2", "3", 1),
+            ]
+        )
+        assert nx.is_weighted(G)
+        assert nx.is_weighted(G, ("1", "0"))
+
+        G = G.to_undirected()
+        assert nx.is_weighted(G)
+        assert nx.is_weighted(G, ("1", "0"))
+
+        pytest.raises(nx.NetworkXError, nx.is_weighted, G, (1, 2))
+
+    def test_is_negatively_weighted(self):
+        G = nx.Graph()
+        assert not nx.is_negatively_weighted(G)
+
+        G.add_node(1)
+        G.add_nodes_from([2, 3, 4, 5])
+        assert not nx.is_negatively_weighted(G)
+
+        G.add_edge(1, 2, weight=4)
+        assert not nx.is_negatively_weighted(G, (1, 2))
+
+        G.add_edges_from([(1, 3), (2, 4), (2, 6)])
+        G[1][3]["color"] = "blue"
+        assert not nx.is_negatively_weighted(G)
+        assert not nx.is_negatively_weighted(G, (1, 3))
+
+        G[2][4]["weight"] = -2
+        assert nx.is_negatively_weighted(G, (2, 4))
+        assert nx.is_negatively_weighted(G)
+
+        G = nx.DiGraph()
+        G.add_weighted_edges_from(
+            [
+                ("0", "3", 3),
+                ("0", "1", -5),
+                ("1", "0", -2),
+                ("0", "2", 2),
+                ("1", "2", -3),
+                ("2", "3", 1),
+            ]
+        )
+        assert nx.is_negatively_weighted(G)
+        assert not nx.is_negatively_weighted(G, ("0", "3"))
+        assert nx.is_negatively_weighted(G, ("1", "0"))
+
+        pytest.raises(nx.NetworkXError, nx.is_negatively_weighted, G, (1, 4))
+
+
+class TestCommonNeighbors:
+    @classmethod
+    def setup_class(cls):
+        cls.func = staticmethod(nx.common_neighbors)
+
+        def test_func(G, u, v, expected):
+            result = sorted(cls.func(G, u, v))
+            assert result == expected
+
+        cls.test = staticmethod(test_func)
+
+    def test_K5(self):
+        G = nx.complete_graph(5)
+        self.test(G, 0, 1, [2, 3, 4])
+
+    def test_P3(self):
+        G = nx.path_graph(3)
+        self.test(G, 0, 2, [1])
+
+    def test_S4(self):
+        G = nx.star_graph(4)
+        self.test(G, 1, 2, [0])
+
+    def test_digraph(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.DiGraph()
+            G.add_edges_from([(0, 1), (1, 2)])
+            self.func(G, 0, 2)
+
+    def test_nonexistent_nodes(self):
+        G = nx.complete_graph(5)
+        pytest.raises(nx.NetworkXError, nx.common_neighbors, G, 5, 4)
+        pytest.raises(nx.NetworkXError, nx.common_neighbors, G, 4, 5)
+        pytest.raises(nx.NetworkXError, nx.common_neighbors, G, 5, 6)
+
+    def test_custom1(self):
+        """Case of no common neighbors."""
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        self.test(G, 0, 1, [])
+
+    def test_custom2(self):
+        """Case of equal nodes."""
+        G = nx.complete_graph(4)
+        self.test(G, 0, 0, [1, 2, 3])
+
+
+@pytest.mark.parametrize(
+    "graph_type", (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+)
+def test_set_node_attributes(graph_type):
+    # Test single value
+    G = nx.path_graph(3, create_using=graph_type)
+    vals = 100
+    attr = "hello"
+    nx.set_node_attributes(G, vals, attr)
+    assert G.nodes[0][attr] == vals
+    assert G.nodes[1][attr] == vals
+    assert G.nodes[2][attr] == vals
+
+    # Test dictionary
+    G = nx.path_graph(3, create_using=graph_type)
+    vals = dict(zip(sorted(G.nodes()), range(len(G))))
+    attr = "hi"
+    nx.set_node_attributes(G, vals, attr)
+    assert G.nodes[0][attr] == 0
+    assert G.nodes[1][attr] == 1
+    assert G.nodes[2][attr] == 2
+
+    # Test dictionary of dictionaries
+    G = nx.path_graph(3, create_using=graph_type)
+    d = {"hi": 0, "hello": 200}
+    vals = dict.fromkeys(G.nodes(), d)
+    vals.pop(0)
+    nx.set_node_attributes(G, vals)
+    assert G.nodes[0] == {}
+    assert G.nodes[1]["hi"] == 0
+    assert G.nodes[2]["hello"] == 200
+
+
+@pytest.mark.parametrize(
+    ("values", "name"),
+    (
+        ({0: "red", 1: "blue"}, "color"),  # values dictionary
+        ({0: {"color": "red"}, 1: {"color": "blue"}}, None),  # dict-of-dict
+    ),
+)
+def test_set_node_attributes_ignores_extra_nodes(values, name):
+    """
+    When `values` is a dict or dict-of-dict keyed by nodes, ensure that keys
+    that correspond to nodes not in G are ignored.
+    """
+    G = nx.Graph()
+    G.add_node(0)
+    nx.set_node_attributes(G, values, name)
+    assert G.nodes[0]["color"] == "red"
+    assert 1 not in G.nodes
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_set_edge_attributes(graph_type):
+    # Test single value
+    G = nx.path_graph(3, create_using=graph_type)
+    attr = "hello"
+    vals = 3
+    nx.set_edge_attributes(G, vals, attr)
+    assert G[0][1][attr] == vals
+    assert G[1][2][attr] == vals
+
+    # Test multiple values
+    G = nx.path_graph(3, create_using=graph_type)
+    attr = "hi"
+    edges = [(0, 1), (1, 2)]
+    vals = dict(zip(edges, range(len(edges))))
+    nx.set_edge_attributes(G, vals, attr)
+    assert G[0][1][attr] == 0
+    assert G[1][2][attr] == 1
+
+    # Test dictionary of dictionaries
+    G = nx.path_graph(3, create_using=graph_type)
+    d = {"hi": 0, "hello": 200}
+    edges = [(0, 1)]
+    vals = dict.fromkeys(edges, d)
+    nx.set_edge_attributes(G, vals)
+    assert G[0][1]["hi"] == 0
+    assert G[0][1]["hello"] == 200
+    assert G[1][2] == {}
+
+
+@pytest.mark.parametrize(
+    ("values", "name"),
+    (
+        ({(0, 1): 1.0, (0, 2): 2.0}, "weight"),  # values dict
+        ({(0, 1): {"weight": 1.0}, (0, 2): {"weight": 2.0}}, None),  # values dod
+    ),
+)
+def test_set_edge_attributes_ignores_extra_edges(values, name):
+    """If `values` is a dict or dict-of-dicts containing edges that are not in
+    G, data associate with these edges should be ignored.
+    """
+    G = nx.Graph([(0, 1)])
+    nx.set_edge_attributes(G, values, name)
+    assert G[0][1]["weight"] == 1.0
+    assert (0, 2) not in G.edges
+
+
+@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
+def test_set_edge_attributes_multi(graph_type):
+    # Test single value
+    G = nx.path_graph(3, create_using=graph_type)
+    attr = "hello"
+    vals = 3
+    nx.set_edge_attributes(G, vals, attr)
+    assert G[0][1][0][attr] == vals
+    assert G[1][2][0][attr] == vals
+
+    # Test multiple values
+    G = nx.path_graph(3, create_using=graph_type)
+    attr = "hi"
+    edges = [(0, 1, 0), (1, 2, 0)]
+    vals = dict(zip(edges, range(len(edges))))
+    nx.set_edge_attributes(G, vals, attr)
+    assert G[0][1][0][attr] == 0
+    assert G[1][2][0][attr] == 1
+
+    # Test dictionary of dictionaries
+    G = nx.path_graph(3, create_using=graph_type)
+    d = {"hi": 0, "hello": 200}
+    edges = [(0, 1, 0)]
+    vals = dict.fromkeys(edges, d)
+    nx.set_edge_attributes(G, vals)
+    assert G[0][1][0]["hi"] == 0
+    assert G[0][1][0]["hello"] == 200
+    assert G[1][2][0] == {}
+
+
+@pytest.mark.parametrize(
+    ("values", "name"),
+    (
+        ({(0, 1, 0): 1.0, (0, 2, 0): 2.0}, "weight"),  # values dict
+        ({(0, 1, 0): {"weight": 1.0}, (0, 2, 0): {"weight": 2.0}}, None),  # values dod
+    ),
+)
+def test_set_edge_attributes_multi_ignores_extra_edges(values, name):
+    """If `values` is a dict or dict-of-dicts containing edges that are not in
+    G, data associate with these edges should be ignored.
+    """
+    G = nx.MultiGraph([(0, 1, 0), (0, 1, 1)])
+    nx.set_edge_attributes(G, values, name)
+    assert G[0][1][0]["weight"] == 1.0
+    assert G[0][1][1] == {}
+    assert (0, 2) not in G.edges()
+
+
+def test_get_node_attributes():
+    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
+    for G in graphs:
+        G = nx.path_graph(3, create_using=G)
+        attr = "hello"
+        vals = 100
+        nx.set_node_attributes(G, vals, attr)
+        attrs = nx.get_node_attributes(G, attr)
+        assert attrs[0] == vals
+        assert attrs[1] == vals
+        assert attrs[2] == vals
+        default_val = 1
+        G.add_node(4)
+        attrs = nx.get_node_attributes(G, attr, default=default_val)
+        assert attrs[4] == default_val
+
+
+def test_get_edge_attributes():
+    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
+    for G in graphs:
+        G = nx.path_graph(3, create_using=G)
+        attr = "hello"
+        vals = 100
+        nx.set_edge_attributes(G, vals, attr)
+        attrs = nx.get_edge_attributes(G, attr)
+        assert len(attrs) == 2
+
+        for edge in G.edges:
+            assert attrs[edge] == vals
+
+        default_val = vals
+        G.add_edge(4, 5)
+        deafult_attrs = nx.get_edge_attributes(G, attr, default=default_val)
+        assert len(deafult_attrs) == 3
+
+        for edge in G.edges:
+            assert deafult_attrs[edge] == vals
+
+
+@pytest.mark.parametrize(
+    "graph_type", (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+)
+def test_remove_node_attributes(graph_type):
+    # Test removing single attribute
+    G = nx.path_graph(3, create_using=graph_type)
+    vals = 100
+    attr = "hello"
+    nx.set_node_attributes(G, vals, attr)
+    nx.remove_node_attributes(G, attr)
+    assert attr not in G.nodes[0]
+    assert attr not in G.nodes[1]
+    assert attr not in G.nodes[2]
+
+    # Test removing single attribute when multiple present
+    G = nx.path_graph(3, create_using=graph_type)
+    other_vals = 200
+    other_attr = "other"
+    nx.set_node_attributes(G, vals, attr)
+    nx.set_node_attributes(G, other_vals, other_attr)
+    nx.remove_node_attributes(G, attr)
+    assert attr not in G.nodes[0]
+    assert G.nodes[0][other_attr] == other_vals
+    assert attr not in G.nodes[1]
+    assert G.nodes[1][other_attr] == other_vals
+    assert attr not in G.nodes[2]
+    assert G.nodes[2][other_attr] == other_vals
+
+    # Test removing multiple attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_node_attributes(G, vals, attr)
+    nx.set_node_attributes(G, other_vals, other_attr)
+    nx.remove_node_attributes(G, attr, other_attr)
+    assert attr not in G.nodes[0] and other_attr not in G.nodes[0]
+    assert attr not in G.nodes[1] and other_attr not in G.nodes[1]
+    assert attr not in G.nodes[2] and other_attr not in G.nodes[2]
+
+    # Test removing multiple (but not all) attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    third_vals = 300
+    third_attr = "three"
+    nx.set_node_attributes(
+        G,
+        {
+            n: {attr: vals, other_attr: other_vals, third_attr: third_vals}
+            for n in G.nodes()
+        },
+    )
+    nx.remove_node_attributes(G, other_attr, third_attr)
+    assert other_attr not in G.nodes[0] and third_attr not in G.nodes[0]
+    assert other_attr not in G.nodes[1] and third_attr not in G.nodes[1]
+    assert other_attr not in G.nodes[2] and third_attr not in G.nodes[2]
+    assert G.nodes[0][attr] == vals
+    assert G.nodes[1][attr] == vals
+    assert G.nodes[2][attr] == vals
+
+    # Test incomplete node attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_node_attributes(
+        G,
+        {
+            1: {attr: vals, other_attr: other_vals},
+            2: {attr: vals, other_attr: other_vals},
+        },
+    )
+    nx.remove_node_attributes(G, attr)
+    assert attr not in G.nodes[0]
+    assert attr not in G.nodes[1]
+    assert attr not in G.nodes[2]
+    assert G.nodes[1][other_attr] == other_vals
+    assert G.nodes[2][other_attr] == other_vals
+
+    # Test removing on a subset of nodes
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_node_attributes(
+        G,
+        {
+            n: {attr: vals, other_attr: other_vals, third_attr: third_vals}
+            for n in G.nodes()
+        },
+    )
+    nx.remove_node_attributes(G, attr, other_attr, nbunch=[0, 1])
+    assert attr not in G.nodes[0] and other_attr not in G.nodes[0]
+    assert attr not in G.nodes[1] and other_attr not in G.nodes[1]
+    assert attr in G.nodes[2] and other_attr in G.nodes[2]
+    assert third_attr in G.nodes[0] and G.nodes[0][third_attr] == third_vals
+    assert third_attr in G.nodes[1] and G.nodes[1][third_attr] == third_vals
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_remove_edge_attributes(graph_type):
+    # Test removing single attribute
+    G = nx.path_graph(3, create_using=graph_type)
+    attr = "hello"
+    vals = 100
+    nx.set_edge_attributes(G, vals, attr)
+    nx.remove_edge_attributes(G, attr)
+    assert len(nx.get_edge_attributes(G, attr)) == 0
+
+    # Test removing only some attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    other_attr = "other"
+    other_vals = 200
+    nx.set_edge_attributes(G, vals, attr)
+    nx.set_edge_attributes(G, other_vals, other_attr)
+    nx.remove_edge_attributes(G, attr)
+
+    assert attr not in G[0][1]
+    assert attr not in G[1][2]
+    assert G[0][1][other_attr] == 200
+    assert G[1][2][other_attr] == 200
+
+    # Test removing multiple attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, vals, attr)
+    nx.set_edge_attributes(G, other_vals, other_attr)
+    nx.remove_edge_attributes(G, attr, other_attr)
+    assert attr not in G[0][1] and other_attr not in G[0][1]
+    assert attr not in G[1][2] and other_attr not in G[1][2]
+
+    # Test removing multiple (not all) attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    third_attr = "third"
+    third_vals = 300
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {attr: vals, other_attr: other_vals, third_attr: third_vals}
+            for u, v in G.edges()
+        },
+    )
+    nx.remove_edge_attributes(G, other_attr, third_attr)
+    assert other_attr not in G[0][1] and third_attr not in G[0][1]
+    assert other_attr not in G[1][2] and third_attr not in G[1][2]
+    assert G[0][1][attr] == vals
+    assert G[1][2][attr] == vals
+
+    # Test removing incomplete edge attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, {(0, 1): {attr: vals, other_attr: other_vals}})
+    nx.remove_edge_attributes(G, other_attr)
+    assert other_attr not in G[0][1] and G[0][1][attr] == vals
+    assert other_attr not in G[1][2]
+
+    # Test removing subset of edge attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {attr: vals, other_attr: other_vals, third_attr: third_vals}
+            for u, v in G.edges()
+        },
+    )
+    nx.remove_edge_attributes(G, other_attr, third_attr, ebunch=[(0, 1)])
+    assert other_attr not in G[0][1] and third_attr not in G[0][1]
+    assert other_attr in G[1][2] and third_attr in G[1][2]
+
+
+@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
+def test_remove_multi_edge_attributes(graph_type):
+    # Test removing single attribute
+    G = nx.path_graph(3, create_using=graph_type)
+    G.add_edge(1, 2)
+    attr = "hello"
+    vals = 100
+    nx.set_edge_attributes(G, vals, attr)
+    nx.remove_edge_attributes(G, attr)
+    assert attr not in G[0][1][0]
+    assert attr not in G[1][2][0]
+    assert attr not in G[1][2][1]
+
+    # Test removing only some attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    G.add_edge(1, 2)
+    other_attr = "other"
+    other_vals = 200
+    nx.set_edge_attributes(G, vals, attr)
+    nx.set_edge_attributes(G, other_vals, other_attr)
+    nx.remove_edge_attributes(G, attr)
+    assert attr not in G[0][1][0]
+    assert attr not in G[1][2][0]
+    assert attr not in G[1][2][1]
+    assert G[0][1][0][other_attr] == other_vals
+    assert G[1][2][0][other_attr] == other_vals
+    assert G[1][2][1][other_attr] == other_vals
+
+    # Test removing multiple attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    G.add_edge(1, 2)
+    nx.set_edge_attributes(G, vals, attr)
+    nx.set_edge_attributes(G, other_vals, other_attr)
+    nx.remove_edge_attributes(G, attr, other_attr)
+    assert attr not in G[0][1][0] and other_attr not in G[0][1][0]
+    assert attr not in G[1][2][0] and other_attr not in G[1][2][0]
+    assert attr not in G[1][2][1] and other_attr not in G[1][2][1]
+
+    # Test removing multiple (not all) attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    G.add_edge(1, 2)
+    third_attr = "third"
+    third_vals = 300
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v, k): {attr: vals, other_attr: other_vals, third_attr: third_vals}
+            for u, v, k in G.edges(keys=True)
+        },
+    )
+    nx.remove_edge_attributes(G, other_attr, third_attr)
+    assert other_attr not in G[0][1][0] and third_attr not in G[0][1][0]
+    assert other_attr not in G[1][2][0] and other_attr not in G[1][2][0]
+    assert other_attr not in G[1][2][1] and other_attr not in G[1][2][1]
+    assert G[0][1][0][attr] == vals
+    assert G[1][2][0][attr] == vals
+    assert G[1][2][1][attr] == vals
+
+    # Test removing incomplete edge attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    G.add_edge(1, 2)
+    nx.set_edge_attributes(
+        G,
+        {
+            (0, 1, 0): {attr: vals, other_attr: other_vals},
+            (1, 2, 1): {attr: vals, other_attr: other_vals},
+        },
+    )
+    nx.remove_edge_attributes(G, other_attr)
+    assert other_attr not in G[0][1][0] and G[0][1][0][attr] == vals
+    assert other_attr not in G[1][2][0]
+    assert other_attr not in G[1][2][1]
+
+    # Test removing subset of edge attributes
+    G = nx.path_graph(3, create_using=graph_type)
+    G.add_edge(1, 2)
+    nx.set_edge_attributes(
+        G,
+        {
+            (0, 1, 0): {attr: vals, other_attr: other_vals},
+            (1, 2, 0): {attr: vals, other_attr: other_vals},
+            (1, 2, 1): {attr: vals, other_attr: other_vals},
+        },
+    )
+    nx.remove_edge_attributes(G, attr, ebunch=[(0, 1, 0), (1, 2, 0)])
+    assert attr not in G[0][1][0] and other_attr in G[0][1][0]
+    assert attr not in G[1][2][0] and other_attr in G[1][2][0]
+    assert attr in G[1][2][1] and other_attr in G[1][2][1]
+
+
+def test_is_empty():
+    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
+    for G in graphs:
+        assert nx.is_empty(G)
+        G.add_nodes_from(range(5))
+        assert nx.is_empty(G)
+        G.add_edges_from([(1, 2), (3, 4)])
+        assert not nx.is_empty(G)
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_selfloops(graph_type):
+    G = nx.complete_graph(3, create_using=graph_type)
+    G.add_edge(0, 0)
+    assert nodes_equal(nx.nodes_with_selfloops(G), [0])
+    assert edges_equal(nx.selfloop_edges(G), [(0, 0)])
+    assert edges_equal(nx.selfloop_edges(G, data=True), [(0, 0, {})])
+    assert nx.number_of_selfloops(G) == 1
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_selfloop_edges_attr(graph_type):
+    G = nx.complete_graph(3, create_using=graph_type)
+    G.add_edge(0, 0)
+    G.add_edge(1, 1, weight=2)
+    assert edges_equal(
+        nx.selfloop_edges(G, data=True), [(0, 0, {}), (1, 1, {"weight": 2})]
+    )
+    assert edges_equal(nx.selfloop_edges(G, data="weight"), [(0, 0, None), (1, 1, 2)])
+
+
+def test_selfloop_edges_multi_with_data_and_keys():
+    G = nx.complete_graph(3, create_using=nx.MultiGraph)
+    G.add_edge(0, 0, weight=10)
+    G.add_edge(0, 0, weight=100)
+    assert edges_equal(
+        nx.selfloop_edges(G, data="weight", keys=True), [(0, 0, 0, 10), (0, 0, 1, 100)]
+    )
+
+
+@pytest.mark.parametrize("graph_type", [nx.Graph, nx.DiGraph])
+def test_selfloops_removal(graph_type):
+    G = nx.complete_graph(3, create_using=graph_type)
+    G.add_edge(0, 0)
+    G.remove_edges_from(nx.selfloop_edges(G, keys=True))
+    G.add_edge(0, 0)
+    G.remove_edges_from(nx.selfloop_edges(G, data=True))
+    G.add_edge(0, 0)
+    G.remove_edges_from(nx.selfloop_edges(G, keys=True, data=True))
+
+
+@pytest.mark.parametrize("graph_type", [nx.MultiGraph, nx.MultiDiGraph])
+def test_selfloops_removal_multi(graph_type):
+    """test removing selfloops behavior vis-a-vis altering a dict while iterating.
+    cf. gh-4068"""
+    G = nx.complete_graph(3, create_using=graph_type)
+    # Defaults - see gh-4080
+    G.add_edge(0, 0)
+    G.add_edge(0, 0)
+    G.remove_edges_from(nx.selfloop_edges(G))
+    assert (0, 0) not in G.edges()
+    # With keys
+    G.add_edge(0, 0)
+    G.add_edge(0, 0)
+    with pytest.raises(RuntimeError):
+        G.remove_edges_from(nx.selfloop_edges(G, keys=True))
+    # With data
+    G.add_edge(0, 0)
+    G.add_edge(0, 0)
+    with pytest.raises(TypeError):
+        G.remove_edges_from(nx.selfloop_edges(G, data=True))
+    # With keys and data
+    G.add_edge(0, 0)
+    G.add_edge(0, 0)
+    with pytest.raises(RuntimeError):
+        G.remove_edges_from(nx.selfloop_edges(G, data=True, keys=True))
+
+
+def test_pathweight():
+    valid_path = [1, 2, 3]
+    invalid_path = [1, 3, 2]
+    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
+    edges = [
+        (1, 2, {"cost": 5, "dist": 6}),
+        (2, 3, {"cost": 3, "dist": 4}),
+        (1, 2, {"cost": 1, "dist": 2}),
+    ]
+    for graph in graphs:
+        graph.add_edges_from(edges)
+        assert nx.path_weight(graph, valid_path, "cost") == 4
+        assert nx.path_weight(graph, valid_path, "dist") == 6
+        pytest.raises(nx.NetworkXNoPath, nx.path_weight, graph, invalid_path, "cost")
+
+
+@pytest.mark.parametrize(
+    "G", (nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph())
+)
+def test_ispath(G):
+    G.add_edges_from([(1, 2), (2, 3), (1, 2), (3, 4)])
+    valid_path = [1, 2, 3, 4]
+    invalid_path = [1, 2, 4, 3]  # wrong node order
+    another_invalid_path = [1, 2, 3, 4, 5]  # contains node not in G
+    assert nx.is_path(G, valid_path)
+    assert not nx.is_path(G, invalid_path)
+    assert not nx.is_path(G, another_invalid_path)
+
+
+@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+def test_restricted_view(G):
+    G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)])
+    G.add_node(4)
+    H = nx.restricted_view(G, [0, 2, 5], [(1, 2), (3, 4)])
+    assert set(H.nodes()) == {1, 3, 4}
+    assert set(H.edges()) == {(1, 1)}
+
+
+@pytest.mark.parametrize("G", (nx.MultiGraph(), nx.MultiDiGraph()))
+def test_restricted_view_multi(G):
+    G.add_edges_from(
+        [(0, 1, 0), (0, 2, 0), (0, 3, 0), (0, 1, 1), (1, 0, 0), (1, 1, 0), (1, 2, 0)]
+    )
+    G.add_node(4)
+    H = nx.restricted_view(G, [0, 2, 5], [(1, 2, 0), (3, 4, 0)])
+    assert set(H.nodes()) == {1, 3, 4}
+    assert set(H.edges()) == {(1, 1)}
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_graph.py b/lib/python3.11/site-packages/networkx/classes/tests/test_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd76f8da72a6609630207fcc24fab36990882513
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_graph.py
@@ -0,0 +1,950 @@
+import gc
+import pickle
+import platform
+import weakref
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+def test_degree_node_not_found_exception_message():
+    """See gh-7740"""
+    G = nx.path_graph(5)
+    with pytest.raises(nx.NetworkXError, match="Node.*is not in the graph"):
+        G.degree(100)
+
+
+class BaseGraphTester:
+    """Tests for data-structure independent graph class features."""
+
+    def test_contains(self):
+        G = self.K3
+        assert 1 in G
+        assert 4 not in G
+        assert "b" not in G
+        assert [] not in G  # no exception for nonhashable
+        assert {1: 1} not in G  # no exception for nonhashable
+
+    def test_order(self):
+        G = self.K3
+        assert len(G) == 3
+        assert G.order() == 3
+        assert G.number_of_nodes() == 3
+
+    def test_nodes(self):
+        G = self.K3
+        assert isinstance(G._node, G.node_dict_factory)
+        assert isinstance(G._adj, G.adjlist_outer_dict_factory)
+        assert all(
+            isinstance(adj, G.adjlist_inner_dict_factory) for adj in G._adj.values()
+        )
+        assert sorted(G.nodes()) == self.k3nodes
+        assert sorted(G.nodes(data=True)) == [(0, {}), (1, {}), (2, {})]
+
+    def test_none_node(self):
+        G = self.Graph()
+        with pytest.raises(ValueError):
+            G.add_node(None)
+        with pytest.raises(ValueError):
+            G.add_nodes_from([None])
+        with pytest.raises(ValueError):
+            G.add_edge(0, None)
+        with pytest.raises(ValueError):
+            G.add_edges_from([(0, None)])
+
+    def test_has_node(self):
+        G = self.K3
+        assert G.has_node(1)
+        assert not G.has_node(4)
+        assert not G.has_node([])  # no exception for nonhashable
+        assert not G.has_node({1: 1})  # no exception for nonhashable
+
+    def test_has_edge(self):
+        G = self.K3
+        assert G.has_edge(0, 1)
+        assert not G.has_edge(0, -1)
+
+    def test_neighbors(self):
+        G = self.K3
+        assert sorted(G.neighbors(0)) == [1, 2]
+        with pytest.raises(nx.NetworkXError):
+            G.neighbors(-1)
+
+    @pytest.mark.skipif(
+        platform.python_implementation() == "PyPy", reason="PyPy gc is different"
+    )
+    def test_memory_leak(self):
+        G = self.Graph()
+
+        def count_objects_of_type(_type):
+            # Iterating over all objects tracked by gc can include weak references
+            # whose weakly-referenced objects may no longer exist. Calling `isinstance`
+            # on such a weak reference will raise ReferenceError. There are at least
+            # three workarounds for this: one is to compare type names instead of using
+            # `isinstance` such as `type(obj).__name__ == typename`, another is to use
+            # `type(obj) == _type`, and the last is to ignore ProxyTypes as we do below.
+            # NOTE: even if this safeguard is deemed unnecessary to pass NetworkX tests,
+            # we should still keep it for maximum safety for other NetworkX backends.
+            return sum(
+                1
+                for obj in gc.get_objects()
+                if not isinstance(obj, weakref.ProxyTypes) and isinstance(obj, _type)
+            )
+
+        gc.collect()
+        before = count_objects_of_type(self.Graph)
+        G.copy()
+        gc.collect()
+        after = count_objects_of_type(self.Graph)
+        assert before == after
+
+        # test a subgraph of the base class
+        class MyGraph(self.Graph):
+            pass
+
+        gc.collect()
+        G = MyGraph()
+        before = count_objects_of_type(MyGraph)
+        G.copy()
+        gc.collect()
+        after = count_objects_of_type(MyGraph)
+        assert before == after
+
+    def test_edges(self):
+        G = self.K3
+        assert isinstance(G._adj, G.adjlist_outer_dict_factory)
+        assert edges_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
+        assert edges_equal(G.edges(0), [(0, 1), (0, 2)])
+        assert edges_equal(G.edges([0, 1]), [(0, 1), (0, 2), (1, 2)])
+        with pytest.raises(nx.NetworkXError):
+            G.edges(-1)
+
+    def test_degree(self):
+        G = self.K3
+        assert sorted(G.degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.degree(0) == 2
+        with pytest.raises(nx.NetworkXError):
+            G.degree(-1)  # node not in graph
+
+    def test_size(self):
+        G = self.K3
+        assert G.size() == 3
+        assert G.number_of_edges() == 3
+
+    def test_nbunch_iter(self):
+        G = self.K3
+        assert nodes_equal(G.nbunch_iter(), self.k3nodes)  # all nodes
+        assert nodes_equal(G.nbunch_iter(0), [0])  # single node
+        assert nodes_equal(G.nbunch_iter([0, 1]), [0, 1])  # sequence
+        # sequence with none in graph
+        assert nodes_equal(G.nbunch_iter([-1]), [])
+        # string sequence with none in graph
+        assert nodes_equal(G.nbunch_iter("foo"), [])
+        # node not in graph doesn't get caught upon creation of iterator
+        bunch = G.nbunch_iter(-1)
+        # but gets caught when iterator used
+        with pytest.raises(nx.NetworkXError, match="is not in the graph"):
+            list(bunch)
+        # unhashable doesn't get caught upon creation of iterator
+        bunch = G.nbunch_iter([0, 1, 2, {}])
+        # but gets caught when iterator hits the unhashable
+        with pytest.raises(
+            nx.NetworkXError, match="in sequence nbunch is not a valid node"
+        ):
+            list(bunch)
+
+    def test_nbunch_iter_node_format_raise(self):
+        # Tests that a node that would have failed string formatting
+        # doesn't cause an error when attempting to raise a
+        # :exc:`nx.NetworkXError`.
+
+        # For more information, see pull request #1813.
+        G = self.Graph()
+        nbunch = [("x", set())]
+        with pytest.raises(nx.NetworkXError):
+            list(G.nbunch_iter(nbunch))
+
+    def test_selfloop_degree(self):
+        G = self.Graph()
+        G.add_edge(1, 1)
+        assert sorted(G.degree()) == [(1, 2)]
+        assert dict(G.degree()) == {1: 2}
+        assert G.degree(1) == 2
+        assert sorted(G.degree([1])) == [(1, 2)]
+        assert G.degree(1, weight="weight") == 2
+
+    def test_selfloops(self):
+        G = self.K3.copy()
+        G.add_edge(0, 0)
+        assert nodes_equal(nx.nodes_with_selfloops(G), [0])
+        assert edges_equal(nx.selfloop_edges(G), [(0, 0)])
+        assert nx.number_of_selfloops(G) == 1
+        G.remove_edge(0, 0)
+        G.add_edge(0, 0)
+        G.remove_edges_from([(0, 0)])
+        G.add_edge(1, 1)
+        G.remove_node(1)
+        G.add_edge(0, 0)
+        G.add_edge(1, 1)
+        G.remove_nodes_from([0, 1])
+
+    def test_cache_reset(self):
+        G = self.K3.copy()
+        old_adj = G.adj
+        assert id(G.adj) == id(old_adj)
+        G._adj = {}
+        assert id(G.adj) != id(old_adj)
+
+        old_nodes = G.nodes
+        assert id(G.nodes) == id(old_nodes)
+        G._node = {}
+        assert id(G.nodes) != id(old_nodes)
+
+    def test_attributes_cached(self):
+        G = self.K3.copy()
+        assert id(G.nodes) == id(G.nodes)
+        assert id(G.edges) == id(G.edges)
+        assert id(G.degree) == id(G.degree)
+        assert id(G.adj) == id(G.adj)
+
+
+class BaseAttrGraphTester(BaseGraphTester):
+    """Tests of graph class attribute features."""
+
+    def test_weighted_degree(self):
+        G = self.Graph()
+        G.add_edge(1, 2, weight=2, other=3)
+        G.add_edge(2, 3, weight=3, other=4)
+        assert sorted(d for n, d in G.degree(weight="weight")) == [2, 3, 5]
+        assert dict(G.degree(weight="weight")) == {1: 2, 2: 5, 3: 3}
+        assert G.degree(1, weight="weight") == 2
+        assert nodes_equal((G.degree([1], weight="weight")), [(1, 2)])
+
+        assert nodes_equal((d for n, d in G.degree(weight="other")), [3, 7, 4])
+        assert dict(G.degree(weight="other")) == {1: 3, 2: 7, 3: 4}
+        assert G.degree(1, weight="other") == 3
+        assert edges_equal((G.degree([1], weight="other")), [(1, 3)])
+
+    def add_attributes(self, G):
+        G.graph["foo"] = []
+        G.nodes[0]["foo"] = []
+        G.remove_edge(1, 2)
+        ll = []
+        G.add_edge(1, 2, foo=ll)
+        G.add_edge(2, 1, foo=ll)
+
+    def test_name(self):
+        G = self.Graph(name="")
+        assert G.name == ""
+        G = self.Graph(name="test")
+        assert G.name == "test"
+
+    def test_str_unnamed(self):
+        G = self.Graph()
+        G.add_edges_from([(1, 2), (2, 3)])
+        assert str(G) == f"{type(G).__name__} with 3 nodes and 2 edges"
+
+    def test_str_named(self):
+        G = self.Graph(name="foo")
+        G.add_edges_from([(1, 2), (2, 3)])
+        assert str(G) == f"{type(G).__name__} named 'foo' with 3 nodes and 2 edges"
+
+    def test_graph_chain(self):
+        G = self.Graph([(0, 1), (1, 2)])
+        DG = G.to_directed(as_view=True)
+        SDG = DG.subgraph([0, 1])
+        RSDG = SDG.reverse(copy=False)
+        assert G is DG._graph
+        assert DG is SDG._graph
+        assert SDG is RSDG._graph
+
+    def test_copy(self):
+        G = self.Graph()
+        G.add_node(0)
+        G.add_edge(1, 2)
+        self.add_attributes(G)
+        # copy edge datadict but any container attr are same
+        H = G.copy()
+        self.graphs_equal(H, G)
+        self.different_attrdict(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+    def test_class_copy(self):
+        G = self.Graph()
+        G.add_node(0)
+        G.add_edge(1, 2)
+        self.add_attributes(G)
+        # copy edge datadict but any container attr are same
+        H = G.__class__(G)
+        self.graphs_equal(H, G)
+        self.different_attrdict(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+    def test_fresh_copy(self):
+        G = self.Graph()
+        G.add_node(0)
+        G.add_edge(1, 2)
+        self.add_attributes(G)
+        # copy graph structure but use fresh datadict
+        H = G.__class__()
+        H.add_nodes_from(G)
+        H.add_edges_from(G.edges())
+        assert len(G.nodes[0]) == 1
+        ddict = G.adj[1][2][0] if G.is_multigraph() else G.adj[1][2]
+        assert len(ddict) == 1
+        assert len(H.nodes[0]) == 0
+        ddict = H.adj[1][2][0] if H.is_multigraph() else H.adj[1][2]
+        assert len(ddict) == 0
+
+    def is_deepcopy(self, H, G):
+        self.graphs_equal(H, G)
+        self.different_attrdict(H, G)
+        self.deep_copy_attrdict(H, G)
+
+    def deep_copy_attrdict(self, H, G):
+        self.deepcopy_graph_attr(H, G)
+        self.deepcopy_node_attr(H, G)
+        self.deepcopy_edge_attr(H, G)
+
+    def deepcopy_graph_attr(self, H, G):
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] != H.graph["foo"]
+
+    def deepcopy_node_attr(self, H, G):
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] != H.nodes[0]["foo"]
+
+    def deepcopy_edge_attr(self, H, G):
+        assert G[1][2]["foo"] == H[1][2]["foo"]
+        G[1][2]["foo"].append(1)
+        assert G[1][2]["foo"] != H[1][2]["foo"]
+
+    def is_shallow_copy(self, H, G):
+        self.graphs_equal(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+    def shallow_copy_attrdict(self, H, G):
+        self.shallow_copy_graph_attr(H, G)
+        self.shallow_copy_node_attr(H, G)
+        self.shallow_copy_edge_attr(H, G)
+
+    def shallow_copy_graph_attr(self, H, G):
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] == H.graph["foo"]
+
+    def shallow_copy_node_attr(self, H, G):
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+
+    def shallow_copy_edge_attr(self, H, G):
+        assert G[1][2]["foo"] == H[1][2]["foo"]
+        G[1][2]["foo"].append(1)
+        assert G[1][2]["foo"] == H[1][2]["foo"]
+
+    def same_attrdict(self, H, G):
+        old_foo = H[1][2]["foo"]
+        H.adj[1][2]["foo"] = "baz"
+        assert G.edges == H.edges
+        H.adj[1][2]["foo"] = old_foo
+        assert G.edges == H.edges
+
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G.nodes == H.nodes
+        H.nodes[0]["foo"] = old_foo
+        assert G.nodes == H.nodes
+
+    def different_attrdict(self, H, G):
+        old_foo = H[1][2]["foo"]
+        H.adj[1][2]["foo"] = "baz"
+        assert G._adj != H._adj
+        H.adj[1][2]["foo"] = old_foo
+        assert G._adj == H._adj
+
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G._node != H._node
+        H.nodes[0]["foo"] = old_foo
+        assert G._node == H._node
+
+    def graphs_equal(self, H, G):
+        assert G._adj == H._adj
+        assert G._node == H._node
+        assert G.graph == H.graph
+        assert G.name == H.name
+        if not G.is_directed() and not H.is_directed():
+            assert H._adj[1][2] is H._adj[2][1]
+            assert G._adj[1][2] is G._adj[2][1]
+        else:  # at least one is directed
+            if not G.is_directed():
+                G._pred = G._adj
+                G._succ = G._adj
+            if not H.is_directed():
+                H._pred = H._adj
+                H._succ = H._adj
+            assert G._pred == H._pred
+            assert G._succ == H._succ
+            assert H._succ[1][2] is H._pred[2][1]
+            assert G._succ[1][2] is G._pred[2][1]
+
+    def test_graph_attr(self):
+        G = self.K3.copy()
+        G.graph["foo"] = "bar"
+        assert isinstance(G.graph, G.graph_attr_dict_factory)
+        assert G.graph["foo"] == "bar"
+        del G.graph["foo"]
+        assert G.graph == {}
+        H = self.Graph(foo="bar")
+        assert H.graph["foo"] == "bar"
+
+    def test_node_attr(self):
+        G = self.K3.copy()
+        G.add_node(1, foo="bar")
+        assert all(
+            isinstance(d, G.node_attr_dict_factory) for u, d in G.nodes(data=True)
+        )
+        assert nodes_equal(G.nodes(), [0, 1, 2])
+        assert nodes_equal(G.nodes(data=True), [(0, {}), (1, {"foo": "bar"}), (2, {})])
+        G.nodes[1]["foo"] = "baz"
+        assert nodes_equal(G.nodes(data=True), [(0, {}), (1, {"foo": "baz"}), (2, {})])
+        assert nodes_equal(G.nodes(data="foo"), [(0, None), (1, "baz"), (2, None)])
+        assert nodes_equal(
+            G.nodes(data="foo", default="bar"), [(0, "bar"), (1, "baz"), (2, "bar")]
+        )
+
+    def test_node_attr2(self):
+        G = self.K3.copy()
+        a = {"foo": "bar"}
+        G.add_node(3, **a)
+        assert nodes_equal(G.nodes(), [0, 1, 2, 3])
+        assert nodes_equal(
+            G.nodes(data=True), [(0, {}), (1, {}), (2, {}), (3, {"foo": "bar"})]
+        )
+
+    def test_edge_lookup(self):
+        G = self.Graph()
+        G.add_edge(1, 2, foo="bar")
+        assert edges_equal(G.edges[1, 2], {"foo": "bar"})
+
+    def test_edge_attr(self):
+        G = self.Graph()
+        G.add_edge(1, 2, foo="bar")
+        assert all(
+            isinstance(d, G.edge_attr_dict_factory) for u, v, d in G.edges(data=True)
+        )
+        assert edges_equal(G.edges(data=True), [(1, 2, {"foo": "bar"})])
+        assert edges_equal(G.edges(data="foo"), [(1, 2, "bar")])
+
+    def test_edge_attr2(self):
+        G = self.Graph()
+        G.add_edges_from([(1, 2), (3, 4)], foo="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"foo": "foo"}), (3, 4, {"foo": "foo"})]
+        )
+        assert edges_equal(G.edges(data="foo"), [(1, 2, "foo"), (3, 4, "foo")])
+
+    def test_edge_attr3(self):
+        G = self.Graph()
+        G.add_edges_from([(1, 2, {"weight": 32}), (3, 4, {"weight": 64})], foo="foo")
+        assert edges_equal(
+            G.edges(data=True),
+            [
+                (1, 2, {"foo": "foo", "weight": 32}),
+                (3, 4, {"foo": "foo", "weight": 64}),
+            ],
+        )
+
+        G.remove_edges_from([(1, 2), (3, 4)])
+        G.add_edge(1, 2, data=7, spam="bar", bar="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 7, "spam": "bar", "bar": "foo"})]
+        )
+
+    def test_edge_attr4(self):
+        G = self.Graph()
+        G.add_edge(1, 2, data=7, spam="bar", bar="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 7, "spam": "bar", "bar": "foo"})]
+        )
+        G[1][2]["data"] = 10  # OK to set data like this
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 10, "spam": "bar", "bar": "foo"})]
+        )
+
+        G.adj[1][2]["data"] = 20
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 20, "spam": "bar", "bar": "foo"})]
+        )
+        G.edges[1, 2]["data"] = 21  # another spelling, "edge"
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 21, "spam": "bar", "bar": "foo"})]
+        )
+        G.adj[1][2]["listdata"] = [20, 200]
+        G.adj[1][2]["weight"] = 20
+        dd = {
+            "data": 21,
+            "spam": "bar",
+            "bar": "foo",
+            "listdata": [20, 200],
+            "weight": 20,
+        }
+        assert edges_equal(G.edges(data=True), [(1, 2, dd)])
+
+    def test_to_undirected(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.Graph(G)
+        self.is_shallow_copy(H, G)
+        self.different_attrdict(H, G)
+        H = G.to_undirected()
+        self.is_deepcopy(H, G)
+
+    def test_to_directed_as_view(self):
+        H = nx.path_graph(2, create_using=self.Graph)
+        H2 = H.to_directed(as_view=True)
+        assert H is H2._graph
+        assert H2.has_edge(0, 1)
+        assert H2.has_edge(1, 0) or H.is_directed()
+        pytest.raises(nx.NetworkXError, H2.add_node, -1)
+        pytest.raises(nx.NetworkXError, H2.add_edge, 1, 2)
+        H.add_edge(1, 2)
+        assert H2.has_edge(1, 2)
+        assert H2.has_edge(2, 1) or H.is_directed()
+
+    def test_to_undirected_as_view(self):
+        H = nx.path_graph(2, create_using=self.Graph)
+        H2 = H.to_undirected(as_view=True)
+        assert H is H2._graph
+        assert H2.has_edge(0, 1)
+        assert H2.has_edge(1, 0)
+        pytest.raises(nx.NetworkXError, H2.add_node, -1)
+        pytest.raises(nx.NetworkXError, H2.add_edge, 1, 2)
+        H.add_edge(1, 2)
+        assert H2.has_edge(1, 2)
+        assert H2.has_edge(2, 1)
+
+    def test_directed_class(self):
+        G = self.Graph()
+
+        class newGraph(G.to_undirected_class()):
+            def to_directed_class(self):
+                return newDiGraph
+
+            def to_undirected_class(self):
+                return newGraph
+
+        class newDiGraph(G.to_directed_class()):
+            def to_directed_class(self):
+                return newDiGraph
+
+            def to_undirected_class(self):
+                return newGraph
+
+        G = newDiGraph() if G.is_directed() else newGraph()
+        H = G.to_directed()
+        assert isinstance(H, newDiGraph)
+        H = G.to_undirected()
+        assert isinstance(H, newGraph)
+
+    def test_to_directed(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.DiGraph(G)
+        self.is_shallow_copy(H, G)
+        self.different_attrdict(H, G)
+        H = G.to_directed()
+        self.is_deepcopy(H, G)
+
+    def test_subgraph(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = G.subgraph([0, 1, 2, 5])
+        self.graphs_equal(H, G)
+        self.same_attrdict(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+        H = G.subgraph(0)
+        assert H.adj == {0: {}}
+        H = G.subgraph([])
+        assert H.adj == {}
+        assert G.adj != {}
+
+    def test_selfloops_attr(self):
+        G = self.K3.copy()
+        G.add_edge(0, 0)
+        G.add_edge(1, 1, weight=2)
+        assert edges_equal(
+            nx.selfloop_edges(G, data=True), [(0, 0, {}), (1, 1, {"weight": 2})]
+        )
+        assert edges_equal(
+            nx.selfloop_edges(G, data="weight"), [(0, 0, None), (1, 1, 2)]
+        )
+
+
+class TestGraph(BaseAttrGraphTester):
+    """Tests specific to dict-of-dict-of-dict graph data structure"""
+
+    def setup_method(self):
+        self.Graph = nx.Graph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3 = ({}, {}, {})
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed1, 2: ed3}, 2: {0: ed2, 1: ed3}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._adj = self.k3adj
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+    def test_pickle(self):
+        G = self.K3
+        pg = pickle.loads(pickle.dumps(G, -1))
+        self.graphs_equal(pg, G)
+        pg = pickle.loads(pickle.dumps(G))
+        self.graphs_equal(pg, G)
+
+    def test_data_input(self):
+        G = self.Graph({1: [2], 2: [1]}, name="test")
+        assert G.name == "test"
+        assert sorted(G.adj.items()) == [(1, {2: {}}), (2, {1: {}})]
+
+    def test_adjacency(self):
+        G = self.K3
+        assert dict(G.adjacency()) == {
+            0: {1: {}, 2: {}},
+            1: {0: {}, 2: {}},
+            2: {0: {}, 1: {}},
+        }
+
+    def test_getitem(self):
+        G = self.K3
+        assert G.adj[0] == {1: {}, 2: {}}
+        assert G[0] == {1: {}, 2: {}}
+        with pytest.raises(KeyError):
+            G.__getitem__("j")
+        with pytest.raises(TypeError):
+            G.__getitem__(["A"])
+
+    def test_add_node(self):
+        G = self.Graph()
+        G.add_node(0)
+        assert G.adj == {0: {}}
+        # test add attributes
+        G.add_node(1, c="red")
+        G.add_node(2, c="blue")
+        G.add_node(3, c="red")
+        assert G.nodes[1]["c"] == "red"
+        assert G.nodes[2]["c"] == "blue"
+        assert G.nodes[3]["c"] == "red"
+        # test updating attributes
+        G.add_node(1, c="blue")
+        G.add_node(2, c="red")
+        G.add_node(3, c="blue")
+        assert G.nodes[1]["c"] == "blue"
+        assert G.nodes[2]["c"] == "red"
+        assert G.nodes[3]["c"] == "blue"
+
+    def test_add_nodes_from(self):
+        G = self.Graph()
+        G.add_nodes_from([0, 1, 2])
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        # test add attributes
+        G.add_nodes_from([0, 1, 2], c="red")
+        assert G.nodes[0]["c"] == "red"
+        assert G.nodes[2]["c"] == "red"
+        # test that attribute dicts are not the same
+        assert G.nodes[0] is not G.nodes[1]
+        # test updating attributes
+        G.add_nodes_from([0, 1, 2], c="blue")
+        assert G.nodes[0]["c"] == "blue"
+        assert G.nodes[2]["c"] == "blue"
+        assert G.nodes[0] is not G.nodes[1]
+        # test tuple input
+        H = self.Graph()
+        H.add_nodes_from(G.nodes(data=True))
+        assert H.nodes[0]["c"] == "blue"
+        assert H.nodes[2]["c"] == "blue"
+        assert H.nodes[0] is not H.nodes[1]
+        # specific overrides general
+        H.add_nodes_from([0, (1, {"c": "green"}), (3, {"c": "cyan"})], c="red")
+        assert H.nodes[0]["c"] == "red"
+        assert H.nodes[1]["c"] == "green"
+        assert H.nodes[2]["c"] == "blue"
+        assert H.nodes[3]["c"] == "cyan"
+
+    def test_remove_node(self):
+        G = self.K3.copy()
+        G.remove_node(0)
+        assert G.adj == {1: {2: {}}, 2: {1: {}}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_node(-1)
+
+        # generator here to implement list,set,string...
+
+    def test_remove_nodes_from(self):
+        G = self.K3.copy()
+        G.remove_nodes_from([0, 1])
+        assert G.adj == {2: {}}
+        G.remove_nodes_from([-1])  # silent fail
+
+    def test_add_edge(self):
+        G = self.Graph()
+        G.add_edge(0, 1)
+        assert G.adj == {0: {1: {}}, 1: {0: {}}}
+        G = self.Graph()
+        G.add_edge(*(0, 1))
+        assert G.adj == {0: {1: {}}, 1: {0: {}}}
+        G = self.Graph()
+        with pytest.raises(ValueError):
+            G.add_edge(None, "anything")
+
+    def test_add_edges_from(self):
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 2, {"weight": 3})])
+        assert G.adj == {
+            0: {1: {}, 2: {"weight": 3}},
+            1: {0: {}},
+            2: {0: {"weight": 3}},
+        }
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 2, {"weight": 3}), (1, 2, {"data": 4})], data=2)
+        assert G.adj == {
+            0: {1: {"data": 2}, 2: {"weight": 3, "data": 2}},
+            1: {0: {"data": 2}, 2: {"data": 4}},
+            2: {0: {"weight": 3, "data": 2}, 1: {"data": 4}},
+        }
+
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0,)])  # too few in tuple
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0, 1, 2, 3)])  # too many in tuple
+        with pytest.raises(TypeError):
+            G.add_edges_from([0])  # not a tuple
+        with pytest.raises(ValueError):
+            G.add_edges_from([(None, 3), (3, 2)])  # None cannot be a node
+
+    def test_remove_edge(self):
+        G = self.K3.copy()
+        G.remove_edge(0, 1)
+        assert G.adj == {0: {2: {}}, 1: {2: {}}, 2: {0: {}, 1: {}}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_edge(-1, 0)
+
+    def test_remove_edges_from(self):
+        G = self.K3.copy()
+        G.remove_edges_from([(0, 1)])
+        assert G.adj == {0: {2: {}}, 1: {2: {}}, 2: {0: {}, 1: {}}}
+        G.remove_edges_from([(0, 0)])  # silent fail
+
+    def test_clear(self):
+        G = self.K3.copy()
+        G.graph["name"] = "K3"
+        G.clear()
+        assert list(G.nodes) == []
+        assert G.adj == {}
+        assert G.graph == {}
+
+    def test_clear_edges(self):
+        G = self.K3.copy()
+        G.graph["name"] = "K3"
+        nodes = list(G.nodes)
+        G.clear_edges()
+        assert list(G.nodes) == nodes
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        assert list(G.edges) == []
+        assert G.graph["name"] == "K3"
+
+    def test_edges_data(self):
+        G = self.K3
+        all_edges = [(0, 1, {}), (0, 2, {}), (1, 2, {})]
+        assert edges_equal(G.edges(data=True), all_edges)
+        assert edges_equal(G.edges(0, data=True), [(0, 1, {}), (0, 2, {})])
+        assert edges_equal(G.edges([0, 1], data=True), all_edges)
+        with pytest.raises(nx.NetworkXError):
+            G.edges(-1, True)
+
+    def test_get_edge_data(self):
+        G = self.K3.copy()
+        assert G.get_edge_data(0, 1) == {}
+        assert G[0][1] == {}
+        assert G.get_edge_data(10, 20) is None
+        assert G.get_edge_data(-1, 0) is None
+        assert G.get_edge_data(-1, 0, default=1) == 1
+
+    def test_update(self):
+        # specify both edges and nodes
+        G = self.K3.copy()
+        G.update(nodes=[3, (4, {"size": 2})], edges=[(4, 5), (6, 7, {"weight": 2})])
+        nlist = [
+            (0, {}),
+            (1, {}),
+            (2, {}),
+            (3, {}),
+            (4, {"size": 2}),
+            (5, {}),
+            (6, {}),
+            (7, {}),
+        ]
+        assert sorted(G.nodes.data()) == nlist
+        if G.is_directed():
+            elist = [
+                (0, 1, {}),
+                (0, 2, {}),
+                (1, 0, {}),
+                (1, 2, {}),
+                (2, 0, {}),
+                (2, 1, {}),
+                (4, 5, {}),
+                (6, 7, {"weight": 2}),
+            ]
+        else:
+            elist = [
+                (0, 1, {}),
+                (0, 2, {}),
+                (1, 2, {}),
+                (4, 5, {}),
+                (6, 7, {"weight": 2}),
+            ]
+        assert sorted(G.edges.data()) == elist
+        assert G.graph == {}
+
+        # no keywords -- order is edges, nodes
+        G = self.K3.copy()
+        G.update([(4, 5), (6, 7, {"weight": 2})], [3, (4, {"size": 2})])
+        assert sorted(G.nodes.data()) == nlist
+        assert sorted(G.edges.data()) == elist
+        assert G.graph == {}
+
+        # update using only a graph
+        G = self.Graph()
+        G.graph["foo"] = "bar"
+        G.add_node(2, data=4)
+        G.add_edge(0, 1, weight=0.5)
+        GG = G.copy()
+        H = self.Graph()
+        GG.update(H)
+        assert graphs_equal(G, GG)
+        H.update(G)
+        assert graphs_equal(H, G)
+
+        # update nodes only
+        H = self.Graph()
+        H.update(nodes=[3, 4])
+        assert H.nodes ^ {3, 4} == set()
+        assert H.size() == 0
+
+        # update edges only
+        H = self.Graph()
+        H.update(edges=[(3, 4)])
+        assert sorted(H.edges.data()) == [(3, 4, {})]
+        assert H.size() == 1
+
+        # No inputs -> exception
+        with pytest.raises(nx.NetworkXError):
+            nx.Graph().update()
+
+
+class TestEdgeSubgraph:
+    """Unit tests for the :meth:`Graph.edge_subgraph` method."""
+
+    def setup_method(self):
+        # Create a path graph on five nodes.
+        G = nx.path_graph(5)
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.edges[0, 1]["name"] = "edge01"
+        G.edges[3, 4]["name"] = "edge34"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by the first and last edges.
+        self.G = G
+        self.H = G.edge_subgraph([(0, 1), (3, 4)])
+
+    def test_correct_nodes(self):
+        """Tests that the subgraph has the correct nodes."""
+        assert [0, 1, 3, 4] == sorted(self.H.nodes())
+
+    def test_correct_edges(self):
+        """Tests that the subgraph has the correct edges."""
+        assert [(0, 1, "edge01"), (3, 4, "edge34")] == sorted(self.H.edges(data="name"))
+
+    def test_add_node(self):
+        """Tests that adding a node to the original graph does not
+        affect the nodes of the subgraph.
+
+        """
+        self.G.add_node(5)
+        assert [0, 1, 3, 4] == sorted(self.H.nodes())
+
+    def test_remove_node(self):
+        """Tests that removing a node in the original graph does
+        affect the nodes of the subgraph.
+
+        """
+        self.G.remove_node(0)
+        assert [1, 3, 4] == sorted(self.H.nodes())
+
+    def test_node_attr_dict(self):
+        """Tests that the node attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for v in self.H:
+            assert self.G.nodes[v] == self.H.nodes[v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.nodes[0]["name"] = "foo"
+        assert self.G.nodes[0] == self.H.nodes[0]
+        self.H.nodes[1]["name"] = "bar"
+        assert self.G.nodes[1] == self.H.nodes[1]
+
+    def test_edge_attr_dict(self):
+        """Tests that the edge attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for u, v in self.H.edges():
+            assert self.G.edges[u, v] == self.H.edges[u, v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.edges[0, 1]["name"] = "foo"
+        assert self.G.edges[0, 1]["name"] == self.H.edges[0, 1]["name"]
+        self.H.edges[3, 4]["name"] = "bar"
+        assert self.G.edges[3, 4]["name"] == self.H.edges[3, 4]["name"]
+
+    def test_graph_attr_dict(self):
+        """Tests that the graph attribute dictionary of the two graphs
+        is the same object.
+
+        """
+        assert self.G.graph is self.H.graph
+
+
+def test_graph_new_extra_args():
+    """Test that subclasses can accept additional arguments.
+
+    See: https://github.com/networkx/networkx/issues/8367
+    """
+
+    class MyGraph(nx.Graph):
+        def __init__(self, incoming_graph_data=None, extra_arg=None, **attr):
+            super().__init__(incoming_graph_data, **attr)
+            self.extra_arg = extra_arg
+
+    G = MyGraph(extra_arg="extra arg")
+    assert G.extra_arg == "extra arg"
+
+    G = MyGraph([], "extra arg")
+    assert G.extra_arg == "extra arg"
+
+    G = MyGraph([(0, 1)], extra_arg="foo", name="bar")
+    assert G.extra_arg == "foo"
+    assert G.graph["name"] == "bar"
+    assert nx.utils.edges_equal(G.edges, [(0, 1)])
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_graph_historical.py b/lib/python3.11/site-packages/networkx/classes/tests/test_graph_historical.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e80878191ee73cd70241a5bab944ab97b7573a9
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_graph_historical.py
@@ -0,0 +1,12 @@
+"""Original NetworkX graph tests"""
+
+import networkx as nx
+
+from .historical_tests import HistoricalTests
+
+
+class TestGraphHistorical(HistoricalTests):
+    @classmethod
+    def setup_class(cls):
+        HistoricalTests.setup_class()
+        cls.G = nx.Graph
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_graphviews.py b/lib/python3.11/site-packages/networkx/classes/tests/test_graphviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..113c1f87b0a983f220f61df4759a2696d8ee17ed
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_graphviews.py
@@ -0,0 +1,349 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+# Note: SubGraph views are not tested here. They have their own testing file
+
+
+class TestReverseView:
+    def setup_method(self):
+        self.G = nx.path_graph(9, create_using=nx.DiGraph())
+        self.rv = nx.reverse_view(self.G)
+
+    def test_pickle(self):
+        import pickle
+
+        rv = self.rv
+        prv = pickle.loads(pickle.dumps(rv, -1))
+        assert rv._node == prv._node
+        assert rv._adj == prv._adj
+        assert rv.graph == prv.graph
+
+    def test_contains(self):
+        assert (2, 3) in self.G.edges
+        assert (3, 2) not in self.G.edges
+        assert (2, 3) not in self.rv.edges
+        assert (3, 2) in self.rv.edges
+
+    def test_iter(self):
+        expected = sorted(tuple(reversed(e)) for e in self.G.edges)
+        assert sorted(self.rv.edges) == expected
+
+    def test_exceptions(self):
+        G = nx.Graph()
+        pytest.raises(nx.NetworkXNotImplemented, nx.reverse_view, G)
+
+    def test_subclass(self):
+        class MyGraph(nx.DiGraph):
+            def my_method(self):
+                return "me"
+
+            def to_directed_class(self):
+                return MyGraph()
+
+        M = MyGraph()
+        M.add_edge(1, 2)
+        RM = nx.reverse_view(M)
+        assert RM.__class__ == MyGraph
+        RMC = RM.copy()
+        assert RMC.__class__ == MyGraph
+        assert RMC.has_edge(2, 1)
+        assert RMC.my_method() == "me"
+
+
+class TestMultiReverseView:
+    def setup_method(self):
+        self.G = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        self.G.add_edge(4, 5)
+        self.rv = nx.reverse_view(self.G)
+
+    def test_pickle(self):
+        import pickle
+
+        rv = self.rv
+        prv = pickle.loads(pickle.dumps(rv, -1))
+        assert rv._node == prv._node
+        assert rv._adj == prv._adj
+        assert rv.graph == prv.graph
+
+    def test_contains(self):
+        assert (2, 3, 0) in self.G.edges
+        assert (3, 2, 0) not in self.G.edges
+        assert (2, 3, 0) not in self.rv.edges
+        assert (3, 2, 0) in self.rv.edges
+        assert (5, 4, 1) in self.rv.edges
+        assert (4, 5, 1) not in self.rv.edges
+
+    def test_iter(self):
+        expected = sorted((v, u, k) for u, v, k in self.G.edges)
+        assert sorted(self.rv.edges) == expected
+
+    def test_exceptions(self):
+        MG = nx.MultiGraph(self.G)
+        pytest.raises(nx.NetworkXNotImplemented, nx.reverse_view, MG)
+
+
+def test_generic_multitype():
+    nxg = nx.graphviews
+    G = nx.DiGraph([(1, 2)])
+    with pytest.raises(nx.NetworkXError):
+        nxg.generic_graph_view(G, create_using=nx.MultiGraph)
+    G = nx.MultiDiGraph([(1, 2)])
+    with pytest.raises(nx.NetworkXError):
+        nxg.generic_graph_view(G, create_using=nx.DiGraph)
+
+
+class TestToDirected:
+    def setup_method(self):
+        self.G = nx.path_graph(9)
+        self.dv = nx.to_directed(self.G)
+        self.MG = nx.path_graph(9, create_using=nx.MultiGraph())
+        self.Mdv = nx.to_directed(self.MG)
+
+    def test_directed(self):
+        assert not self.G.is_directed()
+        assert self.dv.is_directed()
+
+    def test_already_directed(self):
+        dd = nx.to_directed(self.dv)
+        Mdd = nx.to_directed(self.Mdv)
+        assert edges_equal(dd.edges, self.dv.edges, directed=True)
+        assert edges_equal(Mdd.edges, self.Mdv.edges, directed=True)
+
+    def test_pickle(self):
+        import pickle
+
+        dv = self.dv
+        pdv = pickle.loads(pickle.dumps(dv, -1))
+        assert dv._node == pdv._node
+        assert dv._succ == pdv._succ
+        assert dv._pred == pdv._pred
+        assert dv.graph == pdv.graph
+
+    def test_contains(self):
+        assert (2, 3) in self.G.edges
+        assert (3, 2) in self.G.edges
+        assert (2, 3) in self.dv.edges
+        assert (3, 2) in self.dv.edges
+
+    def test_iter(self):
+        revd = [tuple(reversed(e)) for e in self.G.edges]
+        expected = sorted(list(self.G.edges) + revd)
+        assert sorted(self.dv.edges) == expected
+
+
+class TestToUndirected:
+    def setup_method(self):
+        self.DG = nx.path_graph(9, create_using=nx.DiGraph())
+        self.uv = nx.to_undirected(self.DG)
+        self.MDG = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        self.Muv = nx.to_undirected(self.MDG)
+
+    def test_directed(self):
+        assert self.DG.is_directed()
+        assert not self.uv.is_directed()
+
+    def test_already_undirected(self):
+        uu = nx.to_undirected(self.uv)
+        Muu = nx.to_undirected(self.Muv)
+        assert edges_equal(uu.edges, self.uv.edges)
+        assert edges_equal(Muu.edges, self.Muv.edges)
+
+    def test_pickle(self):
+        import pickle
+
+        uv = self.uv
+        puv = pickle.loads(pickle.dumps(uv, -1))
+        assert uv._node == puv._node
+        assert uv._adj == puv._adj
+        assert uv.graph == puv.graph
+        assert hasattr(uv, "_graph")
+
+    def test_contains(self):
+        assert (2, 3) in self.DG.edges
+        assert (3, 2) not in self.DG.edges
+        assert (2, 3) in self.uv.edges
+        assert (3, 2) in self.uv.edges
+
+    def test_iter(self):
+        expected = sorted(self.DG.edges)
+        assert sorted(self.uv.edges) == expected
+
+
+class TestChainsOfViews:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.DG = nx.path_graph(9, create_using=nx.DiGraph())
+        cls.MG = nx.path_graph(9, create_using=nx.MultiGraph())
+        cls.MDG = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        cls.Gv = nx.to_undirected(cls.DG)
+        cls.DGv = nx.to_directed(cls.G)
+        cls.MGv = nx.to_undirected(cls.MDG)
+        cls.MDGv = nx.to_directed(cls.MG)
+        cls.Rv = cls.DG.reverse()
+        cls.MRv = cls.MDG.reverse()
+        cls.graphs = [
+            cls.G,
+            cls.DG,
+            cls.MG,
+            cls.MDG,
+            cls.Gv,
+            cls.DGv,
+            cls.MGv,
+            cls.MDGv,
+            cls.Rv,
+            cls.MRv,
+        ]
+        for G in cls.graphs:
+            G.edges, G.nodes, G.degree
+
+    def test_pickle(self):
+        import pickle
+
+        for G in self.graphs:
+            H = pickle.loads(pickle.dumps(G, -1))
+            assert edges_equal(H.edges, G.edges, directed=G.is_directed())
+            assert nodes_equal(H.nodes, G.nodes)
+
+    def test_subgraph_of_subgraph(self):
+        SGv = nx.subgraph(self.G, range(3, 7))
+        SDGv = nx.subgraph(self.DG, range(3, 7))
+        SMGv = nx.subgraph(self.MG, range(3, 7))
+        SMDGv = nx.subgraph(self.MDG, range(3, 7))
+        for G in self.graphs + [SGv, SDGv, SMGv, SMDGv]:
+            SG = nx.induced_subgraph(G, [4, 5, 6])
+            assert list(SG) == [4, 5, 6]
+            SSG = SG.subgraph([6, 7])
+            assert list(SSG) == [6]
+            # subgraph-subgraph chain is short-cut in base class method
+            assert SSG._graph is G
+
+    def test_restricted_induced_subgraph_chains(self):
+        """Test subgraph chains that both restrict and show nodes/edges.
+
+        A restricted_view subgraph should allow induced subgraphs using
+        G.subgraph that automagically without a chain (meaning the result
+        is a subgraph view of the original graph not a subgraph-of-subgraph.
+        """
+        hide_nodes = [3, 4, 5]
+        hide_edges = [(6, 7)]
+        RG = nx.restricted_view(self.G, hide_nodes, hide_edges)
+        nodes = [4, 5, 6, 7, 8]
+        SG = nx.induced_subgraph(RG, nodes)
+        SSG = RG.subgraph(nodes)
+        assert RG._graph is self.G
+        assert SSG._graph is self.G
+        assert SG._graph is RG
+        assert edges_equal(SG.edges, SSG.edges)
+        # should be same as morphing the graph
+        CG = self.G.copy()
+        CG.remove_nodes_from(hide_nodes)
+        CG.remove_edges_from(hide_edges)
+        assert edges_equal(CG.edges(nodes), SSG.edges)
+        CG.remove_nodes_from([0, 1, 2, 3])
+        assert edges_equal(CG.edges, SSG.edges)
+        # switch order: subgraph first, then restricted view
+        SSSG = self.G.subgraph(nodes)
+        RSG = nx.restricted_view(SSSG, hide_nodes, hide_edges)
+        assert RSG._graph is not self.G
+        assert edges_equal(RSG.edges, CG.edges)
+
+    def test_subgraph_copy(self):
+        for origG in self.graphs:
+            G = nx.Graph(origG)
+            SG = G.subgraph([4, 5, 6])
+            H = SG.copy()
+            assert type(G) is type(H)
+
+    def test_subgraph_todirected(self):
+        SG = nx.induced_subgraph(self.G, [4, 5, 6])
+        SSG = SG.to_directed()
+        assert sorted(SSG) == [4, 5, 6]
+        assert sorted(SSG.edges) == [(4, 5), (5, 4), (5, 6), (6, 5)]
+
+    def test_subgraph_toundirected(self):
+        SG = nx.induced_subgraph(self.G, [4, 5, 6])
+        SSG = SG.to_undirected()
+        assert list(SSG) == [4, 5, 6]
+        assert sorted(SSG.edges) == [(4, 5), (5, 6)]
+
+    def test_reverse_subgraph_toundirected(self):
+        G = self.DG.reverse(copy=False)
+        SG = G.subgraph([4, 5, 6])
+        SSG = SG.to_undirected()
+        assert list(SSG) == [4, 5, 6]
+        assert sorted(SSG.edges) == [(4, 5), (5, 6)]
+
+    def test_reverse_reverse_copy(self):
+        G = self.DG.reverse(copy=False)
+        H = G.reverse(copy=True)
+        assert H.nodes == self.DG.nodes
+        assert H.edges == self.DG.edges
+        G = self.MDG.reverse(copy=False)
+        H = G.reverse(copy=True)
+        assert H.nodes == self.MDG.nodes
+        assert H.edges == self.MDG.edges
+
+    def test_subgraph_edgesubgraph_toundirected(self):
+        G = self.G.copy()
+        SG = G.subgraph([4, 5, 6])
+        SSG = SG.edge_subgraph([(4, 5), (5, 4)])
+        USSG = SSG.to_undirected()
+        assert list(USSG) == [4, 5]
+        assert sorted(USSG.edges) == [(4, 5)]
+
+    def test_copy_subgraph(self):
+        G = self.G.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_disubgraph(self):
+        G = self.DG.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_multidisubgraph(self):
+        G = self.MDG.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_multisubgraph(self):
+        G = self.MG.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_of_view(self):
+        G = nx.MultiGraph(self.MGv)
+        assert G.__class__.__name__ == "MultiGraph"
+        G = G.copy(as_view=True)
+        assert G.__class__.__name__ == "MultiGraph"
+
+    def test_subclass(self):
+        class MyGraph(nx.DiGraph):
+            def my_method(self):
+                return "me"
+
+            def to_directed_class(self):
+                return MyGraph()
+
+        for origG in self.graphs:
+            G = MyGraph(origG)
+            SG = G.subgraph([4, 5, 6])
+            H = SG.copy()
+            assert SG.my_method() == "me"
+            assert H.my_method() == "me"
+            assert 3 not in H or 3 in SG
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_multidigraph.py b/lib/python3.11/site-packages/networkx/classes/tests/test_multidigraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc0bd5467d0a62dc8f533af7a6c5bbc0a57fc010
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_multidigraph.py
@@ -0,0 +1,459 @@
+from collections import UserDict
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal
+
+from .test_multigraph import BaseMultiGraphTester
+from .test_multigraph import TestEdgeSubgraph as _TestMultiGraphEdgeSubgraph
+from .test_multigraph import TestMultiGraph as _TestMultiGraph
+
+
+class BaseMultiDiGraphTester(BaseMultiGraphTester):
+    def test_edges(self):
+        G = self.K3
+        edges = [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.edges()) == edges
+        assert sorted(G.edges(0)) == [(0, 1), (0, 2)]
+        pytest.raises((KeyError, nx.NetworkXError), G.edges, -1)
+
+    def test_edges_data(self):
+        G = self.K3
+        edges = [(0, 1, {}), (0, 2, {}), (1, 0, {}), (1, 2, {}), (2, 0, {}), (2, 1, {})]
+        assert sorted(G.edges(data=True)) == edges
+        assert sorted(G.edges(0, data=True)) == [(0, 1, {}), (0, 2, {})]
+        pytest.raises((KeyError, nx.NetworkXError), G.neighbors, -1)
+
+    def test_edges_multi(self):
+        G = self.K3
+        assert sorted(G.edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.edges(0)) == [(0, 1), (0, 2)]
+        G.add_edge(0, 1)
+        assert sorted(G.edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+
+    def test_out_edges(self):
+        G = self.K3
+        assert sorted(G.out_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)]
+        pytest.raises((KeyError, nx.NetworkXError), G.out_edges, -1)
+        assert sorted(G.out_edges(0, keys=True)) == [(0, 1, 0), (0, 2, 0)]
+
+    def test_out_edges_multi(self):
+        G = self.K3
+        assert sorted(G.out_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)]
+        G.add_edge(0, 1, 2)
+        assert sorted(G.out_edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+
+    def test_out_edges_data(self):
+        G = self.K3
+        assert sorted(G.edges(0, data=True)) == [(0, 1, {}), (0, 2, {})]
+        G.remove_edge(0, 1)
+        G.add_edge(0, 1, data=1)
+        assert sorted(G.edges(0, data=True)) == [(0, 1, {"data": 1}), (0, 2, {})]
+        assert sorted(G.edges(0, data="data")) == [(0, 1, 1), (0, 2, None)]
+        assert sorted(G.edges(0, data="data", default=-1)) == [(0, 1, 1), (0, 2, -1)]
+
+    def test_in_edges(self):
+        G = self.K3
+        assert sorted(G.in_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.in_edges(0)) == [(1, 0), (2, 0)]
+        pytest.raises((KeyError, nx.NetworkXError), G.in_edges, -1)
+        G.add_edge(0, 1, 2)
+        assert sorted(G.in_edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+        assert sorted(G.in_edges(0, keys=True)) == [(1, 0, 0), (2, 0, 0)]
+
+    def test_in_edges_no_keys(self):
+        G = self.K3
+        assert sorted(G.in_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.in_edges(0)) == [(1, 0), (2, 0)]
+        G.add_edge(0, 1, 2)
+        assert sorted(G.in_edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+
+        assert sorted(G.in_edges(data=True, keys=False)) == [
+            (0, 1, {}),
+            (0, 1, {}),
+            (0, 2, {}),
+            (1, 0, {}),
+            (1, 2, {}),
+            (2, 0, {}),
+            (2, 1, {}),
+        ]
+
+    def test_in_edges_data(self):
+        G = self.K3
+        assert sorted(G.in_edges(0, data=True)) == [(1, 0, {}), (2, 0, {})]
+        G.remove_edge(1, 0)
+        G.add_edge(1, 0, data=1)
+        assert sorted(G.in_edges(0, data=True)) == [(1, 0, {"data": 1}), (2, 0, {})]
+        assert sorted(G.in_edges(0, data="data")) == [(1, 0, 1), (2, 0, None)]
+        assert sorted(G.in_edges(0, data="data", default=-1)) == [(1, 0, 1), (2, 0, -1)]
+
+    def is_shallow(self, H, G):
+        # graph
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] == H.graph["foo"]
+        # node
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        # edge
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+        G[1][2][0]["foo"].append(1)
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+
+    def is_deep(self, H, G):
+        # graph
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] != H.graph["foo"]
+        # node
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] != H.nodes[0]["foo"]
+        # edge
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+        G[1][2][0]["foo"].append(1)
+        assert G[1][2][0]["foo"] != H[1][2][0]["foo"]
+
+    def test_to_undirected(self):
+        # MultiDiGraph -> MultiGraph changes number of edges so it is
+        # not a copy operation... use is_shallow, not is_shallow_copy
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.MultiGraph(G)
+        # self.is_shallow(H,G)
+        # the result is traversal order dependent so we
+        # can't use the is_shallow() test here.
+        try:
+            assert edges_equal(H.edges(), [(0, 1), (1, 2), (2, 0)])
+        except AssertionError:
+            assert edges_equal(H.edges(), [(0, 1), (1, 2), (1, 2), (2, 0)])
+        H = G.to_undirected()
+        self.is_deep(H, G)
+
+    def test_has_successor(self):
+        G = self.K3
+        assert G.has_successor(0, 1)
+        assert not G.has_successor(0, -1)
+
+    def test_successors(self):
+        G = self.K3
+        assert sorted(G.successors(0)) == [1, 2]
+        pytest.raises((KeyError, nx.NetworkXError), G.successors, -1)
+
+    def test_has_predecessor(self):
+        G = self.K3
+        assert G.has_predecessor(0, 1)
+        assert not G.has_predecessor(0, -1)
+
+    def test_predecessors(self):
+        G = self.K3
+        assert sorted(G.predecessors(0)) == [1, 2]
+        pytest.raises((KeyError, nx.NetworkXError), G.predecessors, -1)
+
+    def test_degree(self):
+        G = self.K3
+        assert sorted(G.degree()) == [(0, 4), (1, 4), (2, 4)]
+        assert dict(G.degree()) == {0: 4, 1: 4, 2: 4}
+        assert G.degree(0) == 4
+        assert list(G.degree(iter([0]))) == [(0, 4)]
+        G.add_edge(0, 1, weight=0.3, other=1.2)
+        assert sorted(G.degree(weight="weight")) == [(0, 4.3), (1, 4.3), (2, 4)]
+        assert sorted(G.degree(weight="other")) == [(0, 5.2), (1, 5.2), (2, 4)]
+
+    def test_in_degree(self):
+        G = self.K3
+        assert sorted(G.in_degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.in_degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.in_degree(0) == 2
+        assert list(G.in_degree(iter([0]))) == [(0, 2)]
+        assert G.in_degree(0, weight="weight") == 2
+
+    def test_out_degree(self):
+        G = self.K3
+        assert sorted(G.out_degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.out_degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.out_degree(0) == 2
+        assert list(G.out_degree(iter([0]))) == [(0, 2)]
+        assert G.out_degree(0, weight="weight") == 2
+
+    def test_size(self):
+        G = self.K3
+        assert G.size() == 6
+        assert G.number_of_edges() == 6
+        G.add_edge(0, 1, weight=0.3, other=1.2)
+        assert round(G.size(weight="weight"), 2) == 6.3
+        assert round(G.size(weight="other"), 2) == 7.2
+
+    def test_to_undirected_reciprocal(self):
+        G = self.Graph()
+        G.add_edge(1, 2)
+        assert G.to_undirected().has_edge(1, 2)
+        assert not G.to_undirected(reciprocal=True).has_edge(1, 2)
+        G.add_edge(2, 1)
+        assert G.to_undirected(reciprocal=True).has_edge(1, 2)
+
+    def test_reverse_copy(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 1)])
+        R = G.reverse()
+        assert sorted(R.edges()) == [(1, 0), (1, 0)]
+        R.remove_edge(1, 0)
+        assert sorted(R.edges()) == [(1, 0)]
+        assert sorted(G.edges()) == [(0, 1), (0, 1)]
+
+    def test_reverse_nocopy(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 1)])
+        R = G.reverse(copy=False)
+        assert sorted(R.edges()) == [(1, 0), (1, 0)]
+        pytest.raises(nx.NetworkXError, R.remove_edge, 1, 0)
+
+    def test_di_attributes_cached(self):
+        G = self.K3.copy()
+        assert id(G.in_edges) == id(G.in_edges)
+        assert id(G.out_edges) == id(G.out_edges)
+        assert id(G.in_degree) == id(G.in_degree)
+        assert id(G.out_degree) == id(G.out_degree)
+        assert id(G.succ) == id(G.succ)
+        assert id(G.pred) == id(G.pred)
+
+
+class TestMultiDiGraph(BaseMultiDiGraphTester, _TestMultiGraph):
+    def setup_method(self):
+        self.Graph = nx.MultiDiGraph
+        # build K3
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = {0: {}, 1: {}, 2: {}}
+        # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {}, 1: {}, 2: {}}
+        for u in self.k3nodes:
+            for v in self.k3nodes:
+                if u == v:
+                    continue
+                d = {0: {}}
+                self.K3._succ[u][v] = d
+                self.K3._pred[v][u] = d
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+    def test_add_edge(self):
+        G = self.Graph()
+        G.add_edge(0, 1)
+        assert G._adj == {0: {1: {0: {}}}, 1: {}}
+        assert G._succ == {0: {1: {0: {}}}, 1: {}}
+        assert G._pred == {0: {}, 1: {0: {0: {}}}}
+        G = self.Graph()
+        G.add_edge(*(0, 1))
+        assert G._adj == {0: {1: {0: {}}}, 1: {}}
+        assert G._succ == {0: {1: {0: {}}}, 1: {}}
+        assert G._pred == {0: {}, 1: {0: {0: {}}}}
+        with pytest.raises(ValueError, match="None cannot be a node"):
+            G.add_edge(None, 3)
+
+    def test_add_edges_from(self):
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 1, {"weight": 3})])
+        assert G._adj == {0: {1: {0: {}, 1: {"weight": 3}}}, 1: {}}
+        assert G._succ == {0: {1: {0: {}, 1: {"weight": 3}}}, 1: {}}
+        assert G._pred == {0: {}, 1: {0: {0: {}, 1: {"weight": 3}}}}
+
+        G.add_edges_from([(0, 1), (0, 1, {"weight": 3})], weight=2)
+        assert G._succ == {
+            0: {1: {0: {}, 1: {"weight": 3}, 2: {"weight": 2}, 3: {"weight": 3}}},
+            1: {},
+        }
+        assert G._pred == {
+            0: {},
+            1: {0: {0: {}, 1: {"weight": 3}, 2: {"weight": 2}, 3: {"weight": 3}}},
+        }
+
+        G = self.Graph()
+        edges = [
+            (0, 1, {"weight": 3}),
+            (0, 1, (("weight", 2),)),
+            (0, 1, 5),
+            (0, 1, "s"),
+        ]
+        G.add_edges_from(edges)
+        keydict = {0: {"weight": 3}, 1: {"weight": 2}, 5: {}, "s": {}}
+        assert G._succ == {0: {1: keydict}, 1: {}}
+        assert G._pred == {1: {0: keydict}, 0: {}}
+
+        # too few in tuple
+        pytest.raises(nx.NetworkXError, G.add_edges_from, [(0,)])
+        # too many in tuple
+        pytest.raises(nx.NetworkXError, G.add_edges_from, [(0, 1, 2, 3, 4)])
+        # not a tuple
+        pytest.raises(TypeError, G.add_edges_from, [0])
+        with pytest.raises(ValueError, match="None cannot be a node"):
+            G.add_edges_from([(None, 3), (3, 2)])
+
+    def test_remove_edge(self):
+        G = self.K3
+        G.remove_edge(0, 1)
+        assert G._succ == {
+            0: {2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, -1, 0)
+        pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, 0, 2, key=1)
+
+    def test_remove_multiedge(self):
+        G = self.K3
+        G.add_edge(0, 1, key="parallel edge")
+        G.remove_edge(0, 1, key="parallel edge")
+        assert G._adj == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+
+        assert G._succ == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        G.remove_edge(0, 1)
+        assert G._succ == {
+            0: {2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, -1, 0)
+
+    def test_remove_edges_from(self):
+        G = self.K3
+        G.remove_edges_from([(0, 1)])
+        assert G._succ == {
+            0: {2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        G.remove_edges_from([(0, 0)])  # silent fail
+
+
+class TestEdgeSubgraph(_TestMultiGraphEdgeSubgraph):
+    """Unit tests for the :meth:`MultiDiGraph.edge_subgraph` method."""
+
+    def setup_method(self):
+        # Create a quadruply-linked path graph on five nodes.
+        G = nx.MultiDiGraph()
+        nx.add_path(G, range(5))
+        nx.add_path(G, range(5))
+        nx.add_path(G, reversed(range(5)))
+        nx.add_path(G, reversed(range(5)))
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.adj[0][1][0]["name"] = "edge010"
+        G.adj[0][1][1]["name"] = "edge011"
+        G.adj[3][4][0]["name"] = "edge340"
+        G.adj[3][4][1]["name"] = "edge341"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by one of the first edges and one of
+        # the last edges.
+        self.G = G
+        self.H = G.edge_subgraph([(0, 1, 0), (3, 4, 1)])
+
+
+class CustomDictClass(UserDict):
+    pass
+
+
+class MultiDiGraphSubClass(nx.MultiDiGraph):
+    node_dict_factory = CustomDictClass  # type: ignore[assignment]
+    node_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+    adjlist_outer_dict_factory = CustomDictClass  # type: ignore[assignment]
+    adjlist_inner_dict_factory = CustomDictClass  # type: ignore[assignment]
+    edge_key_dict_factory = CustomDictClass  # type: ignore[assignment]
+    edge_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+    graph_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+
+
+class TestMultiDiGraphSubclass(TestMultiDiGraph):
+    def setup_method(self):
+        self.Graph = MultiDiGraphSubClass
+        # build K3
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = self.K3.adjlist_outer_dict_factory(
+            {
+                0: self.K3.adjlist_inner_dict_factory(),
+                1: self.K3.adjlist_inner_dict_factory(),
+                2: self.K3.adjlist_inner_dict_factory(),
+            }
+        )
+        # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {}, 1: {}, 2: {}}
+        for u in self.k3nodes:
+            for v in self.k3nodes:
+                if u == v:
+                    continue
+                d = {0: {}}
+                self.K3._succ[u][v] = d
+                self.K3._pred[v][u] = d
+        self.K3._node = self.K3.node_dict_factory()
+        self.K3._node[0] = self.K3.node_attr_dict_factory()
+        self.K3._node[1] = self.K3.node_attr_dict_factory()
+        self.K3._node[2] = self.K3.node_attr_dict_factory()
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_multigraph.py b/lib/python3.11/site-packages/networkx/classes/tests/test_multigraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd912d1d7c33c056b3c9808221bf7b72cd10fcac
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_multigraph.py
@@ -0,0 +1,528 @@
+from collections import UserDict
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal
+
+from .test_graph import BaseAttrGraphTester
+from .test_graph import TestGraph as _TestGraph
+
+
+class BaseMultiGraphTester(BaseAttrGraphTester):
+    def test_has_edge(self):
+        G = self.K3
+        assert G.has_edge(0, 1)
+        assert not G.has_edge(0, -1)
+        assert G.has_edge(0, 1, 0)
+        assert not G.has_edge(0, 1, 1)
+
+    def test_get_edge_data(self):
+        G = self.K3
+        assert G.get_edge_data(0, 1) == {0: {}}
+        assert G[0][1] == {0: {}}
+        assert G[0][1][0] == {}
+        assert G.get_edge_data(10, 20) is None
+        assert G.get_edge_data(0, 1, 0) == {}
+
+    def test_adjacency(self):
+        G = self.K3
+        assert dict(G.adjacency()) == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+
+    def deepcopy_edge_attr(self, H, G):
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+        G[1][2][0]["foo"].append(1)
+        assert G[1][2][0]["foo"] != H[1][2][0]["foo"]
+
+    def shallow_copy_edge_attr(self, H, G):
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+        G[1][2][0]["foo"].append(1)
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+
+    def graphs_equal(self, H, G):
+        assert G._adj == H._adj
+        assert G._node == H._node
+        assert G.graph == H.graph
+        assert G.name == H.name
+        if not G.is_directed() and not H.is_directed():
+            assert H._adj[1][2][0] is H._adj[2][1][0]
+            assert G._adj[1][2][0] is G._adj[2][1][0]
+        else:  # at least one is directed
+            if not G.is_directed():
+                G._pred = G._adj
+                G._succ = G._adj
+            if not H.is_directed():
+                H._pred = H._adj
+                H._succ = H._adj
+            assert G._pred == H._pred
+            assert G._succ == H._succ
+            assert H._succ[1][2][0] is H._pred[2][1][0]
+            assert G._succ[1][2][0] is G._pred[2][1][0]
+
+    def same_attrdict(self, H, G):
+        # same attrdict in the edgedata
+        old_foo = H[1][2][0]["foo"]
+        H.adj[1][2][0]["foo"] = "baz"
+        assert G._adj == H._adj
+        H.adj[1][2][0]["foo"] = old_foo
+        assert G._adj == H._adj
+
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G._node == H._node
+        H.nodes[0]["foo"] = old_foo
+        assert G._node == H._node
+
+    def different_attrdict(self, H, G):
+        # used by graph_equal_but_different
+        old_foo = H[1][2][0]["foo"]
+        H.adj[1][2][0]["foo"] = "baz"
+        assert G._adj != H._adj
+        H.adj[1][2][0]["foo"] = old_foo
+        assert G._adj == H._adj
+
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G._node != H._node
+        H.nodes[0]["foo"] = old_foo
+        assert G._node == H._node
+
+    def test_to_undirected(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.MultiGraph(G)
+        self.is_shallow_copy(H, G)
+        H = G.to_undirected()
+        self.is_deepcopy(H, G)
+
+    def test_to_directed(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.MultiDiGraph(G)
+        self.is_shallow_copy(H, G)
+        H = G.to_directed()
+        self.is_deepcopy(H, G)
+
+    def test_number_of_edges_selfloops(self):
+        G = self.K3
+        G.add_edge(0, 0)
+        G.add_edge(0, 0)
+        G.add_edge(0, 0, key="parallel edge")
+        G.remove_edge(0, 0, key="parallel edge")
+        assert G.number_of_edges(0, 0) == 2
+        G.remove_edge(0, 0)
+        assert G.number_of_edges(0, 0) == 1
+
+    def test_edge_lookup(self):
+        G = self.Graph()
+        G.add_edge(1, 2, foo="bar")
+        G.add_edge(1, 2, "key", foo="biz")
+        assert edges_equal(G.edges[1, 2, 0], {"foo": "bar"})
+        assert edges_equal(G.edges[1, 2, "key"], {"foo": "biz"})
+
+    def test_edge_attr(self):
+        G = self.Graph()
+        G.add_edge(1, 2, key="k1", foo="bar")
+        G.add_edge(1, 2, key="k2", foo="baz")
+        assert isinstance(G.get_edge_data(1, 2), G.edge_key_dict_factory)
+        assert all(
+            isinstance(d, G.edge_attr_dict_factory) for u, v, d in G.edges(data=True)
+        )
+        assert edges_equal(
+            G.edges(keys=True, data=True),
+            [(1, 2, "k1", {"foo": "bar"}), (1, 2, "k2", {"foo": "baz"})],
+        )
+        assert edges_equal(
+            G.edges(keys=True, data="foo"), [(1, 2, "k1", "bar"), (1, 2, "k2", "baz")]
+        )
+
+    def test_edge_attr4(self):
+        G = self.Graph()
+        G.add_edge(1, 2, key=0, data=7, spam="bar", bar="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 7, "spam": "bar", "bar": "foo"})]
+        )
+        G[1][2][0]["data"] = 10  # OK to set data like this
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 10, "spam": "bar", "bar": "foo"})]
+        )
+
+        G.adj[1][2][0]["data"] = 20
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 20, "spam": "bar", "bar": "foo"})]
+        )
+        G.edges[1, 2, 0]["data"] = 21  # another spelling, "edge"
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 21, "spam": "bar", "bar": "foo"})]
+        )
+        G.adj[1][2][0]["listdata"] = [20, 200]
+        G.adj[1][2][0]["weight"] = 20
+        assert edges_equal(
+            G.edges(data=True),
+            [
+                (
+                    1,
+                    2,
+                    {
+                        "data": 21,
+                        "spam": "bar",
+                        "bar": "foo",
+                        "listdata": [20, 200],
+                        "weight": 20,
+                    },
+                )
+            ],
+        )
+
+
+class TestMultiGraph(BaseMultiGraphTester, _TestGraph):
+    def setup_method(self):
+        self.Graph = nx.MultiGraph
+        # build K3
+        ed1, ed2, ed3 = ({0: {}}, {0: {}}, {0: {}})
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed1, 2: ed3}, 2: {0: ed2, 1: ed3}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._adj = self.k3adj
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+    def test_data_input(self):
+        G = self.Graph({1: [2], 2: [1]}, name="test")
+        assert G.name == "test"
+        expected = [(1, {2: {0: {}}}), (2, {1: {0: {}}})]
+        assert sorted(G.adj.items()) == expected
+
+    def test_data_multigraph_input(self):
+        # standard case with edge keys and edge data
+        edata0 = {"w": 200, "s": "foo"}
+        edata1 = {"w": 201, "s": "bar"}
+        keydict = {0: edata0, 1: edata1}
+        dododod = {"a": {"b": keydict}}
+
+        multiple_edge = [("a", "b", 0, edata0), ("a", "b", 1, edata1)]
+        single_edge = [("a", "b", 0, keydict)]
+
+        G = self.Graph(dododod, multigraph_input=True)
+        assert list(G.edges(keys=True, data=True)) == multiple_edge
+        G = self.Graph(dododod, multigraph_input=None)
+        assert list(G.edges(keys=True, data=True)) == multiple_edge
+        G = self.Graph(dododod, multigraph_input=False)
+        assert list(G.edges(keys=True, data=True)) == single_edge
+
+        # test round-trip to_dict_of_dict and MultiGraph constructor
+        G = self.Graph(dododod, multigraph_input=True)
+        H = self.Graph(nx.to_dict_of_dicts(G))
+        assert nx.is_isomorphic(G, H) is True  # test that default is True
+        for mgi in [True, False]:
+            H = self.Graph(nx.to_dict_of_dicts(G), multigraph_input=mgi)
+            assert nx.is_isomorphic(G, H) == mgi
+
+    # Set up cases for when incoming_graph_data is not multigraph_input
+    etraits = {"w": 200, "s": "foo"}
+    egraphics = {"color": "blue", "shape": "box"}
+    edata = {"traits": etraits, "graphics": egraphics}
+    dodod1 = {"a": {"b": edata}}
+    dodod2 = {"a": {"b": etraits}}
+    dodod3 = {"a": {"b": {"traits": etraits, "s": "foo"}}}
+    dol = {"a": ["b"]}
+
+    multiple_edge = [("a", "b", "traits", etraits), ("a", "b", "graphics", egraphics)]
+    single_edge = [("a", "b", 0, {})]  # type: ignore[var-annotated]
+    single_edge1 = [("a", "b", 0, edata)]
+    single_edge2 = [("a", "b", 0, etraits)]
+    single_edge3 = [("a", "b", 0, {"traits": etraits, "s": "foo"})]
+
+    cases = [  # (dod, mgi, edges)
+        (dodod1, True, multiple_edge),
+        (dodod1, False, single_edge1),
+        (dodod2, False, single_edge2),
+        (dodod3, False, single_edge3),
+        (dol, False, single_edge),
+    ]
+
+    @pytest.mark.parametrize("dod, mgi, edges", cases)
+    def test_non_multigraph_input(self, dod, mgi, edges):
+        G = self.Graph(dod, multigraph_input=mgi)
+        assert list(G.edges(keys=True, data=True)) == edges
+        G = nx.to_networkx_graph(dod, create_using=self.Graph, multigraph_input=mgi)
+        assert list(G.edges(keys=True, data=True)) == edges
+
+    mgi_none_cases = [
+        (dodod1, multiple_edge),
+        (dodod2, single_edge2),
+        (dodod3, single_edge3),
+    ]
+
+    @pytest.mark.parametrize("dod, edges", mgi_none_cases)
+    def test_non_multigraph_input_mgi_none(self, dod, edges):
+        # test constructor without to_networkx_graph for mgi=None
+        G = self.Graph(dod)
+        assert list(G.edges(keys=True, data=True)) == edges
+
+    raise_cases = [dodod2, dodod3, dol]
+
+    @pytest.mark.parametrize("dod", raise_cases)
+    def test_non_multigraph_input_raise(self, dod):
+        # cases where NetworkXError is raised
+        pytest.raises(nx.NetworkXError, self.Graph, dod, multigraph_input=True)
+        pytest.raises(
+            nx.NetworkXError,
+            nx.to_networkx_graph,
+            dod,
+            create_using=self.Graph,
+            multigraph_input=True,
+        )
+
+    def test_getitem(self):
+        G = self.K3
+        assert G[0] == {1: {0: {}}, 2: {0: {}}}
+        with pytest.raises(KeyError):
+            G.__getitem__("j")
+        with pytest.raises(TypeError):
+            G.__getitem__(["A"])
+
+    def test_remove_node(self):
+        G = self.K3
+        G.remove_node(0)
+        assert G.adj == {1: {2: {0: {}}}, 2: {1: {0: {}}}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_node(-1)
+
+    def test_add_edge(self):
+        G = self.Graph()
+        G.add_edge(0, 1)
+        assert G.adj == {0: {1: {0: {}}}, 1: {0: {0: {}}}}
+        G = self.Graph()
+        G.add_edge(*(0, 1))
+        assert G.adj == {0: {1: {0: {}}}, 1: {0: {0: {}}}}
+        G = self.Graph()
+        with pytest.raises(ValueError):
+            G.add_edge(None, "anything")
+
+    def test_add_edge_conflicting_key(self):
+        G = self.Graph()
+        G.add_edge(0, 1, key=1)
+        G.add_edge(0, 1)
+        assert G.number_of_edges() == 2
+        G = self.Graph()
+        G.add_edges_from([(0, 1, 1, {})])
+        G.add_edges_from([(0, 1)])
+        assert G.number_of_edges() == 2
+
+    def test_add_edges_from(self):
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 1, {"weight": 3})])
+        assert G.adj == {
+            0: {1: {0: {}, 1: {"weight": 3}}},
+            1: {0: {0: {}, 1: {"weight": 3}}},
+        }
+        G.add_edges_from([(0, 1), (0, 1, {"weight": 3})], weight=2)
+        assert G.adj == {
+            0: {1: {0: {}, 1: {"weight": 3}, 2: {"weight": 2}, 3: {"weight": 3}}},
+            1: {0: {0: {}, 1: {"weight": 3}, 2: {"weight": 2}, 3: {"weight": 3}}},
+        }
+        G = self.Graph()
+        edges = [
+            (0, 1, {"weight": 3}),
+            (0, 1, (("weight", 2),)),
+            (0, 1, 5),
+            (0, 1, "s"),
+        ]
+        G.add_edges_from(edges)
+        keydict = {0: {"weight": 3}, 1: {"weight": 2}, 5: {}, "s": {}}
+        assert G._adj == {0: {1: keydict}, 1: {0: keydict}}
+
+        # too few in tuple
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0,)])
+        # too many in tuple
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0, 1, 2, 3, 4)])
+        # not a tuple
+        with pytest.raises(TypeError):
+            G.add_edges_from([0])
+
+    def test_multigraph_add_edges_from_four_tuple_misordered(self):
+        """add_edges_from expects 4-tuples of the format (u, v, key, data_dict).
+
+        Ensure 4-tuples of form (u, v, data_dict, key) raise exception.
+        """
+        G = nx.MultiGraph()
+        with pytest.raises(TypeError):
+            # key/data values flipped in 4-tuple
+            G.add_edges_from([(0, 1, {"color": "red"}, 0)])
+
+    def test_remove_edge(self):
+        G = self.K3
+        G.remove_edge(0, 1)
+        assert G.adj == {0: {2: {0: {}}}, 1: {2: {0: {}}}, 2: {0: {0: {}}, 1: {0: {}}}}
+
+        with pytest.raises(nx.NetworkXError):
+            G.remove_edge(-1, 0)
+        with pytest.raises(nx.NetworkXError):
+            G.remove_edge(0, 2, key=1)
+
+    def test_remove_edges_from(self):
+        G = self.K3.copy()
+        G.remove_edges_from([(0, 1)])
+        kd = {0: {}}
+        assert G.adj == {0: {2: kd}, 1: {2: kd}, 2: {0: kd, 1: kd}}
+        G.remove_edges_from([(0, 0)])  # silent fail
+        self.K3.add_edge(0, 1)
+        G = self.K3.copy()
+        G.remove_edges_from(list(G.edges(data=True, keys=True)))
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        G = self.K3.copy()
+        G.remove_edges_from(list(G.edges(data=False, keys=True)))
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        G = self.K3.copy()
+        G.remove_edges_from(list(G.edges(data=False, keys=False)))
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        G = self.K3.copy()
+        G.remove_edges_from([(0, 1, 0), (0, 2, 0, {}), (1, 2)])
+        assert G.adj == {0: {1: {1: {}}}, 1: {0: {1: {}}}, 2: {}}
+
+    def test_remove_multiedge(self):
+        G = self.K3
+        G.add_edge(0, 1, key="parallel edge")
+        G.remove_edge(0, 1, key="parallel edge")
+        assert G.adj == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        G.remove_edge(0, 1)
+        kd = {0: {}}
+        assert G.adj == {0: {2: kd}, 1: {2: kd}, 2: {0: kd, 1: kd}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_edge(-1, 0)
+
+
+class TestEdgeSubgraph:
+    """Unit tests for the :meth:`MultiGraph.edge_subgraph` method."""
+
+    def setup_method(self):
+        # Create a doubly-linked path graph on five nodes.
+        G = nx.MultiGraph()
+        nx.add_path(G, range(5))
+        nx.add_path(G, range(5))
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.adj[0][1][0]["name"] = "edge010"
+        G.adj[0][1][1]["name"] = "edge011"
+        G.adj[3][4][0]["name"] = "edge340"
+        G.adj[3][4][1]["name"] = "edge341"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by one of the first edges and one of
+        # the last edges.
+        self.G = G
+        self.H = G.edge_subgraph([(0, 1, 0), (3, 4, 1)])
+
+    def test_correct_nodes(self):
+        """Tests that the subgraph has the correct nodes."""
+        assert [0, 1, 3, 4] == sorted(self.H.nodes())
+
+    def test_correct_edges(self):
+        """Tests that the subgraph has the correct edges."""
+        assert [(0, 1, 0, "edge010"), (3, 4, 1, "edge341")] == sorted(
+            self.H.edges(keys=True, data="name")
+        )
+
+    def test_add_node(self):
+        """Tests that adding a node to the original graph does not
+        affect the nodes of the subgraph.
+
+        """
+        self.G.add_node(5)
+        assert [0, 1, 3, 4] == sorted(self.H.nodes())
+
+    def test_remove_node(self):
+        """Tests that removing a node in the original graph does
+        affect the nodes of the subgraph.
+
+        """
+        self.G.remove_node(0)
+        assert [1, 3, 4] == sorted(self.H.nodes())
+
+    def test_node_attr_dict(self):
+        """Tests that the node attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for v in self.H:
+            assert self.G.nodes[v] == self.H.nodes[v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.nodes[0]["name"] = "foo"
+        assert self.G.nodes[0] == self.H.nodes[0]
+        self.H.nodes[1]["name"] = "bar"
+        assert self.G.nodes[1] == self.H.nodes[1]
+
+    def test_edge_attr_dict(self):
+        """Tests that the edge attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for u, v, k in self.H.edges(keys=True):
+            assert self.G._adj[u][v][k] == self.H._adj[u][v][k]
+        # Making a change to G should make a change in H and vice versa.
+        self.G._adj[0][1][0]["name"] = "foo"
+        assert self.G._adj[0][1][0]["name"] == self.H._adj[0][1][0]["name"]
+        self.H._adj[3][4][1]["name"] = "bar"
+        assert self.G._adj[3][4][1]["name"] == self.H._adj[3][4][1]["name"]
+
+    def test_graph_attr_dict(self):
+        """Tests that the graph attribute dictionary of the two graphs
+        is the same object.
+
+        """
+        assert self.G.graph is self.H.graph
+
+
+class CustomDictClass(UserDict):
+    pass
+
+
+class MultiGraphSubClass(nx.MultiGraph):
+    node_dict_factory = CustomDictClass  # type: ignore[assignment]
+    node_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+    adjlist_outer_dict_factory = CustomDictClass  # type: ignore[assignment]
+    adjlist_inner_dict_factory = CustomDictClass  # type: ignore[assignment]
+    edge_key_dict_factory = CustomDictClass  # type: ignore[assignment]
+    edge_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+    graph_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+
+
+class TestMultiGraphSubclass(TestMultiGraph):
+    def setup_method(self):
+        self.Graph = MultiGraphSubClass
+        # build K3
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._adj = self.K3.adjlist_outer_dict_factory(
+            {
+                0: self.K3.adjlist_inner_dict_factory(),
+                1: self.K3.adjlist_inner_dict_factory(),
+                2: self.K3.adjlist_inner_dict_factory(),
+            }
+        )
+        self.K3._pred = {0: {}, 1: {}, 2: {}}
+        for u in self.k3nodes:
+            for v in self.k3nodes:
+                if u != v:
+                    d = {0: {}}
+                    self.K3._adj[u][v] = d
+                    self.K3._adj[v][u] = d
+        self.K3._node = self.K3.node_dict_factory()
+        self.K3._node[0] = self.K3.node_attr_dict_factory()
+        self.K3._node[1] = self.K3.node_attr_dict_factory()
+        self.K3._node[2] = self.K3.node_attr_dict_factory()
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_reportviews.py b/lib/python3.11/site-packages/networkx/classes/tests/test_reportviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..8461be21838f34d9a0c49bdf0f5ad81c9fadd00f
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_reportviews.py
@@ -0,0 +1,1421 @@
+import pickle
+from copy import deepcopy
+
+import pytest
+
+import networkx as nx
+from networkx.classes import reportviews as rv
+from networkx.classes.reportviews import NodeDataView
+
+
+# Nodes
+class TestNodeView:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.nv = cls.G.nodes  # NodeView(G)
+
+    def test_pickle(self):
+        import pickle
+
+        nv = self.nv
+        pnv = pickle.loads(pickle.dumps(nv, -1))
+        assert nv == pnv
+        assert nv.__slots__ == pnv.__slots__
+
+    def test_str(self):
+        assert str(self.nv) == "[0, 1, 2, 3, 4, 5, 6, 7, 8]"
+
+    def test_repr(self):
+        assert repr(self.nv) == "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8))"
+
+    def test_contains(self):
+        G = self.G.copy()
+        nv = G.nodes
+        assert 7 in nv
+        assert 9 not in nv
+        G.remove_node(7)
+        G.add_node(9)
+        assert 7 not in nv
+        assert 9 in nv
+
+    def test_getitem(self):
+        G = self.G.copy()
+        nv = G.nodes
+        G.nodes[3]["foo"] = "bar"
+        assert nv[7] == {}
+        assert nv[3] == {"foo": "bar"}
+        # slicing
+        with pytest.raises(nx.NetworkXError):
+            G.nodes[0:5]
+
+    def test_iter(self):
+        nv = self.nv
+        for i, n in enumerate(nv):
+            assert i == n
+        inv = iter(nv)
+        assert next(inv) == 0
+        assert iter(nv) != nv
+        assert iter(inv) == inv
+        inv2 = iter(nv)
+        next(inv2)
+        assert list(inv) == list(inv2)
+        # odd case where NodeView calls NodeDataView with data=False
+        nnv = nv(data=False)
+        for i, n in enumerate(nnv):
+            assert i == n
+
+    def test_call(self):
+        nodes = self.nv
+        assert nodes is nodes()
+        assert nodes is not nodes(data=True)
+        assert nodes is not nodes(data="weight")
+
+
+class TestNodeDataView:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.nv = NodeDataView(cls.G)
+        cls.ndv = cls.G.nodes.data(True)
+        cls.nwv = cls.G.nodes.data("foo")
+
+    def test_viewtype(self):
+        nv = self.G.nodes
+        ndvfalse = nv.data(False)
+        assert nv is ndvfalse
+        assert nv is not self.ndv
+
+    def test_pickle(self):
+        import pickle
+
+        nv = self.nv
+        pnv = pickle.loads(pickle.dumps(nv, -1))
+        assert nv == pnv
+        assert nv.__slots__ == pnv.__slots__
+
+    def test_str(self):
+        msg = str([(n, {}) for n in range(9)])
+        assert str(self.ndv) == msg
+
+    def test_repr(self):
+        expected = "NodeDataView((0, 1, 2, 3, 4, 5, 6, 7, 8))"
+        assert repr(self.nv) == expected
+        expected = (
+            "NodeDataView({0: {}, 1: {}, 2: {}, 3: {}, "
+            + "4: {}, 5: {}, 6: {}, 7: {}, 8: {}})"
+        )
+        assert repr(self.ndv) == expected
+        expected = (
+            "NodeDataView({0: None, 1: None, 2: None, 3: None, 4: None, "
+            + "5: None, 6: None, 7: None, 8: None}, data='foo')"
+        )
+        assert repr(self.nwv) == expected
+
+    def test_contains(self):
+        G = self.G.copy()
+        nv = G.nodes.data()
+        nwv = G.nodes.data("foo")
+        G.nodes[3]["foo"] = "bar"
+        assert (7, {}) in nv
+        assert (3, {"foo": "bar"}) in nv
+        assert (3, "bar") in nwv
+        assert (7, None) in nwv
+        # default
+        nwv_def = G.nodes(data="foo", default="biz")
+        assert (7, "biz") in nwv_def
+        assert (3, "bar") in nwv_def
+
+    def test_getitem(self):
+        G = self.G.copy()
+        nv = G.nodes
+        G.nodes[3]["foo"] = "bar"
+        assert nv[3] == {"foo": "bar"}
+        # default
+        nwv_def = G.nodes(data="foo", default="biz")
+        assert nwv_def[7], "biz"
+        assert nwv_def[3] == "bar"
+        # slicing
+        with pytest.raises(nx.NetworkXError):
+            G.nodes.data()[0:5]
+
+    def test_iter(self):
+        G = self.G.copy()
+        nv = G.nodes.data()
+        ndv = G.nodes.data(True)
+        nwv = G.nodes.data("foo")
+        for i, (n, d) in enumerate(nv):
+            assert i == n
+            assert d == {}
+        inv = iter(nv)
+        assert next(inv) == (0, {})
+        G.nodes[3]["foo"] = "bar"
+        # default
+        for n, d in nv:
+            if n == 3:
+                assert d == {"foo": "bar"}
+            else:
+                assert d == {}
+        # data=True
+        for n, d in ndv:
+            if n == 3:
+                assert d == {"foo": "bar"}
+            else:
+                assert d == {}
+        # data='foo'
+        for n, d in nwv:
+            if n == 3:
+                assert d == "bar"
+            else:
+                assert d is None
+        # data='foo', default=1
+        for n, d in G.nodes.data("foo", default=1):
+            if n == 3:
+                assert d == "bar"
+            else:
+                assert d == 1
+
+
+def test_nodedataview_unhashable():
+    G = nx.path_graph(9)
+    G.nodes[3]["foo"] = "bar"
+    nvs = [G.nodes.data()]
+    nvs.append(G.nodes.data(True))
+    H = G.copy()
+    H.nodes[4]["foo"] = {1, 2, 3}
+    nvs.append(H.nodes.data(True))
+    # raise unhashable
+    for nv in nvs:
+        pytest.raises(TypeError, set, nv)
+        pytest.raises(TypeError, eval, "nv | nv", locals())
+    # no raise... hashable
+    Gn = G.nodes.data(False)
+    set(Gn)
+    Gn | Gn
+    Gn = G.nodes.data("foo")
+    set(Gn)
+    Gn | Gn
+
+
+class TestNodeViewSetOps:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.G.nodes[3]["foo"] = "bar"
+        cls.nv = cls.G.nodes
+
+    def n_its(self, nodes):
+        return set(nodes)
+
+    def test_len(self):
+        G = self.G.copy()
+        nv = G.nodes
+        assert len(nv) == 9
+        G.remove_node(7)
+        assert len(nv) == 8
+        G.add_node(9)
+        assert len(nv) == 9
+
+    def test_and(self):
+        nv = self.nv
+        some_nodes = self.n_its(range(5, 12))
+        assert nv & some_nodes == self.n_its(range(5, 9))
+        assert some_nodes & nv == self.n_its(range(5, 9))
+
+    def test_or(self):
+        nv = self.nv
+        some_nodes = self.n_its(range(5, 12))
+        assert nv | some_nodes == self.n_its(range(12))
+        assert some_nodes | nv == self.n_its(range(12))
+
+    def test_xor(self):
+        nv = self.nv
+        some_nodes = self.n_its(range(5, 12))
+        nodes = {0, 1, 2, 3, 4, 9, 10, 11}
+        assert nv ^ some_nodes == self.n_its(nodes)
+        assert some_nodes ^ nv == self.n_its(nodes)
+
+    def test_sub(self):
+        nv = self.nv
+        some_nodes = self.n_its(range(5, 12))
+        assert nv - some_nodes == self.n_its(range(5))
+        assert some_nodes - nv == self.n_its(range(9, 12))
+
+
+class TestNodeDataViewSetOps(TestNodeViewSetOps):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.G.nodes[3]["foo"] = "bar"
+        cls.nv = cls.G.nodes.data("foo")
+
+    def n_its(self, nodes):
+        return {(node, "bar" if node == 3 else None) for node in nodes}
+
+
+class TestNodeDataViewDefaultSetOps(TestNodeDataViewSetOps):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.G.nodes[3]["foo"] = "bar"
+        cls.nv = cls.G.nodes.data("foo", default=1)
+
+    def n_its(self, nodes):
+        return {(node, "bar" if node == 3 else 1) for node in nodes}
+
+
+# Edges Data View
+class TestEdgeDataView:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.eview = nx.reportviews.EdgeView
+
+    def test_pickle(self):
+        import pickle
+
+        ev = self.eview(self.G)(data=True)
+        pev = pickle.loads(pickle.dumps(ev, -1))
+        assert list(ev) == list(pev)
+        assert ev.__slots__ == pev.__slots__
+
+    def modify_edge(self, G, e, **kwds):
+        G._adj[e[0]][e[1]].update(kwds)
+
+    def test_str(self):
+        ev = self.eview(self.G)(data=True)
+        rep = str([(n, n + 1, {}) for n in range(8)])
+        assert str(ev) == rep
+
+    def test_repr(self):
+        ev = self.eview(self.G)(data=True)
+        rep = (
+            "EdgeDataView([(0, 1, {}), (1, 2, {}), "
+            + "(2, 3, {}), (3, 4, {}), "
+            + "(4, 5, {}), (5, 6, {}), "
+            + "(6, 7, {}), (7, 8, {})])"
+        )
+        assert repr(ev) == rep
+
+    def test_iterdata(self):
+        G = self.G.copy()
+        evr = self.eview(G)
+        ev = evr(data=True)
+        ev_def = evr(data="foo", default=1)
+
+        for u, v, d in ev:
+            pass
+        assert d == {}
+
+        for u, v, wt in ev_def:
+            pass
+        assert wt == 1
+
+        self.modify_edge(G, (2, 3), foo="bar")
+        for e in ev:
+            assert len(e) == 3
+            if set(e[:2]) == {2, 3}:
+                assert e[2] == {"foo": "bar"}
+                checked = True
+            else:
+                assert e[2] == {}
+        assert checked
+
+        for e in ev_def:
+            assert len(e) == 3
+            if set(e[:2]) == {2, 3}:
+                assert e[2] == "bar"
+                checked_wt = True
+            else:
+                assert e[2] == 1
+        assert checked_wt
+
+    def test_iter(self):
+        evr = self.eview(self.G)
+        ev = evr()
+        for u, v in ev:
+            pass
+        iev = iter(ev)
+        assert next(iev) == (0, 1)
+        assert iter(ev) != ev
+        assert iter(iev) == iev
+
+    def test_contains(self):
+        evr = self.eview(self.G)
+        ev = evr()
+        if self.G.is_directed():
+            assert (1, 2) in ev and (2, 1) not in ev
+        else:
+            assert (1, 2) in ev and (2, 1) in ev
+        assert (1, 4) not in ev
+        assert (1, 90) not in ev
+        assert (90, 1) not in ev
+
+    def test_contains_with_nbunch(self):
+        evr = self.eview(self.G)
+        ev = evr(nbunch=[0, 2])
+        if self.G.is_directed():
+            assert (0, 1) in ev
+            assert (1, 2) not in ev
+            assert (2, 3) in ev
+        else:
+            assert (0, 1) in ev
+            assert (1, 2) in ev
+            assert (2, 3) in ev
+        assert (3, 4) not in ev
+        assert (4, 5) not in ev
+        assert (5, 6) not in ev
+        assert (7, 8) not in ev
+        assert (8, 9) not in ev
+
+    def test_len(self):
+        evr = self.eview(self.G)
+        ev = evr(data="foo")
+        assert len(ev) == 8
+        assert len(evr(1)) == 2
+        assert len(evr([1, 2, 3])) == 4
+
+        assert len(self.G.edges(1)) == 2
+        assert len(self.G.edges()) == 8
+        assert len(self.G.edges) == 8
+
+        H = self.G.copy()
+        H.add_edge(1, 1)
+        assert len(H.edges(1)) == 3
+        assert len(H.edges()) == 9
+        assert len(H.edges) == 9
+
+
+class TestOutEdgeDataView(TestEdgeDataView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=nx.DiGraph())
+        cls.eview = nx.reportviews.OutEdgeView
+
+    def test_repr(self):
+        ev = self.eview(self.G)(data=True)
+        rep = (
+            "OutEdgeDataView([(0, 1, {}), (1, 2, {}), "
+            + "(2, 3, {}), (3, 4, {}), "
+            + "(4, 5, {}), (5, 6, {}), "
+            + "(6, 7, {}), (7, 8, {})])"
+        )
+        assert repr(ev) == rep
+
+    def test_len(self):
+        evr = self.eview(self.G)
+        ev = evr(data="foo")
+        assert len(ev) == 8
+        assert len(evr(1)) == 1
+        assert len(evr([1, 2, 3])) == 3
+
+        assert len(self.G.edges(1)) == 1
+        assert len(self.G.edges()) == 8
+        assert len(self.G.edges) == 8
+
+        H = self.G.copy()
+        H.add_edge(1, 1)
+        assert len(H.edges(1)) == 2
+        assert len(H.edges()) == 9
+        assert len(H.edges) == 9
+
+    def test_contains_with_nbunch(self):
+        evr = self.eview(self.G)
+        ev = evr(nbunch=[0, 2])
+        assert (0, 1) in ev
+        assert (1, 2) not in ev
+        assert (2, 3) in ev
+        assert (3, 4) not in ev
+        assert (4, 5) not in ev
+        assert (5, 6) not in ev
+        assert (7, 8) not in ev
+        assert (8, 9) not in ev
+
+
+class TestInEdgeDataView(TestOutEdgeDataView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=nx.DiGraph())
+        cls.eview = nx.reportviews.InEdgeView
+
+    def test_repr(self):
+        ev = self.eview(self.G)(data=True)
+        rep = (
+            "InEdgeDataView([(0, 1, {}), (1, 2, {}), "
+            + "(2, 3, {}), (3, 4, {}), "
+            + "(4, 5, {}), (5, 6, {}), "
+            + "(6, 7, {}), (7, 8, {})])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        evr = self.eview(self.G)
+        ev = evr(nbunch=[0, 2])
+        assert (0, 1) not in ev
+        assert (1, 2) in ev
+        assert (2, 3) not in ev
+        assert (3, 4) not in ev
+        assert (4, 5) not in ev
+        assert (5, 6) not in ev
+        assert (7, 8) not in ev
+        assert (8, 9) not in ev
+
+
+class TestMultiEdgeDataView(TestEdgeDataView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=nx.MultiGraph())
+        cls.eview = nx.reportviews.MultiEdgeView
+
+    def modify_edge(self, G, e, **kwds):
+        G._adj[e[0]][e[1]][0].update(kwds)
+
+    def test_repr(self):
+        ev = self.eview(self.G)(data=True)
+        rep = (
+            "MultiEdgeDataView([(0, 1, {}), (1, 2, {}), "
+            + "(2, 3, {}), (3, 4, {}), "
+            + "(4, 5, {}), (5, 6, {}), "
+            + "(6, 7, {}), (7, 8, {})])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        evr = self.eview(self.G)
+        ev = evr(nbunch=[0, 2])
+        assert (0, 1) in ev
+        assert (1, 2) in ev
+        assert (2, 3) in ev
+        assert (3, 4) not in ev
+        assert (4, 5) not in ev
+        assert (5, 6) not in ev
+        assert (7, 8) not in ev
+        assert (8, 9) not in ev
+
+
+class TestOutMultiEdgeDataView(TestOutEdgeDataView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        cls.eview = nx.reportviews.OutMultiEdgeView
+
+    def modify_edge(self, G, e, **kwds):
+        G._adj[e[0]][e[1]][0].update(kwds)
+
+    def test_repr(self):
+        ev = self.eview(self.G)(data=True)
+        rep = (
+            "OutMultiEdgeDataView([(0, 1, {}), (1, 2, {}), "
+            + "(2, 3, {}), (3, 4, {}), "
+            + "(4, 5, {}), (5, 6, {}), "
+            + "(6, 7, {}), (7, 8, {})])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        evr = self.eview(self.G)
+        ev = evr(nbunch=[0, 2])
+        assert (0, 1) in ev
+        assert (1, 2) not in ev
+        assert (2, 3) in ev
+        assert (3, 4) not in ev
+        assert (4, 5) not in ev
+        assert (5, 6) not in ev
+        assert (7, 8) not in ev
+        assert (8, 9) not in ev
+
+
+class TestInMultiEdgeDataView(TestOutMultiEdgeDataView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        cls.eview = nx.reportviews.InMultiEdgeView
+
+    def test_repr(self):
+        ev = self.eview(self.G)(data=True)
+        rep = (
+            "InMultiEdgeDataView([(0, 1, {}), (1, 2, {}), "
+            + "(2, 3, {}), (3, 4, {}), "
+            + "(4, 5, {}), (5, 6, {}), "
+            + "(6, 7, {}), (7, 8, {})])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        evr = self.eview(self.G)
+        ev = evr(nbunch=[0, 2])
+        assert (0, 1) not in ev
+        assert (1, 2) in ev
+        assert (2, 3) not in ev
+        assert (3, 4) not in ev
+        assert (4, 5) not in ev
+        assert (5, 6) not in ev
+        assert (7, 8) not in ev
+        assert (8, 9) not in ev
+
+
+# Edge Views
+class TestEdgeView:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.eview = nx.reportviews.EdgeView
+
+    def test_pickle(self):
+        import pickle
+
+        ev = self.eview(self.G)
+        pev = pickle.loads(pickle.dumps(ev, -1))
+        assert ev == pev
+        assert ev.__slots__ == pev.__slots__
+
+    def modify_edge(self, G, e, **kwds):
+        G._adj[e[0]][e[1]].update(kwds)
+
+    def test_str(self):
+        ev = self.eview(self.G)
+        rep = str([(n, n + 1) for n in range(8)])
+        assert str(ev) == rep
+
+    def test_repr(self):
+        ev = self.eview(self.G)
+        rep = (
+            "EdgeView([(0, 1), (1, 2), (2, 3), (3, 4), "
+            + "(4, 5), (5, 6), (6, 7), (7, 8)])"
+        )
+        assert repr(ev) == rep
+
+    def test_getitem(self):
+        G = self.G.copy()
+        ev = G.edges
+        G.edges[0, 1]["foo"] = "bar"
+        assert ev[0, 1] == {"foo": "bar"}
+
+        # slicing
+        with pytest.raises(nx.NetworkXError, match=".*does not support slicing"):
+            G.edges[0:5]
+
+        # Invalid edge
+        with pytest.raises(KeyError, match=r".*edge.*is not in the graph."):
+            G.edges[0, 9]
+
+    def test_call(self):
+        ev = self.eview(self.G)
+        assert id(ev) == id(ev())
+        assert id(ev) == id(ev(data=False))
+        assert id(ev) != id(ev(data=True))
+        assert id(ev) != id(ev(nbunch=1))
+
+    def test_data(self):
+        ev = self.eview(self.G)
+        assert id(ev) != id(ev.data())
+        assert id(ev) == id(ev.data(data=False))
+        assert id(ev) != id(ev.data(data=True))
+        assert id(ev) != id(ev.data(nbunch=1))
+
+    def test_iter(self):
+        ev = self.eview(self.G)
+        for u, v in ev:
+            pass
+        iev = iter(ev)
+        assert next(iev) == (0, 1)
+        assert iter(ev) != ev
+        assert iter(iev) == iev
+
+    def test_contains(self):
+        ev = self.eview(self.G)
+        edv = ev()
+        if self.G.is_directed():
+            assert (1, 2) in ev and (2, 1) not in ev
+            assert (1, 2) in edv and (2, 1) not in edv
+        else:
+            assert (1, 2) in ev and (2, 1) in ev
+            assert (1, 2) in edv and (2, 1) in edv
+        assert (1, 4) not in ev
+        assert (1, 4) not in edv
+        # edge not in graph
+        assert (1, 90) not in ev
+        assert (90, 1) not in ev
+        assert (1, 90) not in edv
+        assert (90, 1) not in edv
+
+    def test_contains_with_nbunch(self):
+        ev = self.eview(self.G)
+        evn = ev(nbunch=[0, 2])
+        assert (0, 1) in evn
+        assert (1, 2) in evn
+        assert (2, 3) in evn
+        assert (3, 4) not in evn
+        assert (4, 5) not in evn
+        assert (5, 6) not in evn
+        assert (7, 8) not in evn
+        assert (8, 9) not in evn
+
+    def test_len(self):
+        ev = self.eview(self.G)
+        num_ed = 9 if self.G.is_multigraph() else 8
+        assert len(ev) == num_ed
+
+        H = self.G.copy()
+        H.add_edge(1, 1)
+        assert len(H.edges(1)) == 3 + H.is_multigraph() - H.is_directed()
+        assert len(H.edges()) == num_ed + 1
+        assert len(H.edges) == num_ed + 1
+
+    def test_and(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1), (1, 0), (0, 2)}
+        if self.G.is_directed():
+            assert some_edges & ev, {(0, 1)}
+            assert ev & some_edges, {(0, 1)}
+        else:
+            assert ev & some_edges == {(0, 1), (1, 0)}
+            assert some_edges & ev == {(0, 1), (1, 0)}
+        return
+
+    def test_or(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1), (1, 0), (0, 2)}
+        result1 = {(n, n + 1) for n in range(8)}
+        result1.update(some_edges)
+        result2 = {(n + 1, n) for n in range(8)}
+        result2.update(some_edges)
+        assert (ev | some_edges) in (result1, result2)
+        assert (some_edges | ev) in (result1, result2)
+
+    def test_xor(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1), (1, 0), (0, 2)}
+        if self.G.is_directed():
+            result = {(n, n + 1) for n in range(1, 8)}
+            result.update({(1, 0), (0, 2)})
+            assert ev ^ some_edges == result
+        else:
+            result = {(n, n + 1) for n in range(1, 8)}
+            result.update({(0, 2)})
+            assert ev ^ some_edges == result
+        return
+
+    def test_sub(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1), (1, 0), (0, 2)}
+        result = {(n, n + 1) for n in range(8)}
+        result.remove((0, 1))
+        assert ev - some_edges, result
+
+
+class TestOutEdgeView(TestEdgeView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, nx.DiGraph())
+        cls.eview = nx.reportviews.OutEdgeView
+
+    def test_repr(self):
+        ev = self.eview(self.G)
+        rep = (
+            "OutEdgeView([(0, 1), (1, 2), (2, 3), (3, 4), "
+            + "(4, 5), (5, 6), (6, 7), (7, 8)])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        ev = self.eview(self.G)
+        evn = ev(nbunch=[0, 2])
+        assert (0, 1) in evn
+        assert (1, 2) not in evn
+        assert (2, 3) in evn
+        assert (3, 4) not in evn
+        assert (4, 5) not in evn
+        assert (5, 6) not in evn
+        assert (7, 8) not in evn
+        assert (8, 9) not in evn
+
+
+class TestInEdgeView(TestEdgeView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, nx.DiGraph())
+        cls.eview = nx.reportviews.InEdgeView
+
+    def test_repr(self):
+        ev = self.eview(self.G)
+        rep = (
+            "InEdgeView([(0, 1), (1, 2), (2, 3), (3, 4), "
+            + "(4, 5), (5, 6), (6, 7), (7, 8)])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        ev = self.eview(self.G)
+        evn = ev(nbunch=[0, 2])
+        assert (0, 1) not in evn
+        assert (1, 2) in evn
+        assert (2, 3) not in evn
+        assert (3, 4) not in evn
+        assert (4, 5) not in evn
+        assert (5, 6) not in evn
+        assert (7, 8) not in evn
+        assert (8, 9) not in evn
+
+
+class TestMultiEdgeView(TestEdgeView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, nx.MultiGraph())
+        cls.G.add_edge(1, 2, key=3, foo="bar")
+        cls.eview = nx.reportviews.MultiEdgeView
+
+    def modify_edge(self, G, e, **kwds):
+        if len(e) == 2:
+            e = e + (0,)
+        G._adj[e[0]][e[1]][e[2]].update(kwds)
+
+    def test_str(self):
+        ev = self.eview(self.G)
+        replist = [(n, n + 1, 0) for n in range(8)]
+        replist.insert(2, (1, 2, 3))
+        rep = str(replist)
+        assert str(ev) == rep
+
+    def test_getitem(self):
+        G = self.G.copy()
+        ev = G.edges
+        G.edges[0, 1, 0]["foo"] = "bar"
+        assert ev[0, 1, 0] == {"foo": "bar"}
+
+        # slicing
+        with pytest.raises(nx.NetworkXError):
+            G.edges[0:5]
+
+    def test_repr(self):
+        ev = self.eview(self.G)
+        rep = (
+            "MultiEdgeView([(0, 1, 0), (1, 2, 0), (1, 2, 3), (2, 3, 0), "
+            + "(3, 4, 0), (4, 5, 0), (5, 6, 0), (6, 7, 0), (7, 8, 0)])"
+        )
+        assert repr(ev) == rep
+
+    def test_call(self):
+        ev = self.eview(self.G)
+        assert id(ev) == id(ev(keys=True))
+        assert id(ev) == id(ev(data=False, keys=True))
+        assert id(ev) != id(ev(keys=False))
+        assert id(ev) != id(ev(data=True))
+        assert id(ev) != id(ev(nbunch=1))
+
+    def test_data(self):
+        ev = self.eview(self.G)
+        assert id(ev) != id(ev.data())
+        assert id(ev) == id(ev.data(data=False, keys=True))
+        assert id(ev) != id(ev.data(keys=False))
+        assert id(ev) != id(ev.data(data=True))
+        assert id(ev) != id(ev.data(nbunch=1))
+
+    def test_iter(self):
+        ev = self.eview(self.G)
+        for u, v, k in ev:
+            pass
+        iev = iter(ev)
+        assert next(iev) == (0, 1, 0)
+        assert iter(ev) != ev
+        assert iter(iev) == iev
+
+    def test_iterkeys(self):
+        G = self.G
+        evr = self.eview(G)
+        ev = evr(keys=True)
+        for u, v, k in ev:
+            pass
+        assert k == 0
+        ev = evr(keys=True, data="foo", default=1)
+        for u, v, k, wt in ev:
+            pass
+        assert wt == 1
+
+        self.modify_edge(G, (2, 3, 0), foo="bar")
+        ev = evr(keys=True, data=True)
+        for e in ev:
+            assert len(e) == 4
+            if set(e[:2]) == {2, 3}:
+                assert e[2] == 0
+                assert e[3] == {"foo": "bar"}
+                checked = True
+            elif set(e[:3]) == {1, 2, 3}:
+                assert e[2] == 3
+                assert e[3] == {"foo": "bar"}
+                checked_multi = True
+            else:
+                assert e[2] == 0
+                assert e[3] == {}
+        assert checked
+        assert checked_multi
+        ev = evr(keys=True, data="foo", default=1)
+        for e in ev:
+            if set(e[:2]) == {1, 2} and e[2] == 3:
+                assert e[3] == "bar"
+            if set(e[:2]) == {1, 2} and e[2] == 0:
+                assert e[3] == 1
+            if set(e[:2]) == {2, 3}:
+                assert e[2] == 0
+                assert e[3] == "bar"
+                assert len(e) == 4
+                checked_wt = True
+        assert checked_wt
+        ev = evr(keys=True)
+        for e in ev:
+            assert len(e) == 3
+        elist = sorted([(i, i + 1, 0) for i in range(8)] + [(1, 2, 3)])
+        assert sorted(ev) == elist
+        # test that the keyword arguments are passed correctly
+        ev = evr((1, 2), "foo", keys=True, default=1)
+        with pytest.raises(TypeError):
+            evr((1, 2), "foo", True, 1)
+        with pytest.raises(TypeError):
+            evr((1, 2), "foo", True, default=1)
+        for e in ev:
+            if set(e[:2]) == {1, 2}:
+                assert e[2] in {0, 3}
+                if e[2] == 3:
+                    assert e[3] == "bar"
+                else:  # e[2] == 0
+                    assert e[3] == 1
+        if G.is_directed():
+            assert len(list(ev)) == 3
+        else:
+            assert len(list(ev)) == 4
+
+    def test_or(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1, 0), (1, 0, 0), (0, 2, 0)}
+        result = {(n, n + 1, 0) for n in range(8)}
+        result.update(some_edges)
+        result.update({(1, 2, 3)})
+        assert ev | some_edges == result
+        assert some_edges | ev == result
+
+    def test_sub(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1, 0), (1, 0, 0), (0, 2, 0)}
+        result = {(n, n + 1, 0) for n in range(8)}
+        result.remove((0, 1, 0))
+        result.update({(1, 2, 3)})
+        assert ev - some_edges, result
+        assert some_edges - ev, result
+
+    def test_xor(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1, 0), (1, 0, 0), (0, 2, 0)}
+        if self.G.is_directed():
+            result = {(n, n + 1, 0) for n in range(1, 8)}
+            result.update({(1, 0, 0), (0, 2, 0), (1, 2, 3)})
+            assert ev ^ some_edges == result
+            assert some_edges ^ ev == result
+        else:
+            result = {(n, n + 1, 0) for n in range(1, 8)}
+            result.update({(0, 2, 0), (1, 2, 3)})
+            assert ev ^ some_edges == result
+            assert some_edges ^ ev == result
+
+    def test_and(self):
+        ev = self.eview(self.G)
+        some_edges = {(0, 1, 0), (1, 0, 0), (0, 2, 0)}
+        if self.G.is_directed():
+            assert ev & some_edges == {(0, 1, 0)}
+            assert some_edges & ev == {(0, 1, 0)}
+        else:
+            assert ev & some_edges == {(0, 1, 0), (1, 0, 0)}
+            assert some_edges & ev == {(0, 1, 0), (1, 0, 0)}
+
+    def test_contains_with_nbunch(self):
+        ev = self.eview(self.G)
+        evn = ev(nbunch=[0, 2])
+        assert (0, 1) in evn
+        assert (1, 2) in evn
+        assert (2, 3) in evn
+        assert (3, 4) not in evn
+        assert (4, 5) not in evn
+        assert (5, 6) not in evn
+        assert (7, 8) not in evn
+        assert (8, 9) not in evn
+
+
+class TestOutMultiEdgeView(TestMultiEdgeView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, nx.MultiDiGraph())
+        cls.G.add_edge(1, 2, key=3, foo="bar")
+        cls.eview = nx.reportviews.OutMultiEdgeView
+
+    def modify_edge(self, G, e, **kwds):
+        if len(e) == 2:
+            e = e + (0,)
+        G._adj[e[0]][e[1]][e[2]].update(kwds)
+
+    def test_repr(self):
+        ev = self.eview(self.G)
+        rep = (
+            "OutMultiEdgeView([(0, 1, 0), (1, 2, 0), (1, 2, 3), (2, 3, 0),"
+            + " (3, 4, 0), (4, 5, 0), (5, 6, 0), (6, 7, 0), (7, 8, 0)])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        ev = self.eview(self.G)
+        evn = ev(nbunch=[0, 2])
+        assert (0, 1) in evn
+        assert (1, 2) not in evn
+        assert (2, 3) in evn
+        assert (3, 4) not in evn
+        assert (4, 5) not in evn
+        assert (5, 6) not in evn
+        assert (7, 8) not in evn
+        assert (8, 9) not in evn
+
+
+class TestInMultiEdgeView(TestMultiEdgeView):
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, nx.MultiDiGraph())
+        cls.G.add_edge(1, 2, key=3, foo="bar")
+        cls.eview = nx.reportviews.InMultiEdgeView
+
+    def modify_edge(self, G, e, **kwds):
+        if len(e) == 2:
+            e = e + (0,)
+        G._adj[e[0]][e[1]][e[2]].update(kwds)
+
+    def test_repr(self):
+        ev = self.eview(self.G)
+        rep = (
+            "InMultiEdgeView([(0, 1, 0), (1, 2, 0), (1, 2, 3), (2, 3, 0), "
+            + "(3, 4, 0), (4, 5, 0), (5, 6, 0), (6, 7, 0), (7, 8, 0)])"
+        )
+        assert repr(ev) == rep
+
+    def test_contains_with_nbunch(self):
+        ev = self.eview(self.G)
+        evn = ev(nbunch=[0, 2])
+        assert (0, 1) not in evn
+        assert (1, 2) in evn
+        assert (2, 3) not in evn
+        assert (3, 4) not in evn
+        assert (4, 5) not in evn
+        assert (5, 6) not in evn
+        assert (7, 8) not in evn
+        assert (8, 9) not in evn
+
+
+# Degrees
+class TestDegreeView:
+    GRAPH = nx.Graph
+    dview = nx.reportviews.DegreeView
+
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(6, cls.GRAPH())
+        cls.G.add_edge(1, 3, foo=2)
+        cls.G.add_edge(1, 3, foo=3)
+
+    def test_pickle(self):
+        import pickle
+
+        deg = self.G.degree
+        pdeg = pickle.loads(pickle.dumps(deg, -1))
+        assert dict(deg) == dict(pdeg)
+
+    def test_str(self):
+        dv = self.dview(self.G)
+        rep = str([(0, 1), (1, 3), (2, 2), (3, 3), (4, 2), (5, 1)])
+        assert str(dv) == rep
+        dv = self.G.degree()
+        assert str(dv) == rep
+
+    def test_repr(self):
+        dv = self.dview(self.G)
+        rep = "DegreeView({0: 1, 1: 3, 2: 2, 3: 3, 4: 2, 5: 1})"
+        assert repr(dv) == rep
+
+    def test_iter(self):
+        dv = self.dview(self.G)
+        for n, d in dv:
+            pass
+        idv = iter(dv)
+        assert iter(dv) != dv
+        assert iter(idv) == idv
+        assert next(idv) == (0, dv[0])
+        assert next(idv) == (1, dv[1])
+        # weighted
+        dv = self.dview(self.G, weight="foo")
+        for n, d in dv:
+            pass
+        idv = iter(dv)
+        assert iter(dv) != dv
+        assert iter(idv) == idv
+        assert next(idv) == (0, dv[0])
+        assert next(idv) == (1, dv[1])
+
+    def test_nbunch(self):
+        dv = self.dview(self.G)
+        dvn = dv(0)
+        assert dvn == 1
+        dvn = dv([2, 3])
+        assert sorted(dvn) == [(2, 2), (3, 3)]
+
+    def test_getitem(self):
+        dv = self.dview(self.G)
+        assert dv[0] == 1
+        assert dv[1] == 3
+        assert dv[2] == 2
+        assert dv[3] == 3
+        dv = self.dview(self.G, weight="foo")
+        assert dv[0] == 1
+        assert dv[1] == 5
+        assert dv[2] == 2
+        assert dv[3] == 5
+
+    def test_weight(self):
+        dv = self.dview(self.G)
+        dvw = dv(0, weight="foo")
+        assert dvw == 1
+        dvw = dv(1, weight="foo")
+        assert dvw == 5
+        dvw = dv([2, 3], weight="foo")
+        assert sorted(dvw) == [(2, 2), (3, 5)]
+        dvd = dict(dv(weight="foo"))
+        assert dvd[0] == 1
+        assert dvd[1] == 5
+        assert dvd[2] == 2
+        assert dvd[3] == 5
+
+    def test_len(self):
+        dv = self.dview(self.G)
+        assert len(dv) == 6
+
+
+class TestDiDegreeView(TestDegreeView):
+    GRAPH = nx.DiGraph
+    dview = nx.reportviews.DiDegreeView
+
+    def test_repr(self):
+        dv = self.G.degree()
+        rep = "DiDegreeView({0: 1, 1: 3, 2: 2, 3: 3, 4: 2, 5: 1})"
+        assert repr(dv) == rep
+
+
+class TestOutDegreeView(TestDegreeView):
+    GRAPH = nx.DiGraph
+    dview = nx.reportviews.OutDegreeView
+
+    def test_str(self):
+        dv = self.dview(self.G)
+        rep = str([(0, 1), (1, 2), (2, 1), (3, 1), (4, 1), (5, 0)])
+        assert str(dv) == rep
+        dv = self.G.out_degree()
+        assert str(dv) == rep
+
+    def test_repr(self):
+        dv = self.G.out_degree()
+        rep = "OutDegreeView({0: 1, 1: 2, 2: 1, 3: 1, 4: 1, 5: 0})"
+        assert repr(dv) == rep
+
+    def test_nbunch(self):
+        dv = self.dview(self.G)
+        dvn = dv(0)
+        assert dvn == 1
+        dvn = dv([2, 3])
+        assert sorted(dvn) == [(2, 1), (3, 1)]
+
+    def test_getitem(self):
+        dv = self.dview(self.G)
+        assert dv[0] == 1
+        assert dv[1] == 2
+        assert dv[2] == 1
+        assert dv[3] == 1
+        dv = self.dview(self.G, weight="foo")
+        assert dv[0] == 1
+        assert dv[1] == 4
+        assert dv[2] == 1
+        assert dv[3] == 1
+
+    def test_weight(self):
+        dv = self.dview(self.G)
+        dvw = dv(0, weight="foo")
+        assert dvw == 1
+        dvw = dv(1, weight="foo")
+        assert dvw == 4
+        dvw = dv([2, 3], weight="foo")
+        assert sorted(dvw) == [(2, 1), (3, 1)]
+        dvd = dict(dv(weight="foo"))
+        assert dvd[0] == 1
+        assert dvd[1] == 4
+        assert dvd[2] == 1
+        assert dvd[3] == 1
+
+
+class TestInDegreeView(TestDegreeView):
+    GRAPH = nx.DiGraph
+    dview = nx.reportviews.InDegreeView
+
+    def test_str(self):
+        dv = self.dview(self.G)
+        rep = str([(0, 0), (1, 1), (2, 1), (3, 2), (4, 1), (5, 1)])
+        assert str(dv) == rep
+        dv = self.G.in_degree()
+        assert str(dv) == rep
+
+    def test_repr(self):
+        dv = self.G.in_degree()
+        rep = "InDegreeView({0: 0, 1: 1, 2: 1, 3: 2, 4: 1, 5: 1})"
+        assert repr(dv) == rep
+
+    def test_nbunch(self):
+        dv = self.dview(self.G)
+        dvn = dv(0)
+        assert dvn == 0
+        dvn = dv([2, 3])
+        assert sorted(dvn) == [(2, 1), (3, 2)]
+
+    def test_getitem(self):
+        dv = self.dview(self.G)
+        assert dv[0] == 0
+        assert dv[1] == 1
+        assert dv[2] == 1
+        assert dv[3] == 2
+        dv = self.dview(self.G, weight="foo")
+        assert dv[0] == 0
+        assert dv[1] == 1
+        assert dv[2] == 1
+        assert dv[3] == 4
+
+    def test_weight(self):
+        dv = self.dview(self.G)
+        dvw = dv(0, weight="foo")
+        assert dvw == 0
+        dvw = dv(1, weight="foo")
+        assert dvw == 1
+        dvw = dv([2, 3], weight="foo")
+        assert sorted(dvw) == [(2, 1), (3, 4)]
+        dvd = dict(dv(weight="foo"))
+        assert dvd[0] == 0
+        assert dvd[1] == 1
+        assert dvd[2] == 1
+        assert dvd[3] == 4
+
+
+class TestMultiDegreeView(TestDegreeView):
+    GRAPH = nx.MultiGraph
+    dview = nx.reportviews.MultiDegreeView
+
+    def test_str(self):
+        dv = self.dview(self.G)
+        rep = str([(0, 1), (1, 4), (2, 2), (3, 4), (4, 2), (5, 1)])
+        assert str(dv) == rep
+        dv = self.G.degree()
+        assert str(dv) == rep
+
+    def test_repr(self):
+        dv = self.G.degree()
+        rep = "MultiDegreeView({0: 1, 1: 4, 2: 2, 3: 4, 4: 2, 5: 1})"
+        assert repr(dv) == rep
+
+    def test_nbunch(self):
+        dv = self.dview(self.G)
+        dvn = dv(0)
+        assert dvn == 1
+        dvn = dv([2, 3])
+        assert sorted(dvn) == [(2, 2), (3, 4)]
+
+    def test_getitem(self):
+        dv = self.dview(self.G)
+        assert dv[0] == 1
+        assert dv[1] == 4
+        assert dv[2] == 2
+        assert dv[3] == 4
+        dv = self.dview(self.G, weight="foo")
+        assert dv[0] == 1
+        assert dv[1] == 7
+        assert dv[2] == 2
+        assert dv[3] == 7
+
+    def test_weight(self):
+        dv = self.dview(self.G)
+        dvw = dv(0, weight="foo")
+        assert dvw == 1
+        dvw = dv(1, weight="foo")
+        assert dvw == 7
+        dvw = dv([2, 3], weight="foo")
+        assert sorted(dvw) == [(2, 2), (3, 7)]
+        dvd = dict(dv(weight="foo"))
+        assert dvd[0] == 1
+        assert dvd[1] == 7
+        assert dvd[2] == 2
+        assert dvd[3] == 7
+
+
+class TestDiMultiDegreeView(TestMultiDegreeView):
+    GRAPH = nx.MultiDiGraph
+    dview = nx.reportviews.DiMultiDegreeView
+
+    def test_repr(self):
+        dv = self.G.degree()
+        rep = "DiMultiDegreeView({0: 1, 1: 4, 2: 2, 3: 4, 4: 2, 5: 1})"
+        assert repr(dv) == rep
+
+
+class TestOutMultiDegreeView(TestDegreeView):
+    GRAPH = nx.MultiDiGraph
+    dview = nx.reportviews.OutMultiDegreeView
+
+    def test_str(self):
+        dv = self.dview(self.G)
+        rep = str([(0, 1), (1, 3), (2, 1), (3, 1), (4, 1), (5, 0)])
+        assert str(dv) == rep
+        dv = self.G.out_degree()
+        assert str(dv) == rep
+
+    def test_repr(self):
+        dv = self.G.out_degree()
+        rep = "OutMultiDegreeView({0: 1, 1: 3, 2: 1, 3: 1, 4: 1, 5: 0})"
+        assert repr(dv) == rep
+
+    def test_nbunch(self):
+        dv = self.dview(self.G)
+        dvn = dv(0)
+        assert dvn == 1
+        dvn = dv([2, 3])
+        assert sorted(dvn) == [(2, 1), (3, 1)]
+
+    def test_getitem(self):
+        dv = self.dview(self.G)
+        assert dv[0] == 1
+        assert dv[1] == 3
+        assert dv[2] == 1
+        assert dv[3] == 1
+        dv = self.dview(self.G, weight="foo")
+        assert dv[0] == 1
+        assert dv[1] == 6
+        assert dv[2] == 1
+        assert dv[3] == 1
+
+    def test_weight(self):
+        dv = self.dview(self.G)
+        dvw = dv(0, weight="foo")
+        assert dvw == 1
+        dvw = dv(1, weight="foo")
+        assert dvw == 6
+        dvw = dv([2, 3], weight="foo")
+        assert sorted(dvw) == [(2, 1), (3, 1)]
+        dvd = dict(dv(weight="foo"))
+        assert dvd[0] == 1
+        assert dvd[1] == 6
+        assert dvd[2] == 1
+        assert dvd[3] == 1
+
+
+class TestInMultiDegreeView(TestDegreeView):
+    GRAPH = nx.MultiDiGraph
+    dview = nx.reportviews.InMultiDegreeView
+
+    def test_str(self):
+        dv = self.dview(self.G)
+        rep = str([(0, 0), (1, 1), (2, 1), (3, 3), (4, 1), (5, 1)])
+        assert str(dv) == rep
+        dv = self.G.in_degree()
+        assert str(dv) == rep
+
+    def test_repr(self):
+        dv = self.G.in_degree()
+        rep = "InMultiDegreeView({0: 0, 1: 1, 2: 1, 3: 3, 4: 1, 5: 1})"
+        assert repr(dv) == rep
+
+    def test_nbunch(self):
+        dv = self.dview(self.G)
+        dvn = dv(0)
+        assert dvn == 0
+        dvn = dv([2, 3])
+        assert sorted(dvn) == [(2, 1), (3, 3)]
+
+    def test_getitem(self):
+        dv = self.dview(self.G)
+        assert dv[0] == 0
+        assert dv[1] == 1
+        assert dv[2] == 1
+        assert dv[3] == 3
+        dv = self.dview(self.G, weight="foo")
+        assert dv[0] == 0
+        assert dv[1] == 1
+        assert dv[2] == 1
+        assert dv[3] == 6
+
+    def test_weight(self):
+        dv = self.dview(self.G)
+        dvw = dv(0, weight="foo")
+        assert dvw == 0
+        dvw = dv(1, weight="foo")
+        assert dvw == 1
+        dvw = dv([2, 3], weight="foo")
+        assert sorted(dvw) == [(2, 1), (3, 6)]
+        dvd = dict(dv(weight="foo"))
+        assert dvd[0] == 0
+        assert dvd[1] == 1
+        assert dvd[2] == 1
+        assert dvd[3] == 6
+
+
+@pytest.mark.parametrize(
+    ("reportview", "err_msg_terms"),
+    (
+        (rv.NodeView, "list(G.nodes"),
+        (rv.NodeDataView, "list(G.nodes.data"),
+        (rv.EdgeView, "list(G.edges"),
+        # Directed EdgeViews
+        (rv.InEdgeView, "list(G.in_edges"),
+        (rv.OutEdgeView, "list(G.edges"),
+        # Multi EdgeViews
+        (rv.MultiEdgeView, "list(G.edges"),
+        (rv.InMultiEdgeView, "list(G.in_edges"),
+        (rv.OutMultiEdgeView, "list(G.edges"),
+    ),
+)
+def test_slicing_reportviews(reportview, err_msg_terms):
+    G = nx.complete_graph(3)
+    view = reportview(G)
+    with pytest.raises(nx.NetworkXError) as exc:
+        view[0:2]
+    errmsg = str(exc.value)
+    assert type(view).__name__ in errmsg
+    assert err_msg_terms in errmsg
+
+
+@pytest.mark.parametrize(
+    "graph", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_cache_dict_get_set_state(graph):
+    G = nx.path_graph(5, graph())
+    G.nodes, G.edges, G.adj, G.degree
+    if G.is_directed():
+        G.pred, G.succ, G.in_edges, G.out_edges, G.in_degree, G.out_degree
+    cached_dict = G.__dict__
+    assert "nodes" in cached_dict
+    assert "edges" in cached_dict
+    assert "adj" in cached_dict
+    assert "degree" in cached_dict
+    if G.is_directed():
+        assert "pred" in cached_dict
+        assert "succ" in cached_dict
+        assert "in_edges" in cached_dict
+        assert "out_edges" in cached_dict
+        assert "in_degree" in cached_dict
+        assert "out_degree" in cached_dict
+
+    # Raises error if the cached properties and views do not work
+    pickle.loads(pickle.dumps(G, -1))
+    deepcopy(G)
+
+
+def test_edge_views_inherit_from_EdgeViewABC():
+    all_edge_view_classes = (v for v in dir(nx.reportviews) if "Edge" in v)
+    for eview_class in all_edge_view_classes:
+        assert issubclass(
+            getattr(nx.reportviews, eview_class), nx.reportviews.EdgeViewABC
+        )
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_special.py b/lib/python3.11/site-packages/networkx/classes/tests/test_special.py
new file mode 100644
index 0000000000000000000000000000000000000000..33b61a4b64975bfd0044215825ae5510bec68073
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_special.py
@@ -0,0 +1,131 @@
+import networkx as nx
+
+from .test_digraph import BaseDiGraphTester
+from .test_digraph import TestDiGraph as _TestDiGraph
+from .test_graph import BaseGraphTester
+from .test_graph import TestGraph as _TestGraph
+from .test_multidigraph import TestMultiDiGraph as _TestMultiDiGraph
+from .test_multigraph import TestMultiGraph as _TestMultiGraph
+
+
+def test_factories():
+    class mydict1(dict):
+        pass
+
+    class mydict2(dict):
+        pass
+
+    class mydict3(dict):
+        pass
+
+    class mydict4(dict):
+        pass
+
+    class mydict5(dict):
+        pass
+
+    for Graph in (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph):
+
+        class MyGraph(Graph):
+            node_dict_factory = mydict1
+            adjlist_outer_dict_factory = mydict2
+            adjlist_inner_dict_factory = mydict3
+            edge_key_dict_factory = mydict4
+            edge_attr_dict_factory = mydict5
+
+        G = MyGraph()
+        assert isinstance(G._node, mydict1)
+        assert isinstance(G._adj, mydict2)
+        G.add_node(1)
+        assert isinstance(G._adj[1], mydict3)
+        if G.is_directed():
+            assert isinstance(G._pred, mydict2)
+            assert isinstance(G._succ, mydict2)
+            assert isinstance(G._pred[1], mydict3)
+        G.add_edge(1, 2)
+        if G.is_multigraph():
+            assert isinstance(G._adj[1][2], mydict4)
+            assert isinstance(G._adj[1][2][0], mydict5)
+        else:
+            assert isinstance(G._adj[1][2], mydict5)
+
+
+class TestSpecialGraph(_TestGraph):
+    def setup_method(self):
+        _TestGraph.setup_method(self)
+        self.Graph = nx.Graph
+
+
+class TestThinGraph(BaseGraphTester):
+    def setup_method(self):
+        all_edge_dict = {"weight": 1}
+
+        class MyGraph(nx.Graph):
+            def edge_attr_dict_factory(self):
+                return all_edge_dict
+
+        self.Graph = MyGraph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3 = (all_edge_dict, all_edge_dict, all_edge_dict)
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed1, 2: ed3}, 2: {0: ed2, 1: ed3}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._adj = self.k3adj
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+
+class TestSpecialDiGraph(_TestDiGraph):
+    def setup_method(self):
+        _TestDiGraph.setup_method(self)
+        self.Graph = nx.DiGraph
+
+
+class TestThinDiGraph(BaseDiGraphTester):
+    def setup_method(self):
+        all_edge_dict = {"weight": 1}
+
+        class MyGraph(nx.DiGraph):
+            def edge_attr_dict_factory(self):
+                return all_edge_dict
+
+        self.Graph = MyGraph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3 = (all_edge_dict, all_edge_dict, all_edge_dict)
+        ed4, ed5, ed6 = (all_edge_dict, all_edge_dict, all_edge_dict)
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed3, 2: ed4}, 2: {0: ed5, 1: ed6}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = self.k3adj
+        # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {1: ed3, 2: ed5}, 1: {0: ed1, 2: ed6}, 2: {0: ed2, 1: ed4}}
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+        ed1, ed2 = (all_edge_dict, all_edge_dict)
+        self.P3 = self.Graph()
+        self.P3._succ = {0: {1: ed1}, 1: {2: ed2}, 2: {}}
+        # P3._adj is synced with P3._succ
+        self.P3._pred = {0: {}, 1: {0: ed1}, 2: {1: ed2}}
+        self.P3._node = {}
+        self.P3._node[0] = {}
+        self.P3._node[1] = {}
+        self.P3._node[2] = {}
+
+
+class TestSpecialMultiGraph(_TestMultiGraph):
+    def setup_method(self):
+        _TestMultiGraph.setup_method(self)
+        self.Graph = nx.MultiGraph
+
+
+class TestSpecialMultiDiGraph(_TestMultiDiGraph):
+    def setup_method(self):
+        _TestMultiDiGraph.setup_method(self)
+        self.Graph = nx.MultiDiGraph
diff --git a/lib/python3.11/site-packages/networkx/classes/tests/test_subgraphviews.py b/lib/python3.11/site-packages/networkx/classes/tests/test_subgraphviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..66d570cedcecf4e8bb2f485b0ee892ddae2140dd
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/classes/tests/test_subgraphviews.py
@@ -0,0 +1,371 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal
+
+
+class TestSubGraphView:
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.Graph
+    hide_edges_filter = staticmethod(nx.filters.hide_edges)
+    show_edges_filter = staticmethod(nx.filters.show_edges)
+
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=cls.graph())
+        cls.hide_edges_w_hide_nodes = {(3, 4), (4, 5), (5, 6)}
+
+    def test_hidden_nodes(self):
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        gview = self.gview
+        G = gview(self.G, filter_node=nodes_gone)
+        assert self.G.nodes - G.nodes == {4, 5}
+        assert self.G.edges - G.edges == self.hide_edges_w_hide_nodes
+        if G.is_directed():
+            assert list(G[3]) == []
+            assert list(G[2]) == [3]
+        else:
+            assert list(G[3]) == [2]
+            assert set(G[2]) == {1, 3}
+        pytest.raises(KeyError, G.__getitem__, 4)
+        pytest.raises(KeyError, G.__getitem__, 112)
+        pytest.raises(KeyError, G.__getitem__, 111)
+        assert G.degree(3) == (3 if G.is_multigraph() else 1)
+        assert G.size() == (7 if G.is_multigraph() else 5)
+
+    def test_hidden_edges(self):
+        hide_edges = [(2, 3), (8, 7), (222, 223)]
+        edges_gone = self.hide_edges_filter(hide_edges)
+        gview = self.gview
+        G = gview(self.G, filter_edge=edges_gone)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert self.G.edges - G.edges == {(2, 3)}
+            assert list(G[2]) == []
+            assert list(G.pred[3]) == []
+            assert list(G.pred[2]) == [1]
+            assert G.size() == 7
+        else:
+            assert self.G.edges - G.edges == {(2, 3), (7, 8)}
+            assert list(G[2]) == [1]
+            assert G.size() == 6
+        assert list(G[3]) == [4]
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+        assert G.degree(3) == 1
+
+    def test_shown_node(self):
+        induced_subgraph = nx.filters.show_nodes([2, 3, 111])
+        gview = self.gview
+        G = gview(self.G, filter_node=induced_subgraph)
+        assert set(G.nodes) == {2, 3}
+        if G.is_directed():
+            assert list(G[3]) == []
+        else:
+            assert list(G[3]) == [2]
+        assert list(G[2]) == [3]
+        pytest.raises(KeyError, G.__getitem__, 4)
+        pytest.raises(KeyError, G.__getitem__, 112)
+        pytest.raises(KeyError, G.__getitem__, 111)
+        assert G.degree(3) == (3 if G.is_multigraph() else 1)
+        assert G.size() == (3 if G.is_multigraph() else 1)
+
+    def test_shown_edges(self):
+        show_edges = [(2, 3), (8, 7), (222, 223)]
+        edge_subgraph = self.show_edges_filter(show_edges)
+        G = self.gview(self.G, filter_edge=edge_subgraph)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert G.edges == {(2, 3)}
+            assert list(G[3]) == []
+            assert list(G[2]) == [3]
+            assert list(G.pred[3]) == [2]
+            assert list(G.pred[2]) == []
+            assert G.size() == 1
+        else:
+            assert G.edges == {(2, 3), (7, 8)}
+            assert list(G[3]) == [2]
+            assert list(G[2]) == [3]
+            assert G.size() == 2
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+        assert G.degree(3) == 1
+
+
+class TestSubDiGraphView(TestSubGraphView):
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.DiGraph
+    hide_edges_filter = staticmethod(nx.filters.hide_diedges)
+    show_edges_filter = staticmethod(nx.filters.show_diedges)
+    hide_edges = [(2, 3), (8, 7), (222, 223)]
+    excluded = {(2, 3), (3, 4), (4, 5), (5, 6)}
+
+    def test_inoutedges(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert self.G.in_edges - G.in_edges == self.excluded
+        assert self.G.out_edges - G.out_edges == self.excluded
+
+    def test_pred(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert list(G.pred[2]) == [1]
+        assert list(G.pred[6]) == []
+
+    def test_inout_degree(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert G.degree(2) == 1
+        assert G.out_degree(2) == 0
+        assert G.in_degree(2) == 1
+        assert G.size() == 4
+
+
+# multigraph
+class TestMultiGraphView(TestSubGraphView):
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.MultiGraph
+    hide_edges_filter = staticmethod(nx.filters.hide_multiedges)
+    show_edges_filter = staticmethod(nx.filters.show_multiedges)
+
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=cls.graph())
+        multiedges = {(2, 3, 4), (2, 3, 5)}
+        cls.G.add_edges_from(multiedges)
+        cls.hide_edges_w_hide_nodes = {(3, 4, 0), (4, 5, 0), (5, 6, 0)}
+
+    def test_hidden_edges(self):
+        hide_edges = [(2, 3, 4), (2, 3, 3), (8, 7, 0), (222, 223, 0)]
+        edges_gone = self.hide_edges_filter(hide_edges)
+        G = self.gview(self.G, filter_edge=edges_gone)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert self.G.edges - G.edges == {(2, 3, 4)}
+            assert list(G[3]) == [4]
+            assert list(G[2]) == [3]
+            assert list(G.pred[3]) == [2]  # only one 2 but two edges
+            assert list(G.pred[2]) == [1]
+            assert G.size() == 9
+        else:
+            assert self.G.edges - G.edges == {(2, 3, 4), (7, 8, 0)}
+            assert list(G[3]) == [2, 4]
+            assert list(G[2]) == [1, 3]
+            assert G.size() == 8
+        assert G.degree(3) == 3
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+
+    def test_shown_edges(self):
+        show_edges = [(2, 3, 4), (2, 3, 3), (8, 7, 0), (222, 223, 0)]
+        edge_subgraph = self.show_edges_filter(show_edges)
+        G = self.gview(self.G, filter_edge=edge_subgraph)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert G.edges == {(2, 3, 4)}
+            assert list(G[3]) == []
+            assert list(G.pred[3]) == [2]
+            assert list(G.pred[2]) == []
+            assert G.size() == 1
+        else:
+            assert G.edges == {(2, 3, 4), (7, 8, 0)}
+            assert G.size() == 2
+            assert list(G[3]) == [2]
+        assert G.degree(3) == 1
+        assert list(G[2]) == [3]
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+
+
+# multidigraph
+class TestMultiDiGraphView(TestMultiGraphView, TestSubDiGraphView):
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.MultiDiGraph
+    hide_edges_filter = staticmethod(nx.filters.hide_multidiedges)
+    show_edges_filter = staticmethod(nx.filters.show_multidiedges)
+    hide_edges = [(2, 3, 0), (8, 7, 0), (222, 223, 0)]
+    excluded = {(2, 3, 0), (3, 4, 0), (4, 5, 0), (5, 6, 0)}
+
+    def test_inout_degree(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert G.degree(2) == 3
+        assert G.out_degree(2) == 2
+        assert G.in_degree(2) == 1
+        assert G.size() == 6
+
+
+# induced_subgraph
+class TestInducedSubGraph:
+    @classmethod
+    def setup_class(cls):
+        cls.K3 = G = nx.complete_graph(3)
+        G.graph["foo"] = []
+        G.nodes[0]["foo"] = []
+        G.remove_edge(1, 2)
+        ll = []
+        G.add_edge(1, 2, foo=ll)
+        G.add_edge(2, 1, foo=ll)
+
+    def test_full_graph(self):
+        G = self.K3
+        H = nx.induced_subgraph(G, [0, 1, 2, 5])
+        assert H.name == G.name
+        self.graphs_equal(H, G)
+        self.same_attrdict(H, G)
+
+    def test_partial_subgraph(self):
+        G = self.K3
+        H = nx.induced_subgraph(G, 0)
+        assert dict(H.adj) == {0: {}}
+        assert dict(G.adj) != {0: {}}
+
+        H = nx.induced_subgraph(G, [0, 1])
+        assert dict(H.adj) == {0: {1: {}}, 1: {0: {}}}
+
+    def same_attrdict(self, H, G):
+        old_foo = H[1][2]["foo"]
+        H.edges[1, 2]["foo"] = "baz"
+        assert G.edges == H.edges
+        H.edges[1, 2]["foo"] = old_foo
+        assert G.edges == H.edges
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G.nodes == H.nodes
+        H.nodes[0]["foo"] = old_foo
+        assert G.nodes == H.nodes
+
+    def graphs_equal(self, H, G):
+        assert G._adj == H._adj
+        assert G._node == H._node
+        assert G.graph == H.graph
+        assert G.name == H.name
+        if not G.is_directed() and not H.is_directed():
+            assert H._adj[1][2] is H._adj[2][1]
+            assert G._adj[1][2] is G._adj[2][1]
+        else:  # at least one is directed
+            if not G.is_directed():
+                G._pred = G._adj
+                G._succ = G._adj
+            if not H.is_directed():
+                H._pred = H._adj
+                H._succ = H._adj
+            assert G._pred == H._pred
+            assert G._succ == H._succ
+            assert H._succ[1][2] is H._pred[2][1]
+            assert G._succ[1][2] is G._pred[2][1]
+
+
+# edge_subgraph
+class TestEdgeSubGraph:
+    @classmethod
+    def setup_class(cls):
+        # Create a path graph on five nodes.
+        cls.G = G = nx.path_graph(5)
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.edges[0, 1]["name"] = "edge01"
+        G.edges[3, 4]["name"] = "edge34"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by the first and last edges.
+        cls.H = nx.edge_subgraph(G, [(0, 1), (3, 4)])
+
+    def test_correct_nodes(self):
+        """Tests that the subgraph has the correct nodes."""
+        assert [(0, "node0"), (1, "node1"), (3, "node3"), (4, "node4")] == sorted(
+            self.H.nodes.data("name")
+        )
+
+    def test_correct_edges(self):
+        """Tests that the subgraph has the correct edges."""
+        assert edges_equal(
+            [(0, 1, "edge01"), (3, 4, "edge34")], self.H.edges.data("name")
+        )
+
+    def test_add_node(self):
+        """Tests that adding a node to the original graph does not
+        affect the nodes of the subgraph.
+
+        """
+        self.G.add_node(5)
+        assert [0, 1, 3, 4] == sorted(self.H.nodes)
+        self.G.remove_node(5)
+
+    def test_remove_node(self):
+        """Tests that removing a node in the original graph
+        removes the nodes of the subgraph.
+
+        """
+        self.G.remove_node(0)
+        assert [1, 3, 4] == sorted(self.H.nodes)
+        self.G.add_node(0, name="node0")
+        self.G.add_edge(0, 1, name="edge01")
+
+    def test_node_attr_dict(self):
+        """Tests that the node attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for v in self.H:
+            assert self.G.nodes[v] == self.H.nodes[v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.nodes[0]["name"] = "foo"
+        assert self.G.nodes[0] == self.H.nodes[0]
+        self.H.nodes[1]["name"] = "bar"
+        assert self.G.nodes[1] == self.H.nodes[1]
+        # Revert the change, so tests pass with pytest-randomly
+        self.G.nodes[0]["name"] = "node0"
+        self.H.nodes[1]["name"] = "node1"
+
+    def test_edge_attr_dict(self):
+        """Tests that the edge attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for u, v in self.H.edges():
+            assert self.G.edges[u, v] == self.H.edges[u, v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.edges[0, 1]["name"] = "foo"
+        assert self.G.edges[0, 1]["name"] == self.H.edges[0, 1]["name"]
+        self.H.edges[3, 4]["name"] = "bar"
+        assert self.G.edges[3, 4]["name"] == self.H.edges[3, 4]["name"]
+        # Revert the change, so tests pass with pytest-randomly
+        self.G.edges[0, 1]["name"] = "edge01"
+        self.H.edges[3, 4]["name"] = "edge34"
+
+    def test_graph_attr_dict(self):
+        """Tests that the graph attribute dictionary of the two graphs
+        is the same object.
+
+        """
+        assert self.G.graph is self.H.graph
+
+    def test_readonly(self):
+        """Tests that the subgraph cannot change the graph structure"""
+        pytest.raises(nx.NetworkXError, self.H.add_node, 5)
+        pytest.raises(nx.NetworkXError, self.H.remove_node, 0)
+        pytest.raises(nx.NetworkXError, self.H.add_edge, 5, 6)
+        pytest.raises(nx.NetworkXError, self.H.remove_edge, 0, 1)
+
+    @pytest.mark.parametrize("multigraph", (nx.MultiGraph, nx.MultiDiGraph))
+    def test_multigraph_filtered_edges(self, multigraph):
+        """Check edge visibility in FilterMultiInner on edge_subgraph's of
+        multigraphs. See gh-7724."""
+        G = multigraph([("a", "b"), ("a", "c"), ("c", "b")])
+        H = nx.edge_subgraph(G, [("a", "b", 0), ("c", "b", 0)])
+        assert "c" not in H["a"]
+        assert not H.has_edge("a", "c")
diff --git a/lib/python3.11/site-packages/networkx/drawing/__init__.py b/lib/python3.11/site-packages/networkx/drawing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f53309d4da23a445bcce8cb7570a6de364452b5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/__init__.py
@@ -0,0 +1,7 @@
+# graph drawing and interface to graphviz
+
+from .layout import *
+from .nx_latex import *
+from .nx_pylab import *
+from . import nx_agraph
+from . import nx_pydot
diff --git a/lib/python3.11/site-packages/networkx/drawing/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dc399b4a7835c844fc42d33e1a4975179cd57d76
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/__pycache__/layout.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/__pycache__/layout.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..32e0462037522e6c0136c6b9bd49da0034bec86a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/__pycache__/layout.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_agraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_agraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..138423c229083045ebe15aa93c86f707c9abfd80
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_agraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_latex.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_latex.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1896df3b98c5e4005f95ce817a6fd51786766cc9
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_latex.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_pydot.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_pydot.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..95b5f614ca3c0182c349dd293b92567e3f1f4648
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/__pycache__/nx_pydot.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/layout.py b/lib/python3.11/site-packages/networkx/drawing/layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..b46d9f77b81f27acd8f4eaef2aa5c5a3e87872ed
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/layout.py
@@ -0,0 +1,2036 @@
+"""
+******
+Layout
+******
+
+Node positioning algorithms for graph drawing.
+
+For `random_layout()` the possible resulting shape
+is a square of side [0, scale] (default: [0, 1])
+Changing `center` shifts the layout by that amount.
+
+For the other layout routines, the extent is
+[center - scale, center + scale] (default: [-1, 1]).
+
+Warning: Most layout routines have only been tested in 2-dimensions.
+
+"""
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = [
+    "bipartite_layout",
+    "circular_layout",
+    "forceatlas2_layout",
+    "kamada_kawai_layout",
+    "random_layout",
+    "rescale_layout",
+    "rescale_layout_dict",
+    "shell_layout",
+    "spring_layout",
+    "spectral_layout",
+    "planar_layout",
+    "fruchterman_reingold_layout",
+    "spiral_layout",
+    "multipartite_layout",
+    "bfs_layout",
+    "arf_layout",
+]
+
+
+def _process_params(G, center, dim):
+    # Some boilerplate code.
+    import numpy as np
+
+    if not isinstance(G, nx.Graph):
+        empty_graph = nx.Graph()
+        empty_graph.add_nodes_from(G)
+        G = empty_graph
+
+    if center is None:
+        center = np.zeros(dim)
+    else:
+        center = np.asarray(center)
+
+    if len(center) != dim:
+        msg = "length of center coordinates must match dimension of layout"
+        raise ValueError(msg)
+
+    return G, center
+
+
+@np_random_state(3)
+def random_layout(G, center=None, dim=2, seed=None, store_pos_as=None):
+    """Position nodes uniformly at random in the unit square.
+
+    For every node, a position is generated by choosing each of dim
+    coordinates uniformly at random on the interval [0.0, 1.0).
+
+    NumPy (http://scipy.org) is required for this function.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    seed : int, RandomState instance or None  optional (default=None)
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> pos = nx.random_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.random_layout(G, seed=42, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([0.37454012, 0.9507143 ], dtype=float32),
+     1: array([0.7319939, 0.5986585], dtype=float32),
+     2: array([0.15601864, 0.15599452], dtype=float32),
+     3: array([0.05808361, 0.8661761 ], dtype=float32),
+     4: array([0.601115 , 0.7080726], dtype=float32),
+     5: array([0.02058449, 0.96990985], dtype=float32),
+     6: array([0.83244264, 0.21233912], dtype=float32)}
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+    pos = seed.rand(len(G), dim) + center
+    pos = pos.astype(np.float32)
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+    return pos
+
+
+def circular_layout(G, scale=1, center=None, dim=2, store_pos_as=None):
+    # dim=2 only
+    """Position nodes on a circle.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+        If dim>2, the remaining dimensions are set to zero
+        in the returned positions.
+        If dim<2, a ValueError is raised.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim < 2
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.circular_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.circular_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([9.99999986e-01, 2.18556937e-08]),
+     1: array([-3.57647606e-08,  1.00000000e+00]),
+     2: array([-9.9999997e-01, -6.5567081e-08]),
+     3: array([ 1.98715071e-08, -9.99999956e-01])}
+
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    import numpy as np
+
+    if dim < 2:
+        raise ValueError("cannot handle dimensions < 2")
+
+    G, center = _process_params(G, center, dim)
+
+    paddims = max(0, (dim - 2))
+
+    if len(G) == 0:
+        pos = {}
+    elif len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G): center}
+    else:
+        # Discard the extra angle since it matches 0 radians.
+        theta = np.linspace(0, 1, len(G) + 1)[:-1] * 2 * np.pi
+        theta = theta.astype(np.float32)
+        pos = np.column_stack(
+            [np.cos(theta), np.sin(theta), np.zeros((len(G), paddims))]
+        )
+        pos = rescale_layout(pos, scale=scale) + center
+        pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def shell_layout(
+    G, nlist=None, rotate=None, scale=1, center=None, dim=2, store_pos_as=None
+):
+    """Position nodes in concentric circles.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    nlist : list of lists
+       List of node lists for each shell.
+
+    rotate : angle in radians (default=pi/len(nlist))
+       Angle by which to rotate the starting position of each shell
+       relative to the starting position of the previous shell.
+       To recreate behavior before v2.5 use rotate=0.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout, currently only dim=2 is supported.
+        Other dimension values result in a ValueError.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim != 2
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> shells = [[0], [1, 2, 3]]
+    >>> pos = nx.shell_layout(G, shells)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.shell_layout(G, shells, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([0., 0.]),
+     1: array([-5.00000000e-01, -4.37113883e-08]),
+     2: array([ 0.24999996, -0.43301272]),
+     3: array([0.24999981, 0.43301281])}
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        return {nx.utils.arbitrary_element(G): center}
+
+    if nlist is None:
+        # draw the whole graph in one shell
+        nlist = [list(G)]
+
+    radius_bump = scale / len(nlist)
+
+    if len(nlist[0]) == 1:
+        # single node at center
+        radius = 0.0
+    else:
+        # else start at r=1
+        radius = radius_bump
+
+    if rotate is None:
+        rotate = np.pi / len(nlist)
+    first_theta = rotate
+    npos = {}
+    for nodes in nlist:
+        # Discard the last angle (endpoint=False) since 2*pi matches 0 radians
+        theta = (
+            np.linspace(0, 2 * np.pi, len(nodes), endpoint=False, dtype=np.float32)
+            + first_theta
+        )
+        pos = radius * np.column_stack([np.cos(theta), np.sin(theta)]) + center
+        npos.update(zip(nodes, pos))
+        radius += radius_bump
+        first_theta += rotate
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, npos, store_pos_as)
+    return npos
+
+
+def bipartite_layout(
+    G,
+    nodes=None,
+    align="vertical",
+    scale=1,
+    center=None,
+    aspect_ratio=4 / 3,
+    store_pos_as=None,
+):
+    """Position nodes in two straight lines.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    nodes : collection of nodes
+        Nodes in one node set of the graph. This set will be placed on
+        left or top. If `None` (the default), a node set is chosen arbitrarily
+        if the graph if bipartite.
+
+    align : string (default='vertical')
+        The alignment of nodes. Vertical or horizontal.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    aspect_ratio : number (default=4/3):
+        The ratio of the width to the height of the layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Raises
+    ------
+    NetworkXError
+        If ``nodes=None`` and `G` is not bipartite.
+
+    Examples
+    --------
+    >>> G = nx.complete_bipartite_graph(3, 3)
+    >>> pos = nx.bipartite_layout(G)
+
+    The ordering of the layout (i.e. which nodes appear on the left/top) can
+    be specified with the `nodes` parameter:
+
+    >>> top, bottom = nx.bipartite.sets(G)
+    >>> pos = nx.bipartite_layout(G, nodes=bottom)  # "bottom" set appears on the left
+
+    `store_pos_as` can be used to store the node positions for the computed layout
+    directly on the nodes:
+
+    >>> _ = nx.bipartite_layout(G, nodes=bottom, store_pos_as="pos")
+    >>> from pprint import pprint
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([ 1.  , -0.75]),
+     1: array([1., 0.]),
+     2: array([1.  , 0.75]),
+     3: array([-1.  , -0.75]),
+     4: array([-1.,  0.]),
+     5: array([-1.  ,  0.75])}
+
+
+    The ``bipartite_layout`` function can be used with non-bipartite graphs
+    by explicitly specifying how the layout should be partitioned with `nodes`:
+
+    >>> G = nx.complete_graph(5)  # Non-bipartite
+    >>> pos = nx.bipartite_layout(G, nodes={0, 1, 2})
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+
+    import numpy as np
+
+    if align not in ("vertical", "horizontal"):
+        msg = "align must be either vertical or horizontal."
+        raise ValueError(msg)
+
+    G, center = _process_params(G, center=center, dim=2)
+    if len(G) == 0:
+        return {}
+
+    height = 1
+    width = aspect_ratio * height
+    offset = (width / 2, height / 2)
+
+    if nodes is None:
+        top, bottom = nx.bipartite.sets(G)
+        nodes = list(G)
+    else:
+        top = set(nodes)
+        bottom = set(G) - top
+        # Preserves backward-compatible node ordering in returned pos dict
+        nodes = list(top) + list(bottom)
+
+    left_xs = np.repeat(0, len(top))
+    right_xs = np.repeat(width, len(bottom))
+    left_ys = np.linspace(0, height, len(top))
+    right_ys = np.linspace(0, height, len(bottom))
+
+    top_pos = np.column_stack([left_xs, left_ys]) - offset
+    bottom_pos = np.column_stack([right_xs, right_ys]) - offset
+
+    pos = np.concatenate([top_pos, bottom_pos])
+    pos = rescale_layout(pos, scale=scale) + center
+    if align == "horizontal":
+        pos = pos[:, ::-1]  # swap x and y coords
+    pos = dict(zip(nodes, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+@np_random_state("seed")
+def spring_layout(
+    G,
+    k=None,
+    pos=None,
+    fixed=None,
+    iterations=50,
+    threshold=1e-4,
+    weight="weight",
+    scale=1,
+    center=None,
+    dim=2,
+    seed=None,
+    store_pos_as=None,
+    *,
+    method="auto",
+    gravity=1.0,
+):
+    """Position nodes using Fruchterman-Reingold force-directed algorithm.
+
+    The algorithm simulates a force-directed representation of the network
+    treating edges as springs holding nodes close, while treating nodes
+    as repelling objects, sometimes called an anti-gravity force.
+    Simulation continues until the positions are close to an equilibrium.
+
+    There are some hard-coded values: minimal distance between
+    nodes (0.01) and "temperature" of 0.1 to ensure nodes don't fly away.
+    During the simulation, `k` helps determine the distance between nodes,
+    though `scale` and `center` determine the size and place after
+    rescaling occurs at the end of the simulation.
+
+    Fixing some nodes doesn't allow them to move in the simulation.
+    It also turns off the rescaling feature at the simulation's end.
+    In addition, setting `scale` to `None` turns off rescaling.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    k : float (default=None)
+        Optimal distance between nodes.  If None the distance is set to
+        1/sqrt(n) where n is the number of nodes.  Increase this value
+        to move nodes farther apart.
+
+    pos : dict or None  optional (default=None)
+        Initial positions for nodes as a dictionary with node as keys
+        and values as a coordinate list or tuple.  If None, then use
+        random initial positions.
+
+    fixed : list or None  optional (default=None)
+        Nodes to keep fixed at initial position.
+        Nodes not in ``G.nodes`` are ignored.
+        ValueError raised if `fixed` specified and `pos` not.
+
+    iterations : int  optional (default=50)
+        Maximum number of iterations taken
+
+    threshold: float optional (default = 1e-4)
+        Threshold for relative error in node position changes.
+        The iteration stops if the error is below this threshold.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  Larger means a stronger attractive force.
+        If None, then all edge weights are 1.
+
+    scale : number or None (default: 1)
+        Scale factor for positions. Not used unless `fixed is None`.
+        If scale is None, no rescaling is performed.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+        Not used unless `fixed is None`.
+
+    dim : int
+        Dimension of layout.
+
+    seed : int, RandomState instance or None  optional (default=None)
+        Used only for the initial positions in the algorithm.
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    method : str  optional (default='auto')
+        The method to compute the layout.
+        If 'force', the force-directed Fruchterman-Reingold algorithm [1]_ is used.
+        If 'energy', the energy-based optimization algorithm [2]_ is used with absolute
+        values of edge weights and gravitational forces acting on each connected component.
+        If 'auto', we use 'force' if ``len(G) < 500`` and 'energy' otherwise.
+
+    gravity: float optional (default=1.0)
+        Used only for the method='energy'.
+        The positive coefficient of gravitational forces per connected component.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spring_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.spring_layout(G, seed=123, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-0.61495802, -1.        ]),
+     1: array([-0.21789544, -0.35432583]),
+     2: array([0.21847843, 0.35527369]),
+     3: array([0.61437502, 0.99905215])}
+
+
+    # The same using longer but equivalent function name
+    >>> pos = nx.fruchterman_reingold_layout(G)
+
+    References
+    ----------
+    .. [1] Fruchterman, Thomas MJ, and Edward M. Reingold.
+           "Graph drawing by force-directed placement."
+           Software: Practice and experience 21, no. 11 (1991): 1129-1164.
+           http://dx.doi.org/10.1002/spe.4380211102
+    .. [2] Hamaguchi, Hiroki, Naoki Marumo, and Akiko Takeda.
+           "Initial Placement for Fruchterman--Reingold Force Model With Coordinate Newton Direction."
+           arXiv preprint arXiv:2412.20317 (2024).
+           https://arxiv.org/abs/2412.20317
+    """
+    import numpy as np
+
+    if method not in ("auto", "force", "energy"):
+        raise ValueError("the method must be either auto, force, or energy.")
+    if method == "auto":
+        method = "force" if len(G) < 500 else "energy"
+
+    G, center = _process_params(G, center, dim)
+
+    if fixed is not None:
+        if pos is None:
+            raise ValueError("nodes are fixed without positions given")
+        for node in fixed:
+            if node not in pos:
+                raise ValueError("nodes are fixed without positions given")
+        nfixed = {node: i for i, node in enumerate(G)}
+        fixed = np.asarray([nfixed[node] for node in fixed if node in nfixed])
+
+    if pos is not None:
+        # Determine size of existing domain to adjust initial positions
+        dom_size = max(coord for pos_tup in pos.values() for coord in pos_tup)
+        if dom_size == 0:
+            dom_size = 1
+        pos_arr = seed.rand(len(G), dim) * dom_size + center
+
+        for i, n in enumerate(G):
+            if n in pos:
+                pos_arr[i] = np.asarray(pos[n])
+    else:
+        pos_arr = None
+        dom_size = 1
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G.nodes()): center}
+        if store_pos_as is not None:
+            nx.set_node_attributes(G, pos, store_pos_as)
+        return pos
+
+    # Sparse matrix
+    if len(G) >= 500 or method == "energy":
+        A = nx.to_scipy_sparse_array(G, weight=weight, dtype="f")
+        if k is None and fixed is not None:
+            # We must adjust k by domain size for layouts not near 1x1
+            nnodes, _ = A.shape
+            k = dom_size / np.sqrt(nnodes)
+        pos = _sparse_fruchterman_reingold(
+            A, k, pos_arr, fixed, iterations, threshold, dim, seed, method, gravity
+        )
+    else:
+        A = nx.to_numpy_array(G, weight=weight)
+        if k is None and fixed is not None:
+            # We must adjust k by domain size for layouts not near 1x1
+            nnodes, _ = A.shape
+            k = dom_size / np.sqrt(nnodes)
+        pos = _fruchterman_reingold(
+            A, k, pos_arr, fixed, iterations, threshold, dim, seed
+        )
+    if fixed is None and scale is not None:
+        pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+fruchterman_reingold_layout = spring_layout
+
+
+@np_random_state(7)
+def _fruchterman_reingold(
+    A, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, dim=2, seed=None
+):
+    # Position nodes in adjacency matrix A using Fruchterman-Reingold
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    import numpy as np
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "fruchterman_reingold() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    if pos is None:
+        # random initial positions
+        pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
+    else:
+        # make sure positions are of same type as matrix
+        pos = pos.astype(A.dtype)
+
+    # optimal distance between nodes
+    if k is None:
+        k = np.sqrt(1.0 / nnodes)
+    # the initial "temperature"  is about .1 of domain area (=1x1)
+    # this is the largest step allowed in the dynamics.
+    # We need to calculate this in case our fixed positions force our domain
+    # to be much bigger than 1x1
+    t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1
+    # simple cooling scheme.
+    # linearly step down by dt on each iteration so last iteration is size dt.
+    dt = t / (iterations + 1)
+    delta = np.zeros((pos.shape[0], pos.shape[0], pos.shape[1]), dtype=A.dtype)
+    # the inscrutable (but fast) version
+    # this is still O(V^2)
+    # could use multilevel methods to speed this up significantly
+    for iteration in range(iterations):
+        # matrix of difference between points
+        delta = pos[:, np.newaxis, :] - pos[np.newaxis, :, :]
+        # distance between points
+        distance = np.linalg.norm(delta, axis=-1)
+        # enforce minimum distance of 0.01
+        np.clip(distance, 0.01, None, out=distance)
+        # displacement "force"
+        displacement = np.einsum(
+            "ijk,ij->ik", delta, (k * k / distance**2 - A * distance / k)
+        )
+        # update positions
+        length = np.linalg.norm(displacement, axis=-1)
+        # Threshold the minimum length prior to position scaling
+        # See gh-8113 for detailed discussion of the threshold
+        length = np.clip(length, a_min=0.01, a_max=None)
+        delta_pos = np.einsum("ij,i->ij", displacement, t / length)
+        if fixed is not None:
+            # don't change positions of fixed nodes
+            delta_pos[fixed] = 0.0
+        pos += delta_pos
+        # cool temperature
+        t -= dt
+        if (np.linalg.norm(delta_pos) / nnodes) < threshold:
+            break
+    return pos
+
+
+@np_random_state(7)
+def _sparse_fruchterman_reingold(
+    A,
+    k=None,
+    pos=None,
+    fixed=None,
+    iterations=50,
+    threshold=1e-4,
+    dim=2,
+    seed=None,
+    method="energy",
+    gravity=1.0,
+):
+    # Position nodes in adjacency matrix A using Fruchterman-Reingold
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    # Sparse version
+    import numpy as np
+    import scipy as sp
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "fruchterman_reingold() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    if pos is None:
+        # random initial positions
+        pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
+    else:
+        # make sure positions are of same type as matrix
+        pos = pos.astype(A.dtype)
+
+    # no fixed nodes
+    if fixed is None:
+        fixed = []
+
+    # optimal distance between nodes
+    if k is None:
+        k = np.sqrt(1.0 / nnodes)
+
+    if method == "energy":
+        return _energy_fruchterman_reingold(
+            A, nnodes, k, pos, fixed, iterations, threshold, dim, gravity
+        )
+
+    # make sure we have a LIst of Lists representation
+    try:
+        A = A.tolil()
+    except AttributeError:
+        A = (sp.sparse.coo_array(A)).tolil()
+
+    # the initial "temperature"  is about .1 of domain area (=1x1)
+    # this is the largest step allowed in the dynamics.
+    t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1
+    # simple cooling scheme.
+    # linearly step down by dt on each iteration so last iteration is size dt.
+    dt = t / (iterations + 1)
+
+    displacement = np.zeros((dim, nnodes))
+    for iteration in range(iterations):
+        displacement *= 0
+        # loop over rows
+        for i in range(A.shape[0]):
+            if i in fixed:
+                continue
+            # difference between this row's node position and all others
+            delta = (pos[i] - pos).T
+            # distance between points
+            distance = np.sqrt((delta**2).sum(axis=0))
+            # enforce minimum distance of 0.01
+            distance = np.clip(distance, a_min=0.01, a_max=None)
+            # the adjacency matrix row
+            Ai = A.getrowview(i).toarray()  # TODO: revisit w/ sparse 1D container
+            # displacement "force"
+            displacement[:, i] += (
+                delta * (k * k / distance**2 - Ai * distance / k)
+            ).sum(axis=1)
+        # update positions
+        length = np.sqrt((displacement**2).sum(axis=0))
+        # Threshold the minimum length prior to position scaling
+        # See gh-8113 for detailed discussion of the threshold
+        length = np.clip(length, a_min=0.01, a_max=None)
+        delta_pos = (displacement * t / length).T
+        pos += delta_pos
+        # cool temperature
+        t -= dt
+        if (np.linalg.norm(delta_pos) / nnodes) < threshold:
+            break
+    return pos
+
+
+def _energy_fruchterman_reingold(
+    A, nnodes, k, pos, fixed, iterations, threshold, dim, gravity
+):
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    # energy-based version
+    import numpy as np
+    import scipy as sp
+
+    if gravity <= 0:
+        raise ValueError(f"the gravity must be positive.")
+
+    # make sure we have a Compressed Sparse Row format
+    try:
+        A = A.tocsr()
+    except AttributeError:
+        A = sp.sparse.csr_array(A)
+
+    # Take absolute values of edge weights and symmetrize it
+    A = np.abs(A)
+    A = (A + A.T) / 2
+
+    n_components, labels = sp.sparse.csgraph.connected_components(A, directed=False)
+    bincount = np.bincount(labels)
+    batchsize = 500
+
+    def _cost_FR(x):
+        pos = x.reshape((nnodes, dim))
+        grad = np.zeros((nnodes, dim))
+        cost = 0.0
+        for l in range(0, nnodes, batchsize):
+            r = min(l + batchsize, nnodes)
+            # difference between selected node positions and all others
+            delta = pos[l:r, np.newaxis, :] - pos[np.newaxis, :, :]
+            # distance between points with a minimum distance of 1e-5
+            distance2 = np.sum(delta * delta, axis=2)
+            distance2 = np.maximum(distance2, 1e-10)
+            distance = np.sqrt(distance2)
+            # temporary variable for calculation
+            Ad = A[l:r] * distance
+            # attractive forces and repulsive forces
+            grad[l:r] = 2 * np.einsum("ij,ijk->ik", Ad / k - k**2 / distance2, delta)
+            # integrated attractive forces
+            cost += np.sum(Ad * distance2) / (3 * k)
+            # integrated repulsive forces
+            cost -= k**2 * np.sum(np.log(distance))
+        # gravitational force from the centroids of connected components to (0.5, ..., 0.5)^T
+        centers = np.zeros((n_components, dim))
+        np.add.at(centers, labels, pos)
+        delta0 = centers / bincount[:, np.newaxis] - 0.5
+        grad += gravity * delta0[labels]
+        cost += gravity * 0.5 * np.sum(bincount * np.linalg.norm(delta0, axis=1) ** 2)
+        # fix positions of fixed nodes
+        grad[fixed] = 0.0
+        return cost, grad.ravel()
+
+    # Optimization of the energy function by L-BFGS algorithm
+    options = {"maxiter": iterations, "gtol": threshold}
+    return sp.optimize.minimize(
+        _cost_FR, pos.ravel(), method="L-BFGS-B", jac=True, options=options
+    ).x.reshape((nnodes, dim))
+
+
+def kamada_kawai_layout(
+    G,
+    dist=None,
+    pos=None,
+    weight="weight",
+    scale=1,
+    center=None,
+    dim=2,
+    store_pos_as=None,
+):
+    """Position nodes using Kamada-Kawai path-length cost-function.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    dist : dict (default=None)
+        A two-level dictionary of optimal distances between nodes,
+        indexed by source and destination node.
+        If None, the distance is computed using shortest_path_length().
+
+    pos : dict or None  optional (default=None)
+        Initial positions for nodes as a dictionary with node as keys
+        and values as a coordinate list or tuple.  If None, then use
+        circular_layout() for dim >= 2 and a linear layout for dim == 1.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None, then all edge weights are 1.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.kamada_kawai_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.kamada_kawai_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([0.99996577, 0.99366857]),
+     1: array([0.32913544, 0.33543827]),
+     2: array([-0.33544334, -0.32910684]),
+     3: array([-0.99365787, -1.        ])}
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+    nNodes = len(G)
+    if nNodes == 0:
+        return {}
+
+    if dist is None:
+        dist = dict(nx.shortest_path_length(G, weight=weight))
+    dist_mtx = 1e6 * np.ones((nNodes, nNodes))
+    for row, nr in enumerate(G):
+        if nr not in dist:
+            continue
+        rdist = dist[nr]
+        for col, nc in enumerate(G):
+            if nc not in rdist:
+                continue
+            dist_mtx[row][col] = rdist[nc]
+
+    if pos is None:
+        if dim >= 3:
+            pos = random_layout(G, dim=dim)
+        elif dim == 2:
+            pos = circular_layout(G, dim=dim)
+        else:
+            pos = dict(zip(G, np.linspace(0, 1, len(G))))
+    pos_arr = np.array([pos[n] for n in G])
+
+    pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim)
+
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def _kamada_kawai_solve(dist_mtx, pos_arr, dim):
+    # Anneal node locations based on the Kamada-Kawai cost-function,
+    # using the supplied matrix of preferred inter-node distances,
+    # and starting locations.
+
+    import numpy as np
+    import scipy as sp
+
+    meanwt = 1e-3
+    costargs = (np, 1 / (dist_mtx + np.eye(dist_mtx.shape[0]) * 1e-3), meanwt, dim)
+
+    optresult = sp.optimize.minimize(
+        _kamada_kawai_costfn,
+        pos_arr.ravel(),
+        method="L-BFGS-B",
+        args=costargs,
+        jac=True,
+    )
+
+    return optresult.x.reshape((-1, dim))
+
+
+def _kamada_kawai_costfn(pos_vec, np, invdist, meanweight, dim):
+    # Cost-function and gradient for Kamada-Kawai layout algorithm
+    nNodes = invdist.shape[0]
+    pos_arr = pos_vec.reshape((nNodes, dim))
+
+    delta = pos_arr[:, np.newaxis, :] - pos_arr[np.newaxis, :, :]
+    nodesep = np.linalg.norm(delta, axis=-1)
+    direction = np.einsum("ijk,ij->ijk", delta, 1 / (nodesep + np.eye(nNodes) * 1e-3))
+
+    offset = nodesep * invdist - 1.0
+    offset[np.diag_indices(nNodes)] = 0
+
+    cost = 0.5 * np.sum(offset**2)
+    grad = np.einsum("ij,ij,ijk->ik", invdist, offset, direction) - np.einsum(
+        "ij,ij,ijk->jk", invdist, offset, direction
+    )
+
+    # Additional parabolic term to encourage mean position to be near origin:
+    sumpos = np.sum(pos_arr, axis=0)
+    cost += 0.5 * meanweight * np.sum(sumpos**2)
+    grad += meanweight * sumpos
+
+    return (cost, grad.ravel())
+
+
+def spectral_layout(G, weight="weight", scale=1, center=None, dim=2, store_pos_as=None):
+    """Position nodes using the eigenvectors of the graph Laplacian.
+
+    Using the unnormalized Laplacian, the layout shows possible clusters of
+    nodes which are an approximation of the ratio cut. If dim is the number of
+    dimensions then the positions are the entries of the dim eigenvectors
+    corresponding to the ascending eigenvalues starting from the second one.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None, then all edge weights are 1.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spectral_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.spectral_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-1.        ,  0.76536686]),
+     1: array([-0.41421356, -0.76536686]),
+     2: array([ 0.41421356, -0.76536686]),
+     3: array([1.        , 0.76536686])}
+
+
+    Notes
+    -----
+    Directed graphs will be considered as undirected graphs when
+    positioning the nodes.
+
+    For larger graphs (>500 nodes) this will use the SciPy sparse
+    eigenvalue solver (ARPACK).
+    """
+    # handle some special cases that break the eigensolvers
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) <= 2:
+        if len(G) == 0:
+            pos = np.array([])
+        elif len(G) == 1:
+            pos = np.array([center])
+        else:
+            pos = np.array([np.zeros(dim), np.array(center) * 2.0])
+        return dict(zip(G, pos))
+    try:
+        # Sparse matrix
+        if len(G) < 500:  # dense solver is faster for small graphs
+            raise ValueError
+        A = nx.to_scipy_sparse_array(G, weight=weight, dtype="d")
+        # Symmetrize directed graphs
+        if G.is_directed():
+            A = A + np.transpose(A)
+        pos = _sparse_spectral(A, dim)
+    except (ImportError, ValueError):
+        # Dense matrix
+        A = nx.to_numpy_array(G, weight=weight)
+        # Symmetrize directed graphs
+        if G.is_directed():
+            A += A.T
+        pos = _spectral(A, dim)
+
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def _spectral(A, dim=2):
+    # Input adjacency matrix A
+    # Uses dense eigenvalue solver from numpy
+    import numpy as np
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "spectral() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    # form Laplacian matrix where D is diagonal of degrees
+    D = np.identity(nnodes, dtype=A.dtype) * np.sum(A, axis=1)
+    L = D - A
+
+    eigenvalues, eigenvectors = np.linalg.eig(L)
+    # sort and keep smallest nonzero
+    index = np.argsort(eigenvalues)[1 : dim + 1]  # 0 index is zero eigenvalue
+    return np.real(eigenvectors[:, index])
+
+
+def _sparse_spectral(A, dim=2):
+    # Input adjacency matrix A
+    # Uses sparse eigenvalue solver from scipy
+    # Could use multilevel methods here, see Koren "On spectral graph drawing"
+    import numpy as np
+    import scipy as sp
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "sparse_spectral() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    # form Laplacian matrix
+    D = sp.sparse.dia_array((A.sum(axis=1), 0), shape=(nnodes, nnodes)).tocsr()
+    L = D - A
+
+    k = dim + 1
+    # number of Lanczos vectors for ARPACK solver.What is the right scaling?
+    ncv = max(2 * k + 1, int(np.sqrt(nnodes)))
+    # return smallest k eigenvalues and eigenvectors
+    eigenvalues, eigenvectors = sp.sparse.linalg.eigsh(L, k, which="SM", ncv=ncv)
+    index = np.argsort(eigenvalues)[1:k]  # 0 index is zero eigenvalue
+    return np.real(eigenvectors[:, index])
+
+
+def planar_layout(G, scale=1, center=None, dim=2, store_pos_as=None):
+    """Position nodes without edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G. If G is of type
+        nx.PlanarEmbedding, the positions are selected accordingly.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    NetworkXException
+        If G is not planar
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.planar_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.planar_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-0.77777778, -0.33333333]),
+     1: array([ 1.        , -0.33333333]),
+     2: array([0.11111111, 0.55555556]),
+     3: array([-0.33333333,  0.11111111])}
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+
+    if isinstance(G, nx.PlanarEmbedding):
+        embedding = G
+    else:
+        is_planar, embedding = nx.check_planarity(G)
+        if not is_planar:
+            raise nx.NetworkXException("G is not planar.")
+    pos = nx.combinatorial_embedding_to_pos(embedding)
+    node_list = list(embedding)
+    pos = np.vstack([pos[x] for x in node_list])
+    pos = pos.astype(np.float64)
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(node_list, pos))
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+    return pos
+
+
+def spiral_layout(
+    G,
+    scale=1,
+    center=None,
+    dim=2,
+    resolution=0.35,
+    equidistant=False,
+    store_pos_as=None,
+):
+    """Position nodes in a spiral layout.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int, default=2
+        Dimension of layout, currently only dim=2 is supported.
+        Other dimension values result in a ValueError.
+
+    resolution : float, default=0.35
+        The compactness of the spiral layout returned.
+        Lower values result in more compressed spiral layouts.
+
+    equidistant : bool, default=False
+        If True, nodes will be positioned equidistant from each other
+        by decreasing angle further from center.
+        If False, nodes will be positioned at equal angles
+        from each other by increasing separation further from center.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim != 2
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spiral_layout(G)
+    >>> nx.draw(G, pos=pos)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.spiral_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-0.64153279, -0.68555087]),
+     1: array([-0.03307913, -0.46344795]),
+     2: array([0.34927952, 0.14899882]),
+     3: array([0.32533239, 1.        ])}
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions.
+
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G): center}
+        if store_pos_as is not None:
+            nx.set_node_attributes(G, pos, store_pos_as)
+        return pos
+
+    pos = []
+    if equidistant:
+        chord = 1
+        step = 0.5
+        theta = resolution
+        theta += chord / (step * theta)
+        for _ in range(len(G)):
+            r = step * theta
+            theta += chord / r
+            pos.append([np.cos(theta) * r, np.sin(theta) * r])
+
+    else:
+        dist = np.arange(len(G), dtype=float)
+        angle = resolution * dist
+        pos = np.transpose(dist * np.array([np.cos(angle), np.sin(angle)]))
+
+    pos = rescale_layout(np.array(pos), scale=scale) + center
+
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def multipartite_layout(
+    G, subset_key="subset", align="vertical", scale=1, center=None, store_pos_as=None
+):
+    """Position nodes in layers of straight lines.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    subset_key : string or dict (default='subset')
+        If a string, the key of node data in G that holds the node subset.
+        If a dict, keyed by layer number to the nodes in that layer/subset.
+
+    align : string (default='vertical')
+        The alignment of nodes. Vertical or horizontal.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_multipartite_graph(28, 16, 10)
+    >>> pos = nx.multipartite_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> G = nx.complete_multipartite_graph(28, 16, 10)
+    >>> _ = nx.multipartite_layout(G, store_pos_as="pos")
+
+    or use a dict to provide the layers of the layout
+
+    >>> G = nx.Graph([(0, 1), (1, 2), (1, 3), (3, 4)])
+    >>> layers = {"a": [0], "b": [1], "c": [2, 3], "d": [4]}
+    >>> pos = nx.multipartite_layout(G, subset_key=layers)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    Network does not need to be a complete multipartite graph. As long as nodes
+    have subset_key data, they will be placed in the corresponding layers.
+
+    """
+    import numpy as np
+
+    if align not in ("vertical", "horizontal"):
+        msg = "align must be either vertical or horizontal."
+        raise ValueError(msg)
+
+    G, center = _process_params(G, center=center, dim=2)
+    if len(G) == 0:
+        return {}
+
+    try:
+        # check if subset_key is dict-like
+        if len(G) != sum(len(nodes) for nodes in subset_key.values()):
+            raise nx.NetworkXError(
+                "all nodes must be in one subset of `subset_key` dict"
+            )
+    except AttributeError:
+        # subset_key is not a dict, hence a string
+        node_to_subset = nx.get_node_attributes(G, subset_key)
+        if len(node_to_subset) != len(G):
+            raise nx.NetworkXError(
+                f"all nodes need a subset_key attribute: {subset_key}"
+            )
+        subset_key = nx.utils.groups(node_to_subset)
+
+    # Sort by layer, if possible
+    try:
+        layers = dict(sorted(subset_key.items()))
+    except TypeError:
+        layers = subset_key
+
+    pos = None
+    nodes = []
+    width = len(layers)
+    for i, layer in enumerate(layers.values()):
+        height = len(layer)
+        xs = np.repeat(i, height)
+        ys = np.arange(0, height, dtype=float)
+        offset = ((width - 1) / 2, (height - 1) / 2)
+        layer_pos = np.column_stack([xs, ys]) - offset
+        if pos is None:
+            pos = layer_pos
+        else:
+            pos = np.concatenate([pos, layer_pos])
+        nodes.extend(layer)
+    pos = rescale_layout(pos, scale=scale) + center
+    if align == "horizontal":
+        pos = pos[:, ::-1]  # swap x and y coords
+    pos = dict(zip(nodes, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+@np_random_state("seed")
+def arf_layout(
+    G,
+    pos=None,
+    scaling=1,
+    a=1.1,
+    etol=1e-6,
+    dt=1e-3,
+    max_iter=1000,
+    *,
+    seed=None,
+    store_pos_as=None,
+):
+    """Arf layout for networkx
+
+    The attractive and repulsive forces (arf) layout [1] improves the spring
+    layout in three ways. First, it prevents congestion of highly connected nodes
+    due to strong forcing between nodes. Second, it utilizes the layout space
+    more effectively by preventing large gaps that spring layout tends to create.
+    Lastly, the arf layout represents symmetries in the layout better than the
+    default spring layout.
+
+    Parameters
+    ----------
+    G : nx.Graph or nx.DiGraph
+        Networkx graph.
+    pos : dict
+        Initial  position of  the nodes.  If set  to None  a
+        random layout will be used.
+    scaling : float
+        Scales the radius of the circular layout space.
+    a : float
+        Strength of springs between connected nodes. Should be larger than 1.
+        The greater a, the clearer the separation of unconnected sub clusters.
+    etol : float
+        Gradient sum of spring forces must be larger than `etol` before successful
+        termination.
+    dt : float
+        Time step for force differential equation simulations.
+    max_iter : int
+        Max iterations before termination of the algorithm.
+    seed : int, RandomState instance or None  optional (default=None)
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.grid_graph((5, 5))
+    >>> pos = nx.arf_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> G = nx.grid_graph((5, 5))
+    >>> _ = nx.arf_layout(G, store_pos_as="pos")
+
+    References
+    ----------
+    .. [1] "Self-Organization Applied to Dynamic Network Layout", M. Geipel,
+            International Journal of Modern Physics C, 2007, Vol 18, No 10,
+            pp. 1537-1549.
+            https://doi.org/10.1142/S0129183107011558 https://arxiv.org/abs/0704.1748
+    """
+    import warnings
+
+    import numpy as np
+
+    if a <= 1:
+        msg = "The parameter a should be larger than 1"
+        raise ValueError(msg)
+
+    pos_tmp = nx.random_layout(G, seed=seed)
+    if pos is None:
+        pos = pos_tmp
+    else:
+        for node in G.nodes():
+            if node not in pos:
+                pos[node] = pos_tmp[node].copy()
+
+    # Initialize spring constant matrix
+    N = len(G)
+    # No nodes no computation
+    if N == 0:
+        return pos
+
+    # init force of springs
+    K = np.ones((N, N)) - np.eye(N)
+    node_order = {node: i for i, node in enumerate(G)}
+    for x, y in G.edges():
+        if x != y:
+            idx, jdx = (node_order[i] for i in (x, y))
+            K[idx, jdx] = a
+
+    # vectorize values
+    p = np.asarray(list(pos.values()))
+
+    # equation 10 in [1]
+    rho = scaling * np.sqrt(N)
+
+    # looping variables
+    error = etol + 1
+    n_iter = 0
+    while error > etol:
+        diff = p[:, np.newaxis] - p[np.newaxis]
+        A = np.linalg.norm(diff, axis=-1)[..., np.newaxis]
+        # attraction_force - repulsions force
+        # suppress nans due to division; caused by diagonal set to zero.
+        # Does not affect the computation due to nansum
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            change = K[..., np.newaxis] * diff - rho / A * diff
+        change = np.nansum(change, axis=0)
+        p += change * dt
+
+        error = np.linalg.norm(change, axis=-1).sum()
+        if n_iter > max_iter:
+            break
+        n_iter += 1
+
+    pos = dict(zip(G.nodes(), p))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+@np_random_state("seed")
+@nx._dispatchable(edge_attrs="weight", mutates_input={"store_pos_as": 15})
+def forceatlas2_layout(
+    G,
+    pos=None,
+    *,
+    max_iter=100,
+    jitter_tolerance=1.0,
+    scaling_ratio=2.0,
+    gravity=1.0,
+    distributed_action=False,
+    strong_gravity=False,
+    node_mass=None,
+    node_size=None,
+    weight=None,
+    linlog=False,
+    seed=None,
+    dim=2,
+    store_pos_as=None,
+):
+    """Position nodes using the ForceAtlas2 force-directed layout algorithm.
+
+    This function applies the ForceAtlas2 layout algorithm [1]_ to a NetworkX graph,
+    positioning the nodes in a way that visually represents the structure of the graph.
+    The algorithm uses physical simulation to minimize the energy of the system,
+    resulting in a more readable layout.
+
+    Parameters
+    ----------
+    G : nx.Graph
+        A NetworkX graph to be laid out.
+    pos : dict or None, optional
+        Initial positions of the nodes. If None, random initial positions are used.
+    max_iter : int (default: 100)
+        Number of iterations for the layout optimization.
+    jitter_tolerance : float (default: 1.0)
+        Controls the tolerance for adjusting the speed of layout generation.
+    scaling_ratio : float (default: 2.0)
+        Determines the scaling of attraction and repulsion forces.
+    gravity : float (default: 1.0)
+        Determines the amount of attraction on nodes to the center. Prevents islands
+        (i.e. weakly connected or disconnected parts of the graph)
+        from drifting away.
+    distributed_action : bool (default: False)
+        Distributes the attraction force evenly among nodes.
+    strong_gravity : bool (default: False)
+        Applies a strong gravitational pull towards the center.
+    node_mass : dict or None, optional
+        Maps nodes to their masses, influencing the attraction to other nodes.
+    node_size : dict or None, optional
+        Maps nodes to their sizes, preventing crowding by creating a halo effect.
+    weight : string or None, optional (default: None)
+        The edge attribute that holds the numerical value used for
+        the edge weight. If None, then all edge weights are 1.
+    linlog : bool (default: False)
+        Uses logarithmic attraction instead of linear.
+    seed : int, RandomState instance or None  optional (default=None)
+        Used only for the initial positions in the algorithm.
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+    dim : int (default: 2)
+        Sets the dimensions for the layout. Ignored if `pos` is provided.
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Examples
+    --------
+    >>> import networkx as nx
+    >>> G = nx.florentine_families_graph()
+    >>> pos = nx.forceatlas2_layout(G)
+    >>> nx.draw(G, pos=pos)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> pos = nx.forceatlas2_layout(G, store_pos_as="pos")
+    >>> _ = nx.forceatlas2_layout(G, store_pos_as="pos")
+
+    References
+    ----------
+    .. [1] Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014).
+           ForceAtlas2, a continuous graph layout algorithm for handy network
+           visualization designed for the Gephi software. PloS one, 9(6), e98679.
+           https://doi.org/10.1371/journal.pone.0098679
+    """
+    import numpy as np
+
+    if len(G) == 0:
+        return {}
+    # parse optional pos positions
+    if pos is None:
+        pos = nx.random_layout(G, dim=dim, seed=seed)
+        pos_arr = np.array(list(pos.values()))
+    elif len(pos) == len(G):
+        pos_arr = np.array([pos[node].copy() for node in G])
+    else:
+        # set random node pos within the initial pos values
+        pos_init = np.array(list(pos.values()))
+        max_pos = pos_init.max(axis=0)
+        min_pos = pos_init.min(axis=0)
+        dim = max_pos.size
+        pos_arr = min_pos + seed.rand(len(G), dim) * (max_pos - min_pos)
+        for idx, node in enumerate(G):
+            if node in pos:
+                pos_arr[idx] = pos[node].copy()
+
+    mass = np.zeros(len(G))
+    size = np.zeros(len(G))
+
+    # Only adjust for size when the users specifies size other than default (1)
+    adjust_sizes = False
+    if node_size is None:
+        node_size = {}
+    else:
+        adjust_sizes = True
+
+    if node_mass is None:
+        node_mass = {}
+
+    for idx, node in enumerate(G):
+        mass[idx] = node_mass.get(node, G.degree(node) + 1)
+        size[idx] = node_size.get(node, 1)
+
+    n = len(G)
+    gravities = np.zeros((n, dim))
+    attraction = np.zeros((n, dim))
+    repulsion = np.zeros((n, dim))
+    A = nx.to_numpy_array(G, weight=weight)
+
+    def estimate_factor(n, swing, traction, speed, speed_efficiency, jitter_tolerance):
+        """Computes the scaling factor for the force in the ForceAtlas2 layout algorithm.
+
+        This   helper  function   adjusts   the  speed   and
+        efficiency  of the  layout generation  based on  the
+        current state of  the system, such as  the number of
+        nodes, current swing, and traction forces.
+
+        Parameters
+        ----------
+        n : int
+            Number of nodes in the graph.
+        swing : float
+            The current swing, representing the oscillation of the nodes.
+        traction : float
+            The current traction force, representing the attraction between nodes.
+        speed : float
+            The current speed of the layout generation.
+        speed_efficiency : float
+            The efficiency of the current speed, influencing how fast the layout converges.
+        jitter_tolerance : float
+            The tolerance for jitter, affecting how much speed adjustment is allowed.
+
+        Returns
+        -------
+        tuple
+            A tuple containing the updated speed and speed efficiency.
+
+        Notes
+        -----
+        This function is a part of the ForceAtlas2 layout algorithm and is used to dynamically adjust the
+        layout parameters to achieve an optimal and stable visualization.
+
+        """
+        import numpy as np
+
+        # estimate jitter
+        opt_jitter = 0.05 * np.sqrt(n)
+        min_jitter = np.sqrt(opt_jitter)
+        max_jitter = 10
+        min_speed_efficiency = 0.05
+
+        other = min(max_jitter, opt_jitter * traction / n**2)
+        jitter = jitter_tolerance * max(min_jitter, other)
+
+        if swing / traction > 2.0:
+            if speed_efficiency > min_speed_efficiency:
+                speed_efficiency *= 0.5
+            jitter = max(jitter, jitter_tolerance)
+        if swing == 0:
+            target_speed = np.inf
+        else:
+            target_speed = jitter * speed_efficiency * traction / swing
+
+        if swing > jitter * traction:
+            if speed_efficiency > min_speed_efficiency:
+                speed_efficiency *= 0.7
+        elif speed < 1000:
+            speed_efficiency *= 1.3
+
+        max_rise = 0.5
+        speed = speed + min(target_speed - speed, max_rise * speed)
+        return speed, speed_efficiency
+
+    speed = 1
+    speed_efficiency = 1
+    swing = 1
+    traction = 1
+    for _ in range(max_iter):
+        # compute pairwise difference
+        diff = pos_arr[:, None] - pos_arr[None]
+        # compute pairwise distance
+        distance = np.linalg.norm(diff, axis=-1)
+
+        # linear attraction
+        if linlog:
+            attraction = -np.log(1 + distance) / distance
+            np.fill_diagonal(attraction, 0)
+            attraction = np.einsum("ij, ij -> ij", attraction, A)
+            attraction = np.einsum("ijk, ij -> ik", diff, attraction)
+
+        else:
+            attraction = -np.einsum("ijk, ij -> ik", diff, A)
+
+        if distributed_action:
+            attraction /= mass[:, None]
+
+        # repulsion
+        tmp = mass[:, None] @ mass[None]
+        if adjust_sizes:
+            distance += -size[:, None] - size[None]
+
+        d2 = distance**2
+        # remove self-interaction
+        np.fill_diagonal(tmp, 0)
+        np.fill_diagonal(d2, 1)
+        factor = (tmp / d2) * scaling_ratio
+        repulsion = np.einsum("ijk, ij -> ik", diff, factor)
+
+        # gravity
+        pos_centered = pos_arr - np.mean(pos_arr, axis=0)
+        if strong_gravity:
+            gravities = -gravity * mass[:, None] * pos_centered
+        else:
+            # hide warnings for divide by zero. Then change nan to 0
+            with np.errstate(divide="ignore", invalid="ignore"):
+                unit_vec = pos_centered / np.linalg.norm(pos_centered, axis=-1)[:, None]
+            unit_vec = np.nan_to_num(unit_vec, nan=0)
+            gravities = -gravity * mass[:, None] * unit_vec
+
+        # total forces
+        update = attraction + repulsion + gravities
+
+        # compute total swing and traction
+        swing += (mass * np.linalg.norm(pos_arr - update, axis=-1)).sum()
+        traction += (0.5 * mass * np.linalg.norm(pos_arr + update, axis=-1)).sum()
+
+        speed, speed_efficiency = estimate_factor(
+            n,
+            swing,
+            traction,
+            speed,
+            speed_efficiency,
+            jitter_tolerance,
+        )
+
+        # update pos
+        if adjust_sizes:
+            df = np.linalg.norm(update, axis=-1)
+            swinging = mass * df
+            factor = 0.1 * speed / (1 + np.sqrt(speed * swinging))
+            factor = np.minimum(factor * df, 10.0 * np.ones(df.shape)) / df
+        else:
+            swinging = mass * np.linalg.norm(update, axis=-1)
+            factor = speed / (1 + np.sqrt(speed * swinging))
+
+        factored_update = update * factor[:, None]
+        pos_arr += factored_update
+        if abs(factored_update).sum() < 1e-10:
+            break
+
+    pos = dict(zip(G, pos_arr))
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def rescale_layout(pos, scale=1):
+    """Returns scaled position array to (-scale, scale) in all axes.
+
+    The function acts on NumPy arrays which hold position information.
+    Each position is one row of the array. The dimension of the space
+    equals the number of columns. Each coordinate in one column.
+
+    To rescale, the mean (center) is subtracted from each axis separately.
+    Then all values are scaled so that the largest magnitude value
+    from all axes equals `scale` (thus, the aspect ratio is preserved).
+    The resulting NumPy Array is returned (order of rows unchanged).
+
+    Parameters
+    ----------
+    pos : numpy array
+        positions to be scaled. Each row is a position.
+
+    scale : number (default: 1)
+        The size of the resulting extent in all directions.
+
+    attribute : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute named `attribute` which can be accessed with
+        `G.nodes[...][attribute]`. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : numpy array
+        scaled positions. Each row is a position.
+
+    See Also
+    --------
+    rescale_layout_dict
+    """
+    import numpy as np
+
+    # Find max length over all dimensions
+    pos -= pos.mean(axis=0)
+    lim = np.abs(pos).max()  # max coordinate for all axes
+    # rescale to (-scale, scale) in all directions, preserves aspect
+    if lim > 0:
+        pos *= scale / lim
+    return pos
+
+
+def rescale_layout_dict(pos, scale=1):
+    """Return a dictionary of scaled positions keyed by node
+
+    Parameters
+    ----------
+    pos : A dictionary of positions keyed by node
+
+    scale : number (default: 1)
+        The size of the resulting extent in all directions.
+
+    Returns
+    -------
+    pos : A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> pos = {0: np.array((0, 0)), 1: np.array((1, 1)), 2: np.array((0.5, 0.5))}
+    >>> nx.rescale_layout_dict(pos)
+    {0: array([-1., -1.]), 1: array([1., 1.]), 2: array([0., 0.])}
+
+    >>> pos = {0: np.array((0, 0)), 1: np.array((-1, 1)), 2: np.array((-0.5, 0.5))}
+    >>> nx.rescale_layout_dict(pos, scale=2)
+    {0: array([ 2., -2.]), 1: array([-2.,  2.]), 2: array([0., 0.])}
+
+    See Also
+    --------
+    rescale_layout
+    """
+    import numpy as np
+
+    if not pos:  # empty_graph
+        return {}
+    pos_v = np.array(list(pos.values()))
+    pos_v = rescale_layout(pos_v, scale=scale)
+    return dict(zip(pos, pos_v))
+
+
+def bfs_layout(G, start, *, align="vertical", scale=1, center=None, store_pos_as=None):
+    """Position nodes according to breadth-first search algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A position will be assigned to every node in G.
+
+    start : node in `G`
+        Starting node for bfs
+
+    align : string (default='vertical')
+        The alignment of nodes within a layer, either `"vertical"` or
+        `"horizontal"`.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.bfs_layout(G, 0)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.bfs_layout(G, 0, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-1.,  0.]),
+     1: array([-0.33333333,  0.        ]),
+     2: array([0.33333333, 0.        ]),
+     3: array([1., 0.])}
+
+
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    G, center = _process_params(G, center, 2)
+
+    # Compute layers with BFS
+    layers = dict(enumerate(nx.bfs_layers(G, start)))
+
+    if len(G) != sum(len(nodes) for nodes in layers.values()):
+        raise nx.NetworkXError(
+            "bfs_layout didn't include all nodes. Perhaps use input graph:\n"
+            "        G.subgraph(nx.node_connected_component(G, start))"
+        )
+
+    # Compute node positions with multipartite_layout
+    pos = multipartite_layout(
+        G, subset_key=layers, align=align, scale=scale, center=center
+    )
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
diff --git a/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py b/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..897ab7f38a973bdf3ed1cbf7ac9504f4e93354f3
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/nx_agraph.py
@@ -0,0 +1,470 @@
+"""
+***************
+Graphviz AGraph
+***************
+
+Interface to pygraphviz AGraph class.
+
+Examples
+--------
+>>> G = nx.complete_graph(5)
+>>> A = nx.nx_agraph.to_agraph(G)
+>>> H = nx.nx_agraph.from_agraph(A)
+
+See Also
+--------
+ - Pygraphviz: http://pygraphviz.github.io/
+ - Graphviz:      https://www.graphviz.org
+ - DOT Language:  http://www.graphviz.org/doc/info/lang.html
+"""
+
+import tempfile
+
+import networkx as nx
+
+__all__ = [
+    "from_agraph",
+    "to_agraph",
+    "write_dot",
+    "read_dot",
+    "graphviz_layout",
+    "pygraphviz_layout",
+    "view_pygraphviz",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_agraph(A, create_using=None):
+    """Returns a NetworkX Graph or DiGraph from a PyGraphviz graph.
+
+    Parameters
+    ----------
+    A : PyGraphviz AGraph
+      A graph created with PyGraphviz
+
+    create_using : NetworkX graph constructor, optional (default=None)
+       Graph type to create. If graph instance, then cleared before populated.
+       If `None`, then the appropriate Graph type is inferred from `A`.
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> A = nx.nx_agraph.to_agraph(K5)
+    >>> G = nx.nx_agraph.from_agraph(A)
+
+    Notes
+    -----
+    The Graph G will have a dictionary G.graph_attr containing
+    the default graphviz attributes for graphs, nodes and edges.
+
+    Default node attributes will be in the dictionary G.node_attr
+    which is keyed by node.
+
+    Edge attributes will be returned as edge data in G.  With
+    edge_attr=False the edge data will be the Graphviz edge weight
+    attribute or the value 1 if no edge weight attribute is found.
+
+    """
+    if create_using is None:
+        if A.is_directed():
+            if A.is_strict():
+                create_using = nx.DiGraph
+            else:
+                create_using = nx.MultiDiGraph
+        else:
+            if A.is_strict():
+                create_using = nx.Graph
+            else:
+                create_using = nx.MultiGraph
+
+    # assign defaults
+    N = nx.empty_graph(0, create_using)
+    if A.name is not None:
+        N.name = A.name
+
+    # add graph attributes
+    N.graph.update(A.graph_attr)
+
+    # add nodes, attributes to N.node_attr
+    for n in A.nodes():
+        str_attr = {str(k): v for k, v in n.attr.items()}
+        N.add_node(str(n), **str_attr)
+
+    # add edges, assign edge data as dictionary of attributes
+    for e in A.edges():
+        u, v = str(e[0]), str(e[1])
+        attr = dict(e.attr)
+        str_attr = {str(k): v for k, v in attr.items()}
+        if not N.is_multigraph():
+            if e.name is not None:
+                str_attr["key"] = e.name
+            N.add_edge(u, v, **str_attr)
+        else:
+            N.add_edge(u, v, key=e.name, **str_attr)
+
+    # add default attributes for graph, nodes, and edges
+    # hang them on N.graph_attr
+    graph_default_dict = dict(A.graph_attr)
+    if graph_default_dict:
+        N.graph["graph"] = graph_default_dict
+    node_default_dict = dict(A.node_attr)
+    if node_default_dict and node_default_dict != {"label": "\\N"}:
+        N.graph["node"] = node_default_dict
+    edge_default_dict = dict(A.edge_attr)
+    if edge_default_dict:
+        N.graph["edge"] = edge_default_dict
+    return N
+
+
+def to_agraph(N):
+    """Returns a pygraphviz graph from a NetworkX graph N.
+
+    Parameters
+    ----------
+    N : NetworkX graph
+      A graph created with NetworkX
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> A = nx.nx_agraph.to_agraph(K5)
+
+    Notes
+    -----
+    If N has an dict N.graph_attr an attempt will be made first
+    to copy properties attached to the graph (see from_agraph)
+    and then updated with the calling arguments if any.
+
+    """
+    try:
+        import pygraphviz
+    except ImportError as err:
+        raise ImportError("requires pygraphviz http://pygraphviz.github.io/") from err
+    directed = N.is_directed()
+    strict = nx.number_of_selfloops(N) == 0 and not N.is_multigraph()
+
+    A = pygraphviz.AGraph(name=N.name, strict=strict, directed=directed)
+
+    # default graph attributes
+    A.graph_attr.update(N.graph.get("graph", {}))
+    A.node_attr.update(N.graph.get("node", {}))
+    A.edge_attr.update(N.graph.get("edge", {}))
+
+    A.graph_attr.update(
+        (k, v) for k, v in N.graph.items() if k not in ("graph", "node", "edge")
+    )
+
+    # add nodes
+    for n, nodedata in N.nodes(data=True):
+        A.add_node(n)
+        # Add node data
+        a = A.get_node(n)
+        for key, val in nodedata.items():
+            if key == "pos":
+                a.attr["pos"] = f"{val[0]},{val[1]}!"
+            else:
+                a.attr[key] = str(val)
+
+    # loop over edges
+    if N.is_multigraph():
+        for u, v, key, edgedata in N.edges(data=True, keys=True):
+            str_edgedata = {k: str(v) for k, v in edgedata.items() if k != "key"}
+            A.add_edge(u, v, key=str(key))
+            # Add edge data
+            a = A.get_edge(u, v)
+            a.attr.update(str_edgedata)
+
+    else:
+        for u, v, edgedata in N.edges(data=True):
+            str_edgedata = {k: str(v) for k, v in edgedata.items()}
+            A.add_edge(u, v)
+            # Add edge data
+            a = A.get_edge(u, v)
+            a.attr.update(str_edgedata)
+
+    return A
+
+
+def write_dot(G, path):
+    """Write NetworkX graph G to Graphviz dot format on path.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : filename
+       Filename or file handle to write
+
+    Notes
+    -----
+    To use a specific graph layout, call ``A.layout`` prior to `write_dot`.
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+    """
+    A = to_agraph(G)
+    A.write(path)
+    A.clear()
+    return
+
+
+@nx._dispatchable(name="agraph_read_dot", graphs=None, returns_graph=True)
+def read_dot(path):
+    """Returns a NetworkX graph from a dot file on path.
+
+    Parameters
+    ----------
+    path : file or string
+       File name or file handle to read.
+    """
+    try:
+        import pygraphviz
+    except ImportError as err:
+        raise ImportError(
+            "read_dot() requires pygraphviz http://pygraphviz.github.io/"
+        ) from err
+    A = pygraphviz.AGraph(file=path)
+    gr = from_agraph(A)
+    A.clear()
+    return gr
+
+
+def graphviz_layout(G, prog="neato", root=None, args=""):
+    """Create node positions for G using Graphviz.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      A graph created with NetworkX
+    prog : string
+      Name of Graphviz layout program
+    root : string, optional
+      Root node for twopi layout
+    args : string, optional
+      Extra arguments to Graphviz layout program
+
+    Returns
+    -------
+    Dictionary of x, y, positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.petersen_graph()
+    >>> pos = nx.nx_agraph.graphviz_layout(G)
+    >>> pos = nx.nx_agraph.graphviz_layout(G, prog="dot")
+
+    Notes
+    -----
+    This is a wrapper for pygraphviz_layout.
+
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+    """
+    return pygraphviz_layout(G, prog=prog, root=root, args=args)
+
+
+def pygraphviz_layout(G, prog="neato", root=None, args=""):
+    """Create node positions for G using Graphviz.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      A graph created with NetworkX
+    prog : string
+      Name of Graphviz layout program
+    root : string, optional
+      Root node for twopi layout
+    args : string, optional
+      Extra arguments to Graphviz layout program
+
+    Returns
+    -------
+    node_pos : dict
+      Dictionary of x, y, positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.petersen_graph()
+    >>> pos = nx.nx_agraph.graphviz_layout(G)
+    >>> pos = nx.nx_agraph.graphviz_layout(G, prog="dot")
+
+    Notes
+    -----
+    If you use complex node objects, they may have the same string
+    representation and GraphViz could treat them as the same node.
+    The layout may assign both nodes a single location. See Issue #1568
+    If this occurs in your case, consider relabeling the nodes just
+    for the layout computation using something similar to::
+
+        >>> H = nx.convert_node_labels_to_integers(G, label_attribute="node_label")
+        >>> H_layout = nx.nx_agraph.pygraphviz_layout(H, prog="dot")
+        >>> G_layout = {H.nodes[n]["node_label"]: p for n, p in H_layout.items()}
+
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+    """
+    try:
+        import pygraphviz
+    except ImportError as err:
+        raise ImportError("requires pygraphviz http://pygraphviz.github.io/") from err
+    if root is not None:
+        args += f"-Groot={root}"
+    A = to_agraph(G)
+    A.layout(prog=prog, args=args)
+    node_pos = {}
+    for n in G:
+        node = pygraphviz.Node(A, n)
+        try:
+            xs = node.attr["pos"].split(",")
+            node_pos[n] = tuple(float(x) for x in xs)
+        except:
+            print("no position for node", n)
+            node_pos[n] = (0.0, 0.0)
+    return node_pos
+
+
+@nx.utils.open_file(5, "w+b")
+def view_pygraphviz(
+    G, edgelabel=None, prog="dot", args="", suffix="", path=None, show=True
+):
+    """Views the graph G using the specified layout algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The machine to draw.
+    edgelabel : str, callable, None
+        If a string, then it specifies the edge attribute to be displayed
+        on the edge labels. If a callable, then it is called for each
+        edge and it should return the string to be displayed on the edges.
+        The function signature of `edgelabel` should be edgelabel(data),
+        where `data` is the edge attribute dictionary.
+    prog : string
+        Name of Graphviz layout program.
+    args : str
+        Additional arguments to pass to the Graphviz layout program.
+    suffix : str
+        If `filename` is None, we save to a temporary file.  The value of
+        `suffix` will appear at the tail end of the temporary filename.
+    path : str, None
+        The filename used to save the image.  If None, save to a temporary
+        file.  File formats are the same as those from pygraphviz.agraph.draw.
+        Filenames ending in .gz or .bz2 will be compressed.
+    show : bool, default = True
+        Whether to display the graph with :mod:`PIL.Image.show`,
+        default is `True`. If `False`, the rendered graph is still available
+        at `path`.
+
+    Returns
+    -------
+    path : str
+        The filename of the generated image.
+    A : PyGraphviz graph
+        The PyGraphviz graph instance used to generate the image.
+
+    Notes
+    -----
+    If this function is called in succession too quickly, sometimes the
+    image is not displayed. So you might consider time.sleep(.5) between
+    calls if you experience problems.
+
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+
+    """
+    if not len(G):
+        raise nx.NetworkXException("An empty graph cannot be drawn.")
+
+    # If we are providing default values for graphviz, these must be set
+    # before any nodes or edges are added to the PyGraphviz graph object.
+    # The reason for this is that default values only affect incoming objects.
+    # If you change the default values after the objects have been added,
+    # then they inherit no value and are set only if explicitly set.
+
+    # to_agraph() uses these values.
+    attrs = ["edge", "node", "graph"]
+    for attr in attrs:
+        if attr not in G.graph:
+            G.graph[attr] = {}
+
+    # These are the default values.
+    edge_attrs = {"fontsize": "10"}
+    node_attrs = {
+        "style": "filled",
+        "fillcolor": "#0000FF40",
+        "height": "0.75",
+        "width": "0.75",
+        "shape": "circle",
+    }
+    graph_attrs = {}
+
+    def update_attrs(which, attrs):
+        # Update graph attributes. Return list of those which were added.
+        added = []
+        for k, v in attrs.items():
+            if k not in G.graph[which]:
+                G.graph[which][k] = v
+                added.append(k)
+
+    def clean_attrs(which, added):
+        # Remove added attributes
+        for attr in added:
+            del G.graph[which][attr]
+        if not G.graph[which]:
+            del G.graph[which]
+
+    # Update all default values
+    update_attrs("edge", edge_attrs)
+    update_attrs("node", node_attrs)
+    update_attrs("graph", graph_attrs)
+
+    # Convert to agraph, so we inherit default values
+    A = to_agraph(G)
+
+    # Remove the default values we added to the original graph.
+    clean_attrs("edge", edge_attrs)
+    clean_attrs("node", node_attrs)
+    clean_attrs("graph", graph_attrs)
+
+    # If the user passed in an edgelabel, we update the labels for all edges.
+    if edgelabel is not None:
+        if not callable(edgelabel):
+
+            def func(data):
+                return "".join(["  ", str(data[edgelabel]), "  "])
+
+        else:
+            func = edgelabel
+
+        # update all the edge labels
+        if G.is_multigraph():
+            for u, v, key, data in G.edges(keys=True, data=True):
+                # PyGraphviz doesn't convert the key to a string. See #339
+                edge = A.get_edge(u, v, str(key))
+                edge.attr["label"] = str(func(data))
+        else:
+            for u, v, data in G.edges(data=True):
+                edge = A.get_edge(u, v)
+                edge.attr["label"] = str(func(data))
+
+    if path is None:
+        ext = "png"
+        if suffix:
+            suffix = f"_{suffix}.{ext}"
+        else:
+            suffix = f".{ext}"
+        path = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
+    else:
+        # Assume the decorator worked and it is a file-object.
+        pass
+
+    # Write graph to file
+    A.draw(path=path, format=None, prog=prog, args=args)
+    path.close()
+
+    # Show graph in a new window (depends on platform configuration)
+    if show:
+        from PIL import Image
+
+        Image.open(path.name).show()
+
+    return path.name, A
diff --git a/lib/python3.11/site-packages/networkx/drawing/nx_latex.py b/lib/python3.11/site-packages/networkx/drawing/nx_latex.py
new file mode 100644
index 0000000000000000000000000000000000000000..677def8e75144afe5d73fbdea9f5643222a4ea01
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/nx_latex.py
@@ -0,0 +1,570 @@
+r"""
+*****
+LaTeX
+*****
+
+Export NetworkX graphs in LaTeX format using the TikZ library within TeX/LaTeX.
+Usually, you will want the drawing to appear in a figure environment so
+you use ``to_latex(G, caption="A caption")``. If you want the raw
+drawing commands without a figure environment use :func:`to_latex_raw`.
+And if you want to write to a file instead of just returning the latex
+code as a string, use ``write_latex(G, "filename.tex", caption="A caption")``.
+
+To construct a figure with subfigures for each graph to be shown, provide
+``to_latex`` or ``write_latex`` a list of graphs, a list of subcaptions,
+and a number of rows of subfigures inside the figure.
+
+To be able to refer to the figures or subfigures in latex using ``\\ref``,
+the keyword ``latex_label`` is available for figures and `sub_labels` for
+a list of labels, one for each subfigure.
+
+We intend to eventually provide an interface to the TikZ Graph
+features which include e.g. layout algorithms.
+
+Let us know via github what you'd like to see available, or better yet
+give us some code to do it, or even better make a github pull request
+to add the feature.
+
+The TikZ approach
+=================
+Drawing options can be stored on the graph as node/edge attributes, or
+can be provided as dicts keyed by node/edge to a string of the options
+for that node/edge. Similarly a label can be shown for each node/edge
+by specifying the labels as graph node/edge attributes or by providing
+a dict keyed by node/edge to the text to be written for that node/edge.
+
+Options for the tikzpicture environment (e.g. "[scale=2]") can be provided
+via a keyword argument. Similarly default node and edge options can be
+provided through keywords arguments. The default node options are applied
+to the single TikZ "path" that draws all nodes (and no edges). The default edge
+options are applied to a TikZ "scope" which contains a path for each edge.
+
+Examples
+========
+>>> G = nx.path_graph(3)
+>>> nx.write_latex(G, "just_my_figure.tex", as_document=True)
+>>> nx.write_latex(G, "my_figure.tex", caption="A path graph", latex_label="fig1")
+>>> latex_code = nx.to_latex(G)  # a string rather than a file
+
+You can change many features of the nodes and edges.
+
+>>> G = nx.path_graph(4, create_using=nx.DiGraph)
+>>> pos = {n: (n, n) for n in G}  # nodes set on a line
+
+>>> G.nodes[0]["style"] = "blue"
+>>> G.nodes[2]["style"] = "line width=3,draw"
+>>> G.nodes[3]["label"] = "Stop"
+>>> G.edges[(0, 1)]["label"] = "1st Step"
+>>> G.edges[(0, 1)]["label_opts"] = "near start"
+>>> G.edges[(1, 2)]["style"] = "line width=3"
+>>> G.edges[(1, 2)]["label"] = "2nd Step"
+>>> G.edges[(2, 3)]["style"] = "green"
+>>> G.edges[(2, 3)]["label"] = "3rd Step"
+>>> G.edges[(2, 3)]["label_opts"] = "near end"
+
+>>> nx.write_latex(G, "latex_graph.tex", pos=pos, as_document=True)
+
+Then compile the LaTeX using something like ``pdflatex latex_graph.tex``
+and view the pdf file created: ``latex_graph.pdf``.
+
+If you want **subfigures** each containing one graph, you can input a list of graphs.
+
+>>> H1 = nx.path_graph(4)
+>>> H2 = nx.complete_graph(4)
+>>> H3 = nx.path_graph(8)
+>>> H4 = nx.complete_graph(8)
+>>> graphs = [H1, H2, H3, H4]
+>>> caps = ["Path 4", "Complete graph 4", "Path 8", "Complete graph 8"]
+>>> lbls = ["fig2a", "fig2b", "fig2c", "fig2d"]
+>>> nx.write_latex(graphs, "subfigs.tex", n_rows=2, sub_captions=caps, sub_labels=lbls)
+>>> latex_code = nx.to_latex(graphs, n_rows=2, sub_captions=caps, sub_labels=lbls)
+
+>>> node_color = {0: "red", 1: "orange", 2: "blue", 3: "gray!90"}
+>>> edge_width = {e: "line width=1.5" for e in H3.edges}
+>>> pos = nx.circular_layout(H3)
+>>> latex_code = nx.to_latex(H3, pos, node_options=node_color, edge_options=edge_width)
+>>> print(latex_code)
+\documentclass{report}
+\usepackage{tikz}
+\usepackage{subcaption}
+<BLANKLINE>
+\begin{document}
+\begin{figure}
+  \begin{tikzpicture}
+      \draw
+        (1.0, 0.0) node[red] (0){0}
+        (0.707, 0.707) node[orange] (1){1}
+        (-0.0, 1.0) node[blue] (2){2}
+        (-0.707, 0.707) node[gray!90] (3){3}
+        (-1.0, -0.0) node (4){4}
+        (-0.707, -0.707) node (5){5}
+        (0.0, -1.0) node (6){6}
+        (0.707, -0.707) node (7){7};
+      \begin{scope}[-]
+        \draw[line width=1.5] (0) to (1);
+        \draw[line width=1.5] (1) to (2);
+        \draw[line width=1.5] (2) to (3);
+        \draw[line width=1.5] (3) to (4);
+        \draw[line width=1.5] (4) to (5);
+        \draw[line width=1.5] (5) to (6);
+        \draw[line width=1.5] (6) to (7);
+      \end{scope}
+    \end{tikzpicture}
+\end{figure}
+\end{document}
+
+Notes
+-----
+If you want to change the preamble/postamble of the figure/document/subfigure
+environment, use the keyword arguments: `figure_wrapper`, `document_wrapper`,
+`subfigure_wrapper`. The default values are stored in private variables
+e.g. ``nx.nx_layout._DOCUMENT_WRAPPER``
+
+References
+----------
+TikZ:          https://tikz.dev/
+
+TikZ options details:   https://tikz.dev/tikz-actions
+"""
+
+import networkx as nx
+
+__all__ = [
+    "to_latex_raw",
+    "to_latex",
+    "write_latex",
+]
+
+
+@nx.utils.not_implemented_for("multigraph")
+def to_latex_raw(
+    G,
+    pos="pos",
+    tikz_options="",
+    default_node_options="",
+    node_options="node_options",
+    node_label="label",
+    default_edge_options="",
+    edge_options="edge_options",
+    edge_label="label",
+    edge_label_options="edge_label_options",
+):
+    """Return a string of the LaTeX/TikZ code to draw `G`
+
+    This function produces just the code for the tikzpicture
+    without any enclosing environment.
+
+    Parameters
+    ==========
+    G : NetworkX graph
+        The NetworkX graph to be drawn
+    pos : string or dict (default "pos")
+        The name of the node attribute on `G` that holds the position of each node.
+        Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+        They can also be strings to denote positions in TikZ style, such as (x, y)
+        or (angle:radius).
+        If a dict, it should be keyed by node to a position.
+        If an empty dict, a circular layout is computed by TikZ.
+    tikz_options : string
+        The tikzpicture options description defining the options for the picture.
+        Often large scale options like `[scale=2]`.
+    default_node_options : string
+        The draw options for a path of nodes. Individual node options override these.
+    node_options : string or dict
+        The name of the node attribute on `G` that holds the options for each node.
+        Or a dict keyed by node to a string holding the options for that node.
+    node_label : string or dict
+        The name of the node attribute on `G` that holds the node label (text)
+        displayed for each node. If the attribute is "" or not present, the node
+        itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+        Or a dict keyed by node to a string holding the label for that node.
+    default_edge_options : string
+        The options for the scope drawing all edges. The default is "[-]" for
+        undirected graphs and "[->]" for directed graphs.
+    edge_options : string or dict
+        The name of the edge attribute on `G` that holds the options for each edge.
+        If the edge is a self-loop and ``"loop" not in edge_options`` the option
+        "loop," is added to the options for the self-loop edge. Hence you can
+        use "[loop above]" explicitly, but the default is "[loop]".
+        Or a dict keyed by edge to a string holding the options for that edge.
+    edge_label : string or dict
+        The name of the edge attribute on `G` that holds the edge label (text)
+        displayed for each edge. If the attribute is "" or not present, no edge
+        label is drawn.
+        Or a dict keyed by edge to a string holding the label for that edge.
+    edge_label_options : string or dict
+        The name of the edge attribute on `G` that holds the label options for
+        each edge. For example, "[sloped,above,blue]". The default is no options.
+        Or a dict keyed by edge to a string holding the label options for that edge.
+
+    Returns
+    =======
+    latex_code : string
+       The text string which draws the desired graph(s) when compiled by LaTeX.
+
+    See Also
+    ========
+    to_latex
+    write_latex
+    """
+    i4 = "\n    "
+    i8 = "\n        "
+
+    # set up position dict
+    # TODO allow pos to be None and use a nice TikZ default
+    if not isinstance(pos, dict):
+        pos = nx.get_node_attributes(G, pos)
+    if not pos:
+        # circular layout with radius 2
+        pos = {n: f"({round(360.0 * i / len(G), 3)}:2)" for i, n in enumerate(G)}
+    for node in G:
+        if node not in pos:
+            raise nx.NetworkXError(f"node {node} has no specified pos {pos}")
+        posnode = pos[node]
+        if not isinstance(posnode, str):
+            try:
+                posx, posy = posnode
+                pos[node] = f"({round(posx, 3)}, {round(posy, 3)})"
+            except (TypeError, ValueError):
+                msg = f"position pos[{node}] is not 2-tuple or a string: {posnode}"
+                raise nx.NetworkXError(msg)
+
+    # set up all the dicts
+    if not isinstance(node_options, dict):
+        node_options = nx.get_node_attributes(G, node_options)
+    if not isinstance(node_label, dict):
+        node_label = nx.get_node_attributes(G, node_label)
+    if not isinstance(edge_options, dict):
+        edge_options = nx.get_edge_attributes(G, edge_options)
+    if not isinstance(edge_label, dict):
+        edge_label = nx.get_edge_attributes(G, edge_label)
+    if not isinstance(edge_label_options, dict):
+        edge_label_options = nx.get_edge_attributes(G, edge_label_options)
+
+    # process default options (add brackets or not)
+    topts = "" if tikz_options == "" else f"[{tikz_options.strip('[]')}]"
+    defn = "" if default_node_options == "" else f"[{default_node_options.strip('[]')}]"
+    linestyle = f"{'->' if G.is_directed() else '-'}"
+    if default_edge_options == "":
+        defe = "[" + linestyle + "]"
+    elif "-" in default_edge_options:
+        defe = default_edge_options
+    else:
+        defe = f"[{linestyle},{default_edge_options.strip('[]')}]"
+
+    # Construct the string line by line
+    result = "  \\begin{tikzpicture}" + topts
+    result += i4 + "  \\draw" + defn
+    # load the nodes
+    for n in G:
+        # node options goes inside square brackets
+        nopts = f"[{node_options[n].strip('[]')}]" if n in node_options else ""
+        # node text goes inside curly brackets {}
+        ntext = f"{{{node_label[n]}}}" if n in node_label else f"{{{n}}}"
+
+        result += i8 + f"{pos[n]} node{nopts} ({n}){ntext}"
+    result += ";\n"
+
+    # load the edges
+    result += "      \\begin{scope}" + defe
+    for edge in G.edges:
+        u, v = edge[:2]
+        e_opts = f"{edge_options[edge]}".strip("[]") if edge in edge_options else ""
+        # add loop options for selfloops if not present
+        if u == v and "loop" not in e_opts:
+            e_opts = "loop," + e_opts
+        e_opts = f"[{e_opts}]" if e_opts != "" else ""
+        # TODO -- handle bending of multiedges
+
+        els = edge_label_options[edge] if edge in edge_label_options else ""
+        # edge label options goes inside square brackets []
+        els = f"[{els.strip('[]')}]"
+        # edge text is drawn using the TikZ node command inside curly brackets {}
+        e_label = f" node{els} {{{edge_label[edge]}}}" if edge in edge_label else ""
+
+        result += i8 + f"\\draw{e_opts} ({u}) to{e_label} ({v});"
+
+    result += "\n      \\end{scope}\n    \\end{tikzpicture}\n"
+    return result
+
+
+_DOC_WRAPPER_TIKZ = r"""\documentclass{{report}}
+\usepackage{{tikz}}
+\usepackage{{subcaption}}
+
+\begin{{document}}
+{content}
+\end{{document}}"""
+
+
+_FIG_WRAPPER = r"""\begin{{figure}}
+{content}{caption}{label}
+\end{{figure}}"""
+
+
+_SUBFIG_WRAPPER = r"""  \begin{{subfigure}}{{{size}\textwidth}}
+{content}{caption}{label}
+  \end{{subfigure}}"""
+
+
+def to_latex(
+    Gbunch,
+    pos="pos",
+    tikz_options="",
+    default_node_options="",
+    node_options="node_options",
+    node_label="node_label",
+    default_edge_options="",
+    edge_options="edge_options",
+    edge_label="edge_label",
+    edge_label_options="edge_label_options",
+    caption="",
+    latex_label="",
+    sub_captions=None,
+    sub_labels=None,
+    n_rows=1,
+    as_document=True,
+    document_wrapper=_DOC_WRAPPER_TIKZ,
+    figure_wrapper=_FIG_WRAPPER,
+    subfigure_wrapper=_SUBFIG_WRAPPER,
+):
+    """Return latex code to draw the graph(s) in `Gbunch`
+
+    The TikZ drawing utility in LaTeX is used to draw the graph(s).
+    If `Gbunch` is a graph, it is drawn in a figure environment.
+    If `Gbunch` is an iterable of graphs, each is drawn in a subfigure environment
+    within a single figure environment.
+
+    If `as_document` is True, the figure is wrapped inside a document environment
+    so that the resulting string is ready to be compiled by LaTeX. Otherwise,
+    the string is ready for inclusion in a larger tex document using ``\\include``
+    or ``\\input`` statements.
+
+    Parameters
+    ==========
+    Gbunch : NetworkX graph or iterable of NetworkX graphs
+        The NetworkX graph to be drawn or an iterable of graphs
+        to be drawn inside subfigures of a single figure.
+    pos : string or list of strings
+        The name of the node attribute on `G` that holds the position of each node.
+        Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+        They can also be strings to denote positions in TikZ style, such as (x, y)
+        or (angle:radius).
+        If a dict, it should be keyed by node to a position.
+        If an empty dict, a circular layout is computed by TikZ.
+        If you are drawing many graphs in subfigures, use a list of position dicts.
+    tikz_options : string
+        The tikzpicture options description defining the options for the picture.
+        Often large scale options like `[scale=2]`.
+    default_node_options : string
+        The draw options for a path of nodes. Individual node options override these.
+    node_options : string or dict
+        The name of the node attribute on `G` that holds the options for each node.
+        Or a dict keyed by node to a string holding the options for that node.
+    node_label : string or dict
+        The name of the node attribute on `G` that holds the node label (text)
+        displayed for each node. If the attribute is "" or not present, the node
+        itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+        Or a dict keyed by node to a string holding the label for that node.
+    default_edge_options : string
+        The options for the scope drawing all edges. The default is "[-]" for
+        undirected graphs and "[->]" for directed graphs.
+    edge_options : string or dict
+        The name of the edge attribute on `G` that holds the options for each edge.
+        If the edge is a self-loop and ``"loop" not in edge_options`` the option
+        "loop," is added to the options for the self-loop edge. Hence you can
+        use "[loop above]" explicitly, but the default is "[loop]".
+        Or a dict keyed by edge to a string holding the options for that edge.
+    edge_label : string or dict
+        The name of the edge attribute on `G` that holds the edge label (text)
+        displayed for each edge. If the attribute is "" or not present, no edge
+        label is drawn.
+        Or a dict keyed by edge to a string holding the label for that edge.
+    edge_label_options : string or dict
+        The name of the edge attribute on `G` that holds the label options for
+        each edge. For example, "[sloped,above,blue]". The default is no options.
+        Or a dict keyed by edge to a string holding the label options for that edge.
+    caption : string
+        The caption string for the figure environment
+    latex_label : string
+        The latex label used for the figure for easy referral from the main text
+    sub_captions : list of strings
+        The sub_caption string for each subfigure in the figure
+    sub_latex_labels : list of strings
+        The latex label for each subfigure in the figure
+    n_rows : int
+        The number of rows of subfigures to arrange for multiple graphs
+    as_document : bool
+        Whether to wrap the latex code in a document environment for compiling
+    document_wrapper : formatted text string with variable ``content``.
+        This text is called to evaluate the content embedded in a document
+        environment with a preamble setting up TikZ.
+    figure_wrapper : formatted text string
+        This text is evaluated with variables ``content``, ``caption`` and ``label``.
+        It wraps the content and if a caption is provided, adds the latex code for
+        that caption, and if a label is provided, adds the latex code for a label.
+    subfigure_wrapper : formatted text string
+        This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.
+        It wraps the content and if a caption is provided, adds the latex code for
+        that caption, and if a label is provided, adds the latex code for a label.
+        The size is the vertical size of each row of subfigures as a fraction.
+
+    Returns
+    =======
+    latex_code : string
+        The text string which draws the desired graph(s) when compiled by LaTeX.
+
+    See Also
+    ========
+    write_latex
+    to_latex_raw
+    """
+    if hasattr(Gbunch, "adj"):
+        raw = to_latex_raw(
+            Gbunch,
+            pos,
+            tikz_options,
+            default_node_options,
+            node_options,
+            node_label,
+            default_edge_options,
+            edge_options,
+            edge_label,
+            edge_label_options,
+        )
+    else:  # iterator of graphs
+        sbf = subfigure_wrapper
+        size = 1 / n_rows
+
+        N = len(Gbunch)
+        if isinstance(pos, str | dict):
+            pos = [pos] * N
+        if sub_captions is None:
+            sub_captions = [""] * N
+        if sub_labels is None:
+            sub_labels = [""] * N
+        if not (len(Gbunch) == len(pos) == len(sub_captions) == len(sub_labels)):
+            raise nx.NetworkXError(
+                "length of Gbunch, sub_captions and sub_figures must agree"
+            )
+
+        raw = ""
+        for G, pos, subcap, sublbl in zip(Gbunch, pos, sub_captions, sub_labels):
+            subraw = to_latex_raw(
+                G,
+                pos,
+                tikz_options,
+                default_node_options,
+                node_options,
+                node_label,
+                default_edge_options,
+                edge_options,
+                edge_label,
+                edge_label_options,
+            )
+            cap = f"    \\caption{{{subcap}}}" if subcap else ""
+            lbl = f"\\label{{{sublbl}}}" if sublbl else ""
+            raw += sbf.format(size=size, content=subraw, caption=cap, label=lbl)
+            raw += "\n"
+
+    # put raw latex code into a figure environment and optionally into a document
+    raw = raw[:-1]
+    cap = f"\n  \\caption{{{caption}}}" if caption else ""
+    lbl = f"\\label{{{latex_label}}}" if latex_label else ""
+    fig = figure_wrapper.format(content=raw, caption=cap, label=lbl)
+    if as_document:
+        return document_wrapper.format(content=fig)
+    return fig
+
+
+@nx.utils.open_file(1, mode="w")
+def write_latex(Gbunch, path, **options):
+    """Write the latex code to draw the graph(s) onto `path`.
+
+    This convenience function creates the latex drawing code as a string
+    and writes that to a file ready to be compiled when `as_document` is True
+    or ready to be ``import`` ed or ``include`` ed into your main LaTeX document.
+
+    The `path` argument can be a string filename or a file handle to write to.
+
+    Parameters
+    ----------
+    Gbunch : NetworkX graph or iterable of NetworkX graphs
+        If Gbunch is a graph, it is drawn in a figure environment.
+        If Gbunch is an iterable of graphs, each is drawn in a subfigure
+        environment within a single figure environment.
+    path : string or file
+        Filename or file handle to write to.
+        Filenames ending in .gz or .bz2 will be compressed.
+    options : dict
+        By default, TikZ is used with options: (others are ignored)::
+
+            pos : string or dict or list
+                The name of the node attribute on `G` that holds the position of each node.
+                Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+                They can also be strings to denote positions in TikZ style, such as (x, y)
+                or (angle:radius).
+                If a dict, it should be keyed by node to a position.
+                If an empty dict, a circular layout is computed by TikZ.
+                If you are drawing many graphs in subfigures, use a list of position dicts.
+            tikz_options : string
+                The tikzpicture options description defining the options for the picture.
+                Often large scale options like `[scale=2]`.
+            default_node_options : string
+                The draw options for a path of nodes. Individual node options override these.
+            node_options : string or dict
+                The name of the node attribute on `G` that holds the options for each node.
+                Or a dict keyed by node to a string holding the options for that node.
+            node_label : string or dict
+                The name of the node attribute on `G` that holds the node label (text)
+                displayed for each node. If the attribute is "" or not present, the node
+                itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+                Or a dict keyed by node to a string holding the label for that node.
+            default_edge_options : string
+                The options for the scope drawing all edges. The default is "[-]" for
+                undirected graphs and "[->]" for directed graphs.
+            edge_options : string or dict
+                The name of the edge attribute on `G` that holds the options for each edge.
+                If the edge is a self-loop and ``"loop" not in edge_options`` the option
+                "loop," is added to the options for the self-loop edge. Hence you can
+                use "[loop above]" explicitly, but the default is "[loop]".
+                Or a dict keyed by edge to a string holding the options for that edge.
+            edge_label : string or dict
+                The name of the edge attribute on `G` that holds the edge label (text)
+                displayed for each edge. If the attribute is "" or not present, no edge
+                label is drawn.
+                Or a dict keyed by edge to a string holding the label for that edge.
+            edge_label_options : string or dict
+                The name of the edge attribute on `G` that holds the label options for
+                each edge. For example, "[sloped,above,blue]". The default is no options.
+                Or a dict keyed by edge to a string holding the label options for that edge.
+            caption : string
+                The caption string for the figure environment
+            latex_label : string
+                The latex label used for the figure for easy referral from the main text
+            sub_captions : list of strings
+                The sub_caption string for each subfigure in the figure
+            sub_latex_labels : list of strings
+                The latex label for each subfigure in the figure
+            n_rows : int
+                The number of rows of subfigures to arrange for multiple graphs
+            as_document : bool
+                Whether to wrap the latex code in a document environment for compiling
+            document_wrapper : formatted text string with variable ``content``.
+                This text is called to evaluate the content embedded in a document
+                environment with a preamble setting up the TikZ syntax.
+            figure_wrapper : formatted text string
+                This text is evaluated with variables ``content``, ``caption`` and ``label``.
+                It wraps the content and if a caption is provided, adds the latex code for
+                that caption, and if a label is provided, adds the latex code for a label.
+            subfigure_wrapper : formatted text string
+                This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.
+                It wraps the content and if a caption is provided, adds the latex code for
+                that caption, and if a label is provided, adds the latex code for a label.
+                The size is the vertical size of each row of subfigures as a fraction.
+
+    See Also
+    ========
+    to_latex
+    """
+    path.write(to_latex(Gbunch, **options))
diff --git a/lib/python3.11/site-packages/networkx/drawing/nx_pydot.py b/lib/python3.11/site-packages/networkx/drawing/nx_pydot.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fe5ceec97c5c3c85e5e64e05b4c02eb83978c3a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/nx_pydot.py
@@ -0,0 +1,361 @@
+"""
+*****
+Pydot
+*****
+
+Import and export NetworkX graphs in Graphviz dot format using pydot.
+
+Either this module or nx_agraph can be used to interface with graphviz.
+
+Examples
+--------
+>>> G = nx.complete_graph(5)
+>>> PG = nx.nx_pydot.to_pydot(G)
+>>> H = nx.nx_pydot.from_pydot(PG)
+
+See Also
+--------
+ - pydot:         https://github.com/erocarrera/pydot
+ - Graphviz:      https://www.graphviz.org
+ - DOT Language:  http://www.graphviz.org/doc/info/lang.html
+"""
+
+from locale import getpreferredencoding
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = [
+    "write_dot",
+    "read_dot",
+    "graphviz_layout",
+    "pydot_layout",
+    "to_pydot",
+    "from_pydot",
+]
+
+
+@open_file(1, mode="w")
+def write_dot(G, path):
+    """Write NetworkX graph G to Graphviz dot format on path.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle for data output.
+       Filenames ending in .gz or .bz2 will be compressed.
+    """
+    P = to_pydot(G)
+    path.write(P.to_string())
+    return
+
+
+@open_file(0, mode="r")
+@nx._dispatchable(name="pydot_read_dot", graphs=None, returns_graph=True)
+def read_dot(path):
+    """Returns a NetworkX :class:`MultiGraph` or :class:`MultiDiGraph` from the
+    dot file with the passed path.
+
+    If this file contains multiple graphs, only the first such graph is
+    returned. All graphs _except_ the first are silently ignored.
+
+    Parameters
+    ----------
+    path : str or file
+        Filename or file handle to read.
+        Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : MultiGraph or MultiDiGraph
+        A :class:`MultiGraph` or :class:`MultiDiGraph`.
+
+    Notes
+    -----
+    Use `G = nx.Graph(nx.nx_pydot.read_dot(path))` to return a :class:`Graph` instead of a
+    :class:`MultiGraph`.
+    """
+    import pydot
+
+    data = path.read()
+
+    # List of one or more "pydot.Dot" instances deserialized from this file.
+    P_list = pydot.graph_from_dot_data(data)
+
+    # Convert only the first such instance into a NetworkX graph.
+    return from_pydot(P_list[0])
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_pydot(P):
+    """Returns a NetworkX graph from a Pydot graph.
+
+    Parameters
+    ----------
+    P : Pydot graph
+      A graph created with Pydot
+
+    Returns
+    -------
+    G : NetworkX multigraph
+        A MultiGraph or MultiDiGraph.
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> A = nx.nx_pydot.to_pydot(K5)
+    >>> G = nx.nx_pydot.from_pydot(A)  # return MultiGraph
+
+    # make a Graph instead of MultiGraph
+    >>> G = nx.Graph(nx.nx_pydot.from_pydot(A))
+
+    """
+    # NOTE: Pydot v3 expects a dummy argument whereas Pydot v4 doesn't
+    # Remove the try-except when Pydot v4 becomes the minimum supported version
+    try:
+        strict = P.get_strict()
+    except TypeError:
+        strict = P.get_strict(None)  # pydot bug: get_strict() shouldn't take argument
+    multiedges = not strict
+
+    if P.get_type() == "graph":  # undirected
+        if multiedges:
+            N = nx.MultiGraph()
+        else:
+            N = nx.Graph()
+    else:
+        if multiedges:
+            N = nx.MultiDiGraph()
+        else:
+            N = nx.DiGraph()
+
+    # assign defaults
+    name = P.get_name().strip('"')
+    if name != "":
+        N.name = name
+
+    # add nodes, attributes to N.node_attr
+    for p in P.get_node_list():
+        n = p.get_name().strip('"')
+        if n in ("node", "graph", "edge"):
+            continue
+        N.add_node(n, **p.get_attributes())
+
+    # add edges
+    for e in P.get_edge_list():
+        u = e.get_source()
+        v = e.get_destination()
+        attr = e.get_attributes()
+        s = []
+        d = []
+
+        if isinstance(u, str):
+            s.append(u.strip('"'))
+        else:
+            for unodes in u["nodes"]:
+                s.append(unodes.strip('"'))
+
+        if isinstance(v, str):
+            d.append(v.strip('"'))
+        else:
+            for vnodes in v["nodes"]:
+                d.append(vnodes.strip('"'))
+
+        for source_node in s:
+            for destination_node in d:
+                N.add_edge(source_node, destination_node, **attr)
+
+    # add default attributes for graph, nodes, edges
+    pattr = P.get_attributes()
+    if pattr:
+        N.graph["graph"] = pattr
+    try:
+        N.graph["node"] = P.get_node_defaults()[0]
+    except (IndexError, TypeError):
+        pass  # N.graph['node']={}
+    try:
+        N.graph["edge"] = P.get_edge_defaults()[0]
+    except (IndexError, TypeError):
+        pass  # N.graph['edge']={}
+    return N
+
+
+def to_pydot(N):
+    """Returns a pydot graph from a NetworkX graph N.
+
+    Parameters
+    ----------
+    N : NetworkX graph
+      A graph created with NetworkX
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> P = nx.nx_pydot.to_pydot(K5)
+
+    Notes
+    -----
+
+    """
+    import pydot
+
+    # set Graphviz graph type
+    if N.is_directed():
+        graph_type = "digraph"
+    else:
+        graph_type = "graph"
+    strict = nx.number_of_selfloops(N) == 0 and not N.is_multigraph()
+
+    name = N.name
+    graph_defaults = N.graph.get("graph", {})
+    if name == "":
+        P = pydot.Dot("", graph_type=graph_type, strict=strict, **graph_defaults)
+    else:
+        P = pydot.Dot(
+            f'"{name}"', graph_type=graph_type, strict=strict, **graph_defaults
+        )
+    try:
+        P.set_node_defaults(**N.graph["node"])
+    except KeyError:
+        pass
+    try:
+        P.set_edge_defaults(**N.graph["edge"])
+    except KeyError:
+        pass
+
+    for n, nodedata in N.nodes(data=True):
+        str_nodedata = {str(k): str(v) for k, v in nodedata.items()}
+        n = str(n)
+        p = pydot.Node(n, **str_nodedata)
+        P.add_node(p)
+
+    if N.is_multigraph():
+        for u, v, key, edgedata in N.edges(data=True, keys=True):
+            str_edgedata = {str(k): str(v) for k, v in edgedata.items() if k != "key"}
+            u, v = str(u), str(v)
+            edge = pydot.Edge(u, v, key=str(key), **str_edgedata)
+            P.add_edge(edge)
+
+    else:
+        for u, v, edgedata in N.edges(data=True):
+            str_edgedata = {str(k): str(v) for k, v in edgedata.items()}
+            u, v = str(u), str(v)
+            edge = pydot.Edge(u, v, **str_edgedata)
+            P.add_edge(edge)
+    return P
+
+
+def graphviz_layout(G, prog="neato", root=None):
+    """Create node positions using Pydot and Graphviz.
+
+    Returns a dictionary of positions keyed by node.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph for which the layout is computed.
+    prog : string (default: 'neato')
+        The name of the GraphViz program to use for layout.
+        Options depend on GraphViz version but may include:
+        'dot', 'twopi', 'fdp', 'sfdp', 'circo'
+    root : Node from G or None (default: None)
+        The node of G from which to start some layout algorithms.
+
+    Returns
+    -------
+      Dictionary of (x, y) positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(4)
+    >>> pos = nx.nx_pydot.graphviz_layout(G)
+    >>> pos = nx.nx_pydot.graphviz_layout(G, prog="dot")
+
+    Notes
+    -----
+    This is a wrapper for pydot_layout.
+    """
+    return pydot_layout(G=G, prog=prog, root=root)
+
+
+def pydot_layout(G, prog="neato", root=None):
+    """Create node positions using :mod:`pydot` and Graphviz.
+
+    Parameters
+    ----------
+    G : Graph
+        NetworkX graph to be laid out.
+    prog : string  (default: 'neato')
+        Name of the GraphViz command to use for layout.
+        Options depend on GraphViz version but may include:
+        'dot', 'twopi', 'fdp', 'sfdp', 'circo'
+    root : Node from G or None (default: None)
+        The node of G from which to start some layout algorithms.
+
+    Returns
+    -------
+    dict
+        Dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(4)
+    >>> pos = nx.nx_pydot.pydot_layout(G)
+    >>> pos = nx.nx_pydot.pydot_layout(G, prog="dot")
+
+    Notes
+    -----
+    If you use complex node objects, they may have the same string
+    representation and GraphViz could treat them as the same node.
+    The layout may assign both nodes a single location. See Issue #1568
+    If this occurs in your case, consider relabeling the nodes just
+    for the layout computation using something similar to::
+
+        H = nx.convert_node_labels_to_integers(G, label_attribute="node_label")
+        H_layout = nx.nx_pydot.pydot_layout(H, prog="dot")
+        G_layout = {H.nodes[n]["node_label"]: p for n, p in H_layout.items()}
+
+    """
+    import pydot
+
+    P = to_pydot(G)
+    if root is not None:
+        P.set("root", str(root))
+
+    # List of low-level bytes comprising a string in the dot language converted
+    # from the passed graph with the passed external GraphViz command.
+    D_bytes = P.create_dot(prog=prog)
+
+    # Unique string decoded from these bytes with the preferred locale encoding
+    D = str(D_bytes, encoding=getpreferredencoding())
+
+    if D == "":  # no data returned
+        print(f"Graphviz layout with {prog} failed")
+        print()
+        print("To debug what happened try:")
+        print("P = nx.nx_pydot.to_pydot(G)")
+        print('P.write_dot("file.dot")')
+        print(f"And then run {prog} on file.dot")
+        return
+
+    # List of one or more "pydot.Dot" instances deserialized from this string.
+    Q_list = pydot.graph_from_dot_data(D)
+    assert len(Q_list) == 1
+
+    # The first and only such instance, as guaranteed by the above assertion.
+    Q = Q_list[0]
+
+    node_pos = {}
+    for n in G.nodes():
+        str_n = str(n)
+        node = Q.get_node(pydot.quote_id_if_necessary(str_n))
+
+        if isinstance(node, list):
+            node = node[0]
+        pos = node.get_pos()[1:-1]  # strip leading and trailing double quotes
+        if pos is not None:
+            xx, yy = pos.split(",")
+            node_pos[n] = (float(xx), float(yy))
+    return node_pos
diff --git a/lib/python3.11/site-packages/networkx/drawing/nx_pylab.py b/lib/python3.11/site-packages/networkx/drawing/nx_pylab.py
new file mode 100644
index 0000000000000000000000000000000000000000..143b0a7590811054dd92a1757c64e4bdeb10f320
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/nx_pylab.py
@@ -0,0 +1,2978 @@
+"""
+**********
+Matplotlib
+**********
+
+Draw networks with matplotlib.
+
+Examples
+--------
+>>> G = nx.complete_graph(5)
+>>> nx.draw(G)
+
+See Also
+--------
+ - :doc:`matplotlib <matplotlib:index>`
+ - :func:`matplotlib.pyplot.scatter`
+ - :obj:`matplotlib.patches.FancyArrowPatch`
+"""
+
+import collections
+import itertools
+import math
+from numbers import Number
+
+import networkx as nx
+
+__all__ = [
+    "display",
+    "apply_matplotlib_colors",
+    "draw",
+    "draw_networkx",
+    "draw_networkx_nodes",
+    "draw_networkx_edges",
+    "draw_networkx_labels",
+    "draw_networkx_edge_labels",
+    "draw_bipartite",
+    "draw_circular",
+    "draw_kamada_kawai",
+    "draw_random",
+    "draw_spectral",
+    "draw_spring",
+    "draw_planar",
+    "draw_shell",
+    "draw_forceatlas2",
+]
+
+
+def apply_matplotlib_colors(
+    G, src_attr, dest_attr, map, vmin=None, vmax=None, nodes=True
+):
+    """
+    Apply colors from a matplotlib colormap to a graph.
+
+    Reads values from the `src_attr` and use a matplotlib colormap
+    to produce a color. Write the color to `dest_attr`.
+
+    Parameters
+    ----------
+    G : nx.Graph
+        The graph to read and compute colors for.
+
+    src_attr : str or other attribute name
+        The name of the attribute to read from the graph.
+
+    dest_attr : str or other attribute name
+        The name of the attribute to write to on the graph.
+
+    map : matplotlib.colormap
+        The matplotlib colormap to use.
+
+    vmin : float, default None
+        The minimum value for scaling the colormap. If `None`, find the
+        minimum value of `src_attr`.
+
+    vmax : float, default None
+        The maximum value for scaling the colormap. If `None`, find the
+        maximum value of `src_attr`.
+
+    nodes : bool, default True
+        Whether the attribute names are edge attributes or node attributes.
+    """
+    import matplotlib as mpl
+
+    if nodes:
+        type_iter = G.nodes()
+    elif G.is_multigraph():
+        type_iter = G.edges(keys=True)
+    else:
+        type_iter = G.edges()
+
+    if vmin is None or vmax is None:
+        vals = [type_iter[a][src_attr] for a in type_iter]
+        if vmin is None:
+            vmin = min(vals)
+        if vmax is None:
+            vmax = max(vals)
+
+    mapper = mpl.cm.ScalarMappable(cmap=map)
+    mapper.set_clim(vmin, vmax)
+
+    def do_map(x):
+        # Cast numpy scalars to float
+        return tuple(float(x) for x in mapper.to_rgba(x))
+
+    if nodes:
+        nx.set_node_attributes(
+            G, {n: do_map(G.nodes[n][src_attr]) for n in G.nodes()}, dest_attr
+        )
+    else:
+        nx.set_edge_attributes(
+            G, {e: do_map(G.edges[e][src_attr]) for e in type_iter}, dest_attr
+        )
+
+
+class CurvedArrowTextBase:
+    def __init__(
+        self,
+        arrow,
+        *args,
+        label_pos=0.5,
+        labels_horizontal=False,
+        ax=None,
+        **kwargs,
+    ):
+        # Bind to FancyArrowPatch
+        self.arrow = arrow
+        # how far along the text should be on the curve,
+        # 0 is at start, 1 is at end etc.
+        self.label_pos = label_pos
+        self.labels_horizontal = labels_horizontal
+        if ax is None:
+            ax = plt.gca()
+        self.ax = ax
+        self.x, self.y, self.angle = self._update_text_pos_angle(arrow)
+
+        # Create text object
+        super().__init__(self.x, self.y, *args, rotation=self.angle, **kwargs)
+        # Bind to axis
+        self.ax.add_artist(self)
+
+    def _get_arrow_path_disp(self, arrow):
+        """
+        This is part of FancyArrowPatch._get_path_in_displaycoord
+        It omits the second part of the method where path is converted
+            to polygon based on width
+        The transform is taken from ax, not the object, as the object
+            has not been added yet, and doesn't have transform
+        """
+        dpi_cor = arrow._dpi_cor
+        trans_data = self.ax.transData
+        if arrow._posA_posB is None:
+            raise ValueError(
+                "Can only draw labels for fancy arrows with "
+                "posA and posB inputs, not custom path"
+            )
+        posA = arrow._convert_xy_units(arrow._posA_posB[0])
+        posB = arrow._convert_xy_units(arrow._posA_posB[1])
+        (posA, posB) = trans_data.transform((posA, posB))
+        _path = arrow.get_connectionstyle()(
+            posA,
+            posB,
+            patchA=arrow.patchA,
+            patchB=arrow.patchB,
+            shrinkA=arrow.shrinkA * dpi_cor,
+            shrinkB=arrow.shrinkB * dpi_cor,
+        )
+        # Return is in display coordinates
+        return _path
+
+    def _update_text_pos_angle(self, arrow):
+        # Fractional label position
+        # Text position at a proportion t along the line in display coords
+        # default is 0.5 so text appears at the halfway point
+        import matplotlib as mpl
+        import numpy as np
+
+        t = self.label_pos
+        tt = 1 - t
+        path_disp = self._get_arrow_path_disp(arrow)
+        conn = arrow.get_connectionstyle()
+        # 1. Calculate x and y
+        points = path_disp.vertices
+        if is_curve := isinstance(
+            conn,
+            mpl.patches.ConnectionStyle.Angle3 | mpl.patches.ConnectionStyle.Arc3,
+        ):
+            # Arc3 or Angle3 type Connection Styles - Bezier curve
+            (x1, y1), (cx, cy), (x2, y2) = points
+            x = tt**2 * x1 + 2 * t * tt * cx + t**2 * x2
+            y = tt**2 * y1 + 2 * t * tt * cy + t**2 * y2
+        else:
+            if not isinstance(
+                conn,
+                mpl.patches.ConnectionStyle.Angle
+                | mpl.patches.ConnectionStyle.Arc
+                | mpl.patches.ConnectionStyle.Bar,
+            ):
+                msg = f"invalid connection style: {type(conn)}"
+                raise TypeError(msg)
+            # A. Collect lines
+            codes = path_disp.codes
+            lines = [
+                points[i - 1 : i + 1]
+                for i in range(1, len(points))
+                if codes[i] == mpl.path.Path.LINETO
+            ]
+            # B. If more than one line, find the right one and position in it
+            if (nlines := len(lines)) != 1:
+                dists = [math.dist(*line) for line in lines]
+                dist_tot = sum(dists)
+                cdist = 0
+                last_cut = 0
+                i_last = nlines - 1
+                for i, dist in enumerate(dists):
+                    cdist += dist
+                    cut = cdist / dist_tot
+                    if i == i_last or t < cut:
+                        t = (t - last_cut) / (dist / dist_tot)
+                        tt = 1 - t
+                        lines = [lines[i]]
+                        break
+                    last_cut = cut
+            [[(cx1, cy1), (cx2, cy2)]] = lines
+            x = cx1 * tt + cx2 * t
+            y = cy1 * tt + cy2 * t
+
+        # 2. Calculate Angle
+        if self.labels_horizontal:
+            # Horizontal text labels
+            angle = 0
+        else:
+            # Labels parallel to curve
+            if is_curve:
+                change_x = 2 * tt * (cx - x1) + 2 * t * (x2 - cx)
+                change_y = 2 * tt * (cy - y1) + 2 * t * (y2 - cy)
+            else:
+                change_x = (cx2 - cx1) / 2
+                change_y = (cy2 - cy1) / 2
+            angle = np.arctan2(change_y, change_x) / (2 * np.pi) * 360
+            # Text is "right way up"
+            if angle > 90:
+                angle -= 180
+            elif angle < -90:
+                angle += 180
+        (x, y) = self.ax.transData.inverted().transform((x, y))
+        return x, y, angle
+
+    def draw(self, renderer):
+        # recalculate the text position and angle
+        self.x, self.y, self.angle = self._update_text_pos_angle(self.arrow)
+        self.set_position((self.x, self.y))
+        self.set_rotation(self.angle)
+        # redraw text
+        super().draw(renderer)
+
+
+def display(
+    G,
+    canvas=None,
+    **kwargs,
+):
+    """Draw the graph G.
+
+    Draw the graph as a collection of nodes connected by edges.
+    The exact details of what the graph looks like are controlled by the below
+    attributes. All nodes and nodes at the end of visible edges must have a
+    position set, but nearly all other node and edge attributes are options and
+    nodes or edges missing the attribute will use the default listed below. A more
+    complete description of each parameter is given below this summary.
+
+    .. list-table:: Default Visualization Attributes
+        :widths: 25 25 50
+        :header-rows: 1
+
+        * - Parameter
+          - Default Attribute
+          - Default Value
+        * - node_pos
+          - `"pos"`
+          - If there is not position, a layout will be calculated with `nx.spring_layout`.
+        * - node_visible
+          - `"visible"`
+          - True
+        * - node_color
+          - `"color"`
+          - #1f78b4
+        * - node_size
+          - `"size"`
+          - 300
+        * - node_label
+          - `"label"`
+          - Dict describing the node label. Defaults create a black text with
+            the node name as the label. The dict respects these keys and defaults:
+
+            * size : 12
+            * color : black
+            * family : sans serif
+            * weight : normal
+            * alpha : 1.0
+            * h_align : center
+            * v_align : center
+            * bbox : Dict describing a `matplotlib.patches.FancyBboxPatch`.
+              Default is None.
+
+        * - node_shape
+          - `"shape"`
+          - "o"
+        * - node_alpha
+          - `"alpha"`
+          - 1.0
+        * - node_border_width
+          - `"border_width"`
+          - 1.0
+        * - node_border_color
+          - `"border_color"`
+          - Matching node_color
+        * - edge_visible
+          - `"visible"`
+          - True
+        * - edge_width
+          - `"width"`
+          - 1.0
+        * - edge_color
+          - `"color"`
+          - Black (#000000)
+        * - edge_label
+          - `"label"`
+          - Dict describing the edge label. Defaults create black text with a
+            white bounding box. The dictionary respects these keys and defaults:
+
+            * size : 12
+            * color : black
+            * family : sans serif
+            * weight : normal
+            * alpha : 1.0
+            * bbox : Dict describing a `matplotlib.patches.FancyBboxPatch`.
+              Default {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)}
+            * h_align : "center"
+            * v_align : "center"
+            * pos : 0.5
+            * rotate : True
+
+        * - edge_style
+          - `"style"`
+          - "-"
+        * - edge_alpha
+          - `"alpha"`
+          - 1.0
+        * - edge_arrowstyle
+          - `"arrowstyle"`
+          - ``"-|>"`` if `G` is directed else ``"-"``
+        * - edge_arrowsize
+          - `"arrowsize"`
+          - 10 if `G` is directed else 0
+        * - edge_curvature
+          - `"curvature"`
+          - arc3
+        * - edge_source_margin
+          - `"source_margin"`
+          - 0
+        * - edge_target_margin
+          - `"target_margin"`
+          - 0
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    canvas : Matplotlib Axes object, optional
+        Draw the graph in specified Matplotlib axes
+
+    node_pos : string or function, default "pos"
+        A string naming the node attribute storing the position of nodes as a tuple.
+        Or a function to be called with input `G` which returns the layout as a dict keyed
+        by node to position tuple like the NetworkX layout functions.
+        If no nodes in the graph has the attribute, a spring layout is calculated.
+
+    node_visible : string or bool, default visible
+        A string naming the node attribute which stores if a node should be drawn.
+        If `True`, all nodes will be visible while if `False` no nodes will be visible.
+        If incomplete, nodes missing this attribute will be shown by default.
+
+    node_color : string, default "color"
+        A string naming the node attribute which stores the color of each node.
+        Visible nodes without this attribute will use '#1f78b4' as a default.
+
+    node_size : string or number, default "size"
+        A string naming the node attribute which stores the size of each node.
+        Visible nodes without this attribute will use a default size of 300.
+
+    node_label : string or bool, default "label"
+        A string naming the node attribute which stores the label of each node.
+        The attribute value can be a string, False (no label for that node),
+        True (the node is the label) or a dict keyed by node to the label.
+
+        If a dict is specified, these keys are read to further control the label:
+
+        * label : The text of the label; default: name of the node
+        * size : Font size of the label; default: 12
+        * color : Font color of the label; default: black
+        * family : Font family of the label; default: "sans-serif"
+        * weight : Font weight of the label; default: "normal"
+        * alpha : Alpha value of the label; default: 1.0
+        * h_align : The horizontal alignment of the label.
+            one of "left", "center", "right"; default: "center"
+        * v_align : The vertical alignment of the label.
+            one of "top", "center", "bottom"; default: "center"
+        * bbox : A dict of parameters for `matplotlib.patches.FancyBboxPatch`.
+
+        Visible nodes without this attribute will be treated as if the value was True.
+
+    node_shape : string, default "shape"
+        A string naming the node attribute which stores the label of each node.
+        The values of this attribute are expected to be one of the matplotlib shapes,
+        one of 'so^>v<dph8'. Visible nodes without this attribute will use 'o'.
+
+    node_alpha : string, default "alpha"
+        A string naming the node attribute which stores the alpha of each node.
+        The values of this attribute are expected to be floats between 0.0 and 1.0.
+        Visible nodes without this attribute will be treated as if the value was 1.0.
+
+    node_border_width : string, default "border_width"
+        A string naming the node attribute storing the width of the border of the node.
+        The values of this attribute are expected to be numeric. Visible nodes without
+        this attribute will use the assumed default of 1.0.
+
+    node_border_color : string, default "border_color"
+        A string naming the node attribute which storing the color of the border of the node.
+        Visible nodes missing this attribute will use the final node_color value.
+
+    edge_visible : string or bool, default "visible"
+        A string nameing the edge attribute which stores if an edge should be drawn.
+        If `True`, all edges will be drawn while if `False` no edges will be visible.
+        If incomplete, edges missing this attribute will be shown by default. Values
+        of this attribute are expected to be booleans.
+
+    edge_width : string or int, default "width"
+        A string nameing the edge attribute which stores the width of each edge.
+        Visible edges without this attribute will use a default width of 1.0.
+
+    edge_color : string or color, default "color"
+        A string nameing the edge attribute which stores of color of each edge.
+        Visible edges without this attribute will be drawn black. Each color can be
+        a string or rgb (or rgba) tuple of floats from 0.0 to 1.0.
+
+    edge_label : string, default "label"
+        A string naming the edge attribute which stores the label of each edge.
+        The values of this attribute can be a string, number or False or None. In
+        the latter two cases, no edge label is displayed.
+
+        If a dict is specified, these keys are read to further control the label:
+
+        * label : The text of the label, or the name of an edge attribute holding the label.
+        * size : Font size of the label; default: 12
+        * color : Font color of the label; default: black
+        * family : Font family of the label; default: "sans-serif"
+        * weight : Font weight of the label; default: "normal"
+        * alpha : Alpha value of the label; default: 1.0
+        * h_align : The horizontal alignment of the label.
+            one of "left", "center", "right"; default: "center"
+        * v_align : The vertical alignment of the label.
+            one of "top", "center", "bottom"; default: "center"
+        * bbox : A dict of parameters for `matplotlib.patches.FancyBboxPatch`.
+        * rotate : Whether to rotate labels to lie parallel to the edge, default: True.
+        * pos : A float showing how far along the edge to put the label; default: 0.5.
+
+    edge_style : string, default "style"
+        A string naming the edge attribute which stores the style of each edge.
+        Visible edges without this attribute will be drawn solid. Values of this
+        attribute can be line styles, e.g. '-', '--', '-.' or ':' or words like 'solid'
+        or 'dashed'. If no edge in the graph has this attribute and it is a non-default
+        value, assume that it describes the edge style for all edges in the graph.
+
+    edge_alpha : string or float, default "alpha"
+        A string naming the edge attribute which stores the alpha value of each edge.
+        Visible edges without this attribute will use an alpha value of 1.0.
+
+    edge_arrowstyle : string, default "arrowstyle"
+        A string naming the edge attribute which stores the type of arrowhead to use for
+        each edge. Visible edges without this attribute use ``"-"`` for undirected graphs
+        and ``"-|>"`` for directed graphs.
+
+        See `matplotlib.patches.ArrowStyle` for more options
+
+    edge_arrowsize : string or int, default "arrowsize"
+        A string naming the edge attribute which stores the size of the arrowhead for each
+        edge. Visible edges without this attribute will use a default value of 10.
+
+    edge_curvature : string, default "curvature"
+       A string naming the edge attribute storing the curvature and connection style
+       of each edge. Visible edges without this attribute will use "arc3" as a default
+       value, resulting an a straight line between the two nodes. Curvature can be given
+       as 'arc3,rad=0.2' to specify both the style and radius of curvature.
+
+       Please see `matplotlib.patches.ConnectionStyle` and
+       `matplotlib.patches.FancyArrowPatch` for more information.
+
+    edge_source_margin : string or int, default "source_margin"
+        A string naming the edge attribute which stores the minimum margin (gap) between
+        the source node and the start of the edge. Visible edges without this attribute
+        will use a default value of 0.
+
+    edge_target_margin : string or int, default "target_margin"
+        A string naming the edge attribute which stores the minimumm margin (gap) between
+        the target node and the end of the edge. Visible edges without this attribute
+        will use a default value of 0.
+
+    hide_ticks : bool, default True
+        Weather to remove the ticks from the axes of the matplotlib object.
+
+    Raises
+    ------
+    NetworkXError
+        If a node or edge is missing a required parameter such as `pos` or
+        if `display` receives an argument not listed above.
+
+    ValueError
+        If a node or edge has an invalid color format, i.e. not a color string,
+        rgb tuple or rgba tuple.
+
+    Returns
+    -------
+    The input graph. This is potentially useful for dispatching visualization
+    functions.
+    """
+    from collections import Counter
+
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    defaults = {
+        "node_pos": None,
+        "node_visible": True,
+        "node_color": "#1f78b4",
+        "node_size": 300,
+        "node_label": {
+            "size": 12,
+            "color": "#000000",
+            "family": "sans-serif",
+            "weight": "normal",
+            "alpha": 1.0,
+            "h_align": "center",
+            "v_align": "center",
+            "bbox": None,
+        },
+        "node_shape": "o",
+        "node_alpha": 1.0,
+        "node_border_width": 1.0,
+        "node_border_color": "face",
+        "edge_visible": True,
+        "edge_width": 1.0,
+        "edge_color": "#000000",
+        "edge_label": {
+            "size": 12,
+            "color": "#000000",
+            "family": "sans-serif",
+            "weight": "normal",
+            "alpha": 1.0,
+            "bbox": {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)},
+            "h_align": "center",
+            "v_align": "center",
+            "pos": 0.5,
+            "rotate": True,
+        },
+        "edge_style": "-",
+        "edge_alpha": 1.0,
+        "edge_arrowstyle": "-|>" if G.is_directed() else "-",
+        "edge_arrowsize": 10 if G.is_directed() else 0,
+        "edge_curvature": "arc3",
+        "edge_source_margin": 0,
+        "edge_target_margin": 0,
+        "hide_ticks": True,
+    }
+
+    # Check arguments
+    for kwarg in kwargs:
+        if kwarg not in defaults:
+            raise nx.NetworkXError(
+                f"Unrecognized visualization keyword argument: {kwarg}"
+            )
+
+    if canvas is None:
+        canvas = plt.gca()
+
+    if kwargs.get("hide_ticks", defaults["hide_ticks"]):
+        canvas.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    ### Helper methods and classes
+
+    def node_property_sequence(seq, attr):
+        """Return a list of attribute values for `seq`, using a default if needed"""
+
+        # All node attribute parameters start with "node_"
+        param_name = f"node_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None:
+            # raise instead of using non-existant default value
+            for n in seq:
+                if attr not in node_subgraph.nodes[n]:
+                    raise nx.NetworkXError(f"Attribute '{attr}' missing for node {n}")
+
+        # If `attr` is not a graph attr and was explicitly passed as an argument
+        # it must be a user-default value. Allow attr=None to tell draw to skip
+        # attributes which are on the graph
+        if (
+            attr is not None
+            and nx.get_node_attributes(node_subgraph, attr) == {}
+            and any(attr == v for k, v in kwargs.items() if "node" in k)
+        ):
+            return [attr for _ in seq]
+
+        return [node_subgraph.nodes[n].get(attr, default) for n in seq]
+
+    def compute_colors(color, alpha):
+        if isinstance(color, str):
+            rgba = mpl.colors.colorConverter.to_rgba(color)
+            # Using a non-default alpha value overrides any alpha value in the color
+            if alpha != defaults["node_alpha"]:
+                return (rgba[0], rgba[1], rgba[2], alpha)
+            return rgba
+
+        if isinstance(color, tuple) and len(color) == 3:
+            return (color[0], color[1], color[2], alpha)
+
+        if isinstance(color, tuple) and len(color) == 4:
+            return color
+
+        raise ValueError(f"Invalid format for color: {color}")
+
+    # Find which edges can be plotted as a line collection
+    #
+    # Non-default values for these attributes require fancy arrow patches:
+    # - any arrow style (including the default -|> for directed graphs)
+    # - arrow size (by extension of style)
+    # - connection style
+    # - min_source_margin
+    # - min_target_margin
+
+    def collection_compatible(e):
+        return (
+            get_edge_attr(e, "arrowstyle") == "-"
+            and get_edge_attr(e, "curvature") == "arc3"
+            and get_edge_attr(e, "source_margin") == 0
+            and get_edge_attr(e, "target_margin") == 0
+            # Self-loops will use fancy arrow patches
+            and e[0] != e[1]
+        )
+
+    def edge_property_sequence(seq, attr):
+        """Return a list of attribute values for `seq`, using a default if needed"""
+
+        param_name = f"edge_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None:
+            # raise instead of using non-existant default value
+            for e in seq:
+                if attr not in edge_subgraph.edges[e]:
+                    raise nx.NetworkXError(f"Attribute '{attr}' missing for edge {e}")
+
+        if (
+            attr is not None
+            and nx.get_edge_attributes(edge_subgraph, attr) == {}
+            and any(attr == v for k, v in kwargs.items() if "edge" in k)
+        ):
+            return [attr for _ in seq]
+
+        return [edge_subgraph.edges[e].get(attr, default) for e in seq]
+
+    def get_edge_attr(e, attr):
+        """Return the final edge attribute value, using default if not None"""
+
+        param_name = f"edge_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None and attr not in edge_subgraph.edges[e]:
+            raise nx.NetworkXError(f"Attribute '{attr}' missing from edge {e}")
+
+        if (
+            attr is not None
+            and nx.get_edge_attributes(edge_subgraph, attr) == {}
+            and attr in kwargs.values()
+        ):
+            return attr
+
+        return edge_subgraph.edges[e].get(attr, default)
+
+    def get_node_attr(n, attr, use_edge_subgraph=True):
+        """Return the final node attribute value, using default if not None"""
+        subgraph = edge_subgraph if use_edge_subgraph else node_subgraph
+
+        param_name = f"node_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None and attr not in subgraph.nodes[n]:
+            raise nx.NetworkXError(f"Attribute '{attr}' missing from node {n}")
+
+        if (
+            attr is not None
+            and nx.get_node_attributes(subgraph, attr) == {}
+            and attr in kwargs.values()
+        ):
+            return attr
+
+        return subgraph.nodes[n].get(attr, default)
+
+    # Taken from ConnectionStyleFactory
+    def self_loop(edge_index, node_size):
+        def self_loop_connection(posA, posB, *args, **kwargs):
+            if not np.all(posA == posB):
+                raise nx.NetworkXError(
+                    "`self_loop` connection style method"
+                    "is only to be used for self-loops"
+                )
+            # this is called with _screen space_ values
+            # so convert back to data space
+            data_loc = canvas.transData.inverted().transform(posA)
+            # Scale self loop based on the size of the base node
+            # Size of nodes are given in points ** 2 and each point is 1/72 of an inch
+            v_shift = np.sqrt(node_size) / 72
+            h_shift = v_shift * 0.5
+            # put the top of the loop first so arrow is not hidden by node
+            path = np.asarray(
+                [
+                    # 1
+                    [0, v_shift],
+                    # 4 4 4
+                    [h_shift, v_shift],
+                    [h_shift, 0],
+                    [0, 0],
+                    # 4 4 4
+                    [-h_shift, 0],
+                    [-h_shift, v_shift],
+                    [0, v_shift],
+                ]
+            )
+            # Rotate self loop 90 deg. if more than 1
+            # This will allow for maximum of 4 visible self loops
+            if edge_index % 4:
+                x, y = path.T
+                for _ in range(edge_index % 4):
+                    x, y = y, -x
+                path = np.array([x, y]).T
+            return mpl.path.Path(
+                canvas.transData.transform(data_loc + path), [1, 4, 4, 4, 4, 4, 4]
+            )
+
+        return self_loop_connection
+
+    def to_marker_edge(size, marker):
+        if marker in "s^>v<d":
+            return np.sqrt(2 * size) / 2
+        else:
+            return np.sqrt(size) / 2
+
+    def build_fancy_arrow(e):
+        source_margin = to_marker_edge(
+            get_node_attr(e[0], "size"),
+            get_node_attr(e[0], "shape"),
+        )
+        source_margin = max(
+            source_margin,
+            get_edge_attr(e, "source_margin"),
+        )
+
+        target_margin = to_marker_edge(
+            get_node_attr(e[1], "size"),
+            get_node_attr(e[1], "shape"),
+        )
+        target_margin = max(
+            target_margin,
+            get_edge_attr(e, "target_margin"),
+        )
+        return mpl.patches.FancyArrowPatch(
+            edge_subgraph.nodes[e[0]][pos],
+            edge_subgraph.nodes[e[1]][pos],
+            arrowstyle=get_edge_attr(e, "arrowstyle"),
+            connectionstyle=(
+                get_edge_attr(e, "curvature")
+                if e[0] != e[1]
+                else self_loop(
+                    0 if len(e) == 2 else e[2] % 4,
+                    get_node_attr(e[0], "size"),
+                )
+            ),
+            color=get_edge_attr(e, "color"),
+            linestyle=get_edge_attr(e, "style"),
+            linewidth=get_edge_attr(e, "width"),
+            mutation_scale=get_edge_attr(e, "arrowsize"),
+            shrinkA=source_margin,
+            shrinkB=source_margin,
+            zorder=1,
+        )
+
+    class CurvedArrowText(CurvedArrowTextBase, mpl.text.Text):
+        pass
+
+    ### Draw the nodes first
+    node_visible = kwargs.get("node_visible", "visible")
+    if isinstance(node_visible, bool):
+        if node_visible:
+            visible_nodes = G.nodes()
+        else:
+            visible_nodes = []
+    else:
+        visible_nodes = [
+            n for n, v in nx.get_node_attributes(G, node_visible, True).items() if v
+        ]
+
+    node_subgraph = G.subgraph(visible_nodes)
+
+    # Ignore the default dict value since that's for default values to use, not
+    # default attribute name
+    pos = kwargs.get("node_pos", "pos")
+
+    default_display_pos_attr = "display's position attribute name"
+    if callable(pos):
+        nx.set_node_attributes(
+            node_subgraph, pos(node_subgraph), default_display_pos_attr
+        )
+        pos = default_display_pos_attr
+        kwargs["node_pos"] = default_display_pos_attr
+    elif nx.get_node_attributes(G, pos) == {}:
+        nx.set_node_attributes(
+            node_subgraph, nx.spring_layout(node_subgraph), default_display_pos_attr
+        )
+        pos = default_display_pos_attr
+        kwargs["node_pos"] = default_display_pos_attr
+
+    # Each shape requires a new scatter object since they can't have different
+    # shapes.
+    if len(visible_nodes) > 0:
+        node_shape = kwargs.get("node_shape", "shape")
+        for shape in Counter(
+            nx.get_node_attributes(
+                node_subgraph, node_shape, defaults["node_shape"]
+            ).values()
+        ):
+            # Filter position just on this shape.
+            nodes_with_shape = [
+                n
+                for n, s in node_subgraph.nodes(data=node_shape)
+                if s == shape or (s is None and shape == defaults["node_shape"])
+            ]
+            # There are two property sequences to create before hand.
+            # 1. position, since it is used for x and y parameters to scatter
+            # 2. edgecolor, since the spaeical 'face' parameter value can only be
+            #    be passed in as the sole string, not part of a list of strings.
+            position = np.asarray(node_property_sequence(nodes_with_shape, "pos"))
+            color = np.asarray(
+                [
+                    compute_colors(c, a)
+                    for c, a in zip(
+                        node_property_sequence(nodes_with_shape, "color"),
+                        node_property_sequence(nodes_with_shape, "alpha"),
+                    )
+                ]
+            )
+            border_color = np.asarray(
+                [
+                    (
+                        c
+                        if (
+                            c := get_node_attr(
+                                n,
+                                "border_color",
+                                False,
+                            )
+                        )
+                        != "face"
+                        else color[i]
+                    )
+                    for i, n in enumerate(nodes_with_shape)
+                ]
+            )
+            canvas.scatter(
+                position[:, 0],
+                position[:, 1],
+                s=node_property_sequence(nodes_with_shape, "size"),
+                c=color,
+                marker=shape,
+                linewidths=node_property_sequence(nodes_with_shape, "border_width"),
+                edgecolors=border_color,
+                zorder=2,
+            )
+
+    ### Draw node labels
+    node_label = kwargs.get("node_label", "label")
+    # Plot labels if node_label is not None and not False
+    if node_label is not None and node_label is not False:
+        default_dict = {}
+        if isinstance(node_label, dict):
+            default_dict = node_label
+            node_label = None
+
+        for n, lbl in node_subgraph.nodes(data=node_label):
+            if lbl is False:
+                continue
+
+            # We work with label dicts down here...
+            if not isinstance(lbl, dict):
+                lbl = {"label": lbl if lbl is not None else n}
+
+            lbl_text = lbl.get("label", n)
+            if not isinstance(lbl_text, str):
+                lbl_text = str(lbl_text)
+
+            lbl.update(default_dict)
+            x, y = node_subgraph.nodes[n][pos]
+            canvas.text(
+                x,
+                y,
+                lbl_text,
+                size=lbl.get("size", defaults["node_label"]["size"]),
+                color=lbl.get("color", defaults["node_label"]["color"]),
+                family=lbl.get("family", defaults["node_label"]["family"]),
+                weight=lbl.get("weight", defaults["node_label"]["weight"]),
+                horizontalalignment=lbl.get(
+                    "h_align", defaults["node_label"]["h_align"]
+                ),
+                verticalalignment=lbl.get("v_align", defaults["node_label"]["v_align"]),
+                transform=canvas.transData,
+                bbox=lbl.get("bbox", defaults["node_label"]["bbox"]),
+            )
+
+    ### Draw edges
+
+    edge_visible = kwargs.get("edge_visible", "visible")
+    if isinstance(edge_visible, bool):
+        if edge_visible:
+            visible_edges = G.edges()
+        else:
+            visible_edges = []
+    else:
+        visible_edges = [
+            e for e, v in nx.get_edge_attributes(G, edge_visible, True).items() if v
+        ]
+
+    edge_subgraph = G.edge_subgraph(visible_edges)
+    nx.set_node_attributes(
+        edge_subgraph, nx.get_node_attributes(node_subgraph, pos), name=pos
+    )
+
+    collection_edges = (
+        [e for e in edge_subgraph.edges(keys=True) if collection_compatible(e)]
+        if edge_subgraph.is_multigraph()
+        else [e for e in edge_subgraph.edges() if collection_compatible(e)]
+    )
+    non_collection_edges = (
+        [e for e in edge_subgraph.edges(keys=True) if not collection_compatible(e)]
+        if edge_subgraph.is_multigraph()
+        else [e for e in edge_subgraph.edges() if not collection_compatible(e)]
+    )
+    edge_position = np.asarray(
+        [
+            (
+                get_node_attr(u, "pos", use_edge_subgraph=True),
+                get_node_attr(v, "pos", use_edge_subgraph=True),
+            )
+            for u, v, *_ in collection_edges
+        ]
+    )
+
+    # Only plot a line collection if needed
+    if len(collection_edges) > 0:
+        edge_collection = mpl.collections.LineCollection(
+            edge_position,
+            colors=edge_property_sequence(collection_edges, "color"),
+            linewidths=edge_property_sequence(collection_edges, "width"),
+            linestyle=edge_property_sequence(collection_edges, "style"),
+            alpha=edge_property_sequence(collection_edges, "alpha"),
+            antialiaseds=(1,),
+            zorder=1,
+        )
+        canvas.add_collection(edge_collection)
+
+    fancy_arrows = {}
+    if len(non_collection_edges) > 0:
+        for e in non_collection_edges:
+            # Cache results for use in edge labels
+            fancy_arrows[e] = build_fancy_arrow(e)
+            canvas.add_patch(fancy_arrows[e])
+
+    ### Draw edge labels
+    edge_label = kwargs.get("edge_label", "label")
+    default_dict = {}
+    if isinstance(edge_label, dict):
+        default_dict = edge_label
+        # Restore the default label attribute key of 'label'
+        edge_label = "label"
+
+    # Handle multigraphs
+    edge_label_data = (
+        edge_subgraph.edges(data=edge_label, keys=True)
+        if edge_subgraph.is_multigraph()
+        else edge_subgraph.edges(data=edge_label)
+    )
+    if edge_label is not None and edge_label is not False:
+        for *e, lbl in edge_label_data:
+            e = tuple(e)
+            # I'm not sure how I want to handle None here... For now it means no label
+            if lbl is False or lbl is None:
+                continue
+
+            if not isinstance(lbl, dict):
+                lbl = {"label": lbl}
+
+            lbl.update(default_dict)
+            lbl_text = lbl.get("label")
+            if not isinstance(lbl_text, str):
+                lbl_text = str(lbl_text)
+
+            # In the old code, every non-self-loop is placed via a fancy arrow patch
+            # Only compute a new fancy arrow if needed by caching the results from
+            # edge placement.
+            try:
+                arrow = fancy_arrows[e]
+            except KeyError:
+                arrow = build_fancy_arrow(e)
+
+            if e[0] == e[1]:
+                # Taken directly from draw_networkx_edge_labels
+                connectionstyle_obj = arrow.get_connectionstyle()
+                posA = canvas.transData.transform(edge_subgraph.nodes[e[0]][pos])
+                path_disp = connectionstyle_obj(posA, posA)
+                path_data = canvas.transData.inverted().transform_path(path_disp)
+                x, y = path_data.vertices[0]
+                canvas.text(
+                    x,
+                    y,
+                    lbl_text,
+                    size=lbl.get("size", defaults["edge_label"]["size"]),
+                    color=lbl.get("color", defaults["edge_label"]["color"]),
+                    family=lbl.get("family", defaults["edge_label"]["family"]),
+                    weight=lbl.get("weight", defaults["edge_label"]["weight"]),
+                    alpha=lbl.get("alpha", defaults["edge_label"]["alpha"]),
+                    horizontalalignment=lbl.get(
+                        "h_align", defaults["edge_label"]["h_align"]
+                    ),
+                    verticalalignment=lbl.get(
+                        "v_align", defaults["edge_label"]["v_align"]
+                    ),
+                    rotation=0,
+                    transform=canvas.transData,
+                    bbox=lbl.get("bbox", defaults["edge_label"]["bbox"]),
+                    zorder=1,
+                )
+                continue
+
+            CurvedArrowText(
+                arrow,
+                lbl_text,
+                size=lbl.get("size", defaults["edge_label"]["size"]),
+                color=lbl.get("color", defaults["edge_label"]["color"]),
+                family=lbl.get("family", defaults["edge_label"]["family"]),
+                weight=lbl.get("weight", defaults["edge_label"]["weight"]),
+                alpha=lbl.get("alpha", defaults["edge_label"]["alpha"]),
+                bbox=lbl.get("bbox", defaults["edge_label"]["bbox"]),
+                horizontalalignment=lbl.get(
+                    "h_align", defaults["edge_label"]["h_align"]
+                ),
+                verticalalignment=lbl.get("v_align", defaults["edge_label"]["v_align"]),
+                label_pos=lbl.get("pos", defaults["edge_label"]["pos"]),
+                labels_horizontal=lbl.get("rotate", defaults["edge_label"]["rotate"]),
+                transform=canvas.transData,
+                zorder=1,
+                ax=canvas,
+            )
+
+    # If we had to add an attribute, remove it here
+    if pos == default_display_pos_attr:
+        nx.remove_node_attributes(G, default_display_pos_attr)
+
+    return G
+
+
+def draw(G, pos=None, ax=None, **kwds):
+    """Draw the graph G with Matplotlib.
+
+    Draw the graph as a simple representation with no node
+    labels or edge labels and using the full Matplotlib figure area
+    and no axis labels by default.  See draw_networkx() for more
+    full-featured drawing that allows title, axis labels etc.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary, optional
+        A dictionary with nodes as keys and positions as values.
+        If not specified a spring layout positioning will be computed.
+        See :py:mod:`networkx.drawing.layout` for functions that
+        compute node positions.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in specified Matplotlib axes.
+
+    kwds : optional keywords
+        See networkx.draw_networkx() for a description of optional keywords.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> nx.draw(G)
+    >>> nx.draw(G, pos=nx.spring_layout(G))  # use spring layout
+
+    See Also
+    --------
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_labels
+    draw_networkx_edge_labels
+
+    Notes
+    -----
+    This function has the same name as pylab.draw and pyplot.draw
+    so beware when using `from networkx import *`
+
+    since you might overwrite the pylab.draw function.
+
+    With pyplot use
+
+    >>> import matplotlib.pyplot as plt
+    >>> G = nx.dodecahedral_graph()
+    >>> nx.draw(G)  # networkx draw()
+    >>> plt.draw()  # pyplot draw()
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+    """
+
+    import matplotlib.pyplot as plt
+
+    if ax is None:
+        cf = plt.gcf()
+    else:
+        cf = ax.get_figure()
+    cf.set_facecolor("w")
+    if ax is None:
+        if cf.axes:
+            ax = cf.gca()
+        else:
+            ax = cf.add_axes((0, 0, 1, 1))
+
+    if "with_labels" not in kwds:
+        kwds["with_labels"] = "labels" in kwds
+
+    draw_networkx(G, pos=pos, ax=ax, **kwds)
+    ax.set_axis_off()
+    plt.draw_if_interactive()
+    return
+
+
+def draw_networkx(G, pos=None, arrows=None, with_labels=True, **kwds):
+    r"""Draw the graph G using Matplotlib.
+
+    Draw the graph with Matplotlib with options for node positions,
+    labeling, titles, and many other drawing features.
+    See draw() for simple drawing without labels or axes.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary, optional
+        A dictionary with nodes as keys and positions as values.
+        If not specified a spring layout positioning will be computed.
+        See :py:mod:`networkx.drawing.layout` for functions that
+        compute node positions.
+
+    arrows : bool or None, optional (default=None)
+        If `None`, directed graphs draw arrowheads with
+        `~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
+        via `~matplotlib.collections.LineCollection` for speed.
+        If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
+        If `False`, draw edges using LineCollection (linear and fast).
+        For directed graphs, if True draw arrowheads.
+        Note: Arrows will be the same color as edges.
+
+    arrowstyle : str (default='-\|>' for directed graphs)
+        For directed graphs, choose the style of the arrowsheads.
+        For undirected graphs default to '-'
+
+        See `matplotlib.patches.ArrowStyle` for more options.
+
+    arrowsize : int or list (default=10)
+        For directed graphs, choose the size of the arrow head's length and
+        width. A list of values can be passed in to assign a different size for arrow head's length and width.
+        See `matplotlib.patches.FancyArrowPatch` for attribute `mutation_scale`
+        for more info.
+
+    with_labels :  bool (default=True)
+        Set to True to draw labels on the nodes.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    nodelist : list (default=list(G))
+        Draw only specified nodes
+
+    edgelist : list (default=list(G.edges()))
+        Draw only specified edges
+
+    node_size : scalar or array (default=300)
+        Size of nodes.  If an array is specified it must be the
+        same length as nodelist.
+
+    node_color : color or array of colors (default='#1f78b4')
+        Node color. Can be a single color or a sequence of colors with the same
+        length as nodelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the cmap and vmin,vmax parameters. See
+        matplotlib.scatter for more details.
+
+    node_shape :  string (default='o')
+        The shape of the node.  Specification is as matplotlib.scatter
+        marker, one of 'so^>v<dph8'.
+
+    alpha : float or None (default=None)
+        The node and edge transparency
+
+    cmap : Matplotlib colormap, optional
+        Colormap for mapping intensities of nodes
+
+    vmin,vmax : float, optional
+        Minimum and maximum for node colormap scaling
+
+    linewidths : scalar or sequence (default=1.0)
+        Line width of symbol border
+
+    width : float or array of floats (default=1.0)
+        Line width of edges
+
+    edge_color : color or array of colors (default='k')
+        Edge color. Can be a single color or a sequence of colors with the same
+        length as edgelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
+
+    edge_cmap : Matplotlib colormap, optional
+        Colormap for mapping intensities of edges
+
+    edge_vmin,edge_vmax : floats, optional
+        Minimum and maximum for edge colormap scaling
+
+    style : string (default=solid line)
+        Edge line style e.g.: '-', '--', '-.', ':'
+        or words like 'solid' or 'dashed'.
+        (See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
+
+    labels : dictionary (default=None)
+        Node labels in a dictionary of text labels keyed by node
+
+    font_size : int (default=12 for nodes, 10 for edges)
+        Font size for text labels
+
+    font_color : color (default='k' black)
+        Font color string. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1.
+
+    font_weight : string (default='normal')
+        Font weight
+
+    font_family : string (default='sans-serif')
+        Font family
+
+    label : string, optional
+        Label for graph legend
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    kwds : optional keywords
+        See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and
+        networkx.draw_networkx_labels() for a description of optional keywords.
+
+    Notes
+    -----
+    For directed graphs, arrows  are drawn at the head end.  Arrows can be
+    turned off with keyword arrows=False.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> nx.draw(G)
+    >>> nx.draw(G, pos=nx.spring_layout(G))  # use spring layout
+
+    >>> import matplotlib.pyplot as plt
+    >>> limits = plt.axis("off")  # turn off axis
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_labels
+    draw_networkx_edge_labels
+    """
+    from inspect import signature
+
+    import matplotlib.pyplot as plt
+
+    # Get all valid keywords by inspecting the signatures of draw_networkx_nodes,
+    # draw_networkx_edges, draw_networkx_labels
+
+    valid_node_kwds = signature(draw_networkx_nodes).parameters.keys()
+    valid_edge_kwds = signature(draw_networkx_edges).parameters.keys()
+    valid_label_kwds = signature(draw_networkx_labels).parameters.keys()
+
+    # Create a set with all valid keywords across the three functions and
+    # remove the arguments of this function (draw_networkx)
+    valid_kwds = (valid_node_kwds | valid_edge_kwds | valid_label_kwds) - {
+        "G",
+        "pos",
+        "arrows",
+        "with_labels",
+    }
+
+    if any(k not in valid_kwds for k in kwds):
+        invalid_args = ", ".join([k for k in kwds if k not in valid_kwds])
+        raise ValueError(f"Received invalid argument(s): {invalid_args}")
+
+    node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds}
+    edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds}
+    label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds}
+
+    if pos is None:
+        pos = nx.drawing.spring_layout(G)  # default to spring layout
+
+    draw_networkx_nodes(G, pos, **node_kwds)
+    draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds)
+    if with_labels:
+        draw_networkx_labels(G, pos, **label_kwds)
+    plt.draw_if_interactive()
+
+
+def draw_networkx_nodes(
+    G,
+    pos,
+    nodelist=None,
+    node_size=300,
+    node_color="#1f78b4",
+    node_shape="o",
+    alpha=None,
+    cmap=None,
+    vmin=None,
+    vmax=None,
+    ax=None,
+    linewidths=None,
+    edgecolors=None,
+    label=None,
+    margins=None,
+    hide_ticks=True,
+):
+    """Draw the nodes of the graph G.
+
+    This draws only the nodes of the graph G.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    nodelist : list (default list(G))
+        Draw only specified nodes
+
+    node_size : scalar or array (default=300)
+        Size of nodes.  If an array it must be the same length as nodelist.
+
+    node_color : color or array of colors (default='#1f78b4')
+        Node color. Can be a single color or a sequence of colors with the same
+        length as nodelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the cmap and vmin,vmax parameters. See
+        matplotlib.scatter for more details.
+
+    node_shape :  string (default='o')
+        The shape of the node.  Specification is as matplotlib.scatter
+        marker, one of 'so^>v<dph8'.
+
+    alpha : float or array of floats (default=None)
+        The node transparency.  This can be a single alpha value,
+        in which case it will be applied to all the nodes of color. Otherwise,
+        if it is an array, the elements of alpha will be applied to the colors
+        in order (cycling through alpha multiple times if necessary).
+
+    cmap : Matplotlib colormap (default=None)
+        Colormap for mapping intensities of nodes
+
+    vmin,vmax : floats or None (default=None)
+        Minimum and maximum for node colormap scaling
+
+    linewidths : [None | scalar | sequence] (default=1.0)
+        Line width of symbol border
+
+    edgecolors : [None | scalar | sequence] (default = node_color)
+        Colors of node borders. Can be a single color or a sequence of colors with the
+        same length as nodelist. Color can be string or rgb (or rgba) tuple of floats
+        from 0-1. If numeric values are specified they will be mapped to colors
+        using the cmap and vmin,vmax parameters. See `~matplotlib.pyplot.scatter` for more details.
+
+    label : [None | string]
+        Label for legend
+
+    margins : float or 2-tuple, optional
+        Sets the padding for axis autoscaling. Increase margin to prevent
+        clipping for nodes that are near the edges of an image. Values should
+        be in the range ``[0, 1]``. See :meth:`matplotlib.axes.Axes.margins`
+        for details. The default is `None`, which uses the Matplotlib default.
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+    matplotlib.collections.PathCollection
+        `PathCollection` of the nodes.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_edges
+    draw_networkx_labels
+    draw_networkx_edge_labels
+    """
+    from collections.abc import Iterable
+
+    import matplotlib as mpl
+    import matplotlib.collections  # call as mpl.collections
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    if ax is None:
+        ax = plt.gca()
+
+    if nodelist is None:
+        nodelist = list(G)
+
+    if len(nodelist) == 0:  # empty nodelist, no drawing
+        return mpl.collections.PathCollection(None)
+
+    try:
+        xy = np.asarray([pos[v] for v in nodelist])
+    except KeyError as err:
+        raise nx.NetworkXError(f"Node {err} has no position.") from err
+
+    if isinstance(alpha, Iterable):
+        node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)
+        alpha = None
+
+    if not isinstance(node_shape, np.ndarray) and not isinstance(node_shape, list):
+        node_shape = np.array([node_shape for _ in range(len(nodelist))])
+    elif isinstance(node_shape, list):
+        node_shape = np.asarray(node_shape)
+
+    for shape in np.unique(node_shape):
+        node_collection = ax.scatter(
+            xy[node_shape == shape, 0],
+            xy[node_shape == shape, 1],
+            s=node_size,
+            c=node_color,
+            marker=shape,
+            cmap=cmap,
+            vmin=vmin,
+            vmax=vmax,
+            alpha=alpha,
+            linewidths=linewidths,
+            edgecolors=edgecolors,
+            label=label,
+        )
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    if margins is not None:
+        if isinstance(margins, Iterable):
+            ax.margins(*margins)
+        else:
+            ax.margins(margins)
+
+    node_collection.set_zorder(2)
+    return node_collection
+
+
+class FancyArrowFactory:
+    """Draw arrows with `matplotlib.patches.FancyarrowPatch`"""
+
+    class ConnectionStyleFactory:
+        def __init__(self, connectionstyles, selfloop_height, ax=None):
+            import matplotlib as mpl
+            import matplotlib.path  # call as mpl.path
+            import numpy as np
+
+            self.ax = ax
+            self.mpl = mpl
+            self.np = np
+            self.base_connection_styles = [
+                mpl.patches.ConnectionStyle(cs) for cs in connectionstyles
+            ]
+            self.n = len(self.base_connection_styles)
+            self.selfloop_height = selfloop_height
+
+        def curved(self, edge_index):
+            return self.base_connection_styles[edge_index % self.n]
+
+        def self_loop(self, edge_index):
+            def self_loop_connection(posA, posB, *args, **kwargs):
+                if not self.np.all(posA == posB):
+                    raise nx.NetworkXError(
+                        "`self_loop` connection style method"
+                        "is only to be used for self-loops"
+                    )
+                # this is called with _screen space_ values
+                # so convert back to data space
+                data_loc = self.ax.transData.inverted().transform(posA)
+                v_shift = 0.1 * self.selfloop_height
+                h_shift = v_shift * 0.5
+                # put the top of the loop first so arrow is not hidden by node
+                path = self.np.asarray(
+                    [
+                        # 1
+                        [0, v_shift],
+                        # 4 4 4
+                        [h_shift, v_shift],
+                        [h_shift, 0],
+                        [0, 0],
+                        # 4 4 4
+                        [-h_shift, 0],
+                        [-h_shift, v_shift],
+                        [0, v_shift],
+                    ]
+                )
+                # Rotate self loop 90 deg. if more than 1
+                # This will allow for maximum of 4 visible self loops
+                if edge_index % 4:
+                    x, y = path.T
+                    for _ in range(edge_index % 4):
+                        x, y = y, -x
+                    path = self.np.array([x, y]).T
+                return self.mpl.path.Path(
+                    self.ax.transData.transform(data_loc + path), [1, 4, 4, 4, 4, 4, 4]
+                )
+
+            return self_loop_connection
+
+    def __init__(
+        self,
+        edge_pos,
+        edgelist,
+        nodelist,
+        edge_indices,
+        node_size,
+        selfloop_height,
+        connectionstyle="arc3",
+        node_shape="o",
+        arrowstyle="-",
+        arrowsize=10,
+        edge_color="k",
+        alpha=None,
+        linewidth=1.0,
+        style="solid",
+        min_source_margin=0,
+        min_target_margin=0,
+        ax=None,
+    ):
+        import matplotlib as mpl
+        import matplotlib.patches  # call as mpl.patches
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        if isinstance(connectionstyle, str):
+            connectionstyle = [connectionstyle]
+        elif np.iterable(connectionstyle):
+            connectionstyle = list(connectionstyle)
+        else:
+            msg = "ConnectionStyleFactory arg `connectionstyle` must be str or iterable"
+            raise nx.NetworkXError(msg)
+        self.ax = ax
+        self.mpl = mpl
+        self.np = np
+        self.edge_pos = edge_pos
+        self.edgelist = edgelist
+        self.nodelist = nodelist
+        self.node_shape = node_shape
+        self.min_source_margin = min_source_margin
+        self.min_target_margin = min_target_margin
+        self.edge_indices = edge_indices
+        self.node_size = node_size
+        self.connectionstyle_factory = self.ConnectionStyleFactory(
+            connectionstyle, selfloop_height, ax
+        )
+        self.arrowstyle = arrowstyle
+        self.arrowsize = arrowsize
+        self.arrow_colors = mpl.colors.colorConverter.to_rgba_array(edge_color, alpha)
+        self.linewidth = linewidth
+        self.style = style
+        if isinstance(arrowsize, list) and len(arrowsize) != len(edge_pos):
+            raise ValueError("arrowsize should have the same length as edgelist")
+
+    def __call__(self, i):
+        (x1, y1), (x2, y2) = self.edge_pos[i]
+        shrink_source = 0  # space from source to tail
+        shrink_target = 0  # space from  head to target
+        if (
+            self.np.iterable(self.min_source_margin)
+            and not isinstance(self.min_source_margin, str)
+            and not isinstance(self.min_source_margin, tuple)
+        ):
+            min_source_margin = self.min_source_margin[i]
+        else:
+            min_source_margin = self.min_source_margin
+
+        if (
+            self.np.iterable(self.min_target_margin)
+            and not isinstance(self.min_target_margin, str)
+            and not isinstance(self.min_target_margin, tuple)
+        ):
+            min_target_margin = self.min_target_margin[i]
+        else:
+            min_target_margin = self.min_target_margin
+
+        if self.np.iterable(self.node_size):  # many node sizes
+            source, target = self.edgelist[i][:2]
+            source_node_size = self.node_size[self.nodelist.index(source)]
+            target_node_size = self.node_size[self.nodelist.index(target)]
+            shrink_source = self.to_marker_edge(source_node_size, self.node_shape)
+            shrink_target = self.to_marker_edge(target_node_size, self.node_shape)
+        else:
+            shrink_source = self.to_marker_edge(self.node_size, self.node_shape)
+            shrink_target = shrink_source
+        shrink_source = max(shrink_source, min_source_margin)
+        shrink_target = max(shrink_target, min_target_margin)
+
+        # scale factor of arrow head
+        if isinstance(self.arrowsize, list):
+            mutation_scale = self.arrowsize[i]
+        else:
+            mutation_scale = self.arrowsize
+
+        if len(self.arrow_colors) > i:
+            arrow_color = self.arrow_colors[i]
+        elif len(self.arrow_colors) == 1:
+            arrow_color = self.arrow_colors[0]
+        else:  # Cycle through colors
+            arrow_color = self.arrow_colors[i % len(self.arrow_colors)]
+
+        if self.np.iterable(self.linewidth):
+            if len(self.linewidth) > i:
+                linewidth = self.linewidth[i]
+            else:
+                linewidth = self.linewidth[i % len(self.linewidth)]
+        else:
+            linewidth = self.linewidth
+
+        if (
+            self.np.iterable(self.style)
+            and not isinstance(self.style, str)
+            and not isinstance(self.style, tuple)
+        ):
+            if len(self.style) > i:
+                linestyle = self.style[i]
+            else:  # Cycle through styles
+                linestyle = self.style[i % len(self.style)]
+        else:
+            linestyle = self.style
+
+        if x1 == x2 and y1 == y2:
+            connectionstyle = self.connectionstyle_factory.self_loop(
+                self.edge_indices[i]
+            )
+        else:
+            connectionstyle = self.connectionstyle_factory.curved(self.edge_indices[i])
+
+        if (
+            self.np.iterable(self.arrowstyle)
+            and not isinstance(self.arrowstyle, str)
+            and not isinstance(self.arrowstyle, tuple)
+        ):
+            arrowstyle = self.arrowstyle[i]
+        else:
+            arrowstyle = self.arrowstyle
+
+        return self.mpl.patches.FancyArrowPatch(
+            (x1, y1),
+            (x2, y2),
+            arrowstyle=arrowstyle,
+            shrinkA=shrink_source,
+            shrinkB=shrink_target,
+            mutation_scale=mutation_scale,
+            color=arrow_color,
+            linewidth=linewidth,
+            connectionstyle=connectionstyle,
+            linestyle=linestyle,
+            zorder=1,  # arrows go behind nodes
+        )
+
+    def to_marker_edge(self, marker_size, marker):
+        if marker in "s^>v<d":  # `large` markers need extra space
+            return self.np.sqrt(2 * marker_size) / 2
+        else:
+            return self.np.sqrt(marker_size) / 2
+
+
+def draw_networkx_edges(
+    G,
+    pos,
+    edgelist=None,
+    width=1.0,
+    edge_color="k",
+    style="solid",
+    alpha=None,
+    arrowstyle=None,
+    arrowsize=10,
+    edge_cmap=None,
+    edge_vmin=None,
+    edge_vmax=None,
+    ax=None,
+    arrows=None,
+    label=None,
+    node_size=300,
+    nodelist=None,
+    node_shape="o",
+    connectionstyle="arc3",
+    min_source_margin=0,
+    min_target_margin=0,
+    hide_ticks=True,
+):
+    r"""Draw the edges of the graph G.
+
+    This draws only the edges of the graph G.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    edgelist : collection of edge tuples (default=G.edges())
+        Draw only specified edges
+
+    width : float or array of floats (default=1.0)
+        Line width of edges
+
+    edge_color : color or array of colors (default='k')
+        Edge color. Can be a single color or a sequence of colors with the same
+        length as edgelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
+
+    style : string or array of strings (default='solid')
+        Edge line style e.g.: '-', '--', '-.', ':'
+        or words like 'solid' or 'dashed'.
+        Can be a single style or a sequence of styles with the same
+        length as the edge list.
+        If less styles than edges are given the styles will cycle.
+        If more styles than edges are given the styles will be used sequentially
+        and not be exhausted.
+        Also, `(offset, onoffseq)` tuples can be used as style instead of a strings.
+        (See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
+
+    alpha : float or array of floats (default=None)
+        The edge transparency.  This can be a single alpha value,
+        in which case it will be applied to all specified edges. Otherwise,
+        if it is an array, the elements of alpha will be applied to the colors
+        in order (cycling through alpha multiple times if necessary).
+
+    edge_cmap : Matplotlib colormap, optional
+        Colormap for mapping intensities of edges
+
+    edge_vmin,edge_vmax : floats, optional
+        Minimum and maximum for edge colormap scaling
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    arrows : bool or None, optional (default=None)
+        If `None`, directed graphs draw arrowheads with
+        `~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
+        via `~matplotlib.collections.LineCollection` for speed.
+        If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
+        If `False`, draw edges using LineCollection (linear and fast).
+
+        Note: Arrowheads will be the same color as edges.
+
+    arrowstyle : str or list of strs (default='-\|>' for directed graphs)
+        For directed graphs and `arrows==True` defaults to '-\|>',
+        For undirected graphs default to '-'.
+
+        See `matplotlib.patches.ArrowStyle` for more options.
+
+    arrowsize : int or list of ints(default=10)
+        For directed graphs, choose the size of the arrow head's length and
+        width. See `matplotlib.patches.FancyArrowPatch` for attribute
+        `mutation_scale` for more info.
+
+    connectionstyle : string or iterable of strings (default="arc3")
+        Pass the connectionstyle parameter to create curved arc of rounding
+        radius rad. For example, connectionstyle='arc3,rad=0.2'.
+        See `matplotlib.patches.ConnectionStyle` and
+        `matplotlib.patches.FancyArrowPatch` for more info.
+        If Iterable, index indicates i'th edge key of MultiGraph
+
+    node_size : scalar or array (default=300)
+        Size of nodes. Though the nodes are not drawn with this function, the
+        node size is used in determining edge positioning.
+
+    nodelist : list, optional (default=G.nodes())
+       This provides the node order for the `node_size` array (if it is an array).
+
+    node_shape :  string (default='o')
+        The marker used for nodes, used in determining edge positioning.
+        Specification is as a `matplotlib.markers` marker, e.g. one of 'so^>v<dph8'.
+
+    label : None or string
+        Label for legend
+
+    min_source_margin : int or list of ints (default=0)
+        The minimum margin (gap) at the beginning of the edge at the source.
+
+    min_target_margin : int or list of ints (default=0)
+        The minimum margin (gap) at the end of the edge at the target.
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+     matplotlib.collections.LineCollection or a list of matplotlib.patches.FancyArrowPatch
+        If ``arrows=True``, a list of FancyArrowPatches is returned.
+        If ``arrows=False``, a LineCollection is returned.
+        If ``arrows=None`` (the default), then a LineCollection is returned if
+        `G` is undirected, otherwise returns a list of FancyArrowPatches.
+
+    Notes
+    -----
+    For directed graphs, arrows are drawn at the head end.  Arrows can be
+    turned off with keyword arrows=False or by passing an arrowstyle without
+    an arrow on the end.
+
+    Be sure to include `node_size` as a keyword argument; arrows are
+    drawn considering the size of nodes.
+
+    Self-loops are always drawn with `~matplotlib.patches.FancyArrowPatch`
+    regardless of the value of `arrows` or whether `G` is directed.
+    When ``arrows=False`` or ``arrows=None`` and `G` is undirected, the
+    FancyArrowPatches corresponding to the self-loops are not explicitly
+    returned. They should instead be accessed via the ``Axes.patches``
+    attribute (see examples).
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
+
+    >>> G = nx.DiGraph()
+    >>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
+    >>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
+    >>> alphas = [0.3, 0.4, 0.5]
+    >>> for i, arc in enumerate(arcs):  # change alpha values of arcs
+    ...     arc.set_alpha(alphas[i])
+
+    The FancyArrowPatches corresponding to self-loops are not always
+    returned, but can always be accessed via the ``patches`` attribute of the
+    `matplotlib.Axes` object.
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> G = nx.Graph([(0, 1), (0, 0)])  # Self-loop at node 0
+    >>> edge_collection = nx.draw_networkx_edges(G, pos=nx.circular_layout(G), ax=ax)
+    >>> self_loop_fap = ax.patches[0]
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_labels
+    draw_networkx_edge_labels
+
+    """
+    import warnings
+
+    import matplotlib as mpl
+    import matplotlib.collections  # call as mpl.collections
+    import matplotlib.colors  # call as mpl.colors
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    # The default behavior is to use LineCollection to draw edges for
+    # undirected graphs (for performance reasons) and use FancyArrowPatches
+    # for directed graphs.
+    # The `arrows` keyword can be used to override the default behavior
+    if arrows is None:
+        use_linecollection = not (G.is_directed() or G.is_multigraph())
+    else:
+        if not isinstance(arrows, bool):
+            raise TypeError("Argument `arrows` must be of type bool or None")
+        use_linecollection = not arrows
+
+    if isinstance(connectionstyle, str):
+        connectionstyle = [connectionstyle]
+    elif np.iterable(connectionstyle):
+        connectionstyle = list(connectionstyle)
+    else:
+        msg = "draw_networkx_edges arg `connectionstyle` must be str or iterable"
+        raise nx.NetworkXError(msg)
+
+    # Some kwargs only apply to FancyArrowPatches. Warn users when they use
+    # non-default values for these kwargs when LineCollection is being used
+    # instead of silently ignoring the specified option
+    if use_linecollection:
+        msg = (
+            "\n\nThe {0} keyword argument is not applicable when drawing edges\n"
+            "with LineCollection.\n\n"
+            "To make this warning go away, either specify `arrows=True` to\n"
+            "force FancyArrowPatches or use the default values.\n"
+            "Note that using FancyArrowPatches may be slow for large graphs.\n"
+        )
+        if arrowstyle is not None:
+            warnings.warn(msg.format("arrowstyle"), category=UserWarning, stacklevel=2)
+        if arrowsize != 10:
+            warnings.warn(msg.format("arrowsize"), category=UserWarning, stacklevel=2)
+        if min_source_margin != 0:
+            warnings.warn(
+                msg.format("min_source_margin"), category=UserWarning, stacklevel=2
+            )
+        if min_target_margin != 0:
+            warnings.warn(
+                msg.format("min_target_margin"), category=UserWarning, stacklevel=2
+            )
+        if any(cs != "arc3" for cs in connectionstyle):
+            warnings.warn(
+                msg.format("connectionstyle"), category=UserWarning, stacklevel=2
+            )
+
+    # NOTE: Arrowstyle modification must occur after the warnings section
+    if arrowstyle is None:
+        arrowstyle = "-|>" if G.is_directed() else "-"
+
+    if ax is None:
+        ax = plt.gca()
+
+    if edgelist is None:
+        edgelist = list(G.edges)  # (u, v, k) for multigraph (u, v) otherwise
+
+    if len(edgelist):
+        if G.is_multigraph():
+            key_count = collections.defaultdict(lambda: itertools.count(0))
+            edge_indices = [next(key_count[tuple(e[:2])]) for e in edgelist]
+        else:
+            edge_indices = [0] * len(edgelist)
+    else:  # no edges!
+        return []
+
+    if nodelist is None:
+        nodelist = list(G.nodes())
+
+    # FancyArrowPatch handles color=None different from LineCollection
+    if edge_color is None:
+        edge_color = "k"
+
+    # set edge positions
+    edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
+
+    # Check if edge_color is an array of floats and map to edge_cmap.
+    # This is the only case handled differently from matplotlib
+    if (
+        np.iterable(edge_color)
+        and (len(edge_color) == len(edge_pos))
+        and np.all([isinstance(c, Number) for c in edge_color])
+    ):
+        if edge_cmap is not None:
+            assert isinstance(edge_cmap, mpl.colors.Colormap)
+        else:
+            edge_cmap = plt.get_cmap()
+        if edge_vmin is None:
+            edge_vmin = min(edge_color)
+        if edge_vmax is None:
+            edge_vmax = max(edge_color)
+        color_normal = mpl.colors.Normalize(vmin=edge_vmin, vmax=edge_vmax)
+        edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
+
+    # compute initial view
+    minx = np.amin(np.ravel(edge_pos[:, :, 0]))
+    maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
+    miny = np.amin(np.ravel(edge_pos[:, :, 1]))
+    maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
+    w = maxx - minx
+    h = maxy - miny
+
+    # Self-loops are scaled by view extent, except in cases the extent
+    # is 0, e.g. for a single node. In this case, fall back to scaling
+    # by the maximum node size
+    selfloop_height = h if h != 0 else 0.005 * np.array(node_size).max()
+    fancy_arrow_factory = FancyArrowFactory(
+        edge_pos,
+        edgelist,
+        nodelist,
+        edge_indices,
+        node_size,
+        selfloop_height,
+        connectionstyle,
+        node_shape,
+        arrowstyle,
+        arrowsize,
+        edge_color,
+        alpha,
+        width,
+        style,
+        min_source_margin,
+        min_target_margin,
+        ax=ax,
+    )
+
+    # Draw the edges
+    if use_linecollection:
+        edge_collection = mpl.collections.LineCollection(
+            edge_pos,
+            colors=edge_color,
+            linewidths=width,
+            antialiaseds=(1,),
+            linestyle=style,
+            alpha=alpha,
+        )
+        edge_collection.set_cmap(edge_cmap)
+        edge_collection.set_clim(edge_vmin, edge_vmax)
+        edge_collection.set_zorder(1)  # edges go behind nodes
+        edge_collection.set_label(label)
+        ax.add_collection(edge_collection)
+        edge_viz_obj = edge_collection
+
+        # Make sure selfloop edges are also drawn
+        # ---------------------------------------
+        selfloops_to_draw = [loop for loop in nx.selfloop_edges(G) if loop in edgelist]
+        if selfloops_to_draw:
+            edgelist_tuple = list(map(tuple, edgelist))
+            arrow_collection = []
+            for loop in selfloops_to_draw:
+                i = edgelist_tuple.index(loop)
+                arrow = fancy_arrow_factory(i)
+                arrow_collection.append(arrow)
+                ax.add_patch(arrow)
+    else:
+        edge_viz_obj = []
+        for i in range(len(edgelist)):
+            arrow = fancy_arrow_factory(i)
+            ax.add_patch(arrow)
+            edge_viz_obj.append(arrow)
+
+    # update view after drawing
+    padx, pady = 0.05 * w, 0.05 * h
+    corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
+    ax.update_datalim(corners)
+    ax.autoscale_view()
+
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    return edge_viz_obj
+
+
+def draw_networkx_labels(
+    G,
+    pos,
+    labels=None,
+    font_size=12,
+    font_color="k",
+    font_family="sans-serif",
+    font_weight="normal",
+    alpha=None,
+    bbox=None,
+    horizontalalignment="center",
+    verticalalignment="center",
+    ax=None,
+    clip_on=True,
+    hide_ticks=True,
+):
+    """Draw node labels on the graph G.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    labels : dictionary (default={n: n for n in G})
+        Node labels in a dictionary of text labels keyed by node.
+        Node-keys in labels should appear as keys in `pos`.
+        If needed use: `{n:lab for n,lab in labels.items() if n in pos}`
+
+    font_size : int or dictionary of nodes to ints (default=12)
+        Font size for text labels.
+
+    font_color : color or dictionary of nodes to colors (default='k' black)
+        Font color string. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1.
+
+    font_weight : string or dictionary of nodes to strings (default='normal')
+        Font weight.
+
+    font_family : string or dictionary of nodes to strings (default='sans-serif')
+        Font family.
+
+    alpha : float or None or dictionary of nodes to floats (default=None)
+        The text transparency.
+
+    bbox : Matplotlib bbox, (default is Matplotlib's ax.text default)
+        Specify text box properties (e.g. shape, color etc.) for node labels.
+
+    horizontalalignment : string or array of strings (default='center')
+        Horizontal alignment {'center', 'right', 'left'}. If an array is
+        specified it must be the same length as `nodelist`.
+
+    verticalalignment : string (default='center')
+        Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}.
+        If an array is specified it must be the same length as `nodelist`.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    clip_on : bool (default=True)
+        Turn on clipping of node labels at axis boundaries
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+    dict
+        `dict` of labels keyed on the nodes
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_edge_labels
+    """
+    import matplotlib.pyplot as plt
+
+    if ax is None:
+        ax = plt.gca()
+
+    if labels is None:
+        labels = {n: n for n in G.nodes()}
+
+    individual_params = set()
+
+    def check_individual_params(p_value, p_name):
+        if isinstance(p_value, dict):
+            if len(p_value) != len(labels):
+                raise ValueError(f"{p_name} must have the same length as labels.")
+            individual_params.add(p_name)
+
+    def get_param_value(node, p_value, p_name):
+        if p_name in individual_params:
+            return p_value[node]
+        return p_value
+
+    check_individual_params(font_size, "font_size")
+    check_individual_params(font_color, "font_color")
+    check_individual_params(font_weight, "font_weight")
+    check_individual_params(font_family, "font_family")
+    check_individual_params(alpha, "alpha")
+
+    text_items = {}  # there is no text collection so we'll fake one
+    for n, label in labels.items():
+        (x, y) = pos[n]
+        if not isinstance(label, str):
+            label = str(label)  # this makes "1" and 1 labeled the same
+        t = ax.text(
+            x,
+            y,
+            label,
+            size=get_param_value(n, font_size, "font_size"),
+            color=get_param_value(n, font_color, "font_color"),
+            family=get_param_value(n, font_family, "font_family"),
+            weight=get_param_value(n, font_weight, "font_weight"),
+            alpha=get_param_value(n, alpha, "alpha"),
+            horizontalalignment=horizontalalignment,
+            verticalalignment=verticalalignment,
+            transform=ax.transData,
+            bbox=bbox,
+            clip_on=clip_on,
+        )
+        text_items[n] = t
+
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    return text_items
+
+
+def draw_networkx_edge_labels(
+    G,
+    pos,
+    edge_labels=None,
+    label_pos=0.5,
+    font_size=10,
+    font_color="k",
+    font_family="sans-serif",
+    font_weight="normal",
+    alpha=None,
+    bbox=None,
+    horizontalalignment="center",
+    verticalalignment="center",
+    ax=None,
+    rotate=True,
+    clip_on=True,
+    node_size=300,
+    nodelist=None,
+    connectionstyle="arc3",
+    hide_ticks=True,
+):
+    """Draw edge labels.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    edge_labels : dictionary (default=None)
+        Edge labels in a dictionary of labels keyed by edge two-tuple.
+        Only labels for the keys in the dictionary are drawn.
+
+    label_pos : float (default=0.5)
+        Position of edge label along edge (0=head, 0.5=center, 1=tail)
+
+    font_size : int (default=10)
+        Font size for text labels
+
+    font_color : color (default='k' black)
+        Font color string. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1.
+
+    font_weight : string (default='normal')
+        Font weight
+
+    font_family : string (default='sans-serif')
+        Font family
+
+    alpha : float or None (default=None)
+        The text transparency
+
+    bbox : Matplotlib bbox, optional
+        Specify text box properties (e.g. shape, color etc.) for edge labels.
+        Default is {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}.
+
+    horizontalalignment : string (default='center')
+        Horizontal alignment {'center', 'right', 'left'}
+
+    verticalalignment : string (default='center')
+        Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    rotate : bool (default=True)
+        Rotate edge labels to lie parallel to edges
+
+    clip_on : bool (default=True)
+        Turn on clipping of edge labels at axis boundaries
+
+    node_size : scalar or array (default=300)
+        Size of nodes.  If an array it must be the same length as nodelist.
+
+    nodelist : list, optional (default=G.nodes())
+       This provides the node order for the `node_size` array (if it is an array).
+
+    connectionstyle : string or iterable of strings (default="arc3")
+        Pass the connectionstyle parameter to create curved arc of rounding
+        radius rad. For example, connectionstyle='arc3,rad=0.2'.
+        See `matplotlib.patches.ConnectionStyle` and
+        `matplotlib.patches.FancyArrowPatch` for more info.
+        If Iterable, index indicates i'th edge key of MultiGraph
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+    dict
+        `dict` of labels keyed by edge
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_labels
+    """
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    class CurvedArrowText(CurvedArrowTextBase, mpl.text.Text):
+        pass
+
+    # use default box of white with white border
+    if bbox is None:
+        bbox = {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)}
+
+    if isinstance(connectionstyle, str):
+        connectionstyle = [connectionstyle]
+    elif np.iterable(connectionstyle):
+        connectionstyle = list(connectionstyle)
+    else:
+        raise nx.NetworkXError(
+            "draw_networkx_edges arg `connectionstyle` must be"
+            "string or iterable of strings"
+        )
+
+    if ax is None:
+        ax = plt.gca()
+
+    if edge_labels is None:
+        kwds = {"keys": True} if G.is_multigraph() else {}
+        edge_labels = {tuple(edge): d for *edge, d in G.edges(data=True, **kwds)}
+    # NOTHING TO PLOT
+    if not edge_labels:
+        return {}
+    edgelist, labels = zip(*edge_labels.items())
+
+    if nodelist is None:
+        nodelist = list(G.nodes())
+
+    # set edge positions
+    edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
+
+    if G.is_multigraph():
+        key_count = collections.defaultdict(lambda: itertools.count(0))
+        edge_indices = [next(key_count[tuple(e[:2])]) for e in edgelist]
+    else:
+        edge_indices = [0] * len(edgelist)
+
+    # Used to determine self loop mid-point
+    # Note, that this will not be accurate,
+    #   if not drawing edge_labels for all edges drawn
+    h = 0
+    if edge_labels:
+        miny = np.amin(np.ravel(edge_pos[:, :, 1]))
+        maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
+        h = maxy - miny
+    selfloop_height = h if h != 0 else 0.005 * np.array(node_size).max()
+    fancy_arrow_factory = FancyArrowFactory(
+        edge_pos,
+        edgelist,
+        nodelist,
+        edge_indices,
+        node_size,
+        selfloop_height,
+        connectionstyle,
+        ax=ax,
+    )
+
+    individual_params = {}
+
+    def check_individual_params(p_value, p_name):
+        # TODO should this be list or array (as in a numpy array)?
+        if isinstance(p_value, list):
+            if len(p_value) != len(edgelist):
+                raise ValueError(f"{p_name} must have the same length as edgelist.")
+            individual_params[p_name] = p_value.iter()
+
+    # Don't need to pass in an edge because these are lists, not dicts
+    def get_param_value(p_value, p_name):
+        if p_name in individual_params:
+            return next(individual_params[p_name])
+        return p_value
+
+    check_individual_params(font_size, "font_size")
+    check_individual_params(font_color, "font_color")
+    check_individual_params(font_weight, "font_weight")
+    check_individual_params(alpha, "alpha")
+    check_individual_params(horizontalalignment, "horizontalalignment")
+    check_individual_params(verticalalignment, "verticalalignment")
+    check_individual_params(rotate, "rotate")
+    check_individual_params(label_pos, "label_pos")
+
+    text_items = {}
+    for i, (edge, label) in enumerate(zip(edgelist, labels)):
+        if not isinstance(label, str):
+            label = str(label)  # this makes "1" and 1 labeled the same
+
+        n1, n2 = edge[:2]
+        arrow = fancy_arrow_factory(i)
+        if n1 == n2:
+            connectionstyle_obj = arrow.get_connectionstyle()
+            posA = ax.transData.transform(pos[n1])
+            path_disp = connectionstyle_obj(posA, posA)
+            path_data = ax.transData.inverted().transform_path(path_disp)
+            x, y = path_data.vertices[0]
+            text_items[edge] = ax.text(
+                x,
+                y,
+                label,
+                size=get_param_value(font_size, "font_size"),
+                color=get_param_value(font_color, "font_color"),
+                family=get_param_value(font_family, "font_family"),
+                weight=get_param_value(font_weight, "font_weight"),
+                alpha=get_param_value(alpha, "alpha"),
+                horizontalalignment=get_param_value(
+                    horizontalalignment, "horizontalalignment"
+                ),
+                verticalalignment=get_param_value(
+                    verticalalignment, "verticalalignment"
+                ),
+                rotation=0,
+                transform=ax.transData,
+                bbox=bbox,
+                zorder=1,
+                clip_on=clip_on,
+            )
+        else:
+            text_items[edge] = CurvedArrowText(
+                arrow,
+                label,
+                size=get_param_value(font_size, "font_size"),
+                color=get_param_value(font_color, "font_color"),
+                family=get_param_value(font_family, "font_family"),
+                weight=get_param_value(font_weight, "font_weight"),
+                alpha=get_param_value(alpha, "alpha"),
+                horizontalalignment=get_param_value(
+                    horizontalalignment, "horizontalalignment"
+                ),
+                verticalalignment=get_param_value(
+                    verticalalignment, "verticalalignment"
+                ),
+                transform=ax.transData,
+                bbox=bbox,
+                zorder=1,
+                clip_on=clip_on,
+                label_pos=get_param_value(label_pos, "label_pos"),
+                labels_horizontal=not get_param_value(rotate, "rotate"),
+                ax=ax,
+            )
+
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    return text_items
+
+
+def draw_bipartite(G, **kwargs):
+    """Draw the graph `G` with a bipartite layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.bipartite_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Raises
+    ------
+    NetworkXError :
+        If `G` is not bipartite.
+
+    Notes
+    -----
+    The layout is computed each time this function is called. For
+    repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.bipartite_layout` directly and reuse the result::
+
+        >>> G = nx.complete_bipartite_graph(3, 3)
+        >>> pos = nx.bipartite_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.complete_bipartite_graph(2, 5)
+    >>> nx.draw_bipartite(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.bipartite_layout`
+    """
+    draw(G, pos=nx.bipartite_layout(G), **kwargs)
+
+
+def draw_circular(G, **kwargs):
+    """Draw the graph `G` with a circular layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.circular_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called. For
+    repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.circular_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.circular_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.draw_circular(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.circular_layout`
+    """
+    draw(G, pos=nx.circular_layout(G), **kwargs)
+
+
+def draw_kamada_kawai(G, **kwargs):
+    """Draw the graph `G` with a Kamada-Kawai force-directed layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.kamada_kawai_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.kamada_kawai_layout` directly and reuse the
+    result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.kamada_kawai_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.draw_kamada_kawai(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.kamada_kawai_layout`
+    """
+    draw(G, pos=nx.kamada_kawai_layout(G), **kwargs)
+
+
+def draw_random(G, **kwargs):
+    """Draw the graph `G` with a random layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.random_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.random_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.random_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> nx.draw_random(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.random_layout`
+    """
+    draw(G, pos=nx.random_layout(G), **kwargs)
+
+
+def draw_spectral(G, **kwargs):
+    """Draw the graph `G` with a spectral 2D layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.spectral_layout(G), **kwargs)
+
+    For more information about how node positions are determined, see
+    `~networkx.drawing.layout.spectral_layout`.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.spectral_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.spectral_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.draw_spectral(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.spectral_layout`
+    """
+    draw(G, pos=nx.spectral_layout(G), **kwargs)
+
+
+def draw_spring(G, **kwargs):
+    """Draw the graph `G` with a spring layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.spring_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    `~networkx.drawing.layout.spring_layout` is also the default layout for
+    `draw`, so this function is equivalent to `draw`.
+
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.spring_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.spring_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(20)
+    >>> nx.draw_spring(G)
+
+    See Also
+    --------
+    draw
+    :func:`~networkx.drawing.layout.spring_layout`
+    """
+    draw(G, pos=nx.spring_layout(G), **kwargs)
+
+
+def draw_shell(G, nlist=None, **kwargs):
+    """Draw networkx graph `G` with shell layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    nlist : list of list of nodes, optional
+        A list containing lists of nodes representing the shells.
+        Default is `None`, meaning all nodes are in a single shell.
+        See `~networkx.drawing.layout.shell_layout` for details.
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.shell_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.shell_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> shells = [[0], [1, 2, 3]]
+    >>> nx.draw_shell(G, nlist=shells)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.shell_layout`
+    """
+    draw(G, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
+
+
+def draw_planar(G, **kwargs):
+    """Draw a planar networkx graph `G` with planar layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.planar_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A planar networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Raises
+    ------
+    NetworkXException
+        When `G` is not planar
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.planar_layout` directly and reuse the result::
+
+        >>> G = nx.path_graph(5)
+        >>> pos = nx.planar_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.draw_planar(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.planar_layout`
+    """
+    draw(G, pos=nx.planar_layout(G), **kwargs)
+
+
+def draw_forceatlas2(G, **kwargs):
+    """Draw a networkx graph with forceatlas2 layout.
+
+    This is a convenience function equivalent to::
+
+       nx.draw(G, pos=nx.forceatlas2_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+
+    kwargs : optional keywords
+       See networkx.draw_networkx() for a description of optional keywords,
+       with the exception of the pos parameter which is not used by this
+       function.
+    """
+    draw(G, pos=nx.forceatlas2_layout(G), **kwargs)
+
+
+def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):
+    """Apply an alpha (or list of alphas) to the colors provided.
+
+    Parameters
+    ----------
+
+    colors : color string or array of floats (default='r')
+        Color of element. Can be a single color format string,
+        or a sequence of colors with the same length as nodelist.
+        If numeric values are specified they will be mapped to
+        colors using the cmap and vmin,vmax parameters.  See
+        matplotlib.scatter for more details.
+
+    alpha : float or array of floats
+        Alpha values for elements. This can be a single alpha value, in
+        which case it will be applied to all the elements of color. Otherwise,
+        if it is an array, the elements of alpha will be applied to the colors
+        in order (cycling through alpha multiple times if necessary).
+
+    elem_list : array of networkx objects
+        The list of elements which are being colored. These could be nodes,
+        edges or labels.
+
+    cmap : matplotlib colormap
+        Color map for use if colors is a list of floats corresponding to points
+        on a color mapping.
+
+    vmin, vmax : float
+        Minimum and maximum values for normalizing colors if a colormap is used
+
+    Returns
+    -------
+
+    rgba_colors : numpy ndarray
+        Array containing RGBA format values for each of the node colours.
+
+    """
+    from itertools import cycle, islice
+
+    import matplotlib as mpl
+    import matplotlib.cm  # call as mpl.cm
+    import matplotlib.colors  # call as mpl.colors
+    import numpy as np
+
+    # If we have been provided with a list of numbers as long as elem_list,
+    # apply the color mapping.
+    if len(colors) == len(elem_list) and isinstance(colors[0], Number):
+        mapper = mpl.cm.ScalarMappable(cmap=cmap)
+        mapper.set_clim(vmin, vmax)
+        rgba_colors = mapper.to_rgba(colors)
+    # Otherwise, convert colors to matplotlib's RGB using the colorConverter
+    # object.  These are converted to numpy ndarrays to be consistent with the
+    # to_rgba method of ScalarMappable.
+    else:
+        try:
+            rgba_colors = np.array([mpl.colors.colorConverter.to_rgba(colors)])
+        except ValueError:
+            rgba_colors = np.array(
+                [mpl.colors.colorConverter.to_rgba(color) for color in colors]
+            )
+    # Set the final column of the rgba_colors to have the relevant alpha values
+    try:
+        # If alpha is longer than the number of colors, resize to the number of
+        # elements.  Also, if rgba_colors.size (the number of elements of
+        # rgba_colors) is the same as the number of elements, resize the array,
+        # to avoid it being interpreted as a colormap by scatter()
+        if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):
+            rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))
+            rgba_colors[1:, 0] = rgba_colors[0, 0]
+            rgba_colors[1:, 1] = rgba_colors[0, 1]
+            rgba_colors[1:, 2] = rgba_colors[0, 2]
+        rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))
+    except TypeError:
+        rgba_colors[:, -1] = alpha
+    return rgba_colors
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__init__.py b/lib/python3.11/site-packages/networkx/drawing/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..902db4ada859b03eec44a9f163bbe5ee1858156a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_agraph.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_agraph.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b7606a72afb50fc297a98a3c783c5a0af48a0b7e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_agraph.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_image_comparison_pylab_mpl.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_image_comparison_pylab_mpl.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..42c8d9a77670071b320b8745eb67c67140639a4f
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_image_comparison_pylab_mpl.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_latex.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_latex.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..15672d84a5e6c232dceab4bc532ee99365cb6780
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_latex.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_layout.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_layout.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d0f643d976c1624f09497e2a67c3637fff28cdd2
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_layout.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_pydot.cpython-311.pyc b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_pydot.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ca0894ce4f45b84ef9b1c17e7e41dce5ab728b81
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/drawing/tests/__pycache__/test_pydot.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/test_agraph.py b/lib/python3.11/site-packages/networkx/drawing/tests/test_agraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..3aab7d440f92777c4726cb39bc2c0324beab44ab
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/tests/test_agraph.py
@@ -0,0 +1,237 @@
+"""Unit tests for PyGraphviz interface."""
+
+import warnings
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+pygraphviz = pytest.importorskip("pygraphviz")
+
+
+class TestAGraph:
+    def build_graph(self, G):
+        edges = [("A", "B"), ("A", "C"), ("A", "C"), ("B", "C"), ("A", "D")]
+        G.add_edges_from(edges)
+        G.add_node("E")
+        G.graph["metal"] = "bronze"
+        return G
+
+    def assert_equal(self, G1, G2):
+        assert nodes_equal(G1.nodes(), G2.nodes())
+        assert edges_equal(G1.edges(), G2.edges(), directed=G1.is_directed())
+        assert G1.graph["metal"] == G2.graph["metal"]
+
+    @pytest.mark.parametrize(
+        "G", (nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph())
+    )
+    def test_agraph_roundtripping(self, G, tmp_path):
+        G = self.build_graph(G)
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        self.assert_equal(G, H)
+
+        fname = tmp_path / "test.dot"
+        nx.drawing.nx_agraph.write_dot(H, fname)
+        Hin = nx.nx_agraph.read_dot(fname)
+        self.assert_equal(H, Hin)
+
+        fname = tmp_path / "fh_test.dot"
+        with open(fname, "w") as fh:
+            nx.drawing.nx_agraph.write_dot(H, fh)
+
+        with open(fname) as fh:
+            Hin = nx.nx_agraph.read_dot(fh)
+        self.assert_equal(H, Hin)
+
+    def test_from_agraph_name(self):
+        G = nx.Graph(name="test")
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        assert G.name == "test"
+
+    @pytest.mark.parametrize(
+        "graph_class", (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+    )
+    def test_from_agraph_create_using(self, graph_class):
+        G = nx.path_graph(3)
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A, create_using=graph_class)
+        assert isinstance(H, graph_class)
+
+    def test_from_agraph_named_edges(self):
+        # Create an AGraph from an existing (non-multi) Graph
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        A = nx.nx_agraph.to_agraph(G)
+        # Add edge (+ name, given by key) to the AGraph
+        A.add_edge(0, 1, key="foo")
+        # Verify a.name roundtrips out to 'key' in from_agraph
+        H = nx.nx_agraph.from_agraph(A)
+        assert isinstance(H, nx.Graph)
+        assert ("0", "1", {"key": "foo"}) in H.edges(data=True)
+
+    def test_to_agraph_with_nodedata(self):
+        G = nx.Graph()
+        G.add_node(1, color="red")
+        A = nx.nx_agraph.to_agraph(G)
+        assert dict(A.nodes()[0].attr) == {"color": "red"}
+
+    @pytest.mark.parametrize("graph_class", (nx.Graph, nx.MultiGraph))
+    def test_to_agraph_with_edgedata(self, graph_class):
+        G = graph_class()
+        G.add_nodes_from([0, 1])
+        G.add_edge(0, 1, color="yellow")
+        A = nx.nx_agraph.to_agraph(G)
+        assert dict(A.edges()[0].attr) == {"color": "yellow"}
+
+    def test_view_pygraphviz_path(self, tmp_path):
+        G = nx.complete_graph(3)
+        input_path = str(tmp_path / "graph.png")
+        out_path, A = nx.nx_agraph.view_pygraphviz(G, path=input_path, show=False)
+        assert out_path == input_path
+        # Ensure file is not empty
+        with open(input_path, "rb") as fh:
+            data = fh.read()
+        assert len(data) > 0
+
+    def test_view_pygraphviz_file_suffix(self, tmp_path):
+        G = nx.complete_graph(3)
+        path, A = nx.nx_agraph.view_pygraphviz(G, suffix=1, show=False)
+        assert path[-6:] == "_1.png"
+
+    def test_view_pygraphviz(self):
+        G = nx.Graph()  # "An empty graph cannot be drawn."
+        pytest.raises(nx.NetworkXException, nx.nx_agraph.view_pygraphviz, G)
+        G = nx.barbell_graph(4, 6)
+        nx.nx_agraph.view_pygraphviz(G, show=False)
+
+    def test_view_pygraphviz_edgelabel(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=7)
+        G.add_edge(2, 3, weight=8)
+        path, A = nx.nx_agraph.view_pygraphviz(G, edgelabel="weight", show=False)
+        for edge in A.edges():
+            assert edge.attr["weight"] in ("7", "8")
+
+    def test_view_pygraphviz_callable_edgelabel(self):
+        G = nx.complete_graph(3)
+
+        def foo_label(data):
+            return "foo"
+
+        path, A = nx.nx_agraph.view_pygraphviz(G, edgelabel=foo_label, show=False)
+        for edge in A.edges():
+            assert edge.attr["label"] == "foo"
+
+    def test_view_pygraphviz_multigraph_edgelabels(self):
+        G = nx.MultiGraph()
+        G.add_edge(0, 1, key=0, name="left_fork")
+        G.add_edge(0, 1, key=1, name="right_fork")
+        path, A = nx.nx_agraph.view_pygraphviz(G, edgelabel="name", show=False)
+        edges = A.edges()
+        assert len(edges) == 2
+        for edge in edges:
+            assert edge.attr["label"].strip() in ("left_fork", "right_fork")
+
+    def test_graph_with_reserved_keywords(self):
+        # test attribute/keyword clash case for #1582
+        # node: n
+        # edges: u,v
+        G = nx.Graph()
+        G = self.build_graph(G)
+        G.nodes["E"]["n"] = "keyword"
+        G.edges[("A", "B")]["u"] = "keyword"
+        G.edges[("A", "B")]["v"] = "keyword"
+        A = nx.nx_agraph.to_agraph(G)
+
+    def test_view_pygraphviz_no_added_attrs_to_input(self):
+        G = nx.complete_graph(2)
+        path, A = nx.nx_agraph.view_pygraphviz(G, show=False)
+        assert G.graph == {}
+
+    @pytest.mark.xfail(reason="known bug in clean_attrs")
+    def test_view_pygraphviz_leaves_input_graph_unmodified(self):
+        G = nx.complete_graph(2)
+        # Add entries to graph dict that to_agraph handles specially
+        G.graph["node"] = {"width": "0.80"}
+        G.graph["edge"] = {"fontsize": "14"}
+        path, A = nx.nx_agraph.view_pygraphviz(G, show=False)
+        assert G.graph == {"node": {"width": "0.80"}, "edge": {"fontsize": "14"}}
+
+    def test_graph_with_AGraph_attrs(self):
+        G = nx.complete_graph(2)
+        # Add entries to graph dict that to_agraph handles specially
+        G.graph["node"] = {"width": "0.80"}
+        G.graph["edge"] = {"fontsize": "14"}
+        path, A = nx.nx_agraph.view_pygraphviz(G, show=False)
+        # Ensure user-specified values are not lost
+        assert dict(A.node_attr)["width"] == "0.80"
+        assert dict(A.edge_attr)["fontsize"] == "14"
+
+    def test_round_trip_empty_graph(self):
+        G = nx.Graph()
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        assert graphs_equal(G, H)
+        AA = nx.nx_agraph.to_agraph(H)
+        HH = nx.nx_agraph.from_agraph(AA)
+        assert graphs_equal(H, HH)
+        assert graphs_equal(G, HH)
+
+    @pytest.mark.xfail(reason="integer->string node conversion in round trip")
+    def test_round_trip_integer_nodes(self):
+        G = nx.complete_graph(3)
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        assert graphs_equal(G, H)
+
+    def test_graphviz_alias(self):
+        G = self.build_graph(nx.Graph())
+        pos_graphviz = nx.nx_agraph.graphviz_layout(G)
+        pos_pygraphviz = nx.nx_agraph.pygraphviz_layout(G)
+        assert pos_graphviz == pos_pygraphviz
+
+    @pytest.mark.parametrize("root", range(5))
+    def test_pygraphviz_layout_root(self, root):
+        # NOTE: test depends on layout prog being deterministic
+        G = nx.complete_graph(5)
+        A = nx.nx_agraph.to_agraph(G)
+        # Get layout with root arg is not None
+        pygv_layout = nx.nx_agraph.pygraphviz_layout(G, prog="circo", root=root)
+        # Equivalent layout directly on AGraph
+        A.layout(args=f"-Groot={root}", prog="circo")
+        # Parse AGraph layout
+        a1_pos = tuple(float(v) for v in dict(A.get_node("1").attr)["pos"].split(","))
+        assert pygv_layout[1] == a1_pos
+
+    def test_2d_layout(self):
+        G = nx.Graph()
+        G = self.build_graph(G)
+        G.graph["dimen"] = 2
+        pos = nx.nx_agraph.pygraphviz_layout(G, prog="neato")
+        pos = list(pos.values())
+        assert len(pos) == 5
+        assert len(pos[0]) == 2
+
+    def test_3d_layout(self):
+        G = nx.Graph()
+        G = self.build_graph(G)
+        G.graph["dimen"] = 3
+        pos = nx.nx_agraph.pygraphviz_layout(G, prog="neato")
+        pos = list(pos.values())
+        assert len(pos) == 5
+        assert len(pos[0]) == 3
+
+    def test_no_warnings_raised(self):
+        # Test that no warnings are raised when Networkx graph
+        # is converted to Pygraphviz graph and 'pos'
+        # attribute is given
+        G = nx.Graph()
+        G.add_node(0, pos=(0, 0))
+        G.add_node(1, pos=(1, 1))
+        A = nx.nx_agraph.to_agraph(G)
+        with warnings.catch_warnings(record=True) as record:
+            A.layout()
+        assert len(record) == 0
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/test_image_comparison_pylab_mpl.py b/lib/python3.11/site-packages/networkx/drawing/tests/test_image_comparison_pylab_mpl.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d874f4af1e5fa49e5936fe9940c5c1cefacf708
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/tests/test_image_comparison_pylab_mpl.py
@@ -0,0 +1,229 @@
+"""Unit tests for explicit image comparison with pytest-mpl."""
+
+import pytest
+
+import networkx as nx
+
+pytest.importorskip("pytest_mpl")
+
+mpl = pytest.importorskip("matplotlib")
+mpl.use("PS")
+plt = pytest.importorskip("matplotlib.pyplot")
+plt.rcParams["text.usetex"] = False
+np = pytest.importorskip("numpy")
+
+
+@pytest.mark.mpl_image_compare
+def test_display_house_with_colors():
+    """
+    Originally, I wanted to use the exact samge image as test_house_with_colors.
+    But I can't seem to find the correct value for the margins to get the figures
+    to line up perfectly. To the human eye, these visualizations are basically the
+    same.
+    """
+    G = nx.house_graph()
+    fig, ax = plt.subplots()
+    nx.set_node_attributes(
+        G, {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)}, "pos"
+    )
+    nx.set_node_attributes(
+        G,
+        {
+            n: {
+                "size": 3000 if n != 4 else 2000,
+                "color": "tab:blue" if n != 4 else "tab:orange",
+            }
+            for n in G.nodes()
+        },
+    )
+    nx.display(
+        G,
+        node_pos="pos",
+        edge_alpha=0.5,
+        edge_width=6,
+        node_label=None,
+        node_border_color="k",
+    )
+    ax.margins(0.17)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_labels_and_colors():
+    """See 'Labels and Colors' gallery example"""
+    fig, ax = plt.subplots()
+    G = nx.cubical_graph()
+    pos = nx.spring_layout(G, seed=3113794652)  # positions for all nodes
+    nx.set_node_attributes(G, pos, "pos")  # Will not be needed after PR 7571
+    labels = iter(
+        [
+            r"$a$",
+            r"$b$",
+            r"$c$",
+            r"$d$",
+            r"$\alpha$",
+            r"$\beta$",
+            r"$\gamma$",
+            r"$\delta$",
+        ]
+    )
+    nx.set_node_attributes(
+        G,
+        {
+            n: {
+                "size": 800,
+                "alpha": 0.9,
+                "color": "tab:red" if n < 4 else "tab:blue",
+                "label": {"label": next(labels), "size": 22, "color": "whitesmoke"},
+            }
+            for n in G.nodes()
+        },
+    )
+
+    nx.display(G, node_pos="pos", edge_color="tab:grey")
+
+    # The tricky bit is the highlighted colors for the edges
+    edgelist = [(0, 1), (1, 2), (2, 3), (0, 3)]
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {
+                "width": 8,
+                "alpha": 0.5,
+                "color": "tab:red",
+                "visible": (u, v) in edgelist,
+            }
+            for u, v in G.edges()
+        },
+    )
+    nx.display(G, node_pos="pos", node_visible=False)
+    edgelist = [(4, 5), (5, 6), (6, 7), (4, 7)]
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {
+                "color": "tab:blue",
+                "visible": (u, v) in edgelist,
+            }
+            for u, v in G.edges()
+        },
+    )
+    nx.display(G, node_pos="pos", node_visible=False)
+
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_complex():
+    import itertools as it
+
+    fig, ax = plt.subplots()
+    G = nx.MultiDiGraph()
+    nodes = "ABC"
+    prod = list(it.product(nodes, repeat=2)) * 4
+    G = nx.MultiDiGraph()
+    for i, (u, v) in enumerate(prod):
+        G.add_edge(u, v, w=round(i / 3, 2))
+    nx.set_node_attributes(G, nx.spring_layout(G, seed=3113794652), "pos")
+    csi = it.cycle([f"arc3,rad={r}" for r in it.accumulate([0.15] * 4)])
+    nx.set_edge_attributes(G, {e: next(csi) for e in G.edges(keys=True)}, "curvature")
+    nx.set_edge_attributes(
+        G,
+        {
+            tuple(e): {"label": w, "bbox": {"alpha": 0}}
+            for *e, w in G.edges(keys=True, data="w")
+        },
+        "label",
+    )
+    nx.apply_matplotlib_colors(G, "w", "color", mpl.colormaps["inferno"], nodes=False)
+    nx.display(G, canvas=ax, node_pos="pos")
+
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_shortest_path():
+    fig, ax = plt.subplots()
+    G = nx.Graph()
+    G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
+    G.add_edge("A", "B", weight=4)
+    G.add_edge("A", "H", weight=8)
+    G.add_edge("B", "C", weight=8)
+    G.add_edge("B", "H", weight=11)
+    G.add_edge("C", "D", weight=7)
+    G.add_edge("C", "F", weight=4)
+    G.add_edge("C", "I", weight=2)
+    G.add_edge("D", "E", weight=9)
+    G.add_edge("D", "F", weight=14)
+    G.add_edge("E", "F", weight=10)
+    G.add_edge("F", "G", weight=2)
+    G.add_edge("G", "H", weight=1)
+    G.add_edge("G", "I", weight=6)
+    G.add_edge("H", "I", weight=7)
+
+    # Find the shortest path from node A to node E
+    path = nx.shortest_path(G, "A", "E", weight="weight")
+
+    # Create a list of edges in the shortest path
+    path_edges = list(zip(path, path[1:]))
+    nx.set_node_attributes(G, nx.spring_layout(G, seed=37), "pos")
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {
+                "color": (
+                    "red"
+                    if (u, v) in path_edges or tuple(reversed((u, v))) in path_edges
+                    else "black"
+                ),
+                "label": {"label": d["weight"], "rotate": False},
+            }
+            for u, v, d in G.edges(data=True)
+        },
+    )
+    nx.display(G, canvas=ax)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_empty_graph():
+    G = nx.empty_graph()
+    fig, ax = plt.subplots()
+    nx.display(G, canvas=ax)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_house_with_colors():
+    G = nx.house_graph()
+    # explicitly set positions
+    fig, ax = plt.subplots()
+    pos = {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)}
+
+    # Plot nodes with different properties for the "wall" and "roof" nodes
+    nx.draw_networkx_nodes(
+        G,
+        pos,
+        node_size=3000,
+        nodelist=[0, 1, 2, 3],
+        node_color="tab:blue",
+    )
+    nx.draw_networkx_nodes(
+        G, pos, node_size=2000, nodelist=[4], node_color="tab:orange"
+    )
+    nx.draw_networkx_edges(G, pos, alpha=0.5, width=6)
+    # Customize axes
+    ax.margins(0.11)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/test_latex.py b/lib/python3.11/site-packages/networkx/drawing/tests/test_latex.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ec9b07ab5888d039e719c672be4526597ba8a94
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/tests/test_latex.py
@@ -0,0 +1,285 @@
+import pytest
+
+import networkx as nx
+
+
+def test_tikz_attributes():
+    G = nx.path_graph(4, create_using=nx.DiGraph)
+    pos = {n: (n, n) for n in G}
+
+    G.add_edge(0, 0)
+    G.edges[(0, 0)]["label"] = "Loop"
+    G.edges[(0, 0)]["label_options"] = "midway"
+
+    G.nodes[0]["style"] = "blue"
+    G.nodes[1]["style"] = "line width=3,draw"
+    G.nodes[2]["style"] = "circle,draw,blue!50"
+    G.nodes[3]["label"] = "Stop"
+    G.edges[(0, 1)]["label"] = "1st Step"
+    G.edges[(0, 1)]["label_options"] = "near end"
+    G.edges[(2, 3)]["label"] = "3rd Step"
+    G.edges[(2, 3)]["label_options"] = "near start"
+    G.edges[(2, 3)]["style"] = "bend left,green"
+    G.edges[(1, 2)]["label"] = "2nd"
+    G.edges[(1, 2)]["label_options"] = "pos=0.5"
+    G.edges[(1, 2)]["style"] = ">->,bend right,line width=3,green!90"
+
+    output_tex = nx.to_latex(
+        G,
+        pos=pos,
+        as_document=False,
+        tikz_options="[scale=3]",
+        node_options="style",
+        edge_options="style",
+        node_label="label",
+        edge_label="label",
+        edge_label_options="label_options",
+    )
+    expected_tex = r"""\begin{figure}
+  \begin{tikzpicture}[scale=3]
+      \draw
+        (0, 0) node[blue] (0){0}
+        (1, 1) node[line width=3,draw] (1){1}
+        (2, 2) node[circle,draw,blue!50] (2){2}
+        (3, 3) node (3){Stop};
+      \begin{scope}[->]
+        \draw (0) to node[near end] {1st Step} (1);
+        \draw[loop,] (0) to node[midway] {Loop} (0);
+        \draw[>->,bend right,line width=3,green!90] (1) to node[pos=0.5] {2nd} (2);
+        \draw[bend left,green] (2) to node[near start] {3rd Step} (3);
+      \end{scope}
+    \end{tikzpicture}
+\end{figure}"""
+
+    # First, check for consistency line-by-line - if this fails, the mismatched
+    # line will be shown explicitly in the failure summary
+    for expected, actual in zip(expected_tex.split("\n"), output_tex.split("\n")):
+        assert expected == actual
+
+    assert output_tex == expected_tex
+
+
+def test_basic_multiple_graphs():
+    H1 = nx.path_graph(4)
+    H2 = nx.complete_graph(4)
+    H3 = nx.path_graph(8)
+    H4 = nx.complete_graph(8)
+    captions = [
+        "Path on 4 nodes",
+        "Complete graph on 4 nodes",
+        "Path on 8 nodes",
+        "Complete graph on 8 nodes",
+    ]
+    labels = ["fig2a", "fig2b", "fig2c", "fig2d"]
+    latex_code = nx.to_latex(
+        [H1, H2, H3, H4],
+        n_rows=2,
+        sub_captions=captions,
+        sub_labels=labels,
+    )
+    assert "begin{document}" in latex_code
+    assert "begin{figure}" in latex_code
+    assert latex_code.count("begin{subfigure}") == 4
+    assert latex_code.count("tikzpicture") == 8
+    assert latex_code.count("[-]") == 4
+
+
+def test_basic_tikz():
+    expected_tex = r"""\documentclass{report}
+\usepackage{tikz}
+\usepackage{subcaption}
+
+\begin{document}
+\begin{figure}
+  \begin{subfigure}{0.5\textwidth}
+  \begin{tikzpicture}[scale=2]
+      \draw[gray!90]
+        (0.749, 0.702) node[red!90] (0){0}
+        (1.0, -0.014) node[red!90] (1){1}
+        (-0.777, -0.705) node (2){2}
+        (-0.984, 0.042) node (3){3}
+        (-0.028, 0.375) node[cyan!90] (4){4}
+        (-0.412, 0.888) node (5){5}
+        (0.448, -0.856) node (6){6}
+        (0.003, -0.431) node[cyan!90] (7){7};
+      \begin{scope}[->,gray!90]
+        \draw (0) to (4);
+        \draw (0) to (5);
+        \draw (0) to (6);
+        \draw (0) to (7);
+        \draw (1) to (4);
+        \draw (1) to (5);
+        \draw (1) to (6);
+        \draw (1) to (7);
+        \draw (2) to (4);
+        \draw (2) to (5);
+        \draw (2) to (6);
+        \draw (2) to (7);
+        \draw (3) to (4);
+        \draw (3) to (5);
+        \draw (3) to (6);
+        \draw (3) to (7);
+      \end{scope}
+    \end{tikzpicture}
+    \caption{My tikz number 1 of 2}\label{tikz_1_2}
+  \end{subfigure}
+  \begin{subfigure}{0.5\textwidth}
+  \begin{tikzpicture}[scale=2]
+      \draw[gray!90]
+        (0.749, 0.702) node[green!90] (0){0}
+        (1.0, -0.014) node[green!90] (1){1}
+        (-0.777, -0.705) node (2){2}
+        (-0.984, 0.042) node (3){3}
+        (-0.028, 0.375) node[purple!90] (4){4}
+        (-0.412, 0.888) node (5){5}
+        (0.448, -0.856) node (6){6}
+        (0.003, -0.431) node[purple!90] (7){7};
+      \begin{scope}[->,gray!90]
+        \draw (0) to (4);
+        \draw (0) to (5);
+        \draw (0) to (6);
+        \draw (0) to (7);
+        \draw (1) to (4);
+        \draw (1) to (5);
+        \draw (1) to (6);
+        \draw (1) to (7);
+        \draw (2) to (4);
+        \draw (2) to (5);
+        \draw (2) to (6);
+        \draw (2) to (7);
+        \draw (3) to (4);
+        \draw (3) to (5);
+        \draw (3) to (6);
+        \draw (3) to (7);
+      \end{scope}
+    \end{tikzpicture}
+    \caption{My tikz number 2 of 2}\label{tikz_2_2}
+  \end{subfigure}
+  \caption{A graph generated with python and latex.}
+\end{figure}
+\end{document}"""
+
+    edges = [
+        (0, 4),
+        (0, 5),
+        (0, 6),
+        (0, 7),
+        (1, 4),
+        (1, 5),
+        (1, 6),
+        (1, 7),
+        (2, 4),
+        (2, 5),
+        (2, 6),
+        (2, 7),
+        (3, 4),
+        (3, 5),
+        (3, 6),
+        (3, 7),
+    ]
+    G = nx.DiGraph()
+    G.add_nodes_from(range(8))
+    G.add_edges_from(edges)
+    pos = {
+        0: (0.7490296171687696, 0.702353520257394),
+        1: (1.0, -0.014221357723796535),
+        2: (-0.7765783344161441, -0.7054170966808919),
+        3: (-0.9842690223417624, 0.04177547602465483),
+        4: (-0.02768523817180917, 0.3745724439551441),
+        5: (-0.41154855146767433, 0.8880106515525136),
+        6: (0.44780153389148264, -0.8561492709269164),
+        7: (0.0032499953371383505, -0.43092436645809945),
+    }
+
+    rc_node_color = {0: "red!90", 1: "red!90", 4: "cyan!90", 7: "cyan!90"}
+    gp_node_color = {0: "green!90", 1: "green!90", 4: "purple!90", 7: "purple!90"}
+
+    H = G.copy()
+    nx.set_node_attributes(G, rc_node_color, "color")
+    nx.set_node_attributes(H, gp_node_color, "color")
+
+    sub_captions = ["My tikz number 1 of 2", "My tikz number 2 of 2"]
+    sub_labels = ["tikz_1_2", "tikz_2_2"]
+
+    output_tex = nx.to_latex(
+        [G, H],
+        [pos, pos],
+        tikz_options="[scale=2]",
+        default_node_options="gray!90",
+        default_edge_options="gray!90",
+        node_options="color",
+        sub_captions=sub_captions,
+        sub_labels=sub_labels,
+        caption="A graph generated with python and latex.",
+        n_rows=2,
+        as_document=True,
+    )
+
+    # First, check for consistency line-by-line - if this fails, the mismatched
+    # line will be shown explicitly in the failure summary
+    for expected, actual in zip(expected_tex.split("\n"), output_tex.split("\n")):
+        assert expected == actual
+    # Double-check for document-level consistency
+    assert output_tex == expected_tex
+
+
+def test_exception_pos_single_graph(to_latex=nx.to_latex):
+    # smoke test that pos can be a string
+    G = nx.path_graph(4)
+    to_latex(G, pos="pos")
+
+    # must include all nodes
+    pos = {0: (1, 2), 1: (0, 1), 2: (2, 1)}
+    with pytest.raises(nx.NetworkXError):
+        to_latex(G, pos)
+
+    # must have 2 values
+    pos[3] = (1, 2, 3)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(G, pos)
+    pos[3] = 2
+    with pytest.raises(nx.NetworkXError):
+        to_latex(G, pos)
+
+    # check that passes with 2 values
+    pos[3] = (3, 2)
+    to_latex(G, pos)
+
+
+def test_exception_multiple_graphs(to_latex=nx.to_latex):
+    G = nx.path_graph(3)
+    pos_bad = {0: (1, 2), 1: (0, 1)}
+    pos_OK = {0: (1, 2), 1: (0, 1), 2: (2, 1)}
+    fourG = [G, G, G, G]
+    fourpos = [pos_OK, pos_OK, pos_OK, pos_OK]
+
+    # input single dict to use for all graphs
+    to_latex(fourG, pos_OK)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, pos_bad)
+
+    # input list of dicts to use for all graphs
+    to_latex(fourG, fourpos)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, [pos_bad, pos_bad, pos_bad, pos_bad])
+
+    # every pos dict must include all nodes
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, [pos_OK, pos_OK, pos_bad, pos_OK])
+
+    # test sub_captions and sub_labels (len must match Gbunch)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, fourpos, sub_captions=["hi", "hi"])
+
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, fourpos, sub_labels=["hi", "hi"])
+
+    # all pass
+    to_latex(fourG, fourpos, sub_captions=["hi"] * 4, sub_labels=["lbl"] * 4)
+
+
+def test_exception_multigraph():
+    G = nx.path_graph(4, create_using=nx.MultiGraph)
+    G.add_edge(1, 2)
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.to_latex(G)
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/test_layout.py b/lib/python3.11/site-packages/networkx/drawing/tests/test_layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..34d71efa6050d7ea45df1a5ac3227244a361f84f
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/tests/test_layout.py
@@ -0,0 +1,631 @@
+"""Unit tests for layout functions."""
+
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+
+class TestLayout:
+    @classmethod
+    def setup_class(cls):
+        cls.Gi = nx.grid_2d_graph(5, 5)
+        cls.Gs = nx.Graph()
+        nx.add_path(cls.Gs, "abcdef")
+        cls.bigG = nx.grid_2d_graph(25, 25)  # > 500 nodes for sparse
+
+    def test_spring_fixed_without_pos(self):
+        G = nx.path_graph(4)
+        # No pos dict at all
+        with pytest.raises(ValueError, match="nodes are fixed without positions"):
+            nx.spring_layout(G, fixed=[0])
+
+        pos = {0: (1, 1), 2: (0, 0)}
+        # Node 1 not in pos dict
+        with pytest.raises(ValueError, match="nodes are fixed without positions"):
+            nx.spring_layout(G, fixed=[0, 1], pos=pos)
+
+        # All fixed nodes in pos dict
+        out = nx.spring_layout(G, fixed=[0, 2], pos=pos)  # No ValueError
+        assert all(np.array_equal(out[n], pos[n]) for n in (0, 2))
+
+    def test_spring_init_pos(self):
+        # Tests GH #2448
+        import math
+
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3)])
+
+        init_pos = {0: (0.0, 0.0)}
+        fixed_pos = [0]
+        pos = nx.fruchterman_reingold_layout(G, pos=init_pos, fixed=fixed_pos)
+        has_nan = any(math.isnan(c) for coords in pos.values() for c in coords)
+        assert not has_nan, "values should not be nan"
+
+    def test_smoke_empty_graph(self):
+        G = []
+        nx.random_layout(G)
+        nx.circular_layout(G)
+        nx.planar_layout(G)
+        nx.spring_layout(G)
+        nx.fruchterman_reingold_layout(G)
+        nx.spectral_layout(G)
+        nx.shell_layout(G)
+        nx.bipartite_layout(G, G)
+        nx.spiral_layout(G)
+        nx.multipartite_layout(G)
+        nx.kamada_kawai_layout(G)
+
+    def test_smoke_int(self):
+        G = self.Gi
+        nx.random_layout(G)
+        nx.circular_layout(G)
+        nx.planar_layout(G)
+        nx.spring_layout(G)
+        nx.forceatlas2_layout(G)
+        nx.fruchterman_reingold_layout(G)
+        nx.fruchterman_reingold_layout(self.bigG)
+        nx.spectral_layout(G)
+        nx.spectral_layout(G.to_directed())
+        nx.spectral_layout(self.bigG)
+        nx.spectral_layout(self.bigG.to_directed())
+        nx.shell_layout(G)
+        nx.spiral_layout(G)
+        nx.kamada_kawai_layout(G)
+        nx.kamada_kawai_layout(G, dim=1)
+        nx.kamada_kawai_layout(G, dim=3)
+        nx.arf_layout(G)
+
+    def test_smoke_string(self):
+        G = self.Gs
+        nx.random_layout(G)
+        nx.circular_layout(G)
+        nx.planar_layout(G)
+        nx.spring_layout(G)
+        nx.forceatlas2_layout(G)
+        nx.fruchterman_reingold_layout(G)
+        nx.spectral_layout(G)
+        nx.shell_layout(G)
+        nx.spiral_layout(G)
+        nx.kamada_kawai_layout(G)
+        nx.kamada_kawai_layout(G, dim=1)
+        nx.kamada_kawai_layout(G, dim=3)
+        nx.arf_layout(G)
+
+    def check_scale_and_center(self, pos, scale, center):
+        center = np.array(center)
+        low = center - scale
+        hi = center + scale
+        vpos = np.array(list(pos.values()))
+        length = vpos.max(0) - vpos.min(0)
+        assert (length <= 2 * scale).all()
+        assert (vpos >= low).all()
+        assert (vpos <= hi).all()
+
+    def test_scale_and_center_arg(self):
+        sc = self.check_scale_and_center
+        c = (4, 5)
+        G = nx.complete_graph(9)
+        G.add_node(9)
+        sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5))
+        # rest can have 2*scale length: [-scale, scale]
+        sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.spiral_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c)
+
+        c = (2, 3, 5)
+        sc(nx.kamada_kawai_layout(G, dim=3, scale=2, center=c), scale=2, center=c)
+
+    def test_planar_layout_non_planar_input(self):
+        G = nx.complete_graph(9)
+        pytest.raises(nx.NetworkXException, nx.planar_layout, G)
+
+    def test_smoke_planar_layout_embedding_input(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.set_data({0: [1, 2], 1: [0, 2], 2: [0, 1]})
+        nx.planar_layout(embedding)
+
+    def test_default_scale_and_center(self):
+        sc = self.check_scale_and_center
+        c = (0, 0)
+        G = nx.complete_graph(9)
+        G.add_node(9)
+        sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
+        sc(nx.spring_layout(G), scale=1, center=c)
+        sc(nx.spectral_layout(G), scale=1, center=c)
+        sc(nx.circular_layout(G), scale=1, center=c)
+        sc(nx.shell_layout(G), scale=1, center=c)
+        sc(nx.spiral_layout(G), scale=1, center=c)
+        sc(nx.kamada_kawai_layout(G), scale=1, center=c)
+
+        c = (0, 0, 0)
+        sc(nx.kamada_kawai_layout(G, dim=3), scale=1, center=c)
+
+    def test_circular_planar_and_shell_dim_error(self):
+        G = nx.path_graph(4)
+        pytest.raises(ValueError, nx.circular_layout, G, dim=1)
+        pytest.raises(ValueError, nx.shell_layout, G, dim=1)
+        pytest.raises(ValueError, nx.shell_layout, G, dim=3)
+        pytest.raises(ValueError, nx.planar_layout, G, dim=1)
+        pytest.raises(ValueError, nx.planar_layout, G, dim=3)
+
+    def test_adjacency_interface_numpy(self):
+        A = nx.to_numpy_array(self.Gs)
+        pos = nx.drawing.layout._fruchterman_reingold(A)
+        assert pos.shape == (6, 2)
+        pos = nx.drawing.layout._fruchterman_reingold(A, dim=3)
+        assert pos.shape == (6, 3)
+        pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
+        assert pos.shape == (6, 2)
+
+    def test_adjacency_interface_scipy(self):
+        A = nx.to_scipy_sparse_array(self.Gs, dtype="d")
+        pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
+        assert pos.shape == (6, 2)
+        pos = nx.drawing.layout._sparse_spectral(A)
+        assert pos.shape == (6, 2)
+        pos = nx.drawing.layout._sparse_fruchterman_reingold(A, dim=3)
+        assert pos.shape == (6, 3)
+
+    def test_single_nodes(self):
+        G = nx.path_graph(1)
+        vpos = nx.shell_layout(G)
+        assert not vpos[0].any()
+        G = nx.path_graph(4)
+        vpos = nx.shell_layout(G, [[0], [1, 2], [3]])
+        assert not vpos[0].any()
+        assert vpos[3].any()  # ensure node 3 not at origin (#3188)
+        assert np.linalg.norm(vpos[3]) <= 1  # ensure node 3 fits (#3753)
+        vpos = nx.shell_layout(G, [[0], [1, 2], [3]], rotate=0)
+        assert np.linalg.norm(vpos[3]) <= 1  # ensure node 3 fits (#3753)
+
+    def test_smoke_initial_pos_forceatlas2(self):
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.forceatlas2_layout(self.Gi, pos=pos)
+
+    def test_smoke_initial_pos_fruchterman_reingold(self):
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.fruchterman_reingold_layout(self.Gi, pos=pos)
+
+    def test_smoke_initial_pos_arf(self):
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.arf_layout(self.Gi, pos=pos)
+
+    def test_fixed_node_fruchterman_reingold(self):
+        # Dense version (numpy based)
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.spring_layout(self.Gi, pos=pos, fixed=[(0, 0)])
+        assert tuple(pos[(0, 0)]) == tuple(npos[(0, 0)])
+        # Sparse version (scipy based)
+        pos = nx.circular_layout(self.bigG)
+        npos = nx.spring_layout(self.bigG, pos=pos, fixed=[(0, 0)])
+        for axis in range(2):
+            assert pos[(0, 0)][axis] == pytest.approx(npos[(0, 0)][axis], abs=1e-7)
+
+    def test_center_parameter(self):
+        G = nx.path_graph(1)
+        nx.random_layout(G, center=(1, 1))
+        vpos = nx.circular_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.planar_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.spring_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.spectral_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.shell_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.spiral_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+
+    def test_center_wrong_dimensions(self):
+        G = nx.path_graph(1)
+        assert id(nx.spring_layout) == id(nx.fruchterman_reingold_layout)
+        pytest.raises(ValueError, nx.random_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.circular_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.planar_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spring_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spring_layout, G, dim=3, center=(1, 1))
+        pytest.raises(ValueError, nx.spectral_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spectral_layout, G, dim=3, center=(1, 1))
+        pytest.raises(ValueError, nx.shell_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spiral_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.kamada_kawai_layout, G, center=(1, 1, 1))
+
+    def test_empty_graph(self):
+        G = nx.empty_graph()
+        vpos = nx.random_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.circular_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.planar_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.bipartite_layout(G, G)
+        assert vpos == {}
+        vpos = nx.spring_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.spectral_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.shell_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.spiral_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.multipartite_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.kamada_kawai_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.forceatlas2_layout(G)
+        assert vpos == {}
+        vpos = nx.arf_layout(G)
+        assert vpos == {}
+
+    def test_bipartite_layout(self):
+        G = nx.complete_bipartite_graph(3, 5)
+        top, bottom = nx.bipartite.sets(G)
+
+        vpos = nx.bipartite_layout(G, top)
+        assert len(vpos) == len(G)
+
+        top_x = vpos[list(top)[0]][0]
+        bottom_x = vpos[list(bottom)[0]][0]
+        for node in top:
+            assert vpos[node][0] == top_x
+        for node in bottom:
+            assert vpos[node][0] == bottom_x
+
+        vpos = nx.bipartite_layout(
+            G, top, align="horizontal", center=(2, 2), scale=2, aspect_ratio=1
+        )
+        assert len(vpos) == len(G)
+
+        top_y = vpos[list(top)[0]][1]
+        bottom_y = vpos[list(bottom)[0]][1]
+        for node in top:
+            assert vpos[node][1] == top_y
+        for node in bottom:
+            assert vpos[node][1] == bottom_y
+
+        pytest.raises(ValueError, nx.bipartite_layout, G, top, align="foo")
+
+    def test_multipartite_layout(self):
+        sizes = (0, 5, 7, 2, 8)
+        G = nx.complete_multipartite_graph(*sizes)
+
+        vpos = nx.multipartite_layout(G)
+        assert len(vpos) == len(G)
+
+        start = 0
+        for n in sizes:
+            end = start + n
+            assert all(vpos[start][0] == vpos[i][0] for i in range(start + 1, end))
+            start += n
+
+        vpos = nx.multipartite_layout(G, align="horizontal", scale=2, center=(2, 2))
+        assert len(vpos) == len(G)
+
+        start = 0
+        for n in sizes:
+            end = start + n
+            assert all(vpos[start][1] == vpos[i][1] for i in range(start + 1, end))
+            start += n
+
+        pytest.raises(ValueError, nx.multipartite_layout, G, align="foo")
+
+    def test_kamada_kawai_costfn_1d(self):
+        costfn = nx.drawing.layout._kamada_kawai_costfn
+
+        pos = np.array([4.0, 7.0])
+        invdist = 1 / np.array([[0.1, 2.0], [2.0, 0.3]])
+
+        cost, grad = costfn(pos, np, invdist, meanweight=0, dim=1)
+
+        assert cost == pytest.approx(((3 / 2.0 - 1) ** 2), abs=1e-7)
+        assert grad[0] == pytest.approx((-0.5), abs=1e-7)
+        assert grad[1] == pytest.approx(0.5, abs=1e-7)
+
+    def check_kamada_kawai_costfn(self, pos, invdist, meanwt, dim):
+        costfn = nx.drawing.layout._kamada_kawai_costfn
+
+        cost, grad = costfn(pos.ravel(), np, invdist, meanweight=meanwt, dim=dim)
+
+        expected_cost = 0.5 * meanwt * np.sum(np.sum(pos, axis=0) ** 2)
+        for i in range(pos.shape[0]):
+            for j in range(i + 1, pos.shape[0]):
+                diff = np.linalg.norm(pos[i] - pos[j])
+                expected_cost += (diff * invdist[i][j] - 1.0) ** 2
+
+        assert cost == pytest.approx(expected_cost, abs=1e-7)
+
+        dx = 1e-4
+        for nd in range(pos.shape[0]):
+            for dm in range(pos.shape[1]):
+                idx = nd * pos.shape[1] + dm
+                ps = pos.flatten()
+
+                ps[idx] += dx
+                cplus = costfn(ps, np, invdist, meanweight=meanwt, dim=pos.shape[1])[0]
+
+                ps[idx] -= 2 * dx
+                cminus = costfn(ps, np, invdist, meanweight=meanwt, dim=pos.shape[1])[0]
+
+                assert grad[idx] == pytest.approx((cplus - cminus) / (2 * dx), abs=1e-5)
+
+    def test_kamada_kawai_costfn(self):
+        invdist = 1 / np.array([[0.1, 2.1, 1.7], [2.1, 0.2, 0.6], [1.7, 0.6, 0.3]])
+        meanwt = 0.3
+
+        # 2d
+        pos = np.array([[1.3, -3.2], [2.7, -0.3], [5.1, 2.5]])
+
+        self.check_kamada_kawai_costfn(pos, invdist, meanwt, 2)
+
+        # 3d
+        pos = np.array([[0.9, 8.6, -8.7], [-10, -0.5, -7.1], [9.1, -8.1, 1.6]])
+
+        self.check_kamada_kawai_costfn(pos, invdist, meanwt, 3)
+
+    def test_spiral_layout(self):
+        G = self.Gs
+
+        # a lower value of resolution should result in a more compact layout
+        # intuitively, the total distance from the start and end nodes
+        # via each node in between (transiting through each) will be less,
+        # assuming rescaling does not occur on the computed node positions
+        pos_standard = np.array(list(nx.spiral_layout(G, resolution=0.35).values()))
+        pos_tighter = np.array(list(nx.spiral_layout(G, resolution=0.34).values()))
+        distances = np.linalg.norm(pos_standard[:-1] - pos_standard[1:], axis=1)
+        distances_tighter = np.linalg.norm(pos_tighter[:-1] - pos_tighter[1:], axis=1)
+        assert sum(distances) > sum(distances_tighter)
+
+        # return near-equidistant points after the first value if set to true
+        pos_equidistant = np.array(list(nx.spiral_layout(G, equidistant=True).values()))
+        distances_equidistant = np.linalg.norm(
+            pos_equidistant[:-1] - pos_equidistant[1:], axis=1
+        )
+        assert np.allclose(
+            distances_equidistant[1:], distances_equidistant[-1], atol=0.01
+        )
+
+    def test_spiral_layout_equidistant(self):
+        G = nx.path_graph(10)
+        nx.spiral_layout(G, equidistant=True, store_pos_as="pos")
+        pos = nx.get_node_attributes(G, "pos")
+        # Extract individual node positions as an array
+        p = np.array(list(pos.values()))
+        # Elementwise-distance between node positions
+        dist = np.linalg.norm(p[1:] - p[:-1], axis=1)
+        assert np.allclose(np.diff(dist), 0, atol=1e-3)
+
+    def test_forceatlas2_layout_partial_input_test(self):
+        # check whether partial pos input still returns a full proper position
+        G = self.Gs
+        node = nx.utils.arbitrary_element(G)
+        pos = nx.circular_layout(G)
+        del pos[node]
+        pos = nx.forceatlas2_layout(G, pos=pos)
+        assert len(pos) == len(G)
+
+    def test_rescale_layout_dict(self):
+        G = nx.empty_graph()
+        vpos = nx.random_layout(G, center=(1, 1))
+        assert nx.rescale_layout_dict(vpos) == {}
+
+        G = nx.empty_graph(2)
+        vpos = {0: (0.0, 0.0), 1: (1.0, 1.0)}
+        s_vpos = nx.rescale_layout_dict(vpos)
+        assert np.linalg.norm([sum(x) for x in zip(*s_vpos.values())]) < 1e-6
+
+        G = nx.empty_graph(3)
+        vpos = {0: (0, 0), 1: (1, 1), 2: (0.5, 0.5)}
+        s_vpos = nx.rescale_layout_dict(vpos)
+
+        expectation = {
+            0: np.array((-1, -1)),
+            1: np.array((1, 1)),
+            2: np.array((0, 0)),
+        }
+        for k, v in expectation.items():
+            assert (s_vpos[k] == v).all()
+        s_vpos = nx.rescale_layout_dict(vpos, scale=2)
+        expectation = {
+            0: np.array((-2, -2)),
+            1: np.array((2, 2)),
+            2: np.array((0, 0)),
+        }
+        for k, v in expectation.items():
+            assert (s_vpos[k] == v).all()
+
+    def test_arf_layout_partial_input_test(self):
+        # Checks whether partial pos input still returns a proper position.
+        G = self.Gs
+        node = nx.utils.arbitrary_element(G)
+        pos = nx.circular_layout(G)
+        del pos[node]
+        pos = nx.arf_layout(G, pos=pos)
+        assert len(pos) == len(G)
+
+    def test_arf_layout_negative_a_check(self):
+        """
+        Checks input parameters correctly raises errors. For example,  `a` should be larger than 1
+        """
+        G = self.Gs
+        pytest.raises(ValueError, nx.arf_layout, G=G, a=-1)
+
+    def test_smoke_seed_input(self):
+        G = self.Gs
+        nx.random_layout(G, seed=42)
+        nx.spring_layout(G, seed=42)
+        nx.arf_layout(G, seed=42)
+        nx.forceatlas2_layout(G, seed=42)
+
+    def test_node_at_center(self):
+        # see gh-7791 avoid divide by zero
+        G = nx.path_graph(3)
+        orig_pos = {i: [i - 1, 0.0] for i in range(3)}
+        new_pos = nx.forceatlas2_layout(G, pos=orig_pos)
+
+    def test_initial_only_some_pos(self):
+        G = nx.path_graph(3)
+        orig_pos = {i: [i - 1, 0.0] for i in range(2)}
+        new_pos = nx.forceatlas2_layout(G, pos=orig_pos, seed=42)
+
+
+def test_multipartite_layout_nonnumeric_partition_labels():
+    """See gh-5123."""
+    G = nx.Graph()
+    G.add_node(0, subset="s0")
+    G.add_node(1, subset="s0")
+    G.add_node(2, subset="s1")
+    G.add_node(3, subset="s1")
+    G.add_edges_from([(0, 2), (0, 3), (1, 2)])
+    pos = nx.multipartite_layout(G)
+    assert len(pos) == len(G)
+
+
+def test_multipartite_layout_layer_order():
+    """Return the layers in sorted order if the layers of the multipartite
+    graph are sortable. See gh-5691"""
+    G = nx.Graph()
+    node_group = dict(zip(("a", "b", "c", "d", "e"), (2, 3, 1, 2, 4)))
+    for node, layer in node_group.items():
+        G.add_node(node, subset=layer)
+
+    # Horizontal alignment, therefore y-coord determines layers
+    pos = nx.multipartite_layout(G, align="horizontal")
+
+    layers = nx.utils.groups(node_group)
+    pos_from_layers = nx.multipartite_layout(G, align="horizontal", subset_key=layers)
+    for (n1, p1), (n2, p2) in zip(pos.items(), pos_from_layers.items()):
+        assert n1 == n2 and (p1 == p2).all()
+
+    # Nodes "a" and "d" are in the same layer
+    assert pos["a"][-1] == pos["d"][-1]
+    # positions should be sorted according to layer
+    assert pos["c"][-1] < pos["a"][-1] < pos["b"][-1] < pos["e"][-1]
+
+    # Make sure that multipartite_layout still works when layers are not sortable
+    G.nodes["a"]["subset"] = "layer_0"  # Can't sort mixed strs/ints
+    pos_nosort = nx.multipartite_layout(G)  # smoke test: this should not raise
+    assert pos_nosort.keys() == pos.keys()
+
+
+def _num_nodes_per_bfs_layer(pos):
+    """Helper function to extract the number of nodes in each layer of bfs_layout"""
+    x = np.array(list(pos.values()))[:, 0]  # node positions in layered dimension
+    _, layer_count = np.unique(x, return_counts=True)
+    return layer_count
+
+
+@pytest.mark.parametrize("n", range(2, 7))
+def test_bfs_layout_complete_graph(n):
+    """The complete graph should result in two layers: the starting node and
+    a second layer containing all neighbors."""
+    G = nx.complete_graph(n)
+    nx.bfs_layout(G, start=0, store_pos_as="pos")
+    pos = nx.get_node_attributes(G, "pos")
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), [1, n - 1])
+
+
+def test_bfs_layout_barbell():
+    G = nx.barbell_graph(5, 3)
+    # Start in one of the "bells"
+    pos = nx.bfs_layout(G, start=0)
+    # start, bell-1, [1] * len(bar)+1, bell-1
+    expected_nodes_per_layer = [1, 4, 1, 1, 1, 1, 4]
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), expected_nodes_per_layer)
+    # Start in the other "bell" - expect same layer pattern
+    pos = nx.bfs_layout(G, start=12)
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), expected_nodes_per_layer)
+    # Starting in the center of the bar, expect layers to be symmetric
+    pos = nx.bfs_layout(G, start=6)
+    # Expected layers: {6 (start)}, {5, 7}, {4, 8}, {8 nodes from remainder of bells}
+    expected_nodes_per_layer = [1, 2, 2, 8]
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), expected_nodes_per_layer)
+
+
+def test_bfs_layout_disconnected():
+    G = nx.complete_graph(5)
+    G.add_edges_from([(10, 11), (11, 12)])
+    with pytest.raises(nx.NetworkXError, match="bfs_layout didn't include all nodes"):
+        nx.bfs_layout(G, start=0)
+
+
+def test_bipartite_layout_default_nodes_raises_non_bipartite_input():
+    G = nx.complete_graph(5)
+    with pytest.raises(nx.NetworkXError, match="Graph is not bipartite"):
+        nx.bipartite_layout(G)
+    # No exception if nodes are explicitly specified
+    pos = nx.bipartite_layout(G, nodes=[2, 3])
+
+
+def test_bipartite_layout_default_nodes():
+    G = nx.complete_bipartite_graph(3, 3)
+    pos = nx.bipartite_layout(G)  # no nodes specified
+    # X coords of nodes should be the same within the bipartite sets
+    for nodeset in nx.bipartite.sets(G):
+        xs = [pos[k][0] for k in nodeset]
+        assert all(x == pytest.approx(xs[0]) for x in xs)
+
+
+@pytest.mark.parametrize(
+    "layout",
+    [
+        nx.random_layout,
+        nx.circular_layout,
+        nx.shell_layout,
+        nx.spring_layout,
+        nx.kamada_kawai_layout,
+        nx.spectral_layout,
+        nx.planar_layout,
+        nx.spiral_layout,
+        nx.forceatlas2_layout,
+    ],
+)
+def test_layouts_negative_dim(layout):
+    """Test all layouts that support dim kwarg handle invalid inputs."""
+    G = nx.path_graph(4)
+    valid_err_msgs = "|".join(
+        [
+            "negative dimensions.*not allowed",
+            "can only handle 2",
+            "cannot handle.*2",
+        ]
+    )
+    with pytest.raises(ValueError, match=valid_err_msgs):
+        layout(G, dim=-1)
+
+
+@pytest.mark.parametrize(
+    ("num_nodes", "expected_method"), [(100, "force"), (501, "energy")]
+)
+@pytest.mark.parametrize(
+    "extra_layout_kwargs",
+    [
+        {},  # No extra kwargs
+        {"pos": {0: (0, 0)}, "fixed": [0]},  # Fixed node position
+        {"dim": 3},  # 3D layout
+    ],
+)
+def test_spring_layout_graph_size_heuristic(
+    num_nodes, expected_method, extra_layout_kwargs
+):
+    """Expect 'force' layout for n < 500 and 'energy' for n >= 500"""
+    G = nx.cycle_graph(num_nodes)
+    # Seeded layout to compare explicit method to one determined by "auto"
+    seed = 163674319
+
+    # Compare explicit method to auto method
+    expected = nx.spring_layout(
+        G, method=expected_method, seed=seed, **extra_layout_kwargs
+    )
+    actual = nx.spring_layout(G, method="auto", seed=seed, **extra_layout_kwargs)
+    assert np.allclose(list(expected.values()), list(actual.values()), atol=1e-5)
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/test_pydot.py b/lib/python3.11/site-packages/networkx/drawing/tests/test_pydot.py
new file mode 100644
index 0000000000000000000000000000000000000000..acf93d77ec3e555207f8c02b5a9da00633382eed
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/tests/test_pydot.py
@@ -0,0 +1,146 @@
+"""Unit tests for pydot drawing functions."""
+
+from io import StringIO
+
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+pydot = pytest.importorskip("pydot")
+
+
+class TestPydot:
+    @pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+    @pytest.mark.parametrize("prog", ("neato", "dot"))
+    def test_pydot(self, G, prog, tmp_path):
+        """
+        Validate :mod:`pydot`-based usage of the passed NetworkX graph with the
+        passed basename of an external GraphViz command (e.g., `dot`, `neato`).
+        """
+
+        # Set the name of this graph to... "G". Failing to do so will
+        # subsequently trip an assertion expecting this name.
+        G.graph["name"] = "G"
+
+        # Add arbitrary nodes and edges to the passed empty graph.
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("A", "D")])
+        G.add_node("E")
+
+        # Validate layout of this graph with the passed GraphViz command.
+        graph_layout = nx.nx_pydot.pydot_layout(G, prog=prog)
+        assert isinstance(graph_layout, dict)
+
+        # Convert this graph into a "pydot.Dot" instance.
+        P = nx.nx_pydot.to_pydot(G)
+
+        # Convert this "pydot.Dot" instance back into a graph of the same type.
+        G2 = G.__class__(nx.nx_pydot.from_pydot(P))
+
+        # Validate the original and resulting graphs to be the same.
+        assert graphs_equal(G, G2)
+
+        fname = tmp_path / "out.dot"
+
+        # Serialize this "pydot.Dot" instance to a temporary file in dot format
+        P.write_raw(fname)
+
+        # Deserialize a list of new "pydot.Dot" instances back from this file.
+        Pin_list = pydot.graph_from_dot_file(path=fname, encoding="utf-8")
+
+        # Validate this file to contain only one graph.
+        assert len(Pin_list) == 1
+
+        # The single "pydot.Dot" instance deserialized from this file.
+        Pin = Pin_list[0]
+
+        # Sorted list of all nodes in the original "pydot.Dot" instance.
+        n1 = sorted(p.get_name() for p in P.get_node_list())
+
+        # Sorted list of all nodes in the deserialized "pydot.Dot" instance.
+        n2 = sorted(p.get_name() for p in Pin.get_node_list())
+
+        # Validate these instances to contain the same nodes.
+        assert n1 == n2
+
+        # Sorted list of all edges in the original "pydot.Dot" instance.
+        e1 = sorted((e.get_source(), e.get_destination()) for e in P.get_edge_list())
+
+        # Sorted list of all edges in the original "pydot.Dot" instance.
+        e2 = sorted((e.get_source(), e.get_destination()) for e in Pin.get_edge_list())
+
+        # Validate these instances to contain the same edges.
+        assert e1 == e2
+
+        # Deserialize a new graph of the same type back from this file.
+        Hin = nx.nx_pydot.read_dot(fname)
+        Hin = G.__class__(Hin)
+
+        # Validate the original and resulting graphs to be the same.
+        assert graphs_equal(G, Hin)
+
+    def test_read_write(self):
+        G = nx.MultiGraph()
+        G.graph["name"] = "G"
+        G.add_edge("1", "2", key="0")  # read assumes strings
+        fh = StringIO()
+        nx.nx_pydot.write_dot(G, fh)
+        fh.seek(0)
+        H = nx.nx_pydot.read_dot(fh)
+        assert graphs_equal(G, H)
+
+
+def test_pydot_issue_7581(tmp_path):
+    """Validate that `nx_pydot.pydot_layout` handles nodes
+    with characters like "\n", " ".
+
+    Those characters cause `pydot` to escape and quote them on output,
+    which caused #7581.
+    """
+    G = nx.Graph()
+    G.add_edges_from([("A\nbig test", "B"), ("A\nbig test", "C"), ("B", "C")])
+
+    graph_layout = nx.nx_pydot.pydot_layout(G, prog="dot")
+    assert isinstance(graph_layout, dict)
+
+    # Convert the graph to pydot and back into a graph. There should be no difference.
+    P = nx.nx_pydot.to_pydot(G)
+    G2 = nx.Graph(nx.nx_pydot.from_pydot(P))
+    assert graphs_equal(G, G2)
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_hashable_pydot(graph_type):
+    # gh-5790
+    G = graph_type()
+    G.add_edge("5", frozenset([1]), t='"Example:A"', l=False)
+    G.add_edge("1", 2, w=True, t=("node1",), l=frozenset(["node1"]))
+    G.add_edge("node", (3, 3), w="string")
+
+    assert [
+        {"t": '"Example:A"', "l": "False"},
+        {"w": "True", "t": "('node1',)", "l": "frozenset({'node1'})"},
+        {"w": "string"},
+    ] == [
+        attr
+        for _, _, attr in nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).edges.data()
+    ]
+
+    assert {str(i) for i in G.nodes()} == set(
+        nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).nodes
+    )
+
+
+def test_pydot_numerical_name():
+    G = nx.Graph()
+    G.add_edges_from([("A", "B"), (0, 1)])
+    graph_layout = nx.nx_pydot.pydot_layout(G, prog="dot")
+    assert isinstance(graph_layout, dict)
+    assert "0" not in graph_layout
+    assert 0 in graph_layout
+    assert "1" not in graph_layout
+    assert 1 in graph_layout
+    assert "A" in graph_layout
+    assert "B" in graph_layout
diff --git a/lib/python3.11/site-packages/networkx/drawing/tests/test_pylab.py b/lib/python3.11/site-packages/networkx/drawing/tests/test_pylab.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6cdd1455dfe3c408213c96f4e37728b57f0cd42
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/drawing/tests/test_pylab.py
@@ -0,0 +1,1582 @@
+"""Unit tests for matplotlib drawing functions."""
+
+import itertools
+import os
+import warnings
+
+import pytest
+
+import networkx as nx
+
+mpl = pytest.importorskip("matplotlib")
+np = pytest.importorskip("numpy")
+mpl.use("PS")
+plt = pytest.importorskip("matplotlib.pyplot")
+plt.rcParams["text.usetex"] = False
+
+
+barbell = nx.barbell_graph(4, 6)
+
+defaults = {
+    "node_pos": None,
+    "node_visible": True,
+    "node_color": "#1f78b4",
+    "node_size": 300,
+    "node_label": {
+        "size": 12,
+        "color": "#000000",
+        "family": "sans-serif",
+        "weight": "normal",
+        "alpha": 1.0,
+        "background_color": None,
+        "background_alpha": None,
+        "h_align": "center",
+        "v_align": "center",
+        "bbox": None,
+    },
+    "node_shape": "o",
+    "node_alpha": 1.0,
+    "node_border_width": 1.0,
+    "node_border_color": "face",
+    "edge_visible": True,
+    "edge_width": 1.0,
+    "edge_color": "#000000",
+    "edge_label": {
+        "size": 12,
+        "color": "#000000",
+        "family": "sans-serif",
+        "weight": "normal",
+        "alpha": 1.0,
+        "bbox": {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)},
+        "h_align": "center",
+        "v_align": "center",
+        "pos": 0.5,
+        "rotate": True,
+    },
+    "edge_style": "-",
+    "edge_alpha": 1.0,
+    # These are for undirected-graphs. Directed graphs shouls use "-|>" and 10, respectively
+    "edge_arrowstyle": "-",
+    "edge_arrowsize": 0,
+    "edge_curvature": "arc3",
+    "edge_source_margin": 0,
+    "edge_target_margin": 0,
+}
+
+
+@pytest.mark.parametrize(
+    ("param_name", "param_value", "expected"),
+    (
+        ("node_color", None, defaults["node_color"]),
+        ("node_color", "#FF0000", "red"),
+        ("node_color", "color", "lime"),
+    ),
+)
+def test_display_arg_handling_node_color(param_name, param_value, expected):
+    G = nx.path_graph(4)
+    nx.set_node_attributes(G, "#00FF00", "color")
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas, **{param_name: param_value})
+    assert mpl.colors.same_color(canvas.get_children()[0].get_edgecolors()[0], expected)
+    plt.close()
+
+
+@pytest.mark.parametrize(
+    ("param_value", "expected"),
+    (
+        (None, (1, 1, 1, 1)),  # default value
+        (0.5, (0.5, 0.5, 0.5, 0.5)),
+        ("n_alpha", (1.0, 1 / 2, 1 / 3, 0.25)),
+    ),
+)
+def test_display_arg_handling_node_alpha(param_value, expected):
+    G = nx.path_graph(4)
+    nx.set_node_attributes(G, {n: 1 / (n + 1) for n in G.nodes()}, "n_alpha")
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas, node_alpha=param_value)
+    assert all(
+        canvas.get_children()[0].get_fc()[:, 3] == expected
+    )  # Extract just the alpha from the node colors
+    plt.close()
+
+
+def test_display_node_position():
+    G = nx.path_graph(4)
+    nx.set_node_attributes(G, {n: (n, n) for n in G.nodes()}, "pos")
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas, node_pos="pos")
+    assert np.all(
+        canvas.get_children()[0].get_offsets().data == [[0, 0], [1, 1], [2, 2], [3, 3]]
+    )
+    plt.close()
+
+
+def test_display_line_collection():
+    G = nx.karate_club_graph()
+    nx.set_edge_attributes(
+        G, {(u, v): "-|>" if (u + v) % 2 else "-" for u, v in G.edges()}, "arrowstyle"
+    )
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas, edge_arrowsize=10)
+    # There should only be one line collection in any given visualization
+    lc = [
+        l
+        for l in canvas.get_children()
+        if isinstance(l, mpl.collections.LineCollection)
+    ][0]
+    assert len(lc.get_paths()) == sum([1 for u, v in G.edges() if (u + v) % 2])
+    plt.close()
+
+
+@pytest.mark.parametrize(
+    ("edge_color", "expected"),
+    (
+        (None, "black"),
+        ("r", "red"),
+        ((1.0, 1.0, 0.0), "yellow"),
+        ((0, 1, 0, 1), "lime"),
+        ("color", "blue"),
+        ("#0000FF", "blue"),
+    ),
+)
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_edge_single_color(edge_color, expected, graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, "#0000FF", "color")
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, edge_color=edge_color, canvas=canvas)
+    if G.is_directed():
+        colors = [
+            f.get_fc()
+            for f in canvas.get_children()
+            if isinstance(f, mpl.patches.FancyArrowPatch)
+        ]
+    else:
+        colors = [
+            l
+            for l in canvas.collections
+            if isinstance(l, mpl.collections.LineCollection)
+        ][0].get_colors()
+    assert all(mpl.colors.same_color(c, expected) for c in colors)
+    plt.close()
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_edge_multiple_colors(graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, {(0, 1): "#FF0000", (1, 2): (0, 0, 1)}, "color")
+    ax = plt.figure().add_subplot(111)
+    nx.display(G, canvas=ax)
+    expected = ["red", "blue"]
+    if G.is_directed():
+        colors = [
+            f.get_fc()
+            for f in ax.get_children()
+            if isinstance(f, mpl.patches.FancyArrowPatch)
+        ]
+    else:
+        colors = [
+            l for l in ax.collections if isinstance(l, mpl.collections.LineCollection)
+        ][0].get_colors()
+    assert mpl.colors.same_color(colors, expected)
+    plt.close()
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_edge_position(graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_node_attributes(G, {n: (n, n) for n in G.nodes()}, "pos")
+    ax = plt.figure().add_subplot(111)
+    nx.display(G, canvas=ax)
+    if G.is_directed():
+        end_points = [
+            (f.get_path().vertices[0, :], f.get_path().vertices[-2, :])
+            for f in ax.get_children()
+            if isinstance(f, mpl.patches.FancyArrowPatch)
+        ]
+    else:
+        line_collection = [
+            l for l in ax.collections if isinstance(l, mpl.collections.LineCollection)
+        ][0]
+        end_points = [
+            (p.vertices[0, :], p.vertices[-1, :]) for p in line_collection.get_paths()
+        ]
+    expected = [((0, 0), (1, 1)), ((1, 1), (2, 2))]
+    # Use the threshold to account for slight shifts in FancyArrowPatch margins to
+    # avoid covering the arrow head with the node.
+    threshold = 0.05
+    for a, e in zip(end_points, expected):
+        act_start, act_end = a
+        exp_start, exp_end = e
+        assert all(abs(act_start - exp_start) < (threshold, threshold)) and all(
+            abs(act_end - exp_end) < (threshold, threshold)
+        )
+    plt.close()
+
+
+def test_display_position_function():
+    G = nx.karate_club_graph()
+
+    def fixed_layout(G):
+        return nx.spring_layout(G, seed=314159)
+
+    pos = fixed_layout(G)
+    ax = plt.figure().add_subplot(111)
+    nx.display(G, node_pos=fixed_layout, canvas=ax)
+    # rebuild the position dictionary from the canvas
+    act_pos = {
+        n: tuple(p) for n, p in zip(G.nodes(), ax.get_children()[0].get_offsets().data)
+    }
+    for n in G.nodes():
+        assert all(pos[n] == act_pos[n])
+    plt.close()
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_edge_colormaps(graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, {(0, 1): 0, (1, 2): 1}, "weight")
+    cmap = mpl.colormaps["plasma"]
+    nx.apply_matplotlib_colors(G, "weight", "color", cmap, nodes=False)
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas)
+    mapper = mpl.cm.ScalarMappable(cmap=cmap)
+    mapper.set_clim(0, 1)
+    expected = [mapper.to_rgba(0), mapper.to_rgba(1)]
+    if G.is_directed():
+        colors = [
+            f.get_facecolor()
+            for f in canvas.get_children()
+            if isinstance(f, mpl.patches.FancyArrowPatch)
+        ]
+    else:
+        colors = [
+            l
+            for l in canvas.collections
+            if isinstance(l, mpl.collections.LineCollection)
+        ][0].get_colors()
+    assert mpl.colors.same_color(expected[0], G.edges[0, 1]["color"])
+    assert mpl.colors.same_color(expected[1], G.edges[1, 2]["color"])
+    assert mpl.colors.same_color(expected, colors)
+    plt.close()
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_node_colormaps(graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_node_attributes(G, {0: 0, 1: 0.5, 2: 1}, "weight")
+    cmap = mpl.colormaps["plasma"]
+    nx.apply_matplotlib_colors(G, "weight", "color", cmap)
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas)
+    mapper = mpl.cm.ScalarMappable(cmap=cmap)
+    mapper.set_clim(0, 1)
+    expected = [mapper.to_rgba(0), mapper.to_rgba(0.5), mapper.to_rgba(1)]
+    colors = [
+        s for s in canvas.collections if isinstance(s, mpl.collections.PathCollection)
+    ][0].get_edgecolors()
+    assert mpl.colors.same_color(expected[0], G.nodes[0]["color"])
+    assert mpl.colors.same_color(expected[1], G.nodes[1]["color"])
+    assert mpl.colors.same_color(expected[2], G.nodes[2]["color"])
+    assert mpl.colors.same_color(expected, colors)
+    plt.close()
+
+
+@pytest.mark.parametrize(
+    ("param_value", "expected"),
+    (
+        (None, [defaults["edge_width"], defaults["edge_width"]]),
+        (5, [5, 5]),
+        ("width", [5, 10]),
+    ),
+)
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_edge_width(param_value, expected, graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, {(0, 1): 5, (1, 2): 10}, "width")
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, edge_width=param_value, canvas=canvas)
+    if G.is_directed():
+        widths = [
+            f.get_linewidth()
+            for f in canvas.get_children()
+            if isinstance(f, mpl.patches.FancyArrowPatch)
+        ]
+    else:
+        widths = list(
+            [
+                l
+                for l in canvas.collections
+                if isinstance(l, mpl.collections.LineCollection)
+            ][0].get_linewidths()
+        )
+    assert widths == expected
+
+
+@pytest.mark.parametrize(
+    ("param_value", "expected"),
+    (
+        (None, [defaults["edge_style"], defaults["edge_style"]]),
+        (":", [":", ":"]),
+        ("style", ["-", ":"]),
+    ),
+)
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_display_edge_style(param_value, expected, graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    nx.set_edge_attributes(G, {(0, 1): "-", (1, 2): ":"}, "style")
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, edge_style=param_value, canvas=canvas)
+    if G.is_directed():
+        styles = [
+            f.get_linestyle()
+            for f in canvas.get_children()
+            if isinstance(f, mpl.patches.FancyArrowPatch)
+        ]
+    else:
+        # Convert back from tuple description to character form
+        linestyles = {(0, None): "-", (0, (1, 1.65)): ":"}
+        styles = [
+            linestyles[(s[0], tuple(s[1]) if s[1] is not None else None)]
+            for s in [
+                l
+                for l in canvas.collections
+                if isinstance(l, mpl.collections.LineCollection)
+            ][0].get_linestyles()
+        ]
+    assert styles == expected
+    plt.close()
+
+
+def test_display_node_labels():
+    G = nx.path_graph(4)
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas, node_label={"size": 20})
+    labels = [t for t in canvas.get_children() if isinstance(t, mpl.text.Text)]
+    for n, l in zip(G.nodes(), labels):
+        assert l.get_text() == str(n)
+        assert l.get_size() == 20.0
+    plt.close()
+
+
+def test_display_edge_labels():
+    G = nx.path_graph(4)
+    canvas = plt.figure().add_subplot(111)
+    # While we can pass in dicts for edge label defaults without errors,
+    # this isn't helpful unless we want one label for all edges.
+    nx.set_edge_attributes(G, {(u, v): {"label": u + v} for u, v in G.edges()})
+    nx.display(G, canvas=canvas, edge_label={"color": "r"}, node_label=None)
+    labels = [t for t in canvas.get_children() if isinstance(t, mpl.text.Text)]
+    print(labels)
+    for e, l in zip(G.edges(), labels):
+        assert l.get_text() == str(e[0] + e[1])
+        assert l.get_color() == "r"
+    plt.close()
+
+
+def test_display_multigraph_non_integer_keys():
+    G = nx.MultiGraph()
+    G.add_nodes_from(["A", "B", "C", "D"])
+    G.add_edges_from(
+        [
+            ("A", "B", "0"),
+            ("A", "B", "1"),
+            ("B", "C", "-1"),
+            ("B", "C", "1"),
+            ("C", "D", "-1"),
+            ("C", "D", "0"),
+        ]
+    )
+    nx.set_edge_attributes(
+        G, {e: f"arc3,rad={0.2 * int(e[2])}" for e in G.edges(keys=True)}, "curvature"
+    )
+    canvas = plt.figure().add_subplot(111)
+    nx.display(G, canvas=canvas)
+    rads = [
+        f.get_connectionstyle().rad
+        for f in canvas.get_children()
+        if isinstance(f, mpl.patches.FancyArrowPatch)
+    ]
+    assert rads == [0.0, 0.2, -0.2, 0.2, -0.2, 0.0]
+    plt.close()
+
+
+def test_display_raises_for_bad_arg():
+    G = nx.karate_club_graph()
+    with pytest.raises(nx.NetworkXError):
+        nx.display(G, bad_arg="bad_arg")
+        plt.close()
+
+
+def test_display_arrow_size():
+    G = nx.path_graph(4, create_using=nx.DiGraph)
+    nx.set_edge_attributes(
+        G, {(u, v): (u + v + 2) ** 2 for u, v in G.edges()}, "arrowsize"
+    )
+    ax = plt.axes()
+    nx.display(G, canvas=ax)
+    assert [9, 25, 49] == [
+        f.get_mutation_scale()
+        for f in ax.get_children()
+        if isinstance(f, mpl.patches.FancyArrowPatch)
+    ]
+    plt.close()
+
+
+def test_display_mismatched_edge_position():
+    """
+    This test ensures that a error is raised for incomplete position data.
+    """
+    G = nx.path_graph(5)
+    # Notice that there is no position for node 3
+    nx.set_node_attributes(G, {0: (0, 0), 1: (1, 1), 2: (2, 2), 4: (4, 4)}, "pos")
+    # But that's not a problem since we don't want to show node 4, right?
+    nx.set_node_attributes(G, {n: n < 4 for n in G.nodes()}, "visible")
+    # However, if we try to visualize every edge (including 3 -> 4)...
+    # That's a problem since node 4 doesn't have a position
+    with pytest.raises(nx.NetworkXError):
+        nx.display(G)
+
+
+# NOTE: parametrizing on marker to test both branches of internal
+# to_marker_edge function
+@pytest.mark.parametrize("node_shape", ("o", "s"))
+def test_display_edge_margins(node_shape):
+    """
+    Test that there is a wider gap between the node and the start of an
+    incident edge when min_source_margin is specified.
+
+    This test checks that the use os min_{source/target}_margin edge
+    attributes result is shorter (more padding) between the edges and
+    source and target nodes.
+
+
+    As a crude visual example, let 's' and 't' represent source and target
+    nodes, respectively:
+
+       Default:
+       s-----------------------------t
+
+       With margins:
+       s   -----------------------   t
+
+    """
+    ax = plt.figure().add_subplot(111)
+    G = nx.DiGraph([(0, 1)])
+    nx.set_node_attributes(G, {0: (0, 0), 1: (1, 1)}, "pos")
+    # Get the default patches from the regular visualization
+    nx.display(G, canvas=ax, node_shape=node_shape)
+    default_arrow = [
+        f for f in ax.get_children() if isinstance(f, mpl.patches.FancyArrowPatch)
+    ][0]
+    default_extent = default_arrow.get_extents().corners()[::2, 0]
+    # Now plot again with margins
+    ax = plt.figure().add_subplot(111)
+    nx.display(
+        G,
+        canvas=ax,
+        edge_source_margin=100,
+        edge_target_margin=100,
+        node_shape=node_shape,
+    )
+    padded_arrow = [
+        f for f in ax.get_children() if isinstance(f, mpl.patches.FancyArrowPatch)
+    ][0]
+    padded_extent = padded_arrow.get_extents().corners()[::2, 0]
+
+    # With padding, the left-most extent of the edge should be further to the right
+    assert padded_extent[0] > default_extent[0]
+    # And the rightmost extent of the edge, further to the left
+    assert padded_extent[1] < default_extent[1]
+    plt.close()
+
+
+@pytest.mark.parametrize("ticks", [False, True])
+def test_display_hide_ticks(ticks):
+    G = nx.path_graph(3)
+    nx.set_node_attributes(G, {n: (n, n) for n in G.nodes()}, "pos")
+    ax = plt.axes()
+    nx.display(G, hide_ticks=ticks)
+    for axis in [ax.xaxis, ax.yaxis]:
+        assert bool(axis.get_ticklabels()) != ticks
+
+    plt.close()
+
+
+def test_display_self_loop():
+    ax = plt.axes()
+    G = nx.DiGraph()
+    G.add_node(0)
+    G.add_edge(0, 0)
+    nx.set_node_attributes(G, {0: (0, 0)}, "pos")
+    nx.display(G, canvas=ax)
+    arrow = [
+        f for f in ax.get_children() if isinstance(f, mpl.patches.FancyArrowPatch)
+    ][0]
+    bbox = arrow.get_extents()
+    print(bbox.width)
+    print(bbox.height)
+    assert bbox.width > 0 and bbox.height > 0
+
+    plt.delaxes(ax)
+    plt.close()
+
+
+def test_display_remove_pos_attr():
+    """
+    If the pos attribute isn't provided or is a function, display computes the layout
+    and adds it to the graph. We need to ensure that this new attribute is removed from
+    the returned graph.
+    """
+    G = nx.karate_club_graph()
+    nx.display(G)
+    assert nx.get_node_attributes(G, "display's position attribute name") == {}
+
+
+@pytest.fixture
+def subplots():
+    fig, ax = plt.subplots()
+    yield fig, ax
+    plt.delaxes(ax)
+    plt.close()
+
+
+@pytest.mark.parametrize(
+    "function",
+    [
+        nx.draw_circular,
+        nx.draw_kamada_kawai,
+        nx.draw_planar,
+        nx.draw_random,
+        nx.draw_spectral,
+        nx.draw_spring,
+        nx.draw_shell,
+        nx.draw_forceatlas2,
+    ],
+)
+def test_draw(function, subplots, tmp_path):
+    if function == nx.draw_kamada_kawai:
+        pytest.importorskip("scipy", reason="draw_kamada_kawai requires scipy")
+    fig, _ = subplots
+    options = {"node_color": "black", "node_size": 100, "width": 3}
+    function(barbell, **options)
+    fig.savefig(tmp_path / "test.ps")
+
+
+def test_draw_shell_nlist(subplots, tmp_path):
+    fig, _ = subplots
+    nlist = [list(range(4)), list(range(4, 10)), list(range(10, 14))]
+    nx.draw_shell(barbell, nlist=nlist)
+    fig.savefig(tmp_path / "test.ps")
+
+
+def test_draw_bipartite(subplots, tmp_path):
+    fig, _ = subplots
+    G = nx.complete_bipartite_graph(2, 5)
+    nx.draw_bipartite(G)
+    fig.savefig(tmp_path / "test.ps")
+
+
+def test_edge_colormap():
+    colors = range(barbell.number_of_edges())
+    nx.draw_spring(
+        barbell, edge_color=colors, width=4, edge_cmap=plt.cm.Blues, with_labels=True
+    )
+    # plt.show()
+
+
+def test_draw_networkx_edge_labels(subplots, tmp_path):
+    fig, _ = subplots
+    edge = (0, 1)
+    G = nx.DiGraph([edge])
+    pos = {n: (n, n) for n in G}
+    nx.draw(G, pos=pos)
+    nx.draw_networkx_edge_labels(G, pos, edge_labels={edge: "edge"})
+    fig.savefig(tmp_path / "test.ps")
+
+
+def test_arrows():
+    nx.draw_spring(barbell.to_directed())
+    # plt.show()
+
+
+@pytest.mark.parametrize(
+    ("edge_color", "expected"),
+    (
+        (None, "black"),  # Default
+        ("r", "red"),  # Non-default color string
+        (["r"], "red"),  # Single non-default color in a list
+        ((1.0, 1.0, 0.0), "yellow"),  # single color as rgb tuple
+        ([(1.0, 1.0, 0.0)], "yellow"),  # single color as rgb tuple in list
+        ((0, 1, 0, 1), "lime"),  # single color as rgba tuple
+        ([(0, 1, 0, 1)], "lime"),  # single color as rgba tuple in list
+        ("#0000ff", "blue"),  # single color hex code
+        (["#0000ff"], "blue"),  # hex code in list
+    ),
+)
+@pytest.mark.parametrize("edgelist", (None, [(0, 1)]))
+def test_single_edge_color_undirected(edge_color, expected, edgelist):
+    """Tests ways of specifying all edges have a single color for edges
+    drawn with a LineCollection"""
+
+    G = nx.path_graph(3)
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos=nx.random_layout(G), edgelist=edgelist, edge_color=edge_color
+    )
+    assert mpl.colors.same_color(drawn_edges.get_color(), expected)
+
+
+@pytest.mark.parametrize(
+    ("edge_color", "expected"),
+    (
+        (None, "black"),  # Default
+        ("r", "red"),  # Non-default color string
+        (["r"], "red"),  # Single non-default color in a list
+        ((1.0, 1.0, 0.0), "yellow"),  # single color as rgb tuple
+        ([(1.0, 1.0, 0.0)], "yellow"),  # single color as rgb tuple in list
+        ((0, 1, 0, 1), "lime"),  # single color as rgba tuple
+        ([(0, 1, 0, 1)], "lime"),  # single color as rgba tuple in list
+        ("#0000ff", "blue"),  # single color hex code
+        (["#0000ff"], "blue"),  # hex code in list
+    ),
+)
+@pytest.mark.parametrize("edgelist", (None, [(0, 1)]))
+def test_single_edge_color_directed(edge_color, expected, edgelist):
+    """Tests ways of specifying all edges have a single color for edges drawn
+    with FancyArrowPatches"""
+
+    G = nx.path_graph(3, create_using=nx.DiGraph)
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos=nx.random_layout(G), edgelist=edgelist, edge_color=edge_color
+    )
+    for fap in drawn_edges:
+        assert mpl.colors.same_color(fap.get_edgecolor(), expected)
+
+
+def test_edge_color_tuple_interpretation():
+    """If edge_color is a sequence with the same length as edgelist, then each
+    value in edge_color is mapped onto each edge via colormap."""
+    G = nx.path_graph(6, create_using=nx.DiGraph)
+    pos = {n: (n, n) for n in range(len(G))}
+
+    # num edges != 3 or 4 --> edge_color interpreted as rgb(a)
+    for ec in ((0, 0, 1), (0, 0, 1, 1)):
+        # More than 4 edges
+        drawn_edges = nx.draw_networkx_edges(G, pos, edge_color=ec)
+        for fap in drawn_edges:
+            assert mpl.colors.same_color(fap.get_edgecolor(), ec)
+        # Fewer than 3 edges
+        drawn_edges = nx.draw_networkx_edges(
+            G, pos, edgelist=[(0, 1), (1, 2)], edge_color=ec
+        )
+        for fap in drawn_edges:
+            assert mpl.colors.same_color(fap.get_edgecolor(), ec)
+
+    # num edges == 3, len(edge_color) == 4: interpreted as rgba
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos, edgelist=[(0, 1), (1, 2), (2, 3)], edge_color=(0, 0, 1, 1)
+    )
+    for fap in drawn_edges:
+        assert mpl.colors.same_color(fap.get_edgecolor(), "blue")
+
+    # num edges == 4, len(edge_color) == 3: interpreted as rgb
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos, edgelist=[(0, 1), (1, 2), (2, 3), (3, 4)], edge_color=(0, 0, 1)
+    )
+    for fap in drawn_edges:
+        assert mpl.colors.same_color(fap.get_edgecolor(), "blue")
+
+    # num edges == len(edge_color) == 3: interpreted with cmap, *not* as rgb
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos, edgelist=[(0, 1), (1, 2), (2, 3)], edge_color=(0, 0, 1)
+    )
+    assert mpl.colors.same_color(
+        drawn_edges[0].get_edgecolor(), drawn_edges[1].get_edgecolor()
+    )
+    for fap in drawn_edges:
+        assert not mpl.colors.same_color(fap.get_edgecolor(), "blue")
+
+    # num edges == len(edge_color) == 4: interpreted with cmap, *not* as rgba
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos, edgelist=[(0, 1), (1, 2), (2, 3), (3, 4)], edge_color=(0, 0, 1, 1)
+    )
+    assert mpl.colors.same_color(
+        drawn_edges[0].get_edgecolor(), drawn_edges[1].get_edgecolor()
+    )
+    assert mpl.colors.same_color(
+        drawn_edges[2].get_edgecolor(), drawn_edges[3].get_edgecolor()
+    )
+    for fap in drawn_edges:
+        assert not mpl.colors.same_color(fap.get_edgecolor(), "blue")
+
+
+def test_fewer_edge_colors_than_num_edges_directed():
+    """Test that the edge colors are cycled when there are fewer specified
+    colors than edges."""
+    G = barbell.to_directed()
+    pos = nx.random_layout(barbell)
+    edgecolors = ("r", "g", "b")
+    drawn_edges = nx.draw_networkx_edges(G, pos, edge_color=edgecolors)
+    for fap, expected in zip(drawn_edges, itertools.cycle(edgecolors)):
+        assert mpl.colors.same_color(fap.get_edgecolor(), expected)
+
+
+def test_more_edge_colors_than_num_edges_directed():
+    """Test that extra edge colors are ignored when there are more specified
+    colors than edges."""
+    G = nx.path_graph(4, create_using=nx.DiGraph)  # 3 edges
+    pos = nx.random_layout(barbell)
+    edgecolors = ("r", "g", "b", "c")  # 4 edge colors
+    drawn_edges = nx.draw_networkx_edges(G, pos, edge_color=edgecolors)
+    for fap, expected in zip(drawn_edges, edgecolors[:-1]):
+        assert mpl.colors.same_color(fap.get_edgecolor(), expected)
+
+
+def test_edge_color_string_with_global_alpha_undirected():
+    edge_collection = nx.draw_networkx_edges(
+        barbell,
+        pos=nx.random_layout(barbell),
+        edgelist=[(0, 1), (1, 2)],
+        edge_color="purple",
+        alpha=0.2,
+    )
+    ec = edge_collection.get_color().squeeze()  # as rgba tuple
+    assert len(edge_collection.get_paths()) == 2
+    assert mpl.colors.same_color(ec[:-1], "purple")
+    assert ec[-1] == 0.2
+
+
+def test_edge_color_string_with_global_alpha_directed():
+    drawn_edges = nx.draw_networkx_edges(
+        barbell.to_directed(),
+        pos=nx.random_layout(barbell),
+        edgelist=[(0, 1), (1, 2)],
+        edge_color="purple",
+        alpha=0.2,
+    )
+    assert len(drawn_edges) == 2
+    for fap in drawn_edges:
+        ec = fap.get_edgecolor()  # As rgba tuple
+        assert mpl.colors.same_color(ec[:-1], "purple")
+        assert ec[-1] == 0.2
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+def test_edge_width_default_value(graph_type):
+    """Test the default linewidth for edges drawn either via LineCollection or
+    FancyArrowPatches."""
+    G = nx.path_graph(2, create_using=graph_type)
+    pos = {n: (n, n) for n in range(len(G))}
+    drawn_edges = nx.draw_networkx_edges(G, pos)
+    if isinstance(drawn_edges, list):  # directed case: list of FancyArrowPatch
+        drawn_edges = drawn_edges[0]
+    assert drawn_edges.get_linewidth() == 1
+
+
+@pytest.mark.parametrize(
+    ("edgewidth", "expected"),
+    (
+        (3, 3),  # single-value, non-default
+        ([3], 3),  # Single value as a list
+    ),
+)
+def test_edge_width_single_value_undirected(edgewidth, expected):
+    G = nx.path_graph(4)
+    pos = {n: (n, n) for n in range(len(G))}
+    drawn_edges = nx.draw_networkx_edges(G, pos, width=edgewidth)
+    assert len(drawn_edges.get_paths()) == 3
+    assert drawn_edges.get_linewidth() == expected
+
+
+@pytest.mark.parametrize(
+    ("edgewidth", "expected"),
+    (
+        (3, 3),  # single-value, non-default
+        ([3], 3),  # Single value as a list
+    ),
+)
+def test_edge_width_single_value_directed(edgewidth, expected):
+    G = nx.path_graph(4, create_using=nx.DiGraph)
+    pos = {n: (n, n) for n in range(len(G))}
+    drawn_edges = nx.draw_networkx_edges(G, pos, width=edgewidth)
+    assert len(drawn_edges) == 3
+    for fap in drawn_edges:
+        assert fap.get_linewidth() == expected
+
+
+@pytest.mark.parametrize(
+    "edgelist",
+    (
+        [(0, 1), (1, 2), (2, 3)],  # one width specification per edge
+        None,  #  fewer widths than edges - widths cycle
+        [(0, 1), (1, 2)],  # More widths than edges - unused widths ignored
+    ),
+)
+def test_edge_width_sequence(edgelist):
+    G = barbell.to_directed()
+    pos = nx.random_layout(G)
+    widths = (0.5, 2.0, 12.0)
+    drawn_edges = nx.draw_networkx_edges(G, pos, edgelist=edgelist, width=widths)
+    for fap, expected_width in zip(drawn_edges, itertools.cycle(widths)):
+        assert fap.get_linewidth() == expected_width
+
+
+def test_edge_color_with_edge_vmin_vmax():
+    """Test that edge_vmin and edge_vmax properly set the dynamic range of the
+    color map when num edges == len(edge_colors)."""
+    G = nx.path_graph(3, create_using=nx.DiGraph)
+    pos = nx.random_layout(G)
+    # Extract colors from the original (unscaled) colormap
+    drawn_edges = nx.draw_networkx_edges(G, pos, edge_color=[0, 1.0])
+    orig_colors = [e.get_edgecolor() for e in drawn_edges]
+    # Colors from scaled colormap
+    drawn_edges = nx.draw_networkx_edges(
+        G, pos, edge_color=[0.2, 0.8], edge_vmin=0.2, edge_vmax=0.8
+    )
+    scaled_colors = [e.get_edgecolor() for e in drawn_edges]
+    assert mpl.colors.same_color(orig_colors, scaled_colors)
+
+
+def test_directed_edges_linestyle_default():
+    """Test default linestyle for edges drawn with FancyArrowPatches."""
+    G = nx.path_graph(4, create_using=nx.DiGraph)  # Graph with 3 edges
+    pos = {n: (n, n) for n in range(len(G))}
+
+    # edge with default style
+    drawn_edges = nx.draw_networkx_edges(G, pos)
+    assert len(drawn_edges) == 3
+    for fap in drawn_edges:
+        assert fap.get_linestyle() == "solid"
+
+
+@pytest.mark.parametrize(
+    "style",
+    (
+        "dashed",  # edge with string style
+        "--",  # edge with simplified string style
+        (1, (1, 1)),  # edge with (offset, onoffseq) style
+    ),
+)
+def test_directed_edges_linestyle_single_value(style):
+    """Tests support for specifying linestyles with a single value to be applied to
+    all edges in ``draw_networkx_edges`` for FancyArrowPatch outputs
+    (e.g. directed edges)."""
+
+    G = nx.path_graph(4, create_using=nx.DiGraph)  # Graph with 3 edges
+    pos = {n: (n, n) for n in range(len(G))}
+
+    drawn_edges = nx.draw_networkx_edges(G, pos, style=style)
+    assert len(drawn_edges) == 3
+    for fap in drawn_edges:
+        assert fap.get_linestyle() == style
+
+
+@pytest.mark.parametrize(
+    "style_seq",
+    (
+        ["dashed"],  # edge with string style in list
+        ["--"],  # edge with simplified string style in list
+        [(1, (1, 1))],  # edge with (offset, onoffseq) style in list
+        ["--", "-", ":"],  # edges with styles for each edge
+        ["--", "-"],  # edges with fewer styles than edges (styles cycle)
+        ["--", "-", ":", "-."],  # edges with more styles than edges (extra unused)
+    ),
+)
+def test_directed_edges_linestyle_sequence(style_seq):
+    """Tests support for specifying linestyles with sequences in
+    ``draw_networkx_edges`` for FancyArrowPatch outputs (e.g. directed edges)."""
+
+    G = nx.path_graph(4, create_using=nx.DiGraph)  # Graph with 3 edges
+    pos = {n: (n, n) for n in range(len(G))}
+
+    drawn_edges = nx.draw_networkx_edges(G, pos, style=style_seq)
+    assert len(drawn_edges) == 3
+    for fap, style in zip(drawn_edges, itertools.cycle(style_seq)):
+        assert fap.get_linestyle() == style
+
+
+def test_return_types():
+    from matplotlib.collections import LineCollection, PathCollection
+    from matplotlib.patches import FancyArrowPatch
+
+    G = nx.frucht_graph(create_using=nx.Graph)
+    dG = nx.frucht_graph(create_using=nx.DiGraph)
+    pos = nx.spring_layout(G, seed=42)
+    dpos = nx.spring_layout(dG, seed=42)
+    # nodes
+    nodes = nx.draw_networkx_nodes(G, pos)
+    assert isinstance(nodes, PathCollection)
+    # edges
+    edges = nx.draw_networkx_edges(dG, dpos, arrows=True)
+    assert isinstance(edges, list)
+    if len(edges) > 0:
+        assert isinstance(edges[0], FancyArrowPatch)
+    edges = nx.draw_networkx_edges(dG, dpos, arrows=False)
+    assert isinstance(edges, LineCollection)
+    edges = nx.draw_networkx_edges(G, dpos, arrows=None)
+    assert isinstance(edges, LineCollection)
+    edges = nx.draw_networkx_edges(dG, pos, arrows=None)
+    assert isinstance(edges, list)
+    if len(edges) > 0:
+        assert isinstance(edges[0], FancyArrowPatch)
+
+
+def test_labels_and_colors():
+    G = nx.cubical_graph()
+    pos = nx.spring_layout(G, seed=42)  # positions for all nodes
+    # nodes
+    nx.draw_networkx_nodes(
+        G, pos, nodelist=[0, 1, 2, 3], node_color="r", node_size=500, alpha=0.75
+    )
+    nx.draw_networkx_nodes(
+        G,
+        pos,
+        nodelist=[4, 5, 6, 7],
+        node_color="b",
+        node_size=500,
+        alpha=[0.25, 0.5, 0.75, 1.0],
+    )
+    # edges
+    nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
+    nx.draw_networkx_edges(
+        G,
+        pos,
+        edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)],
+        width=8,
+        alpha=0.5,
+        edge_color="r",
+    )
+    nx.draw_networkx_edges(
+        G,
+        pos,
+        edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)],
+        width=8,
+        alpha=0.5,
+        edge_color="b",
+    )
+    nx.draw_networkx_edges(
+        G,
+        pos,
+        edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)],
+        arrows=True,
+        min_source_margin=0.5,
+        min_target_margin=0.75,
+        width=8,
+        edge_color="b",
+    )
+    # some math labels
+    labels = {}
+    labels[0] = r"$a$"
+    labels[1] = r"$b$"
+    labels[2] = r"$c$"
+    labels[3] = r"$d$"
+    labels[4] = r"$\alpha$"
+    labels[5] = r"$\beta$"
+    labels[6] = r"$\gamma$"
+    labels[7] = r"$\delta$"
+    colors = {n: "k" if n % 2 == 0 else "r" for n in range(8)}
+    nx.draw_networkx_labels(G, pos, labels, font_size=16)
+    nx.draw_networkx_labels(G, pos, labels, font_size=16, font_color=colors)
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=None, rotate=False)
+    nx.draw_networkx_edge_labels(G, pos, edge_labels={(4, 5): "4-5"})
+    # plt.show()
+
+
+def test_axes(subplots):
+    fig, ax = subplots
+    nx.draw(barbell, ax=ax)
+    nx.draw_networkx_edge_labels(barbell, nx.circular_layout(barbell), ax=ax)
+
+
+def test_empty_graph():
+    G = nx.Graph()
+    nx.draw(G)
+
+
+def test_draw_empty_nodes_return_values():
+    # See Issue #3833
+    import matplotlib.collections  # call as mpl.collections
+
+    G = nx.Graph([(1, 2), (2, 3)])
+    DG = nx.DiGraph([(1, 2), (2, 3)])
+    pos = nx.circular_layout(G)
+    assert isinstance(
+        nx.draw_networkx_nodes(G, pos, nodelist=[]), mpl.collections.PathCollection
+    )
+    assert isinstance(
+        nx.draw_networkx_nodes(DG, pos, nodelist=[]), mpl.collections.PathCollection
+    )
+
+    # drawing empty edges used to return an empty LineCollection or empty list.
+    # Now it is always an empty list (because edges are now lists of FancyArrows)
+    assert nx.draw_networkx_edges(G, pos, edgelist=[], arrows=True) == []
+    assert nx.draw_networkx_edges(G, pos, edgelist=[], arrows=False) == []
+    assert nx.draw_networkx_edges(DG, pos, edgelist=[], arrows=False) == []
+    assert nx.draw_networkx_edges(DG, pos, edgelist=[], arrows=True) == []
+
+
+def test_multigraph_edgelist_tuples():
+    # See Issue #3295
+    G = nx.path_graph(3, create_using=nx.MultiDiGraph)
+    nx.draw_networkx(G, edgelist=[(0, 1, 0)])
+    nx.draw_networkx(G, edgelist=[(0, 1, 0)], node_size=[10, 20, 0])
+
+
+def test_alpha_iter():
+    pos = nx.random_layout(barbell)
+    fig = plt.figure()
+    # with fewer alpha elements than nodes
+    fig.add_subplot(131)  # Each test in a new axis object
+    nx.draw_networkx_nodes(barbell, pos, alpha=[0.1, 0.2])
+    # with equal alpha elements and nodes
+    num_nodes = len(barbell.nodes)
+    alpha = [x / num_nodes for x in range(num_nodes)]
+    colors = range(num_nodes)
+    fig.add_subplot(132)
+    nx.draw_networkx_nodes(barbell, pos, node_color=colors, alpha=alpha)
+    # with more alpha elements than nodes
+    alpha.append(1)
+    fig.add_subplot(133)
+    nx.draw_networkx_nodes(barbell, pos, alpha=alpha)
+
+
+def test_multiple_node_shapes(subplots):
+    fig, ax = subplots
+    G = nx.path_graph(4)
+    nx.draw(G, node_shape=["o", "h", "s", "^"], ax=ax)
+    scatters = [
+        s for s in ax.get_children() if isinstance(s, mpl.collections.PathCollection)
+    ]
+    assert len(scatters) == 4
+
+
+def test_individualized_font_attributes(subplots):
+    G = nx.karate_club_graph()
+    fig, ax = subplots
+    nx.draw(
+        G,
+        ax=ax,
+        font_color={n: "k" if n % 2 else "r" for n in G.nodes()},
+        font_size={n: int(n / (34 / 15) + 5) for n in G.nodes()},
+    )
+    for n, t in zip(
+        G.nodes(),
+        [
+            t
+            for t in ax.get_children()
+            if isinstance(t, mpl.text.Text) and len(t.get_text()) > 0
+        ],
+    ):
+        expected = "black" if n % 2 else "red"
+
+        assert mpl.colors.same_color(t.get_color(), expected)
+        assert int(n / (34 / 15) + 5) == t.get_size()
+
+
+def test_individualized_edge_attributes(subplots):
+    G = nx.karate_club_graph()
+    fig, ax = subplots
+    arrowstyles = ["-|>" if (u + v) % 2 == 0 else "-[" for u, v in G.edges()]
+    arrowsizes = [10 * (u % 2 + v % 2) + 10 for u, v in G.edges()]
+    nx.draw(G, ax=ax, arrows=True, arrowstyle=arrowstyles, arrowsize=arrowsizes)
+    arrows = [
+        f for f in ax.get_children() if isinstance(f, mpl.patches.FancyArrowPatch)
+    ]
+    for e, a in zip(G.edges(), arrows):
+        assert a.get_mutation_scale() == 10 * (e[0] % 2 + e[1] % 2) + 10
+        expected = (
+            mpl.patches.ArrowStyle.BracketB
+            if sum(e) % 2
+            else mpl.patches.ArrowStyle.CurveFilledB
+        )
+        assert isinstance(a.get_arrowstyle(), expected)
+
+
+def test_error_invalid_kwds():
+    with pytest.raises(ValueError, match="Received invalid argument"):
+        nx.draw(barbell, foo="bar")
+
+
+def test_draw_networkx_arrowsize_incorrect_size():
+    G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 3)])
+    arrowsize = [1, 2, 3]
+    with pytest.raises(
+        ValueError, match="arrowsize should have the same length as edgelist"
+    ):
+        nx.draw(G, arrowsize=arrowsize)
+
+
+@pytest.mark.parametrize("arrowsize", (30, [10, 20, 30]))
+def test_draw_edges_arrowsize(arrowsize):
+    G = nx.DiGraph([(0, 1), (0, 2), (1, 2)])
+    pos = {0: (0, 0), 1: (0, 1), 2: (1, 0)}
+    edges = nx.draw_networkx_edges(G, pos=pos, arrowsize=arrowsize)
+
+    arrowsize = itertools.repeat(arrowsize) if isinstance(arrowsize, int) else arrowsize
+
+    for fap, expected in zip(edges, arrowsize):
+        assert isinstance(fap, mpl.patches.FancyArrowPatch)
+        assert fap.get_mutation_scale() == expected
+
+
+@pytest.mark.parametrize("arrowstyle", ("-|>", ["-|>", "-[", "<|-|>"]))
+def test_draw_edges_arrowstyle(arrowstyle):
+    G = nx.DiGraph([(0, 1), (0, 2), (1, 2)])
+    pos = {0: (0, 0), 1: (0, 1), 2: (1, 0)}
+    edges = nx.draw_networkx_edges(G, pos=pos, arrowstyle=arrowstyle)
+
+    arrowstyle = (
+        itertools.repeat(arrowstyle) if isinstance(arrowstyle, str) else arrowstyle
+    )
+
+    arrow_objects = {
+        "-|>": mpl.patches.ArrowStyle.CurveFilledB,
+        "-[": mpl.patches.ArrowStyle.BracketB,
+        "<|-|>": mpl.patches.ArrowStyle.CurveFilledAB,
+    }
+
+    for fap, expected in zip(edges, arrowstyle):
+        assert isinstance(fap, mpl.patches.FancyArrowPatch)
+        assert isinstance(fap.get_arrowstyle(), arrow_objects[expected])
+
+
+def test_np_edgelist():
+    # see issue #4129
+    nx.draw_networkx(barbell, edgelist=np.array([(0, 2), (0, 3)]))
+
+
+def test_draw_nodes_missing_node_from_position():
+    G = nx.path_graph(3)
+    pos = {0: (0, 0), 1: (1, 1)}  # No position for node 2
+    with pytest.raises(nx.NetworkXError, match="has no position"):
+        nx.draw_networkx_nodes(G, pos)
+
+
+def test_draw_networkx_nodes_node_shape_list_with_scalar_color(subplots):
+    """Ensure draw_networkx_nodes works when node_shape is a Python list.
+
+    This covers the case where node_shape is a sequence (list) and node_color
+    is a single scalar color, which should be supported.
+    """
+    fig, ax = subplots
+
+    G = nx.empty_graph(5)
+    pos = {i: (i, i) for i in G}
+
+    shapes = ["o", "^", "o", "^", "o"]
+
+    nodes = nx.draw_networkx_nodes(
+        G,
+        pos,
+        node_color="red",  # scalar color (supported)
+        node_shape=shapes,  # list of shapes – this used to be buggy
+        ax=ax,
+    )
+    # Should get a PathCollection with an element in it (same as with numpy arrays)
+    assert len(nodes.get_offsets()) > 0
+    # NOTE: When node_shape is a sequence, draw_networkx_nodes internally calls
+    # ax.scatter multiple times and returns only the last PathCollection.
+    # Therefore, we do NOT assert a value for len(nodes.get_offsets()) here.
+
+
+# NOTE: parametrizing on marker to test both branches of internal
+# nx.draw_networkx_edges.to_marker_edge function
+@pytest.mark.parametrize("node_shape", ("o", "s"))
+def test_draw_edges_min_source_target_margins(node_shape, subplots):
+    """Test that there is a wider gap between the node and the start of an
+    incident edge when min_source_margin is specified.
+
+    This test checks that the use of min_{source/target}_margin kwargs result
+    in shorter (more padding) between the edges and source and target nodes.
+    As a crude visual example, let 's' and 't' represent source and target
+    nodes, respectively:
+
+       Default:
+       s-----------------------------t
+
+       With margins:
+       s   -----------------------   t
+
+    """
+    # Create a single axis object to get consistent pixel coords across
+    # multiple draws
+    fig, ax = subplots
+    G = nx.DiGraph([(0, 1)])
+    pos = {0: (0, 0), 1: (1, 0)}  # horizontal layout
+    # Get leftmost and rightmost points of the FancyArrowPatch object
+    # representing the edge between nodes 0 and 1 (in pixel coordinates)
+    default_patch = nx.draw_networkx_edges(G, pos, ax=ax, node_shape=node_shape)[0]
+    default_extent = default_patch.get_extents().corners()[::2, 0]
+    # Now, do the same but with "padding" for the source and target via the
+    # min_{source/target}_margin kwargs
+    padded_patch = nx.draw_networkx_edges(
+        G,
+        pos,
+        ax=ax,
+        node_shape=node_shape,
+        min_source_margin=100,
+        min_target_margin=100,
+    )[0]
+    padded_extent = padded_patch.get_extents().corners()[::2, 0]
+
+    # With padding, the left-most extent of the edge should be further to the
+    # right
+    assert padded_extent[0] > default_extent[0]
+    # And the rightmost extent of the edge, further to the left
+    assert padded_extent[1] < default_extent[1]
+
+
+# NOTE: parametrizing on marker to test both branches of internal
+# nx.draw_networkx_edges.to_marker_edge function
+@pytest.mark.parametrize("node_shape", ("o", "s"))
+def test_draw_edges_min_source_target_margins_individual(node_shape, subplots):
+    """Test that there is a wider gap between the node and the start of an
+    incident edge when min_source_margin is specified.
+
+    This test checks that the use of min_{source/target}_margin kwargs result
+    in shorter (more padding) between the edges and source and target nodes.
+    As a crude visual example, let 's' and 't' represent source and target
+    nodes, respectively:
+
+       Default:
+       s-----------------------------t
+
+       With margins:
+       s   -----------------------   t
+
+    """
+    # Create a single axis object to get consistent pixel coords across
+    # multiple draws
+    fig, ax = subplots
+    G = nx.DiGraph([(0, 1), (1, 2)])
+    pos = {0: (0, 0), 1: (1, 0), 2: (2, 0)}  # horizontal layout
+    # Get leftmost and rightmost points of the FancyArrowPatch object
+    # representing the edge between nodes 0 and 1 (in pixel coordinates)
+    default_patch = nx.draw_networkx_edges(G, pos, ax=ax, node_shape=node_shape)
+    default_extent = [d.get_extents().corners()[::2, 0] for d in default_patch]
+    # Now, do the same but with "padding" for the source and target via the
+    # min_{source/target}_margin kwargs
+    padded_patch = nx.draw_networkx_edges(
+        G,
+        pos,
+        ax=ax,
+        node_shape=node_shape,
+        min_source_margin=[98, 102],
+        min_target_margin=[98, 102],
+    )
+    padded_extent = [p.get_extents().corners()[::2, 0] for p in padded_patch]
+    for d, p in zip(default_extent, padded_extent):
+        # With padding, the left-most extent of the edge should be further to the
+        # right
+        assert p[0] > d[0]
+        # And the rightmost extent of the edge, further to the left
+        assert p[1] < d[1]
+
+
+def test_nonzero_selfloop_with_single_node(subplots):
+    """Ensure that selfloop extent is non-zero when there is only one node."""
+    # Create explicit axis object for test
+    fig, ax = subplots
+    # Graph with single node + self loop
+    G = nx.DiGraph()
+    G.add_node(0)
+    G.add_edge(0, 0)
+    # Draw
+    patch = nx.draw_networkx_edges(G, {0: (0, 0)})[0]
+    # The resulting patch must have non-zero extent
+    bbox = patch.get_extents()
+    assert bbox.width > 0 and bbox.height > 0
+
+
+def test_nonzero_selfloop_with_single_edge_in_edgelist(subplots):
+    """Ensure that selfloop extent is non-zero when only a single edge is
+    specified in the edgelist.
+    """
+    # Create explicit axis object for test
+    fig, ax = subplots
+    # Graph with selfloop
+    G = nx.path_graph(2, create_using=nx.DiGraph)
+    G.add_edge(1, 1)
+    pos = {n: (n, n) for n in G.nodes}
+    # Draw only the selfloop edge via the `edgelist` kwarg
+    patch = nx.draw_networkx_edges(G, pos, edgelist=[(1, 1)])[0]
+    # The resulting patch must have non-zero extent
+    bbox = patch.get_extents()
+    assert bbox.width > 0 and bbox.height > 0
+
+
+def test_apply_alpha():
+    """Test apply_alpha when there is a mismatch between the number of
+    supplied colors and elements.
+    """
+    nodelist = [0, 1, 2]
+    colorlist = ["r", "g", "b"]
+    alpha = 0.5
+    rgba_colors = nx.drawing.nx_pylab.apply_alpha(colorlist, alpha, nodelist)
+    assert all(rgba_colors[:, -1] == alpha)
+
+
+def test_draw_edges_toggling_with_arrows_kwarg():
+    """
+    The `arrows` keyword argument is used as a 3-way switch to select which
+    type of object to use for drawing edges:
+      - ``arrows=None`` -> default (FancyArrowPatches for directed, else LineCollection)
+      - ``arrows=True`` -> FancyArrowPatches
+      - ``arrows=False`` -> LineCollection
+    """
+    import matplotlib.collections
+    import matplotlib.patches
+
+    UG = nx.path_graph(3)
+    DG = nx.path_graph(3, create_using=nx.DiGraph)
+    pos = {n: (n, n) for n in UG}
+
+    # Use FancyArrowPatches when arrows=True, regardless of graph type
+    for G in (UG, DG):
+        edges = nx.draw_networkx_edges(G, pos, arrows=True)
+        assert len(edges) == len(G.edges)
+        assert isinstance(edges[0], mpl.patches.FancyArrowPatch)
+
+    # Use LineCollection when arrows=False, regardless of graph type
+    for G in (UG, DG):
+        edges = nx.draw_networkx_edges(G, pos, arrows=False)
+        assert isinstance(edges, mpl.collections.LineCollection)
+
+    # Default behavior when arrows=None: FAPs for directed, LC's for undirected
+    edges = nx.draw_networkx_edges(UG, pos)
+    assert isinstance(edges, mpl.collections.LineCollection)
+    edges = nx.draw_networkx_edges(DG, pos)
+    assert len(edges) == len(G.edges)
+    assert isinstance(edges[0], mpl.patches.FancyArrowPatch)
+
+
+@pytest.mark.parametrize("drawing_func", (nx.draw, nx.draw_networkx))
+def test_draw_networkx_arrows_default_undirected(drawing_func, subplots):
+    import matplotlib.collections
+
+    G = nx.path_graph(3)
+    fig, ax = subplots
+    drawing_func(G, ax=ax)
+    assert any(isinstance(c, mpl.collections.LineCollection) for c in ax.collections)
+    assert not ax.patches
+
+
+@pytest.mark.parametrize("drawing_func", (nx.draw, nx.draw_networkx))
+def test_draw_networkx_arrows_default_directed(drawing_func, subplots):
+    import matplotlib.collections
+
+    G = nx.path_graph(3, create_using=nx.DiGraph)
+    fig, ax = subplots
+    drawing_func(G, ax=ax)
+    assert not any(
+        isinstance(c, mpl.collections.LineCollection) for c in ax.collections
+    )
+    assert ax.patches
+
+
+def test_edgelist_kwarg_not_ignored(subplots):
+    # See gh-4994
+    G = nx.path_graph(3)
+    G.add_edge(0, 0)
+    fig, ax = subplots
+    nx.draw(G, edgelist=[(0, 1), (1, 2)], ax=ax)  # Exclude self-loop from edgelist
+    assert not ax.patches
+
+
+@pytest.mark.parametrize(
+    ("G", "expected_n_edges"),
+    ([nx.DiGraph(), 2], [nx.MultiGraph(), 4], [nx.MultiDiGraph(), 4]),
+)
+def test_draw_networkx_edges_multiedge_connectionstyle(G, expected_n_edges):
+    """Draws edges correctly for 3 types of graphs and checks for valid length"""
+    for i, (u, v) in enumerate([(0, 1), (0, 1), (0, 1), (0, 2)]):
+        G.add_edge(u, v, weight=round(i / 3, 2))
+    pos = {n: (n, n) for n in G}
+    # Raises on insufficient connectionstyle length
+    for conn_style in [
+        "arc3,rad=0.1",
+        ["arc3,rad=0.1", "arc3,rad=0.1"],
+        ["arc3,rad=0.1", "arc3,rad=0.1", "arc3,rad=0.2"],
+    ]:
+        nx.draw_networkx_edges(G, pos, connectionstyle=conn_style)
+        arrows = nx.draw_networkx_edges(G, pos, connectionstyle=conn_style)
+        assert len(arrows) == expected_n_edges
+
+
+@pytest.mark.parametrize(
+    ("G", "expected_n_edges"),
+    ([nx.DiGraph(), 2], [nx.MultiGraph(), 4], [nx.MultiDiGraph(), 4]),
+)
+def test_draw_networkx_edge_labels_multiedge_connectionstyle(G, expected_n_edges):
+    """Draws labels correctly for 3 types of graphs and checks for valid length and class names"""
+    for i, (u, v) in enumerate([(0, 1), (0, 1), (0, 1), (0, 2)]):
+        G.add_edge(u, v, weight=round(i / 3, 2))
+    pos = {n: (n, n) for n in G}
+    # Raises on insufficient connectionstyle length
+    arrows = nx.draw_networkx_edges(
+        G, pos, connectionstyle=["arc3,rad=0.1", "arc3,rad=0.1", "arc3,rad=0.1"]
+    )
+    for conn_style in [
+        "arc3,rad=0.1",
+        ["arc3,rad=0.1", "arc3,rad=0.2"],
+        ["arc3,rad=0.1", "arc3,rad=0.1", "arc3,rad=0.1"],
+    ]:
+        text_items = nx.draw_networkx_edge_labels(G, pos, connectionstyle=conn_style)
+        assert len(text_items) == expected_n_edges
+        for ti in text_items.values():
+            assert ti.__class__.__name__ == "CurvedArrowText"
+
+
+def test_draw_networkx_edge_label_multiedge():
+    G = nx.MultiGraph()
+    G.add_edge(0, 1, weight=10)
+    G.add_edge(0, 1, weight=20)
+    edge_labels = nx.get_edge_attributes(G, "weight")  # Includes edge keys
+    pos = {n: (n, n) for n in G}
+    text_items = nx.draw_networkx_edge_labels(
+        G,
+        pos,
+        edge_labels=edge_labels,
+        connectionstyle=["arc3,rad=0.1", "arc3,rad=0.2"],
+    )
+    assert len(text_items) == 2
+
+
+def test_draw_networkx_edge_label_empty_dict():
+    """Regression test for draw_networkx_edge_labels with empty dict. See
+    gh-5372."""
+    G = nx.path_graph(3)
+    pos = {n: (n, n) for n in G.nodes}
+    assert nx.draw_networkx_edge_labels(G, pos, edge_labels={}) == {}
+
+
+def test_draw_networkx_edges_undirected_selfloop_colors(subplots):
+    """When an edgelist is supplied along with a sequence of colors, check that
+    the self-loops have the correct colors."""
+    fig, ax = subplots
+    # Edge list and corresponding colors
+    edgelist = [(1, 3), (1, 2), (2, 3), (1, 1), (3, 3), (2, 2)]
+    edge_colors = ["pink", "cyan", "black", "red", "blue", "green"]
+
+    G = nx.Graph(edgelist)
+    pos = {n: (n, n) for n in G.nodes}
+    nx.draw_networkx_edges(G, pos, ax=ax, edgelist=edgelist, edge_color=edge_colors)
+
+    # Verify that there are three fancy arrow patches (1 per self loop)
+    assert len(ax.patches) == 3
+
+    # These are points that should be contained in the self loops. For example,
+    # sl_points[0] will be (1, 1.1), which is inside the "path" of the first
+    # self-loop but outside the others
+    sl_points = np.array(edgelist[-3:]) + np.array([0, 0.1])
+
+    # Check that the mapping between self-loop locations and their colors is
+    # correct
+    for fap, clr, slp in zip(ax.patches, edge_colors[-3:], sl_points):
+        assert fap.get_path().contains_point(slp)
+        assert mpl.colors.same_color(fap.get_edgecolor(), clr)
+
+
+@pytest.mark.parametrize(
+    "fap_only_kwarg",  # Non-default values for kwargs that only apply to FAPs
+    (
+        {"arrowstyle": "-"},
+        {"arrowsize": 20},
+        {"connectionstyle": "arc3,rad=0.2"},
+        {"min_source_margin": 10},
+        {"min_target_margin": 10},
+    ),
+)
+def test_user_warnings_for_unused_edge_drawing_kwargs(fap_only_kwarg, subplots):
+    """Users should get a warning when they specify a non-default value for
+    one of the kwargs that applies only to edges drawn with FancyArrowPatches,
+    but FancyArrowPatches aren't being used under the hood."""
+    G = nx.path_graph(3)
+    pos = {n: (n, n) for n in G}
+    fig, ax = subplots
+    # By default, an undirected graph will use LineCollection to represent
+    # the edges
+    kwarg_name = list(fap_only_kwarg.keys())[0]
+    with pytest.warns(
+        UserWarning, match=f"\n\nThe {kwarg_name} keyword argument is not applicable"
+    ):
+        nx.draw_networkx_edges(G, pos, ax=ax, **fap_only_kwarg)
+    # FancyArrowPatches are always used when `arrows=True` is specified.
+    # Check that warnings are *not* raised in this case
+    with warnings.catch_warnings():
+        # Escalate warnings -> errors so tests fail if warnings are raised
+        warnings.simplefilter("error")
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        nx.draw_networkx_edges(G, pos, ax=ax, arrows=True, **fap_only_kwarg)
+
+
+@pytest.mark.parametrize("draw_fn", (nx.draw, nx.draw_circular))
+def test_no_warning_on_default_draw_arrowstyle(draw_fn, subplots):
+    # See gh-7284
+    fig, ax = subplots
+    G = nx.cycle_graph(5)
+    with warnings.catch_warnings(record=True) as w:
+        draw_fn(G, ax=ax)
+    assert len(w) == 0
+
+
+@pytest.mark.parametrize("hide_ticks", [False, True])
+@pytest.mark.parametrize(
+    "method",
+    [
+        nx.draw_networkx,
+        nx.draw_networkx_edge_labels,
+        nx.draw_networkx_edges,
+        nx.draw_networkx_labels,
+        nx.draw_networkx_nodes,
+    ],
+)
+def test_hide_ticks(method, hide_ticks, subplots):
+    G = nx.path_graph(3)
+    pos = {n: (n, n) for n in G.nodes}
+    _, ax = subplots
+    method(G, pos=pos, ax=ax, hide_ticks=hide_ticks)
+    for axis in [ax.xaxis, ax.yaxis]:
+        assert bool(axis.get_ticklabels()) != hide_ticks
+
+
+@pytest.mark.parametrize(
+    "style", ["angle", "angle3", "arc", "arc3,rad=0.0", "bar,fraction=0.1"]
+)
+def test_edge_label_all_connectionstyles(subplots, style):
+    """
+    Check that FancyArrowPatches with all `connectionstyle`s are supported
+    in edge label rendering. See gh-7735 and gh-8106.
+    """
+    fig, ax = subplots
+    edge = (0, 1)
+    G = nx.DiGraph([edge])
+    pos = {n: (n, 0) for n in G}
+
+    name = style.split(",")[0]
+    labels = nx.draw_networkx_edge_labels(
+        G, pos, edge_labels={edge: "edge"}, connectionstyle=style
+    )
+
+    hmid = (pos[0][0] + pos[1][0]) / 2
+    vmid = (pos[0][1] + pos[1][1]) / 2
+    if name in {"arc", "arc3"}:  # The label should be at roughly the midpoint.
+        assert labels[edge].x, labels[edge].y == pytest.approx((hmid, vmid))
+    elif name == "bar":  # The label should be below the vertical midpoint.
+        assert labels[edge].y < vmid
+
+
+@pytest.mark.parametrize("label_pos", [-0.1, 1.1])
+def test_edge_label_label_pos(subplots, label_pos):
+    """
+    Check that label positions can be extrapolated outside [0, 1].
+    """
+    fig, ax = subplots
+    edge = (0, 1)
+    G = nx.DiGraph([edge])
+    pos = {n: (n, n) for n in G}
+    lbl = nx.draw_networkx_edge_labels(
+        G, pos, edge_labels={edge: "edge"}, label_pos=label_pos, connectionstyle="angle"
+    )
+
+    assert lbl[edge].x, lbl[edge].y == pytest.approx((label_pos, label_pos))
diff --git a/lib/python3.11/site-packages/networkx/generators/__init__.py b/lib/python3.11/site-packages/networkx/generators/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ec027c2405b6f9de2e7b6a0f7c18d782ac8761c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/__init__.py
@@ -0,0 +1,34 @@
+"""
+A package for generating various graphs in networkx.
+
+"""
+
+from networkx.generators.atlas import *
+from networkx.generators.classic import *
+from networkx.generators.cographs import *
+from networkx.generators.community import *
+from networkx.generators.degree_seq import *
+from networkx.generators.directed import *
+from networkx.generators.duplication import *
+from networkx.generators.ego import *
+from networkx.generators.expanders import *
+from networkx.generators.geometric import *
+from networkx.generators.harary_graph import *
+from networkx.generators.internet_as_graphs import *
+from networkx.generators.intersection import *
+from networkx.generators.interval_graph import *
+from networkx.generators.joint_degree_seq import *
+from networkx.generators.lattice import *
+from networkx.generators.line import *
+from networkx.generators.mycielski import *
+from networkx.generators.nonisomorphic_trees import *
+from networkx.generators.random_clustered import *
+from networkx.generators.random_graphs import *
+from networkx.generators.small import *
+from networkx.generators.social import *
+from networkx.generators.spectral_graph_forge import *
+from networkx.generators.stochastic import *
+from networkx.generators.sudoku import *
+from networkx.generators.time_series import *
+from networkx.generators.trees import *
+from networkx.generators.triads import *
diff --git a/lib/python3.11/site-packages/networkx/generators/atlas.py b/lib/python3.11/site-packages/networkx/generators/atlas.py
new file mode 100644
index 0000000000000000000000000000000000000000..000d478b89210fe65f4a9bfc1a02e35dded889f3
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/atlas.py
@@ -0,0 +1,227 @@
+"""
+Generators for the small graph atlas.
+"""
+
+import gzip
+import importlib.resources
+from itertools import islice
+
+import networkx as nx
+
+__all__ = ["graph_atlas", "graph_atlas_g"]
+
+#: The total number of graphs in the atlas.
+#:
+#: The graphs are labeled starting from 0 and extending to (but not
+#: including) this number.
+NUM_GRAPHS = 1253
+
+#: The path to the data file containing the graph edge lists.
+#:
+#: This is the absolute path of the gzipped text file containing the
+#: edge list for each graph in the atlas. The file contains one entry
+#: per graph in the atlas, in sequential order, starting from graph
+#: number 0 and extending through graph number 1252 (see
+#: :data:`NUM_GRAPHS`). Each entry looks like
+#:
+#: .. sourcecode:: text
+#:
+#:    GRAPH 6
+#:    NODES 3
+#:    0 1
+#:    0 2
+#:
+#: where the first two lines are the graph's index in the atlas and the
+#: number of nodes in the graph, and the remaining lines are the edge
+#: list.
+#:
+#: This file was generated from a Python list of graphs via code like
+#: the following::
+#:
+#:     import gzip
+#:     from networkx.generators.atlas import graph_atlas_g
+#:     from networkx.readwrite.edgelist import write_edgelist
+#:
+#:     with gzip.open('atlas.dat.gz', 'wb') as f:
+#:         for i, G in enumerate(graph_atlas_g()):
+#:             f.write(bytes(f'GRAPH {i}\n', encoding='utf-8'))
+#:             f.write(bytes(f'NODES {len(G)}\n', encoding='utf-8'))
+#:             write_edgelist(G, f, data=False)
+#:
+
+# Path to the atlas file
+ATLAS_FILE = importlib.resources.files("networkx.generators") / "atlas.dat.gz"
+
+
+def _generate_graphs():
+    """Sequentially read the file containing the edge list data for the
+    graphs in the atlas and generate the graphs one at a time.
+
+    This function reads the file given in :data:`.ATLAS_FILE`.
+
+    """
+    with gzip.open(ATLAS_FILE, "rb") as f:
+        line = f.readline()
+        while line and line.startswith(b"GRAPH"):
+            # The first two lines of each entry tell us the index of the
+            # graph in the list and the number of nodes in the graph.
+            # They look like this:
+            #
+            #     GRAPH 3
+            #     NODES 2
+            #
+            graph_index = int(line[6:].rstrip())
+            line = f.readline()
+            num_nodes = int(line[6:].rstrip())
+            # The remaining lines contain the edge list, until the next
+            # GRAPH line (or until the end of the file).
+            edgelist = []
+            line = f.readline()
+            while line and not line.startswith(b"GRAPH"):
+                edgelist.append(line.rstrip())
+                line = f.readline()
+            G = nx.Graph()
+            G.name = f"G{graph_index}"
+            G.add_nodes_from(range(num_nodes))
+            G.add_edges_from(tuple(map(int, e.split())) for e in edgelist)
+            yield G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def graph_atlas(i):
+    """Returns graph number `i` from the Graph Atlas.
+
+    For more information, see :func:`.graph_atlas_g`.
+
+    Parameters
+    ----------
+    i : int
+        The index of the graph from the atlas to get. The graph at index
+        0 is assumed to be the null graph.
+
+    Returns
+    -------
+    list
+        A list of :class:`~networkx.Graph` objects, the one at index *i*
+        corresponding to the graph *i* in the Graph Atlas.
+
+    See also
+    --------
+    graph_atlas_g
+
+    Notes
+    -----
+    The time required by this function increases linearly with the
+    argument `i`, since it reads a large file sequentially in order to
+    generate the graph [1]_.
+
+    References
+    ----------
+    .. [1] Ronald C. Read and Robin J. Wilson, *An Atlas of Graphs*.
+           Oxford University Press, 1998.
+
+    """
+    if not (0 <= i < NUM_GRAPHS):
+        raise ValueError(f"index must be between 0 and {NUM_GRAPHS}")
+    return next(islice(_generate_graphs(), i, None))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def graph_atlas_g():
+    """Returns the list of all graphs with up to seven nodes named in the
+    Graph Atlas.
+
+    The graphs are listed in increasing order by
+
+    1. number of nodes,
+    2. number of edges,
+    3. degree sequence (for example 111223 < 112222),
+    4. number of automorphisms,
+
+    in that order, with three exceptions as described in the *Notes*
+    section below. This causes the list to correspond with the index of
+    the graphs in the Graph Atlas [atlas]_, with the first graph,
+    ``G[0]``, being the null graph.
+
+    Returns
+    -------
+    list
+        A list of :class:`~networkx.Graph` objects, the one at index *i*
+        corresponding to the graph *i* in the Graph Atlas.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> atlas = nx.graph_atlas_g()
+
+    There are 1253 graphs in the atlas
+
+    >>> len(atlas)
+    1253
+
+    The number of graphs with *n* nodes, where *n* ranges from 0 to 7:
+
+    >>> from collections import Counter
+    >>> num_nodes_per_graph = [len(G) for G in atlas]
+    >>> Counter(num_nodes_per_graph)
+    Counter({7: 1044, 6: 156, 5: 34, 4: 11, 3: 4, 2: 2, 0: 1, 1: 1})
+
+    Since the atlas is ordered by the number of nodes in the graph, all graphs
+    with *n* nodes can be obtained by slicing the atlas. For example, all
+    graphs with 5 nodes:
+
+    >>> G5_list = atlas[19:53]
+    >>> all(len(G) == 5 for G in G5_list)
+    True
+
+    Or all graphs with at least 3 nodes but fewer than 7 nodes:
+
+    >>> G3_6_list = atlas[4:209]
+
+    More generally, the indices that partition the atlas by the number of nodes
+    per graph:
+
+    >>> import itertools
+    >>> partition_indices = [0] + list(
+    ...     itertools.accumulate(Counter(num_nodes_per_graph).values())  # cumsum
+    ... )
+    >>> partition_indices
+    [0, 1, 2, 4, 8, 19, 53, 209, 1253]
+    >>> partition_mapping = dict(enumerate(itertools.pairwise(partition_indices)))
+    >>> pprint(partition_mapping)
+    {0: (0, 1),
+     1: (1, 2),
+     2: (2, 4),
+     3: (4, 8),
+     4: (8, 19),
+     5: (19, 53),
+     6: (53, 209),
+     7: (209, 1253)}
+
+    See also
+    --------
+    graph_atlas
+
+    Notes
+    -----
+    This function may be expensive in both time and space, since it
+    reads a large file sequentially in order to populate the list.
+
+    Although the NetworkX atlas functions match the order of graphs
+    given in the "Atlas of Graphs" book, there are (at least) three
+    errors in the ordering described in the book. The following three
+    pairs of nodes violate the lexicographically nondecreasing sorted
+    degree sequence rule:
+
+    - graphs 55 and 56 with degree sequences 001111 and 000112,
+    - graphs 1007 and 1008 with degree sequences 3333444 and 3333336,
+    - graphs 1012 and 1213 with degree sequences 1244555 and 1244456.
+
+    References
+    ----------
+    .. [atlas] Ronald C. Read and Robin J. Wilson,
+               *An Atlas of Graphs*.
+               Oxford University Press, 1998.
+
+    """
+    return list(_generate_graphs())
diff --git a/lib/python3.11/site-packages/networkx/generators/classic.py b/lib/python3.11/site-packages/networkx/generators/classic.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7522195b95679554d22c8627c47b721572f9175
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/classic.py
@@ -0,0 +1,1091 @@
+"""Generators for some classic graphs.
+
+The typical graph builder function is called as follows:
+
+>>> G = nx.complete_graph(100)
+
+returning the complete graph on n nodes labeled 0, .., 99
+as a simple graph. Except for `empty_graph`, all the functions
+in this module return a Graph class (i.e. a simple, undirected graph).
+
+"""
+
+import itertools
+import numbers
+
+import networkx as nx
+from networkx.classes import Graph
+from networkx.exception import NetworkXError
+from networkx.utils import nodes_or_number, pairwise
+
+__all__ = [
+    "balanced_tree",
+    "barbell_graph",
+    "binomial_tree",
+    "complete_graph",
+    "complete_multipartite_graph",
+    "circular_ladder_graph",
+    "circulant_graph",
+    "cycle_graph",
+    "dorogovtsev_goltsev_mendes_graph",
+    "empty_graph",
+    "full_rary_tree",
+    "kneser_graph",
+    "ladder_graph",
+    "lollipop_graph",
+    "null_graph",
+    "path_graph",
+    "star_graph",
+    "tadpole_graph",
+    "trivial_graph",
+    "turan_graph",
+    "wheel_graph",
+]
+
+
+# -------------------------------------------------------------------
+#   Some Classic Graphs
+# -------------------------------------------------------------------
+
+
+def _tree_edges(n, r):
+    if n == 0:
+        return
+    # helper function for trees
+    # yields edges in rooted tree at 0 with n nodes and branching ratio r
+    nodes = iter(range(n))
+    parents = [next(nodes)]  # stack of max length r
+    while parents:
+        source = parents.pop(0)
+        for i in range(r):
+            try:
+                target = next(nodes)
+                parents.append(target)
+                yield source, target
+            except StopIteration:
+                break
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def full_rary_tree(r, n, create_using=None):
+    """Creates a full r-ary tree of `n` nodes.
+
+    Sometimes called a k-ary, n-ary, or m-ary tree.
+    "... all non-leaf nodes have exactly r children and all levels
+    are full except for some rightmost position of the bottom level
+    (if a leaf at the bottom level is missing, then so are all of the
+    leaves to its right." [1]_
+
+    .. plot::
+
+        >>> nx.draw(nx.full_rary_tree(2, 10))
+
+    Parameters
+    ----------
+    r : int
+        branching factor of the tree
+    n : int
+        Number of nodes in the tree
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        An r-ary tree with n nodes
+
+    References
+    ----------
+    .. [1] An introduction to data structures and algorithms,
+           James Andrew Storer,  Birkhauser Boston 2001, (page 225).
+    """
+    G = empty_graph(n, create_using)
+    G.add_edges_from(_tree_edges(n, r))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def kneser_graph(n, k):
+    """Returns the Kneser Graph with parameters `n` and `k`.
+
+    The Kneser Graph has nodes that are k-tuples (subsets) of the integers
+    between 0 and ``n-1``. Nodes are adjacent if their corresponding sets are disjoint.
+
+    Parameters
+    ----------
+    n: int
+        Number of integers from which to make node subsets.
+        Subsets are drawn from ``set(range(n))``.
+    k: int
+        Size of the subsets.
+
+    Returns
+    -------
+    G : NetworkX Graph
+
+    Examples
+    --------
+    >>> G = nx.kneser_graph(5, 2)
+    >>> G.number_of_nodes()
+    10
+    >>> G.number_of_edges()
+    15
+    >>> nx.is_isomorphic(G, nx.petersen_graph())
+    True
+    """
+    if n <= 0:
+        raise NetworkXError("n should be greater than zero")
+    if k <= 0 or k > n:
+        raise NetworkXError("k should be greater than zero and smaller than n")
+
+    G = nx.Graph()
+    # Create all k-subsets of [0, 1, ..., n-1]
+    subsets = list(itertools.combinations(range(n), k))
+
+    if 2 * k > n:
+        G.add_nodes_from(subsets)
+
+    universe = set(range(n))
+    comb = itertools.combinations  # only to make it all fit on one line
+    G.add_edges_from((s, t) for s in subsets for t in comb(universe - set(s), k))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def balanced_tree(r, h, create_using=None):
+    """Returns the perfectly balanced `r`-ary tree of height `h`.
+
+    .. plot::
+
+        >>> nx.draw(nx.balanced_tree(2, 3))
+
+    Parameters
+    ----------
+    r : int
+        Branching factor of the tree; each node will have `r`
+        children.
+
+    h : int
+        Height of the tree.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A balanced `r`-ary tree of height `h`.
+
+    Notes
+    -----
+    This is the rooted tree where all leaves are at distance `h` from
+    the root. The root has degree `r` and all other internal nodes
+    have degree `r + 1`.
+
+    Node labels are integers, starting from zero.
+
+    A balanced tree is also known as a *complete r-ary tree*.
+
+    """
+    # The number of nodes in the balanced tree is `1 + r + ... + r^h`,
+    # which is computed by using the closed-form formula for a geometric
+    # sum with ratio `r`. In the special case that `r` is 1, the number
+    # of nodes is simply `h + 1` (since the tree is actually a path
+    # graph).
+    if r == 1:
+        n = h + 1
+    else:
+        # This must be an integer if both `r` and `h` are integers. If
+        # they are not, we force integer division anyway.
+        n = (1 - r ** (h + 1)) // (1 - r)
+    return full_rary_tree(r, n, create_using=create_using)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def barbell_graph(m1, m2, create_using=None):
+    """Returns the Barbell Graph: two complete graphs connected by a path.
+
+    .. plot::
+
+        >>> nx.draw(nx.barbell_graph(4, 2))
+
+    Parameters
+    ----------
+    m1 : int
+        Size of the left and right barbells, must be greater than 2.
+
+    m2 : int
+        Length of the path connecting the barbells.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+       Only undirected Graphs are supported.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A barbell graph.
+
+    Notes
+    -----
+
+
+    Two identical complete graphs $K_{m1}$ form the left and right bells,
+    and are connected by a path $P_{m2}$.
+
+    The `2*m1+m2`  nodes are numbered
+        `0, ..., m1-1` for the left barbell,
+        `m1, ..., m1+m2-1` for the path,
+        and `m1+m2, ..., 2*m1+m2-1` for the right barbell.
+
+    The 3 subgraphs are joined via the edges `(m1-1, m1)` and
+    `(m1+m2-1, m1+m2)`. If `m2=0`, this is merely two complete
+    graphs joined together.
+
+    This graph is an extremal example in David Aldous
+    and Jim Fill's e-text on Random Walks on Graphs.
+
+    """
+    if m1 < 2:
+        raise NetworkXError("Invalid graph description, m1 should be >=2")
+    if m2 < 0:
+        raise NetworkXError("Invalid graph description, m2 should be >=0")
+
+    # left barbell
+    G = complete_graph(m1, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # connecting path
+    G.add_nodes_from(range(m1, m1 + m2 - 1))
+    if m2 > 1:
+        G.add_edges_from(pairwise(range(m1, m1 + m2)))
+
+    # right barbell
+    G.add_edges_from(
+        (u, v) for u in range(m1 + m2, 2 * m1 + m2) for v in range(u + 1, 2 * m1 + m2)
+    )
+
+    # connect it up
+    G.add_edge(m1 - 1, m1)
+    if m2 > 0:
+        G.add_edge(m1 + m2 - 1, m1 + m2)
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def binomial_tree(n, create_using=None):
+    """Returns the Binomial Tree of order n.
+
+    The binomial tree of order 0 consists of a single node. A binomial tree of order k
+    is defined recursively by linking two binomial trees of order k-1: the root of one is
+    the leftmost child of the root of the other.
+
+    .. plot::
+
+        >>> nx.draw(nx.binomial_tree(3))
+
+    Parameters
+    ----------
+    n : int
+        Order of the binomial tree.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A binomial tree of $2^n$ nodes and $2^n - 1$ edges.
+
+    """
+    G = nx.empty_graph(1, create_using)
+
+    N = 1
+    for i in range(n):
+        # Use G.edges() to ensure 2-tuples. G.edges is 3-tuple for MultiGraph
+        edges = [(u + N, v + N) for (u, v) in G.edges()]
+        G.add_edges_from(edges)
+        G.add_edge(0, N)
+        N *= 2
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def complete_graph(n, create_using=None):
+    """Return the complete graph `K_n` with n nodes.
+
+    A complete graph on `n` nodes means that all pairs
+    of distinct nodes have an edge connecting them.
+
+    .. plot::
+
+        >>> nx.draw(nx.complete_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes
+        If n is an integer, nodes are from range(n).
+        If n is a container of nodes, those nodes appear in the graph.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(9)
+    >>> len(G)
+    9
+    >>> G.size()
+    36
+    >>> G = nx.complete_graph(range(11, 14))
+    >>> list(G.nodes())
+    [11, 12, 13]
+    >>> G = nx.complete_graph(4, nx.DiGraph())
+    >>> G.is_directed()
+    True
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    if len(nodes) > 1:
+        if G.is_directed():
+            edges = itertools.permutations(nodes, 2)
+        else:
+            edges = itertools.combinations(nodes, 2)
+        G.add_edges_from(edges)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def circular_ladder_graph(n, create_using=None):
+    """Returns the circular ladder graph $CL_n$ of length n.
+
+    $CL_n$ consists of two concentric n-cycles in which
+    each of the n pairs of concentric nodes are joined by an edge.
+
+    Node labels are the integers 0 to n-1
+
+    .. plot::
+
+        >>> nx.draw(nx.circular_ladder_graph(5))
+
+    """
+    G = ladder_graph(n, create_using)
+    G.add_edge(0, n - 1)
+    G.add_edge(n, 2 * n - 1)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def circulant_graph(n, offsets, create_using=None):
+    r"""Returns the circulant graph $Ci_n(x_1, x_2, ..., x_m)$ with $n$ nodes.
+
+    The circulant graph $Ci_n(x_1, ..., x_m)$ consists of $n$ nodes $0, ..., n-1$
+    such that node $i$ is connected to nodes $(i + x) \mod n$ and $(i - x) \mod n$
+    for all $x$ in $x_1, ..., x_m$. Thus $Ci_n(1)$ is a cycle graph.
+
+    .. plot::
+
+        >>> nx.draw(nx.circulant_graph(10, [1]))
+
+    Parameters
+    ----------
+    n : integer
+        The number of nodes in the graph.
+    offsets : list of integers
+        A list of node offsets, $x_1$ up to $x_m$, as described above.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX Graph of type create_using
+
+    Examples
+    --------
+    Many well-known graph families are subfamilies of the circulant graphs;
+    for example, to create the cycle graph on n points, we connect every
+    node to nodes on either side (with offset plus or minus one). For n = 10,
+
+    >>> G = nx.circulant_graph(10, [1])
+    >>> edges = [
+    ...     (0, 9),
+    ...     (0, 1),
+    ...     (1, 2),
+    ...     (2, 3),
+    ...     (3, 4),
+    ...     (4, 5),
+    ...     (5, 6),
+    ...     (6, 7),
+    ...     (7, 8),
+    ...     (8, 9),
+    ... ]
+    >>> sorted(edges) == sorted(G.edges())
+    True
+
+    Similarly, we can create the complete graph
+    on 5 points with the set of offsets [1, 2]:
+
+    >>> G = nx.circulant_graph(5, [1, 2])
+    >>> edges = [
+    ...     (0, 1),
+    ...     (0, 2),
+    ...     (0, 3),
+    ...     (0, 4),
+    ...     (1, 2),
+    ...     (1, 3),
+    ...     (1, 4),
+    ...     (2, 3),
+    ...     (2, 4),
+    ...     (3, 4),
+    ... ]
+    >>> sorted(edges) == sorted(G.edges())
+    True
+
+    """
+    G = empty_graph(n, create_using)
+    G.add_edges_from((i, (i - j) % n) for i in range(n) for j in offsets)
+    G.add_edges_from((i, (i + j) % n) for i in range(n) for j in offsets)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def cycle_graph(n, create_using=None):
+    """Returns the cycle graph $C_n$ of cyclically connected nodes.
+
+    $C_n$ is a path with its two end-nodes connected.
+
+    .. plot::
+
+        >>> nx.draw(nx.cycle_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes
+        If n is an integer, nodes are from `range(n)`.
+        If n is a container of nodes, those nodes appear in the graph.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    If create_using is directed, the direction is in increasing order.
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    G.add_edges_from(pairwise(nodes, cyclic=True))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dorogovtsev_goltsev_mendes_graph(n, create_using=None):
+    """Returns the hierarchically constructed Dorogovtsev--Goltsev--Mendes graph.
+
+    The Dorogovtsev--Goltsev--Mendes [1]_ procedure deterministically produces a
+    scale-free graph with ``3/2 * (3**(n-1) + 1)`` nodes
+    and ``3**n`` edges for a given `n`.
+
+    Note that `n` denotes the number of times the state transition is applied,
+    starting from the base graph with ``n = 0`` (no transitions), as in [2]_.
+    This is different from the parameter ``t = n - 1`` in [1]_.
+
+    .. plot::
+
+        >>> nx.draw(nx.dorogovtsev_goltsev_mendes_graph(3))
+
+    Parameters
+    ----------
+    n : integer
+        The generation number.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. Directed graphs and multigraphs are not supported.
+
+    Returns
+    -------
+    G : NetworkX `Graph`
+
+    Raises
+    ------
+    NetworkXError
+        If `n` is less than zero.
+
+        If `create_using` is a directed graph or multigraph.
+
+    Examples
+    --------
+    >>> G = nx.dorogovtsev_goltsev_mendes_graph(3)
+    >>> G.number_of_nodes()
+    15
+    >>> G.number_of_edges()
+    27
+    >>> nx.is_planar(G)
+    True
+
+    References
+    ----------
+    .. [1] S. N. Dorogovtsev, A. V. Goltsev and J. F. F. Mendes,
+        "Pseudofractal scale-free web", Physical Review E 65, 066122, 2002.
+        https://arxiv.org/pdf/cond-mat/0112143.pdf
+    .. [2] Weisstein, Eric W. "Dorogovtsev--Goltsev--Mendes Graph".
+        From MathWorld--A Wolfram Web Resource.
+        https://mathworld.wolfram.com/Dorogovtsev-Goltsev-MendesGraph.html
+    """
+    if n < 0:
+        raise NetworkXError("n must be greater than or equal to 0")
+
+    G = empty_graph(0, create_using)
+    if G.is_directed():
+        raise NetworkXError("directed graph not supported")
+    if G.is_multigraph():
+        raise NetworkXError("multigraph not supported")
+
+    G.add_edge(0, 1)
+    new_node = 2  # next node to be added
+    for _ in range(n):  # iterate over number of generations.
+        new_edges = []
+        for u, v in G.edges():
+            new_edges.append((u, new_node))
+            new_edges.append((v, new_node))
+            new_node += 1
+
+        G.add_edges_from(new_edges)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def empty_graph(n=0, create_using=None, default=Graph):
+    """Returns the empty graph with n nodes and zero edges.
+
+    .. plot::
+
+        >>> nx.draw(nx.empty_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes (default = 0)
+        If n is an integer, nodes are from `range(n)`.
+        If n is a container of nodes, those nodes appear in the graph.
+    create_using : Graph Instance, Constructor or None
+        Indicator of type of graph to return.
+        If a Graph-type instance, then clear and use it.
+        If None, use the `default` constructor.
+        If a constructor, call it to create an empty graph.
+    default : Graph constructor (optional, default = nx.Graph)
+        The constructor to use if create_using is None.
+        If None, then nx.Graph is used.
+        This is used when passing an unknown `create_using` value
+        through your home-grown function to `empty_graph` and
+        you want a default constructor other than nx.Graph.
+
+    Examples
+    --------
+    >>> G = nx.empty_graph(10)
+    >>> G.number_of_nodes()
+    10
+    >>> G.number_of_edges()
+    0
+    >>> G = nx.empty_graph("ABC")
+    >>> G.number_of_nodes()
+    3
+    >>> sorted(G)
+    ['A', 'B', 'C']
+
+    Notes
+    -----
+    The variable create_using should be a Graph Constructor or a
+    "graph"-like object. Constructors, e.g. `nx.Graph` or `nx.MultiGraph`
+    will be used to create the returned graph. "graph"-like objects
+    will be cleared (nodes and edges will be removed) and refitted as
+    an empty "graph" with nodes specified in n. This capability
+    is useful for specifying the class-nature of the resulting empty
+    "graph" (i.e. Graph, DiGraph, MyWeirdGraphClass, etc.).
+
+    The variable create_using has three main uses:
+    Firstly, the variable create_using can be used to create an
+    empty digraph, multigraph, etc.  For example,
+
+    >>> n = 10
+    >>> G = nx.empty_graph(n, create_using=nx.DiGraph)
+
+    will create an empty digraph on n nodes.
+
+    Secondly, one can pass an existing graph (digraph, multigraph,
+    etc.) via create_using. For example, if G is an existing graph
+    (resp. digraph, multigraph, etc.), then empty_graph(n, create_using=G)
+    will empty G (i.e. delete all nodes and edges using G.clear())
+    and then add n nodes and zero edges, and return the modified graph.
+
+    Thirdly, when constructing your home-grown graph creation function
+    you can use empty_graph to construct the graph by passing a user
+    defined create_using to empty_graph. In this case, if you want the
+    default constructor to be other than nx.Graph, specify `default`.
+
+    >>> def mygraph(n, create_using=None):
+    ...     G = nx.empty_graph(n, create_using, nx.MultiGraph)
+    ...     G.add_edges_from([(0, 1), (0, 1)])
+    ...     return G
+    >>> G = mygraph(3)
+    >>> G.is_multigraph()
+    True
+    >>> G = mygraph(3, nx.Graph)
+    >>> G.is_multigraph()
+    False
+
+    See also create_empty_copy(G).
+
+    """
+    if create_using is None:
+        G = default()
+    elif isinstance(create_using, type):
+        G = create_using()
+    elif not hasattr(create_using, "adj"):
+        raise TypeError("create_using is not a valid NetworkX graph type or instance")
+    else:
+        # create_using is a NetworkX style Graph
+        create_using.clear()
+        G = create_using
+
+    _, nodes = n
+    G.add_nodes_from(nodes)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def ladder_graph(n, create_using=None):
+    """Returns the Ladder graph of length n.
+
+    This is two paths of n nodes, with
+    each pair connected by a single edge.
+
+    Node labels are the integers 0 to 2*n - 1.
+
+    .. plot::
+
+        >>> nx.draw(nx.ladder_graph(5))
+
+    """
+    G = empty_graph(2 * n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.add_edges_from(pairwise(range(n)))
+    G.add_edges_from(pairwise(range(n, 2 * n)))
+    G.add_edges_from((v, v + n) for v in range(n))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def lollipop_graph(m, n, create_using=None):
+    """Returns the Lollipop Graph; ``K_m`` connected to ``P_n``.
+
+    This is the Barbell Graph without the right barbell.
+
+    .. plot::
+
+        >>> nx.draw(nx.lollipop_graph(3, 4))
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from ``range(m)`` and ``range(m, m+n)``.
+        If a container of nodes, those nodes appear in the graph.
+        Warning: `m` and `n` are not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+
+        The nodes for `m` appear in the complete graph $K_m$ and the nodes
+        for `n` appear in the path $P_n$
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    Networkx graph
+       A complete graph with `m` nodes connected to a path of length `n`.
+
+    Notes
+    -----
+    The 2 subgraphs are joined via an edge ``(m-1, m)``.
+    If ``n=0``, this is merely a complete graph.
+
+    (This graph is an extremal example in David Aldous and Jim
+    Fill's etext on Random Walks on Graphs.)
+
+    """
+    m, m_nodes = m
+    M = len(m_nodes)
+    if M < 2:
+        raise NetworkXError("Invalid description: m should indicate at least 2 nodes")
+
+    n, n_nodes = n
+    if isinstance(m, numbers.Integral) and isinstance(n, numbers.Integral):
+        n_nodes = list(range(M, M + n))
+    N = len(n_nodes)
+
+    # the ball
+    G = complete_graph(m_nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # the stick
+    G.add_nodes_from(n_nodes)
+    if N > 1:
+        G.add_edges_from(pairwise(n_nodes))
+
+    if len(G) != M + N:
+        raise NetworkXError("Nodes must be distinct in containers m and n")
+
+    # connect ball to stick
+    if M > 0 and N > 0:
+        G.add_edge(m_nodes[-1], n_nodes[0])
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def null_graph(create_using=None):
+    """Returns the Null graph with no nodes or edges.
+
+    See empty_graph for the use of create_using.
+
+    """
+    G = empty_graph(0, create_using)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def path_graph(n, create_using=None):
+    """Returns the Path graph `P_n` of linearly connected nodes.
+
+    .. plot::
+
+        >>> nx.draw(nx.path_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, nodes are 0 to n - 1.
+        If an iterable of nodes, in the order they appear in the path.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    G.add_edges_from(pairwise(nodes))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def star_graph(n, create_using=None):
+    """Return a star graph.
+
+    The star graph consists of one center node connected to `n` outer nodes.
+
+    .. plot::
+
+        >>> nx.draw(nx.star_graph(6))
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, node labels are ``0`` to `n`, with center ``0``.
+        If an iterable of nodes, the center is the first.
+        Warning: `n` is not checked for duplicates and if present, the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    A star graph with 3 spokes can be generated with
+
+    >>> G = nx.star_graph(3)
+    >>> sorted(G.edges)
+    [(0, 1), (0, 2), (0, 3)]
+
+    For directed graphs, the convention is to have edges pointing from the hub
+    to the spokes:
+
+    >>> DG1 = nx.star_graph(3, create_using=nx.DiGraph)
+    >>> sorted(DG1.edges)
+    [(0, 1), (0, 2), (0, 3)]
+
+    Other possible definitions have edges pointing from the spokes to the hub:
+
+    >>> DG2 = nx.star_graph(3, create_using=nx.DiGraph).reverse()
+    >>> sorted(DG2.edges)
+    [(1, 0), (2, 0), (3, 0)]
+
+    or have bidirectional edges:
+
+    >>> DG3 = nx.star_graph(3).to_directed()
+    >>> sorted(DG3.edges)
+    [(0, 1), (0, 2), (0, 3), (1, 0), (2, 0), (3, 0)]
+
+    Notes
+    -----
+    The graph has ``n + 1`` nodes for integer `n`.
+    So ``star_graph(3)`` is the same as ``star_graph(range(4))``.
+    """
+    n, nodes = n
+    if isinstance(n, numbers.Integral):
+        nodes.append(int(n))  # There should be n + 1 nodes.
+    G = empty_graph(nodes, create_using)
+
+    if len(nodes) > 1:
+        hub, *spokes = nodes
+        G.add_edges_from((hub, node) for node in spokes)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def tadpole_graph(m, n, create_using=None):
+    """Returns the (m,n)-tadpole graph; ``C_m`` connected to ``P_n``.
+
+    This graph on m+n nodes connects a cycle of size `m` to a path of length `n`.
+    It looks like a tadpole. It is also called a kite graph or a dragon graph.
+
+    .. plot::
+
+        >>> nx.draw(nx.tadpole_graph(3, 5))
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from ``range(m)`` and ``range(m,m+n)``.
+        If a container of nodes, those nodes appear in the graph.
+        Warning: `m` and `n` are not checked for duplicates and if present the
+        resulting graph may not be as desired.
+
+        The nodes for `m` appear in the cycle graph $C_m$ and the nodes
+        for `n` appear in the path $P_n$.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    Networkx graph
+       A cycle of size `m` connected to a path of length `n`.
+
+    Raises
+    ------
+    NetworkXError
+        If ``m < 2``. The tadpole graph is undefined for ``m<2``.
+
+    Notes
+    -----
+    The 2 subgraphs are joined via an edge ``(m-1, m)``.
+    If ``n=0``, this is a cycle graph.
+    `m` and/or `n` can be a container of nodes instead of an integer.
+
+    """
+    m, m_nodes = m
+    M = len(m_nodes)
+    if M < 2:
+        raise NetworkXError("Invalid description: m should indicate at least 2 nodes")
+
+    n, n_nodes = n
+    if isinstance(m, numbers.Integral) and isinstance(n, numbers.Integral):
+        n_nodes = list(range(M, M + n))
+
+    # the circle
+    G = cycle_graph(m_nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # the stick
+    nx.add_path(G, [m_nodes[-1]] + list(n_nodes))
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def trivial_graph(create_using=None):
+    """Return the Trivial graph with one node (with label 0) and no edges.
+
+    .. plot::
+
+        >>> nx.draw(nx.trivial_graph(), with_labels=True)
+
+    """
+    G = empty_graph(1, create_using)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def turan_graph(n, r):
+    r"""Return the Turan Graph
+
+    The Turan Graph is a complete multipartite graph on $n$ nodes
+    with $r$ disjoint subsets. That is, edges connect each node to
+    every node not in its subset.
+
+    Given $n$ and $r$, we create a complete multipartite graph with
+    $r-(n \mod r)$ partitions of size $n/r$, rounded down, and
+    $n \mod r$ partitions of size $n/r+1$, rounded down.
+
+    .. plot::
+
+        >>> nx.draw(nx.turan_graph(6, 2))
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    r : int
+        The number of partitions.
+        Must be less than or equal to n.
+
+    Notes
+    -----
+    Must satisfy $1 <= r <= n$.
+    The graph has $(r-1)(n^2)/(2r)$ edges, rounded down.
+    """
+
+    if not 1 <= r <= n:
+        raise NetworkXError("Must satisfy 1 <= r <= n")
+
+    partitions = [n // r] * (r - (n % r)) + [n // r + 1] * (n % r)
+    G = complete_multipartite_graph(*partitions)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def wheel_graph(n, create_using=None):
+    """Return the wheel graph
+
+    The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.
+
+    .. plot::
+
+        >>> nx.draw(nx.wheel_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, node labels are 0 to n with center 0.
+        If an iterable of nodes, the center is the first.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Node labels are the integers 0 to n - 1.
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    if len(nodes) > 1:
+        hub, *rim = nodes
+        G.add_edges_from((hub, node) for node in rim)
+        if len(rim) > 1:
+            G.add_edges_from(pairwise(rim, cyclic=True))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def complete_multipartite_graph(*subset_sizes):
+    """Returns the complete multipartite graph with the specified subset sizes.
+
+    .. plot::
+
+        >>> nx.draw(nx.complete_multipartite_graph(1, 2, 3))
+
+    Parameters
+    ----------
+    subset_sizes : tuple of integers or tuple of node iterables
+       The arguments can either all be integer number of nodes or they
+       can all be iterables of nodes. If integers, they represent the
+       number of nodes in each subset of the multipartite graph.
+       If iterables, each is used to create the nodes for that subset.
+       The length of subset_sizes is the number of subsets.
+
+    Returns
+    -------
+    G : NetworkX Graph
+       Returns the complete multipartite graph with the specified subsets.
+
+       For each node, the node attribute 'subset' is an integer
+       indicating which subset contains the node.
+
+    Examples
+    --------
+    Creating a complete tripartite graph, with subsets of one, two, and three
+    nodes, respectively.
+
+    >>> G = nx.complete_multipartite_graph(1, 2, 3)
+    >>> [G.nodes[u]["subset"] for u in G]
+    [0, 1, 1, 2, 2, 2]
+    >>> list(G.edges(0))
+    [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5)]
+    >>> list(G.edges(2))
+    [(2, 0), (2, 3), (2, 4), (2, 5)]
+    >>> list(G.edges(4))
+    [(4, 0), (4, 1), (4, 2)]
+
+    >>> G = nx.complete_multipartite_graph("a", "bc", "def")
+    >>> [G.nodes[u]["subset"] for u in sorted(G)]
+    [0, 1, 1, 2, 2, 2]
+
+    Notes
+    -----
+    This function generalizes several other graph builder functions.
+
+    - If no subset sizes are given, this returns the null graph.
+    - If a single subset size `n` is given, this returns the empty graph on
+      `n` nodes.
+    - If two subset sizes `m` and `n` are given, this returns the complete
+      bipartite graph on `m + n` nodes.
+    - If subset sizes `1` and `n` are given, this returns the star graph on
+      `n + 1` nodes.
+
+    See also
+    --------
+    complete_bipartite_graph
+    """
+    # The complete multipartite graph is an undirected simple graph.
+    G = Graph()
+
+    if len(subset_sizes) == 0:
+        return G
+
+    # set up subsets of nodes
+    try:
+        extents = pairwise(itertools.accumulate((0,) + subset_sizes))
+        subsets = [range(start, end) for start, end in extents]
+    except TypeError:
+        subsets = subset_sizes
+    else:
+        if any(size < 0 for size in subset_sizes):
+            raise NetworkXError(f"Negative number of nodes not valid: {subset_sizes}")
+
+    # add nodes with subset attribute
+    # while checking that ints are not mixed with iterables
+    try:
+        for i, subset in enumerate(subsets):
+            G.add_nodes_from(subset, subset=i)
+    except TypeError as err:
+        raise NetworkXError("Arguments must be all ints or all iterables") from err
+
+    # Across subsets, all nodes should be adjacent.
+    # We can use itertools.combinations() because undirected.
+    for subset1, subset2 in itertools.combinations(subsets, 2):
+        G.add_edges_from(itertools.product(subset1, subset2))
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/cographs.py b/lib/python3.11/site-packages/networkx/generators/cographs.py
new file mode 100644
index 0000000000000000000000000000000000000000..6635b32f691696c1b6f309ad0da81c3cbc43bed9
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/cographs.py
@@ -0,0 +1,68 @@
+r"""Generators for cographs
+
+A cograph is a graph containing no path on four vertices.
+Cographs or $P_4$-free graphs can be obtained from a single vertex
+by disjoint union and complementation operations.
+
+References
+----------
+.. [0] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+    "Complement reducible graphs",
+    Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+    ISSN 0166-218X.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_cograph"]
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_cograph(n, seed=None):
+    r"""Returns a random cograph with $2 ^ n$ nodes.
+
+    A cograph is a graph containing no path on four vertices.
+    Cographs or $P_4$-free graphs can be obtained from a single vertex
+    by disjoint union and complementation operations.
+
+    This generator starts off from a single vertex and performs disjoint
+    union and full join operations on itself.
+    The decision on which operation will take place is random.
+
+    Parameters
+    ----------
+    n : int
+        The order of the cograph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : A random graph containing no path on four vertices.
+
+    See Also
+    --------
+    full_join
+    union
+
+    References
+    ----------
+    .. [1] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+       "Complement reducible graphs",
+       Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+       ISSN 0166-218X.
+    """
+    R = nx.empty_graph(1)
+
+    for i in range(n):
+        RR = nx.relabel_nodes(R.copy(), lambda x: x + len(R))
+
+        if seed.randint(0, 1) == 0:
+            R = nx.full_join(R, RR)
+        else:
+            R = nx.disjoint_union(R, RR)
+
+    return R
diff --git a/lib/python3.11/site-packages/networkx/generators/community.py b/lib/python3.11/site-packages/networkx/generators/community.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f2294c5cf9137dc1fab2a50d7ffcd6c59b6dec
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/community.py
@@ -0,0 +1,1070 @@
+"""Generators for classes of graphs used in studying social networks."""
+
+import itertools
+import math
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "caveman_graph",
+    "connected_caveman_graph",
+    "relaxed_caveman_graph",
+    "random_partition_graph",
+    "planted_partition_graph",
+    "gaussian_random_partition_graph",
+    "ring_of_cliques",
+    "windmill_graph",
+    "stochastic_block_model",
+    "LFR_benchmark_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def caveman_graph(l, k):
+    """Returns a caveman graph of `l` cliques of size `k`.
+
+    Parameters
+    ----------
+    l : int
+      Number of cliques
+    k : int
+      Size of cliques
+
+    Returns
+    -------
+    G : NetworkX Graph
+      caveman graph
+
+    Notes
+    -----
+    This returns an undirected graph, it can be converted to a directed
+    graph using :func:`nx.to_directed`, or a multigraph using
+    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
+    described in [1]_ and it is unclear which of the directed
+    generalizations is most useful.
+
+    Examples
+    --------
+    >>> G = nx.caveman_graph(3, 3)
+
+    See also
+    --------
+
+    connected_caveman_graph
+
+    References
+    ----------
+    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
+       Amer. J. Soc. 105, 493-527, 1999.
+    """
+    # l disjoint cliques of size k
+    G = nx.empty_graph(l * k)
+    if k > 1:
+        for start in range(0, l * k, k):
+            edges = itertools.combinations(range(start, start + k), 2)
+            G.add_edges_from(edges)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def connected_caveman_graph(l, k):
+    """Returns a connected caveman graph of `l` cliques of size `k`.
+
+    The connected caveman graph is formed by creating `n` cliques of size
+    `k`, then a single edge in each clique is rewired to a node in an
+    adjacent clique.
+
+    Parameters
+    ----------
+    l : int
+      number of cliques
+    k : int
+      size of cliques (k at least 2 or NetworkXError is raised)
+
+    Returns
+    -------
+    G : NetworkX Graph
+      connected caveman graph
+
+    Raises
+    ------
+    NetworkXError
+        If the size of cliques `k` is smaller than 2.
+
+    Notes
+    -----
+    This returns an undirected graph, it can be converted to a directed
+    graph using :func:`nx.to_directed`, or a multigraph using
+    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
+    described in [1]_ and it is unclear which of the directed
+    generalizations is most useful.
+
+    Examples
+    --------
+    >>> G = nx.connected_caveman_graph(3, 3)
+
+    References
+    ----------
+    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
+       Amer. J. Soc. 105, 493-527, 1999.
+    """
+    if k < 2:
+        raise nx.NetworkXError(
+            "The size of cliques in a connected caveman graph must be at least 2."
+        )
+
+    G = nx.caveman_graph(l, k)
+    for start in range(0, l * k, k):
+        G.remove_edge(start, start + 1)
+        G.add_edge(start, (start - 1) % (l * k))
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def relaxed_caveman_graph(l, k, p, seed=None):
+    """Returns a relaxed caveman graph.
+
+    A relaxed caveman graph starts with `l` cliques of size `k`.  Edges are
+    then randomly rewired with probability `p` to link different cliques.
+
+    Parameters
+    ----------
+    l : int
+      Number of groups
+    k : int
+      Size of cliques
+    p : float
+      Probability of rewiring each edge.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX Graph
+      Relaxed Caveman Graph
+
+    Raises
+    ------
+    NetworkXError
+     If p is not in [0,1]
+
+    Examples
+    --------
+    >>> G = nx.relaxed_caveman_graph(2, 3, 0.1, seed=42)
+
+    References
+    ----------
+    .. [1] Santo Fortunato, Community Detection in Graphs,
+       Physics Reports Volume 486, Issues 3-5, February 2010, Pages 75-174.
+       https://arxiv.org/abs/0906.0612
+    """
+    G = nx.caveman_graph(l, k)
+    nodes = list(G)
+    for u, v in G.edges():
+        if seed.random() < p:  # rewire the edge
+            x = seed.choice(nodes)
+            if G.has_edge(u, x):
+                continue
+            G.remove_edge(u, v)
+            G.add_edge(u, x)
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_partition_graph(sizes, p_in, p_out, seed=None, directed=False):
+    """Returns the random partition graph with a partition of sizes.
+
+    A partition graph is a graph of communities with sizes defined by
+    s in sizes. Nodes in the same group are connected with probability
+    p_in and nodes of different groups are connected with probability
+    p_out.
+
+    Parameters
+    ----------
+    sizes : list of ints
+      Sizes of groups
+    p_in : float
+      probability of edges with in groups
+    p_out : float
+      probability of edges between groups
+    directed : boolean optional, default=False
+      Whether to create a directed graph
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX Graph or DiGraph
+      random partition graph of size sum(gs)
+
+    Raises
+    ------
+    NetworkXError
+      If p_in or p_out is not in [0,1]
+
+    Examples
+    --------
+    >>> G = nx.random_partition_graph([10, 10, 10], 0.25, 0.01)
+    >>> len(G)
+    30
+    >>> partition = G.graph["partition"]
+    >>> len(partition)
+    3
+
+    Notes
+    -----
+    This is a generalization of the planted-l-partition described in
+    [1]_.  It allows for the creation of groups of any size.
+
+    The partition is store as a graph attribute 'partition'.
+
+    References
+    ----------
+    .. [1] Santo Fortunato 'Community Detection in Graphs' Physical Reports
+       Volume 486, Issue 3-5 p. 75-174. https://arxiv.org/abs/0906.0612
+    """
+    # Use geometric method for O(n+m) complexity algorithm
+    # partition = nx.community_sets(nx.get_node_attributes(G, 'affiliation'))
+    if not 0.0 <= p_in <= 1.0:
+        raise nx.NetworkXError("p_in must be in [0,1]")
+    if not 0.0 <= p_out <= 1.0:
+        raise nx.NetworkXError("p_out must be in [0,1]")
+
+    # create connection matrix
+    num_blocks = len(sizes)
+    p = [[p_out for s in range(num_blocks)] for r in range(num_blocks)]
+    for r in range(num_blocks):
+        p[r][r] = p_in
+
+    return stochastic_block_model(
+        sizes,
+        p,
+        nodelist=None,
+        seed=seed,
+        directed=directed,
+        selfloops=False,
+        sparse=True,
+    )
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def planted_partition_graph(l, k, p_in, p_out, seed=None, directed=False):
+    """Returns the planted l-partition graph.
+
+    This model partitions a graph with n=l*k vertices in
+    l groups with k vertices each. Vertices of the same
+    group are linked with a probability p_in, and vertices
+    of different groups are linked with probability p_out.
+
+    Parameters
+    ----------
+    l : int
+      Number of groups
+    k : int
+      Number of vertices in each group
+    p_in : float
+      probability of connecting vertices within a group
+    p_out : float
+      probability of connected vertices between groups
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool,optional (default=False)
+      If True return a directed graph
+
+    Returns
+    -------
+    G : NetworkX Graph or DiGraph
+      planted l-partition graph
+
+    Raises
+    ------
+    NetworkXError
+      If `p_in`, `p_out` are not in `[0, 1]`
+
+    Examples
+    --------
+    >>> G = nx.planted_partition_graph(4, 3, 0.5, 0.1, seed=42)
+
+    See Also
+    --------
+    random_partition_model
+
+    References
+    ----------
+    .. [1] A. Condon, R.M. Karp, Algorithms for graph partitioning
+        on the planted partition model,
+        Random Struct. Algor. 18 (2001) 116-140.
+
+    .. [2] Santo Fortunato 'Community Detection in Graphs' Physical Reports
+       Volume 486, Issue 3-5 p. 75-174. https://arxiv.org/abs/0906.0612
+    """
+    return random_partition_graph([k] * l, p_in, p_out, seed=seed, directed=directed)
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gaussian_random_partition_graph(n, s, v, p_in, p_out, directed=False, seed=None):
+    """Generate a Gaussian random partition graph.
+
+    A Gaussian random partition graph is created by creating k partitions
+    each with a size drawn from a normal distribution with mean s and variance
+    s/v. Nodes are connected within clusters with probability p_in and
+    between clusters with probability p_out[1]
+
+    Parameters
+    ----------
+    n : int
+      Number of nodes in the graph
+    s : float
+      Mean cluster size
+    v : float
+      Shape parameter. The variance of cluster size distribution is s/v.
+    p_in : float
+      Probability of intra cluster connection.
+    p_out : float
+      Probability of inter cluster connection.
+    directed : boolean, optional default=False
+      Whether to create a directed graph or not
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX Graph or DiGraph
+      gaussian random partition graph
+
+    Raises
+    ------
+    NetworkXError
+      If s is > n
+      If p_in or p_out is not in [0,1]
+
+    Notes
+    -----
+    Note the number of partitions is dependent on s,v and n, and that the
+    last partition may be considerably smaller, as it is sized to simply
+    fill out the nodes [1]
+
+    See Also
+    --------
+    random_partition_graph
+
+    Examples
+    --------
+    >>> G = nx.gaussian_random_partition_graph(100, 10, 10, 0.25, 0.1)
+    >>> len(G)
+    100
+
+    References
+    ----------
+    .. [1] Ulrik Brandes, Marco Gaertler, Dorothea Wagner,
+       Experiments on Graph Clustering Algorithms,
+       In the proceedings of the 11th Europ. Symp. Algorithms, 2003.
+    """
+    if s > n:
+        raise nx.NetworkXError("s must be <= n")
+    assigned = 0
+    sizes = []
+    while True:
+        size = int(seed.gauss(s, s / v + 0.5))
+        if size < 1:  # how to handle 0 or negative sizes?
+            continue
+        if assigned + size >= n:
+            sizes.append(n - assigned)
+            break
+        assigned += size
+        sizes.append(size)
+    return random_partition_graph(sizes, p_in, p_out, seed=seed, directed=directed)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def ring_of_cliques(num_cliques, clique_size):
+    """Defines a "ring of cliques" graph.
+
+    A ring of cliques graph is consisting of cliques, connected through single
+    links. Each clique is a complete graph.
+
+    Parameters
+    ----------
+    num_cliques : int
+        Number of cliques
+    clique_size : int
+        Size of cliques
+
+    Returns
+    -------
+    G : NetworkX Graph
+        ring of cliques graph
+
+    Raises
+    ------
+    NetworkXError
+        If the number of cliques is lower than 2 or
+        if the size of cliques is smaller than 2.
+
+    Examples
+    --------
+    >>> G = nx.ring_of_cliques(8, 4)
+
+    See Also
+    --------
+    connected_caveman_graph
+
+    Notes
+    -----
+    The `connected_caveman_graph` graph removes a link from each clique to
+    connect it with the next clique. Instead, the `ring_of_cliques` graph
+    simply adds the link without removing any link from the cliques.
+    """
+    if num_cliques < 2:
+        raise nx.NetworkXError("A ring of cliques must have at least two cliques")
+    if clique_size < 2:
+        raise nx.NetworkXError("The cliques must have at least two nodes")
+
+    G = nx.Graph()
+    for i in range(num_cliques):
+        edges = itertools.combinations(
+            range(i * clique_size, i * clique_size + clique_size), 2
+        )
+        G.add_edges_from(edges)
+        G.add_edge(
+            i * clique_size + 1, (i + 1) * clique_size % (num_cliques * clique_size)
+        )
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def windmill_graph(n, k):
+    """Generate a windmill graph.
+    A windmill graph is a graph of `n` cliques each of size `k` that are all
+    joined at one node.
+    It can be thought of as taking a disjoint union of `n` cliques of size `k`,
+    selecting one point from each, and contracting all of the selected points.
+    Alternatively, one could generate `n` cliques of size `k-1` and one node
+    that is connected to all other nodes in the graph.
+
+    Parameters
+    ----------
+    n : int
+        Number of cliques
+    k : int
+        Size of cliques
+
+    Returns
+    -------
+    G : NetworkX Graph
+        windmill graph with n cliques of size k
+
+    Raises
+    ------
+    NetworkXError
+        If the number of cliques is less than two
+        If the size of the cliques are less than two
+
+    Examples
+    --------
+    >>> G = nx.windmill_graph(4, 5)
+
+    Notes
+    -----
+    The node labeled `0` will be the node connected to all other nodes.
+    Note that windmill graphs are usually denoted `Wd(k,n)`, so the parameters
+    are in the opposite order as the parameters of this method.
+    """
+    if n < 2:
+        msg = "A windmill graph must have at least two cliques"
+        raise nx.NetworkXError(msg)
+    if k < 2:
+        raise nx.NetworkXError("The cliques must have at least two nodes")
+
+    G = nx.disjoint_union_all(
+        itertools.chain(
+            [nx.complete_graph(k)], (nx.complete_graph(k - 1) for _ in range(n - 1))
+        )
+    )
+    G.add_edges_from((0, i) for i in range(k, G.number_of_nodes()))
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def stochastic_block_model(
+    sizes, p, nodelist=None, seed=None, directed=False, selfloops=False, sparse=True
+):
+    """Returns a stochastic block model graph.
+
+    This model partitions the nodes in blocks of arbitrary sizes, and places
+    edges between pairs of nodes independently, with a probability that depends
+    on the blocks.
+
+    Parameters
+    ----------
+    sizes : list of ints
+        Sizes of blocks
+    p : list of list of floats
+        Element (r,s) gives the density of edges going from the nodes
+        of group r to nodes of group s.
+        p must match the number of groups (len(sizes) == len(p)),
+        and it must be symmetric if the graph is undirected.
+    nodelist : list, optional
+        The block tags are assigned according to the node identifiers
+        in nodelist. If nodelist is None, then the ordering is the
+        range [0,sum(sizes)-1].
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : boolean optional, default=False
+        Whether to create a directed graph or not.
+    selfloops : boolean optional, default=False
+        Whether to include self-loops or not.
+    sparse: boolean optional, default=True
+        Use the sparse heuristic to speed up the generator.
+
+    Returns
+    -------
+    g : NetworkX Graph or DiGraph
+        Stochastic block model graph of size sum(sizes)
+
+    Raises
+    ------
+    NetworkXError
+      If probabilities are not in [0,1].
+      If the probability matrix is not square (directed case).
+      If the probability matrix is not symmetric (undirected case).
+      If the sizes list does not match nodelist or the probability matrix.
+      If nodelist contains duplicate.
+
+    Examples
+    --------
+    >>> sizes = [75, 75, 300]
+    >>> probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    >>> g = nx.stochastic_block_model(sizes, probs, seed=0)
+    >>> len(g)
+    450
+    >>> H = nx.quotient_graph(g, g.graph["partition"], relabel=True)
+    >>> for v in H.nodes(data=True):
+    ...     print(round(v[1]["density"], 3))
+    0.245
+    0.348
+    0.405
+    >>> for v in H.edges(data=True):
+    ...     print(round(1.0 * v[2]["weight"] / (sizes[v[0]] * sizes[v[1]]), 3))
+    0.051
+    0.022
+    0.07
+
+    See Also
+    --------
+    random_partition_graph
+    planted_partition_graph
+    gaussian_random_partition_graph
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] Holland, P. W., Laskey, K. B., & Leinhardt, S.,
+           "Stochastic blockmodels: First steps",
+           Social networks, 5(2), 109-137, 1983.
+    """
+    # Check if dimensions match
+    if len(sizes) != len(p):
+        raise nx.NetworkXException("'sizes' and 'p' do not match.")
+    # Check for probability symmetry (undirected) and shape (directed)
+    for row in p:
+        if len(p) != len(row):
+            raise nx.NetworkXException("'p' must be a square matrix.")
+    if not directed:
+        p_transpose = [list(i) for i in zip(*p)]
+        for i in zip(p, p_transpose):
+            for j in zip(i[0], i[1]):
+                if abs(j[0] - j[1]) > 1e-08:
+                    raise nx.NetworkXException("'p' must be symmetric.")
+    # Check for probability range
+    for row in p:
+        for prob in row:
+            if prob < 0 or prob > 1:
+                raise nx.NetworkXException("Entries of 'p' not in [0,1].")
+    # Check for nodelist consistency
+    if nodelist is not None:
+        if len(nodelist) != sum(sizes):
+            raise nx.NetworkXException("'nodelist' and 'sizes' do not match.")
+        if len(nodelist) != len(set(nodelist)):
+            raise nx.NetworkXException("nodelist contains duplicate.")
+    else:
+        nodelist = range(sum(sizes))
+
+    # Setup the graph conditionally to the directed switch.
+    block_range = range(len(sizes))
+    if directed:
+        g = nx.DiGraph()
+        block_iter = itertools.product(block_range, block_range)
+    else:
+        g = nx.Graph()
+        block_iter = itertools.combinations_with_replacement(block_range, 2)
+    # Split nodelist in a partition (list of sets).
+    size_cumsum = [sum(sizes[0:x]) for x in range(len(sizes) + 1)]
+    g.graph["partition"] = [
+        set(nodelist[size_cumsum[x] : size_cumsum[x + 1]])
+        for x in range(len(size_cumsum) - 1)
+    ]
+    # Setup nodes and graph name
+    for block_id, nodes in enumerate(g.graph["partition"]):
+        for node in nodes:
+            g.add_node(node, block=block_id)
+
+    g.name = "stochastic_block_model"
+
+    # Test for edge existence
+    parts = g.graph["partition"]
+    for i, j in block_iter:
+        if i == j:
+            if directed:
+                if selfloops:
+                    edges = itertools.product(parts[i], parts[i])
+                else:
+                    edges = itertools.permutations(parts[i], 2)
+            else:
+                edges = itertools.combinations(parts[i], 2)
+                if selfloops:
+                    edges = itertools.chain(edges, zip(parts[i], parts[i]))
+            for e in edges:
+                if seed.random() < p[i][j]:
+                    g.add_edge(*e)
+        else:
+            edges = itertools.product(parts[i], parts[j])
+        if sparse:
+            if p[i][j] == 1:  # Test edges cases p_ij = 0 or 1
+                for e in edges:
+                    g.add_edge(*e)
+            elif p[i][j] > 0:
+                while True:
+                    try:
+                        logrand = math.log(seed.random())
+                        skip = math.floor(logrand / math.log(1 - p[i][j]))
+                        # consume "skip" edges
+                        next(itertools.islice(edges, skip, skip), None)
+                        e = next(edges)
+                        g.add_edge(*e)  # __safe
+                    except StopIteration:
+                        break
+        else:
+            for e in edges:
+                if seed.random() < p[i][j]:
+                    g.add_edge(*e)  # __safe
+    return g
+
+
+def _zipf_rv_below(gamma, xmin, threshold, seed):
+    """Returns a random value chosen from the bounded Zipf distribution.
+
+    Repeatedly draws values from the Zipf distribution until the
+    threshold is met, then returns that value.
+    """
+    result = nx.utils.zipf_rv(gamma, xmin, seed)
+    while result > threshold:
+        result = nx.utils.zipf_rv(gamma, xmin, seed)
+    return result
+
+
+def _powerlaw_sequence(gamma, low, high, condition, length, max_iters, seed):
+    """Returns a list of numbers obeying a constrained power law distribution.
+
+    ``gamma`` and ``low`` are the parameters for the Zipf distribution.
+
+    ``high`` is the maximum allowed value for values draw from the Zipf
+    distribution. For more information, see :func:`_zipf_rv_below`.
+
+    ``condition`` and ``length`` are Boolean-valued functions on
+    lists. While generating the list, random values are drawn and
+    appended to the list until ``length`` is satisfied by the created
+    list. Once ``condition`` is satisfied, the sequence generated in
+    this way is returned.
+
+    ``max_iters`` indicates the number of times to generate a list
+    satisfying ``length``. If the number of iterations exceeds this
+    value, :exc:`~networkx.exception.ExceededMaxIterations` is raised.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    """
+    for i in range(max_iters):
+        seq = []
+        while not length(seq):
+            seq.append(_zipf_rv_below(gamma, low, high, seed))
+        if condition(seq):
+            return seq
+    raise nx.ExceededMaxIterations("Could not create power law sequence")
+
+
+def _hurwitz_zeta(x, q, tolerance):
+    """The Hurwitz zeta function, or the Riemann zeta function of two arguments.
+
+    ``x`` must be greater than one and ``q`` must be positive.
+
+    This function repeatedly computes subsequent partial sums until
+    convergence, as decided by ``tolerance``.
+    """
+    z = 0
+    z_prev = -float("inf")
+    k = 0
+    while abs(z - z_prev) > tolerance:
+        z_prev = z
+        z += 1 / ((k + q) ** x)
+        k += 1
+    return z
+
+
+def _generate_min_degree(gamma, average_degree, max_degree, tolerance, max_iters):
+    """Returns a minimum degree from the given average degree."""
+    # Defines zeta function whether or not Scipy is available
+    try:
+        from scipy.special import zeta
+    except ImportError:
+
+        def zeta(x, q):
+            return _hurwitz_zeta(x, q, tolerance)
+
+    min_deg_top = max_degree
+    min_deg_bot = 1
+    min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
+    itrs = 0
+    mid_avg_deg = 0
+    while abs(mid_avg_deg - average_degree) > tolerance:
+        if itrs > max_iters:
+            raise nx.ExceededMaxIterations("Could not match average_degree")
+        mid_avg_deg = 0
+        for x in range(int(min_deg_mid), max_degree + 1):
+            mid_avg_deg += (x ** (-gamma + 1)) / zeta(gamma, min_deg_mid)
+        if mid_avg_deg > average_degree:
+            min_deg_top = min_deg_mid
+            min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
+        else:
+            min_deg_bot = min_deg_mid
+            min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
+        itrs += 1
+    # return int(min_deg_mid + 0.5)
+    return round(min_deg_mid)
+
+
+def _generate_communities(degree_seq, community_sizes, mu, max_iters, seed):
+    """Returns a list of sets, each of which represents a community.
+
+    ``degree_seq`` is the degree sequence that must be met by the
+    graph.
+
+    ``community_sizes`` is the community size distribution that must be
+    met by the generated list of sets.
+
+    ``mu`` is a float in the interval [0, 1] indicating the fraction of
+    intra-community edges incident to each node.
+
+    ``max_iters`` is the number of times to try to add a node to a
+    community. This must be greater than the length of
+    ``degree_seq``, otherwise this function will always fail. If
+    the number of iterations exceeds this value,
+    :exc:`~networkx.exception.ExceededMaxIterations` is raised.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    The communities returned by this are sets of integers in the set {0,
+    ..., *n* - 1}, where *n* is the length of ``degree_seq``.
+
+    """
+    # This assumes the nodes in the graph will be natural numbers.
+    result = [set() for _ in community_sizes]
+    n = len(degree_seq)
+    free = list(range(n))
+    for i in range(max_iters):
+        v = free.pop()
+        c = seed.choice(range(len(community_sizes)))
+        # s = int(degree_seq[v] * (1 - mu) + 0.5)
+        s = round(degree_seq[v] * (1 - mu))
+        # If the community is large enough, add the node to the chosen
+        # community. Otherwise, return it to the list of unaffiliated
+        # nodes.
+        if s < community_sizes[c]:
+            result[c].add(v)
+        else:
+            free.append(v)
+        # If the community is too big, remove a node from it.
+        if len(result[c]) > community_sizes[c]:
+            free.append(result[c].pop())
+        if not free:
+            return result
+    msg = "Could not assign communities; try increasing min_community"
+    raise nx.ExceededMaxIterations(msg)
+
+
+@py_random_state(11)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def LFR_benchmark_graph(
+    n,
+    tau1,
+    tau2,
+    mu,
+    average_degree=None,
+    min_degree=None,
+    max_degree=None,
+    min_community=None,
+    max_community=None,
+    tol=1.0e-7,
+    max_iters=500,
+    seed=None,
+):
+    r"""Returns the LFR benchmark graph.
+
+    This algorithm proceeds as follows:
+
+    1) Find a degree sequence with a power law distribution, and minimum
+       value ``min_degree``, which has approximate average degree
+       ``average_degree``. This is accomplished by either
+
+       a) specifying ``min_degree`` and not ``average_degree``,
+       b) specifying ``average_degree`` and not ``min_degree``, in which
+          case a suitable minimum degree will be found.
+
+       ``max_degree`` can also be specified, otherwise it will be set to
+       ``n``. Each node *u* will have $\mu \mathrm{deg}(u)$ edges
+       joining it to nodes in communities other than its own and $(1 -
+       \mu) \mathrm{deg}(u)$ edges joining it to nodes in its own
+       community.
+    2) Generate community sizes according to a power law distribution
+       with exponent ``tau2``. If ``min_community`` and
+       ``max_community`` are not specified they will be selected to be
+       ``min_degree`` and ``max_degree``, respectively.  Community sizes
+       are generated until the sum of their sizes equals ``n``.
+    3) Each node will be randomly assigned a community with the
+       condition that the community is large enough for the node's
+       intra-community degree, $(1 - \mu) \mathrm{deg}(u)$ as
+       described in step 2. If a community grows too large, a random node
+       will be selected for reassignment to a new community, until all
+       nodes have been assigned a community.
+    4) Each node *u* then adds $(1 - \mu) \mathrm{deg}(u)$
+       intra-community edges and $\mu \mathrm{deg}(u)$ inter-community
+       edges.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes in the created graph.
+
+    tau1 : float
+        Power law exponent for the degree distribution of the created
+        graph. This value must be strictly greater than one.
+
+    tau2 : float
+        Power law exponent for the community size distribution in the
+        created graph. This value must be strictly greater than one.
+
+    mu : float
+        Fraction of inter-community edges incident to each node. This
+        value must be in the interval [0, 1].
+
+    average_degree : float
+        Desired average degree of nodes in the created graph. This value
+        must be in the interval [0, *n*]. Exactly one of this and
+        ``min_degree`` must be specified, otherwise a
+        :exc:`NetworkXError` is raised.
+
+    min_degree : int
+        Minimum degree of nodes in the created graph. This value must be
+        in the interval [0, *n*]. Exactly one of this and
+        ``average_degree`` must be specified, otherwise a
+        :exc:`NetworkXError` is raised.
+
+    max_degree : int
+        Maximum degree of nodes in the created graph. If not specified,
+        this is set to ``n``, the total number of nodes in the graph.
+
+    min_community : int
+        Minimum size of communities in the graph. If not specified, this
+        is set to ``min_degree``.
+
+    max_community : int
+        Maximum size of communities in the graph. If not specified, this
+        is set to ``n``, the total number of nodes in the graph.
+
+    tol : float
+        Tolerance when comparing floats, specifically when comparing
+        average degree values.
+
+    max_iters : int
+        Maximum number of iterations to try to create the community sizes,
+        degree distribution, and community affiliations.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX graph
+        The LFR benchmark graph generated according to the specified
+        parameters.
+
+        Each node in the graph has a node attribute ``'community'`` that
+        stores the community (that is, the set of nodes) that includes
+        it.
+
+    Raises
+    ------
+    NetworkXError
+        If any of the parameters do not meet their upper and lower bounds:
+
+        - ``tau1`` and ``tau2`` must be strictly greater than 1.
+        - ``mu`` must be in [0, 1].
+        - ``max_degree`` must be in {1, ..., *n*}.
+        - ``min_community`` and ``max_community`` must be in {0, ...,
+          *n*}.
+
+        If not exactly one of ``average_degree`` and ``min_degree`` is
+        specified.
+
+        If ``min_degree`` is not specified and a suitable ``min_degree``
+        cannot be found.
+
+    ExceededMaxIterations
+        If a valid degree sequence cannot be created within
+        ``max_iters`` number of iterations.
+
+        If a valid set of community sizes cannot be created within
+        ``max_iters`` number of iterations.
+
+        If a valid community assignment cannot be created within ``10 *
+        n * max_iters`` number of iterations.
+
+    Examples
+    --------
+    Basic usage::
+
+        >>> from networkx.generators.community import LFR_benchmark_graph
+        >>> n = 250
+        >>> tau1 = 3
+        >>> tau2 = 1.5
+        >>> mu = 0.1
+        >>> G = LFR_benchmark_graph(
+        ...     n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10
+        ... )
+
+    Continuing the example above, you can get the communities from the
+    node attributes of the graph::
+
+        >>> communities = {frozenset(G.nodes[v]["community"]) for v in G}
+
+    Notes
+    -----
+    This algorithm differs slightly from the original way it was
+    presented in [1].
+
+    1) Rather than connecting the graph via a configuration model then
+       rewiring to match the intra-community and inter-community
+       degrees, we do this wiring explicitly at the end, which should be
+       equivalent.
+    2) The code posted on the author's website [2] calculates the random
+       power law distributed variables and their average using
+       continuous approximations, whereas we use the discrete
+       distributions here as both degree and community size are
+       discrete.
+
+    Though the authors describe the algorithm as quite robust, testing
+    during development indicates that a somewhat narrower parameter set
+    is likely to successfully produce a graph. Some suggestions have
+    been provided in the event of exceptions.
+
+    References
+    ----------
+    .. [1] "Benchmark graphs for testing community detection algorithms",
+           Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi,
+           Phys. Rev. E 78, 046110 2008
+    .. [2] https://www.santofortunato.net/resources
+
+    """
+    # Perform some basic parameter validation.
+    if not tau1 > 1:
+        raise nx.NetworkXError("tau1 must be greater than one")
+    if not tau2 > 1:
+        raise nx.NetworkXError("tau2 must be greater than one")
+    if not 0 <= mu <= 1:
+        raise nx.NetworkXError("mu must be in the interval [0, 1]")
+
+    # Validate parameters for generating the degree sequence.
+    if max_degree is None:
+        max_degree = n
+    elif not 0 < max_degree <= n:
+        raise nx.NetworkXError("max_degree must be in the interval (0, n]")
+    if not ((min_degree is None) ^ (average_degree is None)):
+        raise nx.NetworkXError(
+            "Must assign exactly one of min_degree and average_degree"
+        )
+    if min_degree is None:
+        min_degree = _generate_min_degree(
+            tau1, average_degree, max_degree, tol, max_iters
+        )
+
+    # Generate a degree sequence with a power law distribution.
+    low, high = min_degree, max_degree
+
+    def condition(seq):
+        return sum(seq) % 2 == 0
+
+    def length(seq):
+        return len(seq) >= n
+
+    deg_seq = _powerlaw_sequence(tau1, low, high, condition, length, max_iters, seed)
+
+    # Validate parameters for generating the community size sequence.
+    if min_community is None:
+        min_community = min(deg_seq)
+    if max_community is None:
+        max_community = max(deg_seq)
+
+    # Generate a community size sequence with a power law distribution.
+    #
+    # TODO The original code incremented the number of iterations each
+    # time a new Zipf random value was drawn from the distribution. This
+    # differed from the way the number of iterations was incremented in
+    # `_powerlaw_degree_sequence`, so this code was changed to match
+    # that one. As a result, this code is allowed many more chances to
+    # generate a valid community size sequence.
+    low, high = min_community, max_community
+
+    def condition(seq):
+        return sum(seq) == n
+
+    def length(seq):
+        return sum(seq) >= n
+
+    comms = _powerlaw_sequence(tau2, low, high, condition, length, max_iters, seed)
+
+    # Generate the communities based on the given degree sequence and
+    # community sizes.
+    max_iters *= 10 * n
+    communities = _generate_communities(deg_seq, comms, mu, max_iters, seed)
+
+    # Finally, generate the benchmark graph based on the given
+    # communities, joining nodes according to the intra- and
+    # inter-community degrees.
+    G = nx.Graph()
+    G.add_nodes_from(range(n))
+    for c in communities:
+        for u in c:
+            while G.degree(u) < round(deg_seq[u] * (1 - mu)):
+                v = seed.choice(list(c))
+                G.add_edge(u, v)
+            while G.degree(u) < deg_seq[u]:
+                v = seed.choice(range(n))
+                if v not in c:
+                    G.add_edge(u, v)
+            G.nodes[u]["community"] = c
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/degree_seq.py b/lib/python3.11/site-packages/networkx/generators/degree_seq.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a374f47c9932fda1757163dbf8868d8c343edb6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/degree_seq.py
@@ -0,0 +1,886 @@
+"""Generate graphs with a given degree sequence or expected degree sequence."""
+
+import heapq
+import math
+from itertools import chain, combinations, zip_longest
+from operator import itemgetter
+
+import networkx as nx
+from networkx.utils import py_random_state, random_weighted_sample
+
+__all__ = [
+    "configuration_model",
+    "directed_configuration_model",
+    "expected_degree_graph",
+    "havel_hakimi_graph",
+    "directed_havel_hakimi_graph",
+    "degree_sequence_tree",
+    "random_degree_sequence_graph",
+]
+
+chaini = chain.from_iterable
+
+
+def _to_stublist(degree_sequence):
+    """Returns a list of degree-repeated node numbers.
+
+    ``degree_sequence`` is a list of nonnegative integers representing
+    the degrees of nodes in a graph.
+
+    This function returns a list of node numbers with multiplicities
+    according to the given degree sequence. For example, if the first
+    element of ``degree_sequence`` is ``3``, then the first node number,
+    ``0``, will appear at the head of the returned list three times. The
+    node numbers are assumed to be the numbers zero through
+    ``len(degree_sequence) - 1``.
+
+    Examples
+    --------
+
+    >>> degree_sequence = [1, 2, 3]
+    >>> _to_stublist(degree_sequence)
+    [0, 1, 1, 2, 2, 2]
+
+    If a zero appears in the sequence, that means the node exists but
+    has degree zero, so that number will be skipped in the returned
+    list::
+
+    >>> degree_sequence = [2, 0, 1]
+    >>> _to_stublist(degree_sequence)
+    [0, 0, 2]
+
+    """
+    return list(chaini([n] * d for n, d in enumerate(degree_sequence)))
+
+
+def _configuration_model(
+    deg_sequence, create_using, directed=False, in_deg_sequence=None, seed=None
+):
+    """Helper function for generating either undirected or directed
+    configuration model graphs.
+
+    ``deg_sequence`` is a list of nonnegative integers representing the
+    degree of the node whose label is the index of the list element.
+
+    ``create_using`` see :func:`~networkx.empty_graph`.
+
+    ``directed`` and ``in_deg_sequence`` are required if you want the
+    returned graph to be generated using the directed configuration
+    model algorithm. If ``directed`` is ``False``, then ``deg_sequence``
+    is interpreted as the degree sequence of an undirected graph and
+    ``in_deg_sequence`` is ignored. Otherwise, if ``directed`` is
+    ``True``, then ``deg_sequence`` is interpreted as the out-degree
+    sequence and ``in_deg_sequence`` as the in-degree sequence of a
+    directed graph.
+
+    .. note::
+
+       ``deg_sequence`` and ``in_deg_sequence`` need not be the same
+       length.
+
+    ``seed`` is a random.Random or numpy.random.RandomState instance
+
+    This function returns a graph, directed if and only if ``directed``
+    is ``True``, generated according to the configuration model
+    algorithm. For more information on the algorithm, see the
+    :func:`configuration_model` or :func:`directed_configuration_model`
+    functions.
+
+    """
+    n = len(deg_sequence)
+    G = nx.empty_graph(n, create_using)
+    # If empty, return the null graph immediately.
+    if n == 0:
+        return G
+    # Build a list of available degree-repeated nodes.  For example,
+    # for degree sequence [3, 2, 1, 1, 1], the "stub list" is
+    # initially [0, 0, 0, 1, 1, 2, 3, 4], that is, node 0 has degree
+    # 3 and thus is repeated 3 times, etc.
+    #
+    # Also, shuffle the stub list in order to get a random sequence of
+    # node pairs.
+    if directed:
+        pairs = zip_longest(deg_sequence, in_deg_sequence, fillvalue=0)
+        # Unzip the list of pairs into a pair of lists.
+        out_deg, in_deg = zip(*pairs)
+
+        out_stublist = _to_stublist(out_deg)
+        in_stublist = _to_stublist(in_deg)
+
+        seed.shuffle(out_stublist)
+        seed.shuffle(in_stublist)
+    else:
+        stublist = _to_stublist(deg_sequence)
+        # Choose a random balanced bipartition of the stublist, which
+        # gives a random pairing of nodes. In this implementation, we
+        # shuffle the list and then split it in half.
+        n = len(stublist)
+        half = n // 2
+        seed.shuffle(stublist)
+        out_stublist, in_stublist = stublist[:half], stublist[half:]
+    G.add_edges_from(zip(out_stublist, in_stublist))
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def configuration_model(deg_sequence, create_using=None, seed=None):
+    """Returns a random graph with the given degree sequence.
+
+    The configuration model generates a random pseudograph (graph with
+    parallel edges and self loops) by randomly assigning edges to
+    match the given degree sequence.
+
+    Parameters
+    ----------
+    deg_sequence :  list of nonnegative integers
+        Each list entry corresponds to the degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequence does not have an even sum.
+
+    See Also
+    --------
+    is_graphical
+
+    Notes
+    -----
+    As described by Newman [1]_.
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the degree
+    sequence does not have an even sum.
+
+    This configuration model construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.
+
+    The density of self-loops and parallel edges tends to decrease as
+    the number of nodes increases. However, typically the number of
+    self-loops will approach a Poisson distribution with a nonzero mean,
+    and similarly for the number of parallel edges.  Consider a node
+    with *k* stubs. The probability of being joined to another stub of
+    the same node is basically (*k* - *1*) / *N*, where *k* is the
+    degree and *N* is the number of nodes. So the probability of a
+    self-loop scales like *c* / *N* for some constant *c*. As *N* grows,
+    this means we expect *c* self-loops. Similarly for parallel edges.
+
+    References
+    ----------
+    .. [1] M.E.J. Newman, "The structure and function of complex networks",
+       SIAM REVIEW 45-2, pp 167-256, 2003.
+
+    Examples
+    --------
+    You can create a degree sequence following a particular distribution
+    by using the one of the distribution functions in
+    :mod:`~networkx.utils.random_sequence` (or one of your own). For
+    example, to create an undirected multigraph on one hundred nodes
+    with degree sequence chosen from the power law distribution:
+
+    >>> sequence = nx.random_powerlaw_tree_sequence(100, tries=5000)
+    >>> G = nx.configuration_model(sequence)
+    >>> len(G)
+    100
+    >>> actual_degrees = [d for v, d in G.degree()]
+    >>> actual_degrees == sequence
+    True
+
+    The returned graph is a multigraph, which may have parallel
+    edges. To remove any parallel edges from the returned graph:
+
+    >>> G = nx.Graph(G)
+
+    Similarly, to remove self-loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    if sum(deg_sequence) % 2 != 0:
+        msg = "Invalid degree sequence: sum of degrees must be even, not odd"
+        raise nx.NetworkXError(msg)
+
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXNotImplemented("not implemented for directed graphs")
+
+    G = _configuration_model(deg_sequence, G, seed=seed)
+
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_configuration_model(
+    in_degree_sequence, out_degree_sequence, create_using=None, seed=None
+):
+    """Returns a directed_random graph with the given degree sequences.
+
+    The configuration model generates a random directed pseudograph
+    (graph with parallel edges and self loops) by randomly assigning
+    edges to match the given degree sequences.
+
+    Parameters
+    ----------
+    in_degree_sequence :  list of nonnegative integers
+       Each list entry corresponds to the in-degree of a node.
+    out_degree_sequence :  list of nonnegative integers
+       Each list entry corresponds to the out-degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiDiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiDiGraph
+        A graph with the specified degree sequences.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequences do not have the same sum.
+
+    See Also
+    --------
+    configuration_model
+
+    Notes
+    -----
+    Algorithm as described by Newman [1]_.
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the degree
+    sequences does not have the same sum.
+
+    This configuration model construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Newman, M. E. J. and Strogatz, S. H. and Watts, D. J.
+       Random graphs with arbitrary degree distributions and their applications
+       Phys. Rev. E, 64, 026118 (2001)
+
+    Examples
+    --------
+    One can modify the in- and out-degree sequences from an existing
+    directed graph in order to create a new directed graph. For example,
+    here we modify the directed path graph:
+
+    >>> D = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+    >>> din = list(d for n, d in D.in_degree())
+    >>> dout = list(d for n, d in D.out_degree())
+    >>> din.append(1)
+    >>> dout[0] = 2
+    >>> # We now expect an edge from node 0 to a new node, node 3.
+    ... D = nx.directed_configuration_model(din, dout)
+
+    The returned graph is a directed multigraph, which may have parallel
+    edges. To remove any parallel edges from the returned graph:
+
+    >>> D = nx.DiGraph(D)
+
+    Similarly, to remove self-loops:
+
+    >>> D.remove_edges_from(nx.selfloop_edges(D))
+
+    """
+    if sum(in_degree_sequence) != sum(out_degree_sequence):
+        msg = "Invalid degree sequences: sequences must have equal sums"
+        raise nx.NetworkXError(msg)
+
+    if create_using is None:
+        create_using = nx.MultiDiGraph
+
+    G = _configuration_model(
+        out_degree_sequence,
+        create_using,
+        directed=True,
+        in_deg_sequence=in_degree_sequence,
+        seed=seed,
+    )
+
+    name = "directed configuration_model {} nodes {} edges"
+    return G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def expected_degree_graph(w, seed=None, selfloops=True):
+    r"""Returns a random graph with given expected degrees.
+
+    Given a sequence of expected degrees $W=(w_0,w_1,\ldots,w_{n-1})$
+    of length $n$ this algorithm assigns an edge between node $u$ and
+    node $v$ with probability
+
+    .. math::
+
+       p_{uv} = \frac{w_u w_v}{\sum_k w_k} .
+
+    Parameters
+    ----------
+    w : list
+        The list of expected degrees.
+    selfloops: bool (default=True)
+        Set to False to remove the possibility of self-loop edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    Graph
+
+    Examples
+    --------
+    >>> z = [10 for i in range(100)]
+    >>> G = nx.expected_degree_graph(z)
+
+    Notes
+    -----
+    The nodes have integer labels corresponding to index of expected degrees
+    input sequence.
+
+    The complexity of this algorithm is $\mathcal{O}(n+m)$ where $n$ is the
+    number of nodes and $m$ is the expected number of edges.
+
+    The model in [1]_ includes the possibility of self-loop edges.
+    Set selfloops=False to produce a graph without self loops.
+
+    For finite graphs this model doesn't produce exactly the given
+    expected degree sequence.  Instead the expected degrees are as
+    follows.
+
+    For the case without self loops (selfloops=False),
+
+    .. math::
+
+       E[deg(u)] = \sum_{v \ne u} p_{uv}
+                = w_u \left( 1 - \frac{w_u}{\sum_k w_k} \right) .
+
+
+    NetworkX uses the standard convention that a self-loop edge counts 2
+    in the degree of a node, so with self loops (selfloops=True),
+
+    .. math::
+
+       E[deg(u)] =  \sum_{v \ne u} p_{uv}  + 2 p_{uu}
+                = w_u \left( 1 + \frac{w_u}{\sum_k w_k} \right) .
+
+    References
+    ----------
+    .. [1] Fan Chung and L. Lu, Connected components in random graphs with
+       given expected degree sequences, Ann. Combinatorics, 6,
+       pp. 125-145, 2002.
+    .. [2] Joel Miller and Aric Hagberg,
+       Efficient generation of networks with given expected degrees,
+       in Algorithms and Models for the Web-Graph (WAW 2011),
+       Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,
+       pp. 115-126, 2011.
+    """
+    n = len(w)
+    G = nx.empty_graph(n)
+
+    # If there are no nodes are no edges in the graph, return the empty graph.
+    if n == 0 or max(w) == 0:
+        return G
+
+    rho = 1 / sum(w)
+    # Sort the weights in decreasing order. The original order of the
+    # weights dictates the order of the (integer) node labels, so we
+    # need to remember the permutation applied in the sorting.
+    order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
+    mapping = {c: u for c, (u, v) in enumerate(order)}
+    seq = [v for u, v in order]
+    last = n
+    if not selfloops:
+        last -= 1
+    for u in range(last):
+        v = u
+        if not selfloops:
+            v += 1
+        factor = seq[u] * rho
+        p = min(seq[v] * factor, 1)
+        while v < n and p > 0:
+            if p != 1:
+                r = seed.random()
+                v += math.floor(math.log(r, 1 - p))
+            if v < n:
+                q = min(seq[v] * factor, 1)
+                if seed.random() < q / p:
+                    G.add_edge(mapping[u], mapping[v])
+                v += 1
+                p = q
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def havel_hakimi_graph(deg_sequence, create_using=None):
+    """Returns a simple graph with given degree sequence constructed
+    using the Havel-Hakimi algorithm.
+
+    Parameters
+    ----------
+    deg_sequence: list of integers
+        Each integer corresponds to the degree of a node (need not be sorted).
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Directed graphs are not allowed.
+
+    Raises
+    ------
+    NetworkXException
+        For a non-graphical degree sequence (i.e. one
+        not realizable by some simple graph).
+
+    Notes
+    -----
+    The Havel-Hakimi algorithm constructs a simple graph by
+    successively connecting the node of highest degree to other nodes
+    of highest degree, resorting remaining nodes by degree, and
+    repeating the process. The resulting graph has a high
+    degree-associativity.  Nodes are labeled 1,.., len(deg_sequence),
+    corresponding to their position in deg_sequence.
+
+    The basic algorithm is from Hakimi [1]_ and was generalized by
+    Kleitman and Wang [2]_.
+
+    References
+    ----------
+    .. [1] Hakimi S., On Realizability of a Set of Integers as
+       Degrees of the Vertices of a Linear Graph. I,
+       Journal of SIAM, 10(3), pp. 496-506 (1962)
+    .. [2] Kleitman D.J. and Wang D.L.
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors  Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    if not nx.is_graphical(deg_sequence):
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    p = len(deg_sequence)
+    G = nx.empty_graph(p, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed graphs are not supported")
+    num_degs = [[] for i in range(p)]
+    dmax, dsum, n = 0, 0, 0
+    for d in deg_sequence:
+        # Process only the non-zero integers
+        if d > 0:
+            num_degs[d].append(n)
+            dmax, dsum, n = max(dmax, d), dsum + d, n + 1
+    # Return graph if no edges
+    if n == 0:
+        return G
+
+    modstubs = [(0, 0)] * (dmax + 1)
+    # Successively reduce degree sequence by removing the maximum degree
+    while n > 0:
+        # Retrieve the maximum degree in the sequence
+        while len(num_degs[dmax]) == 0:
+            dmax -= 1
+        # If there are not enough stubs to connect to, then the sequence is
+        # not graphical
+        if dmax > n - 1:
+            raise nx.NetworkXError("Non-graphical integer sequence")
+
+        # Remove largest stub in list
+        source = num_degs[dmax].pop()
+        n -= 1
+        # Reduce the next dmax largest stubs
+        mslen = 0
+        k = dmax
+        for i in range(dmax):
+            while len(num_degs[k]) == 0:
+                k -= 1
+            target = num_degs[k].pop()
+            G.add_edge(source, target)
+            n -= 1
+            if k > 1:
+                modstubs[mslen] = (k - 1, target)
+                mslen += 1
+        # Add back to the list any nonzero stubs that were removed
+        for i in range(mslen):
+            (stubval, stubtarget) = modstubs[i]
+            num_degs[stubval].append(stubtarget)
+            n += 1
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_havel_hakimi_graph(in_deg_sequence, out_deg_sequence, create_using=None):
+    """Returns a directed graph with the given degree sequences.
+
+    Parameters
+    ----------
+    in_deg_sequence :  list of integers
+        Each list entry corresponds to the in-degree of a node.
+    out_deg_sequence : list of integers
+        Each list entry corresponds to the out-degree of a node.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : DiGraph
+        A graph with the specified degree sequences.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequences are not digraphical.
+
+    See Also
+    --------
+    configuration_model
+
+    Notes
+    -----
+    Algorithm as described by Kleitman and Wang [1]_.
+
+    References
+    ----------
+    .. [1] D.J. Kleitman and D.L. Wang
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    in_deg_sequence = nx.utils.make_list_of_ints(in_deg_sequence)
+    out_deg_sequence = nx.utils.make_list_of_ints(out_deg_sequence)
+
+    # Process the sequences and form two heaps to store degree pairs with
+    # either zero or nonzero out degrees
+    sumin, sumout = 0, 0
+    nin, nout = len(in_deg_sequence), len(out_deg_sequence)
+    maxn = max(nin, nout)
+    G = nx.empty_graph(maxn, create_using, default=nx.DiGraph)
+    if maxn == 0:
+        return G
+    maxin = 0
+    stubheap, zeroheap = [], []
+    for n in range(maxn):
+        in_deg, out_deg = 0, 0
+        if n < nout:
+            out_deg = out_deg_sequence[n]
+        if n < nin:
+            in_deg = in_deg_sequence[n]
+        if in_deg < 0 or out_deg < 0:
+            raise nx.NetworkXError(
+                "Invalid degree sequences. Sequence values must be positive."
+            )
+        sumin, sumout, maxin = sumin + in_deg, sumout + out_deg, max(maxin, in_deg)
+        if in_deg > 0:
+            stubheap.append((-1 * out_deg, -1 * in_deg, n))
+        elif out_deg > 0:
+            zeroheap.append((-1 * out_deg, n))
+    if sumin != sumout:
+        raise nx.NetworkXError(
+            "Invalid degree sequences. Sequences must have equal sums."
+        )
+    heapq.heapify(stubheap)
+    heapq.heapify(zeroheap)
+
+    modstubs = [(0, 0, 0)] * (maxin + 1)
+    # Successively reduce degree sequence by removing the maximum
+    while stubheap:
+        # Remove first value in the sequence with a non-zero in degree
+        (freeout, freein, target) = heapq.heappop(stubheap)
+        freein *= -1
+        if freein > len(stubheap) + len(zeroheap):
+            raise nx.NetworkXError("Non-digraphical integer sequence")
+
+        # Attach arcs from the nodes with the most stubs
+        mslen = 0
+        for i in range(freein):
+            if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0][0]):
+                (stubout, stubsource) = heapq.heappop(zeroheap)
+                stubin = 0
+            else:
+                (stubout, stubin, stubsource) = heapq.heappop(stubheap)
+            if stubout == 0:
+                raise nx.NetworkXError("Non-digraphical integer sequence")
+            G.add_edge(stubsource, target)
+            # Check if source is now totally connected
+            if stubout + 1 < 0 or stubin < 0:
+                modstubs[mslen] = (stubout + 1, stubin, stubsource)
+                mslen += 1
+
+        # Add the nodes back to the heaps that still have available stubs
+        for i in range(mslen):
+            stub = modstubs[i]
+            if stub[1] < 0:
+                heapq.heappush(stubheap, stub)
+            else:
+                heapq.heappush(zeroheap, (stub[0], stub[2]))
+        if freeout < 0:
+            heapq.heappush(zeroheap, (freeout, target))
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def degree_sequence_tree(deg_sequence, create_using=None):
+    """Return a tree with the given degree sequence.
+
+    Two conditions must be met for a degree sequence to be valid for a tree:
+
+    1. The number of nodes must be one more than the number of edges.
+    2. The degree sequence must be trivial or have only strictly positive
+       node degrees.
+
+    Parameters
+    ----------
+    degree_sequence : iterable
+        Iterable of node degrees.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    networkx.Graph
+        A tree with the given degree sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequence is not valid for a tree.
+
+        If `create_using` is directed.
+
+    See Also
+    --------
+    random_degree_sequence_graph
+    """
+    deg_sequence = list(deg_sequence)
+    valid, reason = nx.utils.is_valid_tree_degree_sequence(deg_sequence)
+    if not valid:
+        raise nx.NetworkXError(reason)
+
+    G = nx.empty_graph(0, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    if deg_sequence == [0]:
+        G.add_node(0)
+        return G
+
+    # Sort all degrees greater than 1 in decreasing order.
+    #
+    # TODO Does this need to be sorted in reverse order?
+    deg = sorted((s for s in deg_sequence if s > 1), reverse=True)
+
+    # make path graph as backbone
+    n = len(deg) + 2
+    nx.add_path(G, range(n))
+    last = n
+
+    # add the leaves
+    for source in range(1, n - 1):
+        nedges = deg.pop() - 2
+        G.add_edges_from((source, target) for target in range(last, last + nedges))
+        last += nedges
+    return G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_degree_sequence_graph(sequence, seed=None, tries=10):
+    r"""Returns a simple random graph with the given degree sequence.
+
+    If the maximum degree $d_m$ in the sequence is $O(m^{1/4})$ then the
+    algorithm produces almost uniform random graphs in $O(m d_m)$ time
+    where $m$ is the number of edges.
+
+    Parameters
+    ----------
+    sequence :  list of integers
+        Sequence of degrees
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int, optional
+        Maximum number of tries to create a graph
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified degree sequence.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in the sequence.
+
+    Raises
+    ------
+    NetworkXUnfeasible
+        If the degree sequence is not graphical.
+    NetworkXError
+        If a graph is not produced in specified number of tries
+
+    See Also
+    --------
+    is_graphical, configuration_model
+
+    Notes
+    -----
+    The generator algorithm [1]_ is not guaranteed to produce a graph.
+
+    References
+    ----------
+    .. [1] Moshen Bayati, Jeong Han Kim, and Amin Saberi,
+       A sequential algorithm for generating random graphs.
+       Algorithmica, Volume 58, Number 4, 860-910,
+       DOI: 10.1007/s00453-009-9340-1
+
+    Examples
+    --------
+    >>> sequence = [1, 2, 2, 3]
+    >>> G = nx.random_degree_sequence_graph(sequence, seed=42)
+    >>> sorted(d for n, d in G.degree())
+    [1, 2, 2, 3]
+    """
+    DSRG = DegreeSequenceRandomGraph(sequence, seed)
+    for try_n in range(tries):
+        try:
+            return DSRG.generate()
+        except nx.NetworkXUnfeasible:
+            pass
+    raise nx.NetworkXError(f"failed to generate graph in {tries} tries")
+
+
+class DegreeSequenceRandomGraph:
+    # class to generate random graphs with a given degree sequence
+    # use random_degree_sequence_graph()
+    def __init__(self, degree, rng):
+        self.rng = rng
+        self.degree = list(degree)
+        if not nx.is_graphical(self.degree):
+            raise nx.NetworkXUnfeasible("degree sequence is not graphical")
+        # node labels are integers 0,...,n-1
+        self.m = sum(self.degree) / 2.0  # number of edges
+        try:
+            self.dmax = max(self.degree)  # maximum degree
+        except ValueError:
+            self.dmax = 0
+
+    def generate(self):
+        # remaining_degree is mapping from int->remaining degree
+        self.remaining_degree = dict(enumerate(self.degree))
+        # add all nodes to make sure we get isolated nodes
+        self.graph = nx.Graph()
+        self.graph.add_nodes_from(self.remaining_degree)
+        # remove zero degree nodes
+        for n, d in list(self.remaining_degree.items()):
+            if d == 0:
+                del self.remaining_degree[n]
+        if len(self.remaining_degree) > 0:
+            # build graph in three phases according to how many unmatched edges
+            self.phase1()
+            self.phase2()
+            self.phase3()
+        return self.graph
+
+    def update_remaining(self, u, v, aux_graph=None):
+        # decrement remaining nodes, modify auxiliary graph if in phase3
+        if aux_graph is not None:
+            # remove edges from auxiliary graph
+            aux_graph.remove_edge(u, v)
+        if self.remaining_degree[u] == 1:
+            del self.remaining_degree[u]
+            if aux_graph is not None:
+                aux_graph.remove_node(u)
+        else:
+            self.remaining_degree[u] -= 1
+        if self.remaining_degree[v] == 1:
+            del self.remaining_degree[v]
+            if aux_graph is not None:
+                aux_graph.remove_node(v)
+        else:
+            self.remaining_degree[v] -= 1
+
+    def p(self, u, v):
+        # degree probability
+        return 1 - self.degree[u] * self.degree[v] / (4.0 * self.m)
+
+    def q(self, u, v):
+        # remaining degree probability
+        norm = max(self.remaining_degree.values()) ** 2
+        return self.remaining_degree[u] * self.remaining_degree[v] / norm
+
+    def suitable_edge(self):
+        """Returns True if and only if an arbitrary remaining node can
+        potentially be joined with some other remaining node.
+
+        """
+        nodes = iter(self.remaining_degree)
+        u = next(nodes)
+        return any(v not in self.graph[u] for v in nodes)
+
+    def phase1(self):
+        # choose node pairs from (degree) weighted distribution
+        rem_deg = self.remaining_degree
+        while sum(rem_deg.values()) >= 2 * self.dmax**2:
+            u, v = sorted(random_weighted_sample(rem_deg, 2, self.rng))
+            if self.graph.has_edge(u, v):
+                continue
+            if self.rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v)
+
+    def phase2(self):
+        # choose remaining nodes uniformly at random and use rejection sampling
+        remaining_deg = self.remaining_degree
+        rng = self.rng
+        while len(remaining_deg) >= 2 * self.dmax:
+            while True:
+                u, v = sorted(rng.sample(list(remaining_deg.keys()), 2))
+                if self.graph.has_edge(u, v):
+                    continue
+                if rng.random() < self.q(u, v):
+                    break
+            if rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v)
+
+    def phase3(self):
+        # build potential remaining edges and choose with rejection sampling
+        potential_edges = combinations(self.remaining_degree, 2)
+        # build auxiliary graph of potential edges not already in graph
+        H = nx.Graph(
+            [(u, v) for (u, v) in potential_edges if not self.graph.has_edge(u, v)]
+        )
+        rng = self.rng
+        while self.remaining_degree:
+            if not self.suitable_edge():
+                raise nx.NetworkXUnfeasible("no suitable edges left")
+            while True:
+                u, v = sorted(rng.choice(list(H.edges())))
+                if rng.random() < self.q(u, v):
+                    break
+            if rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v, aux_graph=H)
diff --git a/lib/python3.11/site-packages/networkx/generators/directed.py b/lib/python3.11/site-packages/networkx/generators/directed.py
new file mode 100644
index 0000000000000000000000000000000000000000..759ce2f9d9106d8dbb6c5ca001391c7be74b636a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/directed.py
@@ -0,0 +1,572 @@
+"""
+Generators for some directed graphs, including growing network (GN) graphs and
+scale-free graphs.
+
+"""
+
+import numbers
+from collections import Counter
+
+import networkx as nx
+from networkx.generators.classic import empty_graph
+from networkx.utils import (
+    discrete_sequence,
+    np_random_state,
+    py_random_state,
+    weighted_choice,
+)
+
+__all__ = [
+    "gn_graph",
+    "gnc_graph",
+    "gnr_graph",
+    "random_k_out_graph",
+    "scale_free_graph",
+]
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gn_graph(n, kernel=None, create_using=None, seed=None):
+    """Returns the growing network (GN) digraph with `n` nodes.
+
+    The GN graph is built by adding nodes one at a time with a link to one
+    previously added node.  The target node for the link is chosen with
+    probability based on degree.  The default attachment kernel is a linear
+    function of the degree of a node.
+
+    The graph is always a (directed) tree.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    kernel : function
+        The attachment kernel.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Examples
+    --------
+    To create the undirected GN graph, use the :meth:`~DiGraph.to_directed`
+    method::
+
+    >>> D = nx.gn_graph(10)  # the GN graph
+    >>> G = D.to_undirected()  # the undirected version
+
+    To specify an attachment kernel, use the `kernel` keyword argument::
+
+    >>> D = nx.gn_graph(10, kernel=lambda x: x**1.5)  # A_k = k^1.5
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Organization of Growing Random Networks,
+           Phys. Rev. E, 63, 066123, 2001.
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if kernel is None:
+
+        def kernel(x):
+            return x
+
+    if n == 1:
+        return G
+
+    G.add_edge(1, 0)  # get started
+    ds = [1, 1]  # degree sequence
+
+    for source in range(2, n):
+        # compute distribution from kernel and degree
+        dist = [kernel(d) for d in ds]
+        # choose target from discrete distribution
+        target = discrete_sequence(1, distribution=dist, seed=seed)[0]
+        G.add_edge(source, target)
+        ds.append(1)  # the source has only one link (degree one)
+        ds[target] += 1  # add one to the target link degree
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnr_graph(n, p, create_using=None, seed=None):
+    """Returns the growing network with redirection (GNR) digraph with `n`
+    nodes and redirection probability `p`.
+
+    The GNR graph is built by adding nodes one at a time with a link to one
+    previously added node.  The previous target node is chosen uniformly at
+    random.  With probability `p` the link is instead "redirected" to the
+    successor node of the target.
+
+    The graph is always a (directed) tree.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    p : float
+        The redirection probability.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Examples
+    --------
+    To create the undirected GNR graph, use the :meth:`~DiGraph.to_directed`
+    method::
+
+    >>> D = nx.gnr_graph(10, 0.5)  # the GNR graph
+    >>> G = D.to_undirected()  # the undirected version
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Organization of Growing Random Networks,
+           Phys. Rev. E, 63, 066123, 2001.
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if n == 1:
+        return G
+
+    for source in range(1, n):
+        target = seed.randrange(0, source)
+        if seed.random() < p and target != 0:
+            target = next(G.successors(target))
+        G.add_edge(source, target)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnc_graph(n, create_using=None, seed=None):
+    """Returns the growing network with copying (GNC) digraph with `n` nodes.
+
+    The GNC graph is built by adding nodes one at a time with a link to one
+    previously added node (chosen uniformly at random) and to all of that
+    node's successors.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Network Growth by Copying,
+           Phys. Rev. E, 71, 036118, 2005k.},
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if n == 1:
+        return G
+
+    for source in range(1, n):
+        target = seed.randrange(0, source)
+        for succ in G.successors(target):
+            G.add_edge(source, succ)
+        G.add_edge(source, target)
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def scale_free_graph(
+    n,
+    alpha=0.41,
+    beta=0.54,
+    gamma=0.05,
+    delta_in=0.2,
+    delta_out=0,
+    seed=None,
+    initial_graph=None,
+):
+    """Returns a scale-free directed graph.
+
+    Parameters
+    ----------
+    n : integer
+        Number of nodes in graph
+    alpha : float
+        Probability for adding a new node connected to an existing node
+        chosen randomly according to the in-degree distribution.
+    beta : float
+        Probability for adding an edge between two existing nodes.
+        One existing node is chosen randomly according the in-degree
+        distribution and the other chosen randomly according to the out-degree
+        distribution.
+    gamma : float
+        Probability for adding a new node connected to an existing node
+        chosen randomly according to the out-degree distribution.
+    delta_in : float
+        Bias for choosing nodes from in-degree distribution.
+    delta_out : float
+        Bias for choosing nodes from out-degree distribution.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : MultiDiGraph instance, optional
+        Build the scale-free graph starting from this initial MultiDiGraph,
+        if provided.
+
+    Returns
+    -------
+    MultiDiGraph
+
+    Examples
+    --------
+    Create a scale-free graph on one hundred nodes::
+
+    >>> G = nx.scale_free_graph(100)
+
+    Notes
+    -----
+    The sum of `alpha`, `beta`, and `gamma` must be 1.
+
+    References
+    ----------
+    .. [1] B. Bollobás, C. Borgs, J. Chayes, and O. Riordan,
+           Directed scale-free graphs,
+           Proceedings of the fourteenth annual ACM-SIAM Symposium on
+           Discrete Algorithms, 132--139, 2003.
+    """
+
+    def _choose_node(candidates, node_list, delta):
+        if delta > 0:
+            bias_sum = len(node_list) * delta
+            p_delta = bias_sum / (bias_sum + len(candidates))
+            if seed.random() < p_delta:
+                return seed.choice(node_list)
+        return seed.choice(candidates)
+
+    if initial_graph is not None and hasattr(initial_graph, "_adj"):
+        if not isinstance(initial_graph, nx.MultiDiGraph):
+            raise nx.NetworkXError("initial_graph must be a MultiDiGraph.")
+        G = initial_graph
+    else:
+        # Start with 3-cycle
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (2, 0)])
+
+    if alpha <= 0:
+        raise ValueError("alpha must be > 0.")
+    if beta <= 0:
+        raise ValueError("beta must be > 0.")
+    if gamma <= 0:
+        raise ValueError("gamma must be > 0.")
+
+    if abs(alpha + beta + gamma - 1.0) >= 1e-9:
+        raise ValueError("alpha+beta+gamma must equal 1.")
+
+    if delta_in < 0:
+        raise ValueError("delta_in must be >= 0.")
+
+    if delta_out < 0:
+        raise ValueError("delta_out must be >= 0.")
+
+    # pre-populate degree states
+    vs = sum((count * [idx] for idx, count in G.out_degree()), [])
+    ws = sum((count * [idx] for idx, count in G.in_degree()), [])
+
+    # pre-populate node state
+    node_list = list(G.nodes())
+
+    # see if there already are number-based nodes
+    numeric_nodes = [n for n in node_list if isinstance(n, numbers.Number)]
+    if len(numeric_nodes) > 0:
+        # set cursor for new nodes appropriately
+        cursor = max(int(n.real) for n in numeric_nodes) + 1
+    else:
+        # or start at zero
+        cursor = 0
+
+    while len(G) < n:
+        r = seed.random()
+
+        # random choice in alpha,beta,gamma ranges
+        if r < alpha:
+            # alpha
+            # add new node v
+            v = cursor
+            cursor += 1
+            # also add to node state
+            node_list.append(v)
+            # choose w according to in-degree and delta_in
+            w = _choose_node(ws, node_list, delta_in)
+
+        elif r < alpha + beta:
+            # beta
+            # choose v according to out-degree and delta_out
+            v = _choose_node(vs, node_list, delta_out)
+            # choose w according to in-degree and delta_in
+            w = _choose_node(ws, node_list, delta_in)
+
+        else:
+            # gamma
+            # choose v according to out-degree and delta_out
+            v = _choose_node(vs, node_list, delta_out)
+            # add new node w
+            w = cursor
+            cursor += 1
+            # also add to node state
+            node_list.append(w)
+
+        # add edge to graph
+        G.add_edge(v, w)
+
+        # update degree states
+        vs.append(v)
+        ws.append(w)
+
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_uniform_k_out_graph(n, k, self_loops=True, with_replacement=True, seed=None):
+    """Returns a random `k`-out graph with uniform attachment.
+
+    A random `k`-out graph with uniform attachment is a multidigraph
+    generated by the following algorithm. For each node *u*, choose
+    `k` nodes *v* uniformly at random (with replacement). Add a
+    directed edge joining *u* to *v*.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+
+    k : int
+        The out-degree of each node in the returned graph.
+
+    self_loops : bool
+        If True, self-loops are allowed when generating the graph.
+
+    with_replacement : bool
+        If True, neighbors are chosen with replacement and the
+        returned graph will be a directed multigraph. Otherwise,
+        neighbors are chosen without replacement and the returned graph
+        will be a directed graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    NetworkX graph
+        A `k`-out-regular directed graph generated according to the
+        above algorithm. It will be a multigraph if and only if
+        `with_replacement` is True.
+
+    Raises
+    ------
+    ValueError
+        If `with_replacement` is False and `k` is greater than
+        `n`.
+
+    See also
+    --------
+    random_k_out_graph
+
+    Notes
+    -----
+    The return digraph or multidigraph may not be strongly connected, or
+    even weakly connected.
+
+    If `with_replacement` is True, this function is similar to
+    :func:`random_k_out_graph`, if that function had parameter `alpha`
+    set to positive infinity.
+
+    """
+    if with_replacement:
+        create_using = nx.MultiDiGraph()
+
+        def sample(v, nodes):
+            if not self_loops:
+                nodes = nodes - {v}
+            return (seed.choice(list(nodes)) for i in range(k))
+
+    else:
+        create_using = nx.DiGraph()
+
+        def sample(v, nodes):
+            if not self_loops:
+                nodes = nodes - {v}
+            return seed.sample(list(nodes), k)
+
+    G = nx.empty_graph(n, create_using)
+    nodes = set(G)
+    for u in G:
+        G.add_edges_from((u, v) for v in sample(u, nodes))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_k_out_graph(n, k, alpha, self_loops=True, seed=None):
+    """Returns a random `k`-out graph with preferential attachment.
+
+    .. versionchanged:: 3.5
+       Different implementations will be used based on whether NumPy is
+       available. See Notes for details.
+
+    A random `k`-out graph with preferential attachment is a
+    multidigraph generated by the following algorithm.
+
+    1. Begin with an empty digraph, and initially set each node to have
+       weight `alpha`.
+    2. Choose a node `u` with out-degree less than `k` uniformly at
+       random.
+    3. Choose a node `v` from with probability proportional to its
+       weight.
+    4. Add a directed edge from `u` to `v`, and increase the weight
+       of `v` by one.
+    5. If each node has out-degree `k`, halt, otherwise repeat from
+       step 2.
+
+    For more information on this model of random graph, see [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+
+    k : int
+        The out-degree of each node in the returned graph.
+
+    alpha : float
+        A positive :class:`float` representing the initial weight of
+        each vertex. A higher number means that in step 3 above, nodes
+        will be chosen more like a true uniformly random sample, and a
+        lower number means that nodes are more likely to be chosen as
+        their in-degree increases. If this parameter is not positive, a
+        :exc:`ValueError` is raised.
+
+    self_loops : bool
+        If True, self-loops are allowed when generating the graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`~networkx.classes.MultiDiGraph`
+        A `k`-out-regular multidigraph generated according to the above
+        algorithm.
+
+    Raises
+    ------
+    ValueError
+        If `alpha` is not positive.
+
+    Notes
+    -----
+    The returned multidigraph may not be strongly connected, or even
+    weakly connected.
+
+    `random_k_out_graph` has two implementations: an array-based formulation that
+    uses `numpy` (``_random_k_out_graph_numpy``), and a pure-Python
+    implementation (``_random_k_out_graph_python``).
+    The NumPy implementation is more performant, especially for large `n`, and is
+    therefore used by default. If NumPy is not installed in the environment,
+    then the pure Python implementation is executed.
+    However, you can explicitly control which implementation is executed by directly
+    calling the corresponding function::
+
+        # Use numpy if available, else Python
+        nx.random_k_out_graph(1000, 5, alpha=1)
+
+        # Use the numpy-based implementation (raises ImportError if numpy not installed)
+        nx.generators.directed._random_k_out_graph_numpy(1000, 5, alpha=1)
+
+        # Use the Python-based implementation
+        nx.generators.directed._random_k_out_graph_python(1000, 5, alpha=1)
+
+    References
+    ----------
+    .. [1] Peterson, Nicholas R., and Boris Pittel.
+       "Distance between two random `k`-out digraphs, with and without preferential attachment."
+       arXiv preprint arXiv:1311.5961 (2013) <https://arxiv.org/abs/1311.5961>.
+
+    """
+    if alpha < 0:
+        raise ValueError("alpha must be positive")
+    try:  # Use numpy if available, otherwise fall back to pure Python implementation
+        return _random_k_out_graph_numpy(n, k, alpha, self_loops, seed)
+    except ImportError:
+        return _random_k_out_graph_python(n, k, alpha, self_loops, seed)
+
+
+@np_random_state(4)
+def _random_k_out_graph_numpy(n, k, alpha, self_loops=True, seed=None):
+    import numpy as np
+
+    G = nx.empty_graph(n, create_using=nx.MultiDiGraph)
+    nodes = np.arange(n)
+    remaining_mask = np.full(n, True)
+    weights = np.full(n, alpha)
+    total_weight = n * alpha
+    out_strengths = np.zeros(n)
+
+    for i in range(k * n):
+        u = seed.choice(nodes[remaining_mask])
+
+        if self_loops:
+            v = seed.choice(nodes, p=weights / total_weight)
+        else:  # Ignore weight of u when selecting v
+            u_weight = weights[u]
+            weights[u] = 0
+            v = seed.choice(nodes, p=weights / (total_weight - u_weight))
+            weights[u] = u_weight
+
+        G.add_edge(u.item(), v.item())
+        weights[v] += 1
+        total_weight += 1
+        out_strengths[u] += 1
+        if out_strengths[u] == k:
+            remaining_mask[u] = False
+    return G
+
+
+@py_random_state(4)
+def _random_k_out_graph_python(n, k, alpha, self_loops=True, seed=None):
+    G = nx.empty_graph(n, create_using=nx.MultiDiGraph)
+    weights = Counter({v: alpha for v in G})
+    out_strengths = Counter({v: 0 for v in G})
+
+    for i in range(k * n):
+        u = seed.choice(list(out_strengths.keys()))
+        # If self-loops are not allowed, make the source node `u` have
+        # weight zero.
+        if not self_loops:
+            uweight = weights.pop(u)
+
+        v = weighted_choice(weights, seed=seed)
+
+        if not self_loops:
+            weights[u] = uweight
+
+        G.add_edge(u, v)
+        weights[v] += 1
+        out_strengths[u] += 1
+        if out_strengths[u] == k:
+            out_strengths.pop(u)
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/duplication.py b/lib/python3.11/site-packages/networkx/generators/duplication.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c3ade63f58237eeb927ff631b25f025d7d83fc1
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/duplication.py
@@ -0,0 +1,174 @@
+"""Functions for generating graphs based on the "duplication" method.
+
+These graph generators start with a small initial graph then duplicate
+nodes and (partially) duplicate their edges. These functions are
+generally inspired by biological networks.
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import py_random_state
+from networkx.utils.misc import check_create_using
+
+__all__ = ["partial_duplication_graph", "duplication_divergence_graph"]
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def partial_duplication_graph(N, n, p, q, seed=None, *, create_using=None):
+    """Returns a random graph using the partial duplication model.
+
+    Parameters
+    ----------
+    N : int
+        The total number of nodes in the final graph.
+
+    n : int
+        The number of nodes in the initial clique.
+
+    p : float
+        The probability of joining each neighbor of a node to the
+        duplicate node. Must be a number in the between zero and one,
+        inclusive.
+
+    q : float
+        The probability of joining the source node to the duplicate
+        node. Must be a number in the between zero and one, inclusive.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    A graph of nodes is grown by creating a fully connected graph
+    of size `n`. The following procedure is then repeated until
+    a total of `N` nodes have been reached.
+
+    1. A random node, *u*, is picked and a new node, *v*, is created.
+    2. For each neighbor of *u* an edge from the neighbor to *v* is created
+       with probability `p`.
+    3. An edge from *u* to *v* is created with probability `q`.
+
+    This algorithm appears in [1].
+
+    This implementation allows the possibility of generating
+    disconnected graphs.
+
+    References
+    ----------
+    .. [1] Knudsen Michael, and Carsten Wiuf. "A Markov chain approach to
+           randomly grown graphs." Journal of Applied Mathematics 2008.
+           <https://doi.org/10.1155/2008/190836>
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if p < 0 or p > 1 or q < 0 or q > 1:
+        msg = "partial duplication graph must have 0 <= p, q <= 1."
+        raise NetworkXError(msg)
+    if n > N:
+        raise NetworkXError("partial duplication graph must have n <= N.")
+
+    G = nx.complete_graph(n, create_using)
+    for new_node in range(n, N):
+        # Pick a random vertex, u, already in the graph.
+        src_node = seed.randint(0, new_node - 1)
+
+        # Add a new vertex, v, to the graph.
+        G.add_node(new_node)
+
+        # For each neighbor of u...
+        for nbr_node in list(nx.all_neighbors(G, src_node)):
+            # Add the neighbor to v with probability p.
+            if seed.random() < p:
+                G.add_edge(new_node, nbr_node)
+
+        # Join v and u with probability q.
+        if seed.random() < q:
+            G.add_edge(new_node, src_node)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def duplication_divergence_graph(n, p, seed=None, *, create_using=None):
+    """Returns an undirected graph using the duplication-divergence model.
+
+    A graph of `n` nodes is created by duplicating the initial nodes
+    and retaining edges incident to the original nodes with a retention
+    probability `p`.
+
+    Parameters
+    ----------
+    n : int
+        The desired number of nodes in the graph.
+    p : float
+        The probability for retaining the edge of the replicated node.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `p` is not a valid probability.
+        If `n` is less than 2.
+
+    Notes
+    -----
+    This algorithm appears in [1].
+
+    This implementation disallows the possibility of generating
+    disconnected graphs.
+
+    References
+    ----------
+    .. [1] I. Ispolatov, P. L. Krapivsky, A. Yuryev,
+       "Duplication-divergence model of protein interaction network",
+       Phys. Rev. E, 71, 061911, 2005.
+
+    """
+    if p > 1 or p < 0:
+        msg = f"NetworkXError p={p} is not in [0,1]."
+        raise nx.NetworkXError(msg)
+    if n < 2:
+        msg = "n must be greater than or equal to 2"
+        raise nx.NetworkXError(msg)
+
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    G = nx.empty_graph(create_using=create_using)
+
+    # Initialize the graph with two connected nodes.
+    G.add_edge(0, 1)
+    i = 2
+    while i < n:
+        # Choose a random node from current graph to duplicate.
+        random_node = seed.choice(list(G))
+        # Make the replica.
+        G.add_node(i)
+        # flag indicates whether at least one edge is connected on the replica.
+        flag = False
+        for nbr in G.neighbors(random_node):
+            if seed.random() < p:
+                # Link retention step.
+                G.add_edge(i, nbr)
+                flag = True
+        if not flag:
+            # Delete replica if no edges retained.
+            G.remove_node(i)
+        else:
+            # Successful duplication.
+            i += 1
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/ego.py b/lib/python3.11/site-packages/networkx/generators/ego.py
new file mode 100644
index 0000000000000000000000000000000000000000..91ff3e3fe8573e0764094d030a8656fafa59c929
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/ego.py
@@ -0,0 +1,66 @@
+"""
+Ego graph.
+"""
+
+__all__ = ["ego_graph"]
+
+import networkx as nx
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
+    """Returns induced subgraph of neighbors centered at node n within
+    a given radius.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX Graph or DiGraph
+
+    n : node
+      A single node
+
+    radius : number, optional
+      Include all neighbors of distance<=radius from n.
+
+    center : bool, optional
+      If False, do not include center node in graph
+
+    undirected : bool, optional
+      If True use both in- and out-neighbors of directed graphs.
+
+    distance : key, optional
+      Use specified edge data key as distance.  For example, setting
+      distance='weight' will use the edge weight to measure the
+      distance from the node n.
+
+    Notes
+    -----
+    For directed graphs D this produces the "out" neighborhood
+    or successors.  If you want the neighborhood of predecessors
+    first reverse the graph with D.reverse().  If you want both
+    directions use the keyword argument undirected=True.
+
+    Node, edge, and graph attributes are copied to the returned subgraph.
+    """
+    if undirected:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(
+                G.to_undirected(), n, cutoff=radius, weight=distance
+            )
+        else:
+            sp = dict(
+                nx.single_source_shortest_path_length(
+                    G.to_undirected(), n, cutoff=radius
+                )
+            )
+    else:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(G, n, cutoff=radius, weight=distance)
+        else:
+            sp = nx.single_source_shortest_path_length(G, n, cutoff=radius)
+
+    H = G.subgraph(sp).copy()
+    if not center:
+        H.remove_node(n)
+    return H
diff --git a/lib/python3.11/site-packages/networkx/generators/expanders.py b/lib/python3.11/site-packages/networkx/generators/expanders.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7d6c21f9452f1e8d584864a010ab724cac2f048
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/expanders.py
@@ -0,0 +1,499 @@
+"""Provides explicit constructions of expander graphs."""
+
+import itertools
+
+import networkx as nx
+
+__all__ = [
+    "margulis_gabber_galil_graph",
+    "chordal_cycle_graph",
+    "paley_graph",
+    "maybe_regular_expander",
+    "maybe_regular_expander_graph",
+    "is_regular_expander",
+    "random_regular_expander_graph",
+]
+
+
+# Other discrete torus expanders can be constructed by using the following edge
+# sets. For more information, see Chapter 4, "Expander Graphs", in
+# "Pseudorandomness", by Salil Vadhan.
+#
+# For a directed expander, add edges from (x, y) to:
+#
+#     (x, y),
+#     ((x + 1) % n, y),
+#     (x, (y + 1) % n),
+#     (x, (x + y) % n),
+#     (-y % n, x)
+#
+# For an undirected expander, add the reverse edges.
+#
+# Also appearing in the paper of Gabber and Galil:
+#
+#     (x, y),
+#     (x, (x + y) % n),
+#     (x, (x + y + 1) % n),
+#     ((x + y) % n, y),
+#     ((x + y + 1) % n, y)
+#
+# and:
+#
+#     (x, y),
+#     ((x + 2*y) % n, y),
+#     ((x + (2*y + 1)) % n, y),
+#     ((x + (2*y + 2)) % n, y),
+#     (x, (y + 2*x) % n),
+#     (x, (y + (2*x + 1)) % n),
+#     (x, (y + (2*x + 2)) % n),
+#
+@nx._dispatchable(graphs=None, returns_graph=True)
+def margulis_gabber_galil_graph(n, create_using=None):
+    r"""Returns the Margulis-Gabber-Galil undirected MultiGraph on `n^2` nodes.
+
+    The undirected MultiGraph is regular with degree `8`. Nodes are integer
+    pairs. The second-largest eigenvalue of the adjacency matrix of the graph
+    is at most `5 \sqrt{2}`, regardless of `n`.
+
+    Parameters
+    ----------
+    n : int
+        Determines the number of nodes in the graph: `n^2`.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed or not a multigraph.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x, y in itertools.product(range(n), repeat=2):
+        for u, v in (
+            ((x + 2 * y) % n, y),
+            ((x + (2 * y + 1)) % n, y),
+            (x, (y + 2 * x) % n),
+            (x, (y + (2 * x + 1)) % n),
+        ):
+            G.add_edge((x, y), (u, v))
+    G.graph["name"] = f"margulis_gabber_galil_graph({n})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chordal_cycle_graph(p, create_using=None):
+    """Returns the chordal cycle graph on `p` nodes.
+
+    The returned graph is a cycle graph on `p` nodes with chords joining each
+    vertex `x` to its inverse modulo `p`. This graph is a (mildly explicit)
+    3-regular expander [1]_.
+
+    `p` *must* be a prime number.
+
+    Parameters
+    ----------
+    p : a prime number
+
+        The number of vertices in the graph. This also indicates where the
+        chordal edges in the cycle will be created.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+
+        If `create_using` indicates directed or not a multigraph.
+
+    References
+    ----------
+
+    .. [1] Theorem 4.4.2 in A. Lubotzky. "Discrete groups, expanding graphs and
+           invariant measures", volume 125 of Progress in Mathematics.
+           Birkhäuser Verlag, Basel, 1994.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x in range(p):
+        left = (x - 1) % p
+        right = (x + 1) % p
+        # Here we apply Fermat's Little Theorem to compute the multiplicative
+        # inverse of x in Z/pZ. By Fermat's Little Theorem,
+        #
+        #     x^p = x (mod p)
+        #
+        # Therefore,
+        #
+        #     x * x^(p - 2) = 1 (mod p)
+        #
+        # The number 0 is a special case: we just let its inverse be itself.
+        chord = pow(x, p - 2, p) if x > 0 else 0
+        for y in (left, right, chord):
+            G.add_edge(x, y)
+    G.graph["name"] = f"chordal_cycle_graph({p})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def paley_graph(p, create_using=None):
+    r"""Returns the Paley $\frac{(p-1)}{2}$ -regular graph on $p$ nodes.
+
+    The returned graph is a graph on $\mathbb{Z}/p\mathbb{Z}$ with edges between $x$ and $y$
+    if and only if $x-y$ is a nonzero square in $\mathbb{Z}/p\mathbb{Z}$.
+
+    If $p \equiv 1  \pmod 4$, $-1$ is a square in
+    $\mathbb{Z}/p\mathbb{Z}$ and therefore $x-y$ is a square if and
+    only if $y-x$ is also a square, i.e the edges in the Paley graph are symmetric.
+
+    If $p \equiv 3 \pmod 4$, $-1$ is not a square in $\mathbb{Z}/p\mathbb{Z}$
+    and therefore either $x-y$ or $y-x$ is a square in $\mathbb{Z}/p\mathbb{Z}$ but not both.
+
+    Note that a more general definition of Paley graphs extends this construction
+    to graphs over $q=p^n$ vertices, by using the finite field $F_q$ instead of
+    $\mathbb{Z}/p\mathbb{Z}$.
+    This construction requires to compute squares in general finite fields and is
+    not what is implemented here (i.e `paley_graph(25)` does not return the true
+    Paley graph associated with $5^2$).
+
+    Parameters
+    ----------
+    p : int, an odd prime number.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed directed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is a multigraph.
+
+    References
+    ----------
+    Chapter 13 in B. Bollobas, Random Graphs. Second edition.
+    Cambridge Studies in Advanced Mathematics, 73.
+    Cambridge University Press, Cambridge (2001).
+    """
+    G = nx.empty_graph(0, create_using, default=nx.DiGraph)
+    if G.is_multigraph():
+        msg = "`create_using` cannot be a multigraph."
+        raise nx.NetworkXError(msg)
+
+    # Compute the squares in Z/pZ.
+    # Make it a set to uniquify (there are exactly (p-1)/2 squares in Z/pZ
+    # when is prime).
+    square_set = {(x**2) % p for x in range(1, p) if (x**2) % p != 0}
+
+    for x in range(p):
+        for x2 in square_set:
+            G.add_edge(x, (x + x2) % p)
+    G.graph["name"] = f"paley({p})"
+    return G
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def maybe_regular_expander_graph(n, d, *, create_using=None, max_tries=100, seed=None):
+    r"""Utility for creating a random regular expander.
+
+    Returns a random $d$-regular graph on $n$ nodes which is an expander
+    graph with very good probability.
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    create_using : Graph Instance or Constructor
+      Indicator of type of graph to return.
+      If a Graph-type instance, then clear and use it.
+      If a constructor, call it to create an empty graph.
+      Use the Graph constructor by default.
+    max_tries : int. (default: 100)
+      The number of allowed loops when generating each independent cycle
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    The graph is generated by taking $d / 2$ random independent cycles.
+
+    Joel Friedman proved that in this model the resulting
+    graph is an expander with probability
+    $1 - O(n^{-\tau})$ where $\tau = \lceil (\sqrt{d - 1}) / 2 \rceil - 1$. [1]_
+
+    Examples
+    --------
+    >>> G = nx.maybe_regular_expander_graph(n=200, d=6, seed=8020)
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected graph.
+
+    Raises
+    ------
+    NetworkXError
+        If $d % 2 != 0$ as the degree must be even.
+        If $n - 1$ is less than $ 2d $ as the graph is complete at most.
+        If max_tries is reached
+
+    See Also
+    --------
+    is_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Joel Friedman,
+       A Proof of Alon's Second Eigenvalue Conjecture and Related Problems, 2004
+       https://arxiv.org/abs/cs/0405020
+
+    """
+
+    import numpy as np
+
+    if n < 1:
+        raise nx.NetworkXError("n must be a positive integer")
+
+    if not (d >= 2):
+        raise nx.NetworkXError("d must be greater than or equal to 2")
+
+    if not (d % 2 == 0):
+        raise nx.NetworkXError("d must be even")
+
+    if not (n - 1 >= d):
+        raise nx.NetworkXError(
+            f"Need n-1>= d to have room for {d // 2} independent cycles with {n} nodes"
+        )
+
+    G = nx.empty_graph(n, create_using)
+
+    if n < 2:
+        return G
+
+    cycles = []
+    edges = set()
+
+    # Create d / 2 cycles
+    for i in range(d // 2):
+        iterations = max_tries
+        # Make sure the cycles are independent to have a regular graph
+        while len(edges) != (i + 1) * n:
+            iterations -= 1
+            # Faster than random.permutation(n) since there are only
+            # (n-1)! distinct cycles against n! permutations of size n
+            cycle = seed.permutation(n - 1).tolist()
+            cycle.append(n - 1)
+
+            new_edges = {
+                (u, v)
+                for u, v in nx.utils.pairwise(cycle, cyclic=True)
+                if (u, v) not in edges and (v, u) not in edges
+            }
+            # If the new cycle has no edges in common with previous cycles
+            # then add it to the list otherwise try again
+            if len(new_edges) == n:
+                cycles.append(cycle)
+                edges.update(new_edges)
+
+            if iterations == 0:
+                msg = "Too many iterations in maybe_regular_expander_graph"
+                raise nx.NetworkXError(msg)
+
+    G.add_edges_from(edges)
+
+    return G
+
+
+def maybe_regular_expander(n, d, *, create_using=None, max_tries=100, seed=None):
+    """
+    .. deprecated:: 3.6
+       `maybe_regular_expander` is a deprecated alias
+       for `maybe_regular_expander_graph`.
+       Use `maybe_regular_expander_graph` instead.
+    """
+    import warnings
+
+    warnings.warn(
+        "maybe_regular_expander is deprecated, "
+        "use `maybe_regular_expander_graph` instead.",
+        category=DeprecationWarning,
+        stacklevel=2,
+    )
+    return maybe_regular_expander_graph(
+        n, d, create_using=create_using, max_tries=max_tries, seed=seed
+    )
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}})
+def is_regular_expander(G, *, epsilon=0):
+    r"""Determines whether the graph G is a regular expander. [1]_
+
+    An expander graph is a sparse graph with strong connectivity properties.
+
+    More precisely, this helper checks whether the graph is a
+    regular $(n, d, \lambda)$-expander with $\lambda$ close to
+    the Alon-Boppana bound and given by
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$. [2]_
+
+    In the case where $\epsilon = 0$ then if the graph successfully passes the test
+    it is a Ramanujan graph. [3]_
+
+    A Ramanujan graph has spectral gap almost as large as possible, which makes them
+    excellent expanders.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    epsilon : int, float, default=0
+
+    Returns
+    -------
+    bool
+        Whether the given graph is a regular $(n, d, \lambda)$-expander
+        where $\lambda = 2 \sqrt{d - 1} + \epsilon$.
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    See Also
+    --------
+    maybe_regular_expander_graph
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+
+    import numpy as np
+    import scipy as sp
+
+    if epsilon < 0:
+        raise nx.NetworkXError("epsilon must be non negative")
+
+    if not nx.is_regular(G):
+        return False
+
+    _, d = nx.utils.arbitrary_element(G.degree)
+
+    A = nx.adjacency_matrix(G, dtype=float)
+    lams = sp.sparse.linalg.eigsh(A, which="LM", k=2, return_eigenvectors=False)
+
+    # lambda2 is the second biggest eigenvalue
+    lambda2 = min(lams)
+
+    # Use bool() to convert numpy scalar to Python Boolean
+    return bool(abs(lambda2) < 2 * np.sqrt(d - 1) + epsilon)
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_expander_graph(
+    n, d, *, epsilon=0, create_using=None, max_tries=100, seed=None
+):
+    r"""Returns a random regular expander graph on $n$ nodes with degree $d$.
+
+    An expander graph is a sparse graph with strong connectivity properties. [1]_
+
+    More precisely the returned graph is a $(n, d, \lambda)$-expander with
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$, close to the Alon-Boppana bound. [2]_
+
+    In the case where $\epsilon = 0$ it returns a Ramanujan graph.
+    A Ramanujan graph has spectral gap almost as large as possible,
+    which makes them excellent expanders. [3]_
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    epsilon : int, float, default=0
+    max_tries : int, (default: 100)
+      The number of allowed loops,
+      also used in the `maybe_regular_expander_graph` utility
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Raises
+    ------
+    NetworkXError
+        If max_tries is reached
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    Notes
+    -----
+    This loops over `maybe_regular_expander_graph` and can be slow when
+    $n$ is too big or $\epsilon$ too small.
+
+    See Also
+    --------
+    maybe_regular_expander_graph
+    is_regular_expander
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+    G = maybe_regular_expander_graph(
+        n, d, create_using=create_using, max_tries=max_tries, seed=seed
+    )
+    iterations = max_tries
+
+    while not is_regular_expander(G, epsilon=epsilon):
+        iterations -= 1
+        G = maybe_regular_expander_graph(
+            n=n, d=d, create_using=create_using, max_tries=max_tries, seed=seed
+        )
+
+        if iterations == 0:
+            raise nx.NetworkXError(
+                "Too many iterations in random_regular_expander_graph"
+            )
+
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/geometric.py b/lib/python3.11/site-packages/networkx/generators/geometric.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdd4f627b46a1f0fbd2f6cd1700aecb12549516a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/geometric.py
@@ -0,0 +1,1037 @@
+"""Generators for geometric graphs."""
+
+import math
+from bisect import bisect_left
+from itertools import accumulate, combinations, product
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "geometric_edges",
+    "geographical_threshold_graph",
+    "navigable_small_world_graph",
+    "random_geometric_graph",
+    "soft_random_geometric_graph",
+    "thresholded_random_geometric_graph",
+    "waxman_graph",
+    "geometric_soft_configuration_graph",
+]
+
+
+@nx._dispatchable(node_attrs="pos_name")
+def geometric_edges(G, radius, p=2, *, pos_name="pos"):
+    """Returns edge list of node pairs within `radius` of each other.
+
+    Parameters
+    ----------
+    G : networkx graph
+        The graph from which to generate the edge list. The nodes in `G` should
+        have an attribute ``pos`` corresponding to the node position, which is
+        used to compute the distance to other nodes.
+    radius : scalar
+        The distance threshold. Edges are included in the edge list if the
+        distance between the two nodes is less than `radius`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position of each
+        node in 2D coordinates. Every node in the Graph must have this attribute.
+    p : scalar, default=2
+        The `Minkowski distance metric
+        <https://en.wikipedia.org/wiki/Minkowski_distance>`_ used to compute
+        distances. The default value is 2, i.e. Euclidean distance.
+
+    Returns
+    -------
+    edges : list
+        List of edges whose distances are less than `radius`
+
+    Notes
+    -----
+    Radius uses Minkowski distance metric `p`.
+    If scipy is available, `scipy.spatial.cKDTree` is used to speed computation.
+
+    Examples
+    --------
+    Create a graph with nodes that have a "pos" attribute representing 2D
+    coordinates.
+
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(
+    ...     [
+    ...         (0, {"pos": (0, 0)}),
+    ...         (1, {"pos": (3, 0)}),
+    ...         (2, {"pos": (8, 0)}),
+    ...     ]
+    ... )
+    >>> nx.geometric_edges(G, radius=1)
+    []
+    >>> nx.geometric_edges(G, radius=4)
+    [(0, 1)]
+    >>> nx.geometric_edges(G, radius=6)
+    [(0, 1), (1, 2)]
+    >>> nx.geometric_edges(G, radius=9)
+    [(0, 1), (0, 2), (1, 2)]
+    """
+    # Input validation - every node must have a "pos" attribute
+    for n, pos in G.nodes(data=pos_name):
+        if pos is None:
+            raise nx.NetworkXError(
+                f"Node {n} (and all nodes) must have a '{pos_name}' attribute."
+            )
+
+    # NOTE: See _geometric_edges for the actual implementation. The reason this
+    # is split into two functions is to avoid the overhead of input validation
+    # every time the function is called internally in one of the other
+    # geometric generators
+    return _geometric_edges(G, radius, p, pos_name)
+
+
+def _geometric_edges(G, radius, p, pos_name):
+    """
+    Implements `geometric_edges` without input validation. See `geometric_edges`
+    for complete docstring.
+    """
+    nodes_pos = G.nodes(data=pos_name)
+    try:
+        import scipy as sp
+    except ImportError:
+        # no scipy KDTree so compute by for-loop
+        radius_p = radius**p
+        edges = [
+            (u, v)
+            for (u, pu), (v, pv) in combinations(nodes_pos, 2)
+            if sum(abs(a - b) ** p for a, b in zip(pu, pv)) <= radius_p
+        ]
+        return edges
+    # scipy KDTree is available
+    nodes, coords = list(zip(*nodes_pos))
+    kdtree = sp.spatial.cKDTree(coords)  # Cannot provide generator.
+    edge_indexes = kdtree.query_pairs(radius, p)
+    edges = [(nodes[u], nodes[v]) for u, v in sorted(edge_indexes)]
+    return edges
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_geometric_graph(
+    n, radius, dim=2, pos=None, p=2, seed=None, *, pos_name="pos"
+):
+    """Returns a random geometric graph in the unit cube of dimensions `dim`.
+
+    The random geometric graph model places `n` nodes uniformly at
+    random in the unit cube. Two nodes are joined by an edge if the
+    distance between the nodes is at most `radius`.
+
+    Edges are determined using a KDTree when SciPy is available.
+    This reduces the time complexity from $O(n^2)$ to $O(n)$.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    p : float, optional
+        Which Minkowski distance metric to use.  `p` has to meet the condition
+        ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random geometric graph, undirected and without self-loops.
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function.
+
+    Examples
+    --------
+    Create a random geometric graph on twenty nodes where nodes are joined by
+    an edge if their distance is at most 0.1::
+
+    >>> G = nx.random_geometric_graph(20, 0.1)
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    For example, to use a 2D Gaussian distribution of node positions with mean
+    (0, 0) and standard deviation 2::
+
+    >>> import random
+    >>> n = 20
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> G = nx.random_geometric_graph(n, 0.2, pos=pos)
+
+    References
+    ----------
+    .. [1] Penrose, Mathew, *Random Geometric Graphs*,
+           Oxford Studies in Probability, 5, 2003.
+
+    """
+    # TODO Is this function just a special case of the geographical
+    # threshold graph?
+    #
+    #     half_radius = {v: radius / 2 for v in n}
+    #     return geographical_threshold_graph(nodes, theta=1, alpha=1,
+    #                                         weight=half_radius)
+    #
+    G = nx.empty_graph(n)
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+
+    G.add_edges_from(_geometric_edges(G, radius, p, pos_name))
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def soft_random_geometric_graph(
+    n, radius, dim=2, pos=None, p=2, p_dist=None, seed=None, *, pos_name="pos"
+):
+    r"""Returns a soft random geometric graph in the unit cube.
+
+    The soft random geometric graph [1] model places `n` nodes uniformly at
+    random in the unit cube in dimension `dim`. Two nodes of distance, `dist`,
+    computed by the `p`-Minkowski distance metric are joined by an edge with
+    probability `p_dist` if the computed distance metric value of the nodes
+    is at most `radius`, otherwise they are not joined.
+
+    Edges within `radius` of each other are determined using a KDTree when
+    SciPy is available. This reduces the time complexity from :math:`O(n^2)`
+    to :math:`O(n)`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    p : float, optional
+        Which Minkowski distance metric to use.
+        `p` has to meet the condition ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    p_dist : function, optional
+        A probability density function computing the probability of
+        connecting two nodes that are of distance, dist, computed by the
+        Minkowski distance metric. The probability density function, `p_dist`,
+        must be any function that takes the metric value as input
+        and outputs a single probability value between 0-1. The `scipy.stats`
+        package has many probability distribution functions implemented and
+        tools for custom probability distribution definitions [2], and passing
+        the .pdf method of `scipy.stats` distributions can be used here.  If the
+        probability function, `p_dist`, is not supplied, the default function
+        is an exponential distribution with rate parameter :math:`\lambda=1`.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A soft random geometric graph, undirected and without self-loops.
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function.
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    References
+    ----------
+    .. [1] Penrose, Mathew D. "Connectivity of soft random geometric graphs."
+           The Annals of Applied Probability 26.2 (2016): 986-1028.
+
+    Examples
+    --------
+    Default Graph:
+
+    >>> G = nx.soft_random_geometric_graph(50, 0.2)
+
+    Custom Graph:
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    The `scipy.stats` package can be used to define the probability distribution
+    with the ``.pdf`` method used as `p_dist`.
+
+    For example, create a soft random geometric graph on 100 nodes using a 2D
+    Gaussian distribution of node positions with mean (0, 0) and standard deviation 2,
+    where nodes are joined by an edge with probability computed from an
+    exponential distribution with rate parameter :math:`\lambda=1` if their
+    Euclidean distance is at most 0.2.
+
+    >>> import random
+    >>> from scipy.stats import expon
+    >>> n = 100
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> p_dist = lambda x: expon.pdf(x, scale=1)
+    >>> G = nx.soft_random_geometric_graph(n, 0.2, pos=pos, p_dist=p_dist)
+
+    """
+    G = nx.empty_graph(n)
+    G.name = f"soft_random_geometric_graph({n}, {radius}, {dim})"
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+
+    # if p_dist function not supplied the default function is an exponential
+    # distribution with rate parameter :math:`\lambda=1`.
+    if p_dist is None:
+
+        def p_dist(dist):
+            return math.exp(-dist)
+
+    def should_join(edge):
+        u, v = edge
+        dist = (sum(abs(a - b) ** p for a, b in zip(pos[u], pos[v]))) ** (1 / p)
+        return seed.random() < p_dist(dist)
+
+    G.add_edges_from(filter(should_join, _geometric_edges(G, radius, p, pos_name)))
+    return G
+
+
+@py_random_state(7)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def geographical_threshold_graph(
+    n,
+    theta,
+    dim=2,
+    pos=None,
+    weight=None,
+    metric=None,
+    p_dist=None,
+    seed=None,
+    *,
+    pos_name="pos",
+    weight_name="weight",
+):
+    r"""Returns a geographical threshold graph.
+
+    The geographical threshold graph model places $n$ nodes uniformly at
+    random in a rectangular domain.  Each node $u$ is assigned a weight
+    $w_u$. Two nodes $u$ and $v$ are joined by an edge if
+
+    .. math::
+
+       (w_u + w_v)p_{dist}(r) \ge \theta
+
+    where `r` is the distance between `u` and `v`, `p_dist` is any function of
+    `r`, and :math:`\theta` as the threshold parameter. `p_dist` is used to
+    give weight to the distance between nodes when deciding whether or not
+    they should be connected. The larger `p_dist` is, the more prone nodes
+    separated by `r` are to be connected, and vice versa.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    theta: float
+        Threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict
+        Node positions as a dictionary of tuples keyed by node.
+    weight : dict
+        Node weights as a dictionary of numbers keyed by node.
+    metric : function
+        A metric on vectors of numbers (represented as lists or
+        tuples). This must be a function that accepts two lists (or
+        tuples) as input and yields a number as output. The function
+        must also satisfy the four requirements of a `metric`_.
+        Specifically, if $d$ is the function and $x$, $y$,
+        and $z$ are vectors in the graph, then $d$ must satisfy
+
+        1. $d(x, y) \ge 0$,
+        2. $d(x, y) = 0$ if and only if $x = y$,
+        3. $d(x, y) = d(y, x)$,
+        4. $d(x, z) \le d(x, y) + d(y, z)$.
+
+        If this argument is not specified, the Euclidean distance metric is
+        used.
+
+        .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
+    p_dist : function, optional
+        Any function used to give weight to the distance between nodes when
+        deciding whether or not they should be connected. `p_dist` was
+        originally conceived as a probability density function giving the
+        probability of connecting two nodes that are of metric distance `r`
+        apart. The implementation here allows for more arbitrary definitions
+        of `p_dist` that do not need to correspond to valid probability
+        density functions. The :mod:`scipy.stats` package has many
+        probability density functions implemented and tools for custom
+        probability density definitions, and passing the ``.pdf`` method of
+        `scipy.stats` distributions can be used here. If ``p_dist=None``
+        (the default), the exponential function :math:`r^{-2}` is used.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+    weight_name : string, default="weight"
+        The name of the node attribute which represents the weight
+        of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random geographic threshold graph, undirected and without
+        self-loops.
+
+        Each node has a node attribute ``pos`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function. Similarly, each node has a node
+        attribute ``weight`` that stores the weight of that node as
+        provided or as generated.
+
+    Examples
+    --------
+    Specify an alternate distance metric using the ``metric`` keyword
+    argument. For example, to use the `taxicab metric`_ instead of the
+    default `Euclidean metric`_::
+
+        >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y))
+        >>> G = nx.geographical_threshold_graph(10, 0.1, metric=dist)
+
+    .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry
+    .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance
+
+    Notes
+    -----
+    If weights are not specified they are assigned to nodes by drawing randomly
+    from the exponential distribution with rate parameter $\lambda=1$.
+    To specify weights from a different distribution, use the `weight` keyword
+    argument::
+
+    >>> import random
+    >>> n = 20
+    >>> w = {i: random.expovariate(5.0) for i in range(n)}
+    >>> G = nx.geographical_threshold_graph(20, 50, weight=w)
+
+    If node positions are not specified they are randomly assigned from the
+    uniform distribution.
+
+    References
+    ----------
+    .. [1] Masuda, N., Miwa, H., Konno, N.:
+       Geographical threshold graphs with small-world and scale-free
+       properties.
+       Physical Review E 71, 036108 (2005)
+    .. [2]  Milan Bradonjić, Aric Hagberg and Allon G. Percus,
+       Giant component and connectivity in geographical threshold graphs,
+       in Algorithms and Models for the Web-Graph (WAW 2007),
+       Antony Bonato and Fan Chung (Eds), pp. 209--216, 2007
+    """
+    G = nx.empty_graph(n)
+    # If no weights are provided, choose them from an exponential
+    # distribution.
+    if weight is None:
+        weight = {v: seed.expovariate(1) for v in G}
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    # If no distance metric is provided, use Euclidean distance.
+    if metric is None:
+        metric = math.dist
+    nx.set_node_attributes(G, weight, weight_name)
+    nx.set_node_attributes(G, pos, pos_name)
+
+    # if p_dist is not supplied, use default r^-2
+    if p_dist is None:
+
+        def p_dist(r):
+            return r**-2
+
+    # Returns ``True`` if and only if the nodes whose attributes are
+    # ``du`` and ``dv`` should be joined, according to the threshold
+    # condition.
+    def should_join(pair):
+        u, v = pair
+        u_pos, v_pos = pos[u], pos[v]
+        u_weight, v_weight = weight[u], weight[v]
+        return (u_weight + v_weight) * p_dist(metric(u_pos, v_pos)) >= theta
+
+    G.add_edges_from(filter(should_join, combinations(G, 2)))
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def waxman_graph(
+    n,
+    beta=0.4,
+    alpha=0.1,
+    L=None,
+    domain=(0, 0, 1, 1),
+    metric=None,
+    seed=None,
+    *,
+    pos_name="pos",
+):
+    r"""Returns a Waxman random graph.
+
+    The Waxman random graph model places `n` nodes uniformly at random
+    in a rectangular domain. Each pair of nodes at distance `d` is
+    joined by an edge with probability
+
+    .. math::
+            p = \beta \exp(-d / \alpha L).
+
+    This function implements both Waxman models, using the `L` keyword
+    argument.
+
+    * Waxman-1: if `L` is not specified, it is set to be the maximum distance
+      between any pair of nodes.
+    * Waxman-2: if `L` is specified, the distance between a pair of nodes is
+      chosen uniformly at random from the interval `[0, L]`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    beta: float
+        Model parameter
+    alpha: float
+        Model parameter
+    L : float, optional
+        Maximum distance between nodes.  If not specified, the actual distance
+        is calculated.
+    domain : four-tuple of numbers, optional
+        Domain size, given as a tuple of the form `(x_min, y_min, x_max,
+        y_max)`.
+    metric : function
+        A metric on vectors of numbers (represented as lists or
+        tuples). This must be a function that accepts two lists (or
+        tuples) as input and yields a number as output. The function
+        must also satisfy the four requirements of a `metric`_.
+        Specifically, if $d$ is the function and $x$, $y$,
+        and $z$ are vectors in the graph, then $d$ must satisfy
+
+        1. $d(x, y) \ge 0$,
+        2. $d(x, y) = 0$ if and only if $x = y$,
+        3. $d(x, y) = d(y, x)$,
+        4. $d(x, z) \le d(x, y) + d(y, z)$.
+
+        If this argument is not specified, the Euclidean distance metric is
+        used.
+
+        .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random Waxman graph, undirected and without self-loops. Each
+        node has a node attribute ``'pos'`` that stores the position of
+        that node in Euclidean space as generated by this function.
+
+    Examples
+    --------
+    Specify an alternate distance metric using the ``metric`` keyword
+    argument. For example, to use the "`taxicab metric`_" instead of the
+    default `Euclidean metric`_::
+
+        >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y))
+        >>> G = nx.waxman_graph(10, 0.5, 0.1, metric=dist)
+
+    .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry
+    .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance
+
+    Notes
+    -----
+    Starting in NetworkX 2.0 the parameters alpha and beta align with their
+    usual roles in the probability distribution. In earlier versions their
+    positions in the expression were reversed. Their position in the calling
+    sequence reversed as well to minimize backward incompatibility.
+
+    References
+    ----------
+    .. [1]  B. M. Waxman, *Routing of multipoint connections*.
+       IEEE J. Select. Areas Commun. 6(9),(1988) 1617--1622.
+    """
+    G = nx.empty_graph(n)
+    (xmin, ymin, xmax, ymax) = domain
+    # Each node gets a uniformly random position in the given rectangle.
+    pos = {v: (seed.uniform(xmin, xmax), seed.uniform(ymin, ymax)) for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+    # If no distance metric is provided, use Euclidean distance.
+    if metric is None:
+        metric = math.dist
+    # If the maximum distance L is not specified (that is, we are in the
+    # Waxman-1 model), then find the maximum distance between any pair
+    # of nodes.
+    #
+    # In the Waxman-1 model, join nodes randomly based on distance. In
+    # the Waxman-2 model, join randomly based on random l.
+    if L is None:
+        L = max(metric(x, y) for x, y in combinations(pos.values(), 2))
+
+        def dist(u, v):
+            return metric(pos[u], pos[v])
+
+    else:
+
+        def dist(u, v):
+            return seed.random() * L
+
+    # `pair` is the pair of nodes to decide whether to join.
+    def should_join(pair):
+        return seed.random() < beta * math.exp(-dist(*pair) / (alpha * L))
+
+    G.add_edges_from(filter(should_join, combinations(G, 2)))
+    return G
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def navigable_small_world_graph(n, p=1, q=1, r=2, dim=2, seed=None):
+    r"""Returns a navigable small-world graph.
+
+    A navigable small-world graph is a directed grid with additional long-range
+    connections that are chosen randomly.
+
+      [...] we begin with a set of nodes [...] that are identified with the set
+      of lattice points in an $n \times n$ square,
+      $\{(i, j): i \in \{1, 2, \ldots, n\}, j \in \{1, 2, \ldots, n\}\}$,
+      and we define the *lattice distance* between two nodes $(i, j)$ and
+      $(k, l)$ to be the number of "lattice steps" separating them:
+      $d((i, j), (k, l)) = |k - i| + |l - j|$.
+
+      For a universal constant $p >= 1$, the node $u$ has a directed edge to
+      every other node within lattice distance $p$---these are its *local
+      contacts*. For universal constants $q >= 0$ and $r >= 0$ we also
+      construct directed edges from $u$ to $q$ other nodes (the *long-range
+      contacts*) using independent random trials; the $i$th directed edge from
+      $u$ has endpoint $v$ with probability proportional to $[d(u,v)]^{-r}$.
+
+      -- [1]_
+
+    Parameters
+    ----------
+    n : int
+        The length of one side of the lattice; the number of nodes in
+        the graph is therefore $n^2$.
+    p : int
+        The diameter of short range connections. Each node is joined with every
+        other node within this lattice distance.
+    q : int
+        The number of long-range connections for each node.
+    r : float
+        Exponent for decaying probability of connections.  The probability of
+        connecting to a node at lattice distance $d$ is $1/d^r$.
+    dim : int
+        Dimension of grid
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] J. Kleinberg. The small-world phenomenon: An algorithmic
+       perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000.
+    """
+    if p < 1:
+        raise nx.NetworkXException("p must be >= 1")
+    if q < 0:
+        raise nx.NetworkXException("q must be >= 0")
+    if r < 0:
+        raise nx.NetworkXException("r must be >= 0")
+
+    G = nx.DiGraph()
+    nodes = list(product(range(n), repeat=dim))
+    for p1 in nodes:
+        probs = [0]
+        for p2 in nodes:
+            if p1 == p2:
+                continue
+            d = sum((abs(b - a) for a, b in zip(p1, p2)))
+            if d <= p:
+                G.add_edge(p1, p2)
+            probs.append(d**-r)
+        cdf = list(accumulate(probs))
+        for _ in range(q):
+            target = nodes[bisect_left(cdf, seed.uniform(0, cdf[-1]))]
+            G.add_edge(p1, target)
+    return G
+
+
+@py_random_state(7)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def thresholded_random_geometric_graph(
+    n,
+    radius,
+    theta,
+    dim=2,
+    pos=None,
+    weight=None,
+    p=2,
+    seed=None,
+    *,
+    pos_name="pos",
+    weight_name="weight",
+):
+    r"""Returns a thresholded random geometric graph in the unit cube.
+
+    The thresholded random geometric graph [1] model places `n` nodes
+    uniformly at random in the unit cube of dimensions `dim`. Each node
+    `u` is assigned a weight :math:`w_u`. Two nodes `u` and `v` are
+    joined by an edge if they are within the maximum connection distance,
+    `radius` computed by the `p`-Minkowski distance and the summation of
+    weights :math:`w_u` + :math:`w_v` is greater than or equal
+    to the threshold parameter `theta`.
+
+    Edges within `radius` of each other are determined using a KDTree when
+    SciPy is available. This reduces the time complexity from :math:`O(n^2)`
+    to :math:`O(n)`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    theta: float
+        Threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    weight : dict, optional
+        Node weights as a dictionary of numbers keyed by node.
+    p : float, optional (default 2)
+        Which Minkowski distance metric to use.  `p` has to meet the condition
+        ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+    weight_name : string, default="weight"
+        The name of the node attribute which represents the weight
+        of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A thresholded random geographic graph, undirected and without
+        self-loops.
+
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function. Similarly, each node has a nodethre
+        attribute ``'weight'`` that stores the weight of that node as
+        provided or as generated.
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    References
+    ----------
+    .. [1] http://cole-maclean.github.io/blog/files/thesis.pdf
+
+    Examples
+    --------
+    Default Graph:
+
+    >>> G = nx.thresholded_random_geometric_graph(50, 0.2, 0.1)
+
+    Custom Graph:
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    If weights are not specified they are assigned to nodes by drawing randomly
+    from the exponential distribution with rate parameter :math:`\lambda=1`.
+    To specify weights from a different distribution, use the `weight` keyword
+    argument.
+
+    For example, create a thresholded random geometric graph on 50 nodes using a 2D
+    Gaussian distribution of node positions with mean (0, 0) and standard deviation 2,
+    where nodes are joined by an edge if their sum weights drawn from
+    a exponential distribution with rate = 5 are >= theta = 0.1 and their
+    Euclidean distance is at most 0.2.
+
+    >>> import random
+    >>> n = 50
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> w = {i: random.expovariate(5.0) for i in range(n)}
+    >>> G = nx.thresholded_random_geometric_graph(n, 0.2, 0.1, 2, pos, w)
+
+    """
+    G = nx.empty_graph(n)
+    G.name = f"thresholded_random_geometric_graph({n}, {radius}, {theta}, {dim})"
+    # If no weights are provided, choose them from an exponential
+    # distribution.
+    if weight is None:
+        weight = {v: seed.expovariate(1) for v in G}
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    # If no distance metric is provided, use Euclidean distance.
+    nx.set_node_attributes(G, weight, weight_name)
+    nx.set_node_attributes(G, pos, pos_name)
+
+    edges = (
+        (u, v)
+        for u, v in _geometric_edges(G, radius, p, pos_name)
+        if weight[u] + weight[v] >= theta
+    )
+    G.add_edges_from(edges)
+    return G
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def geometric_soft_configuration_graph(
+    *, beta, n=None, gamma=None, mean_degree=None, kappas=None, seed=None
+):
+    r"""Returns a random graph from the geometric soft configuration model.
+
+    The $\mathbb{S}^1$ model [1]_ is the geometric soft configuration model
+    which is able to explain many fundamental features of real networks such as
+    small-world property, heteregenous degree distributions, high level of
+    clustering, and self-similarity.
+
+    In the geometric soft configuration model, a node $i$ is assigned two hidden
+    variables: a hidden degree $\kappa_i$, quantifying its popularity, influence,
+    or importance, and an angular position $\theta_i$ in a circle abstracting the
+    similarity space, where angular distances between nodes are a proxy for their
+    similarity. Focusing on the angular position, this model is often called
+    the $\mathbb{S}^1$ model (a one-dimensional sphere). The circle's radius is
+    adjusted to $R = N/2\pi$, where $N$ is the number of nodes, so that the density
+    is set to 1 without loss of generality.
+
+    The connection probability between any pair of nodes increases with
+    the product of their hidden degrees (i.e., their combined popularities),
+    and decreases with the angular distance between the two nodes.
+    Specifically, nodes $i$ and $j$ are connected with the probability
+
+    $p_{ij} = \frac{1}{1 + \frac{d_{ij}^\beta}{\left(\mu \kappa_i \kappa_j\right)^{\max(1, \beta)}}}$
+
+    where $d_{ij} = R\Delta\theta_{ij}$ is the arc length of the circle between
+    nodes $i$ and $j$ separated by an angular distance $\Delta\theta_{ij}$.
+    Parameters $\mu$ and $\beta$ (also called inverse temperature) control the
+    average degree and the clustering coefficient, respectively.
+
+    It can be shown [2]_ that the model undergoes a structural phase transition
+    at $\beta=1$ so that for $\beta<1$ networks are unclustered in the thermodynamic
+    limit (when $N\to \infty$) whereas for $\beta>1$ the ensemble generates
+    networks with finite clustering coefficient.
+
+    The $\mathbb{S}^1$ model can be expressed as a purely geometric model
+    $\mathbb{H}^2$ in the hyperbolic plane [3]_ by mapping the hidden degree of
+    each node into a radial coordinate as
+
+    $r_i = \hat{R} - \frac{2 \max(1, \beta)}{\beta \zeta} \ln \left(\frac{\kappa_i}{\kappa_0}\right)$
+
+    where $\hat{R}$ is the radius of the hyperbolic disk and $\zeta$ is the curvature,
+
+    $\hat{R} = \frac{2}{\zeta} \ln \left(\frac{N}{\pi}\right)
+    - \frac{2\max(1, \beta)}{\beta \zeta} \ln (\mu \kappa_0^2)$
+
+    The connection probability then reads
+
+    $p_{ij} = \frac{1}{1 + \exp\left({\frac{\beta\zeta}{2} (x_{ij} - \hat{R})}\right)}$
+
+    where
+
+    $x_{ij} = r_i + r_j + \frac{2}{\zeta} \ln \frac{\Delta\theta_{ij}}{2}$
+
+    is a good approximation of the hyperbolic distance between two nodes separated
+    by an angular distance $\Delta\theta_{ij}$ with radial coordinates $r_i$ and $r_j$.
+    For $\beta > 1$, the curvature $\zeta = 1$, for $\beta < 1$, $\zeta = \beta^{-1}$.
+
+
+    Parameters
+    ----------
+    Either `n`, `gamma`, `mean_degree` are provided or `kappas`. The values of
+    `n`, `gamma`, `mean_degree` (if provided) are used to construct a random
+    kappa-dict keyed by node with values sampled from a power-law distribution.
+
+    beta : positive number
+        Inverse temperature, controlling the clustering coefficient.
+    n : int (default: None)
+        Size of the network (number of nodes).
+        If not provided, `kappas` must be provided and holds the nodes.
+    gamma : float (default: None)
+        Exponent of the power-law distribution for hidden degrees `kappas`.
+        If not provided, `kappas` must be provided directly.
+    mean_degree : float (default: None)
+        The mean degree in the network.
+        If not provided, `kappas` must be provided directly.
+    kappas : dict (default: None)
+        A dict keyed by node to its hidden degree value.
+        If not provided, random values are computed based on a power-law
+        distribution using `n`, `gamma` and `mean_degree`.
+    seed : int, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    Graph
+        A random geometric soft configuration graph (undirected with no self-loops).
+        Each node has three node-attributes:
+
+        - ``kappa`` that represents the hidden degree.
+
+        - ``theta`` the position in the similarity space ($\mathbb{S}^1$) which is
+          also the angular position in the hyperbolic plane.
+
+        - ``radius`` the radial position in the hyperbolic plane
+          (based on the hidden degree).
+
+
+    Examples
+    --------
+    Generate a network with specified parameters:
+
+    >>> G = nx.geometric_soft_configuration_graph(
+    ...     beta=1.5, n=100, gamma=2.7, mean_degree=5
+    ... )
+
+    Create a geometric soft configuration graph with 100 nodes. The $\beta$ parameter
+    is set to 1.5 and the exponent of the powerlaw distribution of the hidden
+    degrees is 2.7 with mean value of 5.
+
+    Generate a network with predefined hidden degrees:
+
+    >>> kappas = {i: 10 for i in range(100)}
+    >>> G = nx.geometric_soft_configuration_graph(beta=2.5, kappas=kappas)
+
+    Create a geometric soft configuration graph with 100 nodes. The $\beta$ parameter
+    is set to 2.5 and all nodes with hidden degree $\kappa=10$.
+
+
+    References
+    ----------
+    .. [1] Serrano, M. Á., Krioukov, D., & Boguñá, M. (2008). Self-similarity
+       of complex networks and hidden metric spaces. Physical review letters, 100(7), 078701.
+
+    .. [2] van der Kolk, J., Serrano, M. Á., & Boguñá, M. (2022). An anomalous
+       topological phase transition in spatial random graphs. Communications Physics, 5(1), 245.
+
+    .. [3] Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., & Boguná, M. (2010).
+       Hyperbolic geometry of complex networks. Physical Review E, 82(3), 036106.
+
+    """
+    if beta <= 0:
+        raise nx.NetworkXError("The parameter beta cannot be smaller or equal to 0.")
+
+    if kappas is not None:
+        if not all((n is None, gamma is None, mean_degree is None)):
+            raise nx.NetworkXError(
+                "When kappas is input, n, gamma and mean_degree must not be."
+            )
+
+        n = len(kappas)
+        mean_degree = sum(kappas) / len(kappas)
+    else:
+        if any((n is None, gamma is None, mean_degree is None)):
+            raise nx.NetworkXError(
+                "Please provide either kappas, or all 3 of: n, gamma and mean_degree."
+            )
+
+        # Generate `n` hidden degrees from a powerlaw distribution
+        # with given exponent `gamma` and mean value `mean_degree`
+        gam_ratio = (gamma - 2) / (gamma - 1)
+        kappa_0 = mean_degree * gam_ratio * (1 - 1 / n) / (1 - 1 / n**gam_ratio)
+        base = 1 - 1 / n
+        power = 1 / (1 - gamma)
+        kappas = {i: kappa_0 * (1 - seed.random() * base) ** power for i in range(n)}
+
+    G = nx.Graph()
+    R = n / (2 * math.pi)
+
+    # Approximate values for mu in the thermodynamic limit (when n -> infinity)
+    if beta > 1:
+        mu = beta * math.sin(math.pi / beta) / (2 * math.pi * mean_degree)
+    elif beta == 1:
+        mu = 1 / (2 * mean_degree * math.log(n))
+    else:
+        mu = (1 - beta) / (2**beta * mean_degree * n ** (1 - beta))
+
+    # Generate random positions on a circle
+    thetas = {k: seed.uniform(0, 2 * math.pi) for k in kappas}
+
+    for u in kappas:
+        for v in list(G):
+            angle = math.pi - math.fabs(math.pi - math.fabs(thetas[u] - thetas[v]))
+            dij = math.pow(R * angle, beta)
+            mu_kappas = math.pow(mu * kappas[u] * kappas[v], max(1, beta))
+            p_ij = 1 / (1 + dij / mu_kappas)
+
+            # Create an edge with a certain connection probability
+            if seed.random() < p_ij:
+                G.add_edge(u, v)
+        G.add_node(u)
+
+    nx.set_node_attributes(G, thetas, "theta")
+    nx.set_node_attributes(G, kappas, "kappa")
+
+    # Map hidden degrees into the radial coordinates
+    zeta = 1 if beta > 1 else 1 / beta
+    kappa_min = min(kappas.values())
+    R_c = 2 * max(1, beta) / (beta * zeta)
+    R_hat = (2 / zeta) * math.log(n / math.pi) - R_c * math.log(mu * kappa_min)
+    radii = {node: R_hat - R_c * math.log(kappa) for node, kappa in kappas.items()}
+    nx.set_node_attributes(G, radii, "radius")
+
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/harary_graph.py b/lib/python3.11/site-packages/networkx/generators/harary_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5dde2e8998f25dbaf68ab8bd7a64f846cd29275
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/harary_graph.py
@@ -0,0 +1,163 @@
+"""Generators for Harary graphs
+
+This module gives two generators for the Harary graph, which was
+introduced by the famous mathematician Frank Harary in his 1962 work [H]_.
+The first generator gives the Harary graph that maximizes the node
+connectivity with given number of nodes and given number of edges.
+The second generator gives the Harary graph that minimizes
+the number of edges in the graph with given node connectivity and
+number of nodes.
+
+References
+----------
+.. [H] Harary, F. "The Maximum Connectivity of a Graph."
+       Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = ["hnm_harary_graph", "hkn_harary_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hnm_harary_graph(n, m, create_using=None):
+    r"""Return the Harary graph with given numbers of nodes and edges.
+
+    The Harary graph $H_{n, m}$ is the graph that maximizes node connectivity
+    with $n$ nodes and $m$ edges.
+
+    This maximum node connectivity is known to be $\lfloor 2m/n \rfloor$. [1]_
+
+    Parameters
+    ----------
+    n: integer
+        The number of nodes the generated graph is to contain.
+
+    m: integer
+        The number of edges the generated graph is to contain.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The Harary graph $H_{n, m}$.
+
+    See Also
+    --------
+    hkn_harary_graph
+
+    Notes
+    -----
+    This algorithm runs in $O(m)$ time.
+    The implementation follows [2]_.
+
+    References
+    ----------
+    .. [1] F. T. Boesch, A. Satyanarayana, and C. L. Suffel,
+       "A Survey of Some Network Reliability Analysis and Synthesis Results,"
+       Networks, pp. 99-107, 2009.
+
+    .. [2] Harary, F. "The Maximum Connectivity of a Graph."
+       Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+    """
+
+    if n < 1:
+        raise NetworkXError("The number of nodes must be >= 1!")
+    if m < n - 1:
+        raise NetworkXError("The number of edges must be >= n - 1 !")
+    if m > n * (n - 1) // 2:
+        raise NetworkXError("The number of edges must be <= n(n-1)/2")
+
+    # Get the floor of average node degree.
+    d = 2 * m // n
+
+    offset = d // 2
+    H = nx.circulant_graph(n, range(1, offset + 1), create_using=create_using)
+
+    half = n // 2
+    if (n % 2 == 0) or (d % 2 == 0):
+        # If d is odd; n must be even.
+        if d % 2 == 1:
+            # Add edges diagonally.
+            H.add_edges_from((i, i + half) for i in range(half))
+
+        r = 2 * m % n
+        # Add remaining edges at offset + 1.
+        H.add_edges_from((i, i + offset + 1) for i in range(r // 2))
+    else:
+        # Add the remaining m - n * offset edges between i and i + half.
+        H.add_edges_from((i, (i + half) % n) for i in range(m - n * offset))
+
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hkn_harary_graph(k, n, create_using=None):
+    r"""Return the Harary graph with given node connectivity and node number.
+
+    The Harary graph $H_{k, n}$ is the graph that minimizes the number of
+    edges needed with given node connectivity $k$ and node number $n$.
+
+    This smallest number of edges is known to be $\lceil kn/2 \rceil$ [1]_.
+
+    Parameters
+    ----------
+    k: integer
+        The node connectivity of the generated graph.
+
+    n: integer
+        The number of nodes the generated graph is to contain.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The Harary graph $H_{k, n}$.
+
+    See Also
+    --------
+    hnm_harary_graph
+
+    Notes
+    -----
+    This algorithm runs in $O(kn)$ time.
+    The implementation follows [2]_.
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Harary Graph." From MathWorld--A Wolfram Web
+     Resource. http://mathworld.wolfram.com/HararyGraph.html.
+
+    .. [2] Harary, F. "The Maximum Connectivity of a Graph."
+      Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+    """
+
+    if k < 1:
+        raise NetworkXError("The node connectivity must be >= 1!")
+    if n < k + 1:
+        raise NetworkXError("The number of nodes must be >= k+1 !")
+
+    # In case of connectivity 1, simply return the path graph.
+    if k == 1:
+        return nx.path_graph(n, create_using)
+
+    offset = k // 2
+    H = nx.circulant_graph(n, range(1, offset + 1), create_using=create_using)
+
+    half = n // 2
+    if (k % 2 == 0) or (n % 2 == 0):
+        # If k is odd; n must be even.
+        if k % 2 == 1:
+            # Add edges diagonally.
+            H.add_edges_from((i, i + half) for i in range(half))
+    else:
+        # Add half + 1 edges between i and i + half.
+        H.add_edges_from((i, (i + half) % n) for i in range(half + 1))
+
+    return H
diff --git a/lib/python3.11/site-packages/networkx/generators/internet_as_graphs.py b/lib/python3.11/site-packages/networkx/generators/internet_as_graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..31fbb6dd03a796aa8abc0dc5afd7c991baf5a993
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/internet_as_graphs.py
@@ -0,0 +1,443 @@
+"""Generates graphs resembling the Internet Autonomous System network"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_internet_as_graph"]
+
+
+def uniform_int_from_avg(a, m, seed):
+    """Pick a random integer with uniform probability.
+
+    Returns a random integer uniformly taken from a distribution with
+    minimum value 'a' and average value 'm', X~U(a,b), E[X]=m, X in N where
+    b = 2*m - a.
+
+    Notes
+    -----
+    p = (b-floor(b))/2
+    X = X1 + X2; X1~U(a,floor(b)), X2~B(p)
+    E[X] = E[X1] + E[X2] = (floor(b)+a)/2 + (b-floor(b))/2 = (b+a)/2 = m
+    """
+
+    from math import floor
+
+    assert m >= a
+    b = 2 * m - a
+    p = (b - floor(b)) / 2
+    X1 = round(seed.random() * (floor(b) - a) + a)
+    if seed.random() < p:
+        X2 = 1
+    else:
+        X2 = 0
+    return X1 + X2
+
+
+@py_random_state("seed")
+def choose_pref_attach(degs, seed):
+    """Pick a random value, with a probability given by its weight.
+
+    Returns a random choice among degs keys, each of which has a
+    probability proportional to the corresponding dictionary value.
+
+    Parameters
+    ----------
+    degs: dictionary
+        It contains the possible values (keys) and the corresponding
+        probabilities (values)
+    seed: random state
+
+    Returns
+    -------
+    v: object
+        A key of degs or None if degs is empty
+    """
+
+    if len(degs) == 0:
+        return None
+    s = sum(degs.values())
+    if s == 0:
+        return seed.choice(list(degs.keys()))
+    v = seed.random() * s
+
+    nodes = list(degs.keys())
+    i = 0
+    acc = degs[nodes[i]]
+    while v > acc:
+        i += 1
+        acc += degs[nodes[i]]
+    return nodes[i]
+
+
+class AS_graph_generator:
+    """Generates random internet AS graphs."""
+
+    @py_random_state("seed")
+    def __init__(self, n, seed):
+        """Initializes variables. Immediate numbers are taken from [1].
+
+        Parameters
+        ----------
+        n: integer
+            Number of graph nodes
+        seed: random state
+            Indicator of random number generation state.
+            See :ref:`Randomness<randomness>`.
+
+        Returns
+        -------
+        GG: AS_graph_generator object
+
+        References
+        ----------
+        [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+        BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+        in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+        """
+
+        self.seed = seed
+        self.n_t = min(n, round(self.seed.random() * 2 + 4))  # num of T nodes
+        self.n_m = round(0.15 * n)  # number of M nodes
+        self.n_cp = round(0.05 * n)  # number of CP nodes
+        self.n_c = max(0, n - self.n_t - self.n_m - self.n_cp)  # number of C nodes
+
+        self.d_m = 2 + (2.5 * n) / 10000  # average multihoming degree for M nodes
+        self.d_cp = 2 + (1.5 * n) / 10000  # avg multihoming degree for CP nodes
+        self.d_c = 1 + (5 * n) / 100000  # average multihoming degree for C nodes
+
+        self.p_m_m = 1 + (2 * n) / 10000  # avg num of peer edges between M and M
+        self.p_cp_m = 0.2 + (2 * n) / 10000  # avg num of peer edges between CP, M
+        self.p_cp_cp = 0.05 + (2 * n) / 100000  # avg num of peer edges btwn CP, CP
+
+        self.t_m = 0.375  # probability M's provider is T
+        self.t_cp = 0.375  # probability CP's provider is T
+        self.t_c = 0.125  # probability C's provider is T
+
+    def t_graph(self):
+        """Generates the core mesh network of tier one nodes of a AS graph.
+
+        Returns
+        -------
+        G: Networkx Graph
+            Core network
+        """
+
+        self.G = nx.Graph()
+        for i in range(self.n_t):
+            self.G.add_node(i, type="T")
+            for r in self.regions:
+                self.regions[r].add(i)
+            for j in self.G.nodes():
+                if i != j:
+                    self.add_edge(i, j, "peer")
+            self.customers[i] = set()
+            self.providers[i] = set()
+        return self.G
+
+    def add_edge(self, i, j, kind):
+        if kind == "transit":
+            customer = str(i)
+        else:
+            customer = "none"
+        self.G.add_edge(i, j, type=kind, customer=customer)
+
+    def choose_peer_pref_attach(self, node_list):
+        """Pick a node with a probability weighted by its peer degree.
+
+        Pick a node from node_list with preferential attachment
+        computed only on their peer degree
+        """
+
+        d = {}
+        for n in node_list:
+            d[n] = self.G.nodes[n]["peers"]
+        return choose_pref_attach(d, self.seed)
+
+    def choose_node_pref_attach(self, node_list):
+        """Pick a node with a probability weighted by its degree.
+
+        Pick a node from node_list with preferential attachment
+        computed on their degree
+        """
+
+        degs = dict(self.G.degree(node_list))
+        return choose_pref_attach(degs, self.seed)
+
+    def add_customer(self, i, j):
+        """Keep the dictionaries 'customers' and 'providers' consistent."""
+
+        self.customers[j].add(i)
+        self.providers[i].add(j)
+        for z in self.providers[j]:
+            self.customers[z].add(i)
+            self.providers[i].add(z)
+
+    def add_node(self, i, kind, reg2prob, avg_deg, t_edge_prob):
+        """Add a node and its customer transit edges to the graph.
+
+        Parameters
+        ----------
+        i: object
+            Identifier of the new node
+        kind: string
+            Type of the new node. Options are: 'M' for middle node, 'CP' for
+            content provider and 'C' for customer.
+        reg2prob: float
+            Probability the new node can be in two different regions.
+        avg_deg: float
+            Average number of transit nodes of which node i is customer.
+        t_edge_prob: float
+            Probability node i establish a customer transit edge with a tier
+            one (T) node
+
+        Returns
+        -------
+        i: object
+            Identifier of the new node
+        """
+
+        regs = 1  # regions in which node resides
+        if self.seed.random() < reg2prob:  # node is in two regions
+            regs = 2
+        node_options = set()
+
+        self.G.add_node(i, type=kind, peers=0)
+        self.customers[i] = set()
+        self.providers[i] = set()
+        self.nodes[kind].add(i)
+        for r in self.seed.sample(list(self.regions), regs):
+            node_options = node_options.union(self.regions[r])
+            self.regions[r].add(i)
+
+        edge_num = uniform_int_from_avg(1, avg_deg, self.seed)
+
+        t_options = node_options.intersection(self.nodes["T"])
+        m_options = node_options.intersection(self.nodes["M"])
+        if i in m_options:
+            m_options.remove(i)
+        d = 0
+        while d < edge_num and (len(t_options) > 0 or len(m_options) > 0):
+            if len(m_options) == 0 or (
+                len(t_options) > 0 and self.seed.random() < t_edge_prob
+            ):  # add edge to a T node
+                j = self.choose_node_pref_attach(t_options)
+                t_options.remove(j)
+            else:
+                j = self.choose_node_pref_attach(m_options)
+                m_options.remove(j)
+            self.add_edge(i, j, "transit")
+            self.add_customer(i, j)
+            d += 1
+
+        return i
+
+    def add_m_peering_link(self, m, to_kind):
+        """Add a peering link between two middle tier (M) nodes.
+
+        Target node j is drawn considering a preferential attachment based on
+        other M node peering degree.
+
+        Parameters
+        ----------
+        m: object
+            Node identifier
+        to_kind: string
+            type for target node j (must be always M)
+
+        Returns
+        -------
+        success: boolean
+        """
+
+        # candidates are of type 'M' and are not customers of m
+        node_options = self.nodes["M"].difference(self.customers[m])
+        # candidates are not providers of m
+        node_options = node_options.difference(self.providers[m])
+        # remove self
+        if m in node_options:
+            node_options.remove(m)
+
+        # remove candidates we are already connected to
+        for j in self.G.neighbors(m):
+            if j in node_options:
+                node_options.remove(j)
+
+        if len(node_options) > 0:
+            j = self.choose_peer_pref_attach(node_options)
+            self.add_edge(m, j, "peer")
+            self.G.nodes[m]["peers"] += 1
+            self.G.nodes[j]["peers"] += 1
+            return True
+        else:
+            return False
+
+    def add_cp_peering_link(self, cp, to_kind):
+        """Add a peering link to a content provider (CP) node.
+
+        Target node j can be CP or M and it is drawn uniformly among the nodes
+        belonging to the same region as cp.
+
+        Parameters
+        ----------
+        cp: object
+            Node identifier
+        to_kind: string
+            type for target node j (must be M or CP)
+
+        Returns
+        -------
+        success: boolean
+        """
+
+        node_options = set()
+        for r in self.regions:  # options include nodes in the same region(s)
+            if cp in self.regions[r]:
+                node_options = node_options.union(self.regions[r])
+
+        # options are restricted to the indicated kind ('M' or 'CP')
+        node_options = self.nodes[to_kind].intersection(node_options)
+
+        # remove self
+        if cp in node_options:
+            node_options.remove(cp)
+
+        # remove nodes that are cp's providers
+        node_options = node_options.difference(self.providers[cp])
+
+        # remove nodes we are already connected to
+        for j in self.G.neighbors(cp):
+            if j in node_options:
+                node_options.remove(j)
+
+        if len(node_options) > 0:
+            j = self.seed.sample(list(node_options), 1)[0]
+            self.add_edge(cp, j, "peer")
+            self.G.nodes[cp]["peers"] += 1
+            self.G.nodes[j]["peers"] += 1
+            return True
+        else:
+            return False
+
+    def graph_regions(self, rn):
+        """Initializes AS network regions.
+
+        Parameters
+        ----------
+        rn: integer
+            Number of regions
+        """
+
+        self.regions = {}
+        for i in range(rn):
+            self.regions["REG" + str(i)] = set()
+
+    def add_peering_links(self, from_kind, to_kind):
+        """Utility function to add peering links among node groups."""
+        peer_link_method = None
+        if from_kind == "M":
+            peer_link_method = self.add_m_peering_link
+            m = self.p_m_m
+        if from_kind == "CP":
+            peer_link_method = self.add_cp_peering_link
+            if to_kind == "M":
+                m = self.p_cp_m
+            else:
+                m = self.p_cp_cp
+
+        for i in self.nodes[from_kind]:
+            num = uniform_int_from_avg(0, m, self.seed)
+            for _ in range(num):
+                peer_link_method(i, to_kind)
+
+    def generate(self):
+        """Generates a random AS network graph as described in [1].
+
+        Returns
+        -------
+        G: Graph object
+
+        Notes
+        -----
+        The process steps are the following: first we create the core network
+        of tier one nodes, then we add the middle tier (M), the content
+        provider (CP) and the customer (C) nodes along with their transit edges
+        (link i,j means i is customer of j). Finally we add peering links
+        between M nodes, between M and CP nodes and between CP node couples.
+        For a detailed description of the algorithm, please refer to [1].
+
+        References
+        ----------
+        [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+        BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+        in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+        """
+
+        self.graph_regions(5)
+        self.customers = {}
+        self.providers = {}
+        self.nodes = {"T": set(), "M": set(), "CP": set(), "C": set()}
+
+        self.t_graph()
+        self.nodes["T"] = set(self.G.nodes())
+
+        i = len(self.nodes["T"])
+        for _ in range(self.n_m):
+            self.nodes["M"].add(self.add_node(i, "M", 0.2, self.d_m, self.t_m))
+            i += 1
+        for _ in range(self.n_cp):
+            self.nodes["CP"].add(self.add_node(i, "CP", 0.05, self.d_cp, self.t_cp))
+            i += 1
+        for _ in range(self.n_c):
+            self.nodes["C"].add(self.add_node(i, "C", 0, self.d_c, self.t_c))
+            i += 1
+
+        self.add_peering_links("M", "M")
+        self.add_peering_links("CP", "M")
+        self.add_peering_links("CP", "CP")
+
+        return self.G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_internet_as_graph(n, seed=None):
+    """Generates a random undirected graph resembling the Internet AS network
+
+    Parameters
+    ----------
+    n: integer in [1000, 10000]
+        Number of graph nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G: Networkx Graph object
+        A randomly generated undirected graph
+
+    Notes
+    -----
+    This algorithm returns an undirected graph resembling the Internet
+    Autonomous System (AS) network, it uses the approach by Elmokashfi et al.
+    [1]_ and it grants the properties described in the related paper [1]_.
+
+    Each node models an autonomous system, with an attribute 'type' specifying
+    its kind; tier-1 (T), mid-level (M), customer (C) or content-provider (CP).
+    Each edge models an ADV communication link (hence, bidirectional) with
+    attributes:
+
+      - type: transit|peer, the kind of commercial agreement between nodes;
+      - customer: <node id>, the identifier of the node acting as customer
+        ('none' if type is peer).
+
+    References
+    ----------
+    .. [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+       BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+       in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+    """
+
+    GG = AS_graph_generator(n, seed)
+    G = GG.generate()
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/intersection.py b/lib/python3.11/site-packages/networkx/generators/intersection.py
new file mode 100644
index 0000000000000000000000000000000000000000..e63af5be8eed2e65b9cced2cc50827ffe6a802ba
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/intersection.py
@@ -0,0 +1,125 @@
+"""
+Generators for random intersection graphs.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "uniform_random_intersection_graph",
+    "k_random_intersection_graph",
+    "general_random_intersection_graph",
+]
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def uniform_random_intersection_graph(n, m, p, seed=None):
+    """Returns a uniform random intersection graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    p : float
+        Probability of connecting nodes between bipartite sets
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] K.B. Singer-Cohen, Random Intersection Graphs, 1995,
+       PhD thesis, Johns Hopkins University
+    .. [2] Fill, J. A., Scheinerman, E. R., and Singer-Cohen, K. B.,
+       Random intersection graphs when m = !(n):
+       An equivalence theorem relating the evolution of the g(n, m, p)
+       and g(n, p) models. Random Struct. Algorithms 16, 2 (2000), 156–176.
+    """
+    from networkx.algorithms import bipartite
+
+    G = bipartite.random_graph(n, m, p, seed)
+    return nx.projected_graph(G, range(n))
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def k_random_intersection_graph(n, m, k, seed=None):
+    """Returns a intersection graph with randomly chosen attribute sets for
+    each node that are of equal size (k).
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    k : float
+        Size of attribute set to assign to each node.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph, uniform_random_intersection_graph
+
+    References
+    ----------
+    .. [1] Godehardt, E., and Jaworski, J.
+       Two models of random intersection graphs and their applications.
+       Electronic Notes in Discrete Mathematics 10 (2001), 129--132.
+    """
+    G = nx.empty_graph(n + m)
+    mset = range(n, n + m)
+    for v in range(n):
+        targets = seed.sample(mset, k)
+        G.add_edges_from(zip([v] * len(targets), targets))
+    return nx.projected_graph(G, range(n))
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def general_random_intersection_graph(n, m, p, seed=None):
+    """Returns a random intersection graph with independent probabilities
+    for connections between node and attribute sets.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    p : list of floats of length m
+        Probabilities for connecting nodes to each attribute
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph, uniform_random_intersection_graph
+
+    References
+    ----------
+    .. [1] Nikoletseas, S. E., Raptopoulos, C., and Spirakis, P. G.
+       The existence and efficient construction of large independent sets
+       in general random intersection graphs. In ICALP (2004), J. D´ıaz,
+       J. Karhum¨aki, A. Lepist¨o, and D. Sannella, Eds., vol. 3142
+       of Lecture Notes in Computer Science, Springer, pp. 1029–1040.
+    """
+    if len(p) != m:
+        raise ValueError("Probability list p must have m elements.")
+    G = nx.empty_graph(n + m)
+    mset = range(n, n + m)
+    for u in range(n):
+        for v, q in zip(mset, p):
+            if seed.random() < q:
+                G.add_edge(u, v)
+    return nx.projected_graph(G, range(n))
diff --git a/lib/python3.11/site-packages/networkx/generators/interval_graph.py b/lib/python3.11/site-packages/networkx/generators/interval_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a3fda45acec52af6a5f060b96d9af1067fc002b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/interval_graph.py
@@ -0,0 +1,70 @@
+"""
+Generators for interval graph.
+"""
+
+from collections.abc import Sequence
+
+import networkx as nx
+
+__all__ = ["interval_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def interval_graph(intervals):
+    """Generates an interval graph for a list of intervals given.
+
+    In graph theory, an interval graph is an undirected graph formed from a set
+    of closed intervals on the real line, with a vertex for each interval
+    and an edge between vertices whose intervals intersect.
+    It is the intersection graph of the intervals.
+
+    More information can be found at:
+    https://en.wikipedia.org/wiki/Interval_graph
+
+    Parameters
+    ----------
+    intervals : a sequence of intervals, say (l, r) where l is the left end,
+    and r is the right end of the closed interval.
+
+    Returns
+    -------
+    G : networkx graph
+
+    Examples
+    --------
+    >>> intervals = [(-2, 3), [1, 4], (2, 3), (4, 6)]
+    >>> G = nx.interval_graph(intervals)
+    >>> sorted(G.edges)
+    [((-2, 3), (1, 4)), ((-2, 3), (2, 3)), ((1, 4), (2, 3)), ((1, 4), (4, 6))]
+
+    Raises
+    ------
+    :exc:`TypeError`
+        if `intervals` contains None or an element which is not
+        collections.abc.Sequence or not a length of 2.
+    :exc:`ValueError`
+        if `intervals` contains an interval such that min1 > max1
+        where min1,max1 = interval
+    """
+    intervals = list(intervals)
+    for interval in intervals:
+        if not (isinstance(interval, Sequence) and len(interval) == 2):
+            raise TypeError(
+                "Each interval must have length 2, and be a "
+                "collections.abc.Sequence such as tuple or list."
+            )
+        if interval[0] > interval[1]:
+            raise ValueError(f"Interval must have lower value first. Got {interval}")
+
+    graph = nx.Graph()
+
+    tupled_intervals = [tuple(interval) for interval in intervals]
+    graph.add_nodes_from(tupled_intervals)
+
+    while tupled_intervals:
+        min1, max1 = interval1 = tupled_intervals.pop()
+        for interval2 in tupled_intervals:
+            min2, max2 = interval2
+            if max1 >= min2 and max2 >= min1:
+                graph.add_edge(interval1, interval2)
+    return graph
diff --git a/lib/python3.11/site-packages/networkx/generators/joint_degree_seq.py b/lib/python3.11/site-packages/networkx/generators/joint_degree_seq.py
new file mode 100644
index 0000000000000000000000000000000000000000..c426df944ad27aef4584371838a6ddb280b90dca
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/joint_degree_seq.py
@@ -0,0 +1,664 @@
+"""Generate graphs with a given joint degree and directed joint degree"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "is_valid_joint_degree",
+    "is_valid_directed_joint_degree",
+    "joint_degree_graph",
+    "directed_joint_degree_graph",
+]
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_joint_degree(joint_degrees):
+    """Checks whether the given joint degree dictionary is realizable.
+
+    A *joint degree dictionary* is a dictionary of dictionaries, in
+    which entry ``joint_degrees[k][l]`` is an integer representing the
+    number of edges joining nodes of degree *k* with nodes of degree
+    *l*. Such a dictionary is realizable as a simple graph if and only
+    if the following conditions are satisfied.
+
+    - each entry must be an integer,
+    - the total number of nodes of degree *k*, computed by
+      ``sum(joint_degrees[k].values()) / k``, must be an integer,
+    - the total number of edges joining nodes of degree *k* with
+      nodes of degree *l* cannot exceed the total number of possible edges,
+    - each diagonal entry ``joint_degrees[k][k]`` must be even (this is
+      a convention assumed by the :func:`joint_degree_graph` function).
+
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]``
+        is the number of edges joining nodes of degree *k* with nodes of
+        degree *l*.
+
+    Returns
+    -------
+    bool
+        Whether the given joint degree dictionary is realizable as a
+        simple graph.
+
+    References
+    ----------
+    .. [1] M. Gjoka, M. Kurant, A. Markopoulou, "2.5K Graphs: from Sampling
+       to Generation", IEEE Infocom, 2013.
+    .. [2] I. Stanton, A. Pinar, "Constructing and sampling graphs with a
+       prescribed joint degree distribution", Journal of Experimental
+       Algorithmics, 2012.
+    """
+
+    degree_count = {}
+    for k in joint_degrees:
+        if k > 0:
+            k_size = sum(joint_degrees[k].values()) / k
+            if not k_size.is_integer():
+                return False
+            degree_count[k] = k_size
+
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            if not float(joint_degrees[k][l]).is_integer():
+                return False
+
+            if (k != l) and (joint_degrees[k][l] > degree_count[k] * degree_count[l]):
+                return False
+            elif k == l:
+                if joint_degrees[k][k] > degree_count[k] * (degree_count[k] - 1):
+                    return False
+                if joint_degrees[k][k] % 2 != 0:
+                    return False
+
+    # if all above conditions have been satisfied then the input
+    # joint degree is realizable as a simple graph.
+    return True
+
+
+def _neighbor_switch(G, w, unsat, h_node_residual, avoid_node_id=None):
+    """Releases one free stub for ``w``, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Graph in which the neighbor switch will take place.
+    w : integer
+        Node id for which we will execute this neighbor switch.
+    unsat : set of integers
+        Set of unsaturated node ids that have the same degree as w.
+    h_node_residual: dictionary of integers
+        Keeps track of the remaining stubs  for a given node.
+    avoid_node_id: integer
+        Node id to avoid when selecting w_prime.
+
+    Notes
+    -----
+    First, it selects *w_prime*, an  unsaturated node that has the same degree
+    as ``w``. Second, it selects *switch_node*, a neighbor node of ``w`` that
+    is not  connected to *w_prime*. Then it executes an edge swap i.e. removes
+    (``w``,*switch_node*) and adds (*w_prime*,*switch_node*). Gjoka et. al. [1]
+    prove that such an edge swap is always possible.
+
+    References
+    ----------
+    .. [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+       Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+    """
+
+    if (avoid_node_id is None) or (h_node_residual[avoid_node_id] > 1):
+        # select unsaturated node w_prime that has the same degree as w
+        w_prime = next(iter(unsat))
+    else:
+        # assume that the node pair (v,w) has been selected for connection. if
+        # - neighbor_switch is called for node w,
+        # - nodes v and w have the same degree,
+        # - node v=avoid_node_id has only one stub left,
+        # then prevent v=avoid_node_id from being selected as w_prime.
+
+        iter_var = iter(unsat)
+        while True:
+            w_prime = next(iter_var)
+            if w_prime != avoid_node_id:
+                break
+
+    # select switch_node, a neighbor of w, that is not connected to w_prime
+    w_prime_neighbs = G[w_prime]  # slightly faster declaring this variable
+    for v in G[w]:
+        if (v not in w_prime_neighbs) and (v != w_prime):
+            switch_node = v
+            break
+
+    # remove edge (w,switch_node), add edge (w_prime,switch_node) and update
+    # data structures
+    G.remove_edge(w, switch_node)
+    G.add_edge(w_prime, switch_node)
+    h_node_residual[w] += 1
+    h_node_residual[w_prime] -= 1
+    if h_node_residual[w_prime] == 0:
+        unsat.remove(w_prime)
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def joint_degree_graph(joint_degrees, seed=None):
+    """Generates a random simple graph with the given joint degree dictionary.
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]`` is the
+        number of edges joining nodes of degree *k* with nodes of degree *l*.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified joint degree dictionary.
+
+    Raises
+    ------
+    NetworkXError
+        If *joint_degrees* dictionary is not realizable.
+
+    Notes
+    -----
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes *v* and *w*, of degree *k* and *l* correspondingly,  for which
+    ``joint_degrees[k][l]`` has not reached its target yet. It then adds
+    edge (*v*, *w*) and increases the number of edges in graph G by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between such disconnected nodes *v* and *w*,
+    even if one or both nodes do not have free stubs. That is made possible by
+    executing a "neighbor switch", an edge rewiring move that releases
+    a free stub while keeping the joint degree of G the same.
+
+    The algorithm continues for E (number of edges) iterations of
+    the "while loop", at the which point all entries of the given
+    ``joint_degrees[k][l]`` have reached their target values and the
+    construction is complete.
+
+    References
+    ----------
+    ..  [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+        Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+
+    Examples
+    --------
+    >>> joint_degrees = {
+    ...     1: {4: 1},
+    ...     2: {2: 2, 3: 2, 4: 2},
+    ...     3: {2: 2, 4: 1},
+    ...     4: {1: 1, 2: 2, 3: 1},
+    ... }
+    >>> G = nx.joint_degree_graph(joint_degrees)
+    >>>
+    """
+
+    if not is_valid_joint_degree(joint_degrees):
+        msg = "Input joint degree dict not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # compute degree count from joint_degrees
+    degree_count = {k: sum(l.values()) // k for k, l in joint_degrees.items() if k > 0}
+
+    # start with empty N-node graph
+    N = sum(degree_count.values())
+    G = nx.empty_graph(N)
+
+    # for a given degree group, keep the list of all node ids
+    h_degree_nodelist = {}
+
+    # for a given node, keep track of the remaining stubs
+    h_node_residual = {}
+
+    # populate h_degree_nodelist and h_node_residual
+    nodeid = 0
+    for degree, num_nodes in degree_count.items():
+        h_degree_nodelist[degree] = range(nodeid, nodeid + num_nodes)
+        for v in h_degree_nodelist[degree]:
+            h_node_residual[v] = degree
+        nodeid += int(num_nodes)
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            # n_edges_add is the number of edges to add for the
+            # degree pair (k,l)
+            n_edges_add = joint_degrees[k][l]
+
+            if (n_edges_add > 0) and (k >= l):
+                # number of nodes with degree k and l
+                k_size = degree_count[k]
+                l_size = degree_count[l]
+
+                # k_nodes and l_nodes consist of all nodes of degree k and l
+                k_nodes = h_degree_nodelist[k]
+                l_nodes = h_degree_nodelist[l]
+
+                # k_unsat and l_unsat consist of nodes of degree k and l that
+                # are unsaturated (nodes that have at least 1 available stub)
+                k_unsat = {v for v in k_nodes if h_node_residual[v] > 0}
+
+                if k != l:
+                    l_unsat = {w for w in l_nodes if h_node_residual[w] > 0}
+                else:
+                    l_unsat = k_unsat
+                    n_edges_add = joint_degrees[k][l] // 2
+
+                while n_edges_add > 0:
+                    # randomly pick nodes v and w that have degrees k and l
+                    v = k_nodes[seed.randrange(k_size)]
+                    w = l_nodes[seed.randrange(l_size)]
+
+                    # if nodes v and w are disconnected then attempt to connect
+                    if not G.has_edge(v, w) and (v != w):
+                        # if node v has no free stubs then do neighbor switch
+                        if h_node_residual[v] == 0:
+                            _neighbor_switch(G, v, k_unsat, h_node_residual)
+
+                        # if node w has no free stubs then do neighbor switch
+                        if h_node_residual[w] == 0:
+                            if k != l:
+                                _neighbor_switch(G, w, l_unsat, h_node_residual)
+                            else:
+                                _neighbor_switch(
+                                    G, w, l_unsat, h_node_residual, avoid_node_id=v
+                                )
+
+                        # add edge (v, w) and update data structures
+                        G.add_edge(v, w)
+                        h_node_residual[v] -= 1
+                        h_node_residual[w] -= 1
+                        n_edges_add -= 1
+
+                        if h_node_residual[v] == 0:
+                            k_unsat.discard(v)
+                        if h_node_residual[w] == 0:
+                            l_unsat.discard(w)
+    return G
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+    """Checks whether the given directed joint degree input is realizable
+
+    Parameters
+    ----------
+    in_degrees :  list of integers
+        in degree sequence contains the in degrees of nodes.
+    out_degrees : list of integers
+        out degree sequence contains the out degrees of nodes.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary. for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+
+    Returns
+    -------
+    boolean
+        returns true if given input is realizable, else returns false.
+
+    Notes
+    -----
+    Here is the list of conditions that the inputs (in/out degree sequences,
+    nkk) need to satisfy for simple directed graph realizability:
+
+    - Condition 0: in_degrees and out_degrees have the same length
+    - Condition 1: nkk[k][l]  is integer for all k,l
+    - Condition 2: sum(nkk[k])/k = number of nodes with partition id k, is an
+                   integer and matching degree sequence
+    - Condition 3: number of edges and non-chords between k and l cannot exceed
+                   maximum possible number of edges
+
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    V = {}  # number of nodes with in/out degree.
+    forbidden = {}
+    if len(in_degrees) != len(out_degrees):
+        return False
+
+    for idx in range(len(in_degrees)):
+        i = in_degrees[idx]
+        o = out_degrees[idx]
+        V[(i, 0)] = V.get((i, 0), 0) + 1
+        V[(o, 1)] = V.get((o, 1), 0) + 1
+
+        forbidden[(o, i)] = forbidden.get((o, i), 0) + 1
+
+    S = {}  # number of edges going from in/out degree nodes.
+    for k in nkk:
+        for l in nkk[k]:
+            val = nkk[k][l]
+            if not float(val).is_integer():  # condition 1
+                return False
+
+            if val > 0:
+                S[(k, 1)] = S.get((k, 1), 0) + val
+                S[(l, 0)] = S.get((l, 0), 0) + val
+                # condition 3
+                if val + forbidden.get((k, l), 0) > V[(k, 1)] * V[(l, 0)]:
+                    return False
+
+    return all(S[s] / s[0] == V[s] for s in S)
+
+
+def _directed_neighbor_switch(
+    G, w, unsat, h_node_residual_out, chords, h_partition_in, partition
+):
+    """Releases one free stub for node w, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_out: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_in: dict of integers
+        for a given node, keeps track of its partition id (in degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    First, it selects node w_prime that (1) has the same degree as w and
+    (2) is unsaturated. Then, it selects node v, a neighbor of w, that is
+    not connected to w_prime and does an edge swap i.e. removes (w,v) and
+    adds (w_prime,v). If neighbor switch is not possible for w using
+    w_prime and v, then return w_prime; in [1] it's proven that
+    such unsaturated nodes can be used.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # select node t, a neighbor of w, that is not connected to w_prime
+    w_neighbs = list(G.successors(w))
+    # slightly faster declaring this variable
+    w_prime_neighbs = list(G.successors(w_prime))
+
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (w,v), add (w_prime,v)  and update data structures
+            G.remove_edge(w, v)
+            G.add_edge(w_prime, v)
+
+            if h_partition_in[v] == partition:
+                chords.add((w, v))
+                chords.discard((w_prime, v))
+
+            h_node_residual_out[w] += 1
+            h_node_residual_out[w_prime] -= 1
+            if h_node_residual_out[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use unsaturated node
+    return w_prime
+
+
+def _directed_neighbor_switch_rev(
+    G, w, unsat, h_node_residual_in, chords, h_partition_out, partition
+):
+    """The reverse of directed_neighbor_switch.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_in: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_out: dict of integers
+        for a given node, keeps track of its partition id (out degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    Same operation as directed_neighbor_switch except it handles this operation
+    for incoming edges instead of outgoing.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # slightly faster declaring these as variables.
+    w_neighbs = list(G.predecessors(w))
+    w_prime_neighbs = list(G.predecessors(w_prime))
+    # select node v, a neighbor of w, that is not connected to w_prime.
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (v,w), add (v,w_prime) and update data structures.
+            G.remove_edge(v, w)
+            G.add_edge(v, w_prime)
+            if h_partition_out[v] == partition:
+                chords.add((v, w))
+                chords.discard((v, w_prime))
+
+            h_node_residual_in[w] += 1
+            h_node_residual_in[w_prime] -= 1
+            if h_node_residual_in[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use the unsaturated node.
+    return w_prime
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_joint_degree_graph(in_degrees, out_degrees, nkk, seed=None):
+    """Generates a random simple directed graph with the joint degree.
+
+    Parameters
+    ----------
+    degree_seq :  list of tuples (of size 3)
+        degree sequence contains tuples of nodes with node id, in degree and
+        out degree.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary, for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+    seed : hashable object, optional
+        Seed for random number generator.
+
+    Returns
+    -------
+    G : Graph
+        A directed graph with the specified inputs.
+
+    Raises
+    ------
+    NetworkXError
+        If degree_seq and nkk are not realizable as a simple directed graph.
+
+
+    Notes
+    -----
+    Similarly to the undirected version:
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes v and w, of degree k and l correspondingly,  for which nkk[k][l] has
+    not reached its target yet i.e. (for given k,l): n_edges_add < nkk[k][l].
+    It then adds edge (v,w) and always increases the number of edges in graph G
+    by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between disconnected nodes v and w, for which
+    nkk[degree(v)][degree(w)] has not reached its target, even if one or both
+    nodes do not have free stubs. If either node v or w does not have a free
+    stub, we perform a "neighbor switch", an edge rewiring move that releases a
+    free stub while keeping nkk the same.
+
+    The difference for the directed version lies in the fact that neighbor
+    switches might not be able to rewire, but in these cases unsaturated nodes
+    can be reassigned to use instead, see [1] for detailed description and
+    proofs.
+
+    The algorithm continues for E (number of edges in the graph) iterations of
+    the "while loop", at which point all entries of the given nkk[k][l] have
+    reached their target values and the construction is complete.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+
+    Examples
+    --------
+    >>> in_degrees = [0, 1, 1, 2]
+    >>> out_degrees = [1, 1, 1, 1]
+    >>> nkk = {1: {1: 2, 2: 2}}
+    >>> G = nx.directed_joint_degree_graph(in_degrees, out_degrees, nkk)
+    >>>
+    """
+    if not is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+        msg = "Input is not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # start with an empty directed graph.
+    G = nx.DiGraph()
+
+    # for a given group, keep the list of all node ids.
+    h_degree_nodelist_in = {}
+    h_degree_nodelist_out = {}
+    # for a given group, keep the list of all unsaturated node ids.
+    h_degree_nodelist_in_unsat = {}
+    h_degree_nodelist_out_unsat = {}
+    # for a given node, keep track of the remaining stubs to be added.
+    h_node_residual_out = {}
+    h_node_residual_in = {}
+    # for a given node, keep track of the partition id.
+    h_partition_out = {}
+    h_partition_in = {}
+    # keep track of non-chords between pairs of partition ids.
+    non_chords = {}
+
+    # populate data structures
+    for idx, i in enumerate(in_degrees):
+        idx = int(idx)
+        if i > 0:
+            h_degree_nodelist_in.setdefault(i, [])
+            h_degree_nodelist_in_unsat.setdefault(i, set())
+            h_degree_nodelist_in[i].append(idx)
+            h_degree_nodelist_in_unsat[i].add(idx)
+            h_node_residual_in[idx] = i
+            h_partition_in[idx] = i
+
+    for idx, o in enumerate(out_degrees):
+        o = out_degrees[idx]
+        non_chords[(o, in_degrees[idx])] = non_chords.get((o, in_degrees[idx]), 0) + 1
+        idx = int(idx)
+        if o > 0:
+            h_degree_nodelist_out.setdefault(o, [])
+            h_degree_nodelist_out_unsat.setdefault(o, set())
+            h_degree_nodelist_out[o].append(idx)
+            h_degree_nodelist_out_unsat[o].add(idx)
+            h_node_residual_out[idx] = o
+            h_partition_out[idx] = o
+
+        G.add_node(idx)
+
+    nk_in = {}
+    nk_out = {}
+    for p in h_degree_nodelist_in:
+        nk_in[p] = len(h_degree_nodelist_in[p])
+    for p in h_degree_nodelist_out:
+        nk_out[p] = len(h_degree_nodelist_out[p])
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair.
+    for k in nkk:
+        for l in nkk[k]:
+            n_edges_add = nkk[k][l]
+
+            if n_edges_add > 0:
+                # chords contains a random set of potential edges.
+                chords = set()
+
+                k_len = nk_out[k]
+                l_len = nk_in[l]
+                chords_sample = seed.sample(
+                    range(k_len * l_len), n_edges_add + non_chords.get((k, l), 0)
+                )
+
+                num = 0
+                while len(chords) < n_edges_add:
+                    i = h_degree_nodelist_out[k][chords_sample[num] % k_len]
+                    j = h_degree_nodelist_in[l][chords_sample[num] // k_len]
+                    num += 1
+                    if i != j:
+                        chords.add((i, j))
+
+                # k_unsat and l_unsat consist of nodes of in/out degree k and l
+                # that are unsaturated i.e. those nodes that have at least one
+                # available stub
+                k_unsat = h_degree_nodelist_out_unsat[k]
+                l_unsat = h_degree_nodelist_in_unsat[l]
+
+                while n_edges_add > 0:
+                    v, w = chords.pop()
+                    chords.add((v, w))
+
+                    # if node v has no free stubs then do neighbor switch.
+                    if h_node_residual_out[v] == 0:
+                        _v = _directed_neighbor_switch(
+                            G,
+                            v,
+                            k_unsat,
+                            h_node_residual_out,
+                            chords,
+                            h_partition_in,
+                            l,
+                        )
+                        if _v is not None:
+                            v = _v
+
+                    # if node w has no free stubs then do neighbor switch.
+                    if h_node_residual_in[w] == 0:
+                        _w = _directed_neighbor_switch_rev(
+                            G,
+                            w,
+                            l_unsat,
+                            h_node_residual_in,
+                            chords,
+                            h_partition_out,
+                            k,
+                        )
+                        if _w is not None:
+                            w = _w
+
+                    # add edge (v,w) and update data structures.
+                    G.add_edge(v, w)
+                    h_node_residual_out[v] -= 1
+                    h_node_residual_in[w] -= 1
+                    n_edges_add -= 1
+                    chords.discard((v, w))
+
+                    if h_node_residual_out[v] == 0:
+                        k_unsat.discard(v)
+                    if h_node_residual_in[w] == 0:
+                        l_unsat.discard(w)
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/lattice.py b/lib/python3.11/site-packages/networkx/generators/lattice.py
new file mode 100644
index 0000000000000000000000000000000000000000..61721c4317995228209305de245c74d64d8095f4
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/lattice.py
@@ -0,0 +1,405 @@
+"""Functions for generating grid graphs and lattices
+
+The :func:`grid_2d_graph`, :func:`triangular_lattice_graph`, and
+:func:`hexagonal_lattice_graph` functions correspond to the three
+`regular tilings of the plane`_, the square, triangular, and hexagonal
+tilings, respectively. :func:`grid_graph` and :func:`hypercube_graph`
+are similar for arbitrary dimensions. Useful relevant discussion can
+be found about `Triangular Tiling`_, and `Square, Hex and Triangle Grids`_
+
+.. _regular tilings of the plane: https://en.wikipedia.org/wiki/List_of_regular_polytopes_and_compounds#Euclidean_tilings
+.. _Square, Hex and Triangle Grids: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+.. _Triangular Tiling: https://en.wikipedia.org/wiki/Triangular_tiling
+
+"""
+
+from itertools import repeat
+from math import sqrt
+
+import networkx as nx
+from networkx.classes import set_node_attributes
+from networkx.exception import NetworkXError
+from networkx.generators.classic import cycle_graph, empty_graph, path_graph
+from networkx.relabel import relabel_nodes
+from networkx.utils import flatten, nodes_or_number, pairwise
+
+__all__ = [
+    "grid_2d_graph",
+    "grid_graph",
+    "hypercube_graph",
+    "triangular_lattice_graph",
+    "hexagonal_lattice_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def grid_2d_graph(m, n, periodic=False, create_using=None):
+    """Returns the two-dimensional grid graph.
+
+    The grid graph has each node connected to its four nearest neighbors.
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from `range(n)`.
+        If a container, elements become the coordinate of the nodes.
+
+    periodic : bool or iterable
+        If `periodic` is True, both dimensions are periodic. If False, none
+        are periodic.  If `periodic` is iterable, it should yield 2 bool
+        values indicating whether the 1st and 2nd axes, respectively, are
+        periodic.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The (possibly periodic) grid graph of the specified dimensions.
+
+    See Also
+    --------
+    triangular_lattice_graph, hexagonal_lattice_graph :
+        Other 2D lattice graphs
+    grid_graph, hypercube_graph :
+        N-dimensional lattice graphs
+    """
+    G = empty_graph(0, create_using)
+    row_name, rows = m
+    col_name, cols = n
+    G.add_nodes_from((i, j) for i in rows for j in cols)
+    G.add_edges_from(((i, j), (pi, j)) for pi, i in pairwise(rows) for j in cols)
+    G.add_edges_from(((i, j), (i, pj)) for i in rows for pj, j in pairwise(cols))
+
+    try:
+        periodic_r, periodic_c = periodic
+    except TypeError:
+        periodic_r = periodic_c = periodic
+
+    if periodic_r and len(rows) > 2:
+        first = rows[0]
+        last = rows[-1]
+        G.add_edges_from(((first, j), (last, j)) for j in cols)
+    if periodic_c and len(cols) > 2:
+        first = cols[0]
+        last = cols[-1]
+        G.add_edges_from(((i, first), (i, last)) for i in rows)
+    # both directions for directed
+    if G.is_directed():
+        G.add_edges_from((v, u) for u, v in G.edges())
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def grid_graph(dim, periodic=False):
+    """Returns the *n*-dimensional grid graph.
+
+    The dimension *n* is the length of the list `dim` and the size in
+    each dimension is the value of the corresponding list element.
+
+    Parameters
+    ----------
+    dim : list or tuple of numbers or iterables of nodes
+        'dim' is a tuple or list with, for each dimension, either a number
+        that is the size of that dimension or an iterable of nodes for
+        that dimension. The dimension of the grid_graph is the length
+        of `dim`.
+
+    periodic : bool or iterable
+        If `periodic` is True, all dimensions are periodic. If False all
+        dimensions are not periodic. If `periodic` is iterable, it should
+        yield `dim` bool values each of which indicates whether the
+        corresponding axis is periodic.
+
+    Returns
+    -------
+    NetworkX graph
+        The (possibly periodic) grid graph of the specified dimensions.
+
+    See Also
+    --------
+    grid_2d_graph, triangular_lattice_graph, hexagonal_lattice_graph :
+        2D lattice graphs
+    hypercube_graph :
+        A special case of `grid_graph` where all elements of `dim` are identical
+
+    Examples
+    --------
+    To produce a 2 by 3 by 4 grid graph, a graph on 24 nodes:
+
+    >>> from networkx import grid_graph
+    >>> G = grid_graph(dim=(2, 3, 4))
+    >>> len(G)
+    24
+    >>> G = grid_graph(dim=(range(7, 9), range(3, 6)))
+    >>> len(G)
+    6
+    """
+    from collections.abc import Iterable
+
+    from networkx.algorithms.operators.product import cartesian_product
+
+    if not dim:
+        return empty_graph(0)
+
+    periodic = repeat(periodic) if not isinstance(periodic, Iterable) else periodic
+    func = (cycle_graph if p else path_graph for p in periodic)
+
+    G = next(func)(dim[0])
+    for current_dim in dim[1:]:
+        Gnew = next(func)(current_dim)
+        G = cartesian_product(Gnew, G)
+    # graph G is done but has labels of the form (1, (2, (3, 1))) so relabel
+    H = relabel_nodes(G, flatten)
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hypercube_graph(n):
+    """Returns the *n*-dimensional hypercube graph.
+
+    The *n*-dimensional hypercube graph [1]_ has ``2**n`` nodes, each represented as
+    a binary integer in the form of a tuple of 0's and 1's. Edges exist between
+    nodes that differ in exactly one bit.
+
+    Parameters
+    ----------
+    n : int
+        Dimension of the hypercube, must be a positive integer.
+
+    Returns
+    -------
+    networkx.Graph
+        The n-dimensional hypercube graph as an undirected graph.
+
+    See Also
+    --------
+    grid_2d_graph, triangular_lattice_graph, hexagonal_lattice_graph :
+        2D lattice graphs
+    grid_graph :
+        A more general N-dimensional grid
+
+    Examples
+    --------
+    >>> G = nx.hypercube_graph(3)
+    >>> list(G.neighbors((0, 0, 0)))
+    [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Hypercube_graph
+    """
+    dim = n * [2]
+    G = grid_graph(dim)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def triangular_lattice_graph(
+    m, n, periodic=False, with_positions=True, create_using=None
+):
+    r"""Returns the $m$ by $n$ triangular lattice graph.
+
+    The `triangular lattice graph`_ is a two-dimensional `grid graph`_ in
+    which each square unit has a diagonal edge (each grid unit has a chord).
+
+    The returned graph has $m$ rows and $n$ columns of triangles. Rows and
+    columns include both triangles pointing up and down. Rows form a strip
+    of constant height. Columns form a series of diamond shapes, staggered
+    with the columns on either side. Another way to state the size is that
+    the nodes form a grid of `m+1` rows and `(n + 1) // 2` columns.
+    The odd row nodes are shifted horizontally relative to the even rows.
+
+    Directed graph types have edges pointed up or right.
+
+    Positions of nodes are computed by default or `with_positions is True`.
+    The position of each node (embedded in a euclidean plane) is stored in
+    the graph using equilateral triangles with sidelength 1.
+    The height between rows of nodes is thus $\sqrt(3)/2$.
+    Nodes lie in the first quadrant with the node $(0, 0)$ at the origin.
+
+    .. _triangular lattice graph: http://mathworld.wolfram.com/TriangularGrid.html
+    .. _grid graph: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+    .. _Triangular Tiling: https://en.wikipedia.org/wiki/Triangular_tiling
+
+    Parameters
+    ----------
+    m : int
+        The number of rows in the lattice.
+
+    n : int
+        The number of columns in the lattice.
+
+    periodic : bool (default: False)
+        If True, join the boundary vertices of the grid using periodic
+        boundary conditions. The join between boundaries is the final row
+        and column of triangles. This means there is one row and one column
+        fewer nodes for the periodic lattice. Periodic lattices require
+        `m >= 3`, `n >= 5` and are allowed but misaligned if `m` or `n` are odd
+
+    with_positions : bool (default: True)
+        Store the coordinates of each node in the graph node attribute 'pos'.
+        The coordinates provide a lattice with equilateral triangles.
+        Periodic positions shift the nodes vertically in a nonlinear way so
+        the edges don't overlap so much.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The *m* by *n* triangular lattice graph.
+
+    See Also
+    --------
+    grid_2d_graph, hexagonal_lattice_graph :
+        Other 2D lattice graphs
+    grid_graph, hypercube_graph :
+        N-dimensional lattice graphs
+    """
+    H = empty_graph(0, create_using)
+    if n == 0 or m == 0:
+        return H
+    if periodic:
+        if n < 5 or m < 3:
+            msg = f"m > 2 and n > 4 required for periodic. m={m}, n={n}"
+            raise NetworkXError(msg)
+
+    N = (n + 1) // 2  # number of nodes in row
+    rows = range(m + 1)
+    cols = range(N + 1)
+    # Make grid
+    H.add_edges_from(((i, j), (i + 1, j)) for j in rows for i in cols[:N])
+    H.add_edges_from(((i, j), (i, j + 1)) for j in rows[:m] for i in cols)
+    # add diagonals
+    H.add_edges_from(((i, j), (i + 1, j + 1)) for j in rows[1:m:2] for i in cols[:N])
+    H.add_edges_from(((i + 1, j), (i, j + 1)) for j in rows[:m:2] for i in cols[:N])
+
+    # identify boundary nodes if periodic
+    if periodic is True:
+        for i in cols:
+            H = nx.contracted_nodes(H, (i, 0), (i, m), store_contraction_as=None)
+        for j in rows[:m]:
+            H = nx.contracted_nodes(H, (0, j), (N, j), store_contraction_as=None)
+    elif n % 2:
+        # remove extra nodes
+        H.remove_nodes_from((N, j) for j in rows[1::2])
+
+    # Add position node attributes
+    if with_positions:
+        ii = (i for i in cols for j in rows)
+        jj = (j for i in cols for j in rows)
+        xx = (0.5 * (j % 2) + i for i in cols for j in rows)
+        h = sqrt(3) / 2
+        if periodic:
+            yy = (h * j + 0.01 * i * i for i in cols for j in rows)
+        else:
+            yy = (h * j for i in cols for j in rows)
+        pos = {(i, j): (x, y) for i, j, x, y in zip(ii, jj, xx, yy) if (i, j) in H}
+        set_node_attributes(H, pos, "pos")
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hexagonal_lattice_graph(
+    m, n, periodic=False, with_positions=True, create_using=None
+):
+    """Returns an `m` by `n` hexagonal lattice graph.
+
+    The *hexagonal lattice graph* is a graph whose nodes and edges are
+    the `hexagonal tiling`_ of the plane.
+
+    The returned graph will have `m` rows and `n` columns of hexagons.
+    `Odd numbered columns`_ are shifted up relative to even numbered columns.
+
+    Positions of nodes are computed by default or `with_positions is True`.
+    Node positions creating the standard embedding in the plane
+    with sidelength 1 and are stored in the node attribute 'pos'.
+    `pos = nx.get_node_attributes(G, 'pos')` creates a dict ready for drawing.
+
+    .. _hexagonal tiling: https://en.wikipedia.org/wiki/Hexagonal_tiling
+    .. _Odd numbered columns: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+
+    Parameters
+    ----------
+    m : int
+        The number of rows of hexagons in the lattice.
+
+    n : int
+        The number of columns of hexagons in the lattice.
+
+    periodic : bool
+        Whether to make a periodic grid by joining the boundary vertices.
+        For this to work `n` must be even and both `n > 1` and `m > 1`.
+        The periodic connections create another row and column of hexagons
+        so these graphs have fewer nodes as boundary nodes are identified.
+
+    with_positions : bool (default: True)
+        Store the coordinates of each node in the graph node attribute 'pos'.
+        The coordinates provide a lattice with vertical columns of hexagons
+        offset to interleave and cover the plane.
+        Periodic positions shift the nodes vertically in a nonlinear way so
+        the edges don't overlap so much.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        If graph is directed, edges will point up or right.
+
+    Returns
+    -------
+    NetworkX graph
+        The *m* by *n* hexagonal lattice graph.
+
+    See Also
+    --------
+    grid_2d_graph, triangular_lattice_graph :
+        Other 2D lattice graphs
+    grid_graph, hypercube_graph :
+        N-dimensional lattice graphs
+    """
+    G = empty_graph(0, create_using)
+    if m == 0 or n == 0:
+        return G
+    if periodic and (n % 2 == 1 or m < 2 or n < 2):
+        msg = "periodic hexagonal lattice needs m > 1, n > 1 and even n"
+        raise NetworkXError(msg)
+
+    M = 2 * m  # twice as many nodes as hexagons vertically
+    rows = range(M + 2)
+    cols = range(n + 1)
+    # make lattice
+    col_edges = (((i, j), (i, j + 1)) for i in cols for j in rows[: M + 1])
+    row_edges = (((i, j), (i + 1, j)) for i in cols[:n] for j in rows if i % 2 == j % 2)
+    G.add_edges_from(col_edges)
+    G.add_edges_from(row_edges)
+    # Remove corner nodes with one edge
+    G.remove_node((0, M + 1))
+    G.remove_node((n, (M + 1) * (n % 2)))
+
+    # identify boundary nodes if periodic
+    if periodic:
+        for i in cols[:n]:
+            G = nx.contracted_nodes(G, (i, 0), (i, M), store_contraction_as=None)
+        for i in cols[1:]:
+            G = nx.contracted_nodes(G, (i, 1), (i, M + 1), store_contraction_as=None)
+        for j in rows[1:M]:
+            G = nx.contracted_nodes(G, (0, j), (n, j), store_contraction_as=None)
+        G.remove_node((n, M))
+
+    # calc position in embedded space
+    if with_positions:
+        ii = (i for i in cols for j in rows)
+        jj = (j for i in cols for j in rows)
+        xx = (0.5 + i + i // 2 + (j % 2) * ((i % 2) - 0.5) for i in cols for j in rows)
+        h = sqrt(3) / 2
+        if periodic:
+            yy = (h * j + 0.01 * i * i for i in cols for j in rows)
+        else:
+            yy = (h * j for i in cols for j in rows)
+        # exclude nodes not in G
+        pos = {(i, j): (x, y) for i, j, x, y in zip(ii, jj, xx, yy) if (i, j) in G}
+        set_node_attributes(G, pos, "pos")
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/line.py b/lib/python3.11/site-packages/networkx/generators/line.py
new file mode 100644
index 0000000000000000000000000000000000000000..90997146bde4a04b4b53c475e300c4a63b987f38
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/line.py
@@ -0,0 +1,501 @@
+"""Functions for generating line graphs."""
+
+from collections import defaultdict
+from functools import partial
+from itertools import combinations
+
+import networkx as nx
+from networkx.utils import arbitrary_element
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = ["line_graph", "inverse_line_graph"]
+
+
+@nx._dispatchable(returns_graph=True)
+def line_graph(G, create_using=None):
+    r"""Returns the line graph of the graph or digraph `G`.
+
+    The line graph of a graph `G` has a node for each edge in `G` and an
+    edge joining those nodes if the two edges in `G` share a common node. For
+    directed graphs, nodes are adjacent exactly when the edges they represent
+    form a directed path of length two.
+
+    The nodes of the line graph are 2-tuples of nodes in the original graph (or
+    3-tuples for multigraphs, with the key of the edge as the third element).
+
+    For information about self-loops and more discussion, see the **Notes**
+    section below.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph, DiGraph, MultiGraph, or MultiDigraph.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    L : graph
+        The line graph of G.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(3)
+    >>> L = nx.line_graph(G)
+    >>> print(sorted(map(sorted, L.edges())))  # makes a 3-clique, K3
+    [[(0, 1), (0, 2)], [(0, 1), (0, 3)], [(0, 2), (0, 3)]]
+
+    Edge attributes from `G` are not copied over as node attributes in `L`, but
+    attributes can be copied manually:
+
+    >>> G = nx.path_graph(4)
+    >>> G.add_edges_from((u, v, {"tot": u + v}) for u, v in G.edges)
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'tot': 1}), (1, 2, {'tot': 3}), (2, 3, {'tot': 5})])
+    >>> H = nx.line_graph(G)
+    >>> H.add_nodes_from((node, G.edges[node]) for node in H)
+    >>> H.nodes(data=True)
+    NodeDataView({(0, 1): {'tot': 1}, (2, 3): {'tot': 5}, (1, 2): {'tot': 3}})
+
+    Notes
+    -----
+    Graph, node, and edge data are not propagated to the new graph. For
+    undirected graphs, the nodes in G must be sortable, otherwise the
+    constructed line graph may not be correct.
+
+    *Self-loops in undirected graphs*
+
+    For an undirected graph `G` without multiple edges, each edge can be
+    written as a set `\{u, v\}`.  Its line graph `L` has the edges of `G` as
+    its nodes. If `x` and `y` are two nodes in `L`, then `\{x, y\}` is an edge
+    in `L` if and only if the intersection of `x` and `y` is nonempty. Thus,
+    the set of all edges is determined by the set of all pairwise intersections
+    of edges in `G`.
+
+    Trivially, every edge in G would have a nonzero intersection with itself,
+    and so every node in `L` should have a self-loop. This is not so
+    interesting, and the original context of line graphs was with simple
+    graphs, which had no self-loops or multiple edges. The line graph was also
+    meant to be a simple graph and thus, self-loops in `L` are not part of the
+    standard definition of a line graph. In a pairwise intersection matrix,
+    this is analogous to excluding the diagonal entries from the line graph
+    definition.
+
+    Self-loops and multiple edges in `G` add nodes to `L` in a natural way, and
+    do not require any fundamental changes to the definition. It might be
+    argued that the self-loops we excluded before should now be included.
+    However, the self-loops are still "trivial" in some sense and thus, are
+    usually excluded.
+
+    *Self-loops in directed graphs*
+
+    For a directed graph `G` without multiple edges, each edge can be written
+    as a tuple `(u, v)`. Its line graph `L` has the edges of `G` as its
+    nodes. If `x` and `y` are two nodes in `L`, then `(x, y)` is an edge in `L`
+    if and only if the tail of `x` matches the head of `y`, for example, if `x
+    = (a, b)` and `y = (b, c)` for some vertices `a`, `b`, and `c` in `G`.
+
+    Due to the directed nature of the edges, it is no longer the case that
+    every edge in `G` should have a self-loop in `L`. Now, the only time
+    self-loops arise is if a node in `G` itself has a self-loop.  So such
+    self-loops are no longer "trivial" but instead, represent essential
+    features of the topology of `G`. For this reason, the historical
+    development of line digraphs is such that self-loops are included. When the
+    graph `G` has multiple edges, once again only superficial changes are
+    required to the definition.
+
+    References
+    ----------
+    * Harary, Frank, and Norman, Robert Z., "Some properties of line digraphs",
+      Rend. Circ. Mat. Palermo, II. Ser. 9 (1960), 161--168.
+    * Hemminger, R. L.; Beineke, L. W. (1978), "Line graphs and line digraphs",
+      in Beineke, L. W.; Wilson, R. J., Selected Topics in Graph Theory,
+      Academic Press Inc., pp. 271--305.
+
+    """
+    if G.is_directed():
+        L = _lg_directed(G, create_using=create_using)
+    else:
+        L = _lg_undirected(G, selfloops=False, create_using=create_using)
+    return L
+
+
+def _lg_directed(G, create_using=None):
+    """Returns the line graph L of the (multi)digraph G.
+
+    Edges in G appear as nodes in L, represented as tuples of the form (u,v)
+    or (u,v,key) if G is a multidigraph. A node in L corresponding to the edge
+    (u,v) is connected to every node corresponding to an edge (v,w).
+
+    Parameters
+    ----------
+    G : digraph
+        A directed graph or directed multigraph.
+    create_using : NetworkX graph constructor, optional
+       Graph type to create. If graph instance, then cleared before populated.
+       Default is to use the same graph class as `G`.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Create a graph specific edge function.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    for from_node in get_edges():
+        # from_node is: (u,v) or (u,v,key)
+        L.add_node(from_node)
+        for to_node in get_edges(from_node[1]):
+            L.add_edge(from_node, to_node)
+
+    return L
+
+
+def _lg_undirected(G, selfloops=False, create_using=None):
+    """Returns the line graph L of the (multi)graph G.
+
+    Edges in G appear as nodes in L, represented as sorted tuples of the form
+    (u,v), or (u,v,key) if G is a multigraph. A node in L corresponding to
+    the edge {u,v} is connected to every node corresponding to an edge that
+    involves u or v.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph or multigraph.
+    selfloops : bool
+        If `True`, then self-loops are included in the line graph. If `False`,
+        they are excluded.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The standard algorithm for line graphs of undirected graphs does not
+    produce self-loops.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Graph specific functions for edges.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    # Determine if we include self-loops or not.
+    shift = 0 if selfloops else 1
+
+    # Introduce numbering of nodes
+    node_index = {n: i for i, n in enumerate(G)}
+
+    # Lift canonical representation of nodes to edges in line graph
+    def edge_key_function(edge):
+        return node_index[edge[0]], node_index[edge[1]]
+
+    edges = set()
+    for u in G:
+        # Label nodes as a sorted tuple of nodes in original graph.
+        # Decide on representation of {u, v} as (u, v) or (v, u) depending on node_index.
+        # -> This ensures a canonical representation and avoids comparing values of different types.
+        nodes = [tuple(sorted(x[:2], key=node_index.get)) + x[2:] for x in get_edges(u)]
+
+        if len(nodes) == 1:
+            # Then the edge will be an isolated node in L.
+            L.add_node(nodes[0])
+
+        # Add a clique of `nodes` to graph. To prevent double adding edges,
+        # especially important for multigraphs, we store the edges in
+        # canonical form in a set.
+        for i, a in enumerate(nodes):
+            edges.update(
+                [
+                    tuple(sorted((a, b), key=edge_key_function))
+                    for b in nodes[i + shift :]
+                ]
+            )
+
+    L.add_edges_from(edges)
+    return L
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def inverse_line_graph(G):
+    """Returns the inverse line graph of graph G.
+
+    If H is a graph, and G is the line graph of H, such that G = L(H).
+    Then H is the inverse line graph of G.
+
+    Not all graphs are line graphs and these do not have an inverse line graph.
+    In these cases this function raises a NetworkXError.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph
+
+    Returns
+    -------
+    H : graph
+        The inverse line graph of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed or a multigraph
+
+    NetworkXError
+        If G is not a line graph
+
+    Notes
+    -----
+    This is an implementation of the Roussopoulos algorithm[1]_.
+
+    If G consists of multiple components, then the algorithm doesn't work.
+    You should invert every component separately:
+
+    >>> K5 = nx.complete_graph(5)
+    >>> P4 = nx.Graph([("a", "b"), ("b", "c"), ("c", "d")])
+    >>> G = nx.union(K5, P4)
+    >>> root_graphs = []
+    >>> for comp in nx.connected_components(G):
+    ...     root_graphs.append(nx.inverse_line_graph(G.subgraph(comp)))
+    >>> len(root_graphs)
+    2
+
+    References
+    ----------
+    .. [1] Roussopoulos, N.D. , "A max {m, n} algorithm for determining the graph H from
+       its line graph G", Information Processing Letters 2, (1973), 108--112, ISSN 0020-0190,
+       `DOI link <https://doi.org/10.1016/0020-0190(73)90029-X>`_
+
+    """
+    if G.number_of_nodes() == 0:
+        return nx.empty_graph(1)
+    elif G.number_of_nodes() == 1:
+        v = arbitrary_element(G)
+        a = (v, 0)
+        b = (v, 1)
+        H = nx.Graph([(a, b)])
+        return H
+    elif G.number_of_nodes() > 1 and G.number_of_edges() == 0:
+        msg = (
+            "inverse_line_graph() doesn't work on an edgeless graph. "
+            "Please use this function on each component separately."
+        )
+        raise nx.NetworkXError(msg)
+
+    if nx.number_of_selfloops(G) != 0:
+        msg = (
+            "A line graph as generated by NetworkX has no selfloops, so G has no "
+            "inverse line graph. Please remove the selfloops from G and try again."
+        )
+        raise nx.NetworkXError(msg)
+
+    starting_cell = _select_starting_cell(G)
+    P = _find_partition(G, starting_cell)
+    # count how many times each vertex appears in the partition set
+    P_count = {u: 0 for u in G.nodes}
+    for p in P:
+        for u in p:
+            P_count[u] += 1
+
+    if max(P_count.values()) > 2:
+        msg = "G is not a line graph (vertex found in more than two partition cells)"
+        raise nx.NetworkXError(msg)
+    W = tuple((u,) for u in P_count if P_count[u] == 1)
+    H = nx.Graph()
+    H.add_nodes_from(P)
+    H.add_nodes_from(W)
+    for a, b in combinations(H.nodes, 2):
+        if any(a_bit in b for a_bit in a):
+            H.add_edge(a, b)
+    return H
+
+
+def _triangles(G, e):
+    """Return list of all triangles containing edge e"""
+    u, v = e
+    if u not in G:
+        raise nx.NetworkXError(f"Vertex {u} not in graph")
+    if v not in G[u]:
+        raise nx.NetworkXError(f"Edge ({u}, {v}) not in graph")
+    triangle_list = []
+    for x in G[u]:
+        if x in G[v]:
+            triangle_list.append((u, v, x))
+    return triangle_list
+
+
+def _odd_triangle(G, T):
+    """Test whether T is an odd triangle in G
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    T : 3-tuple of vertices forming triangle in G
+
+    Returns
+    -------
+    True is T is an odd triangle
+    False otherwise
+
+    Raises
+    ------
+    NetworkXError
+        T is not a triangle in G
+
+    Notes
+    -----
+    An odd triangle is one in which there exists another vertex in G which is
+    adjacent to either exactly one or exactly all three of the vertices in the
+    triangle.
+
+    """
+    for u in T:
+        if u not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {u} not in graph")
+    for e in list(combinations(T, 2)):
+        if e[0] not in G[e[1]]:
+            raise nx.NetworkXError(f"Edge ({e[0]}, {e[1]}) not in graph")
+
+    T_nbrs = defaultdict(int)
+    for t in T:
+        for v in G[t]:
+            if v not in T:
+                T_nbrs[v] += 1
+    return any(T_nbrs[v] in [1, 3] for v in T_nbrs)
+
+
+def _find_partition(G, starting_cell):
+    """Find a partition of the vertices of G into cells of complete graphs
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_cell : tuple of vertices in G which form a cell
+
+    Returns
+    -------
+    List of tuples of vertices of G
+
+    Raises
+    ------
+    NetworkXError
+        If a cell is not a complete subgraph then G is not a line graph
+    """
+    G_partition = G.copy()
+    P = [starting_cell]  # partition set
+    G_partition.remove_edges_from(list(combinations(starting_cell, 2)))
+    # keep list of partitioned nodes which might have an edge in G_partition
+    partitioned_vertices = list(starting_cell)
+    while G_partition.number_of_edges() > 0:
+        # there are still edges left and so more cells to be made
+        u = partitioned_vertices.pop()
+        deg_u = len(G_partition[u])
+        if deg_u != 0:
+            # if u still has edges then we need to find its other cell
+            # this other cell must be a complete subgraph or else G is
+            # not a line graph
+            new_cell = [u] + list(G_partition[u])
+            for u in new_cell:
+                for v in new_cell:
+                    if (u != v) and (v not in G_partition[u]):
+                        msg = (
+                            "G is not a line graph "
+                            "(partition cell not a complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            P.append(tuple(new_cell))
+            G_partition.remove_edges_from(list(combinations(new_cell, 2)))
+            partitioned_vertices += new_cell
+    return P
+
+
+def _select_starting_cell(G, starting_edge=None):
+    """Select a cell to initiate _find_partition
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_edge: an edge to build the starting cell from
+
+    Returns
+    -------
+    Tuple of vertices in G
+
+    Raises
+    ------
+    NetworkXError
+        If it is determined that G is not a line graph
+
+    Notes
+    -----
+    If starting edge not specified then pick an arbitrary edge - doesn't
+    matter which. However, this function may call itself requiring a
+    specific starting edge. Note that the r, s notation for counting
+    triangles is the same as in the Roussopoulos paper cited above.
+    """
+    if starting_edge is None:
+        e = arbitrary_element(G.edges())
+    else:
+        e = starting_edge
+        if e[0] not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {e[0]} not in graph")
+        if e[1] not in G[e[0]]:
+            msg = f"starting_edge ({e[0]}, {e[1]}) is not in the Graph"
+            raise nx.NetworkXError(msg)
+    e_triangles = _triangles(G, e)
+    r = len(e_triangles)
+    if r == 0:
+        # there are no triangles containing e, so the starting cell is just e
+        starting_cell = e
+    elif r == 1:
+        # there is exactly one triangle, T, containing e. If other 2 edges
+        # of T belong only to this triangle then T is starting cell
+        T = e_triangles[0]
+        a, b, c = T
+        # ab was original edge so check the other 2 edges
+        ac_edges = len(_triangles(G, (a, c)))
+        bc_edges = len(_triangles(G, (b, c)))
+        if ac_edges == 1:
+            if bc_edges == 1:
+                starting_cell = T
+            else:
+                return _select_starting_cell(G, starting_edge=(b, c))
+        else:
+            return _select_starting_cell(G, starting_edge=(a, c))
+    else:
+        # r >= 2 so we need to count the number of odd triangles, s
+        s = 0
+        odd_triangles = []
+        for T in e_triangles:
+            if _odd_triangle(G, T):
+                s += 1
+                odd_triangles.append(T)
+        if r == 2 and s == 0:
+            # in this case either triangle works, so just use T
+            starting_cell = T
+        elif r - 1 <= s <= r:
+            # check if odd triangles containing e form complete subgraph
+            triangle_nodes = set()
+            for T in odd_triangles:
+                for x in T:
+                    triangle_nodes.add(x)
+
+            for u in triangle_nodes:
+                for v in triangle_nodes:
+                    if u != v and (v not in G[u]):
+                        msg = (
+                            "G is not a line graph (odd triangles "
+                            "do not form complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            # otherwise then we can use this as the starting cell
+            starting_cell = tuple(triangle_nodes)
+
+        else:
+            msg = (
+                "G is not a line graph (incorrect number of "
+                "odd triangles around starting edge)"
+            )
+            raise nx.NetworkXError(msg)
+    return starting_cell
diff --git a/lib/python3.11/site-packages/networkx/generators/mycielski.py b/lib/python3.11/site-packages/networkx/generators/mycielski.py
new file mode 100644
index 0000000000000000000000000000000000000000..804b903692853d3c45b3b1b20898efeee9b71a5e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/mycielski.py
@@ -0,0 +1,110 @@
+"""Functions related to the Mycielski Operation and the Mycielskian family
+of graphs.
+
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["mycielskian", "mycielski_graph"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def mycielskian(G, iterations=1):
+    r"""Returns the Mycielskian of a simple, undirected graph G
+
+    The Mycielskian of graph preserves a graph's triangle free
+    property while increasing the chromatic number by 1.
+
+    The Mycielski Operation on a graph, :math:`G=(V, E)`, constructs a new
+    graph with :math:`2|V| + 1` nodes and :math:`3|E| + |V|` edges.
+
+    The construction is as follows:
+
+    Let :math:`V = {0, ..., n-1}`. Construct another vertex set
+    :math:`U = {n, ..., 2n}` and a vertex, `w`.
+    Construct a new graph, `M`, with vertices :math:`U \bigcup V \bigcup w`.
+    For edges, :math:`(u, v) \in E` add edges :math:`(u, v), (u, v + n)`, and
+    :math:`(u + n, v)` to M. Finally, for all vertices :math:`u \in U`, add
+    edge :math:`(u, w)` to M.
+
+    The Mycielski Operation can be done multiple times by repeating the above
+    process iteratively.
+
+    More information can be found at https://en.wikipedia.org/wiki/Mycielskian
+
+    Parameters
+    ----------
+    G : graph
+        A simple, undirected NetworkX graph
+    iterations : int
+        The number of iterations of the Mycielski operation to
+        perform on G. Defaults to 1. Must be a non-negative integer.
+
+    Returns
+    -------
+    M : graph
+        The Mycielskian of G after the specified number of iterations.
+
+    Notes
+    -----
+    Graph, node, and edge data are not necessarily propagated to the new graph.
+
+    """
+
+    M = nx.convert_node_labels_to_integers(G)
+
+    for i in range(iterations):
+        n = M.number_of_nodes()
+        M.add_nodes_from(range(n, 2 * n))
+        old_edges = list(M.edges())
+        M.add_edges_from((u, v + n) for u, v in old_edges)
+        M.add_edges_from((u + n, v) for u, v in old_edges)
+        M.add_node(2 * n)
+        M.add_edges_from((u + n, 2 * n) for u in range(n))
+
+    return M
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def mycielski_graph(n):
+    """Generator for the n_th Mycielski Graph.
+
+    The Mycielski family of graphs is an infinite set of graphs.
+    :math:`M_1` is the singleton graph, :math:`M_2` is two vertices with an
+    edge, and, for :math:`i > 2`, :math:`M_i` is the Mycielskian of
+    :math:`M_{i-1}`.
+
+    More information can be found at
+    http://mathworld.wolfram.com/MycielskiGraph.html
+
+    Parameters
+    ----------
+    n : int
+        The desired Mycielski Graph.
+
+    Returns
+    -------
+    M : graph
+        The n_th Mycielski Graph
+
+    Notes
+    -----
+    The first graph in the Mycielski sequence is the singleton graph.
+    The Mycielskian of this graph is not the :math:`P_2` graph, but rather the
+    :math:`P_2` graph with an extra, isolated vertex. The second Mycielski
+    graph is the :math:`P_2` graph, so the first two are hard coded.
+    The remaining graphs are generated using the Mycielski operation.
+
+    """
+
+    if n < 1:
+        raise nx.NetworkXError("must satisfy n >= 1")
+
+    if n == 1:
+        return nx.empty_graph(1)
+
+    else:
+        return mycielskian(nx.path_graph(2), n - 2)
diff --git a/lib/python3.11/site-packages/networkx/generators/nonisomorphic_trees.py b/lib/python3.11/site-packages/networkx/generators/nonisomorphic_trees.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a0f0c996d8915b07b5f87c4e14359bc642b2929
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/nonisomorphic_trees.py
@@ -0,0 +1,259 @@
+"""
+Implementation of the Wright, Richmond, Odlyzko and McKay (WROM)
+algorithm for the enumeration of all non-isomorphic free trees of a
+given order.  Rooted trees are represented by level sequences, i.e.,
+lists in which the i-th element specifies the distance of vertex i to
+the root.
+
+"""
+
+__all__ = ["nonisomorphic_trees", "number_of_nonisomorphic_trees"]
+
+from functools import lru_cache
+
+import networkx as nx
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def nonisomorphic_trees(order):
+    """Generate nonisomorphic trees of specified `order`.
+
+    Parameters
+    ----------
+    order : int
+       order of the desired tree(s)
+
+    Yields
+    ------
+    `networkx.Graph` instances
+       A tree with `order` number of nodes that is not isomorphic to any other
+       yielded tree.
+
+    Raises
+    ------
+    ValueError
+       If `order` is negative.
+
+    Examples
+    --------
+    There are 11 unique (non-isomorphic) trees with 7 nodes.
+
+    >>> n = 7
+    >>> nit_list = list(nx.nonisomorphic_trees(n))
+    >>> len(nit_list) == nx.number_of_nonisomorphic_trees(n) == 11
+    True
+
+    All trees yielded by the generator have the specified order.
+
+    >>> all(len(G) == n for G in nx.nonisomorphic_trees(n))
+    True
+
+    Each tree is nonisomorphic to every other tree yielded by the generator.
+    >>> seen = []
+    >>> for G in nx.nonisomorphic_trees(n):
+    ...     assert not any(nx.is_isomorphic(G, H) for H in seen)
+    ...     seen.append(G)
+
+    See Also
+    --------
+    number_of_nonisomorphic_trees
+    """
+    if order < 0:
+        raise ValueError("order must be non-negative")
+    if order == 0:
+        # Idiom for empty generator, i.e. list(nonisomorphic_trees(0)) == []
+        return
+        yield
+    if order == 1:
+        yield nx.empty_graph(1)
+        return
+    # start at the path graph rooted at its center
+    layout = list(range(order // 2 + 1)) + list(range(1, (order + 1) // 2))
+
+    while layout is not None:
+        layout = _next_tree(layout)
+        if layout is not None:
+            yield _layout_to_graph(layout)
+            layout = _next_rooted_tree(layout)
+
+
+@nx._dispatchable(graphs=None)
+def number_of_nonisomorphic_trees(order):
+    """Returns the number of nonisomorphic trees of the specified `order`.
+
+    Based on an algorithm by Alois P. Heinz in
+    `OEIS entry A000055 <https://oeis.org/A000055>`_. Complexity is ``O(n ** 3)``.
+
+    Parameters
+    ----------
+    order : int
+       Order of the desired tree(s).
+
+    Returns
+    -------
+    int
+       Number of nonisomorphic trees with `order` number of nodes.
+
+    Raises
+    ------
+    ValueError
+       If `order` is negative.
+
+    Examples
+    --------
+    >>> nx.number_of_nonisomorphic_trees(10)
+    106
+
+    See Also
+    --------
+    nonisomorphic_trees
+    """
+    if order < 0:
+        raise ValueError("order must be non-negative")
+    return _unlabeled_trees(order)
+
+
+@lru_cache(None)
+def _unlabeled_trees(n):
+    """Implements OEIS A000055 (number of unlabeled trees)."""
+
+    value = 0
+    for k in range(n + 1):
+        value += _rooted_trees(k) * _rooted_trees(n - k)
+    if n % 2 == 0:
+        value -= _rooted_trees(n // 2)
+    return _rooted_trees(n) - value // 2
+
+
+@lru_cache(None)
+def _rooted_trees(n):
+    """Implements OEIS A000081 (number of unlabeled rooted trees)."""
+
+    if n < 2:
+        return n
+    value = 0
+    for j in range(1, n):
+        for d in range(1, n):
+            if j % d == 0:
+                value += d * _rooted_trees(d) * _rooted_trees(n - j)
+    return value // (n - 1)
+
+
+def _next_rooted_tree(predecessor, p=None):
+    """One iteration of the Beyer-Hedetniemi algorithm."""
+
+    if p is None:
+        p = len(predecessor) - 1
+        while predecessor[p] == 1:
+            p -= 1
+    if p == 0:
+        return None
+
+    q = p - 1
+    while predecessor[q] != predecessor[p] - 1:
+        q -= 1
+    result = list(predecessor)
+    for i in range(p, len(result)):
+        result[i] = result[i - p + q]
+    return result
+
+
+def _next_tree(candidate):
+    """One iteration of the Wright, Richmond, Odlyzko and McKay
+    algorithm."""
+
+    # valid representation of a free tree if:
+    # there are at least two vertices at layer 1
+    # (this is always the case because we start at the path graph)
+    left, rest = _split_tree(candidate)
+
+    # and the left subtree of the root
+    # is less high than the tree with the left subtree removed
+    left_height = max(left)
+    rest_height = max(rest)
+    valid = rest_height >= left_height
+
+    if valid and rest_height == left_height:
+        # and, if left and rest are of the same height,
+        # if left does not encompass more vertices
+        if len(left) > len(rest):
+            valid = False
+        # and, if they have the same number or vertices,
+        # if left does not come after rest lexicographically
+        elif len(left) == len(rest) and left > rest:
+            valid = False
+
+    if valid:
+        return candidate
+    else:
+        # jump to the next valid free tree
+        p = len(left)
+        new_candidate = _next_rooted_tree(candidate, p)
+        if candidate[p] > 2:
+            new_left, new_rest = _split_tree(new_candidate)
+            new_left_height = max(new_left)
+            suffix = range(1, new_left_height + 2)
+            new_candidate[-len(suffix) :] = suffix
+        return new_candidate
+
+
+def _split_tree(layout):
+    """Returns a tuple of two layouts, one containing the left
+    subtree of the root vertex, and one containing the original tree
+    with the left subtree removed."""
+
+    one_found = False
+    m = None
+    for i in range(len(layout)):
+        if layout[i] == 1:
+            if one_found:
+                m = i
+                break
+            else:
+                one_found = True
+
+    if m is None:
+        m = len(layout)
+
+    left = [layout[i] - 1 for i in range(1, m)]
+    rest = [0] + [layout[i] for i in range(m, len(layout))]
+    return (left, rest)
+
+
+def _layout_to_matrix(layout):
+    """Create the adjacency matrix for the tree specified by the
+    given layout (level sequence)."""
+
+    result = [[0] * len(layout) for i in range(len(layout))]
+    stack = []
+    for i in range(len(layout)):
+        i_level = layout[i]
+        if stack:
+            j = stack[-1]
+            j_level = layout[j]
+            while j_level >= i_level:
+                stack.pop()
+                j = stack[-1]
+                j_level = layout[j]
+            result[i][j] = result[j][i] = 1
+        stack.append(i)
+    return result
+
+
+def _layout_to_graph(layout):
+    """Create a NetworkX Graph for the tree specified by the
+    given layout(level sequence)"""
+    G = nx.Graph()
+    stack = []
+    for i in range(len(layout)):
+        i_level = layout[i]
+        if stack:
+            j = stack[-1]
+            j_level = layout[j]
+            while j_level >= i_level:
+                stack.pop()
+                j = stack[-1]
+                j_level = layout[j]
+            G.add_edge(i, j)
+        stack.append(i)
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/random_clustered.py b/lib/python3.11/site-packages/networkx/generators/random_clustered.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbf855e672d3c50f1e74952cc2272143fbac57a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/random_clustered.py
@@ -0,0 +1,117 @@
+"""Generate graphs with given degree and triangle sequence."""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_clustered_graph"]
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_clustered_graph(joint_degree_sequence, create_using=None, seed=None):
+    r"""Generate a random graph with the given joint independent edge degree and
+    triangle degree sequence.
+
+    This uses a configuration model-like approach to generate a random graph
+    (with parallel edges and self-loops) by randomly assigning edges to match
+    the given joint degree sequence.
+
+    The joint degree sequence is a list of pairs of integers of the form
+    $[(d_{1,i}, d_{1,t}), \dotsc, (d_{n,i}, d_{n,t})]$. According to this list,
+    vertex $u$ is a member of $d_{u,t}$ triangles and has $d_{u, i}$ other
+    edges. The number $d_{u,t}$ is the *triangle degree* of $u$ and the number
+    $d_{u,i}$ is the *independent edge degree*.
+
+    Parameters
+    ----------
+    joint_degree_sequence : list of integer pairs
+        Each list entry corresponds to the independent edge degree and
+        triangle degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence. Nodes are labeled
+        starting at 0 with an index corresponding to the position in
+        deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the independent edge degree sequence sum is not even
+        or the triangle degree sequence sum is not divisible by 3.
+
+    Notes
+    -----
+    As described by Miller [1]_ (see also Newman [2]_ for an equivalent
+    description).
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the
+    independent degree sequence does not have an even sum or the
+    triangle degree sequence sum is not divisible by 3.
+
+    This configuration model-like construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Joel C. Miller. "Percolation and epidemics in random clustered
+           networks". In: Physical review. E, Statistical, nonlinear, and soft
+           matter physics 80 (2 Part 1 August 2009).
+    .. [2] M. E. J. Newman. "Random Graphs with Clustering".
+           In: Physical Review Letters 103 (5 July 2009)
+
+    Examples
+    --------
+    >>> deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
+    >>> G = nx.random_clustered_graph(deg)
+
+    To remove parallel edges:
+
+    >>> G = nx.Graph(G)
+
+    To remove self loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    # In Python 3, zip() returns an iterator. Make this into a list.
+    joint_degree_sequence = list(joint_degree_sequence)
+
+    N = len(joint_degree_sequence)
+    G = nx.empty_graph(N, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    ilist = []
+    tlist = []
+    for n in G:
+        degrees = joint_degree_sequence[n]
+        for icount in range(degrees[0]):
+            ilist.append(n)
+        for tcount in range(degrees[1]):
+            tlist.append(n)
+
+    if len(ilist) % 2 != 0 or len(tlist) % 3 != 0:
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    seed.shuffle(ilist)
+    seed.shuffle(tlist)
+    while ilist:
+        G.add_edge(ilist.pop(), ilist.pop())
+    while tlist:
+        n1 = tlist.pop()
+        n2 = tlist.pop()
+        n3 = tlist.pop()
+        G.add_edges_from([(n1, n2), (n1, n3), (n2, n3)])
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/random_graphs.py b/lib/python3.11/site-packages/networkx/generators/random_graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ab0c9813631319ff866ac585926a88e6d76550
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/random_graphs.py
@@ -0,0 +1,1416 @@
+"""
+Generators for random graphs.
+
+"""
+
+import itertools
+import math
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+from ..utils.misc import check_create_using
+from .classic import complete_graph, empty_graph, path_graph, star_graph
+from .degree_seq import degree_sequence_tree
+
+__all__ = [
+    "fast_gnp_random_graph",
+    "gnp_random_graph",
+    "dense_gnm_random_graph",
+    "gnm_random_graph",
+    "erdos_renyi_graph",
+    "binomial_graph",
+    "newman_watts_strogatz_graph",
+    "watts_strogatz_graph",
+    "connected_watts_strogatz_graph",
+    "random_regular_graph",
+    "barabasi_albert_graph",
+    "dual_barabasi_albert_graph",
+    "extended_barabasi_albert_graph",
+    "powerlaw_cluster_graph",
+    "random_lobster",
+    "random_lobster_graph",
+    "random_shell_graph",
+    "random_powerlaw_tree",
+    "random_powerlaw_tree_sequence",
+    "random_kernel_graph",
+]
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def fast_gnp_random_graph(n, p, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph or
+    a binomial graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True, this function returns a directed graph.
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    Notes
+    -----
+    The $G_{n,p}$ graph algorithm chooses each of the $[n (n - 1)] / 2$
+    (undirected) or $n (n - 1)$ (directed) possible edges with probability $p$.
+
+    This algorithm [1]_ runs in $O(n + m)$ time, where `m` is the expected number of
+    edges, which equals $p n (n - 1) / 2$. This should be faster than
+    :func:`gnp_random_graph` when $p$ is small and the expected number of edges
+    is small (that is, the graph is sparse).
+
+    See Also
+    --------
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] Vladimir Batagelj and Ulrik Brandes,
+       "Efficient generation of large random networks",
+       Phys. Rev. E, 71, 036113, 2005.
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if p <= 0 or p >= 1:
+        return nx.gnp_random_graph(
+            n, p, seed=seed, directed=directed, create_using=create_using
+        )
+
+    G = empty_graph(n, create_using=create_using)
+
+    lp = math.log(1.0 - p)
+
+    if directed:
+        v = 1
+        w = -1
+        while v < n:
+            lr = math.log(1.0 - seed.random())
+            w = w + 1 + int(lr / lp)
+            while w >= v and v < n:
+                w = w - v
+                v = v + 1
+            if v < n:
+                G.add_edge(w, v)
+
+    # Nodes in graph are from 0,n-1 (start with v as the second node index).
+    v = 1
+    w = -1
+    while v < n:
+        lr = math.log(1.0 - seed.random())
+        w = w + 1 + int(lr / lp)
+        while w >= v and v < n:
+            w = w - v
+            v = v + 1
+        if v < n:
+            G.add_edge(v, w)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnp_random_graph(n, p, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph
+    or a binomial graph.
+
+    The $G_{n,p}$ model chooses each of the possible edges with probability $p$.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True, this function returns a directed graph.
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    See Also
+    --------
+    fast_gnp_random_graph
+
+    Notes
+    -----
+    This algorithm [2]_ runs in $O(n^2)$ time.  For sparse graphs (that is, for
+    small values of $p$), :func:`fast_gnp_random_graph` is a faster algorithm.
+
+    :func:`binomial_graph` and :func:`erdos_renyi_graph` are
+    aliases for :func:`gnp_random_graph`.
+
+    >>> nx.binomial_graph is nx.gnp_random_graph
+    True
+    >>> nx.erdos_renyi_graph is nx.gnp_random_graph
+    True
+
+    References
+    ----------
+    .. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
+    .. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if p >= 1:
+        return complete_graph(n, create_using=create_using)
+
+    G = nx.empty_graph(n, create_using=create_using)
+    if p <= 0:
+        return G
+
+    edgetool = itertools.permutations if directed else itertools.combinations
+    for e in edgetool(range(n), 2):
+        if seed.random() < p:
+            G.add_edge(*e)
+    return G
+
+
+# add some aliases to common names
+binomial_graph = gnp_random_graph
+erdos_renyi_graph = gnp_random_graph
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dense_gnm_random_graph(n, m, seed=None, *, create_using=None):
+    """Returns a $G_{n,m}$ random graph.
+
+    In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set
+    of all graphs with $n$ nodes and $m$ edges.
+
+    This algorithm should be faster than :func:`gnm_random_graph` for dense
+    graphs.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    m : int
+        The number of edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    See Also
+    --------
+    gnm_random_graph
+
+    Notes
+    -----
+    Algorithm by Keith M. Briggs Mar 31, 2006.
+    Inspired by Knuth's Algorithm S (Selection sampling technique),
+    in section 3.4.2 of [1]_.
+
+    References
+    ----------
+    .. [1] Donald E. Knuth, The Art of Computer Programming,
+        Volume 2/Seminumerical algorithms, Third Edition, Addison-Wesley, 1997.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    mmax = n * (n - 1) // 2
+    if m >= mmax:
+        return complete_graph(n, create_using)
+    G = empty_graph(n, create_using)
+
+    if n == 1:
+        return G
+
+    u = 0
+    v = 1
+    t = 0
+    k = 0
+    while True:
+        if seed.randrange(mmax - t) < m - k:
+            G.add_edge(u, v)
+            k += 1
+            if k == m:
+                return G
+        t += 1
+        v += 1
+        if v == n:  # go to next row of adjacency matrix
+            u += 1
+            v = u + 1
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnm_random_graph(n, m, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,m}$ random graph.
+
+    In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set
+    of all graphs with $n$ nodes and $m$ edges.
+
+    This algorithm should be faster than :func:`dense_gnm_random_graph` for
+    sparse graphs.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    m : int
+        The number of edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True return a directed graph
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    See also
+    --------
+    dense_gnm_random_graph
+
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if n == 1:
+        return nx.empty_graph(n, create_using=create_using)
+    max_edges = n * (n - 1) if directed else n * (n - 1) / 2.0
+    if m >= max_edges:
+        return complete_graph(n, create_using=create_using)
+
+    G = nx.empty_graph(n, create_using=create_using)
+    nlist = list(G)
+    edge_count = 0
+    while edge_count < m:
+        # generate random edge,u,v
+        u = seed.choice(nlist)
+        v = seed.choice(nlist)
+        if u == v or G.has_edge(u, v):
+            continue
+        else:
+            G.add_edge(u, v)
+            edge_count = edge_count + 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def newman_watts_strogatz_graph(n, k, p, seed=None, *, create_using=None):
+    """Returns a Newman–Watts–Strogatz small-world graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of adding a new edge for each edge.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is
+    connected with its $k$ nearest neighbors (or $k - 1$ neighbors if $k$
+    is odd).  Then shortcuts are created by adding new edges as follows: for
+    each edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest
+    neighbors" with probability $p$ add a new edge $(u, w)$ with
+    randomly-chosen existing node $w$.  In contrast with
+    :func:`watts_strogatz_graph`, no edges are removed.
+
+    See Also
+    --------
+    watts_strogatz_graph
+
+    References
+    ----------
+    .. [1] M. E. J. Newman and D. J. Watts,
+       Renormalization group analysis of the small-world network model,
+       Physics Letters A, 263, 341, 1999.
+       https://doi.org/10.1016/S0375-9601(99)00757-4
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if k > n:
+        raise nx.NetworkXError("k>=n, choose smaller k or larger n")
+
+    # If k == n the graph return is a complete graph
+    if k == n:
+        return nx.complete_graph(n, create_using)
+
+    G = empty_graph(n, create_using)
+    nlist = list(G.nodes())
+    fromv = nlist
+    # connect the k/2 neighbors
+    for j in range(1, k // 2 + 1):
+        tov = fromv[j:] + fromv[0:j]  # the first j are now last
+        for i in range(len(fromv)):
+            G.add_edge(fromv[i], tov[i])
+    # for each edge u-v, with probability p, randomly select existing
+    # node w and add new edge u-w
+    e = list(G.edges())
+    for u, v in e:
+        if seed.random() < p:
+            w = seed.choice(nlist)
+            # no self-loops and reject if edge u-w exists
+            # is that the correct NWS model?
+            while w == u or G.has_edge(u, w):
+                w = seed.choice(nlist)
+                if G.degree(u) >= n - 1:
+                    break  # skip this rewiring
+            else:
+                G.add_edge(u, w)
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def watts_strogatz_graph(n, k, p, seed=None, *, create_using=None):
+    """Returns a Watts–Strogatz small-world graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of rewiring each edge
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    See Also
+    --------
+    newman_watts_strogatz_graph
+    connected_watts_strogatz_graph
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is joined
+    to its $k$ nearest neighbors (or $k - 1$ neighbors if $k$ is odd).
+    Then shortcuts are created by replacing some edges as follows: for each
+    edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest neighbors"
+    with probability $p$ replace it with a new edge $(u, w)$ with uniformly
+    random choice of existing node $w$.
+
+    In contrast with :func:`newman_watts_strogatz_graph`, the random rewiring
+    does not increase the number of edges. The rewired graph is not guaranteed
+    to be connected as in :func:`connected_watts_strogatz_graph`.
+
+    References
+    ----------
+    .. [1] Duncan J. Watts and Steven H. Strogatz,
+       Collective dynamics of small-world networks,
+       Nature, 393, pp. 440--442, 1998.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if k > n:
+        raise nx.NetworkXError("k>n, choose smaller k or larger n")
+
+    # If k == n, the graph is complete not Watts-Strogatz
+    if k == n:
+        G = nx.complete_graph(n, create_using)
+        return G
+
+    G = nx.empty_graph(n, create_using=create_using)
+    nodes = list(range(n))  # nodes are labeled 0 to n-1
+    # connect each node to k/2 neighbors
+    for j in range(1, k // 2 + 1):
+        targets = nodes[j:] + nodes[0:j]  # first j nodes are now last in list
+        G.add_edges_from(zip(nodes, targets))
+    # rewire edges from each node
+    # loop over all nodes in order (label) and neighbors in order (distance)
+    # no self loops or multiple edges allowed
+    for j in range(1, k // 2 + 1):  # outer loop is neighbors
+        targets = nodes[j:] + nodes[0:j]  # first j nodes are now last in list
+        # inner loop in node order
+        for u, v in zip(nodes, targets):
+            if seed.random() < p:
+                w = seed.choice(nodes)
+                # Enforce no self-loops or multiple edges
+                while w == u or G.has_edge(u, w):
+                    w = seed.choice(nodes)
+                    if G.degree(u) >= n - 1:
+                        break  # skip this rewiring
+                else:
+                    G.remove_edge(u, v)
+                    G.add_edge(u, w)
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def connected_watts_strogatz_graph(n, k, p, tries=100, seed=None, *, create_using=None):
+    """Returns a connected Watts–Strogatz small-world graph.
+
+    Attempts to generate a connected graph by repeated generation of
+    Watts–Strogatz small-world graphs.  An exception is raised if the maximum
+    number of tries is exceeded.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of rewiring each edge
+    tries : int
+        Number of attempts to generate a connected graph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is joined
+    to its $k$ nearest neighbors (or $k - 1$ neighbors if $k$ is odd).
+    Then shortcuts are created by replacing some edges as follows: for each
+    edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest neighbors"
+    with probability $p$ replace it with a new edge $(u, w)$ with uniformly
+    random choice of existing node $w$.
+    The entire process is repeated until a connected graph results.
+
+    See Also
+    --------
+    newman_watts_strogatz_graph
+    watts_strogatz_graph
+
+    References
+    ----------
+    .. [1] Duncan J. Watts and Steven H. Strogatz,
+       Collective dynamics of small-world networks,
+       Nature, 393, pp. 440--442, 1998.
+    """
+    for i in range(tries):
+        # seed is an RNG so should change sequence each call
+        G = watts_strogatz_graph(n, k, p, seed, create_using=create_using)
+        if nx.is_connected(G):
+            return G
+    raise nx.NetworkXError("Maximum number of tries exceeded")
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_graph(d, n, seed=None, *, create_using=None):
+    r"""Returns a random $d$-regular graph on $n$ nodes.
+
+    A regular graph is a graph where each node has the same number of neighbors.
+
+    The resulting graph has no self-loops or parallel edges.
+
+    Parameters
+    ----------
+    d : int
+      The degree of each node.
+    n : integer
+      The number of nodes. The value of $n \times d$ must be even.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    Kim and Vu's paper [2]_ shows that this algorithm samples in an
+    asymptotically uniform way from the space of random graphs when
+    $d = O(n^{1 / 3 - \epsilon})$.
+
+    Raises
+    ------
+
+    NetworkXError
+        If $n \times d$ is odd or $d$ is greater than or equal to $n$.
+
+    References
+    ----------
+    .. [1] A. Steger and N. Wormald,
+       Generating random regular graphs quickly,
+       Probability and Computing 8 (1999), 377-396, 1999.
+       https://doi.org/10.1017/S0963548399003867
+
+    .. [2] Jeong Han Kim and Van H. Vu,
+       Generating random regular graphs,
+       Proceedings of the thirty-fifth ACM symposium on Theory of computing,
+       San Diego, CA, USA, pp 213--222, 2003.
+       http://portal.acm.org/citation.cfm?id=780542.780576
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if (n * d) % 2 != 0:
+        raise nx.NetworkXError("n * d must be even")
+
+    if not 0 <= d < n:
+        raise nx.NetworkXError("the 0 <= d < n inequality must be satisfied")
+
+    G = nx.empty_graph(n, create_using=create_using)
+
+    if d == 0:
+        return G
+
+    def _suitable(edges, potential_edges):
+        # Helper subroutine to check if there are suitable edges remaining
+        # If False, the generation of the graph has failed
+        if not potential_edges:
+            return True
+        for s1 in potential_edges:
+            for s2 in potential_edges:
+                # Two iterators on the same dictionary are guaranteed
+                # to visit it in the same order if there are no
+                # intervening modifications.
+                if s1 == s2:
+                    # Only need to consider s1-s2 pair one time
+                    break
+                if s1 > s2:
+                    s1, s2 = s2, s1
+                if (s1, s2) not in edges:
+                    return True
+        return False
+
+    def _try_creation():
+        # Attempt to create an edge set
+
+        edges = set()
+        stubs = list(range(n)) * d
+
+        while stubs:
+            potential_edges = defaultdict(lambda: 0)
+            seed.shuffle(stubs)
+            stubiter = iter(stubs)
+            for s1, s2 in zip(stubiter, stubiter):
+                if s1 > s2:
+                    s1, s2 = s2, s1
+                if s1 != s2 and ((s1, s2) not in edges):
+                    edges.add((s1, s2))
+                else:
+                    potential_edges[s1] += 1
+                    potential_edges[s2] += 1
+
+            if not _suitable(edges, potential_edges):
+                return None  # failed to find suitable edge set
+
+            stubs = [
+                node
+                for node, potential in potential_edges.items()
+                for _ in range(potential)
+            ]
+        return edges
+
+    # Even though a suitable edge set exists,
+    # the generation of such a set is not guaranteed.
+    # Try repeatedly to find one.
+    edges = _try_creation()
+    while edges is None:
+        edges = _try_creation()
+    G.add_edges_from(edges)
+
+    return G
+
+
+def _random_subset(seq, m, rng):
+    """Return m unique elements from seq.
+
+    This differs from random.sample which can return repeated
+    elements if seq holds repeated elements.
+
+    Note: rng is a random.Random or numpy.random.RandomState instance.
+    """
+    targets = set()
+    while len(targets) < m:
+        x = rng.choice(seq)
+        targets.add(x)
+    return targets
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def barabasi_albert_graph(n, m, seed=None, initial_graph=None, *, create_using=None):
+    """Returns a random graph using Barabási–Albert preferential attachment
+
+    A graph of $n$ nodes is grown by attaching new nodes each with $m$
+    edges that are preferentially attached to existing nodes with high degree.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m : int
+        Number of edges to attach from a new node to existing nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : Graph or None (default)
+        Initial network for Barabási–Albert algorithm.
+        It should be a connected graph for most use cases.
+        A copy of `initial_graph` is used.
+        If None, starts from a star graph on (m+1) nodes.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m < n``, or
+        the initial graph number of nodes m0 does not satisfy ``m <= m0 <= n``.
+
+    References
+    ----------
+    .. [1] A. L. Barabási and R. Albert "Emergence of scaling in
+       random networks", Science 286, pp 509-512, 1999.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or m >= n:
+        raise nx.NetworkXError(
+            f"Barabási–Albert network must have m >= 1 and m < n, m = {m}, n = {n}"
+        )
+
+    if initial_graph is None:
+        # Default initial graph : star graph on (m + 1) nodes
+        G = star_graph(m, create_using)
+    else:
+        if len(initial_graph) < m or len(initial_graph) > n:
+            raise nx.NetworkXError(
+                f"Barabási–Albert initial graph needs between m={m} and n={n} nodes"
+            )
+        G = initial_graph.copy()
+
+    # List of existing nodes, with nodes repeated once for each adjacent edge
+    repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
+    # Start adding the other n - m0 nodes.
+    source = len(G)
+    while source < n:
+        # Now choose m unique nodes from the existing nodes
+        # Pick uniformly from repeated_nodes (preferential attachment)
+        targets = _random_subset(repeated_nodes, m, seed)
+        # Add edges to m nodes from the source.
+        G.add_edges_from(zip([source] * m, targets))
+        # Add one node to the list for each new edge just created.
+        repeated_nodes.extend(targets)
+        # And the new node "source" has m edges to add to the list.
+        repeated_nodes.extend([source] * m)
+
+        source += 1
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dual_barabasi_albert_graph(
+    n, m1, m2, p, seed=None, initial_graph=None, *, create_using=None
+):
+    """Returns a random graph using dual Barabási–Albert preferential attachment
+
+    A graph of $n$ nodes is grown by attaching new nodes each with either $m_1$
+    edges (with probability $p$) or $m_2$ edges (with probability $1-p$) that
+    are preferentially attached to existing nodes with high degree.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m1 : int
+        Number of edges to link each new node to existing nodes with probability $p$
+    m2 : int
+        Number of edges to link each new node to existing nodes with probability $1-p$
+    p : float
+        The probability of attaching $m_1$ edges (as opposed to $m_2$ edges)
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : Graph or None (default)
+        Initial network for Barabási–Albert algorithm.
+        A copy of `initial_graph` is used.
+        It should be connected for most use cases.
+        If None, starts from an star graph on max(m1, m2) + 1 nodes.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m1` and `m2` do not satisfy ``1 <= m1,m2 < n``, or
+        `p` does not satisfy ``0 <= p <= 1``, or
+        the initial graph number of nodes m0 does not satisfy m1, m2 <= m0 <= n.
+
+    References
+    ----------
+    .. [1] N. Moshiri "The dual-Barabasi-Albert model", arXiv:1810.10538.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m1 < 1 or m1 >= n:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert must have m1 >= 1 and m1 < n, m1 = {m1}, n = {n}"
+        )
+    if m2 < 1 or m2 >= n:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert must have m2 >= 1 and m2 < n, m2 = {m2}, n = {n}"
+        )
+    if p < 0 or p > 1:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert network must have 0 <= p <= 1, p = {p}"
+        )
+
+    # For simplicity, if p == 0 or 1, just return BA
+    if p == 1:
+        return barabasi_albert_graph(n, m1, seed, create_using=create_using)
+    elif p == 0:
+        return barabasi_albert_graph(n, m2, seed, create_using=create_using)
+
+    if initial_graph is None:
+        # Default initial graph : star graph on max(m1, m2) nodes
+        G = star_graph(max(m1, m2), create_using)
+    else:
+        if len(initial_graph) < max(m1, m2) or len(initial_graph) > n:
+            raise nx.NetworkXError(
+                f"Barabási–Albert initial graph must have between "
+                f"max(m1, m2) = {max(m1, m2)} and n = {n} nodes"
+            )
+        G = initial_graph.copy()
+
+    # Target nodes for new edges
+    targets = list(G)
+    # List of existing nodes, with nodes repeated once for each adjacent edge
+    repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
+    # Start adding the remaining nodes.
+    source = len(G)
+    while source < n:
+        # Pick which m to use (m1 or m2)
+        if seed.random() < p:
+            m = m1
+        else:
+            m = m2
+        # Now choose m unique nodes from the existing nodes
+        # Pick uniformly from repeated_nodes (preferential attachment)
+        targets = _random_subset(repeated_nodes, m, seed)
+        # Add edges to m nodes from the source.
+        G.add_edges_from(zip([source] * m, targets))
+        # Add one node to the list for each new edge just created.
+        repeated_nodes.extend(targets)
+        # And the new node "source" has m edges to add to the list.
+        repeated_nodes.extend([source] * m)
+
+        source += 1
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def extended_barabasi_albert_graph(n, m, p, q, seed=None, *, create_using=None):
+    """Returns an extended Barabási–Albert model graph.
+
+    An extended Barabási–Albert model graph is a random graph constructed
+    using preferential attachment. The extended model allows new edges,
+    rewired edges or new nodes. Based on the probabilities $p$ and $q$
+    with $p + q < 1$, the growing behavior of the graph is determined as:
+
+    1) With $p$ probability, $m$ new edges are added to the graph,
+    starting from randomly chosen existing nodes and attached preferentially at the
+    other end.
+
+    2) With $q$ probability, $m$ existing edges are rewired
+    by randomly choosing an edge and rewiring one end to a preferentially chosen node.
+
+    3) With $(1 - p - q)$ probability, $m$ new nodes are added to the graph
+    with edges attached preferentially.
+
+    When $p = q = 0$, the model behaves just like the Barabási–Alber model.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m : int
+        Number of edges with which a new node attaches to existing nodes
+    p : float
+        Probability value for adding an edge between existing nodes. p + q < 1
+    q : float
+        Probability value of rewiring of existing edges. p + q < 1
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m < n`` or ``1 >= p + q``
+
+    References
+    ----------
+    .. [1] Albert, R., & Barabási, A. L. (2000)
+       Topology of evolving networks: local events and universality
+       Physical review letters, 85(24), 5234.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or m >= n:
+        msg = f"Extended Barabasi-Albert network needs m>=1 and m<n, m={m}, n={n}"
+        raise nx.NetworkXError(msg)
+    if p + q >= 1:
+        msg = f"Extended Barabasi-Albert network needs p + q <= 1, p={p}, q={q}"
+        raise nx.NetworkXError(msg)
+
+    # Add m initial nodes (m0 in barabasi-speak)
+    G = empty_graph(m, create_using)
+
+    # List of nodes to represent the preferential attachment random selection.
+    # At the creation of the graph, all nodes are added to the list
+    # so that even nodes that are not connected have a chance to get selected,
+    # for rewiring and adding of edges.
+    # With each new edge, nodes at the ends of the edge are added to the list.
+    attachment_preference = []
+    attachment_preference.extend(range(m))
+
+    # Start adding the other n-m nodes. The first node is m.
+    new_node = m
+    while new_node < n:
+        a_probability = seed.random()
+
+        # Total number of edges of a Clique of all the nodes
+        clique_degree = len(G) - 1
+        clique_size = (len(G) * clique_degree) / 2
+
+        # Adding m new edges, if there is room to add them
+        if a_probability < p and G.size() <= clique_size - m:
+            # Select the nodes where an edge can be added
+            eligible_nodes = [nd for nd, deg in G.degree() if deg < clique_degree]
+            for i in range(m):
+                # Choosing a random source node from eligible_nodes
+                src_node = seed.choice(eligible_nodes)
+
+                # Picking a possible node that is not 'src_node' or
+                # neighbor with 'src_node', with preferential attachment
+                prohibited_nodes = list(G[src_node])
+                prohibited_nodes.append(src_node)
+                # This will raise an exception if the sequence is empty
+                dest_node = seed.choice(
+                    [nd for nd in attachment_preference if nd not in prohibited_nodes]
+                )
+                # Adding the new edge
+                G.add_edge(src_node, dest_node)
+
+                # Appending both nodes to add to their preferential attachment
+                attachment_preference.append(src_node)
+                attachment_preference.append(dest_node)
+
+                # Adjusting the eligible nodes. Degree may be saturated.
+                if G.degree(src_node) == clique_degree:
+                    eligible_nodes.remove(src_node)
+                if G.degree(dest_node) == clique_degree and dest_node in eligible_nodes:
+                    eligible_nodes.remove(dest_node)
+
+        # Rewiring m edges, if there are enough edges
+        elif p <= a_probability < (p + q) and m <= G.size() < clique_size:
+            # Selecting nodes that have at least 1 edge but that are not
+            # fully connected to ALL other nodes (center of star).
+            # These nodes are the pivot nodes of the edges to rewire
+            eligible_nodes = [nd for nd, deg in G.degree() if 0 < deg < clique_degree]
+            for i in range(m):
+                # Choosing a random source node
+                node = seed.choice(eligible_nodes)
+
+                # The available nodes do have a neighbor at least.
+                nbr_nodes = list(G[node])
+
+                # Choosing the other end that will get detached
+                src_node = seed.choice(nbr_nodes)
+
+                # Picking a target node that is not 'node' or
+                # neighbor with 'node', with preferential attachment
+                nbr_nodes.append(node)
+                dest_node = seed.choice(
+                    [nd for nd in attachment_preference if nd not in nbr_nodes]
+                )
+                # Rewire
+                G.remove_edge(node, src_node)
+                G.add_edge(node, dest_node)
+
+                # Adjusting the preferential attachment list
+                attachment_preference.remove(src_node)
+                attachment_preference.append(dest_node)
+
+                # Adjusting the eligible nodes.
+                # nodes may be saturated or isolated.
+                if G.degree(src_node) == 0 and src_node in eligible_nodes:
+                    eligible_nodes.remove(src_node)
+                if dest_node in eligible_nodes:
+                    if G.degree(dest_node) == clique_degree:
+                        eligible_nodes.remove(dest_node)
+                else:
+                    if G.degree(dest_node) == 1:
+                        eligible_nodes.append(dest_node)
+
+        # Adding new node with m edges
+        else:
+            # Select the edges' nodes by preferential attachment
+            targets = _random_subset(attachment_preference, m, seed)
+            G.add_edges_from(zip([new_node] * m, targets))
+
+            # Add one node to the list for each new edge just created.
+            attachment_preference.extend(targets)
+            # The new node has m edges to it, plus itself: m + 1
+            attachment_preference.extend([new_node] * (m + 1))
+            new_node += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def powerlaw_cluster_graph(n, m, p, seed=None, *, create_using=None):
+    """Holme and Kim algorithm for growing graphs with powerlaw
+    degree distribution and approximate average clustering.
+
+    Parameters
+    ----------
+    n : int
+        the number of nodes
+    m : int
+        the number of random edges to add for each new node
+    p : float,
+        Probability of adding a triangle after adding a random edge
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The average clustering has a hard time getting above a certain
+    cutoff that depends on `m`.  This cutoff is often quite low.  The
+    transitivity (fraction of triangles to possible triangles) seems to
+    decrease with network size.
+
+    It is essentially the Barabási–Albert (BA) growth model with an
+    extra step that each random edge is followed by a chance of
+    making an edge to one of its neighbors too (and thus a triangle).
+
+    This algorithm improves on BA in the sense that it enables a
+    higher average clustering to be attained if desired.
+
+    It seems possible to have a disconnected graph with this algorithm
+    since the initial `m` nodes may not be all linked to a new node
+    on the first iteration like the BA model.
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m <= n`` or `p` does not
+        satisfy ``0 <= p <= 1``.
+
+    References
+    ----------
+    .. [1] P. Holme and B. J. Kim,
+       "Growing scale-free networks with tunable clustering",
+       Phys. Rev. E, 65, 026107, 2002.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or n < m:
+        raise nx.NetworkXError(f"NetworkXError must have m>1 and m<n, m={m},n={n}")
+
+    if p > 1 or p < 0:
+        raise nx.NetworkXError(f"NetworkXError p must be in [0,1], p={p}")
+
+    G = empty_graph(m, create_using)  # add m initial nodes (m0 in barabasi-speak)
+    repeated_nodes = list(G)  # list of existing nodes to sample from
+    # with nodes repeated once for each adjacent edge
+    source = m  # next node is m
+    while source < n:  # Now add the other n-1 nodes
+        possible_targets = _random_subset(repeated_nodes, m, seed)
+        # do one preferential attachment for new node
+        target = possible_targets.pop()
+        G.add_edge(source, target)
+        repeated_nodes.append(target)  # add one node to list for each new link
+        count = 1
+        while count < m:  # add m-1 more new links
+            if seed.random() < p:  # clustering step: add triangle
+                neighborhood = [
+                    nbr
+                    for nbr in G.neighbors(target)
+                    if not G.has_edge(source, nbr) and nbr != source
+                ]
+                if neighborhood:  # if there is a neighbor without a link
+                    nbr = seed.choice(neighborhood)
+                    G.add_edge(source, nbr)  # add triangle
+                    repeated_nodes.append(nbr)
+                    count = count + 1
+                    continue  # go to top of while loop
+            # else do preferential attachment step if above fails
+            target = possible_targets.pop()
+            G.add_edge(source, target)
+            repeated_nodes.append(target)
+            count = count + 1
+
+        repeated_nodes.extend([source] * m)  # add source node to list m times
+        source += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_lobster_graph(n, p1, p2, seed=None, *, create_using=None):
+    """Returns a random lobster graph.
+
+    A lobster is a tree that reduces to a caterpillar when pruning all
+    leaf nodes. A caterpillar is a tree that reduces to a path graph
+    when pruning all leaf nodes; setting `p2` to zero produces a caterpillar.
+
+    This implementation iterates on the probabilities `p1` and `p2` to add
+    edges at levels 1 and 2, respectively. Graphs are therefore constructed
+    iteratively with uniform randomness at each level rather than being selected
+    uniformly at random from the set of all possible lobsters.
+
+    Parameters
+    ----------
+    n : int
+        The expected number of nodes in the backbone
+    p1 : float
+        Probability of adding an edge to the backbone
+    p2 : float
+        Probability of adding an edge one level beyond backbone
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Raises
+    ------
+    NetworkXError
+        If `p1` or `p2` parameters are >= 1 because the while loops would never finish.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    p1, p2 = abs(p1), abs(p2)
+    if any(p >= 1 for p in [p1, p2]):
+        raise nx.NetworkXError("Probability values for `p1` and `p2` must both be < 1.")
+
+    # a necessary ingredient in any self-respecting graph library
+    llen = int(2 * seed.random() * n + 0.5)
+    L = path_graph(llen, create_using)
+    # build caterpillar: add edges to path graph with probability p1
+    current_node = llen - 1
+    for n in range(llen):
+        while seed.random() < p1:  # add fuzzy caterpillar parts
+            current_node += 1
+            L.add_edge(n, current_node)
+            cat_node = current_node
+            while seed.random() < p2:  # add crunchy lobster bits
+                current_node += 1
+                L.add_edge(cat_node, current_node)
+    return L  # voila, un lobster!
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_lobster(n, p1, p2, seed=None, *, create_using=None):
+    """
+    .. deprecated:: 3.5
+       `random_lobster` is a deprecated alias
+       for `random_lobster_graph`.
+       Use `random_lobster_graph` instead.
+    """
+    import warnings
+
+    warnings.warn(
+        "`random_lobster` is deprecated, use `random_lobster_graph` instead.",
+        category=DeprecationWarning,
+        stacklevel=2,
+    )
+    return random_lobster_graph(n, p1, p2, seed=seed, create_using=create_using)
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_shell_graph(constructor, seed=None, *, create_using=None):
+    """Returns a random shell graph for the constructor given.
+
+    Parameters
+    ----------
+    constructor : list of three-tuples
+        Represents the parameters for a shell, starting at the center
+        shell.  Each element of the list must be of the form `(n, m,
+        d)`, where `n` is the number of nodes in the shell, `m` is
+        the number of edges in the shell, and `d` is the ratio of
+        inter-shell (next) edges to intra-shell edges. If `d` is zero,
+        there will be no intra-shell edges, and if `d` is one there
+        will be all possible intra-shell edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. Graph instances are not supported.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Examples
+    --------
+    >>> constructor = [(10, 20, 0.8), (20, 40, 0.8)]
+    >>> G = nx.random_shell_graph(constructor)
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    G = empty_graph(0, create_using)
+
+    glist = []
+    intra_edges = []
+    nnodes = 0
+    # create gnm graphs for each shell
+    for n, m, d in constructor:
+        inter_edges = int(m * d)
+        intra_edges.append(m - inter_edges)
+        g = nx.convert_node_labels_to_integers(
+            gnm_random_graph(n, inter_edges, seed=seed, create_using=G.__class__),
+            first_label=nnodes,
+        )
+        glist.append(g)
+        nnodes += n
+        G = nx.operators.union(G, g)
+
+    # connect the shells randomly
+    for gi in range(len(glist) - 1):
+        nlist1 = list(glist[gi])
+        nlist2 = list(glist[gi + 1])
+        total_edges = intra_edges[gi]
+        edge_count = 0
+        while edge_count < total_edges:
+            u = seed.choice(nlist1)
+            v = seed.choice(nlist2)
+            if u == v or G.has_edge(u, v):
+                continue
+            else:
+                G.add_edge(u, v)
+                edge_count = edge_count + 1
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_powerlaw_tree(n, gamma=3, seed=None, tries=100, *, create_using=None):
+    """Returns a tree with a power law degree distribution.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    gamma : float
+        Exponent of the power law.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int
+        Number of attempts to adjust the sequence to make it a tree.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Raises
+    ------
+    NetworkXError
+        If no valid sequence is found within the maximum number of
+        attempts.
+
+    Notes
+    -----
+    A trial power law degree sequence is chosen and then elements are
+    swapped with new elements from a powerlaw distribution until the
+    sequence makes a tree (by checking, for example, that the number of
+    edges is one smaller than the number of nodes).
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    # This call may raise a NetworkXError if the number of tries is succeeded.
+    seq = random_powerlaw_tree_sequence(n, gamma=gamma, seed=seed, tries=tries)
+    G = degree_sequence_tree(seq, create_using)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None)
+def random_powerlaw_tree_sequence(n, gamma=3, seed=None, tries=100):
+    """Returns a degree sequence for a tree with a power law distribution.
+
+    Parameters
+    ----------
+    n : int,
+        The number of nodes.
+    gamma : float
+        Exponent of the power law.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int
+        Number of attempts to adjust the sequence to make it a tree.
+
+    Raises
+    ------
+    NetworkXError
+        If no valid sequence is found within the maximum number of
+        attempts.
+
+    Notes
+    -----
+    A trial power law degree sequence is chosen and then elements are
+    swapped with new elements from a power law distribution until
+    the sequence makes a tree (by checking, for example, that the number of
+    edges is one smaller than the number of nodes).
+
+    """
+    # get trial sequence
+    z = nx.utils.powerlaw_sequence(n, exponent=gamma, seed=seed)
+    # round to integer values in the range [0,n]
+    zseq = [min(n, max(round(s), 0)) for s in z]
+
+    # another sequence to swap values from
+    z = nx.utils.powerlaw_sequence(tries, exponent=gamma, seed=seed)
+    # round to integer values in the range [0,n]
+    swap = [min(n, max(round(s), 0)) for s in z]
+
+    for _ in swap:
+        valid, _ = nx.utils.is_valid_tree_degree_sequence(zseq)
+        if valid:
+            return zseq
+        index = seed.randint(0, n - 1)
+        zseq[index] = swap.pop()
+
+    raise nx.NetworkXError(
+        f"Exceeded max ({tries}) attempts for a valid tree sequence."
+    )
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_kernel_graph(
+    n, kernel_integral, kernel_root=None, seed=None, *, create_using=None
+):
+    r"""Returns an random graph based on the specified kernel.
+
+    The algorithm chooses each of the $[n(n-1)]/2$ possible edges with
+    probability specified by a kernel $\kappa(x,y)$ [1]_.  The kernel
+    $\kappa(x,y)$ must be a symmetric (in $x,y$), non-negative,
+    bounded function.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    kernel_integral : function
+        Function that returns the definite integral of the kernel $\kappa(x,y)$,
+        $F(y,a,b) := \int_a^b \kappa(x,y)dx$
+    kernel_root: function (optional)
+        Function that returns the root $b$ of the equation $F(y,a,b) = r$.
+        If None, the root is found using :func:`scipy.optimize.brentq`
+        (this requires SciPy).
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The kernel is specified through its definite integral which must be
+    provided as one of the arguments. If the integral and root of the
+    kernel integral can be found in $O(1)$ time then this algorithm runs in
+    time $O(n+m)$ where m is the expected number of edges [2]_.
+
+    The nodes are set to integers from $0$ to $n-1$.
+
+    Examples
+    --------
+    Generate an Erdős–Rényi random graph $G(n,c/n)$, with kernel
+    $\kappa(x,y)=c$ where $c$ is the mean expected degree.
+
+    >>> def integral(u, w, z):
+    ...     return c * (z - w)
+    >>> def root(u, w, r):
+    ...     return r / c + w
+    >>> c = 1
+    >>> graph = nx.random_kernel_graph(1000, integral, root)
+
+    See Also
+    --------
+    gnp_random_graph
+    expected_degree_graph
+
+    References
+    ----------
+    .. [1] Bollobás, Béla,  Janson, S. and Riordan, O.
+       "The phase transition in inhomogeneous random graphs",
+       *Random Structures Algorithms*, 31, 3--122, 2007.
+
+    .. [2] Hagberg A, Lemons N (2015),
+       "Fast Generation of Sparse Random Kernel Graphs".
+       PLoS ONE 10(9): e0135177, 2015. doi:10.1371/journal.pone.0135177
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if kernel_root is None:
+        import scipy as sp
+
+        def kernel_root(y, a, r):
+            def my_function(b):
+                return kernel_integral(y, a, b) - r
+
+            return sp.optimize.brentq(my_function, a, 1)
+
+    graph = nx.empty_graph(create_using=create_using)
+    graph.add_nodes_from(range(n))
+    (i, j) = (1, 1)
+    while i < n:
+        r = -math.log(1 - seed.random())  # (1-seed.random()) in (0, 1]
+        if kernel_integral(i / n, j / n, 1) <= r:
+            i, j = i + 1, i + 1
+        else:
+            j = math.ceil(n * kernel_root(i / n, j / n, r))
+            graph.add_edge(i - 1, j - 1)
+    return graph
diff --git a/lib/python3.11/site-packages/networkx/generators/small.py b/lib/python3.11/site-packages/networkx/generators/small.py
new file mode 100644
index 0000000000000000000000000000000000000000..12fc2a3c99c893609ba43ce0af9af5404be19957
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/small.py
@@ -0,0 +1,1070 @@
+"""
+Various small and named graphs, together with some compact generators.
+
+"""
+
+__all__ = [
+    "LCF_graph",
+    "bull_graph",
+    "chvatal_graph",
+    "cubical_graph",
+    "desargues_graph",
+    "diamond_graph",
+    "dodecahedral_graph",
+    "frucht_graph",
+    "generalized_petersen_graph",
+    "heawood_graph",
+    "hoffman_singleton_graph",
+    "house_graph",
+    "house_x_graph",
+    "icosahedral_graph",
+    "krackhardt_kite_graph",
+    "moebius_kantor_graph",
+    "octahedral_graph",
+    "pappus_graph",
+    "petersen_graph",
+    "sedgewick_maze_graph",
+    "tetrahedral_graph",
+    "truncated_cube_graph",
+    "truncated_tetrahedron_graph",
+    "tutte_graph",
+]
+
+from functools import wraps
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.generators.classic import (
+    complete_graph,
+    cycle_graph,
+    empty_graph,
+    path_graph,
+)
+
+
+def _raise_on_directed(func):
+    """
+    A decorator which inspects the `create_using` argument and raises a
+    NetworkX exception when `create_using` is a DiGraph (class or instance) for
+    graph generators that do not support directed outputs.
+
+    `create_using` may be a keyword argument or the first positional argument.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        create_using = args[0] if args else kwargs.get("create_using")
+        if create_using is not None:
+            G = nx.empty_graph(create_using=create_using)
+            if G.is_directed():
+                raise NetworkXError("Directed Graph not supported in create_using")
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def LCF_graph(n, shift_list, repeats, create_using=None):
+    """
+    Return the cubic graph specified in LCF notation.
+
+    LCF (Lederberg-Coxeter-Fruchte) notation[1]_ is a compressed
+    notation used in the generation of various cubic Hamiltonian
+    graphs of high symmetry. See, for example, `dodecahedral_graph`,
+    `desargues_graph`, `heawood_graph` and `pappus_graph`.
+
+    Nodes are drawn from ``range(n)``. Each node ``n_i`` is connected with
+    node ``n_i + shift % n`` where ``shift`` is given by cycling through
+    the input `shift_list` `repeat` s times.
+
+    Parameters
+    ----------
+    n : int
+       The starting graph is the `n`-cycle with nodes ``0, ..., n-1``.
+       The null graph is returned if `n` < 1.
+
+    shift_list : list
+       A list of integer shifts mod `n`, ``[s1, s2, .., sk]``
+
+    repeats : int
+       Integer specifying the number of times that shifts in `shift_list`
+       are successively applied to each current node in the n-cycle
+       to generate an edge between ``n_current`` and ``n_current + shift mod n``.
+
+    Returns
+    -------
+    G : Graph
+       A graph instance created from the specified LCF notation.
+
+    Examples
+    --------
+    The utility graph $K_{3,3}$
+
+    >>> G = nx.LCF_graph(6, [3, -3], 3)
+    >>> G.edges()
+    EdgeView([(0, 1), (0, 5), (0, 3), (1, 2), (1, 4), (2, 3), (2, 5), (3, 4), (4, 5)])
+
+    The Heawood graph:
+
+    >>> G = nx.LCF_graph(14, [5, -5], 7)
+    >>> nx.is_isomorphic(G, nx.heawood_graph())
+    True
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/LCF_notation
+
+    """
+    if n <= 0:
+        return empty_graph(0, create_using)
+
+    # start with the n-cycle
+    G = cycle_graph(n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.name = "LCF_graph"
+    nodes = sorted(G)
+
+    n_extra_edges = repeats * len(shift_list)
+    # edges are added n_extra_edges times
+    # (not all of these need be new)
+    if n_extra_edges < 1:
+        return G
+
+    for i in range(n_extra_edges):
+        shift = shift_list[i % len(shift_list)]  # cycle through shift_list
+        v1 = nodes[i % n]  # cycle repeatedly through nodes
+        v2 = nodes[(i + shift) % n]
+        G.add_edge(v1, v2)
+    return G
+
+
+# -------------------------------------------------------------------------------
+#   Various small and named graphs
+# -------------------------------------------------------------------------------
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def bull_graph(create_using=None):
+    """
+    Returns the Bull Graph
+
+    The Bull Graph has 5 nodes and 5 edges. It is a planar undirected
+    graph in the form of a triangle with two disjoint pendant edges [1]_
+    The name comes from the triangle and pendant edges representing
+    respectively the body and legs of a bull.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A bull graph with 5 nodes
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Bull_graph.
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 4], 3: [1], 4: [2]},
+        create_using=create_using,
+    )
+    G.name = "Bull Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chvatal_graph(create_using=None):
+    """
+    Returns the Chvátal Graph
+
+    The Chvátal Graph is an undirected graph with 12 nodes and 24 edges [1]_.
+    It has 370 distinct (directed) Hamiltonian cycles, giving a unique generalized
+    LCF notation of order 4, two of order 6 , and 43 of order 1 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        The Chvátal graph with 12 nodes and 24 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Chv%C3%A1tal_graph
+    .. [2] https://mathworld.wolfram.com/ChvatalGraph.html
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 6, 9],
+            1: [2, 5, 7],
+            2: [3, 6, 8],
+            3: [4, 7, 9],
+            4: [5, 8],
+            5: [10, 11],
+            6: [10, 11],
+            7: [8, 11],
+            8: [10],
+            9: [10, 11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Chvatal Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def cubical_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Cubical Graph
+
+    The skeleton of the cube (the nodes and edges) form a graph, with 8
+    nodes, and 12 edges. It is a special case of the hypercube graph.
+    It is one of 5 Platonic graphs, each a skeleton of its
+    Platonic solid [1]_.
+    Such graphs arise in parallel processing in computers.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A cubical graph with 8 nodes and 12 edges
+
+    See Also
+    --------
+    tetrahedral_graph, octahedral_graph, dodecahedral_graph, icosahedral_graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Cube#Cubical_graph
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 3, 4],
+            1: [0, 2, 7],
+            2: [1, 3, 6],
+            3: [0, 2, 5],
+            4: [0, 5, 7],
+            5: [3, 4, 6],
+            6: [2, 5, 7],
+            7: [1, 4, 6],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Cubical Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def desargues_graph(create_using=None):
+    """
+    Returns the Desargues Graph
+
+    The Desargues Graph is a non-planar, distance-transitive cubic graph
+    with 20 nodes and 30 edges [1]_. It is isomorphic to the Generalized
+    Petersen Graph GP(10, 3). It is a symmetric graph. It can be represented
+    in LCF notation as [5,-5,9,-9]^5 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Desargues Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Desargues_graph
+    .. [2] https://mathworld.wolfram.com/DesarguesGraph.html
+    """
+    G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
+    G.name = "Desargues Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def diamond_graph(create_using=None):
+    """
+    Returns the Diamond graph
+
+    The Diamond Graph is  planar undirected graph with 4 nodes and 5 edges.
+    It is also sometimes known as the double triangle graph or kite graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Diamond Graph with 4 nodes and 5 edges
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/DiamondGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 3], 3: [1, 2]}, create_using=create_using
+    )
+    G.name = "Diamond Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dodecahedral_graph(create_using=None):
+    """
+    Returns the Platonic Dodecahedral graph.
+
+    The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the
+    dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.
+    It can be described in LCF notation as:
+    ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Dodecahedral Graph with 20 nodes and 30 edges
+
+    See Also
+    --------
+    tetrahedral_graph, cubical_graph, octahedral_graph, icosahedral_graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph
+    .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html
+
+    """
+    G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using)
+    G.name = "Dodecahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def frucht_graph(create_using=None):
+    """
+    Returns the Frucht Graph.
+
+    The Frucht Graph is the smallest cubical graph whose
+    automorphism group consists only of the identity element [1]_.
+    It has 12 nodes and 18 edges and no nontrivial symmetries.
+    It is planar and Hamiltonian [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Frucht Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Frucht_graph
+    .. [2] https://mathworld.wolfram.com/FruchtGraph.html
+
+    """
+    G = cycle_graph(7, create_using)
+    G.add_edges_from(
+        [
+            [0, 7],
+            [1, 7],
+            [2, 8],
+            [3, 9],
+            [4, 9],
+            [5, 10],
+            [6, 10],
+            [7, 11],
+            [8, 11],
+            [8, 9],
+            [10, 11],
+        ]
+    )
+
+    G.name = "Frucht Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def heawood_graph(create_using=None):
+    """
+    Returns the Heawood Graph, a (3,6) cage.
+
+    The Heawood Graph is an undirected graph with 14 nodes and 21 edges,
+    named after Percy John Heawood [1]_.
+    It is cubic symmetric, nonplanar, Hamiltonian, and can be represented
+    in LCF notation as ``[5,-5]^7`` [2]_.
+    It is the unique (3,6)-cage: the regular cubic graph of girth 6 with
+    minimal number of vertices [3]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Heawood Graph with 14 nodes and 21 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Heawood_graph
+    .. [2] https://mathworld.wolfram.com/HeawoodGraph.html
+    .. [3] https://www.win.tue.nl/~aeb/graphs/Heawood.html
+
+    """
+    G = LCF_graph(14, [5, -5], 7, create_using)
+    G.name = "Heawood Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hoffman_singleton_graph():
+    """
+    Returns the Hoffman-Singleton Graph.
+
+    The Hoffman–Singleton graph is a symmetrical undirected graph
+    with 50 nodes and 175 edges.
+    All indices lie in ``Z % 5``: that is, the integers mod 5 [1]_.
+    It is the only regular graph of vertex degree 7, diameter 2, and girth 5.
+    It is the unique (7,5)-cage graph and Moore graph, and contains many
+    copies of the Petersen Graph [2]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Hoffman–Singleton Graph with 50 nodes and 175 edges
+
+    Notes
+    -----
+    Constructed from pentagon and pentagram as follows: Take five pentagons $P_h$
+    and five pentagrams $Q_i$ . Join vertex $j$ of $P_h$ to vertex $h·i+j$ of $Q_i$ [3]_.
+
+    References
+    ----------
+    .. [1] https://blogs.ams.org/visualinsight/2016/02/01/hoffman-singleton-graph/
+    .. [2] https://mathworld.wolfram.com/Hoffman-SingletonGraph.html
+    .. [3] https://en.wikipedia.org/wiki/Hoffman%E2%80%93Singleton_graph
+
+    """
+    G = nx.Graph()
+    for i in range(5):
+        for j in range(5):
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j - 1) % 5))
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j + 1) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j - 2) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j + 2) % 5))
+            for k in range(5):
+                G.add_edge(("pentagon", i, j), ("pentagram", k, (i * k + j) % 5))
+    G = nx.convert_node_labels_to_integers(G)
+    G.name = "Hoffman-Singleton Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_graph(create_using=None):
+    """
+    Returns the House graph (square with triangle on top)
+
+    The house graph is a simple undirected graph with
+    5 nodes and 6 edges [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph in the form of a square with a triangle on top
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 3], 2: [0, 3, 4], 3: [1, 2, 4], 4: [2, 3]},
+        create_using=create_using,
+    )
+    G.name = "House Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_x_graph(create_using=None):
+    """
+    Returns the House graph with a cross inside the house square.
+
+    The House X-graph is the House graph plus the two edges connecting diagonally
+    opposite vertices of the square base. It is also one of the two graphs
+    obtained by removing two edges from the pentatope graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph with diagonal vertices connected
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = house_graph(create_using)
+    G.add_edges_from([(0, 3), (1, 2)])
+    G.name = "House-with-X-inside Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def icosahedral_graph(create_using=None):
+    """
+    Returns the Platonic Icosahedral graph.
+
+    The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph
+    whose nodes have the connectivity of the icosahedron. It is undirected,
+    regular and Hamiltonian [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Icosahedral graph with 12 nodes and 30 edges.
+
+    See Also
+    --------
+    tetrahedral_graph, cubical_graph, octahedral_graph, dodecahedral_graph
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/IcosahedralGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 5, 7, 8, 11],
+            1: [2, 5, 6, 8],
+            2: [3, 6, 8, 9],
+            3: [4, 6, 9, 10],
+            4: [5, 6, 10, 11],
+            5: [6, 11],
+            7: [8, 9, 10, 11],
+            8: [9],
+            9: [10],
+            10: [11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Icosahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def krackhardt_kite_graph(create_using=None):
+    """
+    Returns the Krackhardt Kite Social Network.
+
+    A 10 actor social network introduced by David Krackhardt
+    to illustrate different centrality measures [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Krackhardt Kite graph with 10 nodes and 18 edges
+
+    Notes
+    -----
+    The traditional labeling is:
+    Andre=1, Beverley=2, Carol=3, Diane=4,
+    Ed=5, Fernando=6, Garth=7, Heather=8, Ike=9, Jane=10.
+
+    References
+    ----------
+    .. [1] Krackhardt, David. "Assessing the Political Landscape: Structure,
+       Cognition, and Power in Organizations". Administrative Science Quarterly.
+       35 (2): 342–369. doi:10.2307/2393394. JSTOR 2393394. June 1990.
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3, 5],
+            1: [0, 3, 4, 6],
+            2: [0, 3, 5],
+            3: [0, 1, 2, 4, 5, 6],
+            4: [1, 3, 6],
+            5: [0, 2, 3, 6, 7],
+            6: [1, 3, 4, 5, 7],
+            7: [5, 6, 8],
+            8: [7, 9],
+            9: [8],
+        },
+        create_using=create_using,
+    )
+    G.name = "Krackhardt Kite Social Network"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def moebius_kantor_graph(create_using=None):
+    """
+    Returns the Moebius-Kantor graph.
+
+    The Möbius-Kantor graph is the cubic symmetric graph on 16 nodes.
+    Its LCF notation is [5,-5]^8, and it is isomorphic to the generalized
+    Petersen Graph GP(8, 3) [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Moebius-Kantor graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/M%C3%B6bius%E2%80%93Kantor_graph
+
+    """
+    G = LCF_graph(16, [5, -5], 8, create_using)
+    G.name = "Moebius-Kantor Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def octahedral_graph(create_using=None):
+    """
+    Returns the Platonic Octahedral graph.
+
+    The octahedral graph is the 6-node 12-edge Platonic graph having the
+    connectivity of the octahedron [1]_. If 6 couples go to a party,
+    and each person shakes hands with every person except his or her partner,
+    then this graph describes the set of handshakes that take place;
+    for this reason it is also called the cocktail party graph [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Octahedral graph
+
+    See Also
+    --------
+    tetrahedral_graph, cubical_graph, dodecahedral_graph, icosahedral_graph
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/OctahedralGraph.html
+    .. [2] https://en.wikipedia.org/wiki/Tur%C3%A1n_graph#Special_cases
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2, 3, 4], 1: [2, 3, 5], 2: [4, 5], 3: [4, 5], 4: [5]},
+        create_using=create_using,
+    )
+    G.name = "Platonic Octahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def pappus_graph():
+    """
+    Returns the Pappus graph.
+
+    The Pappus graph is a cubic symmetric distance-regular graph with 18 nodes
+    and 27 edges. It is Hamiltonian and can be represented in LCF notation as
+    [5,7,-7,7,-7,-5]^3 [1]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Pappus graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Pappus_graph
+    """
+    G = LCF_graph(18, [5, 7, -7, 7, -7, -5], 3)
+    G.name = "Pappus Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def petersen_graph(create_using=None):
+    """
+    Returns the Petersen Graph.
+
+    The Peterson Graph is a cubic, undirected graph with 10 nodes and 15 edges [1]_.
+    Julius Petersen constructed the graph as the smallest counterexample
+    against the claim that a connected bridgeless cubic graph
+    has an edge colouring with three colours [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Petersen Graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Petersen_graph
+    .. [2] https://www.win.tue.nl/~aeb/drg/graphs/Petersen.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 5],
+            1: [0, 2, 6],
+            2: [1, 3, 7],
+            3: [2, 4, 8],
+            4: [3, 0, 9],
+            5: [0, 7, 8],
+            6: [1, 8, 9],
+            7: [2, 5, 9],
+            8: [3, 5, 6],
+            9: [4, 6, 7],
+        },
+        create_using=create_using,
+    )
+    G.name = "Petersen Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def generalized_petersen_graph(n, k, *, create_using=None):
+    """
+    Returns the Generalized Petersen Graph GP(n,k).
+
+    The Generalized Peterson Graph consists of an outer cycle of n nodes
+    connected to an inner circulant graph of n nodes, where nodes in the
+    inner circulant are connected to their kth nearest neighbor [1]_ [2]_.
+    A Generalized Petersen Graph is cubic with 2n nodes and 3n edges.
+
+    Some well known graphs are examples of Generalized Petersen Graphs such
+    as the Petersen Graph GP(5, 2), the Desargues graph GP(10, 3), the
+    Moebius-Kantor graph GP(8, 3), and the dodecahedron graph GP(10, 2).
+
+    Parameters
+    ----------
+    n : int
+       Number of nodes in the outer cycle and inner circulant. ``n >= 3`` is required.
+
+    k : int
+       Neighbor to connect in the inner circulant. ``1 <= k <= n/2``.
+       Note that some people require ``k < n/2`` but we and others allow equality.
+       Also, ``k < n/2`` is equivalent to ``k <= floor((n-1)/2)``
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Generalized Petersen Graph n k
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/GeneralizedPetersenGraph.html
+    .. [2] https://en.wikipedia.org/wiki/Generalized_Petersen_graph
+    """
+    if n <= 2:
+        raise NetworkXError(f"n >= 3 required. Got {n=}")
+    if k < 1 or k > n / 2:
+        raise NetworkXError(f" Got {n=} {k=}. Need 1 <= k <= n/2")
+
+    G = nx.cycle_graph(range(n), create_using=create_using)  # u-nodes
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported in create_using")
+    for i in range(n):
+        G.add_edge(i, n + i)  # add v-nodes and u to v edges
+        G.add_edge(n + i, n + (i + k) % n)  # edge from v_i to v_(i+k)%n
+
+    G.name = f"Generalized Petersen Graph GP({n}, {k})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sedgewick_maze_graph(create_using=None):
+    """
+    Return a small maze with a cycle.
+
+    This is the maze used in Sedgewick, 3rd Edition, Part 5, Graph
+    Algorithms, Chapter 18, e.g. Figure 18.2 and following [1]_.
+    Nodes are numbered 0,..,7
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Small maze with a cycle
+
+    References
+    ----------
+    .. [1] Figure 18.2, Chapter 18, Graph Algorithms (3rd Ed), Sedgewick
+    """
+    G = empty_graph(0, create_using)
+    G.add_nodes_from(range(8))
+    G.add_edges_from([[0, 2], [0, 7], [0, 5]])
+    G.add_edges_from([[1, 7], [2, 6]])
+    G.add_edges_from([[3, 4], [3, 5]])
+    G.add_edges_from([[4, 5], [4, 7], [4, 6]])
+    G.name = "Sedgewick Maze"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tetrahedral_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Tetrahedral graph.
+
+    Tetrahedral graph has 4 nodes and 6 edges. It is a
+    special case of the complete graph, K4, and wheel graph, W4.
+    It is one of the 5 platonic graphs [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tetrahedral Graph
+
+    See Also
+    --------
+    cubical_graph, octahedral_graph, dodecahedral_graph, icosahedral_graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tetrahedron#Tetrahedral_graph
+
+    """
+    G = complete_graph(4, create_using)
+    G.name = "Platonic Tetrahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_cube_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated cube.
+
+    The truncated cube is an Archimedean solid with 14 regular
+    faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.
+    The truncated cube is created by truncating (cutting off) the tips
+    of the cube one third of the way into each edge [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated cube
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_cube
+    .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 4],
+            1: [11, 14],
+            2: [3, 4],
+            3: [6, 8],
+            4: [5],
+            5: [16, 18],
+            6: [7, 8],
+            7: [10, 12],
+            8: [9],
+            9: [17, 20],
+            10: [11, 12],
+            11: [14],
+            12: [13],
+            13: [21, 22],
+            14: [15],
+            15: [19, 23],
+            16: [17, 18],
+            17: [20],
+            18: [19],
+            19: [23],
+            20: [21],
+            21: [22],
+            22: [23],
+        },
+        create_using=create_using,
+    )
+    G.name = "Truncated Cube Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_tetrahedron_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated Platonic tetrahedron.
+
+    The truncated tetrahedron is an Archimedean solid with 4 regular hexagonal faces,
+    4 equilateral triangle faces, 12 nodes and 18 edges. It can be constructed by truncating
+    all 4 vertices of a regular tetrahedron at one third of the original edge length [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated tetrahedron
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_tetrahedron
+
+    """
+    G = path_graph(12, create_using)
+    G.add_edges_from([(0, 2), (0, 9), (1, 6), (3, 11), (4, 11), (5, 7), (8, 10)])
+    G.name = "Truncated Tetrahedron Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tutte_graph(create_using=None):
+    """
+    Returns the Tutte graph.
+
+    The Tutte graph is a cubic polyhedral, non-Hamiltonian graph. It has
+    46 nodes and 69 edges.
+    It is a counterexample to Tait's conjecture that every 3-regular polyhedron
+    has a Hamiltonian cycle.
+    It can be realized geometrically from a tetrahedron by multiply truncating
+    three of its vertices [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tutte graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tutte_graph
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3],
+            1: [4, 26],
+            2: [10, 11],
+            3: [18, 19],
+            4: [5, 33],
+            5: [6, 29],
+            6: [7, 27],
+            7: [8, 14],
+            8: [9, 38],
+            9: [10, 37],
+            10: [39],
+            11: [12, 39],
+            12: [13, 35],
+            13: [14, 15],
+            14: [34],
+            15: [16, 22],
+            16: [17, 44],
+            17: [18, 43],
+            18: [45],
+            19: [20, 45],
+            20: [21, 41],
+            21: [22, 23],
+            22: [40],
+            23: [24, 27],
+            24: [25, 32],
+            25: [26, 31],
+            26: [33],
+            27: [28],
+            28: [29, 32],
+            29: [30],
+            30: [31, 33],
+            31: [32],
+            34: [35, 38],
+            35: [36],
+            36: [37, 39],
+            37: [38],
+            40: [41, 44],
+            41: [42],
+            42: [43, 45],
+            43: [44],
+        },
+        create_using=create_using,
+    )
+    G.name = "Tutte's Graph"
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/social.py b/lib/python3.11/site-packages/networkx/generators/social.py
new file mode 100644
index 0000000000000000000000000000000000000000..d06ce00531fb840e0f2a6238aba97f7a40a3b8fc
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/social.py
@@ -0,0 +1,554 @@
+"""
+Famous social networks.
+"""
+
+import networkx as nx
+
+__all__ = [
+    "karate_club_graph",
+    "davis_southern_women_graph",
+    "florentine_families_graph",
+    "les_miserables_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def karate_club_graph():
+    """Returns Zachary's Karate Club graph.
+
+    Each node in the returned graph has a node attribute 'club' that
+    indicates the name of the club to which the member represented by that node
+    belongs, either 'Mr. Hi' or 'Officer'. Each edge has a weight based on the
+    number of contexts in which that edge's incident node members interacted.
+
+    The dataset is derived from the 'Club After Split From Data' column of Table 3 in [1]_.
+    This was in turn derived from the 'Club After Fission' column of Table 1 in the
+    same paper. Note that the nodes are 0-indexed in NetworkX, but 1-indexed in the
+    paper (the 'Individual Number in Matrix C' column of Table 3 starts at 1). This
+    means, for example, that ``G.nodes[9]["club"]`` returns 'Officer', which
+    corresponds to row 10 of Table 3 in the paper.
+
+    Examples
+    --------
+    To get the name of the club to which a node belongs:
+
+    >>> G = nx.karate_club_graph()
+    >>> G.nodes[5]["club"]
+    'Mr. Hi'
+    >>> G.nodes[9]["club"]
+    'Officer'
+
+    References
+    ----------
+    .. [1] Zachary, Wayne W.
+       "An Information Flow Model for Conflict and Fission in Small Groups."
+       *Journal of Anthropological Research*, 33, 452--473, (1977).
+    """
+    # Create the set of all members, and the members of each club.
+    all_members = set(range(34))
+    club1 = {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 16, 17, 19, 21}
+    # club2 = all_members - club1
+
+    G = nx.Graph()
+    G.add_nodes_from(all_members)
+    G.name = "Zachary's Karate Club"
+
+    zacharydat = """\
+0 4 5 3 3 3 3 2 2 0 2 3 2 3 0 0 0 2 0 2 0 2 0 0 0 0 0 0 0 0 0 2 0 0
+4 0 6 3 0 0 0 4 0 0 0 0 0 5 0 0 0 1 0 2 0 2 0 0 0 0 0 0 0 0 2 0 0 0
+5 6 0 3 0 0 0 4 5 1 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 3 0
+3 3 3 0 0 0 0 3 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 2 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 5 0 0 0 3 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 4 3
+0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
+2 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 5 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4
+0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
+2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1
+2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 2 0 0 5 4
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 3 0 0 0 2 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 2 0 0 0 0 0 0 7 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2
+0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 4
+0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 3 2
+0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3
+2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 7 0 0 2 0 0 0 4 4
+0 0 2 0 0 0 0 0 3 0 0 0 0 0 3 3 0 0 1 0 3 0 2 5 0 0 0 0 0 4 3 4 0 5
+0 0 0 0 0 0 0 0 4 2 0 0 0 3 2 4 0 0 2 1 1 0 3 4 0 0 2 4 2 2 3 4 5 0"""
+
+    for row, line in enumerate(zacharydat.split("\n")):
+        thisrow = [int(b) for b in line.split()]
+        for col, entry in enumerate(thisrow):
+            if entry >= 1:
+                G.add_edge(row, col, weight=entry)
+
+    # Add the name of each member's club as a node attribute.
+    for v in G:
+        G.nodes[v]["club"] = "Mr. Hi" if v in club1 else "Officer"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def davis_southern_women_graph():
+    """Returns Davis Southern women social network.
+
+    This is a bipartite graph.
+
+    References
+    ----------
+    .. [1] A. Davis, Gardner, B. B., Gardner, M. R., 1941. Deep South.
+        University of Chicago Press, Chicago, IL.
+    """
+    G = nx.Graph()
+    # Top nodes
+    women = [
+        "Evelyn Jefferson",
+        "Laura Mandeville",
+        "Theresa Anderson",
+        "Brenda Rogers",
+        "Charlotte McDowd",
+        "Frances Anderson",
+        "Eleanor Nye",
+        "Pearl Oglethorpe",
+        "Ruth DeSand",
+        "Verne Sanderson",
+        "Myra Liddel",
+        "Katherina Rogers",
+        "Sylvia Avondale",
+        "Nora Fayette",
+        "Helen Lloyd",
+        "Dorothy Murchison",
+        "Olivia Carleton",
+        "Flora Price",
+    ]
+    G.add_nodes_from(women, bipartite=0)
+    # Bottom nodes
+    events = [
+        "E1",
+        "E2",
+        "E3",
+        "E4",
+        "E5",
+        "E6",
+        "E7",
+        "E8",
+        "E9",
+        "E10",
+        "E11",
+        "E12",
+        "E13",
+        "E14",
+    ]
+    G.add_nodes_from(events, bipartite=1)
+
+    G.add_edges_from(
+        [
+            ("Evelyn Jefferson", "E1"),
+            ("Evelyn Jefferson", "E2"),
+            ("Evelyn Jefferson", "E3"),
+            ("Evelyn Jefferson", "E4"),
+            ("Evelyn Jefferson", "E5"),
+            ("Evelyn Jefferson", "E6"),
+            ("Evelyn Jefferson", "E8"),
+            ("Evelyn Jefferson", "E9"),
+            ("Laura Mandeville", "E1"),
+            ("Laura Mandeville", "E2"),
+            ("Laura Mandeville", "E3"),
+            ("Laura Mandeville", "E5"),
+            ("Laura Mandeville", "E6"),
+            ("Laura Mandeville", "E7"),
+            ("Laura Mandeville", "E8"),
+            ("Theresa Anderson", "E2"),
+            ("Theresa Anderson", "E3"),
+            ("Theresa Anderson", "E4"),
+            ("Theresa Anderson", "E5"),
+            ("Theresa Anderson", "E6"),
+            ("Theresa Anderson", "E7"),
+            ("Theresa Anderson", "E8"),
+            ("Theresa Anderson", "E9"),
+            ("Brenda Rogers", "E1"),
+            ("Brenda Rogers", "E3"),
+            ("Brenda Rogers", "E4"),
+            ("Brenda Rogers", "E5"),
+            ("Brenda Rogers", "E6"),
+            ("Brenda Rogers", "E7"),
+            ("Brenda Rogers", "E8"),
+            ("Charlotte McDowd", "E3"),
+            ("Charlotte McDowd", "E4"),
+            ("Charlotte McDowd", "E5"),
+            ("Charlotte McDowd", "E7"),
+            ("Frances Anderson", "E3"),
+            ("Frances Anderson", "E5"),
+            ("Frances Anderson", "E6"),
+            ("Frances Anderson", "E8"),
+            ("Eleanor Nye", "E5"),
+            ("Eleanor Nye", "E6"),
+            ("Eleanor Nye", "E7"),
+            ("Eleanor Nye", "E8"),
+            ("Pearl Oglethorpe", "E6"),
+            ("Pearl Oglethorpe", "E8"),
+            ("Pearl Oglethorpe", "E9"),
+            ("Ruth DeSand", "E5"),
+            ("Ruth DeSand", "E7"),
+            ("Ruth DeSand", "E8"),
+            ("Ruth DeSand", "E9"),
+            ("Verne Sanderson", "E7"),
+            ("Verne Sanderson", "E8"),
+            ("Verne Sanderson", "E9"),
+            ("Verne Sanderson", "E12"),
+            ("Myra Liddel", "E8"),
+            ("Myra Liddel", "E9"),
+            ("Myra Liddel", "E10"),
+            ("Myra Liddel", "E12"),
+            ("Katherina Rogers", "E8"),
+            ("Katherina Rogers", "E9"),
+            ("Katherina Rogers", "E10"),
+            ("Katherina Rogers", "E12"),
+            ("Katherina Rogers", "E13"),
+            ("Katherina Rogers", "E14"),
+            ("Sylvia Avondale", "E7"),
+            ("Sylvia Avondale", "E8"),
+            ("Sylvia Avondale", "E9"),
+            ("Sylvia Avondale", "E10"),
+            ("Sylvia Avondale", "E12"),
+            ("Sylvia Avondale", "E13"),
+            ("Sylvia Avondale", "E14"),
+            ("Nora Fayette", "E6"),
+            ("Nora Fayette", "E7"),
+            ("Nora Fayette", "E9"),
+            ("Nora Fayette", "E10"),
+            ("Nora Fayette", "E11"),
+            ("Nora Fayette", "E12"),
+            ("Nora Fayette", "E13"),
+            ("Nora Fayette", "E14"),
+            ("Helen Lloyd", "E7"),
+            ("Helen Lloyd", "E8"),
+            ("Helen Lloyd", "E10"),
+            ("Helen Lloyd", "E11"),
+            ("Helen Lloyd", "E12"),
+            ("Dorothy Murchison", "E8"),
+            ("Dorothy Murchison", "E9"),
+            ("Olivia Carleton", "E9"),
+            ("Olivia Carleton", "E11"),
+            ("Flora Price", "E9"),
+            ("Flora Price", "E11"),
+        ]
+    )
+    G.graph["top"] = women
+    G.graph["bottom"] = events
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def florentine_families_graph():
+    """Returns Florentine families graph.
+
+    References
+    ----------
+    .. [1] Ronald L. Breiger and Philippa E. Pattison
+       Cumulated social roles: The duality of persons and their algebras,1
+       Social Networks, Volume 8, Issue 3, September 1986, Pages 215-256
+    """
+    G = nx.Graph()
+    G.add_edge("Acciaiuoli", "Medici")
+    G.add_edge("Castellani", "Peruzzi")
+    G.add_edge("Castellani", "Strozzi")
+    G.add_edge("Castellani", "Barbadori")
+    G.add_edge("Medici", "Barbadori")
+    G.add_edge("Medici", "Ridolfi")
+    G.add_edge("Medici", "Tornabuoni")
+    G.add_edge("Medici", "Albizzi")
+    G.add_edge("Medici", "Salviati")
+    G.add_edge("Salviati", "Pazzi")
+    G.add_edge("Peruzzi", "Strozzi")
+    G.add_edge("Peruzzi", "Bischeri")
+    G.add_edge("Strozzi", "Ridolfi")
+    G.add_edge("Strozzi", "Bischeri")
+    G.add_edge("Ridolfi", "Tornabuoni")
+    G.add_edge("Tornabuoni", "Guadagni")
+    G.add_edge("Albizzi", "Ginori")
+    G.add_edge("Albizzi", "Guadagni")
+    G.add_edge("Bischeri", "Guadagni")
+    G.add_edge("Guadagni", "Lamberteschi")
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def les_miserables_graph():
+    """Returns coappearance network of characters in the novel Les Miserables.
+
+    References
+    ----------
+    .. [1] D. E. Knuth, 1993.
+       The Stanford GraphBase: a platform for combinatorial computing,
+       pp. 74-87. New York: AcM Press.
+    """
+    G = nx.Graph()
+    G.add_edge("Napoleon", "Myriel", weight=1)
+    G.add_edge("MlleBaptistine", "Myriel", weight=8)
+    G.add_edge("MmeMagloire", "Myriel", weight=10)
+    G.add_edge("MmeMagloire", "MlleBaptistine", weight=6)
+    G.add_edge("CountessDeLo", "Myriel", weight=1)
+    G.add_edge("Geborand", "Myriel", weight=1)
+    G.add_edge("Champtercier", "Myriel", weight=1)
+    G.add_edge("Cravatte", "Myriel", weight=1)
+    G.add_edge("Count", "Myriel", weight=2)
+    G.add_edge("OldMan", "Myriel", weight=1)
+    G.add_edge("Valjean", "Labarre", weight=1)
+    G.add_edge("Valjean", "MmeMagloire", weight=3)
+    G.add_edge("Valjean", "MlleBaptistine", weight=3)
+    G.add_edge("Valjean", "Myriel", weight=5)
+    G.add_edge("Marguerite", "Valjean", weight=1)
+    G.add_edge("MmeDeR", "Valjean", weight=1)
+    G.add_edge("Isabeau", "Valjean", weight=1)
+    G.add_edge("Gervais", "Valjean", weight=1)
+    G.add_edge("Listolier", "Tholomyes", weight=4)
+    G.add_edge("Fameuil", "Tholomyes", weight=4)
+    G.add_edge("Fameuil", "Listolier", weight=4)
+    G.add_edge("Blacheville", "Tholomyes", weight=4)
+    G.add_edge("Blacheville", "Listolier", weight=4)
+    G.add_edge("Blacheville", "Fameuil", weight=4)
+    G.add_edge("Favourite", "Tholomyes", weight=3)
+    G.add_edge("Favourite", "Listolier", weight=3)
+    G.add_edge("Favourite", "Fameuil", weight=3)
+    G.add_edge("Favourite", "Blacheville", weight=4)
+    G.add_edge("Dahlia", "Tholomyes", weight=3)
+    G.add_edge("Dahlia", "Listolier", weight=3)
+    G.add_edge("Dahlia", "Fameuil", weight=3)
+    G.add_edge("Dahlia", "Blacheville", weight=3)
+    G.add_edge("Dahlia", "Favourite", weight=5)
+    G.add_edge("Zephine", "Tholomyes", weight=3)
+    G.add_edge("Zephine", "Listolier", weight=3)
+    G.add_edge("Zephine", "Fameuil", weight=3)
+    G.add_edge("Zephine", "Blacheville", weight=3)
+    G.add_edge("Zephine", "Favourite", weight=4)
+    G.add_edge("Zephine", "Dahlia", weight=4)
+    G.add_edge("Fantine", "Tholomyes", weight=3)
+    G.add_edge("Fantine", "Listolier", weight=3)
+    G.add_edge("Fantine", "Fameuil", weight=3)
+    G.add_edge("Fantine", "Blacheville", weight=3)
+    G.add_edge("Fantine", "Favourite", weight=4)
+    G.add_edge("Fantine", "Dahlia", weight=4)
+    G.add_edge("Fantine", "Zephine", weight=4)
+    G.add_edge("Fantine", "Marguerite", weight=2)
+    G.add_edge("Fantine", "Valjean", weight=9)
+    G.add_edge("MmeThenardier", "Fantine", weight=2)
+    G.add_edge("MmeThenardier", "Valjean", weight=7)
+    G.add_edge("Thenardier", "MmeThenardier", weight=13)
+    G.add_edge("Thenardier", "Fantine", weight=1)
+    G.add_edge("Thenardier", "Valjean", weight=12)
+    G.add_edge("Cosette", "MmeThenardier", weight=4)
+    G.add_edge("Cosette", "Valjean", weight=31)
+    G.add_edge("Cosette", "Tholomyes", weight=1)
+    G.add_edge("Cosette", "Thenardier", weight=1)
+    G.add_edge("Javert", "Valjean", weight=17)
+    G.add_edge("Javert", "Fantine", weight=5)
+    G.add_edge("Javert", "Thenardier", weight=5)
+    G.add_edge("Javert", "MmeThenardier", weight=1)
+    G.add_edge("Javert", "Cosette", weight=1)
+    G.add_edge("Fauchelevent", "Valjean", weight=8)
+    G.add_edge("Fauchelevent", "Javert", weight=1)
+    G.add_edge("Bamatabois", "Fantine", weight=1)
+    G.add_edge("Bamatabois", "Javert", weight=1)
+    G.add_edge("Bamatabois", "Valjean", weight=2)
+    G.add_edge("Perpetue", "Fantine", weight=1)
+    G.add_edge("Simplice", "Perpetue", weight=2)
+    G.add_edge("Simplice", "Valjean", weight=3)
+    G.add_edge("Simplice", "Fantine", weight=2)
+    G.add_edge("Simplice", "Javert", weight=1)
+    G.add_edge("Scaufflaire", "Valjean", weight=1)
+    G.add_edge("Woman1", "Valjean", weight=2)
+    G.add_edge("Woman1", "Javert", weight=1)
+    G.add_edge("Judge", "Valjean", weight=3)
+    G.add_edge("Judge", "Bamatabois", weight=2)
+    G.add_edge("Champmathieu", "Valjean", weight=3)
+    G.add_edge("Champmathieu", "Judge", weight=3)
+    G.add_edge("Champmathieu", "Bamatabois", weight=2)
+    G.add_edge("Brevet", "Judge", weight=2)
+    G.add_edge("Brevet", "Champmathieu", weight=2)
+    G.add_edge("Brevet", "Valjean", weight=2)
+    G.add_edge("Brevet", "Bamatabois", weight=1)
+    G.add_edge("Chenildieu", "Judge", weight=2)
+    G.add_edge("Chenildieu", "Champmathieu", weight=2)
+    G.add_edge("Chenildieu", "Brevet", weight=2)
+    G.add_edge("Chenildieu", "Valjean", weight=2)
+    G.add_edge("Chenildieu", "Bamatabois", weight=1)
+    G.add_edge("Cochepaille", "Judge", weight=2)
+    G.add_edge("Cochepaille", "Champmathieu", weight=2)
+    G.add_edge("Cochepaille", "Brevet", weight=2)
+    G.add_edge("Cochepaille", "Chenildieu", weight=2)
+    G.add_edge("Cochepaille", "Valjean", weight=2)
+    G.add_edge("Cochepaille", "Bamatabois", weight=1)
+    G.add_edge("Pontmercy", "Thenardier", weight=1)
+    G.add_edge("Boulatruelle", "Thenardier", weight=1)
+    G.add_edge("Eponine", "MmeThenardier", weight=2)
+    G.add_edge("Eponine", "Thenardier", weight=3)
+    G.add_edge("Anzelma", "Eponine", weight=2)
+    G.add_edge("Anzelma", "Thenardier", weight=2)
+    G.add_edge("Anzelma", "MmeThenardier", weight=1)
+    G.add_edge("Woman2", "Valjean", weight=3)
+    G.add_edge("Woman2", "Cosette", weight=1)
+    G.add_edge("Woman2", "Javert", weight=1)
+    G.add_edge("MotherInnocent", "Fauchelevent", weight=3)
+    G.add_edge("MotherInnocent", "Valjean", weight=1)
+    G.add_edge("Gribier", "Fauchelevent", weight=2)
+    G.add_edge("MmeBurgon", "Jondrette", weight=1)
+    G.add_edge("Gavroche", "MmeBurgon", weight=2)
+    G.add_edge("Gavroche", "Thenardier", weight=1)
+    G.add_edge("Gavroche", "Javert", weight=1)
+    G.add_edge("Gavroche", "Valjean", weight=1)
+    G.add_edge("Gillenormand", "Cosette", weight=3)
+    G.add_edge("Gillenormand", "Valjean", weight=2)
+    G.add_edge("Magnon", "Gillenormand", weight=1)
+    G.add_edge("Magnon", "MmeThenardier", weight=1)
+    G.add_edge("MlleGillenormand", "Gillenormand", weight=9)
+    G.add_edge("MlleGillenormand", "Cosette", weight=2)
+    G.add_edge("MlleGillenormand", "Valjean", weight=2)
+    G.add_edge("MmePontmercy", "MlleGillenormand", weight=1)
+    G.add_edge("MmePontmercy", "Pontmercy", weight=1)
+    G.add_edge("MlleVaubois", "MlleGillenormand", weight=1)
+    G.add_edge("LtGillenormand", "MlleGillenormand", weight=2)
+    G.add_edge("LtGillenormand", "Gillenormand", weight=1)
+    G.add_edge("LtGillenormand", "Cosette", weight=1)
+    G.add_edge("Marius", "MlleGillenormand", weight=6)
+    G.add_edge("Marius", "Gillenormand", weight=12)
+    G.add_edge("Marius", "Pontmercy", weight=1)
+    G.add_edge("Marius", "LtGillenormand", weight=1)
+    G.add_edge("Marius", "Cosette", weight=21)
+    G.add_edge("Marius", "Valjean", weight=19)
+    G.add_edge("Marius", "Tholomyes", weight=1)
+    G.add_edge("Marius", "Thenardier", weight=2)
+    G.add_edge("Marius", "Eponine", weight=5)
+    G.add_edge("Marius", "Gavroche", weight=4)
+    G.add_edge("BaronessT", "Gillenormand", weight=1)
+    G.add_edge("BaronessT", "Marius", weight=1)
+    G.add_edge("Mabeuf", "Marius", weight=1)
+    G.add_edge("Mabeuf", "Eponine", weight=1)
+    G.add_edge("Mabeuf", "Gavroche", weight=1)
+    G.add_edge("Enjolras", "Marius", weight=7)
+    G.add_edge("Enjolras", "Gavroche", weight=7)
+    G.add_edge("Enjolras", "Javert", weight=6)
+    G.add_edge("Enjolras", "Mabeuf", weight=1)
+    G.add_edge("Enjolras", "Valjean", weight=4)
+    G.add_edge("Combeferre", "Enjolras", weight=15)
+    G.add_edge("Combeferre", "Marius", weight=5)
+    G.add_edge("Combeferre", "Gavroche", weight=6)
+    G.add_edge("Combeferre", "Mabeuf", weight=2)
+    G.add_edge("Prouvaire", "Gavroche", weight=1)
+    G.add_edge("Prouvaire", "Enjolras", weight=4)
+    G.add_edge("Prouvaire", "Combeferre", weight=2)
+    G.add_edge("Feuilly", "Gavroche", weight=2)
+    G.add_edge("Feuilly", "Enjolras", weight=6)
+    G.add_edge("Feuilly", "Prouvaire", weight=2)
+    G.add_edge("Feuilly", "Combeferre", weight=5)
+    G.add_edge("Feuilly", "Mabeuf", weight=1)
+    G.add_edge("Feuilly", "Marius", weight=1)
+    G.add_edge("Courfeyrac", "Marius", weight=9)
+    G.add_edge("Courfeyrac", "Enjolras", weight=17)
+    G.add_edge("Courfeyrac", "Combeferre", weight=13)
+    G.add_edge("Courfeyrac", "Gavroche", weight=7)
+    G.add_edge("Courfeyrac", "Mabeuf", weight=2)
+    G.add_edge("Courfeyrac", "Eponine", weight=1)
+    G.add_edge("Courfeyrac", "Feuilly", weight=6)
+    G.add_edge("Courfeyrac", "Prouvaire", weight=3)
+    G.add_edge("Bahorel", "Combeferre", weight=5)
+    G.add_edge("Bahorel", "Gavroche", weight=5)
+    G.add_edge("Bahorel", "Courfeyrac", weight=6)
+    G.add_edge("Bahorel", "Mabeuf", weight=2)
+    G.add_edge("Bahorel", "Enjolras", weight=4)
+    G.add_edge("Bahorel", "Feuilly", weight=3)
+    G.add_edge("Bahorel", "Prouvaire", weight=2)
+    G.add_edge("Bahorel", "Marius", weight=1)
+    G.add_edge("Bossuet", "Marius", weight=5)
+    G.add_edge("Bossuet", "Courfeyrac", weight=12)
+    G.add_edge("Bossuet", "Gavroche", weight=5)
+    G.add_edge("Bossuet", "Bahorel", weight=4)
+    G.add_edge("Bossuet", "Enjolras", weight=10)
+    G.add_edge("Bossuet", "Feuilly", weight=6)
+    G.add_edge("Bossuet", "Prouvaire", weight=2)
+    G.add_edge("Bossuet", "Combeferre", weight=9)
+    G.add_edge("Bossuet", "Mabeuf", weight=1)
+    G.add_edge("Bossuet", "Valjean", weight=1)
+    G.add_edge("Joly", "Bahorel", weight=5)
+    G.add_edge("Joly", "Bossuet", weight=7)
+    G.add_edge("Joly", "Gavroche", weight=3)
+    G.add_edge("Joly", "Courfeyrac", weight=5)
+    G.add_edge("Joly", "Enjolras", weight=5)
+    G.add_edge("Joly", "Feuilly", weight=5)
+    G.add_edge("Joly", "Prouvaire", weight=2)
+    G.add_edge("Joly", "Combeferre", weight=5)
+    G.add_edge("Joly", "Mabeuf", weight=1)
+    G.add_edge("Joly", "Marius", weight=2)
+    G.add_edge("Grantaire", "Bossuet", weight=3)
+    G.add_edge("Grantaire", "Enjolras", weight=3)
+    G.add_edge("Grantaire", "Combeferre", weight=1)
+    G.add_edge("Grantaire", "Courfeyrac", weight=2)
+    G.add_edge("Grantaire", "Joly", weight=2)
+    G.add_edge("Grantaire", "Gavroche", weight=1)
+    G.add_edge("Grantaire", "Bahorel", weight=1)
+    G.add_edge("Grantaire", "Feuilly", weight=1)
+    G.add_edge("Grantaire", "Prouvaire", weight=1)
+    G.add_edge("MotherPlutarch", "Mabeuf", weight=3)
+    G.add_edge("Gueulemer", "Thenardier", weight=5)
+    G.add_edge("Gueulemer", "Valjean", weight=1)
+    G.add_edge("Gueulemer", "MmeThenardier", weight=1)
+    G.add_edge("Gueulemer", "Javert", weight=1)
+    G.add_edge("Gueulemer", "Gavroche", weight=1)
+    G.add_edge("Gueulemer", "Eponine", weight=1)
+    G.add_edge("Babet", "Thenardier", weight=6)
+    G.add_edge("Babet", "Gueulemer", weight=6)
+    G.add_edge("Babet", "Valjean", weight=1)
+    G.add_edge("Babet", "MmeThenardier", weight=1)
+    G.add_edge("Babet", "Javert", weight=2)
+    G.add_edge("Babet", "Gavroche", weight=1)
+    G.add_edge("Babet", "Eponine", weight=1)
+    G.add_edge("Claquesous", "Thenardier", weight=4)
+    G.add_edge("Claquesous", "Babet", weight=4)
+    G.add_edge("Claquesous", "Gueulemer", weight=4)
+    G.add_edge("Claquesous", "Valjean", weight=1)
+    G.add_edge("Claquesous", "MmeThenardier", weight=1)
+    G.add_edge("Claquesous", "Javert", weight=1)
+    G.add_edge("Claquesous", "Eponine", weight=1)
+    G.add_edge("Claquesous", "Enjolras", weight=1)
+    G.add_edge("Montparnasse", "Javert", weight=1)
+    G.add_edge("Montparnasse", "Babet", weight=2)
+    G.add_edge("Montparnasse", "Gueulemer", weight=2)
+    G.add_edge("Montparnasse", "Claquesous", weight=2)
+    G.add_edge("Montparnasse", "Valjean", weight=1)
+    G.add_edge("Montparnasse", "Gavroche", weight=1)
+    G.add_edge("Montparnasse", "Eponine", weight=1)
+    G.add_edge("Montparnasse", "Thenardier", weight=1)
+    G.add_edge("Toussaint", "Cosette", weight=2)
+    G.add_edge("Toussaint", "Javert", weight=1)
+    G.add_edge("Toussaint", "Valjean", weight=1)
+    G.add_edge("Child1", "Gavroche", weight=2)
+    G.add_edge("Child2", "Gavroche", weight=2)
+    G.add_edge("Child2", "Child1", weight=3)
+    G.add_edge("Brujon", "Babet", weight=3)
+    G.add_edge("Brujon", "Gueulemer", weight=3)
+    G.add_edge("Brujon", "Thenardier", weight=3)
+    G.add_edge("Brujon", "Gavroche", weight=1)
+    G.add_edge("Brujon", "Eponine", weight=1)
+    G.add_edge("Brujon", "Claquesous", weight=1)
+    G.add_edge("Brujon", "Montparnasse", weight=1)
+    G.add_edge("MmeHucheloup", "Bossuet", weight=1)
+    G.add_edge("MmeHucheloup", "Joly", weight=1)
+    G.add_edge("MmeHucheloup", "Grantaire", weight=1)
+    G.add_edge("MmeHucheloup", "Bahorel", weight=1)
+    G.add_edge("MmeHucheloup", "Courfeyrac", weight=1)
+    G.add_edge("MmeHucheloup", "Gavroche", weight=1)
+    G.add_edge("MmeHucheloup", "Enjolras", weight=1)
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/spectral_graph_forge.py b/lib/python3.11/site-packages/networkx/generators/spectral_graph_forge.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa8c9194bb31ce15434c728c607bfe0172402406
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/spectral_graph_forge.py
@@ -0,0 +1,120 @@
+"""Generates graphs with a given eigenvector structure"""
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = ["spectral_graph_forge"]
+
+
+@np_random_state(3)
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}}, returns_graph=True)
+def spectral_graph_forge(G, alpha, transformation="identity", seed=None):
+    """Returns a random simple graph with spectrum resembling that of `G`
+
+    This algorithm, called Spectral Graph Forge (SGF), computes the
+    eigenvectors of a given graph adjacency matrix, filters them and
+    builds a random graph with a similar eigenstructure.
+    SGF has been proved to be particularly useful for synthesizing
+    realistic social networks and it can also be used to anonymize
+    graph sensitive data.
+
+    Parameters
+    ----------
+    G : Graph
+    alpha :  float
+        Ratio representing the percentage of eigenvectors of G to consider,
+        values in [0,1].
+    transformation : string, optional
+        Represents the intended matrix linear transformation, possible values
+        are 'identity' and 'modularity'
+    seed : integer, random_state, or None (default)
+        Indicator of numpy random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    H : Graph
+        A graph with a similar eigenvector structure of the input one.
+
+    Raises
+    ------
+    NetworkXError
+        If transformation has a value different from 'identity' or 'modularity'
+
+    Notes
+    -----
+    Spectral Graph Forge (SGF) generates a random simple graph resembling the
+    global properties of the given one.
+    It leverages the low-rank approximation of the associated adjacency matrix
+    driven by the *alpha* precision parameter.
+    SGF preserves the number of nodes of the input graph and their ordering.
+    This way, nodes of output graphs resemble the properties of the input one
+    and attributes can be directly mapped.
+
+    It considers the graph adjacency matrices which can optionally be
+    transformed to other symmetric real matrices (currently transformation
+    options include *identity* and *modularity*).
+    The *modularity* transformation, in the sense of Newman's modularity matrix
+    allows the focusing on community structure related properties of the graph.
+
+    SGF applies a low-rank approximation whose fixed rank is computed from the
+    ratio *alpha* of the input graph adjacency matrix dimension.
+    This step performs a filtering on the input eigenvectors similar to the low
+    pass filtering common in telecommunications.
+
+    The filtered values (after truncation) are used as input to a Bernoulli
+    sampling for constructing a random adjacency matrix.
+
+    References
+    ----------
+    ..  [1] L. Baldesi, C. T. Butts, A. Markopoulou, "Spectral Graph Forge:
+        Graph Generation Targeting Modularity", IEEE Infocom, '18.
+        https://arxiv.org/abs/1801.01715
+    ..  [2] M. Newman, "Networks: an introduction", Oxford university press,
+        2010
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> H = nx.spectral_graph_forge(G, 0.3)
+    >>>
+    """
+    import numpy as np
+    import scipy as sp
+
+    available_transformations = ["identity", "modularity"]
+    alpha = np.clip(alpha, 0, 1)
+    A = nx.to_numpy_array(G)
+    n = A.shape[1]
+    level = round(n * alpha)
+
+    if transformation not in available_transformations:
+        msg = f"{transformation!r} is not a valid transformation. "
+        msg += f"Transformations: {available_transformations}"
+        raise nx.NetworkXError(msg)
+
+    K = np.ones((1, n)) @ A
+
+    B = A
+    if transformation == "modularity":
+        B -= K.T @ K / K.sum()
+
+    # Compute low-rank approximation of B
+    evals, evecs = np.linalg.eigh(B)
+    k = np.argsort(np.abs(evals))[::-1]  # indices of evals in descending order
+    evecs[:, k[np.arange(level, n)]] = 0  # set smallest eigenvectors to 0
+    B = evecs @ np.diag(evals) @ evecs.T
+
+    if transformation == "modularity":
+        B += K.T @ K / K.sum()
+
+    B = np.clip(B, 0, 1)
+    np.fill_diagonal(B, 0)
+
+    for i in range(n - 1):
+        B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed)
+        B[i + 1 :, i] = np.transpose(B[i, i + 1 :])
+
+    H = nx.from_numpy_array(B)
+
+    return H
diff --git a/lib/python3.11/site-packages/networkx/generators/stochastic.py b/lib/python3.11/site-packages/networkx/generators/stochastic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f53e2315470f8ffcdea0380026a933e06ddf6ea7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/stochastic.py
@@ -0,0 +1,54 @@
+"""Functions for generating stochastic graphs from a given weighted directed
+graph.
+
+"""
+
+import networkx as nx
+from networkx.classes import DiGraph, MultiDiGraph
+from networkx.utils import not_implemented_for
+
+__all__ = ["stochastic_graph"]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(
+    edge_attrs="weight", mutates_input={"not copy": 1}, returns_graph=True
+)
+def stochastic_graph(G, copy=True, weight="weight"):
+    """Returns a right-stochastic representation of directed graph `G`.
+
+    A right-stochastic graph is a weighted digraph in which for each
+    node, the sum of the weights of all the out-edges of that node is
+    1. If the graph is already weighted (for example, via a 'weight'
+    edge attribute), the reweighting takes that into account.
+
+    Parameters
+    ----------
+    G : directed graph
+        A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.
+
+    copy : boolean, optional
+        If this is True, then this function returns a new graph with
+        the stochastic reweighting. Otherwise, the original graph is
+        modified in-place (and also returned, for convenience).
+
+    weight : edge attribute key (optional, default='weight')
+        Edge attribute key used for reading the existing weight and
+        setting the new weight.  If no attribute with this key is found
+        for an edge, then the edge weight is assumed to be 1. If an edge
+        has a weight, it must be a positive number.
+
+    """
+    if copy:
+        G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
+    # There is a tradeoff here: the dictionary of node degrees may
+    # require a lot of memory, whereas making a call to `G.out_degree`
+    # inside the loop may be costly in computation time.
+    degree = dict(G.out_degree(weight=weight))
+    for u, v, d in G.edges(data=True):
+        if degree[u] == 0:
+            d[weight] = 0
+        else:
+            d[weight] = d.get(weight, 1) / degree[u]
+    nx._clear_cache(G)
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/sudoku.py b/lib/python3.11/site-packages/networkx/generators/sudoku.py
new file mode 100644
index 0000000000000000000000000000000000000000..f288ed24d1f189588de7e1e0bba61f50bbad0003
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/sudoku.py
@@ -0,0 +1,131 @@
+"""Generator for Sudoku graphs
+
+This module gives a generator for n-Sudoku graphs. It can be used to develop
+algorithms for solving or generating Sudoku puzzles.
+
+A completed Sudoku grid is a 9x9 array of integers between 1 and 9, with no
+number appearing twice in the same row, column, or 3x3 box.
+
++---------+---------+---------+
+| | 8 6 4 | | 3 7 1 | | 2 5 9 |
+| | 3 2 5 | | 8 4 9 | | 7 6 1 |
+| | 9 7 1 | | 2 6 5 | | 8 4 3 |
++---------+---------+---------+
+| | 4 3 6 | | 1 9 2 | | 5 8 7 |
+| | 1 9 8 | | 6 5 7 | | 4 3 2 |
+| | 2 5 7 | | 4 8 3 | | 9 1 6 |
++---------+---------+---------+
+| | 6 8 9 | | 7 3 4 | | 1 2 5 |
+| | 7 1 3 | | 5 2 8 | | 6 9 4 |
+| | 5 4 2 | | 9 1 6 | | 3 7 8 |
++---------+---------+---------+
+
+
+The Sudoku graph is an undirected graph with 81 vertices, corresponding to
+the cells of a Sudoku grid. It is a regular graph of degree 20. Two distinct
+vertices are adjacent if and only if the corresponding cells belong to the
+same row, column, or box. A completed Sudoku grid corresponds to a vertex
+coloring of the Sudoku graph with nine colors.
+
+More generally, the n-Sudoku graph is a graph with n^4 vertices, corresponding
+to the cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and
+only if they belong to the same row, column, or n by n box.
+
+References
+----------
+.. [1] Herzberg, A. M., & Murty, M. R. (2007). Sudoku squares and chromatic
+    polynomials. Notices of the AMS, 54(6), 708-717.
+.. [2] Sander, Torsten (2009), "Sudoku graphs are integral",
+    Electronic Journal of Combinatorics, 16 (1): Note 25, 7pp, MR 2529816
+.. [3] Wikipedia contributors. "Glossary of Sudoku." Wikipedia, The Free
+    Encyclopedia, 3 Dec. 2019. Web. 22 Dec. 2019.
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = ["sudoku_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sudoku_graph(n=3):
+    """Returns the n-Sudoku graph. The default value of n is 3.
+
+    The n-Sudoku graph is a graph with n^4 vertices, corresponding to the
+    cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and
+    only if they belong to the same row, column, or n-by-n box.
+
+    Parameters
+    ----------
+    n: integer
+       The order of the Sudoku graph, equal to the square root of the
+       number of rows. The default is 3.
+
+    Returns
+    -------
+    NetworkX graph
+        The n-Sudoku graph Sud(n).
+
+    Examples
+    --------
+    >>> G = nx.sudoku_graph()
+    >>> G.number_of_nodes()
+    81
+    >>> G.number_of_edges()
+    810
+    >>> sorted(G.neighbors(42))
+    [6, 15, 24, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 51, 52, 53, 60, 69, 78]
+    >>> G = nx.sudoku_graph(2)
+    >>> G.number_of_nodes()
+    16
+    >>> G.number_of_edges()
+    56
+
+    References
+    ----------
+    .. [1] Herzberg, A. M., & Murty, M. R. (2007). Sudoku squares and chromatic
+       polynomials. Notices of the AMS, 54(6), 708-717.
+    .. [2] Sander, Torsten (2009), "Sudoku graphs are integral",
+       Electronic Journal of Combinatorics, 16 (1): Note 25, 7pp, MR 2529816
+    .. [3] Wikipedia contributors. "Glossary of Sudoku." Wikipedia, The Free
+       Encyclopedia, 3 Dec. 2019. Web. 22 Dec. 2019.
+    """
+
+    if n < 0:
+        raise NetworkXError("The order must be greater than or equal to zero.")
+
+    n2 = n * n
+    n3 = n2 * n
+    n4 = n3 * n
+
+    # Construct an empty graph with n^4 nodes
+    G = nx.empty_graph(n4)
+
+    # A Sudoku graph of order 0 or 1 has no edges
+    if n < 2:
+        return G
+
+    # Add edges for cells in the same row
+    for row_no in range(n2):
+        row_start = row_no * n2
+        for j in range(1, n2):
+            for i in range(j):
+                G.add_edge(row_start + i, row_start + j)
+
+    # Add edges for cells in the same column
+    for col_no in range(n2):
+        for j in range(col_no, n4, n2):
+            for i in range(col_no, j, n2):
+                G.add_edge(i, j)
+
+    # Add edges for cells in the same box
+    for band_no in range(n):
+        for stack_no in range(n):
+            box_start = n3 * band_no + n * stack_no
+            for j in range(1, n2):
+                for i in range(j):
+                    u = box_start + (i % n) + n2 * (i // n)
+                    v = box_start + (j % n) + n2 * (j // n)
+                    G.add_edge(u, v)
+
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/tests/__init__.py b/lib/python3.11/site-packages/networkx/generators/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/generators/time_series.py b/lib/python3.11/site-packages/networkx/generators/time_series.py
new file mode 100644
index 0000000000000000000000000000000000000000..592d7734a408bf33e58aad20cb117be674558ad2
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/time_series.py
@@ -0,0 +1,74 @@
+"""
+Time Series Graphs
+"""
+
+import itertools
+
+import networkx as nx
+
+__all__ = ["visibility_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def visibility_graph(series):
+    """
+    Return a Visibility Graph of an input Time Series.
+
+    A visibility graph converts a time series into a graph. The constructed graph
+    uses integer nodes to indicate which event in the series the node represents.
+    Edges are formed as follows: consider a bar plot of the series and view that
+    as a side view of a landscape with a node at the top of each bar. An edge
+    means that the nodes can be connected by a straight "line-of-sight" without
+    being obscured by any bars between the nodes.
+
+    The resulting graph inherits several properties of the series in its structure.
+    Thereby, periodic series convert into regular graphs, random series convert
+    into random graphs, and fractal series convert into scale-free networks [1]_.
+
+    Parameters
+    ----------
+    series : Sequence[Number]
+       A Time Series sequence (iterable and sliceable) of numeric values
+       representing times.
+
+    Returns
+    -------
+    NetworkX Graph
+        The Visibility Graph of the input series
+
+    Examples
+    --------
+    >>> series_list = [range(10), [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3]]
+    >>> for s in series_list:
+    ...     g = nx.visibility_graph(s)
+    ...     print(g)
+    Graph with 10 nodes and 9 edges
+    Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] Lacasa, Lucas, Bartolo Luque, Fernando Ballesteros, Jordi Luque, and Juan Carlos Nuno.
+           "From time series to complex networks: The visibility graph." Proceedings of the
+           National Academy of Sciences 105, no. 13 (2008): 4972-4975.
+           https://www.pnas.org/doi/10.1073/pnas.0709247105
+    """
+
+    # Sequential values are always connected
+    G = nx.path_graph(len(series))
+    nx.set_node_attributes(G, dict(enumerate(series)), "value")
+
+    # Check all combinations of nodes n series
+    for (n1, t1), (n2, t2) in itertools.combinations(enumerate(series), 2):
+        # check if any value between obstructs line of sight
+        slope = (t2 - t1) / (n2 - n1)
+        offset = t2 - slope * n2
+
+        obstructed = any(
+            t >= slope * n + offset
+            for n, t in enumerate(series[n1 + 1 : n2], start=n1 + 1)
+        )
+
+        if not obstructed:
+            G.add_edge(n1, n2)
+
+    return G
diff --git a/lib/python3.11/site-packages/networkx/generators/trees.py b/lib/python3.11/site-packages/networkx/generators/trees.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8b24fad27c59a85daa3b31ecb2e3ddd6fe7875f
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/trees.py
@@ -0,0 +1,1070 @@
+"""Functions for generating trees.
+
+The functions sampling trees at random in this module come
+in two variants: labeled and unlabeled. The labeled variants
+sample from every possible tree with the given number of nodes
+uniformly at random. The unlabeled variants sample from every
+possible *isomorphism class* of trees with the given number
+of nodes uniformly at random.
+
+To understand the difference, consider the following example.
+There are two isomorphism classes of trees with four nodes.
+One is that of the path graph, the other is that of the
+star graph. The unlabeled variant will return a line graph or
+a star graph with probability 1/2.
+
+The labeled variant will return the line graph
+with probability 3/4 and the star graph with probability 1/4,
+because there are more labeled variants of the line graph
+than of the star graph. More precisely, the line graph has
+an automorphism group of order 2, whereas the star graph has
+an automorphism group of order 6, so the line graph has three
+times as many labeled variants as the star graph, and thus
+three more chances to be drawn.
+
+Additionally, some functions in this module can sample rooted
+trees and forests uniformly at random. A rooted tree is a tree
+with a designated root node. A rooted forest is a disjoint union
+of rooted trees.
+"""
+
+from collections import Counter, defaultdict
+from math import comb, factorial
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "prefix_tree",
+    "prefix_tree_recursive",
+    "random_labeled_tree",
+    "random_labeled_rooted_tree",
+    "random_labeled_rooted_forest",
+    "random_unlabeled_tree",
+    "random_unlabeled_rooted_tree",
+    "random_unlabeled_rooted_forest",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def prefix_tree(paths):
+    """Creates a directed prefix tree from a list of paths.
+
+    Usually the paths are described as strings or lists of integers.
+
+    A "prefix tree" represents the prefix structure of the strings.
+    Each node represents a prefix of some string. The root represents
+    the empty prefix with children for the single letter prefixes which
+    in turn have children for each double letter prefix starting with
+    the single letter corresponding to the parent node, and so on.
+
+    More generally the prefixes do not need to be strings. A prefix refers
+    to the start of a sequence. The root has children for each one element
+    prefix and they have children for each two element prefix that starts
+    with the one element sequence of the parent, and so on.
+
+    Note that this implementation uses integer nodes with an attribute.
+    Each node has an attribute "source" whose value is the original element
+    of the path to which this node corresponds. For example, suppose `paths`
+    consists of one path: "can". Then the nodes `[1, 2, 3]` which represent
+    this path have "source" values "c", "a" and "n".
+
+    All the descendants of a node have a common prefix in the sequence/path
+    associated with that node. From the returned tree, the prefix for each
+    node can be constructed by traversing the tree up to the root and
+    accumulating the "source" values along the way.
+
+    The root node is always `0` and has "source" attribute `None`.
+    The root is the only node with in-degree zero.
+    The nil node is always `-1` and has "source" attribute `"NIL"`.
+    The nil node is the only node with out-degree zero.
+
+
+    Parameters
+    ----------
+    paths: iterable of paths
+        An iterable of paths which are themselves sequences.
+        Matching prefixes among these sequences are identified with
+        nodes of the prefix tree. One leaf of the tree is associated
+        with each path. (Identical paths are associated with the same
+        leaf of the tree.)
+
+
+    Returns
+    -------
+    tree: DiGraph
+        A directed graph representing an arborescence consisting of the
+        prefix tree generated by `paths`. Nodes are directed "downward",
+        from parent to child. A special "synthetic" root node is added
+        to be the parent of the first node in each path. A special
+        "synthetic" leaf node, the "nil" node `-1`, is added to be the child
+        of all nodes representing the last element in a path. (The
+        addition of this nil node technically makes this not an
+        arborescence but a directed acyclic graph; removing the nil node
+        makes it an arborescence.)
+
+
+    Notes
+    -----
+    The prefix tree is also known as a *trie*.
+
+
+    Examples
+    --------
+    Create a prefix tree from a list of strings with common prefixes::
+
+        >>> paths = ["ab", "abs", "ad"]
+        >>> T = nx.prefix_tree(paths)
+        >>> list(T.edges)
+        [(0, 1), (1, 2), (1, 4), (2, -1), (2, 3), (3, -1), (4, -1)]
+
+    The leaf nodes can be obtained as predecessors of the nil node::
+
+        >>> root, NIL = 0, -1
+        >>> list(T.predecessors(NIL))
+        [2, 3, 4]
+
+    To recover the original paths that generated the prefix tree,
+    traverse up the tree from the node `-1` to the node `0`::
+
+        >>> recovered = []
+        >>> for v in T.predecessors(NIL):
+        ...     prefix = ""
+        ...     while v != root:
+        ...         prefix = str(T.nodes[v]["source"]) + prefix
+        ...         v = next(T.predecessors(v))  # only one predecessor
+        ...     recovered.append(prefix)
+        >>> sorted(recovered)
+        ['ab', 'abs', 'ad']
+    """
+
+    def get_children(parent, paths):
+        children = defaultdict(list)
+        # Populate dictionary with key(s) as the child/children of the root and
+        # value(s) as the remaining paths of the corresponding child/children
+        for path in paths:
+            # If path is empty, we add an edge to the NIL node.
+            if not path:
+                tree.add_edge(parent, NIL)
+                continue
+            child, *rest = path
+            # `child` may exist as the head of more than one path in `paths`.
+            children[child].append(rest)
+        return children
+
+    # Initialize the prefix tree with a root node and a nil node.
+    tree = nx.DiGraph()
+    root = 0
+    tree.add_node(root, source=None)
+    NIL = -1
+    tree.add_node(NIL, source="NIL")
+    children = get_children(root, paths)
+    stack = [(root, iter(children.items()))]
+    while stack:
+        parent, remaining_children = stack[-1]
+        try:
+            child, remaining_paths = next(remaining_children)
+        # Pop item off stack if there are no remaining children
+        except StopIteration:
+            stack.pop()
+            continue
+        # We relabel each child with an unused name.
+        new_name = len(tree) - 1
+        # The "source" node attribute stores the original node name.
+        tree.add_node(new_name, source=child)
+        tree.add_edge(parent, new_name)
+        children = get_children(new_name, remaining_paths)
+        stack.append((new_name, iter(children.items())))
+
+    return tree
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def prefix_tree_recursive(paths):
+    """Recursively creates a directed prefix tree from a list of paths.
+
+    The original recursive version of prefix_tree for comparison. It is
+    the same algorithm but the recursion is unrolled onto a stack.
+
+    Usually the paths are described as strings or lists of integers.
+
+    A "prefix tree" represents the prefix structure of the strings.
+    Each node represents a prefix of some string. The root represents
+    the empty prefix with children for the single letter prefixes which
+    in turn have children for each double letter prefix starting with
+    the single letter corresponding to the parent node, and so on.
+
+    More generally the prefixes do not need to be strings. A prefix refers
+    to the start of a sequence. The root has children for each one element
+    prefix and they have children for each two element prefix that starts
+    with the one element sequence of the parent, and so on.
+
+    Note that this implementation uses integer nodes with an attribute.
+    Each node has an attribute "source" whose value is the original element
+    of the path to which this node corresponds. For example, suppose `paths`
+    consists of one path: "can". Then the nodes `[1, 2, 3]` which represent
+    this path have "source" values "c", "a" and "n".
+
+    All the descendants of a node have a common prefix in the sequence/path
+    associated with that node. From the returned tree, ehe prefix for each
+    node can be constructed by traversing the tree up to the root and
+    accumulating the "source" values along the way.
+
+    The root node is always `0` and has "source" attribute `None`.
+    The root is the only node with in-degree zero.
+    The nil node is always `-1` and has "source" attribute `"NIL"`.
+    The nil node is the only node with out-degree zero.
+
+
+    Parameters
+    ----------
+    paths: iterable of paths
+        An iterable of paths which are themselves sequences.
+        Matching prefixes among these sequences are identified with
+        nodes of the prefix tree. One leaf of the tree is associated
+        with each path. (Identical paths are associated with the same
+        leaf of the tree.)
+
+
+    Returns
+    -------
+    tree: DiGraph
+        A directed graph representing an arborescence consisting of the
+        prefix tree generated by `paths`. Nodes are directed "downward",
+        from parent to child. A special "synthetic" root node is added
+        to be the parent of the first node in each path. A special
+        "synthetic" leaf node, the "nil" node `-1`, is added to be the child
+        of all nodes representing the last element in a path. (The
+        addition of this nil node technically makes this not an
+        arborescence but a directed acyclic graph; removing the nil node
+        makes it an arborescence.)
+
+
+    Notes
+    -----
+    The prefix tree is also known as a *trie*.
+
+
+    Examples
+    --------
+    Create a prefix tree from a list of strings with common prefixes::
+
+        >>> paths = ["ab", "abs", "ad"]
+        >>> T = nx.prefix_tree(paths)
+        >>> list(T.edges)
+        [(0, 1), (1, 2), (1, 4), (2, -1), (2, 3), (3, -1), (4, -1)]
+
+    The leaf nodes can be obtained as predecessors of the nil node.
+
+        >>> root, NIL = 0, -1
+        >>> list(T.predecessors(NIL))
+        [2, 3, 4]
+
+    To recover the original paths that generated the prefix tree,
+    traverse up the tree from the node `-1` to the node `0`::
+
+        >>> recovered = []
+        >>> for v in T.predecessors(NIL):
+        ...     prefix = ""
+        ...     while v != root:
+        ...         prefix = str(T.nodes[v]["source"]) + prefix
+        ...         v = next(T.predecessors(v))  # only one predecessor
+        ...     recovered.append(prefix)
+        >>> sorted(recovered)
+        ['ab', 'abs', 'ad']
+    """
+
+    def _helper(paths, root, tree):
+        """Recursively create a trie from the given list of paths.
+
+        `paths` is a list of paths, each of which is itself a list of
+        nodes, relative to the given `root` (but not including it). This
+        list of paths will be interpreted as a tree-like structure, in
+        which two paths that share a prefix represent two branches of
+        the tree with the same initial segment.
+
+        `root` is the parent of the node at index 0 in each path.
+
+        `tree` is the "accumulator", the :class:`networkx.DiGraph`
+        representing the branching to which the new nodes and edges will
+        be added.
+
+        """
+        # For each path, remove the first node and make it a child of root.
+        # Any remaining paths then get processed recursively.
+        children = defaultdict(list)
+        for path in paths:
+            # If path is empty, we add an edge to the NIL node.
+            if not path:
+                tree.add_edge(root, NIL)
+                continue
+            child, *rest = path
+            # `child` may exist as the head of more than one path in `paths`.
+            children[child].append(rest)
+        # Add a node for each child, connect root, recurse to remaining paths
+        for child, remaining_paths in children.items():
+            # We relabel each child with an unused name.
+            new_name = len(tree) - 1
+            # The "source" node attribute stores the original node name.
+            tree.add_node(new_name, source=child)
+            tree.add_edge(root, new_name)
+            _helper(remaining_paths, new_name, tree)
+
+    # Initialize the prefix tree with a root node and a nil node.
+    tree = nx.DiGraph()
+    root = 0
+    tree.add_node(root, source=None)
+    NIL = -1
+    tree.add_node(NIL, source="NIL")
+    # Populate the tree.
+    _helper(paths, root, tree)
+    return tree
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_labeled_tree(n, *, seed=None):
+    """Returns a labeled tree on `n` nodes chosen uniformly at random.
+
+    Generating uniformly distributed random Prüfer sequences and
+    converting them into the corresponding trees is a straightforward
+    method of generating uniformly distributed random labeled trees.
+    This function implements this method.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes, greater than zero.
+    seed : random_state
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`
+
+    Returns
+    -------
+     :class:`networkx.Graph`
+        A `networkx.Graph` with nodes in the set {0, …, *n* - 1}.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+
+    Examples
+    --------
+    >>> G = nx.random_labeled_tree(5, seed=42)
+    >>> nx.is_tree(G)
+    True
+    >>> G.edges
+    EdgeView([(0, 1), (0, 3), (0, 2), (2, 4)])
+
+    A tree with *arbitrarily directed* edges can be created by assigning
+    generated edges to a ``DiGraph``:
+
+    >>> DG = nx.DiGraph()
+    >>> DG.add_edges_from(G.edges)
+    >>> nx.is_tree(DG)
+    True
+    >>> DG.edges
+    OutEdgeView([(0, 1), (0, 3), (0, 2), (2, 4)])
+    """
+    # Cannot create a Prüfer sequence unless `n` is at least two.
+    if n == 0:
+        raise nx.NetworkXPointlessConcept("the null graph is not a tree")
+    if n == 1:
+        return nx.empty_graph(1)
+    return nx.from_prufer_sequence([seed.choice(range(n)) for i in range(n - 2)])
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_labeled_rooted_tree(n, *, seed=None):
+    """Returns a labeled rooted tree with `n` nodes.
+
+    The returned tree is chosen uniformly at random from all labeled rooted trees.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph`
+        A `networkx.Graph` with integer nodes 0 <= node <= `n` - 1.
+        The root of the tree is selected uniformly from the nodes.
+        The "root" graph attribute identifies the root of the tree.
+
+    Notes
+    -----
+    This function returns the result of :func:`random_labeled_tree`
+    with a randomly selected root.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+    """
+    t = random_labeled_tree(n, seed=seed)
+    t.graph["root"] = seed.randint(0, n - 1)
+    return t
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_labeled_rooted_forest(n, *, seed=None):
+    """Returns a labeled rooted forest with `n` nodes.
+
+    The returned forest is chosen uniformly at random using a
+    generalization of Prüfer sequences [1]_ in the form described in [2]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    seed : random_state
+       See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph`
+        A `networkx.Graph` with integer nodes 0 <= node <= `n` - 1.
+        The "roots" graph attribute is a set of integers containing the roots.
+
+    References
+    ----------
+    .. [1] Knuth, Donald E. "Another Enumeration of Trees."
+        Canadian Journal of Mathematics, 20 (1968): 1077-1086.
+        https://doi.org/10.4153/CJM-1968-104-8
+    .. [2] Rubey, Martin. "Counting Spanning Trees". Diplomarbeit
+        zur Erlangung des akademischen Grades Magister der
+        Naturwissenschaften an der Formal- und Naturwissenschaftlichen
+        Fakultät der Universität Wien. Wien, May 2000.
+    """
+
+    # Select the number of roots by iterating over the cumulative count of trees
+    # with at most k roots
+    def _select_k(n, seed):
+        r = seed.randint(0, (n + 1) ** (n - 1) - 1)
+        cum_sum = 0
+        for k in range(1, n):
+            cum_sum += (factorial(n - 1) * n ** (n - k)) // (
+                factorial(k - 1) * factorial(n - k)
+            )
+            if r < cum_sum:
+                return k
+
+        return n
+
+    F = nx.empty_graph(n)
+    if n == 0:
+        F.graph["roots"] = {}
+        return F
+    # Select the number of roots k
+    k = _select_k(n, seed)
+    if k == n:
+        F.graph["roots"] = set(range(n))
+        return F  # Nothing to do
+    # Select the roots
+    roots = seed.sample(range(n), k)
+    # Nonroots
+    p = set(range(n)).difference(roots)
+    # Coding sequence
+    N = [seed.randint(0, n - 1) for i in range(n - k - 1)]
+    # Multiset of elements in N also in p
+    degree = Counter([x for x in N if x in p])
+    # Iterator over the elements of p with degree zero
+    iterator = iter(x for x in p if degree[x] == 0)
+    u = last = next(iterator)
+    # This loop is identical to that for Prüfer sequences,
+    # except that we can draw nodes only from p
+    for v in N:
+        F.add_edge(u, v)
+        degree[v] -= 1
+        if v < last and degree[v] == 0:
+            u = v
+        else:
+            last = u = next(iterator)
+
+    F.add_edge(u, roots[0])
+    F.graph["roots"] = set(roots)
+    return F
+
+
+# The following functions support generation of unlabeled trees and forests.
+
+
+def _to_nx(edges, n_nodes, root=None, roots=None):
+    """
+    Converts the (edges, n_nodes) input to a :class:`networkx.Graph`.
+    The (edges, n_nodes) input is a list of even length, where each pair
+    of consecutive integers represents an edge, and an integer `n_nodes`.
+    Integers in the list are elements of `range(n_nodes)`.
+
+    Parameters
+    ----------
+    edges : list of ints
+        The flattened list of edges of the graph.
+    n_nodes : int
+        The number of nodes of the graph.
+    root: int (default=None)
+        If not None, the "root" attribute of the graph will be set to this value.
+    roots: collection of ints (default=None)
+        If not None, he "roots" attribute of the graph will be set to this value.
+
+    Returns
+    -------
+    :class:`networkx.Graph`
+        The graph with `n_nodes` nodes and edges given by `edges`.
+    """
+    G = nx.empty_graph(n_nodes)
+    G.add_edges_from(edges)
+    if root is not None:
+        G.graph["root"] = root
+    if roots is not None:
+        G.graph["roots"] = roots
+    return G
+
+
+def _num_rooted_trees(n, cache_trees):
+    """Returns the number of unlabeled rooted trees with `n` nodes.
+
+    See also https://oeis.org/A000081.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    cache_trees : list of ints
+        The $i$-th element is the number of unlabeled rooted trees with $i$ nodes,
+        which is used as a cache (and is extended to length $n+1$ if needed)
+
+    Returns
+    -------
+    int
+        The number of unlabeled rooted trees with `n` nodes.
+    """
+    for n_i in range(len(cache_trees), n + 1):
+        cache_trees.append(
+            sum(
+                [
+                    d * cache_trees[n_i - j * d] * cache_trees[d]
+                    for d in range(1, n_i)
+                    for j in range(1, (n_i - 1) // d + 1)
+                ]
+            )
+            // (n_i - 1)
+        )
+    return cache_trees[n]
+
+
+def _select_jd_trees(n, cache_trees, seed):
+    """Returns a pair $(j,d)$ with a specific probability
+
+    Given $n$, returns a pair of positive integers $(j,d)$ with the probability
+    specified in formula (5) of Chapter 29 of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    cache_trees : list of ints
+        Cache for :func:`_num_rooted_trees`.
+    seed : random_state
+       See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (int, int)
+        A pair of positive integers $(j,d)$ satisfying formula (5) of
+        Chapter 29 of [1]_.
+
+    References
+    ----------
+    .. [1] Nijenhuis, Albert, and Wilf, Herbert S.
+        "Combinatorial algorithms: for computers and calculators."
+        Academic Press, 1978.
+        https://doi.org/10.1016/C2013-0-11243-3
+    """
+    p = seed.randint(0, _num_rooted_trees(n, cache_trees) * (n - 1) - 1)
+    cumsum = 0
+    for d in range(n - 1, 0, -1):
+        for j in range(1, (n - 1) // d + 1):
+            cumsum += (
+                d
+                * _num_rooted_trees(n - j * d, cache_trees)
+                * _num_rooted_trees(d, cache_trees)
+            )
+            if p < cumsum:
+                return (j, d)
+
+
+def _random_unlabeled_rooted_tree(n, cache_trees, seed):
+    """Returns an unlabeled rooted tree with `n` nodes.
+
+    Returns an unlabeled rooted tree with `n` nodes chosen uniformly
+    at random using the "RANRUT" algorithm from [1]_.
+    The tree is returned in the form: (list_of_edges, number_of_nodes)
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes, greater than zero.
+    cache_trees : list ints
+        Cache for :func:`_num_rooted_trees`.
+    seed : random_state
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (list_of_edges, number_of_nodes) : list, int
+        A random unlabeled rooted tree with `n` nodes as a 2-tuple
+        ``(list_of_edges, number_of_nodes)``.
+        The root is node 0.
+
+    References
+    ----------
+    .. [1] Nijenhuis, Albert, and Wilf, Herbert S.
+        "Combinatorial algorithms: for computers and calculators."
+        Academic Press, 1978.
+        https://doi.org/10.1016/C2013-0-11243-3
+    """
+    if n == 1:
+        edges, n_nodes = [], 1
+        return edges, n_nodes
+    if n == 2:
+        edges, n_nodes = [(0, 1)], 2
+        return edges, n_nodes
+
+    j, d = _select_jd_trees(n, cache_trees, seed)
+    t1, t1_nodes = _random_unlabeled_rooted_tree(n - j * d, cache_trees, seed)
+    t2, t2_nodes = _random_unlabeled_rooted_tree(d, cache_trees, seed)
+    t12 = [(0, t2_nodes * i + t1_nodes) for i in range(j)]
+    t1.extend(t12)
+    for _ in range(j):
+        t1.extend((n1 + t1_nodes, n2 + t1_nodes) for n1, n2 in t2)
+        t1_nodes += t2_nodes
+
+    return t1, t1_nodes
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_unlabeled_rooted_tree(n, *, number_of_trees=None, seed=None):
+    """Returns a number of unlabeled rooted trees uniformly at random
+
+    Returns one or more (depending on `number_of_trees`)
+    unlabeled rooted trees with `n` nodes drawn uniformly
+    at random.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    number_of_trees : int or None (default)
+        If not None, this number of trees is generated and returned.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph` or list of :class:`networkx.Graph`
+        A single `networkx.Graph` (or a list thereof, if `number_of_trees`
+        is specified) with nodes in the set {0, …, *n* - 1}.
+        The "root" graph attribute identifies the root of the tree.
+
+    Notes
+    -----
+    The trees are generated using the "RANRUT" algorithm from [1]_.
+    The algorithm needs to compute some counting functions
+    that are relatively expensive: in case several trees are needed,
+    it is advisable to use the `number_of_trees` optional argument
+    to reuse the counting functions.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+
+    References
+    ----------
+    .. [1] Nijenhuis, Albert, and Wilf, Herbert S.
+        "Combinatorial algorithms: for computers and calculators."
+        Academic Press, 1978.
+        https://doi.org/10.1016/C2013-0-11243-3
+    """
+    if n == 0:
+        raise nx.NetworkXPointlessConcept("the null graph is not a tree")
+    cache_trees = [0, 1]  # initial cache of number of rooted trees
+    if number_of_trees is None:
+        return _to_nx(*_random_unlabeled_rooted_tree(n, cache_trees, seed), root=0)
+    return [
+        _to_nx(*_random_unlabeled_rooted_tree(n, cache_trees, seed), root=0)
+        for i in range(number_of_trees)
+    ]
+
+
+def _num_rooted_forests(n, q, cache_forests):
+    """Returns the number of unlabeled rooted forests with `n` nodes, and with
+    no more than `q` nodes per tree. A recursive formula for this is (2) in
+    [1]_. This function is implemented using dynamic programming instead of
+    recursion.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    q : int
+        The maximum number of nodes for each tree of the forest.
+    cache_forests : list of ints
+        The $i$-th element is the number of unlabeled rooted forests with
+        $i$ nodes, and with no more than `q` nodes per tree; this is used
+        as a cache (and is extended to length `n` + 1 if needed).
+
+    Returns
+    -------
+    int
+        The number of unlabeled rooted forests with `n` nodes with no more than
+        `q` nodes per tree.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    for n_i in range(len(cache_forests), n + 1):
+        q_i = min(n_i, q)
+        cache_forests.append(
+            sum(
+                [
+                    d * cache_forests[n_i - j * d] * cache_forests[d - 1]
+                    for d in range(1, q_i + 1)
+                    for j in range(1, n_i // d + 1)
+                ]
+            )
+            // n_i
+        )
+
+    return cache_forests[n]
+
+
+def _select_jd_forests(n, q, cache_forests, seed):
+    """Given `n` and `q`, returns a pair of positive integers $(j,d)$
+    such that $j\\leq d$, with probability satisfying (F1) of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    q : int
+        The maximum number of nodes for each tree of the forest.
+    cache_forests : list of ints
+        Cache for :func:`_num_rooted_forests`.
+    seed : random_state
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (int, int)
+        A pair of positive integers $(j,d)$
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    p = seed.randint(0, _num_rooted_forests(n, q, cache_forests) * n - 1)
+    cumsum = 0
+    for d in range(q, 0, -1):
+        for j in range(1, n // d + 1):
+            cumsum += (
+                d
+                * _num_rooted_forests(n - j * d, q, cache_forests)
+                * _num_rooted_forests(d - 1, q, cache_forests)
+            )
+            if p < cumsum:
+                return (j, d)
+
+
+def _random_unlabeled_rooted_forest(n, q, cache_trees, cache_forests, seed):
+    """Returns an unlabeled rooted forest with `n` nodes, and with no more
+    than `q` nodes per tree, drawn uniformly at random. It is an implementation
+    of the algorithm "Forest" of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    q : int
+        The maximum number of nodes per tree.
+    cache_trees :
+        Cache for :func:`_num_rooted_trees`.
+    cache_forests :
+        Cache for :func:`_num_rooted_forests`.
+    seed : random_state
+       See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (edges, n, r) : (list, int, list)
+        The forest (edges, n) and a list r of root nodes.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if n == 0:
+        return ([], 0, [])
+
+    j, d = _select_jd_forests(n, q, cache_forests, seed)
+    t1, t1_nodes, r1 = _random_unlabeled_rooted_forest(
+        n - j * d, q, cache_trees, cache_forests, seed
+    )
+    t2, t2_nodes = _random_unlabeled_rooted_tree(d, cache_trees, seed)
+    for _ in range(j):
+        r1.append(t1_nodes)
+        t1.extend((n1 + t1_nodes, n2 + t1_nodes) for n1, n2 in t2)
+        t1_nodes += t2_nodes
+    return t1, t1_nodes, r1
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_unlabeled_rooted_forest(n, *, q=None, number_of_forests=None, seed=None):
+    """Returns a forest or list of forests selected at random.
+
+    Returns one or more (depending on `number_of_forests`)
+    unlabeled rooted forests with `n` nodes, and with no more than
+    `q` nodes per tree, drawn uniformly at random.
+    The "roots" graph attribute identifies the roots of the forest.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    q : int or None (default)
+        The maximum number of nodes per tree.
+    number_of_forests : int or None (default)
+        If not None, this number of forests is generated and returned.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph` or list of :class:`networkx.Graph`
+        A single `networkx.Graph` (or a list thereof, if `number_of_forests`
+        is specified) with nodes in the set {0, …, *n* - 1}.
+        The "roots" graph attribute is a set containing the roots
+        of the trees in the forest.
+
+    Notes
+    -----
+    This function implements the algorithm "Forest" of [1]_.
+    The algorithm needs to compute some counting functions
+    that are relatively expensive: in case several trees are needed,
+    it is advisable to use the `number_of_forests` optional argument
+    to reuse the counting functions.
+
+    Raises
+    ------
+    ValueError
+        If `n` is non-zero but `q` is zero.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if q is None:
+        q = n
+    if q == 0 and n != 0:
+        raise ValueError("q must be a positive integer if n is positive.")
+
+    cache_trees = [0, 1]  # initial cache of number of rooted trees
+    cache_forests = [1]  # initial cache of number of rooted forests
+
+    if number_of_forests is None:
+        g, nodes, rs = _random_unlabeled_rooted_forest(
+            n, q, cache_trees, cache_forests, seed
+        )
+        return _to_nx(g, nodes, roots=set(rs))
+
+    res = []
+    for i in range(number_of_forests):
+        g, nodes, rs = _random_unlabeled_rooted_forest(
+            n, q, cache_trees, cache_forests, seed
+        )
+        res.append(_to_nx(g, nodes, roots=set(rs)))
+    return res
+
+
+def _num_trees(n, cache_trees):
+    """Returns the number of unlabeled trees with `n` nodes.
+
+    See also https://oeis.org/A000055.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    cache_trees : list of ints
+        Cache for :func:`_num_rooted_trees`.
+
+    Returns
+    -------
+    int
+        The number of unlabeled trees with `n` nodes.
+    """
+    r = _num_rooted_trees(n, cache_trees) - sum(
+        [
+            _num_rooted_trees(j, cache_trees) * _num_rooted_trees(n - j, cache_trees)
+            for j in range(1, n // 2 + 1)
+        ]
+    )
+    if n % 2 == 0:
+        r += comb(_num_rooted_trees(n // 2, cache_trees) + 1, 2)
+    return r
+
+
+def _bicenter(n, cache, seed):
+    """Returns a bi-centroidal tree on `n` nodes drawn uniformly at random.
+
+    This function implements the algorithm Bicenter of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes (must be even).
+    cache : list of ints.
+        Cache for :func:`_num_rooted_trees`.
+    seed : random_state
+        See :ref:`Randomness<randomness>`
+
+    Returns
+    -------
+    (edges, n)
+        The tree as a list of edges and number of nodes.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    t, t_nodes = _random_unlabeled_rooted_tree(n // 2, cache, seed)
+    if seed.randint(0, _num_rooted_trees(n // 2, cache)) == 0:
+        t2, t2_nodes = t, t_nodes
+    else:
+        t2, t2_nodes = _random_unlabeled_rooted_tree(n // 2, cache, seed)
+    t.extend([(n1 + (n // 2), n2 + (n // 2)) for n1, n2 in t2])
+    t.append((0, n // 2))
+    return t, t_nodes + t2_nodes
+
+
+def _random_unlabeled_tree(n, cache_trees, cache_forests, seed):
+    """Returns a tree on `n` nodes drawn uniformly at random.
+    It implements the Wilf's algorithm "Free" of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes, greater than zero.
+    cache_trees : list of ints
+        Cache for :func:`_num_rooted_trees`.
+    cache_forests : list of ints
+        Cache for :func:`_num_rooted_forests`.
+    seed : random_state
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`
+
+    Returns
+    -------
+    (edges, n)
+        The tree as a list of edges and number of nodes.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if n % 2 == 1:
+        p = 0
+    else:
+        p = comb(_num_rooted_trees(n // 2, cache_trees) + 1, 2)
+    if seed.randint(0, _num_trees(n, cache_trees) - 1) < p:
+        return _bicenter(n, cache_trees, seed)
+    else:
+        f, n_f, r = _random_unlabeled_rooted_forest(
+            n - 1, (n - 1) // 2, cache_trees, cache_forests, seed
+        )
+        for i in r:
+            f.append((i, n_f))
+        return f, n_f + 1
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_unlabeled_tree(n, *, number_of_trees=None, seed=None):
+    """Returns a tree or list of trees chosen randomly.
+
+    Returns one or more (depending on `number_of_trees`)
+    unlabeled trees with `n` nodes drawn uniformly at random.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    number_of_trees : int or None (default)
+        If not None, this number of trees is generated and returned.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph` or list of :class:`networkx.Graph`
+        A single `networkx.Graph` (or a list thereof, if
+        `number_of_trees` is specified) with nodes in the set {0, …, *n* - 1}.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+
+    Notes
+    -----
+    This function generates an unlabeled tree uniformly at random using
+    Wilf's algorithm "Free" of [1]_. The algorithm needs to
+    compute some counting functions that are relatively expensive:
+    in case several trees are needed, it is advisable to use the
+    `number_of_trees` optional argument to reuse the counting
+    functions.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if n == 0:
+        raise nx.NetworkXPointlessConcept("the null graph is not a tree")
+
+    cache_trees = [0, 1]  # initial cache of number of rooted trees
+    cache_forests = [1]  # initial cache of number of rooted forests
+    if number_of_trees is None:
+        return _to_nx(*_random_unlabeled_tree(n, cache_trees, cache_forests, seed))
+    else:
+        return [
+            _to_nx(*_random_unlabeled_tree(n, cache_trees, cache_forests, seed))
+            for i in range(number_of_trees)
+        ]
diff --git a/lib/python3.11/site-packages/networkx/generators/triads.py b/lib/python3.11/site-packages/networkx/generators/triads.py
new file mode 100644
index 0000000000000000000000000000000000000000..09b722dd1bd49dddae16086115d170ec989f8b06
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/generators/triads.py
@@ -0,0 +1,94 @@
+# See https://github.com/networkx/networkx/pull/1474
+# Copyright 2011 Reya Group <http://www.reyagroup.com>
+# Copyright 2011 Alex Levenson <alex@isnotinvain.com>
+# Copyright 2011 Diederik van Liere <diederik.vanliere@rotman.utoronto.ca>
+"""Functions that generate the triad graphs, that is, the possible
+digraphs on three nodes.
+
+"""
+
+import networkx as nx
+from networkx.classes import DiGraph
+
+__all__ = ["triad_graph"]
+
+#: Dictionary mapping triad name to list of directed edges in the
+#: digraph representation of that triad (with nodes 'a', 'b', and 'c').
+TRIAD_EDGES = {
+    "003": [],
+    "012": ["ab"],
+    "102": ["ab", "ba"],
+    "021D": ["ba", "bc"],
+    "021U": ["ab", "cb"],
+    "021C": ["ab", "bc"],
+    "111D": ["ac", "ca", "bc"],
+    "111U": ["ac", "ca", "cb"],
+    "030T": ["ab", "cb", "ac"],
+    "030C": ["ba", "cb", "ac"],
+    "201": ["ab", "ba", "ac", "ca"],
+    "120D": ["bc", "ba", "ac", "ca"],
+    "120U": ["ab", "cb", "ac", "ca"],
+    "120C": ["ab", "bc", "ac", "ca"],
+    "210": ["ab", "bc", "cb", "ac", "ca"],
+    "300": ["ab", "ba", "bc", "cb", "ac", "ca"],
+}
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def triad_graph(triad_name):
+    """Returns the triad graph with the given name.
+
+    Each string in the following tuple is a valid triad name::
+
+        (
+            "003",
+            "012",
+            "102",
+            "021D",
+            "021U",
+            "021C",
+            "111D",
+            "111U",
+            "030T",
+            "030C",
+            "201",
+            "120D",
+            "120U",
+            "120C",
+            "210",
+            "300",
+        )
+
+    Each triad name corresponds to one of the possible valid digraph on
+    three nodes.
+
+    Parameters
+    ----------
+    triad_name : string
+        The name of a triad, as described above.
+
+    Returns
+    -------
+    :class:`~networkx.DiGraph`
+        The digraph on three nodes with the given name. The nodes of the
+        graph are the single-character strings 'a', 'b', and 'c'.
+
+    Raises
+    ------
+    ValueError
+        If `triad_name` is not the name of a triad.
+
+    See also
+    --------
+    triadic_census
+
+    """
+    if triad_name not in TRIAD_EDGES:
+        raise ValueError(
+            f'unknown triad name "{triad_name}"; use one of the triad names'
+            " in the TRIAD_NAMES constant"
+        )
+    G = DiGraph()
+    G.add_nodes_from("abc")
+    G.add_edges_from(TRIAD_EDGES[triad_name])
+    return G
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..740007185f9d8eb89e2a15f165560591ba23f36b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/algebraicconnectivity.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/algebraicconnectivity.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..92bdfffb848434a1f58ff084bc5f5446b0bde7ba
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/algebraicconnectivity.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/attrmatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/attrmatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..637c4aeb03584077cb4ed6aa44e1492dadaf4796
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/attrmatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/bethehessianmatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/bethehessianmatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..36511b09f0484cefc364e066a0782ac5d4b6b25d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/bethehessianmatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/graphmatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/graphmatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a166c21a34fe1ca702ad071fd0e71c99eea40088
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/graphmatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/laplacianmatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/laplacianmatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b9d76cb5572e7f5ac9ac370eb343caa2d07d5bd6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/laplacianmatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/modularitymatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/modularitymatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d86aba024d793881ceafcd3fed5a5043954efda8
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/modularitymatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/__pycache__/spectrum.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/__pycache__/spectrum.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5b93d901c0394ca17584656ba7891861ca32e32b
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/__pycache__/spectrum.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cbf2c03437fd12c684df00b8ec35c8c430df7f44
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_algebraic_connectivity.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_algebraic_connectivity.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e326aa3a14c539d34b060271ea660c8a378e72fb
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_algebraic_connectivity.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_attrmatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_attrmatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..892b3daafed169d79155505e619cd189ff218acf
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_attrmatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_bethehessian.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_bethehessian.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..062f9eb748e88345b1a664eaf0e0e2df49504386
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_bethehessian.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_graphmatrix.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_graphmatrix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0a71910cc06b55f766a9a2dc5e73d746c99dcc29
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_graphmatrix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_laplacian.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_laplacian.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..764a3202ca7a43aff0b13e1eb35c335cb5f880e4
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_laplacian.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_modularity.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_modularity.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ec1ec191f5790dd112bc15e90c9977c412840b99
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_modularity.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_spectrum.cpython-311.pyc b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_spectrum.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e4d66eadf31c2150d2c2e4f0dff8dd40b954b3d5
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/linalg/tests/__pycache__/test_spectrum.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/test_laplacian.py b/lib/python3.11/site-packages/networkx/linalg/tests/test_laplacian.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1d5c13e8d676c6b9d3e87f148051b7bccf9da30
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/linalg/tests/test_laplacian.py
@@ -0,0 +1,334 @@
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+
+class TestLaplacian:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = nx.havel_hakimi_graph(deg)
+        cls.WG = nx.Graph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
+        )
+        cls.WG.add_node(4)
+        cls.MG = nx.MultiGraph(cls.G)
+
+        # Graph with clsloops
+        cls.Gsl = cls.G.copy()
+        for node in cls.Gsl.nodes():
+            cls.Gsl.add_edge(node, node)
+
+        # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+        # "Google's PageRank and Beyond".
+        cls.DiG = nx.DiGraph()
+        cls.DiG.add_edges_from(
+            (
+                (1, 2),
+                (1, 3),
+                (3, 1),
+                (3, 2),
+                (3, 5),
+                (4, 5),
+                (4, 6),
+                (5, 4),
+                (5, 6),
+                (6, 4),
+            )
+        )
+        cls.DiMG = nx.MultiDiGraph(cls.DiG)
+        cls.DiWG = nx.DiGraph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.DiG.edges()
+        )
+        cls.DiGsl = cls.DiG.copy()
+        for node in cls.DiGsl.nodes():
+            cls.DiGsl.add_edge(node, node)
+
+    def test_laplacian(self):
+        "Graph Laplacian"
+        # fmt: off
+        NL = np.array([[ 3, -1, -1, -1,  0],
+                       [-1,  2, -1,  0,  0],
+                       [-1, -1,  2,  0,  0],
+                       [-1,  0,  0,  1,  0],
+                       [ 0,  0,  0,  0,  0]])
+        # fmt: on
+        WL = 0.5 * NL
+        OL = 0.3 * NL
+        # fmt: off
+        DiNL = np.array([[ 2, -1, -1,  0,  0,  0],
+                         [ 0,  0,  0,  0,  0,  0],
+                         [-1, -1,  3, -1,  0,  0],
+                         [ 0,  0,  0,  2, -1, -1],
+                         [ 0,  0,  0, -1,  2, -1],
+                         [ 0,  0,  0,  0, -1,  1]])
+        # fmt: on
+        DiWL = 0.5 * DiNL
+        DiOL = 0.3 * DiNL
+        np.testing.assert_equal(nx.laplacian_matrix(self.G).todense(), NL)
+        np.testing.assert_equal(nx.laplacian_matrix(self.MG).todense(), NL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.G, nodelist=[0, 1]).todense(),
+            np.array([[1, -1], [-1, 1]]),
+        )
+        np.testing.assert_equal(nx.laplacian_matrix(self.WG).todense(), WL)
+        np.testing.assert_equal(nx.laplacian_matrix(self.WG, weight=None).todense(), NL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.WG, weight="other").todense(), OL
+        )
+
+        np.testing.assert_equal(nx.laplacian_matrix(self.DiG).todense(), DiNL)
+        np.testing.assert_equal(nx.laplacian_matrix(self.DiMG).todense(), DiNL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.DiG, nodelist=[1, 2]).todense(),
+            np.array([[1, -1], [0, 0]]),
+        )
+        np.testing.assert_equal(nx.laplacian_matrix(self.DiWG).todense(), DiWL)
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.DiWG, weight=None).todense(), DiNL
+        )
+        np.testing.assert_equal(
+            nx.laplacian_matrix(self.DiWG, weight="other").todense(), DiOL
+        )
+
+    def test_normalized_laplacian(self):
+        "Generalized Graph Laplacian"
+        # fmt: off
+        G = np.array([[ 1.   , -0.408, -0.408, -0.577,  0.],
+                      [-0.408,  1.   , -0.5  ,  0.   ,  0.],
+                      [-0.408, -0.5  ,  1.   ,  0.   ,  0.],
+                      [-0.577,  0.   ,  0.   ,  1.   ,  0.],
+                      [ 0.   ,  0.   ,  0.   ,  0.   ,  0.]])
+        GL = np.array([[ 1.   , -0.408, -0.408, -0.577,  0.   ],
+                       [-0.408,  1.   , -0.5  ,  0.   ,  0.   ],
+                       [-0.408, -0.5  ,  1.   ,  0.   ,  0.   ],
+                       [-0.577,  0.   ,  0.   ,  1.   ,  0.   ],
+                       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ]])
+        Lsl = np.array([[ 0.75  , -0.2887, -0.2887, -0.3536,  0.    ],
+                        [-0.2887,  0.6667, -0.3333,  0.    ,  0.    ],
+                        [-0.2887, -0.3333,  0.6667,  0.    ,  0.    ],
+                        [-0.3536,  0.    ,  0.    ,  0.5   ,  0.    ],
+                        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ]])
+
+        DiG = np.array([[ 1.    ,  0.    , -0.4082,  0.    ,  0.    ,  0.    ],
+                        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
+                        [-0.4082,  0.    ,  1.    ,  0.    , -0.4082,  0.    ],
+                        [ 0.    ,  0.    ,  0.    ,  1.    , -0.5   , -0.7071],
+                        [ 0.    ,  0.    ,  0.    , -0.5   ,  1.    , -0.7071],
+                        [ 0.    ,  0.    ,  0.    , -0.7071,  0.     , 1.    ]])
+        DiGL = np.array([[ 1.    ,  0.    , -0.4082,  0.    ,  0.    ,  0.    ],
+                         [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
+                         [-0.4082,  0.    ,  1.    , -0.4082,  0.    ,  0.    ],
+                         [ 0.    ,  0.    ,  0.    ,  1.    , -0.5   , -0.7071],
+                         [ 0.    ,  0.    ,  0.    , -0.5   ,  1.    , -0.7071],
+                         [ 0.    ,  0.    ,  0.    ,  0.    , -0.7071,  1.    ]])
+        DiLsl = np.array([[ 0.6667, -0.5774, -0.2887,  0.    ,  0.    ,  0.    ],
+                          [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ],
+                          [-0.2887, -0.5   ,  0.75  , -0.2887,  0.    ,  0.    ],
+                          [ 0.    ,  0.    ,  0.    ,  0.6667, -0.3333, -0.4082],
+                          [ 0.    ,  0.    ,  0.    , -0.3333,  0.6667, -0.4082],
+                          [ 0.    ,  0.    ,  0.    ,  0.    , -0.4082,  0.5   ]])
+        # fmt: on
+
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.G, nodelist=range(5)).todense(),
+            G,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.G).todense(), GL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.MG).todense(), GL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.WG).todense(), GL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.WG, weight="other").todense(),
+            GL,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.Gsl).todense(), Lsl, decimal=3
+        )
+
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(
+                self.DiG,
+                nodelist=range(1, 1 + 6),
+            ).todense(),
+            DiG,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiG).todense(), DiGL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiMG).todense(), DiGL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiWG).todense(), DiGL, decimal=3
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiWG, weight="other").todense(),
+            DiGL,
+            decimal=3,
+        )
+        np.testing.assert_almost_equal(
+            nx.normalized_laplacian_matrix(self.DiGsl).todense(), DiLsl, decimal=3
+        )
+
+
+def test_directed_laplacian():
+    "Directed Laplacian"
+    # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+    # "Google's PageRank and Beyond". The graph contains dangling nodes, so
+    # the pagerank random walk is selected by directed_laplacian
+    G = nx.DiGraph()
+    G.add_edges_from(
+        (
+            (1, 2),
+            (1, 3),
+            (3, 1),
+            (3, 2),
+            (3, 5),
+            (4, 5),
+            (4, 6),
+            (5, 4),
+            (5, 6),
+            (6, 4),
+        )
+    )
+    # fmt: off
+    GL = np.array([[ 0.9833, -0.2941, -0.3882, -0.0291, -0.0231, -0.0261],
+                   [-0.2941,  0.8333, -0.2339, -0.0536, -0.0589, -0.0554],
+                   [-0.3882, -0.2339,  0.9833, -0.0278, -0.0896, -0.0251],
+                   [-0.0291, -0.0536, -0.0278,  0.9833, -0.4878, -0.6675],
+                   [-0.0231, -0.0589, -0.0896, -0.4878,  0.9833, -0.2078],
+                   [-0.0261, -0.0554, -0.0251, -0.6675, -0.2078,  0.9833]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # Make the graph strongly connected, so we can use a random and lazy walk
+    G.add_edges_from(((2, 5), (6, 1)))
+    # fmt: off
+    GL = np.array([[ 1.    , -0.3062, -0.4714,  0.    ,  0.    , -0.3227],
+                   [-0.3062,  1.    , -0.1443,  0.    , -0.3162,  0.    ],
+                   [-0.4714, -0.1443,  1.    ,  0.    , -0.0913,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  1.    , -0.5   , -0.5   ],
+                   [ 0.    , -0.3162, -0.0913, -0.5   ,  1.    , -0.25  ],
+                   [-0.3227,  0.    ,  0.    , -0.5   , -0.25  ,  1.    ]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(
+        G, alpha=0.9, nodelist=sorted(G), walk_type="random"
+    )
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # fmt: off
+    GL = np.array([[ 0.5   , -0.1531, -0.2357,  0.    ,  0.    , -0.1614],
+                   [-0.1531,  0.5   , -0.0722,  0.    , -0.1581,  0.    ],
+                   [-0.2357, -0.0722,  0.5   ,  0.    , -0.0456,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  0.5   , -0.25  , -0.25  ],
+                   [ 0.    , -0.1581, -0.0456, -0.25  ,  0.5   , -0.125 ],
+                   [-0.1614,  0.    ,  0.    , -0.25  , -0.125 ,  0.5   ]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G), walk_type="lazy")
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # Make a strongly connected periodic graph
+    G = nx.DiGraph()
+    G.add_edges_from(((1, 2), (2, 4), (4, 1), (1, 3), (3, 4)))
+    # fmt: off
+    GL = np.array([[ 0.5  , -0.176, -0.176, -0.25 ],
+                   [-0.176,  0.5  ,  0.   , -0.176],
+                   [-0.176,  0.   ,  0.5  , -0.176],
+                   [-0.25 , -0.176, -0.176,  0.5  ]])
+    # fmt: on
+    L = nx.directed_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+
+def test_directed_combinatorial_laplacian():
+    "Directed combinatorial Laplacian"
+    # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+    # "Google's PageRank and Beyond". The graph contains dangling nodes, so
+    # the pagerank random walk is selected by directed_laplacian
+    G = nx.DiGraph()
+    G.add_edges_from(
+        (
+            (1, 2),
+            (1, 3),
+            (3, 1),
+            (3, 2),
+            (3, 5),
+            (4, 5),
+            (4, 6),
+            (5, 4),
+            (5, 6),
+            (6, 4),
+        )
+    )
+    # fmt: off
+    GL = np.array([[ 0.0366, -0.0132, -0.0153, -0.0034, -0.0020, -0.0027],
+                   [-0.0132,  0.0450, -0.0111, -0.0076, -0.0062, -0.0069],
+                   [-0.0153, -0.0111,  0.0408, -0.0035, -0.0083, -0.0027],
+                   [-0.0034, -0.0076, -0.0035,  0.3688, -0.1356, -0.2187],
+                   [-0.0020, -0.0062, -0.0083, -0.1356,  0.2026, -0.0505],
+                   [-0.0027, -0.0069, -0.0027, -0.2187, -0.0505,  0.2815]])
+    # fmt: on
+
+    L = nx.directed_combinatorial_laplacian_matrix(G, alpha=0.9, nodelist=sorted(G))
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # Make the graph strongly connected, so we can use a random and lazy walk
+    G.add_edges_from(((2, 5), (6, 1)))
+
+    # fmt: off
+    GL = np.array([[ 0.1395, -0.0349, -0.0465,  0.    ,  0.    , -0.0581],
+                   [-0.0349,  0.093 , -0.0116,  0.    , -0.0465,  0.    ],
+                   [-0.0465, -0.0116,  0.0698,  0.    , -0.0116,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  0.2326, -0.1163, -0.1163],
+                   [ 0.    , -0.0465, -0.0116, -0.1163,  0.2326, -0.0581],
+                   [-0.0581,  0.    ,  0.    , -0.1163, -0.0581,  0.2326]])
+    # fmt: on
+
+    L = nx.directed_combinatorial_laplacian_matrix(
+        G, alpha=0.9, nodelist=sorted(G), walk_type="random"
+    )
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    # fmt: off
+    GL = np.array([[ 0.0698, -0.0174, -0.0233,  0.    ,  0.    , -0.0291],
+                   [-0.0174,  0.0465, -0.0058,  0.    , -0.0233,  0.    ],
+                   [-0.0233, -0.0058,  0.0349,  0.    , -0.0058,  0.    ],
+                   [ 0.    ,  0.    ,  0.    ,  0.1163, -0.0581, -0.0581],
+                   [ 0.    , -0.0233, -0.0058, -0.0581,  0.1163, -0.0291],
+                   [-0.0291,  0.    ,  0.    , -0.0581, -0.0291,  0.1163]])
+    # fmt: on
+
+    L = nx.directed_combinatorial_laplacian_matrix(
+        G, alpha=0.9, nodelist=sorted(G), walk_type="lazy"
+    )
+    np.testing.assert_almost_equal(L, GL, decimal=3)
+
+    E = nx.DiGraph(nx.margulis_gabber_galil_graph(2))
+    L = nx.directed_combinatorial_laplacian_matrix(E)
+    # fmt: off
+    expected = np.array(
+        [[ 0.16666667, -0.08333333, -0.08333333,  0.        ],
+         [-0.08333333,  0.16666667,  0.        , -0.08333333],
+         [-0.08333333,  0.        ,  0.16666667, -0.08333333],
+         [ 0.        , -0.08333333, -0.08333333,  0.16666667]]
+    )
+    # fmt: on
+    np.testing.assert_almost_equal(L, expected, decimal=6)
+
+    with pytest.raises(nx.NetworkXError):
+        nx.directed_combinatorial_laplacian_matrix(G, walk_type="pagerank", alpha=100)
+    with pytest.raises(nx.NetworkXError):
+        nx.directed_combinatorial_laplacian_matrix(G, walk_type="silly")
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/test_modularity.py b/lib/python3.11/site-packages/networkx/linalg/tests/test_modularity.py
new file mode 100644
index 0000000000000000000000000000000000000000..48dda0063e127bcaa3c47dc05089dd858623f4c1
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/linalg/tests/test_modularity.py
@@ -0,0 +1,86 @@
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+
+class TestModularity:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = nx.havel_hakimi_graph(deg)
+        # Graph used as an example in Sec. 4.1 of Langville and Meyer,
+        # "Google's PageRank and Beyond". (Used for test_directed_laplacian)
+        cls.DG = nx.DiGraph()
+        cls.DG.add_edges_from(
+            (
+                (1, 2),
+                (1, 3),
+                (3, 1),
+                (3, 2),
+                (3, 5),
+                (4, 5),
+                (4, 6),
+                (5, 4),
+                (5, 6),
+                (6, 4),
+            )
+        )
+
+    def test_modularity(self):
+        "Modularity matrix"
+        # fmt: off
+        B = np.array([[-1.125,  0.25,  0.25,  0.625,  0.],
+                      [0.25, -0.5,  0.5, -0.25,  0.],
+                      [0.25,  0.5, -0.5, -0.25,  0.],
+                      [0.625, -0.25, -0.25, -0.125,  0.],
+                      [0.,  0.,  0.,  0.,  0.]])
+        # fmt: on
+
+        permutation = [4, 0, 1, 2, 3]
+        np.testing.assert_equal(nx.modularity_matrix(self.G), B)
+        np.testing.assert_equal(
+            nx.modularity_matrix(self.G, nodelist=permutation),
+            B[np.ix_(permutation, permutation)],
+        )
+
+    def test_modularity_weight(self):
+        "Modularity matrix with weights"
+        # fmt: off
+        B = np.array([[-1.125,  0.25,  0.25,  0.625,  0.],
+                      [0.25, -0.5,  0.5, -0.25,  0.],
+                      [0.25,  0.5, -0.5, -0.25,  0.],
+                      [0.625, -0.25, -0.25, -0.125,  0.],
+                      [0.,  0.,  0.,  0.,  0.]])
+        # fmt: on
+
+        G_weighted = self.G.copy()
+        for n1, n2 in G_weighted.edges():
+            G_weighted.edges[n1, n2]["weight"] = 0.5
+        # The following test would fail in networkx 1.1
+        np.testing.assert_equal(nx.modularity_matrix(G_weighted), B)
+        # The following test that the modularity matrix get rescaled accordingly
+        np.testing.assert_equal(
+            nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B
+        )
+
+    def test_directed_modularity(self):
+        "Directed Modularity matrix"
+        # fmt: off
+        B = np.array([[-0.2,  0.6,  0.8, -0.4, -0.4, -0.4],
+                      [0.,  0.,  0.,  0.,  0.,  0.],
+                      [0.7,  0.4, -0.3, -0.6,  0.4, -0.6],
+                      [-0.2, -0.4, -0.2, -0.4,  0.6,  0.6],
+                      [-0.2, -0.4, -0.2,  0.6, -0.4,  0.6],
+                      [-0.1, -0.2, -0.1,  0.8, -0.2, -0.2]])
+        # fmt: on
+        node_permutation = [5, 1, 2, 3, 4, 6]
+        idx_permutation = [4, 0, 1, 2, 3, 5]
+        mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG))
+        np.testing.assert_equal(mm, B)
+        np.testing.assert_equal(
+            nx.directed_modularity_matrix(self.DG, nodelist=node_permutation),
+            B[np.ix_(idx_permutation, idx_permutation)],
+        )
diff --git a/lib/python3.11/site-packages/networkx/linalg/tests/test_spectrum.py b/lib/python3.11/site-packages/networkx/linalg/tests/test_spectrum.py
new file mode 100644
index 0000000000000000000000000000000000000000..01009f005064b8cd23d221160719c4c5cc8e33f3
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/linalg/tests/test_spectrum.py
@@ -0,0 +1,70 @@
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+
+class TestSpectrum:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = nx.havel_hakimi_graph(deg)
+        cls.P = nx.path_graph(3)
+        cls.WG = nx.Graph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
+        )
+        cls.WG.add_node(4)
+        cls.DG = nx.DiGraph()
+        nx.add_path(cls.DG, [0, 1, 2])
+
+    def test_laplacian_spectrum(self):
+        "Laplacian eigenvalues"
+        evals = np.array([0, 0, 1, 3, 4])
+        e = sorted(nx.laplacian_spectrum(self.G))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.laplacian_spectrum(self.WG, weight=None))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.laplacian_spectrum(self.WG))
+        np.testing.assert_almost_equal(e, 0.5 * evals)
+        e = sorted(nx.laplacian_spectrum(self.WG, weight="other"))
+        np.testing.assert_almost_equal(e, 0.3 * evals)
+
+    def test_normalized_laplacian_spectrum(self):
+        "Normalized Laplacian eigenvalues"
+        evals = np.array([0, 0, 0.7712864461218, 1.5, 1.7287135538781])
+        e = sorted(nx.normalized_laplacian_spectrum(self.G))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight=None))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight="other"))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_adjacency_spectrum(self):
+        "Adjacency eigenvalues"
+        evals = np.array([-np.sqrt(2), 0, np.sqrt(2)])
+        e = sorted(nx.adjacency_spectrum(self.P))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_modularity_spectrum(self):
+        "Modularity eigenvalues"
+        evals = np.array([-1.5, 0.0, 0.0])
+        e = sorted(nx.modularity_spectrum(self.P))
+        np.testing.assert_almost_equal(e, evals)
+        # Directed modularity eigenvalues
+        evals = np.array([-0.5, 0.0, 0.0])
+        e = sorted(nx.modularity_spectrum(self.DG))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_bethe_hessian_spectrum(self):
+        "Bethe Hessian eigenvalues"
+        evals = np.array([0.5 * (9 - np.sqrt(33)), 4, 0.5 * (9 + np.sqrt(33))])
+        e = sorted(nx.bethe_hessian_spectrum(self.P, r=2))
+        np.testing.assert_almost_equal(e, evals)
+        # Collapses back to Laplacian:
+        e1 = sorted(nx.bethe_hessian_spectrum(self.P, r=1))
+        e2 = sorted(nx.laplacian_spectrum(self.P))
+        np.testing.assert_almost_equal(e1, e2)
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__init__.py b/lib/python3.11/site-packages/networkx/readwrite/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a805c50a7b18bc818f7bb0a8978ee1e7e90277b5
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/__init__.py
@@ -0,0 +1,17 @@
+"""
+A package for reading and writing graphs in various formats.
+
+"""
+
+from networkx.readwrite.adjlist import *
+from networkx.readwrite.multiline_adjlist import *
+from networkx.readwrite.edgelist import *
+from networkx.readwrite.pajek import *
+from networkx.readwrite.leda import *
+from networkx.readwrite.sparse6 import *
+from networkx.readwrite.graph6 import *
+from networkx.readwrite.gml import *
+from networkx.readwrite.graphml import *
+from networkx.readwrite.gexf import *
+from networkx.readwrite.json_graph import *
+from networkx.readwrite.text import *
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4606d44ce64b1ec5495783fcd2a89d5c5fb55938
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/adjlist.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/adjlist.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9a4f7e4a5162e57573074cdf3f5d9938c797c2c3
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/adjlist.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/edgelist.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/edgelist.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8dc42340cda680cc7041f2de196b6d33652d7e50
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/edgelist.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/gexf.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/gexf.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..967d3aa803773e49bb15bd8e89d3c32aee057ee2
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/gexf.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/gml.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/gml.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bf23c4f9eacd3c6fd0aeba2912c5c370fe633a66
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/gml.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/graph6.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/graph6.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..99d2b87a6f8744c14702e7f41d8f29ad99fb8704
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/graph6.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/graphml.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/graphml.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9d724e31fd3bbba3214cfcbd52f25550aeadfc9e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/graphml.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/leda.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/leda.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..34964537c4bc1299efe45884da0c36b29ef639f4
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/leda.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/multiline_adjlist.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/multiline_adjlist.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..100e52ac7669377fad7b8a1bb93001cfb7488d28
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/multiline_adjlist.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/p2g.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/p2g.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b686c35dd5e329d5a1cd8a070b0a61d04db1b53e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/p2g.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/pajek.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/pajek.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..26442a8353b15319db46af290bc0e431b3d023b4
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/pajek.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/sparse6.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/sparse6.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3edf8b7bbc569bb393019b462db7ae9fc7cf6292
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/sparse6.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/__pycache__/text.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/text.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0ae2708f19154d5940bfdff413f52ec977baaafb
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/__pycache__/text.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/adjlist.py b/lib/python3.11/site-packages/networkx/readwrite/adjlist.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba3b6e5addff8f39ae5f89610e675304d580787c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/adjlist.py
@@ -0,0 +1,330 @@
+"""
+**************
+Adjacency List
+**************
+Read and write NetworkX graphs as adjacency lists.
+
+Adjacency list format is useful for graphs without data associated
+with nodes or edges and for nodes that can be meaningfully represented
+as strings.
+
+Format
+------
+The adjacency list format consists of lines with node labels.  The
+first label in a line is the source node.  Further labels in the line
+are considered target nodes and are added to the graph along with an edge
+between the source node and target node.
+
+The graph with edges a-b, a-c, d-e can be represented as the following
+adjacency list (anything following the # in a line is a comment)::
+
+     a b c # source target target
+     d e
+"""
+
+__all__ = ["generate_adjlist", "write_adjlist", "parse_adjlist", "read_adjlist"]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_adjlist(G, delimiter=" "):
+    """Generate lines representing a graph in adjacency list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : str, default=" "
+        Separator for node labels.
+
+    Yields
+    ------
+    str
+        Adjacency list for a node in `G`. The first item is the node label,
+        followed by the labels of its neighbors.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> for line in nx.generate_adjlist(G):
+    ...     print(line)
+    0 1 2 3
+    1 2 3
+    2 3
+    3 4
+    4 5
+    5 6
+    6
+
+    When `G` is undirected, each edge is only listed once. For directed graphs,
+    edges appear once for each direction.
+
+    >>> G = nx.complete_graph(3, create_using=nx.DiGraph)
+    >>> for line in nx.generate_adjlist(G):
+    ...     print(line)
+    0 1 2
+    1 0 2
+    2 0 1
+
+    Node labels are shown multiple times for multiedges, but edge data (including keys)
+    are not included in the output.
+
+    >>> G = nx.MultiGraph([(0, 1, {"weight": 1}), (0, 1, {"weight": 2})])
+    >>> for line in nx.generate_adjlist(G):
+    ...     print(line)
+    0 1 1
+    1
+
+    See Also
+    --------
+    write_adjlist, read_adjlist
+
+    Notes
+    -----
+    The default `delimiter=" "` will result in unexpected results if node names contain
+    whitespace characters. To avoid this problem, specify an alternate delimiter when spaces are
+    valid in node names.
+
+    NB: This option is not available for data that isn't user-generated.
+
+    """
+    seen = set()
+    directed = G.is_directed()
+    multigraph = G.is_multigraph()
+    for s, nbrs in G.adjacency():
+        nodes = [str(s)]
+        for t, data in nbrs.items():
+            if t in seen:
+                continue
+            if multigraph and len(data) > 1:
+                nodes.extend((str(t),) * len(data))
+            else:
+                nodes.append(str(t))
+        if not directed:
+            seen.add(s)
+        yield delimiter.join(nodes)
+
+
+@open_file(1, mode="wb")
+def write_adjlist(G, path, comments="#", delimiter=" ", encoding="utf-8"):
+    """Write graph G in single-line adjacency-list format to path.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle for data output.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels
+
+    encoding : string, optional
+       Text encoding.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_adjlist(G, "path4.adjlist")
+
+    The path can be a filehandle or a string with the name of the file. If a
+    filehandle is provided, it has to be opened in 'wb' mode.
+
+    >>> fh = open("path4.adjlist2", "wb")
+    >>> nx.write_adjlist(G, fh)
+
+    Notes
+    -----
+    The default `delimiter=" "` will result in unexpected results if node names contain
+    whitespace characters. To avoid this problem, specify an alternate delimiter when spaces are
+    valid in node names.
+    NB: This option is not available for data that isn't user-generated.
+
+    This format does not store graph, node, or edge data.
+
+    See Also
+    --------
+    read_adjlist, generate_adjlist
+    """
+    import sys
+    import time
+
+    pargs = comments + " ".join(sys.argv) + "\n"
+    header = (
+        pargs
+        + comments
+        + f" GMT {time.asctime(time.gmtime())}\n"
+        + comments
+        + f" {G.name}\n"
+    )
+    path.write(header.encode(encoding))
+
+    for line in generate_adjlist(G, delimiter):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_adjlist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None
+):
+    """Parse lines of a graph adjacency list representation.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in adjlist format
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in adjacency list format.
+
+    Examples
+    --------
+    >>> lines = ["1 2 5", "2 3 4", "3 5", "4", "5"]
+    >>> G = nx.parse_adjlist(lines, nodetype=int)
+    >>> nodes = [1, 2, 3, 4, 5]
+    >>> all(node in G for node in nodes)
+    True
+    >>> edges = [(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)]
+    >>> all((u, v) in G.edges() or (v, u) in G.edges() for (u, v) in edges)
+    True
+
+    See Also
+    --------
+    read_adjlist
+
+    """
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        p = line.find(comments)
+        if p >= 0:
+            line = line[:p]
+        if not len(line):
+            continue
+        vlist = line.rstrip("\n").split(delimiter)
+        u = vlist.pop(0)
+        # convert types
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert node ({u}) to type {nodetype}"
+                ) from err
+        G.add_node(u)
+        if nodetype is not None:
+            try:
+                vlist = list(map(nodetype, vlist))
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert nodes ({','.join(vlist)}) to type {nodetype}"
+                ) from err
+        G.add_edges_from([(u, v) for v in vlist])
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_adjlist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    encoding="utf-8",
+):
+    """Read graph in adjacency list format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in adjacency list format.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_adjlist(G, "test.adjlist")
+    >>> G = nx.read_adjlist("test.adjlist")
+
+    The path can be a filehandle or a string with the name of the file. If a
+    filehandle is provided, it has to be opened in 'rb' mode.
+
+    >>> fh = open("test.adjlist", "rb")
+    >>> G = nx.read_adjlist(fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_adjlist(G, "test.adjlist.gz")
+    >>> G = nx.read_adjlist("test.adjlist.gz")
+
+    The optional nodetype is a function to convert node strings to nodetype.
+
+    For example
+
+    >>> G = nx.read_adjlist("test.adjlist", nodetype=int)
+
+    will attempt to convert all nodes to integer type.
+
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+
+    The optional create_using parameter indicates the type of NetworkX graph
+    created.  The default is `nx.Graph`, an undirected graph.
+    To read the data as a directed graph use
+
+    >>> G = nx.read_adjlist("test.adjlist", create_using=nx.DiGraph)
+
+    Notes
+    -----
+    This format does not store graph or node data.
+
+    See Also
+    --------
+    write_adjlist
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_adjlist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+    )
diff --git a/lib/python3.11/site-packages/networkx/readwrite/edgelist.py b/lib/python3.11/site-packages/networkx/readwrite/edgelist.py
new file mode 100644
index 0000000000000000000000000000000000000000..afdb175e8cf4dfb5d8ddb81b136259d2471ad13e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/edgelist.py
@@ -0,0 +1,489 @@
+"""
+**********
+Edge Lists
+**********
+Read and write NetworkX graphs as edge lists.
+
+The multi-line adjacency list format is useful for graphs with nodes
+that can be meaningfully represented as strings.  With the edgelist
+format simple edge data can be stored but node or graph data is not.
+There is no way of representing isolated nodes unless the node has a
+self-loop edge.
+
+Format
+------
+You can read or write three formats of edge lists with these functions.
+
+Node pairs with no data::
+
+ 1 2
+
+Python dictionary as data::
+
+ 1 2 {'weight':7, 'color':'green'}
+
+Arbitrary data::
+
+ 1 2 7 green
+"""
+
+__all__ = [
+    "generate_edgelist",
+    "write_edgelist",
+    "parse_edgelist",
+    "read_edgelist",
+    "read_weighted_edgelist",
+    "write_weighted_edgelist",
+]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_edgelist(G, delimiter=" ", data=True):
+    """Generate a single line of the graph G in edge list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : string, optional
+       Separator for node labels
+
+    data : bool or list of keys
+       If False generate no edge data.  If True use a dictionary
+       representation of edge data.  If a list of keys use a list of data
+       values corresponding to the keys.
+
+    Returns
+    -------
+    lines : string
+        Lines of data in adjlist format.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> G[1][2]["weight"] = 3
+    >>> G[3][4]["capacity"] = 12
+    >>> for line in nx.generate_edgelist(G, data=False):
+    ...     print(line)
+    0 1
+    0 2
+    0 3
+    1 2
+    1 3
+    2 3
+    3 4
+    4 5
+    5 6
+
+    >>> for line in nx.generate_edgelist(G):
+    ...     print(line)
+    0 1 {}
+    0 2 {}
+    0 3 {}
+    1 2 {'weight': 3}
+    1 3 {}
+    2 3 {}
+    3 4 {'capacity': 12}
+    4 5 {}
+    5 6 {}
+
+    >>> for line in nx.generate_edgelist(G, data=["weight"]):
+    ...     print(line)
+    0 1
+    0 2
+    0 3
+    1 2 3
+    1 3
+    2 3
+    3 4
+    4 5
+    5 6
+
+    See Also
+    --------
+    write_adjlist, read_adjlist
+    """
+    if data is True:
+        for u, v, d in G.edges(data=True):
+            e = u, v, dict(d)
+            yield delimiter.join(map(str, e))
+    elif data is False:
+        for u, v in G.edges(data=False):
+            e = u, v
+            yield delimiter.join(map(str, e))
+    else:
+        for u, v, d in G.edges(data=True):
+            e = [u, v]
+            try:
+                e.extend(d[k] for k in data)
+            except KeyError:
+                pass  # missing data for this edge, should warn?
+            yield delimiter.join(map(str, e))
+
+
+@open_file(1, mode="wb")
+def write_edgelist(G, path, comments="#", delimiter=" ", data=True, encoding="utf-8"):
+    """Write graph as a list of edges.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or filename to write. If a file is provided, it must be
+       opened in 'wb' mode. Filenames ending in .gz or .bz2 will be compressed.
+    comments : string, optional
+       The character used to indicate the start of a comment
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    data : bool or list, optional
+       If False write no edge data.
+       If True write a string representation of the edge data dictionary..
+       If a list (or other iterable) is provided, write the  keys specified
+       in the list.
+    encoding: string, optional
+       Specify which encoding to use when writing file.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_edgelist(G, "test.edgelist")
+    >>> G = nx.path_graph(4)
+    >>> fh = open("test.edgelist", "wb")
+    >>> nx.write_edgelist(G, fh)
+    >>> nx.write_edgelist(G, "test.edgelist.gz")
+    >>> nx.write_edgelist(G, "test.edgelist_nodata.gz", data=False)
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=7, color="red")
+    >>> nx.write_edgelist(G, "test.edgelist_bigger_nodata", data=False)
+    >>> nx.write_edgelist(G, "test.edgelist_color", data=["color"])
+    >>> nx.write_edgelist(G, "test.edgelist_color_weight", data=["color", "weight"])
+
+    See Also
+    --------
+    read_edgelist
+    write_weighted_edgelist
+    """
+
+    for line in generate_edgelist(G, delimiter, data):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_edgelist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None, data=True
+):
+    """Parse lines of an edge list representation of a graph.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in edgelist format
+    comments : string, optional
+       Marker for comment lines. Default is `'#'`. To specify that no character
+       should be treated as a comment, use ``comments=None``.
+    delimiter : string, optional
+       Separator for node labels. Default is `None`, meaning any whitespace.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+    nodetype : Python type, optional
+       Convert nodes to this type. Default is `None`, meaning no conversion is
+       performed.
+    data : bool or list of (label,type) tuples
+       If `False` generate no edge data or if `True` use a dictionary
+       representation of edge data or a list tuples specifying dictionary
+       key names and types for edge data.
+
+    Returns
+    -------
+    G: NetworkX Graph
+        The graph corresponding to lines
+
+    Examples
+    --------
+    Edgelist with no data:
+
+    >>> lines = ["1 2", "2 3", "3 4"]
+    >>> G = nx.parse_edgelist(lines, nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 4]
+    >>> list(G.edges())
+    [(1, 2), (2, 3), (3, 4)]
+
+    Edgelist with data in Python dictionary representation:
+
+    >>> lines = ["1 2 {'weight': 3}", "2 3 {'weight': 27}", "3 4 {'weight': 3.0}"]
+    >>> G = nx.parse_edgelist(lines, nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 4]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3}), (2, 3, {'weight': 27}), (3, 4, {'weight': 3.0})]
+
+    Edgelist with data in a list:
+
+    >>> lines = ["1 2 3", "2 3 27", "3 4 3.0"]
+    >>> G = nx.parse_edgelist(lines, nodetype=int, data=(("weight", float),))
+    >>> list(G)
+    [1, 2, 3, 4]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3.0}), (2, 3, {'weight': 27.0}), (3, 4, {'weight': 3.0})]
+
+    See Also
+    --------
+    read_weighted_edgelist
+    """
+    from ast import literal_eval
+
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        if comments is not None:
+            p = line.find(comments)
+            if p >= 0:
+                line = line[:p]
+            if not line:
+                continue
+        # split line, should have 2 or more
+        s = line.rstrip("\n").split(delimiter)
+        if len(s) < 2:
+            continue
+        u = s.pop(0)
+        v = s.pop(0)
+        d = s
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+                v = nodetype(v)
+            except Exception as err:
+                raise TypeError(
+                    f"Failed to convert nodes {u},{v} to type {nodetype}."
+                ) from err
+
+        if len(d) == 0 or data is False:
+            # no data or data type specified
+            edgedata = {}
+        elif data is True:
+            # no edge types specified
+            try:  # try to evaluate as dictionary
+                if delimiter == ",":
+                    edgedata_str = ",".join(d)
+                else:
+                    edgedata_str = " ".join(d)
+                edgedata = dict(literal_eval(edgedata_str.strip()))
+            except Exception as err:
+                raise TypeError(
+                    f"Failed to convert edge data ({d}) to dictionary."
+                ) from err
+        else:
+            # convert edge data to dictionary with specified keys and type
+            if len(d) != len(data):
+                raise IndexError(
+                    f"Edge data {d} and data_keys {data} are not the same length"
+                )
+            edgedata = {}
+            for (edge_key, edge_type), edge_value in zip(data, d):
+                try:
+                    edge_value = edge_type(edge_value)
+                except Exception as err:
+                    raise TypeError(
+                        f"Failed to convert {edge_key} data {edge_value} "
+                        f"to type {edge_type}."
+                    ) from err
+                edgedata.update({edge_key: edge_value})
+        G.add_edge(u, v, **edgedata)
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_edgelist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    data=True,
+    edgetype=None,
+    encoding="utf-8",
+):
+    """Read a graph from a list of edges.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to read. If a file is provided, it must be
+       opened in 'rb' mode.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    comments : string, optional
+       The character used to indicate the start of a comment. To specify that
+       no character should be treated as a comment, use ``comments=None``.
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+    nodetype : int, float, str, Python type, optional
+       Convert node data from strings to specified type
+    data : bool or list of (label,type) tuples
+       Tuples specifying dictionary key names and types for edge data
+    edgetype : int, float, str, Python type, optional OBSOLETE
+       Convert edge data from strings to specified type and use as 'weight'
+    encoding: string, optional
+       Specify which encoding to use when reading file.
+
+    Returns
+    -------
+    G : graph
+       A networkx Graph or other type specified with create_using
+
+    Examples
+    --------
+    >>> nx.write_edgelist(nx.path_graph(4), "test.edgelist_P4")
+    >>> G = nx.read_edgelist("test.edgelist_P4")
+
+    >>> fh = open("test.edgelist_P4", "rb")
+    >>> G = nx.read_edgelist(fh)
+    >>> fh.close()
+
+    >>> G = nx.read_edgelist("test.edgelist_P4", nodetype=int)
+    >>> G = nx.read_edgelist("test.edgelist_P4", create_using=nx.DiGraph)
+
+    Edgelist with data in a list:
+
+    >>> textline = "1 2 3"
+    >>> fh = open("test.textline", "w")
+    >>> d = fh.write(textline)
+    >>> fh.close()
+    >>> G = nx.read_edgelist("test.textline", nodetype=int, data=(("weight", float),))
+    >>> list(G)
+    [1, 2]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3.0})]
+
+    See parse_edgelist() for more examples of formatting.
+
+    See Also
+    --------
+    parse_edgelist
+    write_edgelist
+
+    Notes
+    -----
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+    """
+    lines = (line if isinstance(line, str) else line.decode(encoding) for line in path)
+    return parse_edgelist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        data=data,
+    )
+
+
+def write_weighted_edgelist(G, path, comments="#", delimiter=" ", encoding="utf-8"):
+    """Write graph G as a list of edges with numeric weights.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or filename to write. If a file is provided, it must be
+       opened in 'wb' mode.
+       Filenames ending in .gz or .bz2 will be compressed.
+    comments : string, optional
+       The character used to indicate the start of a comment
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    encoding: string, optional
+       Specify which encoding to use when writing file.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=7)
+    >>> nx.write_weighted_edgelist(G, "test.weighted.edgelist")
+
+    See Also
+    --------
+    read_edgelist
+    write_edgelist
+    read_weighted_edgelist
+    """
+    write_edgelist(
+        G,
+        path,
+        comments=comments,
+        delimiter=delimiter,
+        data=("weight",),
+        encoding=encoding,
+    )
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_weighted_edgelist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    encoding="utf-8",
+):
+    """Read a graph as list of edges with numeric weights.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to read. If a file is provided, it must be
+       opened in 'rb' mode.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    comments : string, optional
+       The character used to indicate the start of a comment.
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+    nodetype : int, float, str, Python type, optional
+       Convert node data from strings to specified type
+    encoding: string, optional
+       Specify which encoding to use when reading file.
+
+    Returns
+    -------
+    G : graph
+       A networkx Graph or other type specified with create_using
+
+    Notes
+    -----
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+
+    Example edgelist file format.
+
+    With numeric edge data::
+
+     # read with
+     # >>> G=nx.read_weighted_edgelist(fh)
+     # source target data
+     a b 1
+     a c 3.14159
+     d e 42
+
+    See Also
+    --------
+    write_weighted_edgelist
+    """
+    return read_edgelist(
+        path,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        data=(("weight", float),),
+        encoding=encoding,
+    )
diff --git a/lib/python3.11/site-packages/networkx/readwrite/gexf.py b/lib/python3.11/site-packages/networkx/readwrite/gexf.py
new file mode 100644
index 0000000000000000000000000000000000000000..56bc71f0e3c05ea053a10d68425168ddef23f04d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/gexf.py
@@ -0,0 +1,1084 @@
+"""Read and write graphs in GEXF format.
+
+.. warning::
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GEFX files you trust.
+
+GEXF (Graph Exchange XML Format) is a language for describing complex
+network structures, their associated data and dynamics.
+
+This implementation does not support mixed graphs (directed and
+undirected edges together).
+
+Format
+------
+GEXF is an XML format.  See http://gexf.net/schema.html for the
+specification and http://gexf.net/basic.html for examples.
+"""
+
+import itertools
+import time
+from xml.etree.ElementTree import (
+    Element,
+    ElementTree,
+    SubElement,
+    register_namespace,
+    tostring,
+)
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["write_gexf", "read_gexf", "relabel_gexf_graph", "generate_gexf"]
+
+
+@open_file(1, mode="wb")
+def write_gexf(G, path, encoding="utf-8", prettyprint=True, version="1.2draft"):
+    """Write G in GEXF format to path.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Node attributes are checked according to the version of the GEXF
+    schemas used for parameters which are not user defined,
+    e.g. visualization 'viz' [2]_. See example for usage.
+
+    .. warning::
+
+       The `GEXF specification <https://gexf.net/schema.html>`_ reserves some
+       keywords (e.g. ``id``, ``pid``, ``label``, etc.) for specifying node/edge
+       metadata in the file format. Ensure NetworkX node/edge attribute names
+       do not use these special keywords to guarantee all attributes are preserved
+       as expected when roundtripping to/from GEXF format.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or file name to write.
+       File names ending in .gz or .bz2 will be compressed.
+    encoding : string (optional, default: 'utf-8')
+       Encoding for text data.
+    prettyprint : bool (optional, default: True)
+       If True use line breaks and indenting in output XML.
+    version: string (optional, default: '1.2draft')
+       The version of GEXF to be used for nodes attributes checking
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_gexf(G, "test.gexf")
+
+    # visualization data
+    >>> G.nodes[0]["viz"] = {"size": 54}
+    >>> G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1}
+    >>> G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256}
+
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    The node id attribute is set to be the string of the node label.
+    If you want to specify an id use set it as node data, e.g.
+    node['a']['id']=1 to set the id of node 'a' to 1.
+
+    References
+    ----------
+    .. [1] GEXF File Format, http://gexf.net/
+    .. [2] GEXF schema, http://gexf.net/schema.html
+    """
+    writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version)
+    writer.add_graph(G)
+    writer.write(path)
+
+
+def generate_gexf(G, encoding="utf-8", prettyprint=True, version="1.2draft"):
+    """Generate lines of GEXF format representation of G.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Parameters
+    ----------
+    G : graph
+    A NetworkX graph
+    encoding : string (optional, default: 'utf-8')
+    Encoding for text data.
+    prettyprint : bool (optional, default: True)
+    If True use line breaks and indenting in output XML.
+    version : string (default: 1.2draft)
+    Version of GEFX File Format (see http://gexf.net/schema.html)
+    Supported values: "1.1draft", "1.2draft"
+
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed=\n
+    >>> s = linefeed.join(nx.generate_gexf(G))
+    >>> for line in nx.generate_gexf(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    The node id attribute is set to be the string of the node label.
+    If you want to specify an id use set it as node data, e.g.
+    node['a']['id']=1 to set the id of node 'a' to 1.
+
+    References
+    ----------
+    .. [1] GEXF File Format, https://gephi.org/gexf/format/
+    """
+    writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version)
+    writer.add_graph(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_gexf(path, node_type=None, relabel=False, version="1.2draft"):
+    """Read graph in GEXF format from path.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    node_type: Python type (default: None)
+       Convert node ids to this type if not None.
+    relabel : bool (default: False)
+       If True relabel the nodes to use the GEXF node "label" attribute
+       instead of the node "id" attribute as the NetworkX node label.
+    version : string (default: 1.2draft)
+    Version of GEFX File Format (see http://gexf.net/schema.html)
+       Supported values: "1.1draft", "1.2draft"
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    References
+    ----------
+    .. [1] GEXF File Format, http://gexf.net/
+    """
+    reader = GEXFReader(node_type=node_type, version=version)
+    if relabel:
+        G = relabel_gexf_graph(reader(path))
+    else:
+        G = reader(path)
+    return G
+
+
+class GEXF:
+    versions = {
+        "1.1draft": {
+            "NS_GEXF": "http://www.gexf.net/1.1draft",
+            "NS_VIZ": "http://www.gexf.net/1.1draft/viz",
+            "NS_XSI": "http://www.w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://www.gexf.net/1.1draft",
+                    "http://www.gexf.net/1.1draft/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.1",
+        },
+        "1.2draft": {
+            "NS_GEXF": "http://www.gexf.net/1.2draft",
+            "NS_VIZ": "http://www.gexf.net/1.2draft/viz",
+            "NS_XSI": "http://www.w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://www.gexf.net/1.2draft",
+                    "http://www.gexf.net/1.2draft/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.2",
+        },
+        "1.3": {
+            "NS_GEXF": "http://gexf.net/1.3",
+            "NS_VIZ": "http://gexf.net/1.3/viz",
+            "NS_XSI": "http://w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://gexf.net/1.3",
+                    "http://gexf.net/1.3/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.3",
+        },
+    }
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+            (list, "string"),
+            (dict, "string"),
+            (int, "long"),
+            (str, "liststring"),
+            (str, "anyURI"),
+            (str, "string"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except ImportError:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # http://www.w3.org/TR/xmlschema-2/#boolean
+    convert_bool = {
+        "true": True,
+        "false": False,
+        "True": True,
+        "False": False,
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def set_version(self, version):
+        d = self.versions.get(version)
+        if d is None:
+            raise nx.NetworkXError(f"Unknown GEXF version {version}.")
+        self.NS_GEXF = d["NS_GEXF"]
+        self.NS_VIZ = d["NS_VIZ"]
+        self.NS_XSI = d["NS_XSI"]
+        self.SCHEMALOCATION = d["SCHEMALOCATION"]
+        self.VERSION = d["VERSION"]
+        self.version = version
+
+
+class GEXFWriter(GEXF):
+    # class for writing GEXF format files
+    # use write_gexf() function
+    def __init__(
+        self, graph=None, encoding="utf-8", prettyprint=True, version="1.2draft"
+    ):
+        self.construct_types()
+        self.prettyprint = prettyprint
+        self.encoding = encoding
+        self.set_version(version)
+        self.xml = Element(
+            "gexf",
+            {
+                "xmlns": self.NS_GEXF,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+                "version": self.VERSION,
+            },
+        )
+
+        # Make meta element a non-graph element
+        # Also add lastmodifieddate as attribute, not tag
+        meta_element = Element("meta")
+        subelement_text = f"NetworkX {nx.__version__}"
+        SubElement(meta_element, "creator").text = subelement_text
+        meta_element.set("lastmodifieddate", time.strftime("%Y-%m-%d"))
+        self.xml.append(meta_element)
+
+        register_namespace("viz", self.NS_VIZ)
+
+        # counters for edge and attribute identifiers
+        self.edge_id = itertools.count()
+        self.attr_id = itertools.count()
+        self.all_edge_ids = set()
+        # default attributes are stored in dictionaries
+        self.attr = {}
+        self.attr["node"] = {}
+        self.attr["edge"] = {}
+        self.attr["node"]["dynamic"] = {}
+        self.attr["node"]["static"] = {}
+        self.attr["edge"]["dynamic"] = {}
+        self.attr["edge"]["static"] = {}
+
+        if graph is not None:
+            self.add_graph(graph)
+
+    def __str__(self):
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def add_graph(self, G):
+        # first pass through G collecting edge ids
+        for u, v, dd in G.edges(data=True):
+            eid = dd.get("id")
+            if eid is not None:
+                self.all_edge_ids.add(str(eid))
+        # set graph attributes
+        if G.graph.get("mode") == "dynamic":
+            mode = "dynamic"
+        else:
+            mode = "static"
+        # Add a graph element to the XML
+        if G.is_directed():
+            default = "directed"
+        else:
+            default = "undirected"
+        name = G.graph.get("name", "")
+        graph_element = Element("graph", defaultedgetype=default, mode=mode, name=name)
+        self.graph_element = graph_element
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+        self.xml.append(graph_element)
+
+    def add_nodes(self, G, graph_element):
+        nodes_element = Element("nodes")
+        for node, data in G.nodes(data=True):
+            node_data = data.copy()
+            node_id = str(node_data.pop("id", node))
+            kw = {"id": node_id}
+            label = str(node_data.pop("label", node))
+            kw["label"] = label
+            try:
+                pid = node_data.pop("pid")
+                kw["pid"] = str(pid)
+            except KeyError:
+                pass
+            try:
+                start = node_data.pop("start")
+                kw["start"] = str(start)
+                self.alter_graph_mode_timeformat(start)
+            except KeyError:
+                pass
+            try:
+                end = node_data.pop("end")
+                kw["end"] = str(end)
+                self.alter_graph_mode_timeformat(end)
+            except KeyError:
+                pass
+            # add node element with attributes
+            node_element = Element("node", **kw)
+            # add node element and attr subelements
+            default = G.graph.get("node_default", {})
+            node_data = self.add_parents(node_element, node_data)
+            if self.VERSION == "1.1":
+                node_data = self.add_slices(node_element, node_data)
+            else:
+                node_data = self.add_spells(node_element, node_data)
+            node_data = self.add_viz(node_element, node_data)
+            node_data = self.add_attributes("node", node_element, node_data, default)
+            nodes_element.append(node_element)
+        graph_element.append(nodes_element)
+
+    def add_edges(self, G, graph_element):
+        def edge_key_data(G):
+            # helper function to unify multigraph and graph edge iterator
+            if G.is_multigraph():
+                for u, v, key, data in G.edges(data=True, keys=True):
+                    edge_data = data.copy()
+                    edge_data.update(key=key)
+                    edge_id = edge_data.pop("id", None)
+                    if edge_id is None:
+                        edge_id = next(self.edge_id)
+                        while str(edge_id) in self.all_edge_ids:
+                            edge_id = next(self.edge_id)
+                        self.all_edge_ids.add(str(edge_id))
+                    yield u, v, edge_id, edge_data
+            else:
+                for u, v, data in G.edges(data=True):
+                    edge_data = data.copy()
+                    edge_id = edge_data.pop("id", None)
+                    if edge_id is None:
+                        edge_id = next(self.edge_id)
+                        while str(edge_id) in self.all_edge_ids:
+                            edge_id = next(self.edge_id)
+                        self.all_edge_ids.add(str(edge_id))
+                    yield u, v, edge_id, edge_data
+
+        edges_element = Element("edges")
+        for u, v, key, edge_data in edge_key_data(G):
+            kw = {"id": str(key)}
+            try:
+                edge_label = edge_data.pop("label")
+                kw["label"] = str(edge_label)
+            except KeyError:
+                pass
+            try:
+                edge_weight = edge_data.pop("weight")
+                kw["weight"] = str(edge_weight)
+            except KeyError:
+                pass
+            try:
+                edge_type = edge_data.pop("type")
+                kw["type"] = str(edge_type)
+            except KeyError:
+                pass
+            try:
+                start = edge_data.pop("start")
+                kw["start"] = str(start)
+                self.alter_graph_mode_timeformat(start)
+            except KeyError:
+                pass
+            try:
+                end = edge_data.pop("end")
+                kw["end"] = str(end)
+                self.alter_graph_mode_timeformat(end)
+            except KeyError:
+                pass
+            source_id = str(G.nodes[u].get("id", u))
+            target_id = str(G.nodes[v].get("id", v))
+            edge_element = Element("edge", source=source_id, target=target_id, **kw)
+            default = G.graph.get("edge_default", {})
+            if self.VERSION == "1.1":
+                edge_data = self.add_slices(edge_element, edge_data)
+            else:
+                edge_data = self.add_spells(edge_element, edge_data)
+            edge_data = self.add_viz(edge_element, edge_data)
+            edge_data = self.add_attributes("edge", edge_element, edge_data, default)
+            edges_element.append(edge_element)
+        graph_element.append(edges_element)
+
+    def add_attributes(self, node_or_edge, xml_obj, data, default):
+        # Add attrvalues to node or edge
+        attvalues = Element("attvalues")
+        if len(data) == 0:
+            return data
+        mode = "static"
+        for k, v in data.items():
+            # rename generic multigraph key to avoid any name conflict
+            if k == "key":
+                k = "networkx_key"
+            val_type = type(v)
+            if val_type not in self.xml_type:
+                raise TypeError(f"attribute value type is not allowed: {val_type}")
+            if isinstance(v, list):
+                # dynamic data
+                for val, start, end in v:
+                    val_type = type(val)
+                    if start is not None or end is not None:
+                        mode = "dynamic"
+                        self.alter_graph_mode_timeformat(start)
+                        self.alter_graph_mode_timeformat(end)
+                        break
+                attr_id = self.get_attr_id(
+                    str(k), self.xml_type[val_type], node_or_edge, default, mode
+                )
+                for val, start, end in v:
+                    e = Element("attvalue")
+                    e.attrib["for"] = attr_id
+                    e.attrib["value"] = str(val)
+                    # Handle nan, inf, -inf differently
+                    if val_type is float:
+                        if e.attrib["value"] == "inf":
+                            e.attrib["value"] = "INF"
+                        elif e.attrib["value"] == "nan":
+                            e.attrib["value"] = "NaN"
+                        elif e.attrib["value"] == "-inf":
+                            e.attrib["value"] = "-INF"
+                    if start is not None:
+                        e.attrib["start"] = str(start)
+                    if end is not None:
+                        e.attrib["end"] = str(end)
+                    attvalues.append(e)
+            else:
+                # static data
+                mode = "static"
+                attr_id = self.get_attr_id(
+                    str(k), self.xml_type[val_type], node_or_edge, default, mode
+                )
+                e = Element("attvalue")
+                e.attrib["for"] = attr_id
+                if isinstance(v, bool):
+                    e.attrib["value"] = str(v).lower()
+                else:
+                    e.attrib["value"] = str(v)
+                    # Handle float nan, inf, -inf differently
+                    if val_type is float:
+                        if e.attrib["value"] == "inf":
+                            e.attrib["value"] = "INF"
+                        elif e.attrib["value"] == "nan":
+                            e.attrib["value"] = "NaN"
+                        elif e.attrib["value"] == "-inf":
+                            e.attrib["value"] = "-INF"
+                attvalues.append(e)
+        xml_obj.append(attvalues)
+        return data
+
+    def get_attr_id(self, title, attr_type, edge_or_node, default, mode):
+        # find the id of the attribute or generate a new id
+        try:
+            return self.attr[edge_or_node][mode][title]
+        except KeyError:
+            # generate new id
+            new_id = str(next(self.attr_id))
+            self.attr[edge_or_node][mode][title] = new_id
+            attr_kwargs = {"id": new_id, "title": title, "type": attr_type}
+            attribute = Element("attribute", **attr_kwargs)
+            # add subelement for data default value if present
+            default_title = default.get(title)
+            if default_title is not None:
+                default_element = Element("default")
+                default_element.text = str(default_title)
+                attribute.append(default_element)
+            # new insert it into the XML
+            attributes_element = None
+            for a in self.graph_element.findall("attributes"):
+                # find existing attributes element by class and mode
+                a_class = a.get("class")
+                a_mode = a.get("mode", "static")
+                if a_class == edge_or_node and a_mode == mode:
+                    attributes_element = a
+            if attributes_element is None:
+                # create new attributes element
+                attr_kwargs = {"mode": mode, "class": edge_or_node}
+                attributes_element = Element("attributes", **attr_kwargs)
+                self.graph_element.insert(0, attributes_element)
+            attributes_element.append(attribute)
+        return new_id
+
+    def add_viz(self, element, node_data):
+        viz = node_data.pop("viz", False)
+        if viz:
+            color = viz.get("color")
+            if color is not None:
+                if self.VERSION == "1.1":
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}color",
+                        r=str(color.get("r")),
+                        g=str(color.get("g")),
+                        b=str(color.get("b")),
+                    )
+                else:
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}color",
+                        r=str(color.get("r")),
+                        g=str(color.get("g")),
+                        b=str(color.get("b")),
+                        a=str(color.get("a", 1.0)),
+                    )
+                element.append(e)
+
+            size = viz.get("size")
+            if size is not None:
+                e = Element(f"{{{self.NS_VIZ}}}size", value=str(size))
+                element.append(e)
+
+            thickness = viz.get("thickness")
+            if thickness is not None:
+                e = Element(f"{{{self.NS_VIZ}}}thickness", value=str(thickness))
+                element.append(e)
+
+            shape = viz.get("shape")
+            if shape is not None:
+                if shape.startswith("http"):
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}shape", value="image", uri=str(shape)
+                    )
+                else:
+                    e = Element(f"{{{self.NS_VIZ}}}shape", value=str(shape))
+                element.append(e)
+
+            position = viz.get("position")
+            if position is not None:
+                e = Element(
+                    f"{{{self.NS_VIZ}}}position",
+                    x=str(position.get("x")),
+                    y=str(position.get("y")),
+                    z=str(position.get("z")),
+                )
+                element.append(e)
+        return node_data
+
+    def add_parents(self, node_element, node_data):
+        parents = node_data.pop("parents", False)
+        if parents:
+            parents_element = Element("parents")
+            for p in parents:
+                e = Element("parent")
+                e.attrib["for"] = str(p)
+                parents_element.append(e)
+            node_element.append(parents_element)
+        return node_data
+
+    def add_slices(self, node_or_edge_element, node_or_edge_data):
+        slices = node_or_edge_data.pop("slices", False)
+        if slices:
+            slices_element = Element("slices")
+            for start, end in slices:
+                e = Element("slice", start=str(start), end=str(end))
+                slices_element.append(e)
+            node_or_edge_element.append(slices_element)
+        return node_or_edge_data
+
+    def add_spells(self, node_or_edge_element, node_or_edge_data):
+        spells = node_or_edge_data.pop("spells", False)
+        if spells:
+            spells_element = Element("spells")
+            for start, end in spells:
+                e = Element("spell")
+                if start is not None:
+                    e.attrib["start"] = str(start)
+                    self.alter_graph_mode_timeformat(start)
+                if end is not None:
+                    e.attrib["end"] = str(end)
+                    self.alter_graph_mode_timeformat(end)
+                spells_element.append(e)
+            node_or_edge_element.append(spells_element)
+        return node_or_edge_data
+
+    def alter_graph_mode_timeformat(self, start_or_end):
+        # If 'start' or 'end' appears, set timeformat
+        if start_or_end is not None:
+            if isinstance(start_or_end, str):
+                timeformat = "date"
+            elif isinstance(start_or_end, float):
+                timeformat = "double"
+            elif isinstance(start_or_end, int):
+                timeformat = "long"
+            else:
+                raise nx.NetworkXError(
+                    "timeformat should be of the type int, float or str"
+                )
+            self.graph_element.set("timeformat", timeformat)
+            # If Graph mode is static, alter to dynamic
+            if self.graph_element.get("mode") == "static":
+                self.graph_element.set("mode", "dynamic")
+
+    def write(self, fh):
+        # Serialize graph G in GEXF to the open fh
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(fh, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + "  " * level
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class GEXFReader(GEXF):
+    # Class to read GEXF format files
+    # use read_gexf() function
+    def __init__(self, node_type=None, version="1.2draft"):
+        self.construct_types()
+        self.node_type = node_type
+        # assume simple graph and test for multigraph on read
+        self.simple_graph = True
+        self.set_version(version)
+
+    def __call__(self, stream):
+        self.xml = ElementTree(file=stream)
+        g = self.xml.find(f"{{{self.NS_GEXF}}}graph")
+        if g is not None:
+            return self.make_graph(g)
+        # try all the versions
+        for version in self.versions:
+            self.set_version(version)
+            g = self.xml.find(f"{{{self.NS_GEXF}}}graph")
+            if g is not None:
+                return self.make_graph(g)
+        raise nx.NetworkXError("No <graph> element in GEXF file.")
+
+    def make_graph(self, graph_xml):
+        # start with empty DiGraph or MultiDiGraph
+        edgedefault = graph_xml.get("defaultedgetype", None)
+        if edgedefault == "directed":
+            G = nx.MultiDiGraph()
+        else:
+            G = nx.MultiGraph()
+
+        # graph attributes
+        graph_name = graph_xml.get("name", "")
+        if graph_name != "":
+            G.graph["name"] = graph_name
+        graph_start = graph_xml.get("start")
+        if graph_start is not None:
+            G.graph["start"] = graph_start
+        graph_end = graph_xml.get("end")
+        if graph_end is not None:
+            G.graph["end"] = graph_end
+        graph_mode = graph_xml.get("mode", "")
+        if graph_mode == "dynamic":
+            G.graph["mode"] = "dynamic"
+        else:
+            G.graph["mode"] = "static"
+
+        # timeformat
+        self.timeformat = graph_xml.get("timeformat")
+        if self.timeformat == "date":
+            self.timeformat = "string"
+
+        # node and edge attributes
+        attributes_elements = graph_xml.findall(f"{{{self.NS_GEXF}}}attributes")
+        # dictionaries to hold attributes and attribute defaults
+        node_attr = {}
+        node_default = {}
+        edge_attr = {}
+        edge_default = {}
+        for a in attributes_elements:
+            attr_class = a.get("class")
+            if attr_class == "node":
+                na, nd = self.find_gexf_attributes(a)
+                node_attr.update(na)
+                node_default.update(nd)
+                G.graph["node_default"] = node_default
+            elif attr_class == "edge":
+                ea, ed = self.find_gexf_attributes(a)
+                edge_attr.update(ea)
+                edge_default.update(ed)
+                G.graph["edge_default"] = edge_default
+            else:
+                raise  # unknown attribute class
+
+        # Hack to handle Gephi0.7beta bug
+        # add weight attribute
+        ea = {"weight": {"type": "double", "mode": "static", "title": "weight"}}
+        ed = {}
+        edge_attr.update(ea)
+        edge_default.update(ed)
+        G.graph["edge_default"] = edge_default
+
+        # add nodes
+        nodes_element = graph_xml.find(f"{{{self.NS_GEXF}}}nodes")
+        if nodes_element is not None:
+            for node_xml in nodes_element.findall(f"{{{self.NS_GEXF}}}node"):
+                self.add_node(G, node_xml, node_attr)
+
+        # add edges
+        edges_element = graph_xml.find(f"{{{self.NS_GEXF}}}edges")
+        if edges_element is not None:
+            for edge_xml in edges_element.findall(f"{{{self.NS_GEXF}}}edge"):
+                self.add_edge(G, edge_xml, edge_attr)
+
+        # switch to Graph or DiGraph if no parallel edges were found.
+        if self.simple_graph:
+            if G.is_directed():
+                G = nx.DiGraph(G)
+            else:
+                G = nx.Graph(G)
+        return G
+
+    def add_node(self, G, node_xml, node_attr, node_pid=None):
+        # add a single node with attributes to the graph
+
+        # get attributes and subattributues for node
+        data = self.decode_attr_elements(node_attr, node_xml)
+        data = self.add_parents(data, node_xml)  # add any parents
+        if self.VERSION == "1.1":
+            data = self.add_slices(data, node_xml)  # add slices
+        else:
+            data = self.add_spells(data, node_xml)  # add spells
+        data = self.add_viz(data, node_xml)  # add viz
+        data = self.add_start_end(data, node_xml)  # add start/end
+
+        # find the node id and cast it to the appropriate type
+        node_id = node_xml.get("id")
+        if self.node_type is not None:
+            node_id = self.node_type(node_id)
+
+        # every node should have a label
+        node_label = node_xml.get("label")
+        data["label"] = node_label
+
+        # parent node id
+        node_pid = node_xml.get("pid", node_pid)
+        if node_pid is not None:
+            data["pid"] = node_pid
+
+        # check for subnodes, recursive
+        subnodes = node_xml.find(f"{{{self.NS_GEXF}}}nodes")
+        if subnodes is not None:
+            for node_xml in subnodes.findall(f"{{{self.NS_GEXF}}}node"):
+                self.add_node(G, node_xml, node_attr, node_pid=node_id)
+
+        G.add_node(node_id, **data)
+
+    def add_start_end(self, data, xml):
+        # start and end times
+        ttype = self.timeformat
+        node_start = xml.get("start")
+        if node_start is not None:
+            data["start"] = self.python_type[ttype](node_start)
+        node_end = xml.get("end")
+        if node_end is not None:
+            data["end"] = self.python_type[ttype](node_end)
+        return data
+
+    def add_viz(self, data, node_xml):
+        # add viz element for node
+        viz = {}
+        color = node_xml.find(f"{{{self.NS_VIZ}}}color")
+        if color is not None:
+            if self.VERSION == "1.1":
+                viz["color"] = {
+                    "r": int(color.get("r")),
+                    "g": int(color.get("g")),
+                    "b": int(color.get("b")),
+                }
+            else:
+                viz["color"] = {
+                    "r": int(color.get("r")),
+                    "g": int(color.get("g")),
+                    "b": int(color.get("b")),
+                    "a": float(color.get("a", 1)),
+                }
+
+        size = node_xml.find(f"{{{self.NS_VIZ}}}size")
+        if size is not None:
+            viz["size"] = float(size.get("value"))
+
+        thickness = node_xml.find(f"{{{self.NS_VIZ}}}thickness")
+        if thickness is not None:
+            viz["thickness"] = float(thickness.get("value"))
+
+        shape = node_xml.find(f"{{{self.NS_VIZ}}}shape")
+        if shape is not None:
+            viz["shape"] = shape.get("shape")
+            if viz["shape"] == "image":
+                viz["shape"] = shape.get("uri")
+
+        position = node_xml.find(f"{{{self.NS_VIZ}}}position")
+        if position is not None:
+            viz["position"] = {
+                "x": float(position.get("x", 0)),
+                "y": float(position.get("y", 0)),
+                "z": float(position.get("z", 0)),
+            }
+
+        if len(viz) > 0:
+            data["viz"] = viz
+        return data
+
+    def add_parents(self, data, node_xml):
+        parents_element = node_xml.find(f"{{{self.NS_GEXF}}}parents")
+        if parents_element is not None:
+            data["parents"] = []
+            for p in parents_element.findall(f"{{{self.NS_GEXF}}}parent"):
+                parent = p.get("for")
+                data["parents"].append(parent)
+        return data
+
+    def add_slices(self, data, node_or_edge_xml):
+        slices_element = node_or_edge_xml.find(f"{{{self.NS_GEXF}}}slices")
+        if slices_element is not None:
+            data["slices"] = []
+            for s in slices_element.findall(f"{{{self.NS_GEXF}}}slice"):
+                start = s.get("start")
+                end = s.get("end")
+                data["slices"].append((start, end))
+        return data
+
+    def add_spells(self, data, node_or_edge_xml):
+        spells_element = node_or_edge_xml.find(f"{{{self.NS_GEXF}}}spells")
+        if spells_element is not None:
+            data["spells"] = []
+            ttype = self.timeformat
+            for s in spells_element.findall(f"{{{self.NS_GEXF}}}spell"):
+                start = self.python_type[ttype](s.get("start"))
+                end = self.python_type[ttype](s.get("end"))
+                data["spells"].append((start, end))
+        return data
+
+    def add_edge(self, G, edge_element, edge_attr):
+        # add an edge to the graph
+
+        # raise error if we find mixed directed and undirected edges
+        edge_direction = edge_element.get("type")
+        if G.is_directed() and edge_direction == "undirected":
+            raise nx.NetworkXError("Undirected edge found in directed graph.")
+        if (not G.is_directed()) and edge_direction == "directed":
+            raise nx.NetworkXError("Directed edge found in undirected graph.")
+
+        # Get source and target and recast type if required
+        source = edge_element.get("source")
+        target = edge_element.get("target")
+        if self.node_type is not None:
+            source = self.node_type(source)
+            target = self.node_type(target)
+
+        data = self.decode_attr_elements(edge_attr, edge_element)
+        data = self.add_start_end(data, edge_element)
+
+        if self.VERSION == "1.1":
+            data = self.add_slices(data, edge_element)  # add slices
+        else:
+            data = self.add_spells(data, edge_element)  # add spells
+
+        # GEXF stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs
+        # if networkx_key is not specified as an attribute
+        edge_id = edge_element.get("id")
+        if edge_id is not None:
+            data["id"] = edge_id
+
+        # check if there is a 'multigraph_key' and use that as edge_id
+        multigraph_key = data.pop("networkx_key", None)
+        if multigraph_key is not None:
+            edge_id = multigraph_key
+
+        weight = edge_element.get("weight")
+        if weight is not None:
+            data["weight"] = float(weight)
+
+        edge_label = edge_element.get("label")
+        if edge_label is not None:
+            data["label"] = edge_label
+
+        if G.has_edge(source, target):
+            # seen this edge before - this is a multigraph
+            self.simple_graph = False
+        G.add_edge(source, target, key=edge_id, **data)
+        if edge_direction == "mutual":
+            G.add_edge(target, source, key=edge_id, **data)
+
+    def decode_attr_elements(self, gexf_keys, obj_xml):
+        # Use the key information to decode the attr XML
+        attr = {}
+        # look for outer '<attvalues>' element
+        attr_element = obj_xml.find(f"{{{self.NS_GEXF}}}attvalues")
+        if attr_element is not None:
+            # loop over <attvalue> elements
+            for a in attr_element.findall(f"{{{self.NS_GEXF}}}attvalue"):
+                key = a.get("for")  # for is required
+                try:  # should be in our gexf_keys dictionary
+                    title = gexf_keys[key]["title"]
+                except KeyError as err:
+                    raise nx.NetworkXError(f"No attribute defined for={key}.") from err
+                atype = gexf_keys[key]["type"]
+                value = a.get("value")
+                if atype == "boolean":
+                    value = self.convert_bool[value]
+                else:
+                    value = self.python_type[atype](value)
+                if gexf_keys[key]["mode"] == "dynamic":
+                    # for dynamic graphs use list of three-tuples
+                    # [(value1,start1,end1), (value2,start2,end2), etc]
+                    ttype = self.timeformat
+                    start = self.python_type[ttype](a.get("start"))
+                    end = self.python_type[ttype](a.get("end"))
+                    if title in attr:
+                        attr[title].append((value, start, end))
+                    else:
+                        attr[title] = [(value, start, end)]
+                else:
+                    # for static graphs just assign the value
+                    attr[title] = value
+        return attr
+
+    def find_gexf_attributes(self, attributes_element):
+        # Extract all the attributes and defaults
+        attrs = {}
+        defaults = {}
+        mode = attributes_element.get("mode")
+        for k in attributes_element.findall(f"{{{self.NS_GEXF}}}attribute"):
+            attr_id = k.get("id")
+            title = k.get("title")
+            atype = k.get("type")
+            attrs[attr_id] = {"title": title, "type": atype, "mode": mode}
+            # check for the 'default' subelement of key element and add
+            default = k.find(f"{{{self.NS_GEXF}}}default")
+            if default is not None:
+                if atype == "boolean":
+                    value = self.convert_bool[default.text]
+                else:
+                    value = self.python_type[atype](default.text)
+                defaults[title] = value
+        return attrs, defaults
+
+
+def relabel_gexf_graph(G):
+    """Relabel graph using "label" node keyword for node label.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph read from GEXF data
+
+    Returns
+    -------
+    H : graph
+      A NetworkX graph with relabeled nodes
+
+    Raises
+    ------
+    NetworkXError
+        If node labels are missing or not unique while relabel=True.
+
+    Notes
+    -----
+    This function relabels the nodes in a NetworkX graph with the
+    "label" attribute.  It also handles relabeling the specific GEXF
+    node attributes "parents", and "pid".
+    """
+    # build mapping of node labels, do some error checking
+    try:
+        mapping = [(u, G.nodes[u]["label"]) for u in G]
+    except KeyError as err:
+        raise nx.NetworkXError(
+            "Failed to relabel nodes: missing node labels found. Use relabel=False."
+        ) from err
+    x, y = zip(*mapping)
+    if len(set(y)) != len(G):
+        raise nx.NetworkXError(
+            "Failed to relabel nodes: duplicate node labels found. Use relabel=False."
+        )
+    mapping = dict(mapping)
+    H = nx.relabel_nodes(G, mapping)
+    # relabel attributes
+    for n in G:
+        m = mapping[n]
+        H.nodes[m]["id"] = n
+        H.nodes[m].pop("label")
+        if "pid" in H.nodes[m]:
+            H.nodes[m]["pid"] = mapping[G.nodes[n]["pid"]]
+        if "parents" in H.nodes[m]:
+            H.nodes[m]["parents"] = [mapping[p] for p in G.nodes[n]["parents"]]
+    return H
diff --git a/lib/python3.11/site-packages/networkx/readwrite/gml.py b/lib/python3.11/site-packages/networkx/readwrite/gml.py
new file mode 100644
index 0000000000000000000000000000000000000000..c53496c3e7fd2797ce2f786e665d0ad069d8184c
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/gml.py
@@ -0,0 +1,879 @@
+"""
+Read graphs in GML format.
+
+"GML, the Graph Modelling Language, is our proposal for a portable
+file format for graphs. GML's key features are portability, simple
+syntax, extensibility and flexibility. A GML file consists of a
+hierarchical key-value lists. Graphs can be annotated with arbitrary
+data structures. The idea for a common file format was born at the
+GD'95; this proposal is the outcome of many discussions. GML is the
+standard file format in the Graphlet graph editor system. It has been
+overtaken and adapted by several other systems for drawing graphs."
+
+GML files are stored using a 7-bit ASCII encoding with any extended
+ASCII characters (iso8859-1) appearing as HTML character entities.
+You will need to give some thought into how the exported data should
+interact with different languages and even different Python versions.
+Re-importing from gml is also a concern.
+
+Without specifying a `stringizer`/`destringizer`, the code is capable of
+writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+specification.  For writing other data types, and for reading data other
+than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+For additional documentation on the GML file format, please see the
+`GML website <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+Several example graphs in GML format may be found on Mark Newman's
+`Network data page <http://www-personal.umich.edu/~mejn/netdata/>`_.
+"""
+
+import html.entities as htmlentitydefs
+import re
+from ast import literal_eval
+from collections import defaultdict
+from enum import Enum
+from io import StringIO
+from typing import Any, NamedTuple
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import open_file
+
+__all__ = ["read_gml", "parse_gml", "generate_gml", "write_gml"]
+
+
+def escape(text):
+    """Use XML character references to escape characters.
+
+    Use XML character references for unprintable or non-ASCII
+    characters, double quotes and ampersands in a string
+    """
+
+    def fixup(m):
+        ch = m.group(0)
+        return "&#" + str(ord(ch)) + ";"
+
+    text = re.sub('[^ -~]|[&"]', fixup, text)
+    return text if isinstance(text, str) else str(text)
+
+
+def unescape(text):
+    """Replace XML character references with the referenced characters"""
+
+    def fixup(m):
+        text = m.group(0)
+        if text[1] == "#":
+            # Character reference
+            if text[2] == "x":
+                code = int(text[3:-1], 16)
+            else:
+                code = int(text[2:-1])
+        else:
+            # Named entity
+            try:
+                code = htmlentitydefs.name2codepoint[text[1:-1]]
+            except KeyError:
+                return text  # leave unchanged
+        try:
+            return chr(code)
+        except (ValueError, OverflowError):
+            return text  # leave unchanged
+
+    return re.sub("&(?:[0-9A-Za-z]+|#(?:[0-9]+|x[0-9A-Fa-f]+));", fixup, text)
+
+
+def literal_destringizer(rep):
+    """Convert a Python literal to the value it represents.
+
+    Parameters
+    ----------
+    rep : string
+        A Python literal.
+
+    Returns
+    -------
+    value : object
+        The value of the Python literal.
+
+    Raises
+    ------
+    ValueError
+        If `rep` is not a Python literal.
+    """
+    if isinstance(rep, str):
+        orig_rep = rep
+        try:
+            return literal_eval(rep)
+        except SyntaxError as err:
+            raise ValueError(f"{orig_rep!r} is not a valid Python literal") from err
+    else:
+        raise ValueError(f"{rep!r} is not a string")
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_gml(path, label="label", destringizer=None):
+    """Read graph in GML format from `path`.
+
+    Parameters
+    ----------
+    path : file or string
+        Filename or file handle to read.
+        Filenames ending in .gz or .bz2 will be decompressed.
+
+    label : string, optional
+        If not None, the parsed nodes will be renamed according to node
+        attributes indicated by `label`. Default value: 'label'.
+
+    destringizer : callable, optional
+        A `destringizer` that recovers values stored as strings in GML. If it
+        cannot convert a string to a value, a `ValueError` is raised. Default
+        value : None.
+
+    Returns
+    -------
+    G : NetworkX graph
+        The parsed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the input cannot be parsed.
+
+    See Also
+    --------
+    write_gml, parse_gml
+    literal_destringizer
+
+    Notes
+    -----
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_gml(G, "test_path4.gml")
+
+    GML values are interpreted as strings by default:
+
+    >>> H = nx.read_gml("test_path4.gml")
+    >>> H.nodes
+    NodeView(('0', '1', '2', '3'))
+
+    When a `destringizer` is provided, GML values are converted to the provided type.
+    For example, integer nodes can be recovered as shown below:
+
+    >>> J = nx.read_gml("test_path4.gml", destringizer=int)
+    >>> J.nodes
+    NodeView((0, 1, 2, 3))
+
+    """
+
+    def filter_lines(lines):
+        for line in lines:
+            try:
+                line = line.decode("ascii")
+            except UnicodeDecodeError as err:
+                raise NetworkXError("input is not ASCII-encoded") from err
+            if not isinstance(line, str):
+                lines = str(lines)
+            if line and line[-1] == "\n":
+                line = line[:-1]
+            yield line
+
+    G = parse_gml_lines(filter_lines(path), label, destringizer)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_gml(lines, label="label", destringizer=None):
+    """Parse GML graph from a string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable of strings
+       Data in GML format.
+
+    label : string, optional
+        If not None, the parsed nodes will be renamed according to node
+        attributes indicated by `label`. Default value: 'label'.
+
+    destringizer : callable, optional
+        A `destringizer` that recovers values stored as strings in GML. If it
+        cannot convert a string to a value, a `ValueError` is raised. Default
+        value : None.
+
+    Returns
+    -------
+    G : NetworkX graph
+        The parsed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the input cannot be parsed.
+
+    See Also
+    --------
+    write_gml, read_gml
+
+    Notes
+    -----
+    This stores nested GML attributes as dictionaries in the NetworkX graph,
+    node, and edge attribute structures.
+
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+    """
+
+    def decode_line(line):
+        if isinstance(line, bytes):
+            try:
+                line.decode("ascii")
+            except UnicodeDecodeError as err:
+                raise NetworkXError("input is not ASCII-encoded") from err
+        if not isinstance(line, str):
+            line = str(line)
+        return line
+
+    def filter_lines(lines):
+        if isinstance(lines, str):
+            lines = decode_line(lines)
+            lines = lines.splitlines()
+            yield from lines
+        else:
+            for line in lines:
+                line = decode_line(line)
+                if line and line[-1] == "\n":
+                    line = line[:-1]
+                if line.find("\n") != -1:
+                    raise NetworkXError("input line contains newline")
+                yield line
+
+    G = parse_gml_lines(filter_lines(lines), label, destringizer)
+    return G
+
+
+class Pattern(Enum):
+    """encodes the index of each token-matching pattern in `tokenize`."""
+
+    KEYS = 0
+    REALS = 1
+    INTS = 2
+    STRINGS = 3
+    DICT_START = 4
+    DICT_END = 5
+    COMMENT_WHITESPACE = 6
+
+
+class Token(NamedTuple):
+    category: Pattern
+    value: Any
+    line: int
+    position: int
+
+
+LIST_START_VALUE = "_networkx_list_start"
+
+
+def parse_gml_lines(lines, label, destringizer):
+    """Parse GML `lines` into a graph."""
+
+    def tokenize():
+        patterns = [
+            r"[A-Za-z][0-9A-Za-z_]*\b",  # keys
+            # reals
+            r"[+-]?(?:[0-9]*\.[0-9]+|[0-9]+\.[0-9]*|INF)(?:[Ee][+-]?[0-9]+)?",
+            r"[+-]?[0-9]+",  # ints
+            r'".*?"',  # strings
+            r"\[",  # dict start
+            r"\]",  # dict end
+            r"#.*$|\s+",  # comments and whitespaces
+        ]
+        tokens = re.compile("|".join(f"({pattern})" for pattern in patterns))
+        lineno = 0
+        multilines = []  # entries spread across multiple lines
+        for line in lines:
+            pos = 0
+
+            # deal with entries spread across multiple lines
+            #
+            # should we actually have to deal with escaped "s then do it here
+            if multilines:
+                multilines.append(line.strip())
+                if line[-1] == '"':  # closing multiline entry
+                    # multiline entries will be joined by space. cannot
+                    # reintroduce newlines as this will break the tokenizer
+                    line = " ".join(multilines)
+                    multilines = []
+                else:  # continued multiline entry
+                    lineno += 1
+                    continue
+            else:
+                if line.count('"') == 1:  # opening multiline entry
+                    if line.strip()[0] != '"' and line.strip()[-1] != '"':
+                        # since we expect something like key "value", the " should not be found at ends
+                        # otherwise tokenizer will pick up the formatting mistake.
+                        multilines = [line.rstrip()]
+                        lineno += 1
+                        continue
+
+            length = len(line)
+
+            while pos < length:
+                match = tokens.match(line, pos)
+                if match is None:
+                    m = f"cannot tokenize {line[pos:]} at ({lineno + 1}, {pos + 1})"
+                    raise NetworkXError(m)
+                for i in range(len(patterns)):
+                    group = match.group(i + 1)
+                    if group is not None:
+                        if i == 0:  # keys
+                            value = group.rstrip()
+                        elif i == 1:  # reals
+                            value = float(group)
+                        elif i == 2:  # ints
+                            value = int(group)
+                        else:
+                            value = group
+                        if i != 6:  # comments and whitespaces
+                            yield Token(Pattern(i), value, lineno + 1, pos + 1)
+                        pos += len(group)
+                        break
+            lineno += 1
+        yield Token(None, None, lineno + 1, 1)  # EOF
+
+    def unexpected(curr_token, expected):
+        category, value, lineno, pos = curr_token
+        value = repr(value) if value is not None else "EOF"
+        raise NetworkXError(f"expected {expected}, found {value} at ({lineno}, {pos})")
+
+    def consume(curr_token, category, expected):
+        if curr_token.category == category:
+            return next(tokens)
+        unexpected(curr_token, expected)
+
+    def parse_kv(curr_token):
+        dct = defaultdict(list)
+        while curr_token.category == Pattern.KEYS:
+            key = curr_token.value
+            curr_token = next(tokens)
+            category = curr_token.category
+            if category == Pattern.REALS or category == Pattern.INTS:
+                value = curr_token.value
+                curr_token = next(tokens)
+            elif category == Pattern.STRINGS:
+                value = unescape(curr_token.value[1:-1])
+                if destringizer:
+                    try:
+                        value = destringizer(value)
+                    except ValueError:
+                        pass
+                # Special handling for empty lists and tuples
+                if value == "()":
+                    value = ()
+                if value == "[]":
+                    value = []
+                curr_token = next(tokens)
+            elif category == Pattern.DICT_START:
+                curr_token, value = parse_dict(curr_token)
+            else:
+                # Allow for string convertible id and label values
+                if key in ("id", "label", "source", "target"):
+                    try:
+                        # String convert the token value
+                        value = unescape(str(curr_token.value))
+                        if destringizer:
+                            try:
+                                value = destringizer(value)
+                            except ValueError:
+                                pass
+                        curr_token = next(tokens)
+                    except Exception:
+                        msg = (
+                            "an int, float, string, '[' or string"
+                            + " convertible ASCII value for node id or label"
+                        )
+                        unexpected(curr_token, msg)
+                # Special handling for nan and infinity.  Since the gml language
+                # defines unquoted strings as keys, the numeric and string branches
+                # are skipped and we end up in this special branch, so we need to
+                # convert the current token value to a float for NAN and plain INF.
+                # +/-INF are handled in the pattern for 'reals' in tokenize().  This
+                # allows labels and values to be nan or infinity, but not keys.
+                elif curr_token.value in {"NAN", "INF"}:
+                    value = float(curr_token.value)
+                    curr_token = next(tokens)
+                else:  # Otherwise error out
+                    unexpected(curr_token, "an int, float, string or '['")
+            dct[key].append(value)
+
+        def clean_dict_value(value):
+            if not isinstance(value, list):
+                return value
+            if len(value) == 1:
+                return value[0]
+            if value[0] == LIST_START_VALUE:
+                return value[1:]
+            return value
+
+        dct = {key: clean_dict_value(value) for key, value in dct.items()}
+        return curr_token, dct
+
+    def parse_dict(curr_token):
+        # dict start
+        curr_token = consume(curr_token, Pattern.DICT_START, "'['")
+        # dict contents
+        curr_token, dct = parse_kv(curr_token)
+        # dict end
+        curr_token = consume(curr_token, Pattern.DICT_END, "']'")
+        return curr_token, dct
+
+    def parse_graph():
+        curr_token, dct = parse_kv(next(tokens))
+        if curr_token.category is not None:  # EOF
+            unexpected(curr_token, "EOF")
+        if "graph" not in dct:
+            raise NetworkXError("input contains no graph")
+        graph = dct["graph"]
+        if isinstance(graph, list):
+            raise NetworkXError("input contains more than one graph")
+        return graph
+
+    tokens = tokenize()
+    graph = parse_graph()
+
+    directed = graph.pop("directed", False)
+    multigraph = graph.pop("multigraph", False)
+    if not multigraph:
+        G = nx.DiGraph() if directed else nx.Graph()
+    else:
+        G = nx.MultiDiGraph() if directed else nx.MultiGraph()
+    graph_attr = {k: v for k, v in graph.items() if k not in ("node", "edge")}
+    G.graph.update(graph_attr)
+
+    def pop_attr(dct, category, attr, i):
+        try:
+            return dct.pop(attr)
+        except KeyError as err:
+            raise NetworkXError(f"{category} #{i} has no {attr!r} attribute") from err
+
+    nodes = graph.get("node", [])
+    mapping = {}
+    node_labels = set()
+    for i, node in enumerate(nodes if isinstance(nodes, list) else [nodes]):
+        id = pop_attr(node, "node", "id", i)
+        if id in G:
+            raise NetworkXError(f"node id {id!r} is duplicated")
+        if label is not None and label != "id":
+            node_label = pop_attr(node, "node", label, i)
+            if node_label in node_labels:
+                raise NetworkXError(f"node label {node_label!r} is duplicated")
+            node_labels.add(node_label)
+            mapping[id] = node_label
+        G.add_node(id, **node)
+
+    edges = graph.get("edge", [])
+    for i, edge in enumerate(edges if isinstance(edges, list) else [edges]):
+        source = pop_attr(edge, "edge", "source", i)
+        target = pop_attr(edge, "edge", "target", i)
+        if source not in G:
+            raise NetworkXError(f"edge #{i} has undefined source {source!r}")
+        if target not in G:
+            raise NetworkXError(f"edge #{i} has undefined target {target!r}")
+        if not multigraph:
+            if not G.has_edge(source, target):
+                G.add_edge(source, target, **edge)
+            else:
+                arrow = "->" if directed else "--"
+                msg = f"edge #{i} ({source!r}{arrow}{target!r}) is duplicated"
+                raise nx.NetworkXError(msg)
+        else:
+            key = edge.pop("key", None)
+            if key is not None and G.has_edge(source, target, key):
+                arrow = "->" if directed else "--"
+                msg = f"edge #{i} ({source!r}{arrow}{target!r}, {key!r})"
+                msg2 = 'Hint: If multigraph add "multigraph 1" to file header.'
+                raise nx.NetworkXError(msg + " is duplicated\n" + msg2)
+            G.add_edge(source, target, key, **edge)
+
+    if label is not None and label != "id":
+        G = nx.relabel_nodes(G, mapping)
+    return G
+
+
+def literal_stringizer(value):
+    """Convert a `value` to a Python literal in GML representation.
+
+    Parameters
+    ----------
+    value : object
+        The `value` to be converted to GML representation.
+
+    Returns
+    -------
+    rep : string
+        A double-quoted Python literal representing value. Unprintable
+        characters are replaced by XML character references.
+
+    Raises
+    ------
+    ValueError
+        If `value` cannot be converted to GML.
+
+    Notes
+    -----
+    The original value can be recovered using the
+    :func:`networkx.readwrite.gml.literal_destringizer` function.
+    """
+
+    def stringize(value):
+        if isinstance(value, int | bool) or value is None:
+            if value is True:  # GML uses 1/0 for boolean values.
+                buf.write(str(1))
+            elif value is False:
+                buf.write(str(0))
+            else:
+                buf.write(str(value))
+        elif isinstance(value, str):
+            text = repr(value)
+            if text[0] != "u":
+                try:
+                    value.encode("latin1")
+                except UnicodeEncodeError:
+                    text = "u" + text
+            buf.write(text)
+        elif isinstance(value, float | complex | str | bytes):
+            buf.write(repr(value))
+        elif isinstance(value, list):
+            buf.write("[")
+            first = True
+            for item in value:
+                if not first:
+                    buf.write(",")
+                else:
+                    first = False
+                stringize(item)
+            buf.write("]")
+        elif isinstance(value, tuple):
+            if len(value) > 1:
+                buf.write("(")
+                first = True
+                for item in value:
+                    if not first:
+                        buf.write(",")
+                    else:
+                        first = False
+                    stringize(item)
+                buf.write(")")
+            elif value:
+                buf.write("(")
+                stringize(value[0])
+                buf.write(",)")
+            else:
+                buf.write("()")
+        elif isinstance(value, dict):
+            buf.write("{")
+            first = True
+            for key, value in value.items():
+                if not first:
+                    buf.write(",")
+                else:
+                    first = False
+                stringize(key)
+                buf.write(":")
+                stringize(value)
+            buf.write("}")
+        elif isinstance(value, set):
+            buf.write("{")
+            first = True
+            for item in value:
+                if not first:
+                    buf.write(",")
+                else:
+                    first = False
+                stringize(item)
+            buf.write("}")
+        else:
+            msg = f"{value!r} cannot be converted into a Python literal"
+            raise ValueError(msg)
+
+    buf = StringIO()
+    stringize(value)
+    return buf.getvalue()
+
+
+def generate_gml(G, stringizer=None):
+    r"""Generate a single entry of the graph `G` in GML format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph to be converted to GML.
+
+    stringizer : callable, optional
+        A `stringizer` which converts non-int/non-float/non-dict values into
+        strings. If it cannot convert a value into a string, it should raise a
+        `ValueError` to indicate that. Default value: None.
+
+    Returns
+    -------
+    lines: generator of strings
+        Lines of GML data. Newlines are not appended.
+
+    Raises
+    ------
+    NetworkXError
+        If `stringizer` cannot convert a value into a string, or the value to
+        convert is not a string while `stringizer` is None.
+
+    See Also
+    --------
+    literal_stringizer
+
+    Notes
+    -----
+    Graph attributes named 'directed', 'multigraph', 'node' or
+    'edge', node attributes named 'id' or 'label', edge attributes
+    named 'source' or 'target' (or 'key' if `G` is a multigraph)
+    are ignored because these attribute names are used to encode the graph
+    structure.
+
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_node("1")
+    >>> print("\n".join(nx.generate_gml(G)))
+    graph [
+      node [
+        id 0
+        label "1"
+      ]
+    ]
+    >>> G = nx.MultiGraph([("a", "b"), ("a", "b")])
+    >>> print("\n".join(nx.generate_gml(G)))
+    graph [
+      multigraph 1
+      node [
+        id 0
+        label "a"
+      ]
+      node [
+        id 1
+        label "b"
+      ]
+      edge [
+        source 0
+        target 1
+        key 0
+      ]
+      edge [
+        source 0
+        target 1
+        key 1
+      ]
+    ]
+    """
+    valid_keys = re.compile("^[A-Za-z][0-9A-Za-z_]*$")
+
+    def stringize(key, value, ignored_keys, indent, in_list=False):
+        if not isinstance(key, str):
+            raise NetworkXError(f"{key!r} is not a string")
+        if not valid_keys.match(key):
+            raise NetworkXError(f"{key!r} is not a valid key")
+        if not isinstance(key, str):
+            key = str(key)
+        if key not in ignored_keys:
+            if isinstance(value, int | bool):
+                if key == "label":
+                    yield indent + key + ' "' + str(value) + '"'
+                elif value is True:
+                    # python bool is an instance of int
+                    yield indent + key + " 1"
+                elif value is False:
+                    yield indent + key + " 0"
+                # GML only supports signed 32-bit integers
+                elif value < -(2**31) or value >= 2**31:
+                    yield indent + key + ' "' + str(value) + '"'
+                else:
+                    yield indent + key + " " + str(value)
+            elif isinstance(value, float):
+                text = repr(value).upper()
+                # GML matches INF to keys, so prepend + to INF. Use repr(float(*))
+                # instead of string literal to future proof against changes to repr.
+                if text == repr(float("inf")).upper():
+                    text = "+" + text
+                else:
+                    # GML requires that a real literal contain a decimal point, but
+                    # repr may not output a decimal point when the mantissa is
+                    # integral and hence needs fixing.
+                    epos = text.rfind("E")
+                    if epos != -1 and text.find(".", 0, epos) == -1:
+                        text = text[:epos] + "." + text[epos:]
+                if key == "label":
+                    yield indent + key + ' "' + text + '"'
+                else:
+                    yield indent + key + " " + text
+            elif isinstance(value, dict):
+                yield indent + key + " ["
+                next_indent = indent + "  "
+                for key, value in value.items():
+                    yield from stringize(key, value, (), next_indent)
+                yield indent + "]"
+            elif isinstance(value, tuple) and key == "label":
+                yield indent + key + f' "({",".join(repr(v) for v in value)})"'
+            elif isinstance(value, list | tuple) and key != "label" and not in_list:
+                if len(value) == 0:
+                    yield indent + key + " " + f'"{value!r}"'
+                if len(value) == 1:
+                    yield indent + key + " " + f'"{LIST_START_VALUE}"'
+                for val in value:
+                    yield from stringize(key, val, (), indent, True)
+            else:
+                if stringizer:
+                    try:
+                        value = stringizer(value)
+                    except ValueError as err:
+                        raise NetworkXError(
+                            f"{value!r} cannot be converted into a string"
+                        ) from err
+                if not isinstance(value, str):
+                    raise NetworkXError(f"{value!r} is not a string")
+                yield indent + key + ' "' + escape(value) + '"'
+
+    multigraph = G.is_multigraph()
+    yield "graph ["
+
+    # Output graph attributes
+    if G.is_directed():
+        yield "  directed 1"
+    if multigraph:
+        yield "  multigraph 1"
+    ignored_keys = {"directed", "multigraph", "node", "edge"}
+    for attr, value in G.graph.items():
+        yield from stringize(attr, value, ignored_keys, "  ")
+
+    # Output node data
+    node_id = dict(zip(G, range(len(G))))
+    ignored_keys = {"id", "label"}
+    for node, attrs in G.nodes.items():
+        yield "  node ["
+        yield "    id " + str(node_id[node])
+        yield from stringize("label", node, (), "    ")
+        for attr, value in attrs.items():
+            yield from stringize(attr, value, ignored_keys, "    ")
+        yield "  ]"
+
+    # Output edge data
+    ignored_keys = {"source", "target"}
+    kwargs = {"data": True}
+    if multigraph:
+        ignored_keys.add("key")
+        kwargs["keys"] = True
+    for e in G.edges(**kwargs):
+        yield "  edge ["
+        yield "    source " + str(node_id[e[0]])
+        yield "    target " + str(node_id[e[1]])
+        if multigraph:
+            yield from stringize("key", e[2], (), "    ")
+        for attr, value in e[-1].items():
+            yield from stringize(attr, value, ignored_keys, "    ")
+        yield "  ]"
+    yield "]"
+
+
+@open_file(1, mode="wb")
+def write_gml(G, path, stringizer=None):
+    """Write a graph `G` in GML format to the file or file handle `path`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph to be converted to GML.
+
+    path : string or file
+        Filename or file handle to write to.
+        Filenames ending in .gz or .bz2 will be compressed.
+
+    stringizer : callable, optional
+        A `stringizer` which converts non-int/non-float/non-dict values into
+        strings. If it cannot convert a value into a string, it should raise a
+        `ValueError` to indicate that. Default value: None.
+
+    Raises
+    ------
+    NetworkXError
+        If `stringizer` cannot convert a value into a string, or the value to
+        convert is not a string while `stringizer` is None.
+
+    See Also
+    --------
+    read_gml, generate_gml
+    literal_stringizer
+
+    Notes
+    -----
+    Graph attributes named 'directed', 'multigraph', 'node' or
+    'edge', node attributes named 'id' or 'label', edge attributes
+    named 'source' or 'target' (or 'key' if `G` is a multigraph)
+    are ignored because these attribute names are used to encode the graph
+    structure.
+
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    Note that while we allow non-standard GML to be read from a file, we make
+    sure to write GML format. In particular, underscores are not allowed in
+    attribute names.
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.write_gml(G, "test_path5.gml")
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_gml(G, "test_path5.gml.gz")
+    """
+    for line in generate_gml(G, stringizer):
+        path.write((line + "\n").encode("ascii"))
diff --git a/lib/python3.11/site-packages/networkx/readwrite/graph6.py b/lib/python3.11/site-packages/networkx/readwrite/graph6.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdc2925e5fb264a3d3e3235571682e38530d7f69
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/graph6.py
@@ -0,0 +1,427 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *graph6* format.
+
+The *graph6* file format is suitable for small graphs or large dense
+graphs. For large sparse graphs, use the *sparse6* format.
+
+For more information, see the `graph6`_ homepage.
+
+.. _graph6: http://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+
+from itertools import islice
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_graph6_bytes", "read_graph6", "to_graph6_bytes", "write_graph6"]
+
+
+def _generate_graph6_bytes(G, nodes, header):
+    """Yield bytes in the graph6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>graph6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "graph6 is only defined if number of nodes is less than 2 ** 36"
+        )
+    if header:
+        yield b">>graph6<<"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+    # This generates the same as `(v in G[u] for u, v in combinations(G, 2))`,
+    # but in "column-major" order instead of "row-major" order.
+    bits = (nodes[j] in G[nodes[i]] for j in range(1, n) for i in range(j))
+    chunk = list(islice(bits, 6))
+    while chunk:
+        d = sum(b << 5 - i for i, b in enumerate(chunk))
+        yield str.encode(chr(d + 63))
+        chunk = list(islice(bits, 6))
+    yield b"\n"
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_graph6_bytes(bytes_in):
+    """Read a simple undirected graph in graph6 format from bytes.
+
+    Parameters
+    ----------
+    bytes_in : bytes
+       Data in graph6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `bytes_in` is unable to be parsed in graph6 format
+
+    ValueError
+        If any character ``c`` in bytes_in does not satisfy
+        ``63 <= ord(c) < 127``.
+
+    Examples
+    --------
+    >>> G = nx.from_graph6_bytes(b"A_")
+    >>> sorted(G.edges())
+    [(0, 1)]
+
+    Notes
+    -----
+    Per the graph6 spec, the header (e.g. ``b'>>graph6<<'``) must not be
+    followed by a newline character.
+
+    See Also
+    --------
+    read_graph6, write_graph6
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+
+    def bits():
+        """Returns sequence of individual bits from 6-bit-per-value
+        list of data values."""
+        for d in data:
+            for i in [5, 4, 3, 2, 1, 0]:
+                yield (d >> i) & 1
+
+    # Ignore trailing newline
+    bytes_in = bytes_in.rstrip(b"\n")
+
+    if bytes_in.startswith(b">>graph6<<"):
+        bytes_in = bytes_in[10:]
+
+    data = [c - 63 for c in bytes_in]
+    if any(c > 63 for c in data):
+        raise ValueError("each input character must be in range(63, 127)")
+
+    n, data = data_to_n(data)
+    nd = (n * (n - 1) // 2 + 5) // 6
+    if len(data) != nd:
+        raise NetworkXError(
+            f"Expected {n * (n - 1) // 2} bits but got {len(data) * 6} in graph6"
+        )
+
+    G = nx.Graph()
+    G.add_nodes_from(range(n))
+    for (i, j), b in zip(((i, j) for j in range(1, n) for i in range(j)), bits()):
+        if b:
+            G.add_edge(i, j)
+
+    return G
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def to_graph6_bytes(G, nodes=None, header=True):
+    """Convert a simple undirected graph to bytes in graph6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>graph6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the graph6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_graph6_bytes(nx.path_graph(2))
+    b'>>graph6<<A_\\n'
+
+    See Also
+    --------
+    from_graph6_bytes, read_graph6, write_graph6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels, parallel edges or
+    self loops. If self loops are present they are silently ignored.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    H = nx.convert_node_labels_to_integers(G)
+    nodes = sorted(H.nodes())
+    return b"".join(_generate_graph6_bytes(H, nodes, header))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_graph6(path):
+    """Read simple undirected graphs in graph6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : Graph or list of Graphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in graph6 format
+
+    Examples
+    --------
+    You can read a graph6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>graph6<<A_\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_graph6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a graph6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>graph6<<A_\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_graph6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    from_graph6_bytes, write_graph6
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_graph6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@open_file(1, mode="wb")
+def write_graph6(G, path, nodes=None, header=True):
+    """Write a simple undirected graph to a path in graph6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>graph6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the graph6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    You can write a graph6 file by giving the path to a file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_graph6(nx.path_graph(2), f.name)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>graph6<<A_\\n'
+
+    See Also
+    --------
+    from_graph6_bytes, read_graph6
+
+    Notes
+    -----
+    The function writes a newline character after writing the encoding
+    of the graph.
+
+    The format does not support edge or node labels, parallel edges or
+    self loops.  If self loops are present they are silently ignored.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    return write_graph6_file(G, path, nodes=nodes, header=header)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def write_graph6_file(G, f, nodes=None, header=True):
+    """Write a simple undirected graph to a file-like object in graph6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    f : file-like object
+       The file to write.
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>graph6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the graph6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    You can write a graph6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_graph6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>graph6<<A_\\n'
+
+    See Also
+    --------
+    from_graph6_bytes, read_graph6
+
+    Notes
+    -----
+    The function writes a newline character after writing the encoding
+    of the graph.
+
+    The format does not support edge or node labels, parallel edges or
+    self loops.  If self loops are present they are silently ignored.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    H = nx.convert_node_labels_to_integers(G)
+    nodes = sorted(H.nodes())
+    for b in _generate_graph6_bytes(H, nodes, header):
+        f.write(b)
+
+
+def data_to_n(data):
+    """Read initial one-, four- or eight-unit value from graph6
+    integer sequence.
+
+    Return (value, rest of seq.)"""
+    if data[0] <= 62:
+        return data[0], data[1:]
+    if data[1] <= 62:
+        return (data[1] << 12) + (data[2] << 6) + data[3], data[4:]
+    return (
+        (data[2] << 30)
+        + (data[3] << 24)
+        + (data[4] << 18)
+        + (data[5] << 12)
+        + (data[6] << 6)
+        + data[7],
+        data[8:],
+    )
+
+
+def n_to_data(n):
+    """Convert an integer to one-, four- or eight-unit graph6 sequence.
+
+    This function is undefined if `n` is not in ``range(2 ** 36)``.
+
+    """
+    if n <= 62:
+        return [n]
+    elif n <= 258047:
+        return [63, (n >> 12) & 0x3F, (n >> 6) & 0x3F, n & 0x3F]
+    else:  # if n <= 68719476735:
+        return [
+            63,
+            63,
+            (n >> 30) & 0x3F,
+            (n >> 24) & 0x3F,
+            (n >> 18) & 0x3F,
+            (n >> 12) & 0x3F,
+            (n >> 6) & 0x3F,
+            n & 0x3F,
+        ]
diff --git a/lib/python3.11/site-packages/networkx/readwrite/graphml.py b/lib/python3.11/site-packages/networkx/readwrite/graphml.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ca1741452c963d99ffb8bfad2fb09eab8badd6b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/graphml.py
@@ -0,0 +1,1053 @@
+"""
+*******
+GraphML
+*******
+Read and write graphs in GraphML format.
+
+.. warning::
+
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GraphML files you trust.
+
+This implementation does not support mixed graphs (directed and unidirected
+edges together), hyperedges, nested graphs, or ports.
+
+"GraphML is a comprehensive and easy-to-use file format for graphs. It
+consists of a language core to describe the structural properties of a
+graph and a flexible extension mechanism to add application-specific
+data. Its main features include support of
+
+    * directed, undirected, and mixed graphs,
+    * hypergraphs,
+    * hierarchical graphs,
+    * graphical representations,
+    * references to external data,
+    * application-specific attribute data, and
+    * light-weight parsers.
+
+Unlike many other file formats for graphs, GraphML does not use a
+custom syntax. Instead, it is based on XML and hence ideally suited as
+a common denominator for all kinds of services generating, archiving,
+or processing graphs."
+
+http://graphml.graphdrawing.org/
+
+Format
+------
+GraphML is an XML format.  See
+http://graphml.graphdrawing.org/specification.html for the specification and
+http://graphml.graphdrawing.org/primer/graphml-primer.html
+for examples.
+"""
+
+import warnings
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = [
+    "write_graphml",
+    "read_graphml",
+    "generate_graphml",
+    "write_graphml_xml",
+    "write_graphml_lxml",
+    "parse_graphml",
+    "GraphMLWriter",
+    "GraphMLReader",
+]
+
+
+@open_file(1, mode="wb")
+def write_graphml_xml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml(G, "test.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    writer.dump(path)
+
+
+@open_file(1, mode="wb")
+def write_graphml_lxml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    This function uses the LXML framework and should be faster than
+    the version using the xml library.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml_lxml(G, "fourpath.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    try:
+        import lxml.etree as lxmletree
+    except ImportError:
+        return write_graphml_xml(
+            G,
+            path,
+            encoding,
+            prettyprint,
+            infer_numeric_types,
+            named_key_ids,
+            edge_id_from_attribute,
+        )
+
+    writer = GraphMLWriterLxml(
+        path,
+        graph=G,
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.dump()
+
+
+def generate_graphml(
+    G,
+    encoding="utf-8",
+    prettyprint=True,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Generate GraphML lines for G
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> for line in nx.generate_graphml(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_graphml(path, node_type=str, edge_key_type=int, force_multigraph=False):
+    """Read graph in GraphML format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If parallel edges are present or `force_multigraph=True` then
+        a MultiGraph or MultiDiGraph is returned. Otherwise a Graph/DiGraph.
+        The returned graph is directed if the file indicates it should be.
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    Files with the yEd "yfiles" extension can be read. The type of the node's
+    shape is preserved in the `shape_type` node attribute.
+
+    yEd compressed files ("file.graphmlz" extension) can be read by renaming
+    the file to "file.graphml.gz".
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(path=path))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = b'<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        path.seek(0)
+        old_bytes = path.read()
+        new_bytes = old_bytes.replace(b"<graphml>", header)
+        glist = list(reader(string=new_bytes))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_graphml(
+    graphml_string, node_type=str, edge_key_type=int, force_multigraph=False
+):
+    """Read graph in GraphML format from string.
+
+    Parameters
+    ----------
+    graphml_string : string
+       String containing graphml information
+       (e.g., contents of a graphml file).
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> H = nx.parse_graphml(s)
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(string=graphml_string))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = '<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        new_string = graphml_string.replace("<graphml>", header)
+        glist = list(reader(string=new_string))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+class GraphML:
+    NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
+    NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
+    # xmlns:y="http://www.yworks.com/xml/graphml"
+    NS_Y = "http://www.yworks.com/xml/graphml"
+    SCHEMALOCATION = " ".join(
+        [
+            "http://graphml.graphdrawing.org/xmlns",
+            "http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd",
+        ]
+    )
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),  # for Gephi GraphML bug
+            (str, "yfiles"),
+            (str, "string"),
+            (int, "int"),
+            (int, "long"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # This page says that data types in GraphML follow Java(TM).
+    #  http://graphml.graphdrawing.org/primer/graphml-primer.html#AttributesDefinition
+    # true and false are the only boolean literals:
+    #  http://en.wikibooks.org/wiki/Java_Programming/Literals#Boolean_Literals
+    convert_bool = {
+        # We use data.lower() in actual use.
+        "true": True,
+        "false": False,
+        # Include integer strings for convenience.
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def get_xml_type(self, key):
+        """Wrapper around the xml_type dict that raises a more informative
+        exception message when a user attempts to use data of a type not
+        supported by GraphML."""
+        try:
+            return self.xml_type[key]
+        except KeyError as err:
+            raise TypeError(
+                f"GraphML does not support type {key} as data values."
+            ) from err
+
+
+class GraphMLWriter(GraphML):
+    def __init__(
+        self,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        from xml.etree.ElementTree import Element
+
+        self.myElement = Element
+
+        self.infer_numeric_types = infer_numeric_types
+        self.prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.encoding = encoding
+        self.xml = self.myElement(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self.keys = {}
+        self.attributes = defaultdict(list)
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def __str__(self):
+        from xml.etree.ElementTree import tostring
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def attr_type(self, name, scope, value):
+        """Infer the attribute type of data named name. Currently this only
+        supports inference of numeric types.
+
+        If self.infer_numeric_types is false, type is used. Otherwise, pick the
+        most general of types found across all values with name and scope. This
+        means edges with data named 'weight' are treated separately from nodes
+        with data named 'weight'.
+        """
+        if self.infer_numeric_types:
+            types = self.attribute_types[(name, scope)]
+
+            if len(types) > 1:
+                types = {self.get_xml_type(t) for t in types}
+                if "string" in types:
+                    return str
+                elif "float" in types or "double" in types:
+                    return float
+                else:
+                    return int
+            else:
+                return list(types)[0]
+        else:
+            return type(value)
+
+    def get_key(self, name, attr_type, scope, default):
+        keys_key = (name, attr_type, scope)
+        try:
+            return self.keys[keys_key]
+        except KeyError:
+            if self.named_key_ids:
+                new_id = name
+            else:
+                new_id = f"d{len(list(self.keys))}"
+
+            self.keys[keys_key] = new_id
+            key_kwargs = {
+                "id": new_id,
+                "for": scope,
+                "attr.name": name,
+                "attr.type": attr_type,
+            }
+            key_element = self.myElement("key", **key_kwargs)
+            # add subelement for data default value if present
+            if default is not None:
+                default_element = self.myElement("default")
+                default_element.text = str(default)
+                key_element.append(default_element)
+            self.xml.insert(0, key_element)
+        return new_id
+
+    def add_data(self, name, element_type, value, scope="all", default=None):
+        """
+        Make a data element for an edge or a node. Keep a log of the
+        type in the keys table.
+        """
+        if element_type not in self.xml_type:
+            raise nx.NetworkXError(
+                f"GraphML writer does not support {element_type} as data values."
+            )
+        keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
+        data_element = self.myElement("data", key=keyid)
+        data_element.text = str(value)
+        return data_element
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data to edges or nodes, and stores type information
+        to be added later. See add_graph_element.
+        """
+        for k, v in data.items():
+            self.attribute_types[(str(k), scope)].add(type(v))
+            self.attributes[xml_obj].append([k, v, scope, default.get(k)])
+
+    def add_nodes(self, G, graph_element):
+        default = G.graph.get("node_default", {})
+        for node, data in G.nodes(data=True):
+            node_element = self.myElement("node", id=str(node))
+            self.add_attributes("node", node_element, data, default)
+            graph_element.append(node_element)
+
+    def add_edges(self, G, graph_element):
+        if G.is_multigraph():
+            for u, v, key, data in G.edges(data=True, keys=True):
+                edge_element = self.myElement(
+                    "edge",
+                    source=str(u),
+                    target=str(v),
+                    id=str(data.get(self.edge_id_from_attribute))
+                    if self.edge_id_from_attribute
+                    and self.edge_id_from_attribute in data
+                    else str(key),
+                )
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+        else:
+            for u, v, data in G.edges(data=True):
+                if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
+                    # select attribute to be edge id
+                    edge_element = self.myElement(
+                        "edge",
+                        source=str(u),
+                        target=str(v),
+                        id=str(data.get(self.edge_id_from_attribute)),
+                    )
+                else:
+                    # default: no edge id
+                    edge_element = self.myElement("edge", source=str(u), target=str(v))
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self.myElement("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self.myElement(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+        default = {}
+        data = {
+            k: v
+            for (k, v) in G.graph.items()
+            if k not in ["node_default", "edge_default"]
+        }
+        self.add_attributes("graph", graph_element, data, default)
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+
+        # self.attributes contains a mapping from XML Objects to a list of
+        # data that needs to be added to them.
+        # We postpone processing in order to do type inference/generalization.
+        # See self.attr_type
+        for xml_obj, data in self.attributes.items():
+            for k, v, scope, default in data:
+                xml_obj.append(
+                    self.add_data(
+                        str(k), self.attr_type(k, scope, v), str(v), scope, default
+                    )
+                )
+        self.xml.append(graph_element)
+
+    def add_graphs(self, graph_list):
+        """Add many graphs to this GraphML document."""
+        for G in graph_list:
+            self.add_graph_element(G)
+
+    def dump(self, stream):
+        from xml.etree.ElementTree import ElementTree
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(stream, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + level * "  "
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class IncrementalElement:
+    """Wrapper for _IncrementalWriter providing an Element like interface.
+
+    This wrapper does not intend to be a complete implementation but rather to
+    deal with those calls used in GraphMLWriter.
+    """
+
+    def __init__(self, xml, prettyprint):
+        self.xml = xml
+        self.prettyprint = prettyprint
+
+    def append(self, element):
+        self.xml.write(element, pretty_print=self.prettyprint)
+
+
+class GraphMLWriterLxml(GraphMLWriter):
+    def __init__(
+        self,
+        path,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        import lxml.etree as lxmletree
+
+        self.myElement = lxmletree.Element
+
+        self._encoding = encoding
+        self._prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.infer_numeric_types = infer_numeric_types
+
+        self._xml_base = lxmletree.xmlfile(path, encoding=encoding)
+        self._xml = self._xml_base.__enter__()
+        self._xml.write_declaration()
+
+        # We need to have a xml variable that support insertion. This call is
+        # used for adding the keys to the document.
+        # We will store those keys in a plain list, and then after the graph
+        # element is closed we will add them to the main graphml element.
+        self.xml = []
+        self._keys = self.xml
+        self._graphml = self._xml.element(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self._graphml.__enter__()
+        self.keys = {}
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self._xml.element("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self._xml.element(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+
+        # gather attributes types for the whole graph
+        # to find the most general numeric format needed.
+        # Then pass through attributes to create key_id for each.
+        graphdata = {
+            k: v
+            for k, v in G.graph.items()
+            if k not in ("node_default", "edge_default")
+        }
+        node_default = G.graph.get("node_default", {})
+        edge_default = G.graph.get("edge_default", {})
+        # Graph attributes
+        for k, v in graphdata.items():
+            self.attribute_types[(str(k), "graph")].add(type(v))
+        for k, v in graphdata.items():
+            element_type = self.get_xml_type(self.attr_type(k, "graph", v))
+            self.get_key(str(k), element_type, "graph", None)
+        # Nodes and data
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                self.attribute_types[(str(k), "node")].add(type(v))
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                T = self.get_xml_type(self.attr_type(k, "node", v))
+                self.get_key(str(k), T, "node", node_default.get(k))
+        # Edges and data
+        if G.is_multigraph():
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+        else:
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+
+        # Now add attribute keys to the xml file
+        for key in self.xml:
+            self._xml.write(key, pretty_print=self._prettyprint)
+
+        # The incremental_writer writes each node/edge as it is created
+        incremental_writer = IncrementalElement(self._xml, self._prettyprint)
+        with graph_element:
+            self.add_attributes("graph", incremental_writer, graphdata, {})
+            self.add_nodes(G, incremental_writer)  # adds attributes too
+            self.add_edges(G, incremental_writer)  # adds attributes too
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data."""
+        for k, v in data.items():
+            data_element = self.add_data(
+                str(k), self.attr_type(str(k), scope, v), str(v), scope, default.get(k)
+            )
+            xml_obj.append(data_element)
+
+    def __str__(self):
+        return object.__str__(self)
+
+    def dump(self, stream=None):
+        self._graphml.__exit__(None, None, None)
+        self._xml_base.__exit__(None, None, None)
+
+
+# default is lxml is present.
+write_graphml = write_graphml_lxml
+
+
+class GraphMLReader(GraphML):
+    """Read a GraphML document.  Produces NetworkX graph objects."""
+
+    def __init__(self, node_type=str, edge_key_type=int, force_multigraph=False):
+        self.construct_types()
+        self.node_type = node_type
+        self.edge_key_type = edge_key_type
+        self.multigraph = force_multigraph  # If False, test for multiedges
+        self.edge_ids = {}  # dict mapping (u,v) tuples to edge id attributes
+
+    def __call__(self, path=None, string=None):
+        from xml.etree.ElementTree import ElementTree, fromstring
+
+        if path is not None:
+            self.xml = ElementTree(file=path)
+        elif string is not None:
+            self.xml = fromstring(string)
+        else:
+            raise ValueError("Must specify either 'path' or 'string' as kwarg")
+        (keys, defaults) = self.find_graphml_keys(self.xml)
+        for g in self.xml.findall(f"{{{self.NS_GRAPHML}}}graph"):
+            yield self.make_graph(g, keys, defaults)
+
+    def make_graph(self, graph_xml, graphml_keys, defaults, G=None):
+        # set default graph type
+        edgedefault = graph_xml.get("edgedefault", None)
+        if G is None:
+            if edgedefault == "directed":
+                G = nx.MultiDiGraph()
+            else:
+                G = nx.MultiGraph()
+        # set defaults for graph attributes
+        G.graph["node_default"] = {}
+        G.graph["edge_default"] = {}
+        for key_id, value in defaults.items():
+            key_for = graphml_keys[key_id]["for"]
+            name = graphml_keys[key_id]["name"]
+            python_type = graphml_keys[key_id]["type"]
+            if key_for == "node":
+                G.graph["node_default"].update({name: python_type(value)})
+            if key_for == "edge":
+                G.graph["edge_default"].update({name: python_type(value)})
+        # hyperedges are not supported
+        hyperedge = graph_xml.find(f"{{{self.NS_GRAPHML}}}hyperedge")
+        if hyperedge is not None:
+            raise nx.NetworkXError("GraphML reader doesn't support hyperedges")
+        # add nodes
+        for node_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}node"):
+            self.add_node(G, node_xml, graphml_keys, defaults)
+        # add edges
+        for edge_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}edge"):
+            self.add_edge(G, edge_xml, graphml_keys)
+        # add graph data
+        data = self.decode_data_elements(graphml_keys, graph_xml)
+        G.graph.update(data)
+
+        # switch to Graph or DiGraph if no parallel edges were found
+        if self.multigraph:
+            return G
+
+        G = nx.DiGraph(G) if G.is_directed() else nx.Graph(G)
+        # add explicit edge "id" from file as attribute in NX graph.
+        nx.set_edge_attributes(G, values=self.edge_ids, name="id")
+        return G
+
+    def add_node(self, G, node_xml, graphml_keys, defaults):
+        """Add a node to the graph."""
+        # warn on finding unsupported ports tag
+        ports = node_xml.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+        # find the node by id and cast it to the appropriate type
+        node_id = self.node_type(node_xml.get("id"))
+        # get data/attributes for node
+        data = self.decode_data_elements(graphml_keys, node_xml)
+        G.add_node(node_id, **data)
+        # get child nodes
+        if node_xml.attrib.get("yfiles.foldertype") == "group":
+            graph_xml = node_xml.find(f"{{{self.NS_GRAPHML}}}graph")
+            self.make_graph(graph_xml, graphml_keys, defaults, G)
+
+    def add_edge(self, G, edge_element, graphml_keys):
+        """Add an edge to the graph."""
+        # warn on finding unsupported ports tag
+        ports = edge_element.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+
+        # raise error if we find mixed directed and undirected edges
+        directed = edge_element.get("directed")
+        if G.is_directed() and directed == "false":
+            msg = "directed=false edge found in directed graph."
+            raise nx.NetworkXError(msg)
+        if (not G.is_directed()) and directed == "true":
+            msg = "directed=true edge found in undirected graph."
+            raise nx.NetworkXError(msg)
+
+        source = self.node_type(edge_element.get("source"))
+        target = self.node_type(edge_element.get("target"))
+        data = self.decode_data_elements(graphml_keys, edge_element)
+        # GraphML stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs too if no key
+        # attribute is specified
+        edge_id = edge_element.get("id")
+        if edge_id:
+            # self.edge_ids is used by `make_graph` method for non-multigraphs
+            self.edge_ids[source, target] = edge_id
+            try:
+                edge_id = self.edge_key_type(edge_id)
+            except ValueError:  # Could not convert.
+                pass
+        else:
+            edge_id = data.get("key")
+
+        if G.has_edge(source, target):
+            # mark this as a multigraph
+            self.multigraph = True
+
+        # Use add_edges_from to avoid error with add_edge when `'key' in data`
+        # Note there is only one edge here...
+        G.add_edges_from([(source, target, edge_id, data)])
+
+    def decode_data_elements(self, graphml_keys, obj_xml):
+        """Use the key information to decode the data XML if present."""
+        data = {}
+        for data_element in obj_xml.findall(f"{{{self.NS_GRAPHML}}}data"):
+            key = data_element.get("key")
+            try:
+                data_name = graphml_keys[key]["name"]
+                data_type = graphml_keys[key]["type"]
+            except KeyError as err:
+                raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from err
+            text = data_element.text
+            # assume anything with subelements is a yfiles extension
+            if text is not None and len(list(data_element)) == 0:
+                if data_type is bool:
+                    # Ignore cases.
+                    # http://docs.oracle.com/javase/6/docs/api/java/lang/
+                    # Boolean.html#parseBoolean%28java.lang.String%29
+                    data[data_name] = self.convert_bool[text.lower()]
+                else:
+                    data[data_name] = data_type(text)
+            elif len(list(data_element)) > 0:
+                # Assume yfiles as subelements, try to extract node_label
+                node_label = None
+                # set GenericNode's configuration as shape type
+                gn = data_element.find(f"{{{self.NS_Y}}}GenericNode")
+                if gn is not None:
+                    data["shape_type"] = gn.get("configuration")
+                for node_type in ["GenericNode", "ShapeNode", "SVGNode", "ImageNode"]:
+                    pref = f"{{{self.NS_Y}}}{node_type}/{{{self.NS_Y}}}"
+                    geometry = data_element.find(f"{pref}Geometry")
+                    if geometry is not None:
+                        data["x"] = geometry.get("x")
+                        data["y"] = geometry.get("y")
+                    if node_label is None:
+                        node_label = data_element.find(f"{pref}NodeLabel")
+                    shape = data_element.find(f"{pref}Shape")
+                    if shape is not None:
+                        data["shape_type"] = shape.get("type")
+                if node_label is not None:
+                    data["label"] = node_label.text
+
+                # check all the different types of edges available in yEd.
+                for edge_type in [
+                    "PolyLineEdge",
+                    "SplineEdge",
+                    "QuadCurveEdge",
+                    "BezierEdge",
+                    "ArcEdge",
+                ]:
+                    pref = f"{{{self.NS_Y}}}{edge_type}/{{{self.NS_Y}}}"
+                    edge_label = data_element.find(f"{pref}EdgeLabel")
+                    if edge_label is not None:
+                        break
+                if edge_label is not None:
+                    data["label"] = edge_label.text
+            elif text is None:
+                data[data_name] = ""
+        return data
+
+    def find_graphml_keys(self, graph_element):
+        """Extracts all the keys and key defaults from the xml."""
+        graphml_keys = {}
+        graphml_key_defaults = {}
+        for k in graph_element.findall(f"{{{self.NS_GRAPHML}}}key"):
+            attr_id = k.get("id")
+            attr_type = k.get("attr.type")
+            attr_name = k.get("attr.name")
+            yfiles_type = k.get("yfiles.type")
+            if yfiles_type is not None:
+                attr_name = yfiles_type
+                attr_type = "yfiles"
+            if attr_type is None:
+                attr_type = "string"
+                warnings.warn(f"No key type for id {attr_id}. Using string")
+            if attr_name is None:
+                raise nx.NetworkXError(f"Unknown key for id {attr_id}.")
+            graphml_keys[attr_id] = {
+                "name": attr_name,
+                "type": self.python_type[attr_type],
+                "for": k.get("for"),
+            }
+            # check for "default" sub-element of key element
+            default = k.find(f"{{{self.NS_GRAPHML}}}default")
+            if default is not None:
+                # Handle default values identically to data element values
+                python_type = graphml_keys[attr_id]["type"]
+                if python_type is bool:
+                    graphml_key_defaults[attr_id] = self.convert_bool[
+                        default.text.lower()
+                    ]
+                else:
+                    graphml_key_defaults[attr_id] = python_type(default.text)
+        return graphml_keys, graphml_key_defaults
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/__init__.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..532c71d79b7b8936481be8db0defaedf9a96b3e3
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__init__.py
@@ -0,0 +1,19 @@
+"""
+*********
+JSON data
+*********
+Generate and parse JSON serializable data for NetworkX graphs.
+
+These formats are suitable for use with the d3.js examples https://d3js.org/
+
+The three formats that you can generate with NetworkX are:
+
+ - node-link like in the d3.js example https://bl.ocks.org/mbostock/4062045
+ - tree like in the d3.js example https://bl.ocks.org/mbostock/4063550
+ - adjacency like in the d3.js example https://bost.ocks.org/mike/miserables/
+"""
+
+from networkx.readwrite.json_graph.node_link import *
+from networkx.readwrite.json_graph.adjacency import *
+from networkx.readwrite.json_graph.tree import *
+from networkx.readwrite.json_graph.cytoscape import *
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..45f260697345f79837e936b0ab998054cb02f95a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/adjacency.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/adjacency.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..feceb8e5f3438a5d6bcb97ef804952c9699f4d69
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/adjacency.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/cytoscape.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/cytoscape.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..de7bb3f57a70cc1bc365f6014913b4264d213632
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/cytoscape.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/node_link.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/node_link.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7aaf85d7c04c895bceb6150e3e3fcb83ecececba
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/node_link.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/tree.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/tree.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2b2593e2c2c60ab0b5fd4df99a7858068e5203bf
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/__pycache__/tree.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/adjacency.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/adjacency.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b05747565e73388b0871fbb7daf0f85ad2ce98b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/adjacency.py
@@ -0,0 +1,156 @@
+import networkx as nx
+
+__all__ = ["adjacency_data", "adjacency_graph"]
+
+_attrs = {"id": "id", "key": "key"}
+
+
+def adjacency_data(G, attrs=_attrs):
+    """Returns data in adjacency format that is suitable for JSON serialization
+    and use in JavaScript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    attrs : dict
+        A dictionary that contains two keys 'id' and 'key'. The corresponding
+        values provide the attribute names for storing NetworkX-internal graph
+        data. The values should be unique. Default value:
+        :samp:`dict(id='id', key='key')`.
+
+        If some user-defined graph data use these attribute names as data keys,
+        they may be silently dropped.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with adjacency formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If values in attrs are not unique.
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.Graph([(1, 2)])
+    >>> data = json_graph.adjacency_data(G)
+
+    To serialize with json
+
+    >>> import json
+    >>> s = json.dumps(data)
+
+    Notes
+    -----
+    Graph, node, and link attributes will be written when using this format
+    but attribute keys must be strings if you want to serialize the resulting
+    data with JSON.
+
+    The default value of attrs will be changed in a future release of NetworkX.
+
+    See Also
+    --------
+    adjacency_graph, node_link_data, tree_data
+    """
+    multigraph = G.is_multigraph()
+    id_ = attrs["id"]
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else attrs["key"]
+    if id_ == key:
+        raise nx.NetworkXError("Attribute names are not unique.")
+    data = {}
+    data["directed"] = G.is_directed()
+    data["multigraph"] = multigraph
+    data["graph"] = list(G.graph.items())
+    data["nodes"] = []
+    data["adjacency"] = []
+    for n, nbrdict in G.adjacency():
+        data["nodes"].append({**G.nodes[n], id_: n})
+        adj = []
+        if multigraph:
+            for nbr, keys in nbrdict.items():
+                for k, d in keys.items():
+                    adj.append({**d, id_: nbr, key: k})
+        else:
+            for nbr, d in nbrdict.items():
+                adj.append({**d, id_: nbr})
+        data["adjacency"].append(adj)
+    return data
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def adjacency_graph(data, directed=False, multigraph=True, attrs=_attrs):
+    """Returns graph from adjacency data format.
+
+    Parameters
+    ----------
+    data : dict
+        Adjacency list formatted graph data
+
+    directed : bool
+        If True, and direction not specified in data, return a directed graph.
+
+    multigraph : bool
+        If True, and multigraph not specified in data, return a multigraph.
+
+    attrs : dict
+        A dictionary that contains two keys 'id' and 'key'. The corresponding
+        values provide the attribute names for storing NetworkX-internal graph
+        data. The values should be unique. Default value:
+        :samp:`dict(id='id', key='key')`.
+
+    Returns
+    -------
+    G : NetworkX graph
+       A NetworkX graph object
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.Graph([(1, 2)])
+    >>> data = json_graph.adjacency_data(G)
+    >>> H = json_graph.adjacency_graph(data)
+
+    Notes
+    -----
+    The default value of attrs will be changed in a future release of NetworkX.
+
+    See Also
+    --------
+    adjacency_graph, node_link_data, tree_data
+    """
+    multigraph = data.get("multigraph", multigraph)
+    directed = data.get("directed", directed)
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+    id_ = attrs["id"]
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else attrs["key"]
+    graph.graph = dict(data.get("graph", []))
+    mapping = []
+    for d in data["nodes"]:
+        node_data = d.copy()
+        node = node_data.pop(id_)
+        mapping.append(node)
+        graph.add_node(node)
+        graph.nodes[node].update(node_data)
+    for i, d in enumerate(data["adjacency"]):
+        source = mapping[i]
+        for tdata in d:
+            target_data = tdata.copy()
+            target = target_data.pop(id_)
+            if not multigraph:
+                graph.add_edge(source, target)
+                graph[source][target].update(target_data)
+            else:
+                ky = target_data.pop(key, None)
+                graph.add_edge(source, target, key=ky)
+                graph[source][target][ky].update(target_data)
+    return graph
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/cytoscape.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/cytoscape.py
new file mode 100644
index 0000000000000000000000000000000000000000..417e36f72dbb76ae0b1f0cbdc0336ebf50360b9b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/cytoscape.py
@@ -0,0 +1,190 @@
+import networkx as nx
+
+__all__ = ["cytoscape_data", "cytoscape_graph"]
+
+
+def cytoscape_data(G, name="name", ident="id"):
+    """Returns data in Cytoscape JSON format (cyjs).
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to convert to cytoscape format
+    name : string
+        A string which is mapped to the 'name' node element in cyjs format.
+        Must not have the same value as `ident`.
+    ident : string
+        A string which is mapped to the 'id' node element in cyjs format.
+        Must not have the same value as `name`.
+
+    Returns
+    -------
+    data: dict
+        A dictionary with cyjs formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If the values for `name` and `ident` are identical.
+
+    See Also
+    --------
+    cytoscape_graph: convert a dictionary in cyjs format to a graph
+
+    References
+    ----------
+    .. [1] Cytoscape user's manual:
+       http://manual.cytoscape.org/en/stable/index.html
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(2)
+    >>> cyto_data = nx.cytoscape_data(G)
+    >>> pprint(cyto_data, sort_dicts=False)
+    {'data': [],
+     'directed': False,
+     'multigraph': False,
+     'elements': {'nodes': [{'data': {'id': '0', 'value': 0, 'name': '0'}},
+                            {'data': {'id': '1', 'value': 1, 'name': '1'}}],
+                  'edges': [{'data': {'source': 0, 'target': 1}}]}}
+
+    The :mod:`json` package can be used to serialize the resulting data
+
+    >>> import io, json
+    >>> with io.StringIO() as fh:  # replace io with `open(...)` to write to disk
+    ...     json.dump(cyto_data, fh)
+    ...     fh.seek(0)  # doctest: +SKIP
+    ...     print(fh.getvalue()[:64])  # View the first 64 characters
+    {"data": [], "directed": false, "multigraph": false, "elements":
+
+    """
+    if name == ident:
+        raise nx.NetworkXError("name and ident must be different.")
+
+    jsondata = {"data": list(G.graph.items())}
+    jsondata["directed"] = G.is_directed()
+    jsondata["multigraph"] = G.is_multigraph()
+    jsondata["elements"] = {"nodes": [], "edges": []}
+    nodes = jsondata["elements"]["nodes"]
+    edges = jsondata["elements"]["edges"]
+
+    for i, j in G.nodes.items():
+        n = {"data": j.copy()}
+        n["data"]["id"] = j.get(ident) or str(i)
+        n["data"]["value"] = i
+        n["data"]["name"] = j.get(name) or str(i)
+        nodes.append(n)
+
+    if G.is_multigraph():
+        for e in G.edges(keys=True):
+            n = {"data": G.adj[e[0]][e[1]][e[2]].copy()}
+            n["data"]["source"] = e[0]
+            n["data"]["target"] = e[1]
+            n["data"]["key"] = e[2]
+            edges.append(n)
+    else:
+        for e in G.edges():
+            n = {"data": G.adj[e[0]][e[1]].copy()}
+            n["data"]["source"] = e[0]
+            n["data"]["target"] = e[1]
+            edges.append(n)
+    return jsondata
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def cytoscape_graph(data, name="name", ident="id"):
+    """
+    Create a NetworkX graph from a dictionary in cytoscape JSON format.
+
+    Parameters
+    ----------
+    data : dict
+        A dictionary of data conforming to cytoscape JSON format.
+    name : string
+        A string which is mapped to the 'name' node element in cyjs format.
+        Must not have the same value as `ident`.
+    ident : string
+        A string which is mapped to the 'id' node element in cyjs format.
+        Must not have the same value as `name`.
+
+    Returns
+    -------
+    graph : a NetworkX graph instance
+        The `graph` can be an instance of `Graph`, `DiGraph`, `MultiGraph`, or
+        `MultiDiGraph` depending on the input data.
+
+    Raises
+    ------
+    NetworkXError
+        If the `name` and `ident` attributes are identical.
+
+    See Also
+    --------
+    cytoscape_data: convert a NetworkX graph to a dict in cyjs format
+
+    References
+    ----------
+    .. [1] Cytoscape user's manual:
+       http://manual.cytoscape.org/en/stable/index.html
+
+    Examples
+    --------
+    >>> data_dict = {
+    ...     "data": [],
+    ...     "directed": False,
+    ...     "multigraph": False,
+    ...     "elements": {
+    ...         "nodes": [
+    ...             {"data": {"id": "0", "value": 0, "name": "0"}},
+    ...             {"data": {"id": "1", "value": 1, "name": "1"}},
+    ...         ],
+    ...         "edges": [{"data": {"source": 0, "target": 1}}],
+    ...     },
+    ... }
+    >>> G = nx.cytoscape_graph(data_dict)
+    >>> G.name
+    ''
+    >>> G.nodes()
+    NodeView((0, 1))
+    >>> G.nodes(data=True)[0]
+    {'id': '0', 'value': 0, 'name': '0'}
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'source': 0, 'target': 1})])
+    """
+    if name == ident:
+        raise nx.NetworkXError("name and ident must be different.")
+
+    multigraph = data.get("multigraph")
+    directed = data.get("directed")
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+    graph.graph = dict(data.get("data"))
+    for d in data["elements"]["nodes"]:
+        node_data = d["data"].copy()
+        node = d["data"]["value"]
+
+        if d["data"].get(name):
+            node_data[name] = d["data"].get(name)
+        if d["data"].get(ident):
+            node_data[ident] = d["data"].get(ident)
+
+        graph.add_node(node)
+        graph.nodes[node].update(node_data)
+
+    for d in data["elements"]["edges"]:
+        edge_data = d["data"].copy()
+        sour = d["data"]["source"]
+        targ = d["data"]["target"]
+        if multigraph:
+            key = d["data"].get("key", 0)
+            graph.add_edge(sour, targ, key=key)
+            graph.edges[sour, targ, key].update(edge_data)
+        else:
+            graph.add_edge(sour, targ)
+            graph.edges[sour, targ].update(edge_data)
+    return graph
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/node_link.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/node_link.py
new file mode 100644
index 0000000000000000000000000000000000000000..71b74f8549729ea5bc82803397f476baec4a53a9
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/node_link.py
@@ -0,0 +1,261 @@
+import warnings
+from itertools import count
+
+import networkx as nx
+
+__all__ = ["node_link_data", "node_link_graph"]
+
+
+def _to_tuple(x):
+    """Converts lists to tuples, including nested lists.
+
+    All other non-list inputs are passed through unmodified. This function is
+    intended to be used to convert potentially nested lists from json files
+    into valid nodes.
+
+    Examples
+    --------
+    >>> _to_tuple([1, 2, [3, 4]])
+    (1, 2, (3, 4))
+    """
+    if not isinstance(x, tuple | list):
+        return x
+    return tuple(map(_to_tuple, x))
+
+
+def node_link_data(
+    G,
+    *,
+    source="source",
+    target="target",
+    name="id",
+    key="key",
+    edges="edges",
+    nodes="nodes",
+):
+    """Returns data in node-link format that is suitable for JSON serialization
+    and use in JavaScript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    source : string
+        A string that provides the 'source' attribute name for storing NetworkX-internal graph data.
+    target : string
+        A string that provides the 'target' attribute name for storing NetworkX-internal graph data.
+    name : string
+        A string that provides the 'name' attribute name for storing NetworkX-internal graph data.
+    key : string
+        A string that provides the 'key' attribute name for storing NetworkX-internal graph data.
+    edges : string
+        A string that provides the 'edges' attribute name for storing NetworkX-internal graph data.
+    nodes : string
+        A string that provides the 'nodes' attribute name for storing NetworkX-internal graph data.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with node-link formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If the values of 'source', 'target' and 'key' are not unique.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.Graph([("A", "B")])
+    >>> data1 = nx.node_link_data(G)
+    >>> pprint(data1)
+    {'directed': False,
+     'edges': [{'source': 'A', 'target': 'B'}],
+     'graph': {},
+     'multigraph': False,
+     'nodes': [{'id': 'A'}, {'id': 'B'}]}
+
+    To serialize with JSON
+
+    >>> import json
+    >>> s1 = json.dumps(data1)
+    >>> pprint(s1)
+    ('{"directed": false, "multigraph": false, "graph": {}, "nodes": [{"id": "A"}, '
+     '{"id": "B"}], "edges": [{"source": "A", "target": "B"}]}')
+
+
+    A graph can also be serialized by passing `node_link_data` as an encoder function.
+
+    >>> s1 = json.dumps(G, default=nx.node_link_data)
+    >>> pprint(s1)
+    ('{"directed": false, "multigraph": false, "graph": {}, "nodes": [{"id": "A"}, '
+     '{"id": "B"}], "edges": [{"source": "A", "target": "B"}]}')
+
+    The attribute names for storing NetworkX-internal graph data can
+    be specified as keyword options.
+
+    >>> H = nx.gn_graph(2)
+    >>> data2 = nx.node_link_data(
+    ...     H, edges="links", source="from", target="to", nodes="vertices"
+    ... )
+    >>> pprint(data2)
+    {'directed': True,
+     'graph': {},
+     'links': [{'from': 1, 'to': 0}],
+     'multigraph': False,
+     'vertices': [{'id': 0}, {'id': 1}]}
+
+    Notes
+    -----
+    Graph, node, and edge attributes are stored in this format.  Note that
+    attribute keys will be converted to strings in order to comply with JSON.
+
+    Attribute 'key' is only used for multigraphs.
+
+    To use `node_link_data` in conjunction with `node_link_graph`,
+    the keyword names for the attributes must match.
+
+    See Also
+    --------
+    node_link_graph, adjacency_data, tree_data
+    """
+    multigraph = G.is_multigraph()
+
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else key
+    if len({source, target, key}) < 3:
+        raise nx.NetworkXError("Attribute names are not unique.")
+    data = {
+        "directed": G.is_directed(),
+        "multigraph": multigraph,
+        "graph": G.graph,
+        nodes: [{**G.nodes[n], name: n} for n in G],
+    }
+    if multigraph:
+        data[edges] = [
+            {**d, source: u, target: v, key: k}
+            for u, v, k, d in G.edges(keys=True, data=True)
+        ]
+    else:
+        data[edges] = [{**d, source: u, target: v} for u, v, d in G.edges(data=True)]
+    return data
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def node_link_graph(
+    data,
+    directed=False,
+    multigraph=True,
+    *,
+    source="source",
+    target="target",
+    name="id",
+    key="key",
+    edges="edges",
+    nodes="nodes",
+):
+    """Returns graph from node-link data format.
+
+    Useful for de-serialization from JSON.
+
+    Parameters
+    ----------
+    data : dict
+        node-link formatted graph data
+
+    directed : bool
+        If True, and direction not specified in data, return a directed graph.
+
+    multigraph : bool
+        If True, and multigraph not specified in data, return a multigraph.
+
+    source : string
+        A string that provides the 'source' attribute name for storing NetworkX-internal graph data.
+    target : string
+        A string that provides the 'target' attribute name for storing NetworkX-internal graph data.
+    name : string
+        A string that provides the 'name' attribute name for storing NetworkX-internal graph data.
+    key : string
+        A string that provides the 'key' attribute name for storing NetworkX-internal graph data.
+    edges : string
+        A string that provides the 'edges' attribute name for storing NetworkX-internal graph data.
+    nodes : string
+        A string that provides the 'nodes' attribute name for storing NetworkX-internal graph data.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A NetworkX graph object
+
+    Examples
+    --------
+
+    Create data in node-link format by converting a graph.
+
+    >>> from pprint import pprint
+    >>> G = nx.Graph([("A", "B")])
+    >>> data = nx.node_link_data(G)
+    >>> pprint(data)
+    {'directed': False,
+     'edges': [{'source': 'A', 'target': 'B'}],
+     'graph': {},
+     'multigraph': False,
+     'nodes': [{'id': 'A'}, {'id': 'B'}]}
+
+    Revert data in node-link format to a graph.
+
+    >>> H = nx.node_link_graph(data)
+    >>> print(H.edges)
+    [('A', 'B')]
+
+    To serialize and deserialize a graph with JSON,
+
+    >>> import json
+    >>> d = json.dumps(nx.node_link_data(G))
+    >>> H = nx.node_link_graph(json.loads(d))
+    >>> print(G.edges, H.edges)
+    [('A', 'B')] [('A', 'B')]
+
+
+    Notes
+    -----
+    Attribute 'key' is only used for multigraphs.
+
+    To use `node_link_data` in conjunction with `node_link_graph`,
+    the keyword names for the attributes must match.
+
+    See Also
+    --------
+    node_link_data, adjacency_data, tree_data
+    """
+    multigraph = data.get("multigraph", multigraph)
+    directed = data.get("directed", directed)
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else key
+    graph.graph = data.get("graph", {})
+    c = count()
+    for d in data[nodes]:
+        node = _to_tuple(d.get(name, next(c)))
+        nodedata = {str(k): v for k, v in d.items() if k != name}
+        graph.add_node(node, **nodedata)
+    for d in data[edges]:
+        src = tuple(d[source]) if isinstance(d[source], list) else d[source]
+        tgt = tuple(d[target]) if isinstance(d[target], list) else d[target]
+        if not multigraph:
+            edgedata = {str(k): v for k, v in d.items() if k != source and k != target}
+            graph.add_edge(src, tgt, **edgedata)
+        else:
+            ky = d.get(key, None)
+            edgedata = {
+                str(k): v
+                for k, v in d.items()
+                if k != source and k != target and k != key
+            }
+            graph.add_edge(src, tgt, ky, **edgedata)
+    return graph
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__init__.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..74aa33a99082d6bbed909b5c4c6b7880e9f5aeed
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_adjacency.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_adjacency.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5b4ec4e19da82c5031cb276d1a579d391ff3dbec
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_adjacency.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_cytoscape.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_cytoscape.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d2b33d3bde083bef787ebaeb297383443c83ff51
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_cytoscape.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_node_link.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_node_link.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8cd16ec62e2ec968e8f75d5ffcf65c70e1401b19
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_node_link.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_tree.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_tree.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4f98186fea0ad8bc2310bb97073f4a114f6116f1
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/__pycache__/test_tree.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_adjacency.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_adjacency.py
new file mode 100644
index 0000000000000000000000000000000000000000..37506382c55a110b26fdba32a268545d23f4474b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_adjacency.py
@@ -0,0 +1,78 @@
+import copy
+import json
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.json_graph import adjacency_data, adjacency_graph
+from networkx.utils import graphs_equal
+
+
+class TestAdjacency:
+    def test_graph(self):
+        G = nx.path_graph(4)
+        H = adjacency_graph(adjacency_data(G))
+        assert graphs_equal(G, H)
+
+    def test_graph_attributes(self):
+        G = nx.path_graph(4)
+        G.add_node(1, color="red")
+        G.add_edge(1, 2, width=7)
+        G.graph["foo"] = "bar"
+        G.graph[1] = "one"
+
+        H = adjacency_graph(adjacency_data(G))
+        assert graphs_equal(G, H)
+        assert H.graph["foo"] == "bar"
+        assert H.nodes[1]["color"] == "red"
+        assert H[1][2]["width"] == 7
+
+        d = json.dumps(adjacency_data(G))
+        H = adjacency_graph(json.loads(d))
+        assert graphs_equal(G, H)
+        assert H.graph["foo"] == "bar"
+        assert H.graph[1] == "one"
+        assert H.nodes[1]["color"] == "red"
+        assert H[1][2]["width"] == 7
+
+    def test_digraph(self):
+        G = nx.DiGraph()
+        nx.add_path(G, [1, 2, 3])
+        H = adjacency_graph(adjacency_data(G))
+        assert H.is_directed()
+        assert graphs_equal(G, H)
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph()
+        nx.add_path(G, [1, 2, 3])
+        H = adjacency_graph(adjacency_data(G))
+        assert H.is_directed()
+        assert H.is_multigraph()
+        assert graphs_equal(G, H)
+
+    def test_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, key="first")
+        G.add_edge(1, 2, key="second", color="blue")
+        H = adjacency_graph(adjacency_data(G))
+        assert graphs_equal(G, H)
+        assert H[1][2]["second"]["color"] == "blue"
+
+    def test_input_data_is_not_modified_when_building_graph(self):
+        G = nx.path_graph(4)
+        input_data = adjacency_data(G)
+        orig_data = copy.deepcopy(input_data)
+        # Ensure input is unmodified by deserialisation
+        assert graphs_equal(G, adjacency_graph(input_data))
+        assert input_data == orig_data
+
+    def test_adjacency_form_json_serialisable(self):
+        G = nx.path_graph(4)
+        H = adjacency_graph(json.loads(json.dumps(adjacency_data(G))))
+        assert graphs_equal(G, H)
+
+    def test_exception(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.MultiDiGraph()
+            attrs = {"id": "node", "key": "node"}
+            adjacency_data(G, attrs)
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_cytoscape.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_cytoscape.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d47f21f4217d1997165c4f19feb67d283d2dab2
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_cytoscape.py
@@ -0,0 +1,78 @@
+import copy
+import json
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.json_graph import cytoscape_data, cytoscape_graph
+
+
+def test_graph():
+    G = nx.path_graph(4)
+    H = cytoscape_graph(cytoscape_data(G))
+    assert nx.is_isomorphic(G, H)
+
+
+def test_input_data_is_not_modified_when_building_graph():
+    G = nx.path_graph(4)
+    input_data = cytoscape_data(G)
+    orig_data = copy.deepcopy(input_data)
+    # Ensure input is unmodified by cytoscape_graph (gh-4173)
+    cytoscape_graph(input_data)
+    assert input_data == orig_data
+
+
+def test_graph_attributes():
+    G = nx.path_graph(4)
+    G.add_node(1, color="red")
+    G.add_edge(1, 2, width=7)
+    G.graph["foo"] = "bar"
+    G.graph[1] = "one"
+    G.add_node(3, name="node", id="123")
+
+    H = cytoscape_graph(cytoscape_data(G))
+    assert H.graph["foo"] == "bar"
+    assert H.nodes[1]["color"] == "red"
+    assert H[1][2]["width"] == 7
+    assert H.nodes[3]["name"] == "node"
+    assert H.nodes[3]["id"] == "123"
+
+    d = json.dumps(cytoscape_data(G))
+    H = cytoscape_graph(json.loads(d))
+    assert H.graph["foo"] == "bar"
+    assert H.graph[1] == "one"
+    assert H.nodes[1]["color"] == "red"
+    assert H[1][2]["width"] == 7
+    assert H.nodes[3]["name"] == "node"
+    assert H.nodes[3]["id"] == "123"
+
+
+def test_digraph():
+    G = nx.DiGraph()
+    nx.add_path(G, [1, 2, 3])
+    H = cytoscape_graph(cytoscape_data(G))
+    assert H.is_directed()
+    assert nx.is_isomorphic(G, H)
+
+
+def test_multidigraph():
+    G = nx.MultiDiGraph()
+    nx.add_path(G, [1, 2, 3])
+    H = cytoscape_graph(cytoscape_data(G))
+    assert H.is_directed()
+    assert H.is_multigraph()
+
+
+def test_multigraph():
+    G = nx.MultiGraph()
+    G.add_edge(1, 2, key="first")
+    G.add_edge(1, 2, key="second", color="blue")
+    H = cytoscape_graph(cytoscape_data(G))
+    assert nx.is_isomorphic(G, H)
+    assert H[1][2]["second"]["color"] == "blue"
+
+
+def test_exception():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.MultiDiGraph()
+        cytoscape_data(G, name="foo", ident="foo")
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_node_link.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_node_link.py
new file mode 100644
index 0000000000000000000000000000000000000000..f075bb6dd12a5cab7940f79505ccb79ae4aadacc
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_node_link.py
@@ -0,0 +1,109 @@
+import json
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.json_graph import node_link_data, node_link_graph
+
+
+class TestNodeLink:
+    def test_exception_dep(self):
+        G = nx.MultiDiGraph()
+        with pytest.raises(nx.NetworkXError):
+            node_link_data(G, name="node", source="node", target="node", key="node")
+
+    def test_graph(self):
+        G = nx.path_graph(4)
+        H = node_link_graph(node_link_data(G))
+        assert nx.is_isomorphic(G, H)
+
+    def test_graph_attributes(self):
+        G = nx.path_graph(4)
+        G.add_node(1, color="red")
+        G.add_edge(1, 2, width=7)
+        G.graph[1] = "one"
+        G.graph["foo"] = "bar"
+
+        H = node_link_graph(node_link_data(G))
+        assert H.graph["foo"] == "bar"
+        assert H.nodes[1]["color"] == "red"
+        assert H[1][2]["width"] == 7
+
+        d = json.dumps(node_link_data(G))
+        H = node_link_graph(json.loads(d))
+        assert H.graph["foo"] == "bar"
+        assert H.graph["1"] == "one"
+        assert H.nodes[1]["color"] == "red"
+        assert H[1][2]["width"] == 7
+
+    def test_digraph(self):
+        G = nx.DiGraph()
+        H = node_link_graph(node_link_data(G))
+        assert H.is_directed()
+
+    def test_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, key="first")
+        G.add_edge(1, 2, key="second", color="blue")
+        H = node_link_graph(node_link_data(G))
+        assert nx.is_isomorphic(G, H)
+        assert H[1][2]["second"]["color"] == "blue"
+
+    def test_graph_with_tuple_nodes(self):
+        G = nx.Graph()
+        G.add_edge((0, 0), (1, 0), color=[255, 255, 0])
+        d = node_link_data(G)
+        dumped_d = json.dumps(d)
+        dd = json.loads(dumped_d)
+        H = node_link_graph(dd)
+        assert H.nodes[(0, 0)] == G.nodes[(0, 0)]
+        assert H[(0, 0)][(1, 0)]["color"] == [255, 255, 0]
+
+    def test_unicode_keys(self):
+        q = "qualité"
+        G = nx.Graph()
+        G.add_node(1, **{q: q})
+        s = node_link_data(G)
+        output = json.dumps(s, ensure_ascii=False)
+        data = json.loads(output)
+        H = node_link_graph(data)
+        assert H.nodes[1][q] == q
+
+    def test_exception(self):
+        G = nx.MultiDiGraph()
+        attrs = {"name": "node", "source": "node", "target": "node", "key": "node"}
+        with pytest.raises(nx.NetworkXError):
+            node_link_data(G, **attrs)
+
+    def test_string_ids(self):
+        q = "qualité"
+        G = nx.DiGraph()
+        G.add_node("A")
+        G.add_node(q)
+        G.add_edge("A", q)
+        data = node_link_data(G)
+        assert data["edges"][0]["source"] == "A"
+        assert data["edges"][0]["target"] == q
+        H = node_link_graph(data)
+        assert nx.is_isomorphic(G, H)
+
+    def test_custom_attrs(self):
+        G = nx.path_graph(4)
+        G.add_node(1, color="red")
+        G.add_edge(1, 2, width=7)
+        G.graph[1] = "one"
+        G.graph["foo"] = "bar"
+
+        attrs = {
+            "source": "c_source",
+            "target": "c_target",
+            "name": "c_id",
+            "key": "c_key",
+            "edges": "c_links",
+        }
+
+        H = node_link_graph(node_link_data(G, **attrs), multigraph=False, **attrs)
+        assert nx.is_isomorphic(G, H)
+        assert H.graph["foo"] == "bar"
+        assert H.nodes[1]["color"] == "red"
+        assert H[1][2]["width"] == 7
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_tree.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..643a14d89b5211f2d97b98f2e227e68361781b97
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tests/test_tree.py
@@ -0,0 +1,48 @@
+import json
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.json_graph import tree_data, tree_graph
+
+
+def test_graph():
+    G = nx.DiGraph()
+    G.add_nodes_from([1, 2, 3], color="red")
+    G.add_edge(1, 2, foo=7)
+    G.add_edge(1, 3, foo=10)
+    G.add_edge(3, 4, foo=10)
+    H = tree_graph(tree_data(G, 1))
+    assert nx.is_isomorphic(G, H)
+
+
+def test_graph_attributes():
+    G = nx.DiGraph()
+    G.add_nodes_from([1, 2, 3], color="red")
+    G.add_edge(1, 2, foo=7)
+    G.add_edge(1, 3, foo=10)
+    G.add_edge(3, 4, foo=10)
+    H = tree_graph(tree_data(G, 1))
+    assert H.nodes[1]["color"] == "red"
+
+    d = json.dumps(tree_data(G, 1))
+    H = tree_graph(json.loads(d))
+    assert H.nodes[1]["color"] == "red"
+
+
+def test_exceptions():
+    with pytest.raises(TypeError, match="is not a tree."):
+        G = nx.complete_graph(3)
+        tree_data(G, 0)
+    with pytest.raises(TypeError, match="is not directed."):
+        G = nx.path_graph(3)
+        tree_data(G, 0)
+    with pytest.raises(TypeError, match="is not weakly connected."):
+        G = nx.path_graph(3, create_using=nx.DiGraph)
+        G.add_edge(2, 0)
+        G.add_node(3)
+        tree_data(G, 0)
+    with pytest.raises(nx.NetworkXError, match="must be different."):
+        G = nx.MultiDiGraph()
+        G.add_node(0)
+        tree_data(G, 0, ident="node", children="node")
diff --git a/lib/python3.11/site-packages/networkx/readwrite/json_graph/tree.py b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..22b07b09d277815e824b1dd8c5b82a149ed14e1b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/json_graph/tree.py
@@ -0,0 +1,137 @@
+from itertools import chain
+
+import networkx as nx
+
+__all__ = ["tree_data", "tree_graph"]
+
+
+def tree_data(G, root, ident="id", children="children"):
+    """Returns data in tree format that is suitable for JSON serialization
+    and use in JavaScript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       G must be an oriented tree
+
+    root : node
+       The root of the tree
+
+    ident : string
+        Attribute name for storing NetworkX-internal graph data. `ident` must
+        have a different value than `children`. The default is 'id'.
+
+    children : string
+        Attribute name for storing NetworkX-internal graph data. `children`
+        must have a different value than `ident`. The default is 'children'.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with node-link formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If `children` and `ident` attributes are identical.
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.DiGraph([(1, 2)])
+    >>> data = json_graph.tree_data(G, root=1)
+
+    To serialize with json
+
+    >>> import json
+    >>> s = json.dumps(data)
+
+    Notes
+    -----
+    Node attributes are stored in this format but keys
+    for attributes must be strings if you want to serialize with JSON.
+
+    Graph and edge attributes are not stored.
+
+    See Also
+    --------
+    tree_graph, node_link_data, adjacency_data
+    """
+    if G.number_of_nodes() != G.number_of_edges() + 1:
+        raise TypeError("G is not a tree.")
+    if not G.is_directed():
+        raise TypeError("G is not directed.")
+    if not nx.is_weakly_connected(G):
+        raise TypeError("G is not weakly connected.")
+
+    if ident == children:
+        raise nx.NetworkXError("The values for `id` and `children` must be different.")
+
+    def add_children(n, G):
+        nbrs = G[n]
+        if len(nbrs) == 0:
+            return []
+        children_ = []
+        for child in nbrs:
+            d = {**G.nodes[child], ident: child}
+            c = add_children(child, G)
+            if c:
+                d[children] = c
+            children_.append(d)
+        return children_
+
+    return {**G.nodes[root], ident: root, children: add_children(root, G)}
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tree_graph(data, ident="id", children="children"):
+    """Returns graph from tree data format.
+
+    Parameters
+    ----------
+    data : dict
+        Tree formatted graph data
+
+    ident : string
+        Attribute name for storing NetworkX-internal graph data. `ident` must
+        have a different value than `children`. The default is 'id'.
+
+    children : string
+        Attribute name for storing NetworkX-internal graph data. `children`
+        must have a different value than `ident`. The default is 'children'.
+
+    Returns
+    -------
+    G : NetworkX DiGraph
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.DiGraph([(1, 2)])
+    >>> data = json_graph.tree_data(G, root=1)
+    >>> H = json_graph.tree_graph(data)
+
+    See Also
+    --------
+    tree_data, node_link_data, adjacency_data
+    """
+    graph = nx.DiGraph()
+
+    def add_children(parent, children_):
+        for data in children_:
+            child = data[ident]
+            graph.add_edge(parent, child)
+            grandchildren = data.get(children, [])
+            if grandchildren:
+                add_children(child, grandchildren)
+            nodedata = {
+                str(k): v for k, v in data.items() if k != ident and k != children
+            }
+            graph.add_node(child, **nodedata)
+
+    root = data[ident]
+    children_ = data.get(children, [])
+    nodedata = {str(k): v for k, v in data.items() if k != ident and k != children}
+    graph.add_node(root, **nodedata)
+    add_children(root, children_)
+    return graph
diff --git a/lib/python3.11/site-packages/networkx/readwrite/leda.py b/lib/python3.11/site-packages/networkx/readwrite/leda.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d88a67d39d7ff27bf4af36895c52c9546cca329
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/leda.py
@@ -0,0 +1,108 @@
+"""
+Read graphs in LEDA format.
+
+LEDA is a C++ class library for efficient data types and algorithms.
+
+Format
+------
+See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+
+"""
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+
+__all__ = ["read_leda", "parse_leda"]
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import open_file
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_leda(path, encoding="UTF-8"):
+    """Read graph in LEDA format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.read_leda("file.leda")  # doctest: +SKIP
+
+    References
+    ----------
+    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+    """
+    lines = (line.decode(encoding) for line in path)
+    G = parse_leda(lines)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_leda(lines):
+    """Read graph in LEDA format from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in LEDA format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.parse_leda(string)  # doctest: +SKIP
+
+    References
+    ----------
+    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+    """
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter(
+        [
+            line.rstrip("\n")
+            for line in lines
+            if not (line.startswith(("#", "\n")) or line == "")
+        ]
+    )
+    for i in range(3):
+        next(lines)
+    # Graph
+    du = int(next(lines))  # -1=directed, -2=undirected
+    if du == -1:
+        G = nx.DiGraph()
+    else:
+        G = nx.Graph()
+
+    # Nodes
+    n = int(next(lines))  # number of nodes
+    node = {}
+    for i in range(1, n + 1):  # LEDA counts from 1 to n
+        symbol = next(lines).rstrip().strip("|{}|  ")
+        if symbol == "":
+            symbol = str(i)  # use int if no label - could be trouble
+        node[i] = symbol
+
+    G.add_nodes_from([s for i, s in node.items()])
+
+    # Edges
+    m = int(next(lines))  # number of edges
+    for i in range(m):
+        try:
+            s, t, reversal, label = next(lines).split()
+        except BaseException as err:
+            raise NetworkXError(f"Too few fields in LEDA.GRAPH edge {i + 1}") from err
+        # BEWARE: no handling of reversal edges
+        G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
+    return G
diff --git a/lib/python3.11/site-packages/networkx/readwrite/multiline_adjlist.py b/lib/python3.11/site-packages/networkx/readwrite/multiline_adjlist.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5b0b1c153b2f55f22f0fb0e8db9df8bdc0da18d
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/multiline_adjlist.py
@@ -0,0 +1,393 @@
+"""
+*************************
+Multi-line Adjacency List
+*************************
+Read and write NetworkX graphs as multi-line adjacency lists.
+
+The multi-line adjacency list format is useful for graphs with
+nodes that can be meaningfully represented as strings.  With this format
+simple edge data can be stored but node or graph data is not.
+
+Format
+------
+The first label in a line is the source node label followed by the node degree
+d.  The next d lines are target node labels and optional edge data.
+That pattern repeats for all nodes in the graph.
+
+The graph with edges a-b, a-c, d-e can be represented as the following
+adjacency list (anything following the # in a line is a comment)::
+
+     # example.multiline-adjlist
+     a 2
+     b
+     c
+     d 1
+     e
+"""
+
+__all__ = [
+    "generate_multiline_adjlist",
+    "write_multiline_adjlist",
+    "parse_multiline_adjlist",
+    "read_multiline_adjlist",
+]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_multiline_adjlist(G, delimiter=" "):
+    """Generate a single line of the graph G in multiline adjacency list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : string, optional
+       Separator for node labels
+
+    Returns
+    -------
+    lines : string
+        Lines of data in multiline adjlist format.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> for line in nx.generate_multiline_adjlist(G):
+    ...     print(line)
+    0 3
+    1 {}
+    2 {}
+    3 {}
+    1 2
+    2 {}
+    3 {}
+    2 1
+    3 {}
+    3 1
+    4 {}
+    4 1
+    5 {}
+    5 1
+    6 {}
+    6 0
+
+    See Also
+    --------
+    write_multiline_adjlist, read_multiline_adjlist
+    """
+    if G.is_directed():
+        if G.is_multigraph():
+            for s, nbrs in G.adjacency():
+                nbr_edges = [
+                    (u, data)
+                    for u, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                ]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+        else:  # directed single edges
+            for s, nbrs in G.adjacency():
+                deg = len(nbrs)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbrs.items():
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+    else:  # undirected
+        if G.is_multigraph():
+            seen = set()  # helper dict used to avoid duplicate edges
+            for s, nbrs in G.adjacency():
+                nbr_edges = [
+                    (u, data)
+                    for u, datadict in nbrs.items()
+                    if u not in seen
+                    for key, data in datadict.items()
+                ]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+                seen.add(s)
+        else:  # undirected single edges
+            seen = set()  # helper dict used to avoid duplicate edges
+            for s, nbrs in G.adjacency():
+                nbr_edges = [(u, d) for u, d in nbrs.items() if u not in seen]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+                seen.add(s)
+
+
+@open_file(1, mode="wb")
+def write_multiline_adjlist(G, path, delimiter=" ", comments="#", encoding="utf-8"):
+    """Write the graph G in multiline adjacency list format to path
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle to write to.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels
+
+    encoding : string, optional
+       Text encoding.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlist")
+
+    The path can be a file handle or a string with the name of the file. If a
+    file handle is provided, it has to be opened in 'wb' mode.
+
+    >>> fh = open("test.multi_adjlist2", "wb")
+    >>> nx.write_multiline_adjlist(G, fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlist.gz")
+
+    See Also
+    --------
+    read_multiline_adjlist
+    """
+    import sys
+    import time
+
+    pargs = comments + " ".join(sys.argv)
+    header = (
+        f"{pargs}\n"
+        + comments
+        + f" GMT {time.asctime(time.gmtime())}\n"
+        + comments
+        + f" {G.name}\n"
+    )
+    path.write(header.encode(encoding))
+
+    for multiline in generate_multiline_adjlist(G, delimiter):
+        multiline += "\n"
+        path.write(multiline.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_multiline_adjlist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None, edgetype=None
+):
+    """Parse lines of a multiline adjacency list representation of a graph.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in multiline adjlist format
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    edgetype : Python type, optional
+       Convert edges to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in multiline adjacency list format.
+
+    Examples
+    --------
+    >>> lines = [
+    ...     "1 2",
+    ...     "2 {'weight':3, 'name': 'Frodo'}",
+    ...     "3 {}",
+    ...     "2 1",
+    ...     "5 {'weight':6, 'name': 'Saruman'}",
+    ... ]
+    >>> G = nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 5]
+
+    """
+    from ast import literal_eval
+
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        p = line.find(comments)
+        if p >= 0:
+            line = line[:p]
+        if not line:
+            continue
+        try:
+            (u, deg) = line.rstrip("\n").split(delimiter)
+            deg = int(deg)
+        except BaseException as err:
+            raise TypeError(f"Failed to read node and degree on line ({line})") from err
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert node ({u}) to type {nodetype}"
+                ) from err
+        G.add_node(u)
+        for i in range(deg):
+            while True:
+                try:
+                    line = next(lines)
+                except StopIteration as err:
+                    msg = f"Failed to find neighbor for node ({u})"
+                    raise TypeError(msg) from err
+                p = line.find(comments)
+                if p >= 0:
+                    line = line[:p]
+                if line:
+                    break
+            vlist = line.rstrip("\n").split(delimiter)
+            numb = len(vlist)
+            if numb < 1:
+                continue  # isolated node
+            v = vlist.pop(0)
+            data = "".join(vlist)
+            if nodetype is not None:
+                try:
+                    v = nodetype(v)
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert node ({v}) to type {nodetype}"
+                    ) from err
+            if edgetype is not None:
+                try:
+                    edgedata = {"weight": edgetype(data)}
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert edge data ({data}) to type {edgetype}"
+                    ) from err
+            else:
+                try:  # try to evaluate
+                    edgedata = literal_eval(data)
+                except:
+                    edgedata = {}
+            G.add_edge(u, v, **edgedata)
+
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_multiline_adjlist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    edgetype=None,
+    encoding="utf-8",
+):
+    """Read graph in multi-line adjacency list format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    edgetype : Python type, optional
+       Convert edge data to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlistP4")
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4")
+
+    The path can be a file or a string with the name of the file. If a
+    file s provided, it has to be opened in 'rb' mode.
+
+    >>> fh = open("test.multi_adjlistP4", "rb")
+    >>> G = nx.read_multiline_adjlist(fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlistP4.gz")
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4.gz")
+
+    The optional nodetype is a function to convert node strings to nodetype.
+
+    For example
+
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4", nodetype=int)
+
+    will attempt to convert all nodes to integer type.
+
+    The optional edgetype is a function to convert edge data strings to
+    edgetype.
+
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4")
+
+    The optional create_using parameter is a NetworkX graph container.
+    The default is Graph(), an undirected graph.  To read the data as
+    a directed graph use
+
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4", create_using=nx.DiGraph)
+
+    Notes
+    -----
+    This format does not store graph, node, or edge data.
+
+    See Also
+    --------
+    write_multiline_adjlist
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_multiline_adjlist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        edgetype=edgetype,
+    )
diff --git a/lib/python3.11/site-packages/networkx/readwrite/p2g.py b/lib/python3.11/site-packages/networkx/readwrite/p2g.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bde362ea8d511e603ce3b8d95a6206d74091663
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/p2g.py
@@ -0,0 +1,113 @@
+"""
+This module provides the following: read and write of p2g format
+used in metabolic pathway studies.
+
+See:
+<https://web.archive.org/web/20080626113807/http://www.cs.purdue.edu/homes/koyuturk/pathway/>
+for a description.
+
+The summary is included here:
+
+A file that describes a uniquely labeled graph (with extension ".gr")
+format looks like the following:
+
+
+name
+3 4
+a
+1 2
+b
+
+c
+0 2
+
+"name" is simply a description of what the graph corresponds to. The
+second line displays the number of nodes and number of edges,
+respectively. This sample graph contains three nodes labeled "a", "b",
+and "c". The rest of the graph contains two lines for each node. The
+first line for a node contains the node label. After the declaration
+of the node label, the out-edges of that node in the graph are
+provided. For instance, "a" is linked to nodes 1 and 2, which are
+labeled "b" and "c", while the node labeled "b" has no outgoing
+edges. Observe that node labeled "c" has an outgoing edge to
+itself. Indeed, self-loops are allowed. Node index starts from 0.
+
+"""
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+@open_file(1, mode="w")
+def write_p2g(G, path, encoding="utf-8"):
+    """Write NetworkX graph in p2g format.
+
+    Notes
+    -----
+    This format is meant to be used with directed graphs with
+    possible self loops.
+    """
+    path.write((f"{G.name}\n").encode(encoding))
+    path.write((f"{G.order()} {G.size()}\n").encode(encoding))
+    nodes = list(G)
+    # make dictionary mapping nodes to integers
+    nodenumber = dict(zip(nodes, range(len(nodes))))
+    for n in nodes:
+        path.write((f"{n}\n").encode(encoding))
+        for nbr in G.neighbors(n):
+            path.write((f"{nodenumber[nbr]} ").encode(encoding))
+        path.write("\n".encode(encoding))
+
+
+@open_file(0, mode="r")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_p2g(path, encoding="utf-8"):
+    """Read graph in p2g format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    MultiDiGraph
+
+    Notes
+    -----
+    If you want a DiGraph (with no self loops allowed and no edge data)
+    use D=nx.DiGraph(read_p2g(path))
+    """
+    lines = (line.decode(encoding) for line in path)
+    G = parse_p2g(lines)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_p2g(lines):
+    """Parse p2g format graph from string or iterable.
+
+    Returns
+    -------
+    MultiDiGraph
+    """
+    description = next(lines).strip()
+    # are multiedges (parallel edges) allowed?
+    G = nx.MultiDiGraph(name=description, selfloops=True)
+    nnodes, nedges = map(int, next(lines).split())
+    nodelabel = {}
+    nbrs = {}
+    # loop over the nodes keeping track of node labels and out neighbors
+    # defer adding edges until all node labels are known
+    for i in range(nnodes):
+        n = next(lines).strip()
+        nodelabel[i] = n
+        G.add_node(n)
+        nbrs[n] = map(int, next(lines).split())
+    # now we know all of the node labels so we can add the edges
+    # with the correct labels
+    for n in G:
+        for nbr in nbrs[n]:
+            G.add_edge(n, nodelabel[nbr])
+    return G
diff --git a/lib/python3.11/site-packages/networkx/readwrite/pajek.py b/lib/python3.11/site-packages/networkx/readwrite/pajek.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cab6b9ae68ba9c9869f0452394838925ff2a7db
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/pajek.py
@@ -0,0 +1,286 @@
+"""
+*****
+Pajek
+*****
+Read graphs in Pajek format.
+
+This implementation handles directed and undirected graphs including
+those with self loops and parallel edges.
+
+Format
+------
+See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+for format information.
+
+"""
+
+import warnings
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["read_pajek", "parse_pajek", "generate_pajek", "write_pajek"]
+
+
+def generate_pajek(G):
+    """Generate lines in Pajek graph format.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    if G.name == "":
+        name = "NetworkX"
+    else:
+        name = G.name
+    # Apparently many Pajek format readers can't process this line
+    # So we'll leave it out for now.
+    # yield '*network %s'%name
+
+    # write nodes with attributes
+    yield f"*vertices {G.order()}"
+    nodes = list(G)
+    # make dictionary mapping nodes to integers
+    nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
+    for n in nodes:
+        # copy node attributes and pop mandatory attributes
+        # to avoid duplication.
+        na = G.nodes.get(n, {}).copy()
+        x = na.pop("x", 0.0)
+        y = na.pop("y", 0.0)
+        try:
+            id = int(na.pop("id", nodenumber[n]))
+        except ValueError as err:
+            err.args += (
+                (
+                    "Pajek format requires 'id' to be an int()."
+                    " Refer to the 'Relabeling nodes' section."
+                ),
+            )
+            raise
+        nodenumber[n] = id
+        shape = na.pop("shape", "ellipse")
+        s = " ".join(map(make_qstr, (id, n, x, y, shape)))
+        # only optional attributes are left in na.
+        for k, v in na.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Node attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+    # write edges with attributes
+    if G.is_directed():
+        yield "*arcs"
+    else:
+        yield "*edges"
+    for u, v, edgedata in G.edges(data=True):
+        d = edgedata.copy()
+        value = d.pop("weight", 1.0)  # use 1 as default edge value
+        s = " ".join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
+        for k, v in d.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Edge attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+
+@open_file(1, mode="wb")
+def write_pajek(G, path, encoding="UTF-8"):
+    """Write graph in Pajek format to path.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.netP4")
+
+    Warnings
+    --------
+    Optional node attributes and edge attributes must be non-empty strings.
+    Otherwise it will not be written into the file. You will need to
+    convert those attributes to strings if you want to keep them.
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    for line in generate_pajek(G):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_pajek(path, encoding="UTF-8"):
+    """Read graph in Pajek format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : NetworkX MultiGraph or MultiDiGraph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.net")
+    >>> G = nx.read_pajek("test.net")
+
+    To create a Graph instead of a MultiGraph use
+
+    >>> G1 = nx.Graph(G)
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_pajek(lines)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_pajek(lines):
+    """Parse Pajek format graph from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in Pajek format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    See Also
+    --------
+    read_pajek
+
+    """
+    import shlex
+
+    # multigraph=False
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter([line.rstrip("\n") for line in lines])
+    G = nx.MultiDiGraph()  # are multiedges allowed in Pajek? assume yes
+    labels = []  # in the order of the file, needed for matrix
+    while lines:
+        try:
+            l = next(lines)
+        except:  # EOF
+            break
+        if l.lower().startswith("*network"):
+            try:
+                label, name = l.split(None, 1)
+            except ValueError:
+                # Line was not of the form:  *network NAME
+                pass
+            else:
+                G.graph["name"] = name
+        elif l.lower().startswith("*vertices"):
+            nodelabels = {}
+            l, nnodes = l.split()
+            for i in range(int(nnodes)):
+                l = next(lines)
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+                id, label = splitline[0:2]
+                labels.append(label)
+                G.add_node(label)
+                nodelabels[id] = label
+                G.nodes[label]["id"] = id
+                try:
+                    x, y, shape = splitline[2:5]
+                    G.nodes[label].update(
+                        {"x": float(x), "y": float(y), "shape": shape}
+                    )
+                except:
+                    pass
+                extra_attr = zip(splitline[5::2], splitline[6::2])
+                G.nodes[label].update(extra_attr)
+        elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
+            if l.lower().startswith("*edge"):
+                # switch from multidigraph to multigraph
+                G = nx.MultiGraph(G)
+            if l.lower().startswith("*arcs"):
+                # switch to directed with multiple arcs for each existing edge
+                G = G.to_directed()
+            for l in lines:
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+
+                if len(splitline) < 2:
+                    continue
+                ui, vi = splitline[0:2]
+                u = nodelabels.get(ui, ui)
+                v = nodelabels.get(vi, vi)
+                # parse the data attached to this edge and put in a dictionary
+                edge_data = {}
+                try:
+                    # there should always be a single value on the edge?
+                    w = splitline[2:3]
+                    edge_data.update({"weight": float(w[0])})
+                except:
+                    pass
+                    # if there isn't, just assign a 1
+                #                    edge_data.update({'value':1})
+                extra_attr = zip(splitline[3::2], splitline[4::2])
+                edge_data.update(extra_attr)
+                # if G.has_edge(u,v):
+                #     multigraph=True
+                G.add_edge(u, v, **edge_data)
+        elif l.lower().startswith("*matrix"):
+            G = nx.DiGraph(G)
+            adj_list = (
+                (labels[row], labels[col], {"weight": int(data)})
+                for (row, line) in enumerate(lines)
+                for (col, data) in enumerate(line.split())
+                if int(data) != 0
+            )
+            G.add_edges_from(adj_list)
+
+    return G
+
+
+def make_qstr(t):
+    """Returns the string representation of t.
+    Add outer double-quotes if the string has a space.
+    """
+    if not isinstance(t, str):
+        t = str(t)
+    if " " in t:
+        t = f'"{t}"'
+    return t
diff --git a/lib/python3.11/site-packages/networkx/readwrite/sparse6.py b/lib/python3.11/site-packages/networkx/readwrite/sparse6.py
new file mode 100644
index 0000000000000000000000000000000000000000..b82ae5c34d30e1a1e72f0e94e6845072d18296f9
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/sparse6.py
@@ -0,0 +1,379 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at https://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *sparse6* format.
+
+The *sparse6* file format is a space-efficient format for large sparse
+graphs. For small graphs or large dense graphs, use the *graph6* file
+format.
+
+For more information, see the `sparse6`_ homepage.
+
+.. _sparse6: https://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.readwrite.graph6 import data_to_n, n_to_data
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_sparse6_bytes", "read_sparse6", "to_sparse6_bytes", "write_sparse6"]
+
+
+def _generate_sparse6_bytes(G, nodes, header):
+    """Yield bytes in the sparse6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>sparse6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "sparse6 is only defined if number of nodes is less than 2 ** 36"
+        )
+    if header:
+        yield b">>sparse6<<"
+    yield b":"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def enc(x):
+        """Big endian k-bit encoding of x"""
+        return [1 if (x & 1 << (k - 1 - i)) else 0 for i in range(k)]
+
+    edges = sorted((max(u, v), min(u, v)) for u, v in G.edges())
+    bits = []
+    curv = 0
+    for v, u in edges:
+        if v == curv:  # current vertex edge
+            bits.append(0)
+            bits.extend(enc(u))
+        elif v == curv + 1:  # next vertex edge
+            curv += 1
+            bits.append(1)
+            bits.extend(enc(u))
+        else:  # skip to vertex v and then add edge to u
+            curv = v
+            bits.append(1)
+            bits.extend(enc(v))
+            bits.append(0)
+            bits.extend(enc(u))
+    if k < 6 and n == (1 << k) and ((-len(bits)) % 6) >= k and curv < (n - 1):
+        # Padding special case: small k, n=2^k,
+        # more than k bits of padding needed,
+        # current vertex is not (n-1) --
+        # appending 1111... would add a loop on (n-1)
+        bits.append(0)
+        bits.extend([1] * ((-len(bits)) % 6))
+    else:
+        bits.extend([1] * ((-len(bits)) % 6))
+
+    data = [
+        (bits[i + 0] << 5)
+        + (bits[i + 1] << 4)
+        + (bits[i + 2] << 3)
+        + (bits[i + 3] << 2)
+        + (bits[i + 4] << 1)
+        + (bits[i + 5] << 0)
+        for i in range(0, len(bits), 6)
+    ]
+
+    for d in data:
+        yield str.encode(chr(d + 63))
+    yield b"\n"
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_sparse6_bytes(string):
+    """Read an undirected graph in sparse6 format from string.
+
+    Parameters
+    ----------
+    string : string
+       Data in sparse6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    >>> G = nx.from_sparse6_bytes(b":A_")
+    >>> sorted(G.edges())
+    [(0, 1), (0, 1), (0, 1)]
+
+    See Also
+    --------
+    read_sparse6, write_sparse6
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if string.startswith(b">>sparse6<<"):
+        string = string[11:]
+    if not string.startswith(b":"):
+        raise NetworkXError("Expected leading colon in sparse6")
+
+    chars = [c - 63 for c in string[1:]]
+    n, data = data_to_n(chars)
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def parseData():
+        """Returns stream of pairs b[i], x[i] for sparse6 format."""
+        chunks = iter(data)
+        d = None  # partial data word
+        dLen = 0  # how many unparsed bits are left in d
+
+        while 1:
+            if dLen < 1:
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+            dLen -= 1
+            b = (d >> dLen) & 1  # grab top remaining bit
+
+            x = d & ((1 << dLen) - 1)  # partially built up value of x
+            xLen = dLen  # how many bits included so far in x
+            while xLen < k:  # now grab full chunks until we have enough
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+                x = (x << 6) + d
+                xLen += 6
+            x = x >> (xLen - k)  # shift back the extra bits
+            dLen = xLen - k
+            yield b, x
+
+    v = 0
+
+    G = nx.MultiGraph()
+    G.add_nodes_from(range(n))
+
+    multigraph = False
+    for b, x in parseData():
+        if b == 1:
+            v += 1
+        # padding with ones can cause overlarge number here
+        if x >= n or v >= n:
+            break
+        elif x > v:
+            v = x
+        else:
+            if G.has_edge(x, v):
+                multigraph = True
+            G.add_edge(x, v)
+    if not multigraph:
+        G = nx.Graph(G)
+    return G
+
+
+def to_sparse6_bytes(G, nodes=None, header=True):
+    """Convert an undirected graph to bytes in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>sparse6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the sparse6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_sparse6_bytes(nx.path_graph(2))
+    b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    to_sparse6_bytes, read_sparse6, write_sparse6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    return b"".join(_generate_sparse6_bytes(G, nodes, header))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_sparse6(path):
+    """Read an undirected graph in sparse6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : Graph/Multigraph or list of Graphs/MultiGraphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    You can read a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a sparse6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_sparse6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@open_file(1, mode="wb")
+def write_sparse6(G, path, nodes=None, header=True):
+    """Write graph G to given path in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by G.nodes() is used.
+
+    header: bool
+       If True add '>>sparse6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed
+
+    Examples
+    --------
+    You can write a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f.name)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    You can also write a sparse6 file by giving an open file-like object::
+
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    Notes
+    -----
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    for b in _generate_sparse6_bytes(G, nodes, header):
+        path.write(b)
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__init__.py b/lib/python3.11/site-packages/networkx/readwrite/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c42daabfe5f47a8c3191e6f0071f10a40b6ae09d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_adjlist.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_adjlist.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e0eba57189e6b611dad5438be3e104a83973648d
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_adjlist.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_edgelist.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_edgelist.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6096acbcd52117101739a949e19e15e8b0540ba6
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_edgelist.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_gexf.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_gexf.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3b577fd2fb207e2e3e428148c2b748ec1b788265
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_gexf.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_gml.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_gml.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..915b5cbcb6f9069e3d62115281712cf5308209d4
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_gml.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_graph6.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_graph6.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1d4167511b2c8dc93cdc86a57af17cb99079f403
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_graph6.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_graphml.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_graphml.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5bf999276272ac7268ae721bdcf16aa9205a3679
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_graphml.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_leda.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_leda.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6e5f81f78474277ffeee1e849a3bd1daf1641e74
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_leda.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_p2g.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_p2g.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e5a9bc15bda5e62766f9c3339bfffbb2a644ff0
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_p2g.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_pajek.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_pajek.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..13bdc2371ce373417bc4e7540d168a3e36f0d97a
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_pajek.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_sparse6.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_sparse6.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1f9e0061ddb1f67e7faf1ff7cf6877a3d0007b9e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_sparse6.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_text.cpython-311.pyc b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_text.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d127835780a7f4b15a399460276606cbfcf5e51e
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/readwrite/tests/__pycache__/test_text.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_adjlist.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_adjlist.py
new file mode 100644
index 0000000000000000000000000000000000000000..015eaf8276a3b178b0bdb2c67b54ccfdc63256ae
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_adjlist.py
@@ -0,0 +1,354 @@
+"""
+Unit tests for adjlist.
+"""
+
+import io
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+class TestGenerateAdjlist:
+    @pytest.mark.parametrize("graph_type", [nx.Graph, nx.MultiGraph])
+    def test_undirected(self, graph_type):
+        G = nx.complete_graph(5, create_using=graph_type)
+        lines = [
+            "0 1 2 3 4",
+            "1 2 3 4",
+            "2 3 4",
+            "3 4",
+            "4",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+    @pytest.mark.parametrize("graph_type", [nx.DiGraph, nx.MultiDiGraph])
+    def test_directed(self, graph_type):
+        G = nx.complete_graph(5, create_using=graph_type)
+        lines = [
+            "0 1 2 3 4",
+            "1 0 2 3 4",
+            "2 0 1 3 4",
+            "3 0 1 2 4",
+            "4 0 1 2 3",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+        G = nx.path_graph(5, create_using=graph_type)
+        G.add_edge(1, 0)
+        lines = [
+            "0 1",
+            "1 2 0",
+            "2 3",
+            "3 4",
+            "4",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+    @pytest.mark.parametrize("delimiter", [" ", ",", "\t"])
+    def test_delimiter(self, delimiter):
+        G = nx.complete_graph(3)
+        lines = [
+            f"0{delimiter}1{delimiter}2",
+            f"1{delimiter}2",
+            f"2",
+        ]
+        assert list(nx.generate_adjlist(G, delimiter=delimiter)) == lines
+
+    def test_multiple_edges_undirected(self):
+        G = nx.complete_graph(3, create_using=nx.MultiGraph)
+        G.add_edge(0, 1)
+        lines = [
+            "0 1 1 2",
+            "1 2",
+            "2",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+    def test_multiple_edges_directed(self):
+        G = nx.complete_graph(3, create_using=nx.MultiDiGraph)
+        G.add_edge(0, 1)
+        lines = [
+            "0 1 1 2",
+            "1 0 2",
+            "2 0 1",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+        G.add_edge(1, 0)
+        lines[1] = "1 0 0 2"
+        assert list(nx.generate_adjlist(G)) == lines
+
+    def test_multiple_edges_with_data(self):
+        G = nx.complete_graph(3, create_using=nx.MultiGraph)
+        G.add_edge(0, 1, weight=1)
+        G.add_edge(0, 1, weight=2)
+        lines = [
+            "0 1 1 1 2",
+            "1 2",
+            "2",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+    def test_with_self_loop(self):
+        G = nx.complete_graph(3)
+        G.add_edge(0, 0)
+        lines = [
+            "0 1 2 0",
+            "1 2",
+            "2",
+        ]
+        assert list(nx.generate_adjlist(G)) == lines
+
+
+class TestAdjlist:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+        cls.XG = nx.MultiGraph()
+        cls.XG.add_weighted_edges_from([(1, 2, 5), (1, 2, 5), (1, 2, 1), (3, 3, 42)])
+        cls.XDG = nx.MultiDiGraph(cls.XG)
+
+    def test_read_multiline_adjlist_1(self):
+        # Unit test for https://networkx.lanl.gov/trac/ticket/252
+        s = b"""# comment line
+1 2
+# comment line
+2
+3
+"""
+        bytesIO = io.BytesIO(s)
+        G = nx.read_multiline_adjlist(bytesIO)
+        adj = {"1": {"3": {}, "2": {}}, "3": {"1": {}}, "2": {"1": {}}}
+        assert graphs_equal(G, nx.Graph(adj))
+
+    def test_unicode(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname)
+        assert graphs_equal(G, H)
+
+    def test_latin1_err(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "adjlist.txt"
+        with pytest.raises(UnicodeEncodeError):
+            nx.write_multiline_adjlist(G, fname, encoding="latin-1")
+
+    def test_latin1(self, tmp_path):
+        G = nx.Graph()
+        name1 = "Bj" + chr(246) + "rk"
+        name2 = chr(220) + "ber"
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname, encoding="latin-1")
+        H = nx.read_multiline_adjlist(fname, encoding="latin-1")
+        assert graphs_equal(G, H)
+
+    def test_parse_adjlist(self):
+        lines = ["1 2 5", "2 3 4", "3 5", "4", "5"]
+        nx.parse_adjlist(lines, nodetype=int)  # smoke test
+        with pytest.raises(TypeError):
+            nx.parse_adjlist(lines, nodetype="int")
+        lines = ["1 2 5", "2 b", "c"]
+        with pytest.raises(TypeError):
+            nx.parse_adjlist(lines, nodetype=int)
+
+    def test_adjlist_graph(self, tmp_path):
+        G = self.G
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname)
+        H2 = nx.read_adjlist(fname)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_digraph(self, tmp_path):
+        G = self.DG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, create_using=nx.DiGraph())
+        H2 = nx.read_adjlist(fname, create_using=nx.DiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()), directed=True)
+
+    def test_adjlist_integers(self, tmp_path):
+        fname = tmp_path / "adjlist.txt"
+        G = nx.convert_node_labels_to_integers(self.G)
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, nodetype=int)
+        H2 = nx.read_adjlist(fname, nodetype=int)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_multigraph(self, tmp_path):
+        G = self.XG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiGraph())
+        H2 = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_adjlist_multidigraph(self, tmp_path):
+        G = self.XDG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_adjlist(G, fname)
+        H = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        H2 = nx.read_adjlist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()), directed=True)
+
+    def test_adjlist_delimiter(self):
+        fh = io.BytesIO()
+        G = nx.path_graph(3)
+        nx.write_adjlist(G, fh, delimiter=":")
+        fh.seek(0)
+        H = nx.read_adjlist(fh, nodetype=int, delimiter=":")
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+
+class TestMultilineAdjlist:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+        cls.DG.remove_edge("b", "a")
+        cls.DG.remove_edge("b", "c")
+        cls.XG = nx.MultiGraph()
+        cls.XG.add_weighted_edges_from([(1, 2, 5), (1, 2, 5), (1, 2, 1), (3, 3, 42)])
+        cls.XDG = nx.MultiDiGraph(cls.XG)
+
+    def test_parse_multiline_adjlist(self):
+        lines = [
+            "1 2",
+            "b {'weight':3, 'name': 'Frodo'}",
+            "c {}",
+            "d 1",
+            "e {'weight':6, 'name': 'Saruman'}",
+        ]
+        nx.parse_multiline_adjlist(iter(lines))  # smoke test
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+        nx.parse_multiline_adjlist(iter(lines), edgetype=str)  # smoke test
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+        lines = ["1 a"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines))
+        lines = ["a 2"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+        lines = ["1 2"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines))
+        lines = ["1 2", "2 {}"]
+        with pytest.raises(TypeError):
+            nx.parse_multiline_adjlist(iter(lines))
+
+    def test_multiline_adjlist_graph(self, tmp_path):
+        G = self.G
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname)
+        H2 = nx.read_multiline_adjlist(fname)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_digraph(self, tmp_path):
+        G = self.DG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname, create_using=nx.DiGraph())
+        H2 = nx.read_multiline_adjlist(fname, create_using=nx.DiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()), directed=True)
+
+    def test_multiline_adjlist_integers(self, tmp_path):
+        fname = tmp_path / "adjlist.txt"
+        G = nx.convert_node_labels_to_integers(self.G)
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname, nodetype=int)
+        H2 = nx.read_multiline_adjlist(fname, nodetype=int)
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_multigraph(self, tmp_path):
+        G = self.XG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(fname, nodetype=int, create_using=nx.MultiGraph())
+        H2 = nx.read_multiline_adjlist(
+            fname, nodetype=int, create_using=nx.MultiGraph()
+        )
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_multiline_adjlist_multidigraph(self, tmp_path):
+        G = self.XDG
+        fname = tmp_path / "adjlist.txt"
+        nx.write_multiline_adjlist(G, fname)
+        H = nx.read_multiline_adjlist(
+            fname, nodetype=int, create_using=nx.MultiDiGraph()
+        )
+        H2 = nx.read_multiline_adjlist(
+            fname, nodetype=int, create_using=nx.MultiDiGraph()
+        )
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()), directed=True)
+
+    def test_multiline_adjlist_delimiter(self):
+        fh = io.BytesIO()
+        G = nx.path_graph(3)
+        nx.write_multiline_adjlist(G, fh, delimiter=":")
+        fh.seek(0)
+        H = nx.read_multiline_adjlist(fh, nodetype=int, delimiter=":")
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+
+@pytest.mark.parametrize(
+    ("lines", "delim"),
+    (
+        (["1 2 5", "2 3 4", "3 5", "4", "5"], None),  # No extra whitespace
+        (["1\t2\t5", "2\t3\t4", "3\t5", "4", "5"], "\t"),  # tab-delimited
+        (
+            ["1\t2\t5", "2\t3\t4", "3\t5\t", "4\t", "5"],
+            "\t",
+        ),  # tab-delimited, extra delims
+        (
+            ["1\t2\t5", "2\t3\t4", "3\t5\t\t\n", "4\t", "5"],
+            "\t",
+        ),  # extra delim+newlines
+    ),
+)
+def test_adjlist_rstrip_parsing(lines, delim):
+    """Regression test related to gh-7465"""
+    expected = nx.Graph([(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)])
+    nx.utils.graphs_equal(nx.parse_adjlist(lines, delimiter=delim), expected)
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_edgelist.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_edgelist.py
new file mode 100644
index 0000000000000000000000000000000000000000..a185cf83736f0cfe901dd5c2f6cbea941e55c485
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_edgelist.py
@@ -0,0 +1,318 @@
+"""
+Unit tests for edgelists.
+"""
+
+import io
+import textwrap
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+edges_no_data = textwrap.dedent(
+    """
+    # comment line
+    1 2
+    # comment line
+    2 3
+    """
+)
+
+
+edges_with_values = textwrap.dedent(
+    """
+    # comment line
+    1 2 2.0
+    # comment line
+    2 3 3.0
+    """
+)
+
+
+edges_with_weight = textwrap.dedent(
+    """
+    # comment line
+    1 2 {'weight':2.0}
+    # comment line
+    2 3 {'weight':3.0}
+    """
+)
+
+
+edges_with_multiple_attrs = textwrap.dedent(
+    """
+    # comment line
+    1 2 {'weight':2.0, 'color':'green'}
+    # comment line
+    2 3 {'weight':3.0, 'color':'red'}
+    """
+)
+
+
+edges_with_multiple_attrs_csv = textwrap.dedent(
+    """
+    # comment line
+    1, 2, {'weight':2.0, 'color':'green'}
+    # comment line
+    2, 3, {'weight':3.0, 'color':'red'}
+    """
+)
+
+
+_expected_edges_weights = [(1, 2, {"weight": 2.0}), (2, 3, {"weight": 3.0})]
+_expected_edges_multiattr = [
+    (1, 2, {"weight": 2.0, "color": "green"}),
+    (2, 3, {"weight": 3.0, "color": "red"}),
+]
+
+
+@pytest.mark.parametrize(
+    ("data", "extra_kwargs"),
+    (
+        (edges_no_data, {}),
+        (edges_with_values, {}),
+        (edges_with_weight, {}),
+        (edges_with_multiple_attrs, {}),
+        (edges_with_multiple_attrs_csv, {"delimiter": ","}),
+    ),
+)
+def test_read_edgelist_no_data(data, extra_kwargs):
+    bytesIO = io.BytesIO(data.encode("utf-8"))
+    G = nx.read_edgelist(bytesIO, nodetype=int, data=False, **extra_kwargs)
+    assert edges_equal(G.edges(), [(1, 2), (2, 3)])
+
+
+def test_read_weighted_edgelist():
+    bytesIO = io.BytesIO(edges_with_values.encode("utf-8"))
+    G = nx.read_weighted_edgelist(bytesIO, nodetype=int)
+    assert edges_equal(G.edges(data=True), _expected_edges_weights)
+
+
+@pytest.mark.parametrize(
+    ("data", "extra_kwargs", "expected"),
+    (
+        (edges_with_weight, {}, _expected_edges_weights),
+        (edges_with_multiple_attrs, {}, _expected_edges_multiattr),
+        (
+            edges_with_multiple_attrs_csv,
+            {"delimiter": ","},
+            _expected_edges_multiattr,
+        ),
+    ),
+)
+def test_read_edgelist_with_data(data, extra_kwargs, expected):
+    bytesIO = io.BytesIO(data.encode("utf-8"))
+    G = nx.read_edgelist(bytesIO, nodetype=int, **extra_kwargs)
+    assert edges_equal(G.edges(data=True), expected)
+
+
+@pytest.fixture
+def example_graph():
+    G = nx.Graph()
+    G.add_weighted_edges_from([(1, 2, 3.0), (2, 3, 27.0), (3, 4, 3.0)])
+    return G
+
+
+def test_parse_edgelist_no_data(example_graph):
+    G = example_graph
+    H = nx.parse_edgelist(["1 2", "2 3", "3 4"], nodetype=int)
+    assert nodes_equal(G.nodes, H.nodes)
+    assert edges_equal(G.edges, H.edges)
+
+
+def test_parse_edgelist_with_data_dict(example_graph):
+    G = example_graph
+    H = nx.parse_edgelist(
+        ["1 2 {'weight': 3}", "2 3 {'weight': 27}", "3 4 {'weight': 3.0}"], nodetype=int
+    )
+    assert nodes_equal(G.nodes, H.nodes)
+    assert edges_equal(G.edges(data=True), H.edges(data=True))
+
+
+def test_parse_edgelist_with_data_list(example_graph):
+    G = example_graph
+    H = nx.parse_edgelist(
+        ["1 2 3", "2 3 27", "3 4 3.0"], nodetype=int, data=(("weight", float),)
+    )
+    assert nodes_equal(G.nodes, H.nodes)
+    assert edges_equal(G.edges(data=True), H.edges(data=True))
+
+
+def test_parse_edgelist():
+    # ignore lines with less than 2 nodes
+    lines = ["1;2", "2 3", "3 4"]
+    G = nx.parse_edgelist(lines, nodetype=int)
+    assert list(G.edges()) == [(2, 3), (3, 4)]
+    # unknown nodetype
+    with pytest.raises(TypeError, match="Failed to convert nodes"):
+        lines = ["1 2", "2 3", "3 4"]
+        nx.parse_edgelist(lines, nodetype="nope")
+    # lines have invalid edge format
+    with pytest.raises(TypeError, match="Failed to convert edge data"):
+        lines = ["1 2 3", "2 3", "3 4"]
+        nx.parse_edgelist(lines, nodetype=int)
+    # edge data and data_keys not the same length
+    with pytest.raises(IndexError, match="not the same length"):
+        lines = ["1 2 3", "2 3 27", "3 4 3.0"]
+        nx.parse_edgelist(
+            lines, nodetype=int, data=(("weight", float), ("capacity", int))
+        )
+    # edge data can't be converted to edge type
+    with pytest.raises(TypeError, match="Failed to convert"):
+        lines = ["1 2 't1'", "2 3 't3'", "3 4 't3'"]
+        nx.parse_edgelist(lines, nodetype=int, data=(("weight", float),))
+
+
+def test_comments_None():
+    edgelist = ["node#1 node#2", "node#2 node#3"]
+    # comments=None supported to ignore all comment characters
+    G = nx.parse_edgelist(edgelist, comments=None)
+    H = nx.Graph([e.split(" ") for e in edgelist])
+    assert edges_equal(G.edges, H.edges)
+
+
+class TestEdgelist:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+        cls.XG = nx.MultiGraph()
+        cls.XG.add_weighted_edges_from([(1, 2, 5), (1, 2, 5), (1, 2, 1), (3, 3, 42)])
+        cls.XDG = nx.MultiDiGraph(cls.XG)
+
+    def test_write_edgelist_1(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edges_from([(1, 2), (2, 3)])
+        nx.write_edgelist(G, fh, data=False)
+        fh.seek(0)
+        assert fh.read() == b"1 2\n2 3\n"
+
+    def test_write_edgelist_2(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edges_from([(1, 2), (2, 3)])
+        nx.write_edgelist(G, fh, data=True)
+        fh.seek(0)
+        assert fh.read() == b"1 2 {}\n2 3 {}\n"
+
+    def test_write_edgelist_3(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2.0)
+        G.add_edge(2, 3, weight=3.0)
+        nx.write_edgelist(G, fh, data=True)
+        fh.seek(0)
+        assert fh.read() == b"1 2 {'weight': 2.0}\n2 3 {'weight': 3.0}\n"
+
+    def test_write_edgelist_4(self):
+        fh = io.BytesIO()
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2.0)
+        G.add_edge(2, 3, weight=3.0)
+        nx.write_edgelist(G, fh, data=[("weight")])
+        fh.seek(0)
+        assert fh.read() == b"1 2 2.0\n2 3 3.0\n"
+
+    def test_unicode(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname)
+        assert graphs_equal(G, H)
+
+    def test_latin1_issue(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "el.txt"
+        with pytest.raises(UnicodeEncodeError):
+            nx.write_edgelist(G, fname, encoding="latin-1")
+
+    def test_latin1(self, tmp_path):
+        G = nx.Graph()
+        name1 = "Bj" + chr(246) + "rk"
+        name2 = chr(220) + "ber"
+        G.add_edge(name1, "Radiohead", **{name2: 3})
+        fname = tmp_path / "el.txt"
+
+        nx.write_edgelist(G, fname, encoding="latin-1")
+        H = nx.read_edgelist(fname, encoding="latin-1")
+        assert graphs_equal(G, H)
+
+    def test_edgelist_graph(self, tmp_path):
+        G = self.G
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname)
+        H2 = nx.read_edgelist(fname)
+        assert H is not H2  # they should be different graphs
+        G.remove_node("g")  # isolated nodes are not written in edgelist
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_digraph(self, tmp_path):
+        G = self.DG
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, create_using=nx.DiGraph())
+        H2 = nx.read_edgelist(fname, create_using=nx.DiGraph())
+        assert H is not H2  # they should be different graphs
+        G.remove_node("g")  # isolated nodes are not written in edgelist
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()), directed=True)
+
+    def test_edgelist_integers(self, tmp_path):
+        G = nx.convert_node_labels_to_integers(self.G)
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, nodetype=int)
+        # isolated nodes are not written in edgelist
+        G.remove_nodes_from(list(nx.isolates(G)))
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_multigraph(self, tmp_path):
+        G = self.XG
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiGraph())
+        H2 = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()))
+
+    def test_edgelist_multidigraph(self, tmp_path):
+        G = self.XDG
+        fname = tmp_path / "el.txt"
+        nx.write_edgelist(G, fname)
+        H = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        H2 = nx.read_edgelist(fname, nodetype=int, create_using=nx.MultiDiGraph())
+        assert H is not H2  # they should be different graphs
+        assert nodes_equal(list(H), list(G))
+        assert edges_equal(list(H.edges()), list(G.edges()), directed=True)
+
+
+def test_edgelist_consistent_strip_handling():
+    """See gh-7462
+
+    Input when printed looks like::
+
+        1       2       3
+        2       3
+        3       4       3.0
+
+    Note the trailing \\t after the `3` in the second row, indicating an empty
+    data value.
+    """
+    s = io.StringIO("1\t2\t3\n2\t3\t\n3\t4\t3.0")
+    G = nx.parse_edgelist(s, delimiter="\t", nodetype=int, data=[("value", str)])
+    assert sorted(G.edges(data="value")) == [(1, 2, "3"), (2, 3, ""), (3, 4, "3.0")]
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_gexf.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_gexf.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e487cc6f4afe6de138eabb04633b8427f4999da
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_gexf.py
@@ -0,0 +1,612 @@
+import io
+import time
+
+import pytest
+
+import networkx as nx
+
+
+def test_gexf_v1_3(tmp_path):
+    """'Basic graph' example from https://gexf.net/schema.html"""
+    # GEXF file from published example
+    data = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://gexf.net/1.3" version="1.3">
+    <graph mode="static" defaultedgetype="directed">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge source="0" target="1" />
+        </edges>
+    </graph>
+</gexf>
+"""
+    with open(fname := (tmp_path / "basic.gexf"), "w") as fh:
+        fh.write(data)
+
+    # Expected output based on xml input
+    expected = nx.DiGraph([("0", "1")])
+    nx.set_node_attributes(expected, {"0": "Hello", "1": "Word"}, name="label")
+    expected.graph = {"mode": "static", "edge_default": {}}
+
+    # Load example with version explicitly set
+    G = nx.read_gexf(fname, version="1.3")
+    assert nx.utils.graphs_equal(G, expected)
+
+    # And with the "default" version
+    G = nx.read_gexf(fname)
+    assert nx.utils.graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize("time_attr", ("start", "end"))
+@pytest.mark.parametrize("dyn_attr", ("static", "dynamic"))
+def test_dynamic_graph_has_timeformat(time_attr, dyn_attr, tmp_path):
+    """Ensure that graphs which have a 'start' or 'stop' attribute get a
+    'timeformat' attribute upon parsing. See gh-7914."""
+    G = nx.MultiGraph(mode=dyn_attr)
+    G.add_node(0)
+    G.nodes[0][time_attr] = 1
+    # Write out
+    fname = tmp_path / "foo.gexf"
+    nx.write_gexf(G, fname)
+    # Check that timeformat is added to saved data
+    with open(fname) as fh:
+        assert 'timeformat="long"' in fh.read()
+    # Round-trip
+    H = nx.read_gexf(fname)
+    # If any node has a "start" or "end" attr, it is considered dynamic
+    # regardless of the graph "mode" attr
+    assert H.graph["mode"] == "dynamic"
+    assert nx.utils.nodes_equal(G.edges, H.edges)
+
+
+class TestGEXF:
+    @classmethod
+    def setup_class(cls):
+        cls.simple_directed_data = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version="1.2">
+    <graph mode="static" defaultedgetype="directed">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" />
+        </edges>
+    </graph>
+</gexf>
+"""
+        cls.simple_directed_graph = nx.DiGraph()
+        cls.simple_directed_graph.add_node("0", label="Hello")
+        cls.simple_directed_graph.add_node("1", label="World")
+        cls.simple_directed_graph.add_edge("0", "1", id="0")
+
+        cls.simple_directed_fh = io.BytesIO(cls.simple_directed_data.encode("UTF-8"))
+
+        cls.attribute_data = """<?xml version="1.0" encoding="UTF-8"?>\
+<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:xsi="http://www.w3.\
+org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.gexf.net/\
+1.2draft http://www.gexf.net/1.2draft/gexf.xsd" version="1.2">
+  <meta lastmodifieddate="2009-03-20">
+    <creator>Gephi.org</creator>
+    <description>A Web network</description>
+  </meta>
+  <graph defaultedgetype="directed">
+    <attributes class="node">
+      <attribute id="0" title="url" type="string"/>
+      <attribute id="1" title="indegree" type="integer"/>
+      <attribute id="2" title="frog" type="boolean">
+        <default>true</default>
+      </attribute>
+    </attributes>
+    <nodes>
+      <node id="0" label="Gephi">
+        <attvalues>
+          <attvalue for="0" value="https://gephi.org"/>
+          <attvalue for="1" value="1"/>
+          <attvalue for="2" value="false"/>
+        </attvalues>
+      </node>
+      <node id="1" label="Webatlas">
+        <attvalues>
+          <attvalue for="0" value="http://webatlas.fr"/>
+          <attvalue for="1" value="2"/>
+          <attvalue for="2" value="false"/>
+        </attvalues>
+      </node>
+      <node id="2" label="RTGI">
+        <attvalues>
+          <attvalue for="0" value="http://rtgi.fr"/>
+          <attvalue for="1" value="1"/>
+          <attvalue for="2" value="true"/>
+        </attvalues>
+      </node>
+      <node id="3" label="BarabasiLab">
+        <attvalues>
+          <attvalue for="0" value="http://barabasilab.com"/>
+          <attvalue for="1" value="1"/>
+          <attvalue for="2" value="true"/>
+        </attvalues>
+      </node>
+    </nodes>
+    <edges>
+      <edge id="0" source="0" target="1" label="foo"/>
+      <edge id="1" source="0" target="2"/>
+      <edge id="2" source="1" target="0"/>
+      <edge id="3" source="2" target="1"/>
+      <edge id="4" source="0" target="3"/>
+    </edges>
+  </graph>
+</gexf>
+"""
+        cls.attribute_graph = nx.DiGraph()
+        cls.attribute_graph.graph["node_default"] = {"frog": True}
+        cls.attribute_graph.add_node(
+            "0", label="Gephi", url="https://gephi.org", indegree=1, frog=False
+        )
+        cls.attribute_graph.add_node(
+            "1", label="Webatlas", url="http://webatlas.fr", indegree=2, frog=False
+        )
+        cls.attribute_graph.add_node(
+            "2", label="RTGI", url="http://rtgi.fr", indegree=1, frog=True
+        )
+        cls.attribute_graph.add_node(
+            "3",
+            label="BarabasiLab",
+            url="http://barabasilab.com",
+            indegree=1,
+            frog=True,
+        )
+        cls.attribute_graph.add_edge("0", "1", id="0", label="foo")
+        cls.attribute_graph.add_edge("0", "2", id="1")
+        cls.attribute_graph.add_edge("1", "0", id="2")
+        cls.attribute_graph.add_edge("2", "1", id="3")
+        cls.attribute_graph.add_edge("0", "3", id="4")
+        cls.attribute_fh = io.BytesIO(cls.attribute_data.encode("UTF-8"))
+
+        cls.simple_undirected_data = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version="1.2">
+    <graph mode="static" defaultedgetype="undirected">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" />
+        </edges>
+    </graph>
+</gexf>
+"""
+        cls.simple_undirected_graph = nx.Graph()
+        cls.simple_undirected_graph.add_node("0", label="Hello")
+        cls.simple_undirected_graph.add_node("1", label="World")
+        cls.simple_undirected_graph.add_edge("0", "1", id="0")
+
+        cls.simple_undirected_fh = io.BytesIO(
+            cls.simple_undirected_data.encode("UTF-8")
+        )
+
+    def test_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        H = nx.read_gexf(self.simple_directed_fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_write_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_read_simple_undirected_graphml(self):
+        G = self.simple_undirected_graph
+        H = nx.read_gexf(self.simple_undirected_fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+        self.simple_undirected_fh.seek(0)
+
+    def test_read_attribute_graphml(self):
+        G = self.attribute_graph
+        H = nx.read_gexf(self.attribute_fh)
+        assert sorted(G.nodes(True)) == sorted(H.nodes(data=True))
+        ge = sorted(G.edges(data=True))
+        he = sorted(H.edges(data=True))
+        for a, b in zip(ge, he):
+            assert a == b
+        self.attribute_fh.seek(0)
+
+    def test_directed_edge_in_undirected(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="undirected" name="">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" type="directed"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_gexf, fh)
+
+    def test_undirected_edge_in_directed(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="directed" name="">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" type="undirected"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_gexf, fh)
+
+    def test_key_raises(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="directed" name="">
+        <nodes>
+            <node id="0" label="Hello">
+              <attvalues>
+                <attvalue for='0' value='1'/>
+              </attvalues>
+            </node>
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1" type="undirected"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_gexf, fh)
+
+    def test_relabel(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<gexf xmlns="http://www.gexf.net/1.2draft" version='1.2'>
+    <graph mode="static" defaultedgetype="directed" name="">
+        <nodes>
+            <node id="0" label="Hello" />
+            <node id="1" label="Word" />
+        </nodes>
+        <edges>
+            <edge id="0" source="0" target="1"/>
+        </edges>
+    </graph>
+</gexf>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_gexf(fh, relabel=True)
+        assert sorted(G.nodes()) == ["Hello", "Word"]
+
+    def test_default_attribute(self):
+        G = nx.Graph()
+        G.add_node(1, label="1", color="green")
+        nx.add_path(G, [0, 1, 2, 3])
+        G.add_edge(1, 2, foo=3)
+        G.graph["node_default"] = {"color": "yellow"}
+        G.graph["edge_default"] = {"foo": 7}
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+        # Reading a gexf graph always sets mode attribute to either
+        # 'static' or 'dynamic'. Remove the mode attribute from the
+        # read graph for the sake of comparing remaining attributes.
+        del H.graph["mode"]
+        assert G.graph == H.graph
+
+    def test_serialize_ints_to_strings(self):
+        G = nx.Graph()
+        G.add_node(1, id=7, label=77)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert list(H) == [7]
+        assert H.nodes[7]["label"] == "77"
+
+    def test_write_with_node_attributes(self):
+        # Addresses #673.
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (2, 3)])
+        for i in range(4):
+            G.nodes[i]["id"] = i
+            G.nodes[i]["label"] = i
+            G.nodes[i]["pid"] = i
+            G.nodes[i]["start"] = i
+            G.nodes[i]["end"] = i + 1
+
+        expected = f"""<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:xsi\
+="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation=\
+"http://www.gexf.net/1.2draft http://www.gexf.net/1.2draft/\
+gexf.xsd" version="1.2">
+  <meta lastmodifieddate="{time.strftime("%Y-%m-%d")}">
+    <creator>NetworkX {nx.__version__}</creator>
+  </meta>
+  <graph defaultedgetype="undirected" mode="dynamic" name="" timeformat="long">
+    <nodes>
+      <node id="0" label="0" pid="0" start="0" end="1" />
+      <node id="1" label="1" pid="1" start="1" end="2" />
+      <node id="2" label="2" pid="2" start="2" end="3" />
+      <node id="3" label="3" pid="3" start="3" end="4" />
+    </nodes>
+    <edges>
+      <edge source="0" target="1" id="0" />
+      <edge source="1" target="2" id="1" />
+      <edge source="2" target="3" id="2" />
+    </edges>
+  </graph>
+</gexf>"""
+        obtained = "\n".join(nx.generate_gexf(G))
+        assert expected == obtained
+
+    def test_edge_id_construct(self):
+        G = nx.Graph()
+        G.add_edges_from([(0, 1, {"id": 0}), (1, 2, {"id": 2}), (2, 3)])
+
+        expected = f"""<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:xsi\
+="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.\
+gexf.net/1.2draft http://www.gexf.net/1.2draft/gexf.xsd" version="1.2">
+  <meta lastmodifieddate="{time.strftime("%Y-%m-%d")}">
+    <creator>NetworkX {nx.__version__}</creator>
+  </meta>
+  <graph defaultedgetype="undirected" mode="static" name="">
+    <nodes>
+      <node id="0" label="0" />
+      <node id="1" label="1" />
+      <node id="2" label="2" />
+      <node id="3" label="3" />
+    </nodes>
+    <edges>
+      <edge source="0" target="1" id="0" />
+      <edge source="1" target="2" id="2" />
+      <edge source="2" target="3" id="1" />
+    </edges>
+  </graph>
+</gexf>"""
+
+        obtained = "\n".join(nx.generate_gexf(G))
+        assert expected == obtained
+
+    def test_numpy_type(self):
+        np = pytest.importorskip("numpy")
+        G = nx.path_graph(4)
+        nx.set_node_attributes(G, {n: n for n in np.arange(4)}, "number")
+        G[0][1]["edge-number"] = np.float64(1.1)
+
+        expected = f"""<gexf xmlns="http://www.gexf.net/1.2draft"\
+ xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation\
+="http://www.gexf.net/1.2draft http://www.gexf.net/1.2draft/gexf.xsd"\
+ version="1.2">
+  <meta lastmodifieddate="{time.strftime("%Y-%m-%d")}">
+    <creator>NetworkX {nx.__version__}</creator>
+  </meta>
+  <graph defaultedgetype="undirected" mode="static" name="">
+    <attributes mode="static" class="edge">
+      <attribute id="1" title="edge-number" type="float" />
+    </attributes>
+    <attributes mode="static" class="node">
+      <attribute id="0" title="number" type="int" />
+    </attributes>
+    <nodes>
+      <node id="0" label="0">
+        <attvalues>
+          <attvalue for="0" value="0" />
+        </attvalues>
+      </node>
+      <node id="1" label="1">
+        <attvalues>
+          <attvalue for="0" value="1" />
+        </attvalues>
+      </node>
+      <node id="2" label="2">
+        <attvalues>
+          <attvalue for="0" value="2" />
+        </attvalues>
+      </node>
+      <node id="3" label="3">
+        <attvalues>
+          <attvalue for="0" value="3" />
+        </attvalues>
+      </node>
+    </nodes>
+    <edges>
+      <edge source="0" target="1" id="0">
+        <attvalues>
+          <attvalue for="1" value="1.1" />
+        </attvalues>
+      </edge>
+      <edge source="1" target="2" id="1" />
+      <edge source="2" target="3" id="2" />
+    </edges>
+  </graph>
+</gexf>"""
+        obtained = "\n".join(nx.generate_gexf(G))
+        assert expected == obtained
+
+    def test_bool(self):
+        G = nx.Graph()
+        G.add_node(1, testattr=True)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert H.nodes[1]["testattr"]
+
+    # Test for NaN, INF and -INF
+    def test_specials(self):
+        from math import isnan
+
+        inf, nan = float("inf"), float("nan")
+        G = nx.Graph()
+        G.add_node(1, testattr=inf, strdata="inf", key="a")
+        G.add_node(2, testattr=nan, strdata="nan", key="b")
+        G.add_node(3, testattr=-inf, strdata="-inf", key="c")
+
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        filetext = fh.read()
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+
+        assert b"INF" in filetext
+        assert b"NaN" in filetext
+        assert b"-INF" in filetext
+
+        assert H.nodes[1]["testattr"] == inf
+        assert isnan(H.nodes[2]["testattr"])
+        assert H.nodes[3]["testattr"] == -inf
+
+        assert H.nodes[1]["strdata"] == "inf"
+        assert H.nodes[2]["strdata"] == "nan"
+        assert H.nodes[3]["strdata"] == "-inf"
+
+        assert H.nodes[1]["networkx_key"] == "a"
+        assert H.nodes[2]["networkx_key"] == "b"
+        assert H.nodes[3]["networkx_key"] == "c"
+
+    def test_simple_list(self):
+        G = nx.Graph()
+        list_value = [(1, 2, 3), (9, 1, 2)]
+        G.add_node(1, key=list_value)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert H.nodes[1]["networkx_key"] == list_value
+
+    def test_dynamic_mode(self):
+        G = nx.Graph()
+        G.add_node(1, label="1", color="green")
+        G.graph["mode"] = "dynamic"
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_multigraph_with_missing_attributes(self):
+        G = nx.MultiGraph()
+        G.add_node(0, label="1", color="green")
+        G.add_node(1, label="2", color="green")
+        G.add_edge(0, 1, id="0", weight=3, type="undirected", start=0, end=1)
+        G.add_edge(0, 1, id="1", label="foo", start=0, end=1)
+        G.add_edge(0, 1)
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_missing_viz_attributes(self):
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["viz"] = {"size": 54}
+        G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1, "z": 0}
+        G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256}
+        G.nodes[0]["viz"]["shape"] = "http://random.url"
+        G.nodes[0]["viz"]["thickness"] = 2
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh, version="1.1draft")
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+        # Test missing alpha value for version >draft1.1 - set default alpha value
+        # to 1.0 instead of `None` when writing for better general compatibility
+        fh = io.BytesIO()
+        # G.nodes[0]["viz"]["color"] does not have an alpha value explicitly defined
+        # so the default is used instead
+        nx.write_gexf(G, fh, version="1.2draft")
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert H.nodes[0]["viz"]["color"]["a"] == 1.0
+
+        # Second graph for the other branch
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["viz"] = {"size": 54}
+        G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1, "z": 0}
+        G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256, "a": 0.5}
+        G.nodes[0]["viz"]["shape"] = "ftp://random.url"
+        G.nodes[0]["viz"]["thickness"] = 2
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_slice_and_spell(self):
+        # Test spell first, so version = 1.2
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["spells"] = [(1, 2)]
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green")
+        G.nodes[0]["slices"] = [(1, 2)]
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh, version="1.1draft")
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
+
+    def test_add_parent(self):
+        G = nx.Graph()
+        G.add_node(0, label="1", color="green", parents=[1, 2])
+        fh = io.BytesIO()
+        nx.write_gexf(G, fh)
+        fh.seek(0)
+        H = nx.read_gexf(fh, node_type=int)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(sorted(e) for e in G.edges()) == sorted(
+            sorted(e) for e in H.edges()
+        )
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_gml.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_gml.py
new file mode 100644
index 0000000000000000000000000000000000000000..df250979b149b7276babed267d59cb7cbcab22c6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_gml.py
@@ -0,0 +1,744 @@
+import codecs
+import io
+import math
+from ast import literal_eval
+from contextlib import contextmanager
+from textwrap import dedent
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.gml import literal_destringizer, literal_stringizer
+
+
+class TestGraph:
+    @classmethod
+    def setup_class(cls):
+        cls.simple_data = """Creator "me"
+Version "xx"
+graph [
+ comment "This is a sample graph"
+ directed 1
+ IsPlanar 1
+ pos  [ x 0 y 1 ]
+ node [
+   id 1
+   label "Node 1"
+   pos [ x 1 y 1 ]
+ ]
+ node [
+    id 2
+    pos [ x 1 y 2 ]
+    label "Node 2"
+    ]
+  node [
+    id 3
+    label "Node 3"
+    pos [ x 1 y 3 ]
+  ]
+  edge [
+    source 1
+    target 2
+    label "Edge from node 1 to node 2"
+    color [line "blue" thickness 3]
+
+  ]
+  edge [
+    source 2
+    target 3
+    label "Edge from node 2 to node 3"
+  ]
+  edge [
+    source 3
+    target 1
+    label "Edge from node 3 to node 1"
+  ]
+]
+"""
+
+    def test_parse_gml_cytoscape_bug(self):
+        # example from issue #321, originally #324 in trac
+        cytoscape_example = """
+Creator "Cytoscape"
+Version 1.0
+graph   [
+    node    [
+        root_index  -3
+        id  -3
+        graphics    [
+            x   -96.0
+            y   -67.0
+            w   40.0
+            h   40.0
+            fill    "#ff9999"
+            type    "ellipse"
+            outline "#666666"
+            outline_width   1.5
+        ]
+        label   "node2"
+    ]
+    node    [
+        root_index  -2
+        id  -2
+        graphics    [
+            x   63.0
+            y   37.0
+            w   40.0
+            h   40.0
+            fill    "#ff9999"
+            type    "ellipse"
+            outline "#666666"
+            outline_width   1.5
+        ]
+        label   "node1"
+    ]
+    node    [
+        root_index  -1
+        id  -1
+        graphics    [
+            x   -31.0
+            y   -17.0
+            w   40.0
+            h   40.0
+            fill    "#ff9999"
+            type    "ellipse"
+            outline "#666666"
+            outline_width   1.5
+        ]
+        label   "node0"
+    ]
+    edge    [
+        root_index  -2
+        target  -2
+        source  -1
+        graphics    [
+            width   1.5
+            fill    "#0000ff"
+            type    "line"
+            Line    [
+            ]
+            source_arrow    0
+            target_arrow    3
+        ]
+        label   "DirectedEdge"
+    ]
+    edge    [
+        root_index  -1
+        target  -1
+        source  -3
+        graphics    [
+            width   1.5
+            fill    "#0000ff"
+            type    "line"
+            Line    [
+            ]
+            source_arrow    0
+            target_arrow    3
+        ]
+        label   "DirectedEdge"
+    ]
+]
+"""
+        nx.parse_gml(cytoscape_example)
+
+    def test_parse_gml(self):
+        G = nx.parse_gml(self.simple_data, label="label")
+        assert sorted(G.nodes()) == ["Node 1", "Node 2", "Node 3"]
+        assert sorted(G.edges()) == [
+            ("Node 1", "Node 2"),
+            ("Node 2", "Node 3"),
+            ("Node 3", "Node 1"),
+        ]
+
+        assert sorted(G.edges(data=True)) == [
+            (
+                "Node 1",
+                "Node 2",
+                {
+                    "color": {"line": "blue", "thickness": 3},
+                    "label": "Edge from node 1 to node 2",
+                },
+            ),
+            ("Node 2", "Node 3", {"label": "Edge from node 2 to node 3"}),
+            ("Node 3", "Node 1", {"label": "Edge from node 3 to node 1"}),
+        ]
+
+    def test_read_gml(self, tmp_path):
+        fname = tmp_path / "test.gml"
+        with open(fname, "w") as fh:
+            fh.write(self.simple_data)
+        Gin = nx.read_gml(fname, label="label")
+        G = nx.parse_gml(self.simple_data, label="label")
+        assert sorted(G.nodes(data=True)) == sorted(Gin.nodes(data=True))
+        assert sorted(G.edges(data=True)) == sorted(Gin.edges(data=True))
+
+    def test_labels_are_strings(self):
+        # GML requires labels to be strings (i.e., in quotes)
+        answer = """graph [
+  node [
+    id 0
+    label "1203"
+  ]
+]"""
+        G = nx.Graph()
+        G.add_node(1203)
+        data = "\n".join(nx.generate_gml(G, stringizer=literal_stringizer))
+        assert data == answer
+
+    def test_relabel_duplicate(self):
+        data = """
+graph
+[
+        label   ""
+        directed        1
+        node
+        [
+                id      0
+                label   "same"
+        ]
+        node
+        [
+                id      1
+                label   "same"
+        ]
+]
+"""
+        fh = io.BytesIO(data.encode("UTF-8"))
+        fh.seek(0)
+        pytest.raises(nx.NetworkXError, nx.read_gml, fh, label="label")
+
+    @pytest.mark.parametrize("stringizer", (None, literal_stringizer))
+    def test_tuplelabels(self, stringizer):
+        # https://github.com/networkx/networkx/pull/1048
+        # Writing tuple labels to GML failed.
+        G = nx.Graph()
+        G.add_edge((0, 1), (1, 0))
+        data = "\n".join(nx.generate_gml(G, stringizer=stringizer))
+        answer = """graph [
+  node [
+    id 0
+    label "(0,1)"
+  ]
+  node [
+    id 1
+    label "(1,0)"
+  ]
+  edge [
+    source 0
+    target 1
+  ]
+]"""
+        assert data == answer
+
+    def test_quotes(self, tmp_path):
+        # https://github.com/networkx/networkx/issues/1061
+        # Encoding quotes as HTML entities.
+        G = nx.path_graph(1)
+        G.name = "path_graph(1)"
+        attr = 'This is "quoted" and this is a copyright: ' + chr(169)
+        G.nodes[0]["demo"] = attr
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+        answer = """graph [
+  name "path_graph(1)"
+  node [
+    id 0
+    label "0"
+    demo "This is &#34;quoted&#34; and this is a copyright: &#169;"
+  ]
+]"""
+        assert data == answer
+
+    def test_unicode_node(self, tmp_path):
+        node = "node" + chr(169)
+        G = nx.Graph()
+        G.add_node(node)
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+        answer = """graph [
+  node [
+    id 0
+    label "node&#169;"
+  ]
+]"""
+        assert data == answer
+
+    def test_float_label(self, tmp_path):
+        node = 1.0
+        G = nx.Graph()
+        G.add_node(node)
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+        answer = """graph [
+  node [
+    id 0
+    label "1.0"
+  ]
+]"""
+        assert data == answer
+
+    def test_special_float_label(self, tmp_path):
+        special_floats = [float("nan"), float("+inf"), float("-inf")]
+        try:
+            import numpy as np
+
+            special_floats += [np.nan, np.inf, np.inf * -1]
+        except ImportError:
+            special_floats += special_floats
+
+        G = nx.cycle_graph(len(special_floats))
+        attrs = dict(enumerate(special_floats))
+        nx.set_node_attributes(G, attrs, "nodefloat")
+        edges = list(G.edges)
+        attrs = {edges[i]: value for i, value in enumerate(special_floats)}
+        nx.set_edge_attributes(G, attrs, "edgefloat")
+
+        with open(tmp_path / "test.gml", "w+b") as fobj:
+            nx.write_gml(G, fobj)
+            fobj.seek(0)
+            # Should be bytes in 2.x and 3.x
+            data = fobj.read().strip().decode("ascii")
+            answer = """graph [
+  node [
+    id 0
+    label "0"
+    nodefloat NAN
+  ]
+  node [
+    id 1
+    label "1"
+    nodefloat +INF
+  ]
+  node [
+    id 2
+    label "2"
+    nodefloat -INF
+  ]
+  node [
+    id 3
+    label "3"
+    nodefloat NAN
+  ]
+  node [
+    id 4
+    label "4"
+    nodefloat +INF
+  ]
+  node [
+    id 5
+    label "5"
+    nodefloat -INF
+  ]
+  edge [
+    source 0
+    target 1
+    edgefloat NAN
+  ]
+  edge [
+    source 0
+    target 5
+    edgefloat +INF
+  ]
+  edge [
+    source 1
+    target 2
+    edgefloat -INF
+  ]
+  edge [
+    source 2
+    target 3
+    edgefloat NAN
+  ]
+  edge [
+    source 3
+    target 4
+    edgefloat +INF
+  ]
+  edge [
+    source 4
+    target 5
+    edgefloat -INF
+  ]
+]"""
+            assert data == answer
+
+            fobj.seek(0)
+            graph = nx.read_gml(fobj)
+            for indx, value in enumerate(special_floats):
+                node_value = graph.nodes[str(indx)]["nodefloat"]
+                if math.isnan(value):
+                    assert math.isnan(node_value)
+                else:
+                    assert node_value == value
+
+                edge = edges[indx]
+                string_edge = (str(edge[0]), str(edge[1]))
+                edge_value = graph.edges[string_edge]["edgefloat"]
+                if math.isnan(value):
+                    assert math.isnan(edge_value)
+                else:
+                    assert edge_value == value
+
+    def test_name(self):
+        G = nx.parse_gml('graph [ name "x" node [ id 0 label "x" ] ]')
+        assert "x" == G.graph["name"]
+        G = nx.parse_gml('graph [ node [ id 0 label "x" ] ]')
+        assert "" == G.name
+        assert "name" not in G.graph
+
+    def test_graph_types(self):
+        for directed in [None, False, True]:
+            for multigraph in [None, False, True]:
+                gml = "graph ["
+                if directed is not None:
+                    gml += " directed " + str(int(directed))
+                if multigraph is not None:
+                    gml += " multigraph " + str(int(multigraph))
+                gml += ' node [ id 0 label "0" ]'
+                gml += " edge [ source 0 target 0 ]"
+                gml += " ]"
+                G = nx.parse_gml(gml)
+                assert bool(directed) == G.is_directed()
+                assert bool(multigraph) == G.is_multigraph()
+                gml = "graph [\n"
+                if directed is True:
+                    gml += "  directed 1\n"
+                if multigraph is True:
+                    gml += "  multigraph 1\n"
+                gml += """  node [
+    id 0
+    label "0"
+  ]
+  edge [
+    source 0
+    target 0
+"""
+                if multigraph:
+                    gml += "    key 0\n"
+                gml += "  ]\n]"
+                assert gml == "\n".join(nx.generate_gml(G))
+
+    def test_data_types(self):
+        data = [
+            True,
+            False,
+            10**20,
+            -2e33,
+            "'",
+            '"&&amp;&&#34;"',
+            [{(b"\xfd",): "\x7f", chr(0x4444): (1, 2)}, (2, "3")],
+        ]
+        data.append(chr(0x14444))
+        data.append(literal_eval("{2.3j, 1 - 2.3j, ()}"))
+        G = nx.Graph()
+        G.name = data
+        G.graph["data"] = data
+        G.add_node(0, int=-1, data={"data": data})
+        G.add_edge(0, 0, float=-2.5, data=data)
+        gml = "\n".join(nx.generate_gml(G, stringizer=literal_stringizer))
+        G = nx.parse_gml(gml, destringizer=literal_destringizer)
+        assert data == G.name
+        assert {"name": data, "data": data} == G.graph
+        assert list(G.nodes(data=True)) == [(0, {"int": -1, "data": {"data": data}})]
+        assert list(G.edges(data=True)) == [(0, 0, {"float": -2.5, "data": data})]
+        G = nx.Graph()
+        G.graph["data"] = "frozenset([1, 2, 3])"
+        G = nx.parse_gml(nx.generate_gml(G), destringizer=literal_eval)
+        assert G.graph["data"] == "frozenset([1, 2, 3])"
+
+    def test_escape_unescape(self):
+        gml = """graph [
+  name "&amp;&#34;&#xf;&#x4444;&#1234567890;&#x1234567890abcdef;&unknown;"
+]"""
+        G = nx.parse_gml(gml)
+        assert (
+            '&"\x0f' + chr(0x4444) + "&#1234567890;&#x1234567890abcdef;&unknown;"
+            == G.name
+        )
+        gml = "\n".join(nx.generate_gml(G))
+        alnu = "#1234567890;&#38;#x1234567890abcdef"
+        answer = (
+            """graph [
+  name "&#38;&#34;&#15;&#17476;&#38;"""
+            + alnu
+            + """;&#38;unknown;"
+]"""
+        )
+        assert answer == gml
+
+    def test_exceptions(self, tmp_path):
+        pytest.raises(ValueError, literal_destringizer, "(")
+        pytest.raises(ValueError, literal_destringizer, "frozenset([1, 2, 3])")
+        pytest.raises(ValueError, literal_destringizer, literal_destringizer)
+        pytest.raises(ValueError, literal_stringizer, frozenset([1, 2, 3]))
+        pytest.raises(ValueError, literal_stringizer, literal_stringizer)
+        with open(tmp_path / "test.gml", "w+b") as f:
+            f.write(codecs.BOM_UTF8 + b"graph[]")
+            f.seek(0)
+            pytest.raises(nx.NetworkXError, nx.read_gml, f)
+
+        def assert_parse_error(gml):
+            pytest.raises(nx.NetworkXError, nx.parse_gml, gml)
+
+        assert_parse_error(["graph [\n\n", "]"])
+        assert_parse_error("")
+        assert_parse_error('Creator ""')
+        assert_parse_error("0")
+        assert_parse_error("graph ]")
+        assert_parse_error("graph [ 1 ]")
+        assert_parse_error("graph [ 1.E+2 ]")
+        assert_parse_error('graph [ "A" ]')
+        assert_parse_error("graph [ ] graph ]")
+        assert_parse_error("graph [ ] graph [ ]")
+        assert_parse_error("graph [ data [1, 2, 3] ]")
+        assert_parse_error("graph [ node [ ] ]")
+        assert_parse_error("graph [ node [ id 0 ] ]")
+        nx.parse_gml('graph [ node [ id "a" ] ]', label="id")
+        assert_parse_error("graph [ node [ id 0 label 0 ] node [ id 0 label 1 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] node [ id 1 label 0 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ source 0 ] ]")
+        nx.parse_gml("graph [edge [ source 0 target 0 ] node [ id 0 label 0 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ source 1 target 0 ] ]")
+        assert_parse_error("graph [ node [ id 0 label 0 ] edge [ source 0 target 1 ] ]")
+        assert_parse_error(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 ] edge [ source 1 target 0 ] ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 ] edge [ source 1 target 0 ] "
+            "directed 1 ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 ] edge [ source 0 target 1 ]"
+            "multigraph 1 ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 key 0 ] edge [ source 0 target 1 ]"
+            "multigraph 1 ]"
+        )
+        assert_parse_error(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 key 0 ] edge [ source 0 target 1 key 0 ]"
+            "multigraph 1 ]"
+        )
+        nx.parse_gml(
+            "graph [ node [ id 0 label 0 ] node [ id 1 label 1 ] "
+            "edge [ source 0 target 1 key 0 ] edge [ source 1 target 0 key 0 ]"
+            "directed 1 multigraph 1 ]"
+        )
+
+        # Tests for string convertible alphanumeric id and label values
+        nx.parse_gml("graph [edge [ source a target a ] node [ id a label b ] ]")
+        nx.parse_gml(
+            "graph [ node [ id n42 label 0 ] node [ id x43 label 1 ]"
+            "edge [ source n42 target x43 key 0 ]"
+            "edge [ source x43 target n42 key 0 ]"
+            "directed 1 multigraph 1 ]"
+        )
+        assert_parse_error(
+            "graph [edge [ source '\u4200' target '\u4200' ] "
+            + "node [ id '\u4200' label b ] ]"
+        )
+
+        def assert_generate_error(*args, **kwargs):
+            pytest.raises(
+                nx.NetworkXError, lambda: list(nx.generate_gml(*args, **kwargs))
+            )
+
+        G = nx.Graph()
+        G.graph[3] = 3
+        assert_generate_error(G)
+        G = nx.Graph()
+        G.graph["3"] = 3
+        assert_generate_error(G)
+        G = nx.Graph()
+        G.graph["data"] = frozenset([1, 2, 3])
+        assert_generate_error(G, stringizer=literal_stringizer)
+
+    def test_label_kwarg(self):
+        G = nx.parse_gml(self.simple_data, label="id")
+        assert sorted(G.nodes) == [1, 2, 3]
+        labels = [G.nodes[n]["label"] for n in sorted(G.nodes)]
+        assert labels == ["Node 1", "Node 2", "Node 3"]
+
+        G = nx.parse_gml(self.simple_data, label=None)
+        assert sorted(G.nodes) == [1, 2, 3]
+        labels = [G.nodes[n]["label"] for n in sorted(G.nodes)]
+        assert labels == ["Node 1", "Node 2", "Node 3"]
+
+    def test_outofrange_integers(self, tmp_path):
+        # GML restricts integers to 32 signed bits.
+        # Check that we honor this restriction on export
+        G = nx.Graph()
+        # Test export for numbers that barely fit or don't fit into 32 bits,
+        # and 3 numbers in the middle
+        numbers = {
+            "toosmall": (-(2**31)) - 1,
+            "small": -(2**31),
+            "med1": -4,
+            "med2": 0,
+            "med3": 17,
+            "big": (2**31) - 1,
+            "toobig": 2**31,
+        }
+        G.add_node("Node", **numbers)
+
+        fname = tmp_path / "test.gml"
+        nx.write_gml(G, fname)
+        # Check that the export wrote the nonfitting numbers as strings
+        G2 = nx.read_gml(fname)
+        for attr, value in G2.nodes["Node"].items():
+            if attr == "toosmall" or attr == "toobig":
+                assert isinstance(value, str)
+            else:
+                assert isinstance(value, int)
+
+    def test_multiline(self):
+        # example from issue #6836
+        multiline_example = """
+graph
+[
+    node
+    [
+	    id 0
+	    label "multiline node"
+	    label2 "multiline1
+    multiline2
+    multiline3"
+	    alt_name "id 0"
+    ]
+]
+"""
+        G = nx.parse_gml(multiline_example)
+        assert G.nodes["multiline node"] == {
+            "label2": "multiline1 multiline2 multiline3",
+            "alt_name": "id 0",
+        }
+
+
+@contextmanager
+def byte_file():
+    _file_handle = io.BytesIO()
+    yield _file_handle
+    _file_handle.seek(0)
+
+
+class TestPropertyLists:
+    def test_writing_graph_with_multi_element_property_list(self):
+        g = nx.Graph()
+        g.add_node("n1", properties=["element", 0, 1, 2.5, True, False])
+        with byte_file() as f:
+            nx.write_gml(g, f)
+        result = f.read().decode()
+
+        assert result == dedent(
+            """\
+            graph [
+              node [
+                id 0
+                label "n1"
+                properties "element"
+                properties 0
+                properties 1
+                properties 2.5
+                properties 1
+                properties 0
+              ]
+            ]
+        """
+        )
+
+    def test_writing_graph_with_one_element_property_list(self):
+        g = nx.Graph()
+        g.add_node("n1", properties=["element"])
+        with byte_file() as f:
+            nx.write_gml(g, f)
+        result = f.read().decode()
+
+        assert result == dedent(
+            """\
+            graph [
+              node [
+                id 0
+                label "n1"
+                properties "_networkx_list_start"
+                properties "element"
+              ]
+            ]
+        """
+        )
+
+    def test_reading_graph_with_list_property(self):
+        with byte_file() as f:
+            f.write(
+                dedent(
+                    """
+              graph [
+                node [
+                  id 0
+                  label "n1"
+                  properties "element"
+                  properties 0
+                  properties 1
+                  properties 2.5
+                ]
+              ]
+            """
+                ).encode("ascii")
+            )
+            f.seek(0)
+            graph = nx.read_gml(f)
+        assert graph.nodes(data=True)["n1"] == {"properties": ["element", 0, 1, 2.5]}
+
+    def test_reading_graph_with_single_element_list_property(self):
+        with byte_file() as f:
+            f.write(
+                dedent(
+                    """
+              graph [
+                node [
+                  id 0
+                  label "n1"
+                  properties "_networkx_list_start"
+                  properties "element"
+                ]
+              ]
+            """
+                ).encode("ascii")
+            )
+            f.seek(0)
+            graph = nx.read_gml(f)
+        assert graph.nodes(data=True)["n1"] == {"properties": ["element"]}
+
+
+@pytest.mark.parametrize("coll", ([], ()))
+def test_stringize_empty_list_tuple(coll):
+    G = nx.path_graph(2)
+    G.nodes[0]["test"] = coll  # test serializing an empty collection
+    f = io.BytesIO()
+    nx.write_gml(G, f)  # Smoke test - should not raise
+    f.seek(0)
+    H = nx.read_gml(f)
+    assert H.nodes["0"]["test"] == coll  # Check empty list round-trips properly
+    # Check full round-tripping. Note that nodes are loaded as strings by
+    # default, so there needs to be some remapping prior to comparison
+    H = nx.relabel_nodes(H, {"0": 0, "1": 1})
+    assert nx.utils.graphs_equal(G, H)
+    # Same as above, but use destringizer for node remapping. Should have no
+    # effect on node attr
+    f.seek(0)
+    H = nx.read_gml(f, destringizer=int)
+    assert nx.utils.graphs_equal(G, H)
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_graph6.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_graph6.py
new file mode 100644
index 0000000000000000000000000000000000000000..d680c2153d678250b4aa9b32f6ad7d0f6d8e80f6
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_graph6.py
@@ -0,0 +1,181 @@
+from io import BytesIO
+
+import pytest
+
+import networkx as nx
+import networkx.readwrite.graph6 as g6
+from networkx.utils import edges_equal, nodes_equal
+
+
+def test_from_graph6_invariant_to_trailing_newline():
+    """See gh-7557"""
+    G = nx.from_graph6_bytes(b">>graph6<<P~~~~~~~~~~~~~~~~~~~~~~{\n")
+    H = nx.from_graph6_bytes(b">>graph6<<P~~~~~~~~~~~~~~~~~~~~~~{")
+    assert nx.utils.graphs_equal(G, H)
+
+
+def test_from_graph6_raises_header_newline():
+    """graph6 headers must not be followed by a newline. See gh-7557."""
+    with pytest.raises(nx.NetworkXError):
+        G = nx.from_graph6_bytes(b">>graph6<<\nP~~~~~~~~~~~~~~~~~~~~~~{")
+
+
+class TestGraph6Utils:
+    def test_n_data_n_conversion(self):
+        for i in [0, 1, 42, 62, 63, 64, 258047, 258048, 7744773, 68719476735]:
+            assert g6.data_to_n(g6.n_to_data(i))[0] == i
+            assert g6.data_to_n(g6.n_to_data(i))[1] == []
+            assert g6.data_to_n(g6.n_to_data(i) + [42, 43])[1] == [42, 43]
+
+
+class TestFromGraph6Bytes:
+    def test_from_graph6_bytes(self):
+        data = b"DF{"
+        G = nx.from_graph6_bytes(data)
+        assert nodes_equal(G.nodes(), [0, 1, 2, 3, 4])
+        assert edges_equal(
+            G.edges(), [(0, 3), (0, 4), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
+        )
+
+    def test_read_equals_from_bytes(self):
+        data = b"DF{"
+        G = nx.from_graph6_bytes(data)
+        fh = BytesIO(data)
+        Gin = nx.read_graph6(fh)
+        assert nodes_equal(G.nodes(), Gin.nodes())
+        assert edges_equal(G.edges(), Gin.edges())
+
+
+class TestReadGraph6:
+    def test_read_many_graph6(self):
+        """Test for reading many graphs from a file into a list."""
+        data = b"DF{\nD`{\nDqK\nD~{\n"
+        fh = BytesIO(data)
+        glist = nx.read_graph6(fh)
+        assert len(glist) == 4
+        for G in glist:
+            assert sorted(G) == list(range(5))
+
+
+class TestWriteGraph6:
+    """Unit tests for writing a graph to a file in graph6 format."""
+
+    def test_null_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.null_graph(), result)
+        assert result.getvalue() == b">>graph6<<?\n"
+
+    def test_trivial_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.trivial_graph(), result)
+        assert result.getvalue() == b">>graph6<<@\n"
+
+    def test_complete_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.complete_graph(4), result)
+        assert result.getvalue() == b">>graph6<<C~\n"
+
+    def test_large_complete_graph(self):
+        result = BytesIO()
+        nx.write_graph6(nx.complete_graph(67), result, header=False)
+        assert result.getvalue() == b"~?@B" + b"~" * 368 + b"w\n"
+
+    def test_no_header(self):
+        result = BytesIO()
+        nx.write_graph6(nx.complete_graph(4), result, header=False)
+        assert result.getvalue() == b"C~\n"
+
+    def test_complete_bipartite_graph(self):
+        result = BytesIO()
+        G = nx.complete_bipartite_graph(6, 9)
+        nx.write_graph6(G, result, header=False)
+        # The expected encoding here was verified by Sage.
+        assert result.getvalue() == b"N??F~z{~Fw^_~?~?^_?\n"
+
+    @pytest.mark.parametrize("G", (nx.MultiGraph(), nx.DiGraph()))
+    def test_no_directed_or_multi_graphs(self, G):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.write_graph6(G, BytesIO())
+
+    def test_length(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            g = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            gstr = BytesIO()
+            nx.write_graph6(g, gstr, header=False)
+            # Strip the trailing newline.
+            gstr = gstr.getvalue().rstrip()
+            assert len(gstr) == ((i - 1) * i // 2 + 5) // 6 + (1 if i < 63 else 4)
+
+    def test_roundtrip(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            G = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            f = BytesIO()
+            nx.write_graph6(G, f)
+            f.seek(0)
+            H = nx.read_graph6(f)
+            assert nodes_equal(G.nodes(), H.nodes())
+            assert edges_equal(G.edges(), H.edges())
+
+    def test_write_path(self, tmp_path):
+        with open(tmp_path / "test.g6", "w+b") as f:
+            g6.write_graph6_file(nx.null_graph(), f)
+            f.seek(0)
+            assert f.read() == b">>graph6<<?\n"
+
+    @pytest.mark.parametrize("edge", ((0, 1), (1, 2), (1, 42)))
+    def test_relabeling(self, edge):
+        G = nx.Graph([edge])
+        f = BytesIO()
+        nx.write_graph6(G, f)
+        f.seek(0)
+        assert f.read() == b">>graph6<<A_\n"
+
+
+class TestToGraph6Bytes:
+    def test_null_graph(self):
+        G = nx.null_graph()
+        assert g6.to_graph6_bytes(G) == b">>graph6<<?\n"
+
+    def test_trivial_graph(self):
+        G = nx.trivial_graph()
+        assert g6.to_graph6_bytes(G) == b">>graph6<<@\n"
+
+    def test_complete_graph(self):
+        assert g6.to_graph6_bytes(nx.complete_graph(4)) == b">>graph6<<C~\n"
+
+    def test_large_complete_graph(self):
+        G = nx.complete_graph(67)
+        assert g6.to_graph6_bytes(G, header=False) == b"~?@B" + b"~" * 368 + b"w\n"
+
+    def test_no_header(self):
+        G = nx.complete_graph(4)
+        assert g6.to_graph6_bytes(G, header=False) == b"C~\n"
+
+    def test_complete_bipartite_graph(self):
+        G = nx.complete_bipartite_graph(6, 9)
+        assert g6.to_graph6_bytes(G, header=False) == b"N??F~z{~Fw^_~?~?^_?\n"
+
+    @pytest.mark.parametrize("G", (nx.MultiGraph(), nx.DiGraph()))
+    def test_no_directed_or_multi_graphs(self, G):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            g6.to_graph6_bytes(G)
+
+    def test_length(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            G = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            # Strip the trailing newline.
+            gstr = g6.to_graph6_bytes(G, header=False).rstrip()
+            assert len(gstr) == ((i - 1) * i // 2 + 5) // 6 + (1 if i < 63 else 4)
+
+    def test_roundtrip(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            G = nx.random_graphs.gnm_random_graph(i, i * i // 4, seed=i)
+            data = g6.to_graph6_bytes(G)
+            H = nx.from_graph6_bytes(data.rstrip())
+            assert nodes_equal(G.nodes(), H.nodes())
+            assert edges_equal(G.edges(), H.edges())
+
+    @pytest.mark.parametrize("edge", ((0, 1), (1, 2), (1, 42)))
+    def test_relabeling(self, edge):
+        G = nx.Graph([edge])
+        assert g6.to_graph6_bytes(G) == b">>graph6<<A_\n"
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_graphml.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_graphml.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eb96e5eb8d97ee557d94a14f1e0cb769e6b02a7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_graphml.py
@@ -0,0 +1,1531 @@
+import io
+
+import pytest
+
+import networkx as nx
+from networkx.readwrite.graphml import GraphMLWriter
+from networkx.utils import edges_equal, nodes_equal
+
+
+class BaseGraphML:
+    @classmethod
+    def setup_class(cls):
+        cls.simple_directed_data = """<?xml version="1.0" encoding="UTF-8"?>
+<!-- This file was written by the JAVA GraphML Library.-->
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G" edgedefault="directed">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n3"/>
+    <node id="n4"/>
+    <node id="n5"/>
+    <node id="n6"/>
+    <node id="n7"/>
+    <node id="n8"/>
+    <node id="n9"/>
+    <node id="n10"/>
+    <edge id="foo" source="n0" target="n2"/>
+    <edge source="n1" target="n2"/>
+    <edge source="n2" target="n3"/>
+    <edge source="n3" target="n5"/>
+    <edge source="n3" target="n4"/>
+    <edge source="n4" target="n6"/>
+    <edge source="n6" target="n5"/>
+    <edge source="n5" target="n7"/>
+    <edge source="n6" target="n8"/>
+    <edge source="n8" target="n7"/>
+    <edge source="n8" target="n9"/>
+  </graph>
+</graphml>"""
+        cls.simple_directed_graph = nx.DiGraph()
+        cls.simple_directed_graph.add_node("n10")
+        cls.simple_directed_graph.add_edge("n0", "n2", id="foo")
+        cls.simple_directed_graph.add_edge("n0", "n2")
+        cls.simple_directed_graph.add_edges_from(
+            [
+                ("n1", "n2"),
+                ("n2", "n3"),
+                ("n3", "n5"),
+                ("n3", "n4"),
+                ("n4", "n6"),
+                ("n6", "n5"),
+                ("n5", "n7"),
+                ("n6", "n8"),
+                ("n8", "n7"),
+                ("n8", "n9"),
+            ]
+        )
+        cls.simple_directed_fh = io.BytesIO(cls.simple_directed_data.encode("UTF-8"))
+
+        cls.attribute_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+      xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+      xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+        http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="color" attr.type="string">
+    <default>yellow</default>
+  </key>
+  <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">green</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">blue</data>
+    </node>
+    <node id="n3">
+      <data key="d0">red</data>
+    </node>
+    <node id="n4"/>
+    <node id="n5">
+      <data key="d0">turquoise</data>
+    </node>
+    <edge id="e0" source="n0" target="n2">
+      <data key="d1">1.0</data>
+    </edge>
+    <edge id="e1" source="n0" target="n1">
+      <data key="d1">1.0</data>
+    </edge>
+    <edge id="e2" source="n1" target="n3">
+      <data key="d1">2.0</data>
+    </edge>
+    <edge id="e3" source="n3" target="n2"/>
+    <edge id="e4" source="n2" target="n4"/>
+    <edge id="e5" source="n3" target="n5"/>
+    <edge id="e6" source="n5" target="n4">
+      <data key="d1">1.1</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        cls.attribute_graph = nx.DiGraph(id="G")
+        cls.attribute_graph.graph["node_default"] = {"color": "yellow"}
+        cls.attribute_graph.add_node("n0", color="green")
+        cls.attribute_graph.add_node("n2", color="blue")
+        cls.attribute_graph.add_node("n3", color="red")
+        cls.attribute_graph.add_node("n4")
+        cls.attribute_graph.add_node("n5", color="turquoise")
+        cls.attribute_graph.add_edge("n0", "n2", id="e0", weight=1.0)
+        cls.attribute_graph.add_edge("n0", "n1", id="e1", weight=1.0)
+        cls.attribute_graph.add_edge("n1", "n3", id="e2", weight=2.0)
+        cls.attribute_graph.add_edge("n3", "n2", id="e3")
+        cls.attribute_graph.add_edge("n2", "n4", id="e4")
+        cls.attribute_graph.add_edge("n3", "n5", id="e5")
+        cls.attribute_graph.add_edge("n5", "n4", id="e6", weight=1.1)
+        cls.attribute_fh = io.BytesIO(cls.attribute_data.encode("UTF-8"))
+
+        cls.node_attribute_default_data = """<?xml version="1.0" encoding="UTF-8"?>
+        <graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+              xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+              xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+                http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+          <key id="d0" for="node" attr.name="boolean_attribute" attr.type="boolean"><default>false</default></key>
+          <key id="d1" for="node" attr.name="int_attribute" attr.type="int"><default>0</default></key>
+          <key id="d2" for="node" attr.name="long_attribute" attr.type="long"><default>0</default></key>
+          <key id="d3" for="node" attr.name="float_attribute" attr.type="float"><default>0.0</default></key>
+          <key id="d4" for="node" attr.name="double_attribute" attr.type="double"><default>0.0</default></key>
+          <key id="d5" for="node" attr.name="string_attribute" attr.type="string"><default>Foo</default></key>
+          <graph id="G" edgedefault="directed">
+            <node id="n0"/>
+            <node id="n1"/>
+            <edge id="e0" source="n0" target="n1"/>
+          </graph>
+        </graphml>
+        """
+        cls.node_attribute_default_graph = nx.DiGraph(id="G")
+        cls.node_attribute_default_graph.graph["node_default"] = {
+            "boolean_attribute": False,
+            "int_attribute": 0,
+            "long_attribute": 0,
+            "float_attribute": 0.0,
+            "double_attribute": 0.0,
+            "string_attribute": "Foo",
+        }
+        cls.node_attribute_default_graph.add_node("n0")
+        cls.node_attribute_default_graph.add_node("n1")
+        cls.node_attribute_default_graph.add_edge("n0", "n1", id="e0")
+        cls.node_attribute_default_fh = io.BytesIO(
+            cls.node_attribute_default_data.encode("UTF-8")
+        )
+
+        cls.attribute_named_key_ids_data = """<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="edge_prop" for="edge" attr.name="edge_prop" attr.type="string"/>
+  <key id="prop2" for="node" attr.name="prop2" attr.type="string"/>
+  <key id="prop1" for="node" attr.name="prop1" attr.type="string"/>
+  <graph edgedefault="directed">
+    <node id="0">
+      <data key="prop1">val1</data>
+      <data key="prop2">val2</data>
+    </node>
+    <node id="1">
+      <data key="prop1">val_one</data>
+      <data key="prop2">val2</data>
+    </node>
+    <edge source="0" target="1">
+      <data key="edge_prop">edge_value</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        cls.attribute_named_key_ids_graph = nx.DiGraph()
+        cls.attribute_named_key_ids_graph.add_node("0", prop1="val1", prop2="val2")
+        cls.attribute_named_key_ids_graph.add_node("1", prop1="val_one", prop2="val2")
+        cls.attribute_named_key_ids_graph.add_edge("0", "1", edge_prop="edge_value")
+        fh = io.BytesIO(cls.attribute_named_key_ids_data.encode("UTF-8"))
+        cls.attribute_named_key_ids_fh = fh
+
+        cls.attribute_numeric_type_data = """<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key attr.name="weight" attr.type="double" for="node" id="d1" />
+  <key attr.name="weight" attr.type="double" for="edge" id="d0" />
+  <graph edgedefault="directed">
+    <node id="n0">
+      <data key="d1">1</data>
+    </node>
+    <node id="n1">
+      <data key="d1">2.0</data>
+    </node>
+    <edge source="n0" target="n1">
+      <data key="d0">1</data>
+    </edge>
+    <edge source="n1" target="n0">
+      <data key="d0">k</data>
+    </edge>
+    <edge source="n1" target="n1">
+      <data key="d0">1.0</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        cls.attribute_numeric_type_graph = nx.DiGraph()
+        cls.attribute_numeric_type_graph.add_node("n0", weight=1)
+        cls.attribute_numeric_type_graph.add_node("n1", weight=2.0)
+        cls.attribute_numeric_type_graph.add_edge("n0", "n1", weight=1)
+        cls.attribute_numeric_type_graph.add_edge("n1", "n1", weight=1.0)
+        fh = io.BytesIO(cls.attribute_numeric_type_data.encode("UTF-8"))
+        cls.attribute_numeric_type_fh = fh
+
+        cls.simple_undirected_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge id="foo" source="n0" target="n2"/>
+    <edge source="n1" target="n2"/>
+    <edge source="n2" target="n3"/>
+  </graph>
+</graphml>"""
+        #    <edge source="n8" target="n10" directed="false"/>
+        cls.simple_undirected_graph = nx.Graph()
+        cls.simple_undirected_graph.add_node("n10")
+        cls.simple_undirected_graph.add_edge("n0", "n2", id="foo")
+        cls.simple_undirected_graph.add_edges_from([("n1", "n2"), ("n2", "n3")])
+        fh = io.BytesIO(cls.simple_undirected_data.encode("UTF-8"))
+        cls.simple_undirected_fh = fh
+
+        cls.undirected_multigraph_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge id="e0" source="n0" target="n2"/>
+    <edge id="e1" source="n1" target="n2"/>
+    <edge id="e2" source="n2" target="n1"/>
+  </graph>
+</graphml>"""
+        cls.undirected_multigraph = nx.MultiGraph()
+        cls.undirected_multigraph.add_node("n10")
+        cls.undirected_multigraph.add_edge("n0", "n2", id="e0")
+        cls.undirected_multigraph.add_edge("n1", "n2", id="e1")
+        cls.undirected_multigraph.add_edge("n2", "n1", id="e2")
+        fh = io.BytesIO(cls.undirected_multigraph_data.encode("UTF-8"))
+        cls.undirected_multigraph_fh = fh
+
+        cls.undirected_multigraph_no_multiedge_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge id="e0" source="n0" target="n2"/>
+    <edge id="e1" source="n1" target="n2"/>
+    <edge id="e2" source="n2" target="n3"/>
+  </graph>
+</graphml>"""
+        cls.undirected_multigraph_no_multiedge = nx.MultiGraph()
+        cls.undirected_multigraph_no_multiedge.add_node("n10")
+        cls.undirected_multigraph_no_multiedge.add_edge("n0", "n2", id="e0")
+        cls.undirected_multigraph_no_multiedge.add_edge("n1", "n2", id="e1")
+        cls.undirected_multigraph_no_multiedge.add_edge("n2", "n3", id="e2")
+        fh = io.BytesIO(cls.undirected_multigraph_no_multiedge_data.encode("UTF-8"))
+        cls.undirected_multigraph_no_multiedge_fh = fh
+
+        cls.multigraph_only_ids_for_multiedges_data = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <node id="n10"/>
+    <edge source="n0" target="n2"/>
+    <edge id="e1" source="n1" target="n2"/>
+    <edge id="e2" source="n2" target="n1"/>
+  </graph>
+</graphml>"""
+        cls.multigraph_only_ids_for_multiedges = nx.MultiGraph()
+        cls.multigraph_only_ids_for_multiedges.add_node("n10")
+        cls.multigraph_only_ids_for_multiedges.add_edge("n0", "n2")
+        cls.multigraph_only_ids_for_multiedges.add_edge("n1", "n2", id="e1")
+        cls.multigraph_only_ids_for_multiedges.add_edge("n2", "n1", id="e2")
+        fh = io.BytesIO(cls.multigraph_only_ids_for_multiedges_data.encode("UTF-8"))
+        cls.multigraph_only_ids_for_multiedges_fh = fh
+
+
+class TestReadGraphML(BaseGraphML):
+    def test_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        H = nx.read_graphml(self.simple_directed_fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+        PG = nx.parse_graphml(self.simple_directed_data)
+        assert sorted(G.nodes()) == sorted(PG.nodes())
+        assert sorted(G.edges()) == sorted(PG.edges())
+        assert sorted(G.edges(data=True)) == sorted(PG.edges(data=True))
+
+    def test_read_simple_undirected_graphml(self):
+        G = self.simple_undirected_graph
+        H = nx.read_graphml(self.simple_undirected_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.simple_undirected_fh.seek(0)
+
+        PG = nx.parse_graphml(self.simple_undirected_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_undirected_multigraph_graphml(self):
+        G = self.undirected_multigraph
+        H = nx.read_graphml(self.undirected_multigraph_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.undirected_multigraph_fh.seek(0)
+
+        PG = nx.parse_graphml(self.undirected_multigraph_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_undirected_multigraph_no_multiedge_graphml(self):
+        G = self.undirected_multigraph_no_multiedge
+        H = nx.read_graphml(self.undirected_multigraph_no_multiedge_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.undirected_multigraph_no_multiedge_fh.seek(0)
+
+        PG = nx.parse_graphml(self.undirected_multigraph_no_multiedge_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_undirected_multigraph_only_ids_for_multiedges_graphml(self):
+        G = self.multigraph_only_ids_for_multiedges
+        H = nx.read_graphml(self.multigraph_only_ids_for_multiedges_fh)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        self.multigraph_only_ids_for_multiedges_fh.seek(0)
+
+        PG = nx.parse_graphml(self.multigraph_only_ids_for_multiedges_data)
+        assert nodes_equal(G.nodes(), PG.nodes())
+        assert edges_equal(G.edges(), PG.edges())
+
+    def test_read_attribute_graphml(self):
+        G = self.attribute_graph
+        H = nx.read_graphml(self.attribute_fh)
+        assert nodes_equal(G.nodes(True), sorted(H.nodes(data=True)))
+        ge = sorted(G.edges(data=True))
+        he = sorted(H.edges(data=True))
+        for a, b in zip(ge, he):
+            assert a == b
+        self.attribute_fh.seek(0)
+
+        PG = nx.parse_graphml(self.attribute_data)
+        assert sorted(G.nodes(True)) == sorted(PG.nodes(data=True))
+        ge = sorted(G.edges(data=True))
+        he = sorted(PG.edges(data=True))
+        for a, b in zip(ge, he):
+            assert a == b
+
+    def test_node_default_attribute_graphml(self):
+        G = self.node_attribute_default_graph
+        H = nx.read_graphml(self.node_attribute_default_fh)
+        assert G.graph["node_default"] == H.graph["node_default"]
+
+    def test_directed_edge_in_undirected(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G">
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <edge source="n0" target="n1"/>
+    <edge source="n1" target="n2" directed='true'/>
+  </graph>
+</graphml>"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_undirected_edge_in_directed(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G" edgedefault='directed'>
+    <node id="n0"/>
+    <node id="n1"/>
+    <node id="n2"/>
+    <edge source="n0" target="n1"/>
+    <edge source="n1" target="n2" directed='false'/>
+  </graph>
+</graphml>"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_key_raise(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="color" attr.type="string">
+    <default>yellow</default>
+  </key>
+  <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">green</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">blue</data>
+    </node>
+    <edge id="e0" source="n0" target="n2">
+      <data key="d2">1.0</data>
+    </edge>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_hyperedge_raise(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="color" attr.type="string">
+    <default>yellow</default>
+  </key>
+  <key id="d1" for="edge" attr.name="weight" attr.type="double"/>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">green</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">blue</data>
+    </node>
+    <hyperedge id="e0" source="n0" target="n2">
+       <endpoint node="n0"/>
+       <endpoint node="n1"/>
+       <endpoint node="n2"/>
+    </hyperedge>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, s)
+
+    def test_multigraph_keys(self):
+        # Test that reading multigraphs uses edge id attributes as keys
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <graph id="G" edgedefault="directed">
+    <node id="n0"/>
+    <node id="n1"/>
+    <edge id="e0" source="n0" target="n1"/>
+    <edge id="e1" source="n0" target="n1"/>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        expected = [("n0", "n1", "e0"), ("n0", "n1", "e1")]
+        assert sorted(G.edges(keys=True)) == expected
+        fh.seek(0)
+        H = nx.parse_graphml(s)
+        assert sorted(H.edges(keys=True)) == expected
+
+    def test_preserve_multi_edge_data(self):
+        """
+        Test that data and keys of edges are preserved on consequent
+        write and reads
+        """
+        G = nx.MultiGraph()
+        G.add_node(1)
+        G.add_node(2)
+        G.add_edges_from(
+            [
+                # edges with no data, no keys:
+                (1, 2),
+                # edges with only data:
+                (1, 2, {"key": "data_key1"}),
+                (1, 2, {"id": "data_id2"}),
+                (1, 2, {"key": "data_key3", "id": "data_id3"}),
+                # edges with both data and keys:
+                (1, 2, 103, {"key": "data_key4"}),
+                (1, 2, 104, {"id": "data_id5"}),
+                (1, 2, 105, {"key": "data_key6", "id": "data_id7"}),
+            ]
+        )
+        fh = io.BytesIO()
+        nx.write_graphml(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh, node_type=int)
+        assert edges_equal(G.edges(data=True, keys=True), H.edges(data=True, keys=True))
+        assert G._adj == H._adj
+
+        Gadj = {
+            str(node): {
+                str(nbr): {str(ekey): dd for ekey, dd in key_dict.items()}
+                for nbr, key_dict in nbr_dict.items()
+            }
+            for node, nbr_dict in G._adj.items()
+        }
+        fh.seek(0)
+        HH = nx.read_graphml(fh, node_type=str, edge_key_type=str)
+        assert Gadj == HH._adj
+
+        fh.seek(0)
+        string_fh = fh.read()
+        HH = nx.parse_graphml(string_fh, node_type=str, edge_key_type=str)
+        assert Gadj == HH._adj
+
+    def test_yfiles_extension(self):
+        data = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xmlns:y="http://www.yworks.com/xml/graphml"
+         xmlns:yed="http://www.yworks.com/xml/yed/3"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <!--Created by yFiles for Java 2.7-->
+  <key for="graphml" id="d0" yfiles.type="resources"/>
+  <key attr.name="url" attr.type="string" for="node" id="d1"/>
+  <key attr.name="description" attr.type="string" for="node" id="d2"/>
+  <key for="node" id="d3" yfiles.type="nodegraphics"/>
+  <key attr.name="Description" attr.type="string" for="graph" id="d4">
+    <default/>
+  </key>
+  <key attr.name="url" attr.type="string" for="edge" id="d5"/>
+  <key attr.name="description" attr.type="string" for="edge" id="d6"/>
+  <key for="edge" id="d7" yfiles.type="edgegraphics"/>
+  <graph edgedefault="directed" id="G">
+    <node id="n0">
+      <data key="d3">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="125.0" y="100.0"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content"
+           borderDistance="0.0" fontFamily="Dialog" fontSize="13"
+           fontStyle="plain" hasBackgroundColor="false" hasLineColor="false"
+           height="19.1328125" modelName="internal" modelPosition="c"
+           textColor="#000000" visible="true" width="12.27099609375"
+           x="8.864501953125" y="5.43359375">1</y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <node id="n1">
+      <data key="d3">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="183.0" y="205.0"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content"
+          borderDistance="0.0" fontFamily="Dialog" fontSize="13"
+          fontStyle="plain" hasBackgroundColor="false" hasLineColor="false"
+          height="19.1328125" modelName="internal" modelPosition="c"
+          textColor="#000000" visible="true" width="12.27099609375"
+          x="8.864501953125" y="5.43359375">2</y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <node id="n2">
+      <data key="d6" xml:space="preserve"><![CDATA[description
+line1
+line2]]></data>
+      <data key="d3">
+        <y:GenericNode configuration="com.yworks.flowchart.terminator">
+          <y:Geometry height="40.0" width="80.0" x="950.0" y="286.0"/>
+          <y:Fill color="#E8EEF7" color2="#B7C9E3" transparent="false"/>
+          <y:BorderStyle color="#000000" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content"
+          fontFamily="Dialog" fontSize="12" fontStyle="plain"
+          hasBackgroundColor="false" hasLineColor="false" height="17.96875"
+          horizontalTextPosition="center" iconTextGap="4" modelName="custom"
+          textColor="#000000" verticalTextPosition="bottom" visible="true"
+          width="67.984375" x="6.0078125" xml:space="preserve"
+          y="11.015625">3<y:LabelModel>
+          <y:SmartNodeLabelModel distance="4.0"/></y:LabelModel>
+          <y:ModelParameter><y:SmartNodeLabelModelParameter labelRatioX="0.0"
+          labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0"
+          offsetY="0.0" upX="0.0" upY="-1.0"/></y:ModelParameter></y:NodeLabel>
+        </y:GenericNode>
+      </data>
+    </node>
+    <edge id="e0" source="n0" target="n1">
+      <data key="d7">
+        <y:PolyLineEdge>
+          <y:Path sx="0.0" sy="0.0" tx="0.0" ty="0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+  </graph>
+  <data key="d0">
+    <y:Resources/>
+  </data>
+</graphml>
+"""
+        fh = io.BytesIO(data.encode("UTF-8"))
+        G = nx.read_graphml(fh, force_multigraph=True)
+        assert list(G.edges()) == [("n0", "n1")]
+        assert G.has_edge("n0", "n1", key="e0")
+        assert G.nodes["n0"]["label"] == "1"
+        assert G.nodes["n1"]["label"] == "2"
+        assert G.nodes["n2"]["label"] == "3"
+        assert G.nodes["n0"]["shape_type"] == "rectangle"
+        assert G.nodes["n1"]["shape_type"] == "rectangle"
+        assert G.nodes["n2"]["shape_type"] == "com.yworks.flowchart.terminator"
+        assert G.nodes["n2"]["description"] == "description\nline1\nline2"
+        fh.seek(0)
+        G = nx.read_graphml(fh)
+        assert list(G.edges()) == [("n0", "n1")]
+        assert G["n0"]["n1"]["id"] == "e0"
+        assert G.nodes["n0"]["label"] == "1"
+        assert G.nodes["n1"]["label"] == "2"
+        assert G.nodes["n2"]["label"] == "3"
+        assert G.nodes["n0"]["shape_type"] == "rectangle"
+        assert G.nodes["n1"]["shape_type"] == "rectangle"
+        assert G.nodes["n2"]["shape_type"] == "com.yworks.flowchart.terminator"
+        assert G.nodes["n2"]["description"] == "description\nline1\nline2"
+
+        H = nx.parse_graphml(data, force_multigraph=True)
+        assert list(H.edges()) == [("n0", "n1")]
+        assert H.has_edge("n0", "n1", key="e0")
+        assert H.nodes["n0"]["label"] == "1"
+        assert H.nodes["n1"]["label"] == "2"
+        assert H.nodes["n2"]["label"] == "3"
+
+        H = nx.parse_graphml(data)
+        assert list(H.edges()) == [("n0", "n1")]
+        assert H["n0"]["n1"]["id"] == "e0"
+        assert H.nodes["n0"]["label"] == "1"
+        assert H.nodes["n1"]["label"] == "2"
+        assert H.nodes["n2"]["label"] == "3"
+
+    def test_bool(self):
+        s = """<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G" edgedefault="directed">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+    <node id="n1"/>
+    <node id="n2">
+      <data key="d0">false</data>
+    </node>
+    <node id="n3">
+      <data key="d0">FaLsE</data>
+    </node>
+    <node id="n4">
+      <data key="d0">True</data>
+    </node>
+    <node id="n5">
+      <data key="d0">0</data>
+    </node>
+    <node id="n6">
+      <data key="d0">1</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        H = nx.parse_graphml(s)
+        for graph in [G, H]:
+            assert graph.nodes["n0"]["test"]
+            assert not graph.nodes["n2"]["test"]
+            assert not graph.nodes["n3"]["test"]
+            assert graph.nodes["n4"]["test"]
+            assert not graph.nodes["n5"]["test"]
+            assert graph.nodes["n6"]["test"]
+
+    def test_graphml_header_line(self):
+        good = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        bad = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml>
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        ugly = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="https://ghghgh">
+  <key id="d0" for="node" attr.name="test" attr.type="boolean">
+    <default>false</default>
+  </key>
+  <graph id="G">
+    <node id="n0">
+      <data key="d0">true</data>
+    </node>
+  </graph>
+</graphml>
+"""
+        for s in (good, bad):
+            fh = io.BytesIO(s.encode("UTF-8"))
+            G = nx.read_graphml(fh)
+            H = nx.parse_graphml(s)
+            for graph in [G, H]:
+                assert graph.nodes["n0"]["test"]
+
+        fh = io.BytesIO(ugly.encode("UTF-8"))
+        pytest.raises(nx.NetworkXError, nx.read_graphml, fh)
+        pytest.raises(nx.NetworkXError, nx.parse_graphml, ugly)
+
+    def test_read_attributes_with_groups(self):
+        data = """\
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:java="http://www.yworks.com/xml/yfiles-common/1.0/java" xmlns:sys="http://www.yworks.com/xml/yfiles-common/markup/primitives/2.0" xmlns:x="http://www.yworks.com/xml/yfiles-common/markup/2.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:y="http://www.yworks.com/xml/graphml" xmlns:yed="http://www.yworks.com/xml/yed/3" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://www.yworks.com/xml/schema/graphml/1.1/ygraphml.xsd">
+  <!--Created by yEd 3.17-->
+  <key attr.name="Description" attr.type="string" for="graph" id="d0"/>
+  <key for="port" id="d1" yfiles.type="portgraphics"/>
+  <key for="port" id="d2" yfiles.type="portgeometry"/>
+  <key for="port" id="d3" yfiles.type="portuserdata"/>
+  <key attr.name="CustomProperty" attr.type="string" for="node" id="d4">
+    <default/>
+  </key>
+  <key attr.name="url" attr.type="string" for="node" id="d5"/>
+  <key attr.name="description" attr.type="string" for="node" id="d6"/>
+  <key for="node" id="d7" yfiles.type="nodegraphics"/>
+  <key for="graphml" id="d8" yfiles.type="resources"/>
+  <key attr.name="url" attr.type="string" for="edge" id="d9"/>
+  <key attr.name="description" attr.type="string" for="edge" id="d10"/>
+  <key for="edge" id="d11" yfiles.type="edgegraphics"/>
+  <graph edgedefault="directed" id="G">
+    <data key="d0"/>
+    <node id="n0">
+      <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+      <data key="d6"/>
+      <data key="d7">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="125.0" y="-255.4611111111111"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">2<y:LabelModel>
+              <y:SmartNodeLabelModel distance="4.0"/>
+            </y:LabelModel>
+            <y:ModelParameter>
+              <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+            </y:ModelParameter>
+          </y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <node id="n1" yfiles.foldertype="group">
+      <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+      <data key="d5"/>
+      <data key="d6"/>
+      <data key="d7">
+        <y:ProxyAutoBoundsNode>
+          <y:Realizers active="0">
+            <y:GroupNode>
+              <y:Geometry height="250.38333333333333" width="140.0" x="-30.0" y="-330.3833333333333"/>
+              <y:Fill color="#F5F5F5" transparent="false"/>
+              <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+              <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="140.0" x="0.0" y="0.0">Group 3</y:NodeLabel>
+              <y:Shape type="roundrectangle"/>
+              <y:State closed="false" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+              <y:Insets bottom="15" bottomF="15.0" left="15" leftF="15.0" right="15" rightF="15.0" top="15" topF="15.0"/>
+              <y:BorderInsets bottom="1" bottomF="1.0" left="0" leftF="0.0" right="0" rightF="0.0" top="1" topF="1.0001736111111086"/>
+            </y:GroupNode>
+            <y:GroupNode>
+              <y:Geometry height="50.0" width="50.0" x="0.0" y="60.0"/>
+              <y:Fill color="#F5F5F5" transparent="false"/>
+              <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+              <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="65.201171875" x="-7.6005859375" y="0.0">Folder 3</y:NodeLabel>
+              <y:Shape type="roundrectangle"/>
+              <y:State closed="true" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+              <y:Insets bottom="5" bottomF="5.0" left="5" leftF="5.0" right="5" rightF="5.0" top="5" topF="5.0"/>
+              <y:BorderInsets bottom="0" bottomF="0.0" left="0" leftF="0.0" right="0" rightF="0.0" top="0" topF="0.0"/>
+            </y:GroupNode>
+          </y:Realizers>
+        </y:ProxyAutoBoundsNode>
+      </data>
+      <graph edgedefault="directed" id="n1:">
+        <node id="n1::n0" yfiles.foldertype="group">
+          <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+          <data key="d5"/>
+          <data key="d6"/>
+          <data key="d7">
+            <y:ProxyAutoBoundsNode>
+              <y:Realizers active="0">
+                <y:GroupNode>
+                  <y:Geometry height="83.46111111111111" width="110.0" x="-15.0" y="-292.9222222222222"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="110.0" x="0.0" y="0.0">Group 1</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="false" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="15" bottomF="15.0" left="15" leftF="15.0" right="15" rightF="15.0" top="15" topF="15.0"/>
+                  <y:BorderInsets bottom="1" bottomF="1.0" left="0" leftF="0.0" right="0" rightF="0.0" top="1" topF="1.0001736111111086"/>
+                </y:GroupNode>
+                <y:GroupNode>
+                  <y:Geometry height="50.0" width="50.0" x="0.0" y="60.0"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="65.201171875" x="-7.6005859375" y="0.0">Folder 1</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="true" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="5" bottomF="5.0" left="5" leftF="5.0" right="5" rightF="5.0" top="5" topF="5.0"/>
+                  <y:BorderInsets bottom="0" bottomF="0.0" left="0" leftF="0.0" right="0" rightF="0.0" top="0" topF="0.0"/>
+                </y:GroupNode>
+              </y:Realizers>
+            </y:ProxyAutoBoundsNode>
+          </data>
+          <graph edgedefault="directed" id="n1::n0:">
+            <node id="n1::n0::n0">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="50.0" y="-255.4611111111111"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">1<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+            <node id="n1::n0::n1">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="0.0" y="-255.4611111111111"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">3<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+          </graph>
+        </node>
+        <node id="n1::n1" yfiles.foldertype="group">
+          <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+          <data key="d5"/>
+          <data key="d6"/>
+          <data key="d7">
+            <y:ProxyAutoBoundsNode>
+              <y:Realizers active="0">
+                <y:GroupNode>
+                  <y:Geometry height="83.46111111111111" width="110.0" x="-15.0" y="-179.4611111111111"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="110.0" x="0.0" y="0.0">Group 2</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="false" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="15" bottomF="15.0" left="15" leftF="15.0" right="15" rightF="15.0" top="15" topF="15.0"/>
+                  <y:BorderInsets bottom="1" bottomF="1.0" left="0" leftF="0.0" right="0" rightF="0.0" top="1" topF="1.0001736111111086"/>
+                </y:GroupNode>
+                <y:GroupNode>
+                  <y:Geometry height="50.0" width="50.0" x="0.0" y="60.0"/>
+                  <y:Fill color="#F5F5F5" transparent="false"/>
+                  <y:BorderStyle color="#000000" type="dashed" width="1.0"/>
+                  <y:NodeLabel alignment="right" autoSizePolicy="node_width" backgroundColor="#EBEBEB" borderDistance="0.0" fontFamily="Dialog" fontSize="15" fontStyle="plain" hasLineColor="false" height="21.4609375" horizontalTextPosition="center" iconTextGap="4" modelName="internal" modelPosition="t" textColor="#000000" verticalTextPosition="bottom" visible="true" width="65.201171875" x="-7.6005859375" y="0.0">Folder 2</y:NodeLabel>
+                  <y:Shape type="roundrectangle"/>
+                  <y:State closed="true" closedHeight="50.0" closedWidth="50.0" innerGraphDisplayEnabled="false"/>
+                  <y:Insets bottom="5" bottomF="5.0" left="5" leftF="5.0" right="5" rightF="5.0" top="5" topF="5.0"/>
+                  <y:BorderInsets bottom="0" bottomF="0.0" left="0" leftF="0.0" right="0" rightF="0.0" top="0" topF="0.0"/>
+                </y:GroupNode>
+              </y:Realizers>
+            </y:ProxyAutoBoundsNode>
+          </data>
+          <graph edgedefault="directed" id="n1::n1:">
+            <node id="n1::n1::n0">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="0.0" y="-142.0"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">5<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+            <node id="n1::n1::n1">
+              <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+              <data key="d6"/>
+              <data key="d7">
+                <y:ShapeNode>
+                  <y:Geometry height="30.0" width="30.0" x="50.0" y="-142.0"/>
+                  <y:Fill color="#FFCC00" transparent="false"/>
+                  <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+                  <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">6<y:LabelModel>
+                      <y:SmartNodeLabelModel distance="4.0"/>
+                    </y:LabelModel>
+                    <y:ModelParameter>
+                      <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+                    </y:ModelParameter>
+                  </y:NodeLabel>
+                  <y:Shape type="rectangle"/>
+                </y:ShapeNode>
+              </data>
+            </node>
+          </graph>
+        </node>
+      </graph>
+    </node>
+    <node id="n2">
+      <data key="d4"><![CDATA[CustomPropertyValue]]></data>
+      <data key="d6"/>
+      <data key="d7">
+        <y:ShapeNode>
+          <y:Geometry height="30.0" width="30.0" x="125.0" y="-142.0"/>
+          <y:Fill color="#FFCC00" transparent="false"/>
+          <y:BorderStyle color="#000000" raised="false" type="line" width="1.0"/>
+          <y:NodeLabel alignment="center" autoSizePolicy="content" fontFamily="Dialog" fontSize="12" fontStyle="plain" hasBackgroundColor="false" hasLineColor="false" height="17.96875" horizontalTextPosition="center" iconTextGap="4" modelName="custom" textColor="#000000" verticalTextPosition="bottom" visible="true" width="11.634765625" x="9.1826171875" y="6.015625">9<y:LabelModel>
+              <y:SmartNodeLabelModel distance="4.0"/>
+            </y:LabelModel>
+            <y:ModelParameter>
+              <y:SmartNodeLabelModelParameter labelRatioX="0.0" labelRatioY="0.0" nodeRatioX="0.0" nodeRatioY="0.0" offsetX="0.0" offsetY="0.0" upX="0.0" upY="-1.0"/>
+            </y:ModelParameter>
+          </y:NodeLabel>
+          <y:Shape type="rectangle"/>
+        </y:ShapeNode>
+      </data>
+    </node>
+    <edge id="n1::n1::e0" source="n1::n1::n0" target="n1::n1::n1">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+    <edge id="n1::n0::e0" source="n1::n0::n1" target="n1::n0::n0">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+    <edge id="e0" source="n1::n0::n0" target="n0">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+    <edge id="e1" source="n1::n1::n1" target="n2">
+      <data key="d10"/>
+      <data key="d11">
+        <y:PolyLineEdge>
+          <y:Path sx="15.0" sy="-0.0" tx="-15.0" ty="-0.0"/>
+          <y:LineStyle color="#000000" type="line" width="1.0"/>
+          <y:Arrows source="none" target="standard"/>
+          <y:BendStyle smoothed="false"/>
+        </y:PolyLineEdge>
+      </data>
+    </edge>
+  </graph>
+  <data key="d8">
+    <y:Resources/>
+  </data>
+</graphml>
+"""
+        # verify that nodes / attributes are correctly read when part of a group
+        fh = io.BytesIO(data.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        data = [x for _, x in G.nodes(data=True)]
+        assert len(data) == 9
+        for node_data in data:
+            assert node_data["CustomProperty"] != ""
+
+    def test_long_attribute_type(self):
+        # test that graphs with attr.type="long" (as produced by botch and
+        # dose3) can be parsed
+        s = """<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key attr.name="cudfversion" attr.type="long" for="node" id="d6" />
+  <graph edgedefault="directed">
+    <node id="n1">
+      <data key="d6">4284</data>
+    </node>
+  </graph>
+</graphml>"""
+        fh = io.BytesIO(s.encode("UTF-8"))
+        G = nx.read_graphml(fh)
+        expected = [("n1", {"cudfversion": 4284})]
+        assert sorted(G.nodes(data=True)) == expected
+        fh.seek(0)
+        H = nx.parse_graphml(s)
+        assert sorted(H.nodes(data=True)) == expected
+
+
+class TestWriteGraphML(BaseGraphML):
+    writer = staticmethod(nx.write_graphml_lxml)
+
+    @classmethod
+    def setup_class(cls):
+        BaseGraphML.setup_class()
+        _ = pytest.importorskip("lxml.etree")
+
+    def test_write_interface(self):
+        try:
+            import lxml.etree
+
+            assert nx.write_graphml == nx.write_graphml_lxml
+        except ImportError:
+            assert nx.write_graphml == nx.write_graphml_xml
+
+    def test_write_read_simple_directed_graphml(self):
+        G = self.simple_directed_graph
+        G.graph["hi"] = "there"
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_GraphMLWriter_add_graphs(self):
+        gmlw = GraphMLWriter()
+        G = self.simple_directed_graph
+        H = G.copy()
+        gmlw.add_graphs([G, H])
+
+    def test_write_read_simple_no_prettyprint(self):
+        G = self.simple_directed_graph
+        G.graph["hi"] = "there"
+        G.graph["id"] = "1"
+        fh = io.BytesIO()
+        self.writer(G, fh, prettyprint=False)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert sorted(G.nodes()) == sorted(H.nodes())
+        assert sorted(G.edges()) == sorted(H.edges())
+        assert sorted(G.edges(data=True)) == sorted(H.edges(data=True))
+        self.simple_directed_fh.seek(0)
+
+    def test_write_read_attribute_named_key_ids_graphml(self):
+        from xml.etree.ElementTree import parse
+
+        G = self.attribute_named_key_ids_graph
+        fh = io.BytesIO()
+        self.writer(G, fh, named_key_ids=True)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        fh.seek(0)
+
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges(), directed=True)
+        assert edges_equal(G.edges(data=True), H.edges(data=True), directed=True)
+        self.attribute_named_key_ids_fh.seek(0)
+
+        xml = parse(fh)
+        # Children are the key elements, and the graph element
+        children = list(xml.getroot())
+        assert len(children) == 4
+
+        keys = [child.items() for child in children[:3]]
+
+        assert len(keys) == 3
+        assert ("id", "edge_prop") in keys[0]
+        assert ("attr.name", "edge_prop") in keys[0]
+        assert ("id", "prop2") in keys[1]
+        assert ("attr.name", "prop2") in keys[1]
+        assert ("id", "prop1") in keys[2]
+        assert ("attr.name", "prop1") in keys[2]
+
+        # Confirm the read graph nodes/edge are identical when compared to
+        # default writing behavior.
+        default_behavior_fh = io.BytesIO()
+        nx.write_graphml(G, default_behavior_fh)
+        default_behavior_fh.seek(0)
+        H = nx.read_graphml(default_behavior_fh)
+
+        named_key_ids_behavior_fh = io.BytesIO()
+        nx.write_graphml(G, named_key_ids_behavior_fh, named_key_ids=True)
+        named_key_ids_behavior_fh.seek(0)
+        J = nx.read_graphml(named_key_ids_behavior_fh)
+
+        assert all(n1 == n2 for (n1, n2) in zip(H.nodes, J.nodes))
+        assert all(e1 == e2 for (e1, e2) in zip(H.edges, J.edges))
+
+    def test_write_read_attribute_numeric_type_graphml(self):
+        from xml.etree.ElementTree import parse
+
+        G = self.attribute_numeric_type_graph
+        fh = io.BytesIO()
+        self.writer(G, fh, infer_numeric_types=True)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        fh.seek(0)
+
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges(), directed=True)
+        assert edges_equal(G.edges(data=True), H.edges(data=True), directed=True)
+        self.attribute_numeric_type_fh.seek(0)
+
+        xml = parse(fh)
+        # Children are the key elements, and the graph element
+        children = list(xml.getroot())
+        assert len(children) == 3
+
+        keys = [child.items() for child in children[:2]]
+
+        assert len(keys) == 2
+        assert ("attr.type", "double") in keys[0]
+        assert ("attr.type", "double") in keys[1]
+
+    def test_more_multigraph_keys(self, tmp_path):
+        """Writing keys as edge id attributes means keys become strings.
+        The original keys are stored as data, so read them back in
+        if `str(key) == edge_id`
+        This allows the adjacency to remain the same.
+        """
+        G = nx.MultiGraph()
+        G.add_edges_from([("a", "b", 2), ("a", "b", 3)])
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname)
+        assert H.is_multigraph()
+        assert edges_equal(G.edges(keys=True), H.edges(keys=True))
+        assert G._adj == H._adj
+
+    def test_default_attribute(self):
+        G = nx.Graph(name="Fred")
+        G.add_node(1, label=1, color="green")
+        nx.add_path(G, [0, 1, 2, 3])
+        G.add_edge(1, 2, weight=3)
+        G.graph["node_default"] = {"color": "yellow"}
+        G.graph["edge_default"] = {"weight": 7}
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh, node_type=int)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        assert G.graph == H.graph
+
+    def test_mixed_type_attributes(self):
+        G = nx.MultiGraph()
+        G.add_node("n0", special=False)
+        G.add_node("n1", special=0)
+        G.add_edge("n0", "n1", special=False)
+        G.add_edge("n0", "n1", special=0)
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert not H.nodes["n0"]["special"]
+        assert H.nodes["n1"]["special"] == 0
+        assert not H.edges["n0", "n1", 0]["special"]
+        assert H.edges["n0", "n1", 1]["special"] == 0
+
+    def test_str_number_mixed_type_attributes(self):
+        G = nx.MultiGraph()
+        G.add_node("n0", special="hello")
+        G.add_node("n1", special=0)
+        G.add_edge("n0", "n1", special="hello")
+        G.add_edge("n0", "n1", special=0)
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert H.nodes["n0"]["special"] == "hello"
+        assert H.nodes["n1"]["special"] == 0
+        assert H.edges["n0", "n1", 0]["special"] == "hello"
+        assert H.edges["n0", "n1", 1]["special"] == 0
+
+    def test_mixed_int_type_number_attributes(self):
+        np = pytest.importorskip("numpy")
+        G = nx.MultiGraph()
+        G.add_node("n0", special=np.int64(0))
+        G.add_node("n1", special=1)
+        G.add_edge("n0", "n1", special=np.int64(2))
+        G.add_edge("n0", "n1", special=3)
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert H.nodes["n0"]["special"] == 0
+        assert H.nodes["n1"]["special"] == 1
+        assert H.edges["n0", "n1", 0]["special"] == 2
+        assert H.edges["n0", "n1", 1]["special"] == 3
+
+    def test_multigraph_to_graph(self, tmp_path):
+        # test converting multigraph to graph if no parallel edges found
+        G = nx.MultiGraph()
+        G.add_edges_from([("a", "b", 2), ("b", "c", 3)])  # no multiedges
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname)
+        assert not H.is_multigraph()
+        H = nx.read_graphml(fname, force_multigraph=True)
+        assert H.is_multigraph()
+
+        # add a multiedge
+        G.add_edge("a", "b", "e-id")
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname)
+        assert H.is_multigraph()
+        H = nx.read_graphml(fname, force_multigraph=True)
+        assert H.is_multigraph()
+
+    def test_write_generate_edge_id_from_attribute(self, tmp_path):
+        from xml.etree.ElementTree import parse
+
+        G = nx.Graph()
+        G.add_edges_from([("a", "b"), ("b", "c"), ("a", "c")])
+        edge_attributes = {e: str(e) for e in G.edges}
+        nx.set_edge_attributes(G, edge_attributes, "eid")
+        fname = tmp_path / "test.graphml"
+        # set edge_id_from_attribute e.g. "eid" for write_graphml()
+        self.writer(G, fname, edge_id_from_attribute="eid")
+        # set edge_id_from_attribute e.g. "eid" for generate_graphml()
+        generator = nx.generate_graphml(G, edge_id_from_attribute="eid")
+
+        H = nx.read_graphml(fname)
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        # NetworkX adds explicit edge "id" from file as attribute
+        nx.set_edge_attributes(G, edge_attributes, "id")
+        assert edges_equal(G.edges(data=True), H.edges(data=True))
+
+        tree = parse(fname)
+        children = list(tree.getroot())
+        assert len(children) == 2
+        edge_ids = [
+            edge.attrib["id"]
+            for edge in tree.getroot().findall(
+                ".//{http://graphml.graphdrawing.org/xmlns}edge"
+            )
+        ]
+        # verify edge id value is equal to specified attribute value
+        assert sorted(edge_ids) == sorted(edge_attributes.values())
+
+        # check graphml generated from generate_graphml()
+        data = "".join(generator)
+        J = nx.parse_graphml(data)
+        assert sorted(G.nodes()) == sorted(J.nodes())
+        assert sorted(G.edges()) == sorted(J.edges())
+        # NetworkX adds explicit edge "id" from file as attribute
+        nx.set_edge_attributes(G, edge_attributes, "id")
+        assert edges_equal(G.edges(data=True), J.edges(data=True))
+
+    def test_multigraph_write_generate_edge_id_from_attribute(self, tmp_path):
+        from xml.etree.ElementTree import parse
+
+        G = nx.MultiGraph()
+        G.add_edges_from([("a", "b"), ("b", "c"), ("a", "c"), ("a", "b")])
+        edge_attributes = {e: str(e) for e in G.edges}
+        nx.set_edge_attributes(G, edge_attributes, "eid")
+        fname = tmp_path / "test.graphml"
+        # set edge_id_from_attribute e.g. "eid" for write_graphml()
+        self.writer(G, fname, edge_id_from_attribute="eid")
+        # set edge_id_from_attribute e.g. "eid" for generate_graphml()
+        generator = nx.generate_graphml(G, edge_id_from_attribute="eid")
+
+        H = nx.read_graphml(fname)
+        assert H.is_multigraph()
+        H = nx.read_graphml(fname, force_multigraph=True)
+        assert H.is_multigraph()
+
+        assert nodes_equal(G.nodes(), H.nodes())
+        assert edges_equal(G.edges(), H.edges())
+        assert sorted(data.get("eid") for u, v, data in H.edges(data=True)) == sorted(
+            edge_attributes.values()
+        )
+        # NetworkX uses edge_ids as keys in multigraphs if no key
+        assert sorted(key for u, v, key in H.edges(keys=True)) == sorted(
+            edge_attributes.values()
+        )
+
+        tree = parse(fname)
+        children = list(tree.getroot())
+        assert len(children) == 2
+        edge_ids = [
+            edge.attrib["id"]
+            for edge in tree.getroot().findall(
+                ".//{http://graphml.graphdrawing.org/xmlns}edge"
+            )
+        ]
+        # verify edge id value is equal to specified attribute value
+        assert sorted(edge_ids) == sorted(edge_attributes.values())
+
+        # check graphml generated from generate_graphml()
+        graphml_data = "".join(generator)
+        J = nx.parse_graphml(graphml_data)
+        assert J.is_multigraph()
+
+        assert nodes_equal(G.nodes(), J.nodes())
+        assert edges_equal(G.edges(), J.edges())
+        assert sorted(data.get("eid") for u, v, data in J.edges(data=True)) == sorted(
+            edge_attributes.values()
+        )
+        # NetworkX uses edge_ids as keys in multigraphs if no key
+        assert sorted(key for u, v, key in J.edges(keys=True)) == sorted(
+            edge_attributes.values()
+        )
+
+    def test_numpy_float64(self, tmp_path):
+        np = pytest.importorskip("numpy")
+        wt = np.float64(3.4)
+        G = nx.Graph([(1, 2, {"weight": wt})])
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname, node_type=int)
+        assert G.edges == H.edges
+        wtG = G[1][2]["weight"]
+        wtH = H[1][2]["weight"]
+        assert wtG == pytest.approx(wtH, abs=1e-6)
+        assert type(wtG) is np.float64
+        assert type(wtH) is float
+
+    def test_numpy_float32(self, tmp_path):
+        np = pytest.importorskip("numpy")
+        wt = np.float32(3.4)
+        G = nx.Graph([(1, 2, {"weight": wt})])
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname, node_type=int)
+        assert G.edges == H.edges
+        wtG = G[1][2]["weight"]
+        wtH = H[1][2]["weight"]
+        assert wtG == pytest.approx(wtH, abs=1e-6)
+        assert type(wtG) is np.float32
+        assert type(wtH) is float
+
+    def test_numpy_float64_inference(self, tmp_path):
+        np = pytest.importorskip("numpy")
+        G = self.attribute_numeric_type_graph
+        G.edges[("n1", "n1")]["weight"] = np.float64(1.1)
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname, infer_numeric_types=True)
+        H = nx.read_graphml(fname)
+        assert G._adj == H._adj
+
+    def test_unicode_attributes(self, tmp_path):
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        node_type = str
+        G.add_edge(name1, "Radiohead", foo=name2)
+        fname = tmp_path / "test.graphml"
+        self.writer(G, fname)
+        H = nx.read_graphml(fname, node_type=node_type)
+        assert G._adj == H._adj
+
+    def test_unicode_escape(self):
+        # test for handling json escaped strings in python 2 Issue #1880
+        import json
+
+        a = {"a": '{"a": "123"}'}  # an object with many chars to escape
+        sa = json.dumps(a)
+        G = nx.Graph()
+        G.graph["test"] = sa
+        fh = io.BytesIO()
+        self.writer(G, fh)
+        fh.seek(0)
+        H = nx.read_graphml(fh)
+        assert G.graph["test"] == H.graph["test"]
+
+
+class TestXMLGraphML(TestWriteGraphML):
+    writer = staticmethod(nx.write_graphml_xml)
+
+    @classmethod
+    def setup_class(cls):
+        TestWriteGraphML.setup_class()
+
+
+def test_exception_for_unsupported_datatype_node_attr():
+    """Test that a detailed exception is raised when an attribute is of a type
+    not supported by GraphML, e.g. a list"""
+    pytest.importorskip("lxml.etree")
+    # node attribute
+    G = nx.Graph()
+    G.add_node(0, my_list_attribute=[0, 1, 2])
+    fh = io.BytesIO()
+    with pytest.raises(TypeError, match="GraphML does not support"):
+        nx.write_graphml(G, fh)
+
+
+def test_exception_for_unsupported_datatype_edge_attr():
+    """Test that a detailed exception is raised when an attribute is of a type
+    not supported by GraphML, e.g. a list"""
+    pytest.importorskip("lxml.etree")
+    # edge attribute
+    G = nx.Graph()
+    G.add_edge(0, 1, my_list_attribute=[0, 1, 2])
+    fh = io.BytesIO()
+    with pytest.raises(TypeError, match="GraphML does not support"):
+        nx.write_graphml(G, fh)
+
+
+def test_exception_for_unsupported_datatype_graph_attr():
+    """Test that a detailed exception is raised when an attribute is of a type
+    not supported by GraphML, e.g. a list"""
+    pytest.importorskip("lxml.etree")
+    # graph attribute
+    G = nx.Graph()
+    G.graph["my_list_attribute"] = [0, 1, 2]
+    fh = io.BytesIO()
+    with pytest.raises(TypeError, match="GraphML does not support"):
+        nx.write_graphml(G, fh)
+
+
+def test_empty_attribute():
+    """Tests that a GraphML string with an empty attribute can be parsed
+    correctly."""
+    s = """<?xml version='1.0' encoding='utf-8'?>
+    <graphml>
+      <key id="d1" for="node" attr.name="foo" attr.type="string"/>
+      <key id="d2" for="node" attr.name="bar" attr.type="string"/>
+      <graph>
+        <node id="0">
+          <data key="d1">aaa</data>
+          <data key="d2">bbb</data>
+        </node>
+        <node id="1">
+          <data key="d1">ccc</data>
+          <data key="d2"></data>
+        </node>
+      </graph>
+    </graphml>"""
+    fh = io.BytesIO(s.encode("UTF-8"))
+    G = nx.read_graphml(fh)
+    assert G.nodes["0"] == {"foo": "aaa", "bar": "bbb"}
+    assert G.nodes["1"] == {"foo": "ccc", "bar": ""}
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_leda.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_leda.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ac5ecc34bf9b42bd49e316bdc72e0e56c76a616
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_leda.py
@@ -0,0 +1,30 @@
+import io
+
+import networkx as nx
+
+
+class TestLEDA:
+    def test_parse_leda(self):
+        data = """#header section         \nLEDA.GRAPH \nstring\nint\n-1\n#nodes section\n5 \n|{v1}| \n|{v2}| \n|{v3}| \n|{v4}| \n|{v5}| \n\n#edges section\n7 \n1 2 0 |{4}| \n1 3 0 |{3}| \n2 3 0 |{2}| \n3 4 0 |{3}| \n3 5 0 |{7}| \n4 5 0 |{6}| \n5 1 0 |{foo}|"""
+        G = nx.parse_leda(data)
+        G = nx.parse_leda(data.split("\n"))
+        assert sorted(G.nodes()) == ["v1", "v2", "v3", "v4", "v5"]
+        assert sorted(G.edges(data=True)) == [
+            ("v1", "v2", {"label": "4"}),
+            ("v1", "v3", {"label": "3"}),
+            ("v2", "v3", {"label": "2"}),
+            ("v3", "v4", {"label": "3"}),
+            ("v3", "v5", {"label": "7"}),
+            ("v4", "v5", {"label": "6"}),
+            ("v5", "v1", {"label": "foo"}),
+        ]
+
+    def test_read_LEDA(self):
+        fh = io.BytesIO()
+        data = """#header section         \nLEDA.GRAPH \nstring\nint\n-1\n#nodes section\n5 \n|{v1}| \n|{v2}| \n|{v3}| \n|{v4}| \n|{v5}| \n\n#edges section\n7 \n1 2 0 |{4}| \n1 3 0 |{3}| \n2 3 0 |{2}| \n3 4 0 |{3}| \n3 5 0 |{7}| \n4 5 0 |{6}| \n5 1 0 |{foo}|"""
+        G = nx.parse_leda(data)
+        fh.write(data.encode("UTF-8"))
+        fh.seek(0)
+        Gin = nx.read_leda(fh)
+        assert sorted(G.nodes()) == sorted(Gin.nodes())
+        assert sorted(G.edges()) == sorted(Gin.edges())
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_p2g.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_p2g.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6e36bfbca87db83a3732d737355f051e3821a1e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_p2g.py
@@ -0,0 +1,63 @@
+import io
+
+import networkx as nx
+from networkx.readwrite.p2g import read_p2g, write_p2g
+from networkx.utils import edges_equal
+
+
+class TestP2G:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.Graph(name="test")
+        e = [("a", "b"), ("b", "c"), ("c", "d"), ("d", "e"), ("e", "f"), ("a", "f")]
+        cls.G.add_edges_from(e)
+        cls.G.add_node("g")
+        cls.DG = nx.DiGraph(cls.G)
+
+    def test_read_p2g(self):
+        s = b"""\
+name
+3 4
+a
+1 2
+b
+
+c
+0 2
+"""
+        bytesIO = io.BytesIO(s)
+        DG = read_p2g(bytesIO)
+        assert DG.name == "name"
+        assert sorted(DG) == ["a", "b", "c"]
+        assert edges_equal(
+            DG.edges(), [("a", "c"), ("a", "b"), ("c", "a"), ("c", "c")], directed=True
+        )
+
+    def test_write_p2g(self):
+        s = b"""foo
+3 2
+1
+1 
+2
+2 
+3
+
+"""
+        fh = io.BytesIO()
+        G = nx.DiGraph()
+        G.name = "foo"
+        G.add_edges_from([(1, 2), (2, 3)])
+        write_p2g(G, fh)
+        fh.seek(0)
+        r = fh.read()
+        assert r == s
+
+    def test_write_read_p2g(self):
+        fh = io.BytesIO()
+        G = nx.DiGraph()
+        G.name = "foo"
+        G.add_edges_from([("a", "b"), ("b", "c")])
+        write_p2g(G, fh)
+        fh.seek(0)
+        H = read_p2g(fh)
+        assert edges_equal(G.edges(), H.edges(), directed=True)
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_pajek.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_pajek.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cce4d5a8c0fa120fe1239851046c41eb7eb7014
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_pajek.py
@@ -0,0 +1,128 @@
+"""
+Pajek tests
+"""
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestPajek:
+    @classmethod
+    def setup_class(cls):
+        cls.data = """*network Tralala\n*vertices 4\n   1 "A1"         0.0938 0.0896   ellipse x_fact 1 y_fact 1\n   2 "Bb"         0.8188 0.2458   ellipse x_fact 1 y_fact 1\n   3 "C"          0.3688 0.7792   ellipse x_fact 1\n   4 "D2"         0.9583 0.8563   ellipse x_fact 1\n*arcs\n1 1 1  h2 0 w 3 c Blue s 3 a1 -130 k1 0.6 a2 -130 k2 0.6 ap 0.5 l "Bezier loop" lc BlueViolet fos 20 lr 58 lp 0.3 la 360\n2 1 1  h2 0 a1 120 k1 1.3 a2 -120 k2 0.3 ap 25 l "Bezier arc" lphi 270 la 180 lr 19 lp 0.5\n1 2 1  h2 0 a1 40 k1 2.8 a2 30 k2 0.8 ap 25 l "Bezier arc" lphi 90 la 0 lp 0.65\n4 2 -1  h2 0 w 1 k1 -2 k2 250 ap 25 l "Circular arc" c Red lc OrangeRed\n3 4 1  p Dashed h2 0 w 2 c OliveGreen ap 25 l "Straight arc" lc PineGreen\n1 3 1  p Dashed h2 0 w 5 k1 -1 k2 -20 ap 25 l "Oval arc" c Brown lc Black\n3 3 -1  h1 6 w 1 h2 12 k1 -2 k2 -15 ap 0.5 l "Circular loop" c Red lc OrangeRed lphi 270 la 180"""
+        cls.G = nx.MultiDiGraph()
+        cls.G.add_nodes_from(["A1", "Bb", "C", "D2"])
+        cls.G.add_edges_from(
+            [
+                ("A1", "A1"),
+                ("A1", "Bb"),
+                ("A1", "C"),
+                ("Bb", "A1"),
+                ("C", "C"),
+                ("C", "D2"),
+                ("D2", "Bb"),
+            ]
+        )
+
+        cls.G.graph["name"] = "Tralala"
+
+    def test_parse_pajek_simple(self):
+        # Example without node positions or shape
+        data = """*Vertices 2\n1 "1"\n2 "2"\n*Edges\n1 2\n2 1"""
+        G = nx.parse_pajek(data)
+        assert sorted(G.nodes()) == ["1", "2"]
+        assert edges_equal(G.edges(), [("1", "2"), ("1", "2")])
+
+    def test_parse_pajek(self):
+        G = nx.parse_pajek(self.data)
+        assert sorted(G.nodes()) == ["A1", "Bb", "C", "D2"]
+        assert edges_equal(
+            G.edges(),
+            [
+                ("A1", "A1"),
+                ("A1", "Bb"),
+                ("A1", "C"),
+                ("Bb", "A1"),
+                ("C", "C"),
+                ("C", "D2"),
+                ("D2", "Bb"),
+            ],
+            directed=True,
+        )
+
+    def test_parse_pajek_mat(self):
+        data = """*Vertices 3\n1 "one"\n2 "two"\n3 "three"\n*Matrix\n1 1 0\n0 1 0\n0 1 0\n"""
+        G = nx.parse_pajek(data)
+        assert set(G.nodes()) == {"one", "two", "three"}
+        assert G.nodes["two"] == {"id": "2"}
+        assert edges_equal(
+            G.edges(),
+            [("one", "one"), ("one", "two"), ("two", "two"), ("three", "two")],
+            directed=True,
+        )
+
+    def test_read_pajek(self, tmp_path):
+        G = nx.parse_pajek(self.data)
+        # Read data from file
+        fname = tmp_path / "test.pjk"
+        with open(fname, "wb") as fh:
+            fh.write(self.data.encode("UTF-8"))
+
+        Gin = nx.read_pajek(fname)
+        assert sorted(G.nodes()) == sorted(Gin.nodes())
+        assert edges_equal(G.edges(), Gin.edges(), directed=True)
+        assert self.G.graph == Gin.graph
+        for n in G:
+            assert G.nodes[n] == Gin.nodes[n]
+
+    def test_write_pajek(self):
+        import io
+
+        G = nx.parse_pajek(self.data)
+        fh = io.BytesIO()
+        nx.write_pajek(G, fh)
+        fh.seek(0)
+        H = nx.read_pajek(fh)
+        assert nodes_equal(list(G), list(H))
+        assert edges_equal(G.edges(), H.edges(), directed=True)
+        # Graph name is left out for now, therefore it is not tested.
+        # assert_equal(G.graph, H.graph)
+
+    def test_ignored_attribute(self):
+        import io
+
+        G = nx.Graph()
+        fh = io.BytesIO()
+        G.add_node(1, int_attr=1)
+        G.add_node(2, empty_attr="  ")
+        G.add_edge(1, 2, int_attr=2)
+        G.add_edge(2, 3, empty_attr="  ")
+
+        import warnings
+
+        with warnings.catch_warnings(record=True) as w:
+            nx.write_pajek(G, fh)
+            assert len(w) == 4
+
+    def test_noname(self):
+        # Make sure we can parse a line such as:  *network
+        # Issue #952
+        line = "*network\n"
+        other_lines = self.data.split("\n")[1:]
+        data = line + "\n".join(other_lines)
+        G = nx.parse_pajek(data)
+
+    def test_unicode(self):
+        import io
+
+        G = nx.Graph()
+        name1 = chr(2344) + chr(123) + chr(6543)
+        name2 = chr(5543) + chr(1543) + chr(324)
+        G.add_edge(name1, "Radiohead", foo=name2)
+        fh = io.BytesIO()
+        nx.write_pajek(G, fh)
+        fh.seek(0)
+        H = nx.read_pajek(fh)
+        assert nodes_equal(list(G), list(H))
+        assert edges_equal(list(G.edges()), list(H.edges()))
+        assert G.graph == H.graph
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_sparse6.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_sparse6.py
new file mode 100644
index 0000000000000000000000000000000000000000..52cd271d060bd00a4c70e0e21fbdb33990078951
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_sparse6.py
@@ -0,0 +1,166 @@
+from io import BytesIO
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestSparseGraph6:
+    def test_from_sparse6_bytes(self):
+        data = b":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM"
+        G = nx.from_sparse6_bytes(data)
+        assert nodes_equal(
+            sorted(G.nodes()),
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
+        )
+        assert edges_equal(
+            G.edges(),
+            [
+                (0, 1),
+                (0, 2),
+                (0, 3),
+                (1, 12),
+                (1, 14),
+                (2, 13),
+                (2, 15),
+                (3, 16),
+                (3, 17),
+                (4, 7),
+                (4, 9),
+                (4, 11),
+                (5, 6),
+                (5, 8),
+                (5, 9),
+                (6, 10),
+                (6, 11),
+                (7, 8),
+                (7, 10),
+                (8, 12),
+                (9, 15),
+                (10, 14),
+                (11, 13),
+                (12, 16),
+                (13, 17),
+                (14, 17),
+                (15, 16),
+            ],
+        )
+
+    def test_from_bytes_multigraph_graph(self):
+        graph_data = b":An"
+        G = nx.from_sparse6_bytes(graph_data)
+        assert isinstance(G, nx.Graph)
+        multigraph_data = b":Ab"
+        M = nx.from_sparse6_bytes(multigraph_data)
+        assert isinstance(M, nx.MultiGraph)
+
+    def test_read_sparse6(self):
+        data = b":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM"
+        G = nx.from_sparse6_bytes(data)
+        fh = BytesIO(data)
+        Gin = nx.read_sparse6(fh)
+        assert nodes_equal(G.nodes(), Gin.nodes())
+        assert edges_equal(G.edges(), Gin.edges())
+
+    def test_read_many_graph6(self):
+        # Read many graphs into list
+        data = b":Q___eDcdFcDeFcE`GaJ`IaHbKNbLM\n:Q___dCfDEdcEgcbEGbFIaJ`JaHN`IM"
+        fh = BytesIO(data)
+        glist = nx.read_sparse6(fh)
+        assert len(glist) == 2
+        for G in glist:
+            assert nodes_equal(
+                G.nodes(),
+                [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
+            )
+
+
+class TestWriteSparse6:
+    """Unit tests for writing graphs in the sparse6 format.
+
+    Most of the test cases were checked against the sparse6 encoder in Sage.
+
+    """
+
+    def test_null_graph(self):
+        G = nx.null_graph()
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:?\n"
+
+    def test_trivial_graph(self):
+        G = nx.trivial_graph()
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:@\n"
+
+    def test_empty_graph(self):
+        G = nx.empty_graph(5)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:D\n"
+
+    def test_large_empty_graph(self):
+        G = nx.empty_graph(68)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:~?@C\n"
+
+    def test_very_large_empty_graph(self):
+        G = nx.empty_graph(258049)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:~~???~?@\n"
+
+    def test_complete_graph(self):
+        G = nx.complete_graph(4)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        assert result.getvalue() == b">>sparse6<<:CcKI\n"
+
+    def test_no_header(self):
+        G = nx.complete_graph(4)
+        result = BytesIO()
+        nx.write_sparse6(G, result, header=False)
+        assert result.getvalue() == b":CcKI\n"
+
+    def test_padding(self):
+        codes = (b":Cdv", b":DaYn", b":EaYnN", b":FaYnL", b":GaYnLz")
+        for n, code in enumerate(codes, start=4):
+            G = nx.path_graph(n)
+            result = BytesIO()
+            nx.write_sparse6(G, result, header=False)
+            assert result.getvalue() == code + b"\n"
+
+    def test_complete_bipartite(self):
+        G = nx.complete_bipartite_graph(6, 9)
+        result = BytesIO()
+        nx.write_sparse6(G, result)
+        # Compared with sage
+        expected = b">>sparse6<<:Nk" + b"?G`cJ" * 9 + b"\n"
+        assert result.getvalue() == expected
+
+    def test_read_write_inverse(self):
+        for i in list(range(13)) + [31, 47, 62, 63, 64, 72]:
+            m = min(2 * i, i * i // 2)
+            g = nx.random_graphs.gnm_random_graph(i, m, seed=i)
+            gstr = BytesIO()
+            nx.write_sparse6(g, gstr, header=False)
+            # Strip the trailing newline.
+            gstr = gstr.getvalue().rstrip()
+            g2 = nx.from_sparse6_bytes(gstr)
+            assert g2.order() == g.order()
+            assert edges_equal(g2.edges(), g.edges())
+
+    def test_no_directed_graphs(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.write_sparse6(nx.DiGraph(), BytesIO())
+
+    def test_write_path(self, tmp_path):
+        # Get a valid temporary file name
+        fullfilename = str(tmp_path / "test.s6")
+        # file should be closed now, so write_sparse6 can open it
+        nx.write_sparse6(nx.null_graph(), fullfilename)
+        with open(fullfilename, mode="rb") as fh:
+            assert fh.read() == b">>sparse6<<:?\n"
diff --git a/lib/python3.11/site-packages/networkx/readwrite/tests/test_text.py b/lib/python3.11/site-packages/networkx/readwrite/tests/test_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2b744828c916a37784059c869cc990a2473305a
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/tests/test_text.py
@@ -0,0 +1,1742 @@
+import random
+from itertools import product
+from textwrap import dedent
+
+import pytest
+
+import networkx as nx
+
+
+def test_generate_network_text_forest_directed():
+    # Create a directed forest with labels
+    graph = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    for node in graph.nodes:
+        graph.nodes[node]["label"] = "node_" + chr(ord("a") + node)
+
+    node_target = dedent(
+        """
+        ╙── 0
+            ├─╼ 1
+            │   ├─╼ 3
+            │   └─╼ 4
+            └─╼ 2
+                ├─╼ 5
+                └─╼ 6
+        """
+    ).strip()
+
+    label_target = dedent(
+        """
+        ╙── node_a
+            ├─╼ node_b
+            │   ├─╼ node_d
+            │   └─╼ node_e
+            └─╼ node_c
+                ├─╼ node_f
+                └─╼ node_g
+        """
+    ).strip()
+
+    # Basic node case
+    ret = nx.generate_network_text(graph, with_labels=False)
+    assert "\n".join(ret) == node_target
+
+    # Basic label case
+    ret = nx.generate_network_text(graph, with_labels=True)
+    assert "\n".join(ret) == label_target
+
+
+def test_write_network_text_empty_graph():
+    def _graph_str(g, **kw):
+        printbuf = []
+        nx.write_network_text(g, printbuf.append, end="", **kw)
+        return "\n".join(printbuf)
+
+    assert _graph_str(nx.DiGraph()) == "╙"
+    assert _graph_str(nx.Graph()) == "╙"
+    assert _graph_str(nx.DiGraph(), ascii_only=True) == "+"
+    assert _graph_str(nx.Graph(), ascii_only=True) == "+"
+
+
+def test_write_network_text_within_forest_glyph():
+    g = nx.DiGraph()
+    g.add_nodes_from([1, 2, 3, 4])
+    g.add_edge(2, 4)
+    lines = []
+    write = lines.append
+    nx.write_network_text(g, path=write, end="")
+    nx.write_network_text(g, path=write, ascii_only=True, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╟── 1
+        ╟── 2
+        ╎   └─╼ 4
+        ╙── 3
+        +-- 1
+        +-- 2
+        :   L-> 4
+        +-- 3
+        """
+    ).strip()
+    assert text == target
+
+
+def test_generate_network_text_directed_multi_tree():
+    tree1 = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    tree2 = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    forest = nx.disjoint_union_all([tree1, tree2])
+    ret = "\n".join(nx.generate_network_text(forest))
+
+    target = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   │   ├─╼ 3
+        ╎   │   └─╼ 4
+        ╎   └─╼ 2
+        ╎       ├─╼ 5
+        ╎       └─╼ 6
+        ╙── 7
+            ├─╼ 8
+            │   ├─╼ 10
+            │   └─╼ 11
+            └─╼ 9
+                ├─╼ 12
+                └─╼ 13
+        """
+    ).strip()
+    assert ret == target
+
+    tree3 = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    forest = nx.disjoint_union_all([tree1, tree2, tree3])
+    ret = "\n".join(nx.generate_network_text(forest, sources=[0, 14, 7]))
+
+    target = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   │   ├─╼ 3
+        ╎   │   └─╼ 4
+        ╎   └─╼ 2
+        ╎       ├─╼ 5
+        ╎       └─╼ 6
+        ╟── 14
+        ╎   ├─╼ 15
+        ╎   │   ├─╼ 17
+        ╎   │   └─╼ 18
+        ╎   └─╼ 16
+        ╎       ├─╼ 19
+        ╎       └─╼ 20
+        ╙── 7
+            ├─╼ 8
+            │   ├─╼ 10
+            │   └─╼ 11
+            └─╼ 9
+                ├─╼ 12
+                └─╼ 13
+        """
+    ).strip()
+    assert ret == target
+
+    ret = "\n".join(
+        nx.generate_network_text(forest, sources=[0, 14, 7], ascii_only=True)
+    )
+
+    target = dedent(
+        """
+        +-- 0
+        :   |-> 1
+        :   |   |-> 3
+        :   |   L-> 4
+        :   L-> 2
+        :       |-> 5
+        :       L-> 6
+        +-- 14
+        :   |-> 15
+        :   |   |-> 17
+        :   |   L-> 18
+        :   L-> 16
+        :       |-> 19
+        :       L-> 20
+        +-- 7
+            |-> 8
+            |   |-> 10
+            |   L-> 11
+            L-> 9
+                |-> 12
+                L-> 13
+        """
+    ).strip()
+    assert ret == target
+
+
+def test_generate_network_text_undirected_multi_tree():
+    tree1 = nx.balanced_tree(r=2, h=2, create_using=nx.Graph)
+    tree2 = nx.balanced_tree(r=2, h=2, create_using=nx.Graph)
+    tree2 = nx.relabel_nodes(tree2, {n: n + len(tree1) for n in tree2.nodes})
+    forest = nx.union(tree1, tree2)
+    ret = "\n".join(nx.generate_network_text(forest, sources=[0, 7]))
+
+    target = dedent(
+        """
+        ╟── 0
+        ╎   ├── 1
+        ╎   │   ├── 3
+        ╎   │   └── 4
+        ╎   └── 2
+        ╎       ├── 5
+        ╎       └── 6
+        ╙── 7
+            ├── 8
+            │   ├── 10
+            │   └── 11
+            └── 9
+                ├── 12
+                └── 13
+        """
+    ).strip()
+    assert ret == target
+
+    ret = "\n".join(nx.generate_network_text(forest, sources=[0, 7], ascii_only=True))
+
+    target = dedent(
+        """
+        +-- 0
+        :   |-- 1
+        :   |   |-- 3
+        :   |   L-- 4
+        :   L-- 2
+        :       |-- 5
+        :       L-- 6
+        +-- 7
+            |-- 8
+            |   |-- 10
+            |   L-- 11
+            L-- 9
+                |-- 12
+                L-- 13
+        """
+    ).strip()
+    assert ret == target
+
+
+def test_generate_network_text_forest_undirected():
+    # Create a directed forest
+    graph = nx.balanced_tree(r=2, h=2, create_using=nx.Graph)
+
+    node_target0 = dedent(
+        """
+        ╙── 0
+            ├── 1
+            │   ├── 3
+            │   └── 4
+            └── 2
+                ├── 5
+                └── 6
+        """
+    ).strip()
+
+    # defined starting point
+    ret = "\n".join(nx.generate_network_text(graph, sources=[0]))
+    assert ret == node_target0
+
+    # defined starting point
+    node_target2 = dedent(
+        """
+        ╙── 2
+            ├── 0
+            │   └── 1
+            │       ├── 3
+            │       └── 4
+            ├── 5
+            └── 6
+        """
+    ).strip()
+    ret = "\n".join(nx.generate_network_text(graph, sources=[2]))
+    assert ret == node_target2
+
+
+def test_generate_network_text_overspecified_sources():
+    """
+    When sources are directly specified, we won't be able to determine when we
+    are in the last component, so there will always be a trailing, leftmost
+    pipe.
+    """
+    graph = nx.disjoint_union_all(
+        [
+            nx.balanced_tree(r=2, h=1, create_using=nx.DiGraph),
+            nx.balanced_tree(r=1, h=2, create_using=nx.DiGraph),
+            nx.balanced_tree(r=2, h=1, create_using=nx.DiGraph),
+        ]
+    )
+
+    # defined starting point
+    target1 = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   └─╼ 2
+        ╟── 3
+        ╎   └─╼ 4
+        ╎       └─╼ 5
+        ╟── 6
+        ╎   ├─╼ 7
+        ╎   └─╼ 8
+        """
+    ).strip()
+
+    target2 = dedent(
+        """
+        ╟── 0
+        ╎   ├─╼ 1
+        ╎   └─╼ 2
+        ╟── 3
+        ╎   └─╼ 4
+        ╎       └─╼ 5
+        ╙── 6
+            ├─╼ 7
+            └─╼ 8
+        """
+    ).strip()
+
+    got1 = "\n".join(nx.generate_network_text(graph, sources=graph.nodes))
+    got2 = "\n".join(nx.generate_network_text(graph))
+    assert got1 == target1
+    assert got2 == target2
+
+
+def test_write_network_text_iterative_add_directed_edges():
+    """
+    Walk through the cases going from a disconnected to fully connected graph
+    """
+    graph = nx.DiGraph()
+    graph.add_nodes_from([1, 2, 3, 4])
+    lines = []
+    write = lines.append
+    write("--- initial state ---")
+    nx.write_network_text(graph, path=write, end="")
+    for i, j in product(graph.nodes, graph.nodes):
+        write(f"--- add_edge({i}, {j}) ---")
+        graph.add_edge(i, j)
+        nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    # defined starting point
+    target = dedent(
+        """
+        --- initial state ---
+        ╟── 1
+        ╟── 2
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 1) ---
+        ╟── 1 ╾ 1
+        ╎   └─╼  ...
+        ╟── 2
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 2) ---
+        ╟── 1 ╾ 1
+        ╎   ├─╼ 2
+        ╎   └─╼  ...
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 3) ---
+        ╟── 1 ╾ 1
+        ╎   ├─╼ 2
+        ╎   ├─╼ 3
+        ╎   └─╼  ...
+        ╙── 4
+        --- add_edge(1, 4) ---
+        ╙── 1 ╾ 1
+            ├─╼ 2
+            ├─╼ 3
+            ├─╼ 4
+            └─╼  ...
+        --- add_edge(2, 1) ---
+        ╙── 2 ╾ 1
+            └─╼ 1 ╾ 1
+                ├─╼ 3
+                ├─╼ 4
+                └─╼  ...
+        --- add_edge(2, 2) ---
+        ╙── 1 ╾ 1, 2
+            ├─╼ 2 ╾ 2
+            │   └─╼  ...
+            ├─╼ 3
+            ├─╼ 4
+            └─╼  ...
+        --- add_edge(2, 3) ---
+        ╙── 1 ╾ 1, 2
+            ├─╼ 2 ╾ 2
+            │   ├─╼ 3 ╾ 1
+            │   └─╼  ...
+            ├─╼ 4
+            └─╼  ...
+        --- add_edge(2, 4) ---
+        ╙── 1 ╾ 1, 2
+            ├─╼ 2 ╾ 2
+            │   ├─╼ 3 ╾ 1
+            │   ├─╼ 4 ╾ 1
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 1) ---
+        ╙── 2 ╾ 1, 2
+            ├─╼ 1 ╾ 1, 3
+            │   ├─╼ 3 ╾ 2
+            │   │   └─╼  ...
+            │   ├─╼ 4 ╾ 2
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 2) ---
+        ╙── 3 ╾ 1, 2
+            ├─╼ 1 ╾ 1, 2
+            │   ├─╼ 2 ╾ 2, 3
+            │   │   ├─╼ 4 ╾ 1
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 3) ---
+        ╙── 1 ╾ 1, 2, 3
+            ├─╼ 2 ╾ 2, 3
+            │   ├─╼ 3 ╾ 1, 3
+            │   │   └─╼  ...
+            │   ├─╼ 4 ╾ 1
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(3, 4) ---
+        ╙── 1 ╾ 1, 2, 3
+            ├─╼ 2 ╾ 2, 3
+            │   ├─╼ 3 ╾ 1, 3
+            │   │   ├─╼ 4 ╾ 1, 2
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 1) ---
+        ╙── 2 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 1, 3, 4
+            │   ├─╼ 3 ╾ 2, 3
+            │   │   ├─╼ 4 ╾ 1, 2
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 2) ---
+        ╙── 3 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 1, 2, 4
+            │   ├─╼ 2 ╾ 2, 3, 4
+            │   │   ├─╼ 4 ╾ 1, 3
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 3) ---
+        ╙── 4 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 1, 2, 3
+            │   ├─╼ 2 ╾ 2, 3, 4
+            │   │   ├─╼ 3 ╾ 1, 3, 4
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- add_edge(4, 4) ---
+        ╙── 1 ╾ 1, 2, 3, 4
+            ├─╼ 2 ╾ 2, 3, 4
+            │   ├─╼ 3 ╾ 1, 3, 4
+            │   │   ├─╼ 4 ╾ 1, 2, 4
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_iterative_add_undirected_edges():
+    """
+    Walk through the cases going from a disconnected to fully connected graph
+    """
+    graph = nx.Graph()
+    graph.add_nodes_from([1, 2, 3, 4])
+    lines = []
+    write = lines.append
+    write("--- initial state ---")
+    nx.write_network_text(graph, path=write, end="")
+    for i, j in product(graph.nodes, graph.nodes):
+        if i == j:
+            continue
+        write(f"--- add_edge({i}, {j}) ---")
+        graph.add_edge(i, j)
+        nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- initial state ---
+        ╟── 1
+        ╟── 2
+        ╟── 3
+        ╙── 4
+        --- add_edge(1, 2) ---
+        ╟── 3
+        ╟── 4
+        ╙── 1
+            └── 2
+        --- add_edge(1, 3) ---
+        ╟── 4
+        ╙── 2
+            └── 1
+                └── 3
+        --- add_edge(1, 4) ---
+        ╙── 2
+            └── 1
+                ├── 3
+                └── 4
+        --- add_edge(2, 1) ---
+        ╙── 2
+            └── 1
+                ├── 3
+                └── 4
+        --- add_edge(2, 3) ---
+        ╙── 4
+            └── 1
+                ├── 2
+                │   └── 3 ─ 1
+                └──  ...
+        --- add_edge(2, 4) ---
+        ╙── 3
+            ├── 1
+            │   ├── 2 ─ 3
+            │   │   └── 4 ─ 1
+            │   └──  ...
+            └──  ...
+        --- add_edge(3, 1) ---
+        ╙── 3
+            ├── 1
+            │   ├── 2 ─ 3
+            │   │   └── 4 ─ 1
+            │   └──  ...
+            └──  ...
+        --- add_edge(3, 2) ---
+        ╙── 3
+            ├── 1
+            │   ├── 2 ─ 3
+            │   │   └── 4 ─ 1
+            │   └──  ...
+            └──  ...
+        --- add_edge(3, 4) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        --- add_edge(4, 1) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        --- add_edge(4, 2) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        --- add_edge(4, 3) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3 ─ 1
+            │   │   └── 4 ─ 1, 2
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_iterative_add_random_directed_edges():
+    """
+    Walk through the cases going from a disconnected to fully connected graph
+    """
+
+    rng = random.Random(724466096)
+    graph = nx.DiGraph()
+    graph.add_nodes_from([1, 2, 3, 4, 5])
+    possible_edges = list(product(graph.nodes, graph.nodes))
+    rng.shuffle(possible_edges)
+    graph.add_edges_from(possible_edges[0:8])
+    lines = []
+    write = lines.append
+    write("--- initial state ---")
+    nx.write_network_text(graph, path=write, end="")
+    for i, j in possible_edges[8:12]:
+        write(f"--- add_edge({i}, {j}) ---")
+        graph.add_edge(i, j)
+        nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- initial state ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(4, 1) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1, 4
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(2, 1) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1, 4, 2
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(5, 2) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2, 5
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5
+                │   │   ├─╼ 1 ╾ 1, 4, 2
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+        --- add_edge(1, 5) ---
+        ╙── 3 ╾ 5
+            └─╼ 2 ╾ 2, 5
+                ├─╼ 4 ╾ 4
+                │   ├─╼ 5 ╾ 1
+                │   │   ├─╼ 1 ╾ 1, 4, 2
+                │   │   │   └─╼  ...
+                │   │   └─╼  ...
+                │   └─╼  ...
+                └─╼  ...
+
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_nearly_forest():
+    g = nx.DiGraph()
+    g.add_edge(1, 2)
+    g.add_edge(1, 5)
+    g.add_edge(2, 3)
+    g.add_edge(3, 4)
+    g.add_edge(5, 6)
+    g.add_edge(6, 7)
+    g.add_edge(6, 8)
+    orig = g.copy()
+    g.add_edge(1, 8)  # forward edge
+    g.add_edge(4, 2)  # back edge
+    g.add_edge(6, 3)  # cross edge
+    lines = []
+    write = lines.append
+    write("--- directed case ---")
+    nx.write_network_text(orig, path=write, end="")
+    write("--- add (1, 8), (4, 2), (6, 3) ---")
+    nx.write_network_text(g, path=write, end="")
+    write("--- undirected case ---")
+    nx.write_network_text(orig.to_undirected(), path=write, sources=[1], end="")
+    write("--- add (1, 8), (4, 2), (6, 3) ---")
+    nx.write_network_text(g.to_undirected(), path=write, sources=[1], end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case ---
+        ╙── 1
+            ├─╼ 2
+            │   └─╼ 3
+            │       └─╼ 4
+            └─╼ 5
+                └─╼ 6
+                    ├─╼ 7
+                    └─╼ 8
+        --- add (1, 8), (4, 2), (6, 3) ---
+        ╙── 1
+            ├─╼ 2 ╾ 4
+            │   └─╼ 3 ╾ 6
+            │       └─╼ 4
+            │           └─╼  ...
+            ├─╼ 5
+            │   └─╼ 6
+            │       ├─╼ 7
+            │       ├─╼ 8 ╾ 1
+            │       └─╼  ...
+            └─╼  ...
+        --- undirected case ---
+        ╙── 1
+            ├── 2
+            │   └── 3
+            │       └── 4
+            └── 5
+                └── 6
+                    ├── 7
+                    └── 8
+        --- add (1, 8), (4, 2), (6, 3) ---
+        ╙── 1
+            ├── 2
+            │   ├── 3
+            │   │   ├── 4 ─ 2
+            │   │   └── 6
+            │   │       ├── 5 ─ 1
+            │   │       ├── 7
+            │   │       └── 8 ─ 1
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_complete_graph_ascii_only():
+    graph = nx.generators.complete_graph(5, create_using=nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- directed case ---")
+    nx.write_network_text(graph, path=write, ascii_only=True, end="")
+    write("--- undirected case ---")
+    nx.write_network_text(graph.to_undirected(), path=write, ascii_only=True, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case ---
+        +-- 0 <- 1, 2, 3, 4
+            |-> 1 <- 2, 3, 4
+            |   |-> 2 <- 0, 3, 4
+            |   |   |-> 3 <- 0, 1, 4
+            |   |   |   |-> 4 <- 0, 1, 2
+            |   |   |   |   L->  ...
+            |   |   |   L->  ...
+            |   |   L->  ...
+            |   L->  ...
+            L->  ...
+        --- undirected case ---
+        +-- 0
+            |-- 1
+            |   |-- 2 - 0
+            |   |   |-- 3 - 0, 1
+            |   |   |   L-- 4 - 0, 1, 2
+            |   |   L--  ...
+            |   L--  ...
+            L--  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_with_labels():
+    graph = nx.generators.complete_graph(5, create_using=nx.DiGraph)
+    for n in graph.nodes:
+        graph.nodes[n]["label"] = f"Node(n={n})"
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, with_labels=True, ascii_only=False, end="")
+    text = "\n".join(lines)
+    # Non trees with labels can get somewhat out of hand with network text
+    # because we need to immediately show every non-tree edge to the right
+    target = dedent(
+        """
+        ╙── Node(n=0) ╾ Node(n=1), Node(n=2), Node(n=3), Node(n=4)
+            ├─╼ Node(n=1) ╾ Node(n=2), Node(n=3), Node(n=4)
+            │   ├─╼ Node(n=2) ╾ Node(n=0), Node(n=3), Node(n=4)
+            │   │   ├─╼ Node(n=3) ╾ Node(n=0), Node(n=1), Node(n=4)
+            │   │   │   ├─╼ Node(n=4) ╾ Node(n=0), Node(n=1), Node(n=2)
+            │   │   │   │   └─╼  ...
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_complete_graphs():
+    lines = []
+    write = lines.append
+    for k in [0, 1, 2, 3, 4, 5]:
+        g = nx.generators.complete_graph(k)
+        write(f"--- undirected k={k} ---")
+        nx.write_network_text(g, path=write, end="")
+
+    for k in [0, 1, 2, 3, 4, 5]:
+        g = nx.generators.complete_graph(k, nx.DiGraph)
+        write(f"--- directed k={k} ---")
+        nx.write_network_text(g, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- undirected k=0 ---
+        ╙
+        --- undirected k=1 ---
+        ╙── 0
+        --- undirected k=2 ---
+        ╙── 0
+            └── 1
+        --- undirected k=3 ---
+        ╙── 0
+            ├── 1
+            │   └── 2 ─ 0
+            └──  ...
+        --- undirected k=4 ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0
+            │   │   └── 3 ─ 0, 1
+            │   └──  ...
+            └──  ...
+        --- undirected k=5 ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0
+            │   │   ├── 3 ─ 0, 1
+            │   │   │   └── 4 ─ 0, 1, 2
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        --- directed k=0 ---
+        ╙
+        --- directed k=1 ---
+        ╙── 0
+        --- directed k=2 ---
+        ╙── 0 ╾ 1
+            └─╼ 1
+                └─╼  ...
+        --- directed k=3 ---
+        ╙── 0 ╾ 1, 2
+            ├─╼ 1 ╾ 2
+            │   ├─╼ 2 ╾ 0
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- directed k=4 ---
+        ╙── 0 ╾ 1, 2, 3
+            ├─╼ 1 ╾ 2, 3
+            │   ├─╼ 2 ╾ 0, 3
+            │   │   ├─╼ 3 ╾ 0, 1
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- directed k=5 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   ├─╼ 2 ╾ 0, 3, 4
+            │   │   ├─╼ 3 ╾ 0, 1, 4
+            │   │   │   ├─╼ 4 ╾ 0, 1, 2
+            │   │   │   │   └─╼  ...
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_multiple_sources():
+    g = nx.DiGraph()
+    g.add_edge(1, 2)
+    g.add_edge(1, 3)
+    g.add_edge(2, 4)
+    g.add_edge(3, 5)
+    g.add_edge(3, 6)
+    g.add_edge(5, 4)
+    g.add_edge(4, 1)
+    g.add_edge(1, 5)
+    lines = []
+    write = lines.append
+    # Use each node as the starting point to demonstrate how the representation
+    # changes.
+    nodes = sorted(g.nodes())
+    for n in nodes:
+        write(f"--- source node: {n} ---")
+        nx.write_network_text(g, path=write, sources=[n], end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- source node: 1 ---
+        ╙── 1 ╾ 4
+            ├─╼ 2
+            │   └─╼ 4 ╾ 5
+            │       └─╼  ...
+            ├─╼ 3
+            │   ├─╼ 5 ╾ 1
+            │   │   └─╼  ...
+            │   └─╼ 6
+            └─╼  ...
+        --- source node: 2 ---
+        ╙── 2 ╾ 1
+            └─╼ 4 ╾ 5
+                └─╼ 1
+                    ├─╼ 3
+                    │   ├─╼ 5 ╾ 1
+                    │   │   └─╼  ...
+                    │   └─╼ 6
+                    └─╼  ...
+        --- source node: 3 ---
+        ╙── 3 ╾ 1
+            ├─╼ 5 ╾ 1
+            │   └─╼ 4 ╾ 2
+            │       └─╼ 1
+            │           ├─╼ 2
+            │           │   └─╼  ...
+            │           └─╼  ...
+            └─╼ 6
+        --- source node: 4 ---
+        ╙── 4 ╾ 2, 5
+            └─╼ 1
+                ├─╼ 2
+                │   └─╼  ...
+                ├─╼ 3
+                │   ├─╼ 5 ╾ 1
+                │   │   └─╼  ...
+                │   └─╼ 6
+                └─╼  ...
+        --- source node: 5 ---
+        ╙── 5 ╾ 3, 1
+            └─╼ 4 ╾ 2
+                └─╼ 1
+                    ├─╼ 2
+                    │   └─╼  ...
+                    ├─╼ 3
+                    │   ├─╼ 6
+                    │   └─╼  ...
+                    └─╼  ...
+        --- source node: 6 ---
+        ╙── 6 ╾ 3
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_star_graph():
+    graph = nx.star_graph(5, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 1
+            └── 0
+                ├── 2
+                ├── 3
+                ├── 4
+                └── 5
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_path_graph():
+    graph = nx.path_graph(3, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 0
+            └── 1
+                └── 2
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_lollipop_graph():
+    graph = nx.lollipop_graph(4, 2, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 5
+            └── 4
+                └── 3
+                    ├── 0
+                    │   ├── 1 ─ 3
+                    │   │   └── 2 ─ 0, 3
+                    │   └──  ...
+                    └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_wheel_graph():
+    graph = nx.wheel_graph(7, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 1
+            ├── 0
+            │   ├── 2 ─ 1
+            │   │   └── 3 ─ 0
+            │   │       └── 4 ─ 0
+            │   │           └── 5 ─ 0
+            │   │               └── 6 ─ 0, 1
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_circular_ladder_graph():
+    graph = nx.circular_ladder_graph(4, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 0
+            ├── 1
+            │   ├── 2
+            │   │   ├── 3 ─ 0
+            │   │   │   └── 7
+            │   │   │       ├── 6 ─ 2
+            │   │   │       │   └── 5 ─ 1
+            │   │   │       │       └── 4 ─ 0, 7
+            │   │   │       └──  ...
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_dorogovtsev_goltsev_mendes_graph():
+    graph = nx.dorogovtsev_goltsev_mendes_graph(4, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        ╙── 15
+            ├── 0
+            │   ├── 1 ─ 15
+            │   │   ├── 2 ─ 0
+            │   │   │   ├── 4 ─ 0
+            │   │   │   │   ├── 9 ─ 0
+            │   │   │   │   │   ├── 22 ─ 0
+            │   │   │   │   │   └── 38 ─ 4
+            │   │   │   │   ├── 13 ─ 2
+            │   │   │   │   │   ├── 34 ─ 2
+            │   │   │   │   │   └── 39 ─ 4
+            │   │   │   │   ├── 18 ─ 0
+            │   │   │   │   ├── 30 ─ 2
+            │   │   │   │   └──  ...
+            │   │   │   ├── 5 ─ 1
+            │   │   │   │   ├── 12 ─ 1
+            │   │   │   │   │   ├── 29 ─ 1
+            │   │   │   │   │   └── 40 ─ 5
+            │   │   │   │   ├── 14 ─ 2
+            │   │   │   │   │   ├── 35 ─ 2
+            │   │   │   │   │   └── 41 ─ 5
+            │   │   │   │   ├── 25 ─ 1
+            │   │   │   │   ├── 31 ─ 2
+            │   │   │   │   └──  ...
+            │   │   │   ├── 7 ─ 0
+            │   │   │   │   ├── 20 ─ 0
+            │   │   │   │   └── 32 ─ 2
+            │   │   │   ├── 10 ─ 1
+            │   │   │   │   ├── 27 ─ 1
+            │   │   │   │   └── 33 ─ 2
+            │   │   │   ├── 16 ─ 0
+            │   │   │   ├── 23 ─ 1
+            │   │   │   └──  ...
+            │   │   ├── 3 ─ 0
+            │   │   │   ├── 8 ─ 0
+            │   │   │   │   ├── 21 ─ 0
+            │   │   │   │   └── 36 ─ 3
+            │   │   │   ├── 11 ─ 1
+            │   │   │   │   ├── 28 ─ 1
+            │   │   │   │   └── 37 ─ 3
+            │   │   │   ├── 17 ─ 0
+            │   │   │   ├── 24 ─ 1
+            │   │   │   └──  ...
+            │   │   ├── 6 ─ 0
+            │   │   │   ├── 19 ─ 0
+            │   │   │   └── 26 ─ 1
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_tree_max_depth():
+    orig = nx.balanced_tree(r=1, h=3, create_using=nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- directed case, max_depth=0 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=0)
+    write("--- directed case, max_depth=1 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=1)
+    write("--- directed case, max_depth=2 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=2)
+    write("--- directed case, max_depth=3 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=3)
+    write("--- directed case, max_depth=4 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=4)
+    write("--- undirected case, max_depth=0 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=0)
+    write("--- undirected case, max_depth=1 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=1)
+    write("--- undirected case, max_depth=2 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=2)
+    write("--- undirected case, max_depth=3 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=3)
+    write("--- undirected case, max_depth=4 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=4)
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case, max_depth=0 ---
+        ╙ ...
+        --- directed case, max_depth=1 ---
+        ╙── 0
+            └─╼  ...
+        --- directed case, max_depth=2 ---
+        ╙── 0
+            └─╼ 1
+                └─╼  ...
+        --- directed case, max_depth=3 ---
+        ╙── 0
+            └─╼ 1
+                └─╼ 2
+                    └─╼  ...
+        --- directed case, max_depth=4 ---
+        ╙── 0
+            └─╼ 1
+                └─╼ 2
+                    └─╼ 3
+        --- undirected case, max_depth=0 ---
+        ╙ ...
+        --- undirected case, max_depth=1 ---
+        ╙── 0 ─ 1
+            └──  ...
+        --- undirected case, max_depth=2 ---
+        ╙── 0
+            └── 1 ─ 2
+                └──  ...
+        --- undirected case, max_depth=3 ---
+        ╙── 0
+            └── 1
+                └── 2 ─ 3
+                    └──  ...
+        --- undirected case, max_depth=4 ---
+        ╙── 0
+            └── 1
+                └── 2
+                    └── 3
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_graph_max_depth():
+    orig = nx.erdos_renyi_graph(10, 0.15, directed=True, seed=40392)
+    lines = []
+    write = lines.append
+    write("--- directed case, max_depth=None ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=None)
+    write("--- directed case, max_depth=0 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=0)
+    write("--- directed case, max_depth=1 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=1)
+    write("--- directed case, max_depth=2 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=2)
+    write("--- directed case, max_depth=3 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=3)
+    write("--- undirected case, max_depth=None ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=None)
+    write("--- undirected case, max_depth=0 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=0)
+    write("--- undirected case, max_depth=1 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=1)
+    write("--- undirected case, max_depth=2 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=2)
+    write("--- undirected case, max_depth=3 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=3)
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case, max_depth=None ---
+        ╟── 4
+        ╎   ├─╼ 0 ╾ 3
+        ╎   ├─╼ 5 ╾ 7
+        ╎   │   └─╼ 3
+        ╎   │       ├─╼ 1 ╾ 9
+        ╎   │       │   └─╼ 9 ╾ 6
+        ╎   │       │       ├─╼ 6
+        ╎   │       │       │   └─╼  ...
+        ╎   │       │       ├─╼ 7 ╾ 4
+        ╎   │       │       │   ├─╼ 2
+        ╎   │       │       │   └─╼  ...
+        ╎   │       │       └─╼  ...
+        ╎   │       └─╼  ...
+        ╎   └─╼  ...
+        ╙── 8
+        --- directed case, max_depth=0 ---
+        ╙ ...
+        --- directed case, max_depth=1 ---
+        ╟── 4
+        ╎   └─╼  ...
+        ╙── 8
+        --- directed case, max_depth=2 ---
+        ╟── 4
+        ╎   ├─╼ 0 ╾ 3
+        ╎   ├─╼ 5 ╾ 7
+        ╎   │   └─╼  ...
+        ╎   └─╼ 7 ╾ 9
+        ╎       └─╼  ...
+        ╙── 8
+        --- directed case, max_depth=3 ---
+        ╟── 4
+        ╎   ├─╼ 0 ╾ 3
+        ╎   ├─╼ 5 ╾ 7
+        ╎   │   └─╼ 3
+        ╎   │       └─╼  ...
+        ╎   └─╼ 7 ╾ 9
+        ╎       ├─╼ 2
+        ╎       └─╼  ...
+        ╙── 8
+        --- undirected case, max_depth=None ---
+        ╟── 8
+        ╙── 2
+            └── 7
+                ├── 4
+                │   ├── 0
+                │   │   └── 3
+                │   │       ├── 1
+                │   │       │   └── 9 ─ 7
+                │   │       │       └── 6
+                │   │       └── 5 ─ 4, 7
+                │   └──  ...
+                └──  ...
+        --- undirected case, max_depth=0 ---
+        ╙ ...
+        --- undirected case, max_depth=1 ---
+        ╟── 8
+        ╙── 2 ─ 7
+            └──  ...
+        --- undirected case, max_depth=2 ---
+        ╟── 8
+        ╙── 2
+            └── 7 ─ 4, 5, 9
+                └──  ...
+        --- undirected case, max_depth=3 ---
+        ╟── 8
+        ╙── 2
+            └── 7
+                ├── 4 ─ 0, 5
+                │   └──  ...
+                ├── 5 ─ 4, 3
+                │   └──  ...
+                └── 9 ─ 1, 6
+                    └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_clique_max_depth():
+    orig = nx.complete_graph(5, nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- directed case, max_depth=None ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=None)
+    write("--- directed case, max_depth=0 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=0)
+    write("--- directed case, max_depth=1 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=1)
+    write("--- directed case, max_depth=2 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=2)
+    write("--- directed case, max_depth=3 ---")
+    nx.write_network_text(orig, path=write, end="", max_depth=3)
+    write("--- undirected case, max_depth=None ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=None)
+    write("--- undirected case, max_depth=0 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=0)
+    write("--- undirected case, max_depth=1 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=1)
+    write("--- undirected case, max_depth=2 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=2)
+    write("--- undirected case, max_depth=3 ---")
+    nx.write_network_text(orig.to_undirected(), path=write, end="", max_depth=3)
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- directed case, max_depth=None ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   ├─╼ 2 ╾ 0, 3, 4
+            │   │   ├─╼ 3 ╾ 0, 1, 4
+            │   │   │   ├─╼ 4 ╾ 0, 1, 2
+            │   │   │   │   └─╼  ...
+            │   │   │   └─╼  ...
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- directed case, max_depth=0 ---
+        ╙ ...
+        --- directed case, max_depth=1 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            └─╼  ...
+        --- directed case, max_depth=2 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   └─╼  ...
+            ├─╼ 2 ╾ 1, 3, 4
+            │   └─╼  ...
+            ├─╼ 3 ╾ 1, 2, 4
+            │   └─╼  ...
+            └─╼ 4 ╾ 1, 2, 3
+                └─╼  ...
+        --- directed case, max_depth=3 ---
+        ╙── 0 ╾ 1, 2, 3, 4
+            ├─╼ 1 ╾ 2, 3, 4
+            │   ├─╼ 2 ╾ 0, 3, 4
+            │   │   └─╼  ...
+            │   ├─╼ 3 ╾ 0, 2, 4
+            │   │   └─╼  ...
+            │   ├─╼ 4 ╾ 0, 2, 3
+            │   │   └─╼  ...
+            │   └─╼  ...
+            └─╼  ...
+        --- undirected case, max_depth=None ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0
+            │   │   ├── 3 ─ 0, 1
+            │   │   │   └── 4 ─ 0, 1, 2
+            │   │   └──  ...
+            │   └──  ...
+            └──  ...
+        --- undirected case, max_depth=0 ---
+        ╙ ...
+        --- undirected case, max_depth=1 ---
+        ╙── 0 ─ 1, 2, 3, 4
+            └──  ...
+        --- undirected case, max_depth=2 ---
+        ╙── 0
+            ├── 1 ─ 2, 3, 4
+            │   └──  ...
+            ├── 2 ─ 1, 3, 4
+            │   └──  ...
+            ├── 3 ─ 1, 2, 4
+            │   └──  ...
+            └── 4 ─ 1, 2, 3
+        --- undirected case, max_depth=3 ---
+        ╙── 0
+            ├── 1
+            │   ├── 2 ─ 0, 3, 4
+            │   │   └──  ...
+            │   ├── 3 ─ 0, 2, 4
+            │   │   └──  ...
+            │   └── 4 ─ 0, 2, 3
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_custom_label():
+    # Create a directed forest with labels
+    graph = nx.erdos_renyi_graph(5, 0.4, directed=True, seed=359222358)
+    for node in graph.nodes:
+        graph.nodes[node]["label"] = f"Node({node})"
+        graph.nodes[node]["chr"] = chr(node + ord("a") - 1)
+        if node % 2 == 0:
+            graph.nodes[node]["part"] = chr(node + ord("a"))
+
+    lines = []
+    write = lines.append
+    write("--- when with_labels=True, uses the 'label' attr ---")
+    nx.write_network_text(graph, path=write, with_labels=True, end="", max_depth=None)
+    write("--- when with_labels=False, uses str(node) value ---")
+    nx.write_network_text(graph, path=write, with_labels=False, end="", max_depth=None)
+    write("--- when with_labels is a string, use that attr ---")
+    nx.write_network_text(graph, path=write, with_labels="chr", end="", max_depth=None)
+    write("--- fallback to str(node) when the attr does not exist ---")
+    nx.write_network_text(graph, path=write, with_labels="part", end="", max_depth=None)
+
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- when with_labels=True, uses the 'label' attr ---
+        ╙── Node(1)
+            └─╼ Node(3) ╾ Node(2)
+                ├─╼ Node(0)
+                │   ├─╼ Node(2) ╾ Node(3), Node(4)
+                │   │   └─╼  ...
+                │   └─╼ Node(4)
+                │       └─╼  ...
+                └─╼  ...
+        --- when with_labels=False, uses str(node) value ---
+        ╙── 1
+            └─╼ 3 ╾ 2
+                ├─╼ 0
+                │   ├─╼ 2 ╾ 3, 4
+                │   │   └─╼  ...
+                │   └─╼ 4
+                │       └─╼  ...
+                └─╼  ...
+        --- when with_labels is a string, use that attr ---
+        ╙── a
+            └─╼ c ╾ b
+                ├─╼ `
+                │   ├─╼ b ╾ c, d
+                │   │   └─╼  ...
+                │   └─╼ d
+                │       └─╼  ...
+                └─╼  ...
+        --- fallback to str(node) when the attr does not exist ---
+        ╙── 1
+            └─╼ 3 ╾ c
+                ├─╼ a
+                │   ├─╼ c ╾ 3, e
+                │   │   └─╼  ...
+                │   └─╼ e
+                │       └─╼  ...
+                └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_write_network_text_vertical_chains():
+    graph1 = nx.lollipop_graph(4, 2, create_using=nx.Graph)
+    graph1.add_edge(0, -1)
+    graph1.add_edge(-1, -2)
+    graph1.add_edge(-2, -3)
+
+    graph2 = graph1.to_directed()
+    graph2.remove_edges_from([(u, v) for u, v in graph2.edges if v > u])
+
+    lines = []
+    write = lines.append
+    write("--- Undirected UTF ---")
+    nx.write_network_text(graph1, path=write, end="", vertical_chains=True)
+    write("--- Undirected ASCI ---")
+    nx.write_network_text(
+        graph1, path=write, end="", vertical_chains=True, ascii_only=True
+    )
+    write("--- Directed UTF ---")
+    nx.write_network_text(graph2, path=write, end="", vertical_chains=True)
+    write("--- Directed ASCI ---")
+    nx.write_network_text(
+        graph2, path=write, end="", vertical_chains=True, ascii_only=True
+    )
+
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- Undirected UTF ---
+        ╙── 5
+            │
+            4
+            │
+            3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   │
+            │   │   2 ─ 0, 3
+            │   ├── -1
+            │   │   │
+            │   │   -2
+            │   │   │
+            │   │   -3
+            │   └──  ...
+            └──  ...
+        --- Undirected ASCI ---
+        +-- 5
+            |
+            4
+            |
+            3
+            |-- 0
+            |   |-- 1 - 3
+            |   |   |
+            |   |   2 - 0, 3
+            |   |-- -1
+            |   |   |
+            |   |   -2
+            |   |   |
+            |   |   -3
+            |   L--  ...
+            L--  ...
+        --- Directed UTF ---
+        ╙── 5
+            ╽
+            4
+            ╽
+            3
+            ├─╼ 0 ╾ 1, 2
+            │   ╽
+            │   -1
+            │   ╽
+            │   -2
+            │   ╽
+            │   -3
+            ├─╼ 1 ╾ 2
+            │   └─╼  ...
+            └─╼ 2
+                └─╼  ...
+        --- Directed ASCI ---
+        +-- 5
+            !
+            4
+            !
+            3
+            |-> 0 <- 1, 2
+            |   !
+            |   -1
+            |   !
+            |   -2
+            |   !
+            |   -3
+            |-> 1 <- 2
+            |   L->  ...
+            L-> 2
+                L->  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_collapse_directed():
+    graph = nx.balanced_tree(r=2, h=3, create_using=nx.DiGraph)
+    lines = []
+    write = lines.append
+    write("--- Original ---")
+    nx.write_network_text(graph, path=write, end="")
+    graph.nodes[1]["collapse"] = True
+    write("--- Collapse Node 1 ---")
+    nx.write_network_text(graph, path=write, end="")
+    write("--- Add alternate path (5, 3) to collapsed zone")
+    graph.add_edge(5, 3)
+    nx.write_network_text(graph, path=write, end="")
+    write("--- Collapse Node 0 ---")
+    graph.nodes[0]["collapse"] = True
+    nx.write_network_text(graph, path=write, end="")
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- Original ---
+        ╙── 0
+            ├─╼ 1
+            │   ├─╼ 3
+            │   │   ├─╼ 7
+            │   │   └─╼ 8
+            │   └─╼ 4
+            │       ├─╼ 9
+            │       └─╼ 10
+            └─╼ 2
+                ├─╼ 5
+                │   ├─╼ 11
+                │   └─╼ 12
+                └─╼ 6
+                    ├─╼ 13
+                    └─╼ 14
+        --- Collapse Node 1 ---
+        ╙── 0
+            ├─╼ 1
+            │   └─╼  ...
+            └─╼ 2
+                ├─╼ 5
+                │   ├─╼ 11
+                │   └─╼ 12
+                └─╼ 6
+                    ├─╼ 13
+                    └─╼ 14
+        --- Add alternate path (5, 3) to collapsed zone
+        ╙── 0
+            ├─╼ 1
+            │   └─╼  ...
+            └─╼ 2
+                ├─╼ 5
+                │   ├─╼ 11
+                │   ├─╼ 12
+                │   └─╼ 3 ╾ 1
+                │       ├─╼ 7
+                │       └─╼ 8
+                └─╼ 6
+                    ├─╼ 13
+                    └─╼ 14
+        --- Collapse Node 0 ---
+        ╙── 0
+            └─╼  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def test_collapse_undirected():
+    graph = nx.balanced_tree(r=2, h=3, create_using=nx.Graph)
+    lines = []
+    write = lines.append
+    write("--- Original ---")
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    graph.nodes[1]["collapse"] = True
+    write("--- Collapse Node 1 ---")
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    write("--- Add alternate path (5, 3) to collapsed zone")
+    graph.add_edge(5, 3)
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    write("--- Collapse Node 0 ---")
+    graph.nodes[0]["collapse"] = True
+    nx.write_network_text(graph, path=write, end="", sources=[0])
+    text = "\n".join(lines)
+    target = dedent(
+        """
+        --- Original ---
+        ╙── 0
+            ├── 1
+            │   ├── 3
+            │   │   ├── 7
+            │   │   └── 8
+            │   └── 4
+            │       ├── 9
+            │       └── 10
+            └── 2
+                ├── 5
+                │   ├── 11
+                │   └── 12
+                └── 6
+                    ├── 13
+                    └── 14
+        --- Collapse Node 1 ---
+        ╙── 0
+            ├── 1 ─ 3, 4
+            │   └──  ...
+            └── 2
+                ├── 5
+                │   ├── 11
+                │   └── 12
+                └── 6
+                    ├── 13
+                    └── 14
+        --- Add alternate path (5, 3) to collapsed zone
+        ╙── 0
+            ├── 1 ─ 3, 4
+            │   └──  ...
+            └── 2
+                ├── 5
+                │   ├── 11
+                │   ├── 12
+                │   └── 3 ─ 1
+                │       ├── 7
+                │       └── 8
+                └── 6
+                    ├── 13
+                    └── 14
+        --- Collapse Node 0 ---
+        ╙── 0 ─ 1, 2
+            └──  ...
+        """
+    ).strip()
+    assert target == text
+
+
+def generate_test_graphs():
+    """
+    Generate a gauntlet of different test graphs with different properties
+    """
+    import random
+
+    rng = random.Random(976689776)
+    num_randomized = 3
+
+    for directed in [0, 1]:
+        cls = nx.DiGraph if directed else nx.Graph
+
+        for num_nodes in range(17):
+            # Disconnected graph
+            graph = cls()
+            graph.add_nodes_from(range(num_nodes))
+            yield graph
+
+            # Randomize graphs
+            if num_nodes > 0:
+                for p in [0.1, 0.3, 0.5, 0.7, 0.9]:
+                    for seed in range(num_randomized):
+                        graph = nx.erdos_renyi_graph(
+                            num_nodes, p, directed=directed, seed=rng
+                        )
+                        yield graph
+
+                yield nx.complete_graph(num_nodes, cls)
+
+        yield nx.path_graph(3, create_using=cls)
+        yield nx.balanced_tree(r=1, h=3, create_using=cls)
+        if not directed:
+            yield nx.circular_ladder_graph(4, create_using=cls)
+            yield nx.star_graph(5, create_using=cls)
+            yield nx.lollipop_graph(4, 2, create_using=cls)
+            yield nx.wheel_graph(7, create_using=cls)
+            yield nx.dorogovtsev_goltsev_mendes_graph(4, create_using=cls)
+
+
+@pytest.mark.parametrize(
+    ("vertical_chains", "ascii_only"),
+    tuple(
+        [
+            (vertical_chains, ascii_only)
+            for vertical_chains in [0, 1]
+            for ascii_only in [0, 1]
+        ]
+    ),
+)
+def test_network_text_round_trip(vertical_chains, ascii_only):
+    """
+    Write the graph to network text format, then parse it back in, assert it is
+    the same as the original graph. Passing this test is strong validation of
+    both the format generator and parser.
+    """
+    from networkx.readwrite.text import _parse_network_text
+
+    for graph in generate_test_graphs():
+        graph = nx.relabel_nodes(graph, {n: str(n) for n in graph.nodes})
+        lines = list(
+            nx.generate_network_text(
+                graph, vertical_chains=vertical_chains, ascii_only=ascii_only
+            )
+        )
+        new = _parse_network_text(lines)
+        try:
+            assert new.nodes == graph.nodes
+            assert new.edges == graph.edges
+        except Exception:
+            nx.write_network_text(graph)
+            raise
diff --git a/lib/python3.11/site-packages/networkx/readwrite/text.py b/lib/python3.11/site-packages/networkx/readwrite/text.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fce220764d0e3aab0dff0200f3d7b601d03d007
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/readwrite/text.py
@@ -0,0 +1,851 @@
+"""
+Text-based visual representations of graphs
+"""
+
+import sys
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["generate_network_text", "write_network_text"]
+
+
+class BaseGlyphs:
+    @classmethod
+    def as_dict(cls):
+        return {
+            a: getattr(cls, a)
+            for a in dir(cls)
+            if not a.startswith("_") and a != "as_dict"
+        }
+
+
+class AsciiBaseGlyphs(BaseGlyphs):
+    empty: str = "+"
+    newtree_last: str = "+-- "
+    newtree_mid: str = "+-- "
+    endof_forest: str = "    "
+    within_forest: str = ":   "
+    within_tree: str = "|   "
+
+
+class AsciiDirectedGlyphs(AsciiBaseGlyphs):
+    last: str = "L-> "
+    mid: str = "|-> "
+    backedge: str = "<-"
+    vertical_edge: str = "!"
+
+
+class AsciiUndirectedGlyphs(AsciiBaseGlyphs):
+    last: str = "L-- "
+    mid: str = "|-- "
+    backedge: str = "-"
+    vertical_edge: str = "|"
+
+
+class UtfBaseGlyphs(BaseGlyphs):
+    # Notes on available box and arrow characters
+    # https://en.wikipedia.org/wiki/Box-drawing_character
+    # https://stackoverflow.com/questions/2701192/triangle-arrow
+    empty: str = "╙"
+    newtree_last: str = "╙── "
+    newtree_mid: str = "╟── "
+    endof_forest: str = "    "
+    within_forest: str = "╎   "
+    within_tree: str = "│   "
+
+
+class UtfDirectedGlyphs(UtfBaseGlyphs):
+    last: str = "└─╼ "
+    mid: str = "├─╼ "
+    backedge: str = "╾"
+    vertical_edge: str = "╽"
+
+
+class UtfUndirectedGlyphs(UtfBaseGlyphs):
+    last: str = "└── "
+    mid: str = "├── "
+    backedge: str = "─"
+    vertical_edge: str = "│"
+
+
+def generate_network_text(
+    graph,
+    with_labels=True,
+    sources=None,
+    max_depth=None,
+    ascii_only=False,
+    vertical_chains=False,
+):
+    """Generate lines in the "network text" format
+
+    This works via a depth-first traversal of the graph and writing a line for
+    each unique node encountered. Non-tree edges are written to the right of
+    each node, and connection to a non-tree edge is indicated with an ellipsis.
+    This representation works best when the input graph is a forest, but any
+    graph can be represented.
+
+    This notation is original to networkx, although it is simple enough that it
+    may be known in existing literature. See #5602 for details. The procedure
+    is summarized as follows:
+
+    1. Given a set of source nodes (which can be specified, or automatically
+    discovered via finding the (strongly) connected components and choosing one
+    node with minimum degree from each), we traverse the graph in depth first
+    order.
+
+    2. Each reachable node will be printed exactly once on it's own line.
+
+    3. Edges are indicated in one of four ways:
+
+        a. a parent "L-style" connection on the upper left. This corresponds to
+        a traversal in the directed DFS tree.
+
+        b. a backref "<-style" connection shown directly on the right. For
+        directed graphs, these are drawn for any incoming edges to a node that
+        is not a parent edge. For undirected graphs, these are drawn for only
+        the non-parent edges that have already been represented (The edges that
+        have not been represented will be handled in the recursive case).
+
+        c. a child "L-style" connection on the lower right. Drawing of the
+        children are handled recursively.
+
+        d. if ``vertical_chains`` is true, and a parent node only has one child
+        a "vertical-style" edge is drawn between them.
+
+    4. The children of each node (wrt the directed DFS tree) are drawn
+    underneath and to the right of it. In the case that a child node has already
+    been drawn the connection is replaced with an ellipsis ("...") to indicate
+    that there is one or more connections represented elsewhere.
+
+    5. If a maximum depth is specified, an edge to nodes past this maximum
+    depth will be represented by an ellipsis.
+
+    6. If a node has a truthy "collapse" value, then we do not traverse past
+    that node.
+
+    Parameters
+    ----------
+    graph : nx.DiGraph | nx.Graph
+        Graph to represent
+
+    with_labels : bool | str
+        If True will use the "label" attribute of a node to display if it
+        exists otherwise it will use the node value itself. If given as a
+        string, then that attribute name will be used instead of "label".
+        Defaults to True.
+
+    sources : List
+        Specifies which nodes to start traversal from. Note: nodes that are not
+        reachable from one of these sources may not be shown. If unspecified,
+        the minimal set of nodes needed to reach all others will be used.
+
+    max_depth : int | None
+        The maximum depth to traverse before stopping. Defaults to None.
+
+    ascii_only : Boolean
+        If True only ASCII characters are used to construct the visualization
+
+    vertical_chains : Boolean
+        If True, chains of nodes will be drawn vertically when possible.
+
+    Yields
+    ------
+    str : a line of generated text
+
+    Examples
+    --------
+    >>> graph = nx.path_graph(10)
+    >>> graph.add_node("A")
+    >>> graph.add_node("B")
+    >>> graph.add_node("C")
+    >>> graph.add_node("D")
+    >>> graph.add_edge(9, "A")
+    >>> graph.add_edge(9, "B")
+    >>> graph.add_edge(9, "C")
+    >>> graph.add_edge("C", "D")
+    >>> graph.add_edge("C", "E")
+    >>> graph.add_edge("C", "F")
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        └── 1
+            └── 2
+                └── 3
+                    └── 4
+                        └── 5
+                            └── 6
+                                └── 7
+                                    └── 8
+                                        └── 9
+                                            ├── A
+                                            ├── B
+                                            └── C
+                                                ├── D
+                                                ├── E
+                                                └── F
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 0
+        │
+        1
+        │
+        2
+        │
+        3
+        │
+        4
+        │
+        5
+        │
+        6
+        │
+        7
+        │
+        8
+        │
+        9
+        ├── A
+        ├── B
+        └── C
+            ├── D
+            ├── E
+            └── F
+    """
+    from typing import Any, NamedTuple
+
+    class StackFrame(NamedTuple):
+        parent: Any
+        node: Any
+        indents: list
+        this_islast: bool
+        this_vertical: bool
+
+    collapse_attr = "collapse"
+
+    is_directed = graph.is_directed()
+
+    if is_directed:
+        glyphs = AsciiDirectedGlyphs if ascii_only else UtfDirectedGlyphs
+        succ = graph.succ
+        pred = graph.pred
+    else:
+        glyphs = AsciiUndirectedGlyphs if ascii_only else UtfUndirectedGlyphs
+        succ = graph.adj
+        pred = graph.adj
+
+    if isinstance(with_labels, str):
+        label_attr = with_labels
+    elif with_labels:
+        label_attr = "label"
+    else:
+        label_attr = None
+
+    if max_depth == 0:
+        yield glyphs.empty + " ..."
+    elif len(graph.nodes) == 0:
+        yield glyphs.empty
+    else:
+        # If the nodes to traverse are unspecified, find the minimal set of
+        # nodes that will reach the entire graph
+        if sources is None:
+            sources = _find_sources(graph)
+
+        # Populate the stack with each:
+        # 1. parent node in the DFS tree (or None for root nodes),
+        # 2. the current node in the DFS tree
+        # 2. a list of indentations indicating depth
+        # 3. a flag indicating if the node is the final one to be written.
+        # Reverse the stack so sources are popped in the correct order.
+        last_idx = len(sources) - 1
+        stack = [
+            StackFrame(None, node, [], (idx == last_idx), False)
+            for idx, node in enumerate(sources)
+        ][::-1]
+
+        num_skipped_children = defaultdict(lambda: 0)
+        seen_nodes = set()
+        while stack:
+            parent, node, indents, this_islast, this_vertical = stack.pop()
+
+            if node is not Ellipsis:
+                skip = node in seen_nodes
+                if skip:
+                    # Mark that we skipped a parent's child
+                    num_skipped_children[parent] += 1
+
+                if this_islast:
+                    # If we reached the last child of a parent, and we skipped
+                    # any of that parents children, then we should emit an
+                    # ellipsis at the end after this.
+                    if num_skipped_children[parent] and parent is not None:
+                        # Append the ellipsis to be emitted last
+                        next_islast = True
+                        try_frame = StackFrame(
+                            node, Ellipsis, indents, next_islast, False
+                        )
+                        stack.append(try_frame)
+
+                        # Redo this frame, but not as a last object
+                        next_islast = False
+                        try_frame = StackFrame(
+                            parent, node, indents, next_islast, this_vertical
+                        )
+                        stack.append(try_frame)
+                        continue
+
+                if skip:
+                    continue
+                seen_nodes.add(node)
+
+            if not indents:
+                # Top level items (i.e. trees in the forest) get different
+                # glyphs to indicate they are not actually connected
+                if this_islast:
+                    this_vertical = False
+                    this_prefix = indents + [glyphs.newtree_last]
+                    next_prefix = indents + [glyphs.endof_forest]
+                else:
+                    this_prefix = indents + [glyphs.newtree_mid]
+                    next_prefix = indents + [glyphs.within_forest]
+
+            else:
+                # Non-top-level items
+                if this_vertical:
+                    this_prefix = indents
+                    next_prefix = indents
+                else:
+                    if this_islast:
+                        this_prefix = indents + [glyphs.last]
+                        next_prefix = indents + [glyphs.endof_forest]
+                    else:
+                        this_prefix = indents + [glyphs.mid]
+                        next_prefix = indents + [glyphs.within_tree]
+
+            if node is Ellipsis:
+                label = " ..."
+                suffix = ""
+                children = []
+            else:
+                if label_attr is not None:
+                    label = str(graph.nodes[node].get(label_attr, node))
+                else:
+                    label = str(node)
+
+                # Determine if we want to show the children of this node.
+                if collapse_attr is not None:
+                    collapse = graph.nodes[node].get(collapse_attr, False)
+                else:
+                    collapse = False
+
+                # Determine:
+                # (1) children to traverse into after showing this node.
+                # (2) parents to immediately show to the right of this node.
+                if is_directed:
+                    # In the directed case we must show every successor node
+                    # note: it may be skipped later, but we don't have that
+                    # information here.
+                    children = list(succ[node])
+                    # In the directed case we must show every predecessor
+                    # except for parent we directly traversed from.
+                    handled_parents = {parent}
+                else:
+                    # Showing only the unseen children results in a more
+                    # concise representation for the undirected case.
+                    children = [
+                        child for child in succ[node] if child not in seen_nodes
+                    ]
+
+                    # In the undirected case, parents are also children, so we
+                    # only need to immediately show the ones we can no longer
+                    # traverse
+                    handled_parents = {*children, parent}
+
+                if max_depth is not None and len(indents) == max_depth - 1:
+                    # Use ellipsis to indicate we have reached maximum depth
+                    if children:
+                        children = [Ellipsis]
+                    handled_parents = {parent}
+
+                if collapse:
+                    # Collapsing a node is the same as reaching maximum depth
+                    if children:
+                        children = [Ellipsis]
+                    handled_parents = {parent}
+
+                # The other parents are other predecessors of this node that
+                # are not handled elsewhere.
+                other_parents = [p for p in pred[node] if p not in handled_parents]
+                if other_parents:
+                    if label_attr is not None:
+                        other_parents_labels = ", ".join(
+                            [
+                                str(graph.nodes[p].get(label_attr, p))
+                                for p in other_parents
+                            ]
+                        )
+                    else:
+                        other_parents_labels = ", ".join(
+                            [str(p) for p in other_parents]
+                        )
+                    suffix = " ".join(["", glyphs.backedge, other_parents_labels])
+                else:
+                    suffix = ""
+
+            # Emit the line for this node, this will be called for each node
+            # exactly once.
+            if this_vertical:
+                yield "".join(this_prefix + [glyphs.vertical_edge])
+
+            yield "".join(this_prefix + [label, suffix])
+
+            if vertical_chains:
+                if is_directed:
+                    num_children = len(set(children))
+                else:
+                    num_children = len(set(children) - {parent})
+                # The next node can be drawn vertically if it is the only
+                # remaining child of this node.
+                next_is_vertical = num_children == 1
+            else:
+                next_is_vertical = False
+
+            # Push children on the stack in reverse order so they are popped in
+            # the original order.
+            for idx, child in enumerate(children[::-1]):
+                next_islast = idx == 0
+                try_frame = StackFrame(
+                    node, child, next_prefix, next_islast, next_is_vertical
+                )
+                stack.append(try_frame)
+
+
+@open_file(1, "w")
+def write_network_text(
+    graph,
+    path=None,
+    with_labels=True,
+    sources=None,
+    max_depth=None,
+    ascii_only=False,
+    end="\n",
+    vertical_chains=False,
+):
+    """Creates a nice text representation of a graph
+
+    This works via a depth-first traversal of the graph and writing a line for
+    each unique node encountered. Non-tree edges are written to the right of
+    each node, and connection to a non-tree edge is indicated with an ellipsis.
+    This representation works best when the input graph is a forest, but any
+    graph can be represented.
+
+    Parameters
+    ----------
+    graph : nx.DiGraph | nx.Graph
+        Graph to represent
+
+    path : string or file or callable or None
+       Filename or file handle for data output.
+       if a function, then it will be called for each generated line.
+       if None, this will default to "sys.stdout.write"
+
+    with_labels : bool | str
+        If True will use the "label" attribute of a node to display if it
+        exists otherwise it will use the node value itself. If given as a
+        string, then that attribute name will be used instead of "label".
+        Defaults to True.
+
+    sources : List
+        Specifies which nodes to start traversal from. Note: nodes that are not
+        reachable from one of these sources may not be shown. If unspecified,
+        the minimal set of nodes needed to reach all others will be used.
+
+    max_depth : int | None
+        The maximum depth to traverse before stopping. Defaults to None.
+
+    ascii_only : Boolean
+        If True only ASCII characters are used to construct the visualization
+
+    end : string
+        The line ending character
+
+    vertical_chains : Boolean
+        If True, chains of nodes will be drawn vertically when possible.
+
+    Examples
+    --------
+    >>> graph = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├─╼ 1
+        │   ├─╼ 3
+        │   └─╼ 4
+        └─╼ 2
+            ├─╼ 5
+            └─╼ 6
+
+    >>> # A near tree with one non-tree edge
+    >>> graph.add_edge(5, 1)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├─╼ 1 ╾ 5
+        │   ├─╼ 3
+        │   └─╼ 4
+        └─╼ 2
+            ├─╼ 5
+            │   └─╼  ...
+            └─╼ 6
+
+    >>> graph = nx.cycle_graph(5)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├── 1
+        │   └── 2
+        │       └── 3
+        │           └── 4 ─ 0
+        └──  ...
+
+    >>> graph = nx.cycle_graph(5, nx.DiGraph)
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 0 ╾ 4
+        ╽
+        1
+        ╽
+        2
+        ╽
+        3
+        ╽
+        4
+        └─╼  ...
+
+    >>> nx.write_network_text(graph, vertical_chains=True, ascii_only=True)
+    +-- 0 <- 4
+        !
+        1
+        !
+        2
+        !
+        3
+        !
+        4
+        L->  ...
+
+    >>> graph = nx.generators.barbell_graph(4, 2)
+    >>> nx.write_network_text(graph, vertical_chains=False)
+    ╙── 4
+        ├── 5
+        │   └── 6
+        │       ├── 7
+        │       │   ├── 8 ─ 6
+        │       │   │   └── 9 ─ 6, 7
+        │       │   └──  ...
+        │       └──  ...
+        └── 3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   └── 2 ─ 0, 3
+            │   └──  ...
+            └──  ...
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 4
+        ├── 5
+        │   │
+        │   6
+        │   ├── 7
+        │   │   ├── 8 ─ 6
+        │   │   │   │
+        │   │   │   9 ─ 6, 7
+        │   │   └──  ...
+        │   └──  ...
+        └── 3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   │
+            │   │   2 ─ 0, 3
+            │   └──  ...
+            └──  ...
+
+    >>> graph = nx.complete_graph(5, create_using=nx.Graph)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├── 1
+        │   ├── 2 ─ 0
+        │   │   ├── 3 ─ 0, 1
+        │   │   │   └── 4 ─ 0, 1, 2
+        │   │   └──  ...
+        │   └──  ...
+        └──  ...
+
+    >>> graph = nx.complete_graph(3, create_using=nx.DiGraph)
+    >>> nx.write_network_text(graph)
+    ╙── 0 ╾ 1, 2
+        ├─╼ 1 ╾ 2
+        │   ├─╼ 2 ╾ 0
+        │   │   └─╼  ...
+        │   └─╼  ...
+        └─╼  ...
+    """
+    if path is None:
+        # The path is unspecified, write to stdout
+        _write = sys.stdout.write
+    elif hasattr(path, "write"):
+        # The path is already an open file
+        _write = path.write
+    elif callable(path):
+        # The path is a custom callable
+        _write = path
+    else:
+        raise TypeError(type(path))
+
+    for line in generate_network_text(
+        graph,
+        with_labels=with_labels,
+        sources=sources,
+        max_depth=max_depth,
+        ascii_only=ascii_only,
+        vertical_chains=vertical_chains,
+    ):
+        _write(line + end)
+
+
+def _find_sources(graph):
+    """
+    Determine a minimal set of nodes such that the entire graph is reachable
+    """
+    # For each connected part of the graph, choose at least
+    # one node as a starting point, preferably without a parent
+    if graph.is_directed():
+        # Choose one node from each SCC with minimum in_degree
+        sccs = list(nx.strongly_connected_components(graph))
+        # condensing the SCCs forms a dag, the nodes in this graph with
+        # 0 in-degree correspond to the SCCs from which the minimum set
+        # of nodes from which all other nodes can be reached.
+        scc_graph = nx.condensation(graph, sccs)
+        supernode_to_nodes = {sn: [] for sn in scc_graph.nodes()}
+        # Note: the order of mapping differs between pypy and cpython
+        # so we have to loop over graph nodes for consistency
+        mapping = scc_graph.graph["mapping"]
+        for n in graph.nodes:
+            sn = mapping[n]
+            supernode_to_nodes[sn].append(n)
+        sources = []
+        for sn in scc_graph.nodes():
+            if scc_graph.in_degree[sn] == 0:
+                scc = supernode_to_nodes[sn]
+                node = min(scc, key=lambda n: graph.in_degree[n])
+                sources.append(node)
+    else:
+        # For undirected graph, the entire graph will be reachable as
+        # long as we consider one node from every connected component
+        sources = [
+            min(cc, key=lambda n: graph.degree[n])
+            for cc in nx.connected_components(graph)
+        ]
+        sources = sorted(sources, key=lambda n: graph.degree[n])
+    return sources
+
+
+def _parse_network_text(lines):
+    """Reconstructs a graph from a network text representation.
+
+    This is mainly used for testing.  Network text is for display, not
+    serialization, as such this cannot parse all network text representations
+    because node labels can be ambiguous with the glyphs and indentation used
+    to represent edge structure. Additionally, there is no way to determine if
+    disconnected graphs were originally directed or undirected.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in network text format
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in network text format.
+    """
+    from itertools import chain
+    from typing import Any, NamedTuple
+
+    class ParseStackFrame(NamedTuple):
+        node: Any
+        indent: int
+        has_vertical_child: int | None
+
+    initial_line_iter = iter(lines)
+
+    is_ascii = None
+    is_directed = None
+
+    ##############
+    # Initial Pass
+    ##############
+
+    # Do an initial pass over the lines to determine what type of graph it is.
+    # Remember what these lines were, so we can reiterate over them in the
+    # parsing pass.
+    initial_lines = []
+    try:
+        first_line = next(initial_line_iter)
+    except StopIteration:
+        ...
+    else:
+        initial_lines.append(first_line)
+        # The first character indicates if it is an ASCII or UTF graph
+        first_char = first_line[0]
+        if first_char in {
+            UtfBaseGlyphs.empty,
+            UtfBaseGlyphs.newtree_mid[0],
+            UtfBaseGlyphs.newtree_last[0],
+        }:
+            is_ascii = False
+        elif first_char in {
+            AsciiBaseGlyphs.empty,
+            AsciiBaseGlyphs.newtree_mid[0],
+            AsciiBaseGlyphs.newtree_last[0],
+        }:
+            is_ascii = True
+        else:
+            raise AssertionError(f"Unexpected first character: {first_char}")
+
+    if is_ascii:
+        directed_glyphs = AsciiDirectedGlyphs.as_dict()
+        undirected_glyphs = AsciiUndirectedGlyphs.as_dict()
+    else:
+        directed_glyphs = UtfDirectedGlyphs.as_dict()
+        undirected_glyphs = UtfUndirectedGlyphs.as_dict()
+
+    # For both directed / undirected glyphs, determine which glyphs never
+    # appear as substrings in the other undirected / directed glyphs.  Glyphs
+    # with this property unambiguously indicates if a graph is directed /
+    # undirected.
+    directed_items = set(directed_glyphs.values())
+    undirected_items = set(undirected_glyphs.values())
+    unambiguous_directed_items = []
+    for item in directed_items:
+        other_items = undirected_items
+        other_supersets = [other for other in other_items if item in other]
+        if not other_supersets:
+            unambiguous_directed_items.append(item)
+    unambiguous_undirected_items = []
+    for item in undirected_items:
+        other_items = directed_items
+        other_supersets = [other for other in other_items if item in other]
+        if not other_supersets:
+            unambiguous_undirected_items.append(item)
+
+    for line in initial_line_iter:
+        initial_lines.append(line)
+        if any(item in line for item in unambiguous_undirected_items):
+            is_directed = False
+            break
+        elif any(item in line for item in unambiguous_directed_items):
+            is_directed = True
+            break
+
+    if is_directed is None:
+        # Not enough information to determine, choose undirected by default
+        is_directed = False
+
+    glyphs = directed_glyphs if is_directed else undirected_glyphs
+
+    # the backedge symbol by itself can be ambiguous, but with spaces around it
+    # becomes unambiguous.
+    backedge_symbol = " " + glyphs["backedge"] + " "
+
+    # Reconstruct an iterator over all of the lines.
+    parsing_line_iter = chain(initial_lines, initial_line_iter)
+
+    ##############
+    # Parsing Pass
+    ##############
+
+    edges = []
+    nodes = []
+    is_empty = None
+
+    noparent = object()  # sentinel value
+
+    # keep a stack of previous nodes that could be parents of subsequent nodes
+    stack = [ParseStackFrame(noparent, -1, None)]
+
+    for line in parsing_line_iter:
+        if line == glyphs["empty"]:
+            # If the line is the empty glyph, we are done.
+            # There shouldn't be anything else after this.
+            is_empty = True
+            continue
+
+        if backedge_symbol in line:
+            # This line has one or more backedges, separate those out
+            node_part, backedge_part = line.split(backedge_symbol)
+            backedge_nodes = [u.strip() for u in backedge_part.split(", ")]
+            # Now the node can be parsed
+            node_part = node_part.rstrip()
+            prefix, node = node_part.rsplit(" ", 1)
+            node = node.strip()
+            # Add the backedges to the edge list
+            edges.extend([(u, node) for u in backedge_nodes])
+        else:
+            # No backedge, the tail of this line is the node
+            prefix, node = line.rsplit(" ", 1)
+            node = node.strip()
+
+        prev = stack.pop()
+
+        if node in glyphs["vertical_edge"]:
+            # Previous node is still the previous node, but we know it will
+            # have exactly one child, which will need to have its nesting level
+            # adjusted.
+            modified_prev = ParseStackFrame(
+                prev.node,
+                prev.indent,
+                True,
+            )
+            stack.append(modified_prev)
+            continue
+
+        # The length of the string before the node characters give us a hint
+        # about our nesting level. The only case where this doesn't work is
+        # when there are vertical chains, which is handled explicitly.
+        indent = len(prefix)
+        curr = ParseStackFrame(node, indent, None)
+
+        if prev.has_vertical_child:
+            # In this case we know prev must be the parent of our current line,
+            # so we don't have to search the stack. (which is good because the
+            # indentation check wouldn't work in this case).
+            ...
+        else:
+            # If the previous node nesting-level is greater than the current
+            # nodes nesting-level than the previous node was the end of a path,
+            # and is not our parent. We can safely pop nodes off the stack
+            # until we find one with a comparable nesting-level, which is our
+            # parent.
+            while curr.indent <= prev.indent:
+                prev = stack.pop()
+
+        if node == "...":
+            # The current previous node is no longer a valid parent,
+            # keep it popped from the stack.
+            stack.append(prev)
+        else:
+            # The previous and current nodes may still be parents, so add them
+            # back onto the stack.
+            stack.append(prev)
+            stack.append(curr)
+
+            # Add the node and the edge to its parent to the node / edge lists.
+            nodes.append(curr.node)
+            if prev.node is not noparent:
+                edges.append((prev.node, curr.node))
+
+    if is_empty:
+        # Sanity check
+        assert len(nodes) == 0
+
+    # Reconstruct the graph
+    cls = nx.DiGraph if is_directed else nx.Graph
+    new = cls()
+    new.add_nodes_from(nodes)
+    new.add_edges_from(edges)
+    return new
diff --git a/lib/python3.11/site-packages/networkx/tests/__init__.py b/lib/python3.11/site-packages/networkx/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/networkx/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/networkx/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..90118503acffb5cd7807a70afcff2779fb00ff48
Binary files /dev/null and b/lib/python3.11/site-packages/networkx/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/networkx/tests/test_all_random_functions.py b/lib/python3.11/site-packages/networkx/tests/test_all_random_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb3a73d0666b01bd1a90a4759670f912590aaf6b
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_all_random_functions.py
@@ -0,0 +1,248 @@
+import random
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms import approximation as approx
+from networkx.algorithms import threshold
+
+np = pytest.importorskip("numpy")
+
+progress = 0
+
+# store the random numbers after setting a global seed
+np.random.seed(42)
+np_rv = np.random.rand()
+random.seed(42)
+py_rv = random.random()
+
+
+def t(f, *args, **kwds):
+    """call one function and check if global RNG changed"""
+    global progress
+    progress += 1
+    print(progress, ",", end="")
+
+    f(*args, **kwds)
+
+    after_np_rv = np.random.rand()
+    # if np_rv != after_np_rv:
+    #    print(np_rv, after_np_rv, "don't match np!")
+    assert np_rv == after_np_rv
+    np.random.seed(42)
+
+    after_py_rv = random.random()
+    # if py_rv != after_py_rv:
+    #    print(py_rv, after_py_rv, "don't match py!")
+    assert py_rv == after_py_rv
+    random.seed(42)
+
+
+def run_all_random_functions(seed):
+    n = 20
+    m = 10
+    k = l = 2
+    s = v = 10
+    p = q = p1 = p2 = p_in = p_out = 0.4
+    alpha = radius = theta = 0.75
+    sizes = (20, 20, 10)
+    colors = [1, 2, 3]
+    G = nx.barbell_graph(12, 20)
+    H = nx.cycle_graph(3)
+    H.add_weighted_edges_from((u, v, 0.2) for u, v in H.edges)
+    deg_sequence = [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1]
+    in_degree_sequence = w = sequence = aseq = bseq = deg_sequence
+
+    # print("starting...")
+    t(nx.maximal_independent_set, G, seed=seed)
+    t(nx.rich_club_coefficient, G, seed=seed, normalized=False)
+    t(nx.random_reference, G, seed=seed)
+    t(nx.lattice_reference, G, seed=seed)
+    t(nx.sigma, G, 1, 2, seed=seed)
+    t(nx.omega, G, 1, 2, seed=seed)
+    # print("out of smallworld.py")
+    t(nx.double_edge_swap, G, seed=seed)
+    # print("starting connected_double_edge_swap")
+    t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed)
+    # print("ending connected_double_edge_swap")
+    t(nx.random_layout, G, seed=seed)
+    t(nx.fruchterman_reingold_layout, G, seed=seed)
+    t(nx.algebraic_connectivity, G, seed=seed)
+    t(nx.fiedler_vector, G, seed=seed)
+    t(nx.spectral_ordering, G, seed=seed)
+    # print('starting average_clustering')
+    t(approx.average_clustering, G, seed=seed)
+    t(approx.simulated_annealing_tsp, H, "greedy", source=1, seed=seed)
+    t(approx.threshold_accepting_tsp, H, "greedy", source=1, seed=seed)
+    t(
+        approx.traveling_salesman_problem,
+        H,
+        method=lambda G, weight: approx.simulated_annealing_tsp(
+            G, "greedy", weight, seed=seed
+        ),
+    )
+    t(
+        approx.traveling_salesman_problem,
+        H,
+        method=lambda G, weight: approx.threshold_accepting_tsp(
+            G, "greedy", weight, seed=seed
+        ),
+    )
+    t(nx.betweenness_centrality, G, seed=seed)
+    t(nx.edge_betweenness_centrality, G, seed=seed)
+    t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed)
+    # print("kernighan")
+    t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed)
+    # nx.algorithms.community.asyn_lpa_communities(G, seed=seed)
+    t(nx.algorithms.tree.greedy_branching, G, seed=seed)
+    # print('done with graph argument functions')
+
+    t(nx.spectral_graph_forge, G, alpha, seed=seed)
+    t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed)
+    t(
+        nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation,
+        G,
+        k,
+        seed=seed,
+    )
+    t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed)
+
+    t(nx.configuration_model, deg_sequence, seed=seed)
+    t(
+        nx.directed_configuration_model,
+        in_degree_sequence,
+        in_degree_sequence,
+        seed=seed,
+    )
+    t(nx.expected_degree_graph, w, seed=seed)
+    t(nx.random_degree_sequence_graph, sequence, seed=seed)
+    joint_degrees = {
+        1: {4: 1},
+        2: {2: 2, 3: 2, 4: 2},
+        3: {2: 2, 4: 1},
+        4: {1: 1, 2: 2, 3: 1},
+    }
+    t(nx.joint_degree_graph, joint_degrees, seed=seed)
+    joint_degree_sequence = [
+        (1, 0),
+        (1, 0),
+        (1, 0),
+        (2, 0),
+        (1, 0),
+        (2, 1),
+        (0, 1),
+        (0, 1),
+    ]
+    t(nx.random_clustered_graph, joint_degree_sequence, seed=seed)
+    constructor = [(3, 3, 0.5), (10, 10, 0.7)]
+    t(nx.random_shell_graph, constructor, seed=seed)
+    mapping = {1: 0.4, 2: 0.3, 3: 0.3}
+    t(nx.utils.random_weighted_sample, mapping, k, seed=seed)
+    t(nx.utils.weighted_choice, mapping, seed=seed)
+    t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed)
+    t(nx.algorithms.bipartite.preferential_attachment_graph, aseq, p, seed=seed)
+
+    def kernel_integral(u, w, z):
+        return z - w
+
+    t(nx.random_kernel_graph, n, kernel_integral, seed=seed)
+
+    sizes = [75, 75, 300]
+    probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    t(nx.stochastic_block_model, sizes, probs, seed=seed)
+    t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed)
+
+    # print("starting generator functions")
+    t(threshold.random_threshold_sequence, n, p, seed=seed)
+    t(nx.tournament.random_tournament, n, seed=seed)
+    t(nx.relaxed_caveman_graph, l, k, p, seed=seed)
+    t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed)
+    t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed)
+    t(nx.gn_graph, n, seed=seed)
+    t(nx.gnr_graph, n, p, seed=seed)
+    t(nx.gnc_graph, n, seed=seed)
+    t(nx.scale_free_graph, n, seed=seed)
+    t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed)
+    t(nx.random_k_out_graph, n, k, alpha, seed=seed)
+    N = 1000
+    t(nx.partial_duplication_graph, N, n, p, q, seed=seed)
+    t(nx.duplication_divergence_graph, n, p, seed=seed)
+    t(nx.random_geometric_graph, n, radius, seed=seed)
+    t(nx.soft_random_geometric_graph, n, radius, seed=seed)
+    t(nx.geographical_threshold_graph, n, theta, seed=seed)
+    t(nx.waxman_graph, n, seed=seed)
+    t(nx.navigable_small_world_graph, n, seed=seed)
+    t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed)
+    t(nx.uniform_random_intersection_graph, n, m, p, seed=seed)
+    t(nx.k_random_intersection_graph, n, m, k, seed=seed)
+
+    t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed)
+    t(nx.fast_gnp_random_graph, n, p, seed=seed)
+    t(nx.gnp_random_graph, n, p, seed=seed)
+    t(nx.dense_gnm_random_graph, n, m, seed=seed)
+    t(nx.gnm_random_graph, n, m, seed=seed)
+    t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.random_regular_graph, 3, n, seed=seed)
+    t(nx.barabasi_albert_graph, n, m, seed=seed)
+    t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed)
+    t(nx.powerlaw_cluster_graph, n, m, p, seed=seed)
+    t(nx.random_lobster_graph, n, p1, p2, seed=seed)
+    t(nx.random_powerlaw_tree, 5, seed=seed, tries=5000)
+    t(nx.random_powerlaw_tree_sequence, 5, seed=seed, tries=5000)
+    t(nx.random_labeled_tree, n, seed=seed)
+    t(nx.utils.powerlaw_sequence, n, seed=seed)
+    t(nx.utils.zipf_rv, 2.3, seed=seed)
+    cdist = [0.2, 0.4, 0.5, 0.7, 0.9, 1.0]
+    t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed)
+    t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed)
+    t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed)
+    LFR = nx.generators.LFR_benchmark_graph
+    t(
+        LFR,
+        25,
+        3,
+        1.5,
+        0.1,
+        average_degree=3,
+        min_community=10,
+        seed=seed,
+        max_community=20,
+    )
+    t(nx.random_internet_as_graph, n, seed=seed)
+    # print("done")
+
+
+# choose to test an integer seed, or whether a single RNG can be everywhere
+# np_rng = np.random.RandomState(14)
+# seed = np_rng
+# seed = 14
+
+
+@pytest.mark.slow
+# print("NetworkX Version:", nx.__version__)
+def test_rng_interface():
+    global progress
+
+    # try different kinds of seeds
+    for seed in [14, np.random.RandomState(14)]:
+        np.random.seed(42)
+        random.seed(42)
+        run_all_random_functions(seed)
+        progress = 0
+
+        # check that both global RNGs are unaffected
+        after_np_rv = np.random.rand()
+        #        if np_rv != after_np_rv:
+        #            print(np_rv, after_np_rv, "don't match np!")
+        assert np_rv == after_np_rv
+        after_py_rv = random.random()
+        #        if py_rv != after_py_rv:
+        #            print(py_rv, after_py_rv, "don't match py!")
+        assert py_rv == after_py_rv
+
+
+#        print("\nDone testing seed:", seed)
+
+# test_rng_interface()
diff --git a/lib/python3.11/site-packages/networkx/tests/test_convert.py b/lib/python3.11/site-packages/networkx/tests/test_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..44bed9438945a39bb5eb85477301f58cfcd70cf0
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_convert.py
@@ -0,0 +1,321 @@
+import pytest
+
+import networkx as nx
+from networkx.convert import (
+    from_dict_of_dicts,
+    from_dict_of_lists,
+    to_dict_of_dicts,
+    to_dict_of_lists,
+    to_networkx_graph,
+)
+from networkx.generators.classic import barbell_graph, cycle_graph
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+class TestConvert:
+    def edgelists_equal(self, e1, e2):
+        return sorted(sorted(e) for e in e1) == sorted(sorted(e) for e in e2)
+
+    def test_simple_graphs(self):
+        for dest, source in [
+            (to_dict_of_dicts, from_dict_of_dicts),
+            (to_dict_of_lists, from_dict_of_lists),
+        ]:
+            G = barbell_graph(10, 3)
+            G.graph = {}
+            dod = dest(G)
+
+            # Dict of [dicts, lists]
+            GG = source(dod)
+            assert graphs_equal(G, GG)
+            GW = to_networkx_graph(dod)
+            assert graphs_equal(G, GW)
+            GI = nx.Graph(dod)
+            assert graphs_equal(G, GI)
+
+            # With nodelist keyword
+            P4 = nx.path_graph(4)
+            P3 = nx.path_graph(3)
+            P4.graph = {}
+            P3.graph = {}
+            dod = dest(P4, nodelist=[0, 1, 2])
+            Gdod = nx.Graph(dod)
+            assert graphs_equal(Gdod, P3)
+
+    def test_exceptions(self):
+        # NX graph
+        class G:
+            adj = None
+
+        pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+        # pygraphviz  agraph
+        class G:
+            is_strict = None
+
+        pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+        # Dict of [dicts, lists]
+        G = {"a": 0}
+        pytest.raises(TypeError, to_networkx_graph, G)
+
+        # list or generator of edges
+        class G:
+            next = None
+
+        pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+        # no match
+        pytest.raises(nx.NetworkXError, to_networkx_graph, "a")
+
+    def test_digraphs(self):
+        for dest, source in [
+            (to_dict_of_dicts, from_dict_of_dicts),
+            (to_dict_of_lists, from_dict_of_lists),
+        ]:
+            G = cycle_graph(10)
+
+            # Dict of [dicts, lists]
+            dod = dest(G)
+            GG = source(dod)
+            assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+            assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+            GW = to_networkx_graph(dod)
+            assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+            assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+            GI = nx.Graph(dod)
+            assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+            assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
+
+            G = cycle_graph(10, create_using=nx.DiGraph)
+            dod = dest(G)
+            GG = source(dod, create_using=nx.DiGraph)
+            assert sorted(G.nodes()) == sorted(GG.nodes())
+            assert sorted(G.edges()) == sorted(GG.edges())
+            GW = to_networkx_graph(dod, create_using=nx.DiGraph)
+            assert sorted(G.nodes()) == sorted(GW.nodes())
+            assert sorted(G.edges()) == sorted(GW.edges())
+            GI = nx.DiGraph(dod)
+            assert sorted(G.nodes()) == sorted(GI.nodes())
+            assert sorted(G.edges()) == sorted(GI.edges())
+
+    def test_graph(self):
+        g = nx.cycle_graph(10)
+        G = nx.Graph()
+        G.add_nodes_from(g)
+        G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
+
+        # Dict of dicts
+        dod = to_dict_of_dicts(G)
+        GG = from_dict_of_dicts(dod, create_using=nx.Graph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+        GW = to_networkx_graph(dod, create_using=nx.Graph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+        GI = nx.Graph(dod)
+        assert sorted(G.nodes()) == sorted(GI.nodes())
+        assert sorted(G.edges()) == sorted(GI.edges())
+
+        # Dict of lists
+        dol = to_dict_of_lists(G)
+        GG = from_dict_of_lists(dol, create_using=nx.Graph)
+        # dict of lists throws away edge data so set it to none
+        enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
+        assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+        assert edges_equal(enone, sorted(GG.edges(data=True)))
+        GW = to_networkx_graph(dol, create_using=nx.Graph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+        assert edges_equal(enone, sorted(GW.edges(data=True)))
+        GI = nx.Graph(dol)
+        assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+        assert edges_equal(enone, sorted(GI.edges(data=True)))
+
+    def test_with_multiedges_self_loops(self):
+        G = cycle_graph(10)
+        XG = nx.Graph()
+        XG.add_nodes_from(G)
+        XG.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+        XGM = nx.MultiGraph()
+        XGM.add_nodes_from(G)
+        XGM.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+        XGM.add_edge(0, 1, weight=2)  # multiedge
+        XGS = nx.Graph()
+        XGS.add_nodes_from(G)
+        XGS.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+        XGS.add_edge(0, 0, weight=100)  # self loop
+
+        # Dict of dicts
+        # with self loops, OK
+        dod = to_dict_of_dicts(XGS)
+        GG = from_dict_of_dicts(dod, create_using=nx.Graph)
+        assert nodes_equal(XGS.nodes(), GG.nodes())
+        assert edges_equal(XGS.edges(), GG.edges())
+        GW = to_networkx_graph(dod, create_using=nx.Graph)
+        assert nodes_equal(XGS.nodes(), GW.nodes())
+        assert edges_equal(XGS.edges(), GW.edges())
+        GI = nx.Graph(dod)
+        assert nodes_equal(XGS.nodes(), GI.nodes())
+        assert edges_equal(XGS.edges(), GI.edges())
+
+        # Dict of lists
+        # with self loops, OK
+        dol = to_dict_of_lists(XGS)
+        GG = from_dict_of_lists(dol, create_using=nx.Graph)
+        # dict of lists throws away edge data so set it to none
+        enone = [(u, v, {}) for (u, v, d) in XGS.edges(data=True)]
+        assert nodes_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
+        assert edges_equal(enone, sorted(GG.edges(data=True)))
+        GW = to_networkx_graph(dol, create_using=nx.Graph)
+        assert nodes_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
+        assert edges_equal(enone, sorted(GW.edges(data=True)))
+        GI = nx.Graph(dol)
+        assert nodes_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
+        assert edges_equal(enone, sorted(GI.edges(data=True)))
+
+        # Dict of dicts
+        # with multiedges, OK
+        dod = to_dict_of_dicts(XGM)
+        GG = from_dict_of_dicts(dod, create_using=nx.MultiGraph, multigraph_input=True)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GG.nodes()))
+        assert edges_equal(sorted(XGM.edges()), sorted(GG.edges()))
+        GW = to_networkx_graph(dod, create_using=nx.MultiGraph, multigraph_input=True)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GW.nodes()))
+        assert edges_equal(sorted(XGM.edges()), sorted(GW.edges()))
+        GI = nx.MultiGraph(dod)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
+        assert sorted(XGM.edges()) == sorted(GI.edges())
+        GE = from_dict_of_dicts(dod, create_using=nx.MultiGraph, multigraph_input=False)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GE.nodes()))
+        assert sorted(XGM.edges()) != sorted(GE.edges())
+        GI = nx.MultiGraph(XGM)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
+        assert edges_equal(sorted(XGM.edges()), sorted(GI.edges()))
+        GM = nx.MultiGraph(G)
+        assert nodes_equal(sorted(GM.nodes()), sorted(G.nodes()))
+        assert edges_equal(sorted(GM.edges()), sorted(G.edges()))
+
+        # Dict of lists
+        # with multiedges, OK, but better write as DiGraph else you'll
+        # get double edges
+        dol = to_dict_of_lists(G)
+        GG = from_dict_of_lists(dol, create_using=nx.MultiGraph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+        GW = to_networkx_graph(dol, create_using=nx.MultiGraph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+        GI = nx.MultiGraph(dol)
+        assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
+
+    def test_edgelists(self):
+        P = nx.path_graph(4)
+        e = [(0, 1), (1, 2), (2, 3)]
+        G = nx.Graph(e)
+        assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+        assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+        e = [(0, 1, {}), (1, 2, {}), (2, 3, {})]
+        G = nx.Graph(e)
+        assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+        assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+        e = ((n, n + 1) for n in range(3))
+        G = nx.Graph(e)
+        assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+        assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+    def test_directed_to_undirected(self):
+        edges1 = [(0, 1), (1, 2), (2, 0)]
+        edges2 = [(0, 1), (1, 2), (0, 2)]
+        assert self.edgelists_equal(nx.Graph(nx.DiGraph(edges1)).edges(), edges1)
+        assert self.edgelists_equal(nx.Graph(nx.DiGraph(edges2)).edges(), edges1)
+        assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges1)).edges(), edges1)
+        assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges2)).edges(), edges1)
+
+        assert self.edgelists_equal(
+            nx.MultiGraph(nx.MultiDiGraph(edges1)).edges(), edges1
+        )
+        assert self.edgelists_equal(
+            nx.MultiGraph(nx.MultiDiGraph(edges2)).edges(), edges1
+        )
+
+        assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges1)).edges(), edges1)
+        assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges2)).edges(), edges1)
+
+    def test_attribute_dict_integrity(self):
+        # we must not replace dict-like graph data structures with dicts
+        G = nx.Graph()
+        G.add_nodes_from("abc")
+        H = to_networkx_graph(G, create_using=nx.Graph)
+        assert list(H.nodes) == list(G.nodes)
+        H = nx.DiGraph(G)
+        assert list(H.nodes) == list(G.nodes)
+
+    def test_to_edgelist(self):
+        G = nx.Graph([(1, 1)])
+        elist = nx.to_edgelist(G, nodelist=list(G))
+        assert edges_equal(G.edges(data=True), elist)
+
+    def test_custom_node_attr_dict_safekeeping(self):
+        class custom_dict(dict):
+            pass
+
+        class Custom(nx.Graph):
+            node_attr_dict_factory = custom_dict
+
+        g = nx.Graph()
+        g.add_node(1, weight=1)
+
+        h = Custom(g)
+        assert isinstance(g._node[1], dict)
+        assert isinstance(h._node[1], custom_dict)
+
+        # this raise exception
+        # h._node.update((n, dd.copy()) for n, dd in g.nodes.items())
+        # assert isinstance(h._node[1], custom_dict)
+
+
+@pytest.mark.parametrize(
+    "edgelist",
+    (
+        # Graph with no edge data
+        [(0, 1), (1, 2)],
+        # Graph with edge data
+        [(0, 1, {"weight": 1.0}), (1, 2, {"weight": 2.0})],
+    ),
+)
+def test_to_dict_of_dicts_with_edgedata_param(edgelist):
+    G = nx.Graph()
+    G.add_edges_from(edgelist)
+    # Innermost dict value == edge_data when edge_data != None.
+    # In the case when G has edge data, it is overwritten
+    expected = {0: {1: 10}, 1: {0: 10, 2: 10}, 2: {1: 10}}
+    assert nx.to_dict_of_dicts(G, edge_data=10) == expected
+
+
+def test_to_dict_of_dicts_with_edgedata_and_nodelist():
+    G = nx.path_graph(5)
+    nodelist = [2, 3, 4]
+    expected = {2: {3: 10}, 3: {2: 10, 4: 10}, 4: {3: 10}}
+    assert nx.to_dict_of_dicts(G, nodelist=nodelist, edge_data=10) == expected
+
+
+def test_to_dict_of_dicts_with_edgedata_multigraph():
+    """Multi edge data overwritten when edge_data != None"""
+    G = nx.MultiGraph()
+    G.add_edge(0, 1, key="a")
+    G.add_edge(0, 1, key="b")
+    # Multi edge data lost when edge_data is not None
+    expected = {0: {1: 10}, 1: {0: 10}}
+    assert nx.to_dict_of_dicts(G, edge_data=10) == expected
+
+
+def test_to_networkx_graph_non_edgelist():
+    invalid_edgelist = [1, 2, 3]
+    with pytest.raises(nx.NetworkXError, match="Input is not a valid edge list"):
+        nx.to_networkx_graph(invalid_edgelist)
diff --git a/lib/python3.11/site-packages/networkx/tests/test_convert_numpy.py b/lib/python3.11/site-packages/networkx/tests/test_convert_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a554fd4b2d5e00e69a0537fbd04e877a95b6593
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_convert_numpy.py
@@ -0,0 +1,531 @@
+import itertools
+
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+np = pytest.importorskip("numpy")
+npt = pytest.importorskip("numpy.testing")
+
+
+class TestConvertNumpyArray:
+    def setup_method(self):
+        self.G1 = nx.barbell_graph(10, 3)
+        self.G2 = nx.cycle_graph(10, create_using=nx.DiGraph)
+        self.G3 = self.create_weighted(nx.Graph())
+        self.G4 = self.create_weighted(nx.DiGraph())
+
+    def create_weighted(self, G):
+        g = nx.cycle_graph(4)
+        G.add_nodes_from(g)
+        G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges())
+        return G
+
+    def assert_equal(self, G1, G2):
+        assert sorted(G1.nodes()) == sorted(G2.nodes())
+        assert sorted(G1.edges()) == sorted(G2.edges())
+
+    def identity_conversion(self, G, A, create_using):
+        assert A.sum() > 0
+        GG = nx.from_numpy_array(A, create_using=create_using)
+        self.assert_equal(G, GG)
+        GW = nx.to_networkx_graph(A, create_using=create_using)
+        self.assert_equal(G, GW)
+        GI = nx.empty_graph(0, create_using).__class__(A)
+        self.assert_equal(G, GI)
+
+    def test_shape(self):
+        "Conversion from non-square array."
+        A = np.array([[1, 2, 3], [4, 5, 6]])
+        pytest.raises(nx.NetworkXError, nx.from_numpy_array, A)
+
+    def test_identity_graph_array(self):
+        "Conversion from graph to array to graph."
+        A = nx.to_numpy_array(self.G1)
+        self.identity_conversion(self.G1, A, nx.Graph())
+
+    def test_identity_digraph_array(self):
+        """Conversion from digraph to array to digraph."""
+        A = nx.to_numpy_array(self.G2)
+        self.identity_conversion(self.G2, A, nx.DiGraph())
+
+    def test_identity_weighted_graph_array(self):
+        """Conversion from weighted graph to array to weighted graph."""
+        A = nx.to_numpy_array(self.G3)
+        self.identity_conversion(self.G3, A, nx.Graph())
+
+    def test_identity_weighted_digraph_array(self):
+        """Conversion from weighted digraph to array to weighted digraph."""
+        A = nx.to_numpy_array(self.G4)
+        self.identity_conversion(self.G4, A, nx.DiGraph())
+
+    def test_nodelist(self):
+        """Conversion from graph to array to graph with nodelist."""
+        P4 = nx.path_graph(4)
+        P3 = nx.path_graph(3)
+        nodelist = list(P3)
+        A = nx.to_numpy_array(P4, nodelist=nodelist)
+        GA = nx.Graph(A)
+        self.assert_equal(GA, P3)
+
+        # Make nodelist ambiguous by containing duplicates.
+        nodelist += [nodelist[0]]
+        pytest.raises(nx.NetworkXError, nx.to_numpy_array, P3, nodelist=nodelist)
+
+        # Make nodelist invalid by including nonexistent nodes
+        nodelist = [-1, 0, 1]
+        with pytest.raises(
+            nx.NetworkXError,
+            match=f"Nodes {nodelist - P3.nodes} in nodelist is not in G",
+        ):
+            nx.to_numpy_array(P3, nodelist=nodelist)
+
+    def test_weight_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = nx.path_graph(4)
+        A = nx.to_numpy_array(P4)
+        np.testing.assert_equal(A, nx.to_numpy_array(WP4, weight=None))
+        np.testing.assert_equal(0.5 * A, nx.to_numpy_array(WP4))
+        np.testing.assert_equal(0.3 * A, nx.to_numpy_array(WP4, weight="other"))
+
+    def test_from_numpy_array_type(self):
+        A = np.array([[1]])
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], int)
+
+        A = np.array([[1]]).astype(float)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], float)
+
+        A = np.array([[1]]).astype(str)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], str)
+
+        A = np.array([[1]]).astype(bool)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], bool)
+
+        A = np.array([[1]]).astype(complex)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], complex)
+
+        A = np.array([[1]]).astype(object)
+        pytest.raises(TypeError, nx.from_numpy_array, A)
+
+        A = np.array([[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]])
+        with pytest.raises(
+            nx.NetworkXError, match=f"Input array must be 2D, not {A.ndim}"
+        ):
+            g = nx.from_numpy_array(A)
+
+    def test_from_numpy_array_dtype(self):
+        dt = [("weight", float), ("cost", int)]
+        A = np.array([[(1.0, 2)]], dtype=dt)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], float)
+        assert isinstance(G[0][0]["cost"], int)
+        assert G[0][0]["cost"] == 2
+        assert G[0][0]["weight"] == 1.0
+
+    def test_from_numpy_array_parallel_edges(self):
+        """Tests that the :func:`networkx.from_numpy_array` function
+        interprets integer weights as the number of parallel edges when
+        creating a multigraph.
+
+        """
+        A = np.array([[1, 1], [1, 2]])
+        # First, with a simple graph, each integer entry in the adjacency
+        # matrix is interpreted as the weight of a single edge in the graph.
+        expected = nx.DiGraph()
+        edges = [(0, 0), (0, 1), (1, 0)]
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        expected.add_edge(1, 1, weight=2)
+        actual = nx.from_numpy_array(A, parallel_edges=True, create_using=nx.DiGraph)
+        assert graphs_equal(actual, expected)
+        actual = nx.from_numpy_array(A, parallel_edges=False, create_using=nx.DiGraph)
+        assert graphs_equal(actual, expected)
+        # Now each integer entry in the adjacency matrix is interpreted as the
+        # number of parallel edges in the graph if the appropriate keyword
+        # argument is specified.
+        edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
+        expected = nx.MultiDiGraph()
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        actual = nx.from_numpy_array(
+            A, parallel_edges=True, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+        expected = nx.MultiDiGraph()
+        expected.add_edges_from(set(edges), weight=1)
+        # The sole self-loop (edge 0) on vertex 1 should have weight 2.
+        expected[1][1][0]["weight"] = 2
+        actual = nx.from_numpy_array(
+            A, parallel_edges=False, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "dt",
+        (
+            None,  # default
+            int,  # integer dtype
+            np.dtype(
+                [("weight", "f8"), ("color", "i1")]
+            ),  # Structured dtype with named fields
+        ),
+    )
+    def test_from_numpy_array_no_edge_attr(self, dt):
+        A = np.array([[0, 1], [1, 0]], dtype=dt)
+        G = nx.from_numpy_array(A, edge_attr=None)
+        assert "weight" not in G.edges[0, 1]
+        assert len(G.edges[0, 1]) == 0
+
+    def test_from_numpy_array_multiedge_no_edge_attr(self):
+        A = np.array([[0, 2], [2, 0]])
+        G = nx.from_numpy_array(A, create_using=nx.MultiDiGraph, edge_attr=None)
+        assert all("weight" not in e for _, e in G[0][1].items())
+        assert len(G[0][1][0]) == 0
+
+    def test_from_numpy_array_custom_edge_attr(self):
+        A = np.array([[0, 2], [3, 0]])
+        G = nx.from_numpy_array(A, edge_attr="cost")
+        assert "weight" not in G.edges[0, 1]
+        assert G.edges[0, 1]["cost"] == 3
+
+    def test_symmetric(self):
+        """Tests that a symmetric array has edges added only once to an
+        undirected multigraph when using :func:`networkx.from_numpy_array`.
+
+        """
+        A = np.array([[0, 1], [1, 0]])
+        G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
+        expected = nx.MultiGraph()
+        expected.add_edge(0, 1, weight=1)
+        assert graphs_equal(G, expected)
+
+    def test_dtype_int_graph(self):
+        """Test that setting dtype int actually gives an integer array.
+
+        For more information, see GitHub pull request #1363.
+
+        """
+        G = nx.complete_graph(3)
+        A = nx.to_numpy_array(G, dtype=int)
+        assert A.dtype == int
+
+    def test_dtype_int_multigraph(self):
+        """Test that setting dtype int actually gives an integer array.
+
+        For more information, see GitHub pull request #1363.
+
+        """
+        G = nx.MultiGraph(nx.complete_graph(3))
+        A = nx.to_numpy_array(G, dtype=int)
+        assert A.dtype == int
+
+
+@pytest.fixture
+def multigraph_test_graph():
+    G = nx.MultiGraph()
+    G.add_edge(1, 2, weight=7)
+    G.add_edge(1, 2, weight=70)
+    return G
+
+
+@pytest.mark.parametrize(("operator", "expected"), ((sum, 77), (min, 7), (max, 70)))
+def test_numpy_multigraph(multigraph_test_graph, operator, expected):
+    A = nx.to_numpy_array(multigraph_test_graph, multigraph_weight=operator)
+    assert A[1, 0] == expected
+
+
+def test_to_numpy_array_multigraph_nodelist(multigraph_test_graph):
+    G = multigraph_test_graph
+    G.add_edge(0, 1, weight=3)
+    A = nx.to_numpy_array(G, nodelist=[1, 2])
+    assert A.shape == (2, 2)
+    assert A[1, 0] == 77
+
+
+@pytest.mark.parametrize(
+    "G, expected",
+    [
+        (nx.Graph(), np.array([[0, 1 + 2j], [1 + 2j, 0]], dtype=complex)),
+        (nx.DiGraph(), np.array([[0, 1 + 2j], [0, 0]], dtype=complex)),
+    ],
+)
+def test_to_numpy_array_complex_weights(G, expected):
+    G.add_edge(0, 1, weight=1 + 2j)
+    A = nx.to_numpy_array(G, dtype=complex)
+    npt.assert_array_equal(A, expected)
+
+
+def test_to_numpy_array_arbitrary_weights():
+    G = nx.DiGraph()
+    w = 922337203685477580102  # Out of range for int64
+    G.add_edge(0, 1, weight=922337203685477580102)  # val not representable by int64
+    A = nx.to_numpy_array(G, dtype=object)
+    expected = np.array([[0, w], [0, 0]], dtype=object)
+    npt.assert_array_equal(A, expected)
+
+    # Undirected
+    A = nx.to_numpy_array(G.to_undirected(), dtype=object)
+    expected = np.array([[0, w], [w, 0]], dtype=object)
+    npt.assert_array_equal(A, expected)
+
+
+@pytest.mark.parametrize(
+    "func, expected",
+    ((min, -1), (max, 10), (sum, 11), (np.mean, 11 / 3), (np.median, 2)),
+)
+def test_to_numpy_array_multiweight_reduction(func, expected):
+    """Test various functions for reducing multiedge weights."""
+    G = nx.MultiDiGraph()
+    weights = [-1, 2, 10.0]
+    for w in weights:
+        G.add_edge(0, 1, weight=w)
+    A = nx.to_numpy_array(G, multigraph_weight=func, dtype=float)
+    assert np.allclose(A, [[0, expected], [0, 0]])
+
+    # Undirected case
+    A = nx.to_numpy_array(G.to_undirected(), multigraph_weight=func, dtype=float)
+    assert np.allclose(A, [[0, expected], [expected, 0]])
+
+
+@pytest.mark.parametrize(
+    ("G, expected"),
+    [
+        (nx.Graph(), [[(0, 0), (10, 5)], [(10, 5), (0, 0)]]),
+        (nx.DiGraph(), [[(0, 0), (10, 5)], [(0, 0), (0, 0)]]),
+    ],
+)
+def test_to_numpy_array_structured_dtype_attrs_from_fields(G, expected):
+    """When `dtype` is structured (i.e. has names) and `weight` is None, use
+    the named fields of the dtype to look up edge attributes."""
+    G.add_edge(0, 1, weight=10, cost=5.0)
+    dtype = np.dtype([("weight", int), ("cost", int)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    expected = np.asarray(expected, dtype=dtype)
+    npt.assert_array_equal(A, expected)
+
+
+def test_to_numpy_array_structured_dtype_single_attr_default():
+    G = nx.path_graph(3)
+    dtype = np.dtype([("weight", float)])  # A single named field
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    expected = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=float)
+    npt.assert_array_equal(A["weight"], expected)
+
+
+@pytest.mark.parametrize(
+    ("field_name", "expected_attr_val"),
+    [
+        ("weight", 1),
+        ("cost", 3),
+    ],
+)
+def test_to_numpy_array_structured_dtype_single_attr(field_name, expected_attr_val):
+    G = nx.Graph()
+    G.add_edge(0, 1, cost=3)
+    dtype = np.dtype([(field_name, float)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    expected = np.array([[0, expected_attr_val], [expected_attr_val, 0]], dtype=float)
+    npt.assert_array_equal(A[field_name], expected)
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+@pytest.mark.parametrize(
+    "edge",
+    [
+        (0, 1),  # No edge attributes
+        (0, 1, {"weight": 10}),  # One edge attr
+        (0, 1, {"weight": 5, "flow": -4}),  # Multiple but not all edge attrs
+        (0, 1, {"weight": 2.0, "cost": 10, "flow": -45}),  # All attrs
+    ],
+)
+def test_to_numpy_array_structured_dtype_multiple_fields(graph_type, edge):
+    G = graph_type([edge])
+    dtype = np.dtype([("weight", float), ("cost", float), ("flow", float)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    for attr in dtype.names:
+        expected = nx.to_numpy_array(G, dtype=float, weight=attr)
+        npt.assert_array_equal(A[attr], expected)
+
+
+@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+def test_to_numpy_array_structured_dtype_scalar_nonedge(G):
+    G.add_edge(0, 1, weight=10)
+    dtype = np.dtype([("weight", float), ("cost", float)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=np.nan)
+    for attr in dtype.names:
+        expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=np.nan)
+        npt.assert_array_equal(A[attr], expected)
+
+
+@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+def test_to_numpy_array_structured_dtype_nonedge_ary(G):
+    """Similar to the scalar case, except has a different non-edge value for
+    each named field."""
+    G.add_edge(0, 1, weight=10)
+    dtype = np.dtype([("weight", float), ("cost", float)])
+    nonedges = np.array([(0, np.inf)], dtype=dtype)
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=nonedges)
+    for attr in dtype.names:
+        nonedge = nonedges[attr]
+        expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=nonedge)
+        npt.assert_array_equal(A[attr], expected)
+
+
+def test_to_numpy_array_structured_dtype_with_weight_raises():
+    """Using both a structured dtype (with named fields) and specifying a `weight`
+    parameter is ambiguous."""
+    G = nx.path_graph(3)
+    dtype = np.dtype([("weight", int), ("cost", int)])
+    exception_msg = "Specifying `weight` not supported for structured dtypes"
+    with pytest.raises(ValueError, match=exception_msg):
+        nx.to_numpy_array(G, dtype=dtype)  # Default is weight="weight"
+    with pytest.raises(ValueError, match=exception_msg):
+        nx.to_numpy_array(G, dtype=dtype, weight="cost")
+
+
+@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
+def test_to_numpy_array_structured_multigraph_raises(graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    dtype = np.dtype([("weight", int), ("cost", int)])
+    with pytest.raises(nx.NetworkXError, match="Structured arrays are not supported"):
+        nx.to_numpy_array(G, dtype=dtype, weight=None)
+
+
+def test_from_numpy_array_nodelist_bad_size():
+    """An exception is raised when `len(nodelist) != A.shape[0]`."""
+    n = 5  # Number of nodes
+    A = np.diag(np.ones(n - 1), k=1)  # Adj. matrix for P_n
+    expected = nx.path_graph(n)
+
+    assert graphs_equal(nx.from_numpy_array(A, edge_attr=None), expected)
+    nodes = list(range(n))
+    assert graphs_equal(
+        nx.from_numpy_array(A, edge_attr=None, nodelist=nodes), expected
+    )
+
+    # Too many node labels
+    nodes = list(range(n + 1))
+    with pytest.raises(ValueError, match="nodelist must have the same length as A"):
+        nx.from_numpy_array(A, nodelist=nodes)
+
+    # Too few node labels
+    nodes = list(range(n - 1))
+    with pytest.raises(ValueError, match="nodelist must have the same length as A"):
+        nx.from_numpy_array(A, nodelist=nodes)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+def test_from_numpy_array_nodelist(nodes):
+    A = np.diag(np.ones(4), k=1)
+    # Without edge attributes
+    expected = nx.relabel_nodes(
+        nx.path_graph(5), mapping=dict(enumerate(nodes)), copy=True
+    )
+    G = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+    # With edge attributes
+    nx.set_edge_attributes(expected, 1.0, name="weight")
+    G = nx.from_numpy_array(A, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+def test_from_numpy_array_nodelist_directed(nodes):
+    A = np.diag(np.ones(4), k=1)
+    # Without edge attributes
+    H = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 4)])
+    expected = nx.relabel_nodes(H, mapping=dict(enumerate(nodes)), copy=True)
+    G = nx.from_numpy_array(A, create_using=nx.DiGraph, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+    # With edge attributes
+    nx.set_edge_attributes(expected, 1.0, name="weight")
+    G = nx.from_numpy_array(A, create_using=nx.DiGraph, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+def test_from_numpy_array_nodelist_multigraph(nodes):
+    A = np.array(
+        [
+            [0, 1, 0, 0, 0],
+            [1, 0, 2, 0, 0],
+            [0, 2, 0, 3, 0],
+            [0, 0, 3, 0, 4],
+            [0, 0, 0, 4, 0],
+        ]
+    )
+
+    H = nx.MultiGraph()
+    for i, edge in enumerate(((0, 1), (1, 2), (2, 3), (3, 4))):
+        H.add_edges_from(itertools.repeat(edge, i + 1))
+    expected = nx.relabel_nodes(H, mapping=dict(enumerate(nodes)), copy=True)
+
+    G = nx.from_numpy_array(
+        A,
+        parallel_edges=True,
+        create_using=nx.MultiGraph,
+        edge_attr=None,
+        nodelist=nodes,
+    )
+    assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+@pytest.mark.parametrize("graph", (nx.complete_graph, nx.cycle_graph, nx.wheel_graph))
+def test_from_numpy_array_nodelist_rountrip(graph, nodes):
+    G = graph(5)
+    A = nx.to_numpy_array(G)
+    expected = nx.relabel_nodes(G, mapping=dict(enumerate(nodes)), copy=True)
+    H = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(H, expected)
+
+    # With an isolated node
+    G = graph(4)
+    G.add_node("foo")
+    A = nx.to_numpy_array(G)
+    expected = nx.relabel_nodes(G, mapping=dict(zip(G.nodes, nodes)), copy=True)
+    H = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(H, expected)
diff --git a/lib/python3.11/site-packages/networkx/tests/test_convert_pandas.py b/lib/python3.11/site-packages/networkx/tests/test_convert_pandas.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa8d695f868dbb551d3a3819cedd590a25492f8
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_convert_pandas.py
@@ -0,0 +1,349 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+np = pytest.importorskip("numpy")
+pd = pytest.importorskip("pandas")
+
+
+class TestConvertPandas:
+    def setup_method(self):
+        self.rng = np.random.RandomState(seed=5)
+        ints = self.rng.randint(1, 11, size=(3, 2))
+        a = ["A", "B", "C"]
+        b = ["D", "A", "E"]
+        df = pd.DataFrame(ints, columns=["weight", "cost"])
+        df[0] = a  # Column label 0 (int)
+        df["b"] = b  # Column label 'b' (str)
+        self.df = df
+
+        mdf = pd.DataFrame([[4, 16, "A", "D"]], columns=["weight", "cost", 0, "b"])
+        self.mdf = pd.concat([df, mdf])
+
+    def test_exceptions(self):
+        G = pd.DataFrame(["a"])  # adj
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+        G = pd.DataFrame(["a", 0.0])  # elist
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+        df = pd.DataFrame([[1, 1], [1, 0]], dtype=int, index=[1, 2], columns=["a", "b"])
+        pytest.raises(nx.NetworkXError, nx.from_pandas_adjacency, df)
+
+    def test_from_edgelist_all_attr(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"cost": 9, "weight": 10}),
+                ("B", "A", {"cost": 1, "weight": 7}),
+                ("A", "D", {"cost": 7, "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", True)
+        assert graphs_equal(G, Gtrue)
+        # MultiGraph
+        MGtrue = nx.MultiGraph(Gtrue)
+        MGtrue.add_edge("A", "D", cost=16, weight=4)
+        MG = nx.from_pandas_edgelist(self.mdf, 0, "b", True, nx.MultiGraph())
+        assert graphs_equal(MG, MGtrue)
+
+    def test_from_edgelist_multi_attr(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"cost": 9, "weight": 10}),
+                ("B", "A", {"cost": 1, "weight": 7}),
+                ("A", "D", {"cost": 7, "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", ["weight", "cost"])
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_multi_attr_incl_target(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {0: "C", "b": "E", "weight": 10}),
+                ("B", "A", {0: "B", "b": "A", "weight": 7}),
+                ("A", "D", {0: "A", "b": "D", "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", [0, "b", "weight"])
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_multidigraph_and_edge_attr(self):
+        # example from issue #2374
+        edges = [
+            ("X1", "X4", {"Co": "zA", "Mi": 0, "St": "X1"}),
+            ("X1", "X4", {"Co": "zB", "Mi": 54, "St": "X2"}),
+            ("X1", "X4", {"Co": "zB", "Mi": 49, "St": "X3"}),
+            ("X1", "X4", {"Co": "zB", "Mi": 44, "St": "X4"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 0, "St": "Y1"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 34, "St": "Y2"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 29, "St": "X2"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 24, "St": "Y3"}),
+            ("Z1", "Z3", {"Co": "zD", "Mi": 0, "St": "Z1"}),
+            ("Z1", "Z3", {"Co": "zD", "Mi": 14, "St": "X3"}),
+        ]
+        Gtrue = nx.MultiDiGraph(edges)
+        data = {
+            "O": ["X1", "X1", "X1", "X1", "Y1", "Y1", "Y1", "Y1", "Z1", "Z1"],
+            "D": ["X4", "X4", "X4", "X4", "Y3", "Y3", "Y3", "Y3", "Z3", "Z3"],
+            "St": ["X1", "X2", "X3", "X4", "Y1", "Y2", "X2", "Y3", "Z1", "X3"],
+            "Co": ["zA", "zB", "zB", "zB", "zC", "zC", "zC", "zC", "zD", "zD"],
+            "Mi": [0, 54, 49, 44, 0, 34, 29, 24, 0, 14],
+        }
+        df = pd.DataFrame.from_dict(data)
+        G1 = nx.from_pandas_edgelist(
+            df, source="O", target="D", edge_attr=True, create_using=nx.MultiDiGraph
+        )
+        G2 = nx.from_pandas_edgelist(
+            df,
+            source="O",
+            target="D",
+            edge_attr=["St", "Co", "Mi"],
+            create_using=nx.MultiDiGraph,
+        )
+        assert graphs_equal(G1, Gtrue)
+        assert graphs_equal(G2, Gtrue)
+
+    def test_from_edgelist_one_attr(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"weight": 10}),
+                ("B", "A", {"weight": 7}),
+                ("A", "D", {"weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", "weight")
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_int_attr_name(self):
+        # note: this also tests that edge_attr can be `source`
+        Gtrue = nx.Graph(
+            [("E", "C", {0: "C"}), ("B", "A", {0: "B"}), ("A", "D", {0: "A"})]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", 0)
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_invalid_attr(self):
+        pytest.raises(
+            nx.NetworkXError, nx.from_pandas_edgelist, self.df, 0, "b", "misspell"
+        )
+        pytest.raises(nx.NetworkXError, nx.from_pandas_edgelist, self.df, 0, "b", 1)
+        # see Issue #3562
+        edgeframe = pd.DataFrame([[0, 1], [1, 2], [2, 0]], columns=["s", "t"])
+        pytest.raises(
+            nx.NetworkXError, nx.from_pandas_edgelist, edgeframe, "s", "t", True
+        )
+        pytest.raises(
+            nx.NetworkXError, nx.from_pandas_edgelist, edgeframe, "s", "t", "weight"
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            nx.from_pandas_edgelist,
+            edgeframe,
+            "s",
+            "t",
+            ["weight", "size"],
+        )
+
+    def test_from_edgelist_no_attr(self):
+        Gtrue = nx.Graph([("E", "C", {}), ("B", "A", {}), ("A", "D", {})])
+        G = nx.from_pandas_edgelist(self.df, 0, "b")
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist(self):
+        # Pandas DataFrame
+        G = nx.cycle_graph(10)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+
+        edgelist = nx.to_edgelist(G)
+        source = [s for s, t, d in edgelist]
+        target = [t for s, t, d in edgelist]
+        weight = [d["weight"] for s, t, d in edgelist]
+        edges = pd.DataFrame({"source": source, "target": target, "weight": weight})
+
+        GG = nx.from_pandas_edgelist(edges, edge_attr="weight")
+        assert nodes_equal(G.nodes(), GG.nodes())
+        assert edges_equal(G.edges(), GG.edges())
+        GW = nx.to_networkx_graph(edges, create_using=nx.Graph)
+        assert nodes_equal(G.nodes(), GW.nodes())
+        assert edges_equal(G.edges(), GW.edges())
+
+    def test_to_edgelist_default_source_or_target_col_exists(self):
+        G = nx.path_graph(10)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+        nx.set_edge_attributes(G, 0, name="source")
+        pytest.raises(nx.NetworkXError, nx.to_pandas_edgelist, G)
+
+        # drop source column to test an exception raised for the target column
+        for u, v, d in G.edges(data=True):
+            d.pop("source", None)
+
+        nx.set_edge_attributes(G, 0, name="target")
+        pytest.raises(nx.NetworkXError, nx.to_pandas_edgelist, G)
+
+    def test_to_edgelist_custom_source_or_target_col_exists(self):
+        G = nx.path_graph(10)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+        nx.set_edge_attributes(G, 0, name="source_col_name")
+        pytest.raises(
+            nx.NetworkXError, nx.to_pandas_edgelist, G, source="source_col_name"
+        )
+
+        # drop source column to test an exception raised for the target column
+        for u, v, d in G.edges(data=True):
+            d.pop("source_col_name", None)
+
+        nx.set_edge_attributes(G, 0, name="target_col_name")
+        pytest.raises(
+            nx.NetworkXError, nx.to_pandas_edgelist, G, target="target_col_name"
+        )
+
+    def test_to_edgelist_edge_key_col_exists(self):
+        G = nx.path_graph(10, create_using=nx.MultiGraph)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges()))
+        nx.set_edge_attributes(G, 0, name="edge_key_name")
+        pytest.raises(
+            nx.NetworkXError, nx.to_pandas_edgelist, G, edge_key="edge_key_name"
+        )
+
+    def test_from_adjacency(self):
+        nodelist = [1, 2]
+        dftrue = pd.DataFrame(
+            [[1, 1], [1, 0]], dtype=int, index=nodelist, columns=nodelist
+        )
+        G = nx.Graph([(1, 1), (1, 2)])
+        df = nx.to_pandas_adjacency(G, dtype=int)
+        pd.testing.assert_frame_equal(df, dftrue)
+
+    @pytest.mark.parametrize("graph", [nx.Graph, nx.MultiGraph])
+    def test_roundtrip(self, graph):
+        # edgelist
+        Gtrue = graph([(1, 1), (1, 2)])
+        df = nx.to_pandas_edgelist(Gtrue)
+        G = nx.from_pandas_edgelist(df, create_using=graph)
+        assert graphs_equal(Gtrue, G)
+        # adjacency
+        adj = {1: {1: {"weight": 1}, 2: {"weight": 1}}, 2: {1: {"weight": 1}}}
+        Gtrue = graph(adj)
+        df = nx.to_pandas_adjacency(Gtrue, dtype=int)
+        G = nx.from_pandas_adjacency(df, create_using=graph)
+        assert graphs_equal(Gtrue, G)
+
+    def test_from_adjacency_named(self):
+        # example from issue #3105
+        data = {
+            "A": {"A": 0, "B": 0, "C": 0},
+            "B": {"A": 1, "B": 0, "C": 0},
+            "C": {"A": 0, "B": 1, "C": 0},
+        }
+        dftrue = pd.DataFrame(data, dtype=np.intp)
+        df = dftrue[["A", "C", "B"]]
+        G = nx.from_pandas_adjacency(df, create_using=nx.DiGraph())
+        df = nx.to_pandas_adjacency(G, dtype=np.intp)
+        pd.testing.assert_frame_equal(df, dftrue)
+
+    @pytest.mark.parametrize("edge_attr", [["attr2", "attr3"], True])
+    def test_edgekey_with_multigraph(self, edge_attr):
+        df = pd.DataFrame(
+            {
+                "source": {"A": "N1", "B": "N2", "C": "N1", "D": "N1"},
+                "target": {"A": "N2", "B": "N3", "C": "N1", "D": "N2"},
+                "attr1": {"A": "F1", "B": "F2", "C": "F3", "D": "F4"},
+                "attr2": {"A": 1, "B": 0, "C": 0, "D": 0},
+                "attr3": {"A": 0, "B": 1, "C": 0, "D": 1},
+            }
+        )
+        Gtrue = nx.MultiGraph(
+            [
+                ("N1", "N2", "F1", {"attr2": 1, "attr3": 0}),
+                ("N2", "N3", "F2", {"attr2": 0, "attr3": 1}),
+                ("N1", "N1", "F3", {"attr2": 0, "attr3": 0}),
+                ("N1", "N2", "F4", {"attr2": 0, "attr3": 1}),
+            ]
+        )
+        # example from issue #4065
+        G = nx.from_pandas_edgelist(
+            df,
+            source="source",
+            target="target",
+            edge_attr=edge_attr,
+            edge_key="attr1",
+            create_using=nx.MultiGraph(),
+        )
+        assert graphs_equal(G, Gtrue)
+
+        df_roundtrip = nx.to_pandas_edgelist(G, edge_key="attr1")
+        df_roundtrip = df_roundtrip.sort_values("attr1")
+        df_roundtrip.index = ["A", "B", "C", "D"]
+        pd.testing.assert_frame_equal(
+            df, df_roundtrip[["source", "target", "attr1", "attr2", "attr3"]]
+        )
+
+    def test_edgekey_with_normal_graph_no_action(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"cost": 9, "weight": 10}),
+                ("B", "A", {"cost": 1, "weight": 7}),
+                ("A", "D", {"cost": 7, "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", True, edge_key="weight")
+        assert graphs_equal(G, Gtrue)
+
+    def test_nonexisting_edgekey_raises(self):
+        with pytest.raises(nx.exception.NetworkXError):
+            nx.from_pandas_edgelist(
+                self.df,
+                source="source",
+                target="target",
+                edge_key="Not_real",
+                edge_attr=True,
+                create_using=nx.MultiGraph(),
+            )
+
+    def test_multigraph_with_edgekey_no_edgeattrs(self):
+        Gtrue = nx.MultiGraph()
+        Gtrue.add_edge(0, 1, key=0)
+        Gtrue.add_edge(0, 1, key=3)
+        df = nx.to_pandas_edgelist(Gtrue, edge_key="key")
+        expected = pd.DataFrame({"source": [0, 0], "target": [1, 1], "key": [0, 3]})
+        pd.testing.assert_frame_equal(expected, df)
+        G = nx.from_pandas_edgelist(df, edge_key="key", create_using=nx.MultiGraph)
+        assert graphs_equal(Gtrue, G)
+
+
+def test_to_pandas_adjacency_with_nodelist():
+    G = nx.complete_graph(5)
+    nodelist = [1, 4]
+    expected = pd.DataFrame(
+        [[0, 1], [1, 0]], dtype=int, index=nodelist, columns=nodelist
+    )
+    pd.testing.assert_frame_equal(
+        expected, nx.to_pandas_adjacency(G, nodelist, dtype=int)
+    )
+
+
+def test_to_pandas_edgelist_with_nodelist():
+    G = nx.Graph()
+    G.add_edges_from([(0, 1), (1, 2), (1, 3)], weight=2.0)
+    G.add_edge(0, 5, weight=100)
+    df = nx.to_pandas_edgelist(G, nodelist=[1, 2])
+    assert 0 not in df["source"].to_numpy()
+    assert 100 not in df["weight"].to_numpy()
+
+
+def test_from_pandas_adjacency_with_index_collisions():
+    """See gh-7407"""
+    df = pd.DataFrame(
+        [
+            [0, 1, 0, 0],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1],
+            [0, 0, 0, 0],
+        ],
+        index=[1010001, 2, 1, 1010002],
+        columns=[1010001, 2, 1, 1010002],
+    )
+    G = nx.from_pandas_adjacency(df, create_using=nx.DiGraph)
+    expected = nx.DiGraph([(1010001, 2), (2, 1), (1, 1010002)])
+    assert nodes_equal(G.nodes, expected.nodes)
+    assert edges_equal(G.edges, expected.edges, directed=True)
diff --git a/lib/python3.11/site-packages/networkx/tests/test_convert_scipy.py b/lib/python3.11/site-packages/networkx/tests/test_convert_scipy.py
new file mode 100644
index 0000000000000000000000000000000000000000..2575027046c61612c1be3c36a5b7877c28e71d92
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_convert_scipy.py
@@ -0,0 +1,281 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+np = pytest.importorskip("numpy")
+sp = pytest.importorskip("scipy")
+
+
+class TestConvertScipy:
+    def setup_method(self):
+        self.G1 = nx.barbell_graph(10, 3)
+        self.G2 = nx.cycle_graph(10, create_using=nx.DiGraph)
+
+        self.G3 = self.create_weighted(nx.Graph())
+        self.G4 = self.create_weighted(nx.DiGraph())
+
+    def test_exceptions(self):
+        class G:
+            format = None
+
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+
+    def create_weighted(self, G):
+        g = nx.cycle_graph(4)
+        e = list(g.edges())
+        source = [u for u, v in e]
+        dest = [v for u, v in e]
+        weight = [s + 10 for s in source]
+        ex = zip(source, dest, weight)
+        G.add_weighted_edges_from(ex)
+        return G
+
+    def identity_conversion(self, G, A, create_using):
+        GG = nx.from_scipy_sparse_array(A, create_using=create_using)
+        assert nx.is_isomorphic(G, GG)
+
+        GW = nx.to_networkx_graph(A, create_using=create_using)
+        assert nx.is_isomorphic(G, GW)
+
+        GI = nx.empty_graph(0, create_using).__class__(A)
+        assert nx.is_isomorphic(G, GI)
+
+        ACSR = A.tocsr()
+        GI = nx.empty_graph(0, create_using).__class__(ACSR)
+        assert nx.is_isomorphic(G, GI)
+
+        ACOO = A.tocoo()
+        GI = nx.empty_graph(0, create_using).__class__(ACOO)
+        assert nx.is_isomorphic(G, GI)
+
+        ACSC = A.tocsc()
+        GI = nx.empty_graph(0, create_using).__class__(ACSC)
+        assert nx.is_isomorphic(G, GI)
+
+        AD = A.todense()
+        GI = nx.empty_graph(0, create_using).__class__(AD)
+        assert nx.is_isomorphic(G, GI)
+
+        AA = A.toarray()
+        GI = nx.empty_graph(0, create_using).__class__(AA)
+        assert nx.is_isomorphic(G, GI)
+
+    def test_shape(self):
+        "Conversion from non-square sparse array."
+        A = sp.sparse.lil_array([[1, 2, 3], [4, 5, 6]])
+        pytest.raises(nx.NetworkXError, nx.from_scipy_sparse_array, A)
+
+    def test_identity_graph_matrix(self):
+        "Conversion from graph to sparse matrix to graph."
+        A = nx.to_scipy_sparse_array(self.G1)
+        self.identity_conversion(self.G1, A, nx.Graph())
+
+    def test_identity_digraph_matrix(self):
+        "Conversion from digraph to sparse matrix to digraph."
+        A = nx.to_scipy_sparse_array(self.G2)
+        self.identity_conversion(self.G2, A, nx.DiGraph())
+
+    def test_identity_weighted_graph_matrix(self):
+        """Conversion from weighted graph to sparse matrix to weighted graph."""
+        A = nx.to_scipy_sparse_array(self.G3)
+        self.identity_conversion(self.G3, A, nx.Graph())
+
+    def test_identity_weighted_digraph_matrix(self):
+        """Conversion from weighted digraph to sparse matrix to weighted digraph."""
+        A = nx.to_scipy_sparse_array(self.G4)
+        self.identity_conversion(self.G4, A, nx.DiGraph())
+
+    def test_nodelist(self):
+        """Conversion from graph to sparse matrix to graph with nodelist."""
+        P4 = nx.path_graph(4)
+        P3 = nx.path_graph(3)
+        nodelist = list(P3.nodes())
+        A = nx.to_scipy_sparse_array(P4, nodelist=nodelist)
+        GA = nx.Graph(A)
+        assert nx.is_isomorphic(GA, P3)
+
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=[])
+        # Test nodelist duplicates.
+        long_nl = nodelist + [0]
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=long_nl)
+
+        # Test nodelist contains non-nodes
+        non_nl = [-1, 0, 1, 2]
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=non_nl)
+
+    def test_weight_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = nx.path_graph(4)
+        A = nx.to_scipy_sparse_array(P4)
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+        np.testing.assert_equal(
+            0.5 * A.todense(), nx.to_scipy_sparse_array(WP4).todense()
+        )
+        np.testing.assert_equal(
+            0.3 * A.todense(), nx.to_scipy_sparse_array(WP4, weight="other").todense()
+        )
+
+    def test_format_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = nx.path_graph(4)
+        A = nx.to_scipy_sparse_array(P4, format="csr")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="csc")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="coo")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="bsr")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="lil")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="dia")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="dok")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+    def test_format_keyword_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            WP4 = nx.Graph()
+            WP4.add_edges_from(
+                (n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3)
+            )
+            P4 = nx.path_graph(4)
+            nx.to_scipy_sparse_array(P4, format="any_other")
+
+    def test_null_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.to_scipy_sparse_array(nx.Graph())
+
+    def test_empty(self):
+        G = nx.Graph()
+        G.add_node(1)
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[0]]))
+
+    def test_ordering(self):
+        G = nx.DiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(2, 3)
+        G.add_edge(3, 1)
+        M = nx.to_scipy_sparse_array(G, nodelist=[3, 2, 1])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
+        )
+
+    def test_selfloop_graph(self):
+        G = nx.Graph([(1, 1)])
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[1]]))
+
+        G.add_edges_from([(2, 3), (3, 4)])
+        M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
+        )
+
+    def test_selfloop_digraph(self):
+        G = nx.DiGraph([(1, 1)])
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[1]]))
+
+        G.add_edges_from([(2, 3), (3, 4)])
+        M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]])
+        )
+
+    def test_from_scipy_sparse_array_parallel_edges(self):
+        """Tests that the :func:`networkx.from_scipy_sparse_array` function
+        interprets integer weights as the number of parallel edges when
+        creating a multigraph.
+
+        """
+        A = sp.sparse.csr_array([[1, 1], [1, 2]])
+        # First, with a simple graph, each integer entry in the adjacency
+        # matrix is interpreted as the weight of a single edge in the graph.
+        expected = nx.DiGraph()
+        edges = [(0, 0), (0, 1), (1, 0)]
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        expected.add_edge(1, 1, weight=2)
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=True, create_using=nx.DiGraph
+        )
+        assert graphs_equal(actual, expected)
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=False, create_using=nx.DiGraph
+        )
+        assert graphs_equal(actual, expected)
+        # Now each integer entry in the adjacency matrix is interpreted as the
+        # number of parallel edges in the graph if the appropriate keyword
+        # argument is specified.
+        edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
+        expected = nx.MultiDiGraph()
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=True, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+        expected = nx.MultiDiGraph()
+        expected.add_edges_from(set(edges), weight=1)
+        # The sole self-loop (edge 0) on vertex 1 should have weight 2.
+        expected[1][1][0]["weight"] = 2
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=False, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+
+    def test_symmetric(self):
+        """Tests that a symmetric matrix has edges added only once to an
+        undirected multigraph when using
+        :func:`networkx.from_scipy_sparse_array`.
+
+        """
+        A = sp.sparse.csr_array([[0, 1], [1, 0]])
+        G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph)
+        expected = nx.MultiGraph()
+        expected.add_edge(0, 1, weight=1)
+        assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize("sparse_format", ("csr", "csc", "dok"))
+def test_from_scipy_sparse_array_formats(sparse_format):
+    """Test all formats supported by _generate_weighted_edges."""
+    # trinode complete graph with non-uniform edge weights
+    expected = nx.Graph()
+    expected.add_edges_from(
+        [
+            (0, 1, {"weight": 3}),
+            (0, 2, {"weight": 2}),
+            (1, 0, {"weight": 3}),
+            (1, 2, {"weight": 1}),
+            (2, 0, {"weight": 2}),
+            (2, 1, {"weight": 1}),
+        ]
+    )
+    A = sp.sparse.coo_array([[0, 3, 2], [3, 0, 1], [2, 1, 0]]).asformat(sparse_format)
+    assert graphs_equal(expected, nx.from_scipy_sparse_array(A))
diff --git a/lib/python3.11/site-packages/networkx/tests/test_exceptions.py b/lib/python3.11/site-packages/networkx/tests/test_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf59983cb8d12a119f5744ebc8b11e7cb9075366
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_exceptions.py
@@ -0,0 +1,40 @@
+import pytest
+
+import networkx as nx
+
+# smoke tests for exceptions
+
+
+def test_raises_networkxexception():
+    with pytest.raises(nx.NetworkXException):
+        raise nx.NetworkXException
+
+
+def test_raises_networkxerr():
+    with pytest.raises(nx.NetworkXError):
+        raise nx.NetworkXError
+
+
+def test_raises_networkx_pointless_concept():
+    with pytest.raises(nx.NetworkXPointlessConcept):
+        raise nx.NetworkXPointlessConcept
+
+
+def test_raises_networkxalgorithmerr():
+    with pytest.raises(nx.NetworkXAlgorithmError):
+        raise nx.NetworkXAlgorithmError
+
+
+def test_raises_networkx_unfeasible():
+    with pytest.raises(nx.NetworkXUnfeasible):
+        raise nx.NetworkXUnfeasible
+
+
+def test_raises_networkx_no_path():
+    with pytest.raises(nx.NetworkXNoPath):
+        raise nx.NetworkXNoPath
+
+
+def test_raises_networkx_unbounded():
+    with pytest.raises(nx.NetworkXUnbounded):
+        raise nx.NetworkXUnbounded
diff --git a/lib/python3.11/site-packages/networkx/tests/test_import.py b/lib/python3.11/site-packages/networkx/tests/test_import.py
new file mode 100644
index 0000000000000000000000000000000000000000..32aafdf2a4dafc85cee088138590b84f4c627b5e
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_import.py
@@ -0,0 +1,11 @@
+import pytest
+
+
+def test_namespace_alias():
+    with pytest.raises(ImportError):
+        from networkx import nx
+
+
+def test_namespace_nesting():
+    with pytest.raises(ImportError):
+        from networkx import networkx
diff --git a/lib/python3.11/site-packages/networkx/tests/test_relabel.py b/lib/python3.11/site-packages/networkx/tests/test_relabel.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a70ec11e6a4b2ed3e7371e1adaee90ece46f2c7
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_relabel.py
@@ -0,0 +1,349 @@
+import pytest
+
+import networkx as nx
+from networkx.generators.classic import empty_graph
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestRelabel:
+    def test_convert_node_labels_to_integers(self):
+        # test that empty graph converts fine for all options
+        G = empty_graph()
+        H = nx.convert_node_labels_to_integers(G, 100)
+        assert list(H.nodes()) == []
+        assert list(H.edges()) == []
+
+        for opt in ["default", "sorted", "increasing degree", "decreasing degree"]:
+            G = empty_graph()
+            H = nx.convert_node_labels_to_integers(G, 100, ordering=opt)
+            assert list(H.nodes()) == []
+            assert list(H.edges()) == []
+
+        G = empty_graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        H = nx.convert_node_labels_to_integers(G)
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+
+        H = nx.convert_node_labels_to_integers(G, 1000)
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert nodes_equal(H.nodes(), [1000, 1001, 1002, 1003])
+
+        H = nx.convert_node_labels_to_integers(G, ordering="increasing degree")
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert H.degree(0) == 1
+        assert H.degree(1) == 2
+        assert H.degree(2) == 2
+        assert H.degree(3) == 3
+
+        H = nx.convert_node_labels_to_integers(G, ordering="decreasing degree")
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert H.degree(0) == 3
+        assert H.degree(1) == 2
+        assert H.degree(2) == 2
+        assert H.degree(3) == 1
+
+        H = nx.convert_node_labels_to_integers(
+            G, ordering="increasing degree", label_attribute="label"
+        )
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert H.degree(0) == 1
+        assert H.degree(1) == 2
+        assert H.degree(2) == 2
+        assert H.degree(3) == 3
+
+        # check mapping
+        assert H.nodes[3]["label"] == "C"
+        assert H.nodes[0]["label"] == "D"
+        assert H.nodes[1]["label"] == "A" or H.nodes[2]["label"] == "A"
+        assert H.nodes[1]["label"] == "B" or H.nodes[2]["label"] == "B"
+
+    def test_convert_to_integers2(self):
+        G = empty_graph()
+        G.add_edges_from([("C", "D"), ("A", "B"), ("A", "C"), ("B", "C")])
+        H = nx.convert_node_labels_to_integers(G, ordering="sorted")
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+
+        H = nx.convert_node_labels_to_integers(
+            G, ordering="sorted", label_attribute="label"
+        )
+        assert H.nodes[0]["label"] == "A"
+        assert H.nodes[1]["label"] == "B"
+        assert H.nodes[2]["label"] == "C"
+        assert H.nodes[3]["label"] == "D"
+
+    def test_convert_to_integers_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph()
+            H = nx.convert_node_labels_to_integers(G, ordering="increasing age")
+
+    def test_relabel_nodes_copy(self):
+        G = nx.empty_graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {"A": "aardvark", "B": "bear", "C": "cat", "D": "dog"}
+        H = nx.relabel_nodes(G, mapping)
+        assert nodes_equal(H.nodes(), ["aardvark", "bear", "cat", "dog"])
+
+    def test_relabel_nodes_function(self):
+        G = nx.empty_graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        # function mapping no longer encouraged but works
+
+        def mapping(n):
+            return ord(n)
+
+        H = nx.relabel_nodes(G, mapping)
+        assert nodes_equal(H.nodes(), [65, 66, 67, 68])
+
+    def test_relabel_nodes_callable_type(self):
+        G = nx.path_graph(4)
+        H = nx.relabel_nodes(G, str)
+        assert nodes_equal(H.nodes, ["0", "1", "2", "3"])
+
+    @pytest.mark.parametrize("non_mc", ("0123", ["0", "1", "2", "3"]))
+    def test_relabel_nodes_non_mapping_or_callable(self, non_mc):
+        """If `mapping` is neither a Callable or a Mapping, an exception
+        should be raised."""
+        G = nx.path_graph(4)
+        with pytest.raises(AttributeError):
+            nx.relabel_nodes(G, non_mc)
+
+    def test_relabel_nodes_graph(self):
+        G = nx.Graph([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {"A": "aardvark", "B": "bear", "C": "cat", "D": "dog"}
+        H = nx.relabel_nodes(G, mapping)
+        assert nodes_equal(H.nodes(), ["aardvark", "bear", "cat", "dog"])
+
+    def test_relabel_nodes_orderedgraph(self):
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3])
+        G.add_edges_from([(1, 3), (2, 3)])
+        mapping = {1: "a", 2: "b", 3: "c"}
+        H = nx.relabel_nodes(G, mapping)
+        assert list(H.nodes) == ["a", "b", "c"]
+
+    def test_relabel_nodes_digraph(self):
+        G = nx.DiGraph([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {"A": "aardvark", "B": "bear", "C": "cat", "D": "dog"}
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(H.nodes(), ["aardvark", "bear", "cat", "dog"])
+
+    def test_relabel_nodes_multigraph(self):
+        G = nx.MultiGraph([("a", "b"), ("a", "b")])
+        mapping = {"a": "aardvark", "b": "bear"}
+        G = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(G.nodes(), ["aardvark", "bear"])
+        assert edges_equal(G.edges(), [("aardvark", "bear"), ("aardvark", "bear")])
+
+    def test_relabel_nodes_multidigraph(self):
+        G = nx.MultiDiGraph([("a", "b"), ("a", "b")])
+        mapping = {"a": "aardvark", "b": "bear"}
+        G = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(G.nodes(), ["aardvark", "bear"])
+        assert edges_equal(
+            G.edges(), [("aardvark", "bear"), ("aardvark", "bear")], directed=True
+        )
+
+    def test_relabel_isolated_nodes_to_same(self):
+        G = nx.Graph()
+        G.add_nodes_from(range(4))
+        mapping = {1: 1}
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(H.nodes(), list(range(4)))
+
+    def test_relabel_nodes_missing(self):
+        G = nx.Graph([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {0: "aardvark"}
+        # copy=True
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        assert nodes_equal(H.nodes, G.nodes)
+        # copy=False
+        GG = G.copy()
+        nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(G.nodes, GG.nodes)
+
+    def test_relabel_copy_name(self):
+        G = nx.Graph()
+        H = nx.relabel_nodes(G, {}, copy=True)
+        assert H.graph == G.graph
+        H = nx.relabel_nodes(G, {}, copy=False)
+        assert H.graph == G.graph
+        G.name = "first"
+        H = nx.relabel_nodes(G, {}, copy=True)
+        assert H.graph == G.graph
+        H = nx.relabel_nodes(G, {}, copy=False)
+        assert H.graph == G.graph
+
+    def test_relabel_toposort(self):
+        K5 = nx.complete_graph(4)
+        G = nx.complete_graph(4)
+        G = nx.relabel_nodes(G, {i: i + 1 for i in range(4)}, copy=False)
+        assert nx.is_isomorphic(K5, G)
+        G = nx.complete_graph(4)
+        G = nx.relabel_nodes(G, {i: i - 1 for i in range(4)}, copy=False)
+        assert nx.is_isomorphic(K5, G)
+
+    def test_relabel_selfloop(self):
+        G = nx.DiGraph([(1, 1), (1, 2), (2, 3)])
+        G = nx.relabel_nodes(G, {1: "One", 2: "Two", 3: "Three"}, copy=False)
+        assert nodes_equal(G.nodes(), ["One", "Three", "Two"])
+        G = nx.MultiDiGraph([(1, 1), (1, 2), (2, 3)])
+        G = nx.relabel_nodes(G, {1: "One", 2: "Two", 3: "Three"}, copy=False)
+        assert nodes_equal(G.nodes(), ["One", "Three", "Two"])
+        G = nx.MultiDiGraph([(1, 1)])
+        G = nx.relabel_nodes(G, {1: 0}, copy=False)
+        assert nodes_equal(G.nodes(), [0])
+
+    def test_relabel_multidigraph_inout_merge_nodes(self):
+        for MG in (nx.MultiGraph, nx.MultiDiGraph):
+            for cc in (True, False):
+                G = MG([(0, 4), (1, 4), (4, 2), (4, 3)])
+                G[0][4][0]["value"] = "a"
+                G[1][4][0]["value"] = "b"
+                G[4][2][0]["value"] = "c"
+                G[4][3][0]["value"] = "d"
+                G.add_edge(0, 4, key="x", value="e")
+                G.add_edge(4, 3, key="x", value="f")
+                mapping = {0: 9, 1: 9, 2: 9, 3: 9}
+                H = nx.relabel_nodes(G, mapping, copy=cc)
+                # No ordering on keys enforced
+                assert {"value": "a"} in H[9][4].values()
+                assert {"value": "b"} in H[9][4].values()
+                assert {"value": "c"} in H[4][9].values()
+                assert len(H[4][9]) == 3 if G.is_directed() else 6
+                assert {"value": "d"} in H[4][9].values()
+                assert {"value": "e"} in H[9][4].values()
+                assert {"value": "f"} in H[4][9].values()
+                assert len(H[9][4]) == 3 if G.is_directed() else 6
+
+    def test_relabel_multigraph_merge_inplace(self):
+        G = nx.MultiGraph([(0, 1), (0, 2), (0, 3), (0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        nx.relabel_nodes(G, mapping, copy=False)
+        # No ordering on keys enforced
+        assert {"value": "a"} in G[0][4].values()
+        assert {"value": "b"} in G[0][4].values()
+        assert {"value": "c"} in G[0][4].values()
+
+    def test_relabel_multidigraph_merge_inplace(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        nx.relabel_nodes(G, mapping, copy=False)
+        # No ordering on keys enforced
+        assert {"value": "a"} in G[0][4].values()
+        assert {"value": "b"} in G[0][4].values()
+        assert {"value": "c"} in G[0][4].values()
+
+    def test_relabel_multidigraph_inout_copy(self):
+        G = nx.MultiDiGraph([(0, 4), (1, 4), (4, 2), (4, 3)])
+        G[0][4][0]["value"] = "a"
+        G[1][4][0]["value"] = "b"
+        G[4][2][0]["value"] = "c"
+        G[4][3][0]["value"] = "d"
+        G.add_edge(0, 4, key="x", value="e")
+        G.add_edge(4, 3, key="x", value="f")
+        mapping = {0: 9, 1: 9, 2: 9, 3: 9}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        # No ordering on keys enforced
+        assert {"value": "a"} in H[9][4].values()
+        assert {"value": "b"} in H[9][4].values()
+        assert {"value": "c"} in H[4][9].values()
+        assert len(H[4][9]) == 3
+        assert {"value": "d"} in H[4][9].values()
+        assert {"value": "e"} in H[9][4].values()
+        assert {"value": "f"} in H[4][9].values()
+        assert len(H[9][4]) == 3
+
+    def test_relabel_multigraph_merge_copy(self):
+        G = nx.MultiGraph([(0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        assert {"value": "a"} in H[0][4].values()
+        assert {"value": "b"} in H[0][4].values()
+        assert {"value": "c"} in H[0][4].values()
+
+    def test_relabel_multidigraph_merge_copy(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        assert {"value": "a"} in H[0][4].values()
+        assert {"value": "b"} in H[0][4].values()
+        assert {"value": "c"} in H[0][4].values()
+
+    def test_relabel_multigraph_nonnumeric_key(self):
+        for MG in (nx.MultiGraph, nx.MultiDiGraph):
+            for cc in (True, False):
+                G = nx.MultiGraph()
+                G.add_edge(0, 1, key="I", value="a")
+                G.add_edge(0, 2, key="II", value="b")
+                G.add_edge(0, 3, key="II", value="c")
+                mapping = {1: 4, 2: 4, 3: 4}
+                nx.relabel_nodes(G, mapping, copy=False)
+                assert {"value": "a"} in G[0][4].values()
+                assert {"value": "b"} in G[0][4].values()
+                assert {"value": "c"} in G[0][4].values()
+                assert 0 in G[0][4]
+                assert "I" in G[0][4]
+                assert "II" in G[0][4]
+
+    def test_relabel_circular(self):
+        G = nx.path_graph(3)
+        mapping = {0: 1, 1: 0}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        with pytest.raises(nx.NetworkXUnfeasible):
+            H = nx.relabel_nodes(G, mapping, copy=False)
+
+    def test_relabel_preserve_node_order_full_mapping_with_copy_true(self):
+        G = nx.path_graph(3)
+        original_order = list(G.nodes())
+        mapping = {2: "a", 1: "b", 0: "c"}  # dictionary keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        new_order = list(H.nodes())
+        assert [mapping.get(i, i) for i in original_order] == new_order
+
+    def test_relabel_preserve_node_order_full_mapping_with_copy_false(self):
+        G = nx.path_graph(3)
+        original_order = list(G)
+        mapping = {2: "a", 1: "b", 0: "c"}  # dictionary keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        new_order = list(H)
+        assert [mapping.get(i, i) for i in original_order] == new_order
+
+    def test_relabel_preserve_node_order_partial_mapping_with_copy_true(self):
+        G = nx.path_graph(3)
+        original_order = list(G)
+        mapping = {1: "a", 0: "b"}  # partial mapping and keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        new_order = list(H)
+        assert [mapping.get(i, i) for i in original_order] == new_order
+
+    def test_relabel_preserve_node_order_partial_mapping_with_copy_false(self):
+        G = nx.path_graph(3)
+        original_order = list(G)
+        mapping = {1: "a", 0: "b"}  # partial mapping and keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        new_order = list(H)
+        assert [mapping.get(i, i) for i in original_order] != new_order
diff --git a/lib/python3.11/site-packages/networkx/tests/test_removed_functions_exception_messages.py b/lib/python3.11/site-packages/networkx/tests/test_removed_functions_exception_messages.py
new file mode 100644
index 0000000000000000000000000000000000000000..4746345627689ef18d39660f51204622dec5dbdb
--- /dev/null
+++ b/lib/python3.11/site-packages/networkx/tests/test_removed_functions_exception_messages.py
@@ -0,0 +1,8 @@
+import pytest
+
+import networkx as nx
+
+
+def test_random_tree():
+    with pytest.raises(AttributeError, match=".*Use `nx.random_labeled_tree` instead"):
+        nx.random_tree(3)
diff --git a/lib/python3.11/site-packages/numpy/array_api/__init__.py b/lib/python3.11/site-packages/numpy/array_api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edc3205fd5c4e7767b03a6c7d01bc025755e0490
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/__init__.py
@@ -0,0 +1,387 @@
+"""
+A NumPy sub-namespace that conforms to the Python array API standard.
+
+This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It
+is still considered experimental, and will issue a warning when imported.
+
+This is a proof-of-concept namespace that wraps the corresponding NumPy
+functions to give a conforming implementation of the Python array API standard
+(https://data-apis.github.io/array-api/latest/). The standard is currently in
+an RFC phase and comments on it are both welcome and encouraged. Comments
+should be made either at https://github.com/data-apis/array-api or at
+https://github.com/data-apis/consortium-feedback/discussions.
+
+NumPy already follows the proposed spec for the most part, so this module
+serves mostly as a thin wrapper around it. However, NumPy also implements a
+lot of behavior that is not included in the spec, so this serves as a
+restricted subset of the API. Only those functions that are part of the spec
+are included in this namespace, and all functions are given with the exact
+signature given in the spec, including the use of position-only arguments, and
+omitting any extra keyword arguments implemented by NumPy but not part of the
+spec. The behavior of some functions is also modified from the NumPy behavior
+to conform to the standard. Note that the underlying array object itself is
+wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule
+is implemented in pure Python with no C extensions.
+
+The array API spec is designed as a "minimal API subset" and explicitly allows
+libraries to include behaviors not specified by it. But users of this module
+that intend to write portable code should be aware that only those behaviors
+that are listed in the spec are guaranteed to be implemented across libraries.
+Consequently, the NumPy implementation was chosen to be both conforming and
+minimal, so that users can use this implementation of the array API namespace
+and be sure that behaviors that it defines will be available in conforming
+namespaces from other libraries.
+
+A few notes about the current state of this submodule:
+
+- There is a test suite that tests modules against the array API standard at
+  https://github.com/data-apis/array-api-tests. The test suite is still a work
+  in progress, but the existing tests pass on this module, with a few
+  exceptions:
+
+  - DLPack support (see https://github.com/data-apis/array-api/pull/106) is
+    not included here, as it requires a full implementation in NumPy proper
+    first.
+
+  The test suite is not yet complete, and even the tests that exist are not
+  guaranteed to give a comprehensive coverage of the spec. Therefore, when
+  reviewing and using this submodule, you should refer to the standard
+  documents themselves. There are some tests in numpy.array_api.tests, but
+  they primarily focus on things that are not tested by the official array API
+  test suite.
+
+- There is a custom array object, numpy.array_api.Array, which is returned by
+  all functions in this module. All functions in the array API namespace
+  implicitly assume that they will only receive this object as input. The only
+  way to create instances of this object is to use one of the array creation
+  functions. It does not have a public constructor on the object itself. The
+  object is a small wrapper class around numpy.ndarray. The main purpose of it
+  is to restrict the namespace of the array object to only those dtypes and
+  only those methods that are required by the spec, as well as to limit/change
+  certain behavior that differs in the spec. In particular:
+
+  - The array API namespace does not have scalar objects, only 0-D arrays.
+    Operations on Array that would create a scalar in NumPy create a 0-D
+    array.
+
+  - Indexing: Only a subset of indices supported by NumPy are required by the
+    spec. The Array object restricts indexing to only allow those types of
+    indices that are required by the spec. See the docstring of the
+    numpy.array_api.Array._validate_indices helper function for more
+    information.
+
+  - Type promotion: Some type promotion rules are different in the spec. In
+    particular, the spec does not have any value-based casting. The spec also
+    does not require cross-kind casting, like integer -> floating-point. Only
+    those promotions that are explicitly required by the array API
+    specification are allowed in this module. See NEP 47 for more info.
+
+  - Functions do not automatically call asarray() on their input, and will not
+    work if the input type is not Array. The exception is array creation
+    functions, and Python operators on the Array object, which accept Python
+    scalars of the same type as the array dtype.
+
+- All functions include type annotations, corresponding to those given in the
+  spec (see _typing.py for definitions of some custom types). These do not
+  currently fully pass mypy due to some limitations in mypy.
+
+- Dtype objects are just the NumPy dtype objects, e.g., float64 =
+  np.dtype('float64'). The spec does not require any behavior on these dtype
+  objects other than that they be accessible by name and be comparable by
+  equality, but it was considered too much extra complexity to create custom
+  objects to represent dtypes.
+
+- All places where the implementations in this submodule are known to deviate
+  from their corresponding functions in NumPy are marked with "# Note:"
+  comments.
+
+Still TODO in this module are:
+
+- DLPack support for numpy.ndarray is still in progress. See
+  https://github.com/numpy/numpy/pull/19083.
+
+- The copy=False keyword argument to asarray() is not yet implemented. This
+  requires support in numpy.asarray() first.
+
+- Some functions are not yet fully tested in the array API test suite, and may
+  require updates that are not yet known until the tests are written.
+
+- The spec is still in an RFC phase and may still have minor updates, which
+  will need to be reflected here.
+
+- Complex number support in array API spec is planned but not yet finalized,
+  as are the fft extension and certain linear algebra functions such as eig
+  that require complex dtypes.
+
+"""
+
+import warnings
+
+warnings.warn(
+    "The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2
+)
+
+__array_api_version__ = "2022.12"
+
+__all__ = ["__array_api_version__"]
+
+from ._constants import e, inf, nan, pi, newaxis
+
+__all__ += ["e", "inf", "nan", "pi", "newaxis"]
+
+from ._creation_functions import (
+    asarray,
+    arange,
+    empty,
+    empty_like,
+    eye,
+    from_dlpack,
+    full,
+    full_like,
+    linspace,
+    meshgrid,
+    ones,
+    ones_like,
+    tril,
+    triu,
+    zeros,
+    zeros_like,
+)
+
+__all__ += [
+    "asarray",
+    "arange",
+    "empty",
+    "empty_like",
+    "eye",
+    "from_dlpack",
+    "full",
+    "full_like",
+    "linspace",
+    "meshgrid",
+    "ones",
+    "ones_like",
+    "tril",
+    "triu",
+    "zeros",
+    "zeros_like",
+]
+
+from ._data_type_functions import (
+    astype,
+    broadcast_arrays,
+    broadcast_to,
+    can_cast,
+    finfo,
+    isdtype,
+    iinfo,
+    result_type,
+)
+
+__all__ += [
+    "astype",
+    "broadcast_arrays",
+    "broadcast_to",
+    "can_cast",
+    "finfo",
+    "iinfo",
+    "result_type",
+]
+
+from ._dtypes import (
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    complex64,
+    complex128,
+    bool,
+)
+
+__all__ += [
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+    "float32",
+    "float64",
+    "bool",
+]
+
+from ._elementwise_functions import (
+    abs,
+    acos,
+    acosh,
+    add,
+    asin,
+    asinh,
+    atan,
+    atan2,
+    atanh,
+    bitwise_and,
+    bitwise_left_shift,
+    bitwise_invert,
+    bitwise_or,
+    bitwise_right_shift,
+    bitwise_xor,
+    ceil,
+    conj,
+    cos,
+    cosh,
+    divide,
+    equal,
+    exp,
+    expm1,
+    floor,
+    floor_divide,
+    greater,
+    greater_equal,
+    imag,
+    isfinite,
+    isinf,
+    isnan,
+    less,
+    less_equal,
+    log,
+    log1p,
+    log2,
+    log10,
+    logaddexp,
+    logical_and,
+    logical_not,
+    logical_or,
+    logical_xor,
+    multiply,
+    negative,
+    not_equal,
+    positive,
+    pow,
+    real,
+    remainder,
+    round,
+    sign,
+    sin,
+    sinh,
+    square,
+    sqrt,
+    subtract,
+    tan,
+    tanh,
+    trunc,
+)
+
+__all__ += [
+    "abs",
+    "acos",
+    "acosh",
+    "add",
+    "asin",
+    "asinh",
+    "atan",
+    "atan2",
+    "atanh",
+    "bitwise_and",
+    "bitwise_left_shift",
+    "bitwise_invert",
+    "bitwise_or",
+    "bitwise_right_shift",
+    "bitwise_xor",
+    "ceil",
+    "cos",
+    "cosh",
+    "divide",
+    "equal",
+    "exp",
+    "expm1",
+    "floor",
+    "floor_divide",
+    "greater",
+    "greater_equal",
+    "isfinite",
+    "isinf",
+    "isnan",
+    "less",
+    "less_equal",
+    "log",
+    "log1p",
+    "log2",
+    "log10",
+    "logaddexp",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "multiply",
+    "negative",
+    "not_equal",
+    "positive",
+    "pow",
+    "remainder",
+    "round",
+    "sign",
+    "sin",
+    "sinh",
+    "square",
+    "sqrt",
+    "subtract",
+    "tan",
+    "tanh",
+    "trunc",
+]
+
+from ._indexing_functions import take
+
+__all__ += ["take"]
+
+# linalg is an extension in the array API spec, which is a sub-namespace. Only
+# a subset of functions in it are imported into the top-level namespace.
+from . import linalg
+
+__all__ += ["linalg"]
+
+from .linalg import matmul, tensordot, matrix_transpose, vecdot
+
+__all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"]
+
+from ._manipulation_functions import (
+    concat,
+    expand_dims,
+    flip,
+    permute_dims,
+    reshape,
+    roll,
+    squeeze,
+    stack,
+)
+
+__all__ += ["concat", "expand_dims", "flip", "permute_dims", "reshape", "roll", "squeeze", "stack"]
+
+from ._searching_functions import argmax, argmin, nonzero, where
+
+__all__ += ["argmax", "argmin", "nonzero", "where"]
+
+from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values
+
+__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"]
+
+from ._sorting_functions import argsort, sort
+
+__all__ += ["argsort", "sort"]
+
+from ._statistical_functions import max, mean, min, prod, std, sum, var
+
+__all__ += ["max", "mean", "min", "prod", "std", "sum", "var"]
+
+from ._utility_functions import all, any
+
+__all__ += ["all", "any"]
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..41eda323efd5d68d942679ecb8f34c3efd6b4a2e
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_array_object.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_array_object.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bfe8d4cc5603ce62676cb6f4a561efc36390c5e7
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_array_object.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_constants.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_constants.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7fd6a33aa62a46febef87e795799532325f548fc
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_constants.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_creation_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_creation_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..88ef4a9b083d9f3af7a2b67b43020247680a5231
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_creation_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_data_type_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_data_type_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8282e8f22a7b057ccf83d391871e22138d44d506
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_data_type_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_dtypes.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_dtypes.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..10a56d687b25f6dd45cfdadebb1ab9743e0887cb
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_dtypes.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_elementwise_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_elementwise_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f9aa2456c161e27c7c2877a737565ed109ab0d55
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_elementwise_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_indexing_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_indexing_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..663314424f030d809cf42e3a5a542a6072636722
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_indexing_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_manipulation_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_manipulation_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bf6f42b25d92d94225d503a100575702960950b6
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_manipulation_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_searching_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_searching_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dc714c353a881c045526e7262c13cd5267389d12
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_searching_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_set_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_set_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a17ee90e8a0fb7cb75de979875e6d6b527b5d3ac
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_set_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_sorting_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_sorting_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..520f04a6dec1edfbf6c15e69ce224ddebddc309a
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_sorting_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_statistical_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_statistical_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4dbfd5604a2307ca35a7d09007979126fe5d5595
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_statistical_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_typing.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_typing.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3af9b30f3ba3bf403f44f8d7b48b7a7d0255303b
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_typing.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/_utility_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_utility_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ce53a37ff726aae561c826a0fc35c544f19d91c3
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/_utility_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/linalg.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/linalg.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..077914a67cbe81d1b57e7e0d0a845393bc141dd6
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/linalg.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/__pycache__/setup.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/__pycache__/setup.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..020bd4abec7c71db17a5fd17ce2bdfe5580d9f67
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/__pycache__/setup.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/_constants.py b/lib/python3.11/site-packages/numpy/array_api/_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..15ab81d16d932e6c4b4351e54081c5e5301d38c1
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_constants.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+e = np.e
+inf = np.inf
+nan = np.nan
+pi = np.pi
+newaxis = np.newaxis
diff --git a/lib/python3.11/site-packages/numpy/array_api/_creation_functions.py b/lib/python3.11/site-packages/numpy/array_api/_creation_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b014d37b2d64adc70cc76892bbfc96189b6eedf
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_creation_functions.py
@@ -0,0 +1,351 @@
+from __future__ import annotations
+
+
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+if TYPE_CHECKING:
+    from ._typing import (
+        Array,
+        Device,
+        Dtype,
+        NestedSequence,
+        SupportsBufferProtocol,
+    )
+    from collections.abc import Sequence
+from ._dtypes import _all_dtypes
+
+import numpy as np
+
+
+def _check_valid_dtype(dtype):
+    # Note: Only spelling dtypes as the dtype objects is supported.
+
+    # We use this instead of "dtype in _all_dtypes" because the dtype objects
+    # define equality with the sorts of things we want to disallow.
+    for d in (None,) + _all_dtypes:
+        if dtype is d:
+            return
+    raise ValueError("dtype must be one of the supported dtypes")
+
+
+def asarray(
+    obj: Union[
+        Array,
+        bool,
+        int,
+        float,
+        NestedSequence[bool | int | float],
+        SupportsBufferProtocol,
+    ],
+    /,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+    copy: Optional[Union[bool, np._CopyMode]] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`.
+
+    See its docstring for more information.
+    """
+    # _array_object imports in this file are inside the functions to avoid
+    # circular imports
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    if copy in (False, np._CopyMode.IF_NEEDED):
+        # Note: copy=False is not yet implemented in np.asarray
+        raise NotImplementedError("copy=False is not yet implemented")
+    if isinstance(obj, Array):
+        if dtype is not None and obj.dtype != dtype:
+            copy = True
+        if copy in (True, np._CopyMode.ALWAYS):
+            return Array._new(np.array(obj._array, copy=True, dtype=dtype))
+        return obj
+    if dtype is None and isinstance(obj, int) and (obj > 2 ** 64 or obj < -(2 ** 63)):
+        # Give a better error message in this case. NumPy would convert this
+        # to an object array. TODO: This won't handle large integers in lists.
+        raise OverflowError("Integer out of bounds for array dtypes")
+    res = np.asarray(obj, dtype=dtype)
+    return Array._new(res)
+
+
+def arange(
+    start: Union[int, float],
+    /,
+    stop: Optional[Union[int, float]] = None,
+    step: Union[int, float] = 1,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arange <numpy.arange>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype))
+
+
+def empty(
+    shape: Union[int, Tuple[int, ...]],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.empty(shape, dtype=dtype))
+
+
+def empty_like(
+    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.empty_like <numpy.empty_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.empty_like(x._array, dtype=dtype))
+
+
+def eye(
+    n_rows: int,
+    n_cols: Optional[int] = None,
+    /,
+    *,
+    k: int = 0,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.eye <numpy.eye>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))
+
+
+def from_dlpack(x: object, /) -> Array:
+    from ._array_object import Array
+
+    return Array._new(np.from_dlpack(x))
+
+
+def full(
+    shape: Union[int, Tuple[int, ...]],
+    fill_value: Union[int, float],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.full <numpy.full>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    if isinstance(fill_value, Array) and fill_value.ndim == 0:
+        fill_value = fill_value._array
+    res = np.full(shape, fill_value, dtype=dtype)
+    if res.dtype not in _all_dtypes:
+        # This will happen if the fill value is not something that NumPy
+        # coerces to one of the acceptable dtypes.
+        raise TypeError("Invalid input to full")
+    return Array._new(res)
+
+
+def full_like(
+    x: Array,
+    /,
+    fill_value: Union[int, float],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.full_like <numpy.full_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    res = np.full_like(x._array, fill_value, dtype=dtype)
+    if res.dtype not in _all_dtypes:
+        # This will happen if the fill value is not something that NumPy
+        # coerces to one of the acceptable dtypes.
+        raise TypeError("Invalid input to full_like")
+    return Array._new(res)
+
+
+def linspace(
+    start: Union[int, float],
+    stop: Union[int, float],
+    /,
+    num: int,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+    endpoint: bool = True,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linspace <numpy.linspace>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))
+
+
+def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]:
+    """
+    Array API compatible wrapper for :py:func:`np.meshgrid <numpy.meshgrid>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    # Note: unlike np.meshgrid, only inputs with all the same dtype are
+    # allowed
+
+    if len({a.dtype for a in arrays}) > 1:
+        raise ValueError("meshgrid inputs must all have the same dtype")
+
+    return [
+        Array._new(array)
+        for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing)
+    ]
+
+
+def ones(
+    shape: Union[int, Tuple[int, ...]],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ones <numpy.ones>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.ones(shape, dtype=dtype))
+
+
+def ones_like(
+    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ones_like <numpy.ones_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.ones_like(x._array, dtype=dtype))
+
+
+def tril(x: Array, /, *, k: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tril <numpy.tril>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    if x.ndim < 2:
+        # Note: Unlike np.tril, x must be at least 2-D
+        raise ValueError("x must be at least 2-dimensional for tril")
+    return Array._new(np.tril(x._array, k=k))
+
+
+def triu(x: Array, /, *, k: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.triu <numpy.triu>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    if x.ndim < 2:
+        # Note: Unlike np.triu, x must be at least 2-D
+        raise ValueError("x must be at least 2-dimensional for triu")
+    return Array._new(np.triu(x._array, k=k))
+
+
+def zeros(
+    shape: Union[int, Tuple[int, ...]],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.zeros <numpy.zeros>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.zeros(shape, dtype=dtype))
+
+
+def zeros_like(
+    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.zeros_like <numpy.zeros_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.zeros_like(x._array, dtype=dtype))
diff --git a/lib/python3.11/site-packages/numpy/array_api/_data_type_functions.py b/lib/python3.11/site-packages/numpy/array_api/_data_type_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f972c3b54244fdedf2bf2ce77f91c173c09b540
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_data_type_functions.py
@@ -0,0 +1,197 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _signed_integer_dtypes,
+    _unsigned_integer_dtypes,
+    _integer_dtypes,
+    _real_floating_dtypes,
+    _complex_floating_dtypes,
+    _numeric_dtypes,
+    _result_type,
+)
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Tuple, Union
+
+if TYPE_CHECKING:
+    from ._typing import Dtype
+    from collections.abc import Sequence
+
+import numpy as np
+
+
+# Note: astype is a function, not an array method as in NumPy.
+def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array:
+    if not copy and dtype == x.dtype:
+        return x
+    return Array._new(x._array.astype(dtype=dtype, copy=copy))
+
+
+def broadcast_arrays(*arrays: Array) -> List[Array]:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_arrays <numpy.broadcast_arrays>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    return [
+        Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays])
+    ]
+
+
+def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_to <numpy.broadcast_to>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    return Array._new(np.broadcast_to(x._array, shape))
+
+
+def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
+    """
+    Array API compatible wrapper for :py:func:`np.can_cast <numpy.can_cast>`.
+
+    See its docstring for more information.
+    """
+    if isinstance(from_, Array):
+        from_ = from_.dtype
+    elif from_ not in _all_dtypes:
+        raise TypeError(f"{from_=}, but should be an array_api array or dtype")
+    if to not in _all_dtypes:
+        raise TypeError(f"{to=}, but should be a dtype")
+    # Note: We avoid np.can_cast() as it has discrepancies with the array API,
+    # since NumPy allows cross-kind casting (e.g., NumPy allows bool -> int8).
+    # See https://github.com/numpy/numpy/issues/20870
+    try:
+        # We promote `from_` and `to` together. We then check if the promoted
+        # dtype is `to`, which indicates if `from_` can (up)cast to `to`.
+        dtype = _result_type(from_, to)
+        return to == dtype
+    except TypeError:
+        # _result_type() raises if the dtypes don't promote together
+        return False
+
+
+# These are internal objects for the return types of finfo and iinfo, since
+# the NumPy versions contain extra data that isn't part of the spec.
+@dataclass
+class finfo_object:
+    bits: int
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    eps: float
+    max: float
+    min: float
+    smallest_normal: float
+    dtype: Dtype
+
+
+@dataclass
+class iinfo_object:
+    bits: int
+    max: int
+    min: int
+    dtype: Dtype
+
+
+def finfo(type: Union[Dtype, Array], /) -> finfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.finfo <numpy.finfo>`.
+
+    See its docstring for more information.
+    """
+    fi = np.finfo(type)
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    return finfo_object(
+        fi.bits,
+        float(fi.eps),
+        float(fi.max),
+        float(fi.min),
+        float(fi.smallest_normal),
+        fi.dtype,
+    )
+
+
+def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.iinfo <numpy.iinfo>`.
+
+    See its docstring for more information.
+    """
+    ii = np.iinfo(type)
+    return iinfo_object(ii.bits, ii.max, ii.min, ii.dtype)
+
+
+# Note: isdtype is a new function from the 2022.12 array API specification.
+def isdtype(
+    dtype: Dtype, kind: Union[Dtype, str, Tuple[Union[Dtype, str], ...]]
+) -> bool:
+    """
+    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
+    for more details
+    """
+    if isinstance(kind, tuple):
+        # Disallow nested tuples
+        if any(isinstance(k, tuple) for k in kind):
+            raise TypeError("'kind' must be a dtype, str, or tuple of dtypes and strs")
+        return any(isdtype(dtype, k) for k in kind)
+    elif isinstance(kind, str):
+        if kind == 'bool':
+            return dtype in _boolean_dtypes
+        elif kind == 'signed integer':
+            return dtype in _signed_integer_dtypes
+        elif kind == 'unsigned integer':
+            return dtype in _unsigned_integer_dtypes
+        elif kind == 'integral':
+            return dtype in _integer_dtypes
+        elif kind == 'real floating':
+            return dtype in _real_floating_dtypes
+        elif kind == 'complex floating':
+            return dtype in _complex_floating_dtypes
+        elif kind == 'numeric':
+            return dtype in _numeric_dtypes
+        else:
+            raise ValueError(f"Unrecognized data type kind: {kind!r}")
+    elif kind in _all_dtypes:
+        return dtype == kind
+    else:
+        raise TypeError(f"'kind' must be a dtype, str, or tuple of dtypes and strs, not {type(kind).__name__}")
+
+def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype:
+    """
+    Array API compatible wrapper for :py:func:`np.result_type <numpy.result_type>`.
+
+    See its docstring for more information.
+    """
+    # Note: we use a custom implementation that gives only the type promotions
+    # required by the spec rather than using np.result_type. NumPy implements
+    # too many extra type promotions like int64 + uint64 -> float64, and does
+    # value-based casting on scalar arrays.
+    A = []
+    for a in arrays_and_dtypes:
+        if isinstance(a, Array):
+            a = a.dtype
+        elif isinstance(a, np.ndarray) or a not in _all_dtypes:
+            raise TypeError("result_type() inputs must be array_api arrays or dtypes")
+        A.append(a)
+
+    if len(A) == 0:
+        raise ValueError("at least one array or dtype is required")
+    elif len(A) == 1:
+        return A[0]
+    else:
+        t = A[0]
+        for t2 in A[1:]:
+            t = _result_type(t, t2)
+        return t
diff --git a/lib/python3.11/site-packages/numpy/array_api/_indexing_functions.py b/lib/python3.11/site-packages/numpy/array_api/_indexing_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..baf23f7f0b6982115500fa95360d0ca4d2a1caf8
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_indexing_functions.py
@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _integer_dtypes
+
+import numpy as np
+
+def take(x: Array, indices: Array, /, *, axis: Optional[int] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.take <numpy.take>`.
+
+    See its docstring for more information.
+    """
+    if axis is None and x.ndim != 1:
+        raise ValueError("axis must be specified when ndim > 1")
+    if indices.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in indexing")
+    if indices.ndim != 1:
+        raise ValueError("Only 1-dim indices array is supported")
+    return Array._new(np.take(x._array, indices._array, axis=axis))
diff --git a/lib/python3.11/site-packages/numpy/array_api/_set_functions.py b/lib/python3.11/site-packages/numpy/array_api/_set_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b4132cf87f88eba6285bac76f6cfb8aad0b413e
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_set_functions.py
@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import NamedTuple
+
+import numpy as np
+
+# Note: np.unique() is split into four functions in the array API:
+# unique_all, unique_counts, unique_inverse, and unique_values (this is done
+# to remove polymorphic return types).
+
+# Note: The various unique() functions are supposed to return multiple NaNs.
+# This does not match the NumPy behavior, however, this is currently left as a
+# TODO in this implementation as this behavior may be reverted in np.unique().
+# See https://github.com/numpy/numpy/issues/20326.
+
+# Note: The functions here return a namedtuple (np.unique() returns a normal
+# tuple).
+
+class UniqueAllResult(NamedTuple):
+    values: Array
+    indices: Array
+    inverse_indices: Array
+    counts: Array
+
+
+class UniqueCountsResult(NamedTuple):
+    values: Array
+    counts: Array
+
+
+class UniqueInverseResult(NamedTuple):
+    values: Array
+    inverse_indices: Array
+
+
+def unique_all(x: Array, /) -> UniqueAllResult:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    values, indices, inverse_indices, counts = np.unique(
+        x._array,
+        return_counts=True,
+        return_index=True,
+        return_inverse=True,
+        equal_nan=False,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueAllResult(
+        Array._new(values),
+        Array._new(indices),
+        Array._new(inverse_indices),
+        Array._new(counts),
+    )
+
+
+def unique_counts(x: Array, /) -> UniqueCountsResult:
+    res = np.unique(
+        x._array,
+        return_counts=True,
+        return_index=False,
+        return_inverse=False,
+        equal_nan=False,
+    )
+
+    return UniqueCountsResult(*[Array._new(i) for i in res])
+
+
+def unique_inverse(x: Array, /) -> UniqueInverseResult:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    values, inverse_indices = np.unique(
+        x._array,
+        return_counts=False,
+        return_index=False,
+        return_inverse=True,
+        equal_nan=False,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueInverseResult(Array._new(values), Array._new(inverse_indices))
+
+
+def unique_values(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    res = np.unique(
+        x._array,
+        return_counts=False,
+        return_index=False,
+        return_inverse=False,
+        equal_nan=False,
+    )
+    return Array._new(res)
diff --git a/lib/python3.11/site-packages/numpy/array_api/_sorting_functions.py b/lib/python3.11/site-packages/numpy/array_api/_sorting_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b8cb044d88a992da63d6e1a68c5b4c998a49680
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_sorting_functions.py
@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _real_numeric_dtypes
+
+import numpy as np
+
+
+# Note: the descending keyword argument is new in this function
+def argsort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argsort <numpy.argsort>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argsort")
+    # Note: this keyword argument is different, and the default is different.
+    kind = "stable" if stable else "quicksort"
+    if not descending:
+        res = np.argsort(x._array, axis=axis, kind=kind)
+    else:
+        # As NumPy has no native descending sort, we imitate it here. Note that
+        # simply flipping the results of np.argsort(x._array, ...) would not
+        # respect the relative order like it would in native descending sorts.
+        res = np.flip(
+            np.argsort(np.flip(x._array, axis=axis), axis=axis, kind=kind),
+            axis=axis,
+        )
+        # Rely on flip()/argsort() to validate axis
+        normalised_axis = axis if axis >= 0 else x.ndim + axis
+        max_i = x.shape[normalised_axis] - 1
+        res = max_i - res
+    return Array._new(res)
+
+# Note: the descending keyword argument is new in this function
+def sort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sort <numpy.sort>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in sort")
+    # Note: this keyword argument is different, and the default is different.
+    kind = "stable" if stable else "quicksort"
+    res = np.sort(x._array, axis=axis, kind=kind)
+    if descending:
+        res = np.flip(res, axis=axis)
+    return Array._new(res)
diff --git a/lib/python3.11/site-packages/numpy/array_api/_utility_functions.py b/lib/python3.11/site-packages/numpy/array_api/_utility_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ecb4bd9fef732bf5ca6aeec38ce2e7557ed19c2
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/_utility_functions.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+
+def all(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.all <numpy.all>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.all(x._array, axis=axis, keepdims=keepdims)))
+
+
+def any(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.any <numpy.any>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.any(x._array, axis=axis, keepdims=keepdims)))
diff --git a/lib/python3.11/site-packages/numpy/array_api/setup.py b/lib/python3.11/site-packages/numpy/array_api/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8bc2910268e06478d12bffa90024f8f09beb161
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/setup.py
@@ -0,0 +1,12 @@
+def configuration(parent_package="", top_path=None):
+    from numpy.distutils.misc_util import Configuration
+
+    config = Configuration("array_api", parent_package, top_path)
+    config.add_subpackage("tests")
+    return config
+
+
+if __name__ == "__main__":
+    from numpy.distutils.core import setup
+
+    setup(configuration=configuration)
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__init__.py b/lib/python3.11/site-packages/numpy/array_api/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..536062e3827921df637105680ea9e8ea879b8f3e
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/__init__.py
@@ -0,0 +1,7 @@
+"""
+Tests for the array API namespace.
+
+Note, full compliance with the array API can be tested with the official array API test
+suite https://github.com/data-apis/array-api-tests. This test suite primarily
+focuses on those things that are not tested by the official test suite.
+"""
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bc1dda4c96211895bbe3f9567004d5ee824acb3f
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_array_object.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_array_object.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0f5aa5b97ccc7367dad3a0e96ce989357a5cef01
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_array_object.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_creation_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_creation_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..437dc005b921b360585bb7f8a5d9e57147e0eda6
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_creation_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_data_type_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_data_type_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4d86bb8fb2d2b6439e9383db371ceb364e7420e5
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_data_type_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_elementwise_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_elementwise_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7797f130fdaf7716f7b68be7f4c6cf7dd7e4f8c3
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_elementwise_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_indexing_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_indexing_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cc0a29a120e5be919b69d86ec62b7ac49f63a4e7
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_indexing_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_manipulation_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_manipulation_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9987aaeeb733b5764553246df012d613246d3d41
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_manipulation_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_set_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_set_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..be3453326f4538bba1fc229a838ea99a4c8aaeae
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_set_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_sorting_functions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_sorting_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0df1ec05de0b14b101780bc64340a246d47dfa4b
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_sorting_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_validation.cpython-311.pyc b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_validation.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e5e720b5b7364eacfc6150d7be9460902c2acea7
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/array_api/tests/__pycache__/test_validation.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_array_object.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_array_object.py
new file mode 100644
index 0000000000000000000000000000000000000000..0feb72c4ea33a632545e6329854644ac9b817a4a
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_array_object.py
@@ -0,0 +1,395 @@
+import operator
+
+from numpy.testing import assert_raises, suppress_warnings
+import numpy as np
+import pytest
+
+from .. import ones, asarray, reshape, result_type, all, equal
+from .._array_object import Array
+from .._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _real_floating_dtypes,
+    _floating_dtypes,
+    _complex_floating_dtypes,
+    _integer_dtypes,
+    _integer_or_boolean_dtypes,
+    _real_numeric_dtypes,
+    _numeric_dtypes,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint64,
+    bool as bool_,
+)
+
+
+def test_validate_index():
+    # The indexing tests in the official array API test suite test that the
+    # array object correctly handles the subset of indices that are required
+    # by the spec. But the NumPy array API implementation specifically
+    # disallows any index not required by the spec, via Array._validate_index.
+    # This test focuses on testing that non-valid indices are correctly
+    # rejected. See
+    # https://data-apis.org/array-api/latest/API_specification/indexing.html
+    # and the docstring of Array._validate_index for the exact indexing
+    # behavior that should be allowed. This does not test indices that are
+    # already invalid in NumPy itself because Array will generally just pass
+    # such indices directly to the underlying np.ndarray.
+
+    a = ones((3, 4))
+
+    # Out of bounds slices are not allowed
+    assert_raises(IndexError, lambda: a[:4])
+    assert_raises(IndexError, lambda: a[:-4])
+    assert_raises(IndexError, lambda: a[:3:-1])
+    assert_raises(IndexError, lambda: a[:-5:-1])
+    assert_raises(IndexError, lambda: a[4:])
+    assert_raises(IndexError, lambda: a[-4:])
+    assert_raises(IndexError, lambda: a[4::-1])
+    assert_raises(IndexError, lambda: a[-4::-1])
+
+    assert_raises(IndexError, lambda: a[...,:5])
+    assert_raises(IndexError, lambda: a[...,:-5])
+    assert_raises(IndexError, lambda: a[...,:5:-1])
+    assert_raises(IndexError, lambda: a[...,:-6:-1])
+    assert_raises(IndexError, lambda: a[...,5:])
+    assert_raises(IndexError, lambda: a[...,-5:])
+    assert_raises(IndexError, lambda: a[...,5::-1])
+    assert_raises(IndexError, lambda: a[...,-5::-1])
+
+    # Boolean indices cannot be part of a larger tuple index
+    assert_raises(IndexError, lambda: a[a[:,0]==1,0])
+    assert_raises(IndexError, lambda: a[a[:,0]==1,...])
+    assert_raises(IndexError, lambda: a[..., a[0]==1])
+    assert_raises(IndexError, lambda: a[[True, True, True]])
+    assert_raises(IndexError, lambda: a[(True, True, True),])
+
+    # Integer array indices are not allowed (except for 0-D)
+    idx = asarray([[0, 1]])
+    assert_raises(IndexError, lambda: a[idx])
+    assert_raises(IndexError, lambda: a[idx,])
+    assert_raises(IndexError, lambda: a[[0, 1]])
+    assert_raises(IndexError, lambda: a[(0, 1), (0, 1)])
+    assert_raises(IndexError, lambda: a[[0, 1]])
+    assert_raises(IndexError, lambda: a[np.array([[0, 1]])])
+
+    # Multiaxis indices must contain exactly as many indices as dimensions
+    assert_raises(IndexError, lambda: a[()])
+    assert_raises(IndexError, lambda: a[0,])
+    assert_raises(IndexError, lambda: a[0])
+    assert_raises(IndexError, lambda: a[:])
+
+def test_operators():
+    # For every operator, we test that it works for the required type
+    # combinations and raises TypeError otherwise
+    binary_op_dtypes = {
+        "__add__": "numeric",
+        "__and__": "integer_or_boolean",
+        "__eq__": "all",
+        "__floordiv__": "real numeric",
+        "__ge__": "real numeric",
+        "__gt__": "real numeric",
+        "__le__": "real numeric",
+        "__lshift__": "integer",
+        "__lt__": "real numeric",
+        "__mod__": "real numeric",
+        "__mul__": "numeric",
+        "__ne__": "all",
+        "__or__": "integer_or_boolean",
+        "__pow__": "numeric",
+        "__rshift__": "integer",
+        "__sub__": "numeric",
+        "__truediv__": "floating",
+        "__xor__": "integer_or_boolean",
+    }
+    # Recompute each time because of in-place ops
+    def _array_vals():
+        for d in _integer_dtypes:
+            yield asarray(1, dtype=d)
+        for d in _boolean_dtypes:
+            yield asarray(False, dtype=d)
+        for d in _floating_dtypes:
+            yield asarray(1.0, dtype=d)
+
+
+    BIG_INT = int(1e30)
+    for op, dtypes in binary_op_dtypes.items():
+        ops = [op]
+        if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]:
+            rop = "__r" + op[2:]
+            iop = "__i" + op[2:]
+            ops += [rop, iop]
+        for s in [1, 1.0, 1j, BIG_INT, False]:
+            for _op in ops:
+                for a in _array_vals():
+                    # Test array op scalar. From the spec, the following combinations
+                    # are supported:
+
+                    # - Python bool for a bool array dtype,
+                    # - a Python int within the bounds of the given dtype for integer array dtypes,
+                    # - a Python int or float for real floating-point array dtypes
+                    # - a Python int, float, or complex for complex floating-point array dtypes
+
+                    if ((dtypes == "all"
+                         or dtypes == "numeric" and a.dtype in _numeric_dtypes
+                         or dtypes == "real numeric" and a.dtype in _real_numeric_dtypes
+                         or dtypes == "integer" and a.dtype in _integer_dtypes
+                         or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
+                         or dtypes == "boolean" and a.dtype in _boolean_dtypes
+                         or dtypes == "floating" and a.dtype in _floating_dtypes
+                        )
+                        # bool is a subtype of int, which is why we avoid
+                        # isinstance here.
+                        and (a.dtype in _boolean_dtypes and type(s) == bool
+                             or a.dtype in _integer_dtypes and type(s) == int
+                             or a.dtype in _real_floating_dtypes and type(s) in [float, int]
+                             or a.dtype in _complex_floating_dtypes and type(s) in [complex, float, int]
+                        )):
+                        if a.dtype in _integer_dtypes and s == BIG_INT:
+                            assert_raises(OverflowError, lambda: getattr(a, _op)(s))
+                        else:
+                            # Only test for no error
+                            with suppress_warnings() as sup:
+                                # ignore warnings from pow(BIG_INT)
+                                sup.filter(RuntimeWarning,
+                                           "invalid value encountered in power")
+                                getattr(a, _op)(s)
+                    else:
+                        assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+                # Test array op array.
+                for _op in ops:
+                    for x in _array_vals():
+                        for y in _array_vals():
+                            # See the promotion table in NEP 47 or the array
+                            # API spec page on type promotion. Mixed kind
+                            # promotion is not defined.
+                            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                                or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes
+                                or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes
+                                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                                ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure in-place operators only promote to the same dtype as the left operand.
+                            elif (
+                                _op.startswith("__i")
+                                and result_type(x.dtype, y.dtype) != x.dtype
+                            ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure only those dtypes that are required for every operator are allowed.
+                            elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                                      or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or (dtypes == "real numeric" and x.dtype in _real_numeric_dtypes and y.dtype in _real_numeric_dtypes)
+                                or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
+                                or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
+                                                                       or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
+                                or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes
+                            ):
+                                getattr(x, _op)(y)
+                            else:
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+
+    unary_op_dtypes = {
+        "__abs__": "numeric",
+        "__invert__": "integer_or_boolean",
+        "__neg__": "numeric",
+        "__pos__": "numeric",
+    }
+    for op, dtypes in unary_op_dtypes.items():
+        for a in _array_vals():
+            if (
+                dtypes == "numeric"
+                and a.dtype in _numeric_dtypes
+                or dtypes == "integer_or_boolean"
+                and a.dtype in _integer_or_boolean_dtypes
+            ):
+                # Only test for no error
+                getattr(a, op)()
+            else:
+                assert_raises(TypeError, lambda: getattr(a, op)())
+
+    # Finally, matmul() must be tested separately, because it works a bit
+    # different from the other operations.
+    def _matmul_array_vals():
+        for a in _array_vals():
+            yield a
+        for d in _all_dtypes:
+            yield ones((3, 4), dtype=d)
+            yield ones((4, 2), dtype=d)
+            yield ones((4, 4), dtype=d)
+
+    # Scalars always error
+    for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]:
+        for s in [1, 1.0, False]:
+            for a in _matmul_array_vals():
+                if (type(s) in [float, int] and a.dtype in _floating_dtypes
+                    or type(s) == int and a.dtype in _integer_dtypes):
+                    # Type promotion is valid, but @ is not allowed on 0-D
+                    # inputs, so the error is a ValueError
+                    assert_raises(ValueError, lambda: getattr(a, _op)(s))
+                else:
+                    assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+    for x in _matmul_array_vals():
+        for y in _matmul_array_vals():
+            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                or x.dtype in _boolean_dtypes
+                or y.dtype in _boolean_dtypes
+                ):
+                assert_raises(TypeError, lambda: x.__matmul__(y))
+                assert_raises(TypeError, lambda: y.__rmatmul__(x))
+                assert_raises(TypeError, lambda: x.__imatmul__(y))
+            elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]:
+                assert_raises(ValueError, lambda: x.__matmul__(y))
+                assert_raises(ValueError, lambda: y.__rmatmul__(x))
+                if result_type(x.dtype, y.dtype) != x.dtype:
+                    assert_raises(TypeError, lambda: x.__imatmul__(y))
+                else:
+                    assert_raises(ValueError, lambda: x.__imatmul__(y))
+            else:
+                x.__matmul__(y)
+                y.__rmatmul__(x)
+                if result_type(x.dtype, y.dtype) != x.dtype:
+                    assert_raises(TypeError, lambda: x.__imatmul__(y))
+                elif y.shape[0] != y.shape[1]:
+                    # This one fails because x @ y has a different shape from x
+                    assert_raises(ValueError, lambda: x.__imatmul__(y))
+                else:
+                    x.__imatmul__(y)
+
+
+def test_python_scalar_construtors():
+    b = asarray(False)
+    i = asarray(0)
+    f = asarray(0.0)
+    c = asarray(0j)
+
+    assert bool(b) == False
+    assert int(i) == 0
+    assert float(f) == 0.0
+    assert operator.index(i) == 0
+
+    # bool/int/float/complex should only be allowed on 0-D arrays.
+    assert_raises(TypeError, lambda: bool(asarray([False])))
+    assert_raises(TypeError, lambda: int(asarray([0])))
+    assert_raises(TypeError, lambda: float(asarray([0.0])))
+    assert_raises(TypeError, lambda: complex(asarray([0j])))
+    assert_raises(TypeError, lambda: operator.index(asarray([0])))
+
+    # bool should work on all types of arrays
+    assert bool(b) is bool(i) is bool(f) is bool(c) is False
+
+    # int should fail on complex arrays
+    assert int(b) == int(i) == int(f) == 0
+    assert_raises(TypeError, lambda: int(c))
+
+    # float should fail on complex arrays
+    assert float(b) == float(i) == float(f) == 0.0
+    assert_raises(TypeError, lambda: float(c))
+
+    # complex should work on all types of arrays
+    assert complex(b) == complex(i) == complex(f) == complex(c) == 0j
+
+    # index should only work on integer arrays
+    assert operator.index(i) == 0
+    assert_raises(TypeError, lambda: operator.index(b))
+    assert_raises(TypeError, lambda: operator.index(f))
+    assert_raises(TypeError, lambda: operator.index(c))
+
+
+def test_device_property():
+    a = ones((3, 4))
+    assert a.device == 'cpu'
+
+    assert all(equal(a.to_device('cpu'), a))
+    assert_raises(ValueError, lambda: a.to_device('gpu'))
+
+    assert all(equal(asarray(a, device='cpu'), a))
+    assert_raises(ValueError, lambda: asarray(a, device='gpu'))
+
+def test_array_properties():
+    a = ones((1, 2, 3))
+    b = ones((2, 3))
+    assert_raises(ValueError, lambda: a.T)
+
+    assert isinstance(b.T, Array)
+    assert b.T.shape == (3, 2)
+
+    assert isinstance(a.mT, Array)
+    assert a.mT.shape == (1, 3, 2)
+    assert isinstance(b.mT, Array)
+    assert b.mT.shape == (3, 2)
+
+def test___array__():
+    a = ones((2, 3), dtype=int16)
+    assert np.asarray(a) is a._array
+    b = np.asarray(a, dtype=np.float64)
+    assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
+    assert b.dtype == np.float64
+
+def test_allow_newaxis():
+    a = ones(5)
+    indexed_a = a[None, :]
+    assert indexed_a.shape == (1, 5)
+
+def test_disallow_flat_indexing_with_newaxis():
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[None, 0, 0]
+
+def test_disallow_mask_with_newaxis():
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[None, asarray(True)]
+
+@pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)])
+@pytest.mark.parametrize("index", ["string", False, True])
+def test_error_on_invalid_index(shape, index):
+    a = ones(shape)
+    with pytest.raises(IndexError):
+        a[index]
+
+def test_mask_0d_array_without_errors():
+    a = ones(())
+    a[asarray(True)]
+
+@pytest.mark.parametrize(
+    "i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])]
+)
+def test_error_on_invalid_index_with_ellipsis(i):
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[..., i]
+    with pytest.raises(IndexError):
+        a[i, ...]
+
+def test_array_keys_use_private_array():
+    """
+    Indexing operations convert array keys before indexing the internal array
+
+    Fails when array_api array keys are not converted into NumPy-proper arrays
+    in __getitem__(). This is achieved by passing array_api arrays with 0-sized
+    dimensions, which NumPy-proper treats erroneously - not sure why!
+
+    TODO: Find and use appropriate __setitem__() case.
+    """
+    a = ones((0, 0), dtype=bool_)
+    assert a[a].shape == (0,)
+
+    a = ones((0,), dtype=bool_)
+    key = ones((0, 0), dtype=bool_)
+    with pytest.raises(IndexError):
+        a[key]
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_creation_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_creation_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..be9eaa38378cb19f4c445ec4138245564c13b28a
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_creation_functions.py
@@ -0,0 +1,142 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import all
+from .._creation_functions import (
+    asarray,
+    arange,
+    empty,
+    empty_like,
+    eye,
+    full,
+    full_like,
+    linspace,
+    meshgrid,
+    ones,
+    ones_like,
+    zeros,
+    zeros_like,
+)
+from .._dtypes import float32, float64
+from .._array_object import Array
+
+
+def test_asarray_errors():
+    # Test various protections against incorrect usage
+    assert_raises(TypeError, lambda: Array([1]))
+    assert_raises(TypeError, lambda: asarray(["a"]))
+    assert_raises(ValueError, lambda: asarray([1.0], dtype=np.float16))
+    assert_raises(OverflowError, lambda: asarray(2**100))
+    # Preferably this would be OverflowError
+    # assert_raises(OverflowError, lambda: asarray([2**100]))
+    assert_raises(TypeError, lambda: asarray([2**100]))
+    asarray([1], device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: asarray([1], device="gpu"))
+
+    assert_raises(ValueError, lambda: asarray([1], dtype=int))
+    assert_raises(ValueError, lambda: asarray([1], dtype="i"))
+
+
+def test_asarray_copy():
+    a = asarray([1])
+    b = asarray(a, copy=True)
+    a[0] = 0
+    assert all(b[0] == 1)
+    assert all(a[0] == 0)
+    a = asarray([1])
+    b = asarray(a, copy=np._CopyMode.ALWAYS)
+    a[0] = 0
+    assert all(b[0] == 1)
+    assert all(a[0] == 0)
+    a = asarray([1])
+    b = asarray(a, copy=np._CopyMode.NEVER)
+    a[0] = 0
+    assert all(b[0] == 0)
+    assert_raises(NotImplementedError, lambda: asarray(a, copy=False))
+    assert_raises(NotImplementedError,
+                  lambda: asarray(a, copy=np._CopyMode.IF_NEEDED))
+
+
+def test_arange_errors():
+    arange(1, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: arange(1, device="gpu"))
+    assert_raises(ValueError, lambda: arange(1, dtype=int))
+    assert_raises(ValueError, lambda: arange(1, dtype="i"))
+
+
+def test_empty_errors():
+    empty((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: empty((1,), device="gpu"))
+    assert_raises(ValueError, lambda: empty((1,), dtype=int))
+    assert_raises(ValueError, lambda: empty((1,), dtype="i"))
+
+
+def test_empty_like_errors():
+    empty_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: empty_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: empty_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: empty_like(asarray(1), dtype="i"))
+
+
+def test_eye_errors():
+    eye(1, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: eye(1, device="gpu"))
+    assert_raises(ValueError, lambda: eye(1, dtype=int))
+    assert_raises(ValueError, lambda: eye(1, dtype="i"))
+
+
+def test_full_errors():
+    full((1,), 0, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: full((1,), 0, device="gpu"))
+    assert_raises(ValueError, lambda: full((1,), 0, dtype=int))
+    assert_raises(ValueError, lambda: full((1,), 0, dtype="i"))
+
+
+def test_full_like_errors():
+    full_like(asarray(1), 0, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, device="gpu"))
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype=int))
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype="i"))
+
+
+def test_linspace_errors():
+    linspace(0, 1, 10, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, device="gpu"))
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype=float))
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype="f"))
+
+
+def test_ones_errors():
+    ones((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: ones((1,), device="gpu"))
+    assert_raises(ValueError, lambda: ones((1,), dtype=int))
+    assert_raises(ValueError, lambda: ones((1,), dtype="i"))
+
+
+def test_ones_like_errors():
+    ones_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: ones_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: ones_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: ones_like(asarray(1), dtype="i"))
+
+
+def test_zeros_errors():
+    zeros((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: zeros((1,), device="gpu"))
+    assert_raises(ValueError, lambda: zeros((1,), dtype=int))
+    assert_raises(ValueError, lambda: zeros((1,), dtype="i"))
+
+
+def test_zeros_like_errors():
+    zeros_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype="i"))
+
+def test_meshgrid_dtype_errors():
+    # Doesn't raise
+    meshgrid()
+    meshgrid(asarray([1.], dtype=float32))
+    meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float32))
+
+    assert_raises(ValueError, lambda: meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float64)))
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_data_type_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_data_type_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..61d56ca45b1edf9a6dbc4681d1f5ac52448f96c4
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_data_type_functions.py
@@ -0,0 +1,31 @@
+import pytest
+
+from numpy.testing import assert_raises
+from numpy import array_api as xp
+import numpy as np
+
+@pytest.mark.parametrize(
+    "from_, to, expected",
+    [
+        (xp.int8, xp.int16, True),
+        (xp.int16, xp.int8, False),
+        (xp.bool, xp.int8, False),
+        (xp.asarray(0, dtype=xp.uint8), xp.int8, False),
+    ],
+)
+def test_can_cast(from_, to, expected):
+    """
+    can_cast() returns correct result
+    """
+    assert xp.can_cast(from_, to) == expected
+
+def test_isdtype_strictness():
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, 64))
+    assert_raises(ValueError, lambda: xp.isdtype(xp.float64, 'f8'))
+
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, (('integral',),)))
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, np.object_))
+
+    # TODO: These will require https://github.com/numpy/numpy/issues/23883
+    # assert_raises(TypeError, lambda: xp.isdtype(xp.float64, None))
+    # assert_raises(TypeError, lambda: xp.isdtype(xp.float64, np.float64))
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_elementwise_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_elementwise_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..1228d0af2e6a6a8ae504297c7562d3beb5ba9516
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_elementwise_functions.py
@@ -0,0 +1,114 @@
+from inspect import getfullargspec
+
+from numpy.testing import assert_raises
+
+from .. import asarray, _elementwise_functions
+from .._elementwise_functions import bitwise_left_shift, bitwise_right_shift
+from .._dtypes import (
+    _dtype_categories,
+    _boolean_dtypes,
+    _floating_dtypes,
+    _integer_dtypes,
+)
+
+
+def nargs(func):
+    return len(getfullargspec(func).args)
+
+
+def test_function_types():
+    # Test that every function accepts only the required input types. We only
+    # test the negative cases here (error). The positive cases are tested in
+    # the array API test suite.
+
+    elementwise_function_input_types = {
+        "abs": "numeric",
+        "acos": "floating-point",
+        "acosh": "floating-point",
+        "add": "numeric",
+        "asin": "floating-point",
+        "asinh": "floating-point",
+        "atan": "floating-point",
+        "atan2": "real floating-point",
+        "atanh": "floating-point",
+        "bitwise_and": "integer or boolean",
+        "bitwise_invert": "integer or boolean",
+        "bitwise_left_shift": "integer",
+        "bitwise_or": "integer or boolean",
+        "bitwise_right_shift": "integer",
+        "bitwise_xor": "integer or boolean",
+        "ceil": "real numeric",
+        "conj": "complex floating-point",
+        "cos": "floating-point",
+        "cosh": "floating-point",
+        "divide": "floating-point",
+        "equal": "all",
+        "exp": "floating-point",
+        "expm1": "floating-point",
+        "floor": "real numeric",
+        "floor_divide": "real numeric",
+        "greater": "real numeric",
+        "greater_equal": "real numeric",
+        "imag": "complex floating-point",
+        "isfinite": "numeric",
+        "isinf": "numeric",
+        "isnan": "numeric",
+        "less": "real numeric",
+        "less_equal": "real numeric",
+        "log": "floating-point",
+        "logaddexp": "real floating-point",
+        "log10": "floating-point",
+        "log1p": "floating-point",
+        "log2": "floating-point",
+        "logical_and": "boolean",
+        "logical_not": "boolean",
+        "logical_or": "boolean",
+        "logical_xor": "boolean",
+        "multiply": "numeric",
+        "negative": "numeric",
+        "not_equal": "all",
+        "positive": "numeric",
+        "pow": "numeric",
+        "real": "complex floating-point",
+        "remainder": "real numeric",
+        "round": "numeric",
+        "sign": "numeric",
+        "sin": "floating-point",
+        "sinh": "floating-point",
+        "sqrt": "floating-point",
+        "square": "numeric",
+        "subtract": "numeric",
+        "tan": "floating-point",
+        "tanh": "floating-point",
+        "trunc": "real numeric",
+    }
+
+    def _array_vals():
+        for d in _integer_dtypes:
+            yield asarray(1, dtype=d)
+        for d in _boolean_dtypes:
+            yield asarray(False, dtype=d)
+        for d in _floating_dtypes:
+            yield asarray(1.0, dtype=d)
+
+    for x in _array_vals():
+        for func_name, types in elementwise_function_input_types.items():
+            dtypes = _dtype_categories[types]
+            func = getattr(_elementwise_functions, func_name)
+            if nargs(func) == 2:
+                for y in _array_vals():
+                    if x.dtype not in dtypes or y.dtype not in dtypes:
+                        assert_raises(TypeError, lambda: func(x, y))
+            else:
+                if x.dtype not in dtypes:
+                    assert_raises(TypeError, lambda: func(x))
+
+
+def test_bitwise_shift_error():
+    # bitwise shift functions should raise when the second argument is negative
+    assert_raises(
+        ValueError, lambda: bitwise_left_shift(asarray([1, 1]), asarray([1, -1]))
+    )
+    assert_raises(
+        ValueError, lambda: bitwise_right_shift(asarray([1, 1]), asarray([1, -1]))
+    )
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_indexing_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_indexing_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e05c63863a6fca5a24dfaa26e1fd9569dea9580
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_indexing_functions.py
@@ -0,0 +1,24 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "x, indices, axis, expected",
+    [
+        ([2, 3], [1, 1, 0], 0,  [3, 3, 2]),
+        ([2, 3], [1, 1, 0], -1, [3, 3, 2]),
+        ([[2, 3]], [1], -1, [[3]]),
+        ([[2, 3]], [0, 0], 0, [[2, 3], [2, 3]]),
+    ],
+)
+def test_take_function(x, indices, axis, expected):
+    """
+    Indices respect relative order of a descending stable-sort
+
+    See https://github.com/numpy/numpy/issues/20778
+    """
+    x = xp.asarray(x)
+    indices = xp.asarray(indices)
+    out = xp.take(x, indices, axis=axis)
+    assert xp.all(out == xp.asarray(expected))
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_manipulation_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_manipulation_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..aec57c38ba6a60f3e8dbf3e9b8e8241182f788de
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_manipulation_functions.py
@@ -0,0 +1,37 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import all
+from .._creation_functions import asarray
+from .._dtypes import float64, int8
+from .._manipulation_functions import (
+        concat,
+        reshape,
+        stack
+)
+
+
+def test_concat_errors():
+    assert_raises(TypeError, lambda: concat((1, 1), axis=None))
+    assert_raises(TypeError, lambda: concat([asarray([1], dtype=int8),
+                                             asarray([1], dtype=float64)]))
+
+
+def test_stack_errors():
+    assert_raises(TypeError, lambda: stack([asarray([1, 1], dtype=int8),
+                                            asarray([2, 2], dtype=float64)]))
+
+
+def test_reshape_copy():
+    a = asarray(np.ones((2, 3)))
+    b = reshape(a, (3, 2), copy=True)
+    assert not np.shares_memory(a._array, b._array)
+    
+    a = asarray(np.ones((2, 3)))
+    b = reshape(a, (3, 2), copy=False)
+    assert np.shares_memory(a._array, b._array)
+
+    a = asarray(np.ones((2, 3)).T)
+    b = reshape(a, (3, 2), copy=True)
+    assert_raises(AttributeError, lambda: reshape(a, (2, 3), copy=False))
+
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_set_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_set_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8eb65d430dc4f03e2ff662c61823adf17f2d502
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_set_functions.py
@@ -0,0 +1,19 @@
+import pytest
+from hypothesis import given
+from hypothesis.extra.array_api import make_strategies_namespace
+
+from numpy import array_api as xp
+
+xps = make_strategies_namespace(xp)
+
+
+@pytest.mark.parametrize("func", [xp.unique_all, xp.unique_inverse])
+@given(xps.arrays(dtype=xps.scalar_dtypes(), shape=xps.array_shapes()))
+def test_inverse_indices_shape(func, x):
+    """
+    Inverse indices share shape of input array
+
+    See https://github.com/numpy/numpy/issues/20638
+    """
+    out = func(x)
+    assert out.inverse_indices.shape == x.shape
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_sorting_functions.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_sorting_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9848bbfeb7bdee1a7183a6bda641e0611c756ee6
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_sorting_functions.py
@@ -0,0 +1,23 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "obj, axis, expected",
+    [
+        ([0, 0], -1, [0, 1]),
+        ([0, 1, 0], -1, [1, 0, 2]),
+        ([[0, 1], [1, 1]], 0, [[1, 0], [0, 1]]),
+        ([[0, 1], [1, 1]], 1, [[1, 0], [0, 1]]),
+    ],
+)
+def test_stable_desc_argsort(obj, axis, expected):
+    """
+    Indices respect relative order of a descending stable-sort
+
+    See https://github.com/numpy/numpy/issues/20778
+    """
+    x = xp.asarray(obj)
+    out = xp.argsort(x, axis=axis, stable=True, descending=True)
+    assert xp.all(out == xp.asarray(expected))
diff --git a/lib/python3.11/site-packages/numpy/array_api/tests/test_validation.py b/lib/python3.11/site-packages/numpy/array_api/tests/test_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dd100d159242a1dcd0da5277dd655625c602fad
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/array_api/tests/test_validation.py
@@ -0,0 +1,27 @@
+from typing import Callable
+
+import pytest
+
+from numpy import array_api as xp
+
+
+def p(func: Callable, *args, **kwargs):
+    f_sig = ", ".join(
+        [str(a) for a in args] + [f"{k}={v}" for k, v in kwargs.items()]
+    )
+    id_ = f"{func.__name__}({f_sig})"
+    return pytest.param(func, args, kwargs, id=id_)
+
+
+@pytest.mark.parametrize(
+    "func, args, kwargs",
+    [
+        p(xp.can_cast, 42, xp.int8),
+        p(xp.can_cast, xp.int8, 42),
+        p(xp.result_type, 42),
+    ],
+)
+def test_raises_on_invalid_types(func, args, kwargs):
+    """Function raises TypeError when passed invalidly-typed inputs"""
+    with pytest.raises(TypeError):
+        func(*args, **kwargs)
diff --git a/lib/python3.11/site-packages/numpy/random/LICENSE.md b/lib/python3.11/site-packages/numpy/random/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6cf1b17e99725556ac56ce3661498df1ee2276a
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/LICENSE.md
@@ -0,0 +1,71 @@
+**This software is dual-licensed under the The University of Illinois/NCSA
+Open Source License (NCSA) and The 3-Clause BSD License**
+
+# NCSA Open Source License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,
+<kevin.k.sheppard@gmail.com>)
+[http://www.kevinsheppard.com](http://www.kevinsheppard.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal with
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimers.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimers in the documentation and/or
+other materials provided with the distribution.
+
+Neither the names of Kevin Sheppard, nor the names of any contributors may be
+used to endorse or promote products derived from this Software without specific
+prior written permission.
+
+**THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH
+THE SOFTWARE.**
+
+
+# 3-Clause BSD License
+**Copyright (c) 2019 Kevin Sheppard. All rights reserved.**
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+**THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.**
+
+# Components
+
+Many parts of this module have been derived from original sources, 
+often the algorithm's designer. Component licenses are located with 
+the component code.
diff --git a/lib/python3.11/site-packages/numpy/random/__init__.pxd b/lib/python3.11/site-packages/numpy/random/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..1f9057296ba9475574a191cf231dc04ace3f910c
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/__init__.pxd
@@ -0,0 +1,14 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+from numpy.random.bit_generator cimport BitGenerator, SeedSequence
diff --git a/lib/python3.11/site-packages/numpy/random/__init__.py b/lib/python3.11/site-packages/numpy/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e8f99fe3045b9c2b691a8ece67d0f06d9d73b08
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/__init__.py
@@ -0,0 +1,215 @@
+"""
+========================
+Random Number Generation
+========================
+
+Use ``default_rng()`` to create a `Generator` and call its methods.
+
+=============== =========================================================
+Generator
+--------------- ---------------------------------------------------------
+Generator       Class implementing all of the random number distributions
+default_rng     Default constructor for ``Generator``
+=============== =========================================================
+
+============================================= ===
+BitGenerator Streams that work with Generator
+--------------------------------------------- ---
+MT19937
+PCG64
+PCG64DXSM
+Philox
+SFC64
+============================================= ===
+
+============================================= ===
+Getting entropy to initialize a BitGenerator
+--------------------------------------------- ---
+SeedSequence
+============================================= ===
+
+
+Legacy
+------
+
+For backwards compatibility with previous versions of numpy before 1.17, the
+various aliases to the global `RandomState` methods are left alone and do not
+use the new `Generator` API.
+
+==================== =========================================================
+Utility functions
+-------------------- ---------------------------------------------------------
+random               Uniformly distributed floats over ``[0, 1)``
+bytes                Uniformly distributed random bytes.
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+choice               Random sample from 1-D array.
+==================== =========================================================
+
+==================== =========================================================
+Compatibility
+functions - removed
+in the new API
+-------------------- ---------------------------------------------------------
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+random_integers      Uniformly distributed integers in a given range.
+                     (deprecated, use ``integers(..., closed=True)`` instead)
+random_sample        Alias for `random_sample`
+randint              Uniformly distributed integers in a given range
+seed                 Seed the legacy random number generator.
+==================== =========================================================
+
+==================== =========================================================
+Univariate
+distributions
+-------------------- ---------------------------------------------------------
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== ==========================================================
+Multivariate
+distributions
+-------------------- ----------------------------------------------------------
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== ==========================================================
+
+==================== =========================================================
+Standard
+distributions
+-------------------- ---------------------------------------------------------
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+-------------------- ---------------------------------------------------------
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+
+"""
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'choice',
+    'dirichlet',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random',
+    'random_integers',
+    'random_sample',
+    'ranf',
+    'rayleigh',
+    'sample',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf',
+]
+
+# add these for module-freeze analysis (like PyInstaller)
+from . import _pickle
+from . import _common
+from . import _bounded_integers
+
+from ._generator import Generator, default_rng
+from .bit_generator import SeedSequence, BitGenerator
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .mtrand import *
+
+__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
+            'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
+            'BitGenerator']
+
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+
+    This function exists solely to assist (un)pickling.
+
+    Note that the state of the RandomState returned here is irrelevant, as this
+    function's entire purpose is to return a newly allocated RandomState whose
+    state pickle can set.  Consequently the RandomState returned by this function
+    is a freshly allocated copy with a seed=0.
+
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+
+    """
+    return RandomState(seed=0)
+
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/lib/python3.11/site-packages/numpy/random/__init__.pyi b/lib/python3.11/site-packages/numpy/random/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..99ef6f3e2f2a0e45db86589e73c3c8bb36b18ea2
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/__init__.pyi
@@ -0,0 +1,72 @@
+from numpy._pytesttester import PytestTester
+
+from numpy.random._generator import Generator as Generator
+from numpy.random._generator import default_rng as default_rng
+from numpy.random._mt19937 import MT19937 as MT19937
+from numpy.random._pcg64 import (
+    PCG64 as PCG64,
+    PCG64DXSM as PCG64DXSM,
+)
+from numpy.random._philox import Philox as Philox
+from numpy.random._sfc64 import SFC64 as SFC64
+from numpy.random.bit_generator import BitGenerator as BitGenerator
+from numpy.random.bit_generator import SeedSequence as SeedSequence
+from numpy.random.mtrand import (
+    RandomState as RandomState,
+    beta as beta,
+    binomial as binomial,
+    bytes as bytes,
+    chisquare as chisquare,
+    choice as choice,
+    dirichlet as dirichlet,
+    exponential as exponential,
+    f as f,
+    gamma as gamma,
+    geometric as geometric,
+    get_bit_generator as get_bit_generator,
+    get_state as get_state,
+    gumbel as gumbel,
+    hypergeometric as hypergeometric,
+    laplace as laplace,
+    logistic as logistic,
+    lognormal as lognormal,
+    logseries as logseries,
+    multinomial as multinomial,
+    multivariate_normal as multivariate_normal,
+    negative_binomial as negative_binomial,
+    noncentral_chisquare as noncentral_chisquare,
+    noncentral_f as noncentral_f,
+    normal as normal,
+    pareto as pareto,
+    permutation as permutation,
+    poisson as poisson,
+    power as power,
+    rand as rand,
+    randint as randint,
+    randn as randn,
+    random as random,
+    random_integers as random_integers,
+    random_sample as random_sample,
+    ranf as ranf,
+    rayleigh as rayleigh,
+    sample as sample,
+    seed as seed,
+    set_bit_generator as set_bit_generator,
+    set_state as set_state,
+    shuffle as shuffle,
+    standard_cauchy as standard_cauchy,
+    standard_exponential as standard_exponential,
+    standard_gamma as standard_gamma,
+    standard_normal as standard_normal,
+    standard_t as standard_t,
+    triangular as triangular,
+    uniform as uniform,
+    vonmises as vonmises,
+    wald as wald,
+    weibull as weibull,
+    zipf as zipf,
+)
+
+__all__: list[str]
+__path__: list[str]
+test: PytestTester
diff --git a/lib/python3.11/site-packages/numpy/random/_bounded_integers.pxd b/lib/python3.11/site-packages/numpy/random/_bounded_integers.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..7e41463a903e0238d18c553b295c39b6ed8af938
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_bounded_integers.pxd
@@ -0,0 +1,29 @@
+from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
+                          int8_t, int16_t, int32_t, int64_t, intptr_t)
+import numpy as np
+cimport numpy as np
+ctypedef np.npy_bool bool_t
+
+from numpy.random cimport bitgen_t
+
+cdef inline uint64_t _gen_mask(uint64_t max_val) nogil:
+    """Mask generator for use in bounded random numbers"""
+    # Smallest bit mask >= max
+    cdef uint64_t mask = max_val
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+    mask |= mask >> 32
+    return mask
+
+cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
diff --git a/lib/python3.11/site-packages/numpy/random/_common.pxd b/lib/python3.11/site-packages/numpy/random/_common.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..659da0d2daa789089d4d7987c885161237b85141
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_common.pxd
@@ -0,0 +1,106 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t
+
+import numpy as np
+cimport numpy as np
+
+from numpy.random cimport bitgen_t
+
+cdef double POISSON_LAM_MAX
+cdef double LEGACY_POISSON_LAM_MAX
+cdef uint64_t MAXSIZE
+
+cdef enum ConstraintType:
+    CONS_NONE
+    CONS_NON_NEGATIVE
+    CONS_POSITIVE
+    CONS_POSITIVE_NOT_NAN
+    CONS_BOUNDED_0_1
+    CONS_BOUNDED_GT_0_1
+    CONS_BOUNDED_LT_0_1
+    CONS_GT_1
+    CONS_GTE_1
+    CONS_POISSON
+    LEGACY_CONS_POISSON
+
+ctypedef ConstraintType constraint_type
+
+cdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)
+cdef object random_raw(bitgen_t *bitgen, object lock, object size, object output)
+cdef object prepare_cffi(bitgen_t *bitgen)
+cdef object prepare_ctypes(bitgen_t *bitgen)
+cdef int check_constraint(double val, object name, constraint_type cons) except -1
+cdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1
+
+cdef extern from "include/aligned_malloc.h":
+    cdef void *PyArray_realloc_aligned(void *p, size_t n)
+    cdef void *PyArray_malloc_aligned(size_t n)
+    cdef void *PyArray_calloc_aligned(size_t n, size_t s)
+    cdef void PyArray_free_aligned(void *p)
+
+ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out)  noexcept nogil
+ctypedef double (*random_double_0)(void *state)  noexcept nogil
+ctypedef double (*random_double_1)(void *state, double a)  noexcept nogil
+ctypedef double (*random_double_2)(void *state, double a, double b)  noexcept nogil
+ctypedef double (*random_double_3)(void *state, double a, double b, double c)  noexcept nogil
+
+ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out)  noexcept nogil
+ctypedef float (*random_float_0)(bitgen_t *state)  noexcept nogil
+ctypedef float (*random_float_1)(bitgen_t *state, float a)  noexcept nogil
+
+ctypedef int64_t (*random_uint_0)(void *state)  noexcept nogil
+ctypedef int64_t (*random_uint_d)(void *state, double a)  noexcept nogil
+ctypedef int64_t (*random_uint_dd)(void *state, double a, double b)  noexcept nogil
+ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b)  noexcept nogil
+ctypedef int64_t (*random_uint_i)(void *state, int64_t a)  noexcept nogil
+ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c)  noexcept nogil
+
+ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state)  noexcept nogil
+ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a)  noexcept nogil
+
+ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b)  noexcept nogil
+ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b)  noexcept nogil
+
+cdef double kahan_sum(double *darr, np.npy_intp n) noexcept
+
+cdef inline double uint64_to_double(uint64_t rnd) noexcept nogil:
+    return (rnd >> 11) * (1.0 / 9007199254740992.0)
+
+cdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill_from_double(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object wrap_int(object val, object bits)
+
+cdef np.ndarray int_to_array(object value, object name, object bits, object uint_size)
+
+cdef validate_output_shape(iter_shape, np.ndarray output)
+
+cdef object cont(void *func, void *state, object size, object lock, int narg,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint,
+                 object out)
+
+cdef object disc(void *func, void *state, object size, object lock,
+                 int narg_double, int narg_int64,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint)
+
+cdef object cont_f(void *func, bitgen_t *state, object size, object lock,
+                   object a, object a_name, constraint_type a_constraint,
+                   object out)
+
+cdef object cont_broadcast_3(void *func, void *state, object size, object lock,
+                             np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                             np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                             np.ndarray c_arr, object c_name, constraint_type c_constraint)
+
+cdef object discrete_broadcast_iii(void *func, void *state, object size, object lock,
+                                   np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                                   np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                                   np.ndarray c_arr, object c_name, constraint_type c_constraint)
diff --git a/lib/python3.11/site-packages/numpy/random/_generator.pyi b/lib/python3.11/site-packages/numpy/random/_generator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e1cdefb15b772149ee390bf201893718e2191f17
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_generator.pyi
@@ -0,0 +1,681 @@
+from collections.abc import Callable
+from typing import Any, Union, overload, TypeVar, Literal
+
+from numpy import (
+    bool_,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    ndarray,
+    uint,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+from numpy.random import BitGenerator, SeedSequence
+from numpy._typing import (
+    ArrayLike,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DoubleCodes,
+    _DTypeLikeBool,
+    _DTypeLikeInt,
+    _DTypeLikeUInt,
+    _Float32Codes,
+    _Float64Codes,
+    _FloatLike_co,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+)
+
+_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
+
+_DTypeLikeFloat32 = Union[
+    dtype[float32],
+    _SupportsDType[dtype[float32]],
+    type[float32],
+    _Float32Codes,
+    _SingleCodes,
+]
+
+_DTypeLikeFloat64 = Union[
+    dtype[float64],
+    _SupportsDType[dtype[float64]],
+    type[float],
+    type[float64],
+    _Float64Codes,
+    _DoubleCodes,
+]
+
+class Generator:
+    def __init__(self, bit_generator: BitGenerator) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ...
+    @property
+    def bit_generator(self) -> BitGenerator: ...
+    def spawn(self, n_children: int) -> list[Generator]: ...
+    def bytes(self, length: int) -> bytes: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]: ...
+    @overload
+    def standard_exponential(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def random(
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def beta(
+        self,
+        a: _FloatLike_co,
+        b: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def exponential(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+    ) -> int: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeBool = ...,
+        endpoint: bool = ...,
+    ) -> bool: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
+        endpoint: bool = ...,
+    ) -> int: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[bool_]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int8]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int16]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int32]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint8]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint16]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint32]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint]]: ...
+    # TODO: Use a TypeVar _T here to get away from Any output?  Should be int->ndarray[Any,dtype[int64]], ArrayLike[_T] -> _T | ndarray[Any,Any]
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> ndarray[Any, Any]: ...
+    @overload
+    def uniform(
+        self,
+        low: _FloatLike_co = ...,
+        high: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def normal(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: _FloatLike_co,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gamma(self, shape: _FloatLike_co, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_f(self, dfnum: _FloatLike_co, dfden: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def chisquare(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_chisquare(self, df: _FloatLike_co, nonc: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(self, df: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def vonmises(self, mu: _FloatLike_co, kappa: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def pareto(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def weibull(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def power(self, a: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def laplace(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gumbel(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def logistic(
+        self,
+        loc: _FloatLike_co = ...,
+        scale: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def lognormal(
+        self,
+        mean: _FloatLike_co = ...,
+        sigma: _FloatLike_co = ...,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rayleigh(self, scale: _FloatLike_co = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def wald(self, mean: _FloatLike_co, scale: _FloatLike_co, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def triangular(
+        self,
+        left: _FloatLike_co,
+        mode: _FloatLike_co,
+        right: _FloatLike_co,
+        size: None = ...,
+    ) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def binomial(self, n: int, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def negative_binomial(self, n: _FloatLike_co, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def poisson(self, lam: _FloatLike_co = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def zipf(self, a: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def geometric(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def logseries(self, p: _FloatLike_co, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+        *,
+        method: Literal["svd", "eigh", "cholesky"] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co,
+            pvals: _ArrayLikeFloat_co,
+            size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def multivariate_hypergeometric(
+        self,
+        colors: _ArrayLikeInt_co,
+        nsample: int,
+        size: None | _ShapeLike = ...,
+        method: Literal["marginals", "count"] = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def permuted(
+        self, x: ArrayLike, *, axis: None | int = ..., out: None | ndarray[Any, Any] = ...
+    ) -> ndarray[Any, Any]: ...
+    def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...
+
+def default_rng(
+    seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator = ...
+) -> Generator: ...
diff --git a/lib/python3.11/site-packages/numpy/random/_mt19937.pyi b/lib/python3.11/site-packages/numpy/random/_mt19937.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..55cfb2db42b17a80a1e15b6f8eabee1ccbdee282
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_mt19937.pyi
@@ -0,0 +1,22 @@
+from typing import Any, TypedDict
+
+from numpy import dtype, ndarray, uint32
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _MT19937Internal(TypedDict):
+    key: ndarray[Any, dtype[uint32]]
+    pos: int
+
+class _MT19937State(TypedDict):
+    bit_generator: str
+    state: _MT19937Internal
+
+class MT19937(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ...
+    def jumped(self, jumps: int = ...) -> MT19937: ...
+    @property
+    def state(self) -> _MT19937State: ...
+    @state.setter
+    def state(self, value: _MT19937State) -> None: ...
diff --git a/lib/python3.11/site-packages/numpy/random/_pcg64.pyi b/lib/python3.11/site-packages/numpy/random/_pcg64.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..470aee867493b48817670f7c4ff7b24d8be31f26
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_pcg64.pyi
@@ -0,0 +1,42 @@
+from typing import TypedDict
+
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _PCG64Internal(TypedDict):
+    state: int
+    inc: int
+
+class _PCG64State(TypedDict):
+    bit_generator: str
+    state: _PCG64Internal
+    has_uint32: int
+    uinteger: int
+
+class PCG64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64: ...
+
+class PCG64DXSM(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64DXSM: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64DXSM: ...
diff --git a/lib/python3.11/site-packages/numpy/random/_philox.pyi b/lib/python3.11/site-packages/numpy/random/_philox.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..26ce726ecf4a6f0e9fda4d596368a05da5629124
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_philox.pyi
@@ -0,0 +1,36 @@
+from typing import Any, TypedDict
+
+from numpy import dtype, ndarray, uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _PhiloxInternal(TypedDict):
+    counter: ndarray[Any, dtype[uint64]]
+    key: ndarray[Any, dtype[uint64]]
+
+class _PhiloxState(TypedDict):
+    bit_generator: str
+    state: _PhiloxInternal
+    buffer: ndarray[Any, dtype[uint64]]
+    buffer_pos: int
+    has_uint32: int
+    uinteger: int
+
+class Philox(BitGenerator):
+    def __init__(
+        self,
+        seed: None | _ArrayLikeInt_co | SeedSequence = ...,
+        counter: None | _ArrayLikeInt_co = ...,
+        key: None | _ArrayLikeInt_co = ...,
+    ) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _PhiloxState: ...
+    @state.setter
+    def state(
+        self,
+        value: _PhiloxState,
+    ) -> None: ...
+    def jumped(self, jumps: int = ...) -> Philox: ...
+    def advance(self, delta: int) -> Philox: ...
diff --git a/lib/python3.11/site-packages/numpy/random/_pickle.py b/lib/python3.11/site-packages/numpy/random/_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..073993726eb30b48c7e5f9d6eba3299a14829fa3
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_pickle.py
@@ -0,0 +1,80 @@
+from .mtrand import RandomState
+from ._philox import Philox
+from ._pcg64 import PCG64, PCG64DXSM
+from ._sfc64 import SFC64
+
+from ._generator import Generator
+from ._mt19937 import MT19937
+
+BitGenerators = {'MT19937': MT19937,
+                 'PCG64': PCG64,
+                 'PCG64DXSM': PCG64DXSM,
+                 'Philox': Philox,
+                 'SFC64': SFC64,
+                 }
+
+
+def __bit_generator_ctor(bit_generator_name='MT19937'):
+    """
+    Pickling helper function that returns a bit generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the name of the BitGenerator
+
+    Returns
+    -------
+    bit_generator : BitGenerator
+        BitGenerator instance
+    """
+    if bit_generator_name in BitGenerators:
+        bit_generator = BitGenerators[bit_generator_name]
+    else:
+        raise ValueError(str(bit_generator_name) + ' is not a known '
+                                                   'BitGenerator module.')
+
+    return bit_generator()
+
+
+def __generator_ctor(bit_generator_name="MT19937",
+                     bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a Generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator's name
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rg : Generator
+        Generator using the named core BitGenerator
+    """
+    return Generator(bit_generator_ctor(bit_generator_name))
+
+
+def __randomstate_ctor(bit_generator_name="MT19937",
+                       bit_generator_ctor=__bit_generator_ctor):
+    """
+    Pickling helper function that returns a legacy RandomState-like object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator's name
+    bit_generator_ctor : callable, optional
+        Callable function that takes bit_generator_name as its only argument
+        and returns an instantized bit generator.
+
+    Returns
+    -------
+    rs : RandomState
+        Legacy RandomState using the named core BitGenerator
+    """
+
+    return RandomState(bit_generator_ctor(bit_generator_name))
diff --git a/lib/python3.11/site-packages/numpy/random/_sfc64.cpython-311-x86_64-linux-gnu.so b/lib/python3.11/site-packages/numpy/random/_sfc64.cpython-311-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..fd917c457b430126ebfd0a56654bfdc5787ecc2a
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/_sfc64.cpython-311-x86_64-linux-gnu.so differ
diff --git a/lib/python3.11/site-packages/numpy/random/_sfc64.pyi b/lib/python3.11/site-packages/numpy/random/_sfc64.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e1810e7d5261490d83ba1ec8f4f3df863837a5f0
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/_sfc64.pyi
@@ -0,0 +1,28 @@
+from typing import Any, TypedDict
+
+from numpy import dtype as dtype
+from numpy import ndarray as ndarray
+from numpy import uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _SFC64Internal(TypedDict):
+    state: ndarray[Any, dtype[uint64]]
+
+class _SFC64State(TypedDict):
+    bit_generator: str
+    state: _SFC64Internal
+    has_uint32: int
+    uinteger: int
+
+class SFC64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _SFC64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _SFC64State,
+    ) -> None: ...
diff --git a/lib/python3.11/site-packages/numpy/random/bit_generator.pxd b/lib/python3.11/site-packages/numpy/random/bit_generator.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..dfa7d0a71c085dfa3dfb2819f47493cb8501d198
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/bit_generator.pxd
@@ -0,0 +1,35 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+cdef class BitGenerator():
+    cdef readonly object _seed_seq
+    cdef readonly object lock
+    cdef bitgen_t _bitgen
+    cdef readonly object _ctypes
+    cdef readonly object _cffi
+    cdef readonly object capsule
+
+
+cdef class SeedSequence():
+    cdef readonly object entropy
+    cdef readonly tuple spawn_key
+    cdef readonly Py_ssize_t pool_size
+    cdef readonly object pool
+    cdef readonly uint32_t n_children_spawned
+
+    cdef mix_entropy(self, np.ndarray[np.npy_uint32, ndim=1] mixer,
+                     np.ndarray[np.npy_uint32, ndim=1] entropy_array)
+    cdef get_assembled_entropy(self)
+
+cdef class SeedlessSequence():
+    pass
diff --git a/lib/python3.11/site-packages/numpy/random/bit_generator.pyi b/lib/python3.11/site-packages/numpy/random/bit_generator.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8b9779cade317a2e5d213f8cf499cf86fdb1cfec
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/bit_generator.pyi
@@ -0,0 +1,112 @@
+import abc
+from threading import Lock
+from collections.abc import Callable, Mapping, Sequence
+from typing import (
+    Any,
+    NamedTuple,
+    TypedDict,
+    TypeVar,
+    Union,
+    overload,
+    Literal,
+)
+
+from numpy import dtype, ndarray, uint32, uint64
+from numpy._typing import _ArrayLikeInt_co, _ShapeLike, _SupportsDType, _UInt32Codes, _UInt64Codes
+
+_T = TypeVar("_T")
+
+_DTypeLikeUint32 = Union[
+    dtype[uint32],
+    _SupportsDType[dtype[uint32]],
+    type[uint32],
+    _UInt32Codes,
+]
+_DTypeLikeUint64 = Union[
+    dtype[uint64],
+    _SupportsDType[dtype[uint64]],
+    type[uint64],
+    _UInt64Codes,
+]
+
+class _SeedSeqState(TypedDict):
+    entropy: None | int | Sequence[int]
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+
+class _Interface(NamedTuple):
+    state_address: Any
+    state: Any
+    next_uint64: Any
+    next_uint32: Any
+    next_double: Any
+    bit_generator: Any
+
+class ISeedSequence(abc.ABC):
+    @abc.abstractmethod
+    def generate_state(
+        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
+    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
+
+class ISpawnableSeedSequence(ISeedSequence):
+    @abc.abstractmethod
+    def spawn(self: _T, n_children: int) -> list[_T]: ...
+
+class SeedlessSeedSequence(ISpawnableSeedSequence):
+    def generate_state(
+        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
+    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
+    def spawn(self: _T, n_children: int) -> list[_T]: ...
+
+class SeedSequence(ISpawnableSeedSequence):
+    entropy: None | int | Sequence[int]
+    spawn_key: tuple[int, ...]
+    pool_size: int
+    n_children_spawned: int
+    pool: ndarray[Any, dtype[uint32]]
+    def __init__(
+        self,
+        entropy: None | int | Sequence[int] | _ArrayLikeInt_co = ...,
+        *,
+        spawn_key: Sequence[int] = ...,
+        pool_size: int = ...,
+        n_children_spawned: int = ...,
+    ) -> None: ...
+    def __repr__(self) -> str: ...
+    @property
+    def state(
+        self,
+    ) -> _SeedSeqState: ...
+    def generate_state(
+        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
+    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
+    def spawn(self, n_children: int) -> list[SeedSequence]: ...
+
+class BitGenerator(abc.ABC):
+    lock: Lock
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(
+        self,
+    ) -> tuple[Callable[[str], BitGenerator], tuple[str], tuple[dict[str, Any]]]: ...
+    @abc.abstractmethod
+    @property
+    def state(self) -> Mapping[str, Any]: ...
+    @state.setter
+    def state(self, value: Mapping[str, Any]) -> None: ...
+    @property
+    def seed_seq(self) -> ISeedSequence: ...
+    def spawn(self, n_children: int) -> list[BitGenerator]: ...
+    @overload
+    def random_raw(self, size: None = ..., output: Literal[True] = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def random_raw(self, size: _ShapeLike = ..., output: Literal[True] = ...) -> ndarray[Any, dtype[uint64]]: ...  # type: ignore[misc]
+    @overload
+    def random_raw(self, size: None | _ShapeLike = ..., output: Literal[False] = ...) -> None: ...  # type: ignore[misc]
+    def _benchmark(self, cnt: int, method: str = ...) -> None: ...
+    @property
+    def ctypes(self) -> _Interface: ...
+    @property
+    def cffi(self) -> _Interface: ...
diff --git a/lib/python3.11/site-packages/numpy/random/c_distributions.pxd b/lib/python3.11/site-packages/numpy/random/c_distributions.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..b978d13503eaff74ec54a2ad7a9813b24dab68d2
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/c_distributions.pxd
@@ -0,0 +1,120 @@
+#!python
+#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
+from numpy cimport npy_intp
+
+from libc.stdint cimport (uint64_t, int32_t, int64_t)
+from numpy.random cimport bitgen_t
+
+cdef extern from "numpy/random/distributions.h":
+
+    struct s_binomial_t:
+        int has_binomial
+        double psave
+        int64_t nsave
+        double r
+        double q
+        double fm
+        int64_t m
+        double p1
+        double xm
+        double xl
+        double xr
+        double c
+        double laml
+        double lamr
+        double p2
+        double p3
+        double p4
+
+    ctypedef s_binomial_t binomial_t
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    double random_standard_uniform(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_exponential(bitgen_t *bitgen_state) nogil
+    float random_standard_exponential_f(bitgen_t *bitgen_state) nogil
+    void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_normal(bitgen_t* bitgen_state) nogil
+    float random_standard_normal_f(bitgen_t *bitgen_state) nogil
+    void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil
+    void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil
+    double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil
+    float random_standard_normal_f(bitgen_t* bitgen_state) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    int64_t random_positive_int64(bitgen_t *bitgen_state) nogil
+    int32_t random_positive_int32(bitgen_t *bitgen_state) nogil
+    int64_t random_positive_int(bitgen_t *bitgen_state) nogil
+    uint64_t random_uint(bitgen_t *bitgen_state) nogil
+
+    double random_normal(bitgen_t *bitgen_state, double loc, double scale) nogil
+
+    double random_gamma(bitgen_t *bitgen_state, double shape, double scale) nogil
+    float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) nogil
+
+    double random_exponential(bitgen_t *bitgen_state, double scale) nogil
+    double random_uniform(bitgen_t *bitgen_state, double lower, double range) nogil
+    double random_beta(bitgen_t *bitgen_state, double a, double b) nogil
+    double random_chisquare(bitgen_t *bitgen_state, double df) nogil
+    double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) nogil
+    double random_standard_cauchy(bitgen_t *bitgen_state) nogil
+    double random_pareto(bitgen_t *bitgen_state, double a) nogil
+    double random_weibull(bitgen_t *bitgen_state, double a) nogil
+    double random_power(bitgen_t *bitgen_state, double a) nogil
+    double random_laplace(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_gumbel(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_logistic(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) nogil
+    double random_rayleigh(bitgen_t *bitgen_state, double mode) nogil
+    double random_standard_t(bitgen_t *bitgen_state, double df) nogil
+    double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
+                                       double nonc) nogil
+    double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
+                               double dfden, double nonc) nogil
+    double random_wald(bitgen_t *bitgen_state, double mean, double scale) nogil
+    double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa) nogil
+    double random_triangular(bitgen_t *bitgen_state, double left, double mode,
+                             double right) nogil
+
+    int64_t random_poisson(bitgen_t *bitgen_state, double lam) nogil
+    int64_t random_negative_binomial(bitgen_t *bitgen_state, double n, double p) nogil
+    int64_t random_binomial(bitgen_t *bitgen_state, double p, int64_t n, binomial_t *binomial) nogil
+    int64_t random_logseries(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_search(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_zipf(bitgen_t *bitgen_state, double a) nogil
+    int64_t random_hypergeometric(bitgen_t *bitgen_state, int64_t good, int64_t bad,
+                                    int64_t sample) nogil
+
+    uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) nogil
+
+    # Generate random uint64 numbers in closed interval [off, off + rng].
+    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
+                                   uint64_t off, uint64_t rng,
+                                   uint64_t mask, bint use_masked) nogil
+
+    void random_multinomial(bitgen_t *bitgen_state, int64_t n, int64_t *mnix,
+                            double *pix, npy_intp d, binomial_t *binomial) nogil
+
+    int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
+                          int64_t total,
+                          size_t num_colors, int64_t *colors,
+                          int64_t nsample,
+                          size_t num_variates, int64_t *variates) nogil
+    void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
+                               int64_t total,
+                               size_t num_colors, int64_t *colors,
+                               int64_t nsample,
+                               size_t num_variates, int64_t *variates) nogil
+
diff --git a/lib/python3.11/site-packages/numpy/random/lib/libnpyrandom.a b/lib/python3.11/site-packages/numpy/random/lib/libnpyrandom.a
new file mode 100644
index 0000000000000000000000000000000000000000..96bd444f59250fb0219138f3f5729972b78e2220
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/lib/libnpyrandom.a differ
diff --git a/lib/python3.11/site-packages/numpy/random/mtrand.pyi b/lib/python3.11/site-packages/numpy/random/mtrand.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b5f600652b5444ce647108590c89626250f8bae8
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/mtrand.pyi
@@ -0,0 +1,571 @@
+import builtins
+from collections.abc import Callable
+from typing import Any, Union, overload, Literal
+
+from numpy import (
+    bool_,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    ndarray,
+    uint,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+from numpy.random.bit_generator import BitGenerator
+from numpy._typing import (
+    ArrayLike,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DoubleCodes,
+    _DTypeLikeBool,
+    _DTypeLikeInt,
+    _DTypeLikeUInt,
+    _Float32Codes,
+    _Float64Codes,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+)
+
+_DTypeLikeFloat32 = Union[
+    dtype[float32],
+    _SupportsDType[dtype[float32]],
+    type[float32],
+    _Float32Codes,
+    _SingleCodes,
+]
+
+_DTypeLikeFloat64 = Union[
+    dtype[float64],
+    _SupportsDType[dtype[float64]],
+    type[float],
+    type[float64],
+    _Float64Codes,
+    _DoubleCodes,
+]
+
+class RandomState:
+    _bit_generator: BitGenerator
+    def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], RandomState], tuple[str], dict[str, Any]]: ...
+    def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ...
+    @overload
+    def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...
+    @overload
+    def get_state(
+        self, legacy: Literal[True] = ...
+    ) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]: ...
+    def set_state(
+        self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]
+    ) -> None: ...
+    @overload
+    def random_sample(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def tomaxint(self, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeBool = ...,
+    ) -> bool: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
+    ) -> int: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+    ) -> ndarray[Any, dtype[bool_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+    ) -> ndarray[Any, dtype[int8]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+    ) -> ndarray[Any, dtype[int16]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+    ) -> ndarray[Any, dtype[int32]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+    ) -> ndarray[Any, dtype[uint8]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+    ) -> ndarray[Any, dtype[uint16]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+    ) -> ndarray[Any, dtype[uint32]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+    ) -> ndarray[Any, dtype[uint64]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def randint(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+    ) -> ndarray[Any, dtype[uint]]: ...
+    def bytes(self, length: int) -> builtins.bytes: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+    ) -> ndarray[Any, Any]: ...
+    @overload
+    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rand(self) -> float: ...
+    @overload
+    def rand(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def randn(self) -> float: ...
+    @overload
+    def randn(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def random_integers(
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def standard_normal(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: float,
+        size: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int_]]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def shuffle(self, x: ArrayLike) -> None: ...
+    @overload
+    def permutation(self, x: int) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def permutation(self, x: ArrayLike) -> ndarray[Any, Any]: ...
+
+_rand: RandomState
+
+beta = _rand.beta
+binomial = _rand.binomial
+bytes = _rand.bytes
+chisquare = _rand.chisquare
+choice = _rand.choice
+dirichlet = _rand.dirichlet
+exponential = _rand.exponential
+f = _rand.f
+gamma = _rand.gamma
+get_state = _rand.get_state
+geometric = _rand.geometric
+gumbel = _rand.gumbel
+hypergeometric = _rand.hypergeometric
+laplace = _rand.laplace
+logistic = _rand.logistic
+lognormal = _rand.lognormal
+logseries = _rand.logseries
+multinomial = _rand.multinomial
+multivariate_normal = _rand.multivariate_normal
+negative_binomial = _rand.negative_binomial
+noncentral_chisquare = _rand.noncentral_chisquare
+noncentral_f = _rand.noncentral_f
+normal = _rand.normal
+pareto = _rand.pareto
+permutation = _rand.permutation
+poisson = _rand.poisson
+power = _rand.power
+rand = _rand.rand
+randint = _rand.randint
+randn = _rand.randn
+random = _rand.random
+random_integers = _rand.random_integers
+random_sample = _rand.random_sample
+rayleigh = _rand.rayleigh
+seed = _rand.seed
+set_state = _rand.set_state
+shuffle = _rand.shuffle
+standard_cauchy = _rand.standard_cauchy
+standard_exponential = _rand.standard_exponential
+standard_gamma = _rand.standard_gamma
+standard_normal = _rand.standard_normal
+standard_t = _rand.standard_t
+triangular = _rand.triangular
+uniform = _rand.uniform
+vonmises = _rand.vonmises
+wald = _rand.wald
+weibull = _rand.weibull
+zipf = _rand.zipf
+# Two legacy that are trivial wrappers around random_sample
+sample = _rand.random_sample
+ranf = _rand.random_sample
+
+def set_bit_generator(bitgen: BitGenerator) -> None:
+    ...
+
+def get_bit_generator() -> BitGenerator:
+    ...
diff --git a/lib/python3.11/site-packages/numpy/random/tests/__init__.py b/lib/python3.11/site-packages/numpy/random/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/numpy/random/tests/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9597aa32cdf998d5a767cfbecc1d26c228a1140d
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_direct.cpython-311.pyc b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_direct.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..555c117f9779472ec08ab07cbe4cb5b64c55d03a
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_direct.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_extending.cpython-311.pyc b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_extending.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..81e70d534b1b5e8fd10b5fb098373d58d1ec5070
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_extending.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_generator_mt19937_regressions.cpython-311.pyc b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_generator_mt19937_regressions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7963c00b0356ca7e3f845d476c38e39f3490723e
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/tests/__pycache__/test_generator_mt19937_regressions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/__init__.py b/lib/python3.11/site-packages/numpy/random/tests/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/numpy/random/tests/data/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a5371742db80e12b1ec8971cf9c38a594875baf5
Binary files /dev/null and b/lib/python3.11/site-packages/numpy/random/tests/data/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/mt19937-testset-1.csv b/lib/python3.11/site-packages/numpy/random/tests/data/mt19937-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b97bfa66f72f1be31cdb32317905d805fe90f4d5
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/mt19937-testset-1.csv
@@ -0,0 +1,1001 @@
+seed, 0xdeadbeaf
+0, 0xc816921f
+1, 0xb3623c6d
+2, 0x5fa391bb
+3, 0x40178d9
+4, 0x7dcc9811
+5, 0x548eb8e6
+6, 0x92ba3125
+7, 0x65fde68d
+8, 0x2f81ec95
+9, 0xbd94f7a2
+10, 0xdc4d9bcc
+11, 0xa672bf13
+12, 0xb41113e
+13, 0xec7e0066
+14, 0x50239372
+15, 0xd9d66b1d
+16, 0xab72a161
+17, 0xddc2e29f
+18, 0x7ea29ab4
+19, 0x80d141ba
+20, 0xb1c7edf1
+21, 0x44d29203
+22, 0xe224d98
+23, 0x5b3e9d26
+24, 0x14fd567c
+25, 0x27d98c96
+26, 0x838779fc
+27, 0x92a138a
+28, 0x5d08965b
+29, 0x531e0ad6
+30, 0x984ee8f4
+31, 0x1ed78539
+32, 0x32bd6d8d
+33, 0xc37c8516
+34, 0x9aef5c6b
+35, 0x3aacd139
+36, 0xd96ed154
+37, 0x489cd1ed
+38, 0x2cba4b3b
+39, 0x76c6ae72
+40, 0x2dae02b9
+41, 0x52ac5fd6
+42, 0xc2b5e265
+43, 0x630e6a28
+44, 0x3f560d5d
+45, 0x9315bdf3
+46, 0xf1055aba
+47, 0x840e42c6
+48, 0xf2099c6b
+49, 0x15ff7696
+50, 0x7948d146
+51, 0x97342961
+52, 0x7a7a21c
+53, 0xc66f4fb1
+54, 0x23c4103e
+55, 0xd7321f98
+56, 0xeb7efb75
+57, 0xe02490b5
+58, 0x2aa02de
+59, 0x8bee0bf7
+60, 0xfc2da059
+61, 0xae835034
+62, 0x678f2075
+63, 0x6d03094b
+64, 0x56455e05
+65, 0x18b32373
+66, 0x8ff0356b
+67, 0x1fe442fb
+68, 0x3f1ab6c3
+69, 0xb6fd21b
+70, 0xfc310eb2
+71, 0xb19e9a4d
+72, 0x17ddee72
+73, 0xfd534251
+74, 0x9e500564
+75, 0x9013a036
+76, 0xcf08f118
+77, 0x6b6d5969
+78, 0x3ccf1977
+79, 0x7cc11497
+80, 0x651c6ac9
+81, 0x4d6b104b
+82, 0x9a28314e
+83, 0x14c237be
+84, 0x9cfc8d52
+85, 0x2947fad5
+86, 0xd71eff49
+87, 0x5188730e
+88, 0x4b894614
+89, 0xf4fa2a34
+90, 0x42f7cc69
+91, 0x4089c9e8
+92, 0xbf0bbfe4
+93, 0x3cea65c
+94, 0xc6221207
+95, 0x1bb71a8f
+96, 0x54843fe7
+97, 0xbc59de4c
+98, 0x79c6ee64
+99, 0x14e57a26
+100, 0x68d88fe
+101, 0x2b86ef64
+102, 0x8ffff3c1
+103, 0x5bdd573f
+104, 0x85671813
+105, 0xefe32ca2
+106, 0x105ded1e
+107, 0x90ca2769
+108, 0xb33963ac
+109, 0x363fbbc3
+110, 0x3b3763ae
+111, 0x1d50ab88
+112, 0xc9ec01eb
+113, 0xc8bbeada
+114, 0x5d704692
+115, 0x5fd9e40
+116, 0xe61c125
+117, 0x2fe05792
+118, 0xda8afb72
+119, 0x4cbaa653
+120, 0xdd2243df
+121, 0x896fd3f5
+122, 0x5bc23db
+123, 0xa1c4e807
+124, 0x57d1a24d
+125, 0x66503ddc
+126, 0xcf7c0838
+127, 0x19e034fc
+128, 0x66807450
+129, 0xfc219b3b
+130, 0xe8a843e7
+131, 0x9ce61f08
+132, 0x92b950d6
+133, 0xce955ec4
+134, 0xda0d1f0d
+135, 0x960c6250
+136, 0x39552432
+137, 0xde845e84
+138, 0xff3b4b11
+139, 0x5d918e6f
+140, 0xbb930df2
+141, 0x7cfb0993
+142, 0x5400e1e9
+143, 0x3bfa0954
+144, 0x7e2605fb
+145, 0x11941591
+146, 0x887e6994
+147, 0xdc8bed45
+148, 0x45b3fb50
+149, 0xfbdf8358
+150, 0x41507468
+151, 0x34c87166
+152, 0x17f64d77
+153, 0x3bbaf4f8
+154, 0x4f26f37e
+155, 0x4a56ebf2
+156, 0x81100f1
+157, 0x96d94eae
+158, 0xca88fda5
+159, 0x2eef3a60
+160, 0x952afbd3
+161, 0x2bec88c7
+162, 0x52335c4b
+163, 0x8296db8e
+164, 0x4da7d00a
+165, 0xc00ac899
+166, 0xadff8c72
+167, 0xbecf26cf
+168, 0x8835c83c
+169, 0x1d13c804
+170, 0xaa940ddc
+171, 0x68222cfe
+172, 0x4569c0e1
+173, 0x29077976
+174, 0x32d4a5af
+175, 0xd31fcdef
+176, 0xdc60682b
+177, 0x7c95c368
+178, 0x75a70213
+179, 0x43021751
+180, 0x5e52e0a6
+181, 0xf7e190b5
+182, 0xee3e4bb
+183, 0x2fe3b150
+184, 0xcf419c07
+185, 0x478a4570
+186, 0xe5c3ea50
+187, 0x417f30a8
+188, 0xf0cfdaa0
+189, 0xd1f7f738
+190, 0x2c70fc23
+191, 0x54fc89f9
+192, 0x444dcf01
+193, 0xec2a002d
+194, 0xef0c3a88
+195, 0xde21be9
+196, 0x88ab3296
+197, 0x3028897c
+198, 0x264b200b
+199, 0xd8ae0706
+200, 0x9eef901a
+201, 0xbd1b96e0
+202, 0xea71366c
+203, 0x1465b694
+204, 0x5a794650
+205, 0x83df52d4
+206, 0x8262413d
+207, 0x5bc148c0
+208, 0xe0ecd80c
+209, 0x40649571
+210, 0xb4d2ee5f
+211, 0xedfd7d09
+212, 0xa082e25f
+213, 0xc62992d1
+214, 0xbc7e65ee
+215, 0x5499cf8a
+216, 0xac28f775
+217, 0x649840fb
+218, 0xd4c54805
+219, 0x1d166ba6
+220, 0xbeb1171f
+221, 0x45b66703
+222, 0x78c03349
+223, 0x38d2a6ff
+224, 0x935cae8b
+225, 0x1d07dc3f
+226, 0x6c1ed365
+227, 0x579fc585
+228, 0x1320c0ec
+229, 0x632757eb
+230, 0xd265a397
+231, 0x70e9b6c2
+232, 0xc81e322c
+233, 0xa27153cf
+234, 0x2118ba19
+235, 0x514ec400
+236, 0x2bd0ecd6
+237, 0xc3e7dae3
+238, 0xfa39355e
+239, 0x48f23cc1
+240, 0xbcf75948
+241, 0x53ccc70c
+242, 0x75346423
+243, 0x951181e0
+244, 0x348e90df
+245, 0x14365d7f
+246, 0xfbc95d7a
+247, 0xdc98a9e6
+248, 0xed202df7
+249, 0xa59ec913
+250, 0x6b6e9ae2
+251, 0x1697f265
+252, 0x15d322d0
+253, 0xa2e7ee0a
+254, 0x88860b7e
+255, 0x455d8b9d
+256, 0x2f5c59cb
+257, 0xac49c9f1
+258, 0xa6a6a039
+259, 0xc057f56b
+260, 0xf1ff1208
+261, 0x5eb8dc9d
+262, 0xe6702509
+263, 0xe238b0ed
+264, 0x5ae32e3d
+265, 0xa88ebbdf
+266, 0xef885ae7
+267, 0xafa6d49b
+268, 0xc94499e0
+269, 0x1a196325
+270, 0x88938da3
+271, 0x14f4345
+272, 0xd8e33637
+273, 0xa3551bd5
+274, 0x73fe35c7
+275, 0x9561e94b
+276, 0xd673bf68
+277, 0x16134872
+278, 0x68c42f9f
+279, 0xdf7574c8
+280, 0x8809bab9
+281, 0x1432cf69
+282, 0xafb66bf1
+283, 0xc184aa7b
+284, 0xedbf2007
+285, 0xbd420ce1
+286, 0x761033a0
+287, 0xff7e351f
+288, 0xd6c3780e
+289, 0x5844416f
+290, 0xc6c0ee1c
+291, 0xd2e147db
+292, 0x92ac601a
+293, 0x393e846b
+294, 0x18196cca
+295, 0x54a22be
+296, 0x32bab1c4
+297, 0x60365183
+298, 0x64fa342
+299, 0xca24a493
+300, 0xd8cc8b83
+301, 0x3faf102b
+302, 0x6e09bb58
+303, 0x812f0ea
+304, 0x592c95d8
+305, 0xe45ea4c5
+306, 0x23aebf83
+307, 0xbd9691d4
+308, 0xf47b4baa
+309, 0x4ac7b487
+310, 0xcce18803
+311, 0x3377556e
+312, 0x3ff8e6b6
+313, 0x99d22063
+314, 0x23250bec
+315, 0x4e1f9861
+316, 0x8554249b
+317, 0x8635c2fc
+318, 0xe8426e8a
+319, 0x966c29d8
+320, 0x270b6082
+321, 0x3180a8a1
+322, 0xe7e1668b
+323, 0x7f868dc
+324, 0xcf4c17cf
+325, 0xe31de4d1
+326, 0xc8c8aff4
+327, 0xae8db704
+328, 0x3c928cc2
+329, 0xe12cd48
+330, 0xb33ecd04
+331, 0xb93d7cbe
+332, 0x49c69d6a
+333, 0x7d3bce64
+334, 0x86bc219
+335, 0x8408233b
+336, 0x44dc7479
+337, 0xdf80d538
+338, 0xf3db02c3
+339, 0xbbbd31d7
+340, 0x121281f
+341, 0x7521e9a3
+342, 0x8859675a
+343, 0x75aa6502
+344, 0x430ed15b
+345, 0xecf0a28d
+346, 0x659774fd
+347, 0xd58a2311
+348, 0x512389a9
+349, 0xff65e1ff
+350, 0xb6ddf222
+351, 0xe3458895
+352, 0x8b13cd6e
+353, 0xd4a22870
+354, 0xe604c50c
+355, 0x27f54f26
+356, 0x8f7f422f
+357, 0x9735b4cf
+358, 0x414072b0
+359, 0x76a1c6d5
+360, 0xa2208c06
+361, 0x83cd0f61
+362, 0x6c4f7ead
+363, 0x6553cf76
+364, 0xeffcf44
+365, 0x7f434a3f
+366, 0x9dc364bd
+367, 0x3cdf52ed
+368, 0xad597594
+369, 0x9c3e211b
+370, 0x6c04a33f
+371, 0x885dafa6
+372, 0xbbdaca71
+373, 0x7ae5dd5c
+374, 0x37675644
+375, 0x251853c6
+376, 0x130b086b
+377, 0x143fa54b
+378, 0x54cdc282
+379, 0x9faff5b3
+380, 0x502a5c8b
+381, 0xd9524550
+382, 0xae221aa6
+383, 0x55cf759b
+384, 0x24782da4
+385, 0xd715d815
+386, 0x250ea09a
+387, 0x4e0744ac
+388, 0x11e15814
+389, 0xabe5f9df
+390, 0xc8146350
+391, 0xfba67d9b
+392, 0x2b82e42f
+393, 0xd4ea96fc
+394, 0x5ffc179e
+395, 0x1598bafe
+396, 0x7fb6d662
+397, 0x1a12a0db
+398, 0x450cee4a
+399, 0x85f8e12
+400, 0xce71b594
+401, 0xd4bb1d19
+402, 0x968f379d
+403, 0x54cc1d52
+404, 0x467e6066
+405, 0x7da5f9a9
+406, 0x70977034
+407, 0x49e65c4b
+408, 0xd08570d1
+409, 0x7acdf60b
+410, 0xdffa038b
+411, 0x9ce14e4c
+412, 0x107cbbf8
+413, 0xdd746ca0
+414, 0xc6370a46
+415, 0xe7f83312
+416, 0x373fa9ce
+417, 0xd822a2c6
+418, 0x1d4efea6
+419, 0xc53dcadb
+420, 0x9b4e898f
+421, 0x71daa6bf
+422, 0x7a0bc78b
+423, 0xd7b86f50
+424, 0x1b8b3286
+425, 0xcf9425dd
+426, 0xd5263220
+427, 0x4ea0b647
+428, 0xc767fe64
+429, 0xcfc5e67
+430, 0xcc6a2942
+431, 0xa51eff00
+432, 0x76092e1b
+433, 0xf606e80f
+434, 0x824b5e20
+435, 0xebb55e14
+436, 0x783d96a6
+437, 0x10696512
+438, 0x17ee510a
+439, 0x3ab70a1f
+440, 0xcce6b210
+441, 0x8f72f0fb
+442, 0xf0610b41
+443, 0x83d01fb5
+444, 0x6b3de36
+445, 0xe4c2e84f
+446, 0x9c43bb15
+447, 0xddf2905
+448, 0x7dd63556
+449, 0x3662ca09
+450, 0xfb81f35b
+451, 0xc2c8a72a
+452, 0x8e93c37
+453, 0xa93da2d4
+454, 0xa03af8f1
+455, 0x8d75159a
+456, 0x15f010b0
+457, 0xa296ab06
+458, 0xe55962ba
+459, 0xeae700a9
+460, 0xe388964a
+461, 0x917f2bec
+462, 0x1c203fea
+463, 0x792a01ba
+464, 0xa93a80ac
+465, 0x9eb8a197
+466, 0x56c0bc73
+467, 0xb8f05799
+468, 0xf429a8c8
+469, 0xb92cee42
+470, 0xf8864ec
+471, 0x62f2518a
+472, 0x3a7bfa3e
+473, 0x12e56e6d
+474, 0xd7a18313
+475, 0x41fa3899
+476, 0xa09c4956
+477, 0xebcfd94a
+478, 0xc485f90b
+479, 0x4391ce40
+480, 0x742a3333
+481, 0xc932f9e5
+482, 0x75c6c263
+483, 0x80937f0
+484, 0xcf21833c
+485, 0x16027520
+486, 0xd42e669f
+487, 0xb0f01fb7
+488, 0xb35896f1
+489, 0x763737a9
+490, 0x1bb20209
+491, 0x3551f189
+492, 0x56bc2602
+493, 0xb6eacf4
+494, 0x42ec4d11
+495, 0x245cc68
+496, 0xc27ac43b
+497, 0x9d903466
+498, 0xce3f0c05
+499, 0xb708c31c
+500, 0xc0fd37eb
+501, 0x95938b2c
+502, 0xf20175a7
+503, 0x4a86ee9b
+504, 0xbe039a58
+505, 0xd41cabe7
+506, 0x83bc99ba
+507, 0x761d60e1
+508, 0x7737cc2e
+509, 0x2b82fc4b
+510, 0x375aa401
+511, 0xfe9597a0
+512, 0x5543806a
+513, 0x44f31238
+514, 0x7df31538
+515, 0x74cfa770
+516, 0x8755d881
+517, 0x1fde665a
+518, 0xda8bf315
+519, 0x973d8e95
+520, 0x72205228
+521, 0x8fe59717
+522, 0x7bb90b34
+523, 0xef6ed945
+524, 0x16fd4a38
+525, 0x5db44de1
+526, 0xf09f93b3
+527, 0xe84824cc
+528, 0x945bb50e
+529, 0xd0be4aa5
+530, 0x47c277c2
+531, 0xd3800c28
+532, 0xac1c33ec
+533, 0xd3dacce
+534, 0x811c8387
+535, 0x6761b36
+536, 0x70d3882f
+537, 0xd6e62e3a
+538, 0xea25daa2
+539, 0xb07f39d1
+540, 0x391d89d7
+541, 0x84b6fb5e
+542, 0x3dda3fca
+543, 0x229e80a4
+544, 0x3d94a4b7
+545, 0x5d3d576a
+546, 0xad7818a0
+547, 0xce23b03a
+548, 0x7aa2079c
+549, 0x9a6be555
+550, 0x83f3b34a
+551, 0x1848f9d9
+552, 0xd8fefc1c
+553, 0x48e6ce48
+554, 0x52e55750
+555, 0xf41a71cf
+556, 0xba08e259
+557, 0xfaf06a15
+558, 0xeaaac0fb
+559, 0x34f90098
+560, 0xb1dfffbb
+561, 0x718daec2
+562, 0xab4dda21
+563, 0xd27cc1ee
+564, 0x4aafbc4c
+565, 0x356dfb4f
+566, 0x83fcdfd6
+567, 0x8f0bcde0
+568, 0x4363f844
+569, 0xadc0f4d5
+570, 0x3bde994e
+571, 0x3884d452
+572, 0x21876b4a
+573, 0x9c985398
+574, 0xca55a226
+575, 0x3a88c583
+576, 0x916dc33c
+577, 0x8f67d1d7
+578, 0x3b26a667
+579, 0xe4ddeb4b
+580, 0x1a9d8c33
+581, 0x81c9b74f
+582, 0x9ed1e9df
+583, 0x6e61aecf
+584, 0x95e95a5d
+585, 0x68864ff5
+586, 0xb8fa5b9
+587, 0x72b1b3de
+588, 0x5e18a86b
+589, 0xd7f2337d
+590, 0xd70e0925
+591, 0xb573a4c1
+592, 0xc77b3f8a
+593, 0x389b20de
+594, 0x16cf6afb
+595, 0xa39bd275
+596, 0xf491cf01
+597, 0x6f88a802
+598, 0x8510af05
+599, 0xe7cd549a
+600, 0x8603179a
+601, 0xef43f191
+602, 0xf9b64c60
+603, 0xb00254a7
+604, 0xd7c06a2d
+605, 0x17e9380b
+606, 0x529e727b
+607, 0xaaa8fe0a
+608, 0xfb64ff4c
+609, 0xcd75af26
+610, 0xfb717c87
+611, 0xa0789899
+612, 0x10391ec9
+613, 0x7e9b40b3
+614, 0x18536554
+615, 0x728c05f7
+616, 0x787dca98
+617, 0xad948d1
+618, 0x44c18def
+619, 0x3303f2ec
+620, 0xa15acb5
+621, 0xb58d38f4
+622, 0xfe041ef8
+623, 0xd151a956
+624, 0x7b9168e8
+625, 0x5ebeca06
+626, 0x90fe95df
+627, 0xf76875aa
+628, 0xb2e0d664
+629, 0x2e3253b7
+630, 0x68e34469
+631, 0x1f0c2d89
+632, 0x13a34ac2
+633, 0x5ffeb841
+634, 0xe381e91c
+635, 0xb8549a92
+636, 0x3f35cf1
+637, 0xda0f9dcb
+638, 0xdd9828a6
+639, 0xe1428f29
+640, 0xf4db80b5
+641, 0xdac30af5
+642, 0x1af1dd17
+643, 0x9a540254
+644, 0xcab68a38
+645, 0x33560361
+646, 0x2fbf3886
+647, 0xbc785923
+648, 0xe081cd10
+649, 0x8e473356
+650, 0xd102c357
+651, 0xeea4fe48
+652, 0x248d3453
+653, 0x1da79ac
+654, 0x815a65ff
+655, 0x27693e76
+656, 0xb7d5af40
+657, 0x6d245d30
+658, 0x9e06fa8f
+659, 0xb0570dcb
+660, 0x469f0005
+661, 0x3e0ca132
+662, 0xd89bbf3
+663, 0xd61ccd47
+664, 0x6383878
+665, 0x62b5956
+666, 0x4dc83675
+667, 0x93fd8492
+668, 0x5a0091f5
+669, 0xc9f9bc3
+670, 0xa26e7778
+671, 0xeabf2d01
+672, 0xe612dc06
+673, 0x85d89ff9
+674, 0xd1763179
+675, 0xcb88947b
+676, 0x9e8757a5
+677, 0xe100e85c
+678, 0x904166eb
+679, 0x4996243d
+680, 0x4038e1cb
+681, 0x2be2c63d
+682, 0x77017e81
+683, 0x3b1f556b
+684, 0x1c785c77
+685, 0x6869b8bd
+686, 0xe1217ed4
+687, 0x4012ab2f
+688, 0xc06c0d8e
+689, 0x2122eb68
+690, 0xad1783fd
+691, 0x5f0c80e3
+692, 0x828f7efa
+693, 0x29328399
+694, 0xeadf1087
+695, 0x85dc0037
+696, 0x9691ef26
+697, 0xc0947a53
+698, 0x2a178d2a
+699, 0x2a2c7e8f
+700, 0x90378380
+701, 0xaad8d326
+702, 0x9cf1c3c8
+703, 0x84eccd44
+704, 0x79e61808
+705, 0x8b3f454e
+706, 0x209e6e1
+707, 0x51f88378
+708, 0xc210226f
+709, 0xd982adb5
+710, 0x55d44a31
+711, 0x9817d443
+712, 0xa328c626
+713, 0x13455966
+714, 0xb8f681d3
+715, 0x2a3c713b
+716, 0xc186959b
+717, 0x814a74b0
+718, 0xed7bc90
+719, 0xa88d3d6d
+720, 0x88a9f561
+721, 0x73aa1c0a
+722, 0xdfeff404
+723, 0xec037e4b
+724, 0xa5c209f0
+725, 0xb3a223b4
+726, 0x24ce3709
+727, 0x3184c790
+728, 0xa1398c62
+729, 0x2f92034e
+730, 0xbb37a79a
+731, 0x605287b4
+732, 0x8faa772c
+733, 0x6ce56c1d
+734, 0xc035fb4c
+735, 0x7cf5b316
+736, 0x6502645
+737, 0xa283d810
+738, 0x778bc2f1
+739, 0xfdf99313
+740, 0x1f513265
+741, 0xbd3837e2
+742, 0x9b84a9a
+743, 0x2139ce91
+744, 0x61a8e890
+745, 0xf9ff12db
+746, 0xb43d2ea7
+747, 0x88532e61
+748, 0x175a6655
+749, 0x7a6c4f72
+750, 0x6dafc1b7
+751, 0x449b1459
+752, 0x514f654f
+753, 0x9a6731e2
+754, 0x8632da43
+755, 0xc81b0422
+756, 0x81fe9005
+757, 0x15b79618
+758, 0xb5fa629f
+759, 0x987a474f
+760, 0x1c74f54e
+761, 0xf9743232
+762, 0xec4b55f
+763, 0x87d761e5
+764, 0xd1ad78b7
+765, 0x453d9350
+766, 0xc7a7d85
+767, 0xb2576ff5
+768, 0xcdde49b7
+769, 0x8e1f763e
+770, 0x1338583e
+771, 0xfd65b9dc
+772, 0x4f19c4f4
+773, 0x3a52d73d
+774, 0xd3509c4c
+775, 0xda24fe31
+776, 0xe2de56ba
+777, 0x2db5e540
+778, 0x23172734
+779, 0x4db572f
+780, 0xeb941718
+781, 0x84c2649a
+782, 0x3b1e5b6a
+783, 0x4c9c61b9
+784, 0x3bccd11
+785, 0xb4d7b78e
+786, 0x48580ae5
+787, 0xd273ab68
+788, 0x25c11615
+789, 0x470b53f6
+790, 0x329c2068
+791, 0x1693721b
+792, 0xf8c9aacf
+793, 0x4c3d5693
+794, 0xd778284e
+795, 0xae1cb24f
+796, 0x3c11d1b3
+797, 0xddd2b0c0
+798, 0x90269fa7
+799, 0x5666e0a2
+800, 0xf9f195a4
+801, 0x61d78eb2
+802, 0xada5a7c0
+803, 0xaa272fbe
+804, 0xba3bae2f
+805, 0xd0b70fc2
+806, 0x529f32b
+807, 0xda7a3e21
+808, 0x9a776a20
+809, 0xb21f9635
+810, 0xb3acc14e
+811, 0xac55f56
+812, 0x29dccf41
+813, 0x32dabdb3
+814, 0xaa032f58
+815, 0xfa406af4
+816, 0xce3c415d
+817, 0xb44fb4d9
+818, 0x32248d1c
+819, 0x680c6440
+820, 0xae2337b
+821, 0x294cb597
+822, 0x5bca48fe
+823, 0xaef19f40
+824, 0xad60406
+825, 0x4781f090
+826, 0xfd691ffc
+827, 0xb6568268
+828, 0xa56c72cb
+829, 0xf8a9e0fc
+830, 0x9af4fd02
+831, 0x2cd30932
+832, 0x776cefd7
+833, 0xe31f476e
+834, 0x6d94a437
+835, 0xb3cab598
+836, 0xf582d13f
+837, 0x3bf8759d
+838, 0xc3777dc
+839, 0x5e425ea8
+840, 0x1c7ff4ed
+841, 0x1c2e97d1
+842, 0xc062d2b4
+843, 0x46dc80e0
+844, 0xbcdb47e6
+845, 0x32282fe0
+846, 0xaba89063
+847, 0x5e94e9bb
+848, 0x3e667f78
+849, 0xea6eb21a
+850, 0xe56e54e8
+851, 0xa0383510
+852, 0x6768fe2b
+853, 0xb53ac3e0
+854, 0x779569a0
+855, 0xeca83c6a
+856, 0x24db4d2d
+857, 0x4585f696
+858, 0xf84748b2
+859, 0xf6a4dd5b
+860, 0x31fb524d
+861, 0x67ab39fe
+862, 0x5882a899
+863, 0x9a05fcf6
+864, 0x712b5674
+865, 0xe8c6958f
+866, 0x4b448bb3
+867, 0x530b9abf
+868, 0xb491f491
+869, 0x98352c62
+870, 0x2d0a50e3
+871, 0xeb4384da
+872, 0x36246f07
+873, 0xcbc5c1a
+874, 0xae24031d
+875, 0x44d11ed6
+876, 0xf07f1608
+877, 0xf296aadd
+878, 0x3bcfe3be
+879, 0x8fa1e7df
+880, 0xfd317a6e
+881, 0xe4975c44
+882, 0x15205892
+883, 0xa762d4df
+884, 0xf1167365
+885, 0x6811cc00
+886, 0x8315f23
+887, 0xe045b4b1
+888, 0xa8496414
+889, 0xbed313ae
+890, 0xcdae3ddb
+891, 0xa9c22c9
+892, 0x275fab1a
+893, 0xedd65fa
+894, 0x4c188229
+895, 0x63a83e58
+896, 0x18aa9207
+897, 0xa41f2e78
+898, 0xd9f63653
+899, 0xbe2be73b
+900, 0xa3364d39
+901, 0x896d5428
+902, 0xc737539e
+903, 0x745a78c6
+904, 0xf0b2b042
+905, 0x510773b4
+906, 0x92ad8e37
+907, 0x27f2f8c4
+908, 0x23704cc8
+909, 0x3d95a77f
+910, 0xf08587a4
+911, 0xbd696a25
+912, 0x948924f3
+913, 0x8cddb634
+914, 0xcd2a4910
+915, 0x8e0e300e
+916, 0x83815a9b
+917, 0x67383510
+918, 0x3c18f0d0
+919, 0xc7a7bccc
+920, 0x7cc2d3a2
+921, 0x52eb2eeb
+922, 0xe4a257e5
+923, 0xec76160e
+924, 0x63f9ad68
+925, 0x36d0bbbf
+926, 0x957bc4e4
+927, 0xc9ed90ff
+928, 0x4cb6059d
+929, 0x2f86eca1
+930, 0x3e3665a3
+931, 0x9b7eb6f4
+932, 0x492e7e18
+933, 0xa098aa51
+934, 0x7eb568b2
+935, 0x3fd639ba
+936, 0x7bebcf1
+937, 0x99c844ad
+938, 0x43cb5ec7
+939, 0x8dfbbef5
+940, 0x5be413ff
+941, 0xd93b976d
+942, 0xc1c7a86d
+943, 0x1f0e93d0
+944, 0x498204a2
+945, 0xe8fe832a
+946, 0x2236bd7
+947, 0x89953769
+948, 0x2acc3491
+949, 0x2c4f22c6
+950, 0xd7996277
+951, 0x3bcdc349
+952, 0xfc286630
+953, 0x5f8909fd
+954, 0x242677c0
+955, 0x4cb34104
+956, 0xa6ff8100
+957, 0x39ea47ec
+958, 0x9bd54140
+959, 0x7502ffe8
+960, 0x7ebef8ae
+961, 0x1ed8abe4
+962, 0xfaba8450
+963, 0xc197b65f
+964, 0x19431455
+965, 0xe229c176
+966, 0xeb2967da
+967, 0xe0c5dc05
+968, 0xa84e3227
+969, 0x10dd9e0f
+970, 0xbdb70b02
+971, 0xce24808a
+972, 0x423edab8
+973, 0x194caf71
+974, 0x144f150d
+975, 0xf811c2d2
+976, 0xc224ee85
+977, 0x2b217a5b
+978, 0xf78a5a79
+979, 0x6554a4b1
+980, 0x769582df
+981, 0xf4b2cf93
+982, 0x89648483
+983, 0xb3283a3e
+984, 0x82b895db
+985, 0x79388ef0
+986, 0x54bc42a6
+987, 0xc4dd39d9
+988, 0x45b33b7d
+989, 0x8703b2c1
+990, 0x1cc94806
+991, 0xe0f43e49
+992, 0xcaa7b6bc
+993, 0x4f88e9af
+994, 0x1477cce5
+995, 0x347dd115
+996, 0x36e335fa
+997, 0xb93c9a31
+998, 0xaac3a175
+999, 0x68a19647
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/mt19937-testset-2.csv b/lib/python3.11/site-packages/numpy/random/tests/data/mt19937-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cdb8e4794ccdb30fa4fea3045b30a188f6ff5357
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/mt19937-testset-2.csv
@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0x7ab4ea94
+1, 0x9b561119
+2, 0x4957d02e
+3, 0x7dd3fdc2
+4, 0x5affe54
+5, 0x5a01741c
+6, 0x8b9e8c1f
+7, 0xda5bf11a
+8, 0x509226
+9, 0x64e2ea17
+10, 0x82c6dab5
+11, 0xe4302515
+12, 0x8198b873
+13, 0xc3ec9a82
+14, 0x829dff28
+15, 0x5278e44f
+16, 0x994a7d2c
+17, 0xf1c89398
+18, 0xaf2fddec
+19, 0x22abc6ee
+20, 0x963dbd43
+21, 0xc29edffb
+22, 0x41c1ce07
+23, 0x9c90034d
+24, 0x1f17a796
+25, 0x3833caa8
+26, 0xb8795528
+27, 0xebc595a2
+28, 0xf8f5b5dd
+29, 0xc2881f72
+30, 0x18e5d3f0
+31, 0x9b19ac7a
+32, 0xb9992436
+33, 0xc00052b3
+34, 0xb63f4475
+35, 0x962642d9
+36, 0x63506c10
+37, 0x2be6b127
+38, 0x569bdbc6
+39, 0x7f185e01
+40, 0xebb55f53
+41, 0x1c30198c
+42, 0x7c8d75c6
+43, 0xd3f2186b
+44, 0xaca5b9b1
+45, 0xbc49ff45
+46, 0xc4a802af
+47, 0x2cecd86f
+48, 0x8e0da529
+49, 0x1f22b00e
+50, 0x4559ea80
+51, 0x60f587d8
+52, 0x7c7460e9
+53, 0x67be0a4a
+54, 0x987a0183
+55, 0x7bd30f1
+56, 0xab18c4ac
+57, 0xffdbfb64
+58, 0x9ea917f9
+59, 0x1239dab7
+60, 0x38efabeb
+61, 0x5da91888
+62, 0x8f49ed62
+63, 0x83f60b1e
+64, 0x5950a3fc
+65, 0xd8911104
+66, 0x19e8859e
+67, 0x1a4d89ec
+68, 0x968ca180
+69, 0x9e1b6da3
+70, 0x3d99c2c
+71, 0x55f76289
+72, 0x8fa28b9e
+73, 0x9fe01d33
+74, 0xdade4e38
+75, 0x1ea04290
+76, 0xa7263313
+77, 0xaafc762e
+78, 0x460476d6
+79, 0x31226e12
+80, 0x451d3f05
+81, 0xd0d2764b
+82, 0xd06e1ab3
+83, 0x1394e3f4
+84, 0x2fc04ea3
+85, 0x5b8401c
+86, 0xebd6c929
+87, 0xe881687c
+88, 0x94bdd66a
+89, 0xabf85983
+90, 0x223ad12d
+91, 0x2aaeeaa3
+92, 0x1f704934
+93, 0x2db2efb6
+94, 0xf49b8dfb
+95, 0x5bdbbb9d
+96, 0xba0cd0db
+97, 0x4ec4674e
+98, 0xad0129e
+99, 0x7a66129b
+100, 0x50d12c5e
+101, 0x85b1d335
+102, 0x3efda58a
+103, 0xecd886fb
+104, 0x8ecadd3d
+105, 0x60ebac0f
+106, 0x5e10fe79
+107, 0xa84f7e5d
+108, 0x43931288
+109, 0xfacf448
+110, 0x4ee01997
+111, 0xcdc0a651
+112, 0x33c87037
+113, 0x8b50fc03
+114, 0xf52aad34
+115, 0xda6cd856
+116, 0x7585bea0
+117, 0xe947c762
+118, 0x4ddff5d8
+119, 0xe0e79b3b
+120, 0xb804cf09
+121, 0x84765c44
+122, 0x3ff666b4
+123, 0xe31621ad
+124, 0x816f2236
+125, 0x228176bc
+126, 0xfdc14904
+127, 0x635f5077
+128, 0x6981a817
+129, 0xfd9a0300
+130, 0xd3fa8a24
+131, 0xd67c1a77
+132, 0x903fe97a
+133, 0xf7c4a4d5
+134, 0x109f2058
+135, 0x48ab87fe
+136, 0xfd6f1928
+137, 0x707e9452
+138, 0xf327db9e
+139, 0x7b80d76d
+140, 0xfb6ba193
+141, 0x454a1ad0
+142, 0xe20b51e
+143, 0xb774d085
+144, 0x6b1ed574
+145, 0xb1e77de4
+146, 0xe2a83b37
+147, 0x33d3176f
+148, 0x2f0ca0fc
+149, 0x17f51e2
+150, 0x7c1fbf55
+151, 0xf09e9cd0
+152, 0xe3d9bacd
+153, 0x4244db0a
+154, 0x876c09fc
+155, 0x9db4fc2f
+156, 0xd3771d60
+157, 0x25fc6a75
+158, 0xb309915c
+159, 0xc50ee027
+160, 0xaa5b7b38
+161, 0x4c650ded
+162, 0x1acb2879
+163, 0x50db5887
+164, 0x90054847
+165, 0xfef23e5b
+166, 0x2dd7b7d5
+167, 0x990b8c2e
+168, 0x6001a601
+169, 0xb5d314c4
+170, 0xfbfb7bf9
+171, 0x1aba997d
+172, 0x814e7304
+173, 0x989d956a
+174, 0x86d5a29c
+175, 0x70a9fa08
+176, 0xc4ccba87
+177, 0x7e9cb366
+178, 0xee18eb0a
+179, 0x44f5be58
+180, 0x91d4af2d
+181, 0x5ab6e593
+182, 0x9fd6bb4d
+183, 0x85894ce
+184, 0x728a2401
+185, 0xf006f6d4
+186, 0xd782741e
+187, 0x842cd5bd
+188, 0xfb5883aa
+189, 0x7e5a471
+190, 0x83ff6965
+191, 0xc9675c6b
+192, 0xb6ced3c7
+193, 0x3de6425b
+194, 0x25e14db4
+195, 0x69ca3dec
+196, 0x81342d13
+197, 0xd7cd8417
+198, 0x88d15e69
+199, 0xefba17c9
+200, 0x43d595e6
+201, 0x89d4cf25
+202, 0x7cae9b9b
+203, 0x2242c621
+204, 0x27fc3598
+205, 0x467b1d84
+206, 0xe84d4622
+207, 0xa26bf980
+208, 0x80411010
+209, 0xe2c2bfea
+210, 0xbc6ca25a
+211, 0x3ddb592a
+212, 0xdd46eb9e
+213, 0xdfe8f657
+214, 0x2cedc974
+215, 0xf0dc546b
+216, 0xd46be68f
+217, 0x26d8a5aa
+218, 0x76e96ba3
+219, 0x7d5b5353
+220, 0xf532237c
+221, 0x6478b79
+222, 0x9b81a5e5
+223, 0x5fc68e5c
+224, 0x68436e70
+225, 0x2a0043f9
+226, 0x108d523c
+227, 0x7a4c32a3
+228, 0x9c84c742
+229, 0x6f813dae
+230, 0xfcc5bbcc
+231, 0x215b6f3a
+232, 0x84cb321d
+233, 0x7913a248
+234, 0xb1e6b585
+235, 0x49376b31
+236, 0x1dc896b0
+237, 0x347051ad
+238, 0x5524c042
+239, 0xda0eef9d
+240, 0xf2e73342
+241, 0xbeee2f9d
+242, 0x7c702874
+243, 0x9eb3bd34
+244, 0x97b09700
+245, 0xcdbab1d4
+246, 0x4a2f6ed1
+247, 0x2047bda5
+248, 0x3ecc7005
+249, 0x8d0d5e67
+250, 0x40876fb5
+251, 0xb5fd2187
+252, 0xe915d8af
+253, 0x9a2351c7
+254, 0xccc658ae
+255, 0xebb1eddc
+256, 0xc4a83671
+257, 0xffb2548f
+258, 0xe4fe387a
+259, 0x477aaab4
+260, 0x8475a4e4
+261, 0xf8823e46
+262, 0xe4130f71
+263, 0xbdb54482
+264, 0x98fe0462
+265, 0xf36b27b8
+266, 0xed7733da
+267, 0x5f428afc
+268, 0x43a3a21a
+269, 0xf8370b55
+270, 0xfade1de1
+271, 0xd9a038ea
+272, 0x3c69af23
+273, 0x24df7dd0
+274, 0xf66d9353
+275, 0x71d811be
+276, 0xcc4d024b
+277, 0xb8c30bf0
+278, 0x4198509d
+279, 0x8b37ba36
+280, 0xa41ae29a
+281, 0x8cf7799e
+282, 0x5cd0136a
+283, 0xa11324ef
+284, 0x2f8b6d4b
+285, 0x3657cf17
+286, 0x35b6873f
+287, 0xee6e5bd7
+288, 0xbeeaa98
+289, 0x9ad3c581
+290, 0xe2376c3f
+291, 0x738027cc
+292, 0x536ac839
+293, 0xf066227
+294, 0x6c9cb0f9
+295, 0x84082ae6
+296, 0xab38ae9d
+297, 0x493eade9
+298, 0xcb630b3a
+299, 0x64d44250
+300, 0xe5efb557
+301, 0xea2424d9
+302, 0x11a690ba
+303, 0x30a48ae4
+304, 0x58987e53
+305, 0x94ec6076
+306, 0x5d3308fa
+307, 0xf1635ebb
+308, 0x56a5ab90
+309, 0x2b2f2ee4
+310, 0x6f9e6483
+311, 0x8b93e327
+312, 0xa7ce140b
+313, 0x4c8aa42
+314, 0x7657bb3f
+315, 0xf250fd75
+316, 0x1edfcb0f
+317, 0xdb42ace3
+318, 0xf8147e16
+319, 0xd1992bd
+320, 0x64bb14d1
+321, 0x423e724d
+322, 0x7b172f7c
+323, 0x17171696
+324, 0x4acaf83b
+325, 0x7a83527e
+326, 0xfc980c60
+327, 0xc8b56bb
+328, 0x2453f77f
+329, 0x85ad1bf9
+330, 0x62a85dfe
+331, 0x48238c4d
+332, 0xbb3ec1eb
+333, 0x4c1c039c
+334, 0x1f37f571
+335, 0x98aecb63
+336, 0xc3b3ddd6
+337, 0xd22dad4
+338, 0xe49671a3
+339, 0xe3baf945
+340, 0xb9e21680
+341, 0xda562856
+342, 0xe8b88ce4
+343, 0x86f88de2
+344, 0x986faf76
+345, 0x6f0025c3
+346, 0x3fe21234
+347, 0xd8d3f729
+348, 0xc2d11c6f
+349, 0xd4f9e8f
+350, 0xf61a0aa
+351, 0xc48bb313
+352, 0xe944e940
+353, 0xf1801b2e
+354, 0x253590be
+355, 0x981f069d
+356, 0x891454d8
+357, 0xa4f824ad
+358, 0x6dd2cc48
+359, 0x3018827e
+360, 0x3fb329e6
+361, 0x65276517
+362, 0x8d2c0dd2
+363, 0xc965b48e
+364, 0x85d14d90
+365, 0x5a51623c
+366, 0xa9573d6a
+367, 0x82d00edf
+368, 0x5ed7ce07
+369, 0x1d946abc
+370, 0x24fa567b
+371, 0x83ef5ecc
+372, 0x9001724a
+373, 0xc4fe48f3
+374, 0x1e07c25c
+375, 0xf4d5e65e
+376, 0xb734f6e9
+377, 0x327a2df8
+378, 0x766d59b7
+379, 0x625e6b61
+380, 0xe82f32d7
+381, 0x1566c638
+382, 0x2e815871
+383, 0x606514aa
+384, 0x36b7386e
+385, 0xcaa8ce08
+386, 0xb453fe9c
+387, 0x48574e23
+388, 0x71f0da06
+389, 0xa8a79463
+390, 0x6b590210
+391, 0x86e989db
+392, 0x42899f4f
+393, 0x7a654ef9
+394, 0x4c4fe932
+395, 0x77b2fd10
+396, 0xb6b4565c
+397, 0xa2e537a3
+398, 0xef5a3dca
+399, 0x41235ea8
+400, 0x95c90541
+401, 0x50ad32c4
+402, 0xc1b8e0a4
+403, 0x498e9aab
+404, 0xffc965f1
+405, 0x72633485
+406, 0x3a731aef
+407, 0x7cfddd0b
+408, 0xb04d4129
+409, 0x184fc28e
+410, 0x424369b0
+411, 0xf9ae13a1
+412, 0xaf357c8d
+413, 0x7a19228e
+414, 0xb46de2a8
+415, 0xeff2ac76
+416, 0xa6c9357b
+417, 0x614f19c1
+418, 0x8ee1a53f
+419, 0xbe1257b1
+420, 0xf72651fe
+421, 0xd347c298
+422, 0x96dd2f23
+423, 0x5bb1d63e
+424, 0x32e10887
+425, 0x36a144da
+426, 0x9d70e791
+427, 0x5e535a25
+428, 0x214253da
+429, 0x2e43dd40
+430, 0xfc0413f4
+431, 0x1f5ea409
+432, 0x1754c126
+433, 0xcdbeebbe
+434, 0x1fb44a14
+435, 0xaec7926
+436, 0xb9d9a1e
+437, 0x9e4a6577
+438, 0x8b1f04c5
+439, 0x19854e8a
+440, 0x531080cd
+441, 0xc0cbd73
+442, 0x20399d77
+443, 0x7d8e9ed5
+444, 0x66177598
+445, 0x4d18a5c2
+446, 0xe08ebf58
+447, 0xb1f9c87b
+448, 0x66bedb10
+449, 0x26670d21
+450, 0x7a7892da
+451, 0x69b69d86
+452, 0xd04f1d1c
+453, 0xaf469625
+454, 0x7946b813
+455, 0x1ee596bd
+456, 0x7f365d85
+457, 0x795b662b
+458, 0x194ad02d
+459, 0x5a9649b5
+460, 0x6085e278
+461, 0x2cf54550
+462, 0x9c77ea0b
+463, 0x3c6ff8b
+464, 0x2141cd34
+465, 0xb90bc671
+466, 0x35037c4b
+467, 0xd04c0d76
+468, 0xc75bff8
+469, 0x8f52003b
+470, 0xfad3d031
+471, 0x667024bc
+472, 0xcb04ea36
+473, 0x3e03d587
+474, 0x2644d3a0
+475, 0xa8fe99ba
+476, 0x2b9a55fc
+477, 0x45c4d44a
+478, 0xd059881
+479, 0xe07fcd20
+480, 0x4e22046c
+481, 0x7c2cbf81
+482, 0xbf7f23de
+483, 0x69d924c3
+484, 0xe53cd01
+485, 0x3879017c
+486, 0xa590e558
+487, 0x263bc076
+488, 0x245465b1
+489, 0x449212c6
+490, 0x249dcb29
+491, 0x703d42d7
+492, 0x140eb9ec
+493, 0xc86c5741
+494, 0x7992aa5b
+495, 0xb8b76a91
+496, 0x771dac3d
+497, 0x4ecd81e3
+498, 0xe5ac30b3
+499, 0xf4d7a5a6
+500, 0xac24b97
+501, 0x63494d78
+502, 0x627ffa89
+503, 0xfa4f330
+504, 0x8098a1aa
+505, 0xcc0c61dc
+506, 0x34749fa0
+507, 0x7f217822
+508, 0x418d6f15
+509, 0xa4b6e51e
+510, 0x1036de68
+511, 0x1436986e
+512, 0x44df961d
+513, 0x368e4651
+514, 0x6a9e5d8c
+515, 0x27d1597e
+516, 0xa1926c62
+517, 0x8d1f2b55
+518, 0x5797eb42
+519, 0xa90f9e81
+520, 0x57547b10
+521, 0xdbbcca8e
+522, 0x9edd2d86
+523, 0xbb0a7527
+524, 0x7662380c
+525, 0xe7c98590
+526, 0x950fbf3f
+527, 0xdc2b76b3
+528, 0x8a945102
+529, 0x3f0a1a85
+530, 0xeb215834
+531, 0xc59f2802
+532, 0xe2a4610
+533, 0x8b5a8665
+534, 0x8b2d9933
+535, 0x40a4f0bc
+536, 0xaab5bc67
+537, 0x1442a69e
+538, 0xdf531193
+539, 0x698d3db4
+540, 0x2d40324e
+541, 0x1a25feb2
+542, 0xe8cc898f
+543, 0xf12e98f5
+544, 0xc03ad34c
+545, 0xf62fceff
+546, 0xdd827e1e
+547, 0x7d8ccb3b
+548, 0xab2d6bc1
+549, 0xc323a124
+550, 0x8184a19a
+551, 0xc3c4e934
+552, 0x5487424d
+553, 0xd6a81a44
+554, 0x90a8689d
+555, 0xe69c4c67
+556, 0xbdae02dd
+557, 0x72a18a79
+558, 0x2a88e907
+559, 0x31cf4b5d
+560, 0xb157772f
+561, 0x206ba601
+562, 0x18529232
+563, 0x7dac90d8
+564, 0x3a5f8a09
+565, 0x9f4b64a3
+566, 0xae373af9
+567, 0x1d79447c
+568, 0x2a23684b
+569, 0x41fb7ba4
+570, 0x55e4bb9e
+571, 0xd7619d3e
+572, 0xc04e4dd8
+573, 0x8418d516
+574, 0x2b2ca585
+575, 0xfa8eedf
+576, 0x5bafd977
+577, 0x31974fb0
+578, 0x9eb6697b
+579, 0xc8be22f5
+580, 0x173b126a
+581, 0x8809becf
+582, 0x3e41efe1
+583, 0x3d6cbbb8
+584, 0x278c81d8
+585, 0xa6f08434
+586, 0xa0e6601d
+587, 0x2fccd88d
+588, 0x3cbc8beb
+589, 0x5f65d864
+590, 0xa1ff8ddf
+591, 0x609dcb7c
+592, 0x4a4e1663
+593, 0xeae5531
+594, 0x962a7c85
+595, 0x1e110607
+596, 0x8c5db5d0
+597, 0xc7f2337e
+598, 0xc94fcc9c
+599, 0xe7f62629
+600, 0x6c9aa9f8
+601, 0x2e27fe0e
+602, 0x4d0dae12
+603, 0x9eecf588
+604, 0x977ba3f2
+605, 0xed0a51af
+606, 0x3f3ec633
+607, 0xc174b2ec
+608, 0x590be8a9
+609, 0x4f630d18
+610, 0xf579e989
+611, 0xe2a55584
+612, 0xee11edcd
+613, 0x150a4833
+614, 0xc0a0535c
+615, 0xb5e00993
+616, 0xb6435700
+617, 0xa98dbff
+618, 0x315716af
+619, 0x94395776
+620, 0x6cbd48d9
+621, 0xab17f8fc
+622, 0xa794ffb7
+623, 0x6b55e231
+624, 0x89ff5783
+625, 0x431dcb26
+626, 0x270f9bf8
+627, 0x2af1b8d0
+628, 0x881745ed
+629, 0x17e1be4e
+630, 0x132a0ec4
+631, 0x5712df17
+632, 0x2dfb3334
+633, 0xf5a35519
+634, 0xcafbdac6
+635, 0x73b6189d
+636, 0x10107cac
+637, 0x18c1045e
+638, 0xbc19bbad
+639, 0x8b4f05ac
+640, 0x5830d038
+641, 0x468cd98a
+642, 0x5b83a201
+643, 0xf0ccdd9c
+644, 0xcb20c4bd
+645, 0x1ff186c9
+646, 0xcdddb47f
+647, 0x5c65ce6
+648, 0xb748c580
+649, 0x23b6f262
+650, 0xe2ba8e5c
+651, 0x9a164a03
+652, 0x62d3322e
+653, 0x918d8b43
+654, 0x45c8b49d
+655, 0xce172c6e
+656, 0x23febc6
+657, 0x84fdc5b7
+658, 0xe7d1fd82
+659, 0xf0ddf3a6
+660, 0x87050436
+661, 0x13d46375
+662, 0x5b191c78
+663, 0x2cbd99c0
+664, 0x7686c7f
+665, 0xcff56c84
+666, 0x7f9b4486
+667, 0xefc997fe
+668, 0x984d4588
+669, 0xfa44f36a
+670, 0x7a5276c1
+671, 0xcfde6176
+672, 0xcacf7b1d
+673, 0xcffae9a7
+674, 0xe98848d5
+675, 0xd4346001
+676, 0xa2196cac
+677, 0x217f07dc
+678, 0x42d5bef
+679, 0x6f2e8838
+680, 0x4677a24
+681, 0x4ad9cd54
+682, 0x43df42af
+683, 0x2dde417
+684, 0xaef5acb1
+685, 0xf377f4b3
+686, 0x7d870d40
+687, 0xe53df1c2
+688, 0xaeb5be50
+689, 0x7c92eac0
+690, 0x4f00838c
+691, 0x91e05e84
+692, 0x23856c80
+693, 0xc4266fa6
+694, 0x912fddb
+695, 0x34d42d22
+696, 0x6c02ffa
+697, 0xe47d093
+698, 0x183c55b3
+699, 0xc161d142
+700, 0x3d43ff5f
+701, 0xc944a36
+702, 0x27bb9fc6
+703, 0x75c91080
+704, 0x2460d0dc
+705, 0xd2174558
+706, 0x68062dbf
+707, 0x778e5c6e
+708, 0xa4dc9a
+709, 0x7a191e69
+710, 0xc084b2ba
+711, 0xbb391d2
+712, 0x88849be
+713, 0x69c02714
+714, 0x69d4a389
+715, 0x8f51854d
+716, 0xaf10bb82
+717, 0x4d5d1c77
+718, 0x53b53109
+719, 0xa0a92aa0
+720, 0x83ecb757
+721, 0x5325752a
+722, 0x114e466e
+723, 0x4b3f2780
+724, 0xa7a6a39c
+725, 0x5e723357
+726, 0xa6b8be9b
+727, 0x157c32ff
+728, 0x8b898012
+729, 0xd7ff2b1e
+730, 0x69cd8444
+731, 0x6ad8030c
+732, 0xa08a49ec
+733, 0xfbc055d3
+734, 0xedf17e46
+735, 0xc9526200
+736, 0x3849b88a
+737, 0x2746860b
+738, 0xae13d0c1
+739, 0x4f15154f
+740, 0xd65c3975
+741, 0x6a377278
+742, 0x54d501f7
+743, 0x81a054ea
+744, 0x143592ba
+745, 0x97714ad6
+746, 0x4f9926d9
+747, 0x4f7ac56d
+748, 0xe87ca939
+749, 0x58b76f6f
+750, 0x60901ad8
+751, 0x3e401bb6
+752, 0xa058468e
+753, 0xc0bb14f6
+754, 0x2cb8f02a
+755, 0x7c2cf756
+756, 0x34c31de5
+757, 0x9b243e83
+758, 0xa5c85ab4
+759, 0x2741e3b3
+760, 0x1249000e
+761, 0x3fc4e72b
+762, 0xa3e038a2
+763, 0x952dd92c
+764, 0x2b821966
+765, 0xfa81b365
+766, 0x530919b9
+767, 0x4486d66f
+768, 0xccf4f3c1
+769, 0xa8bddd1d
+770, 0xcc295eb9
+771, 0xfccbe42f
+772, 0x38bacd8d
+773, 0x2261854f
+774, 0x56068c62
+775, 0x9bdaeb8
+776, 0x555fa5b6
+777, 0x20fe615e
+778, 0x49fb23d3
+779, 0xd093bad6
+780, 0x54919e86
+781, 0x7373eb24
+782, 0xfbaa7a98
+783, 0x5f62fb39
+784, 0xe03bc9ec
+785, 0xa5074d41
+786, 0xa1cefb1
+787, 0x13912d74
+788, 0xf6421b8
+789, 0xfcb48812
+790, 0x8f1db50b
+791, 0xc1654b87
+792, 0x948b43c2
+793, 0xf503ef77
+794, 0x117d891d
+795, 0x5493ffa
+796, 0x171313b1
+797, 0xa4b62e1e
+798, 0x77454ea6
+799, 0xbea0aff0
+800, 0x13c36389
+801, 0xe3b60bac
+802, 0xa176bed3
+803, 0x2863d428
+804, 0xe2314f46
+805, 0xa85cd3d4
+806, 0x7866e57
+807, 0x8f03f5bc
+808, 0x239ae
+809, 0x46f279fb
+810, 0xcca00559
+811, 0xaa07a104
+812, 0x89123d08
+813, 0x2e6856ba
+814, 0x43a9780d
+815, 0x676cff25
+816, 0x6744b87d
+817, 0xee260d4f
+818, 0xb98d8b77
+819, 0x9b0ca455
+820, 0x659f6fe
+821, 0x28d20d1c
+822, 0x601f2657
+823, 0xdec3073e
+824, 0x61263863
+825, 0x1a13435a
+826, 0x27497d1e
+827, 0x17a8458e
+828, 0xdddc407d
+829, 0x4bb2e8ac
+830, 0x16b2aedb
+831, 0x77ccd696
+832, 0x9d108fcd
+833, 0x25ad233e
+834, 0xaa9bc370
+835, 0xa873ab50
+836, 0xaf19c9d9
+837, 0x696e1e6b
+838, 0x1fdc4bf4
+839, 0x4c2ebc81
+840, 0xde4929ed
+841, 0xf4d0c10c
+842, 0xb6595b76
+843, 0x75cbb1b3
+844, 0xbcb6de49
+845, 0xe23157fd
+846, 0x5e596078
+847, 0xa69b0d29
+848, 0x2118a41
+849, 0x7088c16
+850, 0xc75e1e1
+851, 0x6a4af2d6
+852, 0xf19c6521
+853, 0xaff7b3b1
+854, 0x615295c7
+855, 0xbda3a8d7
+856, 0x5b5ca72e
+857, 0xdad9d80f
+858, 0xfa81c084
+859, 0xf4703fa
+860, 0x3ca54540
+861, 0xa8961d51
+862, 0x53d1ecc2
+863, 0x808d83b6
+864, 0x68e8c48e
+865, 0x89be2039
+866, 0x9088ea11
+867, 0xb8665d12
+868, 0x91272f9
+869, 0x53dddff2
+870, 0xb7a54ab
+871, 0xd2b645ca
+872, 0x99fb8590
+873, 0x5315c8e
+874, 0x2a913806
+875, 0x7f15eb2b
+876, 0xa7f1cc5d
+877, 0xbb2ee836
+878, 0xd9fafd60
+879, 0x17448d6f
+880, 0x999ec436
+881, 0x482ec606
+882, 0x9b403c0e
+883, 0x569eb51b
+884, 0xb275d1a6
+885, 0xadd29c31
+886, 0xb7ebdb15
+887, 0xdfef3662
+888, 0x51aba6db
+889, 0x6d41946d
+890, 0x77bf8896
+891, 0xcafa6fab
+892, 0x976ab40f
+893, 0x49a6d86b
+894, 0x56639e55
+895, 0x9945b996
+896, 0x81459b50
+897, 0xbce97542
+898, 0xe397c9c9
+899, 0x247a5955
+900, 0xb72b1573
+901, 0x86306f86
+902, 0x34f65dc5
+903, 0x909360c0
+904, 0xf3f696ef
+905, 0xcb9faae5
+906, 0x93daecd9
+907, 0xde1af7af
+908, 0x43a1f2d
+909, 0x6d75cde5
+910, 0x9e412b6
+911, 0x5673fed
+912, 0x16bb511a
+913, 0x35ef4cca
+914, 0x4e615aca
+915, 0x5cdaf47a
+916, 0x26676047
+917, 0x8c199325
+918, 0x2adf0cb9
+919, 0x84f2e6fd
+920, 0x5e627f64
+921, 0xb7cee354
+922, 0x542ab4a6
+923, 0xe59cd83b
+924, 0x89cc3f10
+925, 0x92b0f5f
+926, 0xc1328370
+927, 0x8208d9f7
+928, 0x68eb00cf
+929, 0xfadd4ac4
+930, 0x2517784f
+931, 0x4042b99
+932, 0x75ce0230
+933, 0x97c5a1b4
+934, 0x1a97f709
+935, 0x4c62781e
+936, 0xf530a83
+937, 0x75776413
+938, 0x321c7240
+939, 0x6afe4e36
+940, 0xad00a2b4
+941, 0xbc05477d
+942, 0xb0911e80
+943, 0x9935b87d
+944, 0xd535eec5
+945, 0x149af45e
+946, 0x786934b0
+947, 0xbc13cdac
+948, 0x208bfa2e
+949, 0xcf4b39cc
+950, 0x6ac6c172
+951, 0xbfa9a37
+952, 0x42d28db6
+953, 0x2bf1ea63
+954, 0xbed6e677
+955, 0x50325d27
+956, 0xa79d3b8b
+957, 0x52448bb1
+958, 0xefaad1bd
+959, 0x833a2e54
+960, 0xd9de549a
+961, 0x9f59672f
+962, 0x9d5f5f16
+963, 0x1c914489
+964, 0xc08fa058
+965, 0xb188698b
+966, 0xdc4672b5
+967, 0x594f720e
+968, 0x56ed428f
+969, 0x9b0898af
+970, 0x8a64d3d5
+971, 0x773308d6
+972, 0x84d62098
+973, 0x46da7cf9
+974, 0x1114eae7
+975, 0xf9f2a092
+976, 0x5363a28
+977, 0xf2db7b3a
+978, 0x102c71a9
+979, 0xe8e76aaf
+980, 0x77a97b3b
+981, 0x77b090d
+982, 0x1099620e
+983, 0xa6daaae6
+984, 0x86ff4713
+985, 0xc0ef85b8
+986, 0xf621d409
+987, 0xfd1561e2
+988, 0x4bcc687d
+989, 0x596f760
+990, 0x7c8819f9
+991, 0x8cb865b8
+992, 0xadea115a
+993, 0x56609348
+994, 0xb321ac14
+995, 0x1bac7db2
+996, 0x5fe6ee2
+997, 0xe9bfe072
+998, 0x15549e74
+999, 0xad8c191b
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/pcg64-testset-1.csv b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0c8271fab6dff757e0b94443be8bc0e8b5f1e047
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
@@ -0,0 +1,1001 @@
+seed, 0xdeadbeaf
+0, 0x60d24054e17a0698
+1, 0xd5e79d89856e4f12
+2, 0xd254972fe64bd782
+3, 0xf1e3072a53c72571
+4, 0xd7c1d7393d4115c9
+5, 0x77b75928b763e1e2
+6, 0xee6dee05190f7909
+7, 0x15f7b1c51d7fa319
+8, 0x27e44105f26ac2d7
+9, 0xcc0d88b29e5b415
+10, 0xe07b1a90c685e361
+11, 0xd2e430240de95e38
+12, 0x3260bca9a24ca9da
+13, 0x9b3cf2e92385adb7
+14, 0x30b5514548271976
+15, 0xa3a1fa16c124faf9
+16, 0xf53e17e918e45bb6
+17, 0x26f19faaeb833bfc
+18, 0x95e1d605730cce1b
+19, 0xa7b520c5c093c1aa
+20, 0x4b68c010c9b106a3
+21, 0x25e19fe91df703f0
+22, 0x898364bb0bf593cb
+23, 0x5bd6ab7dbaa125db
+24, 0xd1fe47f25152045c
+25, 0x3bb11919addf2409
+26, 0x26a8cb7b3f54af8
+27, 0xe6a27ee11200aa24
+28, 0x7cb585ab01e22000
+29, 0x78e60028676d2ef3
+30, 0x5c32535e5a899528
+31, 0x83e8b6f8c4a46fb3
+32, 0xe56ef7668a161246
+33, 0x36dcbc15aeb73055
+34, 0x5ea247f0bd188acb
+35, 0x438b547b84601a80
+36, 0x8acda2a1273e9e3d
+37, 0x2b05e30a4b40c24c
+38, 0xfd87236bd13af032
+39, 0x471df211d8d985ef
+40, 0x18e8a5609a793292
+41, 0x46f0951fab6dc4e3
+42, 0x6c199c4e700f6795
+43, 0xf04aa16bfb7d22cb
+44, 0xd763d269fbaffc89
+45, 0x9991930cefbe5c2b
+46, 0xb2a11b953f824c96
+47, 0x63fd9f52172c44b0
+48, 0x183bdad907b1d848
+49, 0xe17953cddb931c52
+50, 0x515cf16726ec205a
+51, 0x88c327605150711a
+52, 0xc7090dd79cbc8dc3
+53, 0xcb487cedeb00a350
+54, 0xc8abf254d87b657
+55, 0xd43cc4cbfb493d1a
+56, 0x8705452e5d9ed1e
+57, 0xcecd11446769cf43
+58, 0xde72156c8d65bc69
+59, 0x796a8f0f47d52ee8
+60, 0xb4c0da443917d6c3
+61, 0xe07ad7568a8e3dc3
+62, 0xc24a8da39ce6dc21
+63, 0x92b21ea80a8556eb
+64, 0x572f21e531edf3af
+65, 0x9b917ed56bbed198
+66, 0xe65fd8ddc5ab3d7d
+67, 0xf55a80a8ec84fa18
+68, 0x18fc22e1a5227b61
+69, 0x72305dc7eeaa79d3
+70, 0x47ce58a36e7592cf
+71, 0x14c6374340c0f7cc
+72, 0x6f98273d4eb5a2c
+73, 0x59a8702c46fe8f8a
+74, 0xb67cbd8113cfe57f
+75, 0xaa03c5db5f5b7690
+76, 0x3fb0f77ea4568013
+77, 0x756530990398b26e
+78, 0x4c1952b2a3a6a343
+79, 0x1da15c5383074582
+80, 0xb405b21c81c274f7
+81, 0xbe664677a16788b
+82, 0x9d2e37550bcee656
+83, 0x8b4589f0d9defe02
+84, 0x2935f018ee06a59
+85, 0x3834bf88be97ed11
+86, 0xa610d049cea79b6d
+87, 0xd49ffc0d09a59ea9
+88, 0x4073365b76567adf
+89, 0x499eefb9bb7513e2
+90, 0x74a743ee6b0138a9
+91, 0x3bf0880f2d947594
+92, 0x555d1c0498600a99
+93, 0x923b32a88ef2ffa4
+94, 0x7325411065fbedea
+95, 0x9f4129ff8b79d300
+96, 0xab2b0a9b8a3785dc
+97, 0x11734bdfba3a1713
+98, 0xc8333398841ba585
+99, 0xee2409cc234e6742
+100, 0xf6638e700872ecd2
+101, 0x10875300c13cd284
+102, 0x27a9bbed7c15b2d3
+103, 0x3c87f8fef31ce9bd
+104, 0x92be263cd0914a95
+105, 0xa7b0f11bc742307e
+106, 0x4a56f788cc1c1a3c
+107, 0x4a130fa32257a48b
+108, 0x5d4d9eda16e90286
+109, 0x7cc2af564844bedc
+110, 0x2532867bfe7cda1a
+111, 0xb1c504676611fd17
+112, 0xce8e86cfb4189aee
+113, 0x99685898980d1970
+114, 0x8c3b67db23bcf1e
+115, 0x73e14c93905b135f
+116, 0xf0271b64ac2bd4d3
+117, 0xf4beba82f3ec1b2d
+118, 0x1cdbf3ee9f210af
+119, 0x2e938557c09c3ea6
+120, 0x2d314ccfa6ffd81d
+121, 0x31ad47079950ade4
+122, 0x342b27547b900872
+123, 0x171b0e20b9ef1a76
+124, 0xdf10ce6318b03654
+125, 0x1d625df4aa718897
+126, 0x8712715a9f6e02ec
+127, 0xb4a072da725bca3b
+128, 0x19d346cb7734bd42
+129, 0xfd4281d311cb2958
+130, 0x58274c9519fc8789
+131, 0x4cacf29d885fd544
+132, 0x784b14d1c2523b80
+133, 0x2d25242131bb2373
+134, 0xcd2a5e43a7d9abf9
+135, 0x15eda3806e650ecb
+136, 0xdaac5e277d764d96
+137, 0xdc5a5dd59aaa94e0
+138, 0x40d00237a46d5999
+139, 0x6205dd35a692743f
+140, 0xbbd8236740361f09
+141, 0x1625c9f4e7288bf9
+142, 0xb74f12df1479e3ce
+143, 0xb2d72a51b43d7131
+144, 0xf006a324b3707c83
+145, 0x28e8ab4abe7655b8
+146, 0xfb480093ad7ab55
+147, 0x3f8abd0d6ff8d272
+148, 0xc81a94177ac26bb7
+149, 0x3cdc178307751b14
+150, 0x9de84cc2b10ba025
+151, 0x3f8ab5aefcd046e2
+152, 0x43bdb894e1ee83b2
+153, 0xe288a40f3f06ac9d
+154, 0xdab62a7d04b4f30f
+155, 0x49f4e20295e1a805
+156, 0x3643764805e0edef
+157, 0x9449954618b6b
+158, 0x6c87e0d4508e0ce0
+159, 0x3a334be688a9dd7b
+160, 0xb35c39228776e499
+161, 0xc4118bfff938490e
+162, 0x88cbde3dcbb034b2
+163, 0xf91b287793c417c3
+164, 0x42b15f731a59f5b3
+165, 0xffa27104bbe4814d
+166, 0x1b6789d138beccde
+167, 0x542c2c1440d0ceb9
+168, 0x367294504d18fa0d
+169, 0xf918b60e804a1b58
+170, 0xd390964e33a9d0e3
+171, 0x23bb1be7c4030fe8
+172, 0x9731054d039a8afb
+173, 0x1a6205026b9d139b
+174, 0x2fa13b318254a07e
+175, 0x69571de7d8520626
+176, 0x641a13d7c03332b7
+177, 0x76a6237818f7a441
+178, 0x4e77860d0c660d81
+179, 0x4441448a1c1cbdb2
+180, 0xccd7783a042046e5
+181, 0xf620d8e0805e3200
+182, 0x7de02971367fdd0c
+183, 0x539c263c5914cab1
+184, 0x9c3b9ba1a87bbf08
+185, 0x6d95baa34cda215f
+186, 0x2db3f83ace0bac5f
+187, 0x7f5af1da2dc670a4
+188, 0xfcc098d16c891bfb
+189, 0x81a33df1d7a5ab12
+190, 0x767b0f863c8e9882
+191, 0x7a92983830de483d
+192, 0xfa7598c37a79ac25
+193, 0xb89b3ca42ce03053
+194, 0x457a542b8efed4f7
+195, 0x571b7737fd0eeda7
+196, 0xa0f59e524485c0a
+197, 0x82dca766b7901efd
+198, 0xa68243caf6a3bd5d
+199, 0x1bac981c6c740e5e
+200, 0xbcd51bedf9103e44
+201, 0x4e197efd3ae5a7bf
+202, 0x523568efd782268b
+203, 0x5ec4ef1191fef09
+204, 0xed751ed5e31c9ab
+205, 0x44eac24de03e1b29
+206, 0x9237d57c011d3fb3
+207, 0xa8c6da0f7692f235
+208, 0x9f9eb6bc15d6cac7
+209, 0x34bb8e0c93427aad
+210, 0x115febd738eaac4a
+211, 0xa439991ed139d27a
+212, 0x45c7c2633d8710a2
+213, 0x48b7475f3405a3ce
+214, 0x80158497c77bd00b
+215, 0x935c316a5b1657cb
+216, 0x59c5d54440e9695e
+217, 0x337c78c5b3d0ede2
+218, 0x8c46bb956b93790d
+219, 0xbf1dd03e471d71c5
+220, 0x2d375e90a4bef583
+221, 0xd0365428331b3790
+222, 0xfcd3969ac827ecd4
+223, 0x392fb6c580498410
+224, 0x6d6db4ceab5ea6c0
+225, 0x9bf84f1972e24786
+226, 0x798dfd820959dcc5
+227, 0x2e425095e65e8bfb
+228, 0x8c1aa11536b1c9c3
+229, 0xd28e2ef9b12f6f74
+230, 0x86583bc98c8f78d2
+231, 0x489877530e3f93e7
+232, 0xb1d9430631104a15
+233, 0x1814f6098e6263bd
+234, 0x8e2658a4e0d4cd53
+235, 0x5afe20e2531cdb2a
+236, 0x30d02f7c4755c9bf
+237, 0xe1e217cda16ed2d2
+238, 0xccb4913a42e3b791
+239, 0xfff21363ac183226
+240, 0xe788690bbda147a7
+241, 0x76905cf5917bfc6a
+242, 0x2a8fa58f7916f52c
+243, 0xf903c0cc0357815a
+244, 0x15d20f243a4998d2
+245, 0x5b7decee5a86ea44
+246, 0x114f7fc421211185
+247, 0x328eb21715764c50
+248, 0xaffaa3f45c0678fd
+249, 0x2579e6ef50378393
+250, 0x7610ab7743c19795
+251, 0xf9923d2bd101b197
+252, 0x57e42e7a62ba7e53
+253, 0x9f1dc217b4f02901
+254, 0x88a9ebd86509b234
+255, 0x867fc926aecc8591
+256, 0xaf22c1bfef04c718
+257, 0x39f701f0313f4288
+258, 0x6171ad397e6faab2
+259, 0x239bb5b9abdec4fc
+260, 0xd9a591e25dd01c6e
+261, 0x826dc4a75b628e49
+262, 0xf112b152c408f47
+263, 0x6843a06110f86c0
+264, 0x965e56a7185c1332
+265, 0x8d84492edbc71710
+266, 0xeee8ec111cfd1319
+267, 0xf2858e94ad98e458
+268, 0xbc9589fdf5f3a97e
+269, 0xaf0ceef3bc375130
+270, 0x48f4aaf13fa75c1e
+271, 0x111e9db47bee758f
+272, 0xea3171df130164ba
+273, 0x2a7bbe30bf827ab6
+274, 0xc516c3fdbf758c35
+275, 0xec55097754b04be5
+276, 0x374a997d52b6d3e6
+277, 0x487df5456085ffbc
+278, 0x528883b84df8eafe
+279, 0x805f77ab5ba26f86
+280, 0x8eb81477dc04f213
+281, 0x471ea08ec6794d72
+282, 0x69d3667ecc4d2176
+283, 0x98b7b6e295548a66
+284, 0x3877713c173f8f2
+285, 0xa00542570d0e8de3
+286, 0xf534b1bfa4033e50
+287, 0x7e1fedeac8bf6b26
+288, 0x8043f37c89628af4
+289, 0x1dd7039ec295e86d
+290, 0xce9c05b763a40cc4
+291, 0x246926481e61028f
+292, 0xb7cb0f1babf5893b
+293, 0xefe6b777f37fc63e
+294, 0xebbcabb4cb35cdcb
+295, 0x39fa63cd711eeea9
+296, 0xad5d3ba7aaf30c8d
+297, 0x8e9e78fe46021990
+298, 0xc7eaef6e7d5a3c62
+299, 0xefccdd5495d3f386
+300, 0x2179557ee8cfc76a
+301, 0x88a77f621f0885ce
+302, 0xafda62674543d90c
+303, 0xb8e6fbe2e13e56c0
+304, 0x8bfbbe26a14f9b1a
+305, 0x1404f59f5851f8c3
+306, 0x1140c53a0489566d
+307, 0x3edf2d138b5c3f1d
+308, 0x75d6bb275d817dc
+309, 0x8e660ae27107664e
+310, 0x7a8021038ee303e1
+311, 0x2042ef5eefa9079f
+312, 0xe3e7b90bbf6d457a
+313, 0xf3f819d2bb9405b
+314, 0x522e42155cae0c10
+315, 0xf5bfbb975b40e233
+316, 0x2cf82b614dd95cfa
+317, 0x183ef4a96bc40e55
+318, 0x9f6e351c5ba4e752
+319, 0x37c1110683c90846
+320, 0x1d89b7a996d8a977
+321, 0x18a444f77c7cb4d9
+322, 0xd0a8a971b78dc893
+323, 0x860232fb9e6543f1
+324, 0x60b6097f51002555
+325, 0xca1e5214123e3894
+326, 0xe03fe695c95f99bb
+327, 0x2c7c6779d5f03622
+328, 0xafeeee42f63055d1
+329, 0x670dde905515936a
+330, 0x9a922f42b59fb094
+331, 0xddb5ff49af5a651a
+332, 0xe61b04c9e58ebbf8
+333, 0x4e459dcf272e7fc4
+334, 0xd549e92c16adceeb
+335, 0x7a17dba1299d4a9c
+336, 0x825d756109f2b585
+337, 0xba142e61a9cb203e
+338, 0xc2a19f00e9c04a30
+339, 0x2d0f8140d23d0652
+340, 0x8b866d4d4d6caaf4
+341, 0x4f11d90dd91f8217
+342, 0xf6efc37373b9e0d
+343, 0x248493d6cd6a4736
+344, 0xd12b6ae74a951a3e
+345, 0x56e34722070b70a7
+346, 0x22d3f201cc9fa0eb
+347, 0xbfdcc320008291b7
+348, 0x1a7a6922e9204fbd
+349, 0x831421e0c4945ae4
+350, 0x66316feddddf0e11
+351, 0xa8c86a1517456554
+352, 0x14a9049ad989e335
+353, 0x837022259f141ecd
+354, 0xcb71793a06c261f7
+355, 0x4aeefc07ebe09a79
+356, 0x8982f15aa3b6594b
+357, 0x67bccfa7ed9b0d5b
+358, 0xb377463b523e9dec
+359, 0x53d3d594870fecb7
+360, 0xa5274b1caec5a60a
+361, 0xd6316d0cb643db39
+362, 0xabc1a9b536de88ce
+363, 0xed2fdb1383d2a077
+364, 0x12319c6feb97221b
+365, 0x7e0f6cd40ef47403
+366, 0x86135c84fe26dbf8
+367, 0xc96622d3fbbee19b
+368, 0xe3989d8d8511573f
+369, 0x42cc365554d1fdc7
+370, 0x4c1a1eb8bbce8b4f
+371, 0xfc4e30e7ef2034c1
+372, 0xc490444317a91e76
+373, 0x7ccdf469ff5dc81c
+374, 0xf5a0da4110cc09d7
+375, 0x505227baf34c0fb5
+376, 0xbe58737e8a35cc88
+377, 0xd449bee91b3e8c41
+378, 0x3e590e23299d0e6
+379, 0x291a7d9e0a64caf7
+380, 0xdc6fafbdfebd2293
+381, 0x8223f1e259fe8a65
+382, 0x6186fbc9efd9e3df
+383, 0xfda39b07e4007ffb
+384, 0xfc19aea98574dc02
+385, 0xd0e10d354fcacd8c
+386, 0xc9619916544a55a5
+387, 0xd454d50a8c8558cd
+388, 0xcd94a246712d91e
+389, 0x76a771f5d1231cce
+390, 0xdd20cb2b7b370ee5
+391, 0xa6f4f50feca57c49
+392, 0x78c8fb431f17ab9c
+393, 0x1b692b79a59b43cc
+394, 0x4c45045d287da7e6
+395, 0x522132e18bf43928
+396, 0x25c458983138b41c
+397, 0x2a1fb426ef229796
+398, 0x74dc324c74e5dd3d
+399, 0x6df75e3eb6eb5374
+400, 0xb63f2f4f9ca25b61
+401, 0xac72286112ee54d6
+402, 0x5a966f3d0a6863c4
+403, 0x8d7046bc64a46fc2
+404, 0xa7b740fd6e3087eb
+405, 0xcdbcbe0340cfcdf5
+406, 0xcb632613bf312b65
+407, 0xa91b3f2c2aac238b
+408, 0xa06deb3f5ae555a3
+409, 0x29d72e1f8db69
+410, 0x2d004bae09728ea6
+411, 0xc6eee5dce0736cc1
+412, 0xa7493145500ff60f
+413, 0xc4d68c4aa18ab93c
+414, 0x8210c29e79d48d7f
+415, 0xd0999d7889ecbef6
+416, 0x6e3bd61e66e93566
+417, 0xe6cc13d47d7d7b1f
+418, 0x3d6f181f42e03979
+419, 0xbed4e14fd867604a
+420, 0xbe511c84067bd86d
+421, 0x49a876d89e697d38
+422, 0xc04c3dde8f889c98
+423, 0xaf293eeab0f53e3f
+424, 0x9f6291dd65732cd6
+425, 0xd7811ac01de78c01
+426, 0xe385cf0261d50ec2
+427, 0x5a64134b3542bbf
+428, 0xf9d1302bc6f13a68
+429, 0x5d2aabbea37d8c31
+430, 0xd9842e99a5192970
+431, 0x713eadc4cd30e837
+432, 0xb7b002fc72abb413
+433, 0x276cfeea526af1cf
+434, 0x8519fe79b633a0ce
+435, 0x2f0e87363705a3e2
+436, 0x9adbac0be3c371e7
+437, 0xf3f44ba899a6173c
+438, 0x782d6c29618fde2b
+439, 0x7f61062acec408f
+440, 0x6e79cd836359258f
+441, 0x5c8e9b138df5785a
+442, 0xa54359c9f39a9a84
+443, 0xeec3f033135084b0
+444, 0x883ee717787a535c
+445, 0x9a2422b513a73b00
+446, 0x2dd4beddcdd64a58
+447, 0x90c8a13202239c7b
+448, 0x85b352ab759646d9
+449, 0x139f5cb2e46c53aa
+450, 0xe1d3ba6c721c66d1
+451, 0xaa66e0edc4b60a98
+452, 0x3521275c75be29b6
+453, 0x490a5190b3edfa5d
+454, 0xd2abcdd2ccb2f14e
+455, 0x9d9be8bef4a5857d
+456, 0xde19676f13ef7755
+457, 0xdac2fee2e42615f3
+458, 0xf4239801cb02f2ab
+459, 0xaa8bf923ed91875c
+460, 0x61d18a1940e4c7c0
+461, 0x1eb6aa3d5f077a6d
+462, 0xee7374c063bf29d8
+463, 0x2f0a59e34d76268d
+464, 0xc92e80e17d1eb3e9
+465, 0xafd05b3ec3d2ca72
+466, 0x28a61ad8d6c497b8
+467, 0xa7094d6834ad7d47
+468, 0x57d80ea9eccbb4f
+469, 0xb047e0fee6cdaf16
+470, 0x44f41b5eb48c00bb
+471, 0xd6dc8e1eb9c8c9ba
+472, 0x47adfd2c638c7849
+473, 0x365d63db7d526c68
+474, 0xc21cda439016135d
+475, 0x14d10c3f0f98863c
+476, 0xa93e56f74e037602
+477, 0x3b4e9c8915bdc9
+478, 0xb46f5ae155e54aa2
+479, 0x8e470d21ce1943e1
+480, 0x60b96301b5ba2e8d
+481, 0x1b473a41d381f9ff
+482, 0xabcf5a8e3269e73f
+483, 0xd410f6e94fb21fa1
+484, 0x65d1a47eebf87e5e
+485, 0x48eaa201c61cb843
+486, 0x212c1abc2499bfc5
+487, 0x4255ad8377d2d8d
+488, 0x44caeef472010612
+489, 0xffae764524f572f2
+490, 0x78d374d20c9ee550
+491, 0x6e003206c0511cee
+492, 0x7998a159145bfb82
+493, 0x921239650bda1d4d
+494, 0xae05025509bcfdc5
+495, 0xc6430c980be407b4
+496, 0x78524f1744b153f1
+497, 0x84089e6f468181fe
+498, 0x8d0d21d7dfb6c254
+499, 0x90bad90502a33603
+500, 0x3072a403cbd16315
+501, 0xdfadddf3f1c040c2
+502, 0x22f0b0639d9ff975
+503, 0xb49e48a4cad0765b
+504, 0x95a0a04f8239709d
+505, 0x56e147a24a4c481f
+506, 0xacf16ef61dea4c7e
+507, 0x424040afd2700de6
+508, 0xc67e8096a3c717a9
+509, 0x39f164181dd0a399
+510, 0x2449cedc1d62198c
+511, 0x7a53df11a1f1a61c
+512, 0x5596f1d4a3badae3
+513, 0x38ed4c822072b3d0
+514, 0xf07ef346b3fd730a
+515, 0xfd349c35c3ed51fd
+516, 0x2f15c9c7890f8f32
+517, 0x3b470df52b173c29
+518, 0xd31bfc8981281af7
+519, 0xbbcc9bdf561215bb
+520, 0x5782fffea326574f
+521, 0xb0ebdcfcc5e03290
+522, 0x7fd89d93d2b3fbef
+523, 0x280ea1865d9ba2
+524, 0xe726959845b2c100
+525, 0xd0361f032cd7dbb1
+526, 0x3c65ec2028b81a22
+527, 0x5221e9b2188920bf
+528, 0xeb5ab27c4125ec20
+529, 0x80a32dd48b54f0a4
+530, 0x369b5ced1012bebb
+531, 0x582d35d76530bc6f
+532, 0x7b50dc9b48e1e37d
+533, 0x37fdfe8bbacf8dad
+534, 0x7a0cb7e6e93840ea
+535, 0xa1132c870be0b2ce
+536, 0x9d8ac2c68267cd1a
+537, 0x470969b647fa7df4
+538, 0xabcb7d8adf7e2d24
+539, 0xacdebec9bdf9eb1c
+540, 0xe30f4cbf7eb6a59
+541, 0x746673836c4df41d
+542, 0x75120a6b647bb326
+543, 0x2f4eab556c3f6878
+544, 0xd84651ab05405b7a
+545, 0x9e695808b9622284
+546, 0xc93b71e56aa6e1a5
+547, 0x2be7f3be4a7b7050
+548, 0x6497e910b6733241
+549, 0xcf7050dfd08076fc
+550, 0x4e3cc156eca183f7
+551, 0xf801a33d9326c265
+552, 0x6aa293c8a47d40e6
+553, 0x28c429755faa6230
+554, 0x82b818651f54e7bb
+555, 0xa84d726d7acdbead
+556, 0x5cfa535d5774965d
+557, 0x4a34b7b1cb48d53
+558, 0x86a7b5bce426de84
+559, 0xfcd2307cecdb7318
+560, 0x16dbaaa71181a038
+561, 0x88e7e8cd261c2547
+562, 0x3c09ba6d1d5ea913
+563, 0x5dd3d643734ee5b6
+564, 0x326d725fe8cbb33
+565, 0x7bcca9ca2da8e784
+566, 0x482dcf6b11d7f9a4
+567, 0x1291b605b4cd3e04
+568, 0x6988181b50e2f4a8
+569, 0x649e3c37131fc292
+570, 0x4eeb67b9e21eba54
+571, 0xc051d39073dec45f
+572, 0xc99c52e110270d67
+573, 0xcb813d5d77868add
+574, 0x423a5f13573e7ac0
+575, 0x231ac4cc4fe73616
+576, 0x4c22b888a6e600ea
+577, 0x8059a6dc7c9e25c6
+578, 0x49f498a5b8ad22de
+579, 0xf1e812cc6d1826c8
+580, 0xbbaf60abe8b11e00
+581, 0x1d31d7f4d8be9a6a
+582, 0xfeadce70a9a10c14
+583, 0xb47c635bc136996a
+584, 0xd88e694c8da030cb
+585, 0xc41bbe132aff1364
+586, 0x34249ab18a4b0800
+587, 0xf14b5c825aa736cc
+588, 0x2710be6b08df78e
+589, 0x2ab56bcc9bf9e740
+590, 0x9b7f6e591b5f648
+591, 0xfb665c3772f34135
+592, 0x628a0a5d2db5d8d5
+593, 0xb3e3f251e61b5259
+594, 0x82310ae33faf1b23
+595, 0x24af8723a65cbd0b
+596, 0x671c93282fc4ad97
+597, 0x6cabeaac77270cad
+598, 0xef4643fe38b02b7f
+599, 0x7b011549d1ac6653
+600, 0xe2af87b9fccfe89
+601, 0x36b71ad67197ac8a
+602, 0xdbba55d06f2fd93b
+603, 0xf571dbd764b7f7e5
+604, 0x38ea402501cdbd45
+605, 0xb8ab5b5b1bab2913
+606, 0xfab973c4d45f32bd
+607, 0x9364f1717c2636b9
+608, 0xfad00f4d983e00fe
+609, 0xc90c532a11aef75a
+610, 0x64a6eda96e44783c
+611, 0x35891f2eb84520be
+612, 0x28d216080caed43
+613, 0x129629cc5bd206f6
+614, 0x22c3d39822cbb4b3
+615, 0xf1efbf4cce1eaa2b
+616, 0x7070cba12524ed08
+617, 0xa7ed0be9deabf20d
+618, 0x8ddb4cd6b454f76b
+619, 0xb82814b1db37b63
+620, 0x418e83b36de01876
+621, 0x9a538c7f39c6413
+622, 0xee0cd7abf8a2ecb9
+623, 0xa9222b07e95590f3
+624, 0x6296a415d68341e6
+625, 0x981e0a5a8f811929
+626, 0x4bb372d3b0de283d
+627, 0xa9805b5971866e16
+628, 0xaf3b5f5183497657
+629, 0x2152b0fd23c3d9f
+630, 0xb730c325b7173180
+631, 0x1e3439d231608c19
+632, 0x1c5ba6031379823c
+633, 0x87f5d12d6d365cbc
+634, 0xd3bc7f29614bc594
+635, 0x63102214bb391268
+636, 0x482bbd5bba648a44
+637, 0x6a23604690759dc4
+638, 0x4091d41408d3a39e
+639, 0x7cd017f922101b15
+640, 0x7ce9004ac5f9231
+641, 0x978bc3d8ec7f7fdf
+642, 0x5bd0c4d780580c11
+643, 0x4313c068bb040153
+644, 0x3ab7dab7bc38bf80
+645, 0x3aaf9c187728deea
+646, 0x6633a4ce8efb88d9
+647, 0x7263b089878f00fc
+648, 0xd0d767e96fe00eb8
+649, 0x184a7c0c01908028
+650, 0x1ebdf41e6f76e186
+651, 0xeb740ee1d0402083
+652, 0xfccf4974edb1c339
+653, 0x16e2707aa28306d
+654, 0x1684f0bdb018c3a5
+655, 0x887b6b67b88aa862
+656, 0x923d7810a2bea33a
+657, 0x56b3560babef5d6b
+658, 0xb39a14614c54b8c6
+659, 0x33e4dc545a509fc8
+660, 0x26e21f84142da9b
+661, 0xdd07598125756855
+662, 0x572d49a071d7ae0a
+663, 0xba3c7e3baea28760
+664, 0x7ecdb2d714db4b61
+665, 0x1c62b4920e1b2fe2
+666, 0x71bfafb70092834a
+667, 0xd710a4228f60d56a
+668, 0xeb16277d4ce4e95b
+669, 0x968168c90b16d3a1
+670, 0xac3439dfe8ad0062
+671, 0x5a8226f9dd5876ad
+672, 0xb843affe917291b0
+673, 0xd76d1e67051f8259
+674, 0xb73a6638cce8ccde
+675, 0xa0e6afd3c7295f9
+676, 0xff8857b4bbb5f4c6
+677, 0x99becf78938f0426
+678, 0xfcd17edc1e70f004
+679, 0x6223b8b23f2f50
+680, 0xca875f3e84587b4c
+681, 0x7d1e81e589f87fb9
+682, 0x9eb621586aa826fc
+683, 0xf46fb9ef5b9c2086
+684, 0x2882c9b7092725f3
+685, 0x5493f099bbedcd02
+686, 0x90c1ec979ffa811d
+687, 0x963f765025bcc53
+688, 0x56194e3ec3d9d4e9
+689, 0x7ec4720954cac1f0
+690, 0xfab3145171af7f90
+691, 0x52a0b4e41a13b593
+692, 0x740e2d4d5909d126
+693, 0x98f5339c09c94a28
+694, 0x1700e462fe8dec76
+695, 0x3dbffc2aa4695ac3
+696, 0x5763edacabdfe2a1
+697, 0x7b5b623ce49ef21d
+698, 0x30addc66f49860df
+699, 0xcc7511a6c31bceda
+700, 0x1b25b61ca75db43b
+701, 0x416bc4c298e59046
+702, 0x4cd11fe2d74e4649
+703, 0xb54458a9229fc978
+704, 0x8c21a27882b6ca35
+705, 0x57887c8b5e01639b
+706, 0xf4e893da996680bb
+707, 0x8d601297702c9c0d
+708, 0x2a27904a30aa53af
+709, 0x497800f6917ea8d0
+710, 0xe96db3340ada9c00
+711, 0xcc23166f14c010ee
+712, 0x782690d78fa65ec9
+713, 0xf3e00d74a0878eda
+714, 0xa7cbb683decca0a3
+715, 0xdd2e038e683a94aa
+716, 0xe2096ff8da896ca5
+717, 0xf7c83400afdabe11
+718, 0x395b8c6f6a4086a4
+719, 0x4a164ec05bee71d4
+720, 0xe87aa5d1ca0462fe
+721, 0x8dbc5aed6dff9ceb
+722, 0x12120d1e9552707b
+723, 0x877dca6889b3e6cd
+724, 0xbd65605c01e900fb
+725, 0xbd6b82c4157c3115
+726, 0x8b60282732caf78a
+727, 0x279fcf5e5de9e57f
+728, 0x34b34ebfb6a37eae
+729, 0xd258cc1a14e03b7b
+730, 0x9a528ba3db4a13fb
+731, 0xffa0aea59d057746
+732, 0x27fa7f456cd37c4e
+733, 0xe1117a57a6fdce63
+734, 0xdc8fc903970a1551
+735, 0x492dd104f30faf29
+736, 0x110def0959e5652b
+737, 0x7f8d1997636fdd15
+738, 0xfb77b05e538a9b59
+739, 0x2e41fa35b4b01fc6
+740, 0xbc35ae69a3374085
+741, 0x192c2a681c2d9b4b
+742, 0x12566b8866c189d6
+743, 0x9d88ea785c5185c8
+744, 0x30a621ad5f983c4
+745, 0x8b875efe1206f587
+746, 0x224d25c3af6e3423
+747, 0x7503e976a1ac7bcc
+748, 0x3c98aa869e823859
+749, 0x3d8835304b646892
+750, 0xf6353330ff970bc2
+751, 0x8a673f5e2edb8acb
+752, 0xf2fdcc53493838b9
+753, 0x85ddcd526236af16
+754, 0x60afb99814c676c5
+755, 0x32a1c2749e281ca8
+756, 0x2367a92ae3bee9ca
+757, 0x219fe082703743cc
+758, 0x34d8b74dc85182a9
+759, 0xdd04164c72db23f
+760, 0xe293ac28fe2671a9
+761, 0x9ca7d169cbda6f45
+762, 0x705c47972b4240ed
+763, 0xc10eda9eeb536209
+764, 0xc36ddacd0c94e85d
+765, 0x8eb592c27e8cd0d2
+766, 0x3e815991c76e7cc4
+767, 0xac9cfce31acf7580
+768, 0xbf7a4cb31c7aee94
+769, 0x663077444aceecf6
+770, 0xe7f614ff386eb568
+771, 0x79d7a229c66912c0
+772, 0x161ed4311f63e1f3
+773, 0x308a5faeb9982ede
+774, 0x7b38ddb9b7efd10
+775, 0x1e103a2589b27ecf
+776, 0x67b02baf4259f27e
+777, 0x868921c115ea2eee
+778, 0x959791912200f71e
+779, 0x4dd55f36dec10557
+780, 0xe3464d90080cb99d
+781, 0xfb2d4f6accce652f
+782, 0x109900a9257d77ba
+783, 0x3c4bda8e2c83684c
+784, 0xc9ae040fb7f868c6
+785, 0x78098ffe994f4905
+786, 0x7a94c33eca77f0b4
+787, 0xbe6a2a95e9b5c0e8
+788, 0x797d39cf963f4837
+789, 0x8d2e249e4425d06d
+790, 0x6ae2c30cd5da06f4
+791, 0x904489de762b179f
+792, 0x84713e2dfb591e3b
+793, 0x6405a40da3f6f51b
+794, 0x976b560d663a2df1
+795, 0xed1c544784ba1e22
+796, 0xca658e995ed9344c
+797, 0x2b1c6b8e4db49025
+798, 0x52b1513da528bad
+799, 0x3c63406d256d9968
+800, 0x63a31ca3d423f85e
+801, 0xb05a81f55789a720
+802, 0xd04412992c476c8e
+803, 0x828ec2f77a150a3d
+804, 0xee50926671bb60c6
+805, 0x5aa70f93e2df61b4
+806, 0x94d60fa2e8655858
+807, 0x3f5e5b770703cc7d
+808, 0xc62dfb2688ca7784
+809, 0xaaf02e1e8ba89fe4
+810, 0x4ab74e0d8c047405
+811, 0x31ee04fbac6fcead
+812, 0x1203b78b8228f5af
+813, 0x412a70836f9aa71a
+814, 0xab51cf98c03f1819
+815, 0x783a3ce9ce137f65
+816, 0x8897085b0a072cf2
+817, 0x685dd9bde8798cb
+818, 0x9a1fac7b1705e2c1
+819, 0xf3e9ff98de48e9cb
+820, 0x5c2d3eb1a1fbe917
+821, 0x3bda718b6b54d82e
+822, 0x29f2dd18f22f0821
+823, 0xb992da1572ac3597
+824, 0xacb69e7aa14b34f7
+825, 0xcd36e3ad14f088d1
+826, 0x6aaacc96a1ec55e8
+827, 0xf8ac593f154fe68f
+828, 0x18fc9cbff012339f
+829, 0x2f3368ccbbb99899
+830, 0x7cec7d17f37031f7
+831, 0x96e86bfaadcb8fc2
+832, 0x74f9e7ee3d42a752
+833, 0xbd52f6c7d9b0733
+834, 0xa48e6d96bb6ce1c9
+835, 0xaefa058254b82133
+836, 0xb7a19edfd0929107
+837, 0x6160ce9125b26e26
+838, 0x6537dbbde1d2aed
+839, 0xc567f9a6bec52dde
+840, 0xca29fd3f22443342
+841, 0x7732aa6db6a1c476
+842, 0x8f5a4d7df6b11b3
+843, 0x76649262aa7e31e1
+844, 0x60a13eb125fbc829
+845, 0xc81e4d123dd21ac1
+846, 0x643cbb09bb72f86b
+847, 0xf971a98fb25555a6
+848, 0xffa2774c66692d56
+849, 0xcb33c16c50b13ea9
+850, 0xfabf388dffda0e9b
+851, 0x55d41ec12ca24b9f
+852, 0x91cf693a3467e807
+853, 0x6be2c00b2c31d6dd
+854, 0xc5cf513b5251ae28
+855, 0xffc4384212403dec
+856, 0x45d4e1865255a69d
+857, 0xfb1dcf956972086a
+858, 0xcae946a55c4c55b8
+859, 0x7351ac7720e385c1
+860, 0x19aa8ffd86240254
+861, 0x8f515ae78f4040da
+862, 0x1e1ed2058de50fce
+863, 0x22d006dcdb374243
+864, 0x6e0f0ede7c95b441
+865, 0x70e8aa81b53b4d25
+866, 0x998f309ea41e3814
+867, 0x89ed6598fb66f390
+868, 0xb5997dc3278060df
+869, 0xb2a021eac4f7e046
+870, 0x3705b60aa2fd0768
+871, 0xfc415079ab9200e
+872, 0xf2871ac4cf45ecc9
+873, 0x24bf758d2246175f
+874, 0xac503dd6f8141b3
+875, 0x4e879d12d9f03b3
+876, 0x82034af8cf93b644
+877, 0x59899dd7e478a6c7
+878, 0xae90addb6eb11507
+879, 0x1524ddf76730cdef
+880, 0x6fd4afd5456b1c9d
+881, 0xcddb9221ea001cbc
+882, 0x64ff400bbf2e8604
+883, 0x6dda10549b06ed9b
+884, 0xed2c85104c261527
+885, 0xc7e09217d29929a8
+886, 0x56284df611a428b1
+887, 0x1a7608289c0a61
+888, 0x7cb63db15166ff66
+889, 0xc6013c76fcdcdc72
+890, 0x8e5dd566c7a5a676
+891, 0x5a8e8565f40d133b
+892, 0xe465973455848c44
+893, 0xf92eecbfe0f3c2c0
+894, 0x7d64155d4dcc5cac
+895, 0xf17595706f988dad
+896, 0xd590a001a6a19c5c
+897, 0x82a164475758db3d
+898, 0x6b144993ea1bbe32
+899, 0x22a81a7a6e453779
+900, 0x8e8c298df1a68a73
+901, 0x78056afd6d936b4c
+902, 0xaaceef0325faaf62
+903, 0xe78bb7699f82266f
+904, 0x523a2d283c5a5166
+905, 0x7076d87088f6c6db
+906, 0x6087dd54cff5aeb2
+907, 0x7ef82e62cb851680
+908, 0x4e8bcc8ed84d03d8
+909, 0xd12fa0361df3cfd3
+910, 0xefb89c79f8127297
+911, 0xa9af4e2fbce0b1f8
+912, 0x462136685b70331e
+913, 0xe9e74c93da699b77
+914, 0x9ec69215fb11d0c3
+915, 0xc10f229939e3e111
+916, 0x3f67fa79e41d2374
+917, 0xd5e7c1a9a7185162
+918, 0xa1dcce9ec91492fe
+919, 0xd4e61f0727b5d21b
+920, 0xdf6cdce46551800a
+921, 0xa3f256ce906982d3
+922, 0x209742a6b9ffc27
+923, 0x4006c96958526a57
+924, 0x9606aebc75a1967e
+925, 0x91b9f42fb64189df
+926, 0xb27119defcb938bc
+927, 0x128cc7a84ba05597
+928, 0x6c3df613c62d0d30
+929, 0x3adf69d48b629ec7
+930, 0xda42ee493837b128
+931, 0xb8e770480e760bb5
+932, 0x9feb55d57c99c626
+933, 0x29812d80afdae3ed
+934, 0xae4222a64276a8c7
+935, 0xe3897212a5b4ed53
+936, 0x98bedfd13886e669
+937, 0xca858675d7fc0d0e
+938, 0x28a359f665354234
+939, 0xfac2ccabe4128b35
+940, 0x61373cc5d11ca180
+941, 0x7007605a4512a87a
+942, 0xe71f8eade7b30b3d
+943, 0x3a9e77f9b99bd04d
+944, 0x70d3e42488098866
+945, 0xd30fc159c7cd4d99
+946, 0xe4d3f6600d2e2d6f
+947, 0x1088324dfa955c25
+948, 0x516437acd4764623
+949, 0x38a31abe50d0aa03
+950, 0x72e1054e9dc02ba
+951, 0xe6971dd664d1a2e2
+952, 0xf6698cb095d3b702
+953, 0xad995a5a8c19bd92
+954, 0x34e53c6936f656e6
+955, 0x10de240bc07c757a
+956, 0x3e3b9a6861c2bd1c
+957, 0x9c0b0b97d3712ec9
+958, 0xabf1505a75043aed
+959, 0xbdf93d3de3274179
+960, 0x28fa5904d3f62c28
+961, 0xc3b97b39ef6c5133
+962, 0xf2b2219225b8679d
+963, 0x8be4ec0f930c0aaa
+964, 0x47de5a56aa590643
+965, 0xb6f871b304129856
+966, 0x80a61c06233ab0f9
+967, 0x3ce6c3af8101b055
+968, 0x85b911708274e7d1
+969, 0x4cab65d093a488b7
+970, 0xaabc4b10661fe28e
+971, 0x35b16dea64474a68
+972, 0x1d6eb5b093361223
+973, 0xc39107b92f0fe1fb
+974, 0x1d09e048073c4841
+975, 0xc6a02f43aca8cb2f
+976, 0xaf6613dbc7da909c
+977, 0x5ac2a40c230aa756
+978, 0x33afb5e7c01c39a5
+979, 0xc7b0b20ea8b7d0ef
+980, 0xdf7306c8ccb1bbea
+981, 0x9710efc0c188b2a0
+982, 0xd6303eadb72c873e
+983, 0xa38ca609b118f35a
+984, 0x8390613065c6e535
+985, 0xdf9a0106757e431f
+986, 0x8bcf77039788e143
+987, 0x6026806a986b378e
+988, 0x482ff3b1394cb1dc
+989, 0x2a27d0ccac9ede9c
+990, 0x53c77f26e271b3ab
+991, 0x1ba004cf276cf3f
+992, 0xc135b0517dc81f7c
+993, 0x5d137838db75e442
+994, 0x3fe505f93d1dbdd7
+995, 0x351654ae7d598294
+996, 0x173f8d182af9d84d
+997, 0xf97dfcd164fe11c5
+998, 0xcda423e5ad43b290
+999, 0xa5cb380b8de10d10
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/pcg64-testset-2.csv b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7c13e3172d0e7798200f58cde9362ad0bd4a22e1
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64-testset-2.csv
@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0xa30febcfd9c2825f
+1, 0x4510bdf882d9d721
+2, 0xa7d3da94ecde8b8
+3, 0x43b27b61342f01d
+4, 0xd0327a782cde513b
+5, 0xe9aa5979a6401c4e
+6, 0x9b4c7b7180edb27f
+7, 0xbac0495ff8829a45
+8, 0x8b2b01e7a1dc7fbf
+9, 0xef60e8078f56bfed
+10, 0xd0dbc74d4700374c
+11, 0xb37868abbe90b0
+12, 0xdb7ed8bf64e6f5f0
+13, 0x89910738de7951f
+14, 0xbacab307c3cfd379
+15, 0x2cf7c449d8b927a6
+16, 0xdcf94b3a16db7f0e
+17, 0x8a9d33d905a8792e
+18, 0x4cb9eb2014951238
+19, 0x6c353acf7b26d6f1
+20, 0x73ff53d673aa30c
+21, 0x1fd10760015eca68
+22, 0xabae0aa9021eeba8
+23, 0xa5ae363a868ee2bb
+24, 0x9d89e0f041de6631
+25, 0x6238b133c3991a65
+26, 0xff49267d75fef51a
+27, 0xfb180656ce13c53f
+28, 0xaf7fadf36128712d
+29, 0xa6847fc6f339c63e
+30, 0xb03e0b80d71ea5bc
+31, 0x63905abcb43969af
+32, 0x2295af3ee00a3bba
+33, 0xb8b375b994330415
+34, 0x867d9ef1d8716a3b
+35, 0x4f6c02f5601b4e18
+36, 0x7c5fb4c16c470d18
+37, 0xe3b57986b804b343
+38, 0xef1d79d212aca692
+39, 0x5b98774c8806209c
+40, 0x924fc76bac38a5d1
+41, 0x5266084c412ddeed
+42, 0x98240bf9b831d6a3
+43, 0x5681599e81219442
+44, 0x6441248fc2ba92bc
+45, 0xe3e9051a540349ea
+46, 0x3a2700034390baa3
+47, 0x9f893155b6d402bc
+48, 0x158207910c6d8aef
+49, 0xd5282ab7608c2cbc
+50, 0xc97f4651669dee4f
+51, 0x3d4750d95103ed60
+52, 0xe0614542caac1f04
+53, 0xefe5092144cfc6c
+54, 0x560bc486abd7e9ae
+55, 0x2678b71392daa4b8
+56, 0x734970d3dc2ba416
+57, 0xcbdbe849e51e4aaf
+58, 0x3b0b5e28b491556c
+59, 0xd51449ac45abd88
+60, 0x6790b59991f1b7ab
+61, 0x32d1c039ff2415bc
+62, 0x173b9772f24f72e0
+63, 0x9490a9ca9f883b1b
+64, 0x4c775989e6214222
+65, 0xac07db37e6ee6114
+66, 0x331371b2e3f10aee
+67, 0xf12e5326c21c28e4
+68, 0x5d77dc280c70d614
+69, 0x1b01bd17a2f281ec
+70, 0xa10d3b5882938487
+71, 0xed5a0033c394ae8f
+72, 0x70bc8ea568ea44b4
+73, 0xf4600ae77965e730
+74, 0x7ff92c0b321ce233
+75, 0x6cdbc87d0cc1d670
+76, 0x9ec64f0cf2000eb1
+77, 0xfebea50259800f68
+78, 0xf2edf9019a8fd343
+79, 0x75c584ac042e5468
+80, 0xc1fa8481d5bf9a1d
+81, 0x7f57180168514ac2
+82, 0x878100716b94f81e
+83, 0xc929406e3af17fd2
+84, 0x6a26e2c013e4bf4d
+85, 0xbc071d8848280955
+86, 0xb60d75abbfd1bdac
+87, 0xee9b76afeca9fa69
+88, 0x1d6c399d2f452810
+89, 0xbaa0bc1621e25c83
+90, 0xed6ba792f8671ba5
+91, 0xf7ca02c2ab11d8d7
+92, 0x3c3cadadf0b21e3
+93, 0xdd1784571e864e9c
+94, 0xfb2f992015157509
+95, 0xf50bb9f0d3ced743
+96, 0x261565f75c3e185f
+97, 0xf8fe33b284513e60
+98, 0xe3d2d10b5e024664
+99, 0xd28717566242cf35
+100, 0x7ae07d133ac5b789
+101, 0x3b7ccaaa53ac338e
+102, 0xcd480bace4871650
+103, 0xec6c78f923c080e9
+104, 0x44211d0ff8919d59
+105, 0x89f79af76d2a45fe
+106, 0x71583fd8a837548b
+107, 0xee57269c261511f5
+108, 0xa5ee8f3b128c5d1
+109, 0xbb64c20ed0765a17
+110, 0x9d4790ab2eeaf7e4
+111, 0x742f3db806d9e98
+112, 0xb81ec97aed6a0d1b
+113, 0x41808b34f6a8a23
+114, 0xc20913af175dfd4d
+115, 0x834427db263b22bb
+116, 0xedd9c632e611828a
+117, 0x10eac8524496f571
+118, 0xd76091b97eb00ab7
+119, 0x111298ae9fe95666
+120, 0x5824b2e2a6719c43
+121, 0x6e280ec539e934ed
+122, 0xf74fd832df90083e
+123, 0x8fee6d0f241c2e97
+124, 0x4244f331c2f19c3c
+125, 0x3dde75a845cce97f
+126, 0xe35bb8e635a9915b
+127, 0x39d2943037f7932e
+128, 0x1fe2d134201d0970
+129, 0x49d00b63c749b804
+130, 0x960c2942cd4e4e04
+131, 0x8dd8e009dbc0435f
+132, 0xcf493495c3a055cd
+133, 0x8f7b5a1c0f9fe9cd
+134, 0x49d5f90374641a25
+135, 0x69b3932073d3524c
+136, 0xd170603e7de84ee2
+137, 0xa062ba3ed3539948
+138, 0xf5861cc5b5d56c82
+139, 0x5e914998a30c7e76
+140, 0x8d77f2ad1503c0f1
+141, 0x980b6a9e3b4181fb
+142, 0xd9299cd50694c084
+143, 0x253dc0f8f1cec4c5
+144, 0x68110fb9d1b3e695
+145, 0xe8f3120d0aabc461
+146, 0xb066e7df0dfb042
+147, 0xd29ce0f797e6b60b
+148, 0x6a569bb7ca33bd42
+149, 0xd46e08b2dc2385f8
+150, 0x28c61d11d055767
+151, 0x5d73aa3d1a2bb725
+152, 0x1421191e1c14829a
+153, 0xa711bfb6423df35e
+154, 0x461af97a86308006
+155, 0xb3e1018ff3519367
+156, 0xf19cf866a268ef2b
+157, 0x207715eac9199d1d
+158, 0xdd621c410975b78c
+159, 0xf390aea68683610
+160, 0x617a2d107a0047d9
+161, 0x6e05ac416e5bebf0
+162, 0x7d253e70506c1bed
+163, 0xf9f96f4a7dd53810
+164, 0xc693b29cb1573f73
+165, 0x4f1146b0020ea544
+166, 0x45140608fbd40579
+167, 0xdcf57219828ce6be
+168, 0xe19d58cca37b5b32
+169, 0x82bda95b2a161235
+170, 0x5823c3d8a2b6c9ba
+171, 0xfeb2e74092fdf89a
+172, 0x50e1ad1abc8f869d
+173, 0x2ec63d0c105eb8da
+174, 0xe14e1c4845a3264a
+175, 0xcff53670455eb6aa
+176, 0xaafaccd24619fa3e
+177, 0xf55a988486e2422a
+178, 0xecfba16a90ff4d04
+179, 0xbf8d36c2f644757a
+180, 0xdc56ed75a0dd6249
+181, 0x3f45023eff17c3bb
+182, 0x2428bbfe90023fab
+183, 0xab892c611adcb70c
+184, 0xb6f13d8c0c2b9d74
+185, 0x2ac3fb11d224f2a8
+186, 0x65433dcfae2d9351
+187, 0xe906859ae4b45f82
+188, 0x8fb7f5f093d76a3b
+189, 0x940dd290b5e88d1a
+190, 0x31b27d21bef116e7
+191, 0x86a964e2c83b5296
+192, 0x85ffd17bc079a9e8
+193, 0x16c47c724e7ab7f1
+194, 0xfb6098a9867e7d7f
+195, 0x9246fb69092c6cb2
+196, 0x1a4033572760f32
+197, 0xc5cc568a8b273b84
+198, 0xfa6f9f2fbdd44abc
+199, 0x9701b8e087718ba3
+200, 0x51d6a7dcf73f8f3a
+201, 0x30008172cc6a972d
+202, 0xac2ab49a5ca6ac81
+203, 0x31f28ef79461e54c
+204, 0x93e35a8da8cc6132
+205, 0x9a2c58beeba3d5b9
+206, 0xf6615c1de266ac39
+207, 0x127ff9f8166b766b
+208, 0x7ffe380e80a69556
+209, 0xbe7d2c228e1542f7
+210, 0x2d5ebb4e50ba1746
+211, 0x63585761ae1bf684
+212, 0x1019eb5cee022fea
+213, 0xb9d3540ab58da30d
+214, 0x1677f4cb45620eb9
+215, 0x6524baee51783822
+216, 0xdf9f2ddcfabb0adc
+217, 0x78e8acc43b287935
+218, 0xe9a1974e999222b5
+219, 0xc41324ec2291e780
+220, 0xea52abc9ecdcbc9f
+221, 0x209d7bcd46ec6b04
+222, 0x12d504c09803db2e
+223, 0x1200e6bf21475d81
+224, 0xde6d3c2b35fd2cfc
+225, 0xa2526900ac33bd3c
+226, 0x7f1f5290fc432bc5
+227, 0x29ddfb380a3d69c8
+228, 0xac79cb6942a2909d
+229, 0x516996685b67a92a
+230, 0xb5fc39041cb828bb
+231, 0x75d9d8ca0644a276
+232, 0x81e98b76be92a3e9
+233, 0xca27888fafe12179
+234, 0x17be2ae039925765
+235, 0x9429846c0e6d0342
+236, 0x327dfd50439815e9
+237, 0xcee20cd7bc254aeb
+238, 0x7d250389f453f29e
+239, 0xfd1b232a85c95569
+240, 0x2ed55fac80f3e9e9
+241, 0xf6886c20417a1be7
+242, 0xcd08e61f0b0fdfde
+243, 0x7b33e34da5c27bff
+244, 0xd043c4b7d5603dd5
+245, 0x9a544e4c70a3b686
+246, 0xa7b60398c381f771
+247, 0xe9e7a3487c4bd4f2
+248, 0x10b58fdfe1ff112c
+249, 0xd5c1c9748c0f4ceb
+250, 0x61be9d09159d54ff
+251, 0x5356f51e8239f510
+252, 0xfe7889d9b202ecef
+253, 0xc7fc19ca5d263d5d
+254, 0x7c4c07e61dfd9f69
+255, 0x6c315fe5015f300a
+256, 0xe0a5bc00039747b4
+257, 0x16397fdcf829ee80
+258, 0xb55aee80d16a5169
+259, 0xca0609944d007eea
+260, 0xcc982249f65a02ce
+261, 0x528161feb149c148
+262, 0xcbf08ba49b41c006
+263, 0x39af1ff0b6f14138
+264, 0x5cc036be69799aec
+265, 0x6adde125b1db21c5
+266, 0x8a99d83d6b613b67
+267, 0x1cd43fca9451f74c
+268, 0x682dbb26ecc96365
+269, 0x13b4be2ceb43e3
+270, 0xbe8fbc3b6f4f581e
+271, 0xda148a2f4bda5719
+272, 0x239106ca3319f393
+273, 0xb42b4dde641f0dd5
+274, 0xd233cfdf4cb0af74
+275, 0xfb5919d905589afc
+276, 0xd802a8860c10b66a
+277, 0x6c923e1d00e7b5bc
+278, 0xfacce1134f383b89
+279, 0xf9570abda7a6d553
+280, 0x80f0f9796a208f18
+281, 0xc0e1df5280951c57
+282, 0xe9f143f08257bbe0
+283, 0x79e4c6463123d588
+284, 0xdd2118583f2b1684
+285, 0xb399ff5f2329fa18
+286, 0x4b3e9ebae96f813c
+287, 0xc484dbf247787384
+288, 0x921865eb97603f2c
+289, 0x18063c68e257d300
+290, 0x643181f345e7fc26
+291, 0x12e0b0e8eadf9fa7
+292, 0x79e613fe73dfa354
+293, 0x6db4c59203b7217a
+294, 0x6c7a0e9ba6139eaf
+295, 0x9617c7ac4e3f6d97
+296, 0x1f68a7b4fb1b4b75
+297, 0xef0b7ab24944f466
+298, 0xaf1dee1f4be1bc89
+299, 0xd2e355c959f5fd8d
+300, 0xe594c3fb95d96efc
+301, 0x9554766ca3342906
+302, 0xa4bbdc77d12842c
+303, 0xb62400211ee489a8
+304, 0x91abadaaa3bbe67c
+305, 0xd371eeb91deb42bb
+306, 0x883bab35cbd2b6e5
+307, 0xd030c3d9411a9041
+308, 0xff3c110a858ff000
+309, 0x59bdf5ca47d0bde7
+310, 0x2bc80fa3cdba1853
+311, 0x6444ccb652662cb8
+312, 0xc0c7e256b9e90339
+313, 0x70714ea9c9d72302
+314, 0x96a0142f9d897d27
+315, 0x209a9097c5a91ef7
+316, 0xb9e33afc5171e009
+317, 0x47b37af433a58d40
+318, 0x30cc4ffbfa831d26
+319, 0xdcea4a85ff815466
+320, 0x907d5bd027f2e5cc
+321, 0x7c081f6852e04a4b
+322, 0xe61950749c1d502b
+323, 0x1604e937ee69834a
+324, 0xb2372d952dd25309
+325, 0x53f6a5b834c72577
+326, 0x2ce7a74395e0b694
+327, 0xacbf9ab4fe91f225
+328, 0x5ce1e63d3a2bb90f
+329, 0x54740da3a5ed139b
+330, 0xf194ddb39f29880b
+331, 0x3305374f5d8ec08b
+332, 0x831dd0164927ff4a
+333, 0x625baa78e4458cf
+334, 0x29d27dc0a4a71152
+335, 0xe227bae9a1401034
+336, 0xca0c209831846b2b
+337, 0x8e8cc54b08b5a411
+338, 0x38f2b4acaac27db6
+339, 0x8ec88baac814e86b
+340, 0x31c08e46b007bde
+341, 0xb686c02722794c09
+342, 0xb77cf8fc682e3907
+343, 0xa56334e7f606f4b2
+344, 0x9c80b127bddd5f4f
+345, 0x12df14834cd858bf
+346, 0x3f14762a9cf5fb9f
+347, 0x930a70941ef5779e
+348, 0x64e96c849c30c080
+349, 0xfdf53bfba1300484
+350, 0xec7a9363c21bc616
+351, 0x26e9fd6a115ecb47
+352, 0x9707a84b5bc77fbb
+353, 0xb23b2737b20d5903
+354, 0x22f4825ae80f6501
+355, 0x500644b12be6a01b
+356, 0xb746645b2af082db
+357, 0xe6af051f697892f8
+358, 0x577c724248a1cfc6
+359, 0x3d2b6a434c84eed3
+360, 0xd260f5efd7328314
+361, 0x95c16cc84bb3f55c
+362, 0x7a01b2e4e0e80ca7
+363, 0x41930c3ce70a0935
+364, 0x1299bccf39d4e110
+365, 0x494883ba1a8a87f
+366, 0x9478ecfe2d918e60
+367, 0x30ec9a5670cda8af
+368, 0xf9bc877e833e2b99
+369, 0x1b83a0acfbb4a8db
+370, 0x73bc1740c0d18880
+371, 0x65086ca9773cb3e1
+372, 0x3b78c3ccd63cff2e
+373, 0xbfae748795acfb31
+374, 0xa4c9d5d56a15ba20
+375, 0xb9cb41721e52b71e
+376, 0x1532f15d4dc47748
+377, 0x5a4d647a4b9ee632
+378, 0x8513c7c5a50898d9
+379, 0x6d3d98ccd5461b2e
+380, 0xa65e99be2fe98d6
+381, 0x31abc8855334a0e5
+382, 0xf1ed22a661dca5b8
+383, 0x299e2b63229e03be
+384, 0xda201a06687bce48
+385, 0xd27794b302142c55
+386, 0x642bd3e1c7898a9d
+387, 0x777f1ff00afa1a87
+388, 0xd2f1c84fb3877baa
+389, 0xae417583289191fd
+390, 0xd641f1d88e0e2d55
+391, 0xc1f1d98fb5d18ebf
+392, 0xb0f72aecdadce97b
+393, 0xe9b8abc764f6018a
+394, 0xd2a37cff8e890594
+395, 0x2dd70d631a528771
+396, 0xbf8ba0478c18e336
+397, 0x1630bf47f372ce0a
+398, 0x6d04ea20dc3f46b8
+399, 0x6591881bf34337f2
+400, 0x33c149c7eb5b4103
+401, 0xf01a8c9857c86748
+402, 0x184348cdfc16d215
+403, 0x141168b253d2ed7
+404, 0x52aaf012ef50a6f1
+405, 0xfda1722387e16f4c
+406, 0x43c30f57d6c038fa
+407, 0xd4a8611f5f96d214
+408, 0x2c512ce17e987f2c
+409, 0x961ce450f0fa2822
+410, 0xf55a506ec6cea9cd
+411, 0xb76d694d9c7f5ef6
+412, 0xfb029216dbd8e988
+413, 0x93162501896a0081
+414, 0xfbbbd2c5ab300f5c
+415, 0xd648b6da7387d491
+416, 0xc73b4697471d9d98
+417, 0xe37412bf1c93ee76
+418, 0xa1a96d96570e6637
+419, 0x5b3ab4f82428f65c
+420, 0x873d849b188aa36f
+421, 0x39fbee0ffc9fa9ff
+422, 0xc70d21b744d677fe
+423, 0x2b8a43c23043d209
+424, 0x93c33eaa37370d16
+425, 0x8930ac1880f2b0ef
+426, 0xac01d27707036af0
+427, 0xc2af3fee504343a0
+428, 0x1c1dae2ad5535d97
+429, 0x9ffc21804b76a480
+430, 0x69f903412cc13563
+431, 0x9d3c4e2759a0c47d
+432, 0xb1a8f894be6302b9
+433, 0x95e1fd7951479506
+434, 0xbb9e6c03cd4ae8e3
+435, 0x85206010c9b737cf
+436, 0x767e813694d6238c
+437, 0x4969af329ccbb30a
+438, 0x3aa9af1075aaea5c
+439, 0xb1ff519e8118a993
+440, 0xb21a23a3c91180fe
+441, 0x320b24582ca3fd88
+442, 0xf8ca56415fb4e453
+443, 0xabd0899c07205e77
+444, 0x87fdc7a44b4ad50f
+445, 0xd75744911641a278
+446, 0x7c8c9a65df6fcb95
+447, 0x79d785e3c7a5b695
+448, 0x421e4565ba1f592f
+449, 0x27f87eb2517835cf
+450, 0xb62cc4297441c83e
+451, 0xd817a80ac815ca6d
+452, 0xad84388130df2aa8
+453, 0x5e6b1640452d6ac8
+454, 0x936285e15edce2a3
+455, 0x903bccc4969768e8
+456, 0xefc2cb7b109d3140
+457, 0x633e9dfdda2d903a
+458, 0x2a2f3225925678a1
+459, 0xe07eac91a27f8547
+460, 0xe50ced40eda78cb3
+461, 0xc5b22500e1c7441
+462, 0x32becf61bca3aa72
+463, 0xa2e37c4b30671344
+464, 0xc9f1c1910f45d544
+465, 0x9b50333b2dcdf730
+466, 0x310bfd53a1684b94
+467, 0x1e1dc21e66ac6455
+468, 0x81876c2bfb1ed5a1
+469, 0xd0c54a3e25eadc7b
+470, 0x3791b6fbbd5c7ba0
+471, 0x133be57356c599fc
+472, 0x8d1148eb8e83fdea
+473, 0x311aedba0d8b42cc
+474, 0x1142ae52745f94bb
+475, 0xc5f4ab2fbde8c4a3
+476, 0xd23be827b5b24f6d
+477, 0x65f95194cd122715
+478, 0x4b48969d73125922
+479, 0x46f165052b8ff988
+480, 0x5c689f94b9275ff4
+481, 0x93b03823ff2d536b
+482, 0x871f3775aa4e3523
+483, 0x5af829f7cc0f66a5
+484, 0xa32e05739cbeac8c
+485, 0xacff1856ddace0fe
+486, 0x8eeb5e7f991a5322
+487, 0x6325c2720e0dbdea
+488, 0x9fb817bc4fdf5200
+489, 0x9786f0d850e43d78
+490, 0x571f76dd7f9fb77a
+491, 0x4d9e94e181cbc63f
+492, 0x8bb632d3376c547a
+493, 0x9cc26d9efd1c88b9
+494, 0x9c5d49579df52b0b
+495, 0x6201abf7e1cda07b
+496, 0x90d68f0c6c884963
+497, 0xfc5b66188ef7f561
+498, 0x6d9303cf2e0e0f95
+499, 0xd7cfcff535f5ed07
+500, 0x14d1a1228daa4ac6
+501, 0xe00ef5762f66ae50
+502, 0xf113a79471582978
+503, 0x430985281785dc7a
+504, 0x31914108c206ed5
+505, 0x7ba6707b6419971c
+506, 0x2ec63b033ce112e5
+507, 0xf8bcd36ced3b41e3
+508, 0xe5cf908c8010414b
+509, 0xf5ee224b7c703e30
+510, 0x9a9733af0b12338b
+511, 0x83e18cc00ace34f8
+512, 0xd52cff39e23008b8
+513, 0xa700578136b9c0c5
+514, 0x3fa179d32ac51f99
+515, 0xef2d5eab6d4ad380
+516, 0x709024a5abd032df
+517, 0xc607c7ee349ede87
+518, 0x803d784e9731eb5f
+519, 0x2ef06f4ba769282d
+520, 0x4bc1dca1e9f07eb9
+521, 0x930c958a7a72f94d
+522, 0x249bc8db2cc7a3bf
+523, 0x3845305798f9a5d
+524, 0x6f137eca9ab6f948
+525, 0xc31f5a963d31bd67
+526, 0x9d39693d5383626f
+527, 0x52fb41c335a8b98e
+528, 0xb79d1a29a06006ec
+529, 0x7c0926a7a3eda2cc
+530, 0xffdf5214406fd53e
+531, 0xc6aa02a7e94282b9
+532, 0xd4a4431b4aa301ee
+533, 0x4271cc0f9420d3ab
+534, 0x26fccd7cc7fc2485
+535, 0x330594bb945b8d5a
+536, 0x6ea8eaad12e5cb8c
+537, 0x831c3467726bede3
+538, 0x31d1eb10017eaa61
+539, 0xc7aa75e41508f5cb
+540, 0xde51810f0cadd0b5
+541, 0x50e5b3e73692f80b
+542, 0x82107ec55636e188
+543, 0x9828ef175d843ab4
+544, 0xb8edc6a860dd421e
+545, 0x25c0c138fd537ac3
+546, 0x47e72a771e8eb563
+547, 0xbb0f8c5333f4a2cc
+548, 0x91750d2fb9b2d479
+549, 0xe662d8f6fe38df36
+550, 0x72a6d879fb5619f0
+551, 0x6817c7878dcbf077
+552, 0x4e7741cb484661e8
+553, 0x3b3b3ba0be5711bf
+554, 0xa6989f5d25868765
+555, 0x43c276398997e4e0
+556, 0xdcbe16a94da28870
+557, 0x454936980a699c99
+558, 0xac614bfa8f0266c6
+559, 0x9174841392e213d5
+560, 0xa0e2acffc5fc9d1f
+561, 0xe53a08a7a0e6521a
+562, 0x2b845cf7c24172e0
+563, 0x265a4fc5f7adec0d
+564, 0x1f34fbe5f1e49420
+565, 0x139181f6fb647f20
+566, 0x88c35d46e2fcd05e
+567, 0x2a6d5b55903c0459
+568, 0xcea28eb621ad7bf1
+569, 0x5c9cdc13e7aaa30
+570, 0x5fe63e14746e7103
+571, 0x7923e53d73835db9
+572, 0x376e661210bf1b06
+573, 0x5b1cab85450efdd5
+574, 0x3908dc096c70b452
+575, 0x4825e303cd1f396f
+576, 0xed476bfd702957c3
+577, 0x6acc013aff5db743
+578, 0x62c80b776343d488
+579, 0x9c75edcd5b012697
+580, 0xaa053362a3b9770a
+581, 0xa907e236c7c07e94
+582, 0x15b2c380451692c0
+583, 0x94f79142697bd61f
+584, 0xbc657d31ea98d44f
+585, 0xcbaa5e52517a1f5e
+586, 0x96aa2e44a7c4a03f
+587, 0x216d3c66db2b515d
+588, 0x157001807e3ca88a
+589, 0x52b3a596bdd3859a
+590, 0xed747e7fc5e3adac
+591, 0x78fd765ddb2c448d
+592, 0xe53dc7299ed8614e
+593, 0x75ad41fb1d7a790a
+594, 0xc14f6b944b0e6cb1
+595, 0x7c314b69fce3df1c
+596, 0xb56d82eb740d7abc
+597, 0x5132a93c41251fdb
+598, 0xe3ce35bd2a82f958
+599, 0x440571a981c722f2
+600, 0x194cdfd9f186bc9
+601, 0xb89e522a5db00939
+602, 0xad35f339f68df3c8
+603, 0xa82ab18420322293
+604, 0xaffa6df9b72b27c4
+605, 0x9615694d23beaa2c
+606, 0x1d82ebe563abad91
+607, 0xab50ef65fbd94385
+608, 0x1b070dbd70a9a14
+609, 0x2ececa796abbadf0
+610, 0x6bbeafe9e81ab2a2
+611, 0x60dcd0d2a9b76914
+612, 0x1e748039ef05c33f
+613, 0x6d4d17f2213ccdff
+614, 0x9fa56132957bc987
+615, 0x60a17185de2428eb
+616, 0xb56038ddf306479c
+617, 0x3b1db5df92d06d8b
+618, 0x24d1bba8bdedf580
+619, 0xbfb7e6740ebaa4d9
+620, 0xab31c4473e46f61d
+621, 0x6deb3cdd8fd5869f
+622, 0x23032e47746d72d6
+623, 0xa9e72d734e10f2e8
+624, 0xbffd199b6157bc23
+625, 0x29f8254df273fb62
+626, 0xb076142130ee55ec
+627, 0x5b0b08374126c309
+628, 0xea4536aae979521f
+629, 0xc064e7abec91a174
+630, 0x46133ef80c59d935
+631, 0xf0227e2da1b14160
+632, 0x675a76641e1af5a
+633, 0x2f50a069b33d198c
+634, 0x3ded5a65e1d657eb
+635, 0xbb6999b020694f6b
+636, 0x86b2f2b33487aed7
+637, 0x76e14e85f8bfb4cf
+638, 0x38f7f1e44bd4e0db
+639, 0xc1a7d41b7e80d4ae
+640, 0x1dfaaf80bbceb42e
+641, 0x3f51c11497720c2b
+642, 0xce6da1415ddb8b80
+643, 0x7377d8bcd359b5f3
+644, 0xe077208f3f810aca
+645, 0x9a06a8a2dacbffce
+646, 0xca1f99156b09b735
+647, 0x2ff9a93064d91451
+648, 0x50f3ea93f351a7ef
+649, 0x606fceccb07054de
+650, 0x7e83d6d2f8f6685d
+651, 0x78f3995291c5d407
+652, 0xd28d2460e22d0228
+653, 0x2c5636f68a0054dd
+654, 0xd9fafb1c56c8f6cb
+655, 0xe39889b5f9d74464
+656, 0x1355372bf5db2cc1
+657, 0x26768426b9ac323
+658, 0x4af1dbdc1111fd89
+659, 0x66973587943b927f
+660, 0xf86f5f50684dfb1d
+661, 0x1247d574ff79b534
+662, 0xc8039f3259210fe2
+663, 0x79b573235c92a9f5
+664, 0x213f642d8450e2f0
+665, 0x5db7706973376566
+666, 0x6182c12e69b373d7
+667, 0x3e5ac47300aec07f
+668, 0x4b5b6c57b1574376
+669, 0x6b7fcceefd56b17c
+670, 0xf656c3455cb9d4b8
+671, 0x7577e2e13329721f
+672, 0xf33c0c53ce956e8d
+673, 0x7d0f328ee356174
+674, 0x10ec9a168088686e
+675, 0x71ef1776d062dfa
+676, 0xaa7b590a488a6bc4
+677, 0x38612b6dd8049a1c
+678, 0x939045e36874f731
+679, 0xcb9d1d74c56d5ac9
+680, 0x54f1c1c8fef1d8ff
+681, 0x3ee4b85c8c7e939e
+682, 0xb9b4608e019f352c
+683, 0x79d4701275d12e6a
+684, 0x2632a2d9835c7f19
+685, 0x1662cd9fba293692
+686, 0xbcb70265115ee944
+687, 0xdc43fb9761468604
+688, 0xe3eec4e7d3871352
+689, 0x829531753226989d
+690, 0x2748cc67f540e074
+691, 0x39c4af25d607837d
+692, 0x741a243f4cb5df99
+693, 0xda1353287e18b49a
+694, 0xa6735689d751ea74
+695, 0x46326d587340ce0b
+696, 0xc18531df4550012b
+697, 0x6f7901e05dd4b818
+698, 0xfb966afc4c001d63
+699, 0x6dc10fca67a9cfdb
+700, 0xd6527ffadf0feaae
+701, 0x3b900172045e25d
+702, 0xb7dd594cdded6a46
+703, 0x6602aee7ec1599fc
+704, 0x7fbf12f23747546a
+705, 0x32e63f662bd2de0d
+706, 0xedf47770b67ed641
+707, 0x331bef83481c5c2a
+708, 0x8fc4256fdf05158c
+709, 0x98eba48dabccf5e0
+710, 0xdbc2f2cdb7b1c154
+711, 0x7777755616517ad3
+712, 0xd473c147d2628ac1
+713, 0x861e15d1d760b5a7
+714, 0xf4d25926405ecb07
+715, 0xb7739c69effff86e
+716, 0xe97fbafa6f96830c
+717, 0xf13e8a334e8bede1
+718, 0xcd60010cba4ee4f9
+719, 0x1f537ac2b82e6008
+720, 0x1fda8d781a89140a
+721, 0x9dc204f3f4a463f0
+722, 0x456dcd18eb56a1ab
+723, 0x629957bc87bd16a1
+724, 0x2c8000ddb8c75253
+725, 0xc31dae9ec8449284
+726, 0xdac05c8baa2b691a
+727, 0x21ff7be9ffa3e7ac
+728, 0x844f4b5ed4ee08d0
+729, 0x651f913fd636c994
+730, 0xca3e71a2110b2d49
+731, 0x7709bc42253ed09d
+732, 0xbb164d45b6569d43
+733, 0x90ec2f040c20a112
+734, 0xfa6e77e9166f5be4
+735, 0x6b6d12c1842d587d
+736, 0xfcd7ff8466e25e2a
+737, 0x6a5a2ed8bd971297
+738, 0x2ec35f6bba5adcbc
+739, 0xc83676e16651249a
+740, 0x458f6064cefe10ba
+741, 0x90d54d527e6cd028
+742, 0xa5613e88db27c388
+743, 0x331e0c7d85aa1abc
+744, 0x8cee4977e210358
+745, 0xfcae379aa6cbff8e
+746, 0xd1407afc97a57e86
+747, 0x1fab25c864f094ae
+748, 0xd914864a63004552
+749, 0x4214d226a20f1384
+750, 0x3f4e0d80c488b715
+751, 0xc5ca2f654024b7c8
+752, 0xc1e27a124e7c821c
+753, 0xd890a915ffc7918c
+754, 0x22fba040ce51a9f8
+755, 0xbf61cebd8891617a
+756, 0x7846609ee228e319
+757, 0x536d1854375509b8
+758, 0xbbfb45fc6e666f50
+759, 0xd85b4c0527f9d7d6
+760, 0x528cc9c7fa2a84c8
+761, 0x27a1baece647f2cb
+762, 0xfddf0cb92fe09dc3
+763, 0xeb5008fe965d8d96
+764, 0x4a3307937eb2e5c8
+765, 0xd07d74c240c6c363
+766, 0x16f62290179d1bbf
+767, 0xe99c9bcc9cb1ece7
+768, 0xc64f9be03c8a93be
+769, 0x32659effaf666c1f
+770, 0x4bb228cfb30b6672
+771, 0x98764870842068a5
+772, 0x5b12ef2d2cd8bdcc
+773, 0xbc79d1c1b41f28b8
+774, 0x97a517cf3279fc9a
+775, 0x34ffd46c1d4d6025
+776, 0x9c302307ee25c8f0
+777, 0x399604eed1f18a8
+778, 0x1c9b813c2043142a
+779, 0x2944ea5e55267fe9
+780, 0x5a8a9f5e728ea667
+781, 0x30c8440adb804a0
+782, 0xee0e6b627099a937
+783, 0x3d50757ada3c52da
+784, 0x4548916b32c813ab
+785, 0x602a186fe5bf109b
+786, 0xf0d440a2227ba304
+787, 0x5a10d4e0ca9ea32b
+788, 0x6e5eb90da13ba64c
+789, 0x4c6af8fd04241ab2
+790, 0xf9eb31d26e093006
+791, 0x5d674878839fe3ea
+792, 0x1562b55b2484e47c
+793, 0xa87188c099c1cb61
+794, 0xb7736b8aa02a3392
+795, 0x5f4b301125abb20f
+796, 0x361d566984637f44
+797, 0x68c4b3feac8bd0c3
+798, 0x7066c634dd2503c1
+799, 0xfecbf7c9441eb6ea
+800, 0xdbc26ae0fc81436b
+801, 0x9ef3e2b48252e7a4
+802, 0x31a49b4c339b37c7
+803, 0xb01b2a83cf346cf4
+804, 0xc24dc2347f82fbe3
+805, 0x134cad272dcd410f
+806, 0x61260742823ba59c
+807, 0x53ac4c193a97c730
+808, 0x9207c9833af34b52
+809, 0xa72e7ee77078d1f5
+810, 0x2e6f6e1b05936885
+811, 0x783b99ce5dbf9464
+812, 0xfdfeb6f0d027bb44
+813, 0x40eeb27096f92b0
+814, 0x5ef96ff5d4a4521f
+815, 0x5595806ae873718a
+816, 0x67d449eecf4ca1c3
+817, 0xde837ab611364f3f
+818, 0x7034c24d2b139be9
+819, 0xe21166603e0a9c86
+820, 0x935694435c1f0d51
+821, 0x6cb3bec90c126088
+822, 0x4096ef662b7a9f89
+823, 0xd2d85b8d238d8c15
+824, 0xa4ea533ce3ec59b2
+825, 0x3654729d80a2db29
+826, 0x214c4cc3906d29d4
+827, 0x201c447e7588e373
+828, 0xe8b8f0ae25f683eb
+829, 0x6744aaf5754e38af
+830, 0xd1ffb10d6f27a061
+831, 0xe536733a7b3a6c30
+832, 0x39f0f66e47cbf2c9
+833, 0x856a9593526fde2
+834, 0x2e2a817a0098ea4b
+835, 0xc5e1eeb551a0e3d3
+836, 0x3f21e2f5e2d50b2
+837, 0x906af56c66dd9f8c
+838, 0x30f6dbd70329fac8
+839, 0xc443dfddf3c01a60
+840, 0x7ab85d9aa9675470
+841, 0x8c9080bd39717bfc
+842, 0x4b1ccdb3c3597f6f
+843, 0x74e2542d70ab5d67
+844, 0xbb3d236aad00f74
+845, 0xcf3cadf9a2804774
+846, 0xe851d9750e42bd07
+847, 0xc0ad82029b1c371f
+848, 0x7ee119eb552d6c07
+849, 0xd8024049bd1d784a
+850, 0xfa67a899760363
+851, 0xaa7c2f438b178197
+852, 0xc473674a47ffe064
+853, 0x539fbe3fc674c270
+854, 0xdb48484748a76f3b
+855, 0xc73b2b092060d
+856, 0xa1d2a15345016f5d
+857, 0x4d0fe8599f9bba47
+858, 0xa0edc275e6f8f1d1
+859, 0x40590a8655bc8d72
+860, 0x35b4223161f05f75
+861, 0xa04c0c0f616752dc
+862, 0x7f371ed2ca45432d
+863, 0x2ff1a08f75ac6438
+864, 0xe2dc5c3682282f48
+865, 0xe1e4179fa98d9013
+866, 0x8cb083d6843a73d5
+867, 0xb4c2b5921b706854
+868, 0x738e14c0e7352445
+869, 0xcd2b646f91afd8c7
+870, 0xd5779a5b57a264fd
+871, 0xc39ff855586c7d07
+872, 0x3e3f0098c631a859
+873, 0x644e02fae032110
+874, 0xa8834613c0a45278
+875, 0x69482f2c08e10657
+876, 0xe4ee475bdb87e69a
+877, 0xdc1ef7b25c0d0019
+878, 0x88a3fa2be18d8744
+879, 0x60a02e0b21c5bec7
+880, 0xb6867b88aa19bc1a
+881, 0xb599409affcf10eb
+882, 0xaeaa1778a5e59daa
+883, 0xd7a91a52c16663e3
+884, 0x93cb269affe07b1c
+885, 0x841b6ced3a4ba815
+886, 0x84541768e1540a5c
+887, 0xe3943c84f83b3020
+888, 0x5de366fbd7b45258
+889, 0xd787cc3bde91a661
+890, 0x814071446edecb57
+891, 0x15d8c602a1141514
+892, 0x72f07bc8002d1d0d
+893, 0x4a8bd8dc9a1f0f3e
+894, 0x8723796ae0f20d35
+895, 0xda7283c2051f73b2
+896, 0x2df0cc247f90bd3b
+897, 0x79a8522b968f990a
+898, 0x951ede190c8b9d02
+899, 0xc512f1a5b14b018a
+900, 0xf0e3ddc03b9a4259
+901, 0x8cf4a35ad312e15f
+902, 0xebef28926b11094b
+903, 0x5628ba687325921c
+904, 0xc3aa75e57edc49c3
+905, 0xc38382fa98e762ba
+906, 0x8d209e896285848e
+907, 0x2c7d6adf592b4a3e
+908, 0x62de48e36f8338f3
+909, 0x4a752741e00de30e
+910, 0xf7855b70f1f6ec2b
+911, 0xa505fa4428199e43
+912, 0xe8b6b423b826bbac
+913, 0x4bd1206cf8786d05
+914, 0x6dcf040391fe3bf4
+915, 0x913f500f87e1bba3
+916, 0x5acf775aa180a5d5
+917, 0x74dd28d9432ce739
+918, 0x996c2ff2f0dc2495
+919, 0x73dbfe6c56effe4
+920, 0x56fddd25196f5e40
+921, 0xe87810158f5b7
+922, 0x7b8795e996383f1f
+923, 0x9ba5ee7c777c4c82
+924, 0x17ce3908d270fe1c
+925, 0x3df9e613c1aedfae
+926, 0xcdd26871b32fc8e1
+927, 0xd71cb13afc633979
+928, 0x63427c8ea9b1c79e
+929, 0xd070f7664d3b405d
+930, 0x46f2a9e32d9fb769
+931, 0xb4c3822a45e9fe9b
+932, 0x8ba30b97fe6f5ec7
+933, 0x70aa554ee2fc11f9
+934, 0xa80c99dbe0cfcfaf
+935, 0x36d9250cb2d68ed
+936, 0x2995e4b9e1cd1db4
+937, 0x4b3803ba57fc570f
+938, 0xae3959e7d740eaa5
+939, 0xb4cbd6662adbae08
+940, 0xae46576446e8dbc4
+941, 0xc4828e008a9a8a54
+942, 0x145d7db8e6554b2f
+943, 0x1b1b8916a730c371
+944, 0xdaf84b2bebe31963
+945, 0x5b59b80ef23a2403
+946, 0x9180c7e89cab6fd3
+947, 0x80e58f5411babf34
+948, 0xa06cf55185b9b005
+949, 0x13b2c798424173ad
+950, 0xc510f8e706311d49
+951, 0x1f974b83b6046d3a
+952, 0xae6e8e85e822d1c3
+953, 0x66f2c8dc3274a31a
+954, 0x7e04dbcbf65bd377
+955, 0xabf41ede01ec20a4
+956, 0x5efa0948f6bbb2ea
+957, 0xbc91c99d8592255
+958, 0xf6d6917911d86d75
+959, 0x85ce273d54e9097a
+960, 0xbdfd30f2420fff92
+961, 0x8802f02f610b537c
+962, 0xd1d70037ed543229
+963, 0x908aaf97f9693a46
+964, 0x1f6cfeaa0834d53a
+965, 0xa453fd1648ce04d2
+966, 0x2c38bb85ebc64af9
+967, 0xd2daff551c90c4f8
+968, 0xae5a0d949797d784
+969, 0xf0974c8552ac9593
+970, 0xa10b70499f65c693
+971, 0x39a449ebd594ddff
+972, 0x8ea090f2b17b9b49
+973, 0xc592de318090fd83
+974, 0xb63e4fbc467b6912
+975, 0x57a0c1c5ce0e4dcc
+976, 0xa7c517cf3d436b35
+977, 0xef6dcb0f3fad038b
+978, 0xaf4fb60315b91287
+979, 0x5e0776f67304f331
+980, 0xe927753b8e6f7932
+981, 0xd3df2dd92559e304
+982, 0xdaed52aa6af44413
+983, 0x1b59f4dac1e181f8
+984, 0x4a73c2293877ef39
+985, 0xca45d0d015fe44de
+986, 0x4659c8b7853735a8
+987, 0x12de6466bdf8adeb
+988, 0xaeea857a09bfec15
+989, 0xcc9cf4b3c0b88a23
+990, 0xa44ae52396a5e1bf
+991, 0x5847a724305d137f
+992, 0x8f4d4de223956182
+993, 0x58254dfada867a8
+994, 0x900a98222c2f339e
+995, 0xdb575260935d51d5
+996, 0x13fb4bfbbc0d7b53
+997, 0x62213850186bb92b
+998, 0x2a34823312c00388
+999, 0x6148329042f743b0
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..39cef057f4490a70d7041bcfde4d3fec844732bb
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv
@@ -0,0 +1,1001 @@
+seed, 0xdeadbeaf
+0, 0xdf1ddcf1e22521fe
+1, 0xc71b2f9c706cf151
+2, 0x6922a8cc24ad96b2
+3, 0x82738c549beccc30
+4, 0x5e8415cdb1f17580
+5, 0x64c54ad0c09cb43
+6, 0x361a17a607dce278
+7, 0x4346f6afb7acad68
+8, 0x6e9f14d4f6398d6b
+9, 0xf818d4343f8ed822
+10, 0x6327647daf508ed6
+11, 0xe1d1dbe5496a262a
+12, 0xfc081e619076b2e0
+13, 0x37126563a956ab1
+14, 0x8bb46e155db16b9
+15, 0x56449f006c9f3fb4
+16, 0x34a9273550941803
+17, 0x5b4df62660f99462
+18, 0xb8665cad532e3018
+19, 0x72fc3e5f7f84216a
+20, 0x71d3c47f6fd59939
+21, 0xfd4218afa1de463b
+22, 0xc84054c78e0a9a71
+23, 0xae59034726be61a8
+24, 0xa6a5f21de983654d
+25, 0x3b633acf572009da
+26, 0x6a0884f347ab54c8
+27, 0x7a907ebe9adcab50
+28, 0xbe779be53d7b8d4a
+29, 0xf5976e8c69b9dcd1
+30, 0x1d8302f114699e11
+31, 0x7d37e43042c038a0
+32, 0x2cc1d4edc2a40f35
+33, 0x83e3347bb2d581f1
+34, 0x253f8698651a844d
+35, 0x4312dea0dd4e32f6
+36, 0x10f106439964ea3a
+37, 0x810eb374844868cc
+38, 0x366342a54b1978cc
+39, 0x9fb39b13aaddfb5e
+40, 0xdb91fd0d9482bed7
+41, 0x89f6ea4ca9c68204
+42, 0x146b31ccca461792
+43, 0x203fd9724deb2486
+44, 0x58a84f23748e25cb
+45, 0x2f20eb6aeb94e88
+46, 0x14d3581460e473c
+47, 0xad5bd0d25f37d047
+48, 0x1cf88fa16de258b2
+49, 0x3bcab6485b7a341
+50, 0xb2433b37f227d90c
+51, 0x2cffd7e0a8360cc8
+52, 0x5d2eeff7c9ebc847
+53, 0x6fd7c7ae23f9f64b
+54, 0x381650b2d00f175d
+55, 0x9d93edcedc873cae
+56, 0x56e369a033d4cb49
+57, 0x7547997116a3bac
+58, 0x11debaa897fd4665
+59, 0xdf799d2b73bd6fb8
+60, 0x3747d299c66624d
+61, 0xac9346701afd0cfa
+62, 0xac90e150fa13c7bf
+63, 0x85c56ad2248c2871
+64, 0xdea66bf35c45f195
+65, 0x59cf910ea079fb74
+66, 0x2f841bb782274586
+67, 0x9814df4384d92bd9
+68, 0x15bc70824be09925
+69, 0x16d4d0524c0503a3
+70, 0xf04ea249135c0cc7
+71, 0xa707ab509b7e3032
+72, 0x465459efa869e372
+73, 0x64cbf70a783fab67
+74, 0x36b3541a14ca8ed7
+75, 0x9a4dfae8f4c596bf
+76, 0x11d9a04224281be3
+77, 0xe09bbe6d5e98ec32
+78, 0xa6c60d908973aa0d
+79, 0x7c524c57dd5915c8
+80, 0xa810c170b27f1fdc
+81, 0xce5d409819621583
+82, 0xfe2ee3d5332a3525
+83, 0x162fb7c8b32045eb
+84, 0x4a3327156b0b2d83
+85, 0x808d0282f971064
+86, 0x2e6f04cf5ed27e60
+87, 0xaf6800699cca67a9
+88, 0xc7590aae7244c3bf
+89, 0x7824345f4713f5f9
+90, 0x8f713505f8fd059b
+91, 0x3d5b5b9bb6b1e80e
+92, 0x8674f45e5dc40d79
+93, 0xcb1e36846aa14773
+94, 0xe0ae45b2b9b778c1
+95, 0xd7254ce931eefcfb
+96, 0xef34e15e4f55ac0a
+97, 0xf17cc0ba15a99bc4
+98, 0x77bb0f7ffe7b31f1
+99, 0x6ee86438d2e71d38
+100, 0x584890f86829a455
+101, 0x7baf0d8d30ba70fe
+102, 0xb1ac8f326b8403ae
+103, 0xcc1963435c874ba7
+104, 0x9c483b953d1334ce
+105, 0xc0924bcbf3e10941
+106, 0x21bcc581558717b1
+107, 0x2c5ad1623f8d292b
+108, 0xa8ea110f6124557e
+109, 0x15f24a6c5c4c591
+110, 0x40fe0d9cd7629126
+111, 0xcfe8f2b3b081484d
+112, 0x891383f4b4cac284
+113, 0x76f2fcdef7fa845
+114, 0x4edd12133aed0584
+115, 0xd53c06d12308873d
+116, 0xf7f22882c17f86bf
+117, 0xfbaa4aad72f35e10
+118, 0x627610da2e3c0cc3
+119, 0x582b16a143634d9a
+120, 0x9b4a7f69ed38f4a0
+121, 0x2df694974d1e1cbe
+122, 0xe5be6eaafed5d4b
+123, 0xc48e2a288ad6605e
+124, 0xbcb088149ce27c2b
+125, 0x3cb6a7fb06ceecbe
+126, 0x516735fff3b9e3ac
+127, 0x5cbafc551ee5008d
+128, 0xee27d1ab855c5fd5
+129, 0xc99fb341f6baf846
+130, 0x7ad8891b92058e6d
+131, 0xf50310d03c1ac6c7
+132, 0x947e281d998cbd3e
+133, 0x1d4d94a93824fe80
+134, 0x5568b77289e7ee73
+135, 0x7d82d1b2b41e3c8b
+136, 0x1af462c7abc787b
+137, 0xcfd8dfe80bfae1ef
+138, 0xd314caeb723a63ea
+139, 0x1c63ddcfc1145429
+140, 0x3801b7cc6cbf2437
+141, 0xc327d5b9fdafddd3
+142, 0xe140278430ca3c78
+143, 0x4d0345a685cb6ef8
+144, 0x47640dc86e261ff9
+145, 0xab817f158523ebf4
+146, 0x37c51e35fbe65a6b
+147, 0xab090f475d30a178
+148, 0x4d3ec225bf599fc1
+149, 0xefd517b0041679b1
+150, 0x20ad50bca4da32c5
+151, 0x75e1f7cd07fad86d
+152, 0x348cf781ee655f4b
+153, 0x9375f0e5ffc2d2ec
+154, 0x7689082fd5f7279c
+155, 0x633e56f763561e77
+156, 0x9d1752d70861f9fd
+157, 0xa3c994b4e70b0b0f
+158, 0xabf7276a58701b88
+159, 0xbfa18d1a0540d000
+160, 0xc6a28a2475646d26
+161, 0x7cdf108583f65085
+162, 0x82dcefb9f32104be
+163, 0xc6baadd0adc6b446
+164, 0x7a63cff01075b1b4
+165, 0x67ac62e575c89919
+166, 0x96fa4320a0942035
+167, 0xc4658859385b325f
+168, 0xde22c17ff47808f6
+169, 0xbb952c4d89e2f2ec
+170, 0x638251fbc55bdc37
+171, 0x38918b307a03b3ea
+172, 0xccb60f2cedbb570b
+173, 0x3c06f4086a28f012
+174, 0x4e8d238388986e33
+175, 0x1760b7793514a143
+176, 0xa3f924efe49ee7d6
+177, 0xaf6be2dbaebc0bdf
+178, 0x6782682090dffe09
+179, 0xb63a4d90d848e8ef
+180, 0x5f649c7eaf4c54c5
+181, 0xbe57582426a085ba
+182, 0xb5dd825aa52fb76d
+183, 0x74cb4e6ca4039617
+184, 0x382e578bf0a49588
+185, 0xc043e8ea6e1dcdae
+186, 0xf902addd5c04fa7c
+187, 0xf3337994612528db
+188, 0x4e8fd48d6d15b4e6
+189, 0x7190a509927c07ab
+190, 0x864c2dee5b7108ae
+191, 0xbb9972ddc196f467
+192, 0x1ea02ab3ca10a448
+193, 0xe50a8ffde35ddef9
+194, 0x7bd2f59a67183541
+195, 0x5a940b30d8fcd27a
+196, 0x82b4cea62623d4d3
+197, 0x6fbda76d4afef445
+198, 0x8b1f6880f418328e
+199, 0x8b69a025c72c54b7
+200, 0xb71e0f3986a3835f
+201, 0xa4a7ddb8b9816825
+202, 0x945dcda28228b1d8
+203, 0xb471abf2f8044d72
+204, 0xf07d4af64742b1ba
+205, 0xfca5190bc4dd6a2a
+206, 0xd681497262e11bc5
+207, 0xbe95d5f00c577028
+208, 0x56313439fd8bde19
+209, 0x3f3d9ac9b5ee6522
+210, 0x7b8d457dd2b49bbe
+211, 0xe76b5747885d214b
+212, 0xa8a695b3deb493ea
+213, 0x5292446548c95d71
+214, 0xbf5cdf0d436412df
+215, 0x7936abaed779d28d
+216, 0x659c6e8073b3a06d
+217, 0x86c9ff28f5543b71
+218, 0x6faa748445a99146
+219, 0xdcc1e6ab57904fd7
+220, 0x770bd61233addc5f
+221, 0x16963e041e46d94f
+222, 0x158e6cb2934157ac
+223, 0xb65088a8fd246441
+224, 0x2b12ced6ce8a68c3
+225, 0x59a18d02cd6082b3
+226, 0x4ddbc318cb5488ee
+227, 0x3d4cf520b3ed20a1
+228, 0x7028b3a92e2b292d
+229, 0xf141da264a250e4d
+230, 0x9788d53e86041c37
+231, 0x1bb91238a7c97dbf
+232, 0x81953d0ddb634309
+233, 0xfa39ccfe14d2d46
+234, 0xf7c7861c9b7e8399
+235, 0x18d27ca50d9dc249
+236, 0x258dfdf38510d0d9
+237, 0x9e72d8af910ea76f
+238, 0x4f8ef24b96de50ad
+239, 0xb9d9c12297e03dc9
+240, 0x91994e41b4a1929c
+241, 0x8defa79b2ccc83b9
+242, 0x948566748706dac5
+243, 0x7b0454946e70e4cf
+244, 0x340b7cb298c70ed7
+245, 0x6602005330cebd95
+246, 0xf71cb803aa61f722
+247, 0x4683fb07fc70ae8a
+248, 0xc6db9f0c4de3ed88
+249, 0x3e8dfae2a593cef9
+250, 0x615f7c38e3862b33
+251, 0x676c7996550d857
+252, 0xc6d520d54a5c266a
+253, 0x202b1e8eef14aa2e
+254, 0xa3a84891a27a582
+255, 0x84dbee451658d47f
+256, 0x254c7cd97e777e3a
+257, 0xf50b6e977f0eba50
+258, 0x2898b1d3062a4798
+259, 0x4096f7cbbb019773
+260, 0x9fb8e75548062c50
+261, 0x4647071e5ca318ec
+262, 0x2b4750bdb3b3b01
+263, 0x88ac41cc69a39786
+264, 0x705e25476ef46fa3
+265, 0xc0c1db19884a48a6
+266, 0x1364c0afdbb465e5
+267, 0x58e98534701272a6
+268, 0x746a5ea9701517c0
+269, 0x523a70bc6b300b67
+270, 0x9b1c098eda8564ad
+271, 0xfbaeb28d3637067f
+272, 0xddd9a13551fdba65
+273, 0x56461a670559e832
+274, 0xab4fd79be85570ad
+275, 0xd4b691ecaff8ca55
+276, 0x11a4495939e7f004
+277, 0x40d069d19477eb47
+278, 0xe790783d285cd81e
+279, 0xde8218b16d935bc7
+280, 0x2635e8c65cd4182d
+281, 0xeae402623e3454
+282, 0x9f99c833184e0279
+283, 0x3d0f79a0d52d84e7
+284, 0xc1f8edb10c625b90
+285, 0x9b4546363d1f0489
+286, 0x98d86d0b1212a282
+287, 0x386b53863161200d
+288, 0xbe1165c7fe48a135
+289, 0xb9658b04dbbfdc8c
+290, 0xcea14eddfe84d71a
+291, 0x55d03298be74abe7
+292, 0x5be3b50d961ffd7e
+293, 0xc76b1045dc4b78e1
+294, 0x7830e3ff3f6c3d4c
+295, 0xb617adb36ca3729
+296, 0x4a51bdb194f14aa9
+297, 0x246024e54e6b682a
+298, 0x33d42fc9c6d33083
+299, 0xadccba149f31e1d
+300, 0x5183e66b9002f8b
+301, 0x70eb2416404d51b7
+302, 0x26c25eb225535351
+303, 0xbc2d5b0d23076561
+304, 0x5823019ddead1da
+305, 0x85cfa109fca69f62
+306, 0x26017933e7e1efd9
+307, 0x3ec7be9a32212753
+308, 0x697e8a0697cd6f60
+309, 0x44735f6cca03920f
+310, 0x8cc655eb94ee212e
+311, 0x8b8b74eba84929a0
+312, 0x7708ccedd0c98c80
+313, 0x1b6f21f19777cbe1
+314, 0x363e564bd5fadedb
+315, 0x5921543a641591fe
+316, 0xc390786d68ea8a1b
+317, 0x9b293138dc033fca
+318, 0x45447ca8dc843345
+319, 0xee6ef6755bc49c5e
+320, 0x70a3a1f5163c3be5
+321, 0xf05e25448b6343b0
+322, 0x4739f4f8717b7e69
+323, 0xb006141975bf957
+324, 0x31874a91b707f452
+325, 0x3a07f2c90bae2869
+326, 0xb73dae5499a55c5e
+327, 0x489070893bb51575
+328, 0x7129acf423940575
+329, 0x38c41f4b90130972
+330, 0xc5260ca65f5a84a1
+331, 0x6e76194f39563932
+332, 0x62ca1f9ca3de3ca6
+333, 0xb4a97874e640853f
+334, 0x38ed0f71e311cc02
+335, 0xde183b81099e8f47
+336, 0x9bb8bf8e6694346
+337, 0xd15497b6bf81e0f2
+338, 0xaaae52536c00111
+339, 0x4e4e60d1435aaafd
+340, 0x5a15512e5d6ea721
+341, 0xff0f1ffabfc6664f
+342, 0xba3ffcedc5f97fec
+343, 0xef87f391c0c6bfb6
+344, 0x4a888c5d31eb0f98
+345, 0x559a3fbfd7946e95
+346, 0xe45b44a0db5a9bad
+347, 0x9457898964190af1
+348, 0xd9357dfaab76cd9e
+349, 0xa60e907178d965a1
+350, 0x76b2dc3032dc2f4a
+351, 0x13549b9c2802120
+352, 0x8656b965a66a1800
+353, 0x16802e6e22456a23
+354, 0x23b62edc60efaa9
+355, 0x6832a366e1e4ea3b
+356, 0x46b1b41093ff2b1e
+357, 0x55c857128143f219
+358, 0x7fc35ddf5e138200
+359, 0x790abe78be67467e
+360, 0xa4446fc08babd466
+361, 0xc23d70327999b855
+362, 0x2e019d1597148196
+363, 0xfefd98e560403ab8
+364, 0xbe5f0a33da330d58
+365, 0x3078a4e9d43ca395
+366, 0x511bfedd6f12f2b3
+367, 0x8bc138e335be987c
+368, 0x24640f803465716d
+369, 0xf6530b04d0bd618f
+370, 0x9b7833e5aa782716
+371, 0x778cd35aea5841b1
+372, 0xecea3c458cefbc60
+373, 0x5107ae83fc527f46
+374, 0x278ad83d44bd2d1a
+375, 0x7014a382295aeb16
+376, 0xf326dd762048743f
+377, 0x858633d56279e553
+378, 0x76408154085f01bc
+379, 0x3e77d3364d02e746
+380, 0x2f26cea26cadd50b
+381, 0x6d6846a4ecb84273
+382, 0x4847e96f2df5f76
+383, 0x5a8610f46e13ff61
+384, 0x4e7a7cac403e10dd
+385, 0x754bdf2e20c7bc90
+386, 0x8bdd80e6c51bd0be
+387, 0x61c655fae2b4bc52
+388, 0x60873ef48e3d2f03
+389, 0x9d7d8d3698a0b4a4
+390, 0xdf48e9c355cd5d4b
+391, 0x69ecf03e20be99ac
+392, 0xc1a0c5a339bd1815
+393, 0x2e3263a6a3adccb
+394, 0x23557459719adbdc
+395, 0xd1b709a3b330e5a
+396, 0xade5ab00a5d88b9d
+397, 0x69a6bd644120cfad
+398, 0x40187ecceee92342
+399, 0x1c41964ba1ac78da
+400, 0x9ac5c51cbecabe67
+401, 0xbdc075781cf36d55
+402, 0xeaf5a32246ded56
+403, 0xcda0b67e39c0fb71
+404, 0x4839ee456ef7cc95
+405, 0xf17092fdd41d5658
+406, 0x2b5d422e60ae3253
+407, 0x3effe71102008551
+408, 0x20a47108e83934b7
+409, 0xd02da65fe768a88f
+410, 0xeb046bd56afa4026
+411, 0x70c0509c08e0fbe0
+412, 0x1d35c38d4f8bac6c
+413, 0x9aa8eb6466f392e0
+414, 0x587bd4a430740f30
+415, 0x82978fe4bad4195
+416, 0xdc4ebc4c0feb50ab
+417, 0xd3b7164d0240c06f
+418, 0x6e2ad6e5a5003a63
+419, 0xa24b430e2ee6b59c
+420, 0x2905f49fd5073094
+421, 0x5f209e4de03aa941
+422, 0x57b7da3e0bedb1dc
+423, 0x5e054018875b01f5
+424, 0xb2f2da6145658db3
+425, 0xbd9c94a69a8eb651
+426, 0x9c5f9a07cd6ac749
+427, 0x2296c4af4d529c38
+428, 0x522ed800fafdefab
+429, 0xe2a447ced0c66791
+430, 0x937f10d45e455fef
+431, 0xc882987d9e29a24
+432, 0x4610bfd6a247ee1a
+433, 0x562ba3e50870059
+434, 0x59d8d58793602189
+435, 0xfe9a606e3e34abe
+436, 0x6825f7932a5e9282
+437, 0xe77f7061bab476ad
+438, 0xbf42001da340ace3
+439, 0x9c3e9230f5e47960
+440, 0x2c0f700d96d5ad58
+441, 0x330048b7cd18f1f9
+442, 0xffc08785eca5cca9
+443, 0xb5879046915f07a5
+444, 0xef51fe26f83c988e
+445, 0xfa4c2968e7881a9a
+446, 0xc0a9744455a4aad
+447, 0xbd2ad686d6313928
+448, 0x6b9f0984c127682a
+449, 0xc9aaa00a5da59ed8
+450, 0x762a0c4b98980dbf
+451, 0x52d1a2393d3ca2d1
+452, 0x1e9308f2861db15c
+453, 0xe7b3c74fe4b4a844
+454, 0x485e15704a7fc594
+455, 0x9e7f67ea44c221f6
+456, 0xbab9ad47fde916e0
+457, 0x50e383912b7fc1f4
+458, 0xaad63db8abcef62d
+459, 0xc2f0c5699f47f013
+460, 0xee15b36ada826812
+461, 0x2a1b1cf1e1777142
+462, 0x8adb03ede79e937d
+463, 0xf14105ef65643bf3
+464, 0x752bbaefc374a3c7
+465, 0xa4980a08a5a21d23
+466, 0x418a1c05194b2db7
+467, 0xdd6ff32efe1c3cd6
+468, 0x272473ed1f0d3aa2
+469, 0x1e7fdebadabe6c06
+470, 0xd1baa90c17b3842f
+471, 0xd3d3a778e9c8404a
+472, 0x781ae7fda49fa1a0
+473, 0x61c44fdbdacc672d
+474, 0x6d447d0a1404f257
+475, 0x9303e8bdfbfb894d
+476, 0x3b3482cdec016244
+477, 0xb149bf245d062e7b
+478, 0x96f8d54b14cf992d
+479, 0x4741549a01f8c3d0
+480, 0x48270811b2992af
+481, 0x7b58f175cd25d147
+482, 0x8f19a840b56f4be9
+483, 0x84a77f43c0951a93
+484, 0x34e1a69381f0c374
+485, 0xb158383c9b4040f
+486, 0x372f1abc7cf3a9fa
+487, 0x5439819a84571763
+488, 0xabf8515e9084e2fa
+489, 0xb02312b9387ff99
+490, 0x238a85bb47a68b12
+491, 0x2068cb83857c49bb
+492, 0xc6170e743083664c
+493, 0x745cf8470bcb8467
+494, 0xe3a759a301670300
+495, 0x292c7686ad3e67da
+496, 0x359efedaff192a45
+497, 0x511f2c31a2d8c475
+498, 0x97fd041bf21c20b3
+499, 0x25ef1fe841b7b3f6
+500, 0xbb71739e656f262d
+501, 0x2729b0e989b6b7b8
+502, 0xd2142702ec7dbabf
+503, 0x7008decd2488ee3f
+504, 0x69daa95e303298d7
+505, 0xc35eca4efb8baa5a
+506, 0xf3f16d261cec3b6c
+507, 0x22371c1d75396bd3
+508, 0x7aefa08eccae857e
+509, 0x255b493c5e3c2a2f
+510, 0x779474a077d34241
+511, 0x5199c42686bea241
+512, 0x16c83931e293b8d3
+513, 0xa57fe8db8c0302c7
+514, 0xd7ace619e5312eb1
+515, 0x8740f013306d217c
+516, 0xb6a1ad5e29f4d453
+517, 0x31abf7c964688597
+518, 0xbc3d791daed71e7
+519, 0x31ee4ca67b7056ed
+520, 0x1ab5416bfe290ea3
+521, 0x93db416f6d3b843a
+522, 0xed83bbe5b1dd2fed
+523, 0xece38271470d9b6d
+524, 0x3a620f42663cd8ae
+525, 0x50c87e02acafee5d
+526, 0xcabeb8bedbc6dab5
+527, 0x2880a6d09970c729
+528, 0x4aba5dd3bfc81bc
+529, 0xaba54edf41080cec
+530, 0xb86bb916fc85a169
+531, 0x4c41de87bc79d8ca
+532, 0xcce2a202622945fe
+533, 0x513f086fad94c107
+534, 0x18b3960c11f8cc96
+535, 0x2f0d1cfd1896e236
+536, 0x1702ae3880d79b15
+537, 0x88923749029ae81
+538, 0x84810d4bdec668eb
+539, 0xf85b0a123f4fc68d
+540, 0x93efd68974b6e4d1
+541, 0x5d16d6d993a071c9
+542, 0x94436858f94ca43b
+543, 0xb3dbb9ed0cb180b6
+544, 0x6447030a010b8c99
+545, 0xd7224897c62925d8
+546, 0xb0c13c1d50605d3a
+547, 0xdff02c7cb9d45f30
+548, 0xe8103179f983570d
+549, 0xbc552037d6d0a24e
+550, 0x775e500b01486b0d
+551, 0x2050ac632c694dd6
+552, 0x218910387c4d7ae7
+553, 0xf83e8b68ff885d5d
+554, 0xe3374ec25fca51a3
+555, 0xfa750ffa3a60f3af
+556, 0x29ee40ba6df5592e
+557, 0x70e21a68f48260d2
+558, 0x3805ca72cd40886e
+559, 0x2f23e73f8eabf062
+560, 0x2296f80cdf6531ae
+561, 0x903099ed968db43a
+562, 0xf044445cf9f2929f
+563, 0xcd47fdc2de1b7a1
+564, 0xaab1cbd4f849da99
+565, 0x5fc990688da01acb
+566, 0xa9cee52ea7dab392
+567, 0xecefc3a4349283a8
+568, 0xdd6b572972e3fafc
+569, 0xc1f0b1a2ffb155da
+570, 0xc30d53fc17bd25c8
+571, 0x8afa89c77834db28
+572, 0x5569a596fb32896c
+573, 0x36f207fc8df3e3d4
+574, 0x57c2bd58517d81db
+575, 0xb524693e73d0061c
+576, 0xb69f6eb233f5c48b
+577, 0x4f0fb23cab8dc695
+578, 0x492c1ad0a48df8df
+579, 0xf6dcc348ec8dec1f
+580, 0xa4d8708d6eb2e262
+581, 0x4c2072c2c9766ff1
+582, 0xa9bf27c4304875f0
+583, 0xfc8fb8066d4f9ae2
+584, 0x188095f6235fec3c
+585, 0x1d8227a2938c2864
+586, 0x89ea50c599010378
+587, 0xcac86df0a7c6d56d
+588, 0x47a8c5df84c7d78
+589, 0xe607ae24ea228bfa
+590, 0x36624a7996efe104
+591, 0x5d72881c1227d810
+592, 0x78694a6750374c8
+593, 0x7b9a217d4ab5ff45
+594, 0xd53e5d6f7504becc
+595, 0x197a72d3f4889a0e
+596, 0xfdc70c4755a8df36
+597, 0xd0fda83748c77f74
+598, 0x7ddc919ac9d6dcc9
+599, 0x785c810a6a2dc08b
+600, 0xba4be83e7e36896c
+601, 0x379d6fe80cf2bffe
+602, 0x74cae2dabc429206
+603, 0x1efac32d5d34c917
+604, 0x3cb64e2f98d36e70
+605, 0xc0a7c3cdc3c60aa7
+606, 0x699dfadd38790ebe
+607, 0x4861e61b3ecfbeac
+608, 0x531744826c345baa
+609, 0x5ec26427ad450cba
+610, 0xf2c1741479abdcae
+611, 0xe9328a78b2595458
+612, 0x30cd1bdf087acd7f
+613, 0x7491ced4e009adbe
+614, 0xdcd942df1e2e7023
+615, 0xfe63f01689fee35
+616, 0x80282dfe5eaedc42
+617, 0x6ecdea86495f8427
+618, 0xe0adfdd5e9ed31c3
+619, 0xf32bd2a7418127e
+620, 0x8aabba078db6ee2
+621, 0xa8a8e60499145aca
+622, 0xf76b086ac4e8a0f2
+623, 0x6e55b3c452ff27f8
+624, 0xe18fa7cd025a71bf
+625, 0xeed7b685fde0fa25
+626, 0xba9b6c95867fa721
+627, 0x4c2603bc69de2df2
+628, 0xaac87eee1b58cd66
+629, 0x3c9af6656e01282c
+630, 0x2dfa05ce8ff476b6
+631, 0xeae9143fcf92f23d
+632, 0x3f0699f631be3bc8
+633, 0xa0f5f79f2492bd67
+634, 0x59c47722388131ed
+635, 0x5f6e9d2941cef1de
+636, 0xe9ad915c09788b7b
+637, 0x92c6d37e4f9482f5
+638, 0x57d301b7fdadd911
+639, 0x7e952d23d2a8443
+640, 0xbb2fa5e0704b3871
+641, 0xe5642199be36e2d5
+642, 0x5020b60d54358291
+643, 0xa0b6317ec3f60343
+644, 0xb57b08b99540bc5c
+645, 0x21f1890adc997a88
+646, 0xfcf824200dd9da2d
+647, 0x8146293d83d425d1
+648, 0xdadfbf5fbb99d420
+649, 0x1eb9bbc5e6482b7d
+650, 0xd40ff44f1bbd0f1c
+651, 0xa9f948ba2d08afa5
+652, 0x638cc07c5301e601
+653, 0x1f984baa606e14e8
+654, 0x44e153671081f398
+655, 0xb17882eeb1d77a5d
+656, 0x5fd8dbee995f14c
+657, 0xff3533e87f81b7fe
+658, 0x2f44124293c49795
+659, 0x3bf6b51e9360248
+660, 0x72d615edf1436371
+661, 0x8fc5cf4a38adab9d
+662, 0xfa517e9022078374
+663, 0xf356733f3e26f4d8
+664, 0x20ea099cdc6aad40
+665, 0xe15b977deb37637d
+666, 0xcc85601b89dae88d
+667, 0x5768c62f8dd4905c
+668, 0xa43cc632b4e56ea
+669, 0xc4240cf980e82458
+670, 0xb194e8ffb4b3eeb6
+671, 0xee753cf2219c5fa1
+672, 0xfe2500192181d44d
+673, 0x2d03d7d6493dd821
+674, 0xff0e787bb98e7f9b
+675, 0xa05cf8d3bd810ce7
+676, 0x718d5d6dcbbdcd65
+677, 0x8d0b5343a06931c
+678, 0xae3a00a932e7eaf9
+679, 0x7ed3d8f18f983e18
+680, 0x3bb778ee466dc143
+681, 0x711c685c4e9062c0
+682, 0x104c3af5d7ac9834
+683, 0x17bdbb671fb5d5cf
+684, 0xabf26caead4d2292
+685, 0xa45f02866467c005
+686, 0xf3769a32dc945d2d
+687, 0xe78d0007f6aabb66
+688, 0x34b60be4acbd8d4b
+689, 0x58c0b04b69359084
+690, 0x3a8bb354c212b1
+691, 0x6b82a8f3d70058d5
+692, 0x405bdef80a276a4a
+693, 0xe20ca40ee9195cad
+694, 0xf5dd96ba2446fefd
+695, 0xc1e180c55fe55e3c
+696, 0xa329caf6daa952b3
+697, 0xb4809dd0c84a6b0a
+698, 0xd27f82661070cee7
+699, 0xa7121f15ee2b0d8a
+700, 0x4bdaea70d6b34583
+701, 0xe821dc2f310f7a49
+702, 0x4c00a5a68e76f647
+703, 0x331065b064a2d5ea
+704, 0xac0c2ce3dc04fa37
+705, 0x56b32b37b8229008
+706, 0xe757cdb51534fcfa
+707, 0xd3ff183576b2fad7
+708, 0x179e1f4190f197a7
+709, 0xf874c626a7c9aae5
+710, 0xd58514ffc37c80e4
+711, 0xc65de31d33fa7fd3
+712, 0x6f6637052025769b
+713, 0xca1c6bdadb519cc0
+714, 0xd1f3534cde37828a
+715, 0xc858c339eee4830a
+716, 0x2371eacc215e02f4
+717, 0x84e5022db85bbbe9
+718, 0x5f71c50bba48610e
+719, 0xe420192dad9c323f
+720, 0x2889342721fca003
+721, 0x83e64f63334f501d
+722, 0xac2617172953f2c
+723, 0xfa1f78d8433938ff
+724, 0x5578382760051462
+725, 0x375d7a2e3b90af16
+726, 0xb93ff44e6c07552d
+727, 0xded1d5ad811e818c
+728, 0x7cf256b3b29e3a8c
+729, 0x78d581b8e7bf95e8
+730, 0x5b69192f2caa6ad3
+731, 0xa9e25855a52de3ce
+732, 0x69d8e8fc45cc188d
+733, 0x5dd012c139ad347d
+734, 0xfcb01c07b77db606
+735, 0x56253e36ab3d1cce
+736, 0x1181edbb3ea2192
+737, 0x325bef47ff19a08d
+738, 0xd3e231ceb27e5f7
+739, 0x8e819dd2de7956d2
+740, 0x34a9689fe6f84a51
+741, 0x3e4eeb719a9c2927
+742, 0x5c3b3440581d0aaf
+743, 0x57caf51897d7c920
+744, 0xec6a458130464b40
+745, 0xe98f044e0da40e9b
+746, 0xbe38662020eeb8e7
+747, 0x7b8c407c632724ae
+748, 0x16c7cfa97b33a544
+749, 0xd23359e2e978ae5a
+750, 0x4fdba458250933dd
+751, 0x3c9e0713cfe616ba
+752, 0x6f0df87b13163b42
+753, 0xc460902cb852cc97
+754, 0x289df8fefd6b0bce
+755, 0x4ac2a2a1c3fb8029
+756, 0x2fc3e24d8b68eef7
+757, 0x34564386a59aab9a
+758, 0x31047391ebd67ce4
+759, 0x6c23d070a0564d41
+760, 0xba6387b2b72545f7
+761, 0xcdcf1008058387af
+762, 0xc9308fa98db05192
+763, 0xdbdbb5abd01a9d84
+764, 0x937088275c7804ab
+765, 0x6f6accfefe34ee81
+766, 0x5c33c74c49cfdb2c
+767, 0x5e1a771edfb92bd3
+768, 0x6e89b009069ecae7
+769, 0x34d64e17ec0e8968
+770, 0x841203d0cde0c330
+771, 0x7642cc9d7eb9e9cb
+772, 0xca01d2e8c128b97e
+773, 0x5b8390617b3304ab
+774, 0x52ec4ed10de1eb2d
+775, 0xb90f288b9616f237
+776, 0x5bd43cd49617b2e2
+777, 0x1a53e21d25230596
+778, 0x36ccd15207a21cd6
+779, 0xc8263d780618fd3c
+780, 0x6eb520598c6ce1cb
+781, 0x493c99a3b341564f
+782, 0xab999e9c5aa8764f
+783, 0xab2fa4ceaba84b
+784, 0xbbd2f17e5cb2331b
+785, 0xc8b4d377c0cc4e81
+786, 0x31f71a6e165c4b1e
+787, 0xd1011e55fb3addaa
+788, 0x5f7ec34728dfa59
+789, 0x2aef59e60a84eb0f
+790, 0x5dde6f09aec9ad5f
+791, 0x968c6cdbc0ef0438
+792, 0x1957133afa15b13a
+793, 0xbaf28f27573a64c2
+794, 0xc6f6ddd543ebf862
+795, 0xdd7534315ec9ae1e
+796, 0xd2b80cd2758dd3b
+797, 0xa38c3da00cc81538
+798, 0x15c95b82d3f9b0f9
+799, 0x6704930287ce2571
+800, 0x9c40cc2f6f4ecb0c
+801, 0xc8de91f50b22e94e
+802, 0x39272e8fddbfdf0a
+803, 0x879e0aa810a117d
+804, 0xa312fff4e9e5f3bd
+805, 0x10dd747f2835dfec
+806, 0xeb8466db7171cdae
+807, 0xaa808d87b9ad040a
+808, 0xab4d2229a329243a
+809, 0x7c622f70d46f789c
+810, 0x5d41cef5965b2a8e
+811, 0xce97ec4702410d99
+812, 0x5beba2812c91211b
+813, 0xf134b46c93a3fec7
+814, 0x76401d5630127226
+815, 0xc55fc9d9eacd4ec1
+816, 0xaec8cefaa12f813f
+817, 0x2f845dcfd7b00722
+818, 0x3380ab4c20885921
+819, 0xdb68ad2597691b74
+820, 0x8a7e4951455f563f
+821, 0x2372d007ed761c53
+822, 0xcab691907714c4f1
+823, 0x16bc31d6f3abec1a
+824, 0x7dff639fbcf1824
+825, 0x6666985fbcff543d
+826, 0xb618948e3d8e6d0c
+827, 0x77b87837c794e068
+828, 0xcd48288d54fcb5a8
+829, 0x47a773ed6ae30dc3
+830, 0xba85ae44e203c942
+831, 0xa7a7b21791a25b2d
+832, 0x4029dd92e63f19e0
+833, 0xc2ad66ab85e7d5aa
+834, 0xa0f237c96fdab0db
+835, 0xffefb0ab1ca18ed
+836, 0x90cb4500785fd7d5
+837, 0xa7dd3120f4876435
+838, 0x53f7872624694300
+839, 0xea111326ff0040d9
+840, 0x5f83cb4cce40c83b
+841, 0x918e04936c3b504d
+842, 0x87a8db4c0e15e87c
+843, 0x7cff39da6a0dedd0
+844, 0x36f7de2037f85381
+845, 0xd1d8d94022a1e9a7
+846, 0x2c9930127dc33ec9
+847, 0x6cb4719dcd0101c6
+848, 0xc01868cde76935f7
+849, 0x6b86f2ec1ab50143
+850, 0x68af607d8d94ae61
+851, 0xe216c5b95feedf34
+852, 0x4b866bd91efe2e4b
+853, 0x4bff79df08f92c99
+854, 0x6ff664ea806acfd1
+855, 0x7fce0b3f9ece39bc
+856, 0x29bc90b59cb3db97
+857, 0x833c4b419198607d
+858, 0xf3573e36ca4d4768
+859, 0x50d71c0a3c2a3fa8
+860, 0xd754591aea2017e7
+861, 0x3f9126f1ee1ebf3
+862, 0xe775d7f4b1e43de8
+863, 0xe93d51628c263060
+864, 0x83e77f6fb32d6d82
+865, 0x43dd7eef823408e4
+866, 0x1c843c2c90180662
+867, 0xe924dafb9a16066b
+868, 0x6af3ee96e7b7fbd9
+869, 0x94d5c4f37befcd1f
+870, 0x40ffb04bedef4236
+871, 0x71c17bbc20e553e
+872, 0x101f7a0a6208729f
+873, 0x5ca34570cf923548
+874, 0x8e3139db2e96e814
+875, 0x3ab96d96263d048d
+876, 0x97f3c0bbc6755c3c
+877, 0x31fc72daedaef3dc
+878, 0x71f8d7855d10789b
+879, 0xce6dc97b4662333b
+880, 0xfddc2aabd342bc61
+881, 0xefbd4007ff8c7d2e
+882, 0xf72cd6c689ef8758
+883, 0x932c8b0c0e755137
+884, 0x94cc4dedd58ff69
+885, 0xde4dfd6890535979
+886, 0xdb00dcd2dcb4a50a
+887, 0xb0466240b4548107
+888, 0x9cb9264c7b90d1a3
+889, 0x357e378e9be5766b
+890, 0x6e0316ef03367bbf
+891, 0x201ea18839544ca
+892, 0x803ff3406be5f338
+893, 0xf9d5e82fd4144bb2
+894, 0x1b6b88ca701e9f47
+895, 0xd1fe5ab8e1f89cc0
+896, 0x14171fe176c4bece
+897, 0x887948bdef78beaa
+898, 0x80449ddc3eb9b977
+899, 0x5f4e1f900fb4bcf3
+900, 0xbe30f8701909f8e2
+901, 0xd1f2a2fb5503306d
+902, 0x6b1c77238dc23803
+903, 0x102156a6c9860f66
+904, 0x4cd446e099edf4c1
+905, 0xc79ac6cbc911f33b
+906, 0x3ee096ffe3384f1c
+907, 0xb58f83b18a306dc7
+908, 0x9f76582141de56b2
+909, 0x9ddfa85e02c13866
+910, 0x4d9a19d4ce90a543
+911, 0xbf81ab39fd17d376
+912, 0x5327e5054c6a74f1
+913, 0xd5062dd31db1a9b7
+914, 0x645853735527edc
+915, 0x485393967f91af08
+916, 0xeff9667dcf77ca68
+917, 0xd012313f5fbec464
+918, 0xbeae35bdfae55144
+919, 0x302c41ebac8444a0
+920, 0x9ccdb6c2fe58fba8
+921, 0x567753af68ed23f8
+922, 0xff90f790e43efec3
+923, 0x970cc756fb799696
+924, 0xe59239d1c44915
+925, 0x4d2d189fb3941f05
+926, 0x96f23085db165a9c
+927, 0xa1202dec7a37b1a5
+928, 0xc0c1ee74bcd7dc1a
+929, 0x9edcf2048b30333a
+930, 0xd848588ba7e865fb
+931, 0x8d9f0897317cab40
+932, 0x67b96f15e25924fb
+933, 0xefc8d8536619ee42
+934, 0xf3f621d22bdde0c2
+935, 0x68610a0de862ae32
+936, 0xa22ca5142de24cbd
+937, 0x8815452f4e6b4801
+938, 0x4e9c1b607b2750e5
+939, 0x19b3c09ba6fc9b25
+940, 0x9b2543c8836780ac
+941, 0xe702b8f950e56431
+942, 0xb357cc329cac3917
+943, 0x387bf86a17a31e08
+944, 0x9940b983d331b163
+945, 0xf5d89d7fe9095e18
+946, 0x4362682329e5c4d1
+947, 0xd2132573f6ae7b42
+948, 0xc0a5849e23a61606
+949, 0xdadbddf47265bc02
+950, 0x1b96f00339a705f7
+951, 0x94e6642329288913
+952, 0x825ab3f10e6d330b
+953, 0x1a1c31ac9d883ea0
+954, 0xb49076b7155c6f47
+955, 0x920cf3085dfe3ccb
+956, 0x9743407c9f28e825
+957, 0x6ce8a28622402719
+958, 0xce2fe67e06baf8a6
+959, 0x3a16b34784ecf5e6
+960, 0x140467cc1d162a0c
+961, 0x32d4772692ab625
+962, 0xa4f4b28562f43336
+963, 0x885b4335457bd84a
+964, 0x499d3ed26c87ad8a
+965, 0xc7328bcedb9a545e
+966, 0xc6dd76a6cbf5d2b2
+967, 0xba9c22be404ee1aa
+968, 0x70e6aee45f23521d
+969, 0x61e03a798593c177
+970, 0x171671f809c68213
+971, 0x28d54872fc1d914c
+972, 0x43c2fcd9bd098b53
+973, 0x172ad4c4a98b9d37
+974, 0x330860c9460f2516
+975, 0x49547f472df984f4
+976, 0x873b2436d3f0e114
+977, 0x6f99accf4ea050b6
+978, 0x5968ac874ed51613
+979, 0x4939d70d29a3c611
+980, 0x11f381ed28738d3d
+981, 0xa97430d36ab3a869
+982, 0xe6fa880801129e22
+983, 0xf84decbd8f48c913
+984, 0x4425c0ed1e9a82a5
+985, 0x7a1f9485e9929d5a
+986, 0xc7c51f155dfce1c6
+987, 0x9619a39501d74f2b
+988, 0x7c7035955dbf4c1b
+989, 0xc61ee569cf57c2c9
+990, 0x3eaf7c5b0df734e1
+991, 0xe71cb4064d1ede05
+992, 0x356e3cec80e418b2
+993, 0xca04306243a15be6
+994, 0x941cf3881fa18896
+995, 0x30dbb0e819d644e0
+996, 0xaae22c0bef02859a
+997, 0x7bd30917bbaa8a94
+998, 0x2672547bc8d7d329
+999, 0x4955c92aaa231578
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..878c5ea7c3a5173467a4f945947174149f3913e6
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv
@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0xd97e4a147f788a70
+1, 0x8dfa7bce56e3a253
+2, 0x13556ed9f53d3c10
+3, 0x55dbf1c241341e98
+4, 0xa2cd98f722eb0e0a
+5, 0x83dfc407203ade8
+6, 0xeaa083df518f030d
+7, 0x44968c87e432852b
+8, 0x573107b9cb8d9ecc
+9, 0x9eedd1da50b9daca
+10, 0xb33a6735ca451e3c
+11, 0x72830d2b39677262
+12, 0x9da8c512fd0207e8
+13, 0x1fc5c91954a2672b
+14, 0xd33479437116e08
+15, 0x9ccdd9390cee46f3
+16, 0x1fd39bb01acd9e76
+17, 0xedc1869a42ff7fe5
+18, 0xbd68ca0b42a6e7e9
+19, 0x620b67df09621b1f
+20, 0xfa11d51bd6950221
+21, 0xc8c45b36e7d28d08
+22, 0xe9c91272fbaad777
+23, 0x2dc87a143f220e90
+24, 0x6376a7c82361f49d
+25, 0x552c5e434232fe75
+26, 0x468f7f872ac195bc
+27, 0x32bed6858125cf89
+28, 0xe4f06111494d09d3
+29, 0xa5c166ffea248b80
+30, 0x4e26605b97064a3f
+31, 0xceafd9f6fc5569d
+32, 0xb772f2f9eed9e106
+33, 0x672c65e6a93534e2
+34, 0xcdc5e1a28d1bd6a0
+35, 0x1ed9c96daeebd3e3
+36, 0x4d189dcfc0c93c3f
+37, 0x50df5a95c62f4b43
+38, 0xcccf4949fa65bbb8
+39, 0x19b8073d53cdc984
+40, 0x6fb40bba35483703
+41, 0xb02de4aef86b515a
+42, 0x4d90c63655350310
+43, 0xea44e4089825b16c
+44, 0x8d676958b1f9da2b
+45, 0x6d313940917ae195
+46, 0x1b1d35a4c1dd19f4
+47, 0x117720f8397337ef
+48, 0xcc073cf3ac11eeaa
+49, 0x8331ec58a9ff8acb
+50, 0xf3dc2a308b6b866f
+51, 0x7eba1202663382b6
+52, 0x8269839debeb4e5a
+53, 0x87fd3dc0f9181a8e
+54, 0xabe62ddd3c925f03
+55, 0x7f56f146944fe8d4
+56, 0xc535972150852068
+57, 0x60b252d453bd3a68
+58, 0x4251f0134634490a
+59, 0x338950da210dfeb2
+60, 0xcadfe932971c9471
+61, 0xfb7049457fab470e
+62, 0x9bfb8145a4459dff
+63, 0x4a89dda3898f9d8a
+64, 0x88cc560151483929
+65, 0x277dc820f4b6796e
+66, 0x3524bd07ea0afb88
+67, 0x92eb6ffb2bf14311
+68, 0xf6559be0783f3fe9
+69, 0xf0844f9af54af00d
+70, 0xdd5e0b59adcef8a
+71, 0x4ff7e4f2ab18554c
+72, 0x3fa22c8a02634587
+73, 0x1db8e1a9442fe300
+74, 0x40cf15953ad3d3e7
+75, 0x92af15fe1a9f6f0a
+76, 0xab4a0e466fb0cfd
+77, 0x944f1555a06cca82
+78, 0x10cf48412f1f6066
+79, 0x7f51f9a455f9e8e1
+80, 0x47ee93530f024c7e
+81, 0x36cf2f0413e0f6f2
+82, 0xa315e23731969407
+83, 0xd8e2796327cf5f87
+84, 0xa86072696a555c34
+85, 0xee3f0b8804feaab7
+86, 0x41e80dc858f8360b
+87, 0x31ec2e9b78f5b29
+88, 0xd397fb9b8561344c
+89, 0x28081e724e649b74
+90, 0x5c135fc3fc672348
+91, 0x9a276ca70ce9caa0
+92, 0x9216da059229050a
+93, 0xcf7d375ed68007b0
+94, 0xa68ad1963724a770
+95, 0xd4350de8d3b6787c
+96, 0xee7d2c2cc275b6d2
+97, 0x71645ec738749735
+98, 0x45abdf8c68d33dbb
+99, 0xe71cadb692c705ea
+100, 0x60af6f061fd90622
+101, 0x1eabe2072632c99d
+102, 0x947dda995a402cb6
+103, 0xbb19f49a3454f3b
+104, 0xe6e43e907407758c
+105, 0xfe2b67016bd6873a
+106, 0x7fdb4dd8ab30a722
+107, 0x39d3265b0ff1a45b
+108, 0xed24c0e4fce8d0c2
+109, 0xf6e074f86faf669d
+110, 0x9142040df8dc2a79
+111, 0x9682ab16bc939a9c
+112, 0x6a4e80c378d971c8
+113, 0x31309c2c7fc2d3d6
+114, 0xb7237ec682993339
+115, 0x6a30c06bb83dccd9
+116, 0x21c8e9b6d8e7c382
+117, 0x258a24ae6f086a19
+118, 0xb76edb5be7df5c35
+119, 0x3c11d7d5c16e7175
+120, 0xbdfc34c31eff66e1
+121, 0x8af66e44be8bf3a2
+122, 0x3053292e193dec28
+123, 0xd0cc44545b454995
+124, 0x408ac01a9289d56
+125, 0x4e02d34318ec2e85
+126, 0x9413ff3777c6eb6b
+127, 0xa3a301f8e37eb3df
+128, 0x14e6306bd8d8f9f9
+129, 0xd3ea06ce16c4a653
+130, 0x170abe5429122982
+131, 0x7f9e6fddc6cacb85
+132, 0xa41b93e10a10a4c8
+133, 0x239216f9d5b6d0b5
+134, 0x985fcb6cb4190d98
+135, 0xb45e3e7c68f480c6
+136, 0xc1b2fc2e0446211c
+137, 0x4596adb28858c498
+138, 0x2dd706f3458ddc75
+139, 0x29c988c86f75464
+140, 0xac33a65aa679a60
+141, 0xa28fef762d39d938
+142, 0x541e6fa48647f53
+143, 0x27838d56b2649735
+144, 0x8e143d318a796212
+145, 0xaea6097745f586b8
+146, 0x636143330f8ee2e6
+147, 0xc2d05fd8b945b172
+148, 0x6e355f9eb4353055
+149, 0xeb64ca42e8bf282e
+150, 0xe8202dfd9da0fe5
+151, 0x7305689c9d790cba
+152, 0xf122f8b1bef32970
+153, 0x9562887e38c32ba5
+154, 0xf9cd9be121b738d
+155, 0x6238e0c398307913
+156, 0x5f2e79bb07c30f47
+157, 0x8ce8e45c465006e
+158, 0x39281fe1e99e2441
+159, 0xafb10c2ca2874fea
+160, 0x6e52f91633f83cf
+161, 0x8ff12c1ac73c4494
+162, 0xe48608a09365af59
+163, 0xefd9bbc7e76e6a33
+164, 0xbe16a39d5c38ec92
+165, 0x6a6ffbcaf5a2330f
+166, 0xdd5d6ac7d998d43d
+167, 0x207bf978226d4f11
+168, 0xf8eec56bd2a0f62e
+169, 0xa5bccf05dce0d975
+170, 0x93cf3ec1afe457a6
+171, 0x38651466d201f736
+172, 0x3ad21473985c9184
+173, 0xc6407a3bd38c92a6
+174, 0xb1ec42c7afa90a25
+175, 0xbdeca984df8b7dd3
+176, 0xb6926b1d00aa6c55
+177, 0x86141d0022352d49
+178, 0x169316256135ee09
+179, 0xffb1c7767af02a5c
+180, 0x502af38ad19f5c91
+181, 0xfbf6cbc080086658
+182, 0x33cf9b219edae501
+183, 0x46e69bebd77b8862
+184, 0xf11e0cc91125d041
+185, 0xb4cd1649f85e078f
+186, 0xb49be408db4e952
+187, 0xb0b8db46140cce3c
+188, 0xba647f2174012be7
+189, 0x4f0a09e406970ac9
+190, 0xf868c7aec9890a5c
+191, 0xde4c8fa7498ea090
+192, 0x872ceb197978c1d4
+193, 0x1eb5cd9c3269b258
+194, 0x3ea189f91724f014
+195, 0x41379656f7746f2c
+196, 0x7bd18493aca60e51
+197, 0x5380c23b0cbbf15e
+198, 0x920b72835f88246b
+199, 0x24d7f734a4548b8e
+200, 0x9944edb57e5aa145
+201, 0x4628e136ebb8afe1
+202, 0xb4ee6a776356e2a7
+203, 0x481cbe9744ccf7d7
+204, 0x7e8d67e8b0b995d9
+205, 0xeeacde100af7b47e
+206, 0x103da08f2487dab7
+207, 0x6b9890a91d831459
+208, 0xd0c5beae37b572c7
+209, 0xfdccc371ee73fcc
+210, 0x65438f0a367a2003
+211, 0x5d23b2c818a7e943
+212, 0x9a8ed45ac04b58b3
+213, 0xdaf3c3f1695dce10
+214, 0x5960eec706fa2bc0
+215, 0x98ca652facb80d40
+216, 0x72970ae5e2194143
+217, 0x18c6374d878c5c94
+218, 0x20fa51f997381900
+219, 0x3af253dba26d6e1d
+220, 0x1b23d65db15c7f78
+221, 0x9f53ae976259b0e3
+222, 0x9a6addb28dc92d49
+223, 0x1e085c4accd0a7d7
+224, 0xe9d3f4cc9bad6ce5
+225, 0xe018fad78b5b1059
+226, 0x5ef7682232b4b95
+227, 0xb2242aa649f5de80
+228, 0x8f3e6d8dd99b9e4e
+229, 0xb9be6cc22949d62a
+230, 0xecbdc7beaa5ff1fe
+231, 0xd388db43a855bdf0
+232, 0xd71ee3238852568d
+233, 0x85ab3056304c04b5
+234, 0x2ed7ae7ad3cfc3cb
+235, 0x781d1b03d40b6c48
+236, 0x7d3c740886657e6d
+237, 0x982cfa6828daa6b0
+238, 0x278579599c529464
+239, 0x773adecfae9f0e08
+240, 0x63a243ea4b85c5d7
+241, 0x59940074fc3709e1
+242, 0xc914a2eed58a6363
+243, 0x2602b04274dd724c
+244, 0xdf636eb7636c2c42
+245, 0x891a334d0d26c547
+246, 0xde8cd586d499e22d
+247, 0x3ea1aa4d9b7035b6
+248, 0xd085cff6f9501523
+249, 0xe82a872f374959e
+250, 0x55cb495bbd42cc53
+251, 0x5f42b3226e56ca97
+252, 0xea463f6f203493a3
+253, 0xeef3718e57731737
+254, 0x1bd4f9d62b7f9f3c
+255, 0x19284f5e74817511
+256, 0xaf6e842c7450ca87
+257, 0x1d27d2b08a6b3600
+258, 0xfb4b912b396a52e3
+259, 0x30804d4c5c710121
+260, 0x4907e82564e36338
+261, 0x6441cf3b2900ddb7
+262, 0xd76de6f51988dc66
+263, 0x4f298ef96fd5e6d2
+264, 0x65432960c009f83d
+265, 0x65ebed07e1d2e3df
+266, 0xf83ee8078febca20
+267, 0x7bb18e9d74fc5b29
+268, 0x597b5fbc2261d91
+269, 0xea4f8ed0732b15b2
+270, 0xba2267f74f458268
+271, 0x3f304acabd746bbb
+272, 0x7bd187af85659a82
+273, 0x88e20dbdb7a08ea3
+274, 0x2a2dc948c772fcb4
+275, 0x87784fec2993c867
+276, 0x89163933cd362d4e
+277, 0xfd7b24f04302f957
+278, 0x9bdd544405dfb153
+279, 0xddee0fac58ffc611
+280, 0xa8e8993417e71ec1
+281, 0x55e0ab46ff7757af
+282, 0x53e7645f08d3d7df
+283, 0xbf78e563bc656ba2
+284, 0x1d162253b45ee2de
+285, 0x15e2bfefedf29eb4
+286, 0x4e2a4584aa394702
+287, 0xa89fb12b01525897
+288, 0x825bd98f0544e4df
+289, 0xfc6c50da6750700
+290, 0xc24aaabde7d28423
+291, 0x79d6f4660fcb19e5
+292, 0xee7d4fb40c8d659f
+293, 0x70bc281b462e811d
+294, 0x23ed4dc9636519a7
+295, 0xcb7c3f5a5711b935
+296, 0xe73090e0508c5d9d
+297, 0xb25a331f375952a6
+298, 0xa64c86e0c04740f6
+299, 0xb8f3ffc8d56ac124
+300, 0x2479266fc5ee6b15
+301, 0x8d5792d27f5ffbcb
+302, 0xb064298be946cd52
+303, 0xf0934a98912ffe26
+304, 0xbe805682c6634d98
+305, 0xe0e6e2c010012b4f
+306, 0x58c47d475f75976
+307, 0x358c9a6e646b2b4a
+308, 0x7e7c4ffca5b17ba7
+309, 0x43585c8c9a24a04c
+310, 0x5154ddbcd68d5c2c
+311, 0x4a2b062d3742a5e
+312, 0xca5691191da2b946
+313, 0x696a542109457466
+314, 0x9eb5d658a5022ba5
+315, 0x8158cf6b599ab8dc
+316, 0x1b95391eaa4af4a6
+317, 0x9953e79bd0fc3107
+318, 0x8639690086748123
+319, 0x2d35781c287c6842
+320, 0x393ef0001cd7bc8f
+321, 0xe3a61be8c5f2c22a
+322, 0x5e4ff21b847cc29b
+323, 0x4c9c9389a370eb84
+324, 0xd43a25a8fc3635fa
+325, 0xf6790e4a85385508
+326, 0x37edf0c81cb95e1d
+327, 0x52db00d6e6e79af8
+328, 0x3b202bceeb7f096
+329, 0x2a164a1c776136bb
+330, 0x73e03ee3fd80fd1b
+331, 0xd2c58c0746b8d858
+332, 0x2ed2cb0038153d22
+333, 0x98996d0fc8ceeacc
+334, 0xa4ed0589936b37f
+335, 0x5f61cf41a6d2c172
+336, 0xa6d4afb538c110d7
+337, 0xe85834541baadf1a
+338, 0x4c8967107fd49212
+339, 0x49bafb762ab1a8c1
+340, 0x45d540e2a834bf17
+341, 0x1c0ec8b4ed671dac
+342, 0x3d503ce2c83fe883
+343, 0x437bfffd95f42022
+344, 0xc82d1e3d5c2bc8d2
+345, 0x7a0a9cbfcb0d3f24
+346, 0xc0a4f00251b7a3be
+347, 0xb5be24e74bb6a1c6
+348, 0xa3104b94b57545b1
+349, 0x86de7d0c4b97b361
+350, 0x879c1483f26538a6
+351, 0xd74c87557f6accfb
+352, 0x2f9be40dbf0fe8a1
+353, 0x445a93398f608d89
+354, 0x7b3cb8a7211d7fdc
+355, 0xe86cc51290d031e7
+356, 0x33ef3594052ad79f
+357, 0xc61911d241dbb590
+358, 0x37cccb0c0e3de461
+359, 0xb75259124080b48b
+360, 0xd81e8961beb4abe5
+361, 0xf4542deb84a754e
+362, 0x6ea036d00385f02e
+363, 0xa7b60b0ac3b88681
+364, 0x108a6c36ca30baf5
+365, 0x4a2adc5bbfe2bf07
+366, 0x4079501f892a5342
+367, 0x55e113963c5448f0
+368, 0x8019ff4903b37242
+369, 0x109c6dcdb7ec6618
+370, 0x1239ac50944da450
+371, 0xe1399c7f94c651c1
+372, 0x5a6bbbae388d365a
+373, 0x4d72be57b8810929
+374, 0x3f067df24384e1fb
+375, 0x4f8b9e0f7f6c7be
+376, 0x202492c342a3b08
+377, 0x250753192af93a3
+378, 0xfba1159d9de2cb8e
+379, 0xba964497ab05505c
+380, 0x1329ec5d8a709dca
+381, 0x32927cacb6cd22bb
+382, 0x6b4d7db904187d56
+383, 0xe76adccf8e841e02
+384, 0x8c4bf4b6a788202
+385, 0x3013a3b409831651
+386, 0x7427d125c475412f
+387, 0x84dcc4bb2bf43202
+388, 0x117526f1101372a5
+389, 0xfe95d64b8984bd72
+390, 0x524e129934cc55c1
+391, 0xc3db4b0418c36d30
+392, 0xe1cb2047e9c19f7a
+393, 0xea43d6c8d8982795
+394, 0xe80ac8a37df89ed
+395, 0xfecc2104329ed306
+396, 0xa5c38aac9c1d51ea
+397, 0x3abe5d1c01e4fe17
+398, 0x717a805d97fcc7ac
+399, 0x94441f8207a1fb78
+400, 0x22d7869c5f002607
+401, 0x349e899f28c3a1b9
+402, 0x5639950cdea92b75
+403, 0x7e08450497c375b
+404, 0x94bf898b475d211d
+405, 0x75c761a402375104
+406, 0x1930920ec9d2a1e7
+407, 0xb774ba1bc6f6e4e2
+408, 0xf715602412e5d900
+409, 0x87bb995f4a13f0ba
+410, 0xa3c787868dfa9c8d
+411, 0xa17fd42a5a4f0987
+412, 0x4a9f7d435242b86
+413, 0x240364aff88f8aef
+414, 0xe7cd4cf4bf39f144
+415, 0xd030f313ca4c2692
+416, 0xc46696f4e03ec1e9
+417, 0x22c60f1ec21060b3
+418, 0x16c88058fd68986f
+419, 0x69ca448e8e6bde3f
+420, 0x3466c2cdec218abd
+421, 0x837ac4d05e6b117d
+422, 0x911210e154690191
+423, 0x9ece851d6fa358b7
+424, 0x42f79cb0c45e7897
+425, 0xbf7583babd7c499b
+426, 0x2059fe8031c6e0b9
+427, 0xabbec8fc00f7e51d
+428, 0x88809d86a3a256e1
+429, 0xd36056df829fdcb5
+430, 0x515632b6cb914c64
+431, 0xba76d06c2558874
+432, 0x632c54ca4214d253
+433, 0xadec487adf2cb215
+434, 0x521e663e1940513d
+435, 0xb1b638b548806694
+436, 0xbe2d5bfbe57d2c72
+437, 0x8b89e7719db02f7
+438, 0x90ba5281c1d56e63
+439, 0x899e1b92fceea102
+440, 0xf90d918e15182fa6
+441, 0x94a489ce96c948c4
+442, 0xad34db453517fcd4
+443, 0xc5264eb2de15930f
+444, 0x101b4e6603a21cee
+445, 0xef9b6258d6e85fff
+446, 0x6075c7d6c048bd7a
+447, 0x6f03232c64e438aa
+448, 0x18c983d7105ee469
+449, 0x3ffc23f5c1375879
+450, 0xbc1b4a00afb1f9f
+451, 0x5afa6b2bb8c6b46e
+452, 0xe7fce4af2f2c152a
+453, 0x5b00ab5c4b3982c7
+454, 0x2d4b0c9c0eb4bd0c
+455, 0x61d926270642f1f2
+456, 0x7219c485c23a2377
+457, 0x7e471c752fecd895
+458, 0x23c4d30a4d17ba1f
+459, 0x65cb277fe565ca22
+460, 0xcbb56ed9c701363b
+461, 0xfd04ab3a6eba8282
+462, 0x19c9e5c8bab38500
+463, 0xea4c15227676b65b
+464, 0x20f3412606c8da6f
+465, 0xb06782d3bf61a239
+466, 0xf96e02d5276a9a31
+467, 0x835d256b42aa52a6
+468, 0x25b09151747f39c1
+469, 0x64507386e1103eda
+470, 0x51cbc05716ef88e4
+471, 0x998cd9b7989e81cc
+472, 0x9d7115416bec28d1
+473, 0xc992ca39de97906b
+474, 0xd571e6f7ca598214
+475, 0xafc7fb6ccd9abbf8
+476, 0x88ef456febff7bf4
+477, 0xdbe87ccc55b157d2
+478, 0xaab95e405f8a4f6d
+479, 0xad586a385e74af4f
+480, 0x23cd15225c8485aa
+481, 0x370940bf47900ac7
+482, 0xefd6afda1a4b0ead
+483, 0x9cb1a4c90993dd7a
+484, 0xff7893e8b2f70b11
+485, 0xb09e1807c0638e8e
+486, 0xb10915dcb4978f74
+487, 0x88212ab0051a85eb
+488, 0x7af41b76e1ec793f
+489, 0x2e5c486406d3fefd
+490, 0xebe54eff67f513cc
+491, 0xab6c90d0876a79b8
+492, 0x224df82f93fe9089
+493, 0xc51c1ce053dc9cd2
+494, 0x5ef35a4d8a633ee7
+495, 0x4aca033459c2585f
+496, 0xd066932c6eefb23d
+497, 0x5309768aab9a7591
+498, 0xa2a3e33823df37f9
+499, 0xcec77ff6a359ee9
+500, 0x784dc62d999d3483
+501, 0x84e789fb8acc985d
+502, 0xd590237e86aa60f
+503, 0x737e2ffe1c8ad600
+504, 0xc019c3a39a99eab8
+505, 0x6a39e9836964c516
+506, 0xe0fe43129535d9da
+507, 0xdfc5f603d639d4de
+508, 0x7b9a7d048a9c03b6
+509, 0xbb5aa520faa27fdd
+510, 0x2a09b4200f398fa2
+511, 0x38cc88107904064e
+512, 0xa9a90d0b2d92bb25
+513, 0x9419762f87e987e3
+514, 0x1a52c525153dedcd
+515, 0xc26d9973dd65ae99
+516, 0x8e89bd9d0dc6e6a1
+517, 0x2f30868dc01bfb53
+518, 0x20f09d99b46501c4
+519, 0x78b468a563b8f1e9
+520, 0xcccf34b0b6c380c7
+521, 0xf554e7dc815297e6
+522, 0x332a585cfb4a50ef
+523, 0xa9fb64a2b6da41d7
+524, 0xdcd2a5a337391ce0
+525, 0x8a9bd3e324c6463d
+526, 0x9f4487d725503bdd
+527, 0xf72282d82f1d0ff
+528, 0x308f4160abb72d42
+529, 0x648de1db3a601b08
+530, 0x36cab5192e7ebd39
+531, 0x7975fbe4ab6a1c66
+532, 0xd515b4d72243864e
+533, 0x43a568f8b915e895
+534, 0x15fa9f2057bdb91d
+535, 0x7a43858ef7a222dc
+536, 0x17b4a9175ac074fe
+537, 0xa932c833b8d0f8f8
+538, 0x1d2db93a9a587678
+539, 0x98abd1d146124d27
+540, 0xf0ab0431671740aa
+541, 0xa9d182467540ad33
+542, 0x41c8a6cfc331b7fc
+543, 0xa52c6bd0fcd1d228
+544, 0x2773c29a34dc6fa3
+545, 0x3098230746fc1f37
+546, 0xd63311bb4f23fabe
+547, 0x6712bf530cd2faec
+548, 0x342e8f342e42c4dd
+549, 0xfbd83331851cdcad
+550, 0xe903be1361bbc34d
+551, 0xd94372e5077e3ef9
+552, 0x95aaa234f194bd8
+553, 0x20c0c8fb11e27538
+554, 0xfaf47dc90462b30b
+555, 0x8ddc6d144147682a
+556, 0xf626833fd926af55
+557, 0x5df93c34290d1793
+558, 0xb06a903e6e9fca5e
+559, 0x10c792dc851d77ca
+560, 0xd9b1b817b18e56cb
+561, 0x3a81730c408eb408
+562, 0x65052c04a8d4b63c
+563, 0x3328546598e33742
+564, 0xeca44a13f62d156d
+565, 0x69f83d1d86b20170
+566, 0x937764200412027d
+567, 0xc57eb1b58df0f191
+568, 0xa1c7d67dce81bc41
+569, 0x8e709c59a6a579ce
+570, 0x776a2f5155d46c70
+571, 0xd92906fbbc373aa5
+572, 0xe97ad478a2a98bf6
+573, 0xc296c8819ac815f
+574, 0x613ede67ba70e93e
+575, 0xe145222498f99cde
+576, 0xafcdfa7a3c1cf9bf
+577, 0x1c89252176db670d
+578, 0xad245eda5c0865ff
+579, 0x249463d3053eb917
+580, 0xc9be16d337517c0b
+581, 0xefcc82bf67b8f731
+582, 0x1e01577d029e0d00
+583, 0xad9c24b2a4f3d418
+584, 0xed2cceb510db4d0f
+585, 0xbddadcdb92400c70
+586, 0x67d6b0476ef82186
+587, 0xbc7662ff7bf19f73
+588, 0x9d94452a729e6e92
+589, 0x6b278d8594f55428
+590, 0x6c4b31cceb1b2109
+591, 0xccc6c3a726701e9
+592, 0x6bc28ece07df8925
+593, 0xc0422b7bf150ccc4
+594, 0xab7158f044e73479
+595, 0xdf3347546d9ed83f
+596, 0x3b3235a02c70dff4
+597, 0x2551c49c14ea8d77
+598, 0xee2f7f5bb3cc228e
+599, 0x39b87bfe8c882d39
+600, 0x7dd420fad380b51c
+601, 0xffe64976af093f96
+602, 0x4a4f48dc6e7eaa5f
+603, 0x85f2514d32fdc8cc
+604, 0x1ab1215fd7f94801
+605, 0x4cd1200fc795b774
+606, 0xcf8af463a38942ee
+607, 0x319caa7ce3022721
+608, 0x8cd9798a76d1aea4
+609, 0x2bd3933ac7afd34e
+610, 0x85d4c323403cf811
+611, 0xd7b956d3064efa30
+612, 0x67a078dbf1f13068
+613, 0x665fa6c83e87c290
+614, 0x9333ac2416d2469b
+615, 0xdfb1fd21a0094977
+616, 0xa1962a6e2c25f8ff
+617, 0x1f3b10a7ed5287cf
+618, 0x70641efb3d362713
+619, 0xe527a2cf85d00918
+620, 0x9741e45d3f9890a3
+621, 0x6cb74b5d4d36db4b
+622, 0xf24734d622bd2209
+623, 0xadd6d94f78e9d378
+624, 0xc3bbdb59225cca7f
+625, 0x5ad36614275b30cd
+626, 0x495568dd74eea434
+627, 0xf35de47e0ffe1f2d
+628, 0xefa209dca719ab18
+629, 0x844ddcaeb5b99ae8
+630, 0x37449670a1dc7b19
+631, 0x5a4612c166f845c1
+632, 0xe70f7782f2087947
+633, 0x98d484deac365721
+634, 0x705302198cf52457
+635, 0x7135ae0f5b77df41
+636, 0x342ac6e44a9b6fc3
+637, 0x2713fd2a59af5826
+638, 0x6e1a3f90f84efa75
+639, 0x9fb3b4dd446ca040
+640, 0x530044ae91e6bd49
+641, 0xe984c4183974dc3e
+642, 0x40c1fa961997d066
+643, 0xb7868250d8c21559
+644, 0x8bc929fa085fd1de
+645, 0x7bdb63288dc8733e
+646, 0xac4faad24326a468
+647, 0x1c6e799833aea0b1
+648, 0xcc8a749e94f20f36
+649, 0x4e7abfd0443547c5
+650, 0xb661c73bb8caa358
+651, 0x4a800f5728ff2351
+652, 0x8c15e15189b9f7ed
+653, 0xab367846b811362c
+654, 0x4ba7508f0851ca2a
+655, 0xe9af891acbafc356
+656, 0xbdebe183989601f8
+657, 0x4c665ea496afc061
+658, 0x3ca1d14a5f2ed7c
+659, 0xfbdff10a1027dd21
+660, 0xdfd28f77c8cff968
+661, 0xc4fbaadf8a3e9c77
+662, 0xdac7e448b218c589
+663, 0xb26390b5befd19e2
+664, 0xd2ef14916c66dba9
+665, 0xfab600284b0ff86b
+666, 0xf04a1c229b58dabb
+667, 0xc21c45637e452476
+668, 0xd1435966f75e0791
+669, 0xc1f28522eda4a2d0
+670, 0x52332ae8f1222185
+671, 0x81c6c0790c0bf47e
+672, 0xfebd215e7d8ffb86
+673, 0x68c5dce55dbe962b
+674, 0x231d09cb0d2531d1
+675, 0x3218fba199dbbc6b
+676, 0x8f23c535f8ea0bf6
+677, 0x6c228963e1df8bd9
+678, 0x9843c7722ed153e3
+679, 0xd032d99e419bddec
+680, 0xe2dca88aa7814cab
+681, 0x4d53fb8c6a59cdc2
+682, 0x8fb3abc46157b68b
+683, 0xa3e733087e09b8e
+684, 0x6bdc1aee029d6b96
+685, 0x4089667a8906d65b
+686, 0x8f3026a52d39dd03
+687, 0x6d2e0ccb567bae84
+688, 0x74bad450199e464
+689, 0xf114fb68a8f300d5
+690, 0xc7a5cc7b374c7d10
+691, 0xf0e93da639b279d1
+692, 0xb9943841ad493166
+693, 0x77a69290455a3664
+694, 0x41530da2ebea054b
+695, 0xe8f9fab03ea24abf
+696, 0xaa931f0c9f55a57a
+697, 0xb4d68a75d56f97ae
+698, 0x3d58ff898b6ba297
+699, 0x49d81e08faf5a3f5
+700, 0xfc5207b9f3697f3b
+701, 0xa25911abb3cf19b7
+702, 0x6b8908eb67c3a41
+703, 0xd63ef402e2e3fa33
+704, 0x728e75d3f33b14c5
+705, 0x248cb1b8bc6f379a
+706, 0x3aa3d6d2b8c72996
+707, 0x49cc50bd2d3d2860
+708, 0xb4e1387647c72075
+709, 0x435a1630a4a81ed3
+710, 0xa5ea13005d2460cf
+711, 0xc7a613df37d159ec
+712, 0x95721ccc218b857e
+713, 0xd4b70d8c86b124d3
+714, 0x2b82bcc4b612d494
+715, 0xaf13062885276050
+716, 0xcbd8fcf571a33d9c
+717, 0x3f7f67ca1125fc15
+718, 0xddf4bb45aac81b4c
+719, 0x23606da62de9c040
+720, 0xa3a172375666b636
+721, 0x292f87387a6c6c3c
+722, 0xd1d10d00c5496fe1
+723, 0x86b0411ce8a25550
+724, 0x38e0487872e33976
+725, 0x363e49f88ddfd42c
+726, 0x45bdf1e9f6b66b0a
+727, 0x8a6fff3de394f9b5
+728, 0x8502158bb03f6209
+729, 0x22e24d16dba42907
+730, 0x3fe3ba427cc2b779
+731, 0x77144793f66b3d7e
+732, 0xcf8912ccb29b8af9
+733, 0xdc856caff2abd670
+734, 0xe6d3ae0b0d9d4c8b
+735, 0xb8f5d40e454c539f
+736, 0x79ca953114fbc6b7
+737, 0x478d6f4bbfa38837
+738, 0x9babae1a3ffdc340
+739, 0x40edd56802bae613
+740, 0x97a56c2dcccf0641
+741, 0xafc250257f027f8e
+742, 0x8da41ef1edf69125
+743, 0x6574b0280ff9d309
+744, 0x197c776151b8f820
+745, 0x6b03e077c9dac3b6
+746, 0x24a40ebbc5c341c5
+747, 0x50e585169a6a1c4b
+748, 0x37783a5a6a3e4e02
+749, 0xb3de81ee6fbad647
+750, 0xf4f292f57ca4591e
+751, 0x6214e9e7d44d30a
+752, 0x5920190c56d21c12
+753, 0x9ac163419b5e0c9b
+754, 0xfc2328761ae8ed93
+755, 0xc68f945b545508c6
+756, 0x687c49a17ce0a5e2
+757, 0x276d8f53d30d4ab4
+758, 0x8201804970343ce1
+759, 0x1b5d323cc2e7fb7e
+760, 0x6f351ef04fd904b
+761, 0x6c793a7d455d5198
+762, 0x46f5d108430ae91f
+763, 0xac16a15b2a0cf77f
+764, 0xa0d479d9e4122b9d
+765, 0x3afd94604307f19
+766, 0x2573ed6d39d38dbf
+767, 0xa58e14ba60b4294b
+768, 0xe69c1aed5840d156
+769, 0x4cf6fda7f04855c2
+770, 0x2fb65a56ef5f22da
+771, 0xf95819434d5dc220
+772, 0x29c65133623dafba
+773, 0x8e997bd018467523
+774, 0xfd08ba9d498461a7
+775, 0xdd52243bc78a5592
+776, 0x39c30108f6db88b3
+777, 0x38af8e1894f259b9
+778, 0x97eedf3b4ae5f6de
+779, 0x757825add80c5ece
+780, 0xf0fdd90ac14edb14
+781, 0xbbb19d4cc8cac6d4
+782, 0x9a82234edfae05e3
+783, 0x704401c61d1edf1c
+784, 0x8b0eb481fb3a1fb2
+785, 0xef6f36e7cc06c002
+786, 0x7a208b17e04b8cd7
+787, 0xf20e33d498838fe9
+788, 0xc2bdb22117058326
+789, 0x6ec31939eb4ca543
+790, 0x6f1654838f507a21
+791, 0xc65ab81a955d2b93
+792, 0x40b1420fdd9531b8
+793, 0xe31f221cab9f4f40
+794, 0x798cdd414c1deb7a
+795, 0x9c84e9c7d41cd983
+796, 0x63d6b1ae3b60b7fa
+797, 0xb42bfdd1a2f78ffa
+798, 0x37e431eaccaaa8e9
+799, 0x7508142a0f73eac9
+800, 0x91662a023df5893a
+801, 0x59782070e2fe3031
+802, 0xb2acd589a8ce7961
+803, 0xa224743fa877b292
+804, 0xaa5362aa27e6ed9e
+805, 0xa394a4e520c0c1c7
+806, 0xe49b16d2018ffb6f
+807, 0xb8074b9f2f1e762b
+808, 0xcf5f86143d5c23a7
+809, 0xfd838785db987087
+810, 0x31b1889df389aff8
+811, 0x30aaca876a4383b
+812, 0x1731bb71c4c38d4f
+813, 0x9a83a65395e05458
+814, 0x99cd0c8d67c8f4fc
+815, 0xfbd9fdc849b761a5
+816, 0x82c04834fc466889
+817, 0xdeef9d6e715e8c97
+818, 0x549c281c16da6078
+819, 0x2d70661254ad599d
+820, 0x57995793a72acac
+821, 0xf1727005116183ba
+822, 0xa22bb38945285de3
+823, 0x4f2d687fe45131ff
+824, 0x5666c87ddbbc981f
+825, 0xbcb4b2d4e7a517d0
+826, 0x5e794dd2e20b785d
+827, 0x449ad020149e093c
+828, 0x7704ee0412d106f5
+829, 0x83cbdf257b072ac1
+830, 0xae5c4fc9f638b0da
+831, 0x7b9e5a64e372ed47
+832, 0x7eddbbb22c2cdf57
+833, 0x3f19ebfa155b08e
+834, 0x91d991154dfd7177
+835, 0x611ae74b952d387f
+836, 0x3fdf7a335bda36ee
+837, 0xdf182433fc7a7c05
+838, 0x62c78598d1f8db0a
+839, 0xc3750c69d2c5c1f0
+840, 0xf1318024709efdee
+841, 0xaa3fd360d224dc29
+842, 0x62af53b2f307c19
+843, 0xdf527683c58120c2
+844, 0x3281deecc496f93d
+845, 0x4f704ad31527ef08
+846, 0x127a14a5e07cfdfc
+847, 0x90d0b1f549255c92
+848, 0xbc3406b212c5e1fc
+849, 0x4e89f39379dba91d
+850, 0x1290ef43c4998e6e
+851, 0xecfeb1a1cb1c6e1b
+852, 0x2067e90403003bf1
+853, 0x38ae04be30bdbeba
+854, 0x8a3537f298baedda
+855, 0xd07f3b825cdb2936
+856, 0xea020b5aebae8b45
+857, 0xfcd614ab031132b0
+858, 0x5fb682a4ff2268f5
+859, 0xd1c4662ce65596f4
+860, 0x7026b8270dd0b8dc
+861, 0x8101ec4b4beae45a
+862, 0xa0e9dc87940610a6
+863, 0x83ec33679d83165b
+864, 0x981847ca82e86d41
+865, 0xda84c188a304a0b7
+866, 0x3c37529c5a5bbbb8
+867, 0x34a8491ce3e19a5a
+868, 0xd36ad716a2fa6cb8
+869, 0xfd1d1d6a5189a15c
+870, 0x9716eb47851e8d8d
+871, 0x7dfb13ea3b15c5aa
+872, 0xbdf6e707f45113a5
+873, 0xb8118261b04bd097
+874, 0x6191f9895881bec6
+875, 0x7aac257ae11acf9b
+876, 0x35a491e1537ff120
+877, 0xe078943432efa71c
+878, 0xb3338485dd3dc2b9
+879, 0x456060975d2bb3b5
+880, 0xaddc4c451bdfc44c
+881, 0x18bfa7beacf96430
+882, 0x8802ebcaf0f67498
+883, 0xad922a5a825bd780
+884, 0x9fb4587d748f4efa
+885, 0xdb2a445136cd5e7
+886, 0xb98b3676ea8e96ac
+887, 0xb02d8d244d784878
+888, 0xa1a8442b18860abb
+889, 0x6a3029ba1361e5d1
+890, 0xf426d5fac161eb1
+891, 0xfa5ac2b87acecb23
+892, 0xaa659896e50535df
+893, 0xf40dd7a3d3c5c8ed
+894, 0x3f8367abecb705bc
+895, 0x2d60e7525873358f
+896, 0xc4a9d3948a0c3937
+897, 0x5ecc04fef6003909
+898, 0x7a865004918cba2
+899, 0x47ae110a678ec10b
+900, 0xa0f02f629d91aa67
+901, 0x4848b99e7fac9347
+902, 0xaa858346d63b80ac
+903, 0xeb5bf42ee161eeef
+904, 0x4d35d723d3c6ba37
+905, 0xdf22ca6ca93b64a7
+906, 0x9d198520f97b25b1
+907, 0x3068415350778efe
+908, 0xf3709f2e8793c2fe
+909, 0xd1517bac8dd9f16f
+910, 0xfb99bccaa15861dc
+911, 0xa9ad607d796a2521
+912, 0x55d3793d36bd22e4
+913, 0xf99270d891ff7401
+914, 0x401750a5c4aa8238
+915, 0xd84b3003e6f28309
+916, 0x8a23798b5fa7c98b
+917, 0xadd58bbc8f43e399
+918, 0xbd8c741ada62c6a8
+919, 0xbdc6937bc55b49fa
+920, 0x4aefa82201b8502
+921, 0x17adf29a717b303
+922, 0xa6ed2197be168f6c
+923, 0x1ba47543f4359a95
+924, 0xe34299949ac01ae9
+925, 0x711c76cffc9b62f3
+926, 0xbac259895508a4b7
+927, 0x3c8b3b3626b0d900
+928, 0x1a8d23fbe2ae71bf
+929, 0xca984fa3b5a5c3a1
+930, 0xb1986ab7521a9c93
+931, 0xd6b5b2c8d47a75b5
+932, 0xc7f1c4a88afb4957
+933, 0xdeb58033a3acd6cc
+934, 0xabe49ddfe1167e67
+935, 0x8d559c10205c06e3
+936, 0xea07a1a7de67a651
+937, 0xcbef60db15b6fef8
+938, 0xbfca142cff280e7
+939, 0x362693eba0732221
+940, 0x7463237e134db103
+941, 0x45574ddb5035e17a
+942, 0xfc65e0cb9b94a1aa
+943, 0x3154c55f1d86b36d
+944, 0x2d93a96dd6ab2d8b
+945, 0xbe3bc1d1f2542a25
+946, 0xdd4b541f7385bdaa
+947, 0x3b56b919d914e3f8
+948, 0x82fd51468a21895f
+949, 0x8988cf120731b916
+950, 0xa06a61db5fb93e32
+951, 0x6ed66c1b36f68623
+952, 0x875ae844d2f01c59
+953, 0x17ccd7ac912e5925
+954, 0x12fe2a66b8e40cb1
+955, 0xf843e5e3923ad791
+956, 0xa17560f2fd4ef48
+957, 0x27a2968191a8ee07
+958, 0xa9aab4d22ff44a3c
+959, 0x63cd0dcc3bb083ae
+960, 0x7a30b48c6160bf85
+961, 0x956160fb572503b3
+962, 0xc47f6b7546640257
+963, 0xaf4b625f7f49153
+964, 0x2f5c86a790e0c7e8
+965, 0xb52e0610ae07f0b8
+966, 0x38a589292c3d849e
+967, 0xc3e9ef655d30b4ef
+968, 0xb5695f765cda998a
+969, 0xde5d5e692a028e91
+970, 0x839476721555f72e
+971, 0x48b20679b17d9ebf
+972, 0xe3d4c6b2c26fb0df
+973, 0xce5a9834f0b4e71f
+974, 0x533abb253d5d420e
+975, 0x9eac5ad9aed34627
+976, 0xc0f2a01ab3c90dbb
+977, 0x6528eda93f6a066c
+978, 0xc16a1b625e467ade
+979, 0x1a4a320fb5e8b098
+980, 0x8819cccd8b4ab32f
+981, 0x42daa88531fd0bfd
+982, 0xcf732226409be17c
+983, 0xfddcdb25ccbf378c
+984, 0x9b15b603bf589fc1
+985, 0x2436066b95d366fe
+986, 0x8d42eff2e9cbda90
+987, 0x694b2fc8a4e8303c
+988, 0x8e207f98aaea3ccd
+989, 0x4730d7a620f822d9
+990, 0x468dc9ca30fe2fd4
+991, 0x74b36d8a1c0f031b
+992, 0x3c1aac1c488c1a94
+993, 0x19d0101042444585
+994, 0x8ec50c56d0c8adf4
+995, 0x721ec629e4d66394
+996, 0x3ca5ad93abeac4a4
+997, 0xaaebc76e71592623
+998, 0x969cc319e3ed6058
+999, 0xc0a277e3b2bfc3de
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/philox-testset-1.csv b/lib/python3.11/site-packages/numpy/random/tests/data/philox-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e448cbf73cc0774eca74df0d3d8866d8bd35bab3
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/philox-testset-1.csv
@@ -0,0 +1,1001 @@
+seed, 0xdeadbeaf
+0, 0xedc95200e2bd66a5
+1, 0x581d4e43b7682352
+2, 0x4be7278f5e373eab
+3, 0xee47f17991a9e7ea
+4, 0x38a7d2ae422f2e2c
+5, 0xe2a6730a3b4a8a15
+6, 0x1588b7a841486442
+7, 0x13ad777246700504
+8, 0x14d157e0f5e18204
+9, 0xd87c22a7ee8c13f1
+10, 0x30cc389ce3542ba1
+11, 0xb8a53348955bb2e9
+12, 0xc08802e3c454f74f
+13, 0xb444f627671a5780
+14, 0x4b6dd42b29cbf567
+15, 0x6109c7dc0bc5f7d5
+16, 0x85c954715d6b5b1e
+17, 0x646178d3d9a3a5d5
+18, 0xebbde42b1cd83465
+19, 0x3d015102f6bc9c1a
+20, 0x720fe2ec3798d5fd
+21, 0x93120961289ceb2e
+22, 0xc9207e960a56fae2
+23, 0xa7f042f31d991b98
+24, 0x5fac117415fae74b
+25, 0xd0a970ba8dddc287
+26, 0x84b4e7e51b43106
+27, 0x6ad02bf525ea265f
+28, 0xcdc7e5992b36ef8f
+29, 0x44d4985209261d60
+30, 0x628c02d50f4b902e
+31, 0xc7b1914922d1e76d
+32, 0xfde99ff895cba51d
+33, 0x175a0be050fa985f
+34, 0x47297d3699e03228
+35, 0xccf1e9aeaa3339cd
+36, 0x9fdd18ebeeaf15b1
+37, 0x7c94c9ab68747011
+38, 0x612d8ef22c1fa80f
+39, 0x13f52b860de89ab5
+40, 0x81f264b8c139c43b
+41, 0x8d017ba4ef1e85ba
+42, 0x6d0556f46219951e
+43, 0x8ee7b85663cf67b6
+44, 0x2432fc707645fe67
+45, 0xaf814046051e5941
+46, 0x4d432a83739ac76f
+47, 0x59e5060d0983ccdd
+48, 0xdd20e828b83d9b53
+49, 0x1b891800d7385f4c
+50, 0x10e86a026c52ff5e
+51, 0xb932f11723f7b90c
+52, 0xb2413d0a1f3582d0
+53, 0xe7cd4edda65fc6b5
+54, 0x6d3808848d56593b
+55, 0x192a727c3c7f47d9
+56, 0x9659d8aea5db8c16
+57, 0x4242c79fe2c77c16
+58, 0x605f90c913827cea
+59, 0x53e153c8bfc2138a
+60, 0xed2158fbdef5910e
+61, 0xae9e6e29d4cb5060
+62, 0x7dd51afaad3b11ce
+63, 0x2b9ba533d01a5453
+64, 0x7e0e9cf2b6c72c8
+65, 0x1cc8b3c7747ed147
+66, 0x9b102651e2e11b48
+67, 0x30b0b53cbaac33ea
+68, 0x70c28aec39b99b85
+69, 0x5f1417ff536fdb75
+70, 0x3a1d91abd53acf58
+71, 0xba116a1772168259
+72, 0xf5369bc9bd284151
+73, 0x67bf11373bf183ca
+74, 0xef0b2d44dbd33dc7
+75, 0xbfd567ee1a2953ed
+76, 0x7d373f2579b5e5c6
+77, 0x756eeae7bcdd99be
+78, 0x75f16eb9faa56f3b
+79, 0x96d55ded2b54b9a5
+80, 0x94495191db692c24
+81, 0x32358bdd56bab38c
+82, 0x3f6b64078576579
+83, 0x7177e7948bc064c9
+84, 0x2cbf23f09ba9bc91
+85, 0x9b97cc31c26645f5
+86, 0x5af2d239ff9028b1
+87, 0x316fa920e0332abe
+88, 0x46535b7d1cae10a0
+89, 0x21f0a6869298022c
+90, 0xf395c623b12deb14
+91, 0x8573995180675aa7
+92, 0xc3076509f4dc42d5
+93, 0x15e11e49760c6066
+94, 0xe8a6d311e67a021d
+95, 0x7482f389c883339b
+96, 0xda6f881573cba403
+97, 0xb110ffb847e42f07
+98, 0x2c3393140605ccf9
+99, 0xba1c8ba37d8bdc33
+100, 0x59adf43db7a86fe0
+101, 0xb4fcbf6aa585ca85
+102, 0xd794a93c18033fa6
+103, 0x6e839c01985f9d4
+104, 0x64065bf28222b2c7
+105, 0x6a6359b293fa0640
+106, 0x5ff610969e383e44
+107, 0xa8172c263f05c7f7
+108, 0x62a0172e8bd75d07
+109, 0x7be66e3c453b65ac
+110, 0x6a3b8d5a14014292
+111, 0xa2583e6087450020
+112, 0xd5d3ecc480c627d2
+113, 0xa24e83f1eec8a27c
+114, 0xa23febd2a99ee75a
+115, 0x9a5fbf91c7310366
+116, 0x5b63156932e039b
+117, 0x942af3c569908505
+118, 0x89a850f71ab6a912
+119, 0xfeadc803ac132fe9
+120, 0x67bf60e758250f3
+121, 0x533c25103466a697
+122, 0xb7deede3482f9769
+123, 0x325e043b53bba915
+124, 0x9e8d9e7fde132006
+125, 0x6bacc6860bbc436e
+126, 0xb3ea0534c42b1c53
+127, 0xb2389334db583172
+128, 0xa74b1bfbf5242ee4
+129, 0x53a487e2dc51d15c
+130, 0xe5a3b538d2c7a82e
+131, 0x7b6c70bb0c4cadaf
+132, 0xae20791b2081df1
+133, 0xc685c12e3c61d32c
+134, 0x60110e6b0286e882
+135, 0x49682119c774045c
+136, 0x53dc11a3bbd072e
+137, 0xbdc87c6e732d9c2d
+138, 0xcc4620861ebac8fd
+139, 0x7e9c3558759350cc
+140, 0x157408dee34891ba
+141, 0x9bcad1855b80651b
+142, 0xd81b29141d636908
+143, 0x1ed041a9f319c69d
+144, 0x805b2f541208b490
+145, 0x484ef3bba2eb7c66
+146, 0xb6b5e37d50a99691
+147, 0xabc26a7d9e97e85f
+148, 0xcba2a3cce0417c2f
+149, 0xa030dfffd701993c
+150, 0x2bf2dc50582ebf33
+151, 0xd9df13dd3eb9993e
+152, 0x31ca28b757232ae5
+153, 0x614562a0ccf37263
+154, 0x44d635b01725afbb
+155, 0x5ae230bc9ca9cd
+156, 0xb23a124eb98705c6
+157, 0x6395675444981b11
+158, 0xd97314c34119f9ca
+159, 0x9de61048327dd980
+160, 0x16bac6bded819707
+161, 0xcea3700e3e84b8c7
+162, 0xaa96955e2ee9c408
+163, 0x95361dcc93b5bc99
+164, 0x306921aed3713287
+165, 0x4df87f3130cd302a
+166, 0x37c451daeb6a4af5
+167, 0x8dbbe35f911d5cc1
+168, 0x518157ce61cb10f9
+169, 0x669f577aebc7b35b
+170, 0x4b0a5824a8786040
+171, 0x519bc3528de379f5
+172, 0x6128012516b54e02
+173, 0x98e4f165e5e6a6dd
+174, 0x6404d03618a9b882
+175, 0x15b6aeb3d9cd8dc5
+176, 0x87ed2c1bae83c35b
+177, 0x8377fc0252d41278
+178, 0x843f89d257a9ba02
+179, 0xcdda696ea95d0180
+180, 0xcfc4b23a50a89def
+181, 0xf37fd270d5e29902
+182, 0xafe14418f76b7efa
+183, 0xf984b81577076842
+184, 0xe8c60649ccb5458d
+185, 0x3b7be8e50f8ff27b
+186, 0xaa7506f25cef1464
+187, 0x5e513da59f106688
+188, 0x3c585e1f21a90d91
+189, 0x1df0e2075af292a
+190, 0x29fdd36d4f72795f
+191, 0xb162fe6c24cb4741
+192, 0x45073a8c02bd12c4
+193, 0xcbaaa395c2106f34
+194, 0x5db3c4c6011bc21c
+195, 0x1b02aac4f752e377
+196, 0xa2dfb583eb7bec5
+197, 0xfe1d728805d34bb1
+198, 0xf647fb78bb4601ec
+199, 0xd17be06f0d1f51ef
+200, 0x39ec97c26e3d18a0
+201, 0xb7117c6037e142c8
+202, 0xe3a6ce6e6c71a028
+203, 0xe70a265e5db90bb2
+204, 0x24da4480530def1e
+205, 0xfd82b28ce11d9a90
+206, 0x5bf61ead55074a1d
+207, 0xbe9899c61dec480d
+208, 0xae7d66d21e51ec9e
+209, 0x384ee62c26a08419
+210, 0x6648dccb7c2f4abf
+211, 0xc72aa0c2c708bdc9
+212, 0x205c5946b2b5ba71
+213, 0xd4d8d0b01890a812
+214, 0x56f185493625378d
+215, 0x92f8072c81d39bd0
+216, 0xa60b3ceecb3e4979
+217, 0xfcf41d88b63b5896
+218, 0xf5a49aa845c14003
+219, 0xffcc7e99eee1e705
+220, 0xdd98312a7a43b32d
+221, 0xa6339bd7730b004
+222, 0xdac7874ba7e30386
+223, 0xadf6f0b0d321c8
+224, 0x126a173ae4ffa39f
+225, 0x5c854b137385c1e7
+226, 0x8173d471b1e69c00
+227, 0x23fa34de43581e27
+228, 0x343b373aef4507b1
+229, 0xa482d262b4ea919c
+230, 0xf7fbef1b6f7fbba
+231, 0xd8ce559487976613
+232, 0xbf3c8dd1e6ebc654
+233, 0xda41ed375451e988
+234, 0xf54906371fd4b9b3
+235, 0x5b6bb41231a04230
+236, 0x866d816482b29c17
+237, 0x11315b96941f27dc
+238, 0xff95c79205c47d50
+239, 0x19c4fff96fbdac98
+240, 0xbfb1ae6e4131d0f4
+241, 0x9d20923f3cdb82c9
+242, 0x282175507c865dff
+243, 0xdfd5e58a40fe29be
+244, 0xedbd906ff40c8e4f
+245, 0x11b04fc82614ccb3
+246, 0xeceb8afda76ae49f
+247, 0xa4856913847c2cdf
+248, 0x6f1425f15a627f2a
+249, 0xdf144ffedf60349e
+250, 0x392d7ecfd77cc65f
+251, 0x72b8e2531049b2c6
+252, 0x5a7eb2bdb0ec9529
+253, 0xdcfd4306443e78c1
+254, 0x89ad67ed86cd7583
+255, 0x276b06c0779a6c8f
+256, 0xb2dbb723196a0ac3
+257, 0x66c86a3b65906016
+258, 0x938348768a730b47
+259, 0x5f5282de938d1a96
+260, 0xa4d4588c4b473b1f
+261, 0x8daed5962be4796f
+262, 0x9dde8d796985a56e
+263, 0x46be06dbd9ed9543
+264, 0xdf98286ceb9c5955
+265, 0xa1da1f52d7a7ca2b
+266, 0x5a7f1449f24bbd62
+267, 0x3aedc4e324e525fd
+268, 0xced62464cd0154e1
+269, 0x148fc035e7d88ce3
+270, 0x82f8878948f40d4c
+271, 0x4c04d9cdd6135c17
+272, 0xdf046948d86b3b93
+273, 0x2f0dec84f403fe40
+274, 0xa61954fb71e63c0d
+275, 0x616d8496f00382e8
+276, 0x162c622472746e27
+277, 0x43bcfe48731d2ceb
+278, 0xff22432f9ff16d85
+279, 0xc033ed32bb0ad5a4
+280, 0x5d3717cc91c0ce09
+281, 0x7a39a4852d251075
+282, 0x61cd73d71d6e6a6
+283, 0xe37e2ea4783ab1a5
+284, 0x60e1882162579ea8
+285, 0x9258ec33f1a88e00
+286, 0x24b32acf029f0407
+287, 0x1410fc9aea6d3fac
+288, 0x6054cf2a3c71d8f7
+289, 0x82f7605157a66183
+290, 0x3b34c1c0dff9eac5
+291, 0xfebe01b6d5c61819
+292, 0x7372187c68b777f2
+293, 0xc6923812cda479f0
+294, 0x386613be41b45156
+295, 0x92cfebe8cc4014b
+296, 0x8e13c4595849828b
+297, 0x90e47390d412291f
+298, 0x6b21a1d93d285138
+299, 0xbf5b1f5922f04b12
+300, 0x21e65d1643b3cb69
+301, 0xf7683b131948ac3c
+302, 0xe5d99fc926196ed2
+303, 0x7b138debbec90116
+304, 0x8a2650a75c2c2a5c
+305, 0x20689a768f9b347b
+306, 0xdfa2900cfb72dc6e
+307, 0x98959c3855611cc2
+308, 0x5fdb71b89596cc7c
+309, 0x1c14ac5c49568c7b
+310, 0x958c4293016091fe
+311, 0x7484522eb0087243
+312, 0xc4018dfb34fc190f
+313, 0xca638567e9888860
+314, 0x102cd4805f0c0e89
+315, 0xcc3bc438e04548f8
+316, 0xb808944bb56ea5be
+317, 0xffd4778dbf945c57
+318, 0xfe42617784c0233b
+319, 0x3eccbfeae9b42d3c
+320, 0xd9f1b585fd0bfa60
+321, 0x5c063d1b2705d5dd
+322, 0x8e8bec3519941b64
+323, 0x9e94c36cbec2a42
+324, 0x1cd19f5b64ffd3ad
+325, 0x9632e3aebfc68e66
+326, 0x98960c2d9da4ae45
+327, 0xb76994b1f2bbfc1f
+328, 0xca184a737d3971cc
+329, 0x964d31b07183adfb
+330, 0xe9e0ff351cd276d4
+331, 0xb5747c860b05bbe4
+332, 0x5549ddc3bd3862e2
+333, 0x495496677b27873b
+334, 0x53910baa26e3ea18
+335, 0xaa07a07ad0a688d3
+336, 0xbb43bd1f09ecdb1e
+337, 0xe2ebc105699dd84
+338, 0x6e815a2729584035
+339, 0x2caab1713b17948a
+340, 0x43d39d209fa41c90
+341, 0xfe3e71089d5d1c3a
+342, 0xa778646c32f81177
+343, 0x8d42bfb86e6e92d5
+344, 0x175571f70b4fcfbe
+345, 0x2a66a6fe10dc3b5b
+346, 0xd9545e85235ca709
+347, 0x5642781c77ced48a
+348, 0x24facc40b72ccd09
+349, 0xa800fbacce33f6f8
+350, 0x675f58a0ff19fba
+351, 0x35aedf57bb5cde1b
+352, 0xe5535a6b63f6d068
+353, 0x84dffd0102aaa85d
+354, 0x621faad65467aaa7
+355, 0x596ad85b556b112f
+356, 0x837545fff8894c7a
+357, 0x3d9a4ae1356bc6a6
+358, 0xcd8b7153205d4ad0
+359, 0x98afdd40f1ed09a6
+360, 0xa38b2dc55a5cf87f
+361, 0x484aecce2b6838bc
+362, 0x6af05c26bdab18d9
+363, 0xf418b7399dcf2e4b
+364, 0x1cfa38789b0d2445
+365, 0xfbed23c34166ee67
+366, 0x38e6820039e4912a
+367, 0x1fe94911e963591e
+368, 0x1291c79aee29ad70
+369, 0x65eccfc89506f963
+370, 0x7d14de3b2f55b1f6
+371, 0x82eb79c36cd2a739
+372, 0x41ffe3b75ea0def5
+373, 0x9eba9156470a51d9
+374, 0xd17c00b981db37d1
+375, 0xf688769a75601aa7
+376, 0xbcf738e9e03d571e
+377, 0x14712e56df8f919b
+378, 0xab14e227d156e310
+379, 0xf53d193e993e351e
+380, 0x857fae46bd312141
+381, 0xc2dd71e41b639966
+382, 0x74f8b987a3d00ad1
+383, 0x5bce8526dc527981
+384, 0x94910926c172a379
+385, 0x503c45557688a9d5
+386, 0x244d03834e05807f
+387, 0x6e014cbab9c7a31f
+388, 0xae544c638530facf
+389, 0x9b853aaaf9cbc22d
+390, 0xfb42ab7024d060ed
+391, 0x74cc3fba0dfd7ff2
+392, 0x24ec9e8f62144ad5
+393, 0x72f082954307bbe7
+394, 0x36feda21bbf67577
+395, 0x3222191611b832f1
+396, 0xd0584e81bcac8b0b
+397, 0xdce8d793ef75e771
+398, 0x978824c6c2578fc
+399, 0x6e8f77503b3c2ee4
+400, 0xc85d2d86fecf5d03
+401, 0x3d35b4a5d4d723c4
+402, 0xd3987dfd4727fff3
+403, 0xd3cde63fb6a31add
+404, 0xf6699e86165bdaeb
+405, 0x9d60ba158ec364c4
+406, 0x920c3c18b346bfc9
+407, 0x770fd1fdfbc236ca
+408, 0x45998cfc5fc12ddd
+409, 0xd74a3454e888834b
+410, 0xbf2aa68081a4a28f
+411, 0xea41b26a6f1da1b3
+412, 0x5560a2d24b9d5903
+413, 0xe3791f652a228d8b
+414, 0x365116d3b5a8520c
+415, 0xb1b2bd46528f8969
+416, 0xfcfe14943ef16ae7
+417, 0xf4d43425e8a535dc
+418, 0xe6cf10a78782a7e0
+419, 0x9c7ac0de46556e3e
+420, 0xc667ae0856eed9ef
+421, 0x47dbb532e16f9c7e
+422, 0xdf4785a5d89ee82e
+423, 0xbd014925ce79dbcf
+424, 0xea0d663fb58fa5be
+425, 0x51af07d5cc3821fb
+426, 0x27a1bdcdc4159a9d
+427, 0x520c986c59b1e140
+428, 0x50b73fd9bacd5b39
+429, 0xae5240641f51e4f3
+430, 0x71faecc164ed9681
+431, 0xda95aa35529a7ee
+432, 0xe25ba29b853c1c6d
+433, 0x9871a925cda53735
+434, 0xde481ad8540e114d
+435, 0xa2997f540e8abca0
+436, 0xc9683c5035e28185
+437, 0x1082471b57182bac
+438, 0xbd3ecf0f0b788988
+439, 0xf479760776fbb342
+440, 0x3730929200d91f44
+441, 0xc1762d79ae72809c
+442, 0xfaa0a4c7b1686cb3
+443, 0xd581e6d55afdafcd
+444, 0x6cf57bdfba2dcf6d
+445, 0xdef79d9fe6a5bcef
+446, 0x13ed376e18132bd3
+447, 0xbe67efd72defa2a
+448, 0x5acc176c468966ea
+449, 0x8b35b626af139187
+450, 0x446de3fac0d973ac
+451, 0xe1d49e06dc890317
+452, 0x817bc3fd21fc09b7
+453, 0xb71c3958a13d5579
+454, 0x8746e010f73d7148
+455, 0x1b61c06009922e83
+456, 0xba17e62e6b092316
+457, 0x1375fa23c4db8290
+458, 0x3f071230f51245a6
+459, 0x51c99a086a61cd13
+460, 0x5f0f2ae78589e1fd
+461, 0x604834e114bbbc27
+462, 0x5eb2a7a34814e9a9
+463, 0x77a6907f386bf11e
+464, 0x99525de2bd407eeb
+465, 0xb818348c57b3b98f
+466, 0x25f5f9e702fbe78d
+467, 0x8f66669e6f884473
+468, 0x1e47d46e2af4f919
+469, 0xf6a19df846476833
+470, 0xff00c67bcd06621f
+471, 0xe3dfe069795d72d8
+472, 0x8affc88b2fea4d73
+473, 0x66df747e5f827168
+474, 0xf368ec338d898a0e
+475, 0x9e1f1a739c5984a2
+476, 0x46a1c90e1ca32cbc
+477, 0xc261bc305ed8d762
+478, 0x754d7949f7da9e72
+479, 0x4c8fbbb14ef47b17
+480, 0xccbdc67a3848d80d
+481, 0x3c25e6f58bae751d
+482, 0x7078b163b936d9b6
+483, 0x440e27463c134ecf
+484, 0x6c83ee39f324db0f
+485, 0x27cf901b22aea535
+486, 0x57262dec79a3f366
+487, 0x91db09f1dbb524fb
+488, 0xd7436eefba865df2
+489, 0x16c86b0a275a3f43
+490, 0x689493e6681deaa9
+491, 0x7e1dc536c1a9ac42
+492, 0x1145beac3ac7f5cc
+493, 0x3d05e211a104b2b0
+494, 0x4f9e77ced3c52f44
+495, 0x53de1369354add72
+496, 0x1fb60f835f47cdeb
+497, 0x6ab36f089e40c106
+498, 0xaabffcb0d3d04c7
+499, 0xaa399686d921bd25
+500, 0x2bf8dd8b6d6fa7f0
+501, 0x1ddbf4e124329613
+502, 0x466a740241466a72
+503, 0x98d7381eb68a761
+504, 0x817691510bc4857a
+505, 0x8837622c0171fe33
+506, 0xcba078873179ee16
+507, 0x13adad1ab7b75af4
+508, 0x3bac3f502428840c
+509, 0xbeb3cce138de9a91
+510, 0x30ef556e40b5f0b4
+511, 0x19c22abdf3bbb108
+512, 0x977e66ea4ddc7cf
+513, 0x9f4a505f223d3bf3
+514, 0x6bc3f42ac79ec87b
+515, 0x31e77712158d6c23
+516, 0x6d8de4295a28af0d
+517, 0xee1807dbda72adb7
+518, 0xda54140179cd038f
+519, 0x715aa5cdac38e062
+520, 0x5a7e55e99a22fa16
+521, 0xf190c36aa8edbe4f
+522, 0xccadd93a82c1d044
+523, 0x7070e6d5012c3f15
+524, 0x50a83341a26c1ba5
+525, 0x11bca7cc634142e5
+526, 0x623a0d27867d8b04
+527, 0x75c18acff54fbf6e
+528, 0x455ae7d933497a6f
+529, 0xf624cf27d030c3d3
+530, 0x7a852716f8758bac
+531, 0xe7a497ac1fa2b5b4
+532, 0xf84f097498f57562
+533, 0xc4bb392f87f65943
+534, 0x618e79a5d499fbfb
+535, 0xb3c0b61d82b48b8
+536, 0x4750a10815c78ea7
+537, 0x9cf09cca3ddece69
+538, 0x2a69f1c94cc901a2
+539, 0x347a0e446e1ce86d
+540, 0xb06f3a5a5ab37bb1
+541, 0x8035bd0713d591db
+542, 0x539c9637042c3a1f
+543, 0xd7ba4dc6b273cbd7
+544, 0x12f3f99933444f85
+545, 0x4a9517b9783fb9a4
+546, 0x6422b2ea95093bc5
+547, 0x3a5ecff0f996c2a6
+548, 0x31de504efc76a723
+549, 0x7ccb7c5233c21a9f
+550, 0xc687d9e6ce4186e8
+551, 0x6e40769d6940376a
+552, 0xf51207314f1f7528
+553, 0x67ee3acb190865e3
+554, 0xe08d586270588761
+555, 0xe387fa489af1a75c
+556, 0x73414a52d29d8375
+557, 0x671a38191cf2a357
+558, 0xe00fb25b1aa54008
+559, 0x11a0610e22cf549b
+560, 0xc90cc865d57c75be
+561, 0x90d0863cc15f2b79
+562, 0x8b3e60d32ebcb856
+563, 0xb28cc55af621e04a
+564, 0xcf60bd3cb2a5ab1d
+565, 0x212cb5d421948f86
+566, 0xee297b96e0a3363f
+567, 0x4e9392ff998760d1
+568, 0x61940c8d0105ba3e
+569, 0x14ebcbae72a59a16
+570, 0xdf0f39a3d10c02af
+571, 0xfc047b2b3c1c549d
+572, 0x91718b5b98e3b286
+573, 0x9ea9539b1547d326
+574, 0x7a5a624a89a165e6
+575, 0x145b37dcaa8c4166
+576, 0x63814bbb90e5616c
+577, 0xc4bc3ca6c38bb739
+578, 0x853c3a61ddc6626c
+579, 0xa7ce8481c433829a
+580, 0x8aff426941cc07b
+581, 0x2dc3347ca68d8b95
+582, 0xce69f44f349e9917
+583, 0x2fa5cb8aca009b11
+584, 0xf26bb012115d9aca
+585, 0xafa01c2f2d27235a
+586, 0xabcba21f1b40305e
+587, 0xfec20c896c0c1128
+588, 0xc5f7a71ebacadfa0
+589, 0xc8479ad14bab4eef
+590, 0xad86ec9a3e7d3dc
+591, 0xbbecd65292b915c5
+592, 0xb1f9e28149e67446
+593, 0x708d081c03dad352
+594, 0xaa8a84dbd1de916c
+595, 0x9aa3efb29ba9480b
+596, 0xd3c63969ff11443e
+597, 0x1e9e9ac861315919
+598, 0x4fe227f91e66b41d
+599, 0xefc0212d43d253ab
+600, 0x98341437727c42d1
+601, 0x5ea85c0fe9008adc
+602, 0x7891b15faa808613
+603, 0x32db2d63989aacfd
+604, 0xc92f7f28e88fd7bc
+605, 0x3513545eb6549475
+606, 0x49abe0082906fbf8
+607, 0xcee1e1a6551e729c
+608, 0x38556672b592a28e
+609, 0xc3e61409c4ec2d45
+610, 0x96c67ce2995a0fd4
+611, 0x9b9b0cada870293
+612, 0x82d6dd5dada48037
+613, 0xeea4f415299f1706
+614, 0x371107895f152ab3
+615, 0x2f6686159f4396bb
+616, 0x61005a2ff3680089
+617, 0x9d2f2cafb595e6b6
+618, 0x4a812a920f011672
+619, 0x317554d3a77385d7
+620, 0x24c01086727eb74b
+621, 0xa15ff76d618a3a9e
+622, 0x2121bfd983859940
+623, 0x384d11577eea8114
+624, 0xab0f4299f3c44d88
+625, 0x136fd4b07cfa14d9
+626, 0x665fe45cbfaa972a
+627, 0x76c5a23398a314e9
+628, 0x5507036357ccda98
+629, 0xd9b8c5ac9dce632b
+630, 0x366bc71781da6e27
+631, 0xdd2b2ba1d6be6d15
+632, 0xf33ed0d50ea6f1a6
+633, 0xf05a9b1900174c18
+634, 0x3947e1419e2787cf
+635, 0x6c742b1e029637d0
+636, 0x32aba12196a0d2e8
+637, 0x1b94aab2e82e7df
+638, 0x68b617db19229d6
+639, 0x6c88a95ac0a33f98
+640, 0xdc9b95fd60c2d23e
+641, 0x999e6971d3afc8b3
+642, 0x7071fc6ad8b60129
+643, 0x41a8184ef62485f6
+644, 0xb68e0605c7d5e713
+645, 0x272b961a1d1bbee
+646, 0x23f04e76446187b0
+647, 0x999a7a8f6d33f260
+648, 0xdbd6318df4f168d
+649, 0x8f5e74c84c40711e
+650, 0x8ccc6b04393a19d6
+651, 0xadcd24b782dd8d3d
+652, 0x1a966b4f80ef9499
+653, 0xcb6d4f9ff5a280f0
+654, 0x8095ff2b8484018a
+655, 0xbfd3389611b8e771
+656, 0x278eb670b7d12d51
+657, 0x31df54ca8d65c20f
+658, 0x121c7fb38af6985e
+659, 0x84fb94f38fe1d0a
+660, 0x15ae8af1a6d48f02
+661, 0x8d51e4a62cba1a28
+662, 0x58e6b6b3ae0f9e42
+663, 0x9365a0a85669cc99
+664, 0xe56e92f65a2106df
+665, 0x68fa299c66b428fc
+666, 0x55e51bb0b0a832c6
+667, 0x48b565293f9bc494
+668, 0x73d8132b1cbabb57
+669, 0x9178ac3926c36cbc
+670, 0xe2f22c7b28ea5e0f
+671, 0x6af45322a99afb12
+672, 0x59072fcb486a46f4
+673, 0x166b717b08d3d8e
+674, 0xd4e627a2dfacc4ab
+675, 0x33dad6f2921dedaa
+676, 0x4b13b806834a6704
+677, 0xe5f7971b398ed54d
+678, 0x20bfae65e3e6899b
+679, 0x881dab45d2b4fc98
+680, 0x6f248126b5b885be
+681, 0x7aeb39e986f9deee
+682, 0xf819f9574b8c3a03
+683, 0xff3d93ed6bd9781a
+684, 0x3a31e2e24a2f6385
+685, 0x7888a88f8944a5e
+686, 0x4faee12f5de95537
+687, 0x7f3e4efccdb2ed67
+688, 0x91e0f2fc12593af5
+689, 0xb5be8a4b886a40d3
+690, 0x998e8288ac3a9b1b
+691, 0x85c48fc8b1349e7b
+692, 0xf03af25222d8fae5
+693, 0x45467e805b242c2e
+694, 0xa2350db793dbebdc
+695, 0xfebe5b61d2174553
+696, 0xa9a331f02c54ad0b
+697, 0xe94e49a0f905aef3
+698, 0xe54b4c812b55e3da
+699, 0xdc454114c6bc0278
+700, 0x99c7765ab476baa2
+701, 0xccd9590e47fdff7c
+702, 0xfa2bcae7afd6cb71
+703, 0x2c1bf1a433a6f0f7
+704, 0x53882c62ff0aab28
+705, 0x80ac900f844dacc
+706, 0x27ba8eb5c4a44d54
+707, 0x78f3dfb072a46004
+708, 0x34e00e6ec629edce
+709, 0x5b88d19b552d1fbd
+710, 0xe4df375dc79df432
+711, 0x37446312ff79c3b4
+712, 0xb72256900a95fa6d
+713, 0x89f3171fbdff0bfc
+714, 0xd37885b048687eba
+715, 0xbb033213b283b60e
+716, 0xcf10b523ee769030
+717, 0xbf8070b6cfd7bafb
+718, 0xb7194da81fd1763b
+719, 0xbfc303de88e68d24
+720, 0xb949c7a5aea8a072
+721, 0x844216e7bae90455
+722, 0xf1e7f20840049a33
+723, 0x96e3263ad0cae794
+724, 0x10772d51f6e9ba49
+725, 0xcea24fccae9d23b3
+726, 0xefd378add9dde040
+727, 0xba0c7c5275805976
+728, 0x2e2a04608f64fa8c
+729, 0xafb42ec43aa0fa7
+730, 0x30444b84241ac465
+731, 0x19ef384bac4493ab
+732, 0xfd1ac615d3ba5ab9
+733, 0x6cc781ba38643aff
+734, 0x30ff27ebed875cfd
+735, 0xee1a261aca97ae62
+736, 0xc5a92715202bc940
+737, 0x9e6ec76f93c657ff
+738, 0x9b9fd55f55191ca5
+739, 0x654b13af008d8f03
+740, 0x1b7f030d9bd0719f
+741, 0x6d622e277550cb7f
+742, 0x3f8ee6b8830d0538
+743, 0x475462bcd0de190f
+744, 0x21380e8a513bdbcd
+745, 0x629bf3771b1bd7a4
+746, 0x3b5fd0b62c353709
+747, 0xf95634006ec3867e
+748, 0x1be8bb584a6653c2
+749, 0x2e2d3cfa85320ce8
+750, 0x5b904b692252d11d
+751, 0x4bfd76631d527990
+752, 0xc019571ca2bec4a0
+753, 0xf2eb730cea4cd751
+754, 0xd4571d709530191a
+755, 0x3b5bd947061f5a7d
+756, 0x56e2322cd2d1d1c0
+757, 0xa8830a5f62019f83
+758, 0x901d130c1b873cf3
+759, 0xb5dd29b363c61299
+760, 0xbb710bec3a17b26d
+761, 0xc0c464daca0f2328
+762, 0x4dc8055df02650f5
+763, 0x3d3cd9bbe8b957af
+764, 0xdb79612c2635b828
+765, 0xe25b3a8ad8fa3040
+766, 0xd5875c563cbf236b
+767, 0x46861c1c3849c9bc
+768, 0xf84bf1a2814dff43
+769, 0x6d8103902e0ad5e6
+770, 0x99f51c9be8af79e5
+771, 0xb0bfa8540ff94a96
+772, 0xaf45109a4e06f7d0
+773, 0x281df3e55aea9bfc
+774, 0x6a1155ca8aa40e60
+775, 0x754d32c5de1f5da
+776, 0xce1eafb1c6ca916f
+777, 0xc4f2185fa8577bd1
+778, 0x4a188e9bdb5501d9
+779, 0xbb14107e99bd5550
+780, 0xf0381d8425ec2962
+781, 0x213dbfffc16ec4f6
+782, 0x7a999c5a28ea65bc
+783, 0x23758c2aba7709ff
+784, 0xea7e4bb205e93b44
+785, 0x9c5a31e53911c658
+786, 0x7f04d0bbdc689ddc
+787, 0xe3ed89ab8d78dcb3
+788, 0x73c38bfb43986210
+789, 0x740c7d787eb8e158
+790, 0x5284fafdfb3fb9ec
+791, 0x2e91a58ac1fb1409
+792, 0xb94a600bf0a09af3
+793, 0x533ea4dbe07d81dd
+794, 0x48c3f1a736b3c5fd
+795, 0x56ae3499fa8720ce
+796, 0x526f2def663ca818
+797, 0x2f085759c65665c4
+798, 0xf715f042c69e0db4
+799, 0x110889c399231e60
+800, 0x64584a244866f3a0
+801, 0xf02ec101a39405d3
+802, 0xe73cd5e9a7f17283
+803, 0xfea64869e7028234
+804, 0x97559974ad877891
+805, 0xc8695aba1dc9f2e5
+806, 0x7b62b76ffc2264ec
+807, 0xf5e1df172ec5ccd
+808, 0xafaeb68765e443bd
+809, 0xd3870eb2e8337623
+810, 0x4f944d684138fb39
+811, 0x6977c575038916ad
+812, 0x8ada1a225df95a56
+813, 0xe4044c6c58d15e54
+814, 0x4e5121366681cf2
+815, 0xcf8640b079357b0d
+816, 0xcd5b157d44106fa3
+817, 0x9d7a5481279e25a1
+818, 0xe10e9db41fb4b34f
+819, 0x1052607be1eadff9
+820, 0x3403d67232fe2265
+821, 0xac9358f498c34afc
+822, 0x820172da0dc39c9
+823, 0xe186e91a3b826b6a
+824, 0x1a838e2a40284445
+825, 0x1870b617ebd7bce6
+826, 0xcb7cba4424be1ed7
+827, 0x6a2e56e40fdf9041
+828, 0xace93bbe108f97ee
+829, 0xfeb9bc74ac41ca08
+830, 0x8cb2d05b0f6a1f51
+831, 0x73792309f3fac0a9
+832, 0x2507343d431308ca
+833, 0xd0ea1197be615412
+834, 0xb1870812f1d2fa94
+835, 0x6d067b6935dcd23e
+836, 0xaf161014e5492c31
+837, 0xd4be0dce97064be4
+838, 0xf8edfe3fc75c20f1
+839, 0x894751dc442d2d9c
+840, 0xb4a95f6a6663456c
+841, 0x74e93162e2d805db
+842, 0x784bc5f3a7a2f645
+843, 0xd234d7c5b0582ea9
+844, 0x491f28d0ab6cb97c
+845, 0xa79419e5cf4336c3
+846, 0x66b00141978c849
+847, 0xa7ddbd64698d563f
+848, 0xefc33a4a5d97d4b2
+849, 0x95075514a65aebdc
+850, 0x40eca5b3e28cd25e
+851, 0x90ec7d00e9c9e35d
+852, 0x63e84104d5af417a
+853, 0xdaca0ea32df5744
+854, 0x7ed54f2587795881
+855, 0x5a73931760af4ee0
+856, 0x857d1a185a3081ec
+857, 0x6eac2aabe67fb463
+858, 0xd1f86155d8bfc55f
+859, 0x6d56398f3e7877ef
+860, 0x7642f61dfc62bc17
+861, 0x1d76b12843246ffa
+862, 0xde7817809b8a31d0
+863, 0xbcca9cd091198f9d
+864, 0xf71ca566dddcdfd4
+865, 0xea4386ee8b61d082
+866, 0xe351729d6010bac4
+867, 0xfd685d8a49910dd6
+868, 0xa7a20ea6c686bd3
+869, 0x1cdaf82f4dbd5536
+870, 0xa3da1d1e77dda3e0
+871, 0x4f723b3818ff8b2a
+872, 0x1290669eca152469
+873, 0xb54158b52d30651b
+874, 0xc06b74f2c7f0fee
+875, 0x7d5840bcbf702379
+876, 0x19fa4c1254a82ed
+877, 0xcf5ce090ad0b38ea
+878, 0xd4edd6ac9437e16d
+879, 0xc6ebf25eb623b426
+880, 0xd2b6dbdf00d8fea2
+881, 0x949cf98391cc59e1
+882, 0x380a0c7d0356f7b3
+883, 0x8ffefe32465473bf
+884, 0x637b6542d27c861e
+885, 0x347d12ffc664ecd9
+886, 0xea66e3a0c75a6b37
+887, 0xc3aff6f34fb537a1
+888, 0x67bdf3579959bf49
+889, 0xa17a348e3a74b723
+890, 0x93c9ef26ddadd569
+891, 0x483909059a5ac0b2
+892, 0x26ec9074b56d5a0d
+893, 0x6216000d9a48403a
+894, 0x79b43909eab1ec05
+895, 0xe4a8e8d03649e0de
+896, 0x1435d666f3ccdc08
+897, 0xb9e22ba902650a0e
+898, 0x44dffcccc68b41f8
+899, 0x23e60dcc7a559a17
+900, 0x6fd1735eacd81266
+901, 0xf6bda0745ea20c8e
+902, 0x85efcaefe271e07c
+903, 0x9be996ee931cef42
+904, 0xe78b41c158611d64
+905, 0xd6201df605839830
+906, 0x702e8e47d2769fd3
+907, 0xb8dcf70e18cf14c
+908, 0xac2690bab1bf5c17
+909, 0x92b166b71205d696
+910, 0xb0e73c795fc6df28
+911, 0x4bf2322c8b6b6f0d
+912, 0xa842fbe67918cea0
+913, 0xb01a8675d9294e54
+914, 0xfbe3c94f03ca5af2
+915, 0x51a5c089600c441f
+916, 0x60f0fd7512d85ded
+917, 0xef3113d3bc2cadb0
+918, 0xe1ea128ade300d60
+919, 0xde413b7f8d92d746
+920, 0xfc32c6d43f47c5d8
+921, 0x69d551d8c2b54c68
+922, 0xb9bc68c175777943
+923, 0xb9c79c687f0dae90
+924, 0xd799421ef883c06e
+925, 0xbff553ca95a29a3e
+926, 0xfc9ffac46bd0aca1
+927, 0x4f6c3a30c80c3e5a
+928, 0x8b7245bc6dc4a0a
+929, 0xaf4e191a4575ff60
+930, 0x41218c4a76b90f0b
+931, 0x986052aa51b8e89b
+932, 0x284b464ed5622f9
+933, 0xba6bded912626b40
+934, 0x43cad3ed7443cb5c
+935, 0x21641fa95725f328
+936, 0x6d99d6d09d755822
+937, 0x8246dfa2d4838492
+938, 0xd2ee70b9056f4726
+939, 0x87db515a786fbb8b
+940, 0x7c63e4c1d7786e7d
+941, 0xd1a9d548f10b3e88
+942, 0xa00856475f3b74c9
+943, 0x7f1964ce67148bf4
+944, 0x446650ec71e6018c
+945, 0xb1805ca07d1b6345
+946, 0x869c0a1625b7271b
+947, 0x79d6da06ce2ecfe2
+948, 0xec7b3cafc5e3c85f
+949, 0x1745ce21e39f2c3d
+950, 0xd9a0a7af6ee97825
+951, 0x680e0e52a6e11d5c
+952, 0xd86b3f344ff7f4cd
+953, 0xab56af117c840b9c
+954, 0x5c5404c7e333a10e
+955, 0x4f1eb462f35d990d
+956, 0xf857605a5644458e
+957, 0x3bb87cdf09262f86
+958, 0xd57295baf6da64b
+959, 0xb5993f48472f2894
+960, 0x7d1a501608c060b2
+961, 0x45fabe2d0e54adf0
+962, 0xbb41c3806afb4efe
+963, 0xbfbc506049424c8
+964, 0xb7dd6b67f2203344
+965, 0x389ce52eff883b81
+966, 0xe259c55c0cf6d000
+967, 0x70fb3e3824f7d213
+968, 0x9f36d5599ed55f4b
+969, 0xd14cf6f12f83c4f7
+970, 0x570a09d56aaa0b66
+971, 0x8accafd527f4598
+972, 0xa42d64c62175adfd
+973, 0xddb9c6a87b6e1558
+974, 0xd80b6c69fa1cde2a
+975, 0x44ebaac10082207b
+976, 0xf99be8889552fa1a
+977, 0x38253cd4b38b5dc5
+978, 0x85356c8b02675791
+979, 0xbf91677b2ecdcf55
+980, 0x2316cb85e93f366e
+981, 0x9abf35954db6b053
+982, 0xf49f7425e086b45a
+983, 0x8f5b625e074afde2
+984, 0xe0d614559791b080
+985, 0xbf7b866afab2a525
+986, 0xde89d7e1641a6412
+987, 0x1d10687d8ae5b86f
+988, 0x1f034caa0e904cbd
+989, 0x2086357aec8a7a2c
+990, 0x22dc476b80c56e1e
+991, 0xbef5a73cc0e3a493
+992, 0xddfa3829b26ed797
+993, 0x8917a87ec3d4dc78
+994, 0xfeabe390628c365e
+995, 0x581b0c4f6fb2d642
+996, 0x1ef8c590adbf5b9a
+997, 0x4d8e13aac0cce879
+998, 0xfe38f71e5977fad0
+999, 0x1f83a32d4adfd2ed
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/philox-testset-2.csv b/lib/python3.11/site-packages/numpy/random/tests/data/philox-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..69d24c38c28959361283624e02f8891fa69b6c5f
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/philox-testset-2.csv
@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0x399e5b222b82fa9
+1, 0x41fd08c1f00f3bc5
+2, 0x78b8824162ee4d04
+3, 0x176747919e02739d
+4, 0xfaa88f002a8d3596
+5, 0x418eb6f592e6c227
+6, 0xef83020b8344dd45
+7, 0x30a74a1a6eaa064b
+8, 0x93d43bf97a490c3
+9, 0xe4ba28b442194cc
+10, 0xc829083a168a8656
+11, 0x73f45d50f8e22849
+12, 0xf912db57352824cc
+13, 0xf524216927b12ada
+14, 0x22b7697473b1dfda
+15, 0x311e2a936414b39f
+16, 0xb905abfdcc425be6
+17, 0x4b14630d031eac9c
+18, 0x1cf0c4ae01222bc8
+19, 0xa6c33efc6e82ef3
+20, 0x43b3576937ba0948
+21, 0x1e483d17cdde108a
+22, 0x6722784cac11ac88
+23, 0xee87569a48fc45d7
+24, 0xb821dcbe74d18661
+25, 0xa5d1876ef3da1a81
+26, 0xe4121c2af72a483
+27, 0x2d747e355a52cf43
+28, 0x609059957bd03725
+29, 0xc3327244b49e16c5
+30, 0xb5ae6cb000dde769
+31, 0x774315003209017
+32, 0xa2013397ba8db605
+33, 0x73b228945dbcd957
+34, 0x801af7190375d3c0
+35, 0xae6dca29f24c9c67
+36, 0xd1cc0bcb1ca26249
+37, 0x1defa62a5bd853be
+38, 0x67c2f5557fa89462
+39, 0xf1729b58122fab02
+40, 0xb67eb71949ec6c42
+41, 0x5456366ec1f8f7d7
+42, 0x44492b32eb7966f5
+43, 0xa801804159f175f1
+44, 0x5a416f23cac70d84
+45, 0x186f55293302303d
+46, 0x7339d5d7b6a43639
+47, 0xfc6df38d6a566121
+48, 0xed2fe018f150b39e
+49, 0x508e0b04a781fa1b
+50, 0x8bee9d50f32eaf50
+51, 0x9870015d37e63cc
+52, 0x93c6b12309c14f2d
+53, 0xb571cf798abe93ff
+54, 0x85c35a297a88ae6e
+55, 0x9b1b79afe497a2ae
+56, 0x1ca02e5b95d96b8d
+57, 0x5bb695a666c0a94a
+58, 0x4e3caf9bbab0b208
+59, 0x44a44be1a89f2dc1
+60, 0x4ff37c33445758d1
+61, 0xd0e02875322f35da
+62, 0xfd449a91fb92646b
+63, 0xbe0b49096b95db4d
+64, 0xffa3647cad13ef5d
+65, 0x75c127a61acd10c8
+66, 0xd65f697756f5f98e
+67, 0x3ced84be93d94434
+68, 0x4da3095c2fc46d68
+69, 0x67564e2a771ee9ac
+70, 0x36944775180644a9
+71, 0xf458db1c177cdb60
+72, 0x5b58406dcd034c8
+73, 0x793301a3fdab2a73
+74, 0x1c2a1a16d6db6128
+75, 0xc2dacd4ddddbe56c
+76, 0x2e7d15be2301a111
+77, 0xd4f4a6341b3bcd18
+78, 0x3622996bbe6a9e3b
+79, 0xaf29aa9a7d6d47da
+80, 0x6d7dbb74a4cd68ae
+81, 0xc260a17e0f39f841
+82, 0xdee0170f2af66f0d
+83, 0xf84ae780d7b5a06e
+84, 0x8326247b73f43c3a
+85, 0xd44eef44b4f98b84
+86, 0x3d10aee62ec895e3
+87, 0x4f23fef01bf703b3
+88, 0xf8e50aa57d888df6
+89, 0x7da67411e3bef261
+90, 0x1d00f2769b2f96d7
+91, 0x7ef9a15b7444b84e
+92, 0xcfa16436cc2b7e21
+93, 0x29ab8cfac00460ff
+94, 0x23613de8608b0e70
+95, 0xb1aa0980625798a8
+96, 0xb9256fd29db7df99
+97, 0xdacf311bf3e7fa18
+98, 0xa013c8f9fada20d8
+99, 0xaf5fd4fe8230fe3e
+100, 0xd3d59ca55102bc5c
+101, 0x9d08e2aa5242767f
+102, 0x40278fe131e83b53
+103, 0x56397d03c7c14c98
+104, 0xe874b77b119359b3
+105, 0x926a1ba4304ab19f
+106, 0x1e115d5aa695a91d
+107, 0xc6a459df441f2fe3
+108, 0x2ca842bc1b0b3c6a
+109, 0x24c804cf8e5eed16
+110, 0x7ca00fc4a4c3ebd3
+111, 0x546af7cecc4a4ba6
+112, 0x8faae1fa18fd6e3
+113, 0x40420b0089641a6a
+114, 0x88175a35d9abcb83
+115, 0xf7d746d1b8b1357c
+116, 0x7dae771a651be970
+117, 0x2f6485247ee4df84
+118, 0x6883702fab2d8ec5
+119, 0xeb7eea829a67f9a6
+120, 0x60d5880b485562ed
+121, 0x7d4ca3d7e41a4e7e
+122, 0xbb7fef961ab8de18
+123, 0x3b92452fb810c164
+124, 0x5f4b4755348b338
+125, 0xca45a715a7539806
+126, 0xc33efd9da5399dd
+127, 0x593d665a51d4aedd
+128, 0x75d6b8636563036b
+129, 0x7b57caa55e262082
+130, 0x4ede7427969e0dd5
+131, 0xc3f19b6f78ea00b
+132, 0xeea7bab9be2181ea
+133, 0x652c45fe9c420c04
+134, 0x14ba9e3d175670ee
+135, 0xd2ad156ba6490474
+136, 0x4d65ae41065f614
+137, 0x6ff911c8afa28eb1
+138, 0xedc2b33588f3cb68
+139, 0x437c8bc324666a2f
+140, 0x828cee25457a3f0
+141, 0x530c986091f31b9b
+142, 0x2f34671e8326ade7
+143, 0x4f686a8f4d77f6da
+144, 0xa4c1987083498895
+145, 0xbce5a88b672b0fb1
+146, 0x8476115a9e6a00cc
+147, 0x16de18a55dd2c238
+148, 0xdf38cf4c416232bc
+149, 0x2cb837924e7559f3
+150, 0xfad4727484e982ed
+151, 0x32a55d4b7801e4f
+152, 0x8b9ef96804bd10a5
+153, 0xa1fd422c9b5cf2a9
+154, 0xf46ddb122eb7e442
+155, 0x6e3842547afa3b33
+156, 0x863dee1c34afe5c4
+157, 0x6a43a1935b6db171
+158, 0x1060a5c2f8145821
+159, 0xf783ec9ed34c4607
+160, 0x1da4a86bf5f8c0b0
+161, 0x4c7714041ba12af8
+162, 0x580da7010be2f192
+163, 0xad682fe795a7ea7a
+164, 0x6687b6cb88a9ed2c
+165, 0x3c8d4b175517cd18
+166, 0xe9247c3a524a6b6b
+167, 0x337ca9cfaa02658
+168, 0xed95399481c6feec
+169, 0x58726a088e606062
+170, 0xfe7588a5b4ee342a
+171, 0xee434c7ed146fdee
+172, 0xe2ade8b60fdc4ba5
+173, 0xd57e4c155de4eaab
+174, 0xdefeae12de1137cb
+175, 0xb7a276a241316ac1
+176, 0xeb838b1b1df4ca15
+177, 0x6f78965edea32f6f
+178, 0x18bebd264d7a5d53
+179, 0x3641c691d77005ec
+180, 0xbe70ed7efea8c24c
+181, 0x33047fa8d03ca560
+182, 0x3bed0d2221ff0f87
+183, 0x23083a6ffbcf38a2
+184, 0xc23eb827073d3fa5
+185, 0xc873bb3415e9fb9b
+186, 0xa4645179e54147fe
+187, 0x2c72fb443f66e207
+188, 0x98084915dd89d8f4
+189, 0x88baa2de12c99037
+190, 0x85c74ab238cb795f
+191, 0xe122186469ea3a26
+192, 0x4c3bba99b3249292
+193, 0x85d6845d9a015234
+194, 0x147ddd69c13e6a31
+195, 0x255f4d678c9a570b
+196, 0x2d7c0c410bf962b4
+197, 0x58eb7649e0aa16ca
+198, 0x9d240bf662fe0783
+199, 0x5f74f6fa32d293cc
+200, 0x4928e52f0f79d9b9
+201, 0xe61c2b87146b706d
+202, 0xcfcd90d100cf5431
+203, 0xf15ea8138e6aa178
+204, 0x6ab8287024f9a819
+205, 0xed8942593db74e01
+206, 0xefc00e4ec2ae36dd
+207, 0xc21429fb9387f334
+208, 0xf9a3389e285a9bce
+209, 0xacdee8c43aae49b3
+210, 0xefc382f02ad55c25
+211, 0x1153b50e8d406b72
+212, 0xb00d39ebcc2f89d8
+213, 0xde62f0b9831c8850
+214, 0xc076994662eef6c7
+215, 0x66f08f4752f1e3ef
+216, 0x283b90619796249a
+217, 0x4e4869bc4227499e
+218, 0xb45ad78a49efd7ed
+219, 0xffe19aa77abf5f4b
+220, 0xfce11a0daf913aef
+221, 0x7e4e64450d5cdceb
+222, 0xe9621997cfd62762
+223, 0x4d2c9e156868081
+224, 0x4e2d96eb7cc9a08
+225, 0xda74849bba6e3bd3
+226, 0x6f4621da935e7fde
+227, 0xb94b914aa0497259
+228, 0xd50d03e8b8db1563
+229, 0x1a45c1ce5dca422e
+230, 0xc8d30d33276f843f
+231, 0xb57245774e4176b4
+232, 0x8d36342c05abbbb1
+233, 0x3591ad893ecf9e78
+234, 0x62f4717239ee0ac8
+235, 0x9b71148a1a1d4200
+236, 0x65f8e0f56dd94463
+237, 0x453b1fcfd4fac8c2
+238, 0x4c25e48e54a55865
+239, 0xa866baa05112ace2
+240, 0x7741d3c69c6e79c5
+241, 0x7deb375e8f4f7a8a
+242, 0xc242087ede42abd8
+243, 0x2fa9d1d488750c4b
+244, 0xe8940137a935d3d3
+245, 0x1dab4918ca24b2f2
+246, 0xe2368c782168fe3e
+247, 0x6e8b2d1d73695909
+248, 0x70455ebea268b33e
+249, 0x656a919202e28da1
+250, 0x5a5a8935647da999
+251, 0x428c6f77e118c13c
+252, 0xa87aee2b675bb083
+253, 0x3873a6412b239969
+254, 0x5f72c1e91cb8a2ee
+255, 0xa25af80a1beb5679
+256, 0x1af65d27c7b4abc3
+257, 0x133437060670e067
+258, 0xb1990fa39a97d32e
+259, 0x724adc89ae10ed17
+260, 0x3f682a3f2363a240
+261, 0x29198f8dbd343499
+262, 0xdfaeeaa42bc51105
+263, 0x5baff3901b9480c2
+264, 0x3f760a67043e77f5
+265, 0x610fa7aa355a43ba
+266, 0x394856ac09c4f7a7
+267, 0x1d9229d058aee82e
+268, 0x19c674804c41aeec
+269, 0x74cf12372012f4aa
+270, 0xa5d89b353fa2f6ca
+271, 0x697e4f672ac363dd
+272, 0xde6f55ba73df5af9
+273, 0x679cf537510bd68f
+274, 0x3dc916114ae9ef7e
+275, 0xd7e31a66ec2ee7ba
+276, 0xc21bebb968728495
+277, 0xc5e0781414e2adfd
+278, 0x71147b5412ddd4bd
+279, 0x3b864b410625cca9
+280, 0x433d67c0036cdc6
+281, 0x48083afa0ae20b1b
+282, 0x2d80beecd64ac4e8
+283, 0x2a753c27c3a3ee3e
+284, 0xb2c5e6afd1fe051a
+285, 0xea677930cd66c46b
+286, 0x4c3960932f92810a
+287, 0xf1b367a9e527eaba
+288, 0xb7d92a8a9a69a98e
+289, 0x9f9ad3210bd6b453
+290, 0x817f2889db2dcbd8
+291, 0x4270a665ac15813c
+292, 0x90b85353bd2be4dd
+293, 0x10c0460f7b2d68d
+294, 0x11cef32b94f947f5
+295, 0x3cf29ed8e7d477e8
+296, 0x793aaa9bd50599ef
+297, 0xbac15d1190014aad
+298, 0x987944ae80b5cb13
+299, 0x460aa51f8d57c484
+300, 0xc77df0385f97c2d3
+301, 0x92e743b7293a3822
+302, 0xbc3458bcfbcbb8c0
+303, 0xe277bcf3d04b4ed7
+304, 0xa537ae5cf1c9a31c
+305, 0x95eb00d30bd8cfb2
+306, 0x6376361c24e4f2dd
+307, 0x374477fe87b9ea8e
+308, 0x8210f1a9a039902e
+309, 0xe7628f7031321f68
+310, 0x8b8e9c0888fc1d3d
+311, 0x306be461fdc9e0ed
+312, 0x510009372f9b56f5
+313, 0xa6e6fa486b7a027a
+314, 0x9d3f002025203b5a
+315, 0x7a46e0e81ecbef86
+316, 0x41e280c611d04df0
+317, 0xedcec10418a99e8a
+318, 0x5c27b6327e0b9dbd
+319, 0xa81ed2035b509f07
+320, 0x3581e855983a4cc4
+321, 0x4744594b25e9809d
+322, 0xc737ac7c27fbd0ed
+323, 0x1b523a307045433a
+324, 0x8b4ce9171076f1d9
+325, 0x2db02d817cd5eec0
+326, 0x24a1f1229af50288
+327, 0x5550c0dcf583ff16
+328, 0x3587baaa122ec422
+329, 0xf9d3dc894229e510
+330, 0xf3100430d5cf8e87
+331, 0xc31af79862f8e2fb
+332, 0xd20582063b9f3537
+333, 0xac5e90ac95fcc7ad
+334, 0x107c4c704d5109d4
+335, 0xebc8628906dbfd70
+336, 0x215242776da8c531
+337, 0xa98002f1dcf08b51
+338, 0xbc3bdc07f3b09718
+339, 0x238677062495b512
+340, 0x53b4796f2a3c49e8
+341, 0x6424286467e22f0e
+342, 0x14d0952a11a71bac
+343, 0x2f97098149b82514
+344, 0x3777f2fdc425ad2
+345, 0xa32f2382938876d4
+346, 0xda8a39a021f20ae3
+347, 0x364361ef0a6ac32c
+348, 0x4413eede008ff05a
+349, 0x8dda8ace851aa327
+350, 0x4303cabbdcecd1ee
+351, 0x2e69f06d74aa549f
+352, 0x4797079cd4d9275c
+353, 0xc7b1890917e98307
+354, 0x34031b0e822a4b4c
+355, 0xfc79f76b566303ea
+356, 0x77014adbe255a930
+357, 0xab6c43dd162f3be5
+358, 0xa430041f3463f6b9
+359, 0x5c191a32ada3f84a
+360, 0xe8674a0781645a31
+361, 0x3a11cb667b8d0916
+362, 0xaedc73e80c39fd8a
+363, 0xfde12c1b42328765
+364, 0x97abb7dcccdc1a0b
+365, 0x52475c14d2167bc8
+366, 0x540e8811196d5aff
+367, 0xa867e4ccdb2b4b77
+368, 0x2be04af61e5bcfb9
+369, 0x81b645102bfc5dfd
+370, 0x96a52c9a66c6450f
+371, 0x632ec2d136889234
+372, 0x4ed530c0b36a6c25
+373, 0x6f4851225546b75
+374, 0x2c065d6ba46a1144
+375, 0xf8a3613ff416551d
+376, 0xb5f0fd60e9c971a9
+377, 0x339011a03bb4be65
+378, 0x9439f72b6995ded6
+379, 0xc1b03f3ef3b2292d
+380, 0xad12fd221daab3ae
+381, 0xf615b770f2cf996f
+382, 0x269d0fdcb764172
+383, 0x67837025e8039256
+384, 0x6402831fc823fafa
+385, 0x22854146a4abb964
+386, 0x7b5ad9b5a1bad7a8
+387, 0x67170e7beb6ac935
+388, 0xfc2d1e8e24adfaaa
+389, 0x7ded4395345ff40d
+390, 0x418981760a80dd07
+391, 0xc03bef38022c1d2
+392, 0x3a11850b26eade29
+393, 0xaa56d02c7175c5f4
+394, 0xd83b7917b9bfbff5
+395, 0x3c1df2f8fa6fced3
+396, 0xf3d6e2999c0bb760
+397, 0xc66d683a59a950e3
+398, 0x8e3972a9d73ffabf
+399, 0x97720a0443edffd9
+400, 0xa85f5d2fe198444a
+401, 0xfc5f0458e1b0de5e
+402, 0xe3973f03df632b87
+403, 0xe151073c84c594b3
+404, 0x68eb4e22e7ff8ecf
+405, 0x274f36eaed7cae27
+406, 0x3b87b1eb60896b13
+407, 0xbe0b2f831442d70a
+408, 0x2782ed7a48a1b328
+409, 0xb3619d890310f704
+410, 0xb03926b11b55921a
+411, 0xdb46fc44aa6a0ce4
+412, 0x4b063e2ef2e9453a
+413, 0xe1584f1aeec60fb5
+414, 0x7092bd6a879c5a49
+415, 0xb84e1e7c7d52b0e6
+416, 0x29d09ca48db64dfb
+417, 0x8f6c4a402066e905
+418, 0x77390795eabc36b
+419, 0xcc2dc2e4141cc69f
+420, 0x2727f83beb9e3c7c
+421, 0x1b29868619331de0
+422, 0xd38c571e192c246f
+423, 0x535327479fe37b6f
+424, 0xaff9ce5758617eb3
+425, 0x5658539e9288a4e4
+426, 0x8df91d87126c4c6d
+427, 0xe931cf8fdba6e255
+428, 0x815dfdf25fbee9e8
+429, 0x5c61f4c7cba91697
+430, 0xdd5f5512fe2313a1
+431, 0x499dd918a92a53cd
+432, 0xa7e969d007c97dfd
+433, 0xb8d39c6fc81ac0bb
+434, 0x1d646983def5746c
+435, 0x44d4b3b17432a60c
+436, 0x65664232a14db1e3
+437, 0xda8fae6433e7500b
+438, 0xbe51b94ff2a3fe94
+439, 0xe9b1bd9a9098ef9f
+440, 0xfe47d54176297ef5
+441, 0xb8ab99bc03bb7135
+442, 0xcfad97f608565b38
+443, 0xf05da71f6760d9c1
+444, 0xef8da40a7c70e7b
+445, 0xe0465d58dbd5d138
+446, 0xb54a2d70eb1a938
+447, 0xfdd50c905958f2d8
+448, 0x3c41933c90a57d43
+449, 0x678f6d894c6ad0bb
+450, 0x403e8f4582274e8
+451, 0x5cbbe975668df6b0
+452, 0x297e6520a7902f03
+453, 0x8f6dded33cd1efd7
+454, 0x8e903c97be8d783b
+455, 0x10bd015577e30f77
+456, 0x3fcd69d1c36eab0c
+457, 0xb45989f3ca198d3
+458, 0x507655ce02b491a9
+459, 0xa92cf99bb78602ce
+460, 0xebfb82055fbc2f0f
+461, 0x3334256279289b7a
+462, 0xc19d2a0f740ee0ac
+463, 0x8bb070dea3934905
+464, 0xa4ab57d3a8d1b3eb
+465, 0xfee1b09bcacf7ff4
+466, 0xccc7fb41ceec41fa
+467, 0xd4da49094eb5a74d
+468, 0xed5c693770af02ed
+469, 0x369dabc9bbfaa8e4
+470, 0x7eab9f360d054199
+471, 0xe36dbebf5ee94076
+472, 0xd30840e499b23d7
+473, 0x8678e6cb545015ff
+474, 0x3a47932ca0b336e
+475, 0xeb7c742b6e93d6fe
+476, 0x1404ea51fe5a62a9
+477, 0xa72cd49db978e288
+478, 0xfd7bada020173dcf
+479, 0xc9e74fc7abe50054
+480, 0x93197847bb66808d
+481, 0x25fd5f053dce5698
+482, 0xe198a9b18cc21f4
+483, 0x5cc27b1689452d5d
+484, 0x8b3657af955a98dc
+485, 0xc17f7584f54aa1c0
+486, 0xe821b088246b1427
+487, 0x32b5a9f6b45b6fa0
+488, 0x2aef7c315c2bae0c
+489, 0xe1af8129846b705a
+490, 0x4123b4c091b34614
+491, 0x6999d61ec341c073
+492, 0x14b9a8fcf86831ea
+493, 0xfd4cff6548f46c9f
+494, 0x350c3b7e6cc8d7d6
+495, 0x202a5047fecafcd5
+496, 0xa82509fe496bb57d
+497, 0x835e4b2608b575fe
+498, 0xf3abe3da919f54ec
+499, 0x8705a21e2c9b8796
+500, 0xfd02d1427005c314
+501, 0xa38458faa637f49b
+502, 0x61622f2360e7622a
+503, 0xe89335a773c2963b
+504, 0x481264b659b0e0d0
+505, 0x1e82ae94ebf62f15
+506, 0x8ea7812de49209d4
+507, 0xff963d764680584
+508, 0x418a68bef717f4af
+509, 0x581f0e7621a8ab91
+510, 0x840337e9a0ec4150
+511, 0x951ef61b344be505
+512, 0xc8b1b899feb61ec2
+513, 0x8b78ca13c56f6ed9
+514, 0x3d2fd793715a946f
+515, 0xf1c04fabcd0f4084
+516, 0x92b602614a9a9fcc
+517, 0x7991bd7a94a65be7
+518, 0x5dead10b06cad2d7
+519, 0xda7719b33f722f06
+520, 0x9d87a722b7bff71e
+521, 0xb038e479071409e9
+522, 0xf4e8bbec48054775
+523, 0x4fec2cd7a28a88ea
+524, 0x839e28526aad3e56
+525, 0xd37ec57852a98bf0
+526, 0xdef2cbbe00f3a02d
+527, 0x1aecfe01a9e4d801
+528, 0x59018d3c8beaf067
+529, 0x892753e6ac8bf3cd
+530, 0xefdd3437023d2d1c
+531, 0x447bfbd148c8cb88
+532, 0x282380221bd442b8
+533, 0xfce8658d1347384a
+534, 0x60b211a7ec6bfa8
+535, 0xd21729cfcc692974
+536, 0x162087ecd5038a47
+537, 0x2b17000c4bce39d2
+538, 0x3a1f75ff6adcdce0
+539, 0x721a411d312f1a2c
+540, 0x9c13b6133f66934d
+541, 0xaa975d14978980e5
+542, 0x9403dbd4754203fa
+543, 0x588c15762fdd643
+544, 0xdd1290f8d0ada73a
+545, 0xd9b77380936103f4
+546, 0xb2e2047a356eb829
+547, 0x7019e5e7f76f7a47
+548, 0x3c29a461f62b001d
+549, 0xa07dc6cfab59c116
+550, 0x9b97e278433f8eb
+551, 0x6affc714e7236588
+552, 0x36170aeb32911a73
+553, 0x4a665104d364a789
+554, 0x4be01464ec276c9c
+555, 0x71bb10271a8b4ecf
+556, 0xbf62e1d068bc018
+557, 0xc9ada5db2cbbb413
+558, 0x2bded75e726650e5
+559, 0x33d5a7af2f34385d
+560, 0x8179c46661d85657
+561, 0x324ebcfd29267359
+562, 0xac4c9311dc9f9110
+563, 0xc14bb6a52f9f9c0
+564, 0xc430abe15e7fb9db
+565, 0xf1cce5c14df91c38
+566, 0x651e3efa2c0750d3
+567, 0x38a33604a8be5c75
+568, 0x7aaf77fe7ff56a49
+569, 0xc0d1cc56bbf27706
+570, 0x887aa47324e156c6
+571, 0x12547c004b085e8d
+572, 0xd86a8d6fbbbfd011
+573, 0x57c860188c92d7b4
+574, 0xcd5d3843d361b8ca
+575, 0x8f586ef05a9cb3ef
+576, 0x174456e1ba6267d5
+577, 0xf5dc302c62fe583c
+578, 0xa349442fabcdb71
+579, 0xe5123c1a8b6fd08e
+580, 0x80681552aa318593
+581, 0xb295396deaef1e31
+582, 0xabb626e0b900e32b
+583, 0xf024db8d3f19c15e
+584, 0x1d04bb9548e2fb6c
+585, 0xd8ed2b2214936c2b
+586, 0x618ca1e430a52bc9
+587, 0xccbca44a6088136b
+588, 0xd0481855c8b9ccbe
+589, 0x3c92a2fade28bdf7
+590, 0x855e9fefc38c0816
+591, 0x1269bbfe55a7b27c
+592, 0x1d6c853d83726d43
+593, 0xc8655511cc7fcafc
+594, 0x301503eb125a9b0e
+595, 0xb3108e4532016b11
+596, 0xbb7ab6245da9cb3d
+597, 0x18004c49116d85eb
+598, 0x3480849c20f61129
+599, 0xe28f45157463937b
+600, 0x8e85e61060f2ce1
+601, 0x1673da4ec589ba5e
+602, 0x74b9a6bd1b194712
+603, 0xed39e147fa8b7601
+604, 0x28ce54019102ca77
+605, 0x42e0347f6d7a2f30
+606, 0xb6a908d1c4814731
+607, 0x16c3435e4e9a126d
+608, 0x8880190514c1ad54
+609, 0xfffd86229a6f773c
+610, 0x4f2420cdb0aa1a93
+611, 0xf8e1acb4120fc1fa
+612, 0x63a8c553ab36a2f2
+613, 0x86b88cf3c0a6a190
+614, 0x44d8b2801622c792
+615, 0xf6eae14e93082ff1
+616, 0xd9ed4f5d1b8fac61
+617, 0x1808ce17f4e1f70
+618, 0x446e83ea336f262f
+619, 0xc7c802b04c0917b7
+620, 0x626f45fd64968b73
+621, 0x9ffa540edc9b2c5c
+622, 0xa96a1e219e486af8
+623, 0x2bb8963884e887a1
+624, 0xba7f68a5d029e3c4
+625, 0xefc45f44392d9ca0
+626, 0x98d77762503c5eab
+627, 0xd89bcf62f2da627c
+628, 0xa3cab8347f833151
+629, 0xa095b7595907d5c7
+630, 0x3b3041274286181
+631, 0xb518db8919eb71fa
+632, 0x187036c14fdc9a36
+633, 0xd06e28301e696f5d
+634, 0xdbc71184e0c56492
+635, 0xfe51e9cae6125bfd
+636, 0x3b12d17cd014df24
+637, 0x3b95e4e2c986ac1a
+638, 0x29c1cce59fb2dea2
+639, 0x58c05793182a49d6
+640, 0xc016477e330d8c00
+641, 0x79ef335133ada5d
+642, 0x168e2cad941203f3
+643, 0xf99d0f219d702ef0
+644, 0x655628068f8f135b
+645, 0xdcdea51910ae3f92
+646, 0x8e4505039c567892
+647, 0x91a9ec7e947c89ae
+648, 0x8717172530f93949
+649, 0x1c80aba9a440171a
+650, 0x9c8f83f6ebe7441e
+651, 0x6c05e1efea4aa7f9
+652, 0x10af696b777c01b
+653, 0x5892e9d9a92fc309
+654, 0xd2ba7da71e709432
+655, 0x46378c7c3269a466
+656, 0x942c63dfe18e772c
+657, 0x6245cf02ef2476f
+658, 0x6f265b2759ea2aea
+659, 0x5aa757f17d17f4a6
+660, 0x1ad6a3c44fa09be6
+661, 0xe861af14e7015fb8
+662, 0x86be2e7db388c77
+663, 0x5c7bba32b519e9a0
+664, 0x3feb314850c4437b
+665, 0x97955add60cfb45b
+666, 0xfdb536230a540bdc
+667, 0xdac9d7bf6e58512e
+668, 0x4894c00e474e8120
+669, 0xa1918a37739da366
+670, 0xa8097f2096532807
+671, 0x592afe50e6c5e643
+672, 0xd69050ee6dcb33dc
+673, 0xa6956b262dd3c561
+674, 0x1a55c815555e63f7
+675, 0x2ec7fd37516de2bb
+676, 0x8ec251d9c70e76ba
+677, 0x9b76e4abafd2689
+678, 0x9ce3f5c751a57df1
+679, 0x915c4818bf287bc7
+680, 0x2293a0d1fe07c735
+681, 0x7627dcd5d5a66d3d
+682, 0xb5e4f92cc49c7138
+683, 0x6fc51298731d268c
+684, 0xd19800aa95441f87
+685, 0x14f70f31162fa115
+686, 0x41a3da3752936f59
+687, 0xbec0652be95652ee
+688, 0x7aa4bdb1020a290f
+689, 0x4382d0d9bee899ef
+690, 0xe6d988ae4277d6ff
+691, 0xe618088ccb2a32d1
+692, 0x411669dfaa899e90
+693, 0x234e2bf4ba76d9f
+694, 0xe109fe4cb7828687
+695, 0x1fb96b5022b0b360
+696, 0x6b24ad76c061a716
+697, 0x7e1781d4d7ecee15
+698, 0xf20c2dbe82ba38ba
+699, 0xeda8e8ae1d943655
+700, 0xa58d196e2a77eaec
+701, 0x44564765a5995a0b
+702, 0x11902fe871ecae21
+703, 0x2ea60279900e675d
+704, 0x38427227c18a9a96
+705, 0xe0af01490a1b1b48
+706, 0x826f91997e057824
+707, 0x1e57308e6e50451
+708, 0xb42d469bbbfdc350
+709, 0xb9734cff1109c49b
+710, 0x98967559bb9d364f
+711, 0xd6be360041907c12
+712, 0xa86a1279122a1e21
+713, 0x26f99a8527bfc698
+714, 0xfa8b85758f28f5d6
+715, 0xe3057429940806ae
+716, 0x4bee2d7e84f93b2b
+717, 0x948350a76ea506f4
+718, 0xa139154488045e74
+719, 0x8893579ba5e78085
+720, 0x5f21c215c6a9e397
+721, 0x456134f3a59641dc
+722, 0x92c0273f8e97a9c6
+723, 0xd2936c9c3f0c6936
+724, 0xcfa4221e752c4735
+725, 0x28cd5a7457355dca
+726, 0xecdfdde23d90999f
+727, 0x60631b2d494d032b
+728, 0xf67289df269a827f
+729, 0xcbe8011ef0f5b7ef
+730, 0x20eea973c70a84f5
+731, 0xbe1fd200398557ce
+732, 0xd2279ee030191bba
+733, 0xf2bd4291dedaf819
+734, 0xfc6d167dbe8c402
+735, 0x39ac298da5d0044b
+736, 0xceac026f5f561ce
+737, 0x10a5b0bdd8ad60e6
+738, 0xdeb3c626df6d4bcb
+739, 0x3c128962e77ff6ca
+740, 0xc786262e9c67a0e5
+741, 0x4332855b3febcdc0
+742, 0x7bda9724d1c0e020
+743, 0x6a8c93399bc4df22
+744, 0xa9b20100ac707396
+745, 0xa11a3458502c4eb5
+746, 0xb185461c60478941
+747, 0x13131d56195b7ff6
+748, 0x8d55875ddbd4aa1c
+749, 0xc09b67425f469aa5
+750, 0x39e33786cc7594c4
+751, 0x75e96db8e4b08b93
+752, 0xda01cd12a3275d1e
+753, 0x2c49e7822344fab5
+754, 0x9bd5f10612514ca7
+755, 0x1c801a5c828e7332
+756, 0x29797d3f4f6c7b4c
+757, 0xac992715e21e4e53
+758, 0xe40e89ee887ddb37
+759, 0x15189a2b265a783b
+760, 0xa854159a52af5c5
+761, 0xb9d8a5a81c12bead
+762, 0x3240cdc9d59e2a58
+763, 0x1d0b872234cf8e23
+764, 0xc01224cf6ce12cff
+765, 0x2601e9f3905c8663
+766, 0xd4ecf9890168d6b4
+767, 0xa45db796d89bfdd5
+768, 0x9f389406dad64ab4
+769, 0xa5a851adce43ffe3
+770, 0xd0962c41c26e5aa9
+771, 0x8a671679e48510a4
+772, 0xc196dc0924a6bfeb
+773, 0x3ead661043b549cb
+774, 0x51af4ca737d405ac
+775, 0xf4425b5c62275fb6
+776, 0x71e69d1f818c10f5
+777, 0xacaf4af2d3c70162
+778, 0x2e1f1d4fd7524244
+779, 0xe54fdd8f388890e8
+780, 0xfda0d33e84eb2b83
+781, 0x53965c5e392b81da
+782, 0x5c92288267263097
+783, 0xcac1b431c878c66c
+784, 0x36c0e1cf417241c6
+785, 0x5cc4d9cd1a36bf2c
+786, 0x32e4257bb5d3e470
+787, 0x4aecff904adb44fb
+788, 0x4d91a8e0d1d60cac
+789, 0xa3b478388385b038
+790, 0x48d955f24eba70be
+791, 0x310e4deb07f24f68
+792, 0x8853e73b1f30a5a
+793, 0x278aee45c2a65c5
+794, 0xf6932eedbd62fb0b
+795, 0xafb95958c82fafad
+796, 0x78e807c18616c16c
+797, 0xd7abadda7488ed9f
+798, 0x2dd72e2572aa2ae6
+799, 0x6ec3791982c2be09
+800, 0x6865bb314fac478f
+801, 0xa14dc0ce09000d1a
+802, 0xb8081ad134da10f2
+803, 0xc4ac1534aa825ef5
+804, 0xd83aeb48ae2d538f
+805, 0x38052027e3074be4
+806, 0xa9833e06ef136582
+807, 0x4f02d790ec9fd78
+808, 0xec2f60bc711c5bdc
+809, 0x9253b0d12268e561
+810, 0xa8ac607fdd62c206
+811, 0x895e28ebc920289f
+812, 0xe2fd42b154243ac7
+813, 0xc69cac2f776eee19
+814, 0xf4d4ac11db56d0dc
+815, 0xa8d37049b9f39833
+816, 0x75abbf8a196c337c
+817, 0xb115bb76750d27b8
+818, 0x39426d187839154
+819, 0xd488423e7f38bf83
+820, 0xbb92e0c76ecb6a62
+821, 0x3055a018ce39f4e3
+822, 0xc93fe0e907729bfb
+823, 0x65985d17c5863340
+824, 0x2088ae081b2028e1
+825, 0x6e628de873314057
+826, 0x864377cccf573f0e
+827, 0xae03f4c9aa63d132
+828, 0xb1db766d6404c66d
+829, 0xdce5a22414a374b
+830, 0x622155b777819997
+831, 0x69fe96e620371f3c
+832, 0xa9c67dbc326d94fc
+833, 0x932a84ae5dd43bab
+834, 0xe2301a20f6c48c3f
+835, 0x795d2e79c6477300
+836, 0xd8e3e631289521e7
+837, 0xae2684979002dfd6
+838, 0xc9c2392377550f89
+839, 0xa1b0c99d508ef7ec
+840, 0x593aef3c5a5272ec
+841, 0xe32e511a4b7162cd
+842, 0xab3b81655f5a2857
+843, 0x1b535e1a0aaf053e
+844, 0x5b33f56c1b6a07e2
+845, 0x782dc8cfcac4ef36
+846, 0xb3d4f256eecfd202
+847, 0xf73a6598f58c4f7e
+848, 0xd5722189524870ae
+849, 0x707878de6b995fc0
+850, 0xc3eb6ba73e3d7e8a
+851, 0xca75c017655b75a7
+852, 0x1b29369ea3541e5f
+853, 0x352e98858bdb58a3
+854, 0x1e4412d184b6b27d
+855, 0x2d375ba0304b2d17
+856, 0x56c30fce69a5d08e
+857, 0x6b8c2b0c06584bda
+858, 0xde4dfff228c8c91f
+859, 0xb7c9edd574e6287f
+860, 0xf6078281c9fca2b2
+861, 0xb9b9a51de02a2f1e
+862, 0xa411bef31c0103b0
+863, 0xc5facd8fc5e1d7a3
+864, 0x54e631c05ddf7359
+865, 0x815b42b3fd06c474
+866, 0xc9ac07566fda18ec
+867, 0xd84ea62957bd8e15
+868, 0x5575f74b5cfd8803
+869, 0x5779a8d460c2e304
+870, 0xfd6e87e264a85587
+871, 0xa1d674daa320b26d
+872, 0x2c3c3ec64b35afc4
+873, 0x393a274ff03e6935
+874, 0x1f40ecbac52c50ea
+875, 0xc3de64fa324ffc0c
+876, 0x56ae828b7f9deb04
+877, 0xe7c1a77b5c1f2cb3
+878, 0xa4c4aab19ea921cc
+879, 0xec164c238825822c
+880, 0xa6a3304770c03b03
+881, 0x3a63641d5b1e8123
+882, 0x42677be3a54617ef
+883, 0xa2680423e3a200c0
+884, 0x8b17cf75f3f37277
+885, 0xe7ce65a49242be3d
+886, 0x7f85934271323e4b
+887, 0xcfb0f431f79a4fab
+888, 0x392e4041a8505b65
+889, 0xd3e5daf0d8b25ea6
+890, 0x9447eff675d80f53
+891, 0xea27a9d53cfaeea8
+892, 0xe3f2335945a83ba
+893, 0x8875a43ce216413b
+894, 0xe49941f9eabce33e
+895, 0x9357c1296683a5b1
+896, 0xf0f16439e81ee701
+897, 0x3181515295ffd79a
+898, 0x9d7150fffd169ed8
+899, 0x2d6a1d281e255a72
+900, 0x81bf1286fb3a92b6
+901, 0x566d3079b499e279
+902, 0xc7939ca8f047341
+903, 0xb1f8050e7c2d59f6
+904, 0x605701045e7be192
+905, 0x51b73360e8e31a1c
+906, 0x9f4ad54483ba9fe0
+907, 0xd3085b8fcf69d1c8
+908, 0xc3e7475026dc5f0b
+909, 0x5800f8554b157354
+910, 0x37dfdf858cfcd963
+911, 0x3a1fce05ce385072
+912, 0xf495c062645c20c3
+913, 0xdcbeec2c3492c773
+914, 0xc38f427589d1d0b4
+915, 0x681ead60216a8184
+916, 0x4bd569c40cc88c41
+917, 0x49b0d442e130b7a2
+918, 0xee349156b7d1fa3f
+919, 0x2bde2d2db055135b
+920, 0xc6a460d2fbcb2378
+921, 0xd0f170494ff3dbb
+922, 0xb294422492528a23
+923, 0xfc95873c854e7b86
+924, 0x6c9c3ad1797bb19c
+925, 0xe0c06f2aab65062d
+926, 0x58e32ce0f11e3a81
+927, 0xa745fcd729ff5036
+928, 0x599b249b2fc2cdb2
+929, 0x78f23b5b0dd5b082
+930, 0x6de3e957f549ecfc
+931, 0x9d0712fa6d878756
+932, 0x9076e8554e4a413a
+933, 0xf3185818c0294de8
+934, 0x5de7cdf4b455b9b6
+935, 0xb15f6908ed703f7d
+936, 0x98c654dfedc6818
+937, 0x120502ab0e93ae42
+938, 0x67966a98a58dc120
+939, 0x1caa0fc628989482
+940, 0xd8b2c3cd480a8625
+941, 0x85c70071b3aed671
+942, 0xff385f8473714662
+943, 0xe2868e4bf3773b63
+944, 0x96cf8019b279298e
+945, 0x8511cc930bd74800
+946, 0x5312e48fdd55f5ab
+947, 0xfcdae564b52df78d
+948, 0x9eee48373e652176
+949, 0x953788f6bcbc56b0
+950, 0xd1a3855dbd2f6b37
+951, 0x3ad32acf77f4d1e9
+952, 0x917c7be81b003e30
+953, 0x9ce817da1e2e9dfb
+954, 0x2968983db162d44d
+955, 0x1e005decef5828ad
+956, 0xc38fe59d1aa4f3d5
+957, 0xf357f1710dc02f1d
+958, 0x2613912a4c83ec67
+959, 0x832a11470b9a17cb
+960, 0x5e85508a611f0dad
+961, 0x2781131677f59d56
+962, 0xa82358d7d4b0237f
+963, 0xfbf8b3cc030c3af6
+964, 0x68b2f68ac8a55adb
+965, 0x3b6fcf353add0ada
+966, 0xd1956049bcd15bd5
+967, 0x95b76f31c7f98b6d
+968, 0x814b6690df971a84
+969, 0xdcf7959cddd819e4
+970, 0xcf8c72c5d804fc88
+971, 0x56883769c8945a22
+972, 0x1f034652f658cf46
+973, 0x41df1324cda235a1
+974, 0xeccd32524504a054
+975, 0x974e0910a04ec02c
+976, 0x72104507b821f6db
+977, 0x791f8d089f273044
+978, 0xe0f79a4f567f73c3
+979, 0x52fe5bea3997f024
+980, 0x5f8b9b446494f78
+981, 0xfd9f511947059190
+982, 0x3aea9dac6063bce3
+983, 0xbfdae4dfc24aee60
+984, 0xa82cdbbf0a280318
+985, 0xf460aae18d70aa9d
+986, 0x997367cb204a57c4
+987, 0x616e21ab95ba05ef
+988, 0x9bfc93bec116769f
+989, 0x2b2ee27c37a3fa5b
+990, 0xb25c6ed54006ee38
+991, 0xab04d4a5c69e69a5
+992, 0x6d2f6b45f2d8438f
+993, 0x4ad2f32afc82f092
+994, 0x513d718908f709c0
+995, 0x5272aadc4fffca51
+996, 0xeb3f87e66156ef5d
+997, 0xf8a3d5a46a86ba85
+998, 0xdb4548a86f27abfd
+999, 0x57c05f47ff62380d
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/sfc64-testset-1.csv b/lib/python3.11/site-packages/numpy/random/tests/data/sfc64-testset-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4fffe69591fea3f399f35034057096089d4017fb
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/sfc64-testset-1.csv
@@ -0,0 +1,1001 @@
+seed, 0xdeadbeaf
+0, 0xa475f55fbb6bc638
+1, 0xb2d594b6c29d971c
+2, 0x275bc4ece4484fb1
+3, 0x569be72d9b3492fb
+4, 0x89a5bb9b206a670c
+5, 0xd951bfa06afdc3f9
+6, 0x7ee2e1029d52a265
+7, 0x12ef1d4de0cb4d4c
+8, 0x41658ba8f0ef0280
+9, 0x5b650c82e4fe09c5
+10, 0x638a9f3e30ec4e94
+11, 0x147487fb2ba9233e
+12, 0x89ef035603d2d1fb
+13, 0xe66ca57a190e6cbe
+14, 0x330f673740dd61fc
+15, 0xc71d3dce2f8bb34e
+16, 0x3c07c39ff150b185
+17, 0x5df952b6cae8f099
+18, 0x9f09f2b1f0ceac80
+19, 0x19598eee2d0c4c67
+20, 0x64e06483702e0ebd
+21, 0xda04d1fdb545f7fa
+22, 0xf2cf53b61a0c4f9b
+23, 0xf0bb724ce196f66e
+24, 0x71cefde55d9cf0f
+25, 0x6323f62824a20048
+26, 0x1e93604680f14b4e
+27, 0xd9d8fad1d4654025
+28, 0xf4ee25af2e76ca08
+29, 0x6af3325896befa98
+30, 0xad9e43abf5e04053
+31, 0xbf930e318ce09de3
+32, 0x61f9583b4f9ffe76
+33, 0x9b69d0b3d5ec8958
+34, 0xa608f250f9b2ca41
+35, 0x6fdba7073dc2bb5d
+36, 0xa9d57601efea6d26
+37, 0xc24a88a994954105
+38, 0xc728b1f78d88fe5b
+39, 0x88da88c2b083b3b2
+40, 0xa9e27f7303c76cfd
+41, 0xc4c24608c29176eb
+42, 0x5420b58466b972fd
+43, 0xd2018a661b6756c8
+44, 0x7caed83d9573fc7
+45, 0x562a3d81b849a06a
+46, 0x16588af120c21f2c
+47, 0x658109a7e0eb4837
+48, 0x877aabb14d3822e1
+49, 0x95704c342c3745fe
+50, 0xeeb8a0dc81603616
+51, 0x431bf94889290419
+52, 0xe4a9410ab92a5863
+53, 0xbc6be64ea60f12ba
+54, 0x328a2da920015063
+55, 0x40f6b3bf8271ae07
+56, 0x4068ff00a0e854f8
+57, 0x1b287572ca13fa78
+58, 0xa11624a600490b99
+59, 0x4a04ef29eb7150fa
+60, 0xcc9469ab5ffb739
+61, 0x99a6a9f8d95e782
+62, 0x8e90356573e7a070
+63, 0xa740b8fb415c81c4
+64, 0x47eccef67447f3da
+65, 0x2c720afe3a62a49b
+66, 0xe2a747f0a43eacf4
+67, 0xba063a87ab165576
+68, 0xbc1c78ed27feb5a3
+69, 0x285a19fa3974f9d
+70, 0x489c61e704f5f0e3
+71, 0xf5ab04f6b03f238b
+72, 0x7e25f88138a110dd
+73, 0xc3d1cef3d7c1f1d1
+74, 0xc3de6ec64d0d8e00
+75, 0x73682a15b6cc5088
+76, 0x6fecbeb319163dc5
+77, 0x7e100d5defe570a1
+78, 0xad2af9af076dce57
+79, 0x3c65100e23cd3a9a
+80, 0x4b442cc6cfe521bb
+81, 0xe89dc50f8ab1ef75
+82, 0x8b3c6fdc2496566
+83, 0xdfc50042bc2c308c
+84, 0xe39c5f158b33d2b2
+85, 0x92f6adefdfeb0ac
+86, 0xdf5808a949c85b3e
+87, 0x437384021c9dace9
+88, 0xa7b5ed0d3d67d8f
+89, 0xe1408f8b21da3c34
+90, 0xa1bba125c1e80522
+91, 0x7611dc4710385264
+92, 0xb00a46ea84082917
+93, 0x51bf8002ffa87cef
+94, 0x9bb81013e9810adc
+95, 0xd28f6600013541cd
+96, 0xc2ca3b1fa7791c1f
+97, 0x47f9ad58f099c82c
+98, 0x4d1bb9458469caf9
+99, 0xca0b165b2844257
+100, 0xc3b2e667d075dc66
+101, 0xde22f71136a3dbb1
+102, 0x23b4e3b6f219e4c3
+103, 0x327e0db4c9782f66
+104, 0x9365506a6c7a1807
+105, 0x3e868382dedd3be7
+106, 0xff04fa6534bcaa99
+107, 0x96621a8862995305
+108, 0x81bf39cb5f8e1df7
+109, 0x79b684bb8c37af7a
+110, 0xae3bc073c3cde33c
+111, 0x7805674112c899ac
+112, 0xd95a27995abb20f2
+113, 0x71a503c57b105c40
+114, 0x5ff00d6a73ec8acc
+115, 0x12f96391d91e47c2
+116, 0xd55ca097b3bd4947
+117, 0x794d79d20468b04
+118, 0x35d814efb0d7a07d
+119, 0xfa9ac9bd0aae76d3
+120, 0xa77b8a3711e175cd
+121, 0xe6694fbf421f9489
+122, 0xd8f1756525a1a0aa
+123, 0xe38dfa8426277433
+124, 0x16b640c269bbcd44
+125, 0x2a7a5a67ca24cfeb
+126, 0x669039c28d5344b4
+127, 0x2a445ee81fd596bb
+128, 0x600df94cf25607e0
+129, 0x9358561a7579abff
+130, 0xee1d52ea179fc274
+131, 0x21a8b325e89d31be
+132, 0x36fc0917486eec0a
+133, 0x3d99f40717a6be9f
+134, 0x39ac140051ca55ff
+135, 0xcef7447c26711575
+136, 0xf22666870eff441d
+137, 0x4a53c6134e1c7268
+138, 0xd26de518ad6bdb1b
+139, 0x1a736bf75b8b0e55
+140, 0xef1523f4e6bd0219
+141, 0xb287b32fd615ad92
+142, 0x2583d6af5e841dd5
+143, 0x4b9294aae7ca670c
+144, 0xf5aa4a84174f3ca9
+145, 0x886300f9e0dc6376
+146, 0x3611401e475ef130
+147, 0x69b56432b367e1ac
+148, 0x30c330e9ab36b7c4
+149, 0x1e0e73079a85b8d5
+150, 0x40fdfc7a5bfaecf
+151, 0xd7760f3e8e75a085
+152, 0x1cc1891f7f625313
+153, 0xeece1fe6165b4272
+154, 0xe61111b0c166a3c1
+155, 0x2f1201563312f185
+156, 0xfd10e8ecdd2a57cb
+157, 0x51cdc8c9dd3a89bf
+158, 0xed13cc93938b5496
+159, 0x843816129750526b
+160, 0xd09995cd6819ada
+161, 0x4601e778d40607df
+162, 0xef9df06bd66c2ea0
+163, 0xae0bdecd3db65d69
+164, 0xbb921a3c65a4ae9a
+165, 0xd66698ce8e9361be
+166, 0xacdc91647b6068f4
+167, 0xe505ef68f2a5c1c0
+168, 0xd6e62fd27c6ab137
+169, 0x6a2ba2c6a4641d86
+170, 0x9c89143715c3b81
+171, 0xe408c4e00362601a
+172, 0x986155cbf5d4bd9d
+173, 0xb9e6831728c893a7
+174, 0xb985497c3bf88d8c
+175, 0xd0d729214b727bec
+176, 0x4e557f75fece38a
+177, 0x6572067fdfd623ca
+178, 0x178d49bb4d5cd794
+179, 0xe6baf59f60445d82
+180, 0x5607d53518e3a8d2
+181, 0xba7931adb6ebbd61
+182, 0xe853576172611329
+183, 0xe945daff96000c44
+184, 0x565b9ba3d952a176
+185, 0xcdb54d4f88c584c8
+186, 0x482a7499bee9b5e5
+187, 0x76560dd0affe825b
+188, 0x2a56221faa5ca22c
+189, 0x7729be5b361f5a25
+190, 0xd6f2195795764876
+191, 0x59ef7f8f423f18c5
+192, 0x7ebefed6d02adde1
+193, 0xcfec7265329c73e5
+194, 0x4fd8606a5e59881c
+195, 0x95860982ae370b73
+196, 0xdecfa33b1f902acc
+197, 0xf9b8a57400b7c0a6
+198, 0xd20b822672ec857b
+199, 0x4eb81084096c7364
+200, 0xe535c29a44d9b6ad
+201, 0xdef8b48ebacb2e29
+202, 0x1063bc2b8ba0e915
+203, 0xe4e837fb53d76d02
+204, 0x4df935db53579fb8
+205, 0xa30a0c8053869a89
+206, 0xe891ee58a388a7b5
+207, 0x17931a0c64b8a985
+208, 0xaf2d350b494ce1b3
+209, 0x2ab9345ffbcfed82
+210, 0x7de3fe628a2592f0
+211, 0x85cf54fab8b7e79d
+212, 0x42d221520edab71b
+213, 0x17b695b3af36c233
+214, 0xa4ffe50fe53eb485
+215, 0x1102d242db800e4d
+216, 0xc8dc01f0233b3b6
+217, 0x984a030321053d36
+218, 0x27fa8dc7b7112c0e
+219, 0xba634dd8294e177f
+220, 0xe67ce34b36332eb
+221, 0x8f1351e1894fb41a
+222, 0xb522a3048761fd30
+223, 0xc350ad9bc6729edc
+224, 0xe0ed105bd3c805e1
+225, 0xa14043d2b0825aa7
+226, 0xee7779ce7fc11fdf
+227, 0xc0fa8ba23a60ab25
+228, 0xb596d1ce259afbad
+229, 0xaa9b8445537fdf62
+230, 0x770ab2c700762e13
+231, 0xe812f1183e40cc1
+232, 0x44bc898e57aefbbd
+233, 0xdd8a871df785c996
+234, 0x88836a5e371eb36b
+235, 0xb6081c9152623f27
+236, 0x895acbcd6528ca96
+237, 0xfb67e33ddfbed435
+238, 0xaf7af47d323ce26
+239, 0xe354a510c3c39b2d
+240, 0x5cacdedda0672ba3
+241, 0xa440d9a2c6c22b09
+242, 0x6395099f48d64304
+243, 0xc11cf04c75f655b5
+244, 0x1c4e054d144ddb30
+245, 0x3e0c2db89d336636
+246, 0x127ecf18a5b0b9a7
+247, 0x3b50551a88ea7a73
+248, 0xbd27003e47f1f684
+249, 0xf32d657782baac9b
+250, 0x727f5cabf020bc9
+251, 0x39c1c1c226197dc7
+252, 0x5552c87b35deeb69
+253, 0x64d54067b5ce493f
+254, 0x3494b091fe28dda0
+255, 0xdf0278bc85ee2965
+256, 0xdef16fec25efbd66
+257, 0xe2be09f578c4ce28
+258, 0xd27a9271979d3019
+259, 0x427f6fcd71845e3
+260, 0x26b52c5f81ec142b
+261, 0x98267efc3986ad46
+262, 0x7bf4165ddb7e4374
+263, 0xd05f7996d7941010
+264, 0x3b3991de97b45f14
+265, 0x9068217fb4f27a30
+266, 0xd8fe295160afc7f3
+267, 0x8a159fab4c3bc06f
+268, 0x57855506d19080b6
+269, 0x7636df6b3f2367a4
+270, 0x2844ee3abd1d5ec9
+271, 0xe5788de061f51c16
+272, 0x69e78cc9132a164
+273, 0xacd53cde6d8cd421
+274, 0xb23f3100068e91da
+275, 0x4140070a47f53891
+276, 0xe4a422225a96e53a
+277, 0xb82a8925a272a2ac
+278, 0x7c2f9573590fe3b7
+279, 0xbaf80764db170575
+280, 0x955abffa54358368
+281, 0x355ce7460614a869
+282, 0x3700ede779a4afbf
+283, 0x10a6ec01d92d68cd
+284, 0x3308f5a0a4c0afef
+285, 0x97b892d7601136c9
+286, 0x4955c3b941b8552e
+287, 0xca85aa67e941961d
+288, 0xb1859ae5db28e9d2
+289, 0x305d072ac1521fbd
+290, 0xed52a868996085bb
+291, 0x723bfa6a76358852
+292, 0x78d946ecd97c5fb3
+293, 0x39205b30a8e23e79
+294, 0xb927e3d086baadbe
+295, 0xa18d6946136e1ff5
+296, 0xdab6f0b51c1eb5ff
+297, 0xf0a640bf7a1af60c
+298, 0xf0e81db09004d0d4
+299, 0xfe76cebdbe5a4dde
+300, 0x2dafe9cc3decc376
+301, 0x4c871fdf1af34205
+302, 0xe79617d0c8fa893b
+303, 0xee658aaad3a141f7
+304, 0xfd91aa74863e19f1
+305, 0x841b8f55c103cc22
+306, 0x22766ed65444ad5d
+307, 0x56d03d1beca6c17a
+308, 0x5fd4c112c92036ae
+309, 0x75466ae58a5616dc
+310, 0xfbf98b1081e802a9
+311, 0xdc325e957bf6d8f5
+312, 0xb08da7015ebd19b7
+313, 0xf25a9c0944f0c073
+314, 0xf4625bafa0ced718
+315, 0x4349c9e093a9e692
+316, 0x75a9ccd4dd8935cb
+317, 0x7e6cf9e539361e91
+318, 0x20fdd22fb6edd475
+319, 0x5973021b57c2311f
+320, 0x75392403667edc15
+321, 0xed9b2156ea70d9f1
+322, 0xf40c114db50b64a0
+323, 0xe26bb2c9eef20c62
+324, 0x409c1e3037869f03
+325, 0xcdfd71fdda3b7f91
+326, 0xa0dfae46816777d6
+327, 0xde060a8f61a8deb8
+328, 0x890e082a8b0ca4fc
+329, 0xb9f2958eddf2d0db
+330, 0xd17c148020d20e30
+331, 0xffdc9cc176fe7201
+332, 0xffb83d925b764c1
+333, 0x817ea639e313da8d
+334, 0xa4dd335dd891ca91
+335, 0x1342d25a5e81f488
+336, 0xfa7eb9c3cf466b03
+337, 0xfe0a423d44b185d0
+338, 0x101cfd430ab96049
+339, 0x7b5d3eda9c4504b
+340, 0xe20ccc006e0193f1
+341, 0xf54ccddedebc5df0
+342, 0xc0edd142bd58f1db
+343, 0x3831f40d378d2430
+344, 0x80132353f0a88289
+345, 0x688f23c419d03ef8
+346, 0x4c6837e697884066
+347, 0x699387bb2e9a3a8f
+348, 0x8996f860342448d8
+349, 0xb0f80dff99bfa5cc
+350, 0x3e927a7f9ea12c8e
+351, 0xd7e498d1e5f9dff3
+352, 0x78ecb97bb3f864cc
+353, 0x3c4ffd069a014d38
+354, 0xf8d5073a1e09b4d4
+355, 0x8717e854f9faef23
+356, 0xfbcc5478d8d0ad7
+357, 0xd3cd8b233ca274ff
+358, 0x8bd8f11f79beb265
+359, 0xf64498a832d8fd0e
+360, 0xb01bba75112131ec
+361, 0x55572445a7869781
+362, 0x7b56622f18cb3d7a
+363, 0x7f192c9e075bdb83
+364, 0xd9a112f836b83ff3
+365, 0x68673b37269653dc
+366, 0xe46a9433fb6a0879
+367, 0x127d756ca4779001
+368, 0xc1378e8b1e8eab94
+369, 0x1006edb0f51d078c
+370, 0xc6dd53961232d926
+371, 0x9a4aeef44038256d
+372, 0xd357f4fa652d4f5f
+373, 0x59f3d2cc3378598
+374, 0xe76e6207a824a7fc
+375, 0x5fc5e33712ceffef
+376, 0x77d24aeb0ccb1adc
+377, 0x5be4b2826805659e
+378, 0x257c69d787e64634
+379, 0x58dd52ca6bc727b1
+380, 0x3ab997767235ea33
+381, 0x986a2a7a966fad14
+382, 0xc900f8b27761dcc4
+383, 0x44991bdb13795700
+384, 0xe5c145a4fe733b2
+385, 0x56f041b56bffe0d3
+386, 0x5779c4fef8067996
+387, 0xa0fe8748e829532d
+388, 0x840c1277d78d9dd4
+389, 0x37ebcb315432acbc
+390, 0xf4bc8738433ba3be
+391, 0x8b122993f2e10062
+392, 0xe1fe8481f2681ed5
+393, 0x8e23f1630d9f494a
+394, 0xda24661a01b7d0b3
+395, 0x7a02942a179cee36
+396, 0xf1e08a3c09b71ac
+397, 0x3dec2cc7ee0bd8fd
+398, 0x1f3e480113d805d4
+399, 0xc061b973ad4e3f2c
+400, 0x6bea750f17a66836
+401, 0xbc2add72eac84c25
+402, 0xcff058d3f97934ca
+403, 0x54ccc30987778ec2
+404, 0x93449ec1e1469558
+405, 0xe2ff369eb0c6836
+406, 0x41c2df2d63bf8e55
+407, 0xf9302629b6c71be2
+408, 0xdd30376b8e5ab29a
+409, 0x12db9e04f911d754
+410, 0x8d03d6cd359f1b97
+411, 0xe15956511abf1cee
+412, 0x9b68e10e2c2fd940
+413, 0x2e28de6491c1ce53
+414, 0x52b329b72d0c109d
+415, 0xc2c0b115f9da2a60
+416, 0x6ca084105271bbff
+417, 0x49b92b8676058c1e
+418, 0x767fc92a70f7e5a3
+419, 0x87ba4ed4b65a6aa0
+420, 0xf70b052e0a3975e9
+421, 0x3e925c3306db9eec
+422, 0x43253f1d96ac9513
+423, 0xe3e04f1a1ea454c4
+424, 0x763e3f4cc81ba0c8
+425, 0x2a2721ac69265705
+426, 0xdf3b0ac6416ea214
+427, 0xa6a6b57450f3e000
+428, 0xc3d3b1ac7dbfe6ac
+429, 0xb66e5e6f7d2e4ec0
+430, 0x43c65296f98f0f04
+431, 0xdb0f6e3ff974d842
+432, 0x3d6b48e02ebb203b
+433, 0xd74674ebf09d8f27
+434, 0xbe65243c58fc1200
+435, 0x55eb210a68d42625
+436, 0x87badab097dbe883
+437, 0xada3fda85a53824f
+438, 0xef2791e8f48cd37a
+439, 0x3fe7fceb927a641a
+440, 0xd3bffd3ff031ac78
+441, 0xb94efe03da4d18fb
+442, 0x162a0ad8da65ea68
+443, 0x300f234ef5b7e4a6
+444, 0xa2a8b4c77024e4fb
+445, 0x5950f095ddd7b109
+446, 0xded66dd2b1bb02ba
+447, 0x8ec24b7fa509bcb6
+448, 0x9bede53d924bdad6
+449, 0xa9c3f46423be1930
+450, 0x6dfc90597f8de8b4
+451, 0xb7419ebc65b434f0
+452, 0xa6596949238f58b9
+453, 0x966cbade640829b8
+454, 0x58c74877bdcbf65e
+455, 0xaa103b8f89b0c453
+456, 0x219f0a86e41179a4
+457, 0x90f534fc06ddc57f
+458, 0x8db7cdd644f1affa
+459, 0x38f91de0167127ac
+460, 0xdcd2a65e4df43daa
+461, 0x3e04f34a7e01f834
+462, 0x5b237eea68007768
+463, 0x7ff4d2b015921768
+464, 0xf786b286549d3d51
+465, 0xaefa053fc2c3884c
+466, 0x8e6a8ff381515d36
+467, 0x35b94f3d0a1fce3c
+468, 0x165266d19e9abb64
+469, 0x1deb5caa5f9d8076
+470, 0x13ab91290c7cfe9d
+471, 0x3651ca9856be3e05
+472, 0xe7b705f6e9cccc19
+473, 0xd6e7f79668c127ed
+474, 0xa9faf37154896f92
+475, 0x89fbf190603e0ab1
+476, 0xb34d155a86f942d0
+477, 0xb2d4400a78bfdd76
+478, 0x7c0946aca8cfb3f0
+479, 0x7492771591c9d0e8
+480, 0xd084d95c5ca2eb28
+481, 0xb18d12bd3a6023e
+482, 0xea217ed7b864d80b
+483, 0xe52f69a755dd5c6f
+484, 0x127133993d81c4aa
+485, 0xe07188fcf1670bfb
+486, 0x178fbfe668e4661d
+487, 0x1c9ee14bb0cda154
+488, 0x8d043b96b6668f98
+489, 0xbc858986ec96ca2b
+490, 0x7660f779d528b6b7
+491, 0xd448c6a1f74ae1d3
+492, 0x178e122cfc2a6862
+493, 0x236f000abaf2d23b
+494, 0x171b27f3f0921915
+495, 0x4c3ff07652f50a70
+496, 0x18663e5e7d3a66ca
+497, 0xb38c97946c750cc9
+498, 0xc5031aae6f78f909
+499, 0x4d1514e2925e95c1
+500, 0x4c2184a741dabfbb
+501, 0xfd410364edf77182
+502, 0xc228157f863ee873
+503, 0x9856fdc735cc09fc
+504, 0x660496cd1e41d60e
+505, 0x2edf1d7e01954c32
+506, 0xd32e94639bdd98cf
+507, 0x8e153f48709a77d
+508, 0x89357f332d2d6561
+509, 0x1840d512c97085e6
+510, 0x2f18d035c9e26a85
+511, 0x77b88b1448b26d5b
+512, 0xc1ca6ef4cdae0799
+513, 0xcc203f9e4508165f
+514, 0xeaf762fbc9e0cbbe
+515, 0xc070c687f3c4a290
+516, 0xd49ed321068d5c15
+517, 0x84a55eec17ee64ee
+518, 0x4d8ee685298a8871
+519, 0x9ff5f17d7e029793
+520, 0x791d7d0d62e46302
+521, 0xab218b9114e22bc6
+522, 0x4902b7ab3f7119a7
+523, 0x694930f2e29b049e
+524, 0x1a3c90650848999f
+525, 0x79f1b9d8499c932b
+526, 0xfacb6d3d55e3c92f
+527, 0x8fd8b4f25a5da9f5
+528, 0xd037dcc3a7e62ae7
+529, 0xfecf57300d8f84f4
+530, 0x32079b1e1dc12d48
+531, 0xe5f8f1e62b288f54
+532, 0x97feba3a9c108894
+533, 0xd279a51e1899a9a0
+534, 0xd68eea8e8e363fa8
+535, 0x7394cf2deeca9386
+536, 0x5f70b0c80f1dbf10
+537, 0x8d646916ed40462
+538, 0xd253bb1c8a12bbb6
+539, 0x38f399a821fbd73e
+540, 0x947523a26333ac90
+541, 0xb52e90affbc52a37
+542, 0xcf899cd964654da4
+543, 0xdf66ae9cca8d99e7
+544, 0x6051478e57c21b6a
+545, 0xffa7dc975af3c1da
+546, 0x195c7bff2d1a8f5
+547, 0x64f12b6575cf984d
+548, 0x536034cb842cf9e1
+549, 0x180f247ce5bbfad
+550, 0x8ced45081b134867
+551, 0x532bbfdf426710f3
+552, 0x4747933e74c4f54d
+553, 0x197a890dc4793401
+554, 0x76c7cc2bd42fae2
+555, 0xdabfd67f69675dd0
+556, 0x85c690a68cdb3197
+557, 0xe482cec89ce8f92
+558, 0x20bc9fb7797011b1
+559, 0x76dc85a2185782ad
+560, 0x3df37c164422117a
+561, 0x99211f5d231e0ab0
+562, 0xef7fd794a0a91f4
+563, 0x419577151915f5fe
+564, 0x3ce14a0a7135dae3
+565, 0x389b57598a075d6a
+566, 0x8cc2a9d51b5af9aa
+567, 0xe80a9beffbd13f13
+568, 0x65e96b22ea8a54d8
+569, 0x79f38c4164138ede
+570, 0xd1955846cba03d81
+571, 0x60359fe58e4f26d6
+572, 0x4ea724f585f8d13e
+573, 0x316dfdbadc801a3c
+574, 0x20aa29b7c6dd66fe
+575, 0x65eaf83a6a008caa
+576, 0x407000aff1b9e8cb
+577, 0xb4d49bfb2b268c40
+578, 0xd4e6fe8a7a0f14a9
+579, 0xe34afef924e8f58e
+580, 0xe377b0c891844824
+581, 0x29c2e20c112d30c8
+582, 0x906aad1fe0c18a95
+583, 0x308385f0efbb6474
+584, 0xf23900481bf70445
+585, 0xfdfe3ade7f937a55
+586, 0xf37aae71c33c4f97
+587, 0x1c81e3775a8bed85
+588, 0x7eb5013882ce35ea
+589, 0x37a1c1692495818d
+590, 0x3f90ae118622a0ba
+591, 0x58e4fe6fea29b037
+592, 0xd10ff1d269808825
+593, 0xbce30edb60c21bba
+594, 0x123732329afd6fee
+595, 0x429b4059f797d840
+596, 0x421166568a8c4be1
+597, 0x88f895c424c1bd7f
+598, 0x2adaf7a7b9f781cb
+599, 0xa425644b26cb698
+600, 0x8cc44d2486cc5743
+601, 0xdb9f357a33abf6ba
+602, 0x1a57c4ea77a4d70c
+603, 0x1dea29be75239e44
+604, 0x463141a137121a06
+605, 0x8fecfbbe0b8a9517
+606, 0x92c83984b3566123
+607, 0x3b1c69180ed28665
+608, 0x14a6073425ea8717
+609, 0x71f4c2b3283238d7
+610, 0xb3d491e3152f19f
+611, 0x3a0ba3a11ebac5d2
+612, 0xddb4d1dd4c0f54ac
+613, 0xdb8f36fe02414035
+614, 0x1cf5df5031b1902c
+615, 0x23a20ed12ef95870
+616, 0xf113e573b2dedcbb
+617, 0x308e2395cde0a9fa
+618, 0xd377a22581c3a7da
+619, 0xe0ced97a947a66fb
+620, 0xe44f4de9cd754b00
+621, 0x2344943337d9d1bf
+622, 0x4b5ae5e2ea6e749c
+623, 0x9b8d2e3ef41d1c01
+624, 0x59a5a53ebbd24c6b
+625, 0x4f7611bf9e8a06fb
+626, 0xea38c7b61361cd06
+627, 0xf125a2bfdd2c0c7
+628, 0x2df8dcb5926b9ebb
+629, 0x233e18720cc56988
+630, 0x974c61379b4aa95e
+631, 0xc7fe24c1c868910b
+632, 0x818fd1affc82a842
+633, 0xcee92a952a26d38e
+634, 0x8962f575ebcbf43
+635, 0x7770687e3678c460
+636, 0xdfb1db4ed1298117
+637, 0xb9db54cb03d434d3
+638, 0x34aebbf2244257ad
+639, 0xd836db0cb210c490
+640, 0x935daed7138957cd
+641, 0x3cd914b14e7948fd
+642, 0xd0472e9ed0a0f7f0
+643, 0xa9df33dca697f75e
+644, 0x15e9ea259398721a
+645, 0x23eeba0f970abd60
+646, 0x2217fdf8bbe99a12
+647, 0x5ea490a95717b198
+648, 0xf4e2bfc28280b639
+649, 0x9d19916072d6f05c
+650, 0x5e0387cab1734c6a
+651, 0x93c2c8ac26e5f01e
+652, 0xb0d934354d957eb1
+653, 0xee5099a1eef3188c
+654, 0x8be0abca8edc1115
+655, 0x989a60845dbf5aa3
+656, 0x181c7ed964eee892
+657, 0x49838ea07481288d
+658, 0x17dbc75d66116b2e
+659, 0xa4cafb7a87c0117e
+660, 0xab2d0ae44cdc2e6e
+661, 0xdf802f2457e7da6
+662, 0x4b966c4b9187e124
+663, 0x62de9db6f4811e1a
+664, 0x1e20485968bc62
+665, 0xe9ac288265caca94
+666, 0xc5c694d349aa8c1a
+667, 0x3d67f2083d9bdf10
+668, 0x9a2468e503085486
+669, 0x9d6acd3dc152d1a3
+670, 0xca951e2aeee8df77
+671, 0x2707371af9cdd7b0
+672, 0x2347ae6a4eb5ecbd
+673, 0x16abe5582cb426f
+674, 0x523af4ff980bbccb
+675, 0xb07a0f043e3694aa
+676, 0x14d7c3da81b2de7
+677, 0xf471f1b8ac22305b
+678, 0xdb087ffff9e18520
+679, 0x1a352db3574359e8
+680, 0x48d5431502cc7476
+681, 0x7c9b7e7003dfd1bf
+682, 0x4f43a48aae987169
+683, 0x9a5d3eb66dedb3e9
+684, 0xa7b331af76a9f817
+685, 0xba440154b118ab2d
+686, 0x64d22344ce24c9c6
+687, 0xa22377bd52bd043
+688, 0x9dfa1bb18ca6c5f7
+689, 0xdccf44a92f644c8b
+690, 0xf623d0a49fd18145
+691, 0x556d5c37978e28b3
+692, 0xad96e32ce9d2bb8b
+693, 0x2e479c120be52798
+694, 0x7501cf871af7b2f7
+695, 0xd02536a5d026a5b8
+696, 0x4b37ff53e76ab5a4
+697, 0xdb3a4039caaeab13
+698, 0x6cbd65e3b700c7be
+699, 0x7367abd98761a147
+700, 0xf4f9ba216a35aa77
+701, 0xf88ca25ce921eb86
+702, 0xb211de082ec2cbf2
+703, 0xdd94aa46ec57e12e
+704, 0xa967d74ad8210240
+705, 0xdaa1fada8cfa887
+706, 0x85901d081c4488ee
+707, 0xcf67f79a699ef06
+708, 0x7f2f1f0de921ee14
+709, 0x28bc61e9d3f2328b
+710, 0x3332f2963faf18e5
+711, 0x4167ac71fcf43a6
+712, 0x843c1746b0160b74
+713, 0xd9be80070c578a5e
+714, 0xbd7250c9af1473e7
+715, 0x43f78afaa3647899
+716, 0x91c6b5dd715a75a5
+717, 0x29cc66c8a07bfef3
+718, 0x3f5c667311dc22be
+719, 0x4f49cd47958260cd
+720, 0xbef8be43d920b64e
+721, 0x7a892a5f13061d8b
+722, 0x9532f40125c819b1
+723, 0x924fca3045f8a564
+724, 0x9b2c6442453b0c20
+725, 0x7e21009085b8e793
+726, 0x9b98c17e17af59d2
+727, 0xba61acb73e3ae89a
+728, 0xb9d61a710555c138
+729, 0xc2a425d80978974b
+730, 0xa275e13592da7d67
+731, 0xe962103202d9ad0f
+732, 0xbdf8367a4d6f33fd
+733, 0xe59beb2f8648bdc8
+734, 0xb4c387d8fbc4ac1c
+735, 0x5e3f276b63054b75
+736, 0xf27e616aa54d8464
+737, 0x3f271661d1cd7426
+738, 0x43a69dbee7502c78
+739, 0x8066fcea6df059a1
+740, 0x3c10f19409bdc993
+741, 0x6ba6f43fb21f23e0
+742, 0x9e182d70a5bccf09
+743, 0x1520783d2a63a199
+744, 0xba1dcc0c70b9cace
+745, 0x1009e1e9b1032d8
+746, 0xf632f6a95fb0315
+747, 0x48e711c7114cbfff
+748, 0xef281dcec67debf7
+749, 0x33789894d6abf59b
+750, 0x6c8e541fffbe7f9c
+751, 0x85417f13b08e0a88
+752, 0x9a581e36d589608f
+753, 0x461dca50b1befd35
+754, 0x5a3231680dde6462
+755, 0xcc57acf729780b97
+756, 0x50301efef62e1054
+757, 0x675d042cd4f6bbc9
+758, 0x1652fdd3794384c9
+759, 0x1c93bbeeb763cd4d
+760, 0x44b7240c4b105242
+761, 0x4c6af2a1b606ccfb
+762, 0x18fc43ece2ec1a40
+763, 0x859a5511aeae8acb
+764, 0x2f56826f1996ad2f
+765, 0xa8e95ce8bb363bdf
+766, 0xf4da396054e50e4b
+767, 0x5493865e9895883c
+768, 0x768e4c8b332ac0e3
+769, 0x32195d2aa583fca5
+770, 0xf2f353f21266bc15
+771, 0x43cddf1d021307d
+772, 0x6031e3aa30300e4a
+773, 0x4f1298469ac6088f
+774, 0x4b4d450bafac574e
+775, 0x23e1cf9c0582a22b
+776, 0x2e9036980db49cd0
+777, 0xe4e228b113c411b2
+778, 0x8bddcdb82b51706
+779, 0xd2a7ea8288593629
+780, 0x67fe90e98fdda61
+781, 0x7b63494dba95717b
+782, 0x105625904510d782
+783, 0xdf4aa2242454e50a
+784, 0x32541d6cd7d6c7e3
+785, 0x5661fb432591cf3b
+786, 0xce920a5ed047bce7
+787, 0xed4178a3c96eea8f
+788, 0xe378cd996e39863b
+789, 0x169e1fdc8e2b05e1
+790, 0xaee1812ef7149a96
+791, 0x648571c7453d12c5
+792, 0xb7b6bc9328573c43
+793, 0xe7fb969078e270d7
+794, 0xdfc2b1b8985f6e6f
+795, 0x862b6527ee39a1aa
+796, 0x1ee329aea91d7882
+797, 0x20d25324f2fe704
+798, 0xbfcc47401fc3bbfd
+799, 0x1515cdc8d48b2904
+800, 0xbd6eefe86284261c
+801, 0x9b1f28e3b35f22ee
+802, 0x842a29d35e5aecda
+803, 0xf2346109ad370765
+804, 0x24d68add5a71afd9
+805, 0x4a691421613d91e2
+806, 0x60e3058b3c244051
+807, 0x79194905cdaa5de8
+808, 0xe0e2df35c01e8987
+809, 0xe29b78beffbb5e4a
+810, 0xcdcdbc020218c19e
+811, 0x5ae0af8c16feae43
+812, 0x8109292feeaf14fa
+813, 0x34113f7508dfa521
+814, 0xc062ac163f56730a
+815, 0xf1660e66ec6d4c4c
+816, 0x5966c55f60151c80
+817, 0x3865ae8ec934b17
+818, 0x472a7314afb055ec
+819, 0x7a24277309a44a44
+820, 0x556e02dd35d38baa
+821, 0x9849611a1bc96ec1
+822, 0xd176f5d5a8eb0843
+823, 0x44db12ec60510030
+824, 0x272e3a06a0030078
+825, 0x7c4764dbefc075ea
+826, 0x910712f3735c1183
+827, 0xd49a2da74ae7aff6
+828, 0xcf9b3e6e8f776d71
+829, 0x27789fe3ec481a02
+830, 0x86659f82c6b5912b
+831, 0xe044b3dbf339158c
+832, 0x99d81f6bb62a37b0
+833, 0x5f5830c246fada9a
+834, 0xe68abab1eeb432cb
+835, 0x49c5c5ace04e104
+836, 0x1ac3871b3fc6771b
+837, 0x773b39f32d070652
+838, 0x9c4138c2ae58b1f3
+839, 0xac41c63d7452ac60
+840, 0x9248826b245359e1
+841, 0x99bba1c7a64f1670
+842, 0xe0dc99ff4ebb92f2
+843, 0x113638652740f87c
+844, 0xebf51e94da88cfc
+845, 0x5441c344b81b2585
+846, 0xe1e69e0bc2de652a
+847, 0xe9ab6d64ae42ed1e
+848, 0x879af8730e305f31
+849, 0x36b9ad912c7e00d6
+850, 0x83ef5e9fca853886
+851, 0xda54d48bb20ea974
+852, 0x32c6d93aefa92aa2
+853, 0x4e887b2c3391847d
+854, 0x50966e815f42b1b8
+855, 0x53411ac087832837
+856, 0x46f64fef79df4f29
+857, 0xb34aae3924cd272c
+858, 0xf5ad455869a0adbe
+859, 0x8351ded7144edac8
+860, 0xeb558af089677494
+861, 0x36ed71d69293a8d6
+862, 0x659f90bf5431b254
+863, 0x53349102b7519949
+864, 0x3db83e20b1713610
+865, 0x6d63f96090556254
+866, 0x4cc0467e8f45c645
+867, 0xb8840c4bd5cd4091
+868, 0xbd381463cc93d584
+869, 0x203410d878c2066d
+870, 0x2ebea06213cf71c8
+871, 0x598e8fb75e3fceb4
+872, 0xdcca41ceba0fce02
+873, 0x61bf69212b56aae5
+874, 0x97eed7f70c9114fa
+875, 0xf46f37a8b7a063f9
+876, 0x66c8f4ffe5bd6efa
+877, 0xe43fd6efda2d4e32
+878, 0x12d6c799e5ad01de
+879, 0x9ac83e7f8b709360
+880, 0xbbb7bb3c1957513d
+881, 0x7f87c08d4b3796b0
+882, 0x9a7d1d74b6aa4a5c
+883, 0xa4314530ff741b6f
+884, 0x99a80c6b6f15fca8
+885, 0xd2fec81d6d5fc3ce
+886, 0x15a98be1cc40cea
+887, 0x98693eb7719366f3
+888, 0x36ccdc2a9e9d4de8
+889, 0x3c8208f63d77df25
+890, 0xca2e376e2343df6
+891, 0xcc9b17cbb54420c6
+892, 0x8724c44a64d7dcb8
+893, 0x9d00c6949ff33869
+894, 0xf4f8e584d2699372
+895, 0x88f4748cdd5a2d53
+896, 0xe215072a1205bc6d
+897, 0x190934fe6d740442
+898, 0x7fac5c0ab2af106d
+899, 0x1b86633a0bd84fa1
+900, 0x1293e54318492dfb
+901, 0x433324fd390f34b9
+902, 0x4c5eb2c67a44643b
+903, 0x59a6e281c388b0dd
+904, 0xe78e03f9c44623b7
+905, 0x91307a93c768fc3d
+906, 0xde8867b004d8e3ff
+907, 0xdf52c3f57b7c5862
+908, 0x993f3e1d10358a92
+909, 0x9ccb10bc3e18662d
+910, 0x45093ce48a114c73
+911, 0xd59d05979d26330a
+912, 0x417c0e03300119a9
+913, 0x1c336500f90cde81
+914, 0x1c8ccd29ead9b85b
+915, 0xb76baf3e55d4d950
+916, 0x133ad6196c75fd7e
+917, 0x34200b0cde7ed560
+918, 0x9c7c3dacb213c8d9
+919, 0xd97563c4fd9bf1b6
+920, 0x5d910e871835b6cb
+921, 0x7d46c4733a16bdf9
+922, 0xe41d73194ddc87b2
+923, 0x7d3d8a0855a465a9
+924, 0x70c2a8b5d3f90c0f
+925, 0x9e7565ca5dccfe12
+926, 0x2c0acb4577aa51b1
+927, 0x3d2cd211145b79c7
+928, 0x15a7b17aa6da7732
+929, 0xab44a3730c27d780
+930, 0xf008bd6c802bde3a
+931, 0x82ed86ddf3619f77
+932, 0xaabe982ab15c49f9
+933, 0x9bcad8fa6d8e58a4
+934, 0x8f39ed8243718aa1
+935, 0xe9489340e03e3cb6
+936, 0xc722314f5eefb8d0
+937, 0x870e8869a436df59
+938, 0x4dae75b8087a8204
+939, 0xe1d790f6ec6e425b
+940, 0xafd39ea1b1d0ed09
+941, 0xdf2c99e464ddf08f
+942, 0x74936d859ab9644d
+943, 0x3871302164250e73
+944, 0x764b68921e911886
+945, 0x2a1d024b26bb9d66
+946, 0x797fba43918e75b4
+947, 0x62ec6d24ccca335b
+948, 0xf4bd8b951762b520
+949, 0x9d450dede9119397
+950, 0x5393a26d10f8c124
+951, 0x6b74769392896b57
+952, 0x7f61dbcc0e328581
+953, 0x64e1df3884d0d94
+954, 0xba77dcdf23738c37
+955, 0xf8e288bc0a177475
+956, 0x4a8abfd1702ecb7d
+957, 0x53f22886694736a7
+958, 0x8fc982597ced3e3
+959, 0x1bc46090f820fff7
+960, 0x8bd31f965d02229f
+961, 0x65cd0cb29996ee53
+962, 0x702e0f4fcf8c2e9f
+963, 0x293b77bff307a9a0
+964, 0x125a986b8b305788
+965, 0x416b0eea428ebf3c
+966, 0xeac85421ab0e8469
+967, 0x7f5496095019aa68
+968, 0x1a96d7afbc708e0
+969, 0xb91262e6766e01e1
+970, 0xd0a549cc4ccc6954
+971, 0x75a9a073f50c8a0d
+972, 0xae275d2c1c6cd23c
+973, 0xcf159b5ec5d28fd4
+974, 0x75d0838ce9b92b
+975, 0xd4eddcee6dc4677f
+976, 0x6a0a8ad5df6b75b8
+977, 0x6f3fd0ef0f13ecc4
+978, 0xb75a5826c1a8f8a8
+979, 0xd47098bbc7943766
+980, 0x3d4ddd62d5f23dd1
+981, 0x760a904e4583841c
+982, 0x2afeb5022b4cf1f
+983, 0x66d5f653729f0a13
+984, 0x9a6a5ab62980d30f
+985, 0xc332f5643bbf8d5b
+986, 0x848fb702e4056a90
+987, 0xa057beaf3f9e8c5f
+988, 0x6cc603e4560a6c6a
+989, 0xec761811a7b23211
+990, 0xb14aa4090a82aaa5
+991, 0xe29d9d028a5b2dbb
+992, 0x5564e53738d68f97
+993, 0xfabca36542eaaf3b
+994, 0xb9912fcb782020a2
+995, 0xe865e01b349284fd
+996, 0x540b5ff11c5f9274
+997, 0x3463f64e1e7451dc
+998, 0xe15d3e2f33b735f8
+999, 0xf5433336eadef6e
diff --git a/lib/python3.11/site-packages/numpy/random/tests/data/sfc64-testset-2.csv b/lib/python3.11/site-packages/numpy/random/tests/data/sfc64-testset-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..70aebd5d539256e0abd453bb45828b691c6abdf0
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/data/sfc64-testset-2.csv
@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0x91959e5fb96a6332
+1, 0x3c1dd8a25a7e9f21
+2, 0x657bdffc99798d9e
+3, 0x1a04de320b19e022
+4, 0x65b92af0e5f3c61c
+5, 0x9c84070ce8f743c0
+6, 0xbb10e573693cdb25
+7, 0xd65ea9e76b37fb6b
+8, 0x503efd0e76c8ae66
+9, 0xd711dcd04c26d0f
+10, 0x12f53f435814ac8c
+11, 0xb392cd402cfc82bd
+12, 0x461764550e06c889
+13, 0x716a48b3514e6979
+14, 0xdd0a322213c18ad7
+15, 0x6673a8ca0a05c4d7
+16, 0x2992ef333437f844
+17, 0xc4aaf7e8240b2aad
+18, 0x6ab0a1af1f41474f
+19, 0xb0bae400c226941d
+20, 0xe5f80c2eeeab48c6
+21, 0x3832c6a93a4024bf
+22, 0x280bd824fabe8368
+23, 0x66b626228321e5ff
+24, 0xe0bdfba5325a307e
+25, 0x3a5f65c6ef254e05
+26, 0x99ea12503cb02f94
+27, 0x5d01fd2db77d420b
+28, 0x6959bf5f36b2368d
+29, 0xd856e30c62b5f5be
+30, 0xe33233e1d8140e66
+31, 0xb78be619d415fa8d
+32, 0x4f943bb2cc63d3b
+33, 0x9b1460b290952d81
+34, 0x19205d794826740e
+35, 0x64617bd9d7a6a1ff
+36, 0x30442124b55ea76a
+37, 0xebbbc3b29d0333fc
+38, 0x39235a0fe359751c
+39, 0xf9629768891121aa
+40, 0x32052f53f366e05a
+41, 0x60cc5b412c925bc8
+42, 0xf8b7ecda1c0e5a9
+43, 0x195f036e170a2568
+44, 0xfe06d0381a9ca782
+45, 0x919d89e8b88eebbf
+46, 0xa47fb30148cf0d43
+47, 0x5c983e99d5f9fd56
+48, 0xe7492cdb6a1d42cd
+49, 0xf9cfe5c865b0cfd8
+50, 0x35b653367bbc3b99
+51, 0xb1d92f6f4d4e440b
+52, 0x737e1d5bd87ed9c0
+53, 0x7a880ca1498f8e17
+54, 0x687dae8494f9a3f7
+55, 0x6bae1989f441d5d7
+56, 0x71ad3fa5a9195c2e
+57, 0x16b3969779f5d03
+58, 0xd1bce2ac973f15b3
+59, 0xa114b1ee2ce0dcdd
+60, 0x270d75c11eb1b8d5
+61, 0xc48ffa087c0a7bc
+62, 0xaaf9dc48cda9848d
+63, 0x8111cf10ef6e584d
+64, 0x6736df6af40ee6f4
+65, 0x1a1a111682fbf98d
+66, 0xeb217658e1cb3b5d
+67, 0xcaf58a8b79de9dec
+68, 0x25d0ffd63c88d7a1
+69, 0x4c498cd871b7f176
+70, 0x4069a6156eb0cf3c
+71, 0xdf012f12edcdd867
+72, 0x7734c0ac8edb1689
+73, 0xed6960ac53dbc245
+74, 0x305e20da8868c661
+75, 0x5f0c7a3719956f95
+76, 0x66842bbe3b28895
+77, 0xb608bc9a31eac410
+78, 0xfcb17d5529503abd
+79, 0x829ae5cbc29b92ee
+80, 0x17f2f0027bc24f3a
+81, 0x435926c33d8f44cc
+82, 0x3ab899327098dbec
+83, 0xaf78573b27f8ead8
+84, 0xa8b334fabcf8dc60
+85, 0xcdf3b366a6a303db
+86, 0x8da9379dd62b34c8
+87, 0xb0ba511955f264a7
+88, 0x9d72e21a644f961d
+89, 0xfac28382e2e7e710
+90, 0xd457065f048410aa
+91, 0x1cae57d952563969
+92, 0x5a160a6223253e03
+93, 0x2c45df736d73c8bd
+94, 0x7f651ebc6ad9cec5
+95, 0x77a6be96c7d2e7e7
+96, 0x1721fb1dbfd6546a
+97, 0xf73f433ecff3c997
+98, 0xed1e80f680965bfe
+99, 0x6705ad67a3003b30
+100, 0xac21134efcadb9f7
+101, 0x4d2ba0a91d456ac
+102, 0x59da7b59434eb52b
+103, 0x26c1d070fd414b5f
+104, 0xed7079ddfce83d9a
+105, 0x9277d21f88e0fb7a
+106, 0xfae16b9a8d53d282
+107, 0xb08a0e2e405fdf7d
+108, 0x2ea20df44229d6ec
+109, 0x80e4634cd3612825
+110, 0xbe62e8aeba8f8a1a
+111, 0x4981209769c190fb
+112, 0xcec96ef14c7e1f65
+113, 0x73fe4457b47e7b53
+114, 0x1d66300677315c31
+115, 0xe26821290498c4cc
+116, 0xf6110248fd8fb1c5
+117, 0x30fd7fe32dbd8be3
+118, 0x534ec9b910a2bd72
+119, 0x8f9bfe878bbf7382
+120, 0x4f4eb5295c0c2193
+121, 0xdeb22f03a913be9e
+122, 0x40f716f8e2a8886c
+123, 0xc65007d0e386cdb1
+124, 0x9bdd26d92b143a14
+125, 0xf644b0b77ea44625
+126, 0x75f5a53f6b01993a
+127, 0xfe803e347bf41010
+128, 0x594bff5fa17bc360
+129, 0x3551edfb450373c7
+130, 0x898f9dad433615db
+131, 0x923d2406daa26d49
+132, 0x99e07faccbc33426
+133, 0x7389f9ff4470f807
+134, 0xdc2a25957c6df90b
+135, 0x33c6d8965ef3053f
+136, 0x51a8f07e838f1ab
+137, 0x91c5db369380274f
+138, 0xc37de65ac56b207e
+139, 0xfcc6d2375dde7f14
+140, 0xa4e6418bff505958
+141, 0x4b8b9f78e46953c4
+142, 0x255ab2e0f93cf278
+143, 0xdf650717af3d96ef
+144, 0x2caa21cba3aae2b2
+145, 0xce7e46c6f393daa4
+146, 0x1d5b3573f9997ac7
+147, 0x5280c556e850847d
+148, 0x32edc31bef920ad7
+149, 0xefaa6b0b08cf2c6
+150, 0x5151c99d97b111c5
+151, 0x35ccf4bf53d17590
+152, 0xa210d7bd8697b385
+153, 0xa9419f95738fbe61
+154, 0xdeccf93a1a4fdc90
+155, 0xd0ea3365b18e7a05
+156, 0x84122df6dcd31b9a
+157, 0x33040a2125cea5f5
+158, 0xfe18306a862f6d86
+159, 0xdb97c8392e5c4457
+160, 0xc3e0fa735e80e422
+161, 0x7d106ff36467a0c1
+162, 0xb9825eecc720a76d
+163, 0x7fefc6f771647081
+164, 0xf5df3f5b3977bf13
+165, 0x18fb22736d36f1e0
+166, 0xadc4637b4953abfc
+167, 0x174e66d3e17974bd
+168, 0xf1614c51df4db5db
+169, 0x6664ecde5717b293
+170, 0xd5bc5b6839265c26
+171, 0xf6ca9ce1af3f1832
+172, 0xca696789a9d506ea
+173, 0x7399c246c8f9d53
+174, 0xadf49049626417e2
+175, 0xbcd84af37d09ab91
+176, 0xbb41c177f3a3fa45
+177, 0x592becc814d55302
+178, 0xa88b4e65f6cfe5f7
+179, 0xa0a55e34ff879426
+180, 0x3c2ea6aa725b42b7
+181, 0x65ac4a407b1f9521
+182, 0xde63d53f7e88b556
+183, 0x18bc76696d015f40
+184, 0xd1363f2cd4c116a8
+185, 0x2fe859be19a48e4a
+186, 0x83d6099b1415e656
+187, 0x43f2cbc1a4ee6410
+188, 0xb2eca3d3421c533d
+189, 0xc52b98ea3f031f5d
+190, 0xfe57eb01da07e9d1
+191, 0xf9377883537a6031
+192, 0x364030c05dac7add
+193, 0x6815cb06b35d4404
+194, 0xceae2d4ce31894be
+195, 0xc602bcdf6062bf6a
+196, 0xc8e4bd8dcc6062e3
+197, 0x9c29e87b92a1a791
+198, 0x41e626b871ca9651
+199, 0x325c3d1fb8efbcd8
+200, 0x7dbbacf8e3419fb3
+201, 0x3602e72516bb7319
+202, 0x537a008ebd94d24b
+203, 0xda7714fc9d4d161d
+204, 0x1c8c73700e1b621b
+205, 0x2749b80937d6c939
+206, 0x76ee6abac5b14d33
+207, 0xf18d1e92cb6a8b5c
+208, 0x6ce9579d9291c721
+209, 0x60523c745a40e58
+210, 0x637f837fcc901757
+211, 0x2ff71b19661dc5b3
+212, 0x393ab586326ad16f
+213, 0xa0970ea30fe742b7
+214, 0x570222d7f27fe5ae
+215, 0x3b5806d43fd38629
+216, 0x129a0ad7420180c5
+217, 0x1c4726355778d52c
+218, 0x7c1459cf77656499
+219, 0xfe038a0932132069
+220, 0x4c4cc317a937483a
+221, 0xa333d24067e926ba
+222, 0x401d9b6ab37f6ef2
+223, 0x87ad0e491ebe4a2a
+224, 0xfc02f312e72d121d
+225, 0xfde715b3b99767b2
+226, 0xd111c342ba521c92
+227, 0x83b221b10879c617
+228, 0x6a1bf5c01fdf4277
+229, 0x166bfc0c3f5892ee
+230, 0x4608d556d7c57856
+231, 0x8d786857c95ece49
+232, 0x2d357445a1aca4ac
+233, 0x79620dae28ecd796
+234, 0x90e715dc0f2201c4
+235, 0x173b68b4c9f4b665
+236, 0x4e14d040ebac4eef
+237, 0xbd25960b4b892e
+238, 0x911a199db6f1989d
+239, 0xfe822d7c601fd2e0
+240, 0x9b4c1d58d8223a69
+241, 0x907c1891283843b0
+242, 0xf4868bf54061c4b2
+243, 0x17f8cd1fc24efd85
+244, 0xd44253f9af14c3aa
+245, 0x16d0da0cb911d43c
+246, 0x3c6a46615828e79a
+247, 0x498591c1138e11a5
+248, 0xcc0f26336d0d6141
+249, 0x4d3ebc873212309a
+250, 0x16bad7792d5c2c6a
+251, 0x474215a80b2bbd11
+252, 0x7159848abd8492fc
+253, 0x359341c50973685f
+254, 0x27512ee7bf784a4a
+255, 0x45228ea080f70447
+256, 0x880cab616500d50e
+257, 0x12fae93f9830d56e
+258, 0x6744ee64348d9acd
+259, 0x484dada28cd2a828
+260, 0x98491d0729e41863
+261, 0x2f15aac43c2863b0
+262, 0x5727a34d77a1da0f
+263, 0xa435cebef6a62eed
+264, 0xd211697d57b053b0
+265, 0x65aa757b68bd557
+266, 0xe3a1b7a2d8a3e06a
+267, 0x2adf64e67252a7a9
+268, 0xadadcb75cadee276
+269, 0x7934bc57ac8d97bf
+270, 0xccff0d0f412e0606
+271, 0x101a82aa3e8f3db9
+272, 0xb0f2498094b4575c
+273, 0xba2561d9ef26ed8a
+274, 0xfbcd1268fc3febe1
+275, 0x9aa10bb19eb152e0
+276, 0xf496217a601a6d72
+277, 0xe4be1e4f2fa91363
+278, 0x473a602bf3dd68eb
+279, 0xfe8ed2a48c26f4b5
+280, 0x20e94b1a00159476
+281, 0x93e1cb1c6af86ec7
+282, 0x4fcba3898f7442ba
+283, 0x5150c3a3d94891df
+284, 0x91cfce6c85b033ea
+285, 0x625e8a832a806491
+286, 0x28c97ba72e3ec0b2
+287, 0x8e172de217c71ea1
+288, 0x926b80216c732639
+289, 0x28b19431a649ae3d
+290, 0x57c039a6e95a3795
+291, 0xfbc354182fe52718
+292, 0x819dfd7c7d534cef
+293, 0xabb4093a619ed44f
+294, 0xe785b7ac6f656745
+295, 0xb647b4588b2f942f
+296, 0x64cf870a14c72d27
+297, 0x6d4a4a2a0ba9b37e
+298, 0x78bfb0427d7ce6b0
+299, 0x8dcc72b8bfc79ac6
+300, 0x1c14d915d5e76c99
+301, 0xaf48ddea6f096d79
+302, 0x51b39b67aa130d8
+303, 0x1aeeb39d4def06de
+304, 0xd678092ffedfdd27
+305, 0x8f54787f325111d3
+306, 0xf2ca2e827beaa6bc
+307, 0x339d134099e98545
+308, 0x1f6a8a7b33942e43
+309, 0x952c8065dbef669a
+310, 0xe066aeb6690147f7
+311, 0xed25aa92cf58ebb6
+312, 0x7601edce215ef521
+313, 0xed1c5b396abd9434
+314, 0x4fd1e407535de9d5
+315, 0xccc8315a0d4d1441
+316, 0x85753e250bb86976
+317, 0xf232e469378761c3
+318, 0x81d691b8e9aef3c6
+319, 0x224a2f9cab0ad0e
+320, 0x978f3d3e50007f4e
+321, 0xd3713e6a6c0cbe60
+322, 0xcce8f1eadd41f80d
+323, 0x34bda028a97d469
+324, 0x90e242fdf0f59183
+325, 0x4d749754fbc5f092
+326, 0x4399f5b7851cc87b
+327, 0xcb921a5f25f6c5d7
+328, 0x120bf5d0162101
+329, 0x1304cc2aa352735a
+330, 0xf7236c5d0d5d417b
+331, 0xc31b320fc1654306
+332, 0xb468c6b23f3fb4e7
+333, 0xb5985b5bfaca4166
+334, 0x898285a1cd2f8375
+335, 0xa13493da372aa7c9
+336, 0x15c80c09c12634e7
+337, 0x9b765c5cc9d438bd
+338, 0xee7da816a9201dcb
+339, 0x92e269f73b5a248e
+340, 0xa8086c5de81400ce
+341, 0xe0053901853d42be
+342, 0x821df32c012f433e
+343, 0x17a6d69ca37387c7
+344, 0x2b10044bfba3501f
+345, 0x8dfd262afc2e8515
+346, 0xd68c2c7b60226371
+347, 0xe81ac114e4416774
+348, 0x5896d60061ebc471
+349, 0xa996e3147811dbd1
+350, 0xa819c7b80ecb3661
+351, 0x982ad71b38afbc01
+352, 0xab152b65aa17b7fe
+353, 0x4582bc282ef187ef
+354, 0xab5a17fe8d9bc669
+355, 0x83664fa9cb0284b7
+356, 0x234c4b0091968f52
+357, 0x8ab5f51805688d37
+358, 0xe9e11186e0c53eda
+359, 0x10df37ef1de2eccf
+360, 0x780f1b0d52db968f
+361, 0x50bd4ff292872cd5
+362, 0x51e681c265f5ad0
+363, 0x842c49660a527566
+364, 0x6e56ee026e9eda87
+365, 0x4cf39e40d8c80393
+366, 0x13e466df371f7e1f
+367, 0xf2ce1799f38e028e
+368, 0x833c8db7adc6ff0e
+369, 0xc6e189abc2ec98f
+370, 0xafebb3721283fec5
+371, 0xb49bc1eb5cc17bdc
+372, 0xf1d02e818f5e4488
+373, 0xe5e9d5b41a1dd815
+374, 0xce8aca6573b1bfe5
+375, 0x9b0a5d70e268b1d5
+376, 0xf3c0503a8358f4de
+377, 0x2681605dd755669d
+378, 0xea265ca7601efc70
+379, 0xa93747f0a159439f
+380, 0x62a86ede78a23e50
+381, 0xac8a18935c3d063c
+382, 0x729c0a298f5059f5
+383, 0xbbf195e5b54399f4
+384, 0x38aa9d551f968900
+385, 0x3b3e700c58778caa
+386, 0x68e6e33c4443957a
+387, 0x7c56fc13eb269815
+388, 0xaf7daca39711804a
+389, 0x50fde6d10f9544b3
+390, 0xf3d37159f6f6c03d
+391, 0x82d298f5c1a71685
+392, 0x478661ac54c5002c
+393, 0x6053768e1a324ae0
+394, 0xde8fb4a7e56707ea
+395, 0xaa2809301faa8cf4
+396, 0x690a8d49fedd0722
+397, 0xe17c481b9c217de9
+398, 0x60d1d8a2b57288e3
+399, 0x149adfaadc6b0886
+400, 0xa3c18b6eb79cd5fa
+401, 0x5774e3a091af5f58
+402, 0x2acca57ff30e5712
+403, 0x94454d67367c4b0c
+404, 0x581b2985ac2df5ca
+405, 0x71618e50744f3e70
+406, 0x270a7f3bd9a94ae6
+407, 0x3ef81af9bb36cd7b
+408, 0x8a4a2592875254aa
+409, 0x704ac6086fbb414a
+410, 0xda774d5d3f57414d
+411, 0xe20d3358b918ae9e
+412, 0x934a6b9f7b91e247
+413, 0xf91649cde87ec42c
+414, 0x248cec5f9b6ced30
+415, 0x56791809fd8d64ba
+416, 0xf502b2765c1395f
+417, 0x6b04ec973d75aa7f
+418, 0xb0339f2794bb26f
+419, 0x4c524636efbaea49
+420, 0x6bbf3876e9738748
+421, 0xf686524e754e9e24
+422, 0x8dafa05a42d19cd3
+423, 0xc5f069ab2434008e
+424, 0x4fd64cc713cba76
+425, 0xdbf93450c881ed5f
+426, 0x492e278ebabb59a2
+427, 0x993fddfde4542642
+428, 0xecde68a72c8d4e52
+429, 0xe0760b3074c311fd
+430, 0x68dc0e7e06528707
+431, 0x52b50edf49c0fdc7
+432, 0xb2bd4185c138f412
+433, 0x431496d7e1d86f3
+434, 0xa4e605b037e26c44
+435, 0x58236ae1f0aca2b5
+436, 0x26c72c420fc314d8
+437, 0x20134e982ab99a2b
+438, 0x544b59b8b211374b
+439, 0x1301c42f3a14d993
+440, 0x52a6ea740f763b0f
+441, 0xf209d70c2bebf119
+442, 0xac66a4ebc2aa1be
+443, 0x683713ed35878788
+444, 0x2b5578acec06b80c
+445, 0x86428efa11c45b36
+446, 0xb49010adb17d291e
+447, 0x73b686bd8664b6be
+448, 0x6d28ebf57b6884cc
+449, 0x9712091230ff58d9
+450, 0xc9c91f74c38b286
+451, 0x776310ac41dc008e
+452, 0x2f3739df0bf6a88e
+453, 0x5792dc62b94db675
+454, 0x5715910d024b06af
+455, 0xeb1dd745458da08
+456, 0xfce7b07ccfa851a7
+457, 0xc305f1e983ac368
+458, 0x485aa9519ac00bb0
+459, 0xa5354f6589fb0ea0
+460, 0x32fee02dfdbf4454
+461, 0x4d1ddc304bbefaaa
+462, 0x789a270a1737e57e
+463, 0x9f3072f4b1ed8156
+464, 0x4de3c00e89058120
+465, 0xb00a02529e0a86fa
+466, 0x539f6f0edd845d9a
+467, 0x85e578fe15a8c001
+468, 0xa12c8e1a72cce7d8
+469, 0xc6908abbc2b1828
+470, 0xcf70090774cbb38c
+471, 0x3b636a6977b45d4a
+472, 0xf0a731b220680b57
+473, 0x18973929f51443a8
+474, 0xe93e1fbe7eadabe
+475, 0x8233730f0a6dfa02
+476, 0x66e50b6919b0ab74
+477, 0xb1aba87c97fd08a2
+478, 0xd4dffc1fbc117ad6
+479, 0x6f7fa65724b96e6a
+480, 0x4bd5800dee92e0fa
+481, 0xe18a959db6256da
+482, 0xe53a291bc66df487
+483, 0xb7ec306a08651806
+484, 0x1847a6b80d2821e1
+485, 0xda50391283b14d39
+486, 0xacc4d3cd7cceb97a
+487, 0x57f70185165b7bc6
+488, 0x302b6d597c3aaba7
+489, 0xa47f32d037eab51e
+490, 0xe1509b4408abc559
+491, 0x4f30a1d7c2934157
+492, 0x2ad03e6c60b650b2
+493, 0x334d9c337b0a9064
+494, 0xc7f442821e7aac12
+495, 0xbcdeb09298694cdd
+496, 0xe42402389f8f0fb4
+497, 0xe5de56af539df727
+498, 0x7017f9b2101ee240
+499, 0x1ee5e68d5b10001d
+500, 0x436229051836387a
+501, 0xcd532d6d6ec38fb7
+502, 0x30a66606fdf38272
+503, 0xfdaa2ab9cf798496
+504, 0x4277b4adec70e7df
+505, 0x72cfc30256e0eaef
+506, 0x3c3359fd9bd34917
+507, 0xb7aa89598856efb0
+508, 0xf72226f8bf299ef5
+509, 0x258c499275a4356f
+510, 0x999a56bfc7f20d76
+511, 0x2b3e7432e20c18b
+512, 0x2d1251332f760cb5
+513, 0x7420e0eea62157c5
+514, 0xe85c895aa27cec3d
+515, 0x27a0545c7020d57c
+516, 0xc68638a65b4fff0d
+517, 0xfda473983a4ea747
+518, 0xd19fe65fb4c06062
+519, 0x6b1374e050ee15e4
+520, 0x80065ecd49bc4bef
+521, 0x4ee655954bc838de
+522, 0xe8fb777504a72299
+523, 0x86b652ea70f4bdde
+524, 0xcdc9e0fbde7e4f33
+525, 0x352c0a50cd3ac56
+526, 0x4b8605d368be75dc
+527, 0x1ac9ea8129efbc37
+528, 0x470325faa99f39c5
+529, 0x25dd7ef9adccf7a1
+530, 0x5ae2c7a03e965816
+531, 0xf733d2df59dacc7d
+532, 0xa05bbf0a8a1a7a70
+533, 0xe8aa3f102846ef5f
+534, 0xc9b85ec49ae71789
+535, 0xb904c14ed1cb1936
+536, 0x5ae618230b5f0444
+537, 0x97987fe47b5d7467
+538, 0xabb3aca8865ca761
+539, 0x38bfdf29d4508228
+540, 0x353654f408353330
+541, 0xeb7e92930ae4ef0d
+542, 0xec50f1a7ca526b96
+543, 0xd5e2dc08b5697544
+544, 0x24c7fd69d5ec32df
+545, 0x6f7e1095568b8620
+546, 0x6ed9c16ca13b3c8
+547, 0xe676ef460002130f
+548, 0xa3a01a3992c4b430
+549, 0xe2130406c3b1f202
+550, 0xa8f7263e2aedcd20
+551, 0xc45d71ef2e35f507
+552, 0x37155594021da7ba
+553, 0x22dc94f19de73159
+554, 0x7969fc6bffc5443f
+555, 0x97def7e44faa6bfe
+556, 0x8b940f5e8931d71f
+557, 0xd95b1dd3f1a3fdd5
+558, 0x1c83bfdca615701a
+559, 0xb7fcb56279ceca6b
+560, 0xd84f8950f20dcd0
+561, 0xb03343698de3cbe0
+562, 0xf64565d448d71f71
+563, 0xda52b4676e0ae662
+564, 0xda39c2c05b4ffb91
+565, 0xb35e2560421f6a85
+566, 0x1a7b108d48ac3646
+567, 0xc4e264dc390d79ed
+568, 0xa10727dfd9813256
+569, 0x40d23154e720e4f7
+570, 0xd9fa7cd7e313e119
+571, 0xcbf29107859e6013
+572, 0xc357338553d940b7
+573, 0x2641b7ab0bdfcbaa
+574, 0xd12f2b6060533ae7
+575, 0xd0435aa626411c56
+576, 0x44af4a488a9cec72
+577, 0xb934232ea8fa5696
+578, 0x760a8b12072b572d
+579, 0xfab18f9942cfa9b3
+580, 0x5676834c1fe84d16
+581, 0x9c54e4fddb353236
+582, 0xab49edfc9551f293
+583, 0x567f1fb45a871d
+584, 0x32a967c873998834
+585, 0x99240aad380ef8d1
+586, 0x7f66cbd432859a64
+587, 0x4cdc8a4658166822
+588, 0x984e3984a5766492
+589, 0xa3b2d0a3d64d3d94
+590, 0x177f667172f2affc
+591, 0xb1a90607a73a303f
+592, 0xe600b6c36427f878
+593, 0xf758f9834cb7f466
+594, 0x8ee9fce4a3f36449
+595, 0xcb8f11533e7da347
+596, 0xe7cf647794dabd7c
+597, 0xc9d92cfe6110806
+598, 0xea1335fa9145a1ec
+599, 0xbc6c29821d094552
+600, 0x37b9d6a858cc8bc3
+601, 0xf24e4c694929893e
+602, 0x55d025ce2d7d0004
+603, 0xccdc69acccf4267b
+604, 0xc491c04340c222eb
+605, 0xba50f75ecec9befb
+606, 0x1ec7bd85b8fe3bb9
+607, 0xe4de66498c59ae8a
+608, 0x38aa9e912712c889
+609, 0xcee0e43c5cc31566
+610, 0x72b69aa708fc7ed
+611, 0xdff70b7f6fa96679
+612, 0xd6d71d82112aadc3
+613, 0x365177892cb78531
+614, 0xa54852b39de4f72c
+615, 0x11dd5832bf16dd59
+616, 0x248a0f3369c97097
+617, 0xa14cec0260e26792
+618, 0x3517616ff142bed1
+619, 0x9b693ad39dab7636
+620, 0x739dff825e994434
+621, 0x67711e7356098c9
+622, 0xa81f8515d2fdf458
+623, 0xdac2908113fe568e
+624, 0xe99944ebc6e2806a
+625, 0x671728ca5b030975
+626, 0xfdad20edb2b4a789
+627, 0xedc6e466bd0369d2
+628, 0x88b5d469821f7e1b
+629, 0x2eabf94049a522a5
+630, 0x247794b7a2f5a8e3
+631, 0x278942bdbe02c649
+632, 0xbe5a9a9196ab99c1
+633, 0x75955060866da1b5
+634, 0xdedcfa149273c0b5
+635, 0xdbeb7a57758f3867
+636, 0x7b9053347a2c8d5a
+637, 0xa059b3f2eed338a5
+638, 0x59401a46ded3b79f
+639, 0x38044ba56a6d19fb
+640, 0x72c7221b4e77e779
+641, 0x526df3491a3a34da
+642, 0xc3b31184ba16c0c2
+643, 0xd94c7144488624af
+644, 0xcf966ee4dc373f91
+645, 0x62049e65dd416266
+646, 0x7c2adccb925bf8f
+647, 0xd5fa5c22ed4ef8e1
+648, 0xd00134ebd11f2cd1
+649, 0xfbdf81767bed3634
+650, 0x62e8cc8ff66b6e26
+651, 0x3a72d6bcd4f2dcf7
+652, 0xf1cd45b1b46a86ed
+653, 0x1271f98e0938bb9a
+654, 0x82e6927e83dc31fa
+655, 0x7b9b0e0acb67b92d
+656, 0x6df503e397b2e701
+657, 0x93888f6fb561e0c3
+658, 0x393fb6069a40291
+659, 0x967a7d894cc0754d
+660, 0x6e298996ad866333
+661, 0x5ff3cf5559d6ab46
+662, 0xd0d70508c40349f5
+663, 0xc64c66c0dd426b33
+664, 0x8fea340ee35c64dd
+665, 0xf9cd381eb3060005
+666, 0xfcc37c2799fc0b11
+667, 0x6a37c91d65b489fa
+668, 0x57231000fa0a0c9d
+669, 0x55f6e292c6703f9a
+670, 0xd0508ffbfa55a7a6
+671, 0x885db543276bdac8
+672, 0xc26dbe6a26b0e704
+673, 0x21f884874ebd709e
+674, 0x711f0b6c8f732220
+675, 0x354d0a361eaee195
+676, 0x721344d8d30b006a
+677, 0xa0e090a0d3a56f07
+678, 0x16b3d5d823a4952b
+679, 0x59d7874bc9eae7b6
+680, 0x9bbb32710076455f
+681, 0xd4fb22242ffabafd
+682, 0xe1d4ac6770be1d89
+683, 0xb259cedebc73dc8a
+684, 0x35faaa3b4246ab69
+685, 0x5d26addefdaee89
+686, 0x8e7ec350da0f3545
+687, 0xd0f316eed9f8fc79
+688, 0x98b2a52c9bf291b2
+689, 0xe4d294a8aca6a314
+690, 0x25bd554e6aa7673c
+691, 0xcfde5dcba5be2a6c
+692, 0xb5e01fb48d2d2107
+693, 0xe1caf28948028536
+694, 0xd434aa0a26f3ee9b
+695, 0xd17723381641b8f6
+696, 0xfe73bd1f3f3768a2
+697, 0x1cc6b1abd08d67e9
+698, 0x247e328371a28de0
+699, 0x502e7942e5a9104a
+700, 0x6a030fd242eb4502
+701, 0xa2ffe02744014ce8
+702, 0x59290763b18fe04e
+703, 0xcf14241564271436
+704, 0xb0fb73c3c1503aff
+705, 0x94e27c622f82137a
+706, 0x747a5b406ac3e1f0
+707, 0x9a914e96a732031d
+708, 0x59f68c6c8f078835
+709, 0x809d012c73eb4724
+710, 0x5b3c3b73e1b37d74
+711, 0xdde60ef3ba49cdf7
+712, 0x87a14e1f9c761986
+713, 0x4109b960604522af
+714, 0x122d0e1ed0eb6bb9
+715, 0xadc0d29e80bfe33
+716, 0xa25b1b44f5fc8e4e
+717, 0xbab85d8a9b793f20
+718, 0x825f4cbced0e7d1e
+719, 0x2d6ae8807acb37ea
+720, 0x8234420adce2e39
+721, 0x4a8ad4da6b804807
+722, 0x1e19f9bc215e5245
+723, 0x1d6f4848a916dd5e
+724, 0x9ac40dfcdc2d39cc
+725, 0x9f3524e3086155ec
+726, 0x861fffc43124b2ef
+727, 0xe640e3b756396372
+728, 0x41cb0f0c5e149669
+729, 0xe0bd37e1192e4205
+730, 0x62917d3858f4ce47
+731, 0xa36e7eb4d855820a
+732, 0x204b90255a3bf724
+733, 0x66ee83a0175535bc
+734, 0x2c14ce7c6b0c1423
+735, 0x85d9495fa514f70d
+736, 0x5a4fe45ead874dbc
+737, 0xe72248dcb8cfc863
+738, 0xfc21ff2932ed98cd
+739, 0xcbba1edd735b5cad
+740, 0x91ddc32809679bf5
+741, 0x192cdf2c7631ea1f
+742, 0xbbc451ddf2ea286f
+743, 0xad9e80cae2397a64
+744, 0x6918f0119b95d0e5
+745, 0xa40379017a27d70a
+746, 0x1aaeddb600e61e1
+747, 0x15afd93cbd7adda9
+748, 0x156719bc2b757ff4
+749, 0x13d9a59e2b2df49d
+750, 0x9a490986eaddf0a
+751, 0xef9a350f0b3eb6b4
+752, 0x5de7f6295ba4fa4d
+753, 0x7f37fd087c3fdb49
+754, 0xa9fe3749d6f3f209
+755, 0x50912ac036d9bfb
+756, 0x982cb4d726a441f8
+757, 0x8ca8d8af59b872d0
+758, 0x7f8adfb0ceeade8a
+759, 0xdad390ec742be44
+760, 0xa637944d0045be5b
+761, 0x3569a3b3af807061
+762, 0x9599da8eae14511d
+763, 0xc333e8d19589b01a
+764, 0xfb9b524a20b571e1
+765, 0xbd9dc8b37ce5c3e1
+766, 0x142333005fa389ac
+767, 0x1368bc37cd5bcce1
+768, 0x16094907ad6ecf73
+769, 0xb32c90dbba4c1130
+770, 0x82761d97c1747dd0
+771, 0x599f9f267ae3444d
+772, 0x79ad3382994852e1
+773, 0x2511f06d9ef06e54
+774, 0xb35e6ab7d5bbddae
+775, 0xfca9fa83a2988732
+776, 0x7d4350f0394ac3ba
+777, 0xa52a9527bb176ea3
+778, 0xb49fa0ceb2aa8353
+779, 0x1f62e504d1468cc0
+780, 0xe1a77bfccce6efc3
+781, 0x776cdff4dc0d6797
+782, 0x56612e39b652c1f2
+783, 0x5f096a29294eda04
+784, 0x7978abc3aabd8b23
+785, 0x79dd875e0485b979
+786, 0x8a98aa4d5735d778
+787, 0xcca43940f69d2388
+788, 0xb2d4b156f144f93a
+789, 0xbd528a676e9a862
+790, 0x2a394939c8e7ec5e
+791, 0xb1da900c6efe4abc
+792, 0x9869af479de4c034
+793, 0x78dbdfb88ac7c1db
+794, 0x18cb169143088041
+795, 0xe69e5461c51a3e13
+796, 0x5389fa16ea98183c
+797, 0xed7c80d1be1ea520
+798, 0x87246fc359758ced
+799, 0xab323eba95fae4ed
+800, 0xbc4c0dde7f8a1828
+801, 0xdb739f7955610b1a
+802, 0xecd8c68c3434cc
+803, 0x138c2eb88c477f44
+804, 0x28a65f96727aae41
+805, 0xdee879f2cf5629d
+806, 0x684f0c90ef20070f
+807, 0xa24a819ef5621800
+808, 0x8d0054f870e4fdcb
+809, 0x99e8c6e695b600b
+810, 0x50b705245891f7c3
+811, 0xc02eed3a6e58e51a
+812, 0x443d64e95443606c
+813, 0xca24959cfbd2d120
+814, 0xe072609ea48815bc
+815, 0xbcc715026590315b
+816, 0x3e76df24d7aa5938
+817, 0xd8ff04940d9b79ae
+818, 0x54474ce790059bcd
+819, 0x278390dd6aa70e81
+820, 0xf4df619fe35414e4
+821, 0x757d71270264e615
+822, 0x1e8a373699c11b23
+823, 0xef68c82046e67dd6
+824, 0xe280006599972620
+825, 0x234e095183b0f4d6
+826, 0xe3b7560ed9839749
+827, 0xcd5ec4086572332e
+828, 0xc41c0d4aaa279108
+829, 0x4b9cd6126bc16a6d
+830, 0x4a7252734f3e3dd0
+831, 0xb3132df156cc103a
+832, 0xf9e4abbf7b64464a
+833, 0xf936df27fb3c47b7
+834, 0x9142960873f6d71a
+835, 0x4ba6aa3235cdb10d
+836, 0x3237a2e765ba7766
+837, 0xd62f0b94c8e99e54
+838, 0x26b682f90a3ae41b
+839, 0x40ad5e82072b6f81
+840, 0xd0198101f5484000
+841, 0xe4fac60ba11c332
+842, 0x472d0b0a95ef9d38
+843, 0x8512557aec5a3d8f
+844, 0xef83169d3efd4de9
+845, 0x53fe89283e7a7676
+846, 0x2f50933053d69fc4
+847, 0x76f5e4362e2e53a2
+848, 0x8676fdccce28874a
+849, 0x2737764c1fb1f821
+850, 0x4a6f70afc066ab55
+851, 0x27f8e151e310fca4
+852, 0xd606960ccbe85161
+853, 0xcce51d7ddd270a32
+854, 0xb4235999794875c2
+855, 0x580084e358e884
+856, 0x2159d5e6dc8586d7
+857, 0x87bd54d8599b3ba4
+858, 0x3e9ade6a2181664
+859, 0x5e6e140406d97623
+860, 0x511545d5aa0080a2
+861, 0xf49d78ed219aac57
+862, 0xbece1f9c90b8ea87
+863, 0x1c741cac36a2c514
+864, 0x7453c141047db967
+865, 0xd751832a5037eba2
+866, 0x71370a3f30ada1f7
+867, 0x7c01cf2dcb408631
+868, 0x1052a4fbdccc0fa1
+869, 0x13d525c9df3fb6c
+870, 0xa3aa8dbfee760c55
+871, 0xc0288d200f5155cf
+872, 0x79f4bcd12af567c3
+873, 0x8160d163bb548755
+874, 0x5cf2995fb69fd2df
+875, 0xcc98ed01396639df
+876, 0xad95f1d9cfc8256e
+877, 0xa3df27d9fbdbfb9d
+878, 0x83e5f5dda4d52929
+879, 0x9adc05043009f55b
+880, 0xdfe8329dfde1c001
+881, 0x9980ccdd5298e6a2
+882, 0x636a7bd134f6ef56
+883, 0xef5ff780c4be6ba4
+884, 0x290d71dc77a56d16
+885, 0x6d65db9ff58de1e6
+886, 0x944b063b3805a696
+887, 0xce468ca2cce33008
+888, 0x5ba1ccb840f80f48
+889, 0x28ddce36fc9ad268
+890, 0x4f77ef254d507a21
+891, 0xce9b4057fadf3ab
+892, 0xb518bc68298730e6
+893, 0xd2eb5b8e2ec665b0
+894, 0xe1583303a4f87344
+895, 0x9d5a0df4fbe1bed5
+896, 0x2ba9bc03ec8cfd07
+897, 0x479ed880a96ca669
+898, 0xcedf96338324771a
+899, 0x312f4fc2da41ffaa
+900, 0xa0eb9cf23b5e1ed8
+901, 0xf8f88f975dc3f539
+902, 0x4a37e185d0e96e0f
+903, 0xf829654a5c0b46f9
+904, 0x3909cca7a7f8c7fb
+905, 0x4c2e1d66ceb45105
+906, 0xaffaa19e1db8af87
+907, 0x9ec498246bd18c76
+908, 0x21d51558edc089da
+909, 0xe8984112cd1b1561
+910, 0x7de1d2cf54b0c0e1
+911, 0xa06729aed50bfb9d
+912, 0xcf19f733e5db19e1
+913, 0x70edf2624ab777cd
+914, 0x46685becad10e078
+915, 0x825e0f6add46785
+916, 0x66d4af3b15f70de4
+917, 0xc676614b0666b21
+918, 0x282a916c864f5cb7
+919, 0x2707283a3f512167
+920, 0x37ff3afda7461623
+921, 0xc767eb1205e4ca86
+922, 0x46b359aecc4ea25b
+923, 0x67fbbb797a16dbb1
+924, 0x64fd4ba57122290e
+925, 0x8acc2a8ae59d8fac
+926, 0x64a49298599acc67
+927, 0xedf00de67177ce30
+928, 0x1ea9d8d7e76d2d2c
+929, 0x363fcac323f70eb2
+930, 0x19e6e3ec8a9712eb
+931, 0xca541e96b0961f09
+932, 0x4d8fd34c2822ec46
+933, 0x2fdd56a50b32f705
+934, 0xaac2fcf251e3fd3
+935, 0xb0c600299e57045c
+936, 0xd951ec589e909e38
+937, 0x4dc8414390cae508
+938, 0x537ef9d5e2321344
+939, 0xa57bc21fd31aa2dc
+940, 0xa3a60df564183750
+941, 0xbe69a5ce2e369fb6
+942, 0x7744601f4c053ec8
+943, 0x3838452af42f2612
+944, 0xd4f0dad7115a54e9
+945, 0x629cf68d8009a624
+946, 0x2211c8fa34cb98cb
+947, 0x8040b19e2213db83
+948, 0xb2a86d3ba2384fd
+949, 0x4b85cec4f93f0dab
+950, 0xc8d212d21ea6845d
+951, 0x5b271a03a4fe2be0
+952, 0xff4f671319ad8434
+953, 0x8e615a919d5afa96
+954, 0xea7f47c53161160a
+955, 0x33273930b13c6efc
+956, 0x98eedda27fb59c3c
+957, 0x188dc5e92e939677
+958, 0x9dbd0fa0911430f1
+959, 0x5b3dcf3fa75dfd2b
+960, 0x3f03846febdb275d
+961, 0x20cc24faea9e9cf6
+962, 0x854f3ac66199ff5d
+963, 0x31169ac99d341e6f
+964, 0xa85daed3c0bc1bbe
+965, 0x64633711e71ba5dd
+966, 0x530e79978dc73334
+967, 0x636f2ee6e20aef13
+968, 0xf6220f8b6d9a58fb
+969, 0x425db8fa32141a7b
+970, 0xac7c210f4b02be95
+971, 0x5fe8cfbe197a7754
+972, 0xfff7d40c79420ea
+973, 0x5f8bab9ef4697b77
+974, 0xaf6fe54e45b23fe8
+975, 0xce79456ccc70bbce
+976, 0x645ef680f48f1c00
+977, 0xa4dfac46e2028595
+978, 0x6bece4c41effc5df
+979, 0xd316df886442641f
+980, 0xa4f6ff994edd2a6
+981, 0x30281ae3cc49abe4
+982, 0x39acb7b663dea974
+983, 0x5e8829b01a7c06fb
+984, 0x87bdb08cf027f13e
+985, 0xdfa5ede784e802f6
+986, 0x46d03d55711c38cc
+987, 0xa55a961fc9788306
+988, 0xbf09ded495a2e57a
+989, 0xcd601b29a639cc16
+990, 0x2193ce026bfd1085
+991, 0x25ba27f3f225be13
+992, 0x6f685be82f64f2fe
+993, 0xec8454108229c450
+994, 0x6e79d8d205447a44
+995, 0x9ed7b6a96b9ccd68
+996, 0xae7134b3b7f8ee37
+997, 0x66963de0e5ebcc02
+998, 0x29c8dcd0d17c423f
+999, 0xfb8482c827eb90bc
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_direct.py b/lib/python3.11/site-packages/numpy/random/tests/test_direct.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa2ae866beeb7c36a585eaf9eb04df31a2f2a6c3
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_direct.py
@@ -0,0 +1,518 @@
+import os
+from os.path import join
+import sys
+
+import numpy as np
+from numpy.testing import (assert_equal, assert_allclose, assert_array_equal,
+                           assert_raises)
+import pytest
+
+from numpy.random import (
+    Generator, MT19937, PCG64, PCG64DXSM, Philox, RandomState, SeedSequence,
+    SFC64, default_rng
+)
+from numpy.random._common import interface
+
+try:
+    import cffi  # noqa: F401
+
+    MISSING_CFFI = False
+except ImportError:
+    MISSING_CFFI = True
+
+try:
+    import ctypes  # noqa: F401
+
+    MISSING_CTYPES = False
+except ImportError:
+    MISSING_CTYPES = False
+
+if sys.flags.optimize > 1:
+    # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1
+    # cffi cannot succeed
+    MISSING_CFFI = True
+
+
+pwd = os.path.dirname(os.path.abspath(__file__))
+
+
+def assert_state_equal(actual, target):
+    for key in actual:
+        if isinstance(actual[key], dict):
+            assert_state_equal(actual[key], target[key])
+        elif isinstance(actual[key], np.ndarray):
+            assert_array_equal(actual[key], target[key])
+        else:
+            assert actual[key] == target[key]
+
+
+def uint32_to_float32(u):
+    return ((u >> np.uint32(8)) * (1.0 / 2**24)).astype(np.float32)
+
+
+def uniform32_from_uint64(x):
+    x = np.uint64(x)
+    upper = np.array(x >> np.uint64(32), dtype=np.uint32)
+    lower = np.uint64(0xffffffff)
+    lower = np.array(x & lower, dtype=np.uint32)
+    joined = np.column_stack([lower, upper]).ravel()
+    return uint32_to_float32(joined)
+
+
+def uniform32_from_uint53(x):
+    x = np.uint64(x) >> np.uint64(16)
+    x = np.uint32(x & np.uint64(0xffffffff))
+    return uint32_to_float32(x)
+
+
+def uniform32_from_uint32(x):
+    return uint32_to_float32(x)
+
+
+def uniform32_from_uint(x, bits):
+    if bits == 64:
+        return uniform32_from_uint64(x)
+    elif bits == 53:
+        return uniform32_from_uint53(x)
+    elif bits == 32:
+        return uniform32_from_uint32(x)
+    else:
+        raise NotImplementedError
+
+
+def uniform_from_uint(x, bits):
+    if bits in (64, 63, 53):
+        return uniform_from_uint64(x)
+    elif bits == 32:
+        return uniform_from_uint32(x)
+
+
+def uniform_from_uint64(x):
+    return (x >> np.uint64(11)) * (1.0 / 9007199254740992.0)
+
+
+def uniform_from_uint32(x):
+    out = np.empty(len(x) // 2)
+    for i in range(0, len(x), 2):
+        a = x[i] >> 5
+        b = x[i + 1] >> 6
+        out[i // 2] = (a * 67108864.0 + b) / 9007199254740992.0
+    return out
+
+
+def uniform_from_dsfmt(x):
+    return x.view(np.double) - 1.0
+
+
+def gauss_from_uint(x, n, bits):
+    if bits in (64, 63):
+        doubles = uniform_from_uint64(x)
+    elif bits == 32:
+        doubles = uniform_from_uint32(x)
+    else:  # bits == 'dsfmt'
+        doubles = uniform_from_dsfmt(x)
+    gauss = []
+    loc = 0
+    x1 = x2 = 0.0
+    while len(gauss) < n:
+        r2 = 2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * doubles[loc] - 1.0
+            x2 = 2.0 * doubles[loc + 1] - 1.0
+            r2 = x1 * x1 + x2 * x2
+            loc += 2
+
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        gauss.append(f * x2)
+        gauss.append(f * x1)
+
+    return gauss[:n]
+
+
+def test_seedsequence():
+    from numpy.random.bit_generator import (ISeedSequence,
+                                            ISpawnableSeedSequence,
+                                            SeedlessSeedSequence)
+
+    s1 = SeedSequence(range(10), spawn_key=(1, 2), pool_size=6)
+    s1.spawn(10)
+    s2 = SeedSequence(**s1.state)
+    assert_equal(s1.state, s2.state)
+    assert_equal(s1.n_children_spawned, s2.n_children_spawned)
+
+    # The interfaces cannot be instantiated themselves.
+    assert_raises(TypeError, ISeedSequence)
+    assert_raises(TypeError, ISpawnableSeedSequence)
+    dummy = SeedlessSeedSequence()
+    assert_raises(NotImplementedError, dummy.generate_state, 10)
+    assert len(dummy.spawn(10)) == 10
+
+
+def test_generator_spawning():
+    """ Test spawning new generators and bit_generators directly.
+    """
+    rng = np.random.default_rng()
+    seq = rng.bit_generator.seed_seq
+    new_ss = seq.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5)]
+    assert [c.spawn_key for c in new_ss] == expected_keys
+
+    new_bgs = rng.bit_generator.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(5, 10)]
+    assert [bg.seed_seq.spawn_key for bg in new_bgs] == expected_keys
+
+    new_rngs = rng.spawn(5)
+    expected_keys = [seq.spawn_key + (i,) for i in range(10, 15)]
+    found_keys = [rng.bit_generator.seed_seq.spawn_key for rng in new_rngs]
+    assert found_keys == expected_keys
+
+    # Sanity check that streams are actually different:
+    assert new_rngs[0].uniform() != new_rngs[1].uniform()
+
+
+def test_non_spawnable():
+    from numpy.random.bit_generator import ISeedSequence
+
+    class FakeSeedSequence:
+        def generate_state(self, n_words, dtype=np.uint32):
+            return np.zeros(n_words, dtype=dtype)
+
+    ISeedSequence.register(FakeSeedSequence)
+
+    rng = np.random.default_rng(FakeSeedSequence())
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.spawn(5)
+
+    with pytest.raises(TypeError, match="The underlying SeedSequence"):
+        rng.bit_generator.spawn(5)
+
+
+class Base:
+    dtype = np.uint64
+    data2 = data1 = {}
+
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.seed_error_type = TypeError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = []
+
+    @classmethod
+    def _read_csv(cls, filename):
+        with open(filename) as csv:
+            seed = csv.readline()
+            seed = seed.split(',')
+            seed = [int(s.strip(), 0) for s in seed[1:]]
+            data = []
+            for line in csv:
+                data.append(int(line.split(',')[-1].strip(), 0))
+            return {'seed': seed, 'data': np.array(data, dtype=cls.dtype)}
+
+    def test_raw(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw(1000)
+        assert_equal(uints, self.data1['data'])
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw()
+        assert_equal(uints, self.data1['data'][0])
+
+        bit_generator = self.bit_generator(*self.data2['seed'])
+        uints = bit_generator.random_raw(1000)
+        assert_equal(uints, self.data2['data'])
+
+    def test_random_raw(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        uints = bit_generator.random_raw(output=False)
+        assert uints is None
+        uints = bit_generator.random_raw(1000, output=False)
+        assert uints is None
+
+    def test_gauss_inv(self):
+        n = 25
+        rs = RandomState(self.bit_generator(*self.data1['seed']))
+        gauss = rs.standard_normal(n)
+        assert_allclose(gauss,
+                        gauss_from_uint(self.data1['data'], n, self.bits))
+
+        rs = RandomState(self.bit_generator(*self.data2['seed']))
+        gauss = rs.standard_normal(25)
+        assert_allclose(gauss,
+                        gauss_from_uint(self.data2['data'], n, self.bits))
+
+    def test_uniform_double(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        vals = uniform_from_uint(self.data1['data'], self.bits)
+        uniforms = rs.random(len(vals))
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float64)
+
+        rs = Generator(self.bit_generator(*self.data2['seed']))
+        vals = uniform_from_uint(self.data2['data'], self.bits)
+        uniforms = rs.random(len(vals))
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float64)
+
+    def test_uniform_float(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        vals = uniform32_from_uint(self.data1['data'], self.bits)
+        uniforms = rs.random(len(vals), dtype=np.float32)
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float32)
+
+        rs = Generator(self.bit_generator(*self.data2['seed']))
+        vals = uniform32_from_uint(self.data2['data'], self.bits)
+        uniforms = rs.random(len(vals), dtype=np.float32)
+        assert_allclose(uniforms, vals)
+        assert_equal(uniforms.dtype, np.float32)
+
+    def test_repr(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        assert 'Generator' in repr(rs)
+        assert f'{id(rs):#x}'.upper().replace('X', 'x') in repr(rs)
+
+    def test_str(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        assert 'Generator' in str(rs)
+        assert str(self.bit_generator.__name__) in str(rs)
+        assert f'{id(rs):#x}'.upper().replace('X', 'x') not in str(rs)
+
+    def test_pickle(self):
+        import pickle
+
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        bitgen_pkl = pickle.dumps(bit_generator)
+        reloaded = pickle.loads(bitgen_pkl)
+        reloaded_state = reloaded.state
+        assert_array_equal(Generator(bit_generator).standard_normal(1000),
+                           Generator(reloaded).standard_normal(1000))
+        assert bit_generator is not reloaded
+        assert_state_equal(reloaded_state, state)
+
+        ss = SeedSequence(100)
+        aa = pickle.loads(pickle.dumps(ss))
+        assert_equal(ss.state, aa.state)
+
+    def test_invalid_state_type(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        with pytest.raises(TypeError):
+            bit_generator.state = {'1'}
+
+    def test_invalid_state_value(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        state['bit_generator'] = 'otherBitGenerator'
+        with pytest.raises(ValueError):
+            bit_generator.state = state
+
+    def test_invalid_init_type(self):
+        bit_generator = self.bit_generator
+        for st in self.invalid_init_types:
+            with pytest.raises(TypeError):
+                bit_generator(*st)
+
+    def test_invalid_init_values(self):
+        bit_generator = self.bit_generator
+        for st in self.invalid_init_values:
+            with pytest.raises((ValueError, OverflowError)):
+                bit_generator(*st)
+
+    def test_benchmark(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        bit_generator._benchmark(1)
+        bit_generator._benchmark(1, 'double')
+        with pytest.raises(ValueError):
+            bit_generator._benchmark(1, 'int32')
+
+    @pytest.mark.skipif(MISSING_CFFI, reason='cffi not available')
+    def test_cffi(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        cffi_interface = bit_generator.cffi
+        assert isinstance(cffi_interface, interface)
+        other_cffi_interface = bit_generator.cffi
+        assert other_cffi_interface is cffi_interface
+
+    @pytest.mark.skipif(MISSING_CTYPES, reason='ctypes not available')
+    def test_ctypes(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        ctypes_interface = bit_generator.ctypes
+        assert isinstance(ctypes_interface, interface)
+        other_ctypes_interface = bit_generator.ctypes
+        assert other_ctypes_interface is ctypes_interface
+
+    def test_getstate(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        alt_state = bit_generator.__getstate__()
+        assert_state_equal(state, alt_state)
+
+
+class TestPhilox(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = Philox
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(
+            join(pwd, './data/philox-testset-1.csv'))
+        cls.data2 = cls._read_csv(
+            join(pwd, './data/philox-testset-2.csv'))
+        cls.seed_error_type = TypeError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = [(1, None, 1), (-1,), (None, None, 2 ** 257 + 1)]
+
+    def test_set_key(self):
+        bit_generator = self.bit_generator(*self.data1['seed'])
+        state = bit_generator.state
+        keyed = self.bit_generator(counter=state['state']['counter'],
+                                   key=state['state']['key'])
+        assert_state_equal(bit_generator.state, keyed.state)
+
+
+class TestPCG64(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(join(pwd, './data/pcg64-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/pcg64-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_advance_symmetry(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        state = rs.bit_generator.state
+        step = -0x9e3779b97f4a7c150000000000000000
+        rs.bit_generator.advance(step)
+        val_neg = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(2**128 + step)
+        val_pos = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(10 * 2**128 + step)
+        val_big = rs.integers(10)
+        assert val_neg == val_pos
+        assert val_big == val_pos
+
+    def test_advange_large(self):
+        rs = Generator(self.bit_generator(38219308213743))
+        pcg = rs.bit_generator
+        state = pcg.state["state"]
+        initial_state = 287608843259529770491897792873167516365
+        assert state["state"] == initial_state
+        pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))
+        state = pcg.state["state"]
+        advanced_state = 135275564607035429730177404003164635391
+        assert state["state"] == advanced_state
+
+
+class TestPCG64DXSM(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64DXSM
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/pcg64dxsm-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+    def test_advance_symmetry(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        state = rs.bit_generator.state
+        step = -0x9e3779b97f4a7c150000000000000000
+        rs.bit_generator.advance(step)
+        val_neg = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(2**128 + step)
+        val_pos = rs.integers(10)
+        rs.bit_generator.state = state
+        rs.bit_generator.advance(10 * 2**128 + step)
+        val_big = rs.integers(10)
+        assert val_neg == val_pos
+        assert val_big == val_pos
+
+    def test_advange_large(self):
+        rs = Generator(self.bit_generator(38219308213743))
+        pcg = rs.bit_generator
+        state = pcg.state
+        initial_state = 287608843259529770491897792873167516365
+        assert state["state"]["state"] == initial_state
+        pcg.advance(sum(2**i for i in (96, 64, 32, 16, 8, 4, 2, 1)))
+        state = pcg.state["state"]
+        advanced_state = 277778083536782149546677086420637664879
+        assert state["state"] == advanced_state
+
+
+class TestMT19937(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = MT19937
+        cls.bits = 32
+        cls.dtype = np.uint32
+        cls.data1 = cls._read_csv(join(pwd, './data/mt19937-testset-1.csv'))
+        cls.data2 = cls._read_csv(join(pwd, './data/mt19937-testset-2.csv'))
+        cls.seed_error_type = ValueError
+        cls.invalid_init_types = []
+        cls.invalid_init_values = [(-1,)]
+
+    def test_seed_float_array(self):
+        assert_raises(TypeError, self.bit_generator, np.array([np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([-np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([np.pi, -np.pi]))
+        assert_raises(TypeError, self.bit_generator, np.array([0, np.pi]))
+        assert_raises(TypeError, self.bit_generator, [np.pi])
+        assert_raises(TypeError, self.bit_generator, [0, np.pi])
+
+    def test_state_tuple(self):
+        rs = Generator(self.bit_generator(*self.data1['seed']))
+        bit_generator = rs.bit_generator
+        state = bit_generator.state
+        desired = rs.integers(2 ** 16)
+        tup = (state['bit_generator'], state['state']['key'],
+               state['state']['pos'])
+        bit_generator.state = tup
+        actual = rs.integers(2 ** 16)
+        assert_equal(actual, desired)
+        tup = tup + (0, 0.0)
+        bit_generator.state = tup
+        actual = rs.integers(2 ** 16)
+        assert_equal(actual, desired)
+
+
+class TestSFC64(Base):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = SFC64
+        cls.bits = 64
+        cls.dtype = np.uint64
+        cls.data1 = cls._read_csv(
+            join(pwd, './data/sfc64-testset-1.csv'))
+        cls.data2 = cls._read_csv(
+            join(pwd, './data/sfc64-testset-2.csv'))
+        cls.seed_error_type = (ValueError, TypeError)
+        cls.invalid_init_types = [(3.2,), ([None],), (1, None)]
+        cls.invalid_init_values = [(-1,)]
+
+
+class TestDefaultRNG:
+    def test_seed(self):
+        for args in [(), (None,), (1234,), ([1234, 5678],)]:
+            rg = default_rng(*args)
+            assert isinstance(rg.bit_generator, PCG64)
+
+    def test_passthrough(self):
+        bg = Philox()
+        rg = default_rng(bg)
+        assert rg.bit_generator is bg
+        rg2 = default_rng(rg)
+        assert rg2 is rg
+        assert rg2.bit_generator is bg
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_extending.py b/lib/python3.11/site-packages/numpy/random/tests/test_extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..2783d1cdd9acd183261c30e50d7031f4561fd7bd
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_extending.py
@@ -0,0 +1,118 @@
+from importlib.util import spec_from_file_location, module_from_spec
+import os
+import pathlib
+import pytest
+import shutil
+import subprocess
+import sys
+import sysconfig
+import textwrap
+import warnings
+
+import numpy as np
+from numpy.testing import IS_WASM
+
+
+try:
+    import cffi
+except ImportError:
+    cffi = None
+
+if sys.flags.optimize > 1:
+    # no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1
+    # cffi cannot succeed
+    cffi = None
+
+try:
+    with warnings.catch_warnings(record=True) as w:
+        # numba issue gh-4733
+        warnings.filterwarnings('always', '', DeprecationWarning)
+        import numba
+except (ImportError, SystemError):
+    # Certain numpy/numba versions trigger a SystemError due to a numba bug
+    numba = None
+
+try:
+    import cython
+    from Cython.Compiler.Version import version as cython_version
+except ImportError:
+    cython = None
+else:
+    from numpy._utils import _pep440
+    # Cython 0.29.30 is required for Python 3.11 and there are
+    # other fixes in the 0.29 series that are needed even for earlier
+    # Python versions.
+    # Note: keep in sync with the one in pyproject.toml
+    required_version = '0.29.35'
+    if _pep440.parse(cython_version) < _pep440.Version(required_version):
+        # too old or wrong cython, skip the test
+        cython = None
+
+
+@pytest.mark.skipif(
+        sys.platform == "win32" and sys.maxsize < 2**32,
+        reason="Failing in 32-bit Windows wheel build job, skip for now"
+)
+@pytest.mark.skipif(IS_WASM, reason="Can't start subprocess")
+@pytest.mark.skipif(cython is None, reason="requires cython")
+@pytest.mark.slow
+def test_cython(tmp_path):
+    import glob
+    # build the examples in a temporary directory
+    srcdir = os.path.join(os.path.dirname(__file__), '..')
+    shutil.copytree(srcdir, tmp_path / 'random')
+    build_dir = tmp_path / 'random' / '_examples' / 'cython'
+    target_dir = build_dir / "build"
+    os.makedirs(target_dir, exist_ok=True)
+    if sys.platform == "win32":
+        subprocess.check_call(["meson", "setup",
+                               "--buildtype=release", 
+                               "--vsenv", str(build_dir)],
+                              cwd=target_dir,
+                              )
+    else:
+        subprocess.check_call(["meson", "setup", str(build_dir)],
+                              cwd=target_dir
+                              )
+    subprocess.check_call(["meson", "compile", "-vv"], cwd=target_dir)
+
+    # gh-16162: make sure numpy's __init__.pxd was used for cython
+    # not really part of this test, but it is a convenient place to check
+
+    g = glob.glob(str(target_dir / "*" / "extending.pyx.c"))
+    with open(g[0]) as fid:
+        txt_to_find = 'NumPy API declarations from "numpy/__init__'
+        for i, line in enumerate(fid):
+            if txt_to_find in line:
+                break
+        else:
+            assert False, ("Could not find '{}' in C file, "
+                           "wrong pxd used".format(txt_to_find))
+    # import without adding the directory to sys.path
+    suffix = sysconfig.get_config_var('EXT_SUFFIX')
+
+    def load(modname):
+        so = (target_dir / modname).with_suffix(suffix)
+        spec = spec_from_file_location(modname, so)
+        mod = module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        return mod
+
+    # test that the module can be imported
+    load("extending")
+    load("extending_cpp")
+    # actually test the cython c-extension
+    extending_distributions = load("extending_distributions")
+    from numpy.random import PCG64
+    values = extending_distributions.uniforms_ex(PCG64(0), 10, 'd')
+    assert values.shape == (10,)
+    assert values.dtype == np.float64
+
+@pytest.mark.skipif(numba is None or cffi is None,
+                    reason="requires numba and cffi")
+def test_numba():
+    from numpy.random._examples.numba import extending  # noqa: F401
+
+@pytest.mark.skipif(cffi is None, reason="requires cffi")
+def test_cffi():
+    from numpy.random._examples.cffi import extending  # noqa: F401
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_generator_mt19937.py b/lib/python3.11/site-packages/numpy/random/tests/test_generator_mt19937.py
new file mode 100644
index 0000000000000000000000000000000000000000..e744f5ba611b177b10034cada76f0dd28f63cf16
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_generator_mt19937.py
@@ -0,0 +1,2746 @@
+import sys
+import hashlib
+
+import pytest
+
+import numpy as np
+from numpy.linalg import LinAlgError
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_allclose,
+    assert_warns, assert_no_warnings, assert_array_equal,
+    assert_array_almost_equal, suppress_warnings, IS_WASM)
+
+from numpy.random import Generator, MT19937, SeedSequence, RandomState
+
+random = Generator(MT19937())
+
+JUMP_TEST_DATA = [
+    {
+        "seed": 0,
+        "steps": 10,
+        "initial": {"key_sha256": "bb1636883c2707b51c5b7fc26c6927af4430f2e0785a8c7bc886337f919f9edf", "pos": 9},
+        "jumped": {"key_sha256": "ff682ac12bb140f2d72fba8d3506cf4e46817a0db27aae1683867629031d8d55", "pos": 598},
+    },
+    {
+        "seed":384908324,
+        "steps":312,
+        "initial": {"key_sha256": "16b791a1e04886ccbbb4d448d6ff791267dc458ae599475d08d5cced29d11614", "pos": 311},
+        "jumped": {"key_sha256": "a0110a2cf23b56be0feaed8f787a7fc84bef0cb5623003d75b26bdfa1c18002c", "pos": 276},
+    },
+    {
+        "seed": [839438204, 980239840, 859048019, 821],
+        "steps": 511,
+        "initial": {"key_sha256": "d306cf01314d51bd37892d874308200951a35265ede54d200f1e065004c3e9ea", "pos": 510},
+        "jumped": {"key_sha256": "0e00ab449f01a5195a83b4aee0dfbc2ce8d46466a640b92e33977d2e42f777f8", "pos": 475},
+    },
+]
+
+
+@pytest.fixture(scope='module', params=[True, False])
+def endpoint(request):
+    return request.param
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = Generator(MT19937(0))
+        assert_equal(s.integers(1000), 479)
+        s = Generator(MT19937(4294967295))
+        assert_equal(s.integers(1000), 324)
+
+    def test_array(self):
+        s = Generator(MT19937(range(10)))
+        assert_equal(s.integers(1000), 465)
+        s = Generator(MT19937(np.arange(10)))
+        assert_equal(s.integers(1000), 465)
+        s = Generator(MT19937([0]))
+        assert_equal(s.integers(1000), 479)
+        s = Generator(MT19937([4294967295]))
+        assert_equal(s.integers(1000), 324)
+
+    def test_seedsequence(self):
+        s = MT19937(SeedSequence(0))
+        assert_equal(s.random_raw(1), 2058676884)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, MT19937, -0.5)
+        assert_raises(ValueError, MT19937, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned integer
+        assert_raises(TypeError, MT19937, [-0.5])
+        assert_raises(ValueError, MT19937, [-1])
+        assert_raises(ValueError, MT19937, [1, -2, 4294967296])
+
+    def test_noninstantized_bitgen(self):
+        assert_raises(ValueError, Generator, MT19937)
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.integers(-5, -1) < -1)
+        x = random.integers(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, random.multinomial, 1, p,
+                      float(1))
+
+    def test_invalid_prob(self):
+        assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
+        assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])
+
+    def test_invalid_n(self):
+        assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])
+        assert_raises(ValueError, random.multinomial, [-1] * 10, [0.8, 0.2])
+
+    def test_p_non_contiguous(self):
+        p = np.arange(15.)
+        p /= np.sum(p[1::3])
+        pvals = p[1::3]
+        random = Generator(MT19937(1432985819))
+        non_contig = random.multinomial(100, pvals=pvals)
+        random = Generator(MT19937(1432985819))
+        contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
+        assert_array_equal(non_contig, contig)
+
+    def test_multinomial_pvals_float32(self):
+        x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
+                      1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
+        pvals = x / x.sum()
+        random = Generator(MT19937(1432985819))
+        match = r"[\w\s]*pvals array is cast to 64-bit floating"
+        with pytest.raises(ValueError, match=match):
+            random.multinomial(1, pvals)
+
+
+class TestMultivariateHypergeometric:
+
+    def setup_method(self):
+        self.seed = 8675309
+
+    def test_argument_validation(self):
+        # Error cases...
+
+        # `colors` must be a 1-d sequence
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      10, 4)
+
+        # Negative nsample
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [2, 3, 4], -1)
+
+        # Negative color
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [-1, 2, 3], 2)
+
+        # nsample exceeds sum(colors)
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [2, 3, 4], 10)
+
+        # nsample exceeds sum(colors) (edge case of empty colors)
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [], 1)
+
+        # Validation errors associated with very large values in colors.
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [999999999, 101], 5, 1, 'marginals')
+
+        int64_info = np.iinfo(np.int64)
+        max_int64 = int64_info.max
+        max_int64_index = max_int64 // int64_info.dtype.itemsize
+        assert_raises(ValueError, random.multivariate_hypergeometric,
+                      [max_int64_index - 100, 101], 5, 1, 'count')
+
+    @pytest.mark.parametrize('method', ['count', 'marginals'])
+    def test_edge_cases(self, method):
+        # Set the seed, but in fact, all the results in this test are
+        # deterministic, so we don't really need this.
+        random = Generator(MT19937(self.seed))
+
+        x = random.multivariate_hypergeometric([0, 0, 0], 0, method=method)
+        assert_array_equal(x, [0, 0, 0])
+
+        x = random.multivariate_hypergeometric([], 0, method=method)
+        assert_array_equal(x, [])
+
+        x = random.multivariate_hypergeometric([], 0, size=1, method=method)
+        assert_array_equal(x, np.empty((1, 0), dtype=np.int64))
+
+        x = random.multivariate_hypergeometric([1, 2, 3], 0, method=method)
+        assert_array_equal(x, [0, 0, 0])
+
+        x = random.multivariate_hypergeometric([9, 0, 0], 3, method=method)
+        assert_array_equal(x, [3, 0, 0])
+
+        colors = [1, 1, 0, 1, 1]
+        x = random.multivariate_hypergeometric(colors, sum(colors),
+                                               method=method)
+        assert_array_equal(x, colors)
+
+        x = random.multivariate_hypergeometric([3, 4, 5], 12, size=3,
+                                               method=method)
+        assert_array_equal(x, [[3, 4, 5]]*3)
+
+    # Cases for nsample:
+    #     nsample < 10
+    #     10 <= nsample < colors.sum()/2
+    #     colors.sum()/2 < nsample < colors.sum() - 10
+    #     colors.sum() - 10 < nsample < colors.sum()
+    @pytest.mark.parametrize('nsample', [8, 25, 45, 55])
+    @pytest.mark.parametrize('method', ['count', 'marginals'])
+    @pytest.mark.parametrize('size', [5, (2, 3), 150000])
+    def test_typical_cases(self, nsample, method, size):
+        random = Generator(MT19937(self.seed))
+
+        colors = np.array([10, 5, 20, 25])
+        sample = random.multivariate_hypergeometric(colors, nsample, size,
+                                                    method=method)
+        if isinstance(size, int):
+            expected_shape = (size,) + colors.shape
+        else:
+            expected_shape = size + colors.shape
+        assert_equal(sample.shape, expected_shape)
+        assert_((sample >= 0).all())
+        assert_((sample <= colors).all())
+        assert_array_equal(sample.sum(axis=-1),
+                           np.full(size, fill_value=nsample, dtype=int))
+        if isinstance(size, int) and size >= 100000:
+            # This sample is large enough to compare its mean to
+            # the expected values.
+            assert_allclose(sample.mean(axis=0),
+                            nsample * colors / colors.sum(),
+                            rtol=1e-3, atol=0.005)
+
+    def test_repeatability1(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([3, 4, 5], 5, size=5,
+                                                    method='count')
+        expected = np.array([[2, 1, 2],
+                             [2, 1, 2],
+                             [1, 1, 3],
+                             [2, 0, 3],
+                             [2, 1, 2]])
+        assert_array_equal(sample, expected)
+
+    def test_repeatability2(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([20, 30, 50], 50,
+                                                    size=5,
+                                                    method='marginals')
+        expected = np.array([[ 9, 17, 24],
+                             [ 7, 13, 30],
+                             [ 9, 15, 26],
+                             [ 9, 17, 24],
+                             [12, 14, 24]])
+        assert_array_equal(sample, expected)
+
+    def test_repeatability3(self):
+        random = Generator(MT19937(self.seed))
+        sample = random.multivariate_hypergeometric([20, 30, 50], 12,
+                                                    size=5,
+                                                    method='marginals')
+        expected = np.array([[2, 3, 7],
+                             [5, 3, 4],
+                             [2, 5, 5],
+                             [5, 3, 4],
+                             [1, 5, 6]])
+        assert_array_equal(sample, expected)
+
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.rg = Generator(MT19937(self.seed))
+        self.bit_generator = self.rg.bit_generator
+        self.state = self.bit_generator.state
+        self.legacy_state = (self.state['bit_generator'],
+                             self.state['state']['key'],
+                             self.state['state']['pos'])
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.rg.standard_normal(size=3)
+        self.bit_generator.state = self.state
+        new = self.rg.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.rg.standard_normal()
+        state = self.bit_generator.state
+        old = self.rg.standard_normal(size=3)
+        self.bit_generator.state = state
+        new = self.rg.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.rg.negative_binomial(0.5, 0.5)
+
+
+class TestIntegers:
+    rfunc = random.integers
+
+    # valid integer/boolean types
+    itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self, endpoint):
+        assert_raises(TypeError, self.rfunc, 1, endpoint=endpoint, dtype=float)
+
+    def test_bounds_checking(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, endpoint=endpoint,
+                          dtype=dt)
+
+            assert_raises(ValueError, self.rfunc, [lbnd - 1], ubnd,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [lbnd], [ubnd + 1],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd], [lbnd],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, [0],
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [ubnd+1], [ubnd],
+                          endpoint=endpoint, dtype=dt)
+
+    def test_bounds_checking_array(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + (not endpoint)
+
+            assert_raises(ValueError, self.rfunc, [lbnd - 1] * 2, [ubnd] * 2,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [lbnd] * 2,
+                          [ubnd + 1] * 2, endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, [lbnd] * 2,
+                          endpoint=endpoint, dtype=dt)
+            assert_raises(ValueError, self.rfunc, [1] * 2, 0,
+                          endpoint=endpoint, dtype=dt)
+
+    def test_rng_zero_and_extremes(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            is_open = not endpoint
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc([tgt], tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc(tgt, [tgt + is_open], size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd) // 2
+            assert_equal(self.rfunc(tgt, tgt + is_open, size=1000,
+                                    endpoint=endpoint, dtype=dt), tgt)
+            assert_equal(self.rfunc([tgt], [tgt + is_open],
+                                    size=1000, endpoint=endpoint, dtype=dt),
+                         tgt)
+
+    def test_rng_zero_and_extremes_array(self, endpoint):
+        size = 1000
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd) // 2
+            assert_equal(self.rfunc([tgt], [tgt + 1],
+                                    size=size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, dtype=dt), tgt)
+            assert_equal(self.rfunc(
+                [tgt] * size, [tgt + 1] * size, size=size, dtype=dt), tgt)
+
+    def test_full_range(self, endpoint):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            try:
+                self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_full_range_array(self, endpoint):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            try:
+                self.rfunc([lbnd] * 2, [ubnd], endpoint=endpoint, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self, endpoint):
+        # Don't use fixed seed
+        random = Generator(MT19937())
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd - endpoint, size=2 ** 16,
+                                  endpoint=endpoint, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2 - endpoint, size=2 ** 16, endpoint=endpoint,
+                          dtype=bool)
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_scalar_array_equiv(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            size = 1000
+            random = Generator(MT19937(1234))
+            scalar = random.integers(lbnd, ubnd, size=size, endpoint=endpoint,
+                                dtype=dt)
+
+            random = Generator(MT19937(1234))
+            scalar_array = random.integers([lbnd], [ubnd], size=size,
+                                      endpoint=endpoint, dtype=dt)
+
+            random = Generator(MT19937(1234))
+            array = random.integers([lbnd] * size, [ubnd] *
+                               size, size=size, endpoint=endpoint, dtype=dt)
+            assert_array_equal(scalar, scalar_array)
+            assert_array_equal(scalar, array)
+
+    def test_repeatability(self, endpoint):
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool':   '053594a9b82d656f967c54869bc6970aa0358cf94ad469c81478459c6a90eee3',
+               'int16':  '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
+               'int32':  'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
+               'int64':  '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
+               'int8':   '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1',
+               'uint16': '54de9072b6ee9ff7f20b58329556a46a447a8a29d67db51201bf88baa6e4e5d4',
+               'uint32': 'd3a0d5efb04542b25ac712e50d21f39ac30f312a5052e9bbb1ad3baa791ac84b',
+               'uint64': '14e224389ac4580bfbdccb5697d6190b496f91227cf67df60989de3d546389b1',
+               'uint8':  '0e203226ff3fbbd1580f15da4621e5f7164d0d8d6b51696dd42d004ece2cbec1'}
+
+        for dt in self.itype[1:]:
+            random = Generator(MT19937(1234))
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
+                                 dtype=dt)
+            else:
+                val = random.integers(0, 6 - endpoint, size=1000, endpoint=endpoint,
+                                 dtype=dt).byteswap()
+
+            res = hashlib.sha256(val).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        random = Generator(MT19937(1234))
+        val = random.integers(0, 2 - endpoint, size=1000, endpoint=endpoint,
+                         dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    def test_repeatability_broadcasting(self, endpoint):
+        for dt in self.itype:
+            lbnd = 0 if dt in (bool, np.bool_) else np.iinfo(dt).min
+            ubnd = 2 if dt in (bool, np.bool_) else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            # view as little endian for hash
+            random = Generator(MT19937(1234))
+            val = random.integers(lbnd, ubnd, size=1000, endpoint=endpoint,
+                             dtype=dt)
+
+            random = Generator(MT19937(1234))
+            val_bc = random.integers([lbnd] * 1000, ubnd, endpoint=endpoint,
+                                dtype=dt)
+
+            assert_array_equal(val, val_bc)
+
+            random = Generator(MT19937(1234))
+            val_bc = random.integers([lbnd] * 1000, [ubnd] * 1000,
+                                endpoint=endpoint, dtype=dt)
+
+            assert_array_equal(val, val_bc)
+
+    @pytest.mark.parametrize(
+        'bound, expected',
+        [(2**32 - 1, np.array([517043486, 1364798665, 1733884389, 1353720612,
+                               3769704066, 1170797179, 4108474671])),
+         (2**32, np.array([517043487, 1364798666, 1733884390, 1353720613,
+                           3769704067, 1170797180, 4108474672])),
+         (2**32 + 1, np.array([517043487, 1733884390, 3769704068, 4108474673,
+                               1831631863, 1215661561, 3869512430]))]
+    )
+    def test_repeatability_32bit_boundary(self, bound, expected):
+        for size in [None, len(expected)]:
+            random = Generator(MT19937(1234))
+            x = random.integers(bound, size=size)
+            assert_equal(x, expected if size is not None else expected[0])
+
+    def test_repeatability_32bit_boundary_broadcasting(self):
+        desired = np.array([[[1622936284, 3620788691, 1659384060],
+                             [1417365545,  760222891, 1909653332],
+                             [3788118662,  660249498, 4092002593]],
+                            [[3625610153, 2979601262, 3844162757],
+                             [ 685800658,  120261497, 2694012896],
+                             [1207779440, 1586594375, 3854335050]],
+                            [[3004074748, 2310761796, 3012642217],
+                             [2067714190, 2786677879, 1363865881],
+                             [ 791663441, 1867303284, 2169727960]],
+                            [[1939603804, 1250951100,  298950036],
+                             [1040128489, 3791912209, 3317053765],
+                             [3155528714,   61360675, 2305155588]],
+                            [[ 817688762, 1335621943, 3288952434],
+                             [1770890872, 1102951817, 1957607470],
+                             [3099996017,  798043451,   48334215]]])
+        for size in [None, (5, 3, 3)]:
+            random = Generator(MT19937(12345))
+            x = random.integers([[-1], [0], [1]],
+                                [2**32 - 1, 2**32, 2**32 + 1],
+                                size=size)
+            assert_array_equal(x, desired if size is not None else desired[0])
+
+    def test_int64_uint64_broadcast_exceptions(self, endpoint):
+        configs = {np.uint64: ((0, 2**65), (-1, 2**62), (10, 9), (0, 0)),
+                   np.int64: ((0, 2**64), (-(2**64), 2**62), (10, 9), (0, 0),
+                              (-2**63-1, -2**63-1))}
+        for dtype in configs:
+            for config in configs[dtype]:
+                low, high = config
+                high = high - endpoint
+                low_a = np.array([[low]*10])
+                high_a = np.array([high] * 10)
+                assert_raises(ValueError, random.integers, low, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_a, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low, high_a,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_a, high_a,
+                              endpoint=endpoint, dtype=dtype)
+
+                low_o = np.array([[low]*10], dtype=object)
+                high_o = np.array([high] * 10, dtype=object)
+                assert_raises(ValueError, random.integers, low_o, high,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low, high_o,
+                              endpoint=endpoint, dtype=dtype)
+                assert_raises(ValueError, random.integers, low_o, high_o,
+                              endpoint=endpoint, dtype=dtype)
+
+    def test_int64_uint64_corner_case(self, endpoint):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1 - endpoint)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = random.integers(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            dt = np.bool_ if dt is bool else dt
+
+            sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            assert_equal(sample.dtype, dt)
+
+        for dt in (bool, int):
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, endpoint=endpoint, dtype=dt)
+            assert not hasattr(sample, 'dtype')
+            assert_equal(type(sample), dt)
+
+    def test_respect_dtype_array(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+            ubnd = ubnd - 1 if endpoint else ubnd
+            dt = np.bool_ if dt is bool else dt
+
+            sample = self.rfunc([lbnd], [ubnd], endpoint=endpoint, dtype=dt)
+            assert_equal(sample.dtype, dt)
+            sample = self.rfunc([lbnd] * 2, [ubnd] * 2, endpoint=endpoint,
+                                dtype=dt)
+            assert_equal(sample.dtype, dt)
+
+    def test_zero_size(self, endpoint):
+        # See gh-7203
+        for dt in self.itype:
+            sample = self.rfunc(0, 0, (3, 0, 4), endpoint=endpoint, dtype=dt)
+            assert sample.shape == (3, 0, 4)
+            assert sample.dtype == dt
+            assert self.rfunc(0, -10, 0, endpoint=endpoint,
+                              dtype=dt).shape == (0,)
+            assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape,
+                         (3, 0, 4))
+            assert_equal(random.integers(0, -10, size=0).shape, (0,))
+            assert_equal(random.integers(10, 10, size=0).shape, (0,))
+
+    def test_error_byteorder(self):
+        other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
+        with pytest.raises(ValueError):
+            random.integers(0, 200, size=10, dtype=other_byteord_dt)
+
+    # chi2max is the maximum acceptable chi-squared value.
+    @pytest.mark.slow
+    @pytest.mark.parametrize('sample_size,high,dtype,chi2max',
+        [(5000000, 5, np.int8, 125.0),          # p-value ~4.6e-25
+         (5000000, 7, np.uint8, 150.0),         # p-value ~7.7e-30
+         (10000000, 2500, np.int16, 3300.0),    # p-value ~3.0e-25
+         (50000000, 5000, np.uint16, 6500.0),   # p-value ~3.5e-25
+        ])
+    def test_integers_small_dtype_chisquared(self, sample_size, high,
+                                             dtype, chi2max):
+        # Regression test for gh-14774.
+        samples = random.integers(high, size=sample_size, dtype=dtype)
+
+        values, counts = np.unique(samples, return_counts=True)
+        expected = sample_size / high
+        chi2 = ((counts - expected)**2 / expected).sum()
+        assert chi2 < chi2max
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_integers(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(-99, 99, size=(3, 2))
+        desired = np.array([[-80, -56], [41, 37], [-83, -16]])
+        assert_array_equal(actual, desired)
+
+    def test_integers_masked(self):
+        # Test masked rejection sampling algorithm to generate array of
+        # uint32 in an interval.
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(0, 99, size=(3, 2), dtype=np.uint32)
+        desired = np.array([[9, 21], [70, 68], [8, 41]], dtype=np.uint32)
+        assert_array_equal(actual, desired)
+
+    def test_integers_closed(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.integers(-99, 99, size=(3, 2), endpoint=True)
+        desired = np.array([[-80, -56], [ 41, 38], [-83, -15]])
+        assert_array_equal(actual, desired)
+
+    def test_integers_max_int(self):
+        # Tests whether integers with closed=True can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        actual = random.integers(np.iinfo('l').max, np.iinfo('l').max,
+                                 endpoint=True)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+
+    def test_random(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random((3, 2))
+        desired = np.array([[0.096999199829214, 0.707517457682192],
+                            [0.084364834598269, 0.767731206553125],
+                            [0.665069021359413, 0.715487190596693]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.random()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_random_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random((3, 2))
+        desired = np.array([[0.0969992 , 0.70751746],
+                            [0.08436483, 0.76773121],
+                            [0.66506902, 0.71548719]])
+        assert_array_almost_equal(actual, desired, decimal=7)
+
+    def test_random_float_scalar(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.random(dtype=np.float32)
+        desired = 0.0969992
+        assert_array_almost_equal(actual, desired, decimal=7)
+
+    @pytest.mark.parametrize('dtype, uint_view_type',
+                             [(np.float32, np.uint32),
+                              (np.float64, np.uint64)])
+    def test_random_distribution_of_lsb(self, dtype, uint_view_type):
+        random = Generator(MT19937(self.seed))
+        sample = random.random(100000, dtype=dtype)
+        num_ones_in_lsb = np.count_nonzero(sample.view(uint_view_type) & 1)
+        # The probability of a 1 in the least significant bit is 0.25.
+        # With a sample size of 100000, the probability that num_ones_in_lsb
+        # is outside the following range is less than 5e-11.
+        assert 24100 < num_ones_in_lsb < 25900
+
+    def test_random_unsupported_type(self):
+        assert_raises(TypeError, random.random, dtype='int32')
+
+    def test_choice_uniform_replace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 4)
+        desired = np.array([0, 0, 2, 2], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([0, 1, 0, 1], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 3, replace=False)
+        desired = np.array([2, 0, 3], dtype=np.int64)
+        assert_array_equal(actual, desired)
+        actual = random.choice(4, 4, replace=False, shuffle=False)
+        desired = np.arange(4, dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([0, 2, 3], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['a', 'a', 'c', 'c'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_multidimensional_default_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 3)
+        desired = np.array([[0, 1], [0, 1], [4, 5]])
+        assert_array_equal(actual, desired)
+
+    def test_choice_multidimensional_custom_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.choice([[0, 1], [2, 3], [4, 5], [6, 7]], 1, axis=1)
+        desired = np.array([[0], [2], [4], [6]])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(random.choice(2, replace=True)))
+        assert_(np.isscalar(random.choice(2, replace=False)))
+        assert_(np.isscalar(random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(random.choice([1, 2], replace=True)))
+        assert_(random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
+        assert_(random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(random.choice(6, s, replace=True).shape, s)
+        assert_equal(random.choice(6, s, replace=False).shape, s)
+        assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(random.integers(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(random.integers(0, -10, size=0).shape, (0,))
+        assert_equal(random.integers(10, 10, size=0).shape, (0,))
+        assert_equal(random.choice(0, size=0).shape, (0,))
+        assert_equal(random.choice([], size=(0,)).shape, (0,))
+        assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, random.choice, a, p=p)
+
+    def test_choice_p_non_contiguous(self):
+        p = np.ones(10) / 5
+        p[1::2] = 3.0
+        random = Generator(MT19937(self.seed))
+        non_contig = random.choice(5, 3, p=p[::2])
+        random = Generator(MT19937(self.seed))
+        contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
+        assert_array_equal(non_contig, contig)
+
+    def test_choice_return_type(self):
+        # gh 9867
+        p = np.ones(4) / 4.
+        actual = random.choice(4, 2)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, replace=False)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, p=p)
+        assert actual.dtype == np.int64
+        actual = random.choice(4, 2, p=p, replace=False)
+        assert actual.dtype == np.int64
+
+    def test_choice_large_sample(self):
+        choice_hash = '4266599d12bfcfb815213303432341c06b4349f5455890446578877bb322e222'
+        random = Generator(MT19937(self.seed))
+        actual = random.choice(10000, 5000, replace=False)
+        if sys.byteorder != 'little':
+            actual = actual.byteswap()
+        res = hashlib.sha256(actual.view(np.int8)).hexdigest()
+        assert_(choice_hash == res)
+
+    def test_bytes(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.bytes(10)
+        desired = b'\x86\xf0\xd4\x18\xe1\x81\t8%\xdd'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object, (1,)),
+                                           ("b", np.int32, (1,))])]:
+            random = Generator(MT19937(self.seed))
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            random.shuffle(alist)
+            actual = alist
+            desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_custom_axis(self):
+        random = Generator(MT19937(self.seed))
+        actual = np.arange(16).reshape((4, 4))
+        random.shuffle(actual, axis=1)
+        desired = np.array([[ 0,  3,  1,  2],
+                            [ 4,  7,  5,  6],
+                            [ 8, 11,  9, 10],
+                            [12, 15, 13, 14]])
+        assert_array_equal(actual, desired)
+        random = Generator(MT19937(self.seed))
+        actual = np.arange(16).reshape((4, 4))
+        random.shuffle(actual, axis=-1)
+        assert_array_equal(actual, desired)
+
+    def test_shuffle_custom_axis_empty(self):
+        random = Generator(MT19937(self.seed))
+        desired = np.array([]).reshape((0, 6))
+        for axis in (0, 1):
+            actual = np.array([]).reshape((0, 6))
+            random.shuffle(actual, axis=axis)
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_axis_nonsquare(self):
+        y1 = np.arange(20).reshape(2, 10)
+        y2 = y1.copy()
+        random = Generator(MT19937(self.seed))
+        random.shuffle(y1, axis=1)
+        random = Generator(MT19937(self.seed))
+        random.shuffle(y2.T)
+        assert_array_equal(y1, y2)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+    def test_shuffle_exceptions(self):
+        random = Generator(MT19937(self.seed))
+        arr = np.arange(10)
+        assert_raises(np.AxisError, random.shuffle, arr, 1)
+        arr = np.arange(9).reshape((3, 3))
+        assert_raises(np.AxisError, random.shuffle, arr, 3)
+        assert_raises(TypeError, random.shuffle, arr, slice(1, 2, None))
+        arr = [[1, 2, 3], [4, 5, 6]]
+        assert_raises(NotImplementedError, random.shuffle, arr, 1)
+
+        arr = np.array(3)
+        assert_raises(TypeError, random.shuffle, arr)
+        arr = np.ones((3, 2))
+        assert_raises(np.AxisError, random.shuffle, arr, 2)
+
+    def test_shuffle_not_writeable(self):
+        random = Generator(MT19937(self.seed))
+        a = np.zeros(5)
+        a.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            random.shuffle(a)
+
+    def test_permutation(self):
+        random = Generator(MT19937(self.seed))
+        alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
+        actual = random.permutation(alist)
+        desired = [4, 1, 9, 8, 0, 5, 3, 6, 2, 7]
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
+        actual = random.permutation(arr_2d)
+        assert_array_equal(actual, np.atleast_2d(desired).T)
+
+        bad_x_str = "abcd"
+        assert_raises(np.AxisError, random.permutation, bad_x_str)
+
+        bad_x_float = 1.2
+        assert_raises(np.AxisError, random.permutation, bad_x_float)
+
+        random = Generator(MT19937(self.seed))
+        integer_val = 10
+        desired = [3, 0, 8, 7, 9, 4, 2, 5, 1, 6]
+
+        actual = random.permutation(integer_val)
+        assert_array_equal(actual, desired)
+
+    def test_permutation_custom_axis(self):
+        a = np.arange(16).reshape((4, 4))
+        desired = np.array([[ 0,  3,  1,  2],
+                            [ 4,  7,  5,  6],
+                            [ 8, 11,  9, 10],
+                            [12, 15, 13, 14]])
+        random = Generator(MT19937(self.seed))
+        actual = random.permutation(a, axis=1)
+        assert_array_equal(actual, desired)
+        random = Generator(MT19937(self.seed))
+        actual = random.permutation(a, axis=-1)
+        assert_array_equal(actual, desired)
+
+    def test_permutation_exceptions(self):
+        random = Generator(MT19937(self.seed))
+        arr = np.arange(10)
+        assert_raises(np.AxisError, random.permutation, arr, 1)
+        arr = np.arange(9).reshape((3, 3))
+        assert_raises(np.AxisError, random.permutation, arr, 3)
+        assert_raises(TypeError, random.permutation, arr, slice(1, 2, None))
+
+    @pytest.mark.parametrize("dtype", [int, object])
+    @pytest.mark.parametrize("axis, expected",
+                             [(None, np.array([[3, 7, 0, 9, 10, 11],
+                                               [8, 4, 2, 5,  1,  6]])),
+                              (0, np.array([[6, 1, 2, 9, 10, 11],
+                                            [0, 7, 8, 3,  4,  5]])),
+                              (1, np.array([[ 5, 3,  4, 0, 2, 1],
+                                            [11, 9, 10, 6, 8, 7]]))])
+    def test_permuted(self, dtype, axis, expected):
+        random = Generator(MT19937(self.seed))
+        x = np.arange(12).reshape(2, 6).astype(dtype)
+        random.permuted(x, axis=axis, out=x)
+        assert_array_equal(x, expected)
+
+        random = Generator(MT19937(self.seed))
+        x = np.arange(12).reshape(2, 6).astype(dtype)
+        y = random.permuted(x, axis=axis)
+        assert y.dtype == dtype
+        assert_array_equal(y, expected)
+
+    def test_permuted_with_strides(self):
+        random = Generator(MT19937(self.seed))
+        x0 = np.arange(22).reshape(2, 11)
+        x1 = x0.copy()
+        x = x0[:, ::3]
+        y = random.permuted(x, axis=1, out=x)
+        expected = np.array([[0, 9, 3, 6],
+                             [14, 20, 11, 17]])
+        assert_array_equal(y, expected)
+        x1[:, ::3] = expected
+        # Verify that the original x0 was modified in-place as expected.
+        assert_array_equal(x1, x0)
+
+    def test_permuted_empty(self):
+        y = random.permuted([])
+        assert_array_equal(y, [])
+
+    @pytest.mark.parametrize('outshape', [(2, 3), 5])
+    def test_permuted_out_with_wrong_shape(self, outshape):
+        a = np.array([1, 2, 3])
+        out = np.zeros(outshape, dtype=a.dtype)
+        with pytest.raises(ValueError, match='same shape'):
+            random.permuted(a, out=out)
+
+    def test_permuted_out_with_wrong_type(self):
+        out = np.zeros((3, 5), dtype=np.int32)
+        x = np.ones((3, 5))
+        with pytest.raises(TypeError, match='Cannot cast'):
+            random.permuted(x, axis=1, out=out)
+
+    def test_permuted_not_writeable(self):
+        x = np.zeros((2, 5))
+        x.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            random.permuted(x, axis=1, out=x)
+
+    def test_beta(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+            [[1.083029353267698e-10, 2.449965303168024e-11],
+             [2.397085162969853e-02, 3.590779671820755e-08],
+             [2.830254190078299e-04, 1.744709918330393e-01]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(100.123, .456, size=(3, 2))
+        desired = np.array([[42, 41],
+                            [42, 48],
+                            [44, 50]])
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(100.123, .456)
+        desired = 42
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.chisquare(50, size=(3, 2))
+        desired = np.array([[32.9850547060149, 39.0219480493301],
+                            [56.2006134779419, 57.3474165711485],
+                            [55.4243733880198, 55.4209797925213]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        random = Generator(MT19937(self.seed))
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.5439892869558927,  0.45601071304410745],
+                             [0.5588917345860708,  0.4411082654139292 ]],
+                            [[0.5632074165063435,  0.43679258349365657],
+                             [0.54862581112627,    0.45137418887373015]],
+                            [[0.49961831357047226, 0.5003816864295278 ],
+                             [0.52374806183482,    0.47625193816517997]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        bad_alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, bad_alpha)
+
+        random = Generator(MT19937(self.seed))
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha)
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, alpha)
+
+        # gh-15876
+        assert_raises(ValueError, random.dirichlet, [[5, 1]])
+        assert_raises(ValueError, random.dirichlet, [[5], [1]])
+        assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+        assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
+    def test_dirichlet_alpha_non_contiguous(self):
+        a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
+        alpha = a[::2]
+        random = Generator(MT19937(self.seed))
+        non_contig = random.dirichlet(alpha, size=(3, 2))
+        random = Generator(MT19937(self.seed))
+        contig = random.dirichlet(np.ascontiguousarray(alpha),
+                                  size=(3, 2))
+        assert_array_almost_equal(non_contig, contig)
+
+    def test_dirichlet_small_alpha(self):
+        eps = 1.0e-9  # 1.0e-10 -> runtime x 10; 1e-11 -> runtime x 200, etc.
+        alpha = eps * np.array([1., 1.0e-3])
+        random = Generator(MT19937(self.seed))
+        actual = random.dirichlet(alpha, size=(3, 2))
+        expected = np.array([
+            [[1., 0.],
+             [1., 0.]],
+            [[1., 0.],
+             [1., 0.]],
+            [[1., 0.],
+             [1., 0.]]
+        ])
+        assert_array_almost_equal(actual, expected, decimal=15)
+
+    @pytest.mark.slow
+    def test_dirichlet_moderately_small_alpha(self):
+        # Use alpha.max() < 0.1 to trigger stick breaking code path
+        alpha = np.array([0.02, 0.04, 0.03])
+        exact_mean = alpha / alpha.sum()
+        random = Generator(MT19937(self.seed))
+        sample = random.dirichlet(alpha, size=20000000)
+        sample_mean = sample.mean(axis=0)
+        assert_allclose(sample_mean, exact_mean, rtol=1e-3)
+
+    # This set of parameters includes inputs with alpha.max() >= 0.1 and
+    # alpha.max() < 0.1 to exercise both generation methods within the
+    # dirichlet code.
+    @pytest.mark.parametrize(
+        'alpha',
+        [[5, 9, 0, 8],
+         [0.5, 0, 0, 0],
+         [1, 5, 0, 0, 1.5, 0, 0, 0],
+         [0.01, 0.03, 0, 0.005],
+         [1e-5, 0, 0, 0],
+         [0.002, 0.015, 0, 0, 0.04, 0, 0, 0],
+         [0.0],
+         [0, 0, 0]],
+    )
+    def test_dirichlet_multiple_zeros_in_alpha(self, alpha):
+        alpha = np.array(alpha)
+        y = random.dirichlet(alpha)
+        assert_equal(y[alpha == 0], 0.0)
+
+    def test_exponential(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[0.098845481066258, 1.560752510746964],
+                            [0.075730916041636, 1.769098974710777],
+                            [1.488602544592235, 2.49684815275751 ]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(random.exponential(scale=0), 0)
+        assert_raises(ValueError, random.exponential, scale=-0.)
+
+    def test_f(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.f(12, 77, size=(3, 2))
+        desired = np.array([[0.461720027077085, 1.100441958872451],
+                            [1.100337455217484, 0.91421736740018 ],
+                            [0.500811891303113, 0.826802454552058]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[ 5.03850858902096,  7.9228656732049 ],
+                            [18.73983605132985, 19.57961681699238],
+                            [18.17897755150825, 18.17653912505234]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[1, 11],
+                            [1, 12],
+                            [11, 17]])
+        assert_array_equal(actual, desired)
+
+    def test_geometric_exceptions(self):
+        assert_raises(ValueError, random.geometric, 1.1)
+        assert_raises(ValueError, random.geometric, [1.1] * 10)
+        assert_raises(ValueError, random.geometric, -0.1)
+        assert_raises(ValueError, random.geometric, [-0.1] * 10)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.geometric, np.nan)
+            assert_raises(ValueError, random.geometric, [np.nan] * 10)
+
+    def test_gumbel(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[ 4.688397515056245, -0.289514845417841],
+                            [ 4.981176042584683, -0.633224272589149],
+                            [-0.055915275687488, -0.333962478257953]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(random.gumbel(scale=0), 0)
+        assert_raises(ValueError, random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
+        desired = np.array([[ 9, 9],
+                            [ 9, 9],
+                            [10, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-3.156353949272393,  1.195863024830054],
+                            [-3.435458081645966,  1.656882398925444],
+                            [ 0.924824032467446,  1.251116432209336]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(random.laplace(scale=0), 0)
+        assert_raises(ValueError, random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-4.338584631510999,  1.890171436749954],
+                            [-4.64547787337966 ,  2.514545562919217],
+                            [ 1.495389489198666,  1.967827627577474]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[ 0.0268252166335, 13.9534486483053],
+                            [ 0.1204014788936,  2.2422077497792],
+                            [ 4.2484199496128, 12.0093343977523]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[14, 17],
+                            [3, 18],
+                            [5, 1]])
+        assert_array_equal(actual, desired)
+
+    def test_logseries_zero(self):
+        random = Generator(MT19937(self.seed))
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        random = Generator(MT19937(self.seed))
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
+
+    def test_multinomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[1, 5, 1, 6, 4, 3],
+                             [4, 2, 6, 2, 4, 2]],
+                            [[5, 3, 2, 6, 3, 1],
+                             [4, 4, 0, 2, 3, 7]],
+                            [[6, 3, 1, 5, 3, 2],
+                             [5, 5, 3, 1, 2, 4]]])
+        assert_array_equal(actual, desired)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+    def test_multivariate_normal(self, method):
+        random = Generator(MT19937(self.seed))
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = random.multivariate_normal(mean, cov, size, method=method)
+        desired = np.array([[[-1.747478062846581,  11.25613495182354  ],
+                             [-0.9967333370066214, 10.342002097029821 ]],
+                            [[ 0.7850019631242964, 11.181113712443013 ],
+                             [ 0.8901349653255224,  8.873825399642492 ]],
+                            [[ 0.7130260107430003,  9.551628690083056 ],
+                             [ 0.7127098726541128, 11.991709234143173 ]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = random.multivariate_normal(mean, cov, method=method)
+        desired = np.array([0.233278563284287, 9.424140804347195])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        # Check that non symmetric covariance input raises exception when
+        # check_valid='raises' if using default svd method.
+        mean = [0, 0]
+        cov = [[1, 2], [1, 2]]
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov,
+                     method='eigh')
+        assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+                      method='cholesky')
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise', method='eigh')
+
+        # check degenerate samples from singular covariance matrix
+        cov = [[1, 1], [1, 1]]
+        if method in ('svd', 'eigh'):
+            samples = random.multivariate_normal(mean, cov, size=(3, 2),
+                                                 method=method)
+            assert_array_almost_equal(samples[..., 0], samples[..., 1],
+                                      decimal=6)
+        else:
+            assert_raises(LinAlgError, random.multivariate_normal, mean, cov,
+                          method='cholesky')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            random.multivariate_normal(mean, cov, method=method)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+        mu = np.zeros(2)
+        cov = np.eye(2)
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='other')
+        assert_raises(ValueError, random.multivariate_normal,
+                      np.zeros((2, 1, 1)), cov)
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.empty((3, 2)))
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.eye(3))
+
+    @pytest.mark.parametrize('mean, cov', [([0], [[1+1j]]), ([0j], [[1]])])
+    def test_multivariate_normal_disallow_complex(self, mean, cov):
+        random = Generator(MT19937(self.seed))
+        with pytest.raises(TypeError, match="must not be complex"):
+            random.multivariate_normal(mean, cov)
+
+    @pytest.mark.parametrize("method", ["svd", "eigh", "cholesky"])
+    def test_multivariate_normal_basic_stats(self, method):
+        random = Generator(MT19937(self.seed))
+        n_s = 1000
+        mean = np.array([1, 2])
+        cov = np.array([[2, 1], [1, 2]])
+        s = random.multivariate_normal(mean, cov, size=(n_s,), method=method)
+        s_center = s - mean
+        cov_emp = (s_center.T @ s_center) / (n_s - 1)
+        # these are pretty loose and are only designed to detect major errors
+        assert np.all(np.abs(s_center.mean(-2)) < 0.1)
+        assert np.all(np.abs(cov_emp - cov) < 0.2)
+
+    def test_negative_binomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[543, 727],
+                            [775, 760],
+                            [600, 674]])
+        assert_array_equal(actual, desired)
+
+    def test_negative_binomial_exceptions(self):
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.negative_binomial, 100, np.nan)
+            assert_raises(ValueError, random.negative_binomial, 100,
+                          [np.nan] * 10)
+
+    def test_negative_binomial_p0_exception(self):
+        # Verify that p=0 raises an exception.
+        with assert_raises(ValueError):
+            x = random.negative_binomial(1, 0)
+
+    def test_negative_binomial_invalid_p_n_combination(self):
+        # Verify that values of p and n that would result in an overflow
+        # or infinite loop raise an exception.
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.negative_binomial, 2**62, 0.1)
+            assert_raises(ValueError, random.negative_binomial, [2**62], [0.1])
+
+    def test_noncentral_chisquare(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[ 1.70561552362133, 15.97378184942111],
+                            [13.71483425173724, 20.17859633310629],
+                            [11.3615477156643 ,  3.67891108738029]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[9.41427665607629e-04, 1.70473157518850e-04],
+                            [1.14554372041263e+00, 1.38187755933435e-03],
+                            [1.90659181905387e+00, 1.21772577941822e+00]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[0.82947954590419, 1.80139670767078],
+                            [6.58720057417794, 7.00491463609814],
+                            [6.31101879073157, 6.30982307753005]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                     size=(3, 2))
+        desired = np.array([[0.060310671139  , 0.23866058175939],
+                            [0.86860246709073, 0.2668510459738 ],
+                            [0.23375780078364, 1.88922102885943]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f_nan(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
+        assert np.isnan(actual)
+
+    def test_normal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[-3.618412914693162,  2.635726692647081],
+                            [-2.116923463013243,  0.807460983059643],
+                            [ 1.446547137248593,  2.485684213886024]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(random.normal(scale=0), 0)
+        assert_raises(ValueError, random.normal, scale=-0.)
+
+    def test_pareto(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array([[1.0394926776069018e+00, 7.7142534343505773e+04],
+                            [7.2640150889064703e-01, 3.4650454783825594e+05],
+                            [4.5852344481994740e+04, 6.5851383009539105e+07]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [0, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('int64').max
+        lamneg = -1
+        assert_raises(ValueError, random.poisson, lamneg)
+        assert_raises(ValueError, random.poisson, [lamneg] * 10)
+        assert_raises(ValueError, random.poisson, lambig)
+        assert_raises(ValueError, random.poisson, [lambig] * 10)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, random.poisson, np.nan)
+            assert_raises(ValueError, random.poisson, [np.nan] * 10)
+
+    def test_power(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[1.977857368842754e-09, 9.806792196620341e-02],
+                            [2.482442984543471e-10, 1.527108843266079e-01],
+                            [8.188283434244285e-02, 3.950547209346948e-01]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[4.19494429102666, 16.66920198906598],
+                            [3.67184544902662, 17.74695521962917],
+                            [16.27935397855501, 21.08355560691792]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_cauchy(size=(3, 2))
+        desired = np.array([[-1.489437778266206, -3.275389641569784],
+                            [ 0.560102864910406, -0.680780916282552],
+                            [-1.314912905226277,  0.295852965660225]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_exponential(size=(3, 2), method='inv')
+        desired = np.array([[0.102031839440643, 1.229350298474972],
+                            [0.088137284693098, 1.459859985522667],
+                            [1.093830802293668, 1.256977002164613]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_expoential_type_error(self):
+        assert_raises(TypeError, random.standard_exponential, dtype=np.int32)
+
+    def test_standard_gamma(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[0.62970724056362, 1.22379851271008],
+                            [3.899412530884  , 4.12479964250139],
+                            [3.74994102464584, 3.74929307690815]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gammma_scalar_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(3, dtype=np.float32)
+        desired = 2.9242148399353027
+        assert_array_almost_equal(actual, desired, decimal=6)
+
+    def test_standard_gamma_float(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[0.62971, 1.2238 ],
+                            [3.89941, 4.1248 ],
+                            [3.74994, 3.74929]])
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+    def test_standard_gammma_float_out(self):
+        actual = np.zeros((3, 2), dtype=np.float32)
+        random = Generator(MT19937(self.seed))
+        random.standard_gamma(10.0, out=actual, dtype=np.float32)
+        desired = np.array([[10.14987,  7.87012],
+                             [ 9.46284, 12.56832],
+                             [13.82495,  7.81533]], dtype=np.float32)
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+        random = Generator(MT19937(self.seed))
+        random.standard_gamma(10.0, out=actual, size=(3, 2), dtype=np.float32)
+        assert_array_almost_equal(actual, desired, decimal=5)
+
+    def test_standard_gamma_unknown_type(self):
+        assert_raises(TypeError, random.standard_gamma, 1.,
+                      dtype='int32')
+
+    def test_out_size_mismatch(self):
+        out = np.zeros(10)
+        assert_raises(ValueError, random.standard_gamma, 10.0, size=20,
+                      out=out)
+        assert_raises(ValueError, random.standard_gamma, 10.0, size=(10, 1),
+                      out=out)
+
+    def test_standard_gamma_0(self):
+        assert_equal(random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_normal(size=(3, 2))
+        desired = np.array([[-1.870934851846581,  1.25613495182354 ],
+                            [-1.120190126006621,  0.342002097029821],
+                            [ 0.661545174124296,  1.181113712443012]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_normal_unsupported_type(self):
+        assert_raises(TypeError, random.standard_normal, dtype=np.int32)
+
+    def test_standard_t(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[-1.484666193042647,  0.30597891831161 ],
+                            [ 1.056684299648085, -0.407312602088507],
+                            [ 0.130704414281157, -2.038053410490321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.triangular(left=5.12, mode=10.23, right=20.34,
+                                   size=(3, 2))
+        desired = np.array([[ 7.86664070590917, 13.6313848513185 ],
+                            [ 7.68152445215983, 14.36169131136546],
+                            [13.16105603911429, 13.72341621856971]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[2.13306255040998 , 7.816987531021207],
+                            [2.015436610109887, 8.377577533009589],
+                            [7.421792588856135, 7.891185744455209]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func, 0, np.inf)
+        assert_raises(OverflowError, func, fmin, fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_uniform_zero_range(self):
+        func = random.uniform
+        result = func(1.5, 1.5)
+        assert_allclose(result, 1.5)
+        result = func([0.0, np.pi], [0.0, np.pi])
+        assert_allclose(result, [0.0, np.pi])
+        result = func([[2145.12], [2145.12]], [2145.12, 2145.12])
+        assert_allclose(result, 2145.12 + np.zeros((2, 2)))
+
+    def test_uniform_neg_range(self):
+        func = random.uniform
+        assert_raises(ValueError, func, 2, 1)
+        assert_raises(ValueError, func,  [1, 2], [1, 1])
+        assert_raises(ValueError, func,  [[0, 1],[2, 3]], 2)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[ 1.107972248690106,  2.841536476232361],
+                            [ 1.832602376042457,  1.945511926976032],
+                            [-0.260147475776542,  2.058047492231698]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        assert_(np.isfinite(r).all())
+
+    def test_vonmises_nan(self):
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu=0., kappa=np.nan)
+        assert_(np.isnan(r))
+
+    @pytest.mark.parametrize("kappa", [1e4, 1e15])
+    def test_vonmises_large_kappa(self, kappa):
+        random = Generator(MT19937(self.seed))
+        rs = RandomState(random.bit_generator)
+        state = random.bit_generator.state
+
+        random_state_vals = rs.vonmises(0, kappa, size=10)
+        random.bit_generator.state = state
+        gen_vals = random.vonmises(0, kappa, size=10)
+        if kappa < 1e6:
+            assert_allclose(random_state_vals, gen_vals)
+        else:
+            assert np.all(random_state_vals != gen_vals)
+
+    @pytest.mark.parametrize("mu", [-7., -np.pi, -3.1, np.pi, 3.2])
+    @pytest.mark.parametrize("kappa", [1e-9, 1e-6, 1, 1e3, 1e15])
+    def test_vonmises_large_kappa_range(self, mu, kappa):
+        random = Generator(MT19937(self.seed))
+        r = random.vonmises(mu, kappa, 50)
+        assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_wald(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[0.26871721804551, 3.2233942732115 ],
+                            [2.20328374987066, 2.40958405189353],
+                            [2.07093587449261, 0.73073890064369]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.138613914769468, 1.306463419753191],
+                            [0.111623365934763, 1.446570494646721],
+                            [1.257145775276011, 1.914247725027957]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        random = Generator(MT19937(self.seed))
+        assert_equal(random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, random.weibull, a=-0.)
+
+    def test_zipf(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[  1,   1],
+                            [ 10, 867],
+                            [354,   2]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def test_uniform(self):
+        random = Generator(MT19937(self.seed))
+        low = [0]
+        high = [1]
+        uniform = random.uniform
+        desired = np.array([0.16693771389729, 0.19635129550675, 0.75563050964095])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        random = Generator(MT19937(self.seed))
+        desired = np.array([-0.38736406738527,  0.79594375042255,  0.0197076236097])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.normal, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        normal = random.normal
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        desired = np.array([0.18719338682602, 0.73234824491364, 0.17928615186455])
+
+        random = Generator(MT19937(self.seed))
+        beta = random.beta
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        std_gamma = random.standard_gamma
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        desired = np.array([1.34491986425611, 0.42760990636187, 1.4355697857258])
+
+        random = Generator(MT19937(self.seed))
+        gamma = random.gamma
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        gamma = random.gamma
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        desired = np.array([0.07765056244107, 7.72951397913186, 0.05786093891763])
+
+        random = Generator(MT19937(self.seed))
+        f = random.f
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        random = Generator(MT19937(self.seed))
+        f = random.f
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        desired = np.array([2.02434240411421, 12.91838601070124, 1.24395160354629])
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))
+
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_f = random.noncentral_f
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        random = Generator(MT19937(self.seed))
+        desired = np.array([0.04714867120827, 0.1239390327694])
+        actual = random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        desired = np.array([0.05573640064251, 1.47220224353539, 2.9469379318589])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        desired = np.array([0.07710766249436, 5.27829115110304, 0.630732147399])
+
+        random = Generator(MT19937(self.seed))
+        nonc_chi = random.noncentral_chisquare
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        random = Generator(MT19937(self.seed))
+        nonc_chi = random.noncentral_chisquare
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        desired = np.array([-1.39498829447098, -1.23058658835223, 0.17207021065983])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.standard_t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.standard_t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        desired = np.array([2.25935584988528, 2.23326261461399, -2.84152146503326])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.vonmises, mu * 3, bad_kappa)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.95905052946317, 0.2383810889437 , 1.04988745750013])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.67245993212806, 0.21380495318094, 0.7177848928629])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        desired = np.array([0.48954864361052, 0.19249412888486, 0.51216834058807])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([-1.09698732625119, -0.93470271947368, 0.71592671378202])
+
+        random = Generator(MT19937(self.seed))
+        laplace = random.laplace
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        laplace = random.laplace
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([1.70020068231762, 1.52054354273631, -0.34293267607081])
+
+        random = Generator(MT19937(self.seed))
+        gumbel = random.gumbel
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        gumbel = random.gumbel
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array([-1.607487640433, -1.40925686003678, 1.12887112820397])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.logistic, loc * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.logistic, loc, bad_scale * 3)
+        assert_equal(random.logistic(1.0, 0.0), 1.0)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        desired = np.array([0.67884390500697, 2.21653186290321, 1.01990310084276])
+
+        random = Generator(MT19937(self.seed))
+        lognormal = random.lognormal
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.lognormal(mean, sigma * 3)
+        assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        desired = np.array(
+            [1.1597068009872629,
+             0.6539188836253857,
+             1.1981526554349398]
+        )
+
+        random = Generator(MT19937(self.seed))
+        actual = random.rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        desired = np.array([0.38052407392905, 0.50701641508592, 0.484935249864])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.wald, bad_mean * 3, scale)
+        assert_raises(ValueError, random.wald, mean * 3, bad_scale)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, random.wald, bad_mean, scale * 3)
+        assert_raises(ValueError, random.wald, mean, bad_scale * 3)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        desired = np.array([1.57781954604754, 1.62665986867957, 2.30090130831326])
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        random = Generator(MT19937(self.seed))
+        triangular = random.triangular
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+        assert_raises(ValueError, triangular, 10., 0., 20.)
+        assert_raises(ValueError, triangular, 10., 25., 20.)
+        assert_raises(ValueError, triangular, 10., 10., 10.)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([0, 0, 1])
+
+        random = Generator(MT19937(self.seed))
+        binom = random.binomial
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.binomial(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([0, 2, 1], dtype=np.int64)
+
+        random = Generator(MT19937(self.seed))
+        neg_binom = random.negative_binomial
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        random = Generator(MT19937(self.seed))
+        neg_binom = random.negative_binomial
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+
+        lam = [1]
+        bad_lam_one = [-1]
+        desired = np.array([0, 0, 3])
+
+        random = Generator(MT19937(self.seed))
+        max_lam = random._poisson_lam_max
+        bad_lam_two = [max_lam * 2]
+        poisson = random.poisson
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        desired = np.array([1, 8, 1])
+
+        random = Generator(MT19937(self.seed))
+        zipf = random.zipf
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        desired = np.array([1, 1, 3])
+
+        random = Generator(MT19937(self.seed))
+        geometric = random.geometric
+        actual = geometric(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geometric, bad_p_one * 3)
+        assert_raises(ValueError, geometric, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [-1]
+        bad_nsample_two = [4]
+        desired = np.array([0, 0, 1])
+
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, random.hypergeometric, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, random.hypergeometric, ngood * 3, nbad, bad_nsample_two)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.hypergeometric(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, random.hypergeometric, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, random.hypergeometric, ngood, nbad * 3, bad_nsample_two)
+
+        random = Generator(MT19937(self.seed))
+        hypergeom = random.hypergeometric
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+        assert_raises(ValueError, hypergeom, -1, 10, 20)
+        assert_raises(ValueError, hypergeom, 10, -1, 20)
+        assert_raises(ValueError, hypergeom, 10, 10, -1)
+        assert_raises(ValueError, hypergeom, 10, 10, 25)
+
+        # ValueError for arguments that are too big.
+        assert_raises(ValueError, hypergeom, 2**30, 10, 20)
+        assert_raises(ValueError, hypergeom, 999, 2**31, 50)
+        assert_raises(ValueError, hypergeom, 999, [2**29, 2**30], 1000)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        desired = np.array([1, 1, 1])
+
+        random = Generator(MT19937(self.seed))
+        logseries = random.logseries
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+    def test_multinomial(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[0, 0, 2, 1, 2, 0],
+                             [2, 3, 6, 4, 2, 3]],
+                            [[1, 0, 1, 0, 2, 1],
+                             [7, 2, 2, 1, 4, 4]],
+                            [[0, 2, 0, 1, 2, 0],
+                             [3, 2, 3, 3, 4, 5]]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [1 / 6.] * 6)
+        desired = np.array([[0, 0, 2, 1, 2, 0],
+                            [2, 3, 6, 4, 2, 3]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([5, 20], [[1 / 6.] * 6] * 2)
+        desired = np.array([[0, 0, 2, 1, 2, 0],
+                            [2, 3, 6, 4, 2, 3]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial([[5], [20]], [[1 / 6.] * 6] * 2)
+        desired = np.array([[[0, 0, 2, 1, 2, 0],
+                             [0, 0, 2, 1, 1, 1]],
+                            [[4, 2, 3, 3, 5, 3],
+                             [7, 2, 2, 1, 4, 4]]], dtype=np.int64)
+        assert_array_equal(actual, desired)
+
+    @pytest.mark.parametrize("n", [10,
+                                   np.array([10, 10]),
+                                   np.array([[[10]], [[10]]])
+                                   ]
+                             )
+    def test_multinomial_pval_broadcast(self, n):
+        random = Generator(MT19937(self.seed))
+        pvals = np.array([1 / 4] * 4)
+        actual = random.multinomial(n, pvals)
+        n_shape = tuple() if isinstance(n, int) else n.shape
+        expected_shape = n_shape + (4,)
+        assert actual.shape == expected_shape
+        pvals = np.vstack([pvals, pvals])
+        actual = random.multinomial(n, pvals)
+        expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1]) + (4,)
+        assert actual.shape == expected_shape
+
+        pvals = np.vstack([[pvals], [pvals]])
+        actual = random.multinomial(n, pvals)
+        expected_shape = np.broadcast_shapes(n_shape, pvals.shape[:-1])
+        assert actual.shape == expected_shape + (4,)
+        actual = random.multinomial(n, pvals, size=(3, 2) + expected_shape)
+        assert actual.shape == (3, 2) + expected_shape + (4,)
+
+        with pytest.raises(ValueError):
+            # Ensure that size is not broadcast
+            actual = random.multinomial(n, pvals, size=(1,) * 6)
+
+    def test_invalid_pvals_broadcast(self):
+        random = Generator(MT19937(self.seed))
+        pvals = [[1 / 6] * 6, [1 / 4] * 6]
+        assert_raises(ValueError, random.multinomial, 1, pvals)
+        assert_raises(ValueError, random.multinomial, 6, 0.5)
+
+    def test_empty_outputs(self):
+        random = Generator(MT19937(self.seed))
+        actual = random.multinomial(np.empty((10, 0, 6), "i8"), [1 / 6] * 6)
+        assert actual.shape == (10, 0, 6, 6)
+        actual = random.multinomial(12, np.empty((10, 0, 10)))
+        assert actual.shape == (10, 0, 10)
+        actual = random.multinomial(np.empty((3, 0, 7), "i8"),
+                                    np.empty((3, 0, 7, 4)))
+        assert actual.shape == (3, 0, 7, 4)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(Generator(MT19937(s)), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(Generator(MT19937(s)), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)
+
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (random.exponential, random.standard_gamma,
+                 random.chisquare, random.standard_t,
+                 random.pareto, random.weibull,
+                 random.power, random.rayleigh,
+                 random.poisson, random.zipf,
+                 random.geometric, random.logseries)
+
+        probfuncs = (random.geometric, random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (random.uniform, random.normal,
+                 random.beta, random.gamma,
+                 random.f, random.noncentral_chisquare,
+                 random.vonmises, random.laplace,
+                 random.gumbel, random.logistic,
+                 random.lognormal, random.wald,
+                 random.binomial, random.negative_binomial)
+
+        probfuncs = (random.binomial, random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_integers(self, endpoint):
+        itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
+                 np.int32, np.uint32, np.int64, np.uint64]
+        func = random.integers
+        high = np.array([1])
+        low = np.array([0])
+
+        for dt in itype:
+            out = func(low, high, endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low[0], high, endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low, high[0], endpoint=endpoint, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [random.noncentral_f, random.triangular,
+                 random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+
+@pytest.mark.parametrize("config", JUMP_TEST_DATA)
+def test_jumped(config):
+    # Each config contains the initial seed, a number of raw steps
+    # the sha256 hashes of the initial and the final states' keys and
+    # the position of the initial and the final state.
+    # These were produced using the original C implementation.
+    seed = config["seed"]
+    steps = config["steps"]
+
+    mt19937 = MT19937(seed)
+    # Burn step
+    mt19937.random_raw(steps)
+    key = mt19937.state["state"]["key"]
+    if sys.byteorder == 'big':
+        key = key.byteswap()
+    sha256 = hashlib.sha256(key)
+    assert mt19937.state["state"]["pos"] == config["initial"]["pos"]
+    assert sha256.hexdigest() == config["initial"]["key_sha256"]
+
+    jumped = mt19937.jumped()
+    key = jumped.state["state"]["key"]
+    if sys.byteorder == 'big':
+        key = key.byteswap()
+    sha256 = hashlib.sha256(key)
+    assert jumped.state["state"]["pos"] == config["jumped"]["pos"]
+    assert sha256.hexdigest() == config["jumped"]["key_sha256"]
+
+
+def test_broadcast_size_error():
+    mu = np.ones(3)
+    sigma = np.ones((4, 3))
+    size = (10, 4, 2)
+    assert random.normal(mu, sigma, size=(5, 4, 3)).shape == (5, 4, 3)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=size)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=(1, 3))
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=(4, 1, 1))
+    # 1 arg
+    shape = np.ones((4, 3))
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=size)
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=(3,))
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, size=3)
+    # Check out
+    out = np.empty(size)
+    with pytest.raises(ValueError):
+        random.standard_gamma(shape, out=out)
+
+    # 2 arg
+    with pytest.raises(ValueError):
+        random.binomial(1, [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], 0.3, size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.multinomial([2, 2], [.3, .7], size=(2, 1))
+
+    # 3 arg
+    a = random.chisquare(5, size=3)
+    b = random.chisquare(5, size=(4, 3))
+    c = random.chisquare(5, size=(5, 4, 3))
+    assert random.noncentral_f(a, b, c).shape == (5, 4, 3)
+    with pytest.raises(ValueError, match=r"Output size \(6, 5, 1, 1\) is"):
+        random.noncentral_f(a, b, c, size=(6, 5, 1, 1))
+
+
+def test_broadcast_size_scalar():
+    mu = np.ones(3)
+    sigma = np.ones(3)
+    random.normal(mu, sigma, size=3)
+    with pytest.raises(ValueError):
+        random.normal(mu, sigma, size=2)
+
+
+def test_ragged_shuffle():
+    # GH 18142
+    seq = [[], [], 1]
+    gen = Generator(MT19937(0))
+    assert_no_warnings(gen.shuffle, seq)
+    assert seq == [1, [], []]
+
+
+@pytest.mark.parametrize("high", [-2, [-2]])
+@pytest.mark.parametrize("endpoint", [True, False])
+def test_single_arg_integer_exception(high, endpoint):
+    # GH 14333
+    gen = Generator(MT19937(0))
+    msg = 'high < 0' if endpoint else 'high <= 0'
+    with pytest.raises(ValueError, match=msg):
+        gen.integers(high, endpoint=endpoint)
+    msg = 'low > high' if endpoint else 'low >= high'
+    with pytest.raises(ValueError, match=msg):
+        gen.integers(-1, high, endpoint=endpoint)
+    with pytest.raises(ValueError, match=msg):
+        gen.integers([-1], high, endpoint=endpoint)
+
+
+@pytest.mark.parametrize("dtype", ["f4", "f8"])
+def test_c_contig_req_out(dtype):
+    # GH 18704
+    out = np.empty((2, 3), order="F", dtype=dtype)
+    shape = [1, 2, 3]
+    with pytest.raises(ValueError, match="Supplied output array"):
+        random.standard_gamma(shape, out=out, dtype=dtype)
+    with pytest.raises(ValueError, match="Supplied output array"):
+        random.standard_gamma(shape, out=out, size=out.shape, dtype=dtype)
+
+
+@pytest.mark.parametrize("dtype", ["f4", "f8"])
+@pytest.mark.parametrize("order", ["F", "C"])
+@pytest.mark.parametrize("dist", [random.standard_normal, random.random])
+def test_contig_req_out(dist, order, dtype):
+    # GH 18704
+    out = np.empty((2, 3), dtype=dtype, order=order)
+    variates = dist(out=out, dtype=dtype)
+    assert variates is out
+    variates = dist(out=out, dtype=dtype, size=out.shape)
+    assert variates is out
+
+
+def test_generator_ctor_old_style_pickle():
+    rg = np.random.Generator(np.random.PCG64DXSM(0))
+    rg.standard_normal(1)
+    # Directly call reduce which is used in pickling
+    ctor, args, state_a = rg.__reduce__()
+    # Simulate unpickling an old pickle that only has the name
+    assert args[:1] == ("PCG64DXSM",)
+    b = ctor(*args[:1])
+    b.bit_generator.state = state_a
+    state_b = b.bit_generator.state
+    assert state_a == state_b
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py b/lib/python3.11/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f16af2b293ce21642c32e90af6f3ed22476158e6
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_generator_mt19937_regressions.py
@@ -0,0 +1,165 @@
+from numpy.testing import (assert_, assert_array_equal)
+import numpy as np
+import pytest
+from numpy.random import Generator, MT19937
+
+
+class TestRegression:
+
+    def setup_method(self):
+        self.mt19937 = Generator(MT19937(121263137472525314065))
+
+    def test_vonmises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = self.mt19937.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(self.mt19937.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(self.mt19937.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = (2**20 - 2, 2**20 - 2, 2**20 - 2)  # Check for 32-bit systems
+        assert_(self.mt19937.hypergeometric(*args) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        rvsn = self.mt19937.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            mt19937 = Generator(MT19937(12345))
+            shuffled = np.array(t, dtype=object)
+            mt19937.shuffle(shuffled)
+            expected = np.array([t[2], t[0], t[3], t[1]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom BitGenerator does not call into global state
+        res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4])
+        for i in range(3):
+            mt19937 = Generator(MT19937(i))
+            m = Generator(MT19937(4321))
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        self.mt19937.multivariate_normal([0], [[0]], size=1)
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
+        self.mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        x = self.mt19937.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
+
+    def test_beta_very_small_parameters(self):
+        # gh-24203: beta would hang with very small parameters.
+        self.mt19937.beta(1e-49, 1e-40)
+
+    def test_beta_ridiculously_small_parameters(self):
+        # gh-24266: beta would generate nan when the parameters
+        # were subnormal or a small multiple of the smallest normal.
+        tiny = np.finfo(1.0).tiny
+        x = self.mt19937.beta(tiny/32, tiny/40, size=50)
+        assert not np.any(np.isnan(x))
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = self.mt19937.choice(a, p=probs)
+            assert_(c in a)
+            with pytest.raises(ValueError):
+                self.mt19937.choice(a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            self.mt19937.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            self.mt19937.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+
+        class N(np.ndarray):
+            pass
+
+        mt19937 = Generator(MT19937(1))
+        orig = np.arange(3).view(N)
+        perm = mt19937.permutation(orig)
+        assert_array_equal(perm, np.array([2, 0, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self):
+                return self.a
+
+        mt19937 = Generator(MT19937(1))
+        m = M()
+        perm = mt19937.permutation(m)
+        assert_array_equal(perm, np.array([4, 1, 3, 0, 2]))
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_gamma_0(self):
+        assert self.mt19937.standard_gamma(0.0) == 0.0
+        assert_array_equal(self.mt19937.standard_gamma([0.0]), 0.0)
+
+        actual = self.mt19937.standard_gamma([0.0], dtype='float')
+        expected = np.array([0.], dtype=np.float32)
+        assert_array_equal(actual, expected)
+
+    def test_geometric_tiny_prob(self):
+        # Regression test for gh-17007.
+        # When p = 1e-30, the probability that a sample will exceed 2**63-1
+        # is 0.9999999999907766, so we expect the result to be all 2**63-1.
+        assert_array_equal(self.mt19937.geometric(p=1e-30, size=3),
+                           np.iinfo(np.int64).max)
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_random.py b/lib/python3.11/site-packages/numpy/random/tests/test_random.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d081fe1dbd1c868fe022480330711024804ca20
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_random.py
@@ -0,0 +1,1750 @@
+import warnings
+
+import pytest
+
+import numpy as np
+from numpy.testing import (
+        assert_, assert_raises, assert_equal, assert_warns,
+        assert_no_warnings, assert_array_equal, assert_array_almost_equal,
+        suppress_warnings, IS_WASM
+        )
+from numpy import random
+import sys
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = np.random.RandomState(0)
+        assert_equal(s.randint(1000), 684)
+        s = np.random.RandomState(4294967295)
+        assert_equal(s.randint(1000), 419)
+
+    def test_array(self):
+        s = np.random.RandomState(range(10))
+        assert_equal(s.randint(1000), 468)
+        s = np.random.RandomState(np.arange(10))
+        assert_equal(s.randint(1000), 468)
+        s = np.random.RandomState([0])
+        assert_equal(s.randint(1000), 973)
+        s = np.random.RandomState([4294967295])
+        assert_equal(s.randint(1000), 265)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, np.random.RandomState, -0.5)
+        assert_raises(ValueError, np.random.RandomState, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, np.random.RandomState, [-0.5])
+        assert_raises(ValueError, np.random.RandomState, [-1])
+        assert_raises(ValueError, np.random.RandomState, [4294967296])
+        assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296])
+        assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])
+
+    def test_invalid_array_shape(self):
+        # gh-9832
+        assert_raises(ValueError, np.random.RandomState,
+                      np.array([], dtype=np.int64))
+        assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]])
+        assert_raises(ValueError, np.random.RandomState, [[1, 2, 3],
+                                                          [4, 5, 6]])
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.randint(-5, -1) < -1)
+        x = random.randint(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, np.random.multinomial, 1, p,
+                      float(1))
+
+    def test_multidimensional_pvals(self):
+        assert_raises(ValueError, np.random.multinomial, 10, [[0, 1]])
+        assert_raises(ValueError, np.random.multinomial, 10, [[0], [1]])
+        assert_raises(ValueError, np.random.multinomial, 10, [[[0], [1]], [[1], [0]]])
+        assert_raises(ValueError, np.random.multinomial, 10, np.array([[0, 1], [1, 0]]))
+
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.prng = random.RandomState(self.seed)
+        self.state = self.prng.get_state()
+
+    def test_basic(self):
+        old = self.prng.tomaxint(16)
+        self.prng.set_state(self.state)
+        new = self.prng.tomaxint(16)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.prng.standard_normal(size=3)
+        self.prng.set_state(self.state)
+        new = self.prng.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.prng.standard_normal()
+        state = self.prng.get_state()
+        old = self.prng.standard_normal(size=3)
+        self.prng.set_state(state)
+        new = self.prng.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_backwards_compatibility(self):
+        # Make sure we can accept old state tuples that do not have the
+        # cached Gaussian value.
+        old_state = self.state[:-2]
+        x1 = self.prng.standard_normal(size=16)
+        self.prng.set_state(old_state)
+        x2 = self.prng.standard_normal(size=16)
+        self.prng.set_state(self.state)
+        x3 = self.prng.standard_normal(size=16)
+        assert_(np.all(x1 == x2))
+        assert_(np.all(x1 == x3))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.prng.negative_binomial(0.5, 0.5)
+
+    def test_set_invalid_state(self):
+        # gh-25402
+        with pytest.raises(IndexError):
+            self.prng.set_state(())
+
+
+class TestRandint:
+
+    rfunc = np.random.randint
+
+    # valid integer/boolean types
+    itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self):
+        assert_raises(TypeError, self.rfunc, 1, dtype=float)
+
+    def test_bounds_checking(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
+
+    def test_rng_zero_and_extremes(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd)//2
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+    def test_full_range(self):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            try:
+                self.rfunc(lbnd, ubnd, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self):
+        # Don't use fixed seed
+        np.random.seed()
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2, size=2**16, dtype=np.bool_)
+
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_repeatability(self):
+        import hashlib
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
+               'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
+               'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
+
+        for dt in self.itype[1:]:
+            np.random.seed(1234)
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = self.rfunc(0, 6, size=1000, dtype=dt)
+            else:
+                val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
+
+            res = hashlib.sha256(val.view(np.int8)).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        np.random.seed(1234)
+        val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    def test_int64_uint64_corner_case(self):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = np.random.randint(lbnd, ubnd, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_equal(sample.dtype, np.dtype(dt))
+
+        for dt in (bool, int):
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_(not hasattr(sample, 'dtype'))
+            assert_equal(type(sample), dt)
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_rand(self):
+        np.random.seed(self.seed)
+        actual = np.random.rand(3, 2)
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn(self):
+        np.random.seed(self.seed)
+        actual = np.random.randn(3, 2)
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                           [1.498988344300628, -0.2286433324536169],
+                           [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randint(self):
+        np.random.seed(self.seed)
+        actual = np.random.randint(-99, 99, size=(3, 2))
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers(self):
+        np.random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = np.random.random_integers(-99, 99, size=(3, 2))
+            assert_(len(w) == 1)
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers_max_int(self):
+        # Tests whether random_integers can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = np.random.random_integers(np.iinfo('l').max,
+                                               np.iinfo('l').max)
+            assert_(len(w) == 1)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+
+    def test_random_integers_deprecated(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+
+            # DeprecationWarning raised with high == None
+            assert_raises(DeprecationWarning,
+                          np.random.random_integers,
+                          np.iinfo('l').max)
+
+            # DeprecationWarning raised with high != None
+            assert_raises(DeprecationWarning,
+                          np.random.random_integers,
+                          np.iinfo('l').max, np.iinfo('l').max)
+
+    def test_random(self):
+        np.random.seed(self.seed)
+        actual = np.random.random((3, 2))
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_choice_uniform_replace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 4)
+        desired = np.array([2, 3, 2, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([1, 1, 2, 2])
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 3, replace=False)
+        desired = np.array([0, 1, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(4, 3, replace=False,
+                                  p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([2, 3, 1])
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        np.random.seed(self.seed)
+        actual = np.random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['c', 'd', 'c', 'd'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = np.random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(np.random.choice(2, replace=True)))
+        assert_(np.isscalar(np.random.choice(2, replace=False)))
+        assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(np.random.choice([1, 2], replace=True)))
+        assert_(np.random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(np.random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(np.random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))
+        assert_(np.random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(np.random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(np.random.choice(6, s, replace=True).shape, s)
+        assert_equal(np.random.choice(6, s, replace=False).shape, s)
+        assert_equal(np.random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(np.random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(np.random.randint(0, -10, size=0).shape, (0,))
+        assert_equal(np.random.randint(10, 10, size=0).shape, (0,))
+        assert_equal(np.random.choice(0, size=0).shape, (0,))
+        assert_equal(np.random.choice([], size=(0,)).shape, (0,))
+        assert_equal(np.random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, np.random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, np.random.choice, a, p=p)
+
+    def test_bytes(self):
+        np.random.seed(self.seed)
+        actual = np.random.bytes(10)
+        desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object), ("b", np.int32)])]:
+            np.random.seed(self.seed)
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            np.random.shuffle(alist)
+            actual = alist
+            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            np.random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            np.random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+    @pytest.mark.parametrize("random",
+            [np.random, np.random.RandomState(), np.random.default_rng()])
+    def test_shuffle_untyped_warning(self, random):
+        # Create a dict works like a sequence but isn't one
+        values = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}
+        with pytest.warns(UserWarning,
+                match="you are shuffling a 'dict' object") as rec:
+            random.shuffle(values)
+        assert "test_random" in rec[0].filename
+
+    @pytest.mark.parametrize("random",
+        [np.random, np.random.RandomState(), np.random.default_rng()])
+    @pytest.mark.parametrize("use_array_like", [True, False])
+    def test_shuffle_no_object_unpacking(self, random, use_array_like):
+        class MyArr(np.ndarray):
+            pass
+
+        items = [
+            None, np.array([3]), np.float64(3), np.array(10), np.float64(7)
+        ]
+        arr = np.array(items, dtype=object)
+        item_ids = {id(i) for i in items}
+        if use_array_like:
+            arr = arr.view(MyArr)
+
+        # The array was created fine, and did not modify any objects:
+        assert all(id(i) in item_ids for i in arr)
+
+        if use_array_like and not isinstance(random, np.random.Generator):
+            # The old API gives incorrect results, but warns about it.
+            with pytest.warns(UserWarning,
+                    match="Shuffling a one dimensional array.*"):
+                random.shuffle(arr)
+        else:
+            random.shuffle(arr)
+            assert all(id(i) in item_ids for i in arr)
+
+    def test_shuffle_memoryview(self):
+        # gh-18273
+        # allow graceful handling of memoryviews
+        # (treat the same as arrays)
+        np.random.seed(self.seed)
+        a = np.arange(5).data
+        np.random.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 4, 3, 2])
+        rng = np.random.RandomState(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 2, 3, 4])
+        rng = np.random.default_rng(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [4, 1, 0, 3, 2])
+
+    def test_shuffle_not_writeable(self):
+        a = np.zeros(3)
+        a.flags.writeable = False
+        with pytest.raises(ValueError, match='read-only'):
+            np.random.shuffle(a)
+
+    def test_beta(self):
+        np.random.seed(self.seed)
+        actual = np.random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+                [[1.45341850513746058e-02, 5.31297615662868145e-04],
+                 [1.85366619058432324e-06, 4.19214516800110563e-03],
+                 [1.58405155108498093e-04, 1.26252891949397652e-04]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.binomial(100, .456, size=(3, 2))
+        desired = np.array([[37, 43],
+                            [42, 48],
+                            [46, 45]])
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        np.random.seed(self.seed)
+        actual = np.random.chisquare(50, size=(3, 2))
+        desired = np.array([[63.87858175501090585, 68.68407748911370447],
+                            [65.77116116901505904, 47.09686762438974483],
+                            [72.3828403199695174, 74.18408615260374006]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        np.random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = np.random.mtrand.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.54539444573611562, 0.45460555426388438],
+                             [0.62345816822039413, 0.37654183177960598]],
+                            [[0.55206000085785778, 0.44793999914214233],
+                             [0.58964023305154301, 0.41035976694845688]],
+                            [[0.59266909280647828, 0.40733090719352177],
+                             [0.56974431743975207, 0.43025568256024799]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, np.random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, np.random.mtrand.dirichlet, alpha)
+
+        # gh-15876
+        assert_raises(ValueError, random.dirichlet, [[5, 1]])
+        assert_raises(ValueError, random.dirichlet, [[5], [1]])
+        assert_raises(ValueError, random.dirichlet, [[[5], [1]], [[1], [5]]])
+        assert_raises(ValueError, random.dirichlet, np.array([[5, 1], [1, 5]]))
+
+    def test_exponential(self):
+        np.random.seed(self.seed)
+        actual = np.random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[1.08342649775011624, 1.00607889924557314],
+                            [2.46628830085216721, 2.49668106809923884],
+                            [0.68717433461363442, 1.69175666993575979]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(np.random.exponential(scale=0), 0)
+        assert_raises(ValueError, np.random.exponential, scale=-0.)
+
+    def test_f(self):
+        np.random.seed(self.seed)
+        actual = np.random.f(12, 77, size=(3, 2))
+        desired = np.array([[1.21975394418575878, 1.75135759791559775],
+                            [1.44803115017146489, 1.22108959480396262],
+                            [1.02176975757740629, 1.34431827623300415]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        np.random.seed(self.seed)
+        actual = np.random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[24.60509188649287182, 28.54993563207210627],
+                            [26.13476110204064184, 12.56988482927716078],
+                            [31.71863275789960568, 33.30143302795922011]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(np.random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, np.random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        np.random.seed(self.seed)
+        actual = np.random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[8, 7],
+                            [17, 17],
+                            [5, 12]])
+        assert_array_equal(actual, desired)
+
+    def test_gumbel(self):
+        np.random.seed(self.seed)
+        actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.19591898743416816, 0.34405539668096674],
+                            [-1.4492522252274278, -1.47374816298446865],
+                            [1.10651090478803416, -0.69535848626236174]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(np.random.gumbel(scale=0), 0)
+        assert_raises(ValueError, np.random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        np.random.seed(self.seed)
+        actual = np.random.hypergeometric(10, 5, 14, size=(3, 2))
+        desired = np.array([[10, 10],
+                            [10, 10],
+                            [9, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = np.random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = np.random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = np.random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = np.random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        np.random.seed(self.seed)
+        actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.66599721112760157, 0.52829452552221945],
+                            [3.12791959514407125, 3.18202813572992005],
+                            [-0.05391065675859356, 1.74901336242837324]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(np.random.laplace(scale=0), 0)
+        assert_raises(ValueError, np.random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        np.random.seed(self.seed)
+        actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[1.09232835305011444, 0.8648196662399954],
+                            [4.27818590694950185, 4.33897006346929714],
+                            [-0.21682183359214885, 2.63373365386060332]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        np.random.seed(self.seed)
+        actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[16.50698631688883822, 36.54846706092654784],
+                            [22.67886599981281748, 0.71617561058995771],
+                            [65.72798501792723869, 86.84341601437161273]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(np.random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, np.random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        np.random.seed(self.seed)
+        actual = np.random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[2, 2],
+                            [6, 17],
+                            [3, 6]])
+        assert_array_equal(actual, desired)
+
+    def test_multinomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2))
+        desired = np.array([[[4, 3, 5, 4, 2, 2],
+                             [5, 2, 8, 2, 2, 1]],
+                            [[3, 4, 3, 6, 0, 4],
+                             [2, 1, 4, 3, 6, 4]],
+                            [[4, 4, 2, 5, 2, 3],
+                             [4, 3, 4, 2, 3, 4]]])
+        assert_array_equal(actual, desired)
+
+    def test_multivariate_normal(self):
+        np.random.seed(self.seed)
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = np.random.multivariate_normal(mean, cov, size)
+        desired = np.array([[[1.463620246718631, 11.73759122771936],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = np.random.multivariate_normal(mean, cov)
+        desired = np.array([0.895289569463708, 9.17180864067987])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        mean = [0, 0]
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(np.random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, np.random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            np.random.multivariate_normal(mean, cov)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+    def test_negative_binomial(self):
+        np.random.seed(self.seed)
+        actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[848, 841],
+                            [892, 611],
+                            [779, 647]])
+        assert_array_equal(actual, desired)
+
+    def test_noncentral_chisquare(self):
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[23.91905354498517511, 13.35324692733826346],
+                            [31.22452661329736401, 16.60047399466177254],
+                            [5.03461598262724586, 17.94973089023519464]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[1.47145377828516666,  0.15052899268012659],
+                            [0.00943803056963588,  1.02647251615666169],
+                            [0.332334982684171,  0.15451287602753125]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[9.597154162763948, 11.725484450296079],
+                            [10.413711048138335, 3.694475922923986],
+                            [13.484222138963087, 14.377255424602957]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        np.random.seed(self.seed)
+        actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                        size=(3, 2))
+        desired = np.array([[1.40598099674926669, 0.34207973179285761],
+                            [3.57715069265772545, 7.92632662577829805],
+                            [0.43741599463544162, 1.1774208752428319]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        np.random.seed(self.seed)
+        actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[2.80378370443726244, 3.59863924443872163],
+                            [3.121433477601256, -0.33382987590723379],
+                            [4.18552478636557357, 4.46410668111310471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(np.random.normal(scale=0), 0)
+        assert_raises(ValueError, np.random.normal, scale=-0.)
+
+    def test_pareto(self):
+        np.random.seed(self.seed)
+        actual = np.random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array(
+                [[2.46852460439034849e+03, 1.41286880810518346e+03],
+                 [5.28287797029485181e+07, 6.57720981047328785e+07],
+                 [1.40840323350391515e+02, 1.98390255135251704e+05]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        np.random.seed(self.seed)
+        actual = np.random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [1, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('l').max
+        lamneg = -1
+        assert_raises(ValueError, np.random.poisson, lamneg)
+        assert_raises(ValueError, np.random.poisson, [lamneg]*10)
+        assert_raises(ValueError, np.random.poisson, lambig)
+        assert_raises(ValueError, np.random.poisson, [lambig]*10)
+
+    def test_power(self):
+        np.random.seed(self.seed)
+        actual = np.random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[0.02048932883240791, 0.01424192241128213],
+                            [0.38446073748535298, 0.39499689943484395],
+                            [0.00177699707563439, 0.13115505880863756]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        np.random.seed(self.seed)
+        actual = np.random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[13.8882496494248393, 13.383318339044731],
+                            [20.95413364294492098, 21.08285015800712614],
+                            [11.06066537006854311, 17.35468505778271009]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(np.random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, np.random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_cauchy(size=(3, 2))
+        desired = np.array([[0.77127660196445336, -6.55601161955910605],
+                            [0.93582023391158309, -2.07479293013759447],
+                            [-4.74601644297011926, 0.18338989290760804]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_exponential(size=(3, 2))
+        desired = np.array([[0.96441739162374596, 0.89556604882105506],
+                            [2.1953785836319808, 2.22243285392490542],
+                            [0.6116915921431676, 1.50592546727413201]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_gamma(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[5.50841531318455058, 6.62953470301903103],
+                            [5.93988484943779227, 2.31044849402133989],
+                            [7.54838614231317084, 8.012756093271868]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gamma_0(self):
+        assert_equal(np.random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, np.random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_normal(size=(3, 2))
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                            [1.498988344300628, -0.2286433324536169],
+                            [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_t(self):
+        np.random.seed(self.seed)
+        actual = np.random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[0.97140611862659965, -0.08830486548450577],
+                            [1.36311143689505321, -0.55317463909867071],
+                            [-0.18473749069684214, 0.61181537341755321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        np.random.seed(self.seed)
+        actual = np.random.triangular(left=5.12, mode=10.23, right=20.34,
+                                      size=(3, 2))
+        desired = np.array([[12.68117178949215784, 12.4129206149193152],
+                            [16.20131377335158263, 16.25692138747600524],
+                            [11.20400690911820263, 14.4978144835829923]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        np.random.seed(self.seed)
+        actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[6.99097932346268003, 6.73801597444323974],
+                            [9.50364421400426274, 9.53130618907631089],
+                            [5.48995325769805476, 8.47493103280052118]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = np.random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func,  0,      np.inf)
+        assert_raises(OverflowError, func,  fmin,   fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        np.random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, np.random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+            __index__ = __int__
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, np.random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        np.random.seed(self.seed)
+        actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[2.28567572673902042, 2.89163838442285037],
+                            [0.38198375564286025, 2.57638023113890746],
+                            [1.19153771588353052, 1.83509849681825354]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        np.random.seed(self.seed)
+        r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        np.testing.assert_(np.isfinite(r).all())
+
+    def test_wald(self):
+        np.random.seed(self.seed)
+        actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[3.82935265715889983, 5.13125249184285526],
+                            [0.35045403618358717, 1.50832396872003538],
+                            [0.24124319895843183, 0.22031101461955038]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        np.random.seed(self.seed)
+        actual = np.random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.97097342648766727, 0.91422896443565516],
+                            [1.89517770034962929, 1.91414357960479564],
+                            [0.67057783752390987, 1.39494046635066793]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        np.random.seed(self.seed)
+        assert_equal(np.random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, np.random.weibull, a=-0.)
+
+    def test_zipf(self):
+        np.random.seed(self.seed)
+        actual = np.random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[66, 29],
+                            [1, 1],
+                            [3, 13]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def setSeed(self):
+        np.random.seed(self.seed)
+
+    # TODO: Include test for randint once it can broadcast
+    # Can steal the test written in PR #6938
+
+    def test_uniform(self):
+        low = [0]
+        high = [1]
+        uniform = np.random.uniform
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.setSeed()
+        actual = uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        self.setSeed()
+        actual = uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        normal = np.random.normal
+        desired = np.array([2.2129019979039612,
+                            2.1283977976520019,
+                            1.8417114045748335])
+
+        self.setSeed()
+        actual = normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        beta = np.random.beta
+        desired = np.array([0.19843558305989056,
+                            0.075230336409423643,
+                            0.24976865978980844])
+
+        self.setSeed()
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        self.setSeed()
+        actual = beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a, b * 3)
+        assert_raises(ValueError, beta, a, bad_b * 3)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        exponential = np.random.exponential
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        std_gamma = np.random.standard_gamma
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        gamma = np.random.gamma
+        desired = np.array([1.5221370731769048,
+                            1.5277256455738331,
+                            1.4248762625178359])
+
+        self.setSeed()
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        self.setSeed()
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        f = np.random.f
+        desired = np.array([0.80038951638264799,
+                            0.86768719635363512,
+                            2.7251095168386801])
+
+        self.setSeed()
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        self.setSeed()
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        nonc_f = np.random.noncentral_f
+        desired = np.array([9.1393943263705211,
+                            13.025456344595602,
+                            8.8018098359100545])
+
+        self.setSeed()
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        self.setSeed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = np.random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        chisquare = np.random.chisquare
+        desired = np.array([0.57022801133088286,
+                            0.51947702108840776,
+                            0.1320969254923558])
+
+        self.setSeed()
+        actual = chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        nonc_chi = np.random.noncentral_chisquare
+        desired = np.array([9.0015599467913763,
+                            4.5804135049718742,
+                            6.0872302432834564])
+
+        self.setSeed()
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        self.setSeed()
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        t = np.random.standard_t
+        desired = np.array([3.0702872575217643,
+                            5.8560725167361607,
+                            1.0274791436474273])
+
+        self.setSeed()
+        actual = t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        vonmises = np.random.vonmises
+        desired = np.array([2.9883443664201312,
+                            -2.7064099483995943,
+                            -1.8672476700665914])
+
+        self.setSeed()
+        actual = vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
+
+        self.setSeed()
+        actual = vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        pareto = np.random.pareto
+        desired = np.array([1.1405622680198362,
+                            1.1465519762044529,
+                            1.0389564467453547])
+
+        self.setSeed()
+        actual = pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        weibull = np.random.weibull
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.setSeed()
+        actual = weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        power = np.random.power
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.setSeed()
+        actual = power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        laplace = np.random.laplace
+        desired = np.array([0.067921356028507157,
+                            0.070715642226971326,
+                            0.019290950698972624])
+
+        self.setSeed()
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        gumbel = np.random.gumbel
+        desired = np.array([0.2730318639556768,
+                            0.26936705726291116,
+                            0.33906220393037939])
+
+        self.setSeed()
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        logistic = np.random.logistic
+        desired = np.array([0.13152135837586171,
+                            0.13675915696285773,
+                            0.038216792802833396])
+
+        self.setSeed()
+        actual = logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc * 3, bad_scale)
+
+        self.setSeed()
+        actual = logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc, bad_scale * 3)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        lognormal = np.random.lognormal
+        desired = np.array([9.1422086044848427,
+                            8.4013952870126261,
+                            6.3073234116578671])
+
+        self.setSeed()
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+
+        self.setSeed()
+        actual = lognormal(mean, sigma * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        rayleigh = np.random.rayleigh
+        desired = np.array([1.2337491937897689,
+                            1.2360119924878694,
+                            1.1936818095781789])
+
+        self.setSeed()
+        actual = rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        wald = np.random.wald
+        desired = np.array([0.11873681120271318,
+                            0.12450084820795027,
+                            0.9096122728408238])
+
+        self.setSeed()
+        actual = wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean * 3, scale)
+        assert_raises(ValueError, wald, mean * 3, bad_scale)
+
+        self.setSeed()
+        actual = wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean, scale * 3)
+        assert_raises(ValueError, wald, mean, bad_scale * 3)
+        assert_raises(ValueError, wald, 0.0, 1)
+        assert_raises(ValueError, wald, 0.5, 0.0)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        triangular = np.random.triangular
+        desired = np.array([2.03339048710429,
+                            2.0347400359389356,
+                            2.0095991069536208])
+
+        self.setSeed()
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        self.setSeed()
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        self.setSeed()
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        binom = np.random.binomial
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        self.setSeed()
+        actual = binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        neg_binom = np.random.negative_binomial
+        desired = np.array([1, 0, 1])
+
+        self.setSeed()
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        self.setSeed()
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+        max_lam = np.random.RandomState()._poisson_lam_max
+
+        lam = [1]
+        bad_lam_one = [-1]
+        bad_lam_two = [max_lam * 2]
+        poisson = np.random.poisson
+        desired = np.array([1, 1, 0])
+
+        self.setSeed()
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        zipf = np.random.zipf
+        desired = np.array([2, 2, 1])
+
+        self.setSeed()
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        geom = np.random.geometric
+        desired = np.array([2, 2, 2])
+
+        self.setSeed()
+        actual = geom(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geom, bad_p_one * 3)
+        assert_raises(ValueError, geom, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [0]
+        bad_nsample_two = [4]
+        hypergeom = np.random.hypergeometric
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = hypergeom(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
+
+        self.setSeed()
+        actual = hypergeom(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
+
+        self.setSeed()
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        logseries = np.random.logseries
+        desired = np.array([1, 1, 1])
+
+        self.setSeed()
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(np.random.RandomState(s), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(np.random.RandomState(s), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1/6.]*6, size=10000)
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (np.random.exponential, np.random.standard_gamma,
+                 np.random.chisquare, np.random.standard_t,
+                 np.random.pareto, np.random.weibull,
+                 np.random.power, np.random.rayleigh,
+                 np.random.poisson, np.random.zipf,
+                 np.random.geometric, np.random.logseries)
+
+        probfuncs = (np.random.geometric, np.random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (np.random.uniform, np.random.normal,
+                 np.random.beta, np.random.gamma,
+                 np.random.f, np.random.noncentral_chisquare,
+                 np.random.vonmises, np.random.laplace,
+                 np.random.gumbel, np.random.logistic,
+                 np.random.lognormal, np.random.wald,
+                 np.random.binomial, np.random.negative_binomial)
+
+        probfuncs = (np.random.binomial, np.random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_randint(self):
+        itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
+                 np.int32, np.uint32, np.int64, np.uint64]
+        func = np.random.randint
+        high = np.array([1])
+        low = np.array([0])
+
+        for dt in itype:
+            out = func(low, high, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low[0], high, dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(low, high[0], dtype=dt)
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [np.random.noncentral_f, np.random.triangular,
+                 np.random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_randomstate.py b/lib/python3.11/site-packages/numpy/random/tests/test_randomstate.py
new file mode 100644
index 0000000000000000000000000000000000000000..c77bfce883aea276304c817be9ef93584b59cb28
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_randomstate.py
@@ -0,0 +1,2121 @@
+import hashlib
+import pickle
+import sys
+import warnings
+
+import numpy as np
+import pytest
+from numpy.testing import (
+        assert_, assert_raises, assert_equal, assert_warns,
+        assert_no_warnings, assert_array_equal, assert_array_almost_equal,
+        suppress_warnings, IS_WASM
+        )
+
+from numpy.random import MT19937, PCG64
+from numpy import random
+
+INT_FUNCS = {'binomial': (100.0, 0.6),
+             'geometric': (.5,),
+             'hypergeometric': (20, 20, 10),
+             'logseries': (.5,),
+             'multinomial': (20, np.ones(6) / 6.0),
+             'negative_binomial': (100, .5),
+             'poisson': (10.0,),
+             'zipf': (2,),
+             }
+
+if np.iinfo(int).max < 2**32:
+    # Windows and some 32-bit platforms, e.g., ARM
+    INT_FUNC_HASHES = {'binomial': '2fbead005fc63942decb5326d36a1f32fe2c9d32c904ee61e46866b88447c263',
+                       'logseries': '23ead5dcde35d4cfd4ef2c105e4c3d43304b45dc1b1444b7823b9ee4fa144ebb',
+                       'geometric': '0d764db64f5c3bad48c8c33551c13b4d07a1e7b470f77629bef6c985cac76fcf',
+                       'hypergeometric': '7b59bf2f1691626c5815cdcd9a49e1dd68697251d4521575219e4d2a1b8b2c67',
+                       'multinomial': 'd754fa5b92943a38ec07630de92362dd2e02c43577fc147417dc5b9db94ccdd3',
+                       'negative_binomial': '8eb216f7cb2a63cf55605422845caaff002fddc64a7dc8b2d45acd477a49e824',
+                       'poisson': '70c891d76104013ebd6f6bcf30d403a9074b886ff62e4e6b8eb605bf1a4673b7',
+                       'zipf': '01f074f97517cd5d21747148ac6ca4074dde7fcb7acbaec0a936606fecacd93f',
+                       }
+else:
+    INT_FUNC_HASHES = {'binomial': '8626dd9d052cb608e93d8868de0a7b347258b199493871a1dc56e2a26cacb112',
+                       'geometric': '8edd53d272e49c4fc8fbbe6c7d08d563d62e482921f3131d0a0e068af30f0db9',
+                       'hypergeometric': '83496cc4281c77b786c9b7ad88b74d42e01603a55c60577ebab81c3ba8d45657',
+                       'logseries': '65878a38747c176bc00e930ebafebb69d4e1e16cd3a704e264ea8f5e24f548db',
+                       'multinomial': '7a984ae6dca26fd25374479e118b22f55db0aedccd5a0f2584ceada33db98605',
+                       'negative_binomial': 'd636d968e6a24ae92ab52fe11c46ac45b0897e98714426764e820a7d77602a61',
+                       'poisson': '956552176f77e7c9cb20d0118fc9cf690be488d790ed4b4c4747b965e61b0bb4',
+                       'zipf': 'f84ba7feffda41e606e20b28dfc0f1ea9964a74574513d4a4cbc98433a8bfa45',
+                       }
+
+
+@pytest.fixture(scope='module', params=INT_FUNCS)
+def int_func(request):
+    return (request.param, INT_FUNCS[request.param],
+            INT_FUNC_HASHES[request.param])
+
+
+@pytest.fixture
+def restore_singleton_bitgen():
+    """Ensures that the singleton bitgen is restored after a test"""
+    orig_bitgen = np.random.get_bit_generator()
+    yield
+    np.random.set_bit_generator(orig_bitgen)
+
+
+def assert_mt19937_state_equal(a, b):
+    assert_equal(a['bit_generator'], b['bit_generator'])
+    assert_array_equal(a['state']['key'], b['state']['key'])
+    assert_array_equal(a['state']['pos'], b['state']['pos'])
+    assert_equal(a['has_gauss'], b['has_gauss'])
+    assert_equal(a['gauss'], b['gauss'])
+
+
+class TestSeed:
+    def test_scalar(self):
+        s = random.RandomState(0)
+        assert_equal(s.randint(1000), 684)
+        s = random.RandomState(4294967295)
+        assert_equal(s.randint(1000), 419)
+
+    def test_array(self):
+        s = random.RandomState(range(10))
+        assert_equal(s.randint(1000), 468)
+        s = random.RandomState(np.arange(10))
+        assert_equal(s.randint(1000), 468)
+        s = random.RandomState([0])
+        assert_equal(s.randint(1000), 973)
+        s = random.RandomState([4294967295])
+        assert_equal(s.randint(1000), 265)
+
+    def test_invalid_scalar(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, random.RandomState, -0.5)
+        assert_raises(ValueError, random.RandomState, -1)
+
+    def test_invalid_array(self):
+        # seed must be an unsigned 32 bit integer
+        assert_raises(TypeError, random.RandomState, [-0.5])
+        assert_raises(ValueError, random.RandomState, [-1])
+        assert_raises(ValueError, random.RandomState, [4294967296])
+        assert_raises(ValueError, random.RandomState, [1, 2, 4294967296])
+        assert_raises(ValueError, random.RandomState, [1, -2, 4294967296])
+
+    def test_invalid_array_shape(self):
+        # gh-9832
+        assert_raises(ValueError, random.RandomState, np.array([],
+                                                               dtype=np.int64))
+        assert_raises(ValueError, random.RandomState, [[1, 2, 3]])
+        assert_raises(ValueError, random.RandomState, [[1, 2, 3],
+                                                       [4, 5, 6]])
+
+    def test_cannot_seed(self):
+        rs = random.RandomState(PCG64(0))
+        with assert_raises(TypeError):
+            rs.seed(1234)
+
+    def test_invalid_initialization(self):
+        assert_raises(ValueError, random.RandomState, MT19937)
+
+
+class TestBinomial:
+    def test_n_zero(self):
+        # Tests the corner case of n == 0 for the binomial distribution.
+        # binomial(0, p) should be zero for any p in [0, 1].
+        # This test addresses issue #3480.
+        zeros = np.zeros(2, dtype='int')
+        for p in [0, .5, 1]:
+            assert_(random.binomial(0, p) == 0)
+            assert_array_equal(random.binomial(zeros, p), zeros)
+
+    def test_p_is_nan(self):
+        # Issue #4571.
+        assert_raises(ValueError, random.binomial, 1, np.nan)
+
+
+class TestMultinomial:
+    def test_basic(self):
+        random.multinomial(100, [0.2, 0.8])
+
+    def test_zero_probability(self):
+        random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
+
+    def test_int_negative_interval(self):
+        assert_(-5 <= random.randint(-5, -1) < -1)
+        x = random.randint(-5, -1, 5)
+        assert_(np.all(-5 <= x))
+        assert_(np.all(x < -1))
+
+    def test_size(self):
+        # gh-3173
+        p = [0.5, 0.5]
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.multinomial(1, p, np.array((2, 2))).shape,
+                     (2, 2, 2))
+
+        assert_raises(TypeError, random.multinomial, 1, p,
+                      float(1))
+
+    def test_invalid_prob(self):
+        assert_raises(ValueError, random.multinomial, 100, [1.1, 0.2])
+        assert_raises(ValueError, random.multinomial, 100, [-.1, 0.9])
+
+    def test_invalid_n(self):
+        assert_raises(ValueError, random.multinomial, -1, [0.8, 0.2])
+
+    def test_p_non_contiguous(self):
+        p = np.arange(15.)
+        p /= np.sum(p[1::3])
+        pvals = p[1::3]
+        random.seed(1432985819)
+        non_contig = random.multinomial(100, pvals=pvals)
+        random.seed(1432985819)
+        contig = random.multinomial(100, pvals=np.ascontiguousarray(pvals))
+        assert_array_equal(non_contig, contig)
+
+    def test_multinomial_pvals_float32(self):
+        x = np.array([9.9e-01, 9.9e-01, 1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09,
+                      1.0e-09, 1.0e-09, 1.0e-09, 1.0e-09], dtype=np.float32)
+        pvals = x / x.sum()
+        match = r"[\w\s]*pvals array is cast to 64-bit floating"
+        with pytest.raises(ValueError, match=match):
+            random.multinomial(1, pvals)
+
+    def test_multinomial_n_float(self):
+        # Non-index integer types should gracefully truncate floats
+        random.multinomial(100.5, [0.2, 0.8])
+
+class TestSetState:
+    def setup_method(self):
+        self.seed = 1234567890
+        self.random_state = random.RandomState(self.seed)
+        self.state = self.random_state.get_state()
+
+    def test_basic(self):
+        old = self.random_state.tomaxint(16)
+        self.random_state.set_state(self.state)
+        new = self.random_state.tomaxint(16)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset(self):
+        # Make sure the cached every-other-Gaussian is reset.
+        old = self.random_state.standard_normal(size=3)
+        self.random_state.set_state(self.state)
+        new = self.random_state.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_gaussian_reset_in_media_res(self):
+        # When the state is saved with a cached Gaussian, make sure the
+        # cached Gaussian is restored.
+
+        self.random_state.standard_normal()
+        state = self.random_state.get_state()
+        old = self.random_state.standard_normal(size=3)
+        self.random_state.set_state(state)
+        new = self.random_state.standard_normal(size=3)
+        assert_(np.all(old == new))
+
+    def test_backwards_compatibility(self):
+        # Make sure we can accept old state tuples that do not have the
+        # cached Gaussian value.
+        old_state = self.state[:-2]
+        x1 = self.random_state.standard_normal(size=16)
+        self.random_state.set_state(old_state)
+        x2 = self.random_state.standard_normal(size=16)
+        self.random_state.set_state(self.state)
+        x3 = self.random_state.standard_normal(size=16)
+        assert_(np.all(x1 == x2))
+        assert_(np.all(x1 == x3))
+
+    def test_negative_binomial(self):
+        # Ensure that the negative binomial results take floating point
+        # arguments without truncation.
+        self.random_state.negative_binomial(0.5, 0.5)
+
+    def test_get_state_warning(self):
+        rs = random.RandomState(PCG64())
+        with suppress_warnings() as sup:
+            w = sup.record(RuntimeWarning)
+            state = rs.get_state()
+            assert_(len(w) == 1)
+            assert isinstance(state, dict)
+            assert state['bit_generator'] == 'PCG64'
+
+    def test_invalid_legacy_state_setting(self):
+        state = self.random_state.get_state()
+        new_state = ('Unknown', ) + state[1:]
+        assert_raises(ValueError, self.random_state.set_state, new_state)
+        assert_raises(TypeError, self.random_state.set_state,
+                      np.array(new_state, dtype=object))
+        state = self.random_state.get_state(legacy=False)
+        del state['bit_generator']
+        assert_raises(ValueError, self.random_state.set_state, state)
+
+    def test_pickle(self):
+        self.random_state.seed(0)
+        self.random_state.random_sample(100)
+        self.random_state.standard_normal()
+        pickled = self.random_state.get_state(legacy=False)
+        assert_equal(pickled['has_gauss'], 1)
+        rs_unpick = pickle.loads(pickle.dumps(self.random_state))
+        unpickled = rs_unpick.get_state(legacy=False)
+        assert_mt19937_state_equal(pickled, unpickled)
+
+    def test_state_setting(self):
+        attr_state = self.random_state.__getstate__()
+        self.random_state.standard_normal()
+        self.random_state.__setstate__(attr_state)
+        state = self.random_state.get_state(legacy=False)
+        assert_mt19937_state_equal(attr_state, state)
+
+    def test_repr(self):
+        assert repr(self.random_state).startswith('RandomState(MT19937)')
+
+
+class TestRandint:
+
+    rfunc = random.randint
+
+    # valid integer/boolean types
+    itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
+             np.int32, np.uint32, np.int64, np.uint64]
+
+    def test_unsupported_type(self):
+        assert_raises(TypeError, self.rfunc, 1, dtype=float)
+
+    def test_bounds_checking(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+            assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
+            assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
+            assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
+
+    def test_rng_zero_and_extremes(self):
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            tgt = ubnd - 1
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = lbnd
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+            tgt = (lbnd + ubnd)//2
+            assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
+
+    def test_full_range(self):
+        # Test for ticket #1690
+
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            try:
+                self.rfunc(lbnd, ubnd, dtype=dt)
+            except Exception as e:
+                raise AssertionError("No error should have been raised, "
+                                     "but one was with the following "
+                                     "message:\n\n%s" % str(e))
+
+    def test_in_bounds_fuzz(self):
+        # Don't use fixed seed
+        random.seed()
+
+        for dt in self.itype[1:]:
+            for ubnd in [4, 8, 16]:
+                vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
+                assert_(vals.max() < ubnd)
+                assert_(vals.min() >= 2)
+
+        vals = self.rfunc(0, 2, size=2**16, dtype=np.bool_)
+
+        assert_(vals.max() < 2)
+        assert_(vals.min() >= 0)
+
+    def test_repeatability(self):
+        # We use a sha256 hash of generated sequences of 1000 samples
+        # in the range [0, 6) for all but bool, where the range
+        # is [0, 2). Hashes are for little endian numbers.
+        tgt = {'bool': '509aea74d792fb931784c4b0135392c65aec64beee12b0cc167548a2c3d31e71',
+               'int16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'int32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'int64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'int8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404',
+               'uint16': '7b07f1a920e46f6d0fe02314155a2330bcfd7635e708da50e536c5ebb631a7d4',
+               'uint32': 'e577bfed6c935de944424667e3da285012e741892dcb7051a8f1ce68ab05c92f',
+               'uint64': '0fbead0b06759df2cfb55e43148822d4a1ff953c7eb19a5b08445a63bb64fa9e',
+               'uint8': '001aac3a5acb935a9b186cbe14a1ca064b8bb2dd0b045d48abeacf74d0203404'}
+
+        for dt in self.itype[1:]:
+            random.seed(1234)
+
+            # view as little endian for hash
+            if sys.byteorder == 'little':
+                val = self.rfunc(0, 6, size=1000, dtype=dt)
+            else:
+                val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
+
+            res = hashlib.sha256(val.view(np.int8)).hexdigest()
+            assert_(tgt[np.dtype(dt).name] == res)
+
+        # bools do not depend on endianness
+        random.seed(1234)
+        val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
+        res = hashlib.sha256(val).hexdigest()
+        assert_(tgt[np.dtype(bool).name] == res)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_repeatability_32bit_boundary_broadcasting(self):
+        desired = np.array([[[3992670689, 2438360420, 2557845020],
+                             [4107320065, 4142558326, 3216529513],
+                             [1605979228, 2807061240,  665605495]],
+                            [[3211410639, 4128781000,  457175120],
+                             [1712592594, 1282922662, 3081439808],
+                             [3997822960, 2008322436, 1563495165]],
+                            [[1398375547, 4269260146,  115316740],
+                             [3414372578, 3437564012, 2112038651],
+                             [3572980305, 2260248732, 3908238631]],
+                            [[2561372503,  223155946, 3127879445],
+                             [ 441282060, 3514786552, 2148440361],
+                             [1629275283, 3479737011, 3003195987]],
+                            [[ 412181688,  940383289, 3047321305],
+                             [2978368172,  764731833, 2282559898],
+                             [ 105711276,  720447391, 3596512484]]])
+        for size in [None, (5, 3, 3)]:
+            random.seed(12345)
+            x = self.rfunc([[-1], [0], [1]], [2**32 - 1, 2**32, 2**32 + 1],
+                           size=size)
+            assert_array_equal(x, desired if size is not None else desired[0])
+
+    def test_int64_uint64_corner_case(self):
+        # When stored in Numpy arrays, `lbnd` is casted
+        # as np.int64, and `ubnd` is casted as np.uint64.
+        # Checking whether `lbnd` >= `ubnd` used to be
+        # done solely via direct comparison, which is incorrect
+        # because when Numpy tries to compare both numbers,
+        # it casts both to np.float64 because there is
+        # no integer superset of np.int64 and np.uint64. However,
+        # `ubnd` is too large to be represented in np.float64,
+        # causing it be round down to np.iinfo(np.int64).max,
+        # leading to a ValueError because `lbnd` now equals
+        # the new `ubnd`.
+
+        dt = np.int64
+        tgt = np.iinfo(np.int64).max
+        lbnd = np.int64(np.iinfo(np.int64).max)
+        ubnd = np.uint64(np.iinfo(np.int64).max + 1)
+
+        # None of these function calls should
+        # generate a ValueError now.
+        actual = random.randint(lbnd, ubnd, dtype=dt)
+        assert_equal(actual, tgt)
+
+    def test_respect_dtype_singleton(self):
+        # See gh-7203
+        for dt in self.itype:
+            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
+            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
+
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_equal(sample.dtype, np.dtype(dt))
+
+        for dt in (bool, int):
+            lbnd = 0 if dt is bool else np.iinfo(dt).min
+            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
+
+            # gh-7284: Ensure that we get Python data types
+            sample = self.rfunc(lbnd, ubnd, dtype=dt)
+            assert_(not hasattr(sample, 'dtype'))
+            assert_equal(type(sample), dt)
+
+
+class TestRandomDist:
+    # Make sure the random distribution returns the correct value for a
+    # given seed
+
+    def setup_method(self):
+        self.seed = 1234567890
+
+    def test_rand(self):
+        random.seed(self.seed)
+        actual = random.rand(3, 2)
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rand_singleton(self):
+        random.seed(self.seed)
+        actual = random.rand()
+        desired = 0.61879477158567997
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn(self):
+        random.seed(self.seed)
+        actual = random.randn(3, 2)
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                           [1.498988344300628, -0.2286433324536169],
+                           [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random.seed(self.seed)
+        actual = random.randn()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_randint(self):
+        random.seed(self.seed)
+        actual = random.randint(-99, 99, size=(3, 2))
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+    def test_random_integers(self):
+        random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(-99, 99, size=(3, 2))
+            assert_(len(w) == 1)
+        desired = np.array([[31, 3],
+                            [-52, 41],
+                            [-48, -66]])
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(198, size=(3, 2))
+            assert_(len(w) == 1)
+        assert_array_equal(actual, desired + 100)
+
+    def test_tomaxint(self):
+        random.seed(self.seed)
+        rs = random.RandomState(self.seed)
+        actual = rs.tomaxint(size=(3, 2))
+        if np.iinfo(int).max == 2147483647:
+            desired = np.array([[1328851649,  731237375],
+                                [1270502067,  320041495],
+                                [1908433478,  499156889]], dtype=np.int64)
+        else:
+            desired = np.array([[5707374374421908479, 5456764827585442327],
+                                [8196659375100692377, 8224063923314595285],
+                                [4220315081820346526, 7177518203184491332]],
+                               dtype=np.int64)
+
+        assert_equal(actual, desired)
+
+        rs.seed(self.seed)
+        actual = rs.tomaxint()
+        assert_equal(actual, desired[0, 0])
+
+    def test_random_integers_max_int(self):
+        # Tests whether random_integers can generate the
+        # maximum allowed Python int that can be converted
+        # into a C long. Previous implementations of this
+        # method have thrown an OverflowError when attempting
+        # to generate this integer.
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            actual = random.random_integers(np.iinfo('l').max,
+                                            np.iinfo('l').max)
+            assert_(len(w) == 1)
+
+        desired = np.iinfo('l').max
+        assert_equal(actual, desired)
+        with suppress_warnings() as sup:
+            w = sup.record(DeprecationWarning)
+            typer = np.dtype('l').type
+            actual = random.random_integers(typer(np.iinfo('l').max),
+                                            typer(np.iinfo('l').max))
+            assert_(len(w) == 1)
+        assert_equal(actual, desired)
+
+    def test_random_integers_deprecated(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", DeprecationWarning)
+
+            # DeprecationWarning raised with high == None
+            assert_raises(DeprecationWarning,
+                          random.random_integers,
+                          np.iinfo('l').max)
+
+            # DeprecationWarning raised with high != None
+            assert_raises(DeprecationWarning,
+                          random.random_integers,
+                          np.iinfo('l').max, np.iinfo('l').max)
+
+    def test_random_sample(self):
+        random.seed(self.seed)
+        actual = random.random_sample((3, 2))
+        desired = np.array([[0.61879477158567997, 0.59162362775974664],
+                            [0.88868358904449662, 0.89165480011560816],
+                            [0.4575674820298663, 0.7781880808593471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        random.seed(self.seed)
+        actual = random.random_sample()
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_choice_uniform_replace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 4)
+        desired = np.array([2, 3, 2, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_replace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
+        desired = np.array([1, 1, 2, 2])
+        assert_array_equal(actual, desired)
+
+    def test_choice_uniform_noreplace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 3, replace=False)
+        desired = np.array([0, 1, 3])
+        assert_array_equal(actual, desired)
+
+    def test_choice_nonuniform_noreplace(self):
+        random.seed(self.seed)
+        actual = random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1])
+        desired = np.array([2, 3, 1])
+        assert_array_equal(actual, desired)
+
+    def test_choice_noninteger(self):
+        random.seed(self.seed)
+        actual = random.choice(['a', 'b', 'c', 'd'], 4)
+        desired = np.array(['c', 'd', 'c', 'd'])
+        assert_array_equal(actual, desired)
+
+    def test_choice_exceptions(self):
+        sample = random.choice
+        assert_raises(ValueError, sample, -1, 3)
+        assert_raises(ValueError, sample, 3., 3)
+        assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
+        assert_raises(ValueError, sample, [], 3)
+        assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
+                      p=[[0.25, 0.25], [0.25, 0.25]])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
+        assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
+        assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
+        # gh-13087
+        assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
+        assert_raises(ValueError, sample, [1, 2, 3], 2,
+                      replace=False, p=[1, 0, 0])
+
+    def test_choice_return_shape(self):
+        p = [0.1, 0.9]
+        # Check scalar
+        assert_(np.isscalar(random.choice(2, replace=True)))
+        assert_(np.isscalar(random.choice(2, replace=False)))
+        assert_(np.isscalar(random.choice(2, replace=True, p=p)))
+        assert_(np.isscalar(random.choice(2, replace=False, p=p)))
+        assert_(np.isscalar(random.choice([1, 2], replace=True)))
+        assert_(random.choice([None], replace=True) is None)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, replace=True) is a)
+
+        # Check 0-d array
+        s = tuple()
+        assert_(not np.isscalar(random.choice(2, s, replace=True)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False)))
+        assert_(not np.isscalar(random.choice(2, s, replace=True, p=p)))
+        assert_(not np.isscalar(random.choice(2, s, replace=False, p=p)))
+        assert_(not np.isscalar(random.choice([1, 2], s, replace=True)))
+        assert_(random.choice([None], s, replace=True).ndim == 0)
+        a = np.array([1, 2])
+        arr = np.empty(1, dtype=object)
+        arr[0] = a
+        assert_(random.choice(arr, s, replace=True).item() is a)
+
+        # Check multi dimensional array
+        s = (2, 3)
+        p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
+        assert_equal(random.choice(6, s, replace=True).shape, s)
+        assert_equal(random.choice(6, s, replace=False).shape, s)
+        assert_equal(random.choice(6, s, replace=True, p=p).shape, s)
+        assert_equal(random.choice(6, s, replace=False, p=p).shape, s)
+        assert_equal(random.choice(np.arange(6), s, replace=True).shape, s)
+
+        # Check zero-size
+        assert_equal(random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
+        assert_equal(random.randint(0, -10, size=0).shape, (0,))
+        assert_equal(random.randint(10, 10, size=0).shape, (0,))
+        assert_equal(random.choice(0, size=0).shape, (0,))
+        assert_equal(random.choice([], size=(0,)).shape, (0,))
+        assert_equal(random.choice(['a', 'b'], size=(3, 0, 4)).shape,
+                     (3, 0, 4))
+        assert_raises(ValueError, random.choice, [], 10)
+
+    def test_choice_nan_probabilities(self):
+        a = np.array([42, 1, 2])
+        p = [None, None, None]
+        assert_raises(ValueError, random.choice, a, p=p)
+
+    def test_choice_p_non_contiguous(self):
+        p = np.ones(10) / 5
+        p[1::2] = 3.0
+        random.seed(self.seed)
+        non_contig = random.choice(5, 3, p=p[::2])
+        random.seed(self.seed)
+        contig = random.choice(5, 3, p=np.ascontiguousarray(p[::2]))
+        assert_array_equal(non_contig, contig)
+
+    def test_bytes(self):
+        random.seed(self.seed)
+        actual = random.bytes(10)
+        desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
+        assert_equal(actual, desired)
+
+    def test_shuffle(self):
+        # Test lists, arrays (of various dtypes), and multidimensional versions
+        # of both, c-contiguous or not:
+        for conv in [lambda x: np.array([]),
+                     lambda x: x,
+                     lambda x: np.asarray(x).astype(np.int8),
+                     lambda x: np.asarray(x).astype(np.float32),
+                     lambda x: np.asarray(x).astype(np.complex64),
+                     lambda x: np.asarray(x).astype(object),
+                     lambda x: [(i, i) for i in x],
+                     lambda x: np.asarray([[i, i] for i in x]),
+                     lambda x: np.vstack([x, x]).T,
+                     # gh-11442
+                     lambda x: (np.asarray([(i, i) for i in x],
+                                           [("a", int), ("b", int)])
+                                .view(np.recarray)),
+                     # gh-4270
+                     lambda x: np.asarray([(i, i) for i in x],
+                                          [("a", object, (1,)),
+                                           ("b", np.int32, (1,))])]:
+            random.seed(self.seed)
+            alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
+            random.shuffle(alist)
+            actual = alist
+            desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
+            assert_array_equal(actual, desired)
+
+    def test_shuffle_masked(self):
+        # gh-3263
+        a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
+        b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
+        a_orig = a.copy()
+        b_orig = b.copy()
+        for i in range(50):
+            random.shuffle(a)
+            assert_equal(
+                sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
+            random.shuffle(b)
+            assert_equal(
+                sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+
+        def test_shuffle_invalid_objects(self):
+            x = np.array(3)
+            assert_raises(TypeError, random.shuffle, x)
+
+    def test_permutation(self):
+        random.seed(self.seed)
+        alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
+        actual = random.permutation(alist)
+        desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3]
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        arr_2d = np.atleast_2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]).T
+        actual = random.permutation(arr_2d)
+        assert_array_equal(actual, np.atleast_2d(desired).T)
+
+        random.seed(self.seed)
+        bad_x_str = "abcd"
+        assert_raises(IndexError, random.permutation, bad_x_str)
+
+        random.seed(self.seed)
+        bad_x_float = 1.2
+        assert_raises(IndexError, random.permutation, bad_x_float)
+
+        integer_val = 10
+        desired = [9, 0, 8, 5, 1, 3, 4, 7, 6, 2]
+
+        random.seed(self.seed)
+        actual = random.permutation(integer_val)
+        assert_array_equal(actual, desired)
+
+    def test_beta(self):
+        random.seed(self.seed)
+        actual = random.beta(.1, .9, size=(3, 2))
+        desired = np.array(
+                [[1.45341850513746058e-02, 5.31297615662868145e-04],
+                 [1.85366619058432324e-06, 4.19214516800110563e-03],
+                 [1.58405155108498093e-04, 1.26252891949397652e-04]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_binomial(self):
+        random.seed(self.seed)
+        actual = random.binomial(100.123, .456, size=(3, 2))
+        desired = np.array([[37, 43],
+                            [42, 48],
+                            [46, 45]])
+        assert_array_equal(actual, desired)
+
+        random.seed(self.seed)
+        actual = random.binomial(100.123, .456)
+        desired = 37
+        assert_array_equal(actual, desired)
+
+    def test_chisquare(self):
+        random.seed(self.seed)
+        actual = random.chisquare(50, size=(3, 2))
+        desired = np.array([[63.87858175501090585, 68.68407748911370447],
+                            [65.77116116901505904, 47.09686762438974483],
+                            [72.3828403199695174, 74.18408615260374006]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_dirichlet(self):
+        random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha, size=(3, 2))
+        desired = np.array([[[0.54539444573611562, 0.45460555426388438],
+                             [0.62345816822039413, 0.37654183177960598]],
+                            [[0.55206000085785778, 0.44793999914214233],
+                             [0.58964023305154301, 0.41035976694845688]],
+                            [[0.59266909280647828, 0.40733090719352177],
+                             [0.56974431743975207, 0.43025568256024799]]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+        bad_alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, bad_alpha)
+
+        random.seed(self.seed)
+        alpha = np.array([51.72840233779265162, 39.74494232180943953])
+        actual = random.dirichlet(alpha)
+        assert_array_almost_equal(actual, desired[0, 0], decimal=15)
+
+    def test_dirichlet_size(self):
+        # gh-3173
+        p = np.array([51.72840233779265162, 39.74494232180943953])
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, np.uint32(1)).shape, (1, 2))
+        assert_equal(random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
+        assert_equal(random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
+
+        assert_raises(TypeError, random.dirichlet, p, float(1))
+
+    def test_dirichlet_bad_alpha(self):
+        # gh-2089
+        alpha = np.array([5.4e-01, -1.0e-16])
+        assert_raises(ValueError, random.dirichlet, alpha)
+
+    def test_dirichlet_alpha_non_contiguous(self):
+        a = np.array([51.72840233779265162, -1.0, 39.74494232180943953])
+        alpha = a[::2]
+        random.seed(self.seed)
+        non_contig = random.dirichlet(alpha, size=(3, 2))
+        random.seed(self.seed)
+        contig = random.dirichlet(np.ascontiguousarray(alpha),
+                                  size=(3, 2))
+        assert_array_almost_equal(non_contig, contig)
+
+    def test_exponential(self):
+        random.seed(self.seed)
+        actual = random.exponential(1.1234, size=(3, 2))
+        desired = np.array([[1.08342649775011624, 1.00607889924557314],
+                            [2.46628830085216721, 2.49668106809923884],
+                            [0.68717433461363442, 1.69175666993575979]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_exponential_0(self):
+        assert_equal(random.exponential(scale=0), 0)
+        assert_raises(ValueError, random.exponential, scale=-0.)
+
+    def test_f(self):
+        random.seed(self.seed)
+        actual = random.f(12, 77, size=(3, 2))
+        desired = np.array([[1.21975394418575878, 1.75135759791559775],
+                            [1.44803115017146489, 1.22108959480396262],
+                            [1.02176975757740629, 1.34431827623300415]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gamma(self):
+        random.seed(self.seed)
+        actual = random.gamma(5, 3, size=(3, 2))
+        desired = np.array([[24.60509188649287182, 28.54993563207210627],
+                            [26.13476110204064184, 12.56988482927716078],
+                            [31.71863275789960568, 33.30143302795922011]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_gamma_0(self):
+        assert_equal(random.gamma(shape=0, scale=0), 0)
+        assert_raises(ValueError, random.gamma, shape=-0., scale=-0.)
+
+    def test_geometric(self):
+        random.seed(self.seed)
+        actual = random.geometric(.123456789, size=(3, 2))
+        desired = np.array([[8, 7],
+                            [17, 17],
+                            [5, 12]])
+        assert_array_equal(actual, desired)
+
+    def test_geometric_exceptions(self):
+        assert_raises(ValueError, random.geometric, 1.1)
+        assert_raises(ValueError, random.geometric, [1.1] * 10)
+        assert_raises(ValueError, random.geometric, -0.1)
+        assert_raises(ValueError, random.geometric, [-0.1] * 10)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.geometric, np.nan)
+            assert_raises(ValueError, random.geometric, [np.nan] * 10)
+
+    def test_gumbel(self):
+        random.seed(self.seed)
+        actual = random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.19591898743416816, 0.34405539668096674],
+                            [-1.4492522252274278, -1.47374816298446865],
+                            [1.10651090478803416, -0.69535848626236174]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_gumbel_0(self):
+        assert_equal(random.gumbel(scale=0), 0)
+        assert_raises(ValueError, random.gumbel, scale=-0.)
+
+    def test_hypergeometric(self):
+        random.seed(self.seed)
+        actual = random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
+        desired = np.array([[10, 10],
+                            [10, 10],
+                            [9, 9]])
+        assert_array_equal(actual, desired)
+
+        # Test nbad = 0
+        actual = random.hypergeometric(5, 0, 3, size=4)
+        desired = np.array([3, 3, 3, 3])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(15, 0, 12, size=4)
+        desired = np.array([12, 12, 12, 12])
+        assert_array_equal(actual, desired)
+
+        # Test ngood = 0
+        actual = random.hypergeometric(0, 5, 3, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+        actual = random.hypergeometric(0, 15, 12, size=4)
+        desired = np.array([0, 0, 0, 0])
+        assert_array_equal(actual, desired)
+
+    def test_laplace(self):
+        random.seed(self.seed)
+        actual = random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[0.66599721112760157, 0.52829452552221945],
+                            [3.12791959514407125, 3.18202813572992005],
+                            [-0.05391065675859356, 1.74901336242837324]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_laplace_0(self):
+        assert_equal(random.laplace(scale=0), 0)
+        assert_raises(ValueError, random.laplace, scale=-0.)
+
+    def test_logistic(self):
+        random.seed(self.seed)
+        actual = random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[1.09232835305011444, 0.8648196662399954],
+                            [4.27818590694950185, 4.33897006346929714],
+                            [-0.21682183359214885, 2.63373365386060332]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_lognormal(self):
+        random.seed(self.seed)
+        actual = random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
+        desired = np.array([[16.50698631688883822, 36.54846706092654784],
+                            [22.67886599981281748, 0.71617561058995771],
+                            [65.72798501792723869, 86.84341601437161273]])
+        assert_array_almost_equal(actual, desired, decimal=13)
+
+    def test_lognormal_0(self):
+        assert_equal(random.lognormal(sigma=0), 1)
+        assert_raises(ValueError, random.lognormal, sigma=-0.)
+
+    def test_logseries(self):
+        random.seed(self.seed)
+        actual = random.logseries(p=.923456789, size=(3, 2))
+        desired = np.array([[2, 2],
+                            [6, 17],
+                            [3, 6]])
+        assert_array_equal(actual, desired)
+
+    def test_logseries_zero(self):
+        assert random.logseries(0) == 1
+
+    @pytest.mark.parametrize("value", [np.nextafter(0., -1), 1., np.nan, 5.])
+    def test_logseries_exceptions(self, value):
+        with np.errstate(invalid="ignore"):
+            with pytest.raises(ValueError):
+                random.logseries(value)
+            with pytest.raises(ValueError):
+                # contiguous path:
+                random.logseries(np.array([value] * 10))
+            with pytest.raises(ValueError):
+                # non-contiguous path:
+                random.logseries(np.array([value] * 10)[::2])
+
+    def test_multinomial(self):
+        random.seed(self.seed)
+        actual = random.multinomial(20, [1 / 6.] * 6, size=(3, 2))
+        desired = np.array([[[4, 3, 5, 4, 2, 2],
+                             [5, 2, 8, 2, 2, 1]],
+                            [[3, 4, 3, 6, 0, 4],
+                             [2, 1, 4, 3, 6, 4]],
+                            [[4, 4, 2, 5, 2, 3],
+                             [4, 3, 4, 2, 3, 4]]])
+        assert_array_equal(actual, desired)
+
+    def test_multivariate_normal(self):
+        random.seed(self.seed)
+        mean = (.123456789, 10)
+        cov = [[1, 0], [0, 1]]
+        size = (3, 2)
+        actual = random.multivariate_normal(mean, cov, size)
+        desired = np.array([[[1.463620246718631, 11.73759122771936],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check for default size, was raising deprecation warning
+        actual = random.multivariate_normal(mean, cov)
+        desired = np.array([0.895289569463708, 9.17180864067987])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
+        mean = [0, 0]
+        cov = [[1, 2], [2, 1]]
+        assert_warns(RuntimeWarning, random.multivariate_normal, mean, cov)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        assert_no_warnings(random.multivariate_normal, mean, cov,
+                           check_valid='ignore')
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='raise')
+
+        cov = np.array([[1, 0.1], [0.1, 1]], dtype=np.float32)
+        with suppress_warnings() as sup:
+            random.multivariate_normal(mean, cov)
+            w = sup.record(RuntimeWarning)
+            assert len(w) == 0
+
+        mu = np.zeros(2)
+        cov = np.eye(2)
+        assert_raises(ValueError, random.multivariate_normal, mean, cov,
+                      check_valid='other')
+        assert_raises(ValueError, random.multivariate_normal,
+                      np.zeros((2, 1, 1)), cov)
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.empty((3, 2)))
+        assert_raises(ValueError, random.multivariate_normal,
+                      mu, np.eye(3))
+
+    def test_negative_binomial(self):
+        random.seed(self.seed)
+        actual = random.negative_binomial(n=100, p=.12345, size=(3, 2))
+        desired = np.array([[848, 841],
+                            [892, 611],
+                            [779, 647]])
+        assert_array_equal(actual, desired)
+
+    def test_negative_binomial_exceptions(self):
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.negative_binomial, 100, np.nan)
+            assert_raises(ValueError, random.negative_binomial, 100,
+                          [np.nan] * 10)
+
+    def test_noncentral_chisquare(self):
+        random.seed(self.seed)
+        actual = random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
+        desired = np.array([[23.91905354498517511, 13.35324692733826346],
+                            [31.22452661329736401, 16.60047399466177254],
+                            [5.03461598262724586, 17.94973089023519464]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        actual = random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
+        desired = np.array([[1.47145377828516666,  0.15052899268012659],
+                            [0.00943803056963588,  1.02647251615666169],
+                            [0.332334982684171,  0.15451287602753125]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        random.seed(self.seed)
+        actual = random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
+        desired = np.array([[9.597154162763948, 11.725484450296079],
+                            [10.413711048138335, 3.694475922923986],
+                            [13.484222138963087, 14.377255424602957]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f(self):
+        random.seed(self.seed)
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=1,
+                                     size=(3, 2))
+        desired = np.array([[1.40598099674926669, 0.34207973179285761],
+                            [3.57715069265772545, 7.92632662577829805],
+                            [0.43741599463544162, 1.1774208752428319]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_noncentral_f_nan(self):
+        random.seed(self.seed)
+        actual = random.noncentral_f(dfnum=5, dfden=2, nonc=np.nan)
+        assert np.isnan(actual)
+
+    def test_normal(self):
+        random.seed(self.seed)
+        actual = random.normal(loc=.123456789, scale=2.0, size=(3, 2))
+        desired = np.array([[2.80378370443726244, 3.59863924443872163],
+                            [3.121433477601256, -0.33382987590723379],
+                            [4.18552478636557357, 4.46410668111310471]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_normal_0(self):
+        assert_equal(random.normal(scale=0), 0)
+        assert_raises(ValueError, random.normal, scale=-0.)
+
+    def test_pareto(self):
+        random.seed(self.seed)
+        actual = random.pareto(a=.123456789, size=(3, 2))
+        desired = np.array(
+                [[2.46852460439034849e+03, 1.41286880810518346e+03],
+                 [5.28287797029485181e+07, 6.57720981047328785e+07],
+                 [1.40840323350391515e+02, 1.98390255135251704e+05]])
+        # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
+        # matrix differs by 24 nulps. Discussion:
+        #   https://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
+        # Consensus is that this is probably some gcc quirk that affects
+        # rounding but not in any important way, so we just use a looser
+        # tolerance on this test:
+        np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
+
+    def test_poisson(self):
+        random.seed(self.seed)
+        actual = random.poisson(lam=.123456789, size=(3, 2))
+        desired = np.array([[0, 0],
+                            [1, 0],
+                            [0, 0]])
+        assert_array_equal(actual, desired)
+
+    def test_poisson_exceptions(self):
+        lambig = np.iinfo('l').max
+        lamneg = -1
+        assert_raises(ValueError, random.poisson, lamneg)
+        assert_raises(ValueError, random.poisson, [lamneg] * 10)
+        assert_raises(ValueError, random.poisson, lambig)
+        assert_raises(ValueError, random.poisson, [lambig] * 10)
+        with suppress_warnings() as sup:
+            sup.record(RuntimeWarning)
+            assert_raises(ValueError, random.poisson, np.nan)
+            assert_raises(ValueError, random.poisson, [np.nan] * 10)
+
+    def test_power(self):
+        random.seed(self.seed)
+        actual = random.power(a=.123456789, size=(3, 2))
+        desired = np.array([[0.02048932883240791, 0.01424192241128213],
+                            [0.38446073748535298, 0.39499689943484395],
+                            [0.00177699707563439, 0.13115505880863756]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_rayleigh(self):
+        random.seed(self.seed)
+        actual = random.rayleigh(scale=10, size=(3, 2))
+        desired = np.array([[13.8882496494248393, 13.383318339044731],
+                            [20.95413364294492098, 21.08285015800712614],
+                            [11.06066537006854311, 17.35468505778271009]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_rayleigh_0(self):
+        assert_equal(random.rayleigh(scale=0), 0)
+        assert_raises(ValueError, random.rayleigh, scale=-0.)
+
+    def test_standard_cauchy(self):
+        random.seed(self.seed)
+        actual = random.standard_cauchy(size=(3, 2))
+        desired = np.array([[0.77127660196445336, -6.55601161955910605],
+                            [0.93582023391158309, -2.07479293013759447],
+                            [-4.74601644297011926, 0.18338989290760804]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_exponential(self):
+        random.seed(self.seed)
+        actual = random.standard_exponential(size=(3, 2))
+        desired = np.array([[0.96441739162374596, 0.89556604882105506],
+                            [2.1953785836319808, 2.22243285392490542],
+                            [0.6116915921431676, 1.50592546727413201]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_gamma(self):
+        random.seed(self.seed)
+        actual = random.standard_gamma(shape=3, size=(3, 2))
+        desired = np.array([[5.50841531318455058, 6.62953470301903103],
+                            [5.93988484943779227, 2.31044849402133989],
+                            [7.54838614231317084, 8.012756093271868]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_standard_gamma_0(self):
+        assert_equal(random.standard_gamma(shape=0), 0)
+        assert_raises(ValueError, random.standard_gamma, shape=-0.)
+
+    def test_standard_normal(self):
+        random.seed(self.seed)
+        actual = random.standard_normal(size=(3, 2))
+        desired = np.array([[1.34016345771863121, 1.73759122771936081],
+                            [1.498988344300628, -0.2286433324536169],
+                            [2.031033998682787, 2.17032494605655257]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_randn_singleton(self):
+        random.seed(self.seed)
+        actual = random.randn()
+        desired = np.array(1.34016345771863121)
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_standard_t(self):
+        random.seed(self.seed)
+        actual = random.standard_t(df=10, size=(3, 2))
+        desired = np.array([[0.97140611862659965, -0.08830486548450577],
+                            [1.36311143689505321, -0.55317463909867071],
+                            [-0.18473749069684214, 0.61181537341755321]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_triangular(self):
+        random.seed(self.seed)
+        actual = random.triangular(left=5.12, mode=10.23, right=20.34,
+                                   size=(3, 2))
+        desired = np.array([[12.68117178949215784, 12.4129206149193152],
+                            [16.20131377335158263, 16.25692138747600524],
+                            [11.20400690911820263, 14.4978144835829923]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_uniform(self):
+        random.seed(self.seed)
+        actual = random.uniform(low=1.23, high=10.54, size=(3, 2))
+        desired = np.array([[6.99097932346268003, 6.73801597444323974],
+                            [9.50364421400426274, 9.53130618907631089],
+                            [5.48995325769805476, 8.47493103280052118]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_uniform_range_bounds(self):
+        fmin = np.finfo('float').min
+        fmax = np.finfo('float').max
+
+        func = random.uniform
+        assert_raises(OverflowError, func, -np.inf, 0)
+        assert_raises(OverflowError, func, 0, np.inf)
+        assert_raises(OverflowError, func, fmin, fmax)
+        assert_raises(OverflowError, func, [-np.inf], [0])
+        assert_raises(OverflowError, func, [0], [np.inf])
+
+        # (fmax / 1e17) - fmin is within range, so this should not throw
+        # account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
+        # DBL_MAX by increasing fmin a bit
+        random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
+
+    def test_scalar_exception_propagation(self):
+        # Tests that exceptions are correctly propagated in distributions
+        # when called with objects that throw exceptions when converted to
+        # scalars.
+        #
+        # Regression test for gh: 8865
+
+        class ThrowingFloat(np.ndarray):
+            def __float__(self):
+                raise TypeError
+
+        throwing_float = np.array(1.0).view(ThrowingFloat)
+        assert_raises(TypeError, random.uniform, throwing_float,
+                      throwing_float)
+
+        class ThrowingInteger(np.ndarray):
+            def __int__(self):
+                raise TypeError
+
+        throwing_int = np.array(1).view(ThrowingInteger)
+        assert_raises(TypeError, random.hypergeometric, throwing_int, 1, 1)
+
+    def test_vonmises(self):
+        random.seed(self.seed)
+        actual = random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
+        desired = np.array([[2.28567572673902042, 2.89163838442285037],
+                            [0.38198375564286025, 2.57638023113890746],
+                            [1.19153771588353052, 1.83509849681825354]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_vonmises_small(self):
+        # check infinite loop, gh-4720
+        random.seed(self.seed)
+        r = random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
+        assert_(np.isfinite(r).all())
+
+    def test_vonmises_large(self):
+        # guard against changes in RandomState when Generator is fixed
+        random.seed(self.seed)
+        actual = random.vonmises(mu=0., kappa=1e7, size=3)
+        desired = np.array([4.634253748521111e-04,
+                            3.558873596114509e-04,
+                            -2.337119622577433e-04])
+        assert_array_almost_equal(actual, desired, decimal=8)
+
+    def test_vonmises_nan(self):
+        random.seed(self.seed)
+        r = random.vonmises(mu=0., kappa=np.nan)
+        assert_(np.isnan(r))
+
+    def test_wald(self):
+        random.seed(self.seed)
+        actual = random.wald(mean=1.23, scale=1.54, size=(3, 2))
+        desired = np.array([[3.82935265715889983, 5.13125249184285526],
+                            [0.35045403618358717, 1.50832396872003538],
+                            [0.24124319895843183, 0.22031101461955038]])
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_weibull(self):
+        random.seed(self.seed)
+        actual = random.weibull(a=1.23, size=(3, 2))
+        desired = np.array([[0.97097342648766727, 0.91422896443565516],
+                            [1.89517770034962929, 1.91414357960479564],
+                            [0.67057783752390987, 1.39494046635066793]])
+        assert_array_almost_equal(actual, desired, decimal=15)
+
+    def test_weibull_0(self):
+        random.seed(self.seed)
+        assert_equal(random.weibull(a=0, size=12), np.zeros(12))
+        assert_raises(ValueError, random.weibull, a=-0.)
+
+    def test_zipf(self):
+        random.seed(self.seed)
+        actual = random.zipf(a=1.23, size=(3, 2))
+        desired = np.array([[66, 29],
+                            [1, 1],
+                            [3, 13]])
+        assert_array_equal(actual, desired)
+
+
+class TestBroadcast:
+    # tests that functions that broadcast behave
+    # correctly when presented with non-scalar arguments
+    def setup_method(self):
+        self.seed = 123456789
+
+    def set_seed(self):
+        random.seed(self.seed)
+
+    def test_uniform(self):
+        low = [0]
+        high = [1]
+        uniform = random.uniform
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.set_seed()
+        actual = uniform(low * 3, high)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+        self.set_seed()
+        actual = uniform(low, high * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_normal(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        normal = random.normal
+        desired = np.array([2.2129019979039612,
+                            2.1283977976520019,
+                            1.8417114045748335])
+
+        self.set_seed()
+        actual = normal(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = normal(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, normal, loc, bad_scale * 3)
+
+    def test_beta(self):
+        a = [1]
+        b = [2]
+        bad_a = [-1]
+        bad_b = [-2]
+        beta = random.beta
+        desired = np.array([0.19843558305989056,
+                            0.075230336409423643,
+                            0.24976865978980844])
+
+        self.set_seed()
+        actual = beta(a * 3, b)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a * 3, b)
+        assert_raises(ValueError, beta, a * 3, bad_b)
+
+        self.set_seed()
+        actual = beta(a, b * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, beta, bad_a, b * 3)
+        assert_raises(ValueError, beta, a, bad_b * 3)
+
+    def test_exponential(self):
+        scale = [1]
+        bad_scale = [-1]
+        exponential = random.exponential
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = exponential(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, exponential, bad_scale * 3)
+
+    def test_standard_gamma(self):
+        shape = [1]
+        bad_shape = [-1]
+        std_gamma = random.standard_gamma
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = std_gamma(shape * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, std_gamma, bad_shape * 3)
+
+    def test_gamma(self):
+        shape = [1]
+        scale = [2]
+        bad_shape = [-1]
+        bad_scale = [-2]
+        gamma = random.gamma
+        desired = np.array([1.5221370731769048,
+                            1.5277256455738331,
+                            1.4248762625178359])
+
+        self.set_seed()
+        actual = gamma(shape * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape * 3, scale)
+        assert_raises(ValueError, gamma, shape * 3, bad_scale)
+
+        self.set_seed()
+        actual = gamma(shape, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gamma, bad_shape, scale * 3)
+        assert_raises(ValueError, gamma, shape, bad_scale * 3)
+
+    def test_f(self):
+        dfnum = [1]
+        dfden = [2]
+        bad_dfnum = [-1]
+        bad_dfden = [-2]
+        f = random.f
+        desired = np.array([0.80038951638264799,
+                            0.86768719635363512,
+                            2.7251095168386801])
+
+        self.set_seed()
+        actual = f(dfnum * 3, dfden)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum * 3, dfden)
+        assert_raises(ValueError, f, dfnum * 3, bad_dfden)
+
+        self.set_seed()
+        actual = f(dfnum, dfden * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, f, bad_dfnum, dfden * 3)
+        assert_raises(ValueError, f, dfnum, bad_dfden * 3)
+
+    def test_noncentral_f(self):
+        dfnum = [2]
+        dfden = [3]
+        nonc = [4]
+        bad_dfnum = [0]
+        bad_dfden = [-1]
+        bad_nonc = [-2]
+        nonc_f = random.noncentral_f
+        desired = np.array([9.1393943263705211,
+                            13.025456344595602,
+                            8.8018098359100545])
+
+        self.set_seed()
+        actual = nonc_f(dfnum * 3, dfden, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert np.all(np.isnan(nonc_f(dfnum, dfden, [np.nan] * 3)))
+
+        assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
+        assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_f(dfnum, dfden * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
+        assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_f(dfnum, dfden, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
+        assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
+
+    def test_noncentral_f_small_df(self):
+        self.set_seed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+
+    def test_chisquare(self):
+        df = [1]
+        bad_df = [-1]
+        chisquare = random.chisquare
+        desired = np.array([0.57022801133088286,
+                            0.51947702108840776,
+                            0.1320969254923558])
+
+        self.set_seed()
+        actual = chisquare(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, chisquare, bad_df * 3)
+
+    def test_noncentral_chisquare(self):
+        df = [1]
+        nonc = [2]
+        bad_df = [-1]
+        bad_nonc = [-2]
+        nonc_chi = random.noncentral_chisquare
+        desired = np.array([9.0015599467913763,
+                            4.5804135049718742,
+                            6.0872302432834564])
+
+        self.set_seed()
+        actual = nonc_chi(df * 3, nonc)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
+        assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
+
+        self.set_seed()
+        actual = nonc_chi(df, nonc * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
+        assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
+
+    def test_standard_t(self):
+        df = [1]
+        bad_df = [-1]
+        t = random.standard_t
+        desired = np.array([3.0702872575217643,
+                            5.8560725167361607,
+                            1.0274791436474273])
+
+        self.set_seed()
+        actual = t(df * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, t, bad_df * 3)
+        assert_raises(ValueError, random.standard_t, bad_df * 3)
+
+    def test_vonmises(self):
+        mu = [2]
+        kappa = [1]
+        bad_kappa = [-1]
+        vonmises = random.vonmises
+        desired = np.array([2.9883443664201312,
+                            -2.7064099483995943,
+                            -1.8672476700665914])
+
+        self.set_seed()
+        actual = vonmises(mu * 3, kappa)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
+
+        self.set_seed()
+        actual = vonmises(mu, kappa * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
+
+    def test_pareto(self):
+        a = [1]
+        bad_a = [-1]
+        pareto = random.pareto
+        desired = np.array([1.1405622680198362,
+                            1.1465519762044529,
+                            1.0389564467453547])
+
+        self.set_seed()
+        actual = pareto(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, pareto, bad_a * 3)
+        assert_raises(ValueError, random.pareto, bad_a * 3)
+
+    def test_weibull(self):
+        a = [1]
+        bad_a = [-1]
+        weibull = random.weibull
+        desired = np.array([0.76106853658845242,
+                            0.76386282278691653,
+                            0.71243813125891797])
+
+        self.set_seed()
+        actual = weibull(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, weibull, bad_a * 3)
+        assert_raises(ValueError, random.weibull, bad_a * 3)
+
+    def test_power(self):
+        a = [1]
+        bad_a = [-1]
+        power = random.power
+        desired = np.array([0.53283302478975902,
+                            0.53413660089041659,
+                            0.50955303552646702])
+
+        self.set_seed()
+        actual = power(a * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, power, bad_a * 3)
+        assert_raises(ValueError, random.power, bad_a * 3)
+
+    def test_laplace(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        laplace = random.laplace
+        desired = np.array([0.067921356028507157,
+                            0.070715642226971326,
+                            0.019290950698972624])
+
+        self.set_seed()
+        actual = laplace(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = laplace(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, laplace, loc, bad_scale * 3)
+
+    def test_gumbel(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        gumbel = random.gumbel
+        desired = np.array([0.2730318639556768,
+                            0.26936705726291116,
+                            0.33906220393037939])
+
+        self.set_seed()
+        actual = gumbel(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = gumbel(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, gumbel, loc, bad_scale * 3)
+
+    def test_logistic(self):
+        loc = [0]
+        scale = [1]
+        bad_scale = [-1]
+        logistic = random.logistic
+        desired = np.array([0.13152135837586171,
+                            0.13675915696285773,
+                            0.038216792802833396])
+
+        self.set_seed()
+        actual = logistic(loc * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc * 3, bad_scale)
+
+        self.set_seed()
+        actual = logistic(loc, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, logistic, loc, bad_scale * 3)
+        assert_equal(random.logistic(1.0, 0.0), 1.0)
+
+    def test_lognormal(self):
+        mean = [0]
+        sigma = [1]
+        bad_sigma = [-1]
+        lognormal = random.lognormal
+        desired = np.array([9.1422086044848427,
+                            8.4013952870126261,
+                            6.3073234116578671])
+
+        self.set_seed()
+        actual = lognormal(mean * 3, sigma)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
+        assert_raises(ValueError, random.lognormal, mean * 3, bad_sigma)
+
+        self.set_seed()
+        actual = lognormal(mean, sigma * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
+        assert_raises(ValueError, random.lognormal, mean, bad_sigma * 3)
+
+    def test_rayleigh(self):
+        scale = [1]
+        bad_scale = [-1]
+        rayleigh = random.rayleigh
+        desired = np.array([1.2337491937897689,
+                            1.2360119924878694,
+                            1.1936818095781789])
+
+        self.set_seed()
+        actual = rayleigh(scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, rayleigh, bad_scale * 3)
+
+    def test_wald(self):
+        mean = [0.5]
+        scale = [1]
+        bad_mean = [0]
+        bad_scale = [-2]
+        wald = random.wald
+        desired = np.array([0.11873681120271318,
+                            0.12450084820795027,
+                            0.9096122728408238])
+
+        self.set_seed()
+        actual = wald(mean * 3, scale)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean * 3, scale)
+        assert_raises(ValueError, wald, mean * 3, bad_scale)
+        assert_raises(ValueError, random.wald, bad_mean * 3, scale)
+        assert_raises(ValueError, random.wald, mean * 3, bad_scale)
+
+        self.set_seed()
+        actual = wald(mean, scale * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, wald, bad_mean, scale * 3)
+        assert_raises(ValueError, wald, mean, bad_scale * 3)
+        assert_raises(ValueError, wald, 0.0, 1)
+        assert_raises(ValueError, wald, 0.5, 0.0)
+
+    def test_triangular(self):
+        left = [1]
+        right = [3]
+        mode = [2]
+        bad_left_one = [3]
+        bad_mode_one = [4]
+        bad_left_two, bad_mode_two = right * 2
+        triangular = random.triangular
+        desired = np.array([2.03339048710429,
+                            2.0347400359389356,
+                            2.0095991069536208])
+
+        self.set_seed()
+        actual = triangular(left * 3, mode, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
+        assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
+        assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two,
+                      right)
+
+        self.set_seed()
+        actual = triangular(left, mode * 3, right)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
+        assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3,
+                      right)
+
+        self.set_seed()
+        actual = triangular(left, mode, right * 3)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
+        assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
+        assert_raises(ValueError, triangular, bad_left_two, bad_mode_two,
+                      right * 3)
+
+        assert_raises(ValueError, triangular, 10., 0., 20.)
+        assert_raises(ValueError, triangular, 10., 25., 20.)
+        assert_raises(ValueError, triangular, 10., 10., 10.)
+
+    def test_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        binom = random.binomial
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n * 3, p)
+        assert_raises(ValueError, binom, n * 3, bad_p_one)
+        assert_raises(ValueError, binom, n * 3, bad_p_two)
+
+        self.set_seed()
+        actual = binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, binom, bad_n, p * 3)
+        assert_raises(ValueError, binom, n, bad_p_one * 3)
+        assert_raises(ValueError, binom, n, bad_p_two * 3)
+
+    def test_negative_binomial(self):
+        n = [1]
+        p = [0.5]
+        bad_n = [-1]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        neg_binom = random.negative_binomial
+        desired = np.array([1, 0, 1])
+
+        self.set_seed()
+        actual = neg_binom(n * 3, p)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n * 3, p)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
+        assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
+
+        self.set_seed()
+        actual = neg_binom(n, p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, neg_binom, bad_n, p * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
+        assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
+
+    def test_poisson(self):
+        max_lam = random.RandomState()._poisson_lam_max
+
+        lam = [1]
+        bad_lam_one = [-1]
+        bad_lam_two = [max_lam * 2]
+        poisson = random.poisson
+        desired = np.array([1, 1, 0])
+
+        self.set_seed()
+        actual = poisson(lam * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, poisson, bad_lam_one * 3)
+        assert_raises(ValueError, poisson, bad_lam_two * 3)
+
+    def test_zipf(self):
+        a = [2]
+        bad_a = [0]
+        zipf = random.zipf
+        desired = np.array([2, 2, 1])
+
+        self.set_seed()
+        actual = zipf(a * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, zipf, bad_a * 3)
+        with np.errstate(invalid='ignore'):
+            assert_raises(ValueError, zipf, np.nan)
+            assert_raises(ValueError, zipf, [0, 0, np.nan])
+
+    def test_geometric(self):
+        p = [0.5]
+        bad_p_one = [-1]
+        bad_p_two = [1.5]
+        geom = random.geometric
+        desired = np.array([2, 2, 2])
+
+        self.set_seed()
+        actual = geom(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, geom, bad_p_one * 3)
+        assert_raises(ValueError, geom, bad_p_two * 3)
+
+    def test_hypergeometric(self):
+        ngood = [1]
+        nbad = [2]
+        nsample = [2]
+        bad_ngood = [-1]
+        bad_nbad = [-2]
+        bad_nsample_one = [0]
+        bad_nsample_two = [4]
+        hypergeom = random.hypergeometric
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = hypergeom(ngood * 3, nbad, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
+
+        self.set_seed()
+        actual = hypergeom(ngood, nbad * 3, nsample)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
+        assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
+
+        self.set_seed()
+        actual = hypergeom(ngood, nbad, nsample * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
+        assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
+
+        assert_raises(ValueError, hypergeom, -1, 10, 20)
+        assert_raises(ValueError, hypergeom, 10, -1, 20)
+        assert_raises(ValueError, hypergeom, 10, 10, 0)
+        assert_raises(ValueError, hypergeom, 10, 10, 25)
+
+    def test_logseries(self):
+        p = [0.5]
+        bad_p_one = [2]
+        bad_p_two = [-1]
+        logseries = random.logseries
+        desired = np.array([1, 1, 1])
+
+        self.set_seed()
+        actual = logseries(p * 3)
+        assert_array_equal(actual, desired)
+        assert_raises(ValueError, logseries, bad_p_one * 3)
+        assert_raises(ValueError, logseries, bad_p_two * 3)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start thread")
+class TestThread:
+    # make sure each state produces the same sequence even in threads
+    def setup_method(self):
+        self.seeds = range(4)
+
+    def check_function(self, function, sz):
+        from threading import Thread
+
+        out1 = np.empty((len(self.seeds),) + sz)
+        out2 = np.empty((len(self.seeds),) + sz)
+
+        # threaded generation
+        t = [Thread(target=function, args=(random.RandomState(s), o))
+             for s, o in zip(self.seeds, out1)]
+        [x.start() for x in t]
+        [x.join() for x in t]
+
+        # the same serial
+        for s, o in zip(self.seeds, out2):
+            function(random.RandomState(s), o)
+
+        # these platforms change x87 fpu precision mode in threads
+        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
+            assert_array_almost_equal(out1, out2)
+        else:
+            assert_array_equal(out1, out2)
+
+    def test_normal(self):
+        def gen_random(state, out):
+            out[...] = state.normal(size=10000)
+
+        self.check_function(gen_random, sz=(10000,))
+
+    def test_exp(self):
+        def gen_random(state, out):
+            out[...] = state.exponential(scale=np.ones((100, 1000)))
+
+        self.check_function(gen_random, sz=(100, 1000))
+
+    def test_multinomial(self):
+        def gen_random(state, out):
+            out[...] = state.multinomial(10, [1 / 6.] * 6, size=10000)
+
+        self.check_function(gen_random, sz=(10000, 6))
+
+
+# See Issue #4263
+class TestSingleEltArrayInput:
+    def setup_method(self):
+        self.argOne = np.array([2])
+        self.argTwo = np.array([3])
+        self.argThree = np.array([4])
+        self.tgtShape = (1,)
+
+    def test_one_arg_funcs(self):
+        funcs = (random.exponential, random.standard_gamma,
+                 random.chisquare, random.standard_t,
+                 random.pareto, random.weibull,
+                 random.power, random.rayleigh,
+                 random.poisson, random.zipf,
+                 random.geometric, random.logseries)
+
+        probfuncs = (random.geometric, random.logseries)
+
+        for func in funcs:
+            if func in probfuncs:  # p < 1.0
+                out = func(np.array([0.5]))
+
+            else:
+                out = func(self.argOne)
+
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_two_arg_funcs(self):
+        funcs = (random.uniform, random.normal,
+                 random.beta, random.gamma,
+                 random.f, random.noncentral_chisquare,
+                 random.vonmises, random.laplace,
+                 random.gumbel, random.logistic,
+                 random.lognormal, random.wald,
+                 random.binomial, random.negative_binomial)
+
+        probfuncs = (random.binomial, random.negative_binomial)
+
+        for func in funcs:
+            if func in probfuncs:  # p <= 1
+                argTwo = np.array([0.5])
+
+            else:
+                argTwo = self.argTwo
+
+            out = func(self.argOne, argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], argTwo)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, argTwo[0])
+            assert_equal(out.shape, self.tgtShape)
+
+    def test_three_arg_funcs(self):
+        funcs = [random.noncentral_f, random.triangular,
+                 random.hypergeometric]
+
+        for func in funcs:
+            out = func(self.argOne, self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne[0], self.argTwo, self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+            out = func(self.argOne, self.argTwo[0], self.argThree)
+            assert_equal(out.shape, self.tgtShape)
+
+
+# Ensure returned array dtype is correct for platform
+def test_integer_dtype(int_func):
+    random.seed(123456789)
+    fname, args, sha256 = int_func
+    f = getattr(random, fname)
+    actual = f(*args, size=2)
+    assert_(actual.dtype == np.dtype('l'))
+
+
+def test_integer_repeat(int_func):
+    random.seed(123456789)
+    fname, args, sha256 = int_func
+    f = getattr(random, fname)
+    val = f(*args, size=1000000)
+    if sys.byteorder != 'little':
+        val = val.byteswap()
+    res = hashlib.sha256(val.view(np.int8)).hexdigest()
+    assert_(res == sha256)
+
+
+def test_broadcast_size_error():
+    # GH-16833
+    with pytest.raises(ValueError):
+        random.binomial(1, [0.3, 0.7], size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], 0.3, size=(2, 1))
+    with pytest.raises(ValueError):
+        random.binomial([1, 2], [0.3, 0.7], size=(2, 1))
+
+
+def test_randomstate_ctor_old_style_pickle():
+    rs = np.random.RandomState(MT19937(0))
+    rs.standard_normal(1)
+    # Directly call reduce which is used in pickling
+    ctor, args, state_a = rs.__reduce__()
+    # Simulate unpickling an old pickle that only has the name
+    assert args[:1] == ("MT19937",)
+    b = ctor(*args[:1])
+    b.set_state(state_a)
+    state_b = b.get_state(legacy=False)
+
+    assert_equal(state_a['bit_generator'], state_b['bit_generator'])
+    assert_array_equal(state_a['state']['key'], state_b['state']['key'])
+    assert_array_equal(state_a['state']['pos'], state_b['state']['pos'])
+    assert_equal(state_a['has_gauss'], state_b['has_gauss'])
+    assert_equal(state_a['gauss'], state_b['gauss'])
+
+
+def test_hot_swap(restore_singleton_bitgen):
+    # GH 21808
+    def_bg = np.random.default_rng(0)
+    bg = def_bg.bit_generator
+    np.random.set_bit_generator(bg)
+    assert isinstance(np.random.mtrand._rand._bit_generator, type(bg))
+
+    second_bg = np.random.get_bit_generator()
+    assert bg is second_bg
+
+
+def test_seed_alt_bit_gen(restore_singleton_bitgen):
+    # GH 21808
+    bg = PCG64(0)
+    np.random.set_bit_generator(bg)
+    state = np.random.get_state(legacy=False)
+    np.random.seed(1)
+    new_state = np.random.get_state(legacy=False)
+    print(state)
+    print(new_state)
+    assert state["bit_generator"] == "PCG64"
+    assert state["state"]["state"] != new_state["state"]["state"]
+    assert state["state"]["inc"] != new_state["state"]["inc"]
+
+
+def test_state_error_alt_bit_gen(restore_singleton_bitgen):
+    # GH 21808
+    state = np.random.get_state()
+    bg = PCG64(0)
+    np.random.set_bit_generator(bg)
+    with pytest.raises(ValueError, match="state must be for a PCG64"):
+        np.random.set_state(state)
+
+
+def test_swap_worked(restore_singleton_bitgen):
+    # GH 21808
+    np.random.seed(98765)
+    vals = np.random.randint(0, 2 ** 30, 10)
+    bg = PCG64(0)
+    state = bg.state
+    np.random.set_bit_generator(bg)
+    state_direct = np.random.get_state(legacy=False)
+    for field in state:
+        assert state[field] == state_direct[field]
+    np.random.seed(98765)
+    pcg_vals = np.random.randint(0, 2 ** 30, 10)
+    assert not np.all(vals == pcg_vals)
+    new_state = bg.state
+    assert new_state["state"]["state"] != state["state"]["state"]
+    assert new_state["state"]["inc"] == new_state["state"]["inc"]
+
+
+def test_swapped_singleton_against_direct(restore_singleton_bitgen):
+    np.random.set_bit_generator(PCG64(98765))
+    singleton_vals = np.random.randint(0, 2 ** 30, 10)
+    rg = np.random.RandomState(PCG64(98765))
+    non_singleton_vals = rg.randint(0, 2 ** 30, 10)
+    assert_equal(non_singleton_vals, singleton_vals)
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_randomstate_regression.py b/lib/python3.11/site-packages/numpy/random/tests/test_randomstate_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ad19ab5562b87305a0391c80c602259816a984e
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_randomstate_regression.py
@@ -0,0 +1,216 @@
+import sys
+
+import pytest
+
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises,
+    )
+import numpy as np
+
+from numpy import random
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = random.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        is_64bits = sys.maxsize > 2**32
+        if is_64bits and sys.platform != 'win32':
+            # Check for 64-bit systems
+            args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
+        for arg in args:
+            assert_(random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        random.seed(0)
+        rvsn = random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            random.seed(12345)
+            shuffled = list(t)
+            random.shuffle(shuffled)
+            expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = random.RandomState()
+        res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
+        for i in range(3):
+            random.seed(i)
+            m.seed(4321)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        random.multivariate_normal([0], [[0]], size=1)
+        random.multivariate_normal([0], [[0]], size=np.int_(1))
+        random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        random.seed(1234567890)
+        x = random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        random.seed(1)
+        orig = np.arange(3).view(N)
+        perm = random.permutation(orig)
+        assert_array_equal(perm, np.array([0, 2, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self):
+                return self.a
+
+        random.seed(1)
+        m = M()
+        perm = random.permutation(m)
+        assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_warns_byteorder(self):
+        # GH 13159
+        other_byteord_dt = '<i4' if sys.byteorder == 'big' else '>i4'
+        with pytest.deprecated_call(match='non-native byteorder is not'):
+            random.randint(0, 200, size=10, dtype=other_byteord_dt)
+
+    def test_named_argument_initialization(self):
+        # GH 13669
+        rs1 = np.random.RandomState(123456789)
+        rs2 = np.random.RandomState(seed=123456789)
+        assert rs1.randint(0, 100) == rs2.randint(0, 100)
+
+    def test_choice_retun_dtype(self):
+        # GH 9867
+        c = np.random.choice(10, p=[.1]*10, size=2)
+        assert c.dtype == np.dtype(int)
+        c = np.random.choice(10, p=[.1]*10, replace=False, size=2)
+        assert c.dtype == np.dtype(int)
+        c = np.random.choice(10, size=2)
+        assert c.dtype == np.dtype(int)
+        c = np.random.choice(10, replace=False, size=2)
+        assert c.dtype == np.dtype(int)
+
+    @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+                        reason='Cannot test with 32-bit C long')
+    def test_randint_117(self):
+        # GH 14189
+        random.seed(0)
+        expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
+                             2588848963, 3684848379, 2340255427, 3638918503,
+                             1819583497, 2678185683], dtype='int64')
+        actual = random.randint(2**32, size=10)
+        assert_array_equal(actual, expected)
+
+    def test_p_zero_stream(self):
+        # Regression test for gh-14522.  Ensure that future versions
+        # generate the same variates as version 1.16.
+        np.random.seed(12345)
+        assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),
+                           [0, 0, 0, 1, 1])
+
+    def test_n_zero_stream(self):
+        # Regression test for gh-14522.  Ensure that future versions
+        # generate the same variates as version 1.16.
+        np.random.seed(8675309)
+        expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                             [3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])
+        assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),
+                           expected)
+
+
+def test_multinomial_empty():
+    # gh-20483
+    # Ensure that empty p-vals are correctly handled
+    assert random.multinomial(10, []).shape == (0,)
+    assert random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0)
+
+
+def test_multinomial_1d_pval():
+    # gh-20483
+    with pytest.raises(TypeError, match="pvals must be a 1-d"):
+        random.multinomial(10, 0.3)
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_regression.py b/lib/python3.11/site-packages/numpy/random/tests/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bf419875b3f37cd4cc121030d65be9fc77999a3
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_regression.py
@@ -0,0 +1,149 @@
+import sys
+from numpy.testing import (
+    assert_, assert_array_equal, assert_raises,
+    )
+from numpy import random
+import numpy as np
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7., 7., 5):
+            r = random.mtrand.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        is_64bits = sys.maxsize > 2**32
+        if is_64bits and sys.platform != 'win32':
+            # Check for 64-bit systems
+            args.append((2**40 - 2, 2**40 - 2, 2**40 - 2))
+        for arg in args:
+            assert_(np.random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        np.random.seed(0)
+        rvsn = np.random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f'Frequency was {freq:f}, should be > 0.45'
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f'Frequency was {freq:f}, should be < 0.23'
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        for t in [[1, 2, 3, None],
+                  [(1, 1), (2, 2), (3, 3), None],
+                  [1, (2, 2), (3, 3), None],
+                  [(1, 1), 2, 3, None]]:
+            np.random.seed(12345)
+            shuffled = list(t)
+            random.shuffle(shuffled)
+            expected = np.array([t[0], t[3], t[1], t[2]], dtype=object)
+            assert_array_equal(np.array(shuffled, dtype=object), expected)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = np.random.RandomState()
+        res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
+        for i in range(3):
+            np.random.seed(i)
+            m.seed(4321)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        np.random.multivariate_normal([0], [[0]], size=1)
+        np.random.multivariate_normal([0], [[0]], size=np.int_(1))
+        np.random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        np.random.seed(1234567890)
+        x = np.random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        np.random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = np.random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        np.random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        np.random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        np.random.seed(1)
+        orig = np.arange(3).view(N)
+        perm = np.random.permutation(orig)
+        assert_array_equal(perm, np.array([0, 2, 1]))
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self):
+                return self.a
+
+        np.random.seed(1)
+        m = M()
+        perm = np.random.permutation(m)
+        assert_array_equal(perm, np.array([2, 1, 4, 0, 3]))
+        assert_array_equal(m.__array__(), np.arange(5))
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_seed_sequence.py b/lib/python3.11/site-packages/numpy/random/tests/test_seed_sequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..f08cf80faafa2fc1a369eaf7dd4d6fcccd5e9158
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_seed_sequence.py
@@ -0,0 +1,80 @@
+import numpy as np
+from numpy.testing import assert_array_equal, assert_array_compare
+
+from numpy.random import SeedSequence
+
+
+def test_reference_data():
+    """ Check that SeedSequence generates data the same as the C++ reference.
+
+    https://gist.github.com/imneme/540829265469e673d045
+    """
+    inputs = [
+        [3735928559, 195939070, 229505742, 305419896],
+        [3668361503, 4165561550, 1661411377, 3634257570],
+        [164546577, 4166754639, 1765190214, 1303880213],
+        [446610472, 3941463886, 522937693, 1882353782],
+        [1864922766, 1719732118, 3882010307, 1776744564],
+        [4141682960, 3310988675, 553637289, 902896340],
+        [1134851934, 2352871630, 3699409824, 2648159817],
+        [1240956131, 3107113773, 1283198141, 1924506131],
+        [2669565031, 579818610, 3042504477, 2774880435],
+        [2766103236, 2883057919, 4029656435, 862374500],
+    ]
+    outputs = [
+        [3914649087, 576849849, 3593928901, 2229911004],
+        [2240804226, 3691353228, 1365957195, 2654016646],
+        [3562296087, 3191708229, 1147942216, 3726991905],
+        [1403443605, 3591372999, 1291086759, 441919183],
+        [1086200464, 2191331643, 560336446, 3658716651],
+        [3249937430, 2346751812, 847844327, 2996632307],
+        [2584285912, 4034195531, 3523502488, 169742686],
+        [959045797, 3875435559, 1886309314, 359682705],
+        [3978441347, 432478529, 3223635119, 138903045],
+        [296367413, 4262059219, 13109864, 3283683422],
+    ]
+    outputs64 = [
+        [2477551240072187391, 9577394838764454085],
+        [15854241394484835714, 11398914698975566411],
+        [13708282465491374871, 16007308345579681096],
+        [15424829579845884309, 1898028439751125927],
+        [9411697742461147792, 15714068361935982142],
+        [10079222287618677782, 12870437757549876199],
+        [17326737873898640088, 729039288628699544],
+        [16644868984619524261, 1544825456798124994],
+        [1857481142255628931, 596584038813451439],
+        [18305404959516669237, 14103312907920476776],
+    ]
+    for seed, expected, expected64 in zip(inputs, outputs, outputs64):
+        expected = np.array(expected, dtype=np.uint32)
+        ss = SeedSequence(seed)
+        state = ss.generate_state(len(expected))
+        assert_array_equal(state, expected)
+        state64 = ss.generate_state(len(expected64), dtype=np.uint64)
+        assert_array_equal(state64, expected64)
+
+
+def test_zero_padding():
+    """ Ensure that the implicit zero-padding does not cause problems.
+    """
+    # Ensure that large integers are inserted in little-endian fashion to avoid
+    # trailing 0s.
+    ss0 = SeedSequence(42)
+    ss1 = SeedSequence(42 << 32)
+    assert_array_compare(
+        np.not_equal,
+        ss0.generate_state(4),
+        ss1.generate_state(4))
+
+    # Ensure backwards compatibility with the original 0.17 release for small
+    # integers and no spawn key.
+    expected42 = np.array([3444837047, 2669555309, 2046530742, 3581440988],
+                          dtype=np.uint32)
+    assert_array_equal(SeedSequence(42).generate_state(4), expected42)
+
+    # Regression test for gh-16539 to ensure that the implicit 0s don't
+    # conflict with spawn keys.
+    assert_array_compare(
+        np.not_equal,
+        SeedSequence(42, spawn_key=(0,)).generate_state(4),
+        expected42)
diff --git a/lib/python3.11/site-packages/numpy/random/tests/test_smoke.py b/lib/python3.11/site-packages/numpy/random/tests/test_smoke.py
new file mode 100644
index 0000000000000000000000000000000000000000..9becc434d0d1a66b7c9987d8c5dffdf221fd45b1
--- /dev/null
+++ b/lib/python3.11/site-packages/numpy/random/tests/test_smoke.py
@@ -0,0 +1,818 @@
+import pickle
+from functools import partial
+
+import numpy as np
+import pytest
+from numpy.testing import assert_equal, assert_, assert_array_equal
+from numpy.random import (Generator, MT19937, PCG64, PCG64DXSM, Philox, SFC64)
+
+@pytest.fixture(scope='module',
+                params=(np.bool_, np.int8, np.int16, np.int32, np.int64,
+                        np.uint8, np.uint16, np.uint32, np.uint64))
+def dtype(request):
+    return request.param
+
+
+def params_0(f):
+    val = f()
+    assert_(np.isscalar(val))
+    val = f(10)
+    assert_(val.shape == (10,))
+    val = f((10, 10))
+    assert_(val.shape == (10, 10))
+    val = f((10, 10, 10))
+    assert_(val.shape == (10, 10, 10))
+    val = f(size=(5, 5))
+    assert_(val.shape == (5, 5))
+
+
+def params_1(f, bounded=False):
+    a = 5.0
+    b = np.arange(2.0, 12.0)
+    c = np.arange(2.0, 102.0).reshape((10, 10))
+    d = np.arange(2.0, 1002.0).reshape((10, 10, 10))
+    e = np.array([2.0, 3.0])
+    g = np.arange(2.0, 12.0).reshape((1, 10, 1))
+    if bounded:
+        a = 0.5
+        b = b / (1.5 * b.max())
+        c = c / (1.5 * c.max())
+        d = d / (1.5 * d.max())
+        e = e / (1.5 * e.max())
+        g = g / (1.5 * g.max())
+
+    # Scalar
+    f(a)
+    # Scalar - size
+    f(a, size=(10, 10))
+    # 1d
+    f(b)
+    # 2d
+    f(c)
+    # 3d
+    f(d)
+    # 1d size
+    f(b, size=10)
+    # 2d - size - broadcast
+    f(e, size=(10, 2))
+    # 3d - size
+    f(g, size=(10, 10, 10))
+
+
+def comp_state(state1, state2):
+    identical = True
+    if isinstance(state1, dict):
+        for key in state1:
+            identical &= comp_state(state1[key], state2[key])
+    elif type(state1) != type(state2):
+        identical &= type(state1) == type(state2)
+    else:
+        if (isinstance(state1, (list, tuple, np.ndarray)) and isinstance(
+                state2, (list, tuple, np.ndarray))):
+            for s1, s2 in zip(state1, state2):
+                identical &= comp_state(s1, s2)
+        else:
+            identical &= state1 == state2
+    return identical
+
+
+def warmup(rg, n=None):
+    if n is None:
+        n = 11 + np.random.randint(0, 20)
+    rg.standard_normal(n)
+    rg.standard_normal(n)
+    rg.standard_normal(n, dtype=np.float32)
+    rg.standard_normal(n, dtype=np.float32)
+    rg.integers(0, 2 ** 24, n, dtype=np.uint64)
+    rg.integers(0, 2 ** 48, n, dtype=np.uint64)
+    rg.standard_gamma(11.0, n)
+    rg.standard_gamma(11.0, n, dtype=np.float32)
+    rg.random(n, dtype=np.float64)
+    rg.random(n, dtype=np.float32)
+
+
+class RNG:
+    @classmethod
+    def setup_class(cls):
+        # Overridden in test classes. Place holder to silence IDE noise
+        cls.bit_generator = PCG64
+        cls.advance = None
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+    @classmethod
+    def _extra_setup(cls):
+        cls.vec_1d = np.arange(2.0, 102.0)
+        cls.vec_2d = np.arange(2.0, 102.0)[None, :]
+        cls.mat = np.arange(2.0, 102.0, 0.01).reshape((100, 100))
+        cls.seed_error = TypeError
+
+    def _reset_state(self):
+        self.rg.bit_generator.state = self.initial_state
+
+    def test_init(self):
+        rg = Generator(self.bit_generator())
+        state = rg.bit_generator.state
+        rg.standard_normal(1)
+        rg.standard_normal(1)
+        rg.bit_generator.state = state
+        new_state = rg.bit_generator.state
+        assert_(comp_state(state, new_state))
+
+    def test_advance(self):
+        state = self.rg.bit_generator.state
+        if hasattr(self.rg.bit_generator, 'advance'):
+            self.rg.bit_generator.advance(self.advance)
+            assert_(not comp_state(state, self.rg.bit_generator.state))
+        else:
+            bitgen_name = self.rg.bit_generator.__class__.__name__
+            pytest.skip(f'Advance is not supported by {bitgen_name}')
+
+    def test_jump(self):
+        state = self.rg.bit_generator.state
+        if hasattr(self.rg.bit_generator, 'jumped'):
+            bit_gen2 = self.rg.bit_generator.jumped()
+            jumped_state = bit_gen2.state
+            assert_(not comp_state(state, jumped_state))
+            self.rg.random(2 * 3 * 5 * 7 * 11 * 13 * 17)
+            self.rg.bit_generator.state = state
+            bit_gen3 = self.rg.bit_generator.jumped()
+            rejumped_state = bit_gen3.state
+            assert_(comp_state(jumped_state, rejumped_state))
+        else:
+            bitgen_name = self.rg.bit_generator.__class__.__name__
+            if bitgen_name not in ('SFC64',):
+                raise AttributeError(f'no "jumped" in {bitgen_name}')
+            pytest.skip(f'Jump is not supported by {bitgen_name}')
+
+    def test_uniform(self):
+        r = self.rg.uniform(-1.0, 0.0, size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+
+    def test_uniform_array(self):
+        r = self.rg.uniform(np.array([-1.0] * 10), 0.0, size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+        r = self.rg.uniform(np.array([-1.0] * 10),
+                            np.array([0.0] * 10), size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+        r = self.rg.uniform(-1.0, np.array([0.0] * 10), size=10)
+        assert_(len(r) == 10)
+        assert_((r > -1).all())
+        assert_((r <= 0).all())
+
+    def test_random(self):
+        assert_(len(self.rg.random(10)) == 10)
+        params_0(self.rg.random)
+
+    def test_standard_normal_zig(self):
+        assert_(len(self.rg.standard_normal(10)) == 10)
+
+    def test_standard_normal(self):
+        assert_(len(self.rg.standard_normal(10)) == 10)
+        params_0(self.rg.standard_normal)
+
+    def test_standard_gamma(self):
+        assert_(len(self.rg.standard_gamma(10, 10)) == 10)
+        assert_(len(self.rg.standard_gamma(np.array([10] * 10), 10)) == 10)
+        params_1(self.rg.standard_gamma)
+
+    def test_standard_exponential(self):
+        assert_(len(self.rg.standard_exponential(10)) == 10)
+        params_0(self.rg.standard_exponential)
+
+    def test_standard_exponential_float(self):
+        randoms = self.rg.standard_exponential(10, dtype='float32')
+        assert_(len(randoms) == 10)
+        assert randoms.dtype == np.float32
+        params_0(partial(self.rg.standard_exponential, dtype='float32'))
+
+    def test_standard_exponential_float_log(self):
+        randoms = self.rg.standard_exponential(10, dtype='float32',
+                                               method='inv')
+        assert_(len(randoms) == 10)
+        assert randoms.dtype == np.float32
+        params_0(partial(self.rg.standard_exponential, dtype='float32',
+                         method='inv'))
+
+    def test_standard_cauchy(self):
+        assert_(len(self.rg.standard_cauchy(10)) == 10)
+        params_0(self.rg.standard_cauchy)
+
+    def test_standard_t(self):
+        assert_(len(self.rg.standard_t(10, 10)) == 10)
+        params_1(self.rg.standard_t)
+
+    def test_binomial(self):
+        assert_(self.rg.binomial(10, .5) >= 0)
+        assert_(self.rg.binomial(1000, .5) >= 0)
+
+    def test_reset_state(self):
+        state = self.rg.bit_generator.state
+        int_1 = self.rg.integers(2**31)
+        self.rg.bit_generator.state = state
+        int_2 = self.rg.integers(2**31)
+        assert_(int_1 == int_2)
+
+    def test_entropy_init(self):
+        rg = Generator(self.bit_generator())
+        rg2 = Generator(self.bit_generator())
+        assert_(not comp_state(rg.bit_generator.state,
+                               rg2.bit_generator.state))
+
+    def test_seed(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg2 = Generator(self.bit_generator(*self.seed))
+        rg.random()
+        rg2.random()
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_reset_state_gauss(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.standard_normal()
+        state = rg.bit_generator.state
+        n1 = rg.standard_normal(size=10)
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.standard_normal(size=10)
+        assert_array_equal(n1, n2)
+
+    def test_reset_state_uint32(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.integers(0, 2 ** 24, 120, dtype=np.uint32)
+        state = rg.bit_generator.state
+        n1 = rg.integers(0, 2 ** 24, 10, dtype=np.uint32)
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.integers(0, 2 ** 24, 10, dtype=np.uint32)
+        assert_array_equal(n1, n2)
+
+    def test_reset_state_float(self):
+        rg = Generator(self.bit_generator(*self.seed))
+        rg.random(dtype='float32')
+        state = rg.bit_generator.state
+        n1 = rg.random(size=10, dtype='float32')
+        rg2 = Generator(self.bit_generator())
+        rg2.bit_generator.state = state
+        n2 = rg2.random(size=10, dtype='float32')
+        assert_((n1 == n2).all())
+
+    def test_shuffle(self):
+        original = np.arange(200, 0, -1)
+        permuted = self.rg.permutation(original)
+        assert_((original != permuted).any())
+
+    def test_permutation(self):
+        original = np.arange(200, 0, -1)
+        permuted = self.rg.permutation(original)
+        assert_((original != permuted).any())
+
+    def test_beta(self):
+        vals = self.rg.beta(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), 2.0)
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(2.0, np.array([2.0] * 10))
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), np.array([2.0] * 10))
+        assert_(len(vals) == 10)
+        vals = self.rg.beta(np.array([2.0] * 10), np.array([[2.0]] * 10))
+        assert_(vals.shape == (10, 10))
+
+    def test_bytes(self):
+        vals = self.rg.bytes(10)
+        assert_(len(vals) == 10)
+
+    def test_chisquare(self):
+        vals = self.rg.chisquare(2.0, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.chisquare)
+
+    def test_exponential(self):
+        vals = self.rg.exponential(2.0, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.exponential)
+
+    def test_f(self):
+        vals = self.rg.f(3, 1000, 10)
+        assert_(len(vals) == 10)
+
+    def test_gamma(self):
+        vals = self.rg.gamma(3, 2, 10)
+        assert_(len(vals) == 10)
+
+    def test_geometric(self):
+        vals = self.rg.geometric(0.5, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.exponential, bounded=True)
+
+    def test_gumbel(self):
+        vals = self.rg.gumbel(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_laplace(self):
+        vals = self.rg.laplace(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_logitic(self):
+        vals = self.rg.logistic(2.0, 2.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_logseries(self):
+        vals = self.rg.logseries(0.5, 10)
+        assert_(len(vals) == 10)
+
+    def test_negative_binomial(self):
+        vals = self.rg.negative_binomial(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_noncentral_chisquare(self):
+        vals = self.rg.noncentral_chisquare(10, 2, 10)
+        assert_(len(vals) == 10)
+
+    def test_noncentral_f(self):
+        vals = self.rg.noncentral_f(3, 1000, 2, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(np.array([3] * 10), 1000, 2)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(3, np.array([1000] * 10), 2)
+        assert_(len(vals) == 10)
+        vals = self.rg.noncentral_f(3, 1000, np.array([2] * 10))
+        assert_(len(vals) == 10)
+
+    def test_normal(self):
+        vals = self.rg.normal(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_pareto(self):
+        vals = self.rg.pareto(3.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_poisson(self):
+        vals = self.rg.poisson(10, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.poisson(np.array([10] * 10))
+        assert_(len(vals) == 10)
+        params_1(self.rg.poisson)
+
+    def test_power(self):
+        vals = self.rg.power(0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_integers(self):
+        vals = self.rg.integers(10, 20, 10)
+        assert_(len(vals) == 10)
+
+    def test_rayleigh(self):
+        vals = self.rg.rayleigh(0.2, 10)
+        assert_(len(vals) == 10)
+        params_1(self.rg.rayleigh, bounded=True)
+
+    def test_vonmises(self):
+        vals = self.rg.vonmises(10, 0.2, 10)
+        assert_(len(vals) == 10)
+
+    def test_wald(self):
+        vals = self.rg.wald(1.0, 1.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_weibull(self):
+        vals = self.rg.weibull(1.0, 10)
+        assert_(len(vals) == 10)
+
+    def test_zipf(self):
+        vals = self.rg.zipf(10, 10)
+        assert_(len(vals) == 10)
+        vals = self.rg.zipf(self.vec_1d)
+        assert_(len(vals) == 100)
+        vals = self.rg.zipf(self.vec_2d)
+        assert_(vals.shape == (1, 100))
+        vals = self.rg.zipf(self.mat)
+        assert_(vals.shape == (100, 100))
+
+    def test_hypergeometric(self):
+        vals = self.rg.hypergeometric(25, 25, 20)
+        assert_(np.isscalar(vals))
+        vals = self.rg.hypergeometric(np.array([25] * 10), 25, 20)
+        assert_(vals.shape == (10,))
+
+    def test_triangular(self):
+        vals = self.rg.triangular(-5, 0, 5)
+        assert_(np.isscalar(vals))
+        vals = self.rg.triangular(-5, np.array([0] * 10), 5)
+        assert_(vals.shape == (10,))
+
+    def test_multivariate_normal(self):
+        mean = [0, 0]
+        cov = [[1, 0], [0, 100]]  # diagonal covariance
+        x = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        x_zig = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        x_inv = self.rg.multivariate_normal(mean, cov, 5000)
+        assert_(x.shape == (5000, 2))
+        assert_((x_zig != x_inv).any())
+
+    def test_multinomial(self):
+        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3])
+        assert_(vals.shape == (2,))
+        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3], size=10)
+        assert_(vals.shape == (10, 2))
+
+    def test_dirichlet(self):
+        s = self.rg.dirichlet((10, 5, 3), 20)
+        assert_(s.shape == (20, 3))
+
+    def test_pickle(self):
+        pick = pickle.dumps(self.rg)
+        unpick = pickle.loads(pick)
+        assert_((type(self.rg) == type(unpick)))
+        assert_(comp_state(self.rg.bit_generator.state,
+                           unpick.bit_generator.state))
+
+        pick = pickle.dumps(self.rg)
+        unpick = pickle.loads(pick)
+        assert_((type(self.rg) == type(unpick)))
+        assert_(comp_state(self.rg.bit_generator.state,
+                           unpick.bit_generator.state))
+
+    def test_seed_array(self):
+        if self.seed_vector_bits is None:
+            bitgen_name = self.bit_generator.__name__
+            pytest.skip(f'Vector seeding is not supported by {bitgen_name}')
+
+        if self.seed_vector_bits == 32:
+            dtype = np.uint32
+        else:
+            dtype = np.uint64
+        seed = np.array([1], dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(1)
+        state2 = bg.state
+        assert_(comp_state(state1, state2))
+
+        seed = np.arange(4, dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+        seed = np.arange(1500, dtype=dtype)
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+        seed = 2 ** np.mod(np.arange(1500, dtype=dtype),
+                           self.seed_vector_bits - 1) + 1
+        bg = self.bit_generator(seed)
+        state1 = bg.state
+        bg  = self.bit_generator(seed[0])
+        state2 = bg.state
+        assert_(not comp_state(state1, state2))
+
+    def test_uniform_float(self):
+        rg = Generator(self.bit_generator(12345))
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.random(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.random(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_gamma_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_gamma(4.0, 11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_gamma(4.0, 11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_normal_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_normal(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_normal(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_normal_zig_floats(self):
+        rg = Generator(self.bit_generator())
+        warmup(rg)
+        state = rg.bit_generator.state
+        r1 = rg.standard_normal(11, dtype=np.float32)
+        rg2 = Generator(self.bit_generator())
+        warmup(rg2)
+        rg2.bit_generator.state = state
+        r2 = rg2.standard_normal(11, dtype=np.float32)
+        assert_array_equal(r1, r2)
+        assert_equal(r1.dtype, np.float32)
+        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))
+
+    def test_output_fill(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_normal(size=size)
+        assert_equal(direct, existing)
+
+        sized = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=sized, size=sized.shape)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_normal(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_normal(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_uniform(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.random(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.random(size=size)
+        assert_equal(direct, existing)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.random(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.random(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_exponential(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        rg.bit_generator.state = state
+        rg.standard_exponential(out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_exponential(size=size)
+        assert_equal(direct, existing)
+
+        existing = np.empty(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_exponential(out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_exponential(size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_gamma(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        existing = np.zeros(size)
+        rg.bit_generator.state = state
+        rg.standard_gamma(1.0, out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(1.0, size=size)
+        assert_equal(direct, existing)
+
+        existing = np.zeros(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_gamma(1.0, out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(1.0, size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_filling_gamma_broadcast(self):
+        rg = self.rg
+        state = rg.bit_generator.state
+        size = (31, 7, 97)
+        mu = np.arange(97.0) + 1.0
+        existing = np.zeros(size)
+        rg.bit_generator.state = state
+        rg.standard_gamma(mu, out=existing)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(mu, size=size)
+        assert_equal(direct, existing)
+
+        existing = np.zeros(size, dtype=np.float32)
+        rg.bit_generator.state = state
+        rg.standard_gamma(mu, out=existing, dtype=np.float32)
+        rg.bit_generator.state = state
+        direct = rg.standard_gamma(mu, size=size, dtype=np.float32)
+        assert_equal(direct, existing)
+
+    def test_output_fill_error(self):
+        rg = self.rg
+        size = (31, 7, 97)
+        existing = np.empty(size)
+        with pytest.raises(TypeError):
+            rg.standard_normal(out=existing, dtype=np.float32)
+        with pytest.raises(ValueError):
+            rg.standard_normal(out=existing[::3])
+        existing = np.empty(size, dtype=np.float32)
+        with pytest.raises(TypeError):
+            rg.standard_normal(out=existing, dtype=np.float64)
+
+        existing = np.zeros(size, dtype=np.float32)
+        with pytest.raises(TypeError):
+            rg.standard_gamma(1.0, out=existing, dtype=np.float64)
+        with pytest.raises(ValueError):
+            rg.standard_gamma(1.0, out=existing[::3], dtype=np.float32)
+        existing = np.zeros(size, dtype=np.float64)
+        with pytest.raises(TypeError):
+            rg.standard_gamma(1.0, out=existing, dtype=np.float32)
+        with pytest.raises(ValueError):
+            rg.standard_gamma(1.0, out=existing[::3])
+
+    def test_integers_broadcast(self, dtype):
+        if dtype == np.bool_:
+            upper = 2
+            lower = 0
+        else:
+            info = np.iinfo(dtype)
+            upper = int(info.max) + 1
+            lower = info.min
+        self._reset_state()
+        a = self.rg.integers(lower, [upper] * 10, dtype=dtype)
+        self._reset_state()
+        b = self.rg.integers([lower] * 10, upper, dtype=dtype)
+        assert_equal(a, b)
+        self._reset_state()
+        c = self.rg.integers(lower, upper, size=10, dtype=dtype)
+        assert_equal(a, c)
+        self._reset_state()
+        d = self.rg.integers(np.array(
+            [lower] * 10), np.array([upper], dtype=object), size=10,
+            dtype=dtype)
+        assert_equal(a, d)
+        self._reset_state()
+        e = self.rg.integers(
+            np.array([lower] * 10), np.array([upper] * 10), size=10,
+            dtype=dtype)
+        assert_equal(a, e)
+
+        self._reset_state()
+        a = self.rg.integers(0, upper, size=10, dtype=dtype)
+        self._reset_state()
+        b = self.rg.integers([upper] * 10, dtype=dtype)
+        assert_equal(a, b)
+
+    def test_integers_numpy(self, dtype):
+        high = np.array([1])
+        low = np.array([0])
+
+        out = self.rg.integers(low, high, dtype=dtype)
+        assert out.shape == (1,)
+
+        out = self.rg.integers(low[0], high, dtype=dtype)
+        assert out.shape == (1,)
+
+        out = self.rg.integers(low, high[0], dtype=dtype)
+        assert out.shape == (1,)
+
+    def test_integers_broadcast_errors(self, dtype):
+        if dtype == np.bool_:
+            upper = 2
+            lower = 0
+        else:
+            info = np.iinfo(dtype)
+            upper = int(info.max) + 1
+            lower = info.min
+        with pytest.raises(ValueError):
+            self.rg.integers(lower, [upper + 1] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers(lower - 1, [upper] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers([lower - 1], [upper] * 10, dtype=dtype)
+        with pytest.raises(ValueError):
+            self.rg.integers([0], [0], dtype=dtype)
+
+
+class TestMT19937(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = MT19937
+        cls.advance = None
+        cls.seed = [2 ** 21 + 2 ** 16 + 2 ** 5 + 1]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 32
+        cls._extra_setup()
+        cls.seed_error = ValueError
+
+    def test_numpy_state(self):
+        nprg = np.random.RandomState()
+        nprg.standard_normal(99)
+        state = nprg.get_state()
+        self.rg.bit_generator.state = state
+        state2 = self.rg.bit_generator.state
+        assert_((state[1] == state2['state']['key']).all())
+        assert_((state[2] == state2['state']['pos']))
+
+
+class TestPhilox(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = Philox
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestSFC64(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = SFC64
+        cls.advance = None
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 192
+        cls._extra_setup()
+
+
+class TestPCG64(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestPCG64DXSM(RNG):
+    @classmethod
+    def setup_class(cls):
+        cls.bit_generator = PCG64DXSM
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = Generator(cls.bit_generator(*cls.seed))
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+
+class TestDefaultRNG(RNG):
+    @classmethod
+    def setup_class(cls):
+        # This will duplicate some tests that directly instantiate a fresh
+        # Generator(), but that's okay.
+        cls.bit_generator = PCG64
+        cls.advance = 2**63 + 2**31 + 2**15 + 1
+        cls.seed = [12345]
+        cls.rg = np.random.default_rng(*cls.seed)
+        cls.initial_state = cls.rg.bit_generator.state
+        cls.seed_vector_bits = 64
+        cls._extra_setup()
+
+    def test_default_is_pcg64(self):
+        # In order to change the default BitGenerator, we'll go through
+        # a deprecation cycle to move to a different function.
+        assert_(isinstance(self.rg.bit_generator, PCG64))
+
+    def test_seed(self):
+        np.random.default_rng()
+        np.random.default_rng(None)
+        np.random.default_rng(12345)
+        np.random.default_rng(0)
+        np.random.default_rng(43660444402423911716352051725018508569)
+        np.random.default_rng([43660444402423911716352051725018508569,
+                               279705150948142787361475340226491943209])
+        with pytest.raises(ValueError):
+            np.random.default_rng(-1)
+        with pytest.raises(ValueError):
+            np.random.default_rng([12345, -1])
diff --git a/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..06962f5fc2035519d0cc6fcb6b96b91762969167
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/METADATA
@@ -0,0 +1,49 @@
+Metadata-Version: 2.4
+Name: nvidia-cublas
+Version: 13.1.1.3
+Summary: CUBLAS native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License-Expression: LicenseRef-NVIDIA-Proprietary
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+Description-Content-Type: text/plain
+License-File: License.txt
+Requires-Dist: nvidia-cuda-nvrtc
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: license-expression
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUBLAS native runtime libraries
diff --git a/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..200cb8e2ad5774679a677953891c6736d206de2b
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/RECORD
@@ -0,0 +1,15 @@
+nvidia/cu13/include/cublas.h,sha256=OYeC9ksnq-FOI7GQGQnUzFxxB_94fZ-lNCDY8xVB-cE,41196
+nvidia/cu13/include/cublasLt.h,sha256=i-Q_v0hiW5i4Vi1985Tmrw1-4cBb7rUzNXfV4zGC-j4,109928
+nvidia/cu13/include/cublasXt.h,sha256=CW9dyXYGSUW1wEXrVVyhU6OxBK1PUvMoYdVGlQT7L9A,37380
+nvidia/cu13/include/cublas_api.h,sha256=9rRD9DNY3lkVhNOIENYPz3wQgGd6a6FgWpnotYs0mJA,331797
+nvidia/cu13/include/cublas_v2.h,sha256=eFFL6b2li_hKIes1qNMbZ0RWKmK-q3h1fOIurnxvGyY,15399
+nvidia/cu13/include/nvblas.h,sha256=dXCLR-2oUiJFzLsDtIAK09m42ct4G0HWdYzBUuDPXpc,23341
+nvidia/cu13/lib/libcublas.so.13,sha256=Jc3kMhCFQ2Jt-F2IfHML8sq5aAPKFvllTkq8jStFgz4,54177976
+nvidia/cu13/lib/libcublasLt.so.13,sha256=FImut11V7lV4J5g0OsZvV3cdVhzB_h4EkVaN7voOZRU,541607888
+nvidia/cu13/lib/libnvblas.so.13,sha256=ND8WtXQTnZwlR3KU1MRtQx-uSOYmuTPS3HplDQEuXzQ,737504
+nvidia_cublas-13.1.1.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cublas-13.1.1.3.dist-info/METADATA,sha256=d1sS4LOIWKA-kwGfahgQSxD86A9k-Wn-m7ftYVOrKAQ,1819
+nvidia_cublas-13.1.1.3.dist-info/RECORD,,
+nvidia_cublas-13.1.1.3.dist-info/WHEEL,sha256=KydpMJAloipF2RiFASKxkULpV61umVfoGnA2e3YT9SI,109
+nvidia_cublas-13.1.1.3.dist-info/licenses/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cublas-13.1.1.3.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..2a683b9b325dc92d5b49306e2b46f9aedcccf268
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (82.0.1)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux_2_27_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/licenses/License.txt b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/licenses/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/licenses/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cublas-13.1.1.3.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/License.txt b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..c0d874a42fb5a6bdb8f0cb78bb40f3d9cb708137
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/METADATA
@@ -0,0 +1,35 @@
+Metadata-Version: 2.1
+Name: nvidia-cuda-cupti-cu12
+Version: 12.4.127
+Summary: CUDA profiling tools runtime libs.
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: cuda_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+
+Provides libraries to enable third party tools using GPU profiling APIs.
diff --git a/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..3cbabcdada3c3b305a1c24543f923799a2b82fe8
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/RECORD
@@ -0,0 +1,53 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cuda_cupti/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cuda_cupti/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cuda_cupti/include/Openacc/cupti_openacc.h,sha256=Z0OM5e_hbd3cxdXyn3SCHqBBQawLg4QORnlm57Cr2-M,3513
+nvidia/cuda_cupti/include/Openmp/cupti_openmp.h,sha256=E1WNmeb_7HaUSmBegtUNe4IV1i7pXeNxgzIlyKn1zrM,3491
+nvidia/cuda_cupti/include/Openmp/omp-tools.h,sha256=AmuC_xPC7VPu3B-W4PmXuCNufFawhY8PjNXePaQFAOg,37403
+nvidia/cuda_cupti/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cuda_cupti/include/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cuda_cupti/include/cuda_stdint.h,sha256=XbFOk9CtJjKqk7PpYNqbSVsDxAsVM8avA4rWpPi0BjQ,4093
+nvidia/cuda_cupti/include/cupti.h,sha256=JkVyAGTIMYzwm62dfVqas3nMcILhgP_Wdz6fh4_NED0,4697
+nvidia/cuda_cupti/include/cupti_activity.h,sha256=RB7VYrdiOBpdH_LbXb2o-CcGthnk3-NE2Cqq-jQbN7Q,210611
+nvidia/cuda_cupti/include/cupti_activity_deprecated.h,sha256=rYJsoAJxA2BTT50-olN8EYcSzdlXBpRbR1ATLG3rVIM,121526
+nvidia/cuda_cupti/include/cupti_callbacks.h,sha256=zrEVRb0hubSfD69QUmHsJiL8oAfvqyuKGcTVRihQrnc,29729
+nvidia/cuda_cupti/include/cupti_checkpoint.h,sha256=rTz8JoWxqESBXyZWUhZJGm4xeYcx4OJOtJ7Ld13T_b0,5264
+nvidia/cuda_cupti/include/cupti_common.h,sha256=85m74bxUgXp3tEaPQpezeazmpsNMw41PsjNSYmQdT20,3514
+nvidia/cuda_cupti/include/cupti_driver_cbid.h,sha256=dHKyQYZbBbdlxixzFkIoNHg5IfGXdgriyjN1Bu1i6g4,74462
+nvidia/cuda_cupti/include/cupti_events.h,sha256=f7lLGmD2e8FzvMhRgnn0-v7U0vTpUkiQHIpQxgARGb0,51896
+nvidia/cuda_cupti/include/cupti_metrics.h,sha256=iLAOlDrcbHEsIIUmgq0Tp1ZOY9O3Ot3wj2-bI8iYbSs,32148
+nvidia/cuda_cupti/include/cupti_nvtx_cbid.h,sha256=_azPtR1g4qivvX7qbvHRUg0RHCWF7iEOJyHMN9qZe9E,5912
+nvidia/cuda_cupti/include/cupti_pcsampling.h,sha256=ycJHT36DmPIaVzHsB3xxjXkhFyEfMCJOl3LbCsHFgyA,32144
+nvidia/cuda_cupti/include/cupti_pcsampling_util.h,sha256=lx8CaNXowJe5Zvc06LE-u_Zry_jODs1mM6j9Q5WIX9E,12430
+nvidia/cuda_cupti/include/cupti_profiler_target.h,sha256=JsceoDuhllWNEzaO0xxT81dJ55NrbF0UtRJJgit0P_E,32131
+nvidia/cuda_cupti/include/cupti_result.h,sha256=a-C4Y7LAYCiCT1ngOfoDuTi2stEG1YTafwwn6UfL-LU,12603
+nvidia/cuda_cupti/include/cupti_runtime_cbid.h,sha256=11pXl0MdmTtxUngel-ru4JdqWvF_gEIG14aQExRyfzI,46436
+nvidia/cuda_cupti/include/cupti_sass_metrics.h,sha256=3RW9snJuFQdOhrEn3wDJOru05q0V_zssWrqD7tvVJKw,19674
+nvidia/cuda_cupti/include/cupti_target.h,sha256=x4Vz1Upb6m9ixmVpmGaKQldDWYQI3OZ-ocEXGzNK0EE,1263
+nvidia/cuda_cupti/include/cupti_version.h,sha256=sjd-aUoTGkEWyvA2VUWIpZwXyXAaclqC8gbwNnuK5D0,4425
+nvidia/cuda_cupti/include/generated_cudaGL_meta.h,sha256=dfd2QuaRdEjbStOKvaQLi1Md_qrpRQh8PfyZznJ8bWY,3115
+nvidia/cuda_cupti/include/generated_cudaVDPAU_meta.h,sha256=fAedsoQxaU3hIAApAWDOKsa9kgcuQw4tdyf8klLm-3k,1453
+nvidia/cuda_cupti/include/generated_cuda_gl_interop_meta.h,sha256=LXOqvQCej0sCgAT1LUKKYZ466EFxN4hIwf9oIhXOLF0,2250
+nvidia/cuda_cupti/include/generated_cuda_meta.h,sha256=hawYpDe0xpaDFDnClXI91JjwCRxWb-AS0FS8ydUMgxc,94639
+nvidia/cuda_cupti/include/generated_cuda_runtime_api_meta.h,sha256=D8CbAN3-jLuF2KGfsBHXEELSgL92KrUAiDvugWE8B8M,69706
+nvidia/cuda_cupti/include/generated_cuda_vdpau_interop_meta.h,sha256=8OLqWN26aEYpTWUXtbHJvA5GYhVv3ybYVOTW7yK37z8,1367
+nvidia/cuda_cupti/include/generated_cudart_removed_meta.h,sha256=X3I5WXmhtsJNNlgY7coJ5vg4t11G5FRR6Xo7MboIeck,5172
+nvidia/cuda_cupti/include/generated_nvtx_meta.h,sha256=YHb_RD8g3s4m8PJn7Z0wnxvUHarl7BOAX5ADr-BL3HI,7513
+nvidia/cuda_cupti/include/nvperf_common.h,sha256=BqPml9AxyN10-ptWT3hQzh2JUWqQX57Q5BjQ3ZuaKNs,17255
+nvidia/cuda_cupti/include/nvperf_cuda_host.h,sha256=aBnyIr_hexPDGBkP6WSujN1mI_DYP25sEIXWYY1O7VI,8298
+nvidia/cuda_cupti/include/nvperf_host.h,sha256=afdHG6eraeo4ltlF9ihskqhU7IccxcRCaZDZ6_ikjkg,68506
+nvidia/cuda_cupti/include/nvperf_target.h,sha256=ZDA-JI459tLBW4iLLCQjYYRAMeHwfqDIgXbVqVLDYZ4,22539
+nvidia/cuda_cupti/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cuda_cupti/lib/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cuda_cupti/lib/libcheckpoint.so,sha256=cCTAB7_UNqvoU7zKMCHkklcmM1GGr3atZmZoZksdAKM,1501336
+nvidia/cuda_cupti/lib/libcupti.so.12,sha256=-yp8WxXITflQXdR-VT_kbzEhpX0wOR_KJBedIC9z8_c,7748112
+nvidia/cuda_cupti/lib/libnvperf_host.so,sha256=tZsmsdNdAik8jdiVaro3V8FGa3FzLGaHq6QSxQ2VC2k,28132984
+nvidia/cuda_cupti/lib/libnvperf_target.so,sha256=ztN3NKnf_9XyEogyuHjyOAcTvqYBn6lE0psxejPTeYw,5592368
+nvidia/cuda_cupti/lib/libpcsamplingutil.so,sha256=ZDY0bEGLzy-pA3yfFtc6jfvo-Cu8vWwUCQYatGJrb0Q,912728
+nvidia_cuda_cupti_cu12-12.4.127.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cuda_cupti_cu12-12.4.127.dist-info/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cuda_cupti_cu12-12.4.127.dist-info/METADATA,sha256=UiXYPD5hc55tQSSNiYNq5AqkD68jq1KHNCtG-PJvPds,1553
+nvidia_cuda_cupti_cu12-12.4.127.dist-info/RECORD,,
+nvidia_cuda_cupti_cu12-12.4.127.dist-info/WHEEL,sha256=XDTs3wIbcE-BcRO08VJlZpA6z9OaC1mOKPCGGGwuM2g,109
+nvidia_cuda_cupti_cu12-12.4.127.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..e6c30e957cfb045017a9fef3430bb8ee87c4a074
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.42.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cuda_cupti_cu12-12.4.127.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/License.txt b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0d485c1c82d2c86b62ac0deeb8568fcdb58e0bb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/License.txt
@@ -0,0 +1,154 @@
+LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+This license agreement, including exhibits attached ("Agreement”) is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs your use of a NVIDIA software development kit (“SDK”). 
+
+Each SDK has its own set of software and materials, but here is a description of the types of items that may be included in a SDK: source code, header files, APIs, data sets and assets (examples include images, textures, models, scenes, videos, native API input/output files), binary software, sample code, libraries, utility programs, programming code and documentation. 
+
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the SDK is used. 
+
+If you are entering into this Agreement on behalf of a company or other legal entity, you represent that you have the legal authority to bind the entity to this Agreement, in which case “you” will mean the entity you represent. 
+
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not download, install or use the SDK.  
+
+You agree to use the SDK only for purposes that are permitted by (a) this Agreement, and (b) any applicable law, regulation or generally accepted practices or guidelines in the relevant jurisdictions.
+
+1. License. 
+
+1.1 Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants you a non-exclusive, non-transferable license, without the right to sublicense (except as expressly provided in this Agreement) to: 
+
+(i) Install and use the SDK,
+
+(ii) Modify and create derivative works of sample source code delivered in the SDK, and
+ 
+(iii) Distribute those portions of the SDK that are identified in this Agreement as distributable, as incorporated in object code format into a software application that meets the distribution requirements indicated in this Agreement.
+
+1.2 Distribution Requirements
+
+These are the distribution requirements for you to exercise the distribution grant:
+       
+(i) Your application must have material additional functionality, beyond the included portions of the SDK.
+
+(ii) The distributable portions of the SDK shall only be accessed by your application.  
+
+(iii)  The following notice shall be included in modifications and derivative works of sample source code distributed: “This software contains source code provided by NVIDIA Corporation.”
+
+(iv)  Unless a developer tool is identified in this Agreement as distributable, it is delivered for your internal use only.
+
+(v) The terms under which you distribute your application must be consistent with the terms of this Agreement, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA’s intellectual property rights. Additionally, you agree that you will protect the privacy, security and legal rights of your application users. 
+
+(vi) You agree to notify NVIDIA in writing of any known or suspected distribution or use of the SDK not in compliance with the requirements of this Agreement, and to enforce the terms of your agreements with respect to distributed SDK.
+
+1.3 Authorized Users
+
+You may allow employees and contractors of your entity or of your subsidiary(ies) to access and use the SDK from your secure network to perform work on your behalf. 
+
+If you are an academic institution you may allow users enrolled or employed by the academic institution to access and use the SDK from your secure network. 
+
+You are responsible for the compliance with the terms of this Agreement by your authorized users. If you become aware that your authorized users didn’t follow the terms of this Agreement, you agree to take reasonable steps to resolve the non-compliance and prevent new occurrences. 
+
+1.4 Pre-Release SDK 
+The SDK versions identified as alpha, beta, preview or otherwise as pre-release, may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, accessibility, availability, and reliability standards relative to commercial versions of NVIDIA software and materials. Use of a pre-release SDK may result in unexpected results, loss of data, project delays or other unpredictable damage or loss. 
+You may use a pre-release SDK at your own risk, understanding that pre-release SDKs are not intended for use in production or business-critical systems. 
+NVIDIA may choose not to make available a commercial version of any pre-release SDK. NVIDIA may also choose to abandon development and terminate the availability of a pre-release SDK at any time without liability. 
+1.5	 Updates
+
+NVIDIA may, at its option, make available patches, workarounds or other updates to this SDK. Unless the updates are provided with their separate governing terms, they are deemed part of the SDK licensed to you as provided in this Agreement.
+
+You agree that the form and content of the SDK that NVIDIA provides may change without prior notice to you. While NVIDIA generally maintains compatibility between versions, NVIDIA may in some cases make changes that introduce incompatibilities in future versions of the SDK.
+
+1.6	 Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be distributed with, third party software licensed by a NVIDIA supplier and/or open source software provided under an open source license. Use of third party software is subject to the third-party license terms, or in the absence of third party terms, the terms of this Agreement. Copyright to third party software is held by the copyright holders indicated in the third-party software or license.
+
+1.7 Reservation of Rights
+
+NVIDIA reserves all rights, title and interest in and to the SDK not expressly granted to you under this Agreement.
+
+2. Limitations. 
+
+The following license limitations apply to your use of the SDK:
+
+2.1 You may not reverse engineer, decompile or disassemble, or remove copyright or other proprietary notices from any portion of the SDK or copies of the SDK. 
+
+2.2 Except as expressly provided in this Agreement, you may not copy, sell, rent, sublicense, transfer, distribute, modify, or create derivative works of any portion of the SDK. 
+
+2.3 Unless you have an agreement with NVIDIA for this purpose, you may not indicate that an application created with the SDK is sponsored or endorsed by NVIDIA. 
+
+2.4 You may not bypass, disable, or circumvent any encryption, security, digital rights management or authentication mechanism in the SDK. 
+
+2.5 You may not use the SDK in any manner that would cause it to become subject to an open source software license. As examples, licenses that require as a condition of use, modification, and/or distribution that the SDK be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge.
+
+2.6  Unless you have an agreement with NVIDIA for this purpose, you may not use the SDK with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SDK for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses. 
+
+2.7 You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney’s fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the SDK outside of the scope of this Agreement, or not in compliance with its terms.
+
+3. Ownership. 
+
+3.1 NVIDIA or its licensors hold all rights, title and interest in and to the SDK and its modifications and derivative works, including their respective intellectual property rights, subject to your rights under Section 3.2. This SDK may include software and materials from NVIDIA’s licensors, and these licensors are intended third party beneficiaries that may enforce this Agreement with respect to their intellectual property rights. 
+
+3.2 You hold all rights, title and interest in and to your applications and your derivative works of the sample source code delivered in the SDK, including their respective intellectual property rights, subject to NVIDIA’s rights under section 3.1.
+
+3.3 You may, but don’t have to, provide to NVIDIA suggestions, feature requests or other feedback regarding the SDK, including possible enhancements or modifications to the SDK. For any feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) it without the payment of any royalties or fees to you. NVIDIA will use feedback at its choice. NVIDIA is constantly looking for ways to improve its products, so you may send feedback to NVIDIA through the developer portal at https://developer.nvidia.com.
+
+4.  No Warranties. 
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF DEALING OR COURSE OF TRADE. 
+
+5.	Limitations of Liability. 
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT. 
+
+These exclusions and limitations of liability shall apply regardless if NVIDIA or its affiliates have been advised of the possibility of such damages, and regardless of whether a remedy fails its essential purpose. These exclusions and limitations of liability form an essential basis of the bargain between the parties, and, absent any of these exclusions or limitations of liability, the provisions of this Agreement, including, without limitation, the economic terms, would be substantially different. 
+
+6.   Termination. 
+
+6.1 This Agreement will continue to apply until terminated by either you or NVIDIA as described below. 
+
+6.2 If you want to terminate this Agreement, you may do so by stopping to use the SDK. 
+
+6.3 NVIDIA may, at any time, terminate this Agreement if: (i) you fail to comply with any term of this Agreement and the non-compliance is not fixed within thirty (30) days following notice from NVIDIA (or immediately if you violate NVIDIA’s intellectual property rights); (ii) you commence or participate in any legal proceeding against NVIDIA with respect to the SDK; or (iii) NVIDIA decides to no longer provide the SDK in a country or, in NVIDIA’s sole discretion, the continued use of it is no longer commercially viable. 
+
+6.4 Upon any termination of this Agreement, you agree to promptly discontinue use of the SDK and destroy all copies in your possession or control. Your prior distributions in accordance with this Agreement are not affected by the termination of this Agreement. Upon written request, you will certify in writing that you have complied with your commitments under this section. Upon any termination of this Agreement all provisions survive except for the licenses granted to you. 
+
+7.  General.  
+ 
+If you wish to assign this Agreement or your rights and obligations, including by merger, consolidation, dissolution or operation of law, contact NVIDIA to ask for permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect. NVIDIA may assign, delegate or transfer this Agreement and its rights and obligations, and if to a non-affiliate you will be notified. 
+
+You agree to cooperate with NVIDIA and provide reasonably requested information to verify your compliance with this Agreement.
+
+This Agreement will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language.
+
+The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this Agreement. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction. 
+
+If any court of competent jurisdiction determines that any provision of this Agreement is illegal, invalid or unenforceable, such provision will be construed as limited to the extent necessary to be consistent with and fully enforceable under the law and the remaining provisions will remain in full force and effect. Unless otherwise specified, remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an independent contractor in the performance of this Agreement. 
+
+The SDK has been developed entirely at private expense and is “commercial items” consisting of “commercial computer software” and “commercial computer software documentation” provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this Agreement pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (b)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SDK into any country, or use the SDK in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this Agreement, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement will be delivered via mail, email or fax. You agree that any notices that NVIDIA sends you electronically will satisfy any legal communication requirements. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this Agreement constitute the entire agreement of the parties with respect to the subject matter of this Agreement and supersede all prior negotiations or documentation exchanged between the parties relating to this SDK license. Any additional and/or conflicting terms on documents issued by you are null, void, and invalid. Any amendment or waiver under this Agreement shall be in writing and signed by representatives of both parties.
+
+(v. January 28, 2020)
+
+
+cuDNN SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+The terms in this supplement govern your use of the NVIDIA cuDNN SDK under the terms of your license agreement (“Agreement”) as modified by this supplement. Capitalized terms used but not defined below have the meaning assigned to them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is incorporated as an integral part of the Agreement. In the event of conflict between the terms in this supplement and the terms in the Agreement, the terms in this supplement govern.  
+
+4.1 License Scope. The SDK is licensed for you to develop applications only for use in systems with NVIDIA GPUs.
+
+2. Distribution. The following portions of the SDK are distributable under the Agreement: the runtime files .so and .h, cudnn64_7.dll, and cudnn.lib. 
+
+In addition to the rights above, for parties that are developing software intended solely for use on Jetson development kits or Jetson modules and running Linux for Tegra software the following shall apply: the SDK may be distributed in its entirety, as provided by NVIDIA and without separation of its components, for you and/or your licensees to create software development kits for use only on the Jetson platform and running Linux for Tegra software.
+
+3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com.
+ (v. January 28, 2020)
+
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..dd5524ea6b075288c44a1abc9522181541d96ab9
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/METADATA
@@ -0,0 +1,36 @@
+Metadata-Version: 2.1
+Name: nvidia-cudnn-cu12
+Version: 9.1.0.70
+Summary: cuDNN runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: cuda_installer@nvidia.com
+License: NVIDIA Proprietary Software
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-cublas-cu12
+
+cuDNN runtime libraries containing primitives for deep neural networks.
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..36f08ebd878574972eed2950257da1b6d9baab44
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/RECORD
@@ -0,0 +1,36 @@
+nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cudnn/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cudnn/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cudnn/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cudnn/include/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cudnn/include/cudnn.h,sha256=EILVHTtWS6zo72_G7jNbZosr-lF_V8Bu_UKCY-XCGFU,2841
+nvidia/cudnn/include/cudnn_adv.h,sha256=S-ddXP6m4morsP31Uxk3Thx4VO-FF-t333vq2mY4Zlg,30820
+nvidia/cudnn/include/cudnn_adv_v9.h,sha256=S-ddXP6m4morsP31Uxk3Thx4VO-FF-t333vq2mY4Zlg,30820
+nvidia/cudnn/include/cudnn_backend.h,sha256=rdTyTGHQftsrhWeJS6bQwZDQCzrdyAFhoj0gbRkn1F8,2751
+nvidia/cudnn/include/cudnn_backend_v9.h,sha256=rdTyTGHQftsrhWeJS6bQwZDQCzrdyAFhoj0gbRkn1F8,2751
+nvidia/cudnn/include/cudnn_cnn.h,sha256=BDVryoG36A-oIfj-pPWldDsBoUds58TOCT6eNZVBr-s,36699
+nvidia/cudnn/include/cudnn_cnn_v9.h,sha256=BDVryoG36A-oIfj-pPWldDsBoUds58TOCT6eNZVBr-s,36699
+nvidia/cudnn/include/cudnn_graph.h,sha256=kKH3ylAvBBXYEj5p6rWX63imMBfx-O-dOrwOozFYE-k,37961
+nvidia/cudnn/include/cudnn_graph_v9.h,sha256=kKH3ylAvBBXYEj5p6rWX63imMBfx-O-dOrwOozFYE-k,37961
+nvidia/cudnn/include/cudnn_ops.h,sha256=B56tkXg6lAwKfSPH5-hid3UQi4uhRnHQbbROmBppmZ0,63633
+nvidia/cudnn/include/cudnn_ops_v9.h,sha256=B56tkXg6lAwKfSPH5-hid3UQi4uhRnHQbbROmBppmZ0,63633
+nvidia/cudnn/include/cudnn_v9.h,sha256=EILVHTtWS6zo72_G7jNbZosr-lF_V8Bu_UKCY-XCGFU,2841
+nvidia/cudnn/include/cudnn_version.h,sha256=PGhK3WeGHAhJAJgHBF3RuX5vZSB-ClQHiPYBPPR1EGI,3112
+nvidia/cudnn/include/cudnn_version_v9.h,sha256=PGhK3WeGHAhJAJgHBF3RuX5vZSB-ClQHiPYBPPR1EGI,3112
+nvidia/cudnn/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+nvidia/cudnn/lib/__pycache__/__init__.cpython-311.pyc,,
+nvidia/cudnn/lib/libcudnn.so.9,sha256=dKTElaJtUQQVAkHoShCEJELAJMEpzcJ0rYtqeoeUmA4,104664
+nvidia/cudnn/lib/libcudnn_adv.so.9,sha256=voF0cmqNFSvPbhNrLR-Bdjh23rHStzKbvH0qUmNbAgI,240706416
+nvidia/cudnn/lib/libcudnn_cnn.so.9,sha256=P4w7j3yZS-f7HOYL1WeitJ4WmJHTUVzB5ZUz6XA7byw,4724176
+nvidia/cudnn/lib/libcudnn_engines_precompiled.so.9,sha256=7lSDboo9BIDyq1QRh3CpfLfwLx9CjVpdBHHGuJuQz8w,569645536
+nvidia/cudnn/lib/libcudnn_engines_runtime_compiled.so.9,sha256=UZ_0FROjqBVfQw8IWsxYpfhdwWyGW4KH1UgkCVB-4sA,9585864
+nvidia/cudnn/lib/libcudnn_graph.so.9,sha256=j9hFIHx0d3AOVqA5Wkp0ag3GGBNYU1CAJAgZu2fxoTg,3442456
+nvidia/cudnn/lib/libcudnn_heuristic.so.9,sha256=lPq5jBUEBVjDyA8sGi9f2puqcq_Dmoi9zIIYX0nSQcM,86326864
+nvidia/cudnn/lib/libcudnn_ops.so.9,sha256=JCudupU64uSHjWYDJiQTWpEYoWFsokWI7VhtS8xHXGk,108421928
+nvidia_cudnn_cu12-9.1.0.70.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cudnn_cu12-9.1.0.70.dist-info/License.txt,sha256=Sc95vbNXNLUv5iAwE7O9dZ-B6ZjNMqosZcUduaiMYdI,18174
+nvidia_cudnn_cu12-9.1.0.70.dist-info/METADATA,sha256=V1vE7NqVAZDUF6pjtO5uhb9auIBe9qfRi_tR3rYIK3E,1570
+nvidia_cudnn_cu12-9.1.0.70.dist-info/RECORD,,
+nvidia_cudnn_cu12-9.1.0.70.dist-info/WHEEL,sha256=XDTs3wIbcE-BcRO08VJlZpA6z9OaC1mOKPCGGGwuM2g,109
+nvidia_cudnn_cu12-9.1.0.70.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..e6c30e957cfb045017a9fef3430bb8ee87c4a074
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.42.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu12-9.1.0.70.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..bdf36a789cb553659f8aa887f00c55192408dfad
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/METADATA
@@ -0,0 +1,49 @@
+Metadata-Version: 2.4
+Name: nvidia-cudnn-cu13
+Version: 9.20.0.48
+Summary: cuDNN runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License-Expression: LicenseRef-NVIDIA-Proprietary
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+Description-Content-Type: text/plain
+License-File: License.txt
+Requires-Dist: nvidia-cublas
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: license-expression
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+cuDNN runtime libraries containing primitives for deep neural networks.
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..919cde1c9a6127edbc46734ac5ad2a643d4c6cd3
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/RECORD
@@ -0,0 +1,28 @@
+nvidia/cudnn/include/cudnn.h,sha256=EILVHTtWS6zo72_G7jNbZosr-lF_V8Bu_UKCY-XCGFU,2841
+nvidia/cudnn/include/cudnn_adv.h,sha256=907lSz-lG74QUm8y_i80QUUwp5DQEpRi7XYlTqtPP44,30855
+nvidia/cudnn/include/cudnn_adv_v9.h,sha256=907lSz-lG74QUm8y_i80QUUwp5DQEpRi7XYlTqtPP44,30855
+nvidia/cudnn/include/cudnn_backend.h,sha256=rdTyTGHQftsrhWeJS6bQwZDQCzrdyAFhoj0gbRkn1F8,2751
+nvidia/cudnn/include/cudnn_backend_v9.h,sha256=rdTyTGHQftsrhWeJS6bQwZDQCzrdyAFhoj0gbRkn1F8,2751
+nvidia/cudnn/include/cudnn_cnn.h,sha256=hv5Eso4sNWywCZpHDpiU_i-9sjH96hRMNbEmbpB_Jgo,36700
+nvidia/cudnn/include/cudnn_cnn_v9.h,sha256=hv5Eso4sNWywCZpHDpiU_i-9sjH96hRMNbEmbpB_Jgo,36700
+nvidia/cudnn/include/cudnn_graph.h,sha256=dacFUqB2YNJa2QDdRfGA7FV5Zf1BQzvocju9TGQzcwg,49245
+nvidia/cudnn/include/cudnn_graph_v9.h,sha256=dacFUqB2YNJa2QDdRfGA7FV5Zf1BQzvocju9TGQzcwg,49245
+nvidia/cudnn/include/cudnn_ops.h,sha256=kGlC9k1VQsEiEBQj0aDo2HEe6DbEy2HJg2wBK8tfOMg,63635
+nvidia/cudnn/include/cudnn_ops_v9.h,sha256=kGlC9k1VQsEiEBQj0aDo2HEe6DbEy2HJg2wBK8tfOMg,63635
+nvidia/cudnn/include/cudnn_v9.h,sha256=EILVHTtWS6zo72_G7jNbZosr-lF_V8Bu_UKCY-XCGFU,2841
+nvidia/cudnn/include/cudnn_version.h,sha256=jXk5jO-6INpsqEubnDFgSQNzcJtLyUCJNpWit7wqlSg,3114
+nvidia/cudnn/include/cudnn_version_v9.h,sha256=jXk5jO-6INpsqEubnDFgSQNzcJtLyUCJNpWit7wqlSg,3114
+nvidia/cudnn/lib/libcudnn.so.9,sha256=uIAlr3aXmz8a8gh5AZUJNoT-AcuNGQUohWBY-YgBZmA,129240
+nvidia/cudnn/lib/libcudnn_adv.so.9,sha256=dwIJSlDb6luCRN8Br46dNWUwnTmWu8jKlrbHogfS1r0,109169104
+nvidia/cudnn/lib/libcudnn_cnn.so.9,sha256=6DDFy6h16-uO08_eXMA4onA2ks5xSopP8oZmrI4sVzI,4203904
+nvidia/cudnn/lib/libcudnn_engines_precompiled.so.9,sha256=5RMxCfcQwhXXL9yPHZvhh_R2D18y5XzIxXUV6p6RRV4,245391920
+nvidia/cudnn/lib/libcudnn_engines_runtime_compiled.so.9,sha256=KNsqb0M6GE4mTZJ7GDxH7K9y3GXtWCRabSOA-YDinNM,29386664
+nvidia/cudnn/lib/libcudnn_graph.so.9,sha256=NcCZ0xhDOojSkndWdqUaB9LdFSw655mxwhp1tA945FY,4460192
+nvidia/cudnn/lib/libcudnn_heuristic.so.9,sha256=pB65uqnMyeLWyfLKImAhtUyO4t9KvzqR5BaHhHtIEig,60822040
+nvidia/cudnn/lib/libcudnn_ops.so.9,sha256=cF09z5sgn9vu7Niq7F_ND29V3lZh4cE4i1pLj9Kdt8M,39855888
+nvidia_cudnn_cu13-9.20.0.48.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cudnn_cu13-9.20.0.48.dist-info/METADATA,sha256=kFWYofD6nsmLcxM7c0C7YPs2ZiYuqq-H-PuONGEmHTg,1852
+nvidia_cudnn_cu13-9.20.0.48.dist-info/RECORD,,
+nvidia_cudnn_cu13-9.20.0.48.dist-info/WHEEL,sha256=aph_CRWdwOcwhW1bi5sSowGBGd4s8wQQ8X_Ha2Rqqbg,109
+nvidia_cudnn_cu13-9.20.0.48.dist-info/licenses/License.txt,sha256=Sc95vbNXNLUv5iAwE7O9dZ-B6ZjNMqosZcUduaiMYdI,18174
+nvidia_cudnn_cu13-9.20.0.48.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..46530b525a694df654c1a6284a5c7ae22858cbe7
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (82.0.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux_2_27_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/licenses/License.txt b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/licenses/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0d485c1c82d2c86b62ac0deeb8568fcdb58e0bb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/licenses/License.txt
@@ -0,0 +1,154 @@
+LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+This license agreement, including exhibits attached ("Agreement”) is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs your use of a NVIDIA software development kit (“SDK”). 
+
+Each SDK has its own set of software and materials, but here is a description of the types of items that may be included in a SDK: source code, header files, APIs, data sets and assets (examples include images, textures, models, scenes, videos, native API input/output files), binary software, sample code, libraries, utility programs, programming code and documentation. 
+
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the SDK is used. 
+
+If you are entering into this Agreement on behalf of a company or other legal entity, you represent that you have the legal authority to bind the entity to this Agreement, in which case “you” will mean the entity you represent. 
+
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not download, install or use the SDK.  
+
+You agree to use the SDK only for purposes that are permitted by (a) this Agreement, and (b) any applicable law, regulation or generally accepted practices or guidelines in the relevant jurisdictions.
+
+1. License. 
+
+1.1 Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants you a non-exclusive, non-transferable license, without the right to sublicense (except as expressly provided in this Agreement) to: 
+
+(i) Install and use the SDK,
+
+(ii) Modify and create derivative works of sample source code delivered in the SDK, and
+ 
+(iii) Distribute those portions of the SDK that are identified in this Agreement as distributable, as incorporated in object code format into a software application that meets the distribution requirements indicated in this Agreement.
+
+1.2 Distribution Requirements
+
+These are the distribution requirements for you to exercise the distribution grant:
+       
+(i) Your application must have material additional functionality, beyond the included portions of the SDK.
+
+(ii) The distributable portions of the SDK shall only be accessed by your application.  
+
+(iii)  The following notice shall be included in modifications and derivative works of sample source code distributed: “This software contains source code provided by NVIDIA Corporation.”
+
+(iv)  Unless a developer tool is identified in this Agreement as distributable, it is delivered for your internal use only.
+
+(v) The terms under which you distribute your application must be consistent with the terms of this Agreement, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA’s intellectual property rights. Additionally, you agree that you will protect the privacy, security and legal rights of your application users. 
+
+(vi) You agree to notify NVIDIA in writing of any known or suspected distribution or use of the SDK not in compliance with the requirements of this Agreement, and to enforce the terms of your agreements with respect to distributed SDK.
+
+1.3 Authorized Users
+
+You may allow employees and contractors of your entity or of your subsidiary(ies) to access and use the SDK from your secure network to perform work on your behalf. 
+
+If you are an academic institution you may allow users enrolled or employed by the academic institution to access and use the SDK from your secure network. 
+
+You are responsible for the compliance with the terms of this Agreement by your authorized users. If you become aware that your authorized users didn’t follow the terms of this Agreement, you agree to take reasonable steps to resolve the non-compliance and prevent new occurrences. 
+
+1.4 Pre-Release SDK 
+The SDK versions identified as alpha, beta, preview or otherwise as pre-release, may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, accessibility, availability, and reliability standards relative to commercial versions of NVIDIA software and materials. Use of a pre-release SDK may result in unexpected results, loss of data, project delays or other unpredictable damage or loss. 
+You may use a pre-release SDK at your own risk, understanding that pre-release SDKs are not intended for use in production or business-critical systems. 
+NVIDIA may choose not to make available a commercial version of any pre-release SDK. NVIDIA may also choose to abandon development and terminate the availability of a pre-release SDK at any time without liability. 
+1.5	 Updates
+
+NVIDIA may, at its option, make available patches, workarounds or other updates to this SDK. Unless the updates are provided with their separate governing terms, they are deemed part of the SDK licensed to you as provided in this Agreement.
+
+You agree that the form and content of the SDK that NVIDIA provides may change without prior notice to you. While NVIDIA generally maintains compatibility between versions, NVIDIA may in some cases make changes that introduce incompatibilities in future versions of the SDK.
+
+1.6	 Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be distributed with, third party software licensed by a NVIDIA supplier and/or open source software provided under an open source license. Use of third party software is subject to the third-party license terms, or in the absence of third party terms, the terms of this Agreement. Copyright to third party software is held by the copyright holders indicated in the third-party software or license.
+
+1.7 Reservation of Rights
+
+NVIDIA reserves all rights, title and interest in and to the SDK not expressly granted to you under this Agreement.
+
+2. Limitations. 
+
+The following license limitations apply to your use of the SDK:
+
+2.1 You may not reverse engineer, decompile or disassemble, or remove copyright or other proprietary notices from any portion of the SDK or copies of the SDK. 
+
+2.2 Except as expressly provided in this Agreement, you may not copy, sell, rent, sublicense, transfer, distribute, modify, or create derivative works of any portion of the SDK. 
+
+2.3 Unless you have an agreement with NVIDIA for this purpose, you may not indicate that an application created with the SDK is sponsored or endorsed by NVIDIA. 
+
+2.4 You may not bypass, disable, or circumvent any encryption, security, digital rights management or authentication mechanism in the SDK. 
+
+2.5 You may not use the SDK in any manner that would cause it to become subject to an open source software license. As examples, licenses that require as a condition of use, modification, and/or distribution that the SDK be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge.
+
+2.6  Unless you have an agreement with NVIDIA for this purpose, you may not use the SDK with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SDK for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses. 
+
+2.7 You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney’s fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the SDK outside of the scope of this Agreement, or not in compliance with its terms.
+
+3. Ownership. 
+
+3.1 NVIDIA or its licensors hold all rights, title and interest in and to the SDK and its modifications and derivative works, including their respective intellectual property rights, subject to your rights under Section 3.2. This SDK may include software and materials from NVIDIA’s licensors, and these licensors are intended third party beneficiaries that may enforce this Agreement with respect to their intellectual property rights. 
+
+3.2 You hold all rights, title and interest in and to your applications and your derivative works of the sample source code delivered in the SDK, including their respective intellectual property rights, subject to NVIDIA’s rights under section 3.1.
+
+3.3 You may, but don’t have to, provide to NVIDIA suggestions, feature requests or other feedback regarding the SDK, including possible enhancements or modifications to the SDK. For any feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) it without the payment of any royalties or fees to you. NVIDIA will use feedback at its choice. NVIDIA is constantly looking for ways to improve its products, so you may send feedback to NVIDIA through the developer portal at https://developer.nvidia.com.
+
+4.  No Warranties. 
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF DEALING OR COURSE OF TRADE. 
+
+5.	Limitations of Liability. 
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT. 
+
+These exclusions and limitations of liability shall apply regardless if NVIDIA or its affiliates have been advised of the possibility of such damages, and regardless of whether a remedy fails its essential purpose. These exclusions and limitations of liability form an essential basis of the bargain between the parties, and, absent any of these exclusions or limitations of liability, the provisions of this Agreement, including, without limitation, the economic terms, would be substantially different. 
+
+6.   Termination. 
+
+6.1 This Agreement will continue to apply until terminated by either you or NVIDIA as described below. 
+
+6.2 If you want to terminate this Agreement, you may do so by stopping to use the SDK. 
+
+6.3 NVIDIA may, at any time, terminate this Agreement if: (i) you fail to comply with any term of this Agreement and the non-compliance is not fixed within thirty (30) days following notice from NVIDIA (or immediately if you violate NVIDIA’s intellectual property rights); (ii) you commence or participate in any legal proceeding against NVIDIA with respect to the SDK; or (iii) NVIDIA decides to no longer provide the SDK in a country or, in NVIDIA’s sole discretion, the continued use of it is no longer commercially viable. 
+
+6.4 Upon any termination of this Agreement, you agree to promptly discontinue use of the SDK and destroy all copies in your possession or control. Your prior distributions in accordance with this Agreement are not affected by the termination of this Agreement. Upon written request, you will certify in writing that you have complied with your commitments under this section. Upon any termination of this Agreement all provisions survive except for the licenses granted to you. 
+
+7.  General.  
+ 
+If you wish to assign this Agreement or your rights and obligations, including by merger, consolidation, dissolution or operation of law, contact NVIDIA to ask for permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect. NVIDIA may assign, delegate or transfer this Agreement and its rights and obligations, and if to a non-affiliate you will be notified. 
+
+You agree to cooperate with NVIDIA and provide reasonably requested information to verify your compliance with this Agreement.
+
+This Agreement will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language.
+
+The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this Agreement. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction. 
+
+If any court of competent jurisdiction determines that any provision of this Agreement is illegal, invalid or unenforceable, such provision will be construed as limited to the extent necessary to be consistent with and fully enforceable under the law and the remaining provisions will remain in full force and effect. Unless otherwise specified, remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an independent contractor in the performance of this Agreement. 
+
+The SDK has been developed entirely at private expense and is “commercial items” consisting of “commercial computer software” and “commercial computer software documentation” provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this Agreement pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (b)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SDK into any country, or use the SDK in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this Agreement, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement will be delivered via mail, email or fax. You agree that any notices that NVIDIA sends you electronically will satisfy any legal communication requirements. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this Agreement constitute the entire agreement of the parties with respect to the subject matter of this Agreement and supersede all prior negotiations or documentation exchanged between the parties relating to this SDK license. Any additional and/or conflicting terms on documents issued by you are null, void, and invalid. Any amendment or waiver under this Agreement shall be in writing and signed by representatives of both parties.
+
+(v. January 28, 2020)
+
+
+cuDNN SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+The terms in this supplement govern your use of the NVIDIA cuDNN SDK under the terms of your license agreement (“Agreement”) as modified by this supplement. Capitalized terms used but not defined below have the meaning assigned to them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is incorporated as an integral part of the Agreement. In the event of conflict between the terms in this supplement and the terms in the Agreement, the terms in this supplement govern.  
+
+4.1 License Scope. The SDK is licensed for you to develop applications only for use in systems with NVIDIA GPUs.
+
+2. Distribution. The following portions of the SDK are distributable under the Agreement: the runtime files .so and .h, cudnn64_7.dll, and cudnn.lib. 
+
+In addition to the rights above, for parties that are developing software intended solely for use on Jetson development kits or Jetson modules and running Linux for Tegra software the following shall apply: the SDK may be distributed in its entirety, as provided by NVIDIA and without separation of its components, for you and/or your licensees to create software development kits for use only on the Jetson platform and running Linux for Tegra software.
+
+3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com.
+ (v. January 28, 2020)
+
diff --git a/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cudnn_cu13-9.20.0.48.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..c351679a65e338bc5f6040f1dba72f78d7c37902
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/METADATA
@@ -0,0 +1,47 @@
+Metadata-Version: 2.4
+Name: nvidia-cufft
+Version: 12.0.0.61
+Summary: CUFFT native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: LicenseRef-NVIDIA-Proprietary
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-nvjitlink
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUFFT native runtime libraries
diff --git a/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..816dc73f9706f24316ba7a111605d3b9b8625c71
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/RECORD
@@ -0,0 +1,12 @@
+nvidia/cu13/include/cudalibxt.h,sha256=9GDuRiOzJuO61zRDhIpWpF7XHp8FXSOIlHJNoIMwOZQ,4105
+nvidia/cu13/include/cufft.h,sha256=qzOInYA9gH2BEsn3mIa4Wtg2p1T-mlyd3GBoHzBWBBM,13359
+nvidia/cu13/include/cufftXt.h,sha256=-T0Zaqze9Axxpo9QELtKGdM6enmPrBN63SGvSIXOyxo,12546
+nvidia/cu13/include/cufftw.h,sha256=Uzfj1IVMlLQU_G50u84hXYX1K95HLXIwOcjQoAg5pGE,20051
+nvidia/cu13/lib/libcufft.so.12,sha256=CTP2i7fjv5D4bXC8h80vaQJ_d1fonl3l4Z7DOlP9jTo,286542352
+nvidia/cu13/lib/libcufftw.so.12,sha256=BlJD3uRpjtW4Dk9poL-5nMTg2jG5pwlRb4WEeTfCv3w,991704
+nvidia_cufft-12.0.0.61.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cufft-12.0.0.61.dist-info/METADATA,sha256=9RnmQtufSYP14k0TGlKXIhCzIigAoH4JnUCSbRjRg9g,1755
+nvidia_cufft-12.0.0.61.dist-info/RECORD,,
+nvidia_cufft-12.0.0.61.dist-info/WHEEL,sha256=gy6FWQgpujK_dnYc155G2NL32NQjpi5ebTEXjh8SGZQ,144
+nvidia_cufft-12.0.0.61.dist-info/licenses/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cufft-12.0.0.61.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..29738220b339f398b2cbbc9ac8ccb38a39bdd7b2
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (80.9.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/licenses/License.txt b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/licenses/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/licenses/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cufft-12.0.0.61.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..42d4d21466e2c10d476093336985432c77d1d9fd
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/METADATA
@@ -0,0 +1,49 @@
+Metadata-Version: 2.4
+Name: nvidia-cusolver
+Version: 12.0.4.66
+Summary: CUDA solver native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: LicenseRef-NVIDIA-Proprietary
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-cublas
+Requires-Dist: nvidia-nvjitlink
+Requires-Dist: nvidia-cusparse
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUDA solver native runtime libraries
diff --git a/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..ebc024dd9a3522fdf63a8c8d2f6a069bff4c38be
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/RECORD
@@ -0,0 +1,14 @@
+nvidia/cu13/include/cusolverDn.h,sha256=0iGKU77q741ffBonJLfaOBPcJkYvkNRzowTY6GNgsVg,144917
+nvidia/cu13/include/cusolverMg.h,sha256=eSuHH-ArhbkejSfXsllJdXCBk2Vob95-K5IL5dOL1XM,13293
+nvidia/cu13/include/cusolverRf.h,sha256=EP36tStOeUyi_aODKNoGrPn5wamxjjosVnDLp2iH0VQ,14994
+nvidia/cu13/include/cusolverSp.h,sha256=WY5aub4lteHV2mdtym2tdFVC9kkAZqIW-4XTibwm5Uo,34032
+nvidia/cu13/include/cusolverSp_LOWLEVEL_PREVIEW.h,sha256=mmmXuX_glHBdpgTzIqhXkLMz9UCxxC1uQTbcKA1W6UI,39169
+nvidia/cu13/include/cusolver_common.h,sha256=zGLXd3hIpfXfqaO58Tm2iWcJg6i3N170PncFqS6ceHo,9426
+nvidia/cu13/lib/libcusolver.so.12,sha256=xXHVJPxVcebIpXhNUfdfvozxdZBGPDl6BuSEKMuSbEg,141152872
+nvidia/cu13/lib/libcusolverMg.so.12,sha256=RuwIdDr9ECMgdRBacx8jq5SZjm0GtzmRRO31WgyCfZE,103956840
+nvidia_cusolver-12.0.4.66.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cusolver-12.0.4.66.dist-info/METADATA,sha256=iNKWtEB_giU781hmfbEndty1IRcMf_W47_OOsnL_cp8,1830
+nvidia_cusolver-12.0.4.66.dist-info/RECORD,,
+nvidia_cusolver-12.0.4.66.dist-info/WHEEL,sha256=pQkYcE_zO8O3IUE-PXGJ2qj02_9aLe-AqqPPB3Nn1Xg,109
+nvidia_cusolver-12.0.4.66.dist-info/licenses/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cusolver-12.0.4.66.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..08a681d359db9e13e5679534af805b7d41f40794
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (80.9.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux_2_27_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/licenses/License.txt b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/licenses/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/licenses/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusolver-12.0.4.66.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/INSTALLER b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/METADATA b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..bff70c381a2a391268211a93215ac5845311d6e9
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/METADATA
@@ -0,0 +1,47 @@
+Metadata-Version: 2.4
+Name: nvidia-cusparse
+Version: 12.6.3.3
+Summary: CUSPARSE native runtime libraries
+Home-page: https://developer.nvidia.com/cuda-zone
+Author: Nvidia CUDA Installer Team
+Author-email: compute_installer@nvidia.com
+License: LicenseRef-NVIDIA-Proprietary
+Keywords: cuda,nvidia,runtime,machine learning,deep learning
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: Other/Proprietary License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Requires-Python: >=3
+License-File: License.txt
+Requires-Dist: nvidia-nvjitlink
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: keywords
+Dynamic: license
+Dynamic: license-file
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+CUSPARSE native runtime libraries
diff --git a/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/RECORD b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..344fde2946da25c207104b64a74686ce6a748fe7
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/RECORD
@@ -0,0 +1,9 @@
+nvidia/cu13/include/cusparse.h,sha256=I_C-baSWSU38I2jL3KDzxLGSuOT8qvASdg1KUNUGBSI,296819
+nvidia/cu13/include/cusparse_v2.h,sha256=jkH2A9hYc-TEF0vuQ_SurbhPNEHkYGUIRuxKXhFAqnw,2587
+nvidia/cu13/lib/libcusparse.so.12,sha256=CTOfhI9guxERph7g_pHtDCUTK3_2MpgkTXrBTmG1hGY,162565256
+nvidia_cusparse-12.6.3.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nvidia_cusparse-12.6.3.3.dist-info/METADATA,sha256=17hYLpzzgHKou5lggi6J9BpkvH9omV7seDMEyCjpc04,1763
+nvidia_cusparse-12.6.3.3.dist-info/RECORD,,
+nvidia_cusparse-12.6.3.3.dist-info/WHEEL,sha256=gy6FWQgpujK_dnYc155G2NL32NQjpi5ebTEXjh8SGZQ,144
+nvidia_cusparse-12.6.3.3.dist-info/licenses/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
+nvidia_cusparse-12.6.3.3.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7
diff --git a/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/WHEEL b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..29738220b339f398b2cbbc9ac8ccb38a39bdd7b2
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/WHEEL
@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (80.9.0)
+Root-Is-Purelib: true
+Tag: py3-none-manylinux2014_x86_64
+Tag: py3-none-manylinux_2_17_x86_64
+
diff --git a/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/licenses/License.txt b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/licenses/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/licenses/License.txt
@@ -0,0 +1,1568 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: July 26, 2018
+---------------------------
+
+
+Important NoticeRead before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in avionics, navigation, military,
+    medical, life support or other life critical applications.
+    NVIDIA does not design, test or manufacture the SDK for
+    these critical uses and NVIDIA shall not be liable to you
+    or any third party, in whole or in part, for any claims or
+    damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described in this
+    section. This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described in this section.
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library and Header
+
+All
+
+nvrtc.h
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use (e.g. nvprof) of the PCRE third party
+    component is subject to the following terms and
+    conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/top_level.txt b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb
--- /dev/null
+++ b/lib/python3.11/site-packages/nvidia_cusparse-12.6.3.3.dist-info/top_level.txt
@@ -0,0 +1 @@
+nvidia
diff --git a/lib/python3.11/site-packages/torchaudio/__init__.py b/lib/python3.11/site-packages/torchaudio/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..90b411eae1f27ff5dc8adf4917c1ea8725cd2c9a
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/__init__.py
@@ -0,0 +1,53 @@
+# Initialize extension and backend first
+from . import _extension  # noqa  # usort: skip
+from ._backend import (  # noqa  # usort: skip
+    AudioMetaData,
+    get_audio_backend,
+    info,
+    list_audio_backends,
+    load,
+    save,
+    set_audio_backend,
+)
+
+from . import (  # noqa: F401
+    compliance,
+    datasets,
+    functional,
+    io,
+    kaldi_io,
+    models,
+    pipelines,
+    sox_effects,
+    transforms,
+    utils,
+)
+
+# For BC
+from . import backend  # noqa # usort: skip
+
+try:
+    from .version import __version__, git_version  # noqa: F401
+except ImportError:
+    pass
+
+
+__all__ = [
+    "AudioMetaData",
+    "load",
+    "info",
+    "save",
+    "io",
+    "compliance",
+    "datasets",
+    "functional",
+    "models",
+    "pipelines",
+    "kaldi_io",
+    "utils",
+    "sox_effects",
+    "transforms",
+    "list_audio_backends",
+    "get_audio_backend",
+    "set_audio_backend",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__init__.py b/lib/python3.11/site-packages/torchaudio/_backend/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..27337013ff12edbb9a6b18608a555e5c33031499
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/__init__.py
@@ -0,0 +1,61 @@
+from typing import List, Optional
+
+from torchaudio._internal.module_utils import deprecated
+
+from . import utils
+from .common import AudioMetaData
+
+__all__ = [
+    "AudioMetaData",
+    "load",
+    "info",
+    "save",
+    "list_audio_backends",
+    "get_audio_backend",
+    "set_audio_backend",
+]
+
+
+info = utils.get_info_func()
+load = utils.get_load_func()
+save = utils.get_save_func()
+
+
+def list_audio_backends() -> List[str]:
+    """List available backends
+
+    Returns:
+        list of str: The list of available backends.
+
+        The possible values are; ``"ffmpeg"``, ``"sox"`` and ``"soundfile"``.
+    """
+
+    return list(utils.get_available_backends().keys())
+
+
+# Temporary until global backend is removed
+@deprecated("With dispatcher enabled, this function is no-op. You can remove the function call.")
+def get_audio_backend() -> Optional[str]:
+    """Get the name of the current global backend
+
+    Returns:
+        str or None:
+            If dispatcher mode is enabled, returns ``None`` otherwise,
+            the name of current backend or ``None`` (no backend is set).
+    """
+    return None
+
+
+# Temporary until global backend is removed
+@deprecated("With dispatcher enabled, this function is no-op. You can remove the function call.")
+def set_audio_backend(backend: Optional[str]):  # noqa
+    """Set the global backend.
+
+    This is a no-op when dispatcher mode is enabled.
+
+    Args:
+        backend (str or None): Name of the backend.
+            One of ``"sox_io"`` or ``"soundfile"`` based on availability
+            of the system. If ``None`` is provided the  current backend is unassigned.
+    """
+    pass
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dd5a4f1b376aeba37703a3dc6dcb62efad3a79eb
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d54830ef181d452f316ae14a1ad9b9e837c6c9c6
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/common.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/common.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5c8cc0be0881cca47cef678dd0bbaf34af510fb3
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/common.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/ffmpeg.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/ffmpeg.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b961f5732c32c33a77e6ec7fd00c40b534c7e113
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/ffmpeg.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/soundfile.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/soundfile.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e6056d5febc4c394aacfc61d7f7294aa513266da
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/soundfile.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/soundfile_backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/soundfile_backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..33645572cb75098fe1a4f2678480ce0de0316c84
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/soundfile_backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/sox.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/sox.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d64c77dedc78907048a80b55bcc9b1418aae90b8
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/sox.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/utils.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..39792725344147a6c809c1eba2b438ac78342120
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_backend/__pycache__/utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/backend.py b/lib/python3.11/site-packages/torchaudio/_backend/backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..579340962c42a2210d5d7a5a41a1886b5fb62045
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/backend.py
@@ -0,0 +1,53 @@
+import os
+from abc import ABC, abstractmethod
+from typing import BinaryIO, Optional, Tuple, Union
+
+from torch import Tensor
+from torchaudio.io import CodecConfig
+
+from .common import AudioMetaData
+
+
+class Backend(ABC):
+    @staticmethod
+    @abstractmethod
+    def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[Tensor, int]:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[CodecConfig, float, int]] = None,
+    ) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def can_decode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def can_encode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        raise NotImplementedError
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/common.py b/lib/python3.11/site-packages/torchaudio/_backend/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..804b18d461b93d4a371e02d9cde902b59aba3111
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/common.py
@@ -0,0 +1,52 @@
+class AudioMetaData:
+    """AudioMetaData()
+
+    Return type of ``torchaudio.info`` function.
+
+    :ivar int sample_rate: Sample rate
+    :ivar int num_frames: The number of frames
+    :ivar int num_channels: The number of channels
+    :ivar int bits_per_sample: The number of bits per sample. This is 0 for lossy formats,
+        or when it cannot be accurately inferred.
+    :ivar str encoding: Audio encoding
+        The values encoding can take are one of the following:
+
+            * ``PCM_S``: Signed integer linear PCM
+            * ``PCM_U``: Unsigned integer linear PCM
+            * ``PCM_F``: Floating point linear PCM
+            * ``FLAC``: Flac, Free Lossless Audio Codec
+            * ``ULAW``: Mu-law
+            * ``ALAW``: A-law
+            * ``MP3`` : MP3, MPEG-1 Audio Layer III
+            * ``VORBIS``: OGG Vorbis
+            * ``AMR_WB``: Adaptive Multi-Rate Wideband
+            * ``AMR_NB``: Adaptive Multi-Rate Narrowband
+            * ``OPUS``: Opus
+            * ``HTK``: Single channel 16-bit PCM
+            * ``UNKNOWN`` : None of above
+    """
+
+    def __init__(
+        self,
+        sample_rate: int,
+        num_frames: int,
+        num_channels: int,
+        bits_per_sample: int,
+        encoding: str,
+    ):
+        self.sample_rate = sample_rate
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.bits_per_sample = bits_per_sample
+        self.encoding = encoding
+
+    def __str__(self):
+        return (
+            f"AudioMetaData("
+            f"sample_rate={self.sample_rate}, "
+            f"num_frames={self.num_frames}, "
+            f"num_channels={self.num_channels}, "
+            f"bits_per_sample={self.bits_per_sample}, "
+            f"encoding={self.encoding}"
+            f")"
+        )
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/ffmpeg.py b/lib/python3.11/site-packages/torchaudio/_backend/ffmpeg.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca8374ea07de9c9c06615f773d62b1ca910efb95
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/ffmpeg.py
@@ -0,0 +1,334 @@
+import os
+import re
+import sys
+from typing import BinaryIO, Optional, Tuple, Union
+
+import torch
+import torchaudio
+
+from .backend import Backend
+from .common import AudioMetaData
+
+InputType = Union[BinaryIO, str, os.PathLike]
+
+
+def info_audio(
+    src: InputType,
+    format: Optional[str],
+    buffer_size: int = 4096,
+) -> AudioMetaData:
+    s = torchaudio.io.StreamReader(src, format, None, buffer_size)
+    sinfo = s.get_src_stream_info(s.default_audio_stream)
+    if sinfo.num_frames == 0:
+        waveform = _load_audio(s)
+        num_frames = waveform.size(1)
+    else:
+        num_frames = sinfo.num_frames
+    return AudioMetaData(
+        int(sinfo.sample_rate),
+        num_frames,
+        sinfo.num_channels,
+        sinfo.bits_per_sample,
+        sinfo.codec.upper(),
+    )
+
+
+def _get_load_filter(
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    convert: bool = True,
+) -> Optional[str]:
+    if frame_offset < 0:
+        raise RuntimeError("Invalid argument: frame_offset must be non-negative. Found: {}".format(frame_offset))
+    if num_frames == 0 or num_frames < -1:
+        raise RuntimeError("Invalid argument: num_frames must be -1 or greater than 0. Found: {}".format(num_frames))
+
+    # All default values -> no filter
+    if frame_offset == 0 and num_frames == -1 and not convert:
+        return None
+    # Only convert
+    aformat = "aformat=sample_fmts=fltp"
+    if frame_offset == 0 and num_frames == -1 and convert:
+        return aformat
+    # At least one of frame_offset or num_frames has non-default value
+    if num_frames > 0:
+        atrim = "atrim=start_sample={}:end_sample={}".format(frame_offset, frame_offset + num_frames)
+    else:
+        atrim = "atrim=start_sample={}".format(frame_offset)
+    if not convert:
+        return atrim
+    return "{},{}".format(atrim, aformat)
+
+
+def _load_audio(
+    s: "torchaudio.io.StreamReader",
+    filter: Optional[str] = None,
+    channels_first: bool = True,
+) -> torch.Tensor:
+    s.add_audio_stream(-1, -1, filter_desc=filter)
+    s.process_all_packets()
+    chunk = s.pop_chunks()[0]
+    if chunk is None:
+        raise RuntimeError("Failed to decode audio.")
+    waveform = chunk._elem
+    return waveform.T if channels_first else waveform
+
+
+def load_audio(
+    src: InputType,
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    convert: bool = True,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+    buffer_size: int = 4096,
+) -> Tuple[torch.Tensor, int]:
+    if hasattr(src, "read") and format == "vorbis":
+        format = "ogg"
+    s = torchaudio.io.StreamReader(src, format, None, buffer_size)
+    sample_rate = int(s.get_src_stream_info(s.default_audio_stream).sample_rate)
+    filter = _get_load_filter(frame_offset, num_frames, convert)
+    waveform = _load_audio(s, filter, channels_first)
+    return waveform, sample_rate
+
+
+def _get_sample_format(dtype: torch.dtype) -> str:
+    dtype_to_format = {
+        torch.uint8: "u8",
+        torch.int16: "s16",
+        torch.int32: "s32",
+        torch.int64: "s64",
+        torch.float32: "flt",
+        torch.float64: "dbl",
+    }
+    format = dtype_to_format.get(dtype)
+    if format is None:
+        raise ValueError(f"No format found for dtype {dtype}; dtype must be one of {list(dtype_to_format.keys())}.")
+    return format
+
+
+def _native_endianness() -> str:
+    if sys.byteorder == "little":
+        return "le"
+    else:
+        return "be"
+
+
+def _get_encoder_for_wav(encoding: str, bits_per_sample: int) -> str:
+    if bits_per_sample not in {None, 8, 16, 24, 32, 64}:
+        raise ValueError(f"Invalid bits_per_sample {bits_per_sample} for WAV encoding.")
+    endianness = _native_endianness()
+    if not encoding:
+        if not bits_per_sample:
+            # default to PCM S16
+            return f"pcm_s16{endianness}"
+        if bits_per_sample == 8:
+            return "pcm_u8"
+        return f"pcm_s{bits_per_sample}{endianness}"
+    if encoding == "PCM_S":
+        if not bits_per_sample:
+            bits_per_sample = 16
+        if bits_per_sample == 8:
+            raise ValueError("For WAV signed PCM, 8-bit encoding is not supported.")
+        return f"pcm_s{bits_per_sample}{endianness}"
+    if encoding == "PCM_U":
+        if bits_per_sample in (None, 8):
+            return "pcm_u8"
+        raise ValueError("For WAV unsigned PCM, only 8-bit encoding is supported.")
+    if encoding == "PCM_F":
+        if not bits_per_sample:
+            bits_per_sample = 32
+        if bits_per_sample in (32, 64):
+            return f"pcm_f{bits_per_sample}{endianness}"
+        raise ValueError("For WAV float PCM, only 32- and 64-bit encodings are supported.")
+    if encoding == "ULAW":
+        if bits_per_sample in (None, 8):
+            return "pcm_mulaw"
+        raise ValueError("For WAV PCM mu-law, only 8-bit encoding is supported.")
+    if encoding == "ALAW":
+        if bits_per_sample in (None, 8):
+            return "pcm_alaw"
+        raise ValueError("For WAV PCM A-law, only 8-bit encoding is supported.")
+    raise ValueError(f"WAV encoding {encoding} is not supported.")
+
+
+def _get_flac_sample_fmt(bps):
+    if bps is None or bps == 16:
+        return "s16"
+    if bps == 24:
+        return "s32"
+    raise ValueError(f"FLAC only supports bits_per_sample values of 16 and 24 ({bps} specified).")
+
+
+def _parse_save_args(
+    ext: Optional[str],
+    format: Optional[str],
+    encoding: Optional[str],
+    bps: Optional[int],
+):
+    # torchaudio's save function accepts the followings, which do not 1to1 map
+    # to FFmpeg.
+    #
+    # - format: audio format
+    # - bits_per_sample: encoder sample format
+    # - encoding: such as PCM_U8.
+    #
+    # In FFmpeg, format is specified with the following three (and more)
+    #
+    # - muxer: could be audio format or container format.
+    # the one we passed to the constructor of StreamWriter
+    # - encoder: the audio encoder used to encode audio
+    # - encoder sample format: the format used by encoder to encode audio.
+    #
+    # If encoder sample format is different from source sample format, StreamWriter
+    # will insert a filter automatically.
+    #
+    def _type(spec):
+        # either format is exactly the specified one
+        # or extension matches to the spec AND there is no format override.
+        return format == spec or (format is None and ext == spec)
+
+    if _type("wav") or _type("amb"):
+        # wav is special because it supports different encoding through encoders
+        # each encoder only supports one encoder format
+        #
+        # amb format is a special case originated from libsox.
+        # It is basically a WAV format, with slight modification.
+        # https://github.com/chirlu/sox/commit/4a4ea33edbca5972a1ed8933cc3512c7302fa67a#diff-39171191a858add9df87f5f210a34a776ac2c026842ae6db6ce97f5e68836795
+        # It is a format so that decoders will recognize it as ambisonic.
+        # https://www.ambisonia.com/Members/mleese/file-format-for-b-format/
+        # FFmpeg does not recognize amb because it is basically a WAV format.
+        muxer = "wav"
+        encoder = _get_encoder_for_wav(encoding, bps)
+        sample_fmt = None
+    elif _type("vorbis"):
+        # FFpmeg does not recognize vorbis extension, while libsox used to do.
+        # For the sake of bakward compatibility, (and the simplicity),
+        # we support the case where users want to do save("foo.vorbis")
+        muxer = "ogg"
+        encoder = "vorbis"
+        sample_fmt = None
+    else:
+        muxer = format
+        encoder = None
+        sample_fmt = None
+        if _type("flac"):
+            sample_fmt = _get_flac_sample_fmt(bps)
+        if _type("ogg"):
+            sample_fmt = _get_flac_sample_fmt(bps)
+    return muxer, encoder, sample_fmt
+
+
+def save_audio(
+    uri: InputType,
+    src: torch.Tensor,
+    sample_rate: int,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+    buffer_size: int = 4096,
+    compression: Optional[torchaudio.io.CodecConfig] = None,
+) -> None:
+    ext = None
+    if hasattr(uri, "write"):
+        if format is None:
+            raise RuntimeError("'format' is required when saving to file object.")
+    else:
+        uri = os.path.normpath(uri)
+        if tokens := str(uri).split(".")[1:]:
+            ext = tokens[-1].lower()
+
+    muxer, encoder, enc_fmt = _parse_save_args(ext, format, encoding, bits_per_sample)
+
+    if channels_first:
+        src = src.T
+
+    s = torchaudio.io.StreamWriter(uri, format=muxer, buffer_size=buffer_size)
+    s.add_audio_stream(
+        sample_rate,
+        num_channels=src.size(-1),
+        format=_get_sample_format(src.dtype),
+        encoder=encoder,
+        encoder_format=enc_fmt,
+        codec_config=compression,
+    )
+    with s.open():
+        s.write_audio_chunk(0, src)
+
+
+def _map_encoding(encoding: str) -> str:
+    for dst in ["PCM_S", "PCM_U", "PCM_F"]:
+        if dst in encoding:
+            return dst
+    if encoding == "PCM_MULAW":
+        return "ULAW"
+    elif encoding == "PCM_ALAW":
+        return "ALAW"
+    return encoding
+
+
+def _get_bits_per_sample(encoding: str, bits_per_sample: int) -> str:
+    if m := re.search(r"PCM_\w(\d+)\w*", encoding):
+        return int(m.group(1))
+    elif encoding in ["PCM_ALAW", "PCM_MULAW"]:
+        return 8
+    return bits_per_sample
+
+
+class FFmpegBackend(Backend):
+    @staticmethod
+    def info(uri: InputType, format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        metadata = info_audio(uri, format, buffer_size)
+        metadata.bits_per_sample = _get_bits_per_sample(metadata.encoding, metadata.bits_per_sample)
+        metadata.encoding = _map_encoding(metadata.encoding)
+        return metadata
+
+    @staticmethod
+    def load(
+        uri: InputType,
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[torch.Tensor, int]:
+        return load_audio(uri, frame_offset, num_frames, normalize, channels_first, format)
+
+    @staticmethod
+    def save(
+        uri: InputType,
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[torchaudio.io.CodecConfig, float, int]] = None,
+    ) -> None:
+        if not isinstance(compression, (torchaudio.io.CodecConfig, type(None))):
+            raise ValueError(
+                "FFmpeg backend expects non-`None` value for argument `compression` to be of ",
+                f"type `torchaudio.io.CodecConfig`, but received value of type {type(compression)}",
+            )
+        save_audio(
+            uri,
+            src,
+            sample_rate,
+            channels_first,
+            format,
+            encoding,
+            bits_per_sample,
+            buffer_size,
+            compression,
+        )
+
+    @staticmethod
+    def can_decode(uri: InputType, format: Optional[str]) -> bool:
+        return True
+
+    @staticmethod
+    def can_encode(uri: InputType, format: Optional[str]) -> bool:
+        return True
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/soundfile.py b/lib/python3.11/site-packages/torchaudio/_backend/soundfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4be1f70999db86e4ff70b6b703d5784a891a84c
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/soundfile.py
@@ -0,0 +1,54 @@
+import os
+from typing import BinaryIO, Optional, Tuple, Union
+
+import torch
+from torchaudio.io import CodecConfig
+
+from . import soundfile_backend
+from .backend import Backend
+from .common import AudioMetaData
+
+
+class SoundfileBackend(Backend):
+    @staticmethod
+    def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        return soundfile_backend.info(uri, format)
+
+    @staticmethod
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[torch.Tensor, int]:
+        return soundfile_backend.load(uri, frame_offset, num_frames, normalize, channels_first, format)
+
+    @staticmethod
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[CodecConfig, float, int]] = None,
+    ) -> None:
+        if compression:
+            raise ValueError("soundfile backend does not support argument `compression`.")
+
+        soundfile_backend.save(
+            uri, src, sample_rate, channels_first, format=format, encoding=encoding, bits_per_sample=bits_per_sample
+        )
+
+    @staticmethod
+    def can_decode(uri, format) -> bool:
+        return True
+
+    @staticmethod
+    def can_encode(uri, format) -> bool:
+        return True
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/soundfile_backend.py b/lib/python3.11/site-packages/torchaudio/_backend/soundfile_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e7b0b13cd9adf2106ef4d6885f89341822add13
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/soundfile_backend.py
@@ -0,0 +1,457 @@
+"""The new soundfile backend which will become default in 0.8.0 onward"""
+import warnings
+from typing import Optional, Tuple
+
+import torch
+from torchaudio._internal import module_utils as _mod_utils
+
+from .common import AudioMetaData
+
+
+_IS_SOUNDFILE_AVAILABLE = False
+
+# TODO: import soundfile only when it is used.
+if _mod_utils.is_module_available("soundfile"):
+    try:
+        import soundfile
+
+        _requires_soundfile = _mod_utils.no_op
+        _IS_SOUNDFILE_AVAILABLE = True
+    except Exception:
+        _requires_soundfile = _mod_utils.fail_with_message(
+            "requires soundfile, but we failed to import it. Please check the installation of soundfile."
+        )
+else:
+    _requires_soundfile = _mod_utils.fail_with_message(
+        "requires soundfile, but it is not installed. Please install soundfile."
+    )
+
+
+# Mapping from soundfile subtype to number of bits per sample.
+# This is mostly heuristical and the value is set to 0 when it is irrelevant
+# (lossy formats) or when it can't be inferred.
+# For ADPCM (and G72X) subtypes, it's hard to infer the bit depth because it's not part of the standard:
+# According to https://en.wikipedia.org/wiki/Adaptive_differential_pulse-code_modulation#In_telephony,
+# the default seems to be 8 bits but it can be compressed further to 4 bits.
+# The dict is inspired from
+# https://github.com/bastibe/python-soundfile/blob/744efb4b01abc72498a96b09115b42a4cabd85e4/soundfile.py#L66-L94
+_SUBTYPE_TO_BITS_PER_SAMPLE = {
+    "PCM_S8": 8,  # Signed 8 bit data
+    "PCM_16": 16,  # Signed 16 bit data
+    "PCM_24": 24,  # Signed 24 bit data
+    "PCM_32": 32,  # Signed 32 bit data
+    "PCM_U8": 8,  # Unsigned 8 bit data (WAV and RAW only)
+    "FLOAT": 32,  # 32 bit float data
+    "DOUBLE": 64,  # 64 bit float data
+    "ULAW": 8,  # U-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types
+    "ALAW": 8,  # A-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types
+    "IMA_ADPCM": 0,  # IMA ADPCM.
+    "MS_ADPCM": 0,  # Microsoft ADPCM.
+    "GSM610": 0,  # GSM 6.10 encoding. (Wikipedia says 1.625 bit depth?? https://en.wikipedia.org/wiki/Full_Rate)
+    "VOX_ADPCM": 0,  # OKI / Dialogix ADPCM
+    "G721_32": 0,  # 32kbs G721 ADPCM encoding.
+    "G723_24": 0,  # 24kbs G723 ADPCM encoding.
+    "G723_40": 0,  # 40kbs G723 ADPCM encoding.
+    "DWVW_12": 12,  # 12 bit Delta Width Variable Word encoding.
+    "DWVW_16": 16,  # 16 bit Delta Width Variable Word encoding.
+    "DWVW_24": 24,  # 24 bit Delta Width Variable Word encoding.
+    "DWVW_N": 0,  # N bit Delta Width Variable Word encoding.
+    "DPCM_8": 8,  # 8 bit differential PCM (XI only)
+    "DPCM_16": 16,  # 16 bit differential PCM (XI only)
+    "VORBIS": 0,  # Xiph Vorbis encoding. (lossy)
+    "ALAC_16": 16,  # Apple Lossless Audio Codec (16 bit).
+    "ALAC_20": 20,  # Apple Lossless Audio Codec (20 bit).
+    "ALAC_24": 24,  # Apple Lossless Audio Codec (24 bit).
+    "ALAC_32": 32,  # Apple Lossless Audio Codec (32 bit).
+}
+
+
+def _get_bit_depth(subtype):
+    if subtype not in _SUBTYPE_TO_BITS_PER_SAMPLE:
+        warnings.warn(
+            f"The {subtype} subtype is unknown to TorchAudio. As a result, the bits_per_sample "
+            "attribute will be set to 0. If you are seeing this warning, please "
+            "report by opening an issue on github (after checking for existing/closed ones). "
+            "You may otherwise ignore this warning."
+        )
+    return _SUBTYPE_TO_BITS_PER_SAMPLE.get(subtype, 0)
+
+
+_SUBTYPE_TO_ENCODING = {
+    "PCM_S8": "PCM_S",
+    "PCM_16": "PCM_S",
+    "PCM_24": "PCM_S",
+    "PCM_32": "PCM_S",
+    "PCM_U8": "PCM_U",
+    "FLOAT": "PCM_F",
+    "DOUBLE": "PCM_F",
+    "ULAW": "ULAW",
+    "ALAW": "ALAW",
+    "VORBIS": "VORBIS",
+}
+
+
+def _get_encoding(format: str, subtype: str):
+    if format == "FLAC":
+        return "FLAC"
+    return _SUBTYPE_TO_ENCODING.get(subtype, "UNKNOWN")
+
+
+@_requires_soundfile
+def info(filepath: str, format: Optional[str] = None) -> AudioMetaData:
+    """Get signal information of an audio file.
+
+    Note:
+        ``filepath`` argument is intentionally annotated as ``str`` only, even though it accepts
+        ``pathlib.Path`` object as well. This is for the consistency with ``"sox_io"`` backend,
+        which has a restriction on type annotation due to TorchScript compiler compatiblity.
+
+    Args:
+        filepath (path-like object or file-like object):
+            Source of audio data.
+        format (str or None, optional):
+            Not used. PySoundFile does not accept format hint.
+
+    Returns:
+        AudioMetaData: meta data of the given audio.
+
+    """
+    sinfo = soundfile.info(filepath)
+    return AudioMetaData(
+        sinfo.samplerate,
+        sinfo.frames,
+        sinfo.channels,
+        bits_per_sample=_get_bit_depth(sinfo.subtype),
+        encoding=_get_encoding(sinfo.format, sinfo.subtype),
+    )
+
+
+_SUBTYPE2DTYPE = {
+    "PCM_S8": "int8",
+    "PCM_U8": "uint8",
+    "PCM_16": "int16",
+    "PCM_32": "int32",
+    "FLOAT": "float32",
+    "DOUBLE": "float64",
+}
+
+
+@_requires_soundfile
+def load(
+    filepath: str,
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    normalize: bool = True,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+) -> Tuple[torch.Tensor, int]:
+    """Load audio data from file.
+
+    Note:
+        The formats this function can handle depend on the soundfile installation.
+        This function is tested on the following formats;
+
+        * WAV
+
+            * 32-bit floating-point
+            * 32-bit signed integer
+            * 16-bit signed integer
+            * 8-bit unsigned integer
+
+        * FLAC
+        * OGG/VORBIS
+        * SPHERE
+
+    By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
+    ``float32`` dtype, and the shape of `[channel, time]`.
+
+    .. warning::
+
+       ``normalize`` argument does not perform volume normalization.
+       It only converts the sample type to `torch.float32` from the native sample
+       type.
+
+       When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
+       signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``,
+       this function can return integer Tensor, where the samples are expressed within the whole range
+       of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM,
+       ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not
+       support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors.
+
+       ``normalize`` argument has no effect on 32-bit floating-point WAV and other formats, such as
+       ``flac`` and ``mp3``.
+
+       For these formats, this function always returns ``float32`` Tensor with values.
+
+    Note:
+        ``filepath`` argument is intentionally annotated as ``str`` only, even though it accepts
+        ``pathlib.Path`` object as well. This is for the consistency with ``"sox_io"`` backend,
+        which has a restriction on type annotation due to TorchScript compiler compatiblity.
+
+    Args:
+        filepath (path-like object or file-like object):
+            Source of audio data.
+        frame_offset (int, optional):
+            Number of frames to skip before start reading data.
+        num_frames (int, optional):
+            Maximum number of frames to read. ``-1`` reads all the remaining samples,
+            starting from ``frame_offset``.
+            This function may return the less number of frames if there is not enough
+            frames in the given file.
+        normalize (bool, optional):
+            When ``True``, this function converts the native sample type to ``float32``.
+            Default: ``True``.
+
+            If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
+            integer type.
+            This argument has no effect for formats other than integer WAV type.
+
+        channels_first (bool, optional):
+            When True, the returned Tensor has dimension `[channel, time]`.
+            Otherwise, the returned Tensor's dimension is `[time, channel]`.
+        format (str or None, optional):
+            Not used. PySoundFile does not accept format hint.
+
+    Returns:
+        (torch.Tensor, int): Resulting Tensor and sample rate.
+            If the input file has integer wav format and normalization is off, then it has
+            integer type, else ``float32`` type. If ``channels_first=True``, it has
+            `[channel, time]` else `[time, channel]`.
+    """
+    with soundfile.SoundFile(filepath, "r") as file_:
+        if file_.format != "WAV" or normalize:
+            dtype = "float32"
+        elif file_.subtype not in _SUBTYPE2DTYPE:
+            raise ValueError(f"Unsupported subtype: {file_.subtype}")
+        else:
+            dtype = _SUBTYPE2DTYPE[file_.subtype]
+
+        frames = file_._prepare_read(frame_offset, None, num_frames)
+        waveform = file_.read(frames, dtype, always_2d=True)
+        sample_rate = file_.samplerate
+
+    waveform = torch.from_numpy(waveform)
+    if channels_first:
+        waveform = waveform.t()
+    return waveform, sample_rate
+
+
+def _get_subtype_for_wav(dtype: torch.dtype, encoding: str, bits_per_sample: int):
+    if not encoding:
+        if not bits_per_sample:
+            subtype = {
+                torch.uint8: "PCM_U8",
+                torch.int16: "PCM_16",
+                torch.int32: "PCM_32",
+                torch.float32: "FLOAT",
+                torch.float64: "DOUBLE",
+            }.get(dtype)
+            if not subtype:
+                raise ValueError(f"Unsupported dtype for wav: {dtype}")
+            return subtype
+        if bits_per_sample == 8:
+            return "PCM_U8"
+        return f"PCM_{bits_per_sample}"
+    if encoding == "PCM_S":
+        if not bits_per_sample:
+            return "PCM_32"
+        if bits_per_sample == 8:
+            raise ValueError("wav does not support 8-bit signed PCM encoding.")
+        return f"PCM_{bits_per_sample}"
+    if encoding == "PCM_U":
+        if bits_per_sample in (None, 8):
+            return "PCM_U8"
+        raise ValueError("wav only supports 8-bit unsigned PCM encoding.")
+    if encoding == "PCM_F":
+        if bits_per_sample in (None, 32):
+            return "FLOAT"
+        if bits_per_sample == 64:
+            return "DOUBLE"
+        raise ValueError("wav only supports 32/64-bit float PCM encoding.")
+    if encoding == "ULAW":
+        if bits_per_sample in (None, 8):
+            return "ULAW"
+        raise ValueError("wav only supports 8-bit mu-law encoding.")
+    if encoding == "ALAW":
+        if bits_per_sample in (None, 8):
+            return "ALAW"
+        raise ValueError("wav only supports 8-bit a-law encoding.")
+    raise ValueError(f"wav does not support {encoding}.")
+
+
+def _get_subtype_for_sphere(encoding: str, bits_per_sample: int):
+    if encoding in (None, "PCM_S"):
+        return f"PCM_{bits_per_sample}" if bits_per_sample else "PCM_32"
+    if encoding in ("PCM_U", "PCM_F"):
+        raise ValueError(f"sph does not support {encoding} encoding.")
+    if encoding == "ULAW":
+        if bits_per_sample in (None, 8):
+            return "ULAW"
+        raise ValueError("sph only supports 8-bit for mu-law encoding.")
+    if encoding == "ALAW":
+        return "ALAW"
+    raise ValueError(f"sph does not support {encoding}.")
+
+
+def _get_subtype(dtype: torch.dtype, format: str, encoding: str, bits_per_sample: int):
+    if format == "wav":
+        return _get_subtype_for_wav(dtype, encoding, bits_per_sample)
+    if format == "flac":
+        if encoding:
+            raise ValueError("flac does not support encoding.")
+        if not bits_per_sample:
+            return "PCM_16"
+        if bits_per_sample > 24:
+            raise ValueError("flac does not support bits_per_sample > 24.")
+        return "PCM_S8" if bits_per_sample == 8 else f"PCM_{bits_per_sample}"
+    if format in ("ogg", "vorbis"):
+        if bits_per_sample:
+            raise ValueError("ogg/vorbis does not support bits_per_sample.")
+        if encoding is None or encoding == "vorbis":
+            return "VORBIS"
+        if encoding == "opus":
+            return "OPUS"
+        raise ValueError(f"Unexpected encoding: {encoding}")
+    if format == "mp3":
+        return "MPEG_LAYER_III"
+    if format == "sph":
+        return _get_subtype_for_sphere(encoding, bits_per_sample)
+    if format in ("nis", "nist"):
+        return "PCM_16"
+    raise ValueError(f"Unsupported format: {format}")
+
+
+@_requires_soundfile
+def save(
+    filepath: str,
+    src: torch.Tensor,
+    sample_rate: int,
+    channels_first: bool = True,
+    compression: Optional[float] = None,
+    format: Optional[str] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+):
+    """Save audio data to file.
+
+    Note:
+        The formats this function can handle depend on the soundfile installation.
+        This function is tested on the following formats;
+
+        * WAV
+
+            * 32-bit floating-point
+            * 32-bit signed integer
+            * 16-bit signed integer
+            * 8-bit unsigned integer
+
+        * FLAC
+        * OGG/VORBIS
+        * SPHERE
+
+    Note:
+        ``filepath`` argument is intentionally annotated as ``str`` only, even though it accepts
+        ``pathlib.Path`` object as well. This is for the consistency with ``"sox_io"`` backend,
+        which has a restriction on type annotation due to TorchScript compiler compatiblity.
+
+    Args:
+        filepath (str or pathlib.Path): Path to audio file.
+        src (torch.Tensor): Audio data to save. must be 2D tensor.
+        sample_rate (int): sampling rate
+        channels_first (bool, optional): If ``True``, the given tensor is interpreted as `[channel, time]`,
+            otherwise `[time, channel]`.
+        compression (float of None, optional): Not used.
+            It is here only for interface compatibility reson with "sox_io" backend.
+        format (str or None, optional): Override the audio format.
+            When ``filepath`` argument is path-like object, audio format is
+            inferred from file extension. If the file extension is missing or
+            different, you can specify the correct format with this argument.
+
+            When ``filepath`` argument is file-like object,
+            this argument is required.
+
+            Valid values are ``"wav"``, ``"ogg"``, ``"vorbis"``,
+            ``"flac"`` and ``"sph"``.
+        encoding (str or None, optional): Changes the encoding for supported formats.
+            This argument is effective only for supported formats, sush as
+            ``"wav"``, ``""flac"`` and ``"sph"``. Valid values are;
+
+                - ``"PCM_S"`` (signed integer Linear PCM)
+                - ``"PCM_U"`` (unsigned integer Linear PCM)
+                - ``"PCM_F"`` (floating point PCM)
+                - ``"ULAW"`` (mu-law)
+                - ``"ALAW"`` (a-law)
+
+        bits_per_sample (int or None, optional): Changes the bit depth for the
+            supported formats.
+            When ``format`` is one of ``"wav"``, ``"flac"`` or ``"sph"``,
+            you can change the bit depth.
+            Valid values are ``8``, ``16``, ``24``, ``32`` and ``64``.
+
+    Supported formats/encodings/bit depth/compression are:
+
+    ``"wav"``
+        - 32-bit floating-point PCM
+        - 32-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 8-bit unsigned integer PCM
+        - 8-bit mu-law
+        - 8-bit a-law
+
+        Note:
+            Default encoding/bit depth is determined by the dtype of
+            the input Tensor.
+
+    ``"flac"``
+        - 8-bit
+        - 16-bit (default)
+        - 24-bit
+
+    ``"ogg"``, ``"vorbis"``
+        - Doesn't accept changing configuration.
+
+    ``"sph"``
+        - 8-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 32-bit signed integer PCM (default)
+        - 8-bit mu-law
+        - 8-bit a-law
+        - 16-bit a-law
+        - 24-bit a-law
+        - 32-bit a-law
+
+    """
+    if src.ndim != 2:
+        raise ValueError(f"Expected 2D Tensor, got {src.ndim}D.")
+    if compression is not None:
+        warnings.warn(
+            '`save` function of "soundfile" backend does not support "compression" parameter. '
+            "The argument is silently ignored."
+        )
+    if hasattr(filepath, "write"):
+        if format is None:
+            raise RuntimeError("`format` is required when saving to file object.")
+        ext = format.lower()
+    else:
+        ext = str(filepath).split(".")[-1].lower()
+
+    if bits_per_sample not in (None, 8, 16, 24, 32, 64):
+        raise ValueError("Invalid bits_per_sample.")
+    if bits_per_sample == 24:
+        warnings.warn(
+            "Saving audio with 24 bits per sample might warp samples near -1. "
+            "Using 16 bits per sample might be able to avoid this."
+        )
+    subtype = _get_subtype(src.dtype, ext, encoding, bits_per_sample)
+
+    # sph is a extension used in TED-LIUM but soundfile does not recognize it as NIST format,
+    # so we extend the extensions manually here
+    if ext in ["nis", "nist", "sph"] and format is None:
+        format = "NIST"
+
+    if channels_first:
+        src = src.t()
+
+    soundfile.write(file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format)
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/sox.py b/lib/python3.11/site-packages/torchaudio/_backend/sox.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfcd8a4f8beadcd80787a354d47219f9edfb98e8
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/sox.py
@@ -0,0 +1,91 @@
+import os
+from typing import BinaryIO, Optional, Tuple, Union
+
+import torch
+import torchaudio
+
+from .backend import Backend
+from .common import AudioMetaData
+
+sox_ext = torchaudio._extension.lazy_import_sox_ext()
+
+
+class SoXBackend(Backend):
+    @staticmethod
+    def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        if hasattr(uri, "read"):
+            raise ValueError(
+                "SoX backend does not support reading from file-like objects. ",
+                "Please use an alternative backend that does support reading from file-like objects, e.g. FFmpeg.",
+            )
+        else:
+            sinfo = sox_ext.get_info(uri, format)
+            if sinfo:
+                return AudioMetaData(*sinfo)
+            else:
+                raise RuntimeError(f"Failed to fetch metadata for {uri}.")
+
+    @staticmethod
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[torch.Tensor, int]:
+        if hasattr(uri, "read"):
+            raise ValueError(
+                "SoX backend does not support loading from file-like objects. ",
+                "Please use an alternative backend that does support loading from file-like objects, e.g. FFmpeg.",
+            )
+        else:
+            ret = sox_ext.load_audio_file(uri, frame_offset, num_frames, normalize, channels_first, format)
+            if not ret:
+                raise RuntimeError(f"Failed to load audio from {uri}.")
+            return ret
+
+    @staticmethod
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[torchaudio.io.CodecConfig, float, int]] = None,
+    ) -> None:
+        if not isinstance(compression, (float, int, type(None))):
+            raise ValueError(
+                "SoX backend expects non-`None` value for argument `compression` to be of ",
+                f"type `float` or `int`, but received value of type {type(compression)}",
+            )
+        if hasattr(uri, "write"):
+            raise ValueError(
+                "SoX backend does not support writing to file-like objects. ",
+                "Please use an alternative backend that does support writing to file-like objects, e.g. FFmpeg.",
+            )
+        else:
+            sox_ext.save_audio_file(
+                uri,
+                src,
+                sample_rate,
+                channels_first,
+                compression,
+                format,
+                encoding,
+                bits_per_sample,
+            )
+
+    @staticmethod
+    def can_decode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        # i.e. not a file-like object.
+        return not hasattr(uri, "read")
+
+    @staticmethod
+    def can_encode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        # i.e. not a file-like object.
+        return not hasattr(uri, "write")
diff --git a/lib/python3.11/site-packages/torchaudio/_backend/utils.py b/lib/python3.11/site-packages/torchaudio/_backend/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cde6b1927d2b35e34d4ab0e99124fde146e724d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_backend/utils.py
@@ -0,0 +1,317 @@
+import os
+from functools import lru_cache
+from typing import BinaryIO, Dict, Optional, Tuple, Type, Union
+
+import torch
+
+from torchaudio._extension import lazy_import_sox_ext
+from torchaudio.io import CodecConfig
+from torio._extension import lazy_import_ffmpeg_ext
+
+from . import soundfile_backend
+
+from .backend import Backend
+from .common import AudioMetaData
+from .ffmpeg import FFmpegBackend
+from .soundfile import SoundfileBackend
+from .sox import SoXBackend
+
+
+@lru_cache(None)
+def get_available_backends() -> Dict[str, Type[Backend]]:
+    backend_specs: Dict[str, Type[Backend]] = {}
+    if lazy_import_ffmpeg_ext().is_available():
+        backend_specs["ffmpeg"] = FFmpegBackend
+    if lazy_import_sox_ext().is_available():
+        backend_specs["sox"] = SoXBackend
+    if soundfile_backend._IS_SOUNDFILE_AVAILABLE:
+        backend_specs["soundfile"] = SoundfileBackend
+    return backend_specs
+
+
+def get_backend(backend_name, backends) -> Backend:
+    if backend := backends.get(backend_name):
+        return backend
+    else:
+        raise ValueError(
+            f"Unsupported backend '{backend_name}' specified; ",
+            f"please select one of {list(backends.keys())} instead.",
+        )
+
+
+def get_info_func():
+    backends = get_available_backends()
+
+    def dispatcher(
+        uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], backend_name: Optional[str]
+    ) -> Backend:
+        if backend_name is not None:
+            return get_backend(backend_name, backends)
+
+        for backend in backends.values():
+            if backend.can_decode(uri, format):
+                return backend
+        raise RuntimeError(f"Couldn't find appropriate backend to handle uri {uri} and format {format}.")
+
+    def info(
+        uri: Union[BinaryIO, str, os.PathLike],
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+        backend: Optional[str] = None,
+    ) -> AudioMetaData:
+        """Get signal information of an audio file.
+
+        Note:
+            When the input type is file-like object, this function cannot
+            get the correct length (``num_samples``) for certain formats,
+            such as ``vorbis``.
+            In this case, the value of ``num_samples`` is ``0``.
+
+        Args:
+            uri (path-like object or file-like object):
+                Source of audio data. The following types are accepted:
+
+                * ``path-like``: File path or URL.
+                * ``file-like``: Object with ``read(size: int) -> bytes`` method,
+                  which returns byte string of at most ``size`` length.
+
+            format (str or None, optional):
+                If not ``None``, interpreted as hint that may allow backend to override the detected format.
+                (Default: ``None``)
+
+            buffer_size (int, optional):
+                Size of buffer to use when processing file-like objects, in bytes. (Default: ``4096``)
+
+            backend (str or None, optional):
+                I/O backend to use.
+                If ``None``, function selects backend given input and available backends.
+                Otherwise, must be one of [``"ffmpeg"``, ``"sox"``, ``"soundfile"``],
+                with the corresponding backend available.
+                (Default: ``None``)
+
+                .. seealso::
+                   :ref:`backend`
+
+        Returns:
+            AudioMetaData
+        """
+        backend = dispatcher(uri, format, backend)
+        return backend.info(uri, format, buffer_size)
+
+    return info
+
+
+def get_load_func():
+    backends = get_available_backends()
+
+    def dispatcher(
+        uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], backend_name: Optional[str]
+    ) -> Backend:
+        if backend_name is not None:
+            return get_backend(backend_name, backends)
+
+        for backend in backends.values():
+            if backend.can_decode(uri, format):
+                return backend
+        raise RuntimeError(f"Couldn't find appropriate backend to handle uri {uri} and format {format}.")
+
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+        backend: Optional[str] = None,
+    ) -> Tuple[torch.Tensor, int]:
+        """Load audio data from source.
+
+        By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
+        ``float32`` dtype, and the shape of `[channel, time]`.
+
+        Note:
+            The formats this function can handle depend on the availability of backends.
+            Please use the following functions to fetch the supported formats.
+
+            - FFmpeg: :py:func:`torchaudio.utils.ffmpeg_utils.get_audio_decoders`
+            - Sox: :py:func:`torchaudio.utils.sox_utils.list_read_formats`
+            - SoundFile: Refer to `the official document <https://pysoundfile.readthedocs.io/>`__.
+
+        .. warning::
+
+            ``normalize`` argument does not perform volume normalization.
+            It only converts the sample type to `torch.float32` from the native sample
+            type.
+
+            When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
+            signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``,
+            this function can return integer Tensor, where the samples are expressed within the whole range
+            of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM,
+            ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not
+            support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors.
+
+            ``normalize`` argument has no effect on 32-bit floating-point WAV and other formats, such as
+            ``flac`` and ``mp3``.
+
+            For these formats, this function always returns ``float32`` Tensor with values.
+
+
+        Args:
+            uri (path-like object or file-like object):
+                Source of audio data.
+            frame_offset (int, optional):
+                Number of frames to skip before start reading data.
+            num_frames (int, optional):
+                Maximum number of frames to read. ``-1`` reads all the remaining samples,
+                starting from ``frame_offset``.
+                This function may return the less number of frames if there is not enough
+                frames in the given file.
+            normalize (bool, optional):
+                When ``True``, this function converts the native sample type to ``float32``.
+                Default: ``True``.
+
+                If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
+                integer type.
+                This argument has no effect for formats other than integer WAV type.
+
+            channels_first (bool, optional):
+                When True, the returned Tensor has dimension `[channel, time]`.
+                Otherwise, the returned Tensor's dimension is `[time, channel]`.
+
+            format (str or None, optional):
+                If not ``None``, interpreted as hint that may allow backend to override the detected format.
+                (Default: ``None``)
+
+            buffer_size (int, optional):
+                Size of buffer to use when processing file-like objects, in bytes. (Default: ``4096``)
+
+            backend (str or None, optional):
+                I/O backend to use.
+                If ``None``, function selects backend given input and available backends.
+                Otherwise, must be one of [``"ffmpeg"``, ``"sox"``, ``"soundfile"``],
+                with the corresponding backend being available. (Default: ``None``)
+
+                .. seealso::
+                   :ref:`backend`
+
+        Returns:
+            (torch.Tensor, int): Resulting Tensor and sample rate.
+                If the input file has integer wav format and normalization is off, then it has
+                integer type, else ``float32`` type. If ``channels_first=True``, it has
+                `[channel, time]` else `[time, channel]`.
+        """
+        backend = dispatcher(uri, format, backend)
+        return backend.load(uri, frame_offset, num_frames, normalize, channels_first, format, buffer_size)
+
+    return load
+
+
+def get_save_func():
+    backends = get_available_backends()
+
+    def dispatcher(
+        uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], backend_name: Optional[str]
+    ) -> Backend:
+        if backend_name is not None:
+            return get_backend(backend_name, backends)
+
+        for backend in backends.values():
+            if backend.can_encode(uri, format):
+                return backend
+        raise RuntimeError(f"Couldn't find appropriate backend to handle uri {uri} and format {format}.")
+
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        backend: Optional[str] = None,
+        compression: Optional[Union[CodecConfig, float, int]] = None,
+    ):
+        """Save audio data to file.
+
+        Note:
+            The formats this function can handle depend on the availability of backends.
+            Please use the following functions to fetch the supported formats.
+
+            - FFmpeg: :py:func:`torchaudio.utils.ffmpeg_utils.get_audio_encoders`
+            - Sox: :py:func:`torchaudio.utils.sox_utils.list_write_formats`
+            - SoundFile: Refer to `the official document <https://pysoundfile.readthedocs.io/>`__.
+
+        Args:
+            uri (str or pathlib.Path): Path to audio file.
+            src (torch.Tensor): Audio data to save. must be 2D tensor.
+            sample_rate (int): sampling rate
+            channels_first (bool, optional): If ``True``, the given tensor is interpreted as `[channel, time]`,
+                otherwise `[time, channel]`.
+            format (str or None, optional): Override the audio format.
+                When ``uri`` argument is path-like object, audio format is
+                inferred from file extension. If the file extension is missing or
+                different, you can specify the correct format with this argument.
+
+                When ``uri`` argument is file-like object,
+                this argument is required.
+
+                Valid values are ``"wav"``, ``"ogg"``, and ``"flac"``.
+            encoding (str or None, optional): Changes the encoding for supported formats.
+                This argument is effective only for supported formats, i.e.
+                ``"wav"`` and ``""flac"```. Valid values are
+
+                - ``"PCM_S"`` (signed integer Linear PCM)
+                - ``"PCM_U"`` (unsigned integer Linear PCM)
+                - ``"PCM_F"`` (floating point PCM)
+                - ``"ULAW"`` (mu-law)
+                - ``"ALAW"`` (a-law)
+
+            bits_per_sample (int or None, optional): Changes the bit depth for the
+                supported formats.
+                When ``format`` is one of ``"wav"`` and ``"flac"``,
+                you can change the bit depth.
+                Valid values are ``8``, ``16``, ``24``, ``32`` and ``64``.
+
+            buffer_size (int, optional):
+                Size of buffer to use when processing file-like objects, in bytes. (Default: ``4096``)
+
+            backend (str or None, optional):
+                I/O backend to use.
+                If ``None``, function selects backend given input and available backends.
+                Otherwise, must be one of [``"ffmpeg"``, ``"sox"``, ``"soundfile"``],
+                with the corresponding backend being available.
+                (Default: ``None``)
+
+                .. seealso::
+                   :ref:`backend`
+
+            compression (CodecConfig, float, int, or None, optional):
+                Compression configuration to apply.
+
+                If the selected backend is FFmpeg, an instance of :py:class:`CodecConfig` must be provided.
+
+                Otherwise, if the selected backend is SoX, a float or int value corresponding to option ``-C`` of the
+                ``sox`` command line interface must be provided. For instance:
+
+                ``"mp3"``
+                    Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or
+                    VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``.
+
+                ``"flac"``
+                    Whole number from ``0`` to ``8``. ``8`` is default and highest compression.
+
+                ``"ogg"``, ``"vorbis"``
+                    Number from ``-1`` to ``10``; ``-1`` is the highest compression
+                    and lowest quality. Default: ``3``.
+
+                Refer to http://sox.sourceforge.net/soxformat.html for more details.
+
+        """
+        backend = dispatcher(uri, format, backend)
+        return backend.save(
+            uri, src, sample_rate, channels_first, format, encoding, bits_per_sample, buffer_size, compression
+        )
+
+    return save
diff --git a/lib/python3.11/site-packages/torchaudio/_extension/__init__.py b/lib/python3.11/site-packages/torchaudio/_extension/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c2ff55583ba48aa5859e26bfceaba9a78ee23e2
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_extension/__init__.py
@@ -0,0 +1,74 @@
+import logging
+import os
+import sys
+
+from torchaudio._internal.module_utils import fail_with_message, is_module_available, no_op
+
+from .utils import _check_cuda_version, _init_dll_path, _init_sox, _LazyImporter, _load_lib
+
+_LG = logging.getLogger(__name__)
+
+
+# Note:
+# `_check_cuda_version` is not meant to be used by regular users.
+# Builder uses it for debugging purpose, so we export it.
+# https://github.com/pytorch/builder/blob/e2e4542b8eb0bdf491214451a1a4128bd606cce2/test/smoke_test/smoke_test.py#L80
+__all__ = [
+    "_check_cuda_version",
+    "_IS_TORCHAUDIO_EXT_AVAILABLE",
+    "_IS_RIR_AVAILABLE",
+    "lazy_import_sox_ext",
+]
+
+
+if os.name == "nt" and (3, 8) <= sys.version_info < (3, 9):
+    _init_dll_path()
+
+
+# When the extension module is built, we initialize it.
+# In case of an error, we do not catch the failure as it suggests there is something
+# wrong with the installation.
+_IS_TORCHAUDIO_EXT_AVAILABLE = is_module_available("torchaudio.lib._torchaudio")
+# RIR features are implemented in _torchaudio extension, but they can be individually
+# turned on/off at build time. Available means that _torchaudio is loaded properly, and
+# RIR features are found there.
+_IS_RIR_AVAILABLE = False
+_IS_ALIGN_AVAILABLE = False
+if _IS_TORCHAUDIO_EXT_AVAILABLE:
+    _load_lib("libtorchaudio")
+
+    import torchaudio.lib._torchaudio  # noqa
+
+    _check_cuda_version()
+    _IS_RIR_AVAILABLE = torchaudio.lib._torchaudio.is_rir_available()
+    _IS_ALIGN_AVAILABLE = torchaudio.lib._torchaudio.is_align_available()
+
+
+_SOX_EXT = None
+
+
+def lazy_import_sox_ext():
+    """Load SoX integration based on availability in lazy manner"""
+
+    global _SOX_EXT
+    if _SOX_EXT is None:
+        _SOX_EXT = _LazyImporter("_torchaudio_sox", _init_sox)
+    return _SOX_EXT
+
+
+fail_if_no_rir = (
+    no_op
+    if _IS_RIR_AVAILABLE
+    else fail_with_message(
+        "requires RIR extension, but TorchAudio is not compiled with it. Please build TorchAudio with RIR support."
+    )
+)
+
+fail_if_no_align = (
+    no_op
+    if _IS_ALIGN_AVAILABLE
+    else fail_with_message(
+        "Requires alignment extension, but TorchAudio is not compiled with it. \
+        Please build TorchAudio with alignment support."
+    )
+)
diff --git a/lib/python3.11/site-packages/torchaudio/_extension/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_extension/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2f2f58b0e16307c2a921cbcb5519c79e68b964df
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_extension/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_extension/__pycache__/utils.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_extension/__pycache__/utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3dc2c6dd707aef1947c5234acd5058bd2856ca72
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_extension/__pycache__/utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_extension/utils.py b/lib/python3.11/site-packages/torchaudio/_extension/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5660a1e22c2b7f73ee6f351fb8b37cde4c3260f
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_extension/utils.py
@@ -0,0 +1,180 @@
+"""Module to implement logics used for initializing extensions.
+
+The implementations here should be stateless.
+They should not depend on external state.
+Anything that depends on external state should happen in __init__.py
+"""
+import importlib
+import logging
+import os
+import types
+from pathlib import Path
+
+import torch
+from torchaudio._internal.module_utils import eval_env
+
+_LG = logging.getLogger(__name__)
+_LIB_DIR = Path(__file__).parent.parent / "lib"
+
+
+def _get_lib_path(lib: str):
+    suffix = "pyd" if os.name == "nt" else "so"
+    path = _LIB_DIR / f"{lib}.{suffix}"
+    return path
+
+
+def _load_lib(lib: str) -> bool:
+    """Load extension module
+
+    Note:
+        In case `torchaudio` is deployed with `pex` format, the library file
+        is not in a standard location.
+        In this case, we expect that `libtorchaudio` is available somewhere
+        in the search path of dynamic loading mechanism, so that importing
+        `_torchaudio` will have library loader find and load `libtorchaudio`.
+        This is the reason why the function should not raising an error when the library
+        file is not found.
+
+    Returns:
+        bool:
+            True if the library file is found AND the library loaded without failure.
+            False if the library file is not found (like in the case where torchaudio
+            is deployed with pex format, thus the shared library file is
+            in a non-standard location.).
+            If the library file is found but there is an issue loading the library,
+            (such as missing dependency) then this function raises the exception as-is.
+
+    Raises:
+        Exception:
+            If the library file is found, but there is an issue loading the library file,
+            (when underlying `ctype.DLL` throws an exception), this function will pass
+            the exception as-is, instead of catching it and returning bool.
+            The expected case is `OSError` thrown by `ctype.DLL` when a dynamic dependency
+            is not found.
+            This behavior was chosen because the expected failure case is not recoverable.
+            If a dependency is missing, then users have to install it.
+    """
+    path = _get_lib_path(lib)
+    if not path.exists():
+        return False
+    torch.ops.load_library(path)
+    return True
+
+
+def _import_sox_ext():
+    if os.name == "nt":
+        raise RuntimeError("sox extension is not supported on Windows")
+    if not eval_env("TORCHAUDIO_USE_SOX", True):
+        raise RuntimeError("sox extension is disabled. (TORCHAUDIO_USE_SOX=0)")
+
+    ext = "torchaudio.lib._torchaudio_sox"
+
+    if not importlib.util.find_spec(ext):
+        raise RuntimeError(
+            # fmt: off
+            "TorchAudio is not built with sox extension. "
+            "Please build TorchAudio with libsox support. (BUILD_SOX=1)"
+            # fmt: on
+        )
+
+    _load_lib("libtorchaudio_sox")
+    return importlib.import_module(ext)
+
+
+def _init_sox():
+    ext = _import_sox_ext()
+    ext.set_verbosity(0)
+
+    import atexit
+
+    torch.ops.torchaudio_sox.initialize_sox_effects()
+    atexit.register(torch.ops.torchaudio_sox.shutdown_sox_effects)
+
+    # Bundle functions registered with TORCH_LIBRARY into extension
+    # so that they can also be accessed in the same (lazy) manner
+    # from the extension.
+    keys = [
+        "get_info",
+        "load_audio_file",
+        "save_audio_file",
+        "apply_effects_tensor",
+        "apply_effects_file",
+    ]
+    for key in keys:
+        setattr(ext, key, getattr(torch.ops.torchaudio_sox, key))
+
+    return ext
+
+
+class _LazyImporter(types.ModuleType):
+    """Lazily import module/extension."""
+
+    def __init__(self, name, import_func):
+        super().__init__(name)
+        self.import_func = import_func
+        self.module = None
+
+    # Note:
+    # Python caches what was retrieved with `__getattr__`, so this method will not be
+    # called again for the same item.
+    def __getattr__(self, item):
+        self._import_once()
+        return getattr(self.module, item)
+
+    def __repr__(self):
+        if self.module is None:
+            return f"<module '{self.__module__}.{self.__class__.__name__}(\"{self.name}\")'>"
+        return repr(self.module)
+
+    def __dir__(self):
+        self._import_once()
+        return dir(self.module)
+
+    def _import_once(self):
+        if self.module is None:
+            self.module = self.import_func()
+            # Note:
+            # By attaching the module attributes to self,
+            # module attributes are directly accessible.
+            # This allows to avoid calling __getattr__ for every attribute access.
+            self.__dict__.update(self.module.__dict__)
+
+    def is_available(self):
+        try:
+            self._import_once()
+        except Exception:
+            return False
+        return True
+
+
+def _init_dll_path():
+    # On Windows Python-3.8+ has `os.add_dll_directory` call,
+    # which is called to configure dll search path.
+    # To find cuda related dlls we need to make sure the
+    # conda environment/bin path is configured Please take a look:
+    # https://stackoverflow.com/questions/59330863/cant-import-dll-module-in-python
+    # Please note: if some path can't be added using add_dll_directory we simply ignore this path
+    for path in os.environ.get("PATH", "").split(";"):
+        if os.path.exists(path):
+            try:
+                os.add_dll_directory(path)
+            except Exception:
+                pass
+
+
+def _check_cuda_version():
+    import torchaudio.lib._torchaudio
+
+    version = torchaudio.lib._torchaudio.cuda_version()
+    if version is not None and torch.version.cuda is not None:
+        version_str = str(version)
+        ta_version = f"{version_str[:-3]}.{version_str[-2]}"
+        t_version = torch.version.cuda.split(".")
+        t_version = f"{t_version[0]}.{t_version[1]}"
+        if ta_version != t_version:
+            raise RuntimeError(
+                "Detected that PyTorch and TorchAudio were compiled with different CUDA versions. "
+                f"PyTorch has CUDA version {t_version} whereas TorchAudio has CUDA version {ta_version}. "
+                "Please install the TorchAudio version that matches your PyTorch version."
+            )
+    return version
diff --git a/lib/python3.11/site-packages/torchaudio/_internal/__init__.py b/lib/python3.11/site-packages/torchaudio/_internal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..363e94f13bb5059ab6888af2fb60314699f1ab1e
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_internal/__init__.py
@@ -0,0 +1,10 @@
+try:
+    from .fb import download_url_to_file, load_state_dict_from_url
+except ImportError:
+    from torch.hub import download_url_to_file, load_state_dict_from_url
+
+
+__all__ = [
+    "load_state_dict_from_url",
+    "download_url_to_file",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/_internal/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_internal/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c076d0ee5706b1ed91c03c01f82ddc1aaecb5f5d
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_internal/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_internal/__pycache__/module_utils.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/_internal/__pycache__/module_utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4bd8cf3d6f3b0ee87b7d6456fd3c8309e287efb5
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/_internal/__pycache__/module_utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/_internal/module_utils.py b/lib/python3.11/site-packages/torchaudio/_internal/module_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc484104bfd86460c57b2f773146ea1a14e47984
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/_internal/module_utils.py
@@ -0,0 +1,113 @@
+import importlib.util
+import os
+import warnings
+from functools import wraps
+from typing import Optional
+
+
+def eval_env(var, default):
+    """Check if environment varable has True-y value"""
+    if var not in os.environ:
+        return default
+
+    val = os.environ.get(var, "0")
+    trues = ["1", "true", "TRUE", "on", "ON", "yes", "YES"]
+    falses = ["0", "false", "FALSE", "off", "OFF", "no", "NO"]
+    if val in trues:
+        return True
+    if val not in falses:
+        # fmt: off
+        raise RuntimeError(
+            f"Unexpected environment variable value `{var}={val}`. "
+            f"Expected one of {trues + falses}")
+        # fmt: on
+    return False
+
+
+def is_module_available(*modules: str) -> bool:
+    r"""Returns if a top-level module with :attr:`name` exists *without**
+    importing it. This is generally safer than try-catch block around a
+    `import X`. It avoids third party libraries breaking assumptions of some of
+    our tests, e.g., setting multiprocessing start method when imported
+    (see librosa/#747, torchvision/#544).
+    """
+    return all(importlib.util.find_spec(m) is not None for m in modules)
+
+
+def requires_module(*modules: str):
+    """Decorate function to give error message if invoked without required optional modules.
+
+    This decorator is to give better error message to users rather
+    than raising ``NameError:  name 'module' is not defined`` at random places.
+    """
+    missing = [m for m in modules if not is_module_available(m)]
+
+    if not missing:
+        # fall through. If all the modules are available, no need to decorate
+        def decorator(func):
+            return func
+
+    else:
+        req = f"module: {missing[0]}" if len(missing) == 1 else f"modules: {missing}"
+
+        def decorator(func):
+            @wraps(func)
+            def wrapped(*args, **kwargs):
+                raise RuntimeError(f"{func.__module__}.{func.__name__} requires {req}")
+
+            return wrapped
+
+    return decorator
+
+
+def deprecated(direction: str, version: Optional[str] = None, remove: bool = False):
+    """Decorator to add deprecation message
+
+    Args:
+        direction (str): Migration steps to be given to users.
+        version (str or int): The version when the object will be removed
+        remove (bool): If enabled, append future removal message.
+    """
+
+    def decorator(func):
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            message = f"{func.__module__}.{func.__name__} has been deprecated. {direction}"
+            if remove:
+                message += f' It will be removed from {"future" if version is None else version} release. '
+            warnings.warn(message, stacklevel=2)
+            return func(*args, **kwargs)
+
+        message = "This function has been deprecated. "
+        if remove:
+            message += f'It will be removed from {"future" if version is None else version} release. '
+
+        wrapped.__doc__ = f"""DEPRECATED: {func.__doc__}
+
+    .. warning::
+
+       {message}
+       {direction}
+        """
+
+        return wrapped
+
+    return decorator
+
+
+def fail_with_message(message):
+    """Generate decorator to give users message about missing TorchAudio extension."""
+
+    def decorator(func):
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            raise RuntimeError(f"{func.__module__}.{func.__name__} {message}")
+
+        return wrapped
+
+    return decorator
+
+
+def no_op(func):
+    """Op-op decorator. Used in place of fail_with_message when a functionality that requires extension works fine."""
+    return func
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__init__.py b/lib/python3.11/site-packages/torchaudio/backend/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..84df7e7d697616076d549dc4163b55cd34335a25
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/__init__.py
@@ -0,0 +1,8 @@
+# NOTE:
+# The entire `torchaudio.backend` module is deprecated.
+# New things should be added to `torchaudio._backend`.
+# Only things related to backward compatibility should be placed here.
+
+from . import common, no_backend, soundfile_backend, sox_io_backend  # noqa
+
+__all__ = []
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..00bf10f6b6fae645d361e002ace223c1daa9242f
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/_no_backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/_no_backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d1c0161e39a1deaa02b81c6ffef6be3b218a5dc1
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/_no_backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/_sox_io_backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/_sox_io_backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..305017ea6221cdd30cce5a2c9faa32f123706e82
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/_sox_io_backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/common.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/common.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fb4129a0a5fd1d6687335c40e17cc55f36714fe7
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/common.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/no_backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/no_backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..46dea023a0ddb03f93066e8bfb9fb53ea83dbc85
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/no_backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/soundfile_backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/soundfile_backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2e0108907fc21400f27eec0231137b0e9bdd8611
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/soundfile_backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/__pycache__/sox_io_backend.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/sox_io_backend.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..886d1eea841ffc6cb2a35e1b9e045e2850193d54
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/backend/__pycache__/sox_io_backend.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/backend/_no_backend.py b/lib/python3.11/site-packages/torchaudio/backend/_no_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcbb2ad84aefcf33b181b686ee1105e532a8661d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/_no_backend.py
@@ -0,0 +1,25 @@
+from pathlib import Path
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+from torchaudio import AudioMetaData
+
+
+def load(
+    filepath: Union[str, Path],
+    out: Optional[Tensor] = None,
+    normalization: Union[bool, float, Callable] = True,
+    channels_first: bool = True,
+    num_frames: int = 0,
+    offset: int = 0,
+    filetype: Optional[str] = None,
+) -> Tuple[Tensor, int]:
+    raise RuntimeError("No audio I/O backend is available.")
+
+
+def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None:
+    raise RuntimeError("No audio I/O backend is available.")
+
+
+def info(filepath: str) -> AudioMetaData:
+    raise RuntimeError("No audio I/O backend is available.")
diff --git a/lib/python3.11/site-packages/torchaudio/backend/_sox_io_backend.py b/lib/python3.11/site-packages/torchaudio/backend/_sox_io_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..6af267b17a48d330c699e72dd3e31bc336a7d3da
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/_sox_io_backend.py
@@ -0,0 +1,294 @@
+import os
+from typing import Optional, Tuple
+
+import torch
+import torchaudio
+from torchaudio import AudioMetaData
+
+sox_ext = torchaudio._extension.lazy_import_sox_ext()
+
+
+def info(
+    filepath: str,
+    format: Optional[str] = None,
+) -> AudioMetaData:
+    """Get signal information of an audio file.
+
+    Args:
+        filepath (str):
+            Source of audio data.
+
+        format (str or None, optional):
+            Override the format detection with the given format.
+            Providing the argument might help when libsox can not infer the format
+            from header or extension.
+
+    Returns:
+        AudioMetaData: Metadata of the given audio.
+    """
+    if not torch.jit.is_scripting():
+        if hasattr(filepath, "read"):
+            raise RuntimeError("sox_io backend does not support file-like object.")
+        filepath = os.fspath(filepath)
+    sinfo = sox_ext.get_info(filepath, format)
+    return AudioMetaData(*sinfo)
+
+
+def load(
+    filepath: str,
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    normalize: bool = True,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+) -> Tuple[torch.Tensor, int]:
+    """Load audio data from file.
+
+    Note:
+        This function can handle all the codecs that underlying libsox can handle,
+        however it is tested on the following formats;
+
+        * WAV, AMB
+
+            * 32-bit floating-point
+            * 32-bit signed integer
+            * 24-bit signed integer
+            * 16-bit signed integer
+            * 8-bit unsigned integer (WAV only)
+
+        * MP3
+        * FLAC
+        * OGG/VORBIS
+        * OPUS
+        * SPHERE
+        * AMR-NB
+
+        To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not
+        handle natively, your installation of ``torchaudio`` has to be linked to ``libsox``
+        and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc.
+
+    By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
+    ``float32`` dtype, and the shape of `[channel, time]`.
+
+    .. warning::
+
+       ``normalize`` argument does not perform volume normalization.
+       It only converts the sample type to `torch.float32` from the native sample
+       type.
+
+       When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
+       signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``,
+       this function can return integer Tensor, where the samples are expressed within the whole range
+       of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM,
+       ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not
+       support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors.
+
+       ``normalize`` argument has no effect on 32-bit floating-point WAV and other formats, such as
+       ``flac`` and ``mp3``.
+
+       For these formats, this function always returns ``float32`` Tensor with values.
+
+    Args:
+        filepath (path-like object): Source of audio data.
+        frame_offset (int):
+            Number of frames to skip before start reading data.
+        num_frames (int, optional):
+            Maximum number of frames to read. ``-1`` reads all the remaining samples,
+            starting from ``frame_offset``.
+            This function may return the less number of frames if there is not enough
+            frames in the given file.
+        normalize (bool, optional):
+            When ``True``, this function converts the native sample type to ``float32``.
+            Default: ``True``.
+
+            If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
+            integer type.
+            This argument has no effect for formats other than integer WAV type.
+
+        channels_first (bool, optional):
+            When True, the returned Tensor has dimension `[channel, time]`.
+            Otherwise, the returned Tensor's dimension is `[time, channel]`.
+        format (str or None, optional):
+            Override the format detection with the given format.
+            Providing the argument might help when libsox can not infer the format
+            from header or extension.
+
+    Returns:
+        (torch.Tensor, int): Resulting Tensor and sample rate.
+            If the input file has integer wav format and ``normalize=False``, then it has
+            integer type, else ``float32`` type. If ``channels_first=True``, it has
+            `[channel, time]` else `[time, channel]`.
+    """
+    if not torch.jit.is_scripting():
+        if hasattr(filepath, "read"):
+            raise RuntimeError("sox_io backend does not support file-like object.")
+        filepath = os.fspath(filepath)
+    return sox_ext.load_audio_file(filepath, frame_offset, num_frames, normalize, channels_first, format)
+
+
+def save(
+    filepath: str,
+    src: torch.Tensor,
+    sample_rate: int,
+    channels_first: bool = True,
+    compression: Optional[float] = None,
+    format: Optional[str] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+):
+    """Save audio data to file.
+
+    Args:
+        filepath (path-like object): Path to save file.
+        src (torch.Tensor): Audio data to save. must be 2D tensor.
+        sample_rate (int): sampling rate
+        channels_first (bool, optional): If ``True``, the given tensor is interpreted as `[channel, time]`,
+            otherwise `[time, channel]`.
+        compression (float or None, optional): Used for formats other than WAV.
+            This corresponds to ``-C`` option of ``sox`` command.
+
+            ``"mp3"``
+                Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or
+                VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``.
+
+            ``"flac"``
+                Whole number from ``0`` to ``8``. ``8`` is default and highest compression.
+
+            ``"ogg"``, ``"vorbis"``
+                Number from ``-1`` to ``10``; ``-1`` is the highest compression
+                and lowest quality. Default: ``3``.
+
+            See the detail at http://sox.sourceforge.net/soxformat.html.
+        format (str or None, optional): Override the audio format.
+            When ``filepath`` argument is path-like object, audio format is infered from
+            file extension. If file extension is missing or different, you can specify the
+            correct format with this argument.
+
+            When ``filepath`` argument is file-like object, this argument is required.
+
+            Valid values are ``"wav"``, ``"mp3"``, ``"ogg"``, ``"vorbis"``, ``"amr-nb"``,
+            ``"amb"``, ``"flac"``, ``"sph"``, ``"gsm"``, and ``"htk"``.
+
+        encoding (str or None, optional): Changes the encoding for the supported formats.
+            This argument is effective only for supported formats, such as ``"wav"``, ``""amb"``
+            and ``"sph"``. Valid values are;
+
+                - ``"PCM_S"`` (signed integer Linear PCM)
+                - ``"PCM_U"`` (unsigned integer Linear PCM)
+                - ``"PCM_F"`` (floating point PCM)
+                - ``"ULAW"`` (mu-law)
+                - ``"ALAW"`` (a-law)
+
+            Default values
+                If not provided, the default value is picked based on ``format`` and ``bits_per_sample``.
+
+                ``"wav"``, ``"amb"``
+                    - | If both ``encoding`` and ``bits_per_sample`` are not provided, the ``dtype`` of the
+                      | Tensor is used to determine the default value.
+
+                        - ``"PCM_U"`` if dtype is ``uint8``
+                        - ``"PCM_S"`` if dtype is ``int16`` or ``int32``
+                        - ``"PCM_F"`` if dtype is ``float32``
+
+                    - ``"PCM_U"`` if ``bits_per_sample=8``
+                    - ``"PCM_S"`` otherwise
+
+                ``"sph"`` format;
+                    - the default value is ``"PCM_S"``
+
+        bits_per_sample (int or None, optional): Changes the bit depth for the supported formats.
+            When ``format`` is one of ``"wav"``, ``"flac"``, ``"sph"``, or ``"amb"``, you can change the
+            bit depth. Valid values are ``8``, ``16``, ``32`` and ``64``.
+
+            Default Value;
+                If not provided, the default values are picked based on ``format`` and ``"encoding"``;
+
+                ``"wav"``, ``"amb"``;
+                    - | If both ``encoding`` and ``bits_per_sample`` are not provided, the ``dtype`` of the
+                      | Tensor is used.
+
+                        - ``8`` if dtype is ``uint8``
+                        - ``16`` if dtype is ``int16``
+                        - ``32`` if dtype is  ``int32`` or ``float32``
+
+                    - ``8`` if ``encoding`` is ``"PCM_U"``, ``"ULAW"`` or ``"ALAW"``
+                    - ``16`` if ``encoding`` is ``"PCM_S"``
+                    - ``32`` if ``encoding`` is ``"PCM_F"``
+
+                ``"flac"`` format;
+                    - the default value is ``24``
+
+                ``"sph"`` format;
+                    - ``16`` if ``encoding`` is ``"PCM_U"``, ``"PCM_S"``, ``"PCM_F"`` or not provided.
+                    - ``8`` if ``encoding`` is ``"ULAW"`` or ``"ALAW"``
+
+                ``"amb"`` format;
+                    - ``8`` if ``encoding`` is ``"PCM_U"``, ``"ULAW"`` or ``"ALAW"``
+                    - ``16`` if ``encoding`` is ``"PCM_S"`` or not provided.
+                    - ``32`` if ``encoding`` is ``"PCM_F"``
+
+    Supported formats/encodings/bit depth/compression are;
+
+    ``"wav"``, ``"amb"``
+        - 32-bit floating-point PCM
+        - 32-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 8-bit unsigned integer PCM
+        - 8-bit mu-law
+        - 8-bit a-law
+
+        Note: Default encoding/bit depth is determined by the dtype of the input Tensor.
+
+    ``"mp3"``
+        Fixed bit rate (such as 128kHz) and variable bit rate compression.
+        Default: VBR with high quality.
+
+    ``"flac"``
+        - 8-bit
+        - 16-bit
+        - 24-bit (default)
+
+    ``"ogg"``, ``"vorbis"``
+        - Different quality level. Default: approx. 112kbps
+
+    ``"sph"``
+        - 8-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 32-bit signed integer PCM (default)
+        - 8-bit mu-law
+        - 8-bit a-law
+        - 16-bit a-law
+        - 24-bit a-law
+        - 32-bit a-law
+
+    ``"amr-nb"``
+        Bitrate ranging from 4.75 kbit/s to 12.2 kbit/s. Default: 4.75 kbit/s
+
+    ``"gsm"``
+        Lossy Speech Compression, CPU intensive.
+
+    ``"htk"``
+        Uses a default single-channel 16-bit PCM format.
+
+    Note:
+        To save into formats that ``libsox`` does not handle natively, (such as ``"mp3"``,
+        ``"flac"``, ``"ogg"`` and ``"vorbis"``), your installation of ``torchaudio`` has
+        to be linked to ``libsox`` and corresponding codec libraries such as ``libmad``
+        or ``libmp3lame`` etc.
+    """
+    if not torch.jit.is_scripting():
+        if hasattr(filepath, "write"):
+            raise RuntimeError("sox_io backend does not handle file-like object.")
+        filepath = os.fspath(filepath)
+    sox_ext.save_audio_file(
+        filepath,
+        src,
+        sample_rate,
+        channels_first,
+        compression,
+        format,
+        encoding,
+        bits_per_sample,
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/backend/common.py b/lib/python3.11/site-packages/torchaudio/backend/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f736bf4017c952c850dcbb3cc5fe1fe14f2715f
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/common.py
@@ -0,0 +1,13 @@
+def __getattr__(name: str):
+    if name == "AudioMetaData":
+        import warnings
+
+        warnings.warn(
+            "`torchaudio.backend.common.AudioMetaData` has been moved to "
+            "`torchaudio.AudioMetaData`. Please update the import path.",
+            stacklevel=2,
+        )
+        from torchaudio import AudioMetaData
+
+        return AudioMetaData
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/lib/python3.11/site-packages/torchaudio/backend/no_backend.py b/lib/python3.11/site-packages/torchaudio/backend/no_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..2645a86bc80538fa0522f5eb80e304881f30acc7
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/no_backend.py
@@ -0,0 +1,14 @@
+def __getattr__(name: str):
+    import warnings
+
+    warnings.warn(
+        "Torchaudio's I/O functions now support par-call bakcend dispatch. "
+        "Importing backend implementation directly is no longer guaranteed to work. "
+        "Please use `backend` keyword with load/save/info function, instead of "
+        "calling the udnerlying implementation directly.",
+        stacklevel=2,
+    )
+
+    from . import _no_backend
+
+    return getattr(_no_backend, name)
diff --git a/lib/python3.11/site-packages/torchaudio/backend/soundfile_backend.py b/lib/python3.11/site-packages/torchaudio/backend/soundfile_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e81db372a12800a869f4a48291a77739c4f07e6
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/soundfile_backend.py
@@ -0,0 +1,14 @@
+def __getattr__(name: str):
+    import warnings
+
+    warnings.warn(
+        "Torchaudio's I/O functions now support par-call bakcend dispatch. "
+        "Importing backend implementation directly is no longer guaranteed to work. "
+        "Please use `backend` keyword with load/save/info function, instead of "
+        "calling the udnerlying implementation directly.",
+        stacklevel=2,
+    )
+
+    from torchaudio._backend import soundfile_backend
+
+    return getattr(soundfile_backend, name)
diff --git a/lib/python3.11/site-packages/torchaudio/backend/sox_io_backend.py b/lib/python3.11/site-packages/torchaudio/backend/sox_io_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..a361ab87a5dba694e247f7f2205c0f2a25b19686
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/backend/sox_io_backend.py
@@ -0,0 +1,14 @@
+def __getattr__(name: str):
+    import warnings
+
+    warnings.warn(
+        "Torchaudio's I/O functions now support par-call bakcend dispatch. "
+        "Importing backend implementation directly is no longer guaranteed to work. "
+        "Please use `backend` keyword with load/save/info function, instead of "
+        "calling the udnerlying implementation directly.",
+        stacklevel=2,
+    )
+
+    from . import _sox_io_backend
+
+    return getattr(_sox_io_backend, name)
diff --git a/lib/python3.11/site-packages/torchaudio/compliance/__init__.py b/lib/python3.11/site-packages/torchaudio/compliance/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..65579b4f01ba09695860717f1e6cd90d6e42b631
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/compliance/__init__.py
@@ -0,0 +1,5 @@
+from . import kaldi
+
+__all__ = [
+    "kaldi",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/compliance/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/compliance/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dd2622f531c140963425f19ea217e309fe7f1a84
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/compliance/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/compliance/__pycache__/kaldi.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/compliance/__pycache__/kaldi.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7d425a68a932cfe16a3a49c7b5b6ecd994e07d53
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/compliance/__pycache__/kaldi.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/compliance/kaldi.py b/lib/python3.11/site-packages/torchaudio/compliance/kaldi.py
new file mode 100644
index 0000000000000000000000000000000000000000..98358f40b522facc0abdfbaceec45f5887e00e54
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/compliance/kaldi.py
@@ -0,0 +1,813 @@
+import math
+from typing import Tuple
+
+import torch
+import torchaudio
+from torch import Tensor
+
+__all__ = [
+    "get_mel_banks",
+    "inverse_mel_scale",
+    "inverse_mel_scale_scalar",
+    "mel_scale",
+    "mel_scale_scalar",
+    "spectrogram",
+    "fbank",
+    "mfcc",
+    "vtln_warp_freq",
+    "vtln_warp_mel_freq",
+]
+
+# numeric_limits<float>::epsilon() 1.1920928955078125e-07
+EPSILON = torch.tensor(torch.finfo(torch.float).eps)
+# 1 milliseconds = 0.001 seconds
+MILLISECONDS_TO_SECONDS = 0.001
+
+# window types
+HAMMING = "hamming"
+HANNING = "hanning"
+POVEY = "povey"
+RECTANGULAR = "rectangular"
+BLACKMAN = "blackman"
+WINDOWS = [HAMMING, HANNING, POVEY, RECTANGULAR, BLACKMAN]
+
+
+def _get_epsilon(device, dtype):
+    return EPSILON.to(device=device, dtype=dtype)
+
+
+def _next_power_of_2(x: int) -> int:
+    r"""Returns the smallest power of 2 that is greater than x"""
+    return 1 if x == 0 else 2 ** (x - 1).bit_length()
+
+
+def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edges: bool) -> Tensor:
+    r"""Given a waveform (1D tensor of size ``num_samples``), it returns a 2D tensor (m, ``window_size``)
+    representing how the window is shifted along the waveform. Each row is a frame.
+
+    Args:
+        waveform (Tensor): Tensor of size ``num_samples``
+        window_size (int): Frame length
+        window_shift (int): Frame shift
+        snip_edges (bool): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends.
+
+    Returns:
+        Tensor: 2D tensor of size (m, ``window_size``) where each row is a frame
+    """
+    assert waveform.dim() == 1
+    num_samples = waveform.size(0)
+    strides = (window_shift * waveform.stride(0), waveform.stride(0))
+
+    if snip_edges:
+        if num_samples < window_size:
+            return torch.empty((0, 0), dtype=waveform.dtype, device=waveform.device)
+        else:
+            m = 1 + (num_samples - window_size) // window_shift
+    else:
+        reversed_waveform = torch.flip(waveform, [0])
+        m = (num_samples + (window_shift // 2)) // window_shift
+        pad = window_size // 2 - window_shift // 2
+        pad_right = reversed_waveform
+        if pad > 0:
+            # torch.nn.functional.pad returns [2,1,0,1,2] for 'reflect'
+            # but we want [2, 1, 0, 0, 1, 2]
+            pad_left = reversed_waveform[-pad:]
+            waveform = torch.cat((pad_left, waveform, pad_right), dim=0)
+        else:
+            # pad is negative so we want to trim the waveform at the front
+            waveform = torch.cat((waveform[-pad:], pad_right), dim=0)
+
+    sizes = (m, window_size)
+    return waveform.as_strided(sizes, strides)
+
+
+def _feature_window_function(
+    window_type: str,
+    window_size: int,
+    blackman_coeff: float,
+    device: torch.device,
+    dtype: int,
+) -> Tensor:
+    r"""Returns a window function with the given type and size"""
+    if window_type == HANNING:
+        return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype)
+    elif window_type == HAMMING:
+        return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46, device=device, dtype=dtype)
+    elif window_type == POVEY:
+        # like hanning but goes to zero at edges
+        return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype).pow(0.85)
+    elif window_type == RECTANGULAR:
+        return torch.ones(window_size, device=device, dtype=dtype)
+    elif window_type == BLACKMAN:
+        a = 2 * math.pi / (window_size - 1)
+        window_function = torch.arange(window_size, device=device, dtype=dtype)
+        # can't use torch.blackman_window as they use different coefficients
+        return (
+            blackman_coeff
+            - 0.5 * torch.cos(a * window_function)
+            + (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)
+        ).to(device=device, dtype=dtype)
+    else:
+        raise Exception("Invalid window type " + window_type)
+
+
+def _get_log_energy(strided_input: Tensor, epsilon: Tensor, energy_floor: float) -> Tensor:
+    r"""Returns the log energy of size (m) for a strided_input (m,*)"""
+    device, dtype = strided_input.device, strided_input.dtype
+    log_energy = torch.max(strided_input.pow(2).sum(1), epsilon).log()  # size (m)
+    if energy_floor == 0.0:
+        return log_energy
+    return torch.max(log_energy, torch.tensor(math.log(energy_floor), device=device, dtype=dtype))
+
+
+def _get_waveform_and_window_properties(
+    waveform: Tensor,
+    channel: int,
+    sample_frequency: float,
+    frame_shift: float,
+    frame_length: float,
+    round_to_power_of_two: bool,
+    preemphasis_coefficient: float,
+) -> Tuple[Tensor, int, int, int]:
+    r"""Gets the waveform and window properties"""
+    channel = max(channel, 0)
+    assert channel < waveform.size(0), "Invalid channel {} for size {}".format(channel, waveform.size(0))
+    waveform = waveform[channel, :]  # size (n)
+    window_shift = int(sample_frequency * frame_shift * MILLISECONDS_TO_SECONDS)
+    window_size = int(sample_frequency * frame_length * MILLISECONDS_TO_SECONDS)
+    padded_window_size = _next_power_of_2(window_size) if round_to_power_of_two else window_size
+
+    assert 2 <= window_size <= len(waveform), "choose a window size {} that is [2, {}]".format(
+        window_size, len(waveform)
+    )
+    assert 0 < window_shift, "`window_shift` must be greater than 0"
+    assert padded_window_size % 2 == 0, (
+        "the padded `window_size` must be divisible by two." " use `round_to_power_of_two` or change `frame_length`"
+    )
+    assert 0.0 <= preemphasis_coefficient <= 1.0, "`preemphasis_coefficient` must be between [0,1]"
+    assert sample_frequency > 0, "`sample_frequency` must be greater than zero"
+    return waveform, window_shift, window_size, padded_window_size
+
+
+def _get_window(
+    waveform: Tensor,
+    padded_window_size: int,
+    window_size: int,
+    window_shift: int,
+    window_type: str,
+    blackman_coeff: float,
+    snip_edges: bool,
+    raw_energy: bool,
+    energy_floor: float,
+    dither: float,
+    remove_dc_offset: bool,
+    preemphasis_coefficient: float,
+) -> Tuple[Tensor, Tensor]:
+    r"""Gets a window and its log energy
+
+    Returns:
+        (Tensor, Tensor): strided_input of size (m, ``padded_window_size``) and signal_log_energy of size (m)
+    """
+    device, dtype = waveform.device, waveform.dtype
+    epsilon = _get_epsilon(device, dtype)
+
+    # size (m, window_size)
+    strided_input = _get_strided(waveform, window_size, window_shift, snip_edges)
+
+    if dither != 0.0:
+        rand_gauss = torch.randn(strided_input.shape, device=device, dtype=dtype)
+        strided_input = strided_input + rand_gauss * dither
+
+    if remove_dc_offset:
+        # Subtract each row/frame by its mean
+        row_means = torch.mean(strided_input, dim=1).unsqueeze(1)  # size (m, 1)
+        strided_input = strided_input - row_means
+
+    if raw_energy:
+        # Compute the log energy of each row/frame before applying preemphasis and
+        # window function
+        signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor)  # size (m)
+
+    if preemphasis_coefficient != 0.0:
+        # strided_input[i,j] -= preemphasis_coefficient * strided_input[i, max(0, j-1)] for all i,j
+        offset_strided_input = torch.nn.functional.pad(strided_input.unsqueeze(0), (1, 0), mode="replicate").squeeze(
+            0
+        )  # size (m, window_size + 1)
+        strided_input = strided_input - preemphasis_coefficient * offset_strided_input[:, :-1]
+
+    # Apply window_function to each row/frame
+    window_function = _feature_window_function(window_type, window_size, blackman_coeff, device, dtype).unsqueeze(
+        0
+    )  # size (1, window_size)
+    strided_input = strided_input * window_function  # size (m, window_size)
+
+    # Pad columns with zero until we reach size (m, padded_window_size)
+    if padded_window_size != window_size:
+        padding_right = padded_window_size - window_size
+        strided_input = torch.nn.functional.pad(
+            strided_input.unsqueeze(0), (0, padding_right), mode="constant", value=0
+        ).squeeze(0)
+
+    # Compute energy after window function (not the raw one)
+    if not raw_energy:
+        signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor)  # size (m)
+
+    return strided_input, signal_log_energy
+
+
+def _subtract_column_mean(tensor: Tensor, subtract_mean: bool) -> Tensor:
+    # subtracts the column mean of the tensor size (m, n) if subtract_mean=True
+    # it returns size (m, n)
+    if subtract_mean:
+        col_means = torch.mean(tensor, dim=0).unsqueeze(0)
+        tensor = tensor - col_means
+    return tensor
+
+
+def spectrogram(
+    waveform: Tensor,
+    blackman_coeff: float = 0.42,
+    channel: int = -1,
+    dither: float = 0.0,
+    energy_floor: float = 1.0,
+    frame_length: float = 25.0,
+    frame_shift: float = 10.0,
+    min_duration: float = 0.0,
+    preemphasis_coefficient: float = 0.97,
+    raw_energy: bool = True,
+    remove_dc_offset: bool = True,
+    round_to_power_of_two: bool = True,
+    sample_frequency: float = 16000.0,
+    snip_edges: bool = True,
+    subtract_mean: bool = False,
+    window_type: str = POVEY,
+) -> Tensor:
+    r"""Create a spectrogram from a raw audio signal. This matches the input/output of Kaldi's
+    compute-spectrogram-feats.
+
+    Args:
+        waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
+        blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
+        channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
+        dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
+            the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
+        energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
+            this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
+            individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
+        frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
+        frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
+        min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
+        preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
+        raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
+        remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
+        round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
+            to FFT. (Default: ``True``)
+        sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
+            specified there) (Default: ``16000.0``)
+        snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
+        subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
+            it this way.  (Default: ``False``)
+        window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
+         (Default: ``'povey'``)
+
+    Returns:
+        Tensor: A spectrogram identical to what Kaldi would output. The shape is
+        (m, ``padded_window_size // 2 + 1``) where m is calculated in _get_strided
+    """
+    device, dtype = waveform.device, waveform.dtype
+    epsilon = _get_epsilon(device, dtype)
+
+    waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
+        waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient
+    )
+
+    if len(waveform) < min_duration * sample_frequency:
+        # signal is too short
+        return torch.empty(0)
+
+    strided_input, signal_log_energy = _get_window(
+        waveform,
+        padded_window_size,
+        window_size,
+        window_shift,
+        window_type,
+        blackman_coeff,
+        snip_edges,
+        raw_energy,
+        energy_floor,
+        dither,
+        remove_dc_offset,
+        preemphasis_coefficient,
+    )
+
+    # size (m, padded_window_size // 2 + 1, 2)
+    fft = torch.fft.rfft(strided_input)
+
+    # Convert the FFT into a power spectrum
+    power_spectrum = torch.max(fft.abs().pow(2.0), epsilon).log()  # size (m, padded_window_size // 2 + 1)
+    power_spectrum[:, 0] = signal_log_energy
+
+    power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean)
+    return power_spectrum
+
+
+def inverse_mel_scale_scalar(mel_freq: float) -> float:
+    return 700.0 * (math.exp(mel_freq / 1127.0) - 1.0)
+
+
+def inverse_mel_scale(mel_freq: Tensor) -> Tensor:
+    return 700.0 * ((mel_freq / 1127.0).exp() - 1.0)
+
+
+def mel_scale_scalar(freq: float) -> float:
+    return 1127.0 * math.log(1.0 + freq / 700.0)
+
+
+def mel_scale(freq: Tensor) -> Tensor:
+    return 1127.0 * (1.0 + freq / 700.0).log()
+
+
+def vtln_warp_freq(
+    vtln_low_cutoff: float,
+    vtln_high_cutoff: float,
+    low_freq: float,
+    high_freq: float,
+    vtln_warp_factor: float,
+    freq: Tensor,
+) -> Tensor:
+    r"""This computes a VTLN warping function that is not the same as HTK's one,
+    but has similar inputs (this function has the advantage of never producing
+    empty bins).
+
+    This function computes a warp function F(freq), defined between low_freq
+    and high_freq inclusive, with the following properties:
+        F(low_freq) == low_freq
+        F(high_freq) == high_freq
+    The function is continuous and piecewise linear with two inflection
+        points.
+    The lower inflection point (measured in terms of the unwarped
+        frequency) is at frequency l, determined as described below.
+    The higher inflection point is at a frequency h, determined as
+        described below.
+    If l <= f <= h, then F(f) = f/vtln_warp_factor.
+    If the higher inflection point (measured in terms of the unwarped
+        frequency) is at h, then max(h, F(h)) == vtln_high_cutoff.
+        Since (by the last point) F(h) == h/vtln_warp_factor, then
+        max(h, h/vtln_warp_factor) == vtln_high_cutoff, so
+        h = vtln_high_cutoff / max(1, 1/vtln_warp_factor).
+          = vtln_high_cutoff * min(1, vtln_warp_factor).
+    If the lower inflection point (measured in terms of the unwarped
+        frequency) is at l, then min(l, F(l)) == vtln_low_cutoff
+        This implies that l = vtln_low_cutoff / min(1, 1/vtln_warp_factor)
+                            = vtln_low_cutoff * max(1, vtln_warp_factor)
+    Args:
+        vtln_low_cutoff (float): Lower frequency cutoffs for VTLN
+        vtln_high_cutoff (float): Upper frequency cutoffs for VTLN
+        low_freq (float): Lower frequency cutoffs in mel computation
+        high_freq (float): Upper frequency cutoffs in mel computation
+        vtln_warp_factor (float): Vtln warp factor
+        freq (Tensor): given frequency in Hz
+
+    Returns:
+        Tensor: Freq after vtln warp
+    """
+    assert vtln_low_cutoff > low_freq, "be sure to set the vtln_low option higher than low_freq"
+    assert vtln_high_cutoff < high_freq, "be sure to set the vtln_high option lower than high_freq [or negative]"
+    l = vtln_low_cutoff * max(1.0, vtln_warp_factor)
+    h = vtln_high_cutoff * min(1.0, vtln_warp_factor)
+    scale = 1.0 / vtln_warp_factor
+    Fl = scale * l  # F(l)
+    Fh = scale * h  # F(h)
+    assert l > low_freq and h < high_freq
+    # slope of left part of the 3-piece linear function
+    scale_left = (Fl - low_freq) / (l - low_freq)
+    # [slope of center part is just "scale"]
+
+    # slope of right part of the 3-piece linear function
+    scale_right = (high_freq - Fh) / (high_freq - h)
+
+    res = torch.empty_like(freq)
+
+    outside_low_high_freq = torch.lt(freq, low_freq) | torch.gt(freq, high_freq)  # freq < low_freq || freq > high_freq
+    before_l = torch.lt(freq, l)  # freq < l
+    before_h = torch.lt(freq, h)  # freq < h
+    after_h = torch.ge(freq, h)  # freq >= h
+
+    # order of operations matter here (since there is overlapping frequency regions)
+    res[after_h] = high_freq + scale_right * (freq[after_h] - high_freq)
+    res[before_h] = scale * freq[before_h]
+    res[before_l] = low_freq + scale_left * (freq[before_l] - low_freq)
+    res[outside_low_high_freq] = freq[outside_low_high_freq]
+
+    return res
+
+
+def vtln_warp_mel_freq(
+    vtln_low_cutoff: float,
+    vtln_high_cutoff: float,
+    low_freq,
+    high_freq: float,
+    vtln_warp_factor: float,
+    mel_freq: Tensor,
+) -> Tensor:
+    r"""
+    Args:
+        vtln_low_cutoff (float): Lower frequency cutoffs for VTLN
+        vtln_high_cutoff (float): Upper frequency cutoffs for VTLN
+        low_freq (float): Lower frequency cutoffs in mel computation
+        high_freq (float): Upper frequency cutoffs in mel computation
+        vtln_warp_factor (float): Vtln warp factor
+        mel_freq (Tensor): Given frequency in Mel
+
+    Returns:
+        Tensor: ``mel_freq`` after vtln warp
+    """
+    return mel_scale(
+        vtln_warp_freq(
+            vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq, vtln_warp_factor, inverse_mel_scale(mel_freq)
+        )
+    )
+
+
+def get_mel_banks(
+    num_bins: int,
+    window_length_padded: int,
+    sample_freq: float,
+    low_freq: float,
+    high_freq: float,
+    vtln_low: float,
+    vtln_high: float,
+    vtln_warp_factor: float,
+) -> Tuple[Tensor, Tensor]:
+    """
+    Returns:
+        (Tensor, Tensor): The tuple consists of ``bins`` (which is
+        melbank of size (``num_bins``, ``num_fft_bins``)) and ``center_freqs`` (which is
+        center frequencies of bins of size (``num_bins``)).
+    """
+    assert num_bins > 3, "Must have at least 3 mel bins"
+    assert window_length_padded % 2 == 0
+    num_fft_bins = window_length_padded / 2
+    nyquist = 0.5 * sample_freq
+
+    if high_freq <= 0.0:
+        high_freq += nyquist
+
+    assert (
+        (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq)
+    ), "Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(low_freq, high_freq, nyquist)
+
+    # fft-bin width [think of it as Nyquist-freq / half-window-length]
+    fft_bin_width = sample_freq / window_length_padded
+    mel_low_freq = mel_scale_scalar(low_freq)
+    mel_high_freq = mel_scale_scalar(high_freq)
+
+    # divide by num_bins+1 in next line because of end-effects where the bins
+    # spread out to the sides.
+    mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
+
+    if vtln_high < 0.0:
+        vtln_high += nyquist
+
+    assert vtln_warp_factor == 1.0 or (
+        (low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)
+    ), "Bad values in options: vtln-low {} and vtln-high {}, versus " "low-freq {} and high-freq {}".format(
+        vtln_low, vtln_high, low_freq, high_freq
+    )
+
+    bin = torch.arange(num_bins).unsqueeze(1)
+    left_mel = mel_low_freq + bin * mel_freq_delta  # size(num_bins, 1)
+    center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta  # size(num_bins, 1)
+    right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta  # size(num_bins, 1)
+
+    if vtln_warp_factor != 1.0:
+        left_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, left_mel)
+        center_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, center_mel)
+        right_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, right_mel)
+
+    center_freqs = inverse_mel_scale(center_mel)  # size (num_bins)
+    # size(1, num_fft_bins)
+    mel = mel_scale(fft_bin_width * torch.arange(num_fft_bins)).unsqueeze(0)
+
+    # size (num_bins, num_fft_bins)
+    up_slope = (mel - left_mel) / (center_mel - left_mel)
+    down_slope = (right_mel - mel) / (right_mel - center_mel)
+
+    if vtln_warp_factor == 1.0:
+        # left_mel < center_mel < right_mel so we can min the two slopes and clamp negative values
+        bins = torch.max(torch.zeros(1), torch.min(up_slope, down_slope))
+    else:
+        # warping can move the order of left_mel, center_mel, right_mel anywhere
+        bins = torch.zeros_like(up_slope)
+        up_idx = torch.gt(mel, left_mel) & torch.le(mel, center_mel)  # left_mel < mel <= center_mel
+        down_idx = torch.gt(mel, center_mel) & torch.lt(mel, right_mel)  # center_mel < mel < right_mel
+        bins[up_idx] = up_slope[up_idx]
+        bins[down_idx] = down_slope[down_idx]
+
+    return bins, center_freqs
+
+
+def fbank(
+    waveform: Tensor,
+    blackman_coeff: float = 0.42,
+    channel: int = -1,
+    dither: float = 0.0,
+    energy_floor: float = 1.0,
+    frame_length: float = 25.0,
+    frame_shift: float = 10.0,
+    high_freq: float = 0.0,
+    htk_compat: bool = False,
+    low_freq: float = 20.0,
+    min_duration: float = 0.0,
+    num_mel_bins: int = 23,
+    preemphasis_coefficient: float = 0.97,
+    raw_energy: bool = True,
+    remove_dc_offset: bool = True,
+    round_to_power_of_two: bool = True,
+    sample_frequency: float = 16000.0,
+    snip_edges: bool = True,
+    subtract_mean: bool = False,
+    use_energy: bool = False,
+    use_log_fbank: bool = True,
+    use_power: bool = True,
+    vtln_high: float = -500.0,
+    vtln_low: float = 100.0,
+    vtln_warp: float = 1.0,
+    window_type: str = POVEY,
+) -> Tensor:
+    r"""Create a fbank from a raw audio signal. This matches the input/output of Kaldi's
+    compute-fbank-feats.
+
+    Args:
+        waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
+        blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
+        channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
+        dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
+            the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
+        energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
+            this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
+            individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
+        frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
+        frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
+        high_freq (float, optional): High cutoff frequency for mel bins (if <= 0, offset from Nyquist)
+         (Default: ``0.0``)
+        htk_compat (bool, optional): If true, put energy last.  Warning: not sufficient to get HTK compatible features
+         (need to change other parameters). (Default: ``False``)
+        low_freq (float, optional): Low cutoff frequency for mel bins (Default: ``20.0``)
+        min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
+        num_mel_bins (int, optional): Number of triangular mel-frequency bins (Default: ``23``)
+        preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
+        raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
+        remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
+        round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
+            to FFT. (Default: ``True``)
+        sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
+            specified there) (Default: ``16000.0``)
+        snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
+        subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
+            it this way.  (Default: ``False``)
+        use_energy (bool, optional): Add an extra dimension with energy to the FBANK output. (Default: ``False``)
+        use_log_fbank (bool, optional):If true, produce log-filterbank, else produce linear. (Default: ``True``)
+        use_power (bool, optional): If true, use power, else use magnitude. (Default: ``True``)
+        vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function (if
+            negative, offset from high-mel-freq (Default: ``-500.0``)
+        vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function (Default: ``100.0``)
+        vtln_warp (float, optional): Vtln warp factor (only applicable if vtln_map not specified) (Default: ``1.0``)
+        window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
+         (Default: ``'povey'``)
+
+    Returns:
+        Tensor: A fbank identical to what Kaldi would output. The shape is (m, ``num_mel_bins + use_energy``)
+        where m is calculated in _get_strided
+    """
+    device, dtype = waveform.device, waveform.dtype
+
+    waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
+        waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient
+    )
+
+    if len(waveform) < min_duration * sample_frequency:
+        # signal is too short
+        return torch.empty(0, device=device, dtype=dtype)
+
+    # strided_input, size (m, padded_window_size) and signal_log_energy, size (m)
+    strided_input, signal_log_energy = _get_window(
+        waveform,
+        padded_window_size,
+        window_size,
+        window_shift,
+        window_type,
+        blackman_coeff,
+        snip_edges,
+        raw_energy,
+        energy_floor,
+        dither,
+        remove_dc_offset,
+        preemphasis_coefficient,
+    )
+
+    # size (m, padded_window_size // 2 + 1)
+    spectrum = torch.fft.rfft(strided_input).abs()
+    if use_power:
+        spectrum = spectrum.pow(2.0)
+
+    # size (num_mel_bins, padded_window_size // 2)
+    mel_energies, _ = get_mel_banks(
+        num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp
+    )
+    mel_energies = mel_energies.to(device=device, dtype=dtype)
+
+    # pad right column with zeros and add dimension, size (num_mel_bins, padded_window_size // 2 + 1)
+    mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode="constant", value=0)
+
+    # sum with mel fiterbanks over the power spectrum, size (m, num_mel_bins)
+    mel_energies = torch.mm(spectrum, mel_energies.T)
+    if use_log_fbank:
+        # avoid log of zero (which should be prevented anyway by dithering)
+        mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log()
+
+    # if use_energy then add it as the last column for htk_compat == true else first column
+    if use_energy:
+        signal_log_energy = signal_log_energy.unsqueeze(1)  # size (m, 1)
+        # returns size (m, num_mel_bins + 1)
+        if htk_compat:
+            mel_energies = torch.cat((mel_energies, signal_log_energy), dim=1)
+        else:
+            mel_energies = torch.cat((signal_log_energy, mel_energies), dim=1)
+
+    mel_energies = _subtract_column_mean(mel_energies, subtract_mean)
+    return mel_energies
+
+
+def _get_dct_matrix(num_ceps: int, num_mel_bins: int) -> Tensor:
+    # returns a dct matrix of size (num_mel_bins, num_ceps)
+    # size (num_mel_bins, num_mel_bins)
+    dct_matrix = torchaudio.functional.create_dct(num_mel_bins, num_mel_bins, "ortho")
+    # kaldi expects the first cepstral to be weighted sum of factor sqrt(1/num_mel_bins)
+    # this would be the first column in the dct_matrix for torchaudio as it expects a
+    # right multiply (which would be the first column of the kaldi's dct_matrix as kaldi
+    # expects a left multiply e.g. dct_matrix * vector).
+    dct_matrix[:, 0] = math.sqrt(1 / float(num_mel_bins))
+    dct_matrix = dct_matrix[:, :num_ceps]
+    return dct_matrix
+
+
+def _get_lifter_coeffs(num_ceps: int, cepstral_lifter: float) -> Tensor:
+    # returns size (num_ceps)
+    # Compute liftering coefficients (scaling on cepstral coeffs)
+    # coeffs are numbered slightly differently from HTK: the zeroth index is C0, which is not affected.
+    i = torch.arange(num_ceps)
+    return 1.0 + 0.5 * cepstral_lifter * torch.sin(math.pi * i / cepstral_lifter)
+
+
+def mfcc(
+    waveform: Tensor,
+    blackman_coeff: float = 0.42,
+    cepstral_lifter: float = 22.0,
+    channel: int = -1,
+    dither: float = 0.0,
+    energy_floor: float = 1.0,
+    frame_length: float = 25.0,
+    frame_shift: float = 10.0,
+    high_freq: float = 0.0,
+    htk_compat: bool = False,
+    low_freq: float = 20.0,
+    num_ceps: int = 13,
+    min_duration: float = 0.0,
+    num_mel_bins: int = 23,
+    preemphasis_coefficient: float = 0.97,
+    raw_energy: bool = True,
+    remove_dc_offset: bool = True,
+    round_to_power_of_two: bool = True,
+    sample_frequency: float = 16000.0,
+    snip_edges: bool = True,
+    subtract_mean: bool = False,
+    use_energy: bool = False,
+    vtln_high: float = -500.0,
+    vtln_low: float = 100.0,
+    vtln_warp: float = 1.0,
+    window_type: str = POVEY,
+) -> Tensor:
+    r"""Create a mfcc from a raw audio signal. This matches the input/output of Kaldi's
+    compute-mfcc-feats.
+
+    Args:
+        waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
+        blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
+        cepstral_lifter (float, optional): Constant that controls scaling of MFCCs (Default: ``22.0``)
+        channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
+        dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
+            the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
+        energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
+            this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
+            individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
+        frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
+        frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
+        high_freq (float, optional): High cutoff frequency for mel bins (if <= 0, offset from Nyquist)
+         (Default: ``0.0``)
+        htk_compat (bool, optional): If true, put energy last.  Warning: not sufficient to get HTK compatible
+         features (need to change other parameters). (Default: ``False``)
+        low_freq (float, optional): Low cutoff frequency for mel bins (Default: ``20.0``)
+        num_ceps (int, optional): Number of cepstra in MFCC computation (including C0) (Default: ``13``)
+        min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
+        num_mel_bins (int, optional): Number of triangular mel-frequency bins (Default: ``23``)
+        preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
+        raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
+        remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
+        round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
+            to FFT. (Default: ``True``)
+        sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
+            specified there) (Default: ``16000.0``)
+        snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
+        subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
+            it this way.  (Default: ``False``)
+        use_energy (bool, optional): Add an extra dimension with energy to the FBANK output. (Default: ``False``)
+        vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function (if
+            negative, offset from high-mel-freq (Default: ``-500.0``)
+        vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function (Default: ``100.0``)
+        vtln_warp (float, optional): Vtln warp factor (only applicable if vtln_map not specified) (Default: ``1.0``)
+        window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
+         (Default: ``"povey"``)
+
+    Returns:
+        Tensor: A mfcc identical to what Kaldi would output. The shape is (m, ``num_ceps``)
+        where m is calculated in _get_strided
+    """
+    assert num_ceps <= num_mel_bins, "num_ceps cannot be larger than num_mel_bins: %d vs %d" % (num_ceps, num_mel_bins)
+
+    device, dtype = waveform.device, waveform.dtype
+
+    # The mel_energies should not be squared (use_power=True), not have mean subtracted
+    # (subtract_mean=False), and use log (use_log_fbank=True).
+    # size (m, num_mel_bins + use_energy)
+    feature = fbank(
+        waveform=waveform,
+        blackman_coeff=blackman_coeff,
+        channel=channel,
+        dither=dither,
+        energy_floor=energy_floor,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        high_freq=high_freq,
+        htk_compat=htk_compat,
+        low_freq=low_freq,
+        min_duration=min_duration,
+        num_mel_bins=num_mel_bins,
+        preemphasis_coefficient=preemphasis_coefficient,
+        raw_energy=raw_energy,
+        remove_dc_offset=remove_dc_offset,
+        round_to_power_of_two=round_to_power_of_two,
+        sample_frequency=sample_frequency,
+        snip_edges=snip_edges,
+        subtract_mean=False,
+        use_energy=use_energy,
+        use_log_fbank=True,
+        use_power=True,
+        vtln_high=vtln_high,
+        vtln_low=vtln_low,
+        vtln_warp=vtln_warp,
+        window_type=window_type,
+    )
+
+    if use_energy:
+        # size (m)
+        signal_log_energy = feature[:, num_mel_bins if htk_compat else 0]
+        # offset is 0 if htk_compat==True else 1
+        mel_offset = int(not htk_compat)
+        feature = feature[:, mel_offset : (num_mel_bins + mel_offset)]
+
+    # size (num_mel_bins, num_ceps)
+    dct_matrix = _get_dct_matrix(num_ceps, num_mel_bins).to(dtype=dtype, device=device)
+
+    # size (m, num_ceps)
+    feature = feature.matmul(dct_matrix)
+
+    if cepstral_lifter != 0.0:
+        # size (1, num_ceps)
+        lifter_coeffs = _get_lifter_coeffs(num_ceps, cepstral_lifter).unsqueeze(0)
+        feature *= lifter_coeffs.to(device=device, dtype=dtype)
+
+    # if use_energy then replace the last column for htk_compat == true else first column
+    if use_energy:
+        feature[:, 0] = signal_log_energy
+
+    if htk_compat:
+        energy = feature[:, 0].unsqueeze(1)  # size (m, 1)
+        feature = feature[:, 1:]  # size (m, num_ceps - 1)
+        if not use_energy:
+            # scale on C0 (actually removing a scale we previously added that's
+            # part of one common definition of the cosine transform.)
+            energy *= math.sqrt(2)
+
+        feature = torch.cat((feature, energy), dim=1)
+
+    feature = _subtract_column_mean(feature, subtract_mean)
+    return feature
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__init__.py b/lib/python3.11/site-packages/torchaudio/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..609cb14fdcc38c48270acd5803f4bfe603c39e71
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/__init__.py
@@ -0,0 +1,47 @@
+from .cmuarctic import CMUARCTIC
+from .cmudict import CMUDict
+from .commonvoice import COMMONVOICE
+from .dr_vctk import DR_VCTK
+from .fluentcommands import FluentSpeechCommands
+from .gtzan import GTZAN
+from .iemocap import IEMOCAP
+from .librilight_limited import LibriLightLimited
+from .librimix import LibriMix
+from .librispeech import LIBRISPEECH
+from .librispeech_biasing import LibriSpeechBiasing
+from .libritts import LIBRITTS
+from .ljspeech import LJSPEECH
+from .musdb_hq import MUSDB_HQ
+from .quesst14 import QUESST14
+from .snips import Snips
+from .speechcommands import SPEECHCOMMANDS
+from .tedlium import TEDLIUM
+from .vctk import VCTK_092
+from .voxceleb1 import VoxCeleb1Identification, VoxCeleb1Verification
+from .yesno import YESNO
+
+
+__all__ = [
+    "COMMONVOICE",
+    "LIBRISPEECH",
+    "LibriSpeechBiasing",
+    "LibriLightLimited",
+    "SPEECHCOMMANDS",
+    "VCTK_092",
+    "DR_VCTK",
+    "YESNO",
+    "LJSPEECH",
+    "GTZAN",
+    "CMUARCTIC",
+    "CMUDict",
+    "LibriMix",
+    "LIBRITTS",
+    "TEDLIUM",
+    "QUESST14",
+    "MUSDB_HQ",
+    "FluentSpeechCommands",
+    "VoxCeleb1Identification",
+    "VoxCeleb1Verification",
+    "IEMOCAP",
+    "Snips",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fe5c377856f62451850319d331070b3f458b735d
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/cmuarctic.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/cmuarctic.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..755ffc5988d9d059885e99a701657bdf8574a9ad
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/cmuarctic.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/cmudict.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/cmudict.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..468be8b62e196e14d8c00858ce432eaebea1bfde
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/cmudict.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/commonvoice.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/commonvoice.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..69c6b0c83113a48383ff9b253418925bcc03b96c
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/commonvoice.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/dr_vctk.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/dr_vctk.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..869264a8f25dd1961dc5209abd05244c00f97257
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/dr_vctk.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/fluentcommands.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/fluentcommands.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b108eb32e78a87fb6532441169b4d9db9b77eee6
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/fluentcommands.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/gtzan.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/gtzan.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dfa98f69974ab17a55c3191b3f5d85feaae3c822
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/gtzan.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/iemocap.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/iemocap.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7e20e70b18c66e68c2f3d19eccaa3f395973b145
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/iemocap.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librilight_limited.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librilight_limited.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a4ade92331757d9c114d4bb1b66967e3cb33ebcb
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librilight_limited.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librimix.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librimix.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..51da390786f9372f8e027e8e539a74c30bca8c42
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librimix.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librispeech.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librispeech.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5a01c98b6c2769fa1d6289281141b20851db481b
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librispeech.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librispeech_biasing.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librispeech_biasing.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b8c90cdedaedb9eb6a2853b5c7d2e1a80c210a54
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/librispeech_biasing.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/libritts.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/libritts.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..99c349613b345957b77d97f9d4816e8bd5bc4e88
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/libritts.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/ljspeech.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/ljspeech.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a420e89ef4753f01479eab044b0c8f063ed82fbe
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/ljspeech.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/musdb_hq.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/musdb_hq.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..601106fb9087e4b58b01cbafee5f59aec8a6c97f
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/musdb_hq.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/quesst14.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/quesst14.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..824dfff6955b3e5c79a5d65d359efda99983d6e1
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/quesst14.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/snips.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/snips.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..194f254f2f6e351bf4bb143d9d1d42affa10d767
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/snips.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/speechcommands.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/speechcommands.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6adbfca273d2f5a5711d5b94d29af43d66bfa982
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/speechcommands.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/tedlium.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/tedlium.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..935ff079c931a37a0e97338ec916bc77bc95b330
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/tedlium.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/utils.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..75d706c7bbf1e806fa9fb2dd56d4795b46688420
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/vctk.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/vctk.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f8ea941a8f3a665c2600324f177e9759e9c410c3
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/vctk.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/voxceleb1.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/voxceleb1.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..41891b7d3aec832b255bba057952596482099c69
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/voxceleb1.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/yesno.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/yesno.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d49b9e6b353a145af3e99b4fb372784bedfc07c6
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/datasets/__pycache__/yesno.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/cmuarctic.py b/lib/python3.11/site-packages/torchaudio/datasets/cmuarctic.py
new file mode 100644
index 0000000000000000000000000000000000000000..96f498f00f04a2f1b6d7d3e33510eafcb9ffe6bc
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/cmuarctic.py
@@ -0,0 +1,157 @@
+import csv
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+URL = "aew"
+FOLDER_IN_ARCHIVE = "ARCTIC"
+_CHECKSUMS = {
+    "http://festvox.org/cmu_arctic/packed/cmu_us_aew_arctic.tar.bz2": "645cb33c0f0b2ce41384fdd8d3db2c3f5fc15c1e688baeb74d2e08cab18ab406",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_ahw_arctic.tar.bz2": "024664adeb892809d646a3efd043625b46b5bfa3e6189b3500b2d0d59dfab06c",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_aup_arctic.tar.bz2": "2c55bc3050caa996758869126ad10cf42e1441212111db034b3a45189c18b6fc",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_awb_arctic.tar.bz2": "d74a950c9739a65f7bfc4dfa6187f2730fa03de5b8eb3f2da97a51b74df64d3c",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_axb_arctic.tar.bz2": "dd65c3d2907d1ee52f86e44f578319159e60f4bf722a9142be01161d84e330ff",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_bdl_arctic.tar.bz2": "26b91aaf48b2799b2956792b4632c2f926cd0542f402b5452d5adecb60942904",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_clb_arctic.tar.bz2": "3f16dc3f3b97955ea22623efb33b444341013fc660677b2e170efdcc959fa7c6",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_eey_arctic.tar.bz2": "8a0ee4e5acbd4b2f61a4fb947c1730ab3adcc9dc50b195981d99391d29928e8a",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_fem_arctic.tar.bz2": "3fcff629412b57233589cdb058f730594a62c4f3a75c20de14afe06621ef45e2",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_gka_arctic.tar.bz2": "dc82e7967cbd5eddbed33074b0699128dbd4482b41711916d58103707e38c67f",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_jmk_arctic.tar.bz2": "3a37c0e1dfc91e734fdbc88b562d9e2ebca621772402cdc693bbc9b09b211d73",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_ksp_arctic.tar.bz2": "8029cafce8296f9bed3022c44ef1e7953332b6bf6943c14b929f468122532717",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_ljm_arctic.tar.bz2": "b23993765cbf2b9e7bbc3c85b6c56eaf292ac81ee4bb887b638a24d104f921a0",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_lnh_arctic.tar.bz2": "4faf34d71aa7112813252fb20c5433e2fdd9a9de55a00701ffcbf05f24a5991a",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_rms_arctic.tar.bz2": "c6dc11235629c58441c071a7ba8a2d067903dfefbaabc4056d87da35b72ecda4",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_rxr_arctic.tar.bz2": "1fa4271c393e5998d200e56c102ff46fcfea169aaa2148ad9e9469616fbfdd9b",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_slp_arctic.tar.bz2": "54345ed55e45c23d419e9a823eef427f1cc93c83a710735ec667d068c916abf1",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_slt_arctic.tar.bz2": "7c173297916acf3cc7fcab2713be4c60b27312316765a90934651d367226b4ea",  # noqa: E501
+}
+
+
+def load_cmuarctic_item(line: str, path: str, folder_audio: str, ext_audio: str) -> Tuple[Tensor, int, str, str]:
+
+    utterance_id, transcript = line[0].strip().split(" ", 2)[1:]
+
+    # Remove space, double quote, and single parenthesis from transcript
+    transcript = transcript[1:-3]
+
+    file_audio = os.path.join(path, folder_audio, utterance_id + ext_audio)
+
+    # Load audio
+    waveform, sample_rate = torchaudio.load(file_audio)
+
+    return (waveform, sample_rate, transcript, utterance_id.split("_")[1])
+
+
+class CMUARCTIC(Dataset):
+    """*CMU ARCTIC* :cite:`Kominek03cmuarctic` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional):
+            The URL to download the dataset from or the type of the dataset to download.
+            (default: ``"aew"``)
+            Allowed type values are ``"aew"``, ``"ahw"``, ``"aup"``, ``"awb"``, ``"axb"``, ``"bdl"``,
+            ``"clb"``, ``"eey"``, ``"fem"``, ``"gka"``, ``"jmk"``, ``"ksp"``, ``"ljm"``, ``"lnh"``,
+            ``"rms"``, ``"rxr"``, ``"slp"`` or ``"slt"``.
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"ARCTIC"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _file_text = "txt.done.data"
+    _folder_text = "etc"
+    _ext_audio = ".wav"
+    _folder_audio = "wav"
+
+    def __init__(
+        self, root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False
+    ) -> None:
+
+        if url in [
+            "aew",
+            "ahw",
+            "aup",
+            "awb",
+            "axb",
+            "bdl",
+            "clb",
+            "eey",
+            "fem",
+            "gka",
+            "jmk",
+            "ksp",
+            "ljm",
+            "lnh",
+            "rms",
+            "rxr",
+            "slp",
+            "slt",
+        ]:
+
+            url = "cmu_us_" + url + "_arctic"
+            ext_archive = ".tar.bz2"
+            base_url = "http://www.festvox.org/cmu_arctic/packed/"
+
+            url = os.path.join(base_url, url + ext_archive)
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        basename = os.path.basename(url)
+        root = os.path.join(root, folder_in_archive)
+        if not os.path.isdir(root):
+            os.mkdir(root)
+        archive = os.path.join(root, basename)
+
+        basename = basename.split(".")[0]
+
+        self._path = os.path.join(root, basename)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+        self._text = os.path.join(self._path, self._folder_text, self._file_text)
+
+        with open(self._text, "r") as text:
+            walker = csv.reader(text, delimiter="\n")
+            self._walker = list(walker)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            str:
+                Utterance ID
+        """
+        line = self._walker[n]
+        return load_cmuarctic_item(line, self._path, self._folder_audio, self._ext_audio)
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/cmudict.py b/lib/python3.11/site-packages/torchaudio/datasets/cmudict.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1038f48badde6f5db589691c5aee2ddf1f1d5de
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/cmudict.py
@@ -0,0 +1,186 @@
+import os
+import re
+from pathlib import Path
+from typing import Iterable, List, Tuple, Union
+
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+
+
+_CHECKSUMS = {
+    "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b": "209a8b4cd265013e96f4658632a9878103b0c5abf62b50d4ef3ae1be226b29e4",  # noqa: E501
+    "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols": "408ccaae803641c6d7b626b6299949320c2dbca96b2220fd3fb17887b023b027",  # noqa: E501
+}
+_PUNCTUATIONS = {
+    "!EXCLAMATION-POINT",
+    '"CLOSE-QUOTE',
+    '"DOUBLE-QUOTE',
+    '"END-OF-QUOTE',
+    '"END-QUOTE',
+    '"IN-QUOTES',
+    '"QUOTE',
+    '"UNQUOTE',
+    "#HASH-MARK",
+    "#POUND-SIGN",
+    "#SHARP-SIGN",
+    "%PERCENT",
+    "&AMPERSAND",
+    "'END-INNER-QUOTE",
+    "'END-QUOTE",
+    "'INNER-QUOTE",
+    "'QUOTE",
+    "'SINGLE-QUOTE",
+    "(BEGIN-PARENS",
+    "(IN-PARENTHESES",
+    "(LEFT-PAREN",
+    "(OPEN-PARENTHESES",
+    "(PAREN",
+    "(PARENS",
+    "(PARENTHESES",
+    ")CLOSE-PAREN",
+    ")CLOSE-PARENTHESES",
+    ")END-PAREN",
+    ")END-PARENS",
+    ")END-PARENTHESES",
+    ")END-THE-PAREN",
+    ")PAREN",
+    ")PARENS",
+    ")RIGHT-PAREN",
+    ")UN-PARENTHESES",
+    "+PLUS",
+    ",COMMA",
+    "--DASH",
+    "-DASH",
+    "-HYPHEN",
+    "...ELLIPSIS",
+    ".DECIMAL",
+    ".DOT",
+    ".FULL-STOP",
+    ".PERIOD",
+    ".POINT",
+    "/SLASH",
+    ":COLON",
+    ";SEMI-COLON",
+    ";SEMI-COLON(1)",
+    "?QUESTION-MARK",
+    "{BRACE",
+    "{LEFT-BRACE",
+    "{OPEN-BRACE",
+    "}CLOSE-BRACE",
+    "}RIGHT-BRACE",
+}
+
+
+def _parse_dictionary(lines: Iterable[str], exclude_punctuations: bool) -> List[str]:
+    _alt_re = re.compile(r"\([0-9]+\)")
+    cmudict: List[Tuple[str, List[str]]] = []
+    for line in lines:
+        if not line or line.startswith(";;;"):  # ignore comments
+            continue
+
+        word, phones = line.strip().split("  ")
+        if word in _PUNCTUATIONS:
+            if exclude_punctuations:
+                continue
+            # !EXCLAMATION-POINT -> !
+            # --DASH -> --
+            # ...ELLIPSIS -> ...
+            if word.startswith("..."):
+                word = "..."
+            elif word.startswith("--"):
+                word = "--"
+            else:
+                word = word[0]
+
+        # if a word have multiple pronunciations, there will be (number) appended to it
+        # for example, DATAPOINTS and DATAPOINTS(1),
+        # the regular expression `_alt_re` removes the '(1)' and change the word DATAPOINTS(1) to DATAPOINTS
+        word = re.sub(_alt_re, "", word)
+        phones = phones.split(" ")
+        cmudict.append((word, phones))
+
+    return cmudict
+
+
+class CMUDict(Dataset):
+    """*CMU Pronouncing Dictionary* :cite:`cmudict` (CMUDict) dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        exclude_punctuations (bool, optional):
+            When enabled, exclude the pronounciation of punctuations, such as
+            `!EXCLAMATION-POINT` and `#HASH-MARK`.
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        url (str, optional):
+            The URL to download the dictionary from.
+            (default: ``"http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b"``)
+        url_symbols (str, optional):
+            The URL to download the list of symbols from.
+            (default: ``"http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols"``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        exclude_punctuations: bool = True,
+        *,
+        download: bool = False,
+        url: str = "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b",
+        url_symbols: str = "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols",
+    ) -> None:
+
+        self.exclude_punctuations = exclude_punctuations
+
+        self._root_path = Path(root)
+        if not os.path.isdir(self._root_path):
+            raise RuntimeError(f"The root directory does not exist; {root}")
+
+        dict_file = self._root_path / os.path.basename(url)
+        symbol_file = self._root_path / os.path.basename(url_symbols)
+        if not os.path.exists(dict_file):
+            if not download:
+                raise RuntimeError(
+                    "The dictionary file is not found in the following location. "
+                    f"Set `download=True` to download it. {dict_file}"
+                )
+            checksum = _CHECKSUMS.get(url, None)
+            download_url_to_file(url, dict_file, checksum)
+        if not os.path.exists(symbol_file):
+            if not download:
+                raise RuntimeError(
+                    "The symbol file is not found in the following location. "
+                    f"Set `download=True` to download it. {symbol_file}"
+                )
+            checksum = _CHECKSUMS.get(url_symbols, None)
+            download_url_to_file(url_symbols, symbol_file, checksum)
+
+        with open(symbol_file, "r") as text:
+            self._symbols = [line.strip() for line in text.readlines()]
+
+        with open(dict_file, "r", encoding="latin-1") as text:
+            self._dictionary = _parse_dictionary(text.readlines(), exclude_punctuations=self.exclude_punctuations)
+
+    def __getitem__(self, n: int) -> Tuple[str, List[str]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded.
+
+        Returns:
+            Tuple of a word and its phonemes
+
+            str:
+                Word
+            List[str]:
+                Phonemes
+        """
+        return self._dictionary[n]
+
+    def __len__(self) -> int:
+        return len(self._dictionary)
+
+    @property
+    def symbols(self) -> List[str]:
+        """list[str]: A list of phonemes symbols, such as ``"AA"``, ``"AE"``, ``"AH"``."""
+        return self._symbols.copy()
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/commonvoice.py b/lib/python3.11/site-packages/torchaudio/datasets/commonvoice.py
new file mode 100644
index 0000000000000000000000000000000000000000..db0e035c6116487a87efcffaeea31a19212be458
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/commonvoice.py
@@ -0,0 +1,86 @@
+import csv
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+
+
+def load_commonvoice_item(
+    line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
+) -> Tuple[Tensor, int, Dict[str, str]]:
+    # Each line as the following data:
+    # client_id, path, sentence, up_votes, down_votes, age, gender, accent
+
+    if header[1] != "path":
+        raise ValueError(f"expect `header[1]` to be 'path', but got {header[1]}")
+    fileid = line[1]
+    filename = os.path.join(path, folder_audio, fileid)
+    if not filename.endswith(ext_audio):
+        filename += ext_audio
+    waveform, sample_rate = torchaudio.load(filename)
+
+    dic = dict(zip(header, line))
+
+    return waveform, sample_rate, dic
+
+
+class COMMONVOICE(Dataset):
+    """*CommonVoice* :cite:`ardila2020common` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is located.
+             (Where the ``tsv`` file is present.)
+        tsv (str, optional):
+            The name of the tsv file used to construct the metadata, such as
+            ``"train.tsv"``, ``"test.tsv"``, ``"dev.tsv"``, ``"invalidated.tsv"``,
+            ``"validated.tsv"`` and ``"other.tsv"``. (default: ``"train.tsv"``)
+    """
+
+    _ext_txt = ".txt"
+    _ext_audio = ".mp3"
+    _folder_audio = "clips"
+
+    def __init__(self, root: Union[str, Path], tsv: str = "train.tsv") -> None:
+
+        # Get string representation of 'root' in case Path object is passed
+        self._path = os.fspath(root)
+        self._tsv = os.path.join(self._path, tsv)
+
+        with open(self._tsv, "r") as tsv_:
+            walker = csv.reader(tsv_, delimiter="\t")
+            self._header = next(walker)
+            self._walker = list(walker)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            Dict[str, str]:
+                Dictionary containing the following items from the corresponding TSV file;
+
+                * ``"client_id"``
+                * ``"path"``
+                * ``"sentence"``
+                * ``"up_votes"``
+                * ``"down_votes"``
+                * ``"age"``
+                * ``"gender"``
+                * ``"accent"``
+        """
+        line = self._walker[n]
+        return load_commonvoice_item(line, self._header, self._path, self._folder_audio, self._ext_audio)
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/dr_vctk.py b/lib/python3.11/site-packages/torchaudio/datasets/dr_vctk.py
new file mode 100644
index 0000000000000000000000000000000000000000..a634b968949480738eefef926d25b05846f0b67d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/dr_vctk.py
@@ -0,0 +1,121 @@
+from pathlib import Path
+from typing import Dict, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_zip
+
+
+_URL = "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"
+_CHECKSUM = "781f12f4406ed36ed27ae3bce55da47ba176e2d8bae67319e389e07b2c9bd769"
+_SUPPORTED_SUBSETS = {"train", "test"}
+
+
+class DR_VCTK(Dataset):
+    """*Device Recorded VCTK (Small subset version)* :cite:`Sarfjoo2018DeviceRV` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found.
+        subset (str): The subset to use. Can be one of ``"train"`` and ``"test"``. (default: ``"train"``).
+        download (bool):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        url (str): The URL to download the dataset from.
+            (default: ``"https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str = "train",
+        *,
+        download: bool = False,
+        url: str = _URL,
+    ) -> None:
+        if subset not in _SUPPORTED_SUBSETS:
+            raise RuntimeError(
+                f"The subset '{subset}' does not match any of the supported subsets: {_SUPPORTED_SUBSETS}"
+            )
+
+        root = Path(root).expanduser()
+        archive = root / "DR-VCTK.zip"
+
+        self._subset = subset
+        self._path = root / "DR-VCTK" / "DR-VCTK"
+        self._clean_audio_dir = self._path / f"clean_{self._subset}set_wav_16k"
+        self._noisy_audio_dir = self._path / f"device-recorded_{self._subset}set_wav_16k"
+        self._config_filepath = self._path / "configurations" / f"{self._subset}_ch_log.txt"
+
+        if not self._path.is_dir():
+            if not archive.is_file():
+                if not download:
+                    raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
+                download_url_to_file(url, archive, hash_prefix=_CHECKSUM)
+            _extract_zip(archive, root)
+
+        self._config = self._load_config(self._config_filepath)
+        self._filename_list = sorted(self._config)
+
+    def _load_config(self, filepath: str) -> Dict[str, Tuple[str, int]]:
+        # Skip header
+        skip_rows = 2 if self._subset == "train" else 1
+
+        config = {}
+        with open(filepath) as f:
+            for i, line in enumerate(f):
+                if i < skip_rows or not line:
+                    continue
+                filename, source, channel_id = line.strip().split("\t")
+                config[filename] = (source, int(channel_id))
+        return config
+
+    def _load_dr_vctk_item(self, filename: str) -> Tuple[Tensor, int, Tensor, int, str, str, str, int]:
+        speaker_id, utterance_id = filename.split(".")[0].split("_")
+        source, channel_id = self._config[filename]
+        file_clean_audio = self._clean_audio_dir / filename
+        file_noisy_audio = self._noisy_audio_dir / filename
+        waveform_clean, sample_rate_clean = torchaudio.load(file_clean_audio)
+        waveform_noisy, sample_rate_noisy = torchaudio.load(file_noisy_audio)
+        return (
+            waveform_clean,
+            sample_rate_clean,
+            waveform_noisy,
+            sample_rate_noisy,
+            speaker_id,
+            utterance_id,
+            source,
+            channel_id,
+        )
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, Tensor, int, str, str, str, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Clean waveform
+            int:
+                Sample rate of the clean waveform
+            Tensor:
+                Noisy waveform
+            int:
+                Sample rate of the noisy waveform
+            str:
+                Speaker ID
+            str:
+                Utterance ID
+            str:
+                Source
+            int:
+                Channel ID
+        """
+        filename = self._filename_list[n]
+        return self._load_dr_vctk_item(filename)
+
+    def __len__(self) -> int:
+        return len(self._filename_list)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/fluentcommands.py b/lib/python3.11/site-packages/torchaudio/datasets/fluentcommands.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cdee398d6e31a5e622321d1f73177606d9c8640
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/fluentcommands.py
@@ -0,0 +1,108 @@
+import csv
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+SAMPLE_RATE = 16000
+
+
+class FluentSpeechCommands(Dataset):
+    """*Fluent Speech Commands* :cite:`fluent` dataset
+
+    Args:
+        root (str of Path): Path to the directory where the dataset is found.
+        subset (str, optional): subset of the dataset to use.
+            Options: [``"train"``, ``"valid"``, ``"test"``].
+            (Default: ``"train"``)
+    """
+
+    def __init__(self, root: Union[str, Path], subset: str = "train"):
+        if subset not in ["train", "valid", "test"]:
+            raise ValueError("`subset` must be one of ['train', 'valid', 'test']")
+
+        root = os.fspath(root)
+        self._path = os.path.join(root, "fluent_speech_commands_dataset")
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found.")
+
+        subset_path = os.path.join(self._path, "data", f"{subset}_data.csv")
+        with open(subset_path) as subset_csv:
+            subset_reader = csv.reader(subset_csv)
+            data = list(subset_reader)
+
+        self.header = data[0]
+        self.data = data[1:]
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, int, str, str, str, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                File name
+            int:
+                Speaker ID
+            str:
+                Transcription
+            str:
+                Action
+            str:
+                Object
+            str:
+                Location
+        """
+        sample = self.data[n]
+
+        file_name = sample[self.header.index("path")].split("/")[-1]
+        file_name = file_name.split(".")[0]
+        speaker_id, transcription, action, obj, location = sample[2:]
+        file_path = os.path.join("wavs", "speakers", speaker_id, f"{file_name}.wav")
+
+        return file_path, SAMPLE_RATE, file_name, speaker_id, transcription, action, obj, location
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, str, str, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                File name
+            int:
+                Speaker ID
+            str:
+                Transcription
+            str:
+                Action
+            str:
+                Object
+            str:
+                Location
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/gtzan.py b/lib/python3.11/site-packages/torchaudio/datasets/gtzan.py
new file mode 100644
index 0000000000000000000000000000000000000000..347e7e71831770f42d7fdaf0b3c63a09409f659d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/gtzan.py
@@ -0,0 +1,1118 @@
+import os
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+# The following lists prefixed with `filtered_` provide a filtered split
+# that:
+#
+# a. Mitigate a known issue with GTZAN (duplication)
+#
+# b. Provide a standard split for testing it against other
+#    methods (e.g. the one in jordipons/sklearn-audio-transfer-learning).
+#
+# Those are used when GTZAN is initialised with the `filtered` keyword.
+# The split was taken from (github) jordipons/sklearn-audio-transfer-learning.
+
+gtzan_genres = [
+    "blues",
+    "classical",
+    "country",
+    "disco",
+    "hiphop",
+    "jazz",
+    "metal",
+    "pop",
+    "reggae",
+    "rock",
+]
+
+filtered_test = [
+    "blues.00012",
+    "blues.00013",
+    "blues.00014",
+    "blues.00015",
+    "blues.00016",
+    "blues.00017",
+    "blues.00018",
+    "blues.00019",
+    "blues.00020",
+    "blues.00021",
+    "blues.00022",
+    "blues.00023",
+    "blues.00024",
+    "blues.00025",
+    "blues.00026",
+    "blues.00027",
+    "blues.00028",
+    "blues.00061",
+    "blues.00062",
+    "blues.00063",
+    "blues.00064",
+    "blues.00065",
+    "blues.00066",
+    "blues.00067",
+    "blues.00068",
+    "blues.00069",
+    "blues.00070",
+    "blues.00071",
+    "blues.00072",
+    "blues.00098",
+    "blues.00099",
+    "classical.00011",
+    "classical.00012",
+    "classical.00013",
+    "classical.00014",
+    "classical.00015",
+    "classical.00016",
+    "classical.00017",
+    "classical.00018",
+    "classical.00019",
+    "classical.00020",
+    "classical.00021",
+    "classical.00022",
+    "classical.00023",
+    "classical.00024",
+    "classical.00025",
+    "classical.00026",
+    "classical.00027",
+    "classical.00028",
+    "classical.00029",
+    "classical.00034",
+    "classical.00035",
+    "classical.00036",
+    "classical.00037",
+    "classical.00038",
+    "classical.00039",
+    "classical.00040",
+    "classical.00041",
+    "classical.00049",
+    "classical.00077",
+    "classical.00078",
+    "classical.00079",
+    "country.00030",
+    "country.00031",
+    "country.00032",
+    "country.00033",
+    "country.00034",
+    "country.00035",
+    "country.00036",
+    "country.00037",
+    "country.00038",
+    "country.00039",
+    "country.00040",
+    "country.00043",
+    "country.00044",
+    "country.00046",
+    "country.00047",
+    "country.00048",
+    "country.00050",
+    "country.00051",
+    "country.00053",
+    "country.00054",
+    "country.00055",
+    "country.00056",
+    "country.00057",
+    "country.00058",
+    "country.00059",
+    "country.00060",
+    "country.00061",
+    "country.00062",
+    "country.00063",
+    "country.00064",
+    "disco.00001",
+    "disco.00021",
+    "disco.00058",
+    "disco.00062",
+    "disco.00063",
+    "disco.00064",
+    "disco.00065",
+    "disco.00066",
+    "disco.00069",
+    "disco.00076",
+    "disco.00077",
+    "disco.00078",
+    "disco.00079",
+    "disco.00080",
+    "disco.00081",
+    "disco.00082",
+    "disco.00083",
+    "disco.00084",
+    "disco.00085",
+    "disco.00086",
+    "disco.00087",
+    "disco.00088",
+    "disco.00091",
+    "disco.00092",
+    "disco.00093",
+    "disco.00094",
+    "disco.00096",
+    "disco.00097",
+    "disco.00099",
+    "hiphop.00000",
+    "hiphop.00026",
+    "hiphop.00027",
+    "hiphop.00030",
+    "hiphop.00040",
+    "hiphop.00043",
+    "hiphop.00044",
+    "hiphop.00045",
+    "hiphop.00051",
+    "hiphop.00052",
+    "hiphop.00053",
+    "hiphop.00054",
+    "hiphop.00062",
+    "hiphop.00063",
+    "hiphop.00064",
+    "hiphop.00065",
+    "hiphop.00066",
+    "hiphop.00067",
+    "hiphop.00068",
+    "hiphop.00069",
+    "hiphop.00070",
+    "hiphop.00071",
+    "hiphop.00072",
+    "hiphop.00073",
+    "hiphop.00074",
+    "hiphop.00075",
+    "hiphop.00099",
+    "jazz.00073",
+    "jazz.00074",
+    "jazz.00075",
+    "jazz.00076",
+    "jazz.00077",
+    "jazz.00078",
+    "jazz.00079",
+    "jazz.00080",
+    "jazz.00081",
+    "jazz.00082",
+    "jazz.00083",
+    "jazz.00084",
+    "jazz.00085",
+    "jazz.00086",
+    "jazz.00087",
+    "jazz.00088",
+    "jazz.00089",
+    "jazz.00090",
+    "jazz.00091",
+    "jazz.00092",
+    "jazz.00093",
+    "jazz.00094",
+    "jazz.00095",
+    "jazz.00096",
+    "jazz.00097",
+    "jazz.00098",
+    "jazz.00099",
+    "metal.00012",
+    "metal.00013",
+    "metal.00014",
+    "metal.00015",
+    "metal.00022",
+    "metal.00023",
+    "metal.00025",
+    "metal.00026",
+    "metal.00027",
+    "metal.00028",
+    "metal.00029",
+    "metal.00030",
+    "metal.00031",
+    "metal.00032",
+    "metal.00033",
+    "metal.00038",
+    "metal.00039",
+    "metal.00067",
+    "metal.00070",
+    "metal.00073",
+    "metal.00074",
+    "metal.00075",
+    "metal.00078",
+    "metal.00083",
+    "metal.00085",
+    "metal.00087",
+    "metal.00088",
+    "pop.00000",
+    "pop.00001",
+    "pop.00013",
+    "pop.00014",
+    "pop.00043",
+    "pop.00063",
+    "pop.00064",
+    "pop.00065",
+    "pop.00066",
+    "pop.00069",
+    "pop.00070",
+    "pop.00071",
+    "pop.00072",
+    "pop.00073",
+    "pop.00074",
+    "pop.00075",
+    "pop.00076",
+    "pop.00077",
+    "pop.00078",
+    "pop.00079",
+    "pop.00082",
+    "pop.00088",
+    "pop.00089",
+    "pop.00090",
+    "pop.00091",
+    "pop.00092",
+    "pop.00093",
+    "pop.00094",
+    "pop.00095",
+    "pop.00096",
+    "reggae.00034",
+    "reggae.00035",
+    "reggae.00036",
+    "reggae.00037",
+    "reggae.00038",
+    "reggae.00039",
+    "reggae.00040",
+    "reggae.00046",
+    "reggae.00047",
+    "reggae.00048",
+    "reggae.00052",
+    "reggae.00053",
+    "reggae.00064",
+    "reggae.00065",
+    "reggae.00066",
+    "reggae.00067",
+    "reggae.00068",
+    "reggae.00071",
+    "reggae.00079",
+    "reggae.00082",
+    "reggae.00083",
+    "reggae.00084",
+    "reggae.00087",
+    "reggae.00088",
+    "reggae.00089",
+    "reggae.00090",
+    "rock.00010",
+    "rock.00011",
+    "rock.00012",
+    "rock.00013",
+    "rock.00014",
+    "rock.00015",
+    "rock.00027",
+    "rock.00028",
+    "rock.00029",
+    "rock.00030",
+    "rock.00031",
+    "rock.00032",
+    "rock.00033",
+    "rock.00034",
+    "rock.00035",
+    "rock.00036",
+    "rock.00037",
+    "rock.00039",
+    "rock.00040",
+    "rock.00041",
+    "rock.00042",
+    "rock.00043",
+    "rock.00044",
+    "rock.00045",
+    "rock.00046",
+    "rock.00047",
+    "rock.00048",
+    "rock.00086",
+    "rock.00087",
+    "rock.00088",
+    "rock.00089",
+    "rock.00090",
+]
+
+filtered_train = [
+    "blues.00029",
+    "blues.00030",
+    "blues.00031",
+    "blues.00032",
+    "blues.00033",
+    "blues.00034",
+    "blues.00035",
+    "blues.00036",
+    "blues.00037",
+    "blues.00038",
+    "blues.00039",
+    "blues.00040",
+    "blues.00041",
+    "blues.00042",
+    "blues.00043",
+    "blues.00044",
+    "blues.00045",
+    "blues.00046",
+    "blues.00047",
+    "blues.00048",
+    "blues.00049",
+    "blues.00073",
+    "blues.00074",
+    "blues.00075",
+    "blues.00076",
+    "blues.00077",
+    "blues.00078",
+    "blues.00079",
+    "blues.00080",
+    "blues.00081",
+    "blues.00082",
+    "blues.00083",
+    "blues.00084",
+    "blues.00085",
+    "blues.00086",
+    "blues.00087",
+    "blues.00088",
+    "blues.00089",
+    "blues.00090",
+    "blues.00091",
+    "blues.00092",
+    "blues.00093",
+    "blues.00094",
+    "blues.00095",
+    "blues.00096",
+    "blues.00097",
+    "classical.00030",
+    "classical.00031",
+    "classical.00032",
+    "classical.00033",
+    "classical.00043",
+    "classical.00044",
+    "classical.00045",
+    "classical.00046",
+    "classical.00047",
+    "classical.00048",
+    "classical.00050",
+    "classical.00051",
+    "classical.00052",
+    "classical.00053",
+    "classical.00054",
+    "classical.00055",
+    "classical.00056",
+    "classical.00057",
+    "classical.00058",
+    "classical.00059",
+    "classical.00060",
+    "classical.00061",
+    "classical.00062",
+    "classical.00063",
+    "classical.00064",
+    "classical.00065",
+    "classical.00066",
+    "classical.00067",
+    "classical.00080",
+    "classical.00081",
+    "classical.00082",
+    "classical.00083",
+    "classical.00084",
+    "classical.00085",
+    "classical.00086",
+    "classical.00087",
+    "classical.00088",
+    "classical.00089",
+    "classical.00090",
+    "classical.00091",
+    "classical.00092",
+    "classical.00093",
+    "classical.00094",
+    "classical.00095",
+    "classical.00096",
+    "classical.00097",
+    "classical.00098",
+    "classical.00099",
+    "country.00019",
+    "country.00020",
+    "country.00021",
+    "country.00022",
+    "country.00023",
+    "country.00024",
+    "country.00025",
+    "country.00026",
+    "country.00028",
+    "country.00029",
+    "country.00065",
+    "country.00066",
+    "country.00067",
+    "country.00068",
+    "country.00069",
+    "country.00070",
+    "country.00071",
+    "country.00072",
+    "country.00073",
+    "country.00074",
+    "country.00075",
+    "country.00076",
+    "country.00077",
+    "country.00078",
+    "country.00079",
+    "country.00080",
+    "country.00081",
+    "country.00082",
+    "country.00083",
+    "country.00084",
+    "country.00085",
+    "country.00086",
+    "country.00087",
+    "country.00088",
+    "country.00089",
+    "country.00090",
+    "country.00091",
+    "country.00092",
+    "country.00093",
+    "country.00094",
+    "country.00095",
+    "country.00096",
+    "country.00097",
+    "country.00098",
+    "country.00099",
+    "disco.00005",
+    "disco.00015",
+    "disco.00016",
+    "disco.00017",
+    "disco.00018",
+    "disco.00019",
+    "disco.00020",
+    "disco.00022",
+    "disco.00023",
+    "disco.00024",
+    "disco.00025",
+    "disco.00026",
+    "disco.00027",
+    "disco.00028",
+    "disco.00029",
+    "disco.00030",
+    "disco.00031",
+    "disco.00032",
+    "disco.00033",
+    "disco.00034",
+    "disco.00035",
+    "disco.00036",
+    "disco.00037",
+    "disco.00039",
+    "disco.00040",
+    "disco.00041",
+    "disco.00042",
+    "disco.00043",
+    "disco.00044",
+    "disco.00045",
+    "disco.00047",
+    "disco.00049",
+    "disco.00053",
+    "disco.00054",
+    "disco.00056",
+    "disco.00057",
+    "disco.00059",
+    "disco.00061",
+    "disco.00070",
+    "disco.00073",
+    "disco.00074",
+    "disco.00089",
+    "hiphop.00002",
+    "hiphop.00003",
+    "hiphop.00004",
+    "hiphop.00005",
+    "hiphop.00006",
+    "hiphop.00007",
+    "hiphop.00008",
+    "hiphop.00009",
+    "hiphop.00010",
+    "hiphop.00011",
+    "hiphop.00012",
+    "hiphop.00013",
+    "hiphop.00014",
+    "hiphop.00015",
+    "hiphop.00016",
+    "hiphop.00017",
+    "hiphop.00018",
+    "hiphop.00019",
+    "hiphop.00020",
+    "hiphop.00021",
+    "hiphop.00022",
+    "hiphop.00023",
+    "hiphop.00024",
+    "hiphop.00025",
+    "hiphop.00028",
+    "hiphop.00029",
+    "hiphop.00031",
+    "hiphop.00032",
+    "hiphop.00033",
+    "hiphop.00034",
+    "hiphop.00035",
+    "hiphop.00036",
+    "hiphop.00037",
+    "hiphop.00038",
+    "hiphop.00041",
+    "hiphop.00042",
+    "hiphop.00055",
+    "hiphop.00056",
+    "hiphop.00057",
+    "hiphop.00058",
+    "hiphop.00059",
+    "hiphop.00060",
+    "hiphop.00061",
+    "hiphop.00077",
+    "hiphop.00078",
+    "hiphop.00079",
+    "hiphop.00080",
+    "jazz.00000",
+    "jazz.00001",
+    "jazz.00011",
+    "jazz.00012",
+    "jazz.00013",
+    "jazz.00014",
+    "jazz.00015",
+    "jazz.00016",
+    "jazz.00017",
+    "jazz.00018",
+    "jazz.00019",
+    "jazz.00020",
+    "jazz.00021",
+    "jazz.00022",
+    "jazz.00023",
+    "jazz.00024",
+    "jazz.00041",
+    "jazz.00047",
+    "jazz.00048",
+    "jazz.00049",
+    "jazz.00050",
+    "jazz.00051",
+    "jazz.00052",
+    "jazz.00053",
+    "jazz.00054",
+    "jazz.00055",
+    "jazz.00056",
+    "jazz.00057",
+    "jazz.00058",
+    "jazz.00059",
+    "jazz.00060",
+    "jazz.00061",
+    "jazz.00062",
+    "jazz.00063",
+    "jazz.00064",
+    "jazz.00065",
+    "jazz.00066",
+    "jazz.00067",
+    "jazz.00068",
+    "jazz.00069",
+    "jazz.00070",
+    "jazz.00071",
+    "jazz.00072",
+    "metal.00002",
+    "metal.00003",
+    "metal.00005",
+    "metal.00021",
+    "metal.00024",
+    "metal.00035",
+    "metal.00046",
+    "metal.00047",
+    "metal.00048",
+    "metal.00049",
+    "metal.00050",
+    "metal.00051",
+    "metal.00052",
+    "metal.00053",
+    "metal.00054",
+    "metal.00055",
+    "metal.00056",
+    "metal.00057",
+    "metal.00059",
+    "metal.00060",
+    "metal.00061",
+    "metal.00062",
+    "metal.00063",
+    "metal.00064",
+    "metal.00065",
+    "metal.00066",
+    "metal.00069",
+    "metal.00071",
+    "metal.00072",
+    "metal.00079",
+    "metal.00080",
+    "metal.00084",
+    "metal.00086",
+    "metal.00089",
+    "metal.00090",
+    "metal.00091",
+    "metal.00092",
+    "metal.00093",
+    "metal.00094",
+    "metal.00095",
+    "metal.00096",
+    "metal.00097",
+    "metal.00098",
+    "metal.00099",
+    "pop.00002",
+    "pop.00003",
+    "pop.00004",
+    "pop.00005",
+    "pop.00006",
+    "pop.00007",
+    "pop.00008",
+    "pop.00009",
+    "pop.00011",
+    "pop.00012",
+    "pop.00016",
+    "pop.00017",
+    "pop.00018",
+    "pop.00019",
+    "pop.00020",
+    "pop.00023",
+    "pop.00024",
+    "pop.00025",
+    "pop.00026",
+    "pop.00027",
+    "pop.00028",
+    "pop.00029",
+    "pop.00031",
+    "pop.00032",
+    "pop.00033",
+    "pop.00034",
+    "pop.00035",
+    "pop.00036",
+    "pop.00038",
+    "pop.00039",
+    "pop.00040",
+    "pop.00041",
+    "pop.00042",
+    "pop.00044",
+    "pop.00046",
+    "pop.00049",
+    "pop.00050",
+    "pop.00080",
+    "pop.00097",
+    "pop.00098",
+    "pop.00099",
+    "reggae.00000",
+    "reggae.00001",
+    "reggae.00002",
+    "reggae.00004",
+    "reggae.00006",
+    "reggae.00009",
+    "reggae.00011",
+    "reggae.00012",
+    "reggae.00014",
+    "reggae.00015",
+    "reggae.00016",
+    "reggae.00017",
+    "reggae.00018",
+    "reggae.00019",
+    "reggae.00020",
+    "reggae.00021",
+    "reggae.00022",
+    "reggae.00023",
+    "reggae.00024",
+    "reggae.00025",
+    "reggae.00026",
+    "reggae.00027",
+    "reggae.00028",
+    "reggae.00029",
+    "reggae.00030",
+    "reggae.00031",
+    "reggae.00032",
+    "reggae.00042",
+    "reggae.00043",
+    "reggae.00044",
+    "reggae.00045",
+    "reggae.00049",
+    "reggae.00050",
+    "reggae.00051",
+    "reggae.00054",
+    "reggae.00055",
+    "reggae.00056",
+    "reggae.00057",
+    "reggae.00058",
+    "reggae.00059",
+    "reggae.00060",
+    "reggae.00063",
+    "reggae.00069",
+    "rock.00000",
+    "rock.00001",
+    "rock.00002",
+    "rock.00003",
+    "rock.00004",
+    "rock.00005",
+    "rock.00006",
+    "rock.00007",
+    "rock.00008",
+    "rock.00009",
+    "rock.00016",
+    "rock.00017",
+    "rock.00018",
+    "rock.00019",
+    "rock.00020",
+    "rock.00021",
+    "rock.00022",
+    "rock.00023",
+    "rock.00024",
+    "rock.00025",
+    "rock.00026",
+    "rock.00057",
+    "rock.00058",
+    "rock.00059",
+    "rock.00060",
+    "rock.00061",
+    "rock.00062",
+    "rock.00063",
+    "rock.00064",
+    "rock.00065",
+    "rock.00066",
+    "rock.00067",
+    "rock.00068",
+    "rock.00069",
+    "rock.00070",
+    "rock.00091",
+    "rock.00092",
+    "rock.00093",
+    "rock.00094",
+    "rock.00095",
+    "rock.00096",
+    "rock.00097",
+    "rock.00098",
+    "rock.00099",
+]
+
+filtered_valid = [
+    "blues.00000",
+    "blues.00001",
+    "blues.00002",
+    "blues.00003",
+    "blues.00004",
+    "blues.00005",
+    "blues.00006",
+    "blues.00007",
+    "blues.00008",
+    "blues.00009",
+    "blues.00010",
+    "blues.00011",
+    "blues.00050",
+    "blues.00051",
+    "blues.00052",
+    "blues.00053",
+    "blues.00054",
+    "blues.00055",
+    "blues.00056",
+    "blues.00057",
+    "blues.00058",
+    "blues.00059",
+    "blues.00060",
+    "classical.00000",
+    "classical.00001",
+    "classical.00002",
+    "classical.00003",
+    "classical.00004",
+    "classical.00005",
+    "classical.00006",
+    "classical.00007",
+    "classical.00008",
+    "classical.00009",
+    "classical.00010",
+    "classical.00068",
+    "classical.00069",
+    "classical.00070",
+    "classical.00071",
+    "classical.00072",
+    "classical.00073",
+    "classical.00074",
+    "classical.00075",
+    "classical.00076",
+    "country.00000",
+    "country.00001",
+    "country.00002",
+    "country.00003",
+    "country.00004",
+    "country.00005",
+    "country.00006",
+    "country.00007",
+    "country.00009",
+    "country.00010",
+    "country.00011",
+    "country.00012",
+    "country.00013",
+    "country.00014",
+    "country.00015",
+    "country.00016",
+    "country.00017",
+    "country.00018",
+    "country.00027",
+    "country.00041",
+    "country.00042",
+    "country.00045",
+    "country.00049",
+    "disco.00000",
+    "disco.00002",
+    "disco.00003",
+    "disco.00004",
+    "disco.00006",
+    "disco.00007",
+    "disco.00008",
+    "disco.00009",
+    "disco.00010",
+    "disco.00011",
+    "disco.00012",
+    "disco.00013",
+    "disco.00014",
+    "disco.00046",
+    "disco.00048",
+    "disco.00052",
+    "disco.00067",
+    "disco.00068",
+    "disco.00072",
+    "disco.00075",
+    "disco.00090",
+    "disco.00095",
+    "hiphop.00081",
+    "hiphop.00082",
+    "hiphop.00083",
+    "hiphop.00084",
+    "hiphop.00085",
+    "hiphop.00086",
+    "hiphop.00087",
+    "hiphop.00088",
+    "hiphop.00089",
+    "hiphop.00090",
+    "hiphop.00091",
+    "hiphop.00092",
+    "hiphop.00093",
+    "hiphop.00094",
+    "hiphop.00095",
+    "hiphop.00096",
+    "hiphop.00097",
+    "hiphop.00098",
+    "jazz.00002",
+    "jazz.00003",
+    "jazz.00004",
+    "jazz.00005",
+    "jazz.00006",
+    "jazz.00007",
+    "jazz.00008",
+    "jazz.00009",
+    "jazz.00010",
+    "jazz.00025",
+    "jazz.00026",
+    "jazz.00027",
+    "jazz.00028",
+    "jazz.00029",
+    "jazz.00030",
+    "jazz.00031",
+    "jazz.00032",
+    "metal.00000",
+    "metal.00001",
+    "metal.00006",
+    "metal.00007",
+    "metal.00008",
+    "metal.00009",
+    "metal.00010",
+    "metal.00011",
+    "metal.00016",
+    "metal.00017",
+    "metal.00018",
+    "metal.00019",
+    "metal.00020",
+    "metal.00036",
+    "metal.00037",
+    "metal.00068",
+    "metal.00076",
+    "metal.00077",
+    "metal.00081",
+    "metal.00082",
+    "pop.00010",
+    "pop.00053",
+    "pop.00055",
+    "pop.00058",
+    "pop.00059",
+    "pop.00060",
+    "pop.00061",
+    "pop.00062",
+    "pop.00081",
+    "pop.00083",
+    "pop.00084",
+    "pop.00085",
+    "pop.00086",
+    "reggae.00061",
+    "reggae.00062",
+    "reggae.00070",
+    "reggae.00072",
+    "reggae.00074",
+    "reggae.00076",
+    "reggae.00077",
+    "reggae.00078",
+    "reggae.00085",
+    "reggae.00092",
+    "reggae.00093",
+    "reggae.00094",
+    "reggae.00095",
+    "reggae.00096",
+    "reggae.00097",
+    "reggae.00098",
+    "reggae.00099",
+    "rock.00038",
+    "rock.00049",
+    "rock.00050",
+    "rock.00051",
+    "rock.00052",
+    "rock.00053",
+    "rock.00054",
+    "rock.00055",
+    "rock.00056",
+    "rock.00071",
+    "rock.00072",
+    "rock.00073",
+    "rock.00074",
+    "rock.00075",
+    "rock.00076",
+    "rock.00077",
+    "rock.00078",
+    "rock.00079",
+    "rock.00080",
+    "rock.00081",
+    "rock.00082",
+    "rock.00083",
+    "rock.00084",
+    "rock.00085",
+]
+
+
+URL = "http://opihi.cs.uvic.ca/sound/genres.tar.gz"
+FOLDER_IN_ARCHIVE = "genres"
+_CHECKSUMS = {
+    "http://opihi.cs.uvic.ca/sound/genres.tar.gz": "24347e0223d2ba798e0a558c4c172d9d4a19c00bb7963fe055d183dadb4ef2c6"
+}
+
+
+def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str]:
+    """
+    Loads a file from the dataset and returns the raw waveform
+    as a Torch Tensor, its sample rate as an integer, and its
+    genre as a string.
+    """
+    # Filenames are of the form label.id, e.g. blues.00078
+    label, _ = fileid.split(".")
+
+    # Read wav
+    file_audio = os.path.join(path, label, fileid + ext_audio)
+    waveform, sample_rate = torchaudio.load(file_audio)
+
+    return waveform, sample_rate, label
+
+
+class GTZAN(Dataset):
+    """*GTZAN* :cite:`tzanetakis_essl_cook_2001` dataset.
+
+    Note:
+        Please see http://marsyas.info/downloads/datasets.html if you are planning to use
+        this dataset to publish results.
+
+    Note:
+        As of October 2022, the download link is not currently working. Setting ``download=True``
+        in GTZAN dataset will result in a URL connection error.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from.
+            (default: ``"http://opihi.cs.uvic.ca/sound/genres.tar.gz"``)
+        folder_in_archive (str, optional): The top-level directory of the dataset.
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        subset (str or None, optional): Which subset of the dataset to use.
+            One of ``"training"``, ``"validation"``, ``"testing"`` or ``None``.
+            If ``None``, the entire dataset is used. (default: ``None``).
+    """
+
+    _ext_audio = ".wav"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+        subset: Optional[str] = None,
+    ) -> None:
+
+        # super(GTZAN, self).__init__()
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        self.root = root
+        self.url = url
+        self.folder_in_archive = folder_in_archive
+        self.download = download
+        self.subset = subset
+
+        if subset is not None and subset not in ["training", "validation", "testing"]:
+            raise ValueError("When `subset` is not None, it must be one of ['training', 'validation', 'testing'].")
+
+        archive = os.path.basename(url)
+        archive = os.path.join(root, archive)
+        self._path = os.path.join(root, folder_in_archive)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
+
+        if self.subset is None:
+            # Check every subdirectory under dataset root
+            # which has the same name as the genres in
+            # GTZAN (e.g. `root_dir'/blues/, `root_dir'/rock, etc.)
+            # This lets users remove or move around song files,
+            # useful when e.g. they want to use only some of the files
+            # in a genre or want to label other files with a different
+            # genre.
+            self._walker = []
+
+            root = os.path.expanduser(self._path)
+
+            for directory in gtzan_genres:
+                fulldir = os.path.join(root, directory)
+
+                if not os.path.exists(fulldir):
+                    continue
+
+                songs_in_genre = os.listdir(fulldir)
+                songs_in_genre.sort()
+                for fname in songs_in_genre:
+                    name, ext = os.path.splitext(fname)
+                    if ext.lower() == ".wav" and "." in name:
+                        # Check whether the file is of the form
+                        # `gtzan_genre`.`5 digit number`.wav
+                        genre, num = name.split(".")
+                        if genre in gtzan_genres and len(num) == 5 and num.isdigit():
+                            self._walker.append(name)
+        else:
+            if self.subset == "training":
+                self._walker = filtered_train
+            elif self.subset == "validation":
+                self._walker = filtered_valid
+            elif self.subset == "testing":
+                self._walker = filtered_test
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Label
+        """
+        fileid = self._walker[n]
+        item = load_gtzan_item(fileid, self._path, self._ext_audio)
+        waveform, sample_rate, label = item
+        return waveform, sample_rate, label
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/iemocap.py b/lib/python3.11/site-packages/torchaudio/datasets/iemocap.py
new file mode 100644
index 0000000000000000000000000000000000000000..224300a84f5ec3ae217f030783c825fc3db56c8a
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/iemocap.py
@@ -0,0 +1,147 @@
+import os
+import re
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+
+_SAMPLE_RATE = 16000
+
+
+def _get_wavs_paths(data_dir):
+    wav_dir = data_dir / "sentences" / "wav"
+    wav_paths = sorted(str(p) for p in wav_dir.glob("*/*.wav"))
+    relative_paths = []
+    for wav_path in wav_paths:
+        start = wav_path.find("Session")
+        wav_path = wav_path[start:]
+        relative_paths.append(wav_path)
+    return relative_paths
+
+
+class IEMOCAP(Dataset):
+    """*IEMOCAP* :cite:`iemocap` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found
+        sessions (Tuple[int]): Tuple of sessions (1-5) to use. (Default: ``(1, 2, 3, 4, 5)``)
+        utterance_type (str or None, optional): Which type(s) of utterances to include in the dataset.
+            Options: ("scripted", "improvised", ``None``). If ``None``, both scripted and improvised
+            data are used.
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        sessions: Tuple[str] = (1, 2, 3, 4, 5),
+        utterance_type: Optional[str] = None,
+    ):
+        root = Path(root)
+        self._path = root / "IEMOCAP"
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found.")
+
+        if utterance_type not in ["scripted", "improvised", None]:
+            raise ValueError("utterance_type must be one of ['scripted', 'improvised', or None]")
+
+        all_data = []
+        self.data = []
+        self.mapping = {}
+
+        for session in sessions:
+            session_name = f"Session{session}"
+            session_dir = self._path / session_name
+
+            # get wav paths
+            wav_paths = _get_wavs_paths(session_dir)
+            for wav_path in wav_paths:
+                wav_stem = str(Path(wav_path).stem)
+                all_data.append(wav_stem)
+
+            # add labels
+            label_dir = session_dir / "dialog" / "EmoEvaluation"
+            query = "*.txt"
+            if utterance_type == "scripted":
+                query = "*script*.txt"
+            elif utterance_type == "improvised":
+                query = "*impro*.txt"
+            label_paths = label_dir.glob(query)
+
+            for label_path in label_paths:
+                with open(label_path, "r") as f:
+                    for line in f:
+                        if not line.startswith("["):
+                            continue
+                        line = re.split("[\t\n]", line)
+                        wav_stem = line[1]
+                        label = line[2]
+                        if wav_stem not in all_data:
+                            continue
+                        if label not in ["neu", "hap", "ang", "sad", "exc", "fru"]:
+                            continue
+                        self.mapping[wav_stem] = {}
+                        self.mapping[wav_stem]["label"] = label
+
+            for wav_path in wav_paths:
+                wav_stem = str(Path(wav_path).stem)
+                if wav_stem in self.mapping:
+                    self.data.append(wav_stem)
+                    self.mapping[wav_stem]["path"] = wav_path
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, str, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:meth:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                File name
+            str:
+                Label (one of ``"neu"``, ``"hap"``, ``"ang"``, ``"sad"``, ``"exc"``, ``"fru"``)
+            str:
+                Speaker
+        """
+        wav_stem = self.data[n]
+        wav_path = self.mapping[wav_stem]["path"]
+        label = self.mapping[wav_stem]["label"]
+        speaker = wav_stem.split("_")[0]
+        return (wav_path, _SAMPLE_RATE, wav_stem, label, speaker)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                File name
+            str:
+                Label (one of ``"neu"``, ``"hap"``, ``"ang"``, ``"sad"``, ``"exc"``, ``"fru"``)
+            str:
+                Speaker
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self):
+        return len(self.data)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/librilight_limited.py b/lib/python3.11/site-packages/torchaudio/datasets/librilight_limited.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0cb3100f7c4ad2e488c20bdfaac3833e0a136dd
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/librilight_limited.py
@@ -0,0 +1,111 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.librispeech import _get_librispeech_metadata
+from torchaudio.datasets.utils import _extract_tar
+
+
+_ARCHIVE_NAME = "librispeech_finetuning"
+_URL = "https://dl.fbaipublicfiles.com/librilight/data/librispeech_finetuning.tgz"
+_CHECKSUM = "5d1efdc777b548194d7e09ba89126e2188026df9fd57aa57eb14408d2b2342af"
+_SUBSET_MAP = {"10min": ["1h/0"], "1h": ["1h/*"], "10h": ["1h/*", "9h"]}
+
+
+def _get_fileids_paths(path: Path, folders: List[str], _ext_audio: str) -> List[Tuple[str, str]]:
+    """Get the file names and the corresponding file paths without `speaker_id`
+    and `chapter_id` directories.
+    The format of path is like:
+        {root}/{_ARCHIVE_NAME}/1h/[0-5]/[clean, other] or
+        {root}/{_ARCHIVE_NAME}/9h/[clean, other]
+
+    Args:
+        path (Path): Root path to the dataset.
+        folders (List[str]): Folders that contain the desired audio files.
+        _ext_audio (str): Extension of audio files.
+
+    Returns:
+        List[Tuple[str, str]]:
+            List of tuples where the first element is the relative path to the audio file.
+            The format of relative path is like:
+            1h/[0-5]/[clean, other] or 9h/[clean, other]
+            The second element is the file name without audio extension.
+    """
+
+    path = Path(path)
+    files_paths = []
+    for folder in folders:
+        paths = [p.relative_to(path) for p in path.glob(f"{folder}/*/*/*/*{_ext_audio}")]
+        files_paths += [(str(p.parent.parent.parent), str(p.stem)) for p in paths]  # get subset folder and file name
+    files_paths.sort(key=lambda x: x[0] + x[1])
+    return files_paths
+
+
+class LibriLightLimited(Dataset):
+    """Subset of Libri-light :cite:`librilight` dataset,
+    which was used in HuBERT :cite:`hsu2021hubert` for supervised fine-tuning.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        subset (str, optional): The subset to use. Options: [``"10min"``, ``"1h"``, ``"10h"``]
+            (Default: ``"10min"``).
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _ext_txt = ".trans.txt"
+    _ext_audio = ".flac"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str = "10min",
+        download: bool = False,
+    ) -> None:
+        if subset not in _SUBSET_MAP:
+            raise ValueError(f"`subset` must be one of {_SUBSET_MAP.keys()}. Found: {subset}")
+        folders = _SUBSET_MAP[subset]
+
+        root = os.fspath(root)
+        self._path = os.path.join(root, _ARCHIVE_NAME)
+        archive = os.path.join(root, f"{_ARCHIVE_NAME}.tgz")
+        if not os.path.isdir(self._path):
+            if not download:
+                raise RuntimeError("Dataset not found. Please use `download=True` to download")
+            if not os.path.isfile(archive):
+                download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM)
+            _extract_tar(archive)
+        self._fileids_paths = _get_fileids_paths(self._path, folders, self._ext_audio)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+        """
+        file_path, fileid = self._fileids_paths[n]
+        metadata = _get_librispeech_metadata(fileid, self._path, file_path, self._ext_audio, self._ext_txt)
+        waveform, _ = torchaudio.load(os.path.join(self._path, metadata[0]))
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._fileids_paths)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/librimix.py b/lib/python3.11/site-packages/torchaudio/datasets/librimix.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c6c6f18600ab35f037dda11f9f5bc32c8a5cbf5
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/librimix.py
@@ -0,0 +1,133 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+import torch
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+_TASKS_TO_MIXTURE = {
+    "sep_clean": "mix_clean",
+    "enh_single": "mix_single",
+    "enh_both": "mix_both",
+    "sep_noisy": "mix_both",
+}
+
+
+class LibriMix(Dataset):
+    r"""*LibriMix* :cite:`cosentino2020librimix` dataset.
+
+    Args:
+        root (str or Path): The path where the directory ``Libri2Mix`` or
+            ``Libri3Mix`` is stored. Not the path of those directories.
+        subset (str, optional): The subset to use. Options: [``"train-360"``, ``"train-100"``,
+            ``"dev"``, and ``"test"``] (Default: ``"train-360"``).
+        num_speakers (int, optional): The number of speakers, which determines the directories
+            to traverse. The Dataset will traverse ``s1`` to ``sN`` directories to collect
+            N source audios. (Default: 2)
+        sample_rate (int, optional): Sample rate of audio files. The ``sample_rate`` determines
+            which subdirectory the audio are fetched. If any of the audio has a different sample
+            rate, raises ``ValueError``. Options: [8000, 16000] (Default: 8000)
+        task (str, optional): The task of LibriMix.
+            Options: [``"enh_single"``, ``"enh_both"``, ``"sep_clean"``, ``"sep_noisy"``]
+            (Default: ``"sep_clean"``)
+        mode (str, optional): The mode when creating the mixture. If set to ``"min"``, the lengths of mixture
+            and sources are the minimum length of all sources. If set to ``"max"``, the lengths of mixture and
+            sources are zero padded to the maximum length of all sources.
+            Options: [``"min"``, ``"max"``]
+            (Default: ``"min"``)
+
+    Note:
+        The LibriMix dataset needs to be manually generated. Please check https://github.com/JorisCos/LibriMix
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str = "train-360",
+        num_speakers: int = 2,
+        sample_rate: int = 8000,
+        task: str = "sep_clean",
+        mode: str = "min",
+    ):
+        self.root = Path(root) / f"Libri{num_speakers}Mix"
+        if not os.path.exists(self.root):
+            raise RuntimeError(
+                f"The path {self.root} doesn't exist. "
+                "Please check the ``root`` path and ``num_speakers`` or download the dataset manually."
+            )
+        if mode not in ["max", "min"]:
+            raise ValueError(f'Expect ``mode`` to be one in ["min", "max"]. Found {mode}.')
+        if sample_rate == 8000:
+            mix_dir = self.root / "wav8k" / mode / subset
+        elif sample_rate == 16000:
+            mix_dir = self.root / "wav16k" / mode / subset
+        else:
+            raise ValueError(f"Unsupported sample rate. Found {sample_rate}.")
+        self.sample_rate = sample_rate
+        self.task = task
+
+        self.mix_dir = mix_dir / _TASKS_TO_MIXTURE[task]
+        if task == "enh_both":
+            self.src_dirs = [(mix_dir / "mix_clean")]
+        else:
+            self.src_dirs = [(mix_dir / f"s{i+1}") for i in range(num_speakers)]
+
+        self.files = [p.name for p in self.mix_dir.glob("*.wav")]
+        self.files.sort()
+
+    def _load_sample(self, key) -> Tuple[int, torch.Tensor, List[torch.Tensor]]:
+        metadata = self.get_metadata(key)
+        mixed = _load_waveform(self.root, metadata[1], metadata[0])
+        srcs = []
+        for i, path_ in enumerate(metadata[2]):
+            src = _load_waveform(self.root, path_, metadata[0])
+            if mixed.shape != src.shape:
+                raise ValueError(f"Different waveform shapes. mixed: {mixed.shape}, src[{i}]: {src.shape}")
+            srcs.append(src)
+        return self.sample_rate, mixed, srcs
+
+    def get_metadata(self, key: int) -> Tuple[int, str, List[str]]:
+        """Get metadata for the n-th sample from the dataset.
+
+        Args:
+            key (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            int:
+                Sample rate
+            str:
+                Path to mixed audio
+            List of str:
+                List of paths to source audios
+        """
+        filename = self.files[key]
+        mixed_path = os.path.relpath(self.mix_dir / filename, self.root)
+        srcs_paths = []
+        for dir_ in self.src_dirs:
+            src = os.path.relpath(dir_ / filename, self.root)
+            srcs_paths.append(src)
+        return self.sample_rate, mixed_path, srcs_paths
+
+    def __len__(self) -> int:
+        return len(self.files)
+
+    def __getitem__(self, key: int) -> Tuple[int, torch.Tensor, List[torch.Tensor]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            key (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            int:
+                Sample rate
+            Tensor:
+                Mixture waveform
+            List of Tensors:
+                List of source waveforms
+        """
+        return self._load_sample(key)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/librispeech.py b/lib/python3.11/site-packages/torchaudio/datasets/librispeech.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cf05dbecb5cce24c91e3bbcf232935e1f6d8cd9
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/librispeech.py
@@ -0,0 +1,174 @@
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar, _load_waveform
+
+URL = "train-clean-100"
+FOLDER_IN_ARCHIVE = "LibriSpeech"
+SAMPLE_RATE = 16000
+_DATA_SUBSETS = [
+    "dev-clean",
+    "dev-other",
+    "test-clean",
+    "test-other",
+    "train-clean-100",
+    "train-clean-360",
+    "train-other-500",
+]
+_CHECKSUMS = {
+    "http://www.openslr.org/resources/12/dev-clean.tar.gz": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3",  # noqa: E501
+    "http://www.openslr.org/resources/12/dev-other.tar.gz": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-clean.tar.gz": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-other.tar.gz": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-100.tar.gz": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-360.tar.gz": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-other-500.tar.gz": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2",  # noqa: E501
+}
+
+
+def _download_librispeech(root, url):
+    base_url = "http://www.openslr.org/resources/12/"
+    ext_archive = ".tar.gz"
+
+    filename = url + ext_archive
+    archive = os.path.join(root, filename)
+    download_url = os.path.join(base_url, filename)
+    if not os.path.isfile(archive):
+        checksum = _CHECKSUMS.get(download_url, None)
+        download_url_to_file(download_url, archive, hash_prefix=checksum)
+    _extract_tar(archive)
+
+
+def _get_librispeech_metadata(
+    fileid: str, root: str, folder: str, ext_audio: str, ext_txt: str
+) -> Tuple[str, int, str, int, int, int]:
+    speaker_id, chapter_id, utterance_id = fileid.split("-")
+
+    # Get audio path and sample rate
+    fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}"
+    filepath = os.path.join(folder, speaker_id, chapter_id, f"{fileid_audio}{ext_audio}")
+
+    # Load text
+    file_text = f"{speaker_id}-{chapter_id}{ext_txt}"
+    file_text = os.path.join(root, folder, speaker_id, chapter_id, file_text)
+    with open(file_text) as ft:
+        for line in ft:
+            fileid_text, transcript = line.strip().split(" ", 1)
+            if fileid_audio == fileid_text:
+                break
+        else:
+            # Translation not found
+            raise FileNotFoundError(f"Translation not found for {fileid_audio}")
+
+    return (
+        filepath,
+        SAMPLE_RATE,
+        transcript,
+        int(speaker_id),
+        int(chapter_id),
+        int(utterance_id),
+    )
+
+
+class LIBRISPEECH(Dataset):
+    """*LibriSpeech* :cite:`7178964` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
+            ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
+            ``"train-other-500"``. (default: ``"train-clean-100"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"LibriSpeech"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _ext_txt = ".trans.txt"
+    _ext_audio = ".flac"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+    ) -> None:
+        self._url = url
+        if url not in _DATA_SUBSETS:
+            raise ValueError(f"Invalid url '{url}' given; please provide one of {_DATA_SUBSETS}.")
+
+        root = os.fspath(root)
+        self._archive = os.path.join(root, folder_in_archive)
+        self._path = os.path.join(root, folder_in_archive, url)
+
+        if not os.path.isdir(self._path):
+            if download:
+                _download_librispeech(root, url)
+            else:
+                raise RuntimeError(
+                    f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
+                )
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*/*/*" + self._ext_audio))
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, int, int, int]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+        """
+        fileid = self._walker[n]
+        return _get_librispeech_metadata(fileid, self._archive, self._url, self._ext_audio, self._ext_txt)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._archive, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/librispeech_biasing.py b/lib/python3.11/site-packages/torchaudio/datasets/librispeech_biasing.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd518cf2b69094728f8693fe2cb8a2a535bd7d3c
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/librispeech_biasing.py
@@ -0,0 +1,189 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar, _load_waveform
+
+URL = "train-clean-100"
+FOLDER_IN_ARCHIVE = "LibriSpeech"
+SAMPLE_RATE = 16000
+_DATA_SUBSETS = [
+    "dev-clean",
+    "dev-other",
+    "test-clean",
+    "test-other",
+    "train-clean-100",
+    "train-clean-360",
+    "train-other-500",
+]
+_CHECKSUMS = {
+    "http://www.openslr.org/resources/12/dev-clean.tar.gz": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3",  # noqa: E501
+    "http://www.openslr.org/resources/12/dev-other.tar.gz": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-clean.tar.gz": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-other.tar.gz": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-100.tar.gz": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-360.tar.gz": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-other-500.tar.gz": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2",  # noqa: E501
+}
+
+
+def _download_librispeech(root, url):
+    base_url = "http://www.openslr.org/resources/12/"
+    ext_archive = ".tar.gz"
+
+    filename = url + ext_archive
+    archive = os.path.join(root, filename)
+    download_url = os.path.join(base_url, filename)
+    if not os.path.isfile(archive):
+        checksum = _CHECKSUMS.get(download_url, None)
+        download_url_to_file(download_url, archive, hash_prefix=checksum)
+    _extract_tar(archive)
+
+
+def _get_librispeech_metadata(
+    fileid: str, root: str, folder: str, ext_audio: str, ext_txt: str, blist: List[str]
+) -> Tuple[str, int, str, int, int, int]:
+    blist = blist or []
+    speaker_id, chapter_id, utterance_id = fileid.split("-")
+
+    # Get audio path and sample rate
+    fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}"
+    filepath = os.path.join(folder, speaker_id, chapter_id, f"{fileid_audio}{ext_audio}")
+
+    # Load text
+    file_text = f"{speaker_id}-{chapter_id}{ext_txt}"
+    file_text = os.path.join(root, folder, speaker_id, chapter_id, file_text)
+    uttblist = []
+    with open(file_text) as ft:
+        for line in ft:
+            fileid_text, transcript = line.strip().split(" ", 1)
+            if fileid_audio == fileid_text:
+                # get utterance biasing list
+                for word in transcript.split():
+                    if word in blist and word not in uttblist:
+                        uttblist.append(word)
+                break
+        else:
+            # Translation not found
+            raise FileNotFoundError(f"Translation not found for {fileid_audio}")
+
+    return (
+        filepath,
+        SAMPLE_RATE,
+        transcript,
+        int(speaker_id),
+        int(chapter_id),
+        int(utterance_id),
+        uttblist,
+    )
+
+
+class LibriSpeechBiasing(Dataset):
+    """*LibriSpeech* :cite:`7178964` dataset with prefix-tree construction and biasing support.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
+            ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
+            ``"train-other-500"``. (default: ``"train-clean-100"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"LibriSpeech"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        blist (list, optional):
+            The list of biasing words (default: ``[]``).
+    """
+
+    _ext_txt = ".trans.txt"
+    _ext_audio = ".flac"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+        blist: List[str] = None,
+    ) -> None:
+        self._url = url
+        if url not in _DATA_SUBSETS:
+            raise ValueError(f"Invalid url '{url}' given; please provide one of {_DATA_SUBSETS}.")
+
+        root = os.fspath(root)
+        self._archive = os.path.join(root, folder_in_archive)
+        self._path = os.path.join(root, folder_in_archive, url)
+
+        if not os.path.isdir(self._path):
+            if download:
+                _download_librispeech(root, url)
+            else:
+                raise RuntimeError(
+                    f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
+                )
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*/*/*" + self._ext_audio))
+        self.blist = blist
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, int, int, int]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+            list:
+                List of biasing words in the utterance
+        """
+        fileid = self._walker[n]
+        return _get_librispeech_metadata(fileid, self._archive, self._url, self._ext_audio, self._ext_txt, self.blist)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+            list:
+                List of biasing words in the utterance
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._archive, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/libritts.py b/lib/python3.11/site-packages/torchaudio/datasets/libritts.py
new file mode 100644
index 0000000000000000000000000000000000000000..829ce9572920c31ec7a4b393379f779a7df14ea9
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/libritts.py
@@ -0,0 +1,168 @@
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+URL = "train-clean-100"
+FOLDER_IN_ARCHIVE = "LibriTTS"
+_CHECKSUMS = {
+    "http://www.openslr.org/resources/60/dev-clean.tar.gz": "da0864e1bd26debed35da8a869dd5c04dfc27682921936de7cff9c8a254dbe1a",  # noqa: E501
+    "http://www.openslr.org/resources/60/dev-other.tar.gz": "d413eda26f3a152ac7c9cf3658ef85504dfb1b625296e5fa83727f5186cca79c",  # noqa: E501
+    "http://www.openslr.org/resources/60/test-clean.tar.gz": "234ea5b25859102a87024a4b9b86641f5b5aaaf1197335c95090cde04fe9a4f5",  # noqa: E501
+    "http://www.openslr.org/resources/60/test-other.tar.gz": "33a5342094f3bba7ccc2e0500b9e72d558f72eb99328ac8debe1d9080402f10d",  # noqa: E501
+    "http://www.openslr.org/resources/60/train-clean-100.tar.gz": "c5608bf1ef74bb621935382b8399c5cdd51cd3ee47cec51f00f885a64c6c7f6b",  # noqa: E501
+    "http://www.openslr.org/resources/60/train-clean-360.tar.gz": "ce7cff44dcac46009d18379f37ef36551123a1dc4e5c8e4eb73ae57260de4886",  # noqa: E501
+    "http://www.openslr.org/resources/60/train-other-500.tar.gz": "e35f7e34deeb2e2bdfe4403d88c8fdd5fbf64865cae41f027a185a6965f0a5df",  # noqa: E501
+}
+
+
+def load_libritts_item(
+    fileid: str,
+    path: str,
+    ext_audio: str,
+    ext_original_txt: str,
+    ext_normalized_txt: str,
+) -> Tuple[Tensor, int, str, str, int, int, str]:
+    speaker_id, chapter_id, segment_id, utterance_id = fileid.split("_")
+    utterance_id = fileid
+
+    normalized_text = utterance_id + ext_normalized_txt
+    normalized_text = os.path.join(path, speaker_id, chapter_id, normalized_text)
+
+    original_text = utterance_id + ext_original_txt
+    original_text = os.path.join(path, speaker_id, chapter_id, original_text)
+
+    file_audio = utterance_id + ext_audio
+    file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)
+
+    # Load audio
+    waveform, sample_rate = torchaudio.load(file_audio)
+
+    # Load original text
+    with open(original_text) as ft:
+        original_text = ft.readline()
+
+    # Load normalized text
+    with open(normalized_text, "r") as ft:
+        normalized_text = ft.readline()
+
+    return (
+        waveform,
+        sample_rate,
+        original_text,
+        normalized_text,
+        int(speaker_id),
+        int(chapter_id),
+        utterance_id,
+    )
+
+
+class LIBRITTS(Dataset):
+    """*LibriTTS* :cite:`Zen2019LibriTTSAC` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
+            ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
+            ``"train-other-500"``. (default: ``"train-clean-100"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"LibriTTS"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _ext_original_txt = ".original.txt"
+    _ext_normalized_txt = ".normalized.txt"
+    _ext_audio = ".wav"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+    ) -> None:
+
+        if url in [
+            "dev-clean",
+            "dev-other",
+            "test-clean",
+            "test-other",
+            "train-clean-100",
+            "train-clean-360",
+            "train-other-500",
+        ]:
+
+            ext_archive = ".tar.gz"
+            base_url = "http://www.openslr.org/resources/60/"
+
+            url = os.path.join(base_url, url + ext_archive)
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        basename = os.path.basename(url)
+        archive = os.path.join(root, basename)
+
+        basename = basename.split(".")[0]
+        folder_in_archive = os.path.join(folder_in_archive, basename)
+
+        self._path = os.path.join(root, folder_in_archive)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*/*/*" + self._ext_audio))
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Original text
+            str:
+                Normalized text
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            str:
+                Utterance ID
+        """
+        fileid = self._walker[n]
+        return load_libritts_item(
+            fileid,
+            self._path,
+            self._ext_audio,
+            self._ext_original_txt,
+            self._ext_normalized_txt,
+        )
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/ljspeech.py b/lib/python3.11/site-packages/torchaudio/datasets/ljspeech.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cdaeeb0f3e67a29fc57e9d0e9ed3056d98c24df
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/ljspeech.py
@@ -0,0 +1,107 @@
+import csv
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+
+_RELEASE_CONFIGS = {
+    "release1": {
+        "folder_in_archive": "wavs",
+        "url": "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2",
+        "checksum": "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5",
+    }
+}
+
+
+class LJSPEECH(Dataset):
+    """*LJSpeech-1.1* :cite:`ljspeech17` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from.
+            (default: ``"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"wavs"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = _RELEASE_CONFIGS["release1"]["url"],
+        folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"],
+        download: bool = False,
+    ) -> None:
+
+        self._parse_filesystem(root, url, folder_in_archive, download)
+
+    def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None:
+        root = Path(root)
+
+        basename = os.path.basename(url)
+        archive = root / basename
+
+        basename = Path(basename.split(".tar.bz2")[0])
+        folder_in_archive = basename / folder_in_archive
+
+        self._path = root / folder_in_archive
+        self._metadata_path = root / basename / "metadata.csv"
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _RELEASE_CONFIGS["release1"]["checksum"]
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+
+        with open(self._metadata_path, "r", newline="") as metadata:
+            flist = csv.reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE)
+            self._flist = list(flist)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            str:
+                Normalized Transcript
+        """
+        line = self._flist[n]
+        fileid, transcript, normalized_transcript = line
+        fileid_audio = self._path / (fileid + ".wav")
+
+        # Load audio
+        waveform, sample_rate = torchaudio.load(fileid_audio)
+
+        return (
+            waveform,
+            sample_rate,
+            transcript,
+            normalized_transcript,
+        )
+
+    def __len__(self) -> int:
+        return len(self._flist)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/musdb_hq.py b/lib/python3.11/site-packages/torchaudio/datasets/musdb_hq.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd4bc9f340f3fde076ea31a683a7b41b7b3741d7
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/musdb_hq.py
@@ -0,0 +1,139 @@
+import os
+from pathlib import Path
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torchaudio
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_zip
+
+_URL = "https://zenodo.org/record/3338373/files/musdb18hq.zip"
+_CHECKSUM = "baac80d0483c61d74b2e5f3be75fa557eec52898339e6aa45c1fa48833c5d21d"
+_EXT = ".wav"
+_SAMPLE_RATE = 44100
+_VALIDATION_SET = [
+    "Actions - One Minute Smile",
+    "Clara Berry And Wooldog - Waltz For My Victims",
+    "Johnny Lokke - Promises & Lies",
+    "Patrick Talbot - A Reason To Leave",
+    "Triviul - Angelsaint",
+    "Alexander Ross - Goodbye Bolero",
+    "Fergessen - Nos Palpitants",
+    "Leaf - Summerghost",
+    "Skelpolu - Human Mistakes",
+    "Young Griffo - Pennies",
+    "ANiMAL - Rockshow",
+    "James May - On The Line",
+    "Meaxic - Take A Step",
+    "Traffic Experiment - Sirens",
+]
+
+
+class MUSDB_HQ(Dataset):
+    """*MUSDB_HQ* :cite:`MUSDB18HQ` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found
+        subset (str): Subset of the dataset to use. Options: [``"train"``, ``"test"``].
+        sources (List[str] or None, optional): Sources extract data from.
+            List can contain the following options: [``"bass"``, ``"drums"``, ``"other"``, ``"mixture"``, ``"vocals"``].
+            If ``None``, dataset consists of tracks except mixture.
+            (default: ``None``)
+        split (str or None, optional): Whether to split training set into train and validation set.
+            If ``None``, no splitting occurs. If ``train`` or ``validation``, returns respective set.
+            (default: ``None``)
+        download (bool, optional): Whether to download the dataset if it is not found at root path.
+            (default: ``False``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str,
+        sources: Optional[List[str]] = None,
+        split: Optional[str] = None,
+        download: bool = False,
+    ) -> None:
+        self.sources = ["bass", "drums", "other", "vocals"] if not sources else sources
+        self.split = split
+
+        basename = os.path.basename(_URL)
+        archive = os.path.join(root, basename)
+        basename = basename.rsplit(".", 2)[0]
+
+        if subset not in ["test", "train"]:
+            raise ValueError("`subset` must be one of ['test', 'train']")
+        if self.split is not None and self.split not in ["train", "validation"]:
+            raise ValueError("`split` must be one of ['train', 'validation']")
+        base_path = os.path.join(root, basename)
+        self._path = os.path.join(base_path, subset)
+        if not os.path.isdir(self._path):
+            if not os.path.isfile(archive):
+                if not download:
+                    raise RuntimeError("Dataset not found. Please use `download=True` to download")
+                download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM)
+            os.makedirs(base_path, exist_ok=True)
+            _extract_zip(archive, base_path)
+
+        self.names = self._collect_songs()
+
+    def _get_track(self, name, source):
+        return Path(self._path) / name / f"{source}{_EXT}"
+
+    def _load_sample(self, n: int) -> Tuple[torch.Tensor, int, int, str]:
+        name = self.names[n]
+        wavs = []
+        num_frames = None
+        for source in self.sources:
+            track = self._get_track(name, source)
+            wav, sr = torchaudio.load(str(track))
+            if sr != _SAMPLE_RATE:
+                raise ValueError(f"expected sample rate {_SAMPLE_RATE}, but got {sr}")
+            if num_frames is None:
+                num_frames = wav.shape[-1]
+            else:
+                if wav.shape[-1] != num_frames:
+                    raise ValueError("num_frames do not match across sources")
+            wavs.append(wav)
+
+        stacked = torch.stack(wavs)
+
+        return stacked, _SAMPLE_RATE, num_frames, name
+
+    def _collect_songs(self):
+        if self.split == "validation":
+            return _VALIDATION_SET
+        path = Path(self._path)
+        names = []
+        for root, folders, _ in os.walk(path, followlinks=True):
+            root = Path(root)
+            if root.name.startswith(".") or folders or root == path:
+                continue
+            name = str(root.relative_to(path))
+            if self.split and name in _VALIDATION_SET:
+                continue
+            names.append(name)
+        return sorted(names)
+
+    def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            int:
+                Num frames
+            str:
+                Track name
+        """
+        return self._load_sample(n)
+
+    def __len__(self) -> int:
+        return len(self.names)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/quesst14.py b/lib/python3.11/site-packages/torchaudio/datasets/quesst14.py
new file mode 100644
index 0000000000000000000000000000000000000000..064423c4494850f2ad8f43fb00a956be21fcb95e
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/quesst14.py
@@ -0,0 +1,136 @@
+import os
+import re
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
+import torch
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar, _load_waveform
+
+
+URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
+SAMPLE_RATE = 8000
+_CHECKSUM = "4f869e06bc066bbe9c5dde31dbd3909a0870d70291110ebbb38878dcbc2fc5e4"
+_LANGUAGES = [
+    "albanian",
+    "basque",
+    "czech",
+    "nnenglish",
+    "romanian",
+    "slovak",
+]
+
+
+class QUESST14(Dataset):
+    """*QUESST14* :cite:`Mir2015QUESST2014EQ` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found
+        subset (str): Subset of the dataset to use. Options: [``"docs"``, ``"dev"``, ``"eval"``].
+        language (str or None, optional): Language to get dataset for.
+            Options: [``None``, ``albanian``, ``basque``, ``czech``, ``nnenglish``, ``romanian``, ``slovak``].
+            If ``None``, dataset consists of all languages. (default: ``"nnenglish"``)
+        download (bool, optional): Whether to download the dataset if it is not found at root path.
+            (default: ``False``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str,
+        language: Optional[str] = "nnenglish",
+        download: bool = False,
+    ) -> None:
+        if subset not in ["docs", "dev", "eval"]:
+            raise ValueError("`subset` must be one of ['docs', 'dev', 'eval']")
+
+        if language is not None and language not in _LANGUAGES:
+            raise ValueError(f"`language` must be None or one of {str(_LANGUAGES)}")
+
+        # Get string representation of 'root'
+        root = os.fspath(root)
+
+        basename = os.path.basename(URL)
+        archive = os.path.join(root, basename)
+
+        basename = basename.rsplit(".", 2)[0]
+        self._path = os.path.join(root, basename)
+
+        if not os.path.isdir(self._path):
+            if not os.path.isfile(archive):
+                if not download:
+                    raise RuntimeError("Dataset not found. Please use `download=True` to download")
+                download_url_to_file(URL, archive, hash_prefix=_CHECKSUM)
+            _extract_tar(archive, root)
+
+        if subset == "docs":
+            self.data = filter_audio_paths(self._path, language, "language_key_utterances.lst")
+        elif subset == "dev":
+            self.data = filter_audio_paths(self._path, language, "language_key_dev.lst")
+        elif subset == "eval":
+            self.data = filter_audio_paths(self._path, language, "language_key_eval.lst")
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                File name
+        """
+        audio_path = self.data[n]
+        relpath = os.path.relpath(audio_path, self._path)
+        return relpath, SAMPLE_RATE, audio_path.with_suffix("").name
+
+    def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                File name
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+
+def filter_audio_paths(
+    path: str,
+    language: str,
+    lst_name: str,
+):
+    """Extract audio paths for the given language."""
+    audio_paths = []
+
+    path = Path(path)
+    with open(path / "scoring" / lst_name) as f:
+        for line in f:
+            audio_path, lang = line.strip().split()
+            if language is not None and lang != language:
+                continue
+            audio_path = re.sub(r"^.*?\/", "", audio_path)
+            audio_paths.append(path / audio_path)
+
+    return audio_paths
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/snips.py b/lib/python3.11/site-packages/torchaudio/datasets/snips.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b15d677f7fa1f9c1baccad7625a6fa14c73d70f
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/snips.py
@@ -0,0 +1,157 @@
+import os
+from pathlib import Path
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+
+_SAMPLE_RATE = 16000
+_SPEAKERS = [
+    "Aditi",
+    "Amy",
+    "Brian",
+    "Emma",
+    "Geraint",
+    "Ivy",
+    "Joanna",
+    "Joey",
+    "Justin",
+    "Kendra",
+    "Kimberly",
+    "Matthew",
+    "Nicole",
+    "Raveena",
+    "Russell",
+    "Salli",
+]
+
+
+def _load_labels(file: Path, subset: str):
+    """Load transcirpt, iob, and intent labels for all utterances.
+
+    Args:
+        file (Path): The path to the label file.
+        subset (str): Subset of the dataset to use. Options: [``"train"``, ``"valid"``, ``"test"``].
+
+    Returns:
+        Dictionary of labels, where the key is the filename of the audio,
+            and the label is a Tuple of transcript, Inside–outside–beginning (IOB) label, and intention label.
+    """
+    labels = {}
+    with open(file, "r") as f:
+        for line in f:
+            line = line.strip().split(" ")
+            index = line[0]
+            trans, iob_intent = " ".join(line[1:]).split("\t")
+            trans = " ".join(trans.split(" ")[1:-1])
+            iob = " ".join(iob_intent.split(" ")[1:-1])
+            intent = iob_intent.split(" ")[-1]
+            if subset in index:
+                labels[index] = (trans, iob, intent)
+    return labels
+
+
+class Snips(Dataset):
+    """*Snips* :cite:`coucke2018snips` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found.
+        subset (str): Subset of the dataset to use. Options: [``"train"``, ``"valid"``, ``"test"``].
+        speakers (List[str] or None, optional): The speaker list to include in the dataset. If ``None``,
+            include all speakers in the subset. (Default: ``None``)
+        audio_format (str, optional): The extension of the audios. Options: [``"mp3"``, ``"wav"``].
+            (Default: ``"mp3"``)
+    """
+
+    _trans_file = "all.iob.snips.txt"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str,
+        speakers: Optional[List[str]] = None,
+        audio_format: str = "mp3",
+    ) -> None:
+        if subset not in ["train", "valid", "test"]:
+            raise ValueError('`subset` must be one of ["train", "valid", "test"].')
+        if audio_format not in ["mp3", "wav"]:
+            raise ValueError('`audio_format` must be one of ["mp3", "wav].')
+
+        root = Path(root)
+        self._path = root / "SNIPS"
+        self.audio_path = self._path / subset
+        if speakers is None:
+            speakers = _SPEAKERS
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found.")
+
+        self.audio_paths = self.audio_path.glob(f"*.{audio_format}")
+        self.data = []
+        for audio_path in sorted(self.audio_paths):
+            audio_name = str(audio_path.name)
+            speaker = audio_name.split("-")[0]
+            if speaker in speakers:
+                self.data.append(audio_path)
+        transcript_path = self._path / self._trans_file
+        self.labels = _load_labels(transcript_path, subset)
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, str, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded.
+
+        Returns:
+            Tuple of the following items:
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                File name
+            str:
+                Transcription of audio
+            str:
+                Inside–outside–beginning (IOB) label of transcription
+            str:
+                Intention label of the audio.
+        """
+        audio_path = self.data[n]
+        relpath = os.path.relpath(audio_path, self._path)
+        file_name = audio_path.with_suffix("").name
+        transcript, iob, intent = self.labels[file_name]
+        return relpath, _SAMPLE_RATE, file_name, transcript, iob, intent
+
+    def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, str, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items:
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                File name
+            str:
+                Transcription of audio
+            str:
+                Inside–outside–beginning (IOB) label of transcription
+            str:
+                Intention label of the audio.
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self.data)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/speechcommands.py b/lib/python3.11/site-packages/torchaudio/datasets/speechcommands.py
new file mode 100644
index 0000000000000000000000000000000000000000..1945fc75c18b474404b733e43d50156f3c3d6652
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/speechcommands.py
@@ -0,0 +1,183 @@
+import os
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar, _load_waveform
+
+FOLDER_IN_ARCHIVE = "SpeechCommands"
+URL = "speech_commands_v0.02"
+HASH_DIVIDER = "_nohash_"
+EXCEPT_FOLDER = "_background_noise_"
+SAMPLE_RATE = 16000
+_CHECKSUMS = {
+    "http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz": "743935421bb51cccdb6bdd152e04c5c70274e935c82119ad7faeec31780d811d",  # noqa: E501
+    "http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz": "af14739ee7dc311471de98f5f9d2c9191b18aedfe957f4a6ff791c709868ff58",  # noqa: E501
+}
+
+
+def _load_list(root, *filenames):
+    output = []
+    for filename in filenames:
+        filepath = os.path.join(root, filename)
+        with open(filepath) as fileobj:
+            output += [os.path.normpath(os.path.join(root, line.strip())) for line in fileobj]
+    return output
+
+
+def _get_speechcommands_metadata(filepath: str, path: str) -> Tuple[str, int, str, str, int]:
+    relpath = os.path.relpath(filepath, path)
+    reldir, filename = os.path.split(relpath)
+    _, label = os.path.split(reldir)
+    # Besides the officially supported split method for datasets defined by "validation_list.txt"
+    # and "testing_list.txt" over "speech_commands_v0.0x.tar.gz" archives, an alternative split
+    # method referred to in paragraph 2-3 of Section 7.1, references 13 and 14 of the original
+    # paper, and the checksums file from the tensorflow_datasets package [1] is also supported.
+    # Some filenames in those "speech_commands_test_set_v0.0x.tar.gz" archives have the form
+    # "xxx.wav.wav", so file extensions twice needs to be stripped twice.
+    # [1] https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/url_checksums/speech_commands.txt
+    speaker, _ = os.path.splitext(filename)
+    speaker, _ = os.path.splitext(speaker)
+
+    speaker_id, utterance_number = speaker.split(HASH_DIVIDER)
+    utterance_number = int(utterance_number)
+
+    return relpath, SAMPLE_RATE, label, speaker_id, utterance_number
+
+
+class SPEECHCOMMANDS(Dataset):
+    """*Speech Commands* :cite:`speechcommandsv2` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"speech_commands_v0.01"`` and ``"speech_commands_v0.02"``
+            (default: ``"speech_commands_v0.02"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"SpeechCommands"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        subset (str or None, optional):
+            Select a subset of the dataset [None, "training", "validation", "testing"]. None means
+            the whole dataset. "validation" and "testing" are defined in "validation_list.txt" and
+            "testing_list.txt", respectively, and "training" is the rest. Details for the files
+            "validation_list.txt" and "testing_list.txt" are explained in the README of the dataset
+            and in the introduction of Section 7 of the original paper and its reference 12. The
+            original paper can be found `here <https://arxiv.org/pdf/1804.03209.pdf>`_. (Default: ``None``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+        subset: Optional[str] = None,
+    ) -> None:
+
+        if subset is not None and subset not in ["training", "validation", "testing"]:
+            raise ValueError("When `subset` is not None, it must be one of ['training', 'validation', 'testing'].")
+
+        if url in [
+            "speech_commands_v0.01",
+            "speech_commands_v0.02",
+        ]:
+            base_url = "http://download.tensorflow.org/data/"
+            ext_archive = ".tar.gz"
+
+            url = os.path.join(base_url, url + ext_archive)
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+        self._archive = os.path.join(root, folder_in_archive)
+
+        basename = os.path.basename(url)
+        archive = os.path.join(root, basename)
+
+        basename = basename.rsplit(".", 2)[0]
+        folder_in_archive = os.path.join(folder_in_archive, basename)
+
+        self._path = os.path.join(root, folder_in_archive)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive, self._path)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+
+        if subset == "validation":
+            self._walker = _load_list(self._path, "validation_list.txt")
+        elif subset == "testing":
+            self._walker = _load_list(self._path, "testing_list.txt")
+        elif subset == "training":
+            excludes = set(_load_list(self._path, "validation_list.txt", "testing_list.txt"))
+            walker = sorted(str(p) for p in Path(self._path).glob("*/*.wav"))
+            self._walker = [
+                w
+                for w in walker
+                if HASH_DIVIDER in w and EXCEPT_FOLDER not in w and os.path.normpath(w) not in excludes
+            ]
+        else:
+            walker = sorted(str(p) for p in Path(self._path).glob("*/*.wav"))
+            self._walker = [w for w in walker if HASH_DIVIDER in w and EXCEPT_FOLDER not in w]
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, str, int]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to the audio
+            int:
+                Sample rate
+            str:
+                Label
+            str:
+                Speaker ID
+            int:
+                Utterance number
+        """
+        fileid = self._walker[n]
+        return _get_speechcommands_metadata(fileid, self._archive)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Label
+            str:
+                Speaker ID
+            int:
+                Utterance number
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._archive, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/tedlium.py b/lib/python3.11/site-packages/torchaudio/datasets/tedlium.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e7d22195a772d18770f6db3253d83672743c81c
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/tedlium.py
@@ -0,0 +1,218 @@
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+
+_RELEASE_CONFIGS = {
+    "release1": {
+        "folder_in_archive": "TEDLIUM_release1",
+        "url": "http://www.openslr.org/resources/7/TEDLIUM_release1.tar.gz",
+        "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27",
+        "data_path": "",
+        "subset": "train",
+        "supported_subsets": ["train", "test", "dev"],
+        "dict": "TEDLIUM.150K.dic",
+    },
+    "release2": {
+        "folder_in_archive": "TEDLIUM_release2",
+        "url": "http://www.openslr.org/resources/19/TEDLIUM_release2.tar.gz",
+        "checksum": "93281b5fcaaae5c88671c9d000b443cb3c7ea3499ad12010b3934ca41a7b9c58",
+        "data_path": "",
+        "subset": "train",
+        "supported_subsets": ["train", "test", "dev"],
+        "dict": "TEDLIUM.152k.dic",
+    },
+    "release3": {
+        "folder_in_archive": "TEDLIUM_release-3",
+        "url": "http://www.openslr.org/resources/51/TEDLIUM_release-3.tgz",
+        "checksum": "ad1e454d14d1ad550bc2564c462d87c7a7ec83d4dc2b9210f22ab4973b9eccdb",
+        "data_path": "data/",
+        "subset": "train",
+        "supported_subsets": ["train", "test", "dev"],
+        "dict": "TEDLIUM.152k.dic",
+    },
+}
+
+
+class TEDLIUM(Dataset):
+    """*Tedlium* :cite:`rousseau2012tedlium` dataset (releases 1,2 and 3).
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        release (str, optional): Release version.
+            Allowed values are ``"release1"``, ``"release2"`` or ``"release3"``.
+            (default: ``"release1"``).
+        subset (str, optional): The subset of dataset to use. Valid options are ``"train"``, ``"dev"``,
+            and ``"test"``. Defaults to ``"train"``.
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        audio_ext (str, optional): extension for audio file (default: ``".sph"``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        release: str = "release1",
+        subset: str = "train",
+        download: bool = False,
+        audio_ext: str = ".sph",
+    ) -> None:
+        self._ext_audio = audio_ext
+        if release in _RELEASE_CONFIGS.keys():
+            folder_in_archive = _RELEASE_CONFIGS[release]["folder_in_archive"]
+            url = _RELEASE_CONFIGS[release]["url"]
+            subset = subset if subset else _RELEASE_CONFIGS[release]["subset"]
+        else:
+            # Raise warning
+            raise RuntimeError(
+                "The release {} does not match any of the supported tedlium releases{} ".format(
+                    release,
+                    _RELEASE_CONFIGS.keys(),
+                )
+            )
+        if subset not in _RELEASE_CONFIGS[release]["supported_subsets"]:
+            # Raise warning
+            raise RuntimeError(
+                "The subset {} does not match any of the supported tedlium subsets{} ".format(
+                    subset,
+                    _RELEASE_CONFIGS[release]["supported_subsets"],
+                )
+            )
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        basename = os.path.basename(url)
+        archive = os.path.join(root, basename)
+
+        basename = basename.split(".")[0]
+
+        if release == "release3":
+            if subset == "train":
+                self._path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["data_path"])
+            else:
+                self._path = os.path.join(root, folder_in_archive, "legacy", subset)
+        else:
+            self._path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["data_path"], subset)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _RELEASE_CONFIGS[release]["checksum"]
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+
+        # Create list for all samples
+        self._filelist = []
+        stm_path = os.path.join(self._path, "stm")
+        for file in sorted(os.listdir(stm_path)):
+            if file.endswith(".stm"):
+                stm_path = os.path.join(self._path, "stm", file)
+                with open(stm_path) as f:
+                    l = len(f.readlines())
+                    file = file.replace(".stm", "")
+                    self._filelist.extend((file, line) for line in range(l))
+        # Create dict path for later read
+        self._dict_path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["dict"])
+        self._phoneme_dict = None
+
+    def _load_tedlium_item(self, fileid: str, line: int, path: str) -> Tuple[Tensor, int, str, int, int, int]:
+        """Loads a TEDLIUM dataset sample given a file name and corresponding sentence name.
+
+        Args:
+            fileid (str): File id to identify both text and audio files corresponding to the sample
+            line (int): Line identifier for the sample inside the text file
+            path (str): Dataset root path
+
+        Returns:
+            (Tensor, int, str, int, int, int):
+            ``(waveform, sample_rate, transcript, talk_id, speaker_id, identifier)``
+        """
+        transcript_path = os.path.join(path, "stm", fileid)
+        with open(transcript_path + ".stm") as f:
+            transcript = f.readlines()[line]
+            talk_id, _, speaker_id, start_time, end_time, identifier, transcript = transcript.split(" ", 6)
+
+        wave_path = os.path.join(path, "sph", fileid)
+        waveform, sample_rate = self._load_audio(wave_path + self._ext_audio, start_time=start_time, end_time=end_time)
+
+        return (waveform, sample_rate, transcript, talk_id, speaker_id, identifier)
+
+    def _load_audio(self, path: str, start_time: float, end_time: float, sample_rate: int = 16000) -> [Tensor, int]:
+        """Default load function used in TEDLIUM dataset, you can overwrite this function to customize functionality
+        and load individual sentences from a full ted audio talk file.
+
+        Args:
+            path (str): Path to audio file
+            start_time (int): Time in seconds where the sample sentence stars
+            end_time (int): Time in seconds where the sample sentence finishes
+            sample_rate (float, optional): Sampling rate
+
+        Returns:
+            [Tensor, int]: Audio tensor representation and sample rate
+        """
+        start_time = int(float(start_time) * sample_rate)
+        end_time = int(float(end_time) * sample_rate)
+
+        kwargs = {"frame_offset": start_time, "num_frames": end_time - start_time}
+
+        return torchaudio.load(path, **kwargs)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Talk ID
+            int:
+                Speaker ID
+            int:
+                Identifier
+        """
+        fileid, line = self._filelist[n]
+        return self._load_tedlium_item(fileid, line, self._path)
+
+    def __len__(self) -> int:
+        """TEDLIUM dataset custom function overwritting len default behaviour.
+
+        Returns:
+            int: TEDLIUM dataset length
+        """
+        return len(self._filelist)
+
+    @property
+    def phoneme_dict(self):
+        """dict[str, tuple[str]]: Phonemes. Mapping from word to tuple of phonemes.
+        Note that some words have empty phonemes.
+        """
+        # Read phoneme dictionary
+        if not self._phoneme_dict:
+            self._phoneme_dict = {}
+            with open(self._dict_path, "r", encoding="utf-8") as f:
+                for line in f.readlines():
+                    content = line.strip().split()
+                    self._phoneme_dict[content[0]] = tuple(content[1:])  # content[1:] can be empty list
+        return self._phoneme_dict.copy()
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/utils.py b/lib/python3.11/site-packages/torchaudio/datasets/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4599f83aae535d5c4126b5d0bab4ed325f494f3
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/utils.py
@@ -0,0 +1,54 @@
+import logging
+import os
+import tarfile
+import zipfile
+from typing import Any, List, Optional
+
+import torchaudio
+
+_LG = logging.getLogger(__name__)
+
+
+def _extract_tar(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
+    if to_path is None:
+        to_path = os.path.dirname(from_path)
+    with tarfile.open(from_path, "r") as tar:
+        files = []
+        for file_ in tar:  # type: Any
+            file_path = os.path.join(to_path, file_.name)
+            if file_.isfile():
+                files.append(file_path)
+                if os.path.exists(file_path):
+                    _LG.info("%s already extracted.", file_path)
+                    if not overwrite:
+                        continue
+            tar.extract(file_, to_path)
+        return files
+
+
+def _extract_zip(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
+    if to_path is None:
+        to_path = os.path.dirname(from_path)
+
+    with zipfile.ZipFile(from_path, "r") as zfile:
+        files = zfile.namelist()
+        for file_ in files:
+            file_path = os.path.join(to_path, file_)
+            if os.path.exists(file_path):
+                _LG.info("%s already extracted.", file_path)
+                if not overwrite:
+                    continue
+            zfile.extract(file_, to_path)
+    return files
+
+
+def _load_waveform(
+    root: str,
+    filename: str,
+    exp_sample_rate: int,
+):
+    path = os.path.join(root, filename)
+    waveform, sample_rate = torchaudio.load(path)
+    if exp_sample_rate != sample_rate:
+        raise ValueError(f"sample rate should be {exp_sample_rate}, but got {sample_rate}")
+    return waveform
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/vctk.py b/lib/python3.11/site-packages/torchaudio/datasets/vctk.py
new file mode 100644
index 0000000000000000000000000000000000000000..3195b9b4276b643e934baadc26c872fc690383df
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/vctk.py
@@ -0,0 +1,143 @@
+import os
+from typing import Tuple
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_zip
+
+URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"
+_CHECKSUMS = {
+    "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip": "f96258be9fdc2cbff6559541aae7ea4f59df3fcaf5cf963aae5ca647357e359c"  # noqa: E501
+}
+
+
+SampleType = Tuple[Tensor, int, str, str, str]
+
+
+class VCTK_092(Dataset):
+    """*VCTK 0.92* :cite:`yamagishi2019vctk` dataset
+
+    Args:
+        root (str): Root directory where the dataset's top level directory is found.
+        mic_id (str, optional): Microphone ID. Either ``"mic1"`` or ``"mic2"``. (default: ``"mic2"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        url (str, optional): The URL to download the dataset from.
+            (default: ``"https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"``)
+        audio_ext (str, optional): Custom audio extension if dataset is converted to non-default audio format.
+
+    Note:
+        * All the speeches from speaker ``p315`` will be skipped due to the lack of the corresponding text files.
+        * All the speeches from ``p280`` will be skipped for ``mic_id="mic2"`` due to the lack of the audio files.
+        * Some of the speeches from speaker ``p362`` will be skipped due to the lack of  the audio files.
+        * See Also: https://datashare.is.ed.ac.uk/handle/10283/3443
+    """
+
+    def __init__(
+        self,
+        root: str,
+        mic_id: str = "mic2",
+        download: bool = False,
+        url: str = URL,
+        audio_ext=".flac",
+    ):
+        if mic_id not in ["mic1", "mic2"]:
+            raise RuntimeError(f'`mic_id` has to be either "mic1" or "mic2". Found: {mic_id}')
+
+        archive = os.path.join(root, "VCTK-Corpus-0.92.zip")
+
+        self._path = os.path.join(root, "VCTK-Corpus-0.92")
+        self._txt_dir = os.path.join(self._path, "txt")
+        self._audio_dir = os.path.join(self._path, "wav48_silence_trimmed")
+        self._mic_id = mic_id
+        self._audio_ext = audio_ext
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_zip(archive, self._path)
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
+
+        # Extracting speaker IDs from the folder structure
+        self._speaker_ids = sorted(os.listdir(self._txt_dir))
+        self._sample_ids = []
+
+        """
+        Due to some insufficient data complexity in the 0.92 version of this dataset,
+        we start traversing the audio folder structure in accordance with the text folder.
+        As some of the audio files are missing of either ``mic_1`` or ``mic_2`` but the
+        text is present for the same, we first check for the existence of the audio file
+        before adding it to the ``sample_ids`` list.
+
+        Once the ``audio_ids`` are loaded into memory we can quickly access the list for
+        different parameters required by the user.
+        """
+        for speaker_id in self._speaker_ids:
+            if speaker_id == "p280" and mic_id == "mic2":
+                continue
+            utterance_dir = os.path.join(self._txt_dir, speaker_id)
+            for utterance_file in sorted(f for f in os.listdir(utterance_dir) if f.endswith(".txt")):
+                utterance_id = os.path.splitext(utterance_file)[0]
+                audio_path_mic = os.path.join(
+                    self._audio_dir,
+                    speaker_id,
+                    f"{utterance_id}_{mic_id}{self._audio_ext}",
+                )
+                if speaker_id == "p362" and not os.path.isfile(audio_path_mic):
+                    continue
+                self._sample_ids.append(utterance_id.split("_"))
+
+    def _load_text(self, file_path) -> str:
+        with open(file_path) as file_path:
+            return file_path.readlines()[0]
+
+    def _load_audio(self, file_path) -> Tuple[Tensor, int]:
+        return torchaudio.load(file_path)
+
+    def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> SampleType:
+        transcript_path = os.path.join(self._txt_dir, speaker_id, f"{speaker_id}_{utterance_id}.txt")
+        audio_path = os.path.join(
+            self._audio_dir,
+            speaker_id,
+            f"{speaker_id}_{utterance_id}_{mic_id}{self._audio_ext}",
+        )
+
+        # Reading text
+        transcript = self._load_text(transcript_path)
+
+        # Reading FLAC
+        waveform, sample_rate = self._load_audio(audio_path)
+
+        return (waveform, sample_rate, transcript, speaker_id, utterance_id)
+
+    def __getitem__(self, n: int) -> SampleType:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            str:
+                Speaker ID
+            std:
+                Utterance ID
+        """
+        speaker_id, utterance_id = self._sample_ids[n]
+        return self._load_sample(speaker_id, utterance_id, self._mic_id)
+
+    def __len__(self) -> int:
+        return len(self._sample_ids)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/voxceleb1.py b/lib/python3.11/site-packages/torchaudio/datasets/voxceleb1.py
new file mode 100644
index 0000000000000000000000000000000000000000..5112fff0898a88adb1d2c33acf9bdd905ca883f3
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/voxceleb1.py
@@ -0,0 +1,309 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_zip, _load_waveform
+
+
+SAMPLE_RATE = 16000
+_ARCHIVE_CONFIGS = {
+    "dev": {
+        "archive_name": "vox1_dev_wav.zip",
+        "urls": [
+            "https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa",
+            "https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partab",
+            "https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partac",
+            "https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partad",
+        ],
+        "checksums": [
+            "21ec6ca843659ebc2fdbe04b530baa4f191ad4b0971912672d92c158f32226a0",
+            "311d21e0c8cbf33573a4fce6c80e5a279d80736274b381c394319fc557159a04",
+            "92b64465f2b2a3dc0e4196ae8dd6828cbe9ddd1f089419a11e4cbfe2e1750df0",
+            "00e6190c770b27f27d2a3dd26ee15596b17066b715ac111906861a7d09a211a5",
+        ],
+    },
+    "test": {
+        "archive_name": "vox1_test_wav.zip",
+        "url": "https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_test_wav.zip",
+        "checksum": "8de57f347fe22b2c24526e9f444f689ecf5096fc2a92018cf420ff6b5b15eaea",
+    },
+}
+_IDEN_SPLIT_URL = "https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/iden_split.txt"
+_VERI_TEST_URL = "https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test.txt"
+
+
+def _download_extract_wavs(root: str):
+    for archive in ["dev", "test"]:
+        archive_name = _ARCHIVE_CONFIGS[archive]["archive_name"]
+        archive_path = os.path.join(root, archive_name)
+        # The zip file of dev data is splited to 4 chunks.
+        # Download and combine them into one file before extraction.
+        if archive == "dev":
+            urls = _ARCHIVE_CONFIGS[archive]["urls"]
+            checksums = _ARCHIVE_CONFIGS[archive]["checksums"]
+            with open(archive_path, "wb") as f:
+                for url, checksum in zip(urls, checksums):
+                    file_path = os.path.join(root, os.path.basename(url))
+                    download_url_to_file(url, file_path, hash_prefix=checksum)
+                    with open(file_path, "rb") as f_split:
+                        f.write(f_split.read())
+        else:
+            url = _ARCHIVE_CONFIGS[archive]["url"]
+            checksum = _ARCHIVE_CONFIGS[archive]["checksum"]
+            download_url_to_file(url, archive_path, hash_prefix=checksum)
+        _extract_zip(archive_path)
+
+
+def _get_flist(root: str, file_path: str, subset: str) -> List[str]:
+    f_list = []
+    if subset == "train":
+        index = 1
+    elif subset == "dev":
+        index = 2
+    else:
+        index = 3
+    with open(file_path, "r") as f:
+        for line in f:
+            id, path = line.split()
+            if int(id) == index:
+                f_list.append(path)
+    return sorted(f_list)
+
+
+def _get_paired_flist(root: str, veri_test_path: str):
+    f_list = []
+    with open(veri_test_path, "r") as f:
+        for line in f:
+            label, path1, path2 = line.split()
+            f_list.append((label, path1, path2))
+    return f_list
+
+
+def _get_file_id(file_path: str, _ext_audio: str):
+    speaker_id, youtube_id, utterance_id = file_path.split("/")[-3:]
+    utterance_id = utterance_id.replace(_ext_audio, "")
+    file_id = "-".join([speaker_id, youtube_id, utterance_id])
+    return file_id
+
+
+class VoxCeleb1(Dataset):
+    """*VoxCeleb1* :cite:`nagrani2017voxceleb` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (Default: ``False``).
+    """
+
+    _ext_audio = ".wav"
+
+    def __init__(self, root: Union[str, Path], download: bool = False) -> None:
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+        self._path = os.path.join(root, "wav")
+        if not os.path.isdir(self._path):
+            if not download:
+                raise RuntimeError(
+                    f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
+                )
+            _download_extract_wavs(root)
+
+    def get_metadata(self, n: int):
+        raise NotImplementedError
+
+    def __getitem__(self, n: int):
+        raise NotImplementedError
+
+    def __len__(self) -> int:
+        raise NotImplementedError
+
+
+class VoxCeleb1Identification(VoxCeleb1):
+    """*VoxCeleb1* :cite:`nagrani2017voxceleb` dataset for speaker identification task.
+
+    Each data sample contains the waveform, sample rate, speaker id, and the file id.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        subset (str, optional): Subset of the dataset to use. Options: ["train", "dev", "test"]. (Default: ``"train"``)
+        meta_url (str, optional): The url of meta file that contains the list of subset labels and file paths.
+            The format of each row is ``subset file_path". For example: ``1 id10006/nLEBBc9oIFs/00003.wav``.
+            ``1``, ``2``, ``3`` mean ``train``, ``dev``, and ``test`` subest, respectively.
+            (Default: ``"https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/iden_split.txt"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (Default: ``False``).
+
+    Note:
+        The file structure of `VoxCeleb1Identification` dataset is as follows:
+
+        └─ root/
+
+         └─ wav/
+
+         └─ speaker_id folders
+
+        Users who pre-downloaded the ``"vox1_dev_wav.zip"`` and ``"vox1_test_wav.zip"`` files need to move
+        the extracted files into the same ``root`` directory.
+    """
+
+    def __init__(
+        self, root: Union[str, Path], subset: str = "train", meta_url: str = _IDEN_SPLIT_URL, download: bool = False
+    ) -> None:
+        super().__init__(root, download)
+        if subset not in ["train", "dev", "test"]:
+            raise ValueError("`subset` must be one of ['train', 'dev', 'test']")
+        # download the iden_split.txt to get the train, dev, test lists.
+        meta_list_path = os.path.join(root, os.path.basename(meta_url))
+        if not os.path.exists(meta_list_path):
+            download_url_to_file(meta_url, meta_list_path)
+        self._flist = _get_flist(self._path, meta_list_path, subset)
+
+    def get_metadata(self, n: int) -> Tuple[str, int, int, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            int:
+                Speaker ID
+            str:
+                File ID
+        """
+        file_path = self._flist[n]
+        file_id = _get_file_id(file_path, self._ext_audio)
+        speaker_id = file_id.split("-")[0]
+        speaker_id = int(speaker_id[3:])
+        return file_path, SAMPLE_RATE, speaker_id, file_id
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            int:
+                Speaker ID
+            str:
+                File ID
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._flist)
+
+
+class VoxCeleb1Verification(VoxCeleb1):
+    """*VoxCeleb1* :cite:`nagrani2017voxceleb` dataset for speaker verification task.
+
+    Each data sample contains a pair of waveforms, sample rate, the label indicating if they are
+    from the same speaker, and the file ids.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        meta_url (str, optional): The url of meta file that contains a list of utterance pairs
+            and the corresponding labels. The format of each row is ``label file_path1 file_path2".
+            For example: ``1 id10270/x6uYqmx31kE/00001.wav id10270/8jEAjG6SegY/00008.wav``.
+            ``1`` means the two utterances are from the same speaker, ``0`` means not.
+            (Default: ``"https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test.txt"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (Default: ``False``).
+
+    Note:
+        The file structure of `VoxCeleb1Verification` dataset is as follows:
+
+        └─ root/
+
+         └─ wav/
+
+         └─ speaker_id folders
+
+        Users who pre-downloaded the ``"vox1_dev_wav.zip"`` and ``"vox1_test_wav.zip"`` files need to move
+        the extracted files into the same ``root`` directory.
+    """
+
+    def __init__(self, root: Union[str, Path], meta_url: str = _VERI_TEST_URL, download: bool = False) -> None:
+        super().__init__(root, download)
+        # download the veri_test.txt to get the list of training pairs and labels.
+        meta_list_path = os.path.join(root, os.path.basename(meta_url))
+        if not os.path.exists(meta_list_path):
+            download_url_to_file(meta_url, meta_list_path)
+        self._flist = _get_paired_flist(self._path, meta_list_path)
+
+    def get_metadata(self, n: int) -> Tuple[str, str, int, int, str, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepaths instead of waveforms,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio file of speaker 1
+            str:
+                Path to audio file of speaker 2
+            int:
+                Sample rate
+            int:
+                Label
+            str:
+                File ID of speaker 1
+            str:
+                File ID of speaker 2
+        """
+        label, file_path_spk1, file_path_spk2 = self._flist[n]
+        label = int(label)
+        file_id_spk1 = _get_file_id(file_path_spk1, self._ext_audio)
+        file_id_spk2 = _get_file_id(file_path_spk2, self._ext_audio)
+        return file_path_spk1, file_path_spk2, SAMPLE_RATE, label, file_id_spk1, file_id_spk2
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, Tensor, int, int, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded.
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform of speaker 1
+            Tensor:
+                Waveform of speaker 2
+            int:
+                Sample rate
+            int:
+                Label
+            str:
+                File ID of speaker 1
+            str:
+                File ID of speaker 2
+        """
+        metadata = self.get_metadata(n)
+        waveform_spk1 = _load_waveform(self._path, metadata[0], metadata[2])
+        waveform_spk2 = _load_waveform(self._path, metadata[1], metadata[2])
+        return (waveform_spk1, waveform_spk2) + metadata[2:]
+
+    def __len__(self) -> int:
+        return len(self._flist)
diff --git a/lib/python3.11/site-packages/torchaudio/datasets/yesno.py b/lib/python3.11/site-packages/torchaudio/datasets/yesno.py
new file mode 100644
index 0000000000000000000000000000000000000000..baad08f1593a49af5f95658e8d4b67be6d3deeb9
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/datasets/yesno.py
@@ -0,0 +1,89 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+
+_RELEASE_CONFIGS = {
+    "release1": {
+        "folder_in_archive": "waves_yesno",
+        "url": "http://www.openslr.org/resources/1/waves_yesno.tar.gz",
+        "checksum": "c3f49e0cca421f96b75b41640749167b52118f232498667ca7a5f9416aef8e73",
+    }
+}
+
+
+class YESNO(Dataset):
+    """*YesNo* :cite:`YesNo` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from.
+            (default: ``"http://www.openslr.org/resources/1/waves_yesno.tar.gz"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"waves_yesno"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = _RELEASE_CONFIGS["release1"]["url"],
+        folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"],
+        download: bool = False,
+    ) -> None:
+
+        self._parse_filesystem(root, url, folder_in_archive, download)
+
+    def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None:
+        root = Path(root)
+        archive = os.path.basename(url)
+        archive = root / archive
+
+        self._path = root / folder_in_archive
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _RELEASE_CONFIGS["release1"]["checksum"]
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*.wav"))
+
+    def _load_item(self, fileid: str, path: str):
+        labels = [int(c) for c in fileid.split("_")]
+        file_audio = os.path.join(path, fileid + ".wav")
+        waveform, sample_rate = torchaudio.load(file_audio)
+        return waveform, sample_rate, labels
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            List[int]:
+                labels
+        """
+        fileid = self._walker[n]
+        item = self._load_item(fileid, self._path)
+        return item
+
+    def __len__(self) -> int:
+        return len(self._walker)
diff --git a/lib/python3.11/site-packages/torchaudio/functional/__init__.py b/lib/python3.11/site-packages/torchaudio/functional/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b866977c67c9cbcb6098c39ea5c26ed7b19d5979
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/functional/__init__.py
@@ -0,0 +1,127 @@
+from ._alignment import forced_align, merge_tokens, TokenSpan
+from .filtering import (
+    allpass_biquad,
+    band_biquad,
+    bandpass_biquad,
+    bandreject_biquad,
+    bass_biquad,
+    biquad,
+    contrast,
+    dcshift,
+    deemph_biquad,
+    dither,
+    equalizer_biquad,
+    filtfilt,
+    flanger,
+    gain,
+    highpass_biquad,
+    lfilter,
+    lowpass_biquad,
+    overdrive,
+    phaser,
+    riaa_biquad,
+    treble_biquad,
+    vad,
+)
+from .functional import (
+    add_noise,
+    amplitude_to_DB,
+    apply_beamforming,
+    apply_codec,
+    compute_deltas,
+    convolve,
+    create_dct,
+    DB_to_amplitude,
+    deemphasis,
+    detect_pitch_frequency,
+    edit_distance,
+    fftconvolve,
+    frechet_distance,
+    griffinlim,
+    inverse_spectrogram,
+    linear_fbanks,
+    loudness,
+    mask_along_axis,
+    mask_along_axis_iid,
+    melscale_fbanks,
+    mu_law_decoding,
+    mu_law_encoding,
+    mvdr_weights_rtf,
+    mvdr_weights_souden,
+    phase_vocoder,
+    pitch_shift,
+    preemphasis,
+    psd,
+    resample,
+    rnnt_loss,
+    rtf_evd,
+    rtf_power,
+    sliding_window_cmn,
+    spectral_centroid,
+    spectrogram,
+    speed,
+)
+
+__all__ = [
+    "amplitude_to_DB",
+    "compute_deltas",
+    "create_dct",
+    "melscale_fbanks",
+    "linear_fbanks",
+    "DB_to_amplitude",
+    "loudness",
+    "detect_pitch_frequency",
+    "griffinlim",
+    "mask_along_axis",
+    "mask_along_axis_iid",
+    "mu_law_encoding",
+    "mu_law_decoding",
+    "phase_vocoder",
+    "sliding_window_cmn",
+    "spectrogram",
+    "inverse_spectrogram",
+    "spectral_centroid",
+    "allpass_biquad",
+    "band_biquad",
+    "bandpass_biquad",
+    "bandreject_biquad",
+    "bass_biquad",
+    "biquad",
+    "contrast",
+    "dither",
+    "dcshift",
+    "deemph_biquad",
+    "equalizer_biquad",
+    "filtfilt",
+    "flanger",
+    "forced_align",
+    "merge_tokens",
+    "TokenSpan",
+    "gain",
+    "highpass_biquad",
+    "lfilter",
+    "lowpass_biquad",
+    "overdrive",
+    "phaser",
+    "riaa_biquad",
+    "treble_biquad",
+    "vad",
+    "apply_codec",
+    "resample",
+    "edit_distance",
+    "pitch_shift",
+    "rnnt_loss",
+    "psd",
+    "mvdr_weights_souden",
+    "mvdr_weights_rtf",
+    "rtf_evd",
+    "rtf_power",
+    "apply_beamforming",
+    "fftconvolve",
+    "convolve",
+    "add_noise",
+    "speed",
+    "preemphasis",
+    "deemphasis",
+    "frechet_distance",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/functional/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/functional/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..52487b264c905e49767250498b16a602b1571c1c
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/functional/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/functional/__pycache__/_alignment.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/functional/__pycache__/_alignment.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c3994d22ee5a79005a3b48d4ae9f2218a898b1a2
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/functional/__pycache__/_alignment.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/functional/__pycache__/filtering.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/functional/__pycache__/filtering.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a5a3cb1ba2aa3a9428eead729b26e52ea8516d6c
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/functional/__pycache__/filtering.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/functional/_alignment.py b/lib/python3.11/site-packages/torchaudio/functional/_alignment.py
new file mode 100644
index 0000000000000000000000000000000000000000..70d1e995e41b8e6817b2dcc25f6a2d0ec3d83752
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/functional/_alignment.py
@@ -0,0 +1,128 @@
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import torch
+from torch import Tensor
+from torchaudio._extension import fail_if_no_align
+
+__all__ = []
+
+
+@fail_if_no_align
+def forced_align(
+    log_probs: Tensor,
+    targets: Tensor,
+    input_lengths: Optional[Tensor] = None,
+    target_lengths: Optional[Tensor] = None,
+    blank: int = 0,
+) -> Tuple[Tensor, Tensor]:
+    r"""Align a CTC label sequence to an emission.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        log_probs (Tensor): log probability of CTC emission output.
+            Tensor of shape `(B, T, C)`. where `B` is the batch size, `T` is the input length,
+            `C` is the number of characters in alphabet including blank.
+        targets (Tensor): Target sequence. Tensor of shape `(B, L)`,
+            where `L` is the target length.
+        input_lengths (Tensor or None, optional):
+            Lengths of the inputs (max value must each be <= `T`). 1-D Tensor of shape `(B,)`.
+        target_lengths (Tensor or None, optional):
+            Lengths of the targets. 1-D Tensor of shape `(B,)`.
+        blank_id (int, optional): The index of blank symbol in CTC emission. (Default: 0)
+
+    Returns:
+        Tuple(Tensor, Tensor):
+            Tensor: Label for each time step in the alignment path computed using forced alignment.
+
+            Tensor: Log probability scores of the labels for each time step.
+
+    Note:
+        The sequence length of `log_probs` must satisfy:
+
+
+        .. math::
+            L_{\text{log\_probs}} \ge L_{\text{label}} + N_{\text{repeat}}
+
+        where :math:`N_{\text{repeat}}` is the number of consecutively repeated tokens.
+        For example, in str `"aabbc"`, the number of repeats are `2`.
+
+    Note:
+        The current version only supports ``batch_size==1``.
+    """
+    if blank in targets:
+        raise ValueError(f"targets Tensor shouldn't contain blank index. Found {targets}.")
+    if torch.max(targets) >= log_probs.shape[-1]:
+        raise ValueError("targets values must be less than the CTC dimension")
+
+    if input_lengths is None:
+        batch_size, length = log_probs.size(0), log_probs.size(1)
+        input_lengths = torch.full((batch_size,), length, dtype=torch.int64, device=log_probs.device)
+    if target_lengths is None:
+        batch_size, length = targets.size(0), targets.size(1)
+        target_lengths = torch.full((batch_size,), length, dtype=torch.int64, device=targets.device)
+
+    # For TorchScript compatibility
+    assert input_lengths is not None
+    assert target_lengths is not None
+
+    paths, scores = torch.ops.torchaudio.forced_align(log_probs, targets, input_lengths, target_lengths, blank)
+    return paths, scores
+
+
+@dataclass
+class TokenSpan:
+    """TokenSpan()
+    Token with time stamps and score. Returned by :py:func:`merge_tokens`.
+    """
+
+    token: int
+    """The token"""
+    start: int
+    """The start time (inclusive) in emission time axis."""
+    end: int
+    """The end time (exclusive) in emission time axis."""
+    score: float
+    """The score of the this token."""
+
+    def __len__(self) -> int:
+        """Returns the time span"""
+        return self.end - self.start
+
+
+def merge_tokens(tokens: Tensor, scores: Tensor, blank: int = 0) -> List[TokenSpan]:
+    """Removes repeated tokens and blank tokens from the given CTC token sequence.
+
+    Args:
+        tokens (Tensor): Alignment tokens (unbatched) returned from :py:func:`forced_align`.
+            Shape: `(time, )`.
+        scores (Tensor): Alignment scores (unbatched) returned from :py:func:`forced_align`.
+            Shape: `(time, )`. When computing the token-size score, the given score is averaged
+            across the corresponding time span.
+
+    Returns:
+        list of TokenSpan
+
+    Example:
+        >>> aligned_tokens, scores = forced_align(emission, targets, input_lengths, target_lengths)
+        >>> token_spans = merge_tokens(aligned_tokens[0], scores[0])
+    """
+    if tokens.ndim != 1 or scores.ndim != 1:
+        raise ValueError("`tokens` and `scores` must be 1D Tensor.")
+    if len(tokens) != len(scores):
+        raise ValueError("`tokens` and `scores` must be the same length.")
+
+    diff = torch.diff(
+        tokens, prepend=torch.tensor([-1], device=tokens.device), append=torch.tensor([-1], device=tokens.device)
+    )
+    changes_wo_blank = torch.nonzero((diff != 0)).squeeze().tolist()
+    tokens = tokens.tolist()
+    spans = [
+        TokenSpan(token=token, start=start, end=end, score=scores[start:end].mean().item())
+        for start, end in zip(changes_wo_blank[:-1], changes_wo_blank[1:])
+        if (token := tokens[start]) != blank
+    ]
+    return spans
diff --git a/lib/python3.11/site-packages/torchaudio/functional/filtering.py b/lib/python3.11/site-packages/torchaudio/functional/filtering.py
new file mode 100644
index 0000000000000000000000000000000000000000..1628d82c33c2a33a48ae49842cf012f4054c509c
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/functional/filtering.py
@@ -0,0 +1,1670 @@
+import math
+import warnings
+from typing import Optional
+
+import torch
+from torch import Tensor
+
+from torchaudio._extension import _IS_TORCHAUDIO_EXT_AVAILABLE
+
+
+def _dB2Linear(x: float) -> float:
+    return math.exp(x * math.log(10) / 20.0)
+
+
+def _generate_wave_table(
+    wave_type: str,
+    data_type: str,
+    table_size: int,
+    min: float,
+    max: float,
+    phase: float,
+    device: torch.device,
+) -> Tensor:
+    r"""A helper function for phaser. Generates a table with given parameters.
+
+    Args:
+        wave_type (str): SINE or TRIANGULAR
+        data_type (str): desired data_type ( `INT` or `FLOAT` )
+        table_size (int): desired table size
+        min (float): desired min value
+        max (float): desired max value
+        phase (float): desired phase
+        device (torch.device): Torch device on which table must be generated
+    Returns:
+        Tensor: A 1D tensor with wave table values
+    """
+
+    phase_offset = int(phase / math.pi / 2 * table_size + 0.5)
+
+    t = torch.arange(table_size, device=device, dtype=torch.int32)
+
+    point = (t + phase_offset) % table_size
+
+    d = torch.zeros_like(point, device=device, dtype=torch.float64)
+
+    if wave_type == "SINE":
+        d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2
+    elif wave_type == "TRIANGLE":
+        d = point.to(torch.float64) * 2 / table_size
+        value = torch.div(4 * point, table_size, rounding_mode="floor")
+        d[value == 0] = d[value == 0] + 0.5
+        d[value == 1] = 1.5 - d[value == 1]
+        d[value == 2] = 1.5 - d[value == 2]
+        d[value == 3] = d[value == 3] - 1.5
+
+    d = d * (max - min) + min
+
+    if data_type == "INT":
+        mask = d < 0
+        d[mask] = d[mask] - 0.5
+        d[~mask] = d[~mask] + 0.5
+        d = d.to(torch.int32)
+    elif data_type == "FLOAT":
+        d = d.to(torch.float32)
+
+    return d
+
+
+def allpass_biquad(waveform: Tensor, sample_rate: int, central_freq: float, Q: float = 0.707) -> Tensor:
+    r"""Design two-pole all-pass filter.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform(torch.Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        central_freq (float or torch.Tensor): central frequency (in Hz)
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+
+    alpha = torch.sin(w0) / 2 / Q
+
+    b0 = 1 - alpha
+    b1 = -2 * torch.cos(w0)
+    b2 = 1 + alpha
+    a0 = 1 + alpha
+    a1 = -2 * torch.cos(w0)
+    a2 = 1 - alpha
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def band_biquad(
+    waveform: Tensor,
+    sample_rate: int,
+    central_freq: float,
+    Q: float = 0.707,
+    noise: bool = False,
+) -> Tensor:
+    r"""Design two-pole band filter.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        central_freq (float or torch.Tensor): central frequency (in Hz)
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).
+        noise (bool, optional) : If ``True``, uses the alternate mode for un-pitched audio (e.g. percussion).
+            If ``False``, uses mode oriented to pitched audio, i.e. voice, singing,
+            or instrumental music (Default: ``False``).
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+    bw_Hz = central_freq / Q
+
+    a0 = 1.0
+    a2 = torch.exp(-2 * math.pi * bw_Hz / sample_rate)
+    a1 = -4 * a2 / (1 + a2) * torch.cos(w0)
+
+    b0 = torch.sqrt(1 - a1 * a1 / (4 * a2)) * (1 - a2)
+
+    if noise:
+        mult = torch.sqrt(((1 + a2) * (1 + a2) - a1 * a1) * (1 - a2) / (1 + a2)) / b0
+        b0 = mult * b0
+
+    b1 = 0.0
+    b2 = 0.0
+
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def bandpass_biquad(
+    waveform: Tensor,
+    sample_rate: int,
+    central_freq: float,
+    Q: float = 0.707,
+    const_skirt_gain: bool = False,
+) -> Tensor:
+    r"""Design two-pole band-pass filter.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        central_freq (float or torch.Tensor): central frequency (in Hz)
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
+        const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q).
+            If ``False``, uses a constant 0dB peak gain. (Default: ``False``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+    alpha = torch.sin(w0) / 2 / Q
+
+    temp = torch.sin(w0) / 2 if const_skirt_gain else alpha
+    b0 = temp
+    b1 = 0.0
+    b2 = -temp
+    a0 = 1 + alpha
+    a1 = -2 * torch.cos(w0)
+    a2 = 1 - alpha
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def bandreject_biquad(waveform: Tensor, sample_rate: int, central_freq: float, Q: float = 0.707) -> Tensor:
+    r"""Design two-pole band-reject filter.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        central_freq (float or torch.Tensor): central frequency (in Hz)
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+    alpha = torch.sin(w0) / 2 / Q
+
+    b0 = 1.0
+    b1 = -2 * torch.cos(w0)
+    b2 = 1.0
+    a0 = 1 + alpha
+    a1 = -2 * torch.cos(w0)
+    a2 = 1 - alpha
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def bass_biquad(
+    waveform: Tensor,
+    sample_rate: int,
+    gain: float,
+    central_freq: float = 100,
+    Q: float = 0.707,
+) -> Tensor:
+    r"""Design a bass tone-control effect.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB.
+        central_freq (float or torch.Tensor, optional): central frequency (in Hz). (Default: ``100``)
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+    gain = torch.as_tensor(gain, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+    alpha = torch.sin(w0) / 2 / Q
+    A = torch.exp(gain / 40 * math.log(10))
+
+    temp1 = 2 * torch.sqrt(A) * alpha
+    temp2 = (A - 1) * torch.cos(w0)
+    temp3 = (A + 1) * torch.cos(w0)
+
+    b0 = A * ((A + 1) - temp2 + temp1)
+    b1 = 2 * A * ((A - 1) - temp3)
+    b2 = A * ((A + 1) - temp2 - temp1)
+    a0 = (A + 1) + temp2 + temp1
+    a1 = -2 * ((A - 1) + temp3)
+    a2 = (A + 1) + temp2 - temp1
+
+    return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0)
+
+
+def biquad(waveform: Tensor, b0: float, b1: float, b2: float, a0: float, a1: float, a2: float) -> Tensor:
+    r"""Perform a biquad filter of input tensor.  Initial conditions set to 0.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        b0 (float or torch.Tensor): numerator coefficient of current input, x[n]
+        b1 (float or torch.Tensor): numerator coefficient of input one time step ago x[n-1]
+        b2 (float or torch.Tensor): numerator coefficient of input two time steps ago x[n-2]
+        a0 (float or torch.Tensor): denominator coefficient of current output y[n], typically 1
+        a1 (float or torch.Tensor): denominator coefficient of current output y[n-1]
+        a2 (float or torch.Tensor): denominator coefficient of current output y[n-2]
+
+    Returns:
+        Tensor: Waveform with dimension of `(..., time)`
+
+    Reference:
+       - https://en.wikipedia.org/wiki/Digital_biquad_filter
+    """
+
+    device = waveform.device
+    dtype = waveform.dtype
+
+    b0 = torch.as_tensor(b0, dtype=dtype, device=device).view(1)
+    b1 = torch.as_tensor(b1, dtype=dtype, device=device).view(1)
+    b2 = torch.as_tensor(b2, dtype=dtype, device=device).view(1)
+    a0 = torch.as_tensor(a0, dtype=dtype, device=device).view(1)
+    a1 = torch.as_tensor(a1, dtype=dtype, device=device).view(1)
+    a2 = torch.as_tensor(a2, dtype=dtype, device=device).view(1)
+
+    output_waveform = lfilter(
+        waveform,
+        torch.cat([a0, a1, a2]),
+        torch.cat([b0, b1, b2]),
+    )
+    return output_waveform
+
+
+def contrast(waveform: Tensor, enhancement_amount: float = 75.0) -> Tensor:
+    r"""Apply contrast effect.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Comparable with compression, this effect modifies an audio signal to make it sound louder
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        enhancement_amount (float, optional): controls the amount of the enhancement
+            Allowed range of values for enhancement_amount : 0-100
+            Note that enhancement_amount = 0 still gives a significant contrast enhancement
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+    """
+
+    if not 0 <= enhancement_amount <= 100:
+        raise ValueError("Allowed range of values for enhancement_amount : 0-100")
+
+    contrast = enhancement_amount / 750.0
+
+    temp1 = waveform * (math.pi / 2)
+    temp2 = contrast * torch.sin(temp1 * 4)
+    output_waveform = torch.sin(temp1 + temp2)
+
+    return output_waveform
+
+
+def dcshift(waveform: Tensor, shift: float, limiter_gain: Optional[float] = None) -> Tensor:
+    r"""Apply a DC shift to the audio. Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    This can be useful to remove a DC offset
+    (caused perhaps by a hardware problem in the recording chain) from the audio
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        shift (float): indicates the amount to shift the audio
+            Allowed range of values for shift : -2.0 to +2.0
+        limiter_gain (float of None, optional): It is used only on peaks to prevent clipping
+            It should have a value much less than 1 (e.g. 0.05 or 0.02)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+    """
+    output_waveform = waveform
+    limiter_threshold = 0.0
+
+    if limiter_gain is not None:
+        limiter_threshold = 1.0 - (abs(shift) - limiter_gain)
+
+    # Note:
+    # the following index-based update breaks auto-grad support
+    if limiter_gain is not None and shift > 0:
+        mask = waveform > limiter_threshold
+        temp = (waveform[mask] - limiter_threshold) * limiter_gain / (1 - limiter_threshold)
+        output_waveform[mask] = (temp + limiter_threshold + shift).clamp(max=limiter_threshold)
+        output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1)
+    elif limiter_gain is not None and shift < 0:
+        mask = waveform < -limiter_threshold
+        temp = (waveform[mask] + limiter_threshold) * limiter_gain / (1 - limiter_threshold)
+        output_waveform[mask] = (temp - limiter_threshold + shift).clamp(min=-limiter_threshold)
+        output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1)
+    else:
+        output_waveform = (waveform + shift).clamp(min=-1, max=1)
+
+    return output_waveform
+
+
+def deemph_biquad(waveform: Tensor, sample_rate: int) -> Tensor:
+    r"""Apply ISO 908 CD de-emphasis (shelving) IIR filter.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, Allowed sample rate ``44100`` or ``48000``
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+
+    if sample_rate == 44100:
+        central_freq = 5283
+        width_slope = 0.4845
+        gain = -9.477
+    elif sample_rate == 48000:
+        central_freq = 5356
+        width_slope = 0.479
+        gain = -9.62
+    else:
+        raise ValueError("Sample rate must be 44100 (audio-CD) or 48000 (DAT)")
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+    A = math.exp(gain / 40.0 * math.log(10))
+    alpha = math.sin(w0) / 2 * math.sqrt((A + 1 / A) * (1 / width_slope - 1) + 2)
+
+    temp1 = 2 * math.sqrt(A) * alpha
+    temp2 = (A - 1) * math.cos(w0)
+    temp3 = (A + 1) * math.cos(w0)
+
+    b0 = A * ((A + 1) + temp2 + temp1)
+    b1 = -2 * A * ((A - 1) + temp3)
+    b2 = A * ((A + 1) + temp2 - temp1)
+    a0 = (A + 1) - temp2 + temp1
+    a1 = 2 * ((A - 1) - temp3)
+    a2 = (A + 1) - temp2 - temp1
+
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def _add_noise_shaping(dithered_waveform: Tensor, waveform: Tensor) -> Tensor:
+    r"""Noise shaping is calculated by error:
+    error[n] = dithered[n] - original[n]
+    noise_shaped_waveform[n] = dithered[n] + error[n-1]
+    """
+    wf_shape = waveform.size()
+    waveform = waveform.reshape(-1, wf_shape[-1])
+
+    dithered_shape = dithered_waveform.size()
+    dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1])
+
+    error = dithered_waveform - waveform
+
+    # add error[n-1] to dithered_waveform[n], so offset the error by 1 index
+    zeros = torch.zeros(1, dtype=error.dtype, device=error.device)
+    for index in range(error.size()[0]):
+        err = error[index]
+        error_offset = torch.cat((zeros, err))
+        error[index] = error_offset[: waveform.size()[1]]
+
+    noise_shaped = dithered_waveform + error
+    return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:])
+
+
+def _apply_probability_distribution(waveform: Tensor, density_function: str = "TPDF") -> Tensor:
+    r"""Apply a probability distribution function on a waveform.
+
+    Triangular probability density function (TPDF) dither noise has a
+    triangular distribution; values in the center of the range have a higher
+    probability of occurring.
+
+    Rectangular probability density function (RPDF) dither noise has a
+    uniform distribution; any value in the specified range has the same
+    probability of occurring.
+
+    Gaussian probability density function (GPDF) has a normal distribution.
+    The relationship of probabilities of results follows a bell-shaped,
+    or Gaussian curve, typical of dither generated by analog sources.
+    Args:
+        waveform (Tensor): Tensor of audio of dimension (..., time)
+        density_function (str, optional): The density function of a
+           continuous random variable (Default: ``"TPDF"``)
+           Options: Triangular Probability Density Function - `TPDF`
+                    Rectangular Probability Density Function - `RPDF`
+                    Gaussian Probability Density Function - `GPDF`
+    Returns:
+        Tensor: waveform dithered with TPDF
+    """
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.reshape(-1, shape[-1])
+
+    channel_size = waveform.size()[0] - 1
+    time_size = waveform.size()[-1] - 1
+
+    random_channel = (
+        int(
+            torch.randint(
+                channel_size,
+                [
+                    1,
+                ],
+            ).item()
+        )
+        if channel_size > 0
+        else 0
+    )
+    random_time = (
+        int(
+            torch.randint(
+                time_size,
+                [
+                    1,
+                ],
+            ).item()
+        )
+        if time_size > 0
+        else 0
+    )
+
+    number_of_bits = 16
+    up_scaling = 2 ** (number_of_bits - 1) - 2
+    signal_scaled = waveform * up_scaling
+    down_scaling = 2 ** (number_of_bits - 1)
+
+    signal_scaled_dis = waveform
+    if density_function == "RPDF":
+        RPDF = waveform[random_channel][random_time] - 0.5
+
+        signal_scaled_dis = signal_scaled + RPDF
+    elif density_function == "GPDF":
+        # TODO Replace by distribution code once
+        # https://github.com/pytorch/pytorch/issues/29843 is resolved
+        # gaussian = torch.distributions.normal.Normal(torch.mean(waveform, -1), 1).sample()
+
+        num_rand_variables = 6
+
+        gaussian = waveform[random_channel][random_time]
+        for ws in num_rand_variables * [time_size]:
+            rand_chan = int(
+                torch.randint(
+                    channel_size,
+                    [
+                        1,
+                    ],
+                ).item()
+            )
+            gaussian += waveform[rand_chan][
+                int(
+                    torch.randint(
+                        ws,
+                        [
+                            1,
+                        ],
+                    ).item()
+                )
+            ]
+
+        signal_scaled_dis = signal_scaled + gaussian
+    else:
+        # dtype needed for https://github.com/pytorch/pytorch/issues/32358
+        TPDF = torch.bartlett_window(time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device)
+        TPDF = TPDF.repeat((channel_size + 1), 1)
+        signal_scaled_dis = signal_scaled + TPDF
+
+    quantised_signal_scaled = torch.round(signal_scaled_dis)
+    quantised_signal = quantised_signal_scaled / down_scaling
+
+    # unpack batch
+    return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:])
+
+
+def dither(waveform: Tensor, density_function: str = "TPDF", noise_shaping: bool = False) -> Tensor:
+    r"""Apply dither
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Dither increases the perceived dynamic range of audio stored at a
+    particular bit-depth by eliminating nonlinear truncation distortion
+    (i.e. adding minimally perceived noise to mask distortion caused by quantization).
+
+    Args:
+        waveform (Tensor): Tensor of audio of dimension (..., time)
+        density_function (str, optional):
+            The density function of a continuous random variable. One of
+            ``"TPDF"`` (Triangular Probability Density Function),
+            ``"RPDF"`` (Rectangular Probability Density Function) or
+            ``"GPDF"`` (Gaussian Probability Density Function) (Default: ``"TPDF"``).
+        noise_shaping (bool, optional): a filtering process that shapes the spectral
+            energy of quantisation error (Default: ``False``)
+
+    Returns:
+       Tensor: waveform dithered
+    """
+    dithered = _apply_probability_distribution(waveform, density_function=density_function)
+
+    if noise_shaping:
+        return _add_noise_shaping(dithered, waveform)
+    else:
+        return dithered
+
+
+def equalizer_biquad(
+    waveform: Tensor,
+    sample_rate: int,
+    center_freq: float,
+    gain: float,
+    Q: float = 0.707,
+) -> Tensor:
+    r"""Design biquad peaking equalizer filter and perform filtering.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        center_freq (float): filter's central frequency
+        gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    center_freq = torch.as_tensor(center_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+    gain = torch.as_tensor(gain, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * center_freq / sample_rate
+    A = torch.exp(gain / 40.0 * math.log(10))
+    alpha = torch.sin(w0) / 2 / Q
+
+    b0 = 1 + alpha * A
+    b1 = -2 * torch.cos(w0)
+    b2 = 1 - alpha * A
+    a0 = 1 + alpha / A
+    a1 = -2 * torch.cos(w0)
+    a2 = 1 - alpha / A
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def filtfilt(
+    waveform: Tensor,
+    a_coeffs: Tensor,
+    b_coeffs: Tensor,
+    clamp: bool = True,
+) -> Tensor:
+    r"""Apply an IIR filter forward and backward to a waveform.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Inspired by https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.filtfilt.html
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`.  Must be normalized to -1 to 1.
+        a_coeffs (Tensor): denominator coefficients of difference equation of dimension of either
+                                1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
+                                Lower delay coefficients are first, e.g. ``[a0, a1, a2, ...]``.
+                                Must be same size as b_coeffs (pad with 0's as necessary).
+        b_coeffs (Tensor): numerator coefficients of difference equation of dimension of either
+                                1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
+                                Lower delay coefficients are first, e.g. ``[b0, b1, b2, ...]``.
+                                Must be same size as a_coeffs (pad with 0's as necessary).
+        clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``)
+
+    Returns:
+        Tensor: Waveform with dimension of either `(..., num_filters, time)` if ``a_coeffs`` and ``b_coeffs``
+        are 2D Tensors, or `(..., time)` otherwise.
+    """
+    forward_filtered = lfilter(waveform, a_coeffs, b_coeffs, clamp=False, batching=True)
+    backward_filtered = lfilter(
+        forward_filtered.flip(-1),
+        a_coeffs,
+        b_coeffs,
+        clamp=clamp,
+        batching=True,
+    ).flip(-1)
+    return backward_filtered
+
+
+def flanger(
+    waveform: Tensor,
+    sample_rate: int,
+    delay: float = 0.0,
+    depth: float = 2.0,
+    regen: float = 0.0,
+    width: float = 71.0,
+    speed: float = 0.5,
+    phase: float = 25.0,
+    modulation: str = "sinusoidal",
+    interpolation: str = "linear",
+) -> Tensor:
+    r"""Apply a flanger effect to the audio. Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., channel, time)` .
+            Max 4 channels allowed
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        delay (float, optional): desired delay in milliseconds(ms)
+            Allowed range of values are 0 to 30
+        depth (float, optional): desired delay depth in milliseconds(ms)
+            Allowed range of values are 0 to 10
+        regen (float, optional): desired regen(feedback gain) in dB
+            Allowed range of values are -95 to 95
+        width (float, optional):  desired width(delay gain) in dB
+            Allowed range of values are 0 to 100
+        speed (float, optional):  modulation speed in Hz
+            Allowed range of values are 0.1 to 10
+        phase (float, optional):  percentage phase-shift for multi-channel
+            Allowed range of values are 0 to 100
+        modulation (str, optional):  Use either "sinusoidal" or "triangular" modulation. (Default: ``sinusoidal``)
+        interpolation (str, optional): Use either "linear" or "quadratic" for delay-line interpolation.
+            (Default: ``linear``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., channel, time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+
+        - Scott Lehman, `Effects Explained`_,
+
+    .. _Effects Explained:
+        https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html
+    """
+
+    if modulation not in ("sinusoidal", "triangular"):
+        raise ValueError('Only "sinusoidal" or "triangular" modulation allowed')
+
+    if interpolation not in ("linear", "quadratic"):
+        raise ValueError('Only "linear" or "quadratic" interpolation allowed')
+
+    actual_shape = waveform.shape
+    device, dtype = waveform.device, waveform.dtype
+
+    if actual_shape[-2] > 4:
+        raise ValueError("Max 4 channels allowed")
+
+    # convert to 3D (batch, channels, time)
+    waveform = waveform.view(-1, actual_shape[-2], actual_shape[-1])
+
+    # Scaling
+    feedback_gain = regen / 100
+    delay_gain = width / 100
+    channel_phase = phase / 100
+    delay_min = delay / 1000
+    delay_depth = depth / 1000
+
+    n_channels = waveform.shape[-2]
+
+    if modulation == "sinusoidal":
+        wave_type = "SINE"
+    else:
+        wave_type = "TRIANGLE"
+
+    # Balance output:
+    in_gain = 1.0 / (1 + delay_gain)
+    delay_gain = delay_gain / (1 + delay_gain)
+
+    # Balance feedback loop:
+    delay_gain = delay_gain * (1 - abs(feedback_gain))
+
+    delay_buf_length = int((delay_min + delay_depth) * sample_rate + 0.5)
+    delay_buf_length = delay_buf_length + 2
+
+    delay_bufs = torch.zeros(waveform.shape[0], n_channels, delay_buf_length, dtype=dtype, device=device)
+    delay_last = torch.zeros(waveform.shape[0], n_channels, dtype=dtype, device=device)
+
+    lfo_length = int(sample_rate / speed)
+
+    table_min = math.floor(delay_min * sample_rate + 0.5)
+    table_max = delay_buf_length - 2.0
+
+    lfo = _generate_wave_table(
+        wave_type=wave_type,
+        data_type="FLOAT",
+        table_size=lfo_length,
+        min=float(table_min),
+        max=float(table_max),
+        phase=3 * math.pi / 2,
+        device=device,
+    )
+
+    output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device)
+
+    delay_buf_pos = 0
+    lfo_pos = 0
+    channel_idxs = torch.arange(0, n_channels, device=device)
+
+    for i in range(waveform.shape[-1]):
+
+        delay_buf_pos = (delay_buf_pos + delay_buf_length - 1) % delay_buf_length
+
+        cur_channel_phase = (channel_idxs * lfo_length * channel_phase + 0.5).to(torch.int64)
+        delay_tensor = lfo[(lfo_pos + cur_channel_phase) % lfo_length]
+        frac_delay = torch.frac(delay_tensor)
+        delay_tensor = torch.floor(delay_tensor)
+
+        int_delay = delay_tensor.to(torch.int64)
+
+        temp = waveform[:, :, i]
+
+        delay_bufs[:, :, delay_buf_pos] = temp + delay_last * feedback_gain
+
+        delayed_0 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length]
+
+        int_delay = int_delay + 1
+
+        delayed_1 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length]
+
+        int_delay = int_delay + 1
+
+        if interpolation == "linear":
+            delayed = delayed_0 + (delayed_1 - delayed_0) * frac_delay
+        else:
+            delayed_2 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length]
+
+            int_delay = int_delay + 1
+
+            delayed_2 = delayed_2 - delayed_0
+            delayed_1 = delayed_1 - delayed_0
+            a = delayed_2 * 0.5 - delayed_1
+            b = delayed_1 * 2 - delayed_2 * 0.5
+
+            delayed = delayed_0 + (a * frac_delay + b) * frac_delay
+
+        delay_last = delayed
+        output_waveform[:, :, i] = waveform[:, :, i] * in_gain + delayed * delay_gain
+
+        lfo_pos = (lfo_pos + 1) % lfo_length
+
+    return output_waveform.clamp(min=-1, max=1).view(actual_shape)
+
+
+def gain(waveform: Tensor, gain_db: float = 1.0) -> Tensor:
+    r"""Apply amplification or attenuation to the whole waveform.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+       waveform (Tensor): Tensor of audio of dimension (..., time).
+       gain_db (float, optional) Gain adjustment in decibels (dB) (Default: ``1.0``).
+
+    Returns:
+       Tensor: the whole waveform amplified by gain_db.
+    """
+    if gain_db == 0:
+        return waveform
+
+    ratio = 10 ** (gain_db / 20)
+
+    return waveform * ratio
+
+
+def highpass_biquad(waveform: Tensor, sample_rate: int, cutoff_freq: float, Q: float = 0.707) -> Tensor:
+    r"""Design biquad highpass filter and perform filtering.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        cutoff_freq (float or torch.Tensor): filter cutoff frequency
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
+
+    Returns:
+        Tensor: Waveform dimension of `(..., time)`
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    cutoff_freq = torch.as_tensor(cutoff_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * cutoff_freq / sample_rate
+    alpha = torch.sin(w0) / 2.0 / Q
+
+    b0 = (1 + torch.cos(w0)) / 2
+    b1 = -1 - torch.cos(w0)
+    b2 = b0
+    a0 = 1 + alpha
+    a1 = -2 * torch.cos(w0)
+    a2 = 1 - alpha
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def _lfilter_core_generic_loop(input_signal_windows: Tensor, a_coeffs_flipped: Tensor, padded_output_waveform: Tensor):
+    n_order = a_coeffs_flipped.size(1)
+    a_coeffs_flipped = a_coeffs_flipped.unsqueeze(2)
+    for i_sample, o0 in enumerate(input_signal_windows.permute(2, 0, 1)):
+        windowed_output_signal = padded_output_waveform[:, :, i_sample : i_sample + n_order]
+        o0 -= (windowed_output_signal.transpose(0, 1) @ a_coeffs_flipped)[..., 0].t()
+        padded_output_waveform[:, :, i_sample + n_order - 1] = o0
+
+
+if _IS_TORCHAUDIO_EXT_AVAILABLE:
+    _lfilter_core_cpu_loop = torch.ops.torchaudio._lfilter_core_loop
+else:
+    _lfilter_core_cpu_loop = _lfilter_core_generic_loop
+
+
+def _lfilter_core(
+    waveform: Tensor,
+    a_coeffs: Tensor,
+    b_coeffs: Tensor,
+) -> Tensor:
+
+    if a_coeffs.size() != b_coeffs.size():
+        raise ValueError(
+            "Expected coeffs to be the same size."
+            f"Found a_coeffs size: {a_coeffs.size()}, b_coeffs size: {b_coeffs.size()}"
+        )
+    if waveform.ndim != 3:
+        raise ValueError(f"Expected waveform to be 3 dimensional. Found: {waveform.ndim}")
+    if not (waveform.device == a_coeffs.device == b_coeffs.device):
+        raise ValueError(
+            "Expected waveform and coeffs to be on the same device."
+            f"Found: waveform device:{waveform.device}, a_coeffs device: {a_coeffs.device}, "
+            f"b_coeffs device: {b_coeffs.device}"
+        )
+
+    n_batch, n_channel, n_sample = waveform.size()
+    n_order = a_coeffs.size(1)
+    if n_order <= 0:
+        raise ValueError(f"Expected n_order to be positive. Found: {n_order}")
+
+    # Pad the input and create output
+
+    padded_waveform = torch.nn.functional.pad(waveform, [n_order - 1, 0])
+    padded_output_waveform = torch.zeros_like(padded_waveform)
+
+    # Set up the coefficients matrix
+    # Flip coefficients' order
+    a_coeffs_flipped = a_coeffs.flip(1)
+    b_coeffs_flipped = b_coeffs.flip(1)
+
+    # calculate windowed_input_signal in parallel using convolution
+    input_signal_windows = torch.nn.functional.conv1d(padded_waveform, b_coeffs_flipped.unsqueeze(1), groups=n_channel)
+
+    input_signal_windows.div_(a_coeffs[:, :1])
+    a_coeffs_flipped.div_(a_coeffs[:, :1])
+
+    if (
+        input_signal_windows.device == torch.device("cpu")
+        and a_coeffs_flipped.device == torch.device("cpu")
+        and padded_output_waveform.device == torch.device("cpu")
+    ):
+        _lfilter_core_cpu_loop(input_signal_windows, a_coeffs_flipped, padded_output_waveform)
+    else:
+        _lfilter_core_generic_loop(input_signal_windows, a_coeffs_flipped, padded_output_waveform)
+
+    output = padded_output_waveform[:, :, n_order - 1 :]
+    return output
+
+
+if _IS_TORCHAUDIO_EXT_AVAILABLE:
+    _lfilter = torch.ops.torchaudio._lfilter
+else:
+    _lfilter = _lfilter_core
+
+
+def lfilter(waveform: Tensor, a_coeffs: Tensor, b_coeffs: Tensor, clamp: bool = True, batching: bool = True) -> Tensor:
+    r"""Perform an IIR filter by evaluating difference equation, using differentiable implementation
+    developed separately by *Yu et al.* :cite:`ismir_YuF23` and *Forgione et al.* :cite:`forgione2021dynonet`.
+    The gradients of ``a_coeffs`` are computed based on a faster algorithm from :cite:`ycy2024diffapf`.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Note:
+        To avoid numerical problems, small filter order is preferred.
+        Using double precision could also minimize numerical precision errors.
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`.  Must be normalized to -1 to 1.
+        a_coeffs (Tensor): denominator coefficients of difference equation of dimension of either
+                                1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
+                                Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``.
+                                Must be same size as b_coeffs (pad with 0's as necessary).
+        b_coeffs (Tensor): numerator coefficients of difference equation of dimension of either
+                                1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
+                                Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``.
+                                Must be same size as a_coeffs (pad with 0's as necessary).
+        clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``)
+        batching (bool, optional): Effective only when coefficients are 2D. If ``True``, then waveform should be at
+                                    least 2D, and the size of second axis from last should equals to ``num_filters``.
+                                    The output can be expressed as ``output[..., i, :] = lfilter(waveform[..., i, :],
+                                    a_coeffs[i], b_coeffs[i], clamp=clamp, batching=False)``. (Default: ``True``)
+
+    Returns:
+        Tensor: Waveform with dimension of either `(..., num_filters, time)` if ``a_coeffs`` and ``b_coeffs``
+        are 2D Tensors, or `(..., time)` otherwise.
+    """
+    if a_coeffs.size() != b_coeffs.size():
+        raise ValueError(
+            "Expected coeffs to be the same size."
+            f"Found: a_coeffs size: {a_coeffs.size()}, b_coeffs size: {b_coeffs.size()}"
+        )
+    if a_coeffs.ndim > 2:
+        raise ValueError(f"Expected coeffs to have greater than 1 dimension. Found: {a_coeffs.ndim}")
+
+    if a_coeffs.ndim > 1:
+        if batching:
+            if waveform.ndim <= 0:
+                raise ValueError("Expected waveform to have a positive number of dimensions." f"Found: {waveform.ndim}")
+            if waveform.shape[-2] != a_coeffs.shape[0]:
+                raise ValueError(
+                    "Expected number of batches in waveform and coeffs to be the same."
+                    f"Found: coeffs batches: {a_coeffs.shape[0]}, waveform batches: {waveform.shape[-2]}"
+                )
+        else:
+            waveform = torch.stack([waveform] * a_coeffs.shape[0], -2)
+    else:
+        a_coeffs = a_coeffs.unsqueeze(0)
+        b_coeffs = b_coeffs.unsqueeze(0)
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.reshape(-1, a_coeffs.shape[0], shape[-1])
+    output = _lfilter(waveform, a_coeffs, b_coeffs)
+
+    if clamp:
+        output = torch.clamp(output, min=-1.0, max=1.0)
+
+    # unpack batch
+    output = output.reshape(shape[:-1] + output.shape[-1:])
+
+    return output
+
+
+def lowpass_biquad(waveform: Tensor, sample_rate: int, cutoff_freq: float, Q: float = 0.707) -> Tensor:
+    r"""Design biquad lowpass filter and perform filtering.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        cutoff_freq (float or torch.Tensor): filter cutoff frequency
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    cutoff_freq = torch.as_tensor(cutoff_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * cutoff_freq / sample_rate
+    alpha = torch.sin(w0) / 2 / Q
+
+    b0 = (1 - torch.cos(w0)) / 2
+    b1 = 1 - torch.cos(w0)
+    b2 = b0
+    a0 = 1 + alpha
+    a1 = -2 * torch.cos(w0)
+    a2 = 1 - alpha
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def _overdrive_core_loop_generic(
+    waveform: Tensor, temp: Tensor, last_in: Tensor, last_out: Tensor, output_waveform: Tensor
+):
+    for i in range(waveform.shape[-1]):
+        last_out = temp[:, i] - last_in + 0.995 * last_out
+        last_in = temp[:, i]
+        output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75
+
+
+if _IS_TORCHAUDIO_EXT_AVAILABLE:
+    _overdrive_core_loop_cpu = torch.ops.torchaudio._overdrive_core_loop
+else:
+    _overdrive_core_loop_cpu = _overdrive_core_loop_generic
+
+
+def overdrive(waveform: Tensor, gain: float = 20, colour: float = 20) -> Tensor:
+    r"""Apply a overdrive effect to the audio. Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    This effect applies a non linear distortion to the audio signal.
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        gain (float, optional): desired gain at the boost (or attenuation) in dB
+            Allowed range of values are 0 to 100
+        colour (float, optional):  controls the amount of even harmonic content in the over-driven output
+            Allowed range of values are 0 to 100
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+    """
+    actual_shape = waveform.shape
+    device, dtype = waveform.device, waveform.dtype
+
+    # convert to 2D (..,time)
+    waveform = waveform.view(-1, actual_shape[-1])
+
+    gain = _dB2Linear(gain)
+    colour = colour / 200
+    last_in = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device)
+    last_out = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device)
+
+    temp = waveform * gain + colour
+
+    mask1 = temp < -1
+    temp[mask1] = torch.tensor(-2.0 / 3.0, dtype=dtype, device=device)
+    # Wrapping the constant with Tensor is required for Torchscript
+
+    mask2 = temp > 1
+    temp[mask2] = torch.tensor(2.0 / 3.0, dtype=dtype, device=device)
+
+    mask3 = ~mask1 & ~mask2
+    temp[mask3] = temp[mask3] - (temp[mask3] ** 3) * (1.0 / 3)
+
+    output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device)
+
+    # Uses CPU optimized loop function if available for CPU device
+    if device == torch.device("cpu"):
+        _overdrive_core_loop_cpu(waveform, temp, last_in, last_out, output_waveform)
+    else:
+        _overdrive_core_loop_generic(waveform, temp, last_in, last_out, output_waveform)
+
+    return output_waveform.clamp(min=-1, max=1).view(actual_shape)
+
+
+def phaser(
+    waveform: Tensor,
+    sample_rate: int,
+    gain_in: float = 0.4,
+    gain_out: float = 0.74,
+    delay_ms: float = 3.0,
+    decay: float = 0.4,
+    mod_speed: float = 0.5,
+    sinusoidal: bool = True,
+) -> Tensor:
+    r"""Apply a phasing effect to the audio. Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        gain_in (float, optional): desired input gain at the boost (or attenuation) in dB
+            Allowed range of values are 0 to 1
+        gain_out (float, optional): desired output gain at the boost (or attenuation) in dB
+            Allowed range of values are 0 to 1e9
+        delay_ms (float, optional): desired delay in milliseconds
+            Allowed range of values are 0 to 5.0
+        decay (float, optional):  desired decay relative to gain-in
+            Allowed range of values are 0 to 0.99
+        mod_speed (float, optional):  modulation speed in Hz
+            Allowed range of values are 0.1 to 2
+        sinusoidal (bool, optional):  If ``True``, uses sinusoidal modulation (preferable for multiple instruments)
+            If ``False``, uses triangular modulation (gives single instruments a sharper phasing effect)
+            (Default: ``True``)
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - Scott Lehman, `Effects Explained`_.
+
+    .. _Effects Explained:
+        https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html
+    """
+    actual_shape = waveform.shape
+    device, dtype = waveform.device, waveform.dtype
+
+    # convert to 2D (channels,time)
+    waveform = waveform.view(-1, actual_shape[-1])
+
+    delay_buf_len = int((delay_ms * 0.001 * sample_rate) + 0.5)
+    delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device)
+
+    mod_buf_len = int(sample_rate / mod_speed + 0.5)
+
+    if sinusoidal:
+        wave_type = "SINE"
+    else:
+        wave_type = "TRIANGLE"
+
+    mod_buf = _generate_wave_table(
+        wave_type=wave_type,
+        data_type="INT",
+        table_size=mod_buf_len,
+        min=1.0,
+        max=float(delay_buf_len),
+        phase=math.pi / 2,
+        device=device,
+    )
+
+    delay_pos = 0
+    mod_pos = 0
+
+    output_waveform_pre_gain_list = []
+    waveform = waveform * gain_in
+    delay_buf = delay_buf * decay
+    waveform_list = [waveform[:, i] for i in range(waveform.size(1))]
+    delay_buf_list = [delay_buf[:, i] for i in range(delay_buf.size(1))]
+    mod_buf_list = [mod_buf[i] for i in range(mod_buf.size(0))]
+
+    for i in range(waveform.shape[-1]):
+        idx = int((delay_pos + mod_buf_list[mod_pos]) % delay_buf_len)
+        mod_pos = (mod_pos + 1) % mod_buf_len
+        delay_pos = (delay_pos + 1) % delay_buf_len
+        temp = (waveform_list[i]) + (delay_buf_list[idx])
+        delay_buf_list[delay_pos] = temp * decay
+        output_waveform_pre_gain_list.append(temp)
+
+    output_waveform = torch.stack(output_waveform_pre_gain_list, dim=1).to(dtype=dtype, device=device)
+    output_waveform.mul_(gain_out)
+
+    return output_waveform.clamp(min=-1, max=1).view(actual_shape)
+
+
+def riaa_biquad(waveform: Tensor, sample_rate: int) -> Tensor:
+    r"""Apply RIAA vinyl playback equalization.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz).
+            Allowed sample rates in Hz : ``44100``,``48000``,``88200``,``96000``
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+
+    if sample_rate == 44100:
+        zeros = [-0.2014898, 0.9233820]
+        poles = [0.7083149, 0.9924091]
+
+    elif sample_rate == 48000:
+        zeros = [-0.1766069, 0.9321590]
+        poles = [0.7396325, 0.9931330]
+
+    elif sample_rate == 88200:
+        zeros = [-0.1168735, 0.9648312]
+        poles = [0.8590646, 0.9964002]
+
+    elif sample_rate == 96000:
+        zeros = [-0.1141486, 0.9676817]
+        poles = [0.8699137, 0.9966946]
+
+    else:
+        raise ValueError("Sample rate must be 44.1k, 48k, 88.2k, or 96k")
+
+    # polynomial coefficients with roots zeros[0] and zeros[1]
+    b0 = 1.0
+    b1 = -(zeros[0] + zeros[1])
+    b2 = zeros[0] * zeros[1]
+
+    # polynomial coefficients with roots poles[0] and poles[1]
+    a0 = 1.0
+    a1 = -(poles[0] + poles[1])
+    a2 = poles[0] * poles[1]
+
+    # Normalize to 0dB at 1kHz
+    y = 2 * math.pi * 1000 / sample_rate
+    b_re = b0 + b1 * math.cos(-y) + b2 * math.cos(-2 * y)
+    a_re = a0 + a1 * math.cos(-y) + a2 * math.cos(-2 * y)
+    b_im = b1 * math.sin(-y) + b2 * math.sin(-2 * y)
+    a_im = a1 * math.sin(-y) + a2 * math.sin(-2 * y)
+    g = 1 / math.sqrt((b_re**2 + b_im**2) / (a_re**2 + a_im**2))
+
+    b0 *= g
+    b1 *= g
+    b2 *= g
+
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def treble_biquad(
+    waveform: Tensor,
+    sample_rate: int,
+    gain: float,
+    central_freq: float = 3000,
+    Q: float = 0.707,
+) -> Tensor:
+    r"""Design a treble tone-control effect.  Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): audio waveform of dimension of `(..., time)`
+        sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
+        gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB.
+        central_freq (float or torch.Tensor, optional): central frequency (in Hz). (Default: ``3000``)
+        Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).
+
+    Returns:
+        Tensor: Waveform of dimension of `(..., time)`
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+        - https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
+    """
+    dtype = waveform.dtype
+    device = waveform.device
+    central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
+    Q = torch.as_tensor(Q, dtype=dtype, device=device)
+    gain = torch.as_tensor(gain, dtype=dtype, device=device)
+
+    w0 = 2 * math.pi * central_freq / sample_rate
+    alpha = torch.sin(w0) / 2 / Q
+    A = torch.exp(gain / 40 * math.log(10))
+
+    temp1 = 2 * torch.sqrt(A) * alpha
+    temp2 = (A - 1) * torch.cos(w0)
+    temp3 = (A + 1) * torch.cos(w0)
+
+    b0 = A * ((A + 1) + temp2 + temp1)
+    b1 = -2 * A * ((A - 1) + temp3)
+    b2 = A * ((A + 1) + temp2 - temp1)
+    a0 = (A + 1) - temp2 + temp1
+    a1 = 2 * ((A - 1) - temp3)
+    a2 = (A + 1) - temp2 - temp1
+
+    return biquad(waveform, b0, b1, b2, a0, a1, a2)
+
+
+def _measure(
+    measure_len_ws: int,
+    samples: Tensor,
+    spectrum: Tensor,
+    noise_spectrum: Tensor,
+    spectrum_window: Tensor,
+    spectrum_start: int,
+    spectrum_end: int,
+    cepstrum_window: Tensor,
+    cepstrum_start: int,
+    cepstrum_end: int,
+    noise_reduction_amount: float,
+    measure_smooth_time_mult: float,
+    noise_up_time_mult: Tensor,
+    noise_down_time_mult: Tensor,
+    boot_count: int,
+) -> float:
+    device = samples.device
+
+    if spectrum.size(-1) != noise_spectrum.size(-1):
+        raise ValueError(
+            "Expected spectrum size to match noise spectrum size in final dimension."
+            f"Found: spectrum size: {spectrum.size()}, noise_spectrum size: {noise_spectrum.size()}"
+        )
+
+    dft_len_ws = spectrum.size()[-1]
+
+    dftBuf = torch.zeros(dft_len_ws, device=device)
+
+    dftBuf[:measure_len_ws] = samples * spectrum_window[:measure_len_ws]
+
+    # lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf);
+    _dftBuf = torch.fft.rfft(dftBuf)
+
+    mult: float = boot_count / (1.0 + boot_count) if boot_count >= 0 else measure_smooth_time_mult
+
+    _d = _dftBuf[spectrum_start:spectrum_end].abs()
+    spectrum[spectrum_start:spectrum_end].mul_(mult).add_(_d * (1 - mult))
+    _d = spectrum[spectrum_start:spectrum_end] ** 2
+
+    _zeros = torch.zeros(spectrum_end - spectrum_start, device=device)
+    _mult = (
+        _zeros
+        if boot_count >= 0
+        else torch.where(
+            _d > noise_spectrum[spectrum_start:spectrum_end],
+            noise_up_time_mult,  # if
+            noise_down_time_mult,  # else,
+        )
+    )
+
+    noise_spectrum[spectrum_start:spectrum_end].mul_(_mult).add_(_d * (1 - _mult))
+    _d = torch.sqrt(
+        torch.max(
+            _zeros,
+            _d - noise_reduction_amount * noise_spectrum[spectrum_start:spectrum_end],
+        ),
+    )
+
+    _cepstrum_Buf: Tensor = torch.zeros(dft_len_ws >> 1, device=device)
+    _cepstrum_Buf[spectrum_start:spectrum_end] = _d * cepstrum_window
+    _cepstrum_Buf[spectrum_end : dft_len_ws >> 1].zero_()
+
+    # lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf);
+    _cepstrum_Buf = torch.fft.rfft(_cepstrum_Buf)
+
+    result: float = float(torch.sum(_cepstrum_Buf[cepstrum_start:cepstrum_end].abs().pow(2)))
+    result = math.log(result / (cepstrum_end - cepstrum_start)) if result > 0 else -math.inf
+    return max(0, 21 + result)
+
+
+def vad(
+    waveform: Tensor,
+    sample_rate: int,
+    trigger_level: float = 7.0,
+    trigger_time: float = 0.25,
+    search_time: float = 1.0,
+    allowed_gap: float = 0.25,
+    pre_trigger_time: float = 0.0,
+    # Fine-tuning parameters
+    boot_time: float = 0.35,
+    noise_up_time: float = 0.1,
+    noise_down_time: float = 0.01,
+    noise_reduction_amount: float = 1.35,
+    measure_freq: float = 20.0,
+    measure_duration: Optional[float] = None,
+    measure_smooth_time: float = 0.4,
+    hp_filter_freq: float = 50.0,
+    lp_filter_freq: float = 6000.0,
+    hp_lifter_freq: float = 150.0,
+    lp_lifter_freq: float = 2000.0,
+) -> Tensor:
+    r"""Voice Activity Detector. Similar to SoX implementation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Attempts to trim silence and quiet background sounds from the ends of recordings of speech.
+    The algorithm currently uses a simple cepstral power measurement to detect voice,
+    so may be fooled by other things, especially music.
+
+    The effect can trim only from the front of the audio,
+    so in order to trim from the back, the reverse effect must also be used.
+
+    Args:
+        waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)`
+            Tensor of shape `(channels, time)` is treated as a multi-channel recording
+            of the same event and the resulting output will be trimmed to the earliest
+            voice activity in any channel.
+        sample_rate (int): Sample rate of audio signal.
+        trigger_level (float, optional): The measurement level used to trigger activity detection.
+            This may need to be cahnged depending on the noise level, signal level,
+            and other characteristics of the input audio. (Default: 7.0)
+        trigger_time (float, optional): The time constant (in seconds)
+            used to help ignore short bursts of sound. (Default: 0.25)
+        search_time (float, optional): The amount of audio (in seconds)
+            to search for quieter/shorter bursts of audio to include prior
+            to the detected trigger point. (Default: 1.0)
+        allowed_gap (float, optional): The allowed gap (in seconds) between
+            quieter/shorter bursts of audio to include prior
+            to the detected trigger point. (Default: 0.25)
+        pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve
+            before the trigger point and any found quieter/shorter bursts. (Default: 0.0)
+        boot_time (float, optional) The algorithm (internally) uses adaptive noise
+            estimation/reduction in order to detect the start of the wanted audio.
+            This option sets the time for the initial noise estimate. (Default: 0.35)
+        noise_up_time (float, optional) Time constant used by the adaptive noise estimator
+            for when the noise level is increasing. (Default: 0.1)
+        noise_down_time (float, optional) Time constant used by the adaptive noise estimator
+            for when the noise level is decreasing. (Default: 0.01)
+        noise_reduction_amount (float, optional) Amount of noise reduction to use in
+            the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35)
+        measure_freq (float, optional) Frequency of the algorithm's
+            processing/measurements. (Default: 20.0)
+        measure_duration: (float, optional) Measurement duration.
+            (Default: Twice the measurement period; i.e. with overlap.)
+        measure_smooth_time (float, optional) Time constant used to smooth
+            spectral measurements. (Default: 0.4)
+        hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied
+            at the input to the detector algorithm. (Default: 50.0)
+        lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied
+            at the input to the detector algorithm. (Default: 6000.0)
+        hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used
+            in the detector algorithm. (Default: 150.0)
+        lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used
+            in the detector algorithm. (Default: 2000.0)
+
+    Returns:
+        Tensor: Tensor of audio of dimension `(..., time)`.
+
+    Reference:
+        - http://sox.sourceforge.net/sox.html
+    """
+    device = waveform.device
+
+    if waveform.ndim > 2:
+        warnings.warn(
+            "Expected input tensor dimension of 1 for single channel"
+            f" or 2 for multi-channel. Got {waveform.ndim} instead. "
+            "Batch semantics is not supported. "
+            "Please refer to https://github.com/pytorch/audio/issues/1348"
+            " and https://github.com/pytorch/audio/issues/1468."
+        )
+
+    measure_duration: float = 2.0 / measure_freq if measure_duration is None else measure_duration
+
+    measure_len_ws = int(sample_rate * measure_duration + 0.5)
+    measure_len_ns = measure_len_ws
+    # for (dft_len_ws = 16; dft_len_ws < measure_len_ws; dft_len_ws <<= 1);
+    dft_len_ws = 16
+    while dft_len_ws < measure_len_ws:
+        dft_len_ws *= 2
+
+    measure_period_ns = int(sample_rate / measure_freq + 0.5)
+    measures_len = math.ceil(search_time * measure_freq)
+    search_pre_trigger_len_ns = measures_len * measure_period_ns
+    gap_len = int(allowed_gap * measure_freq + 0.5)
+
+    fixed_pre_trigger_len_ns = int(pre_trigger_time * sample_rate + 0.5)
+    samplesLen_ns = fixed_pre_trigger_len_ns + search_pre_trigger_len_ns + measure_len_ns
+
+    spectrum_window = torch.zeros(measure_len_ws, device=device)
+    for i in range(measure_len_ws):
+        # sox.h:741 define SOX_SAMPLE_MIN (sox_sample_t)SOX_INT_MIN(32)
+        spectrum_window[i] = 2.0 / math.sqrt(float(measure_len_ws))
+    # lsx_apply_hann(spectrum_window, (int)measure_len_ws);
+    spectrum_window *= torch.hann_window(measure_len_ws, device=device, dtype=torch.float)
+
+    spectrum_start: int = int(hp_filter_freq / sample_rate * dft_len_ws + 0.5)
+    spectrum_start: int = max(spectrum_start, 1)
+    spectrum_end: int = int(lp_filter_freq / sample_rate * dft_len_ws + 0.5)
+    spectrum_end: int = min(spectrum_end, dft_len_ws // 2)
+
+    cepstrum_window = torch.zeros(spectrum_end - spectrum_start, device=device)
+    for i in range(spectrum_end - spectrum_start):
+        cepstrum_window[i] = 2.0 / math.sqrt(float(spectrum_end) - spectrum_start)
+    # lsx_apply_hann(cepstrum_window,(int)(spectrum_end - spectrum_start));
+    cepstrum_window *= torch.hann_window(spectrum_end - spectrum_start, device=device, dtype=torch.float)
+
+    cepstrum_start = math.ceil(sample_rate * 0.5 / lp_lifter_freq)
+    cepstrum_end = math.floor(sample_rate * 0.5 / hp_lifter_freq)
+    cepstrum_end = min(cepstrum_end, dft_len_ws // 4)
+
+    if cepstrum_end <= cepstrum_start:
+        raise ValueError(
+            "Expected cepstrum_start to be smaller than cepstrum_end."
+            f"Found: cepstrum_start: {cepstrum_start}, cepstrum_end: {cepstrum_end}."
+        )
+
+    noise_up_time_mult = torch.tensor(math.exp(-1.0 / (noise_up_time * measure_freq)), device=device)
+    noise_down_time_mult = torch.tensor(math.exp(-1.0 / (noise_down_time * measure_freq)), device=device)
+    measure_smooth_time_mult = math.exp(-1.0 / (measure_smooth_time * measure_freq))
+    trigger_meas_time_mult = math.exp(-1.0 / (trigger_time * measure_freq))
+
+    boot_count_max = int(boot_time * measure_freq - 0.5)
+    boot_count = measures_index = flushedLen_ns = 0
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.view(-1, shape[-1])
+
+    n_channels, ilen = waveform.size()
+
+    mean_meas = torch.zeros(n_channels, device=device)
+    spectrum = torch.zeros(n_channels, dft_len_ws, device=device)
+    noise_spectrum = torch.zeros(n_channels, dft_len_ws, device=device)
+    measures = torch.zeros(n_channels, measures_len, device=device)
+
+    has_triggered: bool = False
+    num_measures_to_flush: int = 0
+
+    pos = 0
+    for pos in range(measure_len_ns, ilen, measure_period_ns):
+        for i in range(n_channels):
+            meas: float = _measure(
+                measure_len_ws=measure_len_ws,
+                samples=waveform[i, pos - measure_len_ws : pos],
+                spectrum=spectrum[i],
+                noise_spectrum=noise_spectrum[i],
+                spectrum_window=spectrum_window,
+                spectrum_start=spectrum_start,
+                spectrum_end=spectrum_end,
+                cepstrum_window=cepstrum_window,
+                cepstrum_start=cepstrum_start,
+                cepstrum_end=cepstrum_end,
+                noise_reduction_amount=noise_reduction_amount,
+                measure_smooth_time_mult=measure_smooth_time_mult,
+                noise_up_time_mult=noise_up_time_mult,
+                noise_down_time_mult=noise_down_time_mult,
+                boot_count=boot_count,
+            )
+            measures[i, measures_index] = meas
+            mean_meas[i] = mean_meas[i] * trigger_meas_time_mult + meas * (1.0 - trigger_meas_time_mult)
+
+            has_triggered = has_triggered or (mean_meas[i] >= trigger_level)
+            if has_triggered:
+                n: int = measures_len
+                k: int = measures_index
+                jTrigger: int = n
+                jZero: int = n
+                j: int = 0
+
+                for j in range(n):
+                    if (measures[i, k] >= trigger_level) and (j <= jTrigger + gap_len):
+                        jZero = jTrigger = j
+                    elif (measures[i, k] == 0) and (jTrigger >= jZero):
+                        jZero = j
+                    k = (k + n - 1) % n
+                j = min(j, jZero)
+                # num_measures_to_flush = range_limit(j, num_measures_to_flush, n);
+                num_measures_to_flush = min(max(num_measures_to_flush, j), n)
+            # end if has_triggered
+        # end for channel
+        measures_index += 1
+        measures_index = measures_index % measures_len
+        if boot_count >= 0:
+            boot_count = -1 if boot_count == boot_count_max else boot_count + 1
+
+        if has_triggered:
+            flushedLen_ns = (measures_len - num_measures_to_flush) * measure_period_ns
+            break
+    # end for window
+    if not has_triggered:
+        return waveform[..., :0].view(shape[:-1] + torch.Size([0]))
+
+    res = waveform[:, max(pos - samplesLen_ns + flushedLen_ns, 0) :]
+    # unpack batch
+    return res.view(shape[:-1] + res.shape[-1:])
diff --git a/lib/python3.11/site-packages/torchaudio/functional/functional.py b/lib/python3.11/site-packages/torchaudio/functional/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..af34e707e552d6eae403278743b406988e16e9db
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/functional/functional.py
@@ -0,0 +1,2535 @@
+# -*- coding: utf-8 -*-
+
+import math
+import tempfile
+import warnings
+from collections.abc import Sequence
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torchaudio
+from torch import Tensor
+from torchaudio._internal.module_utils import deprecated
+
+from .filtering import highpass_biquad, treble_biquad
+
+__all__ = [
+    "spectrogram",
+    "inverse_spectrogram",
+    "griffinlim",
+    "amplitude_to_DB",
+    "DB_to_amplitude",
+    "compute_deltas",
+    "melscale_fbanks",
+    "linear_fbanks",
+    "create_dct",
+    "compute_deltas",
+    "detect_pitch_frequency",
+    "DB_to_amplitude",
+    "mu_law_encoding",
+    "mu_law_decoding",
+    "phase_vocoder",
+    "mask_along_axis",
+    "mask_along_axis_iid",
+    "sliding_window_cmn",
+    "spectral_centroid",
+    "apply_codec",
+    "resample",
+    "edit_distance",
+    "loudness",
+    "pitch_shift",
+    "rnnt_loss",
+    "psd",
+    "mvdr_weights_souden",
+    "mvdr_weights_rtf",
+    "rtf_evd",
+    "rtf_power",
+    "apply_beamforming",
+    "fftconvolve",
+    "convolve",
+    "add_noise",
+    "speed",
+    "preemphasis",
+    "deemphasis",
+]
+
+
+def spectrogram(
+    waveform: Tensor,
+    pad: int,
+    window: Tensor,
+    n_fft: int,
+    hop_length: int,
+    win_length: int,
+    power: Optional[float],
+    normalized: Union[bool, str],
+    center: bool = True,
+    pad_mode: str = "reflect",
+    onesided: bool = True,
+    return_complex: Optional[bool] = None,
+) -> Tensor:
+    r"""Create a spectrogram or a batch of spectrograms from a raw audio signal.
+    The spectrogram can be either magnitude-only or complex.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (Tensor): Tensor of audio of dimension `(..., time)`
+        pad (int): Two sided padding of signal
+        window (Tensor): Window tensor that is applied/multiplied to each frame/window
+        n_fft (int): Size of FFT
+        hop_length (int): Length of hop between STFT windows
+        win_length (int): Window size
+        power (float or None): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
+            If None, then the complex spectrum is returned instead.
+        normalized (bool or str): Whether to normalize by magnitude after stft. If input is str, choices are
+            ``"window"`` and ``"frame_length"``, if specific normalization type is desirable. ``True`` maps to
+            ``"window"``. When normalized on ``"window"``, waveform is normalized upon the window's L2 energy. If
+            normalized on ``"frame_length"``, waveform is normalized by dividing by
+            :math:`(\text{frame\_length})^{0.5}`.
+        center (bool, optional): whether to pad :attr:`waveform` on both sides so
+            that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
+            Default: ``True``
+        pad_mode (string, optional): controls the padding method used when
+            :attr:`center` is ``True``. Default: ``"reflect"``
+        onesided (bool, optional): controls whether to return half of results to
+            avoid redundancy. Default: ``True``
+        return_complex (bool, optional):
+            Deprecated and not used.
+
+    Returns:
+        Tensor: Dimension `(..., freq, time)`, freq is
+        ``n_fft // 2 + 1`` and ``n_fft`` is the number of
+        Fourier bins, and time is the number of window hops (n_frame).
+    """
+    if return_complex is not None:
+        warnings.warn(
+            "`return_complex` argument is now deprecated and is not effective."
+            "`torchaudio.functional.spectrogram(power=None)` always returns a tensor with "
+            "complex dtype. Please remove the argument in the function call."
+        )
+
+    if pad > 0:
+        # TODO add "with torch.no_grad():" back when JIT supports it
+        waveform = torch.nn.functional.pad(waveform, (pad, pad), "constant")
+
+    frame_length_norm, window_norm = _get_spec_norms(normalized)
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.reshape(-1, shape[-1])
+
+    # default values are consistent with librosa.core.spectrum._spectrogram
+    spec_f = torch.stft(
+        input=waveform,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        window=window,
+        center=center,
+        pad_mode=pad_mode,
+        normalized=frame_length_norm,
+        onesided=onesided,
+        return_complex=True,
+    )
+
+    # unpack batch
+    spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-2:])
+
+    if window_norm:
+        spec_f /= window.pow(2.0).sum().sqrt()
+    if power is not None:
+        if power == 1.0:
+            return spec_f.abs()
+        return spec_f.abs().pow(power)
+    return spec_f
+
+
+def inverse_spectrogram(
+    spectrogram: Tensor,
+    length: Optional[int],
+    pad: int,
+    window: Tensor,
+    n_fft: int,
+    hop_length: int,
+    win_length: int,
+    normalized: Union[bool, str],
+    center: bool = True,
+    pad_mode: str = "reflect",
+    onesided: bool = True,
+) -> Tensor:
+    r"""Create an inverse spectrogram or a batch of inverse spectrograms from the provided
+    complex-valued spectrogram.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        spectrogram (Tensor): Complex tensor of audio of dimension (..., freq, time).
+        length (int or None): The output length of the waveform.
+        pad (int): Two sided padding of signal. It is only effective when ``length`` is provided.
+        window (Tensor): Window tensor that is applied/multiplied to each frame/window
+        n_fft (int): Size of FFT
+        hop_length (int): Length of hop between STFT windows
+        win_length (int): Window size
+        normalized (bool or str): Whether the stft output was normalized by magnitude. If input is str, choices are
+            ``"window"`` and ``"frame_length"``, dependent on normalization mode. ``True`` maps to
+            ``"window"``.
+        center (bool, optional): whether the waveform was padded on both sides so
+            that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
+            Default: ``True``
+        pad_mode (string, optional): controls the padding method used when
+            :attr:`center` is ``True``. This parameter is provided for compatibility with the
+            spectrogram function and is not used. Default: ``"reflect"``
+        onesided (bool, optional): controls whether spectrogram was done in onesided mode.
+            Default: ``True``
+
+    Returns:
+        Tensor: Dimension `(..., time)`. Least squares estimation of the original signal.
+    """
+
+    frame_length_norm, window_norm = _get_spec_norms(normalized)
+
+    if not spectrogram.is_complex():
+        raise ValueError("Expected `spectrogram` to be complex dtype.")
+
+    if window_norm:
+        spectrogram = spectrogram * window.pow(2.0).sum().sqrt()
+
+    # pack batch
+    shape = spectrogram.size()
+    spectrogram = spectrogram.reshape(-1, shape[-2], shape[-1])
+
+    # default values are consistent with librosa.core.spectrum._spectrogram
+    waveform = torch.istft(
+        input=spectrogram,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        window=window,
+        center=center,
+        normalized=frame_length_norm,
+        onesided=onesided,
+        length=length + 2 * pad if length is not None else None,
+        return_complex=False,
+    )
+
+    if length is not None and pad > 0:
+        # remove padding from front and back
+        waveform = waveform[:, pad:-pad]
+
+    # unpack batch
+    waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:])
+
+    return waveform
+
+
+def _get_spec_norms(normalized: Union[str, bool]):
+    frame_length_norm, window_norm = False, False
+    if torch.jit.isinstance(normalized, str):
+        if normalized not in ["frame_length", "window"]:
+            raise ValueError("Invalid normalized parameter: {}".format(normalized))
+        if normalized == "frame_length":
+            frame_length_norm = True
+        elif normalized == "window":
+            window_norm = True
+    elif torch.jit.isinstance(normalized, bool):
+        if normalized:
+            window_norm = True
+    else:
+        raise TypeError("Input type not supported")
+    return frame_length_norm, window_norm
+
+
+def _get_complex_dtype(real_dtype: torch.dtype):
+    if real_dtype == torch.double:
+        return torch.cdouble
+    if real_dtype == torch.float:
+        return torch.cfloat
+    if real_dtype == torch.half:
+        return torch.complex32
+    raise ValueError(f"Unexpected dtype {real_dtype}")
+
+
+def griffinlim(
+    specgram: Tensor,
+    window: Tensor,
+    n_fft: int,
+    hop_length: int,
+    win_length: int,
+    power: float,
+    n_iter: int,
+    momentum: float,
+    length: Optional[int],
+    rand_init: bool,
+) -> Tensor:
+    r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Implementation ported from
+    *librosa* :cite:`brian_mcfee-proc-scipy-2015`, *A fast Griffin-Lim algorithm* :cite:`6701851`
+    and *Signal estimation from modified short-time Fourier transform* :cite:`1172092`.
+
+    Args:
+        specgram (Tensor): A magnitude-only STFT spectrogram of dimension `(..., freq, frames)`
+            where freq is ``n_fft // 2 + 1``.
+        window (Tensor): Window tensor that is applied/multiplied to each frame/window
+        n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins
+        hop_length (int): Length of hop between STFT windows. (
+            Default: ``win_length // 2``)
+        win_length (int): Window size. (Default: ``n_fft``)
+        power (float): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
+        n_iter (int): Number of iteration for phase recovery process.
+        momentum (float): The momentum parameter for fast Griffin-Lim.
+            Setting this to 0 recovers the original Griffin-Lim method.
+            Values near 1 can lead to faster convergence, but above 1 may not converge.
+        length (int or None): Array length of the expected output.
+        rand_init (bool): Initializes phase randomly if True, to zero otherwise.
+
+    Returns:
+        Tensor: waveform of `(..., time)`, where time equals the ``length`` parameter if given.
+    """
+    if not 0 <= momentum < 1:
+        raise ValueError("momentum must be in range [0, 1). Found: {}".format(momentum))
+
+    momentum = momentum / (1 + momentum)
+
+    # pack batch
+    shape = specgram.size()
+    specgram = specgram.reshape([-1] + list(shape[-2:]))
+
+    specgram = specgram.pow(1 / power)
+
+    # initialize the phase
+    if rand_init:
+        angles = torch.rand(specgram.size(), dtype=_get_complex_dtype(specgram.dtype), device=specgram.device)
+    else:
+        angles = torch.full(specgram.size(), 1, dtype=_get_complex_dtype(specgram.dtype), device=specgram.device)
+
+    # And initialize the previous iterate to 0
+    tprev = torch.tensor(0.0, dtype=specgram.dtype, device=specgram.device)
+    for _ in range(n_iter):
+        # Invert with our current estimate of the phases
+        inverse = torch.istft(
+            specgram * angles, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=length
+        )
+
+        # Rebuild the spectrogram
+        rebuilt = torch.stft(
+            input=inverse,
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            window=window,
+            center=True,
+            pad_mode="reflect",
+            normalized=False,
+            onesided=True,
+            return_complex=True,
+        )
+
+        # Update our phase estimates
+        angles = rebuilt
+        if momentum:
+            angles = angles - tprev.mul_(momentum)
+        angles = angles.div(angles.abs().add(1e-16))
+
+        # Store the previous iterate
+        tprev = rebuilt
+
+    # Return the final phase estimates
+    waveform = torch.istft(
+        specgram * angles, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=length
+    )
+
+    # unpack batch
+    waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:])
+
+    return waveform
+
+
+def amplitude_to_DB(
+    x: Tensor, multiplier: float, amin: float, db_multiplier: float, top_db: Optional[float] = None
+) -> Tensor:
+    r"""Turn a spectrogram from the power/amplitude scale to the decibel scale.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    The output of each tensor in a batch depends on the maximum value of that tensor,
+    and so may return different values for an audio clip split into snippets vs. a full clip.
+
+    Args:
+
+        x (Tensor): Input spectrogram(s) before being converted to decibel scale.
+            The expected shapes are ``(freq, time)``, ``(channel, freq, time)`` or
+            ``(..., batch, channel, freq, time)``.
+
+            .. note::
+
+               When ``top_db`` is specified, cut-off values are computed for each audio
+               in the batch. Therefore if the input shape is 4D (or larger), different
+               cut-off values are used for audio data in the batch.
+               If the input shape is 2D or 3D, a single cutoff value is used.
+
+        multiplier (float): Use 10. for power and 20. for amplitude
+        amin (float): Number to clamp ``x``
+        db_multiplier (float): Log10(max(reference value and amin))
+        top_db (float or None, optional): Minimum negative cut-off in decibels. A reasonable number
+            is 80. (Default: ``None``)
+
+    Returns:
+        Tensor: Output tensor in decibel scale
+    """
+    x_db = multiplier * torch.log10(torch.clamp(x, min=amin))
+    x_db -= multiplier * db_multiplier
+
+    if top_db is not None:
+        # Expand batch
+        shape = x_db.size()
+        packed_channels = shape[-3] if x_db.dim() > 2 else 1
+        x_db = x_db.reshape(-1, packed_channels, shape[-2], shape[-1])
+
+        x_db = torch.max(x_db, (x_db.amax(dim=(-3, -2, -1)) - top_db).view(-1, 1, 1, 1))
+
+        # Repack batch
+        x_db = x_db.reshape(shape)
+
+    return x_db
+
+
+def DB_to_amplitude(x: Tensor, ref: float, power: float) -> Tensor:
+    r"""Turn a tensor from the decibel scale to the power/amplitude scale.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        x (Tensor): Input tensor before being converted to power/amplitude scale.
+        ref (float): Reference which the output will be scaled by.
+        power (float): If power equals 1, will compute DB to power. If 0.5, will compute DB to amplitude.
+
+    Returns:
+        Tensor: Output tensor in power/amplitude scale.
+    """
+    return ref * torch.pow(torch.pow(10.0, 0.1 * x), power)
+
+
+def _hz_to_mel(freq: float, mel_scale: str = "htk") -> float:
+    r"""Convert Hz to Mels.
+
+    Args:
+        freqs (float): Frequencies in Hz
+        mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+
+    Returns:
+        mels (float): Frequency in Mels
+    """
+
+    if mel_scale not in ["slaney", "htk"]:
+        raise ValueError('mel_scale should be one of "htk" or "slaney".')
+
+    if mel_scale == "htk":
+        return 2595.0 * math.log10(1.0 + (freq / 700.0))
+
+    # Fill in the linear part
+    f_min = 0.0
+    f_sp = 200.0 / 3
+
+    mels = (freq - f_min) / f_sp
+
+    # Fill in the log-scale part
+    min_log_hz = 1000.0
+    min_log_mel = (min_log_hz - f_min) / f_sp
+    logstep = math.log(6.4) / 27.0
+
+    if freq >= min_log_hz:
+        mels = min_log_mel + math.log(freq / min_log_hz) / logstep
+
+    return mels
+
+
+def _mel_to_hz(mels: Tensor, mel_scale: str = "htk") -> Tensor:
+    """Convert mel bin numbers to frequencies.
+
+    Args:
+        mels (Tensor): Mel frequencies
+        mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+
+    Returns:
+        freqs (Tensor): Mels converted in Hz
+    """
+
+    if mel_scale not in ["slaney", "htk"]:
+        raise ValueError('mel_scale should be one of "htk" or "slaney".')
+
+    if mel_scale == "htk":
+        return 700.0 * (10.0 ** (mels / 2595.0) - 1.0)
+
+    # Fill in the linear scale
+    f_min = 0.0
+    f_sp = 200.0 / 3
+    freqs = f_min + f_sp * mels
+
+    # And now the nonlinear scale
+    min_log_hz = 1000.0
+    min_log_mel = (min_log_hz - f_min) / f_sp
+    logstep = math.log(6.4) / 27.0
+
+    log_t = mels >= min_log_mel
+    freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel))
+
+    return freqs
+
+
+def _create_triangular_filterbank(
+    all_freqs: Tensor,
+    f_pts: Tensor,
+) -> Tensor:
+    """Create a triangular filter bank.
+
+    Args:
+        all_freqs (Tensor): STFT freq points of size (`n_freqs`).
+        f_pts (Tensor): Filter mid points of size (`n_filter`).
+
+    Returns:
+        fb (Tensor): The filter bank of size (`n_freqs`, `n_filter`).
+    """
+    # Adopted from Librosa
+    # calculate the difference between each filter mid point and each stft freq point in hertz
+    f_diff = f_pts[1:] - f_pts[:-1]  # (n_filter + 1)
+    slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(1)  # (n_freqs, n_filter + 2)
+    # create overlapping triangles
+    zero = torch.zeros(1)
+    down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1]  # (n_freqs, n_filter)
+    up_slopes = slopes[:, 2:] / f_diff[1:]  # (n_freqs, n_filter)
+    fb = torch.max(zero, torch.min(down_slopes, up_slopes))
+
+    return fb
+
+
+def melscale_fbanks(
+    n_freqs: int,
+    f_min: float,
+    f_max: float,
+    n_mels: int,
+    sample_rate: int,
+    norm: Optional[str] = None,
+    mel_scale: str = "htk",
+) -> Tensor:
+    r"""Create a frequency bin conversion matrix.
+
+    .. devices:: CPU
+
+    .. properties:: TorchScript
+
+    Note:
+        For the sake of the numerical compatibility with librosa, not all the coefficients
+        in the resulting filter bank has magnitude of 1.
+
+        .. image:: https://download.pytorch.org/torchaudio/doc-assets/mel_fbanks.png
+           :alt: Visualization of generated filter bank
+
+    Args:
+        n_freqs (int): Number of frequencies to highlight/apply
+        f_min (float): Minimum frequency (Hz)
+        f_max (float): Maximum frequency (Hz)
+        n_mels (int): Number of mel filterbanks
+        sample_rate (int): Sample rate of the audio waveform
+        norm (str or None, optional): If "slaney", divide the triangular mel weights by the width of the mel band
+            (area normalization). (Default: ``None``)
+        mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+
+    Returns:
+        Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``)
+        meaning number of frequencies to highlight/apply to x the number of filterbanks.
+        Each column is a filterbank so that assuming there is a matrix A of
+        size (..., ``n_freqs``), the applied result would be
+        ``A @ melscale_fbanks(A.size(-1), ...)``.
+
+    """
+
+    if norm is not None and norm != "slaney":
+        raise ValueError('norm must be one of None or "slaney"')
+
+    # freq bins
+    all_freqs = torch.linspace(0, sample_rate // 2, n_freqs)
+
+    # calculate mel freq bins
+    m_min = _hz_to_mel(f_min, mel_scale=mel_scale)
+    m_max = _hz_to_mel(f_max, mel_scale=mel_scale)
+
+    m_pts = torch.linspace(m_min, m_max, n_mels + 2)
+    f_pts = _mel_to_hz(m_pts, mel_scale=mel_scale)
+
+    # create filterbank
+    fb = _create_triangular_filterbank(all_freqs, f_pts)
+
+    if norm is not None and norm == "slaney":
+        # Slaney-style mel is scaled to be approx constant energy per channel
+        enorm = 2.0 / (f_pts[2 : n_mels + 2] - f_pts[:n_mels])
+        fb *= enorm.unsqueeze(0)
+
+    if (fb.max(dim=0).values == 0.0).any():
+        warnings.warn(
+            "At least one mel filterbank has all zero values. "
+            f"The value for `n_mels` ({n_mels}) may be set too high. "
+            f"Or, the value for `n_freqs` ({n_freqs}) may be set too low."
+        )
+
+    return fb
+
+
+def linear_fbanks(
+    n_freqs: int,
+    f_min: float,
+    f_max: float,
+    n_filter: int,
+    sample_rate: int,
+) -> Tensor:
+    r"""Creates a linear triangular filterbank.
+
+    .. devices:: CPU
+
+    .. properties:: TorchScript
+
+    Note:
+        For the sake of the numerical compatibility with librosa, not all the coefficients
+        in the resulting filter bank has magnitude of 1.
+
+        .. image:: https://download.pytorch.org/torchaudio/doc-assets/lin_fbanks.png
+           :alt: Visualization of generated filter bank
+
+    Args:
+        n_freqs (int): Number of frequencies to highlight/apply
+        f_min (float): Minimum frequency (Hz)
+        f_max (float): Maximum frequency (Hz)
+        n_filter (int): Number of (linear) triangular filter
+        sample_rate (int): Sample rate of the audio waveform
+
+    Returns:
+        Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_filter``)
+        meaning number of frequencies to highlight/apply to x the number of filterbanks.
+        Each column is a filterbank so that assuming there is a matrix A of
+        size (..., ``n_freqs``), the applied result would be
+        ``A * linear_fbanks(A.size(-1), ...)``.
+    """
+    # freq bins
+    all_freqs = torch.linspace(0, sample_rate // 2, n_freqs)
+
+    # filter mid-points
+    f_pts = torch.linspace(f_min, f_max, n_filter + 2)
+
+    # create filterbank
+    fb = _create_triangular_filterbank(all_freqs, f_pts)
+
+    return fb
+
+
+def create_dct(n_mfcc: int, n_mels: int, norm: Optional[str]) -> Tensor:
+    r"""Create a DCT transformation matrix with shape (``n_mels``, ``n_mfcc``),
+    normalized depending on norm.
+
+    .. devices:: CPU
+
+    .. properties:: TorchScript
+
+    Args:
+        n_mfcc (int): Number of mfc coefficients to retain
+        n_mels (int): Number of mel filterbanks
+        norm (str or None): Norm to use (either "ortho" or None)
+
+    Returns:
+        Tensor: The transformation matrix, to be right-multiplied to
+        row-wise data of size (``n_mels``, ``n_mfcc``).
+    """
+
+    if norm is not None and norm != "ortho":
+        raise ValueError('norm must be either "ortho" or None')
+
+    # http://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II
+    n = torch.arange(float(n_mels))
+    k = torch.arange(float(n_mfcc)).unsqueeze(1)
+    dct = torch.cos(math.pi / float(n_mels) * (n + 0.5) * k)  # size (n_mfcc, n_mels)
+
+    if norm is None:
+        dct *= 2.0
+    else:
+        dct[0] *= 1.0 / math.sqrt(2.0)
+        dct *= math.sqrt(2.0 / float(n_mels))
+    return dct.t()
+
+
+def mu_law_encoding(x: Tensor, quantization_channels: int) -> Tensor:
+    r"""Encode signal based on mu-law companding.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    For more info see the
+    `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
+
+    This algorithm expects the signal has been scaled to between -1 and 1 and
+    returns a signal encoded with values from 0 to quantization_channels - 1.
+
+    Args:
+        x (Tensor): Input tensor
+        quantization_channels (int): Number of channels
+
+    Returns:
+        Tensor: Input after mu-law encoding
+    """
+    mu = quantization_channels - 1.0
+    if not x.is_floating_point():
+        warnings.warn(
+            "The input Tensor must be of floating type. \
+            This will be an error in the v0.12 release."
+        )
+        x = x.to(torch.float)
+    mu = torch.tensor(mu, dtype=x.dtype)
+    x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu)
+    x_mu = ((x_mu + 1) / 2 * mu + 0.5).to(torch.int64)
+    return x_mu
+
+
+def mu_law_decoding(x_mu: Tensor, quantization_channels: int) -> Tensor:
+    r"""Decode mu-law encoded signal.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    For more info see the
+    `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
+
+    This expects an input with values between 0 and quantization_channels - 1
+    and returns a signal scaled between -1 and 1.
+
+    Args:
+        x_mu (Tensor): Input tensor
+        quantization_channels (int): Number of channels
+
+    Returns:
+        Tensor: Input after mu-law decoding
+    """
+    mu = quantization_channels - 1.0
+    if not x_mu.is_floating_point():
+        x_mu = x_mu.to(torch.float)
+    mu = torch.tensor(mu, dtype=x_mu.dtype)
+    x = ((x_mu) / mu) * 2 - 1.0
+    x = torch.sign(x) * (torch.exp(torch.abs(x) * torch.log1p(mu)) - 1.0) / mu
+    return x
+
+
+def phase_vocoder(complex_specgrams: Tensor, rate: float, phase_advance: Tensor) -> Tensor:
+    r"""Given a STFT tensor, speed up in time without modifying pitch by a factor of ``rate``.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        complex_specgrams (Tensor):
+            A tensor of dimension `(..., freq, num_frame)` with complex dtype.
+        rate (float): Speed-up factor
+        phase_advance (Tensor): Expected phase advance in each bin. Dimension of `(freq, 1)`
+
+    Returns:
+        Tensor:
+            Stretched spectrogram. The resulting tensor is of the same dtype as the input
+            spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``.
+
+    Example
+        >>> freq, hop_length = 1025, 512
+        >>> # (channel, freq, time)
+        >>> complex_specgrams = torch.randn(2, freq, 300, dtype=torch.cfloat)
+        >>> rate = 1.3 # Speed up by 30%
+        >>> phase_advance = torch.linspace(
+        >>>    0, math.pi * hop_length, freq)[..., None]
+        >>> x = phase_vocoder(complex_specgrams, rate, phase_advance)
+        >>> x.shape # with 231 == ceil(300 / 1.3)
+        torch.Size([2, 1025, 231])
+    """
+    if rate == 1.0:
+        return complex_specgrams
+
+    # pack batch
+    shape = complex_specgrams.size()
+    complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-2:]))
+
+    # Figures out the corresponding real dtype, i.e. complex128 -> float64, complex64 -> float32
+    # Note torch.real is a view so it does not incur any memory copy.
+    real_dtype = torch.real(complex_specgrams).dtype
+    time_steps = torch.arange(0, complex_specgrams.size(-1), rate, device=complex_specgrams.device, dtype=real_dtype)
+
+    alphas = time_steps % 1.0
+    phase_0 = complex_specgrams[..., :1].angle()
+
+    # Time Padding
+    complex_specgrams = torch.nn.functional.pad(complex_specgrams, [0, 2])
+
+    # (new_bins, freq, 2)
+    complex_specgrams_0 = complex_specgrams.index_select(-1, time_steps.long())
+    complex_specgrams_1 = complex_specgrams.index_select(-1, (time_steps + 1).long())
+
+    angle_0 = complex_specgrams_0.angle()
+    angle_1 = complex_specgrams_1.angle()
+
+    norm_0 = complex_specgrams_0.abs()
+    norm_1 = complex_specgrams_1.abs()
+
+    phase = angle_1 - angle_0 - phase_advance
+    phase = phase - 2 * math.pi * torch.round(phase / (2 * math.pi))
+
+    # Compute Phase Accum
+    phase = phase + phase_advance
+    phase = torch.cat([phase_0, phase[..., :-1]], dim=-1)
+    phase_acc = torch.cumsum(phase, -1)
+
+    mag = alphas * norm_1 + (1 - alphas) * norm_0
+
+    complex_specgrams_stretch = torch.polar(mag, phase_acc)
+
+    # unpack batch
+    complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-2] + complex_specgrams_stretch.shape[1:])
+    return complex_specgrams_stretch
+
+
+def _get_mask_param(mask_param: int, p: float, axis_length: int) -> int:
+    if p == 1.0:
+        return mask_param
+    else:
+        return min(mask_param, int(axis_length * p))
+
+
+def mask_along_axis_iid(
+    specgrams: Tensor,
+    mask_param: int,
+    mask_value: float,
+    axis: int,
+    p: float = 1.0,
+) -> Tensor:
+    r"""Apply a mask along ``axis``.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Mask will be applied from indices ``[v_0, v_0 + v)``,
+    where ``v`` is sampled from ``uniform(0, max_v)`` and
+    ``v_0`` from ``uniform(0, specgrams.size(axis) - v)``,
+    with ``max_v = mask_param`` when ``p = 1.0`` and
+    ``max_v = min(mask_param, floor(specgrams.size(axis) * p))`` otherwise.
+
+    Args:
+        specgrams (Tensor): Real spectrograms `(..., freq, time)`, with at least 3 dimensions.
+        mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
+        mask_value (float): Value to assign to the masked columns
+        axis (int): Axis to apply masking on, which should be the one of the last two dimensions.
+        p (float, optional): maximum proportion of columns that can be masked. (Default: 1.0)
+
+    Returns:
+        Tensor: Masked spectrograms with the same dimensions as input specgrams Tensor`
+    """
+
+    dim = specgrams.dim()
+
+    if dim < 3:
+        raise ValueError(f"Spectrogram must have at least three dimensions ({dim} given).")
+
+    if axis not in [dim - 2, dim - 1]:
+        raise ValueError(
+            f"Only Frequency and Time masking are supported (axis {dim-2} and axis {dim-1} supported; {axis} given)."
+        )
+
+    if not 0.0 <= p <= 1.0:
+        raise ValueError(f"The value of p must be between 0.0 and 1.0 ({p} given).")
+
+    mask_param = _get_mask_param(mask_param, p, specgrams.shape[axis])
+    if mask_param < 1:
+        return specgrams
+
+    device = specgrams.device
+    dtype = specgrams.dtype
+
+    value = torch.rand(specgrams.shape[: (dim - 2)], device=device, dtype=dtype) * mask_param
+    min_value = torch.rand(specgrams.shape[: (dim - 2)], device=device, dtype=dtype) * (specgrams.size(axis) - value)
+
+    # Create broadcastable mask
+    mask_start = min_value.long()[..., None, None]
+    mask_end = (min_value.long() + value.long())[..., None, None]
+    mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype)
+
+    # Per batch example masking
+    specgrams = specgrams.transpose(axis, -1)
+    specgrams = specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value)
+    specgrams = specgrams.transpose(axis, -1)
+
+    return specgrams
+
+
+def mask_along_axis(
+    specgram: Tensor,
+    mask_param: int,
+    mask_value: float,
+    axis: int,
+    p: float = 1.0,
+) -> Tensor:
+    r"""Apply a mask along ``axis``.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Mask will be applied from indices ``[v_0, v_0 + v)``,
+    where ``v`` is sampled from ``uniform(0, max_v)`` and
+    ``v_0`` from ``uniform(0, specgram.size(axis) - v)``, with
+    ``max_v = mask_param`` when ``p = 1.0`` and
+    ``max_v = min(mask_param, floor(specgram.size(axis) * p))``
+    otherwise.
+    All examples will have the same mask interval.
+
+    Args:
+        specgram (Tensor): Real spectrograms `(..., freq, time)`, with at least 2 dimensions.
+        mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
+        mask_value (float): Value to assign to the masked columns
+        axis (int): Axis to apply masking on, which should be the one of the last two dimensions.
+        p (float, optional): maximum proportion of columns that can be masked. (Default: 1.0)
+
+    Returns:
+        Tensor: Masked spectrograms with the same dimensions as input specgram Tensor
+    """
+    dim = specgram.dim()
+
+    if dim < 2:
+        raise ValueError(f"Spectrogram must have at least two dimensions (time and frequency) ({dim} given).")
+
+    if axis not in [dim - 2, dim - 1]:
+        raise ValueError(
+            f"Only Frequency and Time masking are supported (axis {dim-2} and axis {dim-1} supported; {axis} given)."
+        )
+
+    if not 0.0 <= p <= 1.0:
+        raise ValueError(f"The value of p must be between 0.0 and 1.0 ({p} given).")
+
+    mask_param = _get_mask_param(mask_param, p, specgram.shape[axis])
+    if mask_param < 1:
+        return specgram
+
+    # pack batch
+    shape = specgram.size()
+    specgram = specgram.reshape([-1] + list(shape[-2:]))
+    # After packing, specgram is a 3D tensor, and the axis corresponding to the to-be-masked dimension
+    # is now (axis - dim + 3), e.g. a tensor of shape (10, 2, 50, 10, 2) becomes a tensor of shape (1000, 10, 2).
+    value = torch.rand(1) * mask_param
+    min_value = torch.rand(1) * (specgram.size(axis - dim + 3) - value)
+
+    mask_start = (min_value.long()).squeeze()
+    mask_end = (min_value.long() + value.long()).squeeze()
+    mask = torch.arange(0, specgram.shape[axis - dim + 3], device=specgram.device, dtype=specgram.dtype)
+    mask = (mask >= mask_start) & (mask < mask_end)
+    # unsqueeze the mask if the axis is frequency
+    if axis == dim - 2:
+        mask = mask.unsqueeze(-1)
+
+    if mask_end - mask_start >= mask_param:
+        raise ValueError("Number of columns to be masked should be less than mask_param")
+
+    specgram = specgram.masked_fill(mask, mask_value)
+
+    # unpack batch
+    specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:])
+
+    return specgram
+
+
+def compute_deltas(specgram: Tensor, win_length: int = 5, mode: str = "replicate") -> Tensor:
+    r"""Compute delta coefficients of a tensor, usually a spectrogram:
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    .. math::
+       d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N}} n^2}
+
+    where :math:`d_t` is the deltas at time :math:`t`,
+    :math:`c_t` is the spectrogram coeffcients at time :math:`t`,
+    :math:`N` is ``(win_length-1)//2``.
+
+    Args:
+        specgram (Tensor): Tensor of audio of dimension `(..., freq, time)`
+        win_length (int, optional): The window length used for computing delta (Default: ``5``)
+        mode (str, optional): Mode parameter passed to padding (Default: ``"replicate"``)
+
+    Returns:
+        Tensor: Tensor of deltas of dimension `(..., freq, time)`
+
+    Example
+        >>> specgram = torch.randn(1, 40, 1000)
+        >>> delta = compute_deltas(specgram)
+        >>> delta2 = compute_deltas(delta)
+    """
+    device = specgram.device
+    dtype = specgram.dtype
+
+    # pack batch
+    shape = specgram.size()
+    specgram = specgram.reshape(1, -1, shape[-1])
+
+    if win_length < 3:
+        raise ValueError(f"Window length should be greater than or equal to 3. Found win_length {win_length}")
+
+    n = (win_length - 1) // 2
+
+    # twice sum of integer squared
+    denom = n * (n + 1) * (2 * n + 1) / 3
+
+    specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode)
+
+    kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1)
+
+    output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom
+
+    # unpack batch
+    output = output.reshape(shape)
+
+    return output
+
+
+def _compute_nccf(waveform: Tensor, sample_rate: int, frame_time: float, freq_low: int) -> Tensor:
+    r"""
+    Compute Normalized Cross-Correlation Function (NCCF).
+
+    .. math::
+        \phi_i(m) = \frac{\sum_{n=b_i}^{b_i + N-1} w(n) w(m+n)}{\sqrt{E(b_i) E(m+b_i)}},
+
+    where
+    :math:`\phi_i(m)` is the NCCF at frame :math:`i` with lag :math:`m`,
+    :math:`w` is the waveform,
+    :math:`N` is the length of a frame,
+    :math:`b_i` is the beginning of frame :math:`i`,
+    :math:`E(j)` is the energy :math:`\sum_{n=j}^{j+N-1} w^2(n)`.
+    """
+
+    EPSILON = 10 ** (-9)
+
+    # Number of lags to check
+    lags = int(math.ceil(sample_rate / freq_low))
+
+    frame_size = int(math.ceil(sample_rate * frame_time))
+
+    waveform_length = waveform.size()[-1]
+    num_of_frames = int(math.ceil(waveform_length / frame_size))
+
+    p = lags + num_of_frames * frame_size - waveform_length
+    waveform = torch.nn.functional.pad(waveform, (0, p))
+
+    # Compute lags
+    output_lag = []
+    for lag in range(1, lags + 1):
+        s1 = waveform[..., :-lag].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :]
+        s2 = waveform[..., lag:].unfold(-1, frame_size, frame_size)[..., :num_of_frames, :]
+
+        output_frames = (
+            (s1 * s2).sum(-1)
+            / (EPSILON + torch.linalg.vector_norm(s1, ord=2, dim=-1)).pow(2)
+            / (EPSILON + torch.linalg.vector_norm(s2, ord=2, dim=-1)).pow(2)
+        )
+
+        output_lag.append(output_frames.unsqueeze(-1))
+
+    nccf = torch.cat(output_lag, -1)
+
+    return nccf
+
+
+def _combine_max(a: Tuple[Tensor, Tensor], b: Tuple[Tensor, Tensor], thresh: float = 0.99) -> Tuple[Tensor, Tensor]:
+    """
+    Take value from first if bigger than a multiplicative factor of the second, elementwise.
+    """
+    mask = a[0] > thresh * b[0]
+    values = mask * a[0] + ~mask * b[0]
+    indices = mask * a[1] + ~mask * b[1]
+    return values, indices
+
+
+def _find_max_per_frame(nccf: Tensor, sample_rate: int, freq_high: int) -> Tensor:
+    r"""
+    For each frame, take the highest value of NCCF,
+    apply centered median smoothing, and convert to frequency.
+
+    Note: If the max among all the lags is very close
+    to the first half of lags, then the latter is taken.
+    """
+
+    lag_min = int(math.ceil(sample_rate / freq_high))
+
+    # Find near enough max that is smallest
+
+    best = torch.max(nccf[..., lag_min:], -1)
+
+    half_size = nccf.shape[-1] // 2
+    half = torch.max(nccf[..., lag_min:half_size], -1)
+
+    best = _combine_max(half, best)
+    indices = best[1]
+
+    # Add back minimal lag
+    indices += lag_min
+    # Add 1 empirical calibration offset
+    indices += 1
+
+    return indices
+
+
+def _median_smoothing(indices: Tensor, win_length: int) -> Tensor:
+    r"""
+    Apply median smoothing to the 1D tensor over the given window.
+    """
+
+    # Centered windowed
+    pad_length = (win_length - 1) // 2
+
+    # "replicate" padding in any dimension
+    indices = torch.nn.functional.pad(indices, (pad_length, 0), mode="constant", value=0.0)
+
+    indices[..., :pad_length] = torch.cat(pad_length * [indices[..., pad_length].unsqueeze(-1)], dim=-1)
+    roll = indices.unfold(-1, win_length, 1)
+
+    values, _ = torch.median(roll, -1)
+    return values
+
+
+def detect_pitch_frequency(
+    waveform: Tensor,
+    sample_rate: int,
+    frame_time: float = 10 ** (-2),
+    win_length: int = 30,
+    freq_low: int = 85,
+    freq_high: int = 3400,
+) -> Tensor:
+    r"""Detect pitch frequency.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    It is implemented using normalized cross-correlation function and median smoothing.
+
+    Args:
+        waveform (Tensor): Tensor of audio of dimension `(..., freq, time)`
+        sample_rate (int): The sample rate of the waveform (Hz)
+        frame_time (float, optional): Duration of a frame (Default: ``10 ** (-2)``).
+        win_length (int, optional): The window length for median smoothing (in number of frames) (Default: ``30``).
+        freq_low (int, optional): Lowest frequency that can be detected (Hz) (Default: ``85``).
+        freq_high (int, optional): Highest frequency that can be detected (Hz) (Default: ``3400``).
+
+    Returns:
+        Tensor: Tensor of freq of dimension `(..., frame)`
+    """
+    # pack batch
+    shape = list(waveform.size())
+    waveform = waveform.reshape([-1] + shape[-1:])
+
+    nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low)
+    indices = _find_max_per_frame(nccf, sample_rate, freq_high)
+    indices = _median_smoothing(indices, win_length)
+
+    # Convert indices to frequency
+    EPSILON = 10 ** (-9)
+    freq = sample_rate / (EPSILON + indices.to(torch.float))
+
+    # unpack batch
+    freq = freq.reshape(shape[:-1] + list(freq.shape[-1:]))
+
+    return freq
+
+
+def sliding_window_cmn(
+    specgram: Tensor,
+    cmn_window: int = 600,
+    min_cmn_window: int = 100,
+    center: bool = False,
+    norm_vars: bool = False,
+) -> Tensor:
+    r"""
+    Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        specgram (Tensor): Tensor of spectrogram of dimension `(..., time, freq)`
+        cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600)
+        min_cmn_window (int, optional):  Minimum CMN window used at start of decoding (adds latency only at start).
+            Only applicable if center == false, ignored if center==true (int, default = 100)
+        center (bool, optional): If true, use a window centered on the current frame
+            (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false)
+        norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false)
+
+    Returns:
+        Tensor: Tensor matching input shape `(..., freq, time)`
+    """
+    input_shape = specgram.shape
+    num_frames, num_feats = input_shape[-2:]
+    specgram = specgram.view(-1, num_frames, num_feats)
+    num_channels = specgram.shape[0]
+
+    dtype = specgram.dtype
+    device = specgram.device
+    last_window_start = last_window_end = -1
+    cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device)
+    cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device)
+    cmn_specgram = torch.zeros(num_channels, num_frames, num_feats, dtype=dtype, device=device)
+    for t in range(num_frames):
+        window_start = 0
+        window_end = 0
+        if center:
+            window_start = t - cmn_window // 2
+            window_end = window_start + cmn_window
+        else:
+            window_start = t - cmn_window
+            window_end = t + 1
+        if window_start < 0:
+            window_end -= window_start
+            window_start = 0
+        if not center:
+            if window_end > t:
+                window_end = max(t + 1, min_cmn_window)
+        if window_end > num_frames:
+            window_start -= window_end - num_frames
+            window_end = num_frames
+            if window_start < 0:
+                window_start = 0
+        if last_window_start == -1:
+            input_part = specgram[:, window_start : window_end - window_start, :]
+            cur_sum += torch.sum(input_part, 1)
+            if norm_vars:
+                cur_sumsq += torch.cumsum(input_part**2, 1)[:, -1, :]
+        else:
+            if window_start > last_window_start:
+                frame_to_remove = specgram[:, last_window_start, :]
+                cur_sum -= frame_to_remove
+                if norm_vars:
+                    cur_sumsq -= frame_to_remove**2
+            if window_end > last_window_end:
+                frame_to_add = specgram[:, last_window_end, :]
+                cur_sum += frame_to_add
+                if norm_vars:
+                    cur_sumsq += frame_to_add**2
+        window_frames = window_end - window_start
+        last_window_start = window_start
+        last_window_end = window_end
+        cmn_specgram[:, t, :] = specgram[:, t, :] - cur_sum / window_frames
+        if norm_vars:
+            if window_frames == 1:
+                cmn_specgram[:, t, :] = torch.zeros(num_channels, num_feats, dtype=dtype, device=device)
+            else:
+                variance = cur_sumsq
+                variance = variance / window_frames
+                variance -= (cur_sum**2) / (window_frames**2)
+                variance = torch.pow(variance, -0.5)
+                cmn_specgram[:, t, :] *= variance
+
+    cmn_specgram = cmn_specgram.view(input_shape[:-2] + (num_frames, num_feats))
+    if len(input_shape) == 2:
+        cmn_specgram = cmn_specgram.squeeze(0)
+    return cmn_specgram
+
+
+def spectral_centroid(
+    waveform: Tensor,
+    sample_rate: int,
+    pad: int,
+    window: Tensor,
+    n_fft: int,
+    hop_length: int,
+    win_length: int,
+) -> Tensor:
+    r"""Compute the spectral centroid for each channel along the time axis.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    The spectral centroid is defined as the weighted average of the
+    frequency values, weighted by their magnitude.
+
+    Args:
+        waveform (Tensor): Tensor of audio of dimension `(..., time)`
+        sample_rate (int): Sample rate of the audio waveform
+        pad (int): Two sided padding of signal
+        window (Tensor): Window tensor that is applied/multiplied to each frame/window
+        n_fft (int): Size of FFT
+        hop_length (int): Length of hop between STFT windows
+        win_length (int): Window size
+
+    Returns:
+        Tensor: Dimension `(..., time)`
+    """
+    specgram = spectrogram(
+        waveform,
+        pad=pad,
+        window=window,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        power=1.0,
+        normalized=False,
+    )
+    freqs = torch.linspace(0, sample_rate // 2, steps=1 + n_fft // 2, device=specgram.device).reshape((-1, 1))
+    freq_dim = -2
+    return (freqs * specgram).sum(dim=freq_dim) / specgram.sum(dim=freq_dim)
+
+
+@deprecated("Please migrate to :py:class:`torchaudio.io.AudioEffector`.", remove=False)
+def apply_codec(
+    waveform: Tensor,
+    sample_rate: int,
+    format: str,
+    channels_first: bool = True,
+    compression: Optional[float] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+) -> Tensor:
+    r"""
+    Apply codecs as a form of augmentation.
+
+    .. devices:: CPU
+
+    Args:
+        waveform (Tensor): Audio data. Must be 2 dimensional. See also ```channels_first```.
+        sample_rate (int): Sample rate of the audio waveform.
+        format (str): File format.
+        channels_first (bool, optional):
+            When True, both the input and output Tensor have dimension `(channel, time)`.
+            Otherwise, they have dimension `(time, channel)`.
+        compression (float or None, optional): Used for formats other than WAV.
+            For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
+        encoding (str or None, optional): Changes the encoding for the supported formats.
+            For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
+        bits_per_sample (int or None, optional): Changes the bit depth for the supported formats.
+            For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
+
+    Returns:
+        Tensor: Resulting Tensor.
+        If ``channels_first=True``, it has `(channel, time)` else `(time, channel)`.
+    """
+    from torchaudio.backend import _sox_io_backend
+
+    with tempfile.NamedTemporaryFile() as f:
+        torchaudio.backend._sox_io_backend.save(
+            f.name, waveform, sample_rate, channels_first, compression, format, encoding, bits_per_sample
+        )
+        augmented, sr = _sox_io_backend.load(f.name, channels_first=channels_first, format=format)
+    if sr != sample_rate:
+        augmented = resample(augmented, sr, sample_rate)
+    return augmented
+
+
+_CPU = torch.device("cpu")
+
+
+def _get_sinc_resample_kernel(
+    orig_freq: int,
+    new_freq: int,
+    gcd: int,
+    lowpass_filter_width: int = 6,
+    rolloff: float = 0.99,
+    resampling_method: str = "sinc_interp_hann",
+    beta: Optional[float] = None,
+    device: torch.device = _CPU,
+    dtype: Optional[torch.dtype] = None,
+):
+    if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq):
+        raise Exception(
+            "Frequencies must be of integer type to ensure quality resampling computation. "
+            "To work around this, manually convert both frequencies to integer values "
+            "that maintain their resampling rate ratio before passing them into the function. "
+            "Example: To downsample a 44100 hz waveform by a factor of 8, use "
+            "`orig_freq=8` and `new_freq=1` instead of `orig_freq=44100` and `new_freq=5512.5`. "
+            "For more information, please refer to https://github.com/pytorch/audio/issues/1487."
+        )
+
+    if resampling_method in ["sinc_interpolation", "kaiser_window"]:
+        method_map = {
+            "sinc_interpolation": "sinc_interp_hann",
+            "kaiser_window": "sinc_interp_kaiser",
+        }
+        warnings.warn(
+            f'"{resampling_method}" resampling method name is being deprecated and replaced by '
+            f'"{method_map[resampling_method]}" in the next release. '
+            "The default behavior remains unchanged.",
+            stacklevel=3,
+        )
+    elif resampling_method not in ["sinc_interp_hann", "sinc_interp_kaiser"]:
+        raise ValueError("Invalid resampling method: {}".format(resampling_method))
+
+    orig_freq = int(orig_freq) // gcd
+    new_freq = int(new_freq) // gcd
+
+    if lowpass_filter_width <= 0:
+        raise ValueError("Low pass filter width should be positive.")
+    base_freq = min(orig_freq, new_freq)
+    # This will perform antialiasing filtering by removing the highest frequencies.
+    # At first I thought I only needed this when downsampling, but when upsampling
+    # you will get edge artifacts without this, as the edge is equivalent to zero padding,
+    # which will add high freq artifacts.
+    base_freq *= rolloff
+
+    # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor)
+    # using the sinc interpolation formula:
+    #   x(t) = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - t))
+    # We can then sample the function x(t) with a different sample rate:
+    #    y[j] = x(j / new_freq)
+    # or,
+    #    y[j] = sum_i x[i] sinc(pi * orig_freq * (i / orig_freq - j / new_freq))
+
+    # We see here that y[j] is the convolution of x[i] with a specific filter, for which
+    # we take an FIR approximation, stopping when we see at least `lowpass_filter_width` zeros crossing.
+    # But y[j+1] is going to have a different set of weights and so on, until y[j + new_freq].
+    # Indeed:
+    # y[j + new_freq] = sum_i x[i] sinc(pi * orig_freq * ((i / orig_freq - (j + new_freq) / new_freq))
+    #                 = sum_i x[i] sinc(pi * orig_freq * ((i - orig_freq) / orig_freq - j / new_freq))
+    #                 = sum_i x[i + orig_freq] sinc(pi * orig_freq * (i / orig_freq - j / new_freq))
+    # so y[j+new_freq] uses the same filter as y[j], but on a shifted version of x by `orig_freq`.
+    # This will explain the F.conv1d after, with a stride of orig_freq.
+    width = math.ceil(lowpass_filter_width * orig_freq / base_freq)
+    # If orig_freq is still big after GCD reduction, most filters will be very unbalanced, i.e.,
+    # they will have a lot of almost zero values to the left or to the right...
+    # There is probably a way to evaluate those filters more efficiently, but this is kept for
+    # future work.
+    idx_dtype = dtype if dtype is not None else torch.float64
+
+    idx = torch.arange(-width, width + orig_freq, dtype=idx_dtype, device=device)[None, None] / orig_freq
+
+    t = torch.arange(0, -new_freq, -1, dtype=dtype, device=device)[:, None, None] / new_freq + idx
+    t *= base_freq
+    t = t.clamp_(-lowpass_filter_width, lowpass_filter_width)
+
+    # we do not use built in torch windows here as we need to evaluate the window
+    # at specific positions, not over a regular grid.
+    if resampling_method == "sinc_interp_hann":
+        window = torch.cos(t * math.pi / lowpass_filter_width / 2) ** 2
+    else:
+        # sinc_interp_kaiser
+        if beta is None:
+            beta = 14.769656459379492
+        beta_tensor = torch.tensor(float(beta))
+        window = torch.i0(beta_tensor * torch.sqrt(1 - (t / lowpass_filter_width) ** 2)) / torch.i0(beta_tensor)
+
+    t *= math.pi
+
+    scale = base_freq / orig_freq
+    kernels = torch.where(t == 0, torch.tensor(1.0).to(t), t.sin() / t)
+    kernels *= window * scale
+
+    if dtype is None:
+        kernels = kernels.to(dtype=torch.float32)
+
+    return kernels, width
+
+
+def _apply_sinc_resample_kernel(
+    waveform: Tensor,
+    orig_freq: int,
+    new_freq: int,
+    gcd: int,
+    kernel: Tensor,
+    width: int,
+):
+    if not waveform.is_floating_point():
+        raise TypeError(f"Expected floating point type for waveform tensor, but received {waveform.dtype}.")
+
+    orig_freq = int(orig_freq) // gcd
+    new_freq = int(new_freq) // gcd
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.view(-1, shape[-1])
+
+    num_wavs, length = waveform.shape
+    waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq))
+    resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq)
+    resampled = resampled.transpose(1, 2).reshape(num_wavs, -1)
+    target_length = torch.ceil(torch.as_tensor(new_freq * length / orig_freq)).long()
+    resampled = resampled[..., :target_length]
+
+    # unpack batch
+    resampled = resampled.view(shape[:-1] + resampled.shape[-1:])
+    return resampled
+
+
+def resample(
+    waveform: Tensor,
+    orig_freq: int,
+    new_freq: int,
+    lowpass_filter_width: int = 6,
+    rolloff: float = 0.99,
+    resampling_method: str = "sinc_interp_hann",
+    beta: Optional[float] = None,
+) -> Tensor:
+    r"""Resamples the waveform at the new frequency using bandlimited interpolation. :cite:`RESAMPLE`.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Note:
+        ``transforms.Resample`` precomputes and reuses the resampling kernel, so using it will result in
+        more efficient computation if resampling multiple waveforms with the same resampling parameters.
+
+    Args:
+        waveform (Tensor): The input signal of dimension `(..., time)`
+        orig_freq (int): The original frequency of the signal
+        new_freq (int): The desired frequency
+        lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper
+            but less efficient. (Default: ``6``)
+        rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist.
+            Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``)
+        resampling_method (str, optional): The resampling method to use.
+            Options: [``"sinc_interp_hann"``, ``"sinc_interp_kaiser"``] (Default: ``"sinc_interp_hann"``)
+        beta (float or None, optional): The shape parameter used for kaiser window.
+
+    Returns:
+        Tensor: The waveform at the new frequency of dimension `(..., time).`
+    """
+
+    if orig_freq <= 0.0 or new_freq <= 0.0:
+        raise ValueError("Original frequency and desired frequecy should be positive")
+
+    if orig_freq == new_freq:
+        return waveform
+
+    gcd = math.gcd(int(orig_freq), int(new_freq))
+
+    kernel, width = _get_sinc_resample_kernel(
+        orig_freq,
+        new_freq,
+        gcd,
+        lowpass_filter_width,
+        rolloff,
+        resampling_method,
+        beta,
+        waveform.device,
+        waveform.dtype,
+    )
+    resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width)
+    return resampled
+
+
+@torch.jit.unused
+def edit_distance(seq1: Sequence, seq2: Sequence) -> int:
+    """
+    Calculate the word level edit (Levenshtein) distance between two sequences.
+
+    .. devices:: CPU
+
+    The function computes an edit distance allowing deletion, insertion and
+    substitution. The result is an integer.
+
+    For most applications, the two input sequences should be the same type. If
+    two strings are given, the output is the edit distance between the two
+    strings (character edit distance). If two lists of strings are given, the
+    output is the edit distance between sentences (word edit distance). Users
+    may want to normalize the output by the length of the reference sequence.
+
+    Args:
+        seq1 (Sequence): the first sequence to compare.
+        seq2 (Sequence): the second sequence to compare.
+    Returns:
+        int: The distance between the first and second sequences.
+    """
+    len_sent2 = len(seq2)
+    dold = list(range(len_sent2 + 1))
+    dnew = [0 for _ in range(len_sent2 + 1)]
+
+    for i in range(1, len(seq1) + 1):
+        dnew[0] = i
+        for j in range(1, len_sent2 + 1):
+            if seq1[i - 1] == seq2[j - 1]:
+                dnew[j] = dold[j - 1]
+            else:
+                substitution = dold[j - 1] + 1
+                insertion = dnew[j - 1] + 1
+                deletion = dold[j] + 1
+                dnew[j] = min(substitution, insertion, deletion)
+
+        dnew, dold = dold, dnew
+
+    return int(dold[-1])
+
+
+def loudness(waveform: Tensor, sample_rate: int):
+    r"""Measure audio loudness according to the ITU-R BS.1770-4 recommendation.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        waveform(torch.Tensor): audio waveform of dimension `(..., channels, time)`
+        sample_rate (int): sampling rate of the waveform
+
+    Returns:
+        Tensor: loudness estimates (LKFS)
+
+    Reference:
+        - https://www.itu.int/rec/R-REC-BS.1770-4-201510-I/en
+    """
+
+    if waveform.size(-2) > 5:
+        raise ValueError("Only up to 5 channels are supported.")
+
+    gate_duration = 0.4
+    overlap = 0.75
+    gamma_abs = -70.0
+    kweight_bias = -0.691
+    gate_samples = int(round(gate_duration * sample_rate))
+    step = int(round(gate_samples * (1 - overlap)))
+
+    # Apply K-weighting
+    waveform = treble_biquad(waveform, sample_rate, 4.0, 1500.0, 1 / math.sqrt(2))
+    waveform = highpass_biquad(waveform, sample_rate, 38.0, 0.5)
+
+    # Compute the energy for each block
+    energy = torch.square(waveform).unfold(-1, gate_samples, step)
+    energy = torch.mean(energy, dim=-1)
+
+    # Compute channel-weighted summation
+    g = torch.tensor([1.0, 1.0, 1.0, 1.41, 1.41], dtype=waveform.dtype, device=waveform.device)
+    g = g[: energy.size(-2)]
+
+    energy_weighted = torch.sum(g.unsqueeze(-1) * energy, dim=-2)
+    loudness = -0.691 + 10 * torch.log10(energy_weighted)
+
+    # Apply absolute gating of the blocks
+    gated_blocks = loudness > gamma_abs
+    gated_blocks = gated_blocks.unsqueeze(-2)
+
+    energy_filtered = torch.sum(gated_blocks * energy, dim=-1) / torch.count_nonzero(gated_blocks, dim=-1)
+    energy_weighted = torch.sum(g * energy_filtered, dim=-1)
+    gamma_rel = kweight_bias + 10 * torch.log10(energy_weighted) - 10
+
+    # Apply relative gating of the blocks
+    gated_blocks = torch.logical_and(gated_blocks.squeeze(-2), loudness > gamma_rel.unsqueeze(-1))
+    gated_blocks = gated_blocks.unsqueeze(-2)
+
+    energy_filtered = torch.sum(gated_blocks * energy, dim=-1) / torch.count_nonzero(gated_blocks, dim=-1)
+    energy_weighted = torch.sum(g * energy_filtered, dim=-1)
+    LKFS = kweight_bias + 10 * torch.log10(energy_weighted)
+    return LKFS
+
+
+def pitch_shift(
+    waveform: Tensor,
+    sample_rate: int,
+    n_steps: int,
+    bins_per_octave: int = 12,
+    n_fft: int = 512,
+    win_length: Optional[int] = None,
+    hop_length: Optional[int] = None,
+    window: Optional[Tensor] = None,
+) -> Tensor:
+    """
+    Shift the pitch of a waveform by ``n_steps`` steps.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        waveform (Tensor): The input waveform of shape `(..., time)`.
+        sample_rate (int): Sample rate of `waveform`.
+        n_steps (int): The (fractional) steps to shift `waveform`.
+        bins_per_octave (int, optional): The number of steps per octave (Default: ``12``).
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins (Default: ``512``).
+        win_length (int or None, optional): Window size. If None, then ``n_fft`` is used. (Default: ``None``).
+        hop_length (int or None, optional): Length of hop between STFT windows. If None, then
+            ``win_length // 4`` is used (Default: ``None``).
+        window (Tensor or None, optional): Window tensor that is applied/multiplied to each frame/window.
+            If None, then ``torch.hann_window(win_length)`` is used (Default: ``None``).
+
+
+    Returns:
+        Tensor: The pitch-shifted audio waveform of shape `(..., time)`.
+    """
+    waveform_stretch = _stretch_waveform(
+        waveform,
+        n_steps,
+        bins_per_octave,
+        n_fft,
+        win_length,
+        hop_length,
+        window,
+    )
+    rate = 2.0 ** (-float(n_steps) / bins_per_octave)
+    waveform_shift = resample(waveform_stretch, int(sample_rate / rate), sample_rate)
+
+    return _fix_waveform_shape(waveform_shift, waveform.size())
+
+
+def _stretch_waveform(
+    waveform: Tensor,
+    n_steps: int,
+    bins_per_octave: int = 12,
+    n_fft: int = 512,
+    win_length: Optional[int] = None,
+    hop_length: Optional[int] = None,
+    window: Optional[Tensor] = None,
+) -> Tensor:
+    """
+    Pitch shift helper function to preprocess and stretch waveform before resampling step.
+
+    Args:
+        See pitch_shift arg descriptions.
+
+    Returns:
+        Tensor: The preprocessed waveform stretched prior to resampling.
+    """
+    if hop_length is None:
+        hop_length = n_fft // 4
+    if win_length is None:
+        win_length = n_fft
+    if window is None:
+        window = torch.hann_window(window_length=win_length, device=waveform.device)
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.reshape(-1, shape[-1])
+
+    ori_len = shape[-1]
+    rate = 2.0 ** (-float(n_steps) / bins_per_octave)
+    spec_f = torch.stft(
+        input=waveform,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        window=window,
+        center=True,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+    phase_advance = torch.linspace(0, math.pi * hop_length, spec_f.shape[-2], device=spec_f.device)[..., None]
+    spec_stretch = phase_vocoder(spec_f, rate, phase_advance)
+    len_stretch = int(round(ori_len / rate))
+    waveform_stretch = torch.istft(
+        spec_stretch, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=len_stretch
+    )
+    return waveform_stretch
+
+
+def _fix_waveform_shape(
+    waveform_shift: Tensor,
+    shape: List[int],
+) -> Tensor:
+    """
+    PitchShift helper function to process after resampling step to fix the shape back.
+
+    Args:
+        waveform_shift(Tensor): The waveform after stretch and resample
+        shape (List[int]): The shape of initial waveform
+
+    Returns:
+        Tensor: The pitch-shifted audio waveform of shape `(..., time)`.
+    """
+    ori_len = shape[-1]
+    shift_len = waveform_shift.size()[-1]
+    if shift_len > ori_len:
+        waveform_shift = waveform_shift[..., :ori_len]
+    else:
+        waveform_shift = torch.nn.functional.pad(waveform_shift, [0, ori_len - shift_len])
+
+    # unpack batch
+    waveform_shift = waveform_shift.view(shape[:-1] + waveform_shift.shape[-1:])
+    return waveform_shift
+
+
+def rnnt_loss(
+    logits: Tensor,
+    targets: Tensor,
+    logit_lengths: Tensor,
+    target_lengths: Tensor,
+    blank: int = -1,
+    clamp: float = -1,
+    reduction: str = "mean",
+    fused_log_softmax: bool = True,
+):
+    """Compute the RNN Transducer loss from *Sequence Transduction with Recurrent Neural Networks*
+    :cite:`graves2012sequence`.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    The RNN Transducer loss extends the CTC loss by defining a distribution over output
+    sequences of all lengths, and by jointly modelling both input-output and output-output
+    dependencies.
+
+    Args:
+        logits (Tensor): Tensor of dimension `(batch, max seq length, max target length + 1, class)`
+            containing output from joiner
+        targets (Tensor): Tensor of dimension `(batch, max target length)` containing targets with zero padded
+        logit_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of each sequence from encoder
+        target_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of targets for each sequence
+        blank (int, optional): blank label (Default: ``-1``)
+        clamp (float, optional): clamp for gradients (Default: ``-1``)
+        reduction (string, optional): Specifies the reduction to apply to the output:
+            ``"none"`` | ``"mean"`` | ``"sum"``. (Default: ``"mean"``)
+        fused_log_softmax (bool): set to False if calling log_softmax outside of loss (Default: ``True``)
+    Returns:
+        Tensor: Loss with the reduction option applied. If ``reduction`` is  ``"none"``, then size `(batch)`,
+        otherwise scalar.
+    """
+    if reduction not in ["none", "mean", "sum"]:
+        raise ValueError('reduction should be one of "none", "mean", or "sum"')
+
+    if blank < 0:  # reinterpret blank index if blank < 0.
+        blank = logits.shape[-1] + blank
+
+    costs, _ = torch.ops.torchaudio.rnnt_loss(
+        logits=logits,
+        targets=targets,
+        logit_lengths=logit_lengths,
+        target_lengths=target_lengths,
+        blank=blank,
+        clamp=clamp,
+        fused_log_softmax=fused_log_softmax,
+    )
+
+    if reduction == "mean":
+        return costs.mean()
+    elif reduction == "sum":
+        return costs.sum()
+
+    return costs
+
+
+def psd(
+    specgram: Tensor,
+    mask: Optional[Tensor] = None,
+    normalize: bool = True,
+    eps: float = 1e-10,
+) -> Tensor:
+    """Compute cross-channel power spectral density (PSD) matrix.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        specgram (torch.Tensor): Multi-channel complex-valued spectrum.
+            Tensor with dimensions `(..., channel, freq, time)`.
+        mask (torch.Tensor or None, optional): Time-Frequency mask for normalization.
+            Tensor with dimensions `(..., freq, time)`. (Default: ``None``)
+        normalize (bool, optional): If ``True``, normalize the mask along the time dimension. (Default: ``True``)
+        eps (float, optional): Value to add to the denominator in mask normalization. (Default: ``1e-15``)
+
+    Returns:
+        torch.Tensor: The complex-valued PSD matrix of the input spectrum.
+        Tensor with dimensions `(..., freq, channel, channel)`
+    """
+    specgram = specgram.transpose(-3, -2)  # shape (freq, channel, time)
+    # outer product:
+    # (..., ch_1, time) x (..., ch_2, time) -> (..., time, ch_1, ch_2)
+    psd = torch.einsum("...ct,...et->...tce", [specgram, specgram.conj()])
+
+    if mask is not None:
+        if mask.shape[:-1] != specgram.shape[:-2] or mask.shape[-1] != specgram.shape[-1]:
+            raise ValueError(
+                "The dimensions of mask except the channel dimension should be the same as specgram."
+                f"Found {mask.shape} for mask and {specgram.shape} for specgram."
+            )
+        # Normalized mask along time dimension:
+        if normalize:
+            mask = mask / (mask.sum(dim=-1, keepdim=True) + eps)
+
+        psd = psd * mask[..., None, None]
+
+    psd = psd.sum(dim=-3)
+    return psd
+
+
+def _compute_mat_trace(input: torch.Tensor, dim1: int = -1, dim2: int = -2) -> torch.Tensor:
+    r"""Compute the trace of a Tensor along ``dim1`` and ``dim2`` dimensions.
+
+    Args:
+        input (torch.Tensor): Tensor with dimensions `(..., channel, channel)`.
+        dim1 (int, optional): The first dimension of the diagonal matrix.
+            (Default: ``-1``)
+        dim2 (int, optional): The second dimension of the diagonal matrix.
+            (Default: ``-2``)
+
+    Returns:
+        Tensor: The trace of the input Tensor.
+    """
+    if input.ndim < 2:
+        raise ValueError("The dimension of the tensor must be at least 2.")
+    if input.shape[dim1] != input.shape[dim2]:
+        raise ValueError("The size of ``dim1`` and ``dim2`` must be the same.")
+    input = torch.diagonal(input, 0, dim1=dim1, dim2=dim2)
+    return input.sum(dim=-1)
+
+
+def _tik_reg(mat: torch.Tensor, reg: float = 1e-7, eps: float = 1e-8) -> torch.Tensor:
+    """Perform Tikhonov regularization (only modifying real part).
+
+    Args:
+        mat (torch.Tensor): Input matrix with dimensions `(..., channel, channel)`.
+        reg (float, optional): Regularization factor. (Default: 1e-8)
+        eps (float, optional): Value to avoid the correlation matrix is all-zero. (Default: ``1e-8``)
+
+    Returns:
+        Tensor: Regularized matrix with dimensions `(..., channel, channel)`.
+    """
+    # Add eps
+    C = mat.size(-1)
+    eye = torch.eye(C, dtype=mat.dtype, device=mat.device)
+    epsilon = _compute_mat_trace(mat).real[..., None, None] * reg
+    # in case that correlation_matrix is all-zero
+    epsilon = epsilon + eps
+    mat = mat + epsilon * eye[..., :, :]
+    return mat
+
+
+def _assert_psd_matrices(psd_s: torch.Tensor, psd_n: torch.Tensor) -> None:
+    """Assertion checks of the PSD matrices of target speech and noise.
+
+    Args:
+        psd_s (torch.Tensor): The complex-valued power spectral density (PSD) matrix of target speech.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+        psd_n (torch.Tensor): The complex-valued power spectral density (PSD) matrix of noise.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+    """
+    if psd_s.ndim < 3 or psd_n.ndim < 3:
+        raise ValueError(
+            "Expected at least 3D Tensor (..., freq, channel, channel) for psd_s and psd_n. "
+            f"Found {psd_s.shape} for psd_s and {psd_n.shape} for psd_n."
+        )
+    if not (psd_s.is_complex() and psd_n.is_complex()):
+        raise TypeError(
+            "The type of psd_s and psd_n must be ``torch.cfloat`` or ``torch.cdouble``. "
+            f"Found {psd_s.dtype} for psd_s and {psd_n.dtype} for psd_n."
+        )
+    if psd_s.shape != psd_n.shape:
+        raise ValueError(
+            f"The dimensions of psd_s and psd_n should be the same. Found {psd_s.shape} and {psd_n.shape}."
+        )
+    if psd_s.shape[-1] != psd_s.shape[-2]:
+        raise ValueError(f"The last two dimensions of psd_s should be the same. Found {psd_s.shape}.")
+
+
+def mvdr_weights_souden(
+    psd_s: Tensor,
+    psd_n: Tensor,
+    reference_channel: Union[int, Tensor],
+    diagonal_loading: bool = True,
+    diag_eps: float = 1e-7,
+    eps: float = 1e-8,
+) -> Tensor:
+    r"""Compute the Minimum Variance Distortionless Response (*MVDR* :cite:`capon1969high`) beamforming weights
+    by the method proposed by *Souden et, al.* :cite:`souden2009optimal`.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Given the power spectral density (PSD) matrix of target speech :math:`\bf{\Phi}_{\textbf{SS}}`,
+    the PSD matrix of noise :math:`\bf{\Phi}_{\textbf{NN}}`, and a one-hot vector that represents the
+    reference channel :math:`\bf{u}`, the method computes the MVDR beamforming weight martrix
+    :math:`\textbf{w}_{\text{MVDR}}`. The formula is defined as:
+
+    .. math::
+        \textbf{w}_{\text{MVDR}}(f) =
+        \frac{{{\bf{\Phi}_{\textbf{NN}}^{-1}}(f){\bf{\Phi}_{\textbf{SS}}}}(f)}
+        {\text{Trace}({{{\bf{\Phi}_{\textbf{NN}}^{-1}}(f) \bf{\Phi}_{\textbf{SS}}}(f))}}\bm{u}
+
+    Args:
+        psd_s (torch.Tensor): The complex-valued power spectral density (PSD) matrix of target speech.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+        psd_n (torch.Tensor): The complex-valued power spectral density (PSD) matrix of noise.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+        reference_channel (int or torch.Tensor): Specifies the reference channel.
+            If the dtype is ``int``, it represents the reference channel index.
+            If the dtype is ``torch.Tensor``, its shape is `(..., channel)`, where the ``channel`` dimension
+            is one-hot.
+        diagonal_loading (bool, optional): If ``True``, enables applying diagonal loading to ``psd_n``.
+            (Default: ``True``)
+        diag_eps (float, optional): The coefficient multiplied to the identity matrix for diagonal loading.
+            It is only effective when ``diagonal_loading`` is set to ``True``. (Default: ``1e-7``)
+        eps (float, optional): Value to add to the denominator in the beamforming weight formula.
+            (Default: ``1e-8``)
+
+    Returns:
+        torch.Tensor: The complex-valued MVDR beamforming weight matrix with dimensions `(..., freq, channel)`.
+    """
+    _assert_psd_matrices(psd_s, psd_n)
+
+    if diagonal_loading:
+        psd_n = _tik_reg(psd_n, reg=diag_eps)
+    numerator = torch.linalg.solve(psd_n, psd_s)  # psd_n.inv() @ psd_s
+    # ws: (..., C, C) / (...,) -> (..., C, C)
+    ws = numerator / (_compute_mat_trace(numerator)[..., None, None] + eps)
+    if torch.jit.isinstance(reference_channel, int):
+        beamform_weights = ws[..., :, reference_channel]
+    elif torch.jit.isinstance(reference_channel, Tensor):
+        reference_channel = reference_channel.to(psd_n.dtype)
+        # h: (..., F, C_1, C_2) x (..., C_2) -> (..., F, C_1)
+        beamform_weights = torch.einsum("...c,...c->...", [ws, reference_channel[..., None, None, :]])
+    else:
+        raise TypeError(f'Expected "int" or "Tensor" for reference_channel. Found: {type(reference_channel)}.')
+
+    return beamform_weights
+
+
+def mvdr_weights_rtf(
+    rtf: Tensor,
+    psd_n: Tensor,
+    reference_channel: Optional[Union[int, Tensor]] = None,
+    diagonal_loading: bool = True,
+    diag_eps: float = 1e-7,
+    eps: float = 1e-8,
+) -> Tensor:
+    r"""Compute the Minimum Variance Distortionless Response (*MVDR* :cite:`capon1969high`) beamforming weights
+    based on the relative transfer function (RTF) and power spectral density (PSD) matrix of noise.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Given the relative transfer function (RTF) matrix or the steering vector of target speech :math:`\bm{v}`,
+    the PSD matrix of noise :math:`\bf{\Phi}_{\textbf{NN}}`, and a one-hot vector that represents the
+    reference channel :math:`\bf{u}`, the method computes the MVDR beamforming weight martrix
+    :math:`\textbf{w}_{\text{MVDR}}`. The formula is defined as:
+
+    .. math::
+        \textbf{w}_{\text{MVDR}}(f) =
+        \frac{{{\bf{\Phi}_{\textbf{NN}}^{-1}}(f){\bm{v}}(f)}}
+        {{\bm{v}^{\mathsf{H}}}(f){\bf{\Phi}_{\textbf{NN}}^{-1}}(f){\bm{v}}(f)}
+
+    where :math:`(.)^{\mathsf{H}}` denotes the Hermitian Conjugate operation.
+
+    Args:
+        rtf (torch.Tensor): The complex-valued RTF vector of target speech.
+            Tensor with dimensions `(..., freq, channel)`.
+        psd_n (torch.Tensor): The complex-valued power spectral density (PSD) matrix of noise.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+        reference_channel (int or torch.Tensor): Specifies the reference channel.
+            If the dtype is ``int``, it represents the reference channel index.
+            If the dtype is ``torch.Tensor``, its shape is `(..., channel)`, where the ``channel`` dimension
+            is one-hot.
+        diagonal_loading (bool, optional): If ``True``, enables applying diagonal loading to ``psd_n``.
+            (Default: ``True``)
+        diag_eps (float, optional): The coefficient multiplied to the identity matrix for diagonal loading.
+            It is only effective when ``diagonal_loading`` is set to ``True``. (Default: ``1e-7``)
+        eps (float, optional): Value to add to the denominator in the beamforming weight formula.
+            (Default: ``1e-8``)
+
+    Returns:
+        torch.Tensor: The complex-valued MVDR beamforming weight matrix with dimensions `(..., freq, channel)`.
+    """
+    if rtf.ndim < 2:
+        raise ValueError(f"Expected at least 2D Tensor (..., freq, channel) for rtf. Found {rtf.shape}.")
+    if psd_n.ndim < 3:
+        raise ValueError(f"Expected at least 3D Tensor (..., freq, channel, channel) for psd_n. Found {psd_n.shape}.")
+    if not (rtf.is_complex() and psd_n.is_complex()):
+        raise TypeError(
+            "The type of rtf and psd_n must be ``torch.cfloat`` or ``torch.cdouble``. "
+            f"Found {rtf.dtype} for rtf and {psd_n.dtype} for psd_n."
+        )
+    if rtf.shape != psd_n.shape[:-1]:
+        raise ValueError(
+            "The dimensions of rtf and the dimensions withou the last dimension of psd_n should be the same. "
+            f"Found {rtf.shape} for rtf and {psd_n.shape} for psd_n."
+        )
+    if psd_n.shape[-1] != psd_n.shape[-2]:
+        raise ValueError(f"The last two dimensions of psd_n should be the same. Found {psd_n.shape}.")
+
+    if diagonal_loading:
+        psd_n = _tik_reg(psd_n, reg=diag_eps)
+    # numerator = psd_n.inv() @ stv
+    numerator = torch.linalg.solve(psd_n, rtf.unsqueeze(-1)).squeeze(-1)  # (..., freq, channel)
+    # denominator = stv^H @ psd_n.inv() @ stv
+    denominator = torch.einsum("...d,...d->...", [rtf.conj(), numerator])
+    beamform_weights = numerator / (denominator.real.unsqueeze(-1) + eps)
+    # normalize the numerator
+    if reference_channel is not None:
+        if torch.jit.isinstance(reference_channel, int):
+            scale = rtf[..., reference_channel].conj()
+        elif torch.jit.isinstance(reference_channel, Tensor):
+            reference_channel = reference_channel.to(psd_n.dtype)
+            scale = torch.einsum("...c,...c->...", [rtf.conj(), reference_channel[..., None, :]])
+        else:
+            raise TypeError(f'Expected "int" or "Tensor" for reference_channel. Found: {type(reference_channel)}.')
+
+        beamform_weights = beamform_weights * scale[..., None]
+
+    return beamform_weights
+
+
+def rtf_evd(psd_s: Tensor) -> Tensor:
+    r"""Estimate the relative transfer function (RTF) or the steering vector by eigenvalue decomposition.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: TorchScript
+
+    Args:
+        psd_s (Tensor): The complex-valued power spectral density (PSD) matrix of target speech.
+            Tensor of dimension `(..., freq, channel, channel)`
+
+    Returns:
+        Tensor: The estimated complex-valued RTF of target speech.
+        Tensor of dimension `(..., freq, channel)`
+    """
+    if not psd_s.is_complex():
+        raise TypeError(f"The type of psd_s must be ``torch.cfloat`` or ``torch.cdouble``. Found {psd_s.dtype}.")
+    if psd_s.shape[-1] != psd_s.shape[-2]:
+        raise ValueError(f"The last two dimensions of psd_s should be the same. Found {psd_s.shape}.")
+    _, v = torch.linalg.eigh(psd_s)  # v is sorted along with eigenvalues in ascending order
+    rtf = v[..., -1]  # choose the eigenvector with max eigenvalue
+    return rtf
+
+
+def rtf_power(
+    psd_s: Tensor,
+    psd_n: Tensor,
+    reference_channel: Union[int, Tensor],
+    n_iter: int = 3,
+    diagonal_loading: bool = True,
+    diag_eps: float = 1e-7,
+) -> Tensor:
+    r"""Estimate the relative transfer function (RTF) or the steering vector by the power method.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        psd_s (torch.Tensor): The complex-valued power spectral density (PSD) matrix of target speech.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+        psd_n (torch.Tensor): The complex-valued power spectral density (PSD) matrix of noise.
+            Tensor with dimensions `(..., freq, channel, channel)`.
+        reference_channel (int or torch.Tensor): Specifies the reference channel.
+            If the dtype is ``int``, it represents the reference channel index.
+            If the dtype is ``torch.Tensor``, its shape is `(..., channel)`, where the ``channel`` dimension
+            is one-hot.
+        diagonal_loading (bool, optional): If ``True``, enables applying diagonal loading to ``psd_n``.
+            (Default: ``True``)
+        diag_eps (float, optional): The coefficient multiplied to the identity matrix for diagonal loading.
+            It is only effective when ``diagonal_loading`` is set to ``True``. (Default: ``1e-7``)
+
+    Returns:
+        torch.Tensor: The estimated complex-valued RTF of target speech.
+        Tensor of dimension `(..., freq, channel)`.
+    """
+    _assert_psd_matrices(psd_s, psd_n)
+    if n_iter <= 0:
+        raise ValueError("The number of iteration must be greater than 0.")
+
+    # Apply diagonal loading to psd_n to improve robustness.
+    if diagonal_loading:
+        psd_n = _tik_reg(psd_n, reg=diag_eps)
+    # phi is regarded as the first iteration
+    phi = torch.linalg.solve(psd_n, psd_s)  # psd_n.inv() @ psd_s
+    if torch.jit.isinstance(reference_channel, int):
+        rtf = phi[..., reference_channel]
+    elif torch.jit.isinstance(reference_channel, Tensor):
+        reference_channel = reference_channel.to(psd_n.dtype)
+        rtf = torch.einsum("...c,...c->...", [phi, reference_channel[..., None, None, :]])
+    else:
+        raise TypeError(f'Expected "int" or "Tensor" for reference_channel. Found: {type(reference_channel)}.')
+    rtf = rtf.unsqueeze(-1)  # (..., freq, channel, 1)
+    if n_iter >= 2:
+        # The number of iterations in the for loop is `n_iter - 2`
+        # because the `phi` above and `torch.matmul(psd_s, rtf)` are regarded as
+        # two iterations.
+        for _ in range(n_iter - 2):
+            rtf = torch.matmul(phi, rtf)
+        rtf = torch.matmul(psd_s, rtf)
+    else:
+        # if there is only one iteration, the rtf is the psd_s[..., referenc_channel]
+        # which is psd_n @ phi @ ref_channel
+        rtf = torch.matmul(psd_n, rtf)
+    return rtf.squeeze(-1)
+
+
+def apply_beamforming(beamform_weights: Tensor, specgram: Tensor) -> Tensor:
+    r"""Apply the beamforming weight to the multi-channel noisy spectrum to obtain the single-channel enhanced spectrum.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    .. math::
+        \hat{\textbf{S}}(f) = \textbf{w}_{\text{bf}}(f)^{\mathsf{H}} \textbf{Y}(f)
+
+    where :math:`\textbf{w}_{\text{bf}}(f)` is the beamforming weight for the :math:`f`-th frequency bin,
+    :math:`\textbf{Y}` is the multi-channel spectrum for the :math:`f`-th frequency bin.
+
+    Args:
+        beamform_weights (Tensor): The complex-valued beamforming weight matrix.
+            Tensor of dimension `(..., freq, channel)`
+        specgram (Tensor): The multi-channel complex-valued noisy spectrum.
+            Tensor of dimension `(..., channel, freq, time)`
+
+    Returns:
+        Tensor: The single-channel complex-valued enhanced spectrum.
+            Tensor of dimension `(..., freq, time)`
+    """
+    if beamform_weights.shape[:-2] != specgram.shape[:-3]:
+        raise ValueError(
+            "The dimensions except the last two dimensions of beamform_weights should be the same "
+            "as the dimensions except the last three dimensions of specgram. "
+            f"Found {beamform_weights.shape} for beamform_weights and {specgram.shape} for specgram."
+        )
+
+    if not (beamform_weights.is_complex() and specgram.is_complex()):
+        raise TypeError(
+            "The type of beamform_weights and specgram must be ``torch.cfloat`` or ``torch.cdouble``. "
+            f"Found {beamform_weights.dtype} for beamform_weights and {specgram.dtype} for specgram."
+        )
+
+    # (..., freq, channel) x (..., channel, freq, time) -> (..., freq, time)
+    specgram_enhanced = torch.einsum("...fc,...cft->...ft", [beamform_weights.conj(), specgram])
+    return specgram_enhanced
+
+
+def _check_shape_compatible(x: torch.Tensor, y: torch.Tensor) -> None:
+    if x.ndim != y.ndim:
+        raise ValueError(f"The operands must be the same dimension (got {x.ndim} and {y.ndim}).")
+
+    for i in range(x.ndim - 1):
+        xi = x.size(i)
+        yi = y.size(i)
+        if xi == yi or xi == 1 or yi == 1:
+            continue
+        raise ValueError(f"Leading dimensions of x and y are not broadcastable (got {x.shape} and {y.shape}).")
+
+
+def _check_convolve_mode(mode: str) -> None:
+    valid_convolve_modes = ["full", "valid", "same"]
+    if mode not in valid_convolve_modes:
+        raise ValueError(f"Unrecognized mode value '{mode}'. Please specify one of {valid_convolve_modes}.")
+
+
+def _apply_convolve_mode(conv_result: torch.Tensor, x_length: int, y_length: int, mode: str) -> torch.Tensor:
+    valid_convolve_modes = ["full", "valid", "same"]
+    if mode == "full":
+        return conv_result
+    elif mode == "valid":
+        target_length = max(x_length, y_length) - min(x_length, y_length) + 1
+        start_idx = (conv_result.size(-1) - target_length) // 2
+        return conv_result[..., start_idx : start_idx + target_length]
+    elif mode == "same":
+        start_idx = (conv_result.size(-1) - x_length) // 2
+        return conv_result[..., start_idx : start_idx + x_length]
+    else:
+        raise ValueError(f"Unrecognized mode value '{mode}'. Please specify one of {valid_convolve_modes}.")
+
+
+def fftconvolve(x: torch.Tensor, y: torch.Tensor, mode: str = "full") -> torch.Tensor:
+    r"""
+    Convolves inputs along their last dimension using FFT. For inputs with large last dimensions, this function
+    is generally much faster than :meth:`convolve`.
+    Note that, in contrast to :meth:`torch.nn.functional.conv1d`, which actually applies the valid cross-correlation
+    operator, this function applies the true `convolution`_ operator.
+    Also note that this function can only output float tensors (int tensor inputs will be cast to float).
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        x (torch.Tensor): First convolution operand, with shape `(..., N)`.
+        y (torch.Tensor): Second convolution operand, with shape `(..., M)`
+            (leading dimensions must be broadcast-able with those of ``x``).
+        mode (str, optional): Must be one of ("full", "valid", "same").
+
+            * "full": Returns the full convolution result, with shape `(..., N + M - 1)`. (Default)
+            * "valid": Returns the segment of the full convolution result corresponding to where
+              the two inputs overlap completely, with shape `(..., max(N, M) - min(N, M) + 1)`.
+            * "same": Returns the center segment of the full convolution result, with shape `(..., N)`.
+
+    Returns:
+        torch.Tensor: Result of convolving ``x`` and ``y``, with shape `(..., L)`, where
+        the leading dimensions match those of ``x`` and `L` is dictated by ``mode``.
+
+    .. _convolution:
+        https://en.wikipedia.org/wiki/Convolution
+    """
+    _check_shape_compatible(x, y)
+    _check_convolve_mode(mode)
+
+    n = x.size(-1) + y.size(-1) - 1
+    fresult = torch.fft.rfft(x, n=n) * torch.fft.rfft(y, n=n)
+    result = torch.fft.irfft(fresult, n=n)
+    return _apply_convolve_mode(result, x.size(-1), y.size(-1), mode)
+
+
+def convolve(x: torch.Tensor, y: torch.Tensor, mode: str = "full") -> torch.Tensor:
+    r"""
+    Convolves inputs along their last dimension using the direct method.
+    Note that, in contrast to :meth:`torch.nn.functional.conv1d`, which actually applies the valid cross-correlation
+    operator, this function applies the true `convolution`_ operator.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        x (torch.Tensor): First convolution operand, with shape `(..., N)`.
+        y (torch.Tensor): Second convolution operand, with shape `(..., M)`
+            (leading dimensions must be broadcast-able with those of ``x``).
+        mode (str, optional): Must be one of ("full", "valid", "same").
+
+            * "full": Returns the full convolution result, with shape `(..., N + M - 1)`. (Default)
+            * "valid": Returns the segment of the full convolution result corresponding to where
+              the two inputs overlap completely, with shape `(..., max(N, M) - min(N, M) + 1)`.
+            * "same": Returns the center segment of the full convolution result, with shape `(..., N)`.
+
+    Returns:
+        torch.Tensor: Result of convolving ``x`` and ``y``, with shape `(..., L)`, where
+        the leading dimensions match those of ``x`` and `L` is dictated by ``mode``.
+
+    .. _convolution:
+        https://en.wikipedia.org/wiki/Convolution
+    """
+    _check_shape_compatible(x, y)
+    _check_convolve_mode(mode)
+
+    x_size, y_size = x.size(-1), y.size(-1)
+
+    if x.size(-1) < y.size(-1):
+        x, y = y, x
+
+    if x.shape[:-1] != y.shape[:-1]:
+        new_shape = [max(i, j) for i, j in zip(x.shape[:-1], y.shape[:-1])]
+        x = x.broadcast_to(new_shape + [x.shape[-1]])
+        y = y.broadcast_to(new_shape + [y.shape[-1]])
+
+    num_signals = torch.tensor(x.shape[:-1]).prod()
+    reshaped_x = x.reshape((int(num_signals), x.size(-1)))
+    reshaped_y = y.reshape((int(num_signals), y.size(-1)))
+    output = torch.nn.functional.conv1d(
+        input=reshaped_x,
+        weight=reshaped_y.flip(-1).unsqueeze(1),
+        stride=1,
+        groups=reshaped_x.size(0),
+        padding=reshaped_y.size(-1) - 1,
+    )
+    output_shape = x.shape[:-1] + (-1,)
+    result = output.reshape(output_shape)
+    return _apply_convolve_mode(result, x_size, y_size, mode)
+
+
+def add_noise(
+    waveform: torch.Tensor, noise: torch.Tensor, snr: torch.Tensor, lengths: Optional[torch.Tensor] = None
+) -> torch.Tensor:
+    r"""Scales and adds noise to waveform per signal-to-noise ratio.
+
+    Specifically, for each pair of waveform vector :math:`x \in \mathbb{R}^L` and noise vector
+    :math:`n \in \mathbb{R}^L`, the function computes output :math:`y` as
+
+    .. math::
+        y = x + a n \, \text{,}
+
+    where
+
+    .. math::
+        a = \sqrt{ \frac{ ||x||_{2}^{2} }{ ||n||_{2}^{2} } \cdot 10^{-\frac{\text{SNR}}{10}} } \, \text{,}
+
+    with :math:`\text{SNR}` being the desired signal-to-noise ratio between :math:`x` and :math:`n`, in dB.
+
+    Note that this function broadcasts singleton leading dimensions in its inputs in a manner that is
+    consistent with the above formulae and PyTorch's broadcasting semantics.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (torch.Tensor): Input waveform, with shape `(..., L)`.
+        noise (torch.Tensor): Noise, with shape `(..., L)` (same shape as ``waveform``).
+        snr (torch.Tensor): Signal-to-noise ratios in dB, with shape `(...,)`.
+        lengths (torch.Tensor or None, optional): Valid lengths of signals in ``waveform`` and ``noise``, with shape
+            `(...,)` (leading dimensions must match those of ``waveform``). If ``None``, all elements in ``waveform``
+            and ``noise`` are treated as valid. (Default: ``None``)
+
+    Returns:
+        torch.Tensor: Result of scaling and adding ``noise`` to ``waveform``, with shape `(..., L)`
+        (same shape as ``waveform``).
+    """
+
+    if not (waveform.ndim - 1 == noise.ndim - 1 == snr.ndim and (lengths is None or lengths.ndim == snr.ndim)):
+        raise ValueError("Input leading dimensions don't match.")
+
+    L = waveform.size(-1)
+
+    if L != noise.size(-1):
+        raise ValueError(f"Length dimensions of waveform and noise don't match (got {L} and {noise.size(-1)}).")
+
+    # compute scale
+    if lengths is not None:
+        mask = torch.arange(0, L, device=lengths.device).expand(waveform.shape) < lengths.unsqueeze(
+            -1
+        )  # (*, L) < (*, 1) = (*, L)
+        masked_waveform = waveform * mask
+        masked_noise = noise * mask
+    else:
+        masked_waveform = waveform
+        masked_noise = noise
+
+    energy_signal = torch.linalg.vector_norm(masked_waveform, ord=2, dim=-1) ** 2  # (*,)
+    energy_noise = torch.linalg.vector_norm(masked_noise, ord=2, dim=-1) ** 2  # (*,)
+    original_snr_db = 10 * (torch.log10(energy_signal) - torch.log10(energy_noise))
+    scale = 10 ** ((original_snr_db - snr) / 20.0)  # (*,)
+
+    # scale noise
+    scaled_noise = scale.unsqueeze(-1) * noise  # (*, 1) * (*, L) = (*, L)
+
+    return waveform + scaled_noise  # (*, L)
+
+
+def speed(
+    waveform: torch.Tensor, orig_freq: int, factor: float, lengths: Optional[torch.Tensor] = None
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    r"""Adjusts waveform speed.
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (torch.Tensor): Input signals, with shape `(..., time)`.
+        orig_freq (int): Original frequency of the signals in ``waveform``.
+        factor (float): Factor by which to adjust speed of input. Values greater than 1.0
+            compress ``waveform`` in time, whereas values less than 1.0 stretch ``waveform`` in time.
+        lengths (torch.Tensor or None, optional): Valid lengths of signals in ``waveform``, with shape `(...)`.
+            If ``None``, all elements in ``waveform`` are treated as valid. (Default: ``None``)
+
+    Returns:
+        (torch.Tensor, torch.Tensor or None):
+            torch.Tensor
+                Speed-adjusted waveform, with shape `(..., new_time).`
+            torch.Tensor or None
+                If ``lengths`` is not ``None``, valid lengths of signals in speed-adjusted waveform,
+                with shape `(...)`; otherwise, ``None``.
+    """
+
+    source_sample_rate = int(factor * orig_freq)
+    target_sample_rate = int(orig_freq)
+
+    gcd = math.gcd(source_sample_rate, target_sample_rate)
+    source_sample_rate = source_sample_rate // gcd
+    target_sample_rate = target_sample_rate // gcd
+
+    if lengths is None:
+        out_lengths = None
+    else:
+        out_lengths = torch.ceil(lengths * target_sample_rate / source_sample_rate).to(lengths.dtype)
+
+    return resample(waveform, source_sample_rate, target_sample_rate), out_lengths
+
+
+def preemphasis(waveform, coeff: float = 0.97) -> torch.Tensor:
+    r"""Pre-emphasizes a waveform along its last dimension, i.e.
+    for each signal :math:`x` in ``waveform``, computes
+    output :math:`y` as
+
+    .. math::
+        y[i] = x[i] - \text{coeff} \cdot x[i - 1]
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (torch.Tensor): Waveform, with shape `(..., N)`.
+        coeff (float, optional): Pre-emphasis coefficient. Typically between 0.0 and 1.0.
+            (Default: 0.97)
+
+    Returns:
+        torch.Tensor: Pre-emphasized waveform, with shape `(..., N)`.
+    """
+    waveform = waveform.clone()
+    waveform[..., 1:] -= coeff * waveform[..., :-1]
+    return waveform
+
+
+def deemphasis(waveform, coeff: float = 0.97) -> torch.Tensor:
+    r"""De-emphasizes a waveform along its last dimension.
+    Inverse of :meth:`preemphasis`. Concretely, for each signal
+    :math:`x` in ``waveform``, computes output :math:`y` as
+
+    .. math::
+        y[i] = x[i] + \text{coeff} \cdot y[i - 1]
+
+    .. devices:: CPU CUDA
+
+    .. properties:: Autograd TorchScript
+
+    Args:
+        waveform (torch.Tensor): Waveform, with shape `(..., N)`.
+        coeff (float, optional): De-emphasis coefficient. Typically between 0.0 and 1.0.
+            (Default: 0.97)
+
+    Returns:
+        torch.Tensor: De-emphasized waveform, with shape `(..., N)`.
+    """
+    a_coeffs = torch.tensor([1.0, -coeff], dtype=waveform.dtype, device=waveform.device)
+    b_coeffs = torch.tensor([1.0, 0.0], dtype=waveform.dtype, device=waveform.device)
+    return torchaudio.functional.lfilter(waveform, a_coeffs=a_coeffs, b_coeffs=b_coeffs)
+
+
+def frechet_distance(mu_x, sigma_x, mu_y, sigma_y):
+    r"""Computes the Fréchet distance between two multivariate normal distributions :cite:`dowson1982frechet`.
+
+    Concretely, for multivariate Gaussians :math:`X(\mu_X, \Sigma_X)`
+    and :math:`Y(\mu_Y, \Sigma_Y)`, the function computes and returns :math:`F` as
+
+    .. math::
+        F(X, Y) = || \mu_X - \mu_Y ||_2^2
+        + \text{Tr}\left( \Sigma_X + \Sigma_Y - 2 \sqrt{\Sigma_X \Sigma_Y} \right)
+
+    Args:
+        mu_x (torch.Tensor): mean :math:`\mu_X` of multivariate Gaussian :math:`X`, with shape `(N,)`.
+        sigma_x (torch.Tensor): covariance matrix :math:`\Sigma_X` of :math:`X`, with shape `(N, N)`.
+        mu_y (torch.Tensor): mean :math:`\mu_Y` of multivariate Gaussian :math:`Y`, with shape `(N,)`.
+        sigma_y (torch.Tensor): covariance matrix :math:`\Sigma_Y` of :math:`Y`, with shape `(N, N)`.
+
+    Returns:
+        torch.Tensor: the Fréchet distance between :math:`X` and :math:`Y`.
+    """
+    if len(mu_x.size()) != 1:
+        raise ValueError(f"Input mu_x must be one-dimensional; got dimension {len(mu_x.size())}.")
+    if len(sigma_x.size()) != 2:
+        raise ValueError(f"Input sigma_x must be two-dimensional; got dimension {len(sigma_x.size())}.")
+    if sigma_x.size(0) != sigma_x.size(1) != mu_x.size(0):
+        raise ValueError("Each of sigma_x's dimensions must match mu_x's size.")
+    if mu_x.size() != mu_y.size():
+        raise ValueError(f"Inputs mu_x and mu_y must have the same shape; got {mu_x.size()} and {mu_y.size()}.")
+    if sigma_x.size() != sigma_y.size():
+        raise ValueError(
+            f"Inputs sigma_x and sigma_y must have the same shape; got {sigma_x.size()} and {sigma_y.size()}."
+        )
+
+    a = (mu_x - mu_y).square().sum()
+    b = sigma_x.trace() + sigma_y.trace()
+    c = torch.linalg.eigvals(sigma_x @ sigma_y).sqrt().real.sum()
+    return a + b - 2 * c
diff --git a/lib/python3.11/site-packages/torchaudio/io/__init__.py b/lib/python3.11/site-packages/torchaudio/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5486424dd21a0f1aeb8ea2cead737309fb7f9a7a
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/io/__init__.py
@@ -0,0 +1,13 @@
+from torio.io import CodecConfig, StreamingMediaDecoder as StreamReader, StreamingMediaEncoder as StreamWriter
+
+from ._effector import AudioEffector
+from ._playback import play_audio
+
+
+__all__ = [
+    "AudioEffector",
+    "StreamReader",
+    "StreamWriter",
+    "CodecConfig",
+    "play_audio",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/io/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/io/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9af6b976d7671e9644973925f9b093a01416ab3a
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/io/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/io/__pycache__/_effector.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/io/__pycache__/_effector.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e9ab7b1b823af4200e822622c93a65a4a25e4779
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/io/__pycache__/_effector.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/io/__pycache__/_playback.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/io/__pycache__/_playback.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2c700860c0813df3fb40d298464f3b4d4d866e6e
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/io/__pycache__/_playback.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/io/_effector.py b/lib/python3.11/site-packages/torchaudio/io/_effector.py
new file mode 100644
index 0000000000000000000000000000000000000000..74255684c8fa75789e88fc224bcdac12aa1b29cf
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/io/_effector.py
@@ -0,0 +1,347 @@
+import io
+from typing import Iterator, List, Optional
+
+import torch
+from torch import Tensor
+
+from torio.io._streaming_media_decoder import _get_afilter_desc, StreamingMediaDecoder as StreamReader
+from torio.io._streaming_media_encoder import CodecConfig, StreamingMediaEncoder as StreamWriter
+
+
+class _StreamingIOBuffer:
+    """Streaming Bytes IO buffer. Data are dropped when read."""
+
+    def __init__(self):
+        self._buffer: List(bytes) = []
+
+    def write(self, b: bytes):
+        if b:
+            self._buffer.append(b)
+        return len(b)
+
+    def pop(self, n):
+        """Pop the oldest byte string. It does not necessary return the requested amount"""
+        if not self._buffer:
+            return b""
+        if len(self._buffer[0]) <= n:
+            return self._buffer.pop(0)
+        ret = self._buffer[0][:n]
+        self._buffer[0] = self._buffer[0][n:]
+        return ret
+
+
+def _get_sample_fmt(dtype: torch.dtype):
+    types = {
+        torch.uint8: "u8",
+        torch.int16: "s16",
+        torch.int32: "s32",
+        torch.float32: "flt",
+        torch.float64: "dbl",
+    }
+    if dtype not in types:
+        raise ValueError(f"Unsupported dtype is provided {dtype}. Supported dtypes are: {types.keys()}")
+    return types[dtype]
+
+
+class _AudioStreamingEncoder:
+    """Given a waveform, encode on-demand and return bytes"""
+
+    def __init__(
+        self,
+        src: Tensor,
+        sample_rate: int,
+        effect: str,
+        muxer: str,
+        encoder: Optional[str],
+        codec_config: Optional[CodecConfig],
+        frames_per_chunk: int,
+    ):
+        self.src = src
+        self.buffer = _StreamingIOBuffer()
+        self.writer = StreamWriter(self.buffer, format=muxer)
+        self.writer.add_audio_stream(
+            num_channels=src.size(1),
+            sample_rate=sample_rate,
+            format=_get_sample_fmt(src.dtype),
+            encoder=encoder,
+            filter_desc=effect,
+            codec_config=codec_config,
+        )
+        self.writer.open()
+        self.fpc = frames_per_chunk
+
+        # index on the input tensor (along time-axis)
+        # we use -1 to indicate that we finished iterating the tensor and
+        # the writer is closed.
+        self.i_iter = 0
+
+    def read(self, n):
+        while not self.buffer._buffer and self.i_iter >= 0:
+            self.writer.write_audio_chunk(0, self.src[self.i_iter : self.i_iter + self.fpc])
+            self.i_iter += self.fpc
+            if self.i_iter >= self.src.size(0):
+                self.writer.flush()
+                self.writer.close()
+                self.i_iter = -1
+        return self.buffer.pop(n)
+
+
+def _encode(
+    src: Tensor,
+    sample_rate: int,
+    effect: str,
+    muxer: str,
+    encoder: Optional[str],
+    codec_config: Optional[CodecConfig],
+):
+    buffer = io.BytesIO()
+    writer = StreamWriter(buffer, format=muxer)
+    writer.add_audio_stream(
+        num_channels=src.size(1),
+        sample_rate=sample_rate,
+        format=_get_sample_fmt(src.dtype),
+        encoder=encoder,
+        filter_desc=effect,
+        codec_config=codec_config,
+    )
+    with writer.open():
+        writer.write_audio_chunk(0, src)
+    buffer.seek(0)
+    return buffer
+
+
+def _get_muxer(dtype: torch.dtype):
+    # TODO: check if this works in Windows.
+    types = {
+        torch.uint8: "u8",
+        torch.int16: "s16le",
+        torch.int32: "s32le",
+        torch.float32: "f32le",
+        torch.float64: "f64le",
+    }
+    if dtype not in types:
+        raise ValueError(f"Unsupported dtype is provided {dtype}. Supported dtypes are: {types.keys()}")
+    return types[dtype]
+
+
+class AudioEffector:
+    """Apply various filters and/or codecs to waveforms.
+
+    .. versionadded:: 2.1
+
+    Args:
+        effect (str or None, optional): Filter expressions or ``None`` to apply no filter.
+            See https://ffmpeg.org/ffmpeg-filters.html#Audio-Filters for the
+            details of filter syntax.
+
+        format (str or None, optional): When provided, encode the audio into the
+            corresponding format. Default: ``None``.
+
+        encoder (str or None, optional): When provided, override the encoder used
+            by the ``format``. Default: ``None``.
+
+        codec_config (CodecConfig or None, optional): When provided, configure the encoding codec.
+            Should be provided in conjunction with ``format`` option.
+
+        pad_end (bool, optional): When enabled, and if the waveform becomes shorter after applying
+            effects/codec, then pad the end with silence.
+
+    Example - Basic usage
+        To use ``AudioEffector``, first instantiate it with a set of
+        ``effect`` and ``format``.
+
+        >>> # instantiate the effector
+        >>> effector = AudioEffector(effect=..., format=...)
+
+        Then, use :py:meth:`~AudioEffector.apply` or :py:meth:`~AudioEffector.stream`
+        method to apply them.
+
+        >>> # Apply the effect to the whole waveform
+        >>> applied = effector.apply(waveform, sample_rate)
+
+        >>> # Apply the effect chunk-by-chunk
+        >>> for chunk in effector.stream(waveform, sample_rate):
+        >>>    ...
+
+    Example - Applying effects
+        Please refer to
+        https://ffmpeg.org/ffmpeg-filters.html#Filtergraph-description
+        for the overview of filter description, and
+        https://ffmpeg.org/ffmpeg-filters.html#toc-Audio-Filters
+        for the list of available filters.
+
+        Tempo - https://ffmpeg.org/ffmpeg-filters.html#atempo
+
+        >>> AudioEffector(effect="atempo=1.5")
+
+        Echo - https://ffmpeg.org/ffmpeg-filters.html#aecho
+
+        >>> AudioEffector(effect="aecho=0.8:0.88:60:0.4")
+
+        Flanger - https://ffmpeg.org/ffmpeg-filters.html#flanger
+
+        >>> AudioEffector(effect="aflanger")
+
+        Vibrato - https://ffmpeg.org/ffmpeg-filters.html#vibrato
+
+        >>> AudioEffector(effect="vibrato")
+
+        Tremolo - https://ffmpeg.org/ffmpeg-filters.html#tremolo
+
+        >>> AudioEffector(effect="vibrato")
+
+        You can also apply multiple effects at once.
+
+        >>> AudioEffector(effect="")
+
+    Example - Applying codec
+        One can apply codec using ``format`` argument. ``format`` can be
+        audio format or container format. If the container format supports
+        multiple encoders, you can specify it with ``encoder`` argument.
+
+        Wav format
+        (no compression is applied but samples are converted to
+        16-bit signed integer)
+
+        >>> AudioEffector(format="wav")
+
+        Ogg format with default encoder
+
+        >>> AudioEffector(format="ogg")
+
+        Ogg format with vorbis
+
+        >>> AudioEffector(format="ogg", encoder="vorbis")
+
+        Ogg format with opus
+
+        >>> AudioEffector(format="ogg", encoder="opus")
+
+        Webm format with opus
+
+        >>> AudioEffector(format="webm", encoder="opus")
+
+    Example - Applying codec with configuration
+        Reference: https://trac.ffmpeg.org/wiki/Encode/MP3
+
+        MP3 with default config
+
+        >>> AudioEffector(format="mp3")
+
+        MP3 with variable bitrate
+
+        >>> AudioEffector(format="mp3", codec_config=CodecConfig(qscale=5))
+
+        MP3 with constant bitrate
+
+        >>> AudioEffector(format="mp3", codec_config=CodecConfig(bit_rate=32_000))
+    """
+
+    def __init__(
+        self,
+        effect: Optional[str] = None,
+        format: Optional[str] = None,
+        *,
+        encoder: Optional[str] = None,
+        codec_config: Optional[CodecConfig] = None,
+        pad_end: bool = True,
+    ):
+        if format is None:
+            if encoder is not None or codec_config is not None:
+                raise ValueError("`encoder` and/or `condec_config` opions are provided without `format` option.")
+        self.effect = effect
+        self.format = format
+        self.encoder = encoder
+        self.codec_config = codec_config
+        self.pad_end = pad_end
+
+    def _get_reader(self, waveform, sample_rate, output_sample_rate, frames_per_chunk=None):
+        num_frames, num_channels = waveform.shape
+
+        if self.format is not None:
+            muxer = self.format
+            encoder = self.encoder
+            option = {}
+            # Some formats are headerless, so need to provide these infomation.
+            if self.format == "mulaw":
+                option = {"sample_rate": f"{sample_rate}", "channels": f"{num_channels}"}
+
+        else:  # PCM
+            muxer = _get_muxer(waveform.dtype)
+            encoder = None
+            option = {"sample_rate": f"{sample_rate}", "channels": f"{num_channels}"}
+
+        if frames_per_chunk is None:
+            src = _encode(waveform, sample_rate, self.effect, muxer, encoder, self.codec_config)
+        else:
+            src = _AudioStreamingEncoder(
+                waveform, sample_rate, self.effect, muxer, encoder, self.codec_config, frames_per_chunk
+            )
+
+        output_sr = sample_rate if output_sample_rate is None else output_sample_rate
+        filter_desc = _get_afilter_desc(output_sr, _get_sample_fmt(waveform.dtype), num_channels)
+        if self.pad_end:
+            filter_desc = f"{filter_desc},apad=whole_len={num_frames}"
+
+        reader = StreamReader(src, format=muxer, option=option)
+        reader.add_audio_stream(frames_per_chunk or -1, -1, filter_desc=filter_desc)
+        return reader
+
+    def apply(self, waveform: Tensor, sample_rate: int, output_sample_rate: Optional[int] = None) -> Tensor:
+        """Apply the effect and/or codecs to the whole tensor.
+
+        Args:
+            waveform (Tensor): The input waveform. Shape: ``(time, channel)``
+            sample_rate (int): Sample rate of the input waveform.
+            output_sample_rate (int or None, optional): Output sample rate.
+                If provided, override the output sample rate.
+                Otherwise, the resulting tensor is resampled to have
+                the same sample rate as the input.
+                Default: ``None``.
+
+        Returns:
+            Tensor:
+                Resulting Tensor. Shape: ``(time, channel)``. The number of frames
+                could be different from that of the input.
+        """
+        if waveform.ndim != 2:
+            raise ValueError(f"Expected the input waveform to be 2D. Found: {waveform.ndim}")
+
+        if waveform.numel() == 0:
+            return waveform
+
+        reader = self._get_reader(waveform, sample_rate, output_sample_rate)
+        reader.process_all_packets()
+        (applied,) = reader.pop_chunks()
+        return Tensor(applied)
+
+    def stream(
+        self, waveform: Tensor, sample_rate: int, frames_per_chunk: int, output_sample_rate: Optional[int] = None
+    ) -> Iterator[Tensor]:
+        """Apply the effect and/or codecs to the given tensor chunk by chunk.
+
+        Args:
+            waveform (Tensor): The input waveform. Shape: ``(time, channel)``
+            sample_rate (int): Sample rate of the waveform.
+            frames_per_chunk (int): The number of frames to return at a time.
+            output_sample_rate (int or None, optional): Output sample rate.
+                If provided, override the output sample rate.
+                Otherwise, the resulting tensor is resampled to have
+                the same sample rate as the input.
+                Default: ``None``.
+
+        Returns:
+            Iterator[Tensor]:
+                Series of processed chunks. Shape: ``(time, channel)``, where the
+                the number of frames matches ``frames_per_chunk`` except the
+                last chunk, which could be shorter.
+        """
+        if waveform.ndim != 2:
+            raise ValueError(f"Expected the input waveform to be 2D. Found: {waveform.ndim}")
+
+        if waveform.numel() == 0:
+            return waveform
+
+        reader = self._get_reader(waveform, sample_rate, output_sample_rate, frames_per_chunk)
+        for (applied,) in reader.stream():
+            yield Tensor(applied)
diff --git a/lib/python3.11/site-packages/torchaudio/io/_playback.py b/lib/python3.11/site-packages/torchaudio/io/_playback.py
new file mode 100644
index 0000000000000000000000000000000000000000..7183ee3ba8cad8e842d066f6aaa9067687b9476b
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/io/_playback.py
@@ -0,0 +1,72 @@
+import warnings
+from sys import platform
+from typing import Optional
+
+import torch
+import torchaudio
+
+dict_format = {
+    torch.uint8: "u8",
+    torch.int16: "s16",
+    torch.int32: "s32",
+    torch.int64: "s64",
+    torch.float32: "flt",
+    torch.float64: "dbl",
+}
+
+
+def play_audio(
+    waveform: torch.Tensor,
+    sample_rate: Optional[float],
+    device: Optional[str] = None,
+) -> None:
+    """Plays audio through specified or available output device.
+
+    .. warning::
+       This function is currently only supported on MacOS, and requires
+       libavdevice (FFmpeg) with ``audiotoolbox`` output device.
+
+    .. note::
+       This function can play up to two audio channels.
+
+    Args:
+        waveform: Tensor containing the audio to play.
+            Expected shape: `(time, num_channels)`.
+        sample_rate: Sample rate of the audio to play.
+        device: Output device to use. If None, the default device is used.
+    """
+
+    if platform == "darwin":
+        device = device or "audiotoolbox"
+        path = "-"
+    else:
+        raise ValueError(f"This function only supports MacOS, but current OS is {platform}")
+
+    available_devices = list(torchaudio.utils.ffmpeg_utils.get_output_devices().keys())
+    if device not in available_devices:
+        raise ValueError(f"Device {device} is not available. Available devices are: {available_devices}")
+
+    if waveform.dtype not in dict_format:
+        raise ValueError(f"Unsupported type {waveform.dtype}. The list of supported types is: {dict_format.keys()}")
+    format = dict_format[waveform.dtype]
+
+    if waveform.ndim != 2:
+        raise ValueError(f"Expected 2D tensor with shape `(time, num_channels)`, got {waveform.ndim}D tensor instead")
+
+    time, num_channels = waveform.size()
+    if num_channels > 2:
+        warnings.warn(
+            f"Expected up to 2 channels, got {num_channels} channels instead. "
+            "Only the first 2 channels will be played.",
+            stacklevel=2,
+        )
+
+    # Write to speaker device
+    s = torchaudio.io.StreamWriter(dst=path, format=device)
+    s.add_audio_stream(sample_rate, num_channels, format=format)
+
+    # write audio to the device
+    block_size = 256
+    with s.open():
+        for i in range(0, time, block_size):
+            s.write_audio_chunk(0, waveform[i : i + block_size, :])
diff --git a/lib/python3.11/site-packages/torchaudio/kaldi_io.py b/lib/python3.11/site-packages/torchaudio/kaldi_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d372429dcfd23fdcbe8cd0f38abef1086d8a5eb
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/kaldi_io.py
@@ -0,0 +1,144 @@
+# To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python)
+# needs to be installed. This is a light wrapper around kaldi_io that returns
+# torch.Tensors.
+from typing import Any, Callable, Iterable, Tuple
+
+import torch
+from torch import Tensor
+from torchaudio._internal import module_utils as _mod_utils
+
+if _mod_utils.is_module_available("numpy"):
+    import numpy as np
+
+
+__all__ = [
+    "read_vec_int_ark",
+    "read_vec_flt_scp",
+    "read_vec_flt_ark",
+    "read_mat_scp",
+    "read_mat_ark",
+]
+
+
+def _convert_method_output_to_tensor(
+    file_or_fd: Any, fn: Callable, convert_contiguous: bool = False
+) -> Iterable[Tuple[str, Tensor]]:
+    r"""Takes a method invokes it. The output is converted to a tensor.
+
+    Args:
+        file_or_fd (str/FileDescriptor): File name or file descriptor
+        fn (Callable): Function that has the signature (file name/descriptor) and converts it to
+            Iterable[Tuple[str, Tensor]].
+        convert_contiguous (bool, optional): Determines whether the array should be converted into a
+            contiguous layout. (Default: ``False``)
+
+    Returns:
+        Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is vec/mat
+    """
+    for key, np_arr in fn(file_or_fd):
+        if convert_contiguous:
+            np_arr = np.ascontiguousarray(np_arr)
+        yield key, torch.from_numpy(np_arr)
+
+
+@_mod_utils.requires_module("kaldi_io", "numpy")
+def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
+    r"""Create generator of (key,vector<int>) tuples, which reads from the ark file/stream.
+
+    Args:
+        file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor
+
+    Returns:
+        Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file
+
+    Example
+        >>> # read ark to a 'dictionary'
+        >>> d = { u:d for u,d in torchaudio.kaldi_io.read_vec_int_ark(file) }
+    """
+
+    import kaldi_io
+
+    # Requires convert_contiguous to be True because elements from int32 vector are
+    # sorted in tuples: (sizeof(int32), value) so strides are (5,) instead of (4,) which will throw an error
+    # in from_numpy as it expects strides to be a multiple of 4 (int32).
+    return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True)
+
+
+@_mod_utils.requires_module("kaldi_io", "numpy")
+def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
+    r"""Create generator of (key,vector<float32/float64>) tuples, read according to Kaldi scp.
+
+    Args:
+        file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor
+
+    Returns:
+        Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file
+
+    Example
+        >>> # read scp to a 'dictionary'
+        >>> # d = { u:d for u,d in torchaudio.kaldi_io.read_vec_flt_scp(file) }
+    """
+
+    import kaldi_io
+
+    return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp)
+
+
+@_mod_utils.requires_module("kaldi_io", "numpy")
+def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
+    r"""Create generator of (key,vector<float32/float64>) tuples, which reads from the ark file/stream.
+
+    Args:
+        file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor
+
+    Returns:
+        Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file
+
+    Example
+        >>> # read ark to a 'dictionary'
+        >>> d = { u:d for u,d in torchaudio.kaldi_io.read_vec_flt_ark(file) }
+    """
+
+    import kaldi_io
+
+    return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark)
+
+
+@_mod_utils.requires_module("kaldi_io", "numpy")
+def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
+    r"""Create generator of (key,matrix<float32/float64>) tuples, read according to Kaldi scp.
+
+    Args:
+        file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor
+
+    Returns:
+        Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the matrix read from file
+
+    Example
+        >>> # read scp to a 'dictionary'
+        >>> d = { u:d for u,d in torchaudio.kaldi_io.read_mat_scp(file) }
+    """
+
+    import kaldi_io
+
+    return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp)
+
+
+@_mod_utils.requires_module("kaldi_io", "numpy")
+def read_mat_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]:
+    r"""Create generator of (key,matrix<float32/float64>) tuples, which reads from the ark file/stream.
+
+    Args:
+        file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor
+
+    Returns:
+        Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the matrix read from file
+
+    Example
+        >>> # read ark to a 'dictionary'
+        >>> d = { u:d for u,d in torchaudio.kaldi_io.read_mat_ark(file) }
+    """
+
+    import kaldi_io
+
+    return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_ark)
diff --git a/lib/python3.11/site-packages/torchaudio/lib/__init__.py b/lib/python3.11/site-packages/torchaudio/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/torchaudio/lib/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/lib/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0ec1b138af8e048a23e7618bb42a7493b17353ba
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/lib/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/__init__.py b/lib/python3.11/site-packages/torchaudio/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d344400d3b8771d2c1b93ba48def361615a132f
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/__init__.py
@@ -0,0 +1,85 @@
+from ._hdemucs import HDemucs, hdemucs_high, hdemucs_low, hdemucs_medium
+from .conformer import Conformer
+from .conv_tasnet import conv_tasnet_base, ConvTasNet
+from .deepspeech import DeepSpeech
+from .emformer import Emformer
+from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
+from .rnnt_decoder import Hypothesis, RNNTBeamSearch
+from .squim import (
+    squim_objective_base,
+    squim_objective_model,
+    squim_subjective_base,
+    squim_subjective_model,
+    SquimObjective,
+    SquimSubjective,
+)
+from .tacotron2 import Tacotron2
+from .wav2letter import Wav2Letter
+from .wav2vec2 import (
+    hubert_base,
+    hubert_large,
+    hubert_pretrain_base,
+    hubert_pretrain_large,
+    hubert_pretrain_model,
+    hubert_pretrain_xlarge,
+    hubert_xlarge,
+    HuBERTPretrainModel,
+    wav2vec2_base,
+    wav2vec2_large,
+    wav2vec2_large_lv60k,
+    wav2vec2_model,
+    wav2vec2_xlsr_1b,
+    wav2vec2_xlsr_2b,
+    wav2vec2_xlsr_300m,
+    Wav2Vec2Model,
+    wavlm_base,
+    wavlm_large,
+    wavlm_model,
+)
+from .wavernn import WaveRNN
+
+
+__all__ = [
+    "Wav2Letter",
+    "WaveRNN",
+    "ConvTasNet",
+    "conv_tasnet_base",
+    "DeepSpeech",
+    "Wav2Vec2Model",
+    "HuBERTPretrainModel",
+    "wavlm_model",
+    "wavlm_base",
+    "wavlm_large",
+    "wav2vec2_model",
+    "wav2vec2_base",
+    "wav2vec2_large",
+    "wav2vec2_large_lv60k",
+    "hubert_base",
+    "hubert_large",
+    "hubert_xlarge",
+    "hubert_pretrain_model",
+    "hubert_pretrain_base",
+    "hubert_pretrain_large",
+    "hubert_pretrain_xlarge",
+    "wav2vec2_xlsr_300m",
+    "wav2vec2_xlsr_1b",
+    "wav2vec2_xlsr_2b",
+    "Tacotron2",
+    "Conformer",
+    "Emformer",
+    "Hypothesis",
+    "RNNT",
+    "RNNTBeamSearch",
+    "emformer_rnnt_base",
+    "emformer_rnnt_model",
+    "HDemucs",
+    "hdemucs_low",
+    "hdemucs_medium",
+    "hdemucs_high",
+    "squim_objective_base",
+    "squim_objective_model",
+    "squim_subjective_base",
+    "squim_subjective_model",
+    "SquimObjective",
+    "SquimSubjective",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/models/_hdemucs.py b/lib/python3.11/site-packages/torchaudio/models/_hdemucs.py
new file mode 100644
index 0000000000000000000000000000000000000000..74a3ebd1d609e67edd09f4356a8cefa305c1fc49
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/_hdemucs.py
@@ -0,0 +1,1008 @@
+# *****************************************************************************
+# MIT License
+#
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+# *****************************************************************************
+
+
+import math
+import typing as tp
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+class _ScaledEmbedding(torch.nn.Module):
+    r"""Make continuous embeddings and boost learning rate
+
+    Args:
+        num_embeddings (int): number of embeddings
+        embedding_dim (int): embedding dimensions
+        scale (float, optional): amount to scale learning rate (Default: 10.0)
+        smooth (bool, optional): choose to apply smoothing (Default: ``False``)
+    """
+
+    def __init__(self, num_embeddings: int, embedding_dim: int, scale: float = 10.0, smooth: bool = False):
+        super().__init__()
+        self.embedding = nn.Embedding(num_embeddings, embedding_dim)
+        if smooth:
+            weight = torch.cumsum(self.embedding.weight.data, dim=0)
+            # when summing gaussian, scale raises as sqrt(n), so we normalize by that.
+            weight = weight / torch.arange(1, num_embeddings + 1).sqrt()[:, None]
+            self.embedding.weight.data[:] = weight
+        self.embedding.weight.data /= scale
+        self.scale = scale
+
+    @property
+    def weight(self) -> torch.Tensor:
+        return self.embedding.weight * self.scale
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        r"""Forward pass for embedding with scale.
+        Args:
+            x (torch.Tensor): input tensor of shape `(num_embeddings)`
+
+        Returns:
+            (Tensor):
+                Embedding output of shape `(num_embeddings, embedding_dim)`
+        """
+        out = self.embedding(x) * self.scale
+        return out
+
+
+class _HEncLayer(torch.nn.Module):
+
+    r"""Encoder layer. This used both by the time and the frequency branch.
+    Args:
+        chin (int): number of input channels.
+        chout (int): number of output channels.
+        kernel_size (int, optional): Kernel size for encoder (Default: 8)
+        stride (int, optional): Stride for encoder layer (Default: 4)
+        norm_groups (int, optional): number of groups for group norm. (Default: 4)
+        empty (bool, optional): used to make a layer with just the first conv. this is used
+            before merging the time and freq. branches. (Default: ``False``)
+        freq (bool, optional): boolean for whether conv layer is for frequency domain (Default: ``True``)
+        norm_type (string, optional): Norm type, either ``group_norm `` or ``none`` (Default: ``group_norm``)
+        context (int, optional): context size for the 1x1 conv. (Default: 0)
+        dconv_kw (Dict[str, Any] or None, optional): dictionary of kwargs for the DConv class. (Default: ``None``)
+        pad (bool, optional): true to pad the input. Padding is done so that the output size is
+            always the input size / stride. (Default: ``True``)
+    """
+
+    def __init__(
+        self,
+        chin: int,
+        chout: int,
+        kernel_size: int = 8,
+        stride: int = 4,
+        norm_groups: int = 4,
+        empty: bool = False,
+        freq: bool = True,
+        norm_type: str = "group_norm",
+        context: int = 0,
+        dconv_kw: Optional[Dict[str, Any]] = None,
+        pad: bool = True,
+    ):
+        super().__init__()
+        if dconv_kw is None:
+            dconv_kw = {}
+        norm_fn = lambda d: nn.Identity()  # noqa
+        if norm_type == "group_norm":
+            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa
+        pad_val = kernel_size // 4 if pad else 0
+        klass = nn.Conv1d
+        self.freq = freq
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.empty = empty
+        self.pad = pad_val
+        if freq:
+            kernel_size = [kernel_size, 1]
+            stride = [stride, 1]
+            pad_val = [pad_val, 0]
+            klass = nn.Conv2d
+        self.conv = klass(chin, chout, kernel_size, stride, pad_val)
+        self.norm1 = norm_fn(chout)
+
+        if self.empty:
+            self.rewrite = nn.Identity()
+            self.norm2 = nn.Identity()
+            self.dconv = nn.Identity()
+        else:
+            self.rewrite = klass(chout, 2 * chout, 1 + 2 * context, 1, context)
+            self.norm2 = norm_fn(2 * chout)
+            self.dconv = _DConv(chout, **dconv_kw)
+
+    def forward(self, x: torch.Tensor, inject: Optional[torch.Tensor] = None) -> torch.Tensor:
+        r"""Forward pass for encoding layer.
+
+        Size depends on whether frequency or time
+
+        Args:
+            x (torch.Tensor): tensor input of shape `(B, C, F, T)` for frequency and shape
+                `(B, C, T)` for time
+            inject (torch.Tensor, optional): on last layer, combine frequency and time branches through inject param,
+                same shape as x (default: ``None``)
+
+        Returns:
+            Tensor
+                output tensor after encoder layer of shape `(B, C, F / stride, T)` for frequency
+                    and shape `(B, C, ceil(T / stride))` for time
+        """
+
+        if not self.freq and x.dim() == 4:
+            B, C, Fr, T = x.shape
+            x = x.view(B, -1, T)
+
+        if not self.freq:
+            le = x.shape[-1]
+            if not le % self.stride == 0:
+                x = F.pad(x, (0, self.stride - (le % self.stride)))
+        y = self.conv(x)
+        if self.empty:
+            return y
+        if inject is not None:
+            if inject.shape[-1] != y.shape[-1]:
+                raise ValueError("Injection shapes do not align")
+            if inject.dim() == 3 and y.dim() == 4:
+                inject = inject[:, :, None]
+            y = y + inject
+        y = F.gelu(self.norm1(y))
+        if self.freq:
+            B, C, Fr, T = y.shape
+            y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
+            y = self.dconv(y)
+            y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
+        else:
+            y = self.dconv(y)
+        z = self.norm2(self.rewrite(y))
+        z = F.glu(z, dim=1)
+        return z
+
+
+class _HDecLayer(torch.nn.Module):
+    r"""Decoder layer. This used both by the time and the frequency branches.
+    Args:
+        chin (int): number of input channels.
+        chout (int): number of output channels.
+        last (bool, optional): whether current layer is final layer (Default: ``False``)
+        kernel_size (int, optional): Kernel size for encoder (Default: 8)
+        stride (int): Stride for encoder layer (Default: 4)
+        norm_groups (int, optional): number of groups for group norm. (Default: 1)
+        empty (bool, optional): used to make a layer with just the first conv. this is used
+            before merging the time and freq. branches. (Default: ``False``)
+        freq (bool, optional): boolean for whether conv layer is for frequency (Default: ``True``)
+        norm_type (str, optional): Norm type, either ``group_norm `` or ``none`` (Default: ``group_norm``)
+        context (int, optional): context size for the 1x1 conv. (Default: 1)
+        dconv_kw (Dict[str, Any] or None, optional): dictionary of kwargs for the DConv class. (Default: ``None``)
+        pad (bool, optional): true to pad the input. Padding is done so that the output size is
+            always the input size / stride. (Default: ``True``)
+    """
+
+    def __init__(
+        self,
+        chin: int,
+        chout: int,
+        last: bool = False,
+        kernel_size: int = 8,
+        stride: int = 4,
+        norm_groups: int = 1,
+        empty: bool = False,
+        freq: bool = True,
+        norm_type: str = "group_norm",
+        context: int = 1,
+        dconv_kw: Optional[Dict[str, Any]] = None,
+        pad: bool = True,
+    ):
+        super().__init__()
+        if dconv_kw is None:
+            dconv_kw = {}
+        norm_fn = lambda d: nn.Identity()  # noqa
+        if norm_type == "group_norm":
+            norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa
+        if pad:
+            if (kernel_size - stride) % 2 != 0:
+                raise ValueError("Kernel size and stride do not align")
+            pad = (kernel_size - stride) // 2
+        else:
+            pad = 0
+        self.pad = pad
+        self.last = last
+        self.freq = freq
+        self.chin = chin
+        self.empty = empty
+        self.stride = stride
+        self.kernel_size = kernel_size
+        klass = nn.Conv1d
+        klass_tr = nn.ConvTranspose1d
+        if freq:
+            kernel_size = [kernel_size, 1]
+            stride = [stride, 1]
+            klass = nn.Conv2d
+            klass_tr = nn.ConvTranspose2d
+        self.conv_tr = klass_tr(chin, chout, kernel_size, stride)
+        self.norm2 = norm_fn(chout)
+        if self.empty:
+            self.rewrite = nn.Identity()
+            self.norm1 = nn.Identity()
+        else:
+            self.rewrite = klass(chin, 2 * chin, 1 + 2 * context, 1, context)
+            self.norm1 = norm_fn(2 * chin)
+
+    def forward(self, x: torch.Tensor, skip: Optional[torch.Tensor], length):
+        r"""Forward pass for decoding layer.
+
+        Size depends on whether frequency or time
+
+        Args:
+            x (torch.Tensor): tensor input of shape `(B, C, F, T)` for frequency and shape
+                `(B, C, T)` for time
+            skip (torch.Tensor, optional): on first layer, separate frequency and time branches using param
+                (default: ``None``)
+            length (int): Size of tensor for output
+
+        Returns:
+            (Tensor, Tensor):
+                Tensor
+                    output tensor after decoder layer of shape `(B, C, F * stride, T)` for frequency domain except last
+                        frequency layer shape is `(B, C, kernel_size, T)`. Shape is `(B, C, stride * T)`
+                        for time domain.
+                Tensor
+                    contains the output just before final transposed convolution, which is used when the
+                        freq. and time branch separate. Otherwise, does not matter. Shape is
+                        `(B, C, F, T)` for frequency and `(B, C, T)` for time.
+        """
+        if self.freq and x.dim() == 3:
+            B, C, T = x.shape
+            x = x.view(B, self.chin, -1, T)
+
+        if not self.empty:
+            x = x + skip
+            y = F.glu(self.norm1(self.rewrite(x)), dim=1)
+        else:
+            y = x
+            if skip is not None:
+                raise ValueError("Skip must be none when empty is true.")
+
+        z = self.norm2(self.conv_tr(y))
+        if self.freq:
+            if self.pad:
+                z = z[..., self.pad : -self.pad, :]
+        else:
+            z = z[..., self.pad : self.pad + length]
+            if z.shape[-1] != length:
+                raise ValueError("Last index of z must be equal to length")
+        if not self.last:
+            z = F.gelu(z)
+
+        return z, y
+
+
+class HDemucs(torch.nn.Module):
+    r"""Hybrid Demucs model from
+    *Hybrid Spectrogram and Waveform Source Separation* :cite:`defossez2021hybrid`.
+
+    See Also:
+        * :class:`torchaudio.pipelines.SourceSeparationBundle`: Source separation pipeline with pre-trained models.
+
+    Args:
+        sources (List[str]): list of source names. List can contain the following source
+            options: [``"bass"``, ``"drums"``, ``"other"``, ``"mixture"``, ``"vocals"``].
+        audio_channels (int, optional): input/output audio channels. (Default: 2)
+        channels (int, optional): initial number of hidden channels. (Default: 48)
+        growth (int, optional): increase the number of hidden channels by this factor at each layer. (Default: 2)
+        nfft (int, optional): number of fft bins. Note that changing this requires careful computation of
+            various shape parameters and will not work out of the box for hybrid models. (Default: 4096)
+        depth (int, optional): number of layers in encoder and decoder (Default: 6)
+        freq_emb (float, optional): add frequency embedding after the first frequency layer if > 0,
+            the actual value controls the weight of the embedding. (Default: 0.2)
+        emb_scale (int, optional): equivalent to scaling the embedding learning rate (Default: 10)
+        emb_smooth (bool, optional): initialize the embedding with a smooth one (with respect to frequencies).
+            (Default: ``True``)
+        kernel_size (int, optional): kernel_size for encoder and decoder layers. (Default: 8)
+        time_stride (int, optional): stride for the final time layer, after the merge. (Default: 2)
+        stride (int, optional): stride for encoder and decoder layers. (Default: 4)
+        context (int, optional): context for 1x1 conv in the decoder. (Default: 4)
+        context_enc (int, optional): context for 1x1 conv in the encoder. (Default: 0)
+        norm_starts (int, optional): layer at which group norm starts being used.
+            decoder layers are numbered in reverse order. (Default: 4)
+        norm_groups (int, optional): number of groups for group norm. (Default: 4)
+        dconv_depth (int, optional): depth of residual DConv branch. (Default: 2)
+        dconv_comp (int, optional): compression of DConv branch. (Default: 4)
+        dconv_attn (int, optional): adds attention layers in DConv branch starting at this layer. (Default: 4)
+        dconv_lstm (int, optional): adds a LSTM layer in DConv branch starting at this layer. (Default: 4)
+        dconv_init (float, optional): initial scale for the DConv branch LayerScale. (Default: 1e-4)
+    """
+
+    def __init__(
+        self,
+        sources: List[str],
+        audio_channels: int = 2,
+        channels: int = 48,
+        growth: int = 2,
+        nfft: int = 4096,
+        depth: int = 6,
+        freq_emb: float = 0.2,
+        emb_scale: int = 10,
+        emb_smooth: bool = True,
+        kernel_size: int = 8,
+        time_stride: int = 2,
+        stride: int = 4,
+        context: int = 1,
+        context_enc: int = 0,
+        norm_starts: int = 4,
+        norm_groups: int = 4,
+        dconv_depth: int = 2,
+        dconv_comp: int = 4,
+        dconv_attn: int = 4,
+        dconv_lstm: int = 4,
+        dconv_init: float = 1e-4,
+    ):
+        super().__init__()
+        self.depth = depth
+        self.nfft = nfft
+        self.audio_channels = audio_channels
+        self.sources = sources
+        self.kernel_size = kernel_size
+        self.context = context
+        self.stride = stride
+        self.channels = channels
+
+        self.hop_length = self.nfft // 4
+        self.freq_emb = None
+
+        self.freq_encoder = nn.ModuleList()
+        self.freq_decoder = nn.ModuleList()
+
+        self.time_encoder = nn.ModuleList()
+        self.time_decoder = nn.ModuleList()
+
+        chin = audio_channels
+        chin_z = chin * 2  # number of channels for the freq branch
+        chout = channels
+        chout_z = channels
+        freqs = self.nfft // 2
+
+        for index in range(self.depth):
+            lstm = index >= dconv_lstm
+            attn = index >= dconv_attn
+            norm_type = "group_norm" if index >= norm_starts else "none"
+            freq = freqs > 1
+            stri = stride
+            ker = kernel_size
+            if not freq:
+                if freqs != 1:
+                    raise ValueError("When freq is false, freqs must be 1.")
+                ker = time_stride * 2
+                stri = time_stride
+
+            pad = True
+            last_freq = False
+            if freq and freqs <= kernel_size:
+                ker = freqs
+                pad = False
+                last_freq = True
+
+            kw = {
+                "kernel_size": ker,
+                "stride": stri,
+                "freq": freq,
+                "pad": pad,
+                "norm_type": norm_type,
+                "norm_groups": norm_groups,
+                "dconv_kw": {
+                    "lstm": lstm,
+                    "attn": attn,
+                    "depth": dconv_depth,
+                    "compress": dconv_comp,
+                    "init": dconv_init,
+                },
+            }
+            kwt = dict(kw)
+            kwt["freq"] = 0
+            kwt["kernel_size"] = kernel_size
+            kwt["stride"] = stride
+            kwt["pad"] = True
+            kw_dec = dict(kw)
+
+            if last_freq:
+                chout_z = max(chout, chout_z)
+                chout = chout_z
+
+            enc = _HEncLayer(chin_z, chout_z, context=context_enc, **kw)
+            if freq:
+                if last_freq is True and nfft == 2048:
+                    kwt["stride"] = 2
+                    kwt["kernel_size"] = 4
+                tenc = _HEncLayer(chin, chout, context=context_enc, empty=last_freq, **kwt)
+                self.time_encoder.append(tenc)
+
+            self.freq_encoder.append(enc)
+            if index == 0:
+                chin = self.audio_channels * len(self.sources)
+                chin_z = chin * 2
+            dec = _HDecLayer(chout_z, chin_z, last=index == 0, context=context, **kw_dec)
+            if freq:
+                tdec = _HDecLayer(chout, chin, empty=last_freq, last=index == 0, context=context, **kwt)
+                self.time_decoder.insert(0, tdec)
+            self.freq_decoder.insert(0, dec)
+
+            chin = chout
+            chin_z = chout_z
+            chout = int(growth * chout)
+            chout_z = int(growth * chout_z)
+            if freq:
+                if freqs <= kernel_size:
+                    freqs = 1
+                else:
+                    freqs //= stride
+            if index == 0 and freq_emb:
+                self.freq_emb = _ScaledEmbedding(freqs, chin_z, smooth=emb_smooth, scale=emb_scale)
+                self.freq_emb_scale = freq_emb
+
+        _rescale_module(self)
+
+    def _spec(self, x):
+        hl = self.hop_length
+        nfft = self.nfft
+        x0 = x  # noqa
+
+        # We re-pad the signal in order to keep the property
+        # that the size of the output is exactly the size of the input
+        # divided by the stride (here hop_length), when divisible.
+        # This is achieved by padding by 1/4th of the kernel size (here nfft).
+        # which is not supported by torch.stft.
+        # Having all convolution operations follow this convention allow to easily
+        # align the time and frequency branches later on.
+        if hl != nfft // 4:
+            raise ValueError("Hop length must be nfft // 4")
+        le = int(math.ceil(x.shape[-1] / hl))
+        pad = hl // 2 * 3
+        x = self._pad1d(x, pad, pad + le * hl - x.shape[-1], mode="reflect")
+
+        z = _spectro(x, nfft, hl)[..., :-1, :]
+        if z.shape[-1] != le + 4:
+            raise ValueError("Spectrogram's last dimension must be 4 + input size divided by stride")
+        z = z[..., 2 : 2 + le]
+        return z
+
+    def _ispec(self, z, length=None):
+        hl = self.hop_length
+        z = F.pad(z, [0, 0, 0, 1])
+        z = F.pad(z, [2, 2])
+        pad = hl // 2 * 3
+        le = hl * int(math.ceil(length / hl)) + 2 * pad
+        x = _ispectro(z, hl, length=le)
+        x = x[..., pad : pad + length]
+        return x
+
+    def _pad1d(self, x: torch.Tensor, padding_left: int, padding_right: int, mode: str = "zero", value: float = 0.0):
+        """Wrapper around F.pad, in order for reflect padding when num_frames is shorter than max_pad.
+        Add extra zero padding around in order for padding to not break."""
+        length = x.shape[-1]
+        if mode == "reflect":
+            max_pad = max(padding_left, padding_right)
+            if length <= max_pad:
+                x = F.pad(x, (0, max_pad - length + 1))
+        return F.pad(x, (padding_left, padding_right), mode, value)
+
+    def _magnitude(self, z):
+        # move the complex dimension to the channel one.
+        B, C, Fr, T = z.shape
+        m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)
+        m = m.reshape(B, C * 2, Fr, T)
+        return m
+
+    def _mask(self, m):
+        # `m` is a full spectrogram and `z` is ignored.
+        B, S, C, Fr, T = m.shape
+        out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)
+        out = torch.view_as_complex(out.contiguous())
+        return out
+
+    def forward(self, input: torch.Tensor):
+
+        r"""HDemucs forward call
+
+        Args:
+            input (torch.Tensor): input mixed tensor of shape `(batch_size, channel, num_frames)`
+
+        Returns:
+            Tensor
+                output tensor split into sources of shape `(batch_size, num_sources, channel, num_frames)`
+        """
+
+        if input.ndim != 3:
+            raise ValueError(f"Expected 3D tensor with dimensions (batch, channel, frames). Found: {input.shape}")
+
+        if input.shape[1] != self.audio_channels:
+            raise ValueError(
+                f"The channel dimension of input Tensor must match `audio_channels` of HDemucs model. "
+                f"Found:{input.shape[1]}."
+            )
+
+        x = input
+        length = x.shape[-1]
+
+        z = self._spec(input)
+        mag = self._magnitude(z)
+        x = mag
+
+        B, C, Fq, T = x.shape
+
+        # unlike previous Demucs, we always normalize because it is easier.
+        mean = x.mean(dim=(1, 2, 3), keepdim=True)
+        std = x.std(dim=(1, 2, 3), keepdim=True)
+        x = (x - mean) / (1e-5 + std)
+        # x will be the freq. branch input.
+
+        # Prepare the time branch input.
+        xt = input
+        meant = xt.mean(dim=(1, 2), keepdim=True)
+        stdt = xt.std(dim=(1, 2), keepdim=True)
+        xt = (xt - meant) / (1e-5 + stdt)
+
+        saved = []  # skip connections, freq.
+        saved_t = []  # skip connections, time.
+        lengths: List[int] = []  # saved lengths to properly remove padding, freq branch.
+        lengths_t: List[int] = []  # saved lengths for time branch.
+
+        for idx, encode in enumerate(self.freq_encoder):
+            lengths.append(x.shape[-1])
+            inject = None
+            if idx < len(self.time_encoder):
+                # we have not yet merged branches.
+                lengths_t.append(xt.shape[-1])
+                tenc = self.time_encoder[idx]
+                xt = tenc(xt)
+                if not tenc.empty:
+                    # save for skip connection
+                    saved_t.append(xt)
+                else:
+                    # tenc contains just the first conv., so that now time and freq.
+                    # branches have the same shape and can be merged.
+                    inject = xt
+            x = encode(x, inject)
+            if idx == 0 and self.freq_emb is not None:
+                # add frequency embedding to allow for non equivariant convolutions
+                # over the frequency axis.
+                frs = torch.arange(x.shape[-2], device=x.device)
+                emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)
+                x = x + self.freq_emb_scale * emb
+
+            saved.append(x)
+
+        x = torch.zeros_like(x)
+        xt = torch.zeros_like(x)
+        # initialize everything to zero (signal will go through u-net skips).
+
+        for idx, decode in enumerate(self.freq_decoder):
+            skip = saved.pop(-1)
+            x, pre = decode(x, skip, lengths.pop(-1))
+            # `pre` contains the output just before final transposed convolution,
+            # which is used when the freq. and time branch separate.
+            offset = self.depth - len(self.time_decoder)
+            if idx >= offset:
+                tdec = self.time_decoder[idx - offset]
+                length_t = lengths_t.pop(-1)
+                if tdec.empty:
+                    if pre.shape[2] != 1:
+                        raise ValueError(f"If tdec empty is True, pre shape does not match {pre.shape}")
+                    pre = pre[:, :, 0]
+                    xt, _ = tdec(pre, None, length_t)
+                else:
+                    skip = saved_t.pop(-1)
+                    xt, _ = tdec(xt, skip, length_t)
+
+        if len(saved) != 0:
+            raise AssertionError("saved is not empty")
+        if len(lengths_t) != 0:
+            raise AssertionError("lengths_t is not empty")
+        if len(saved_t) != 0:
+            raise AssertionError("saved_t is not empty")
+
+        S = len(self.sources)
+        x = x.view(B, S, -1, Fq, T)
+        x = x * std[:, None] + mean[:, None]
+
+        zout = self._mask(x)
+        x = self._ispec(zout, length)
+
+        xt = xt.view(B, S, -1, length)
+        xt = xt * stdt[:, None] + meant[:, None]
+        x = xt + x
+        return x
+
+
+class _DConv(torch.nn.Module):
+    r"""
+    New residual branches in each encoder layer.
+    This alternates dilated convolutions, potentially with LSTMs and attention.
+    Also before entering each residual branch, dimension is projected on a smaller subspace,
+    e.g. of dim `channels // compress`.
+
+    Args:
+        channels (int): input/output channels for residual branch.
+        compress (float, optional): amount of channel compression inside the branch. (default: 4)
+        depth (int, optional): number of layers in the residual branch. Each layer has its own
+            projection, and potentially LSTM and attention.(default: 2)
+        init (float, optional): initial scale for LayerNorm. (default: 1e-4)
+        norm_type (bool, optional): Norm type, either ``group_norm `` or ``none`` (Default: ``group_norm``)
+        attn (bool, optional): use LocalAttention. (Default: ``False``)
+        heads (int, optional): number of heads for the LocalAttention.  (default: 4)
+        ndecay (int, optional): number of decay controls in the LocalAttention. (default: 4)
+        lstm (bool, optional): use LSTM. (Default: ``False``)
+        kernel_size (int, optional): kernel size for the (dilated) convolutions. (default: 3)
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        compress: float = 4,
+        depth: int = 2,
+        init: float = 1e-4,
+        norm_type: str = "group_norm",
+        attn: bool = False,
+        heads: int = 4,
+        ndecay: int = 4,
+        lstm: bool = False,
+        kernel_size: int = 3,
+    ):
+
+        super().__init__()
+        if kernel_size % 2 == 0:
+            raise ValueError("Kernel size should not be divisible by 2")
+        self.channels = channels
+        self.compress = compress
+        self.depth = abs(depth)
+        dilate = depth > 0
+
+        norm_fn: tp.Callable[[int], nn.Module]
+        norm_fn = lambda d: nn.Identity()  # noqa
+        if norm_type == "group_norm":
+            norm_fn = lambda d: nn.GroupNorm(1, d)  # noqa
+
+        hidden = int(channels / compress)
+
+        act = nn.GELU
+
+        self.layers = nn.ModuleList([])
+        for d in range(self.depth):
+            dilation = pow(2, d) if dilate else 1
+            padding = dilation * (kernel_size // 2)
+            mods = [
+                nn.Conv1d(channels, hidden, kernel_size, dilation=dilation, padding=padding),
+                norm_fn(hidden),
+                act(),
+                nn.Conv1d(hidden, 2 * channels, 1),
+                norm_fn(2 * channels),
+                nn.GLU(1),
+                _LayerScale(channels, init),
+            ]
+            if attn:
+                mods.insert(3, _LocalState(hidden, heads=heads, ndecay=ndecay))
+            if lstm:
+                mods.insert(3, _BLSTM(hidden, layers=2, skip=True))
+            layer = nn.Sequential(*mods)
+            self.layers.append(layer)
+
+    def forward(self, x):
+        r"""DConv forward call
+
+        Args:
+            x (torch.Tensor): input tensor for convolution
+
+        Returns:
+            Tensor
+                Output after being run through layers.
+        """
+        for layer in self.layers:
+            x = x + layer(x)
+        return x
+
+
+class _BLSTM(torch.nn.Module):
+    r"""
+    BiLSTM with same hidden units as input dim.
+    If `max_steps` is not None, input will be splitting in overlapping
+    chunks and the LSTM applied separately on each chunk.
+    Args:
+        dim (int): dimensions at LSTM layer.
+        layers (int, optional): number of LSTM layers. (default: 1)
+        skip (bool, optional): (default: ``False``)
+    """
+
+    def __init__(self, dim, layers: int = 1, skip: bool = False):
+        super().__init__()
+        self.max_steps = 200
+        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
+        self.linear = nn.Linear(2 * dim, dim)
+        self.skip = skip
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        r"""BLSTM forward call
+
+        Args:
+            x (torch.Tensor): input tensor for BLSTM shape is `(batch_size, dim, time_steps)`
+
+        Returns:
+            Tensor
+                Output after being run through bidirectional LSTM. Shape is `(batch_size, dim, time_steps)`
+        """
+        B, C, T = x.shape
+        y = x
+        framed = False
+        width = 0
+        stride = 0
+        nframes = 0
+        if self.max_steps is not None and T > self.max_steps:
+            width = self.max_steps
+            stride = width // 2
+            frames = _unfold(x, width, stride)
+            nframes = frames.shape[2]
+            framed = True
+            x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)
+
+        x = x.permute(2, 0, 1)
+
+        x = self.lstm(x)[0]
+        x = self.linear(x)
+        x = x.permute(1, 2, 0)
+        if framed:
+            out = []
+            frames = x.reshape(B, -1, C, width)
+            limit = stride // 2
+            for k in range(nframes):
+                if k == 0:
+                    out.append(frames[:, k, :, :-limit])
+                elif k == nframes - 1:
+                    out.append(frames[:, k, :, limit:])
+                else:
+                    out.append(frames[:, k, :, limit:-limit])
+            out = torch.cat(out, -1)
+            out = out[..., :T]
+            x = out
+        if self.skip:
+            x = x + y
+
+        return x
+
+
+class _LocalState(nn.Module):
+    """Local state allows to have attention based only on data (no positional embedding),
+    but while setting a constraint on the time window (e.g. decaying penalty term).
+    Also a failed experiments with trying to provide some frequency based attention.
+    """
+
+    def __init__(self, channels: int, heads: int = 4, ndecay: int = 4):
+        r"""
+        Args:
+            channels (int): Size of Conv1d layers.
+            heads (int, optional):  (default: 4)
+            ndecay (int, optional): (default: 4)
+        """
+        super(_LocalState, self).__init__()
+        if channels % heads != 0:
+            raise ValueError("Channels must be divisible by heads.")
+        self.heads = heads
+        self.ndecay = ndecay
+        self.content = nn.Conv1d(channels, channels, 1)
+        self.query = nn.Conv1d(channels, channels, 1)
+        self.key = nn.Conv1d(channels, channels, 1)
+
+        self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
+        if ndecay:
+            # Initialize decay close to zero (there is a sigmoid), for maximum initial window.
+            self.query_decay.weight.data *= 0.01
+            if self.query_decay.bias is None:
+                raise ValueError("bias must not be None.")
+            self.query_decay.bias.data[:] = -2
+        self.proj = nn.Conv1d(channels + heads * 0, channels, 1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        r"""LocalState forward call
+
+        Args:
+            x (torch.Tensor): input tensor for LocalState
+
+        Returns:
+            Tensor
+                Output after being run through LocalState layer.
+        """
+        B, C, T = x.shape
+        heads = self.heads
+        indexes = torch.arange(T, device=x.device, dtype=x.dtype)
+        # left index are keys, right index are queries
+        delta = indexes[:, None] - indexes[None, :]
+
+        queries = self.query(x).view(B, heads, -1, T)
+        keys = self.key(x).view(B, heads, -1, T)
+        # t are keys, s are queries
+        dots = torch.einsum("bhct,bhcs->bhts", keys, queries)
+        dots /= math.sqrt(keys.shape[2])
+        if self.ndecay:
+            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)
+            decay_q = self.query_decay(x).view(B, heads, -1, T)
+            decay_q = torch.sigmoid(decay_q) / 2
+            decay_kernel = -decays.view(-1, 1, 1) * delta.abs() / math.sqrt(self.ndecay)
+            dots += torch.einsum("fts,bhfs->bhts", decay_kernel, decay_q)
+
+        # Kill self reference.
+        dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)
+        weights = torch.softmax(dots, dim=2)
+
+        content = self.content(x).view(B, heads, -1, T)
+        result = torch.einsum("bhts,bhct->bhcs", weights, content)
+        result = result.reshape(B, -1, T)
+        return x + self.proj(result)
+
+
+class _LayerScale(nn.Module):
+    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
+    This rescales diagonally residual outputs close to 0 initially, then learnt.
+    """
+
+    def __init__(self, channels: int, init: float = 0):
+        r"""
+        Args:
+            channels (int): Size of  rescaling
+            init (float, optional): Scale to default to (default: 0)
+        """
+        super().__init__()
+        self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
+        self.scale.data[:] = init
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        r"""LayerScale forward call
+
+        Args:
+            x (torch.Tensor): input tensor for LayerScale
+
+        Returns:
+            Tensor
+                Output after rescaling tensor.
+        """
+        return self.scale[:, None] * x
+
+
+def _unfold(a: torch.Tensor, kernel_size: int, stride: int) -> torch.Tensor:
+    """Given input of size [*OT, T], output Tensor of size [*OT, F, K]
+    with K the kernel size, by extracting frames with the given stride.
+    This will pad the input so that `F = ceil(T / K)`.
+    see https://github.com/pytorch/pytorch/issues/60466
+    """
+    shape = list(a.shape[:-1])
+    length = int(a.shape[-1])
+    n_frames = math.ceil(length / stride)
+    tgt_length = (n_frames - 1) * stride + kernel_size
+    a = F.pad(input=a, pad=[0, tgt_length - length])
+    strides = [a.stride(dim) for dim in range(a.dim())]
+    if strides[-1] != 1:
+        raise ValueError("Data should be contiguous.")
+    strides = strides[:-1] + [stride, 1]
+    shape.append(n_frames)
+    shape.append(kernel_size)
+    return a.as_strided(shape, strides)
+
+
+def _rescale_module(module):
+    r"""
+    Rescales initial weight scale for all models within the module.
+    """
+    for sub in module.modules():
+        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):
+            std = sub.weight.std().detach()
+            scale = (std / 0.1) ** 0.5
+            sub.weight.data /= scale
+            if sub.bias is not None:
+                sub.bias.data /= scale
+
+
+def _spectro(x: torch.Tensor, n_fft: int = 512, hop_length: int = 0, pad: int = 0) -> torch.Tensor:
+    other = list(x.shape[:-1])
+    length = int(x.shape[-1])
+    x = x.reshape(-1, length)
+    z = torch.stft(
+        x,
+        n_fft * (1 + pad),
+        hop_length,
+        window=torch.hann_window(n_fft).to(x),
+        win_length=n_fft,
+        normalized=True,
+        center=True,
+        return_complex=True,
+        pad_mode="reflect",
+    )
+    _, freqs, frame = z.shape
+    other.extend([freqs, frame])
+    return z.view(other)
+
+
+def _ispectro(z: torch.Tensor, hop_length: int = 0, length: int = 0, pad: int = 0) -> torch.Tensor:
+    other = list(z.shape[:-2])
+    freqs = int(z.shape[-2])
+    frames = int(z.shape[-1])
+
+    n_fft = 2 * freqs - 2
+    z = z.view(-1, freqs, frames)
+    win_length = n_fft // (1 + pad)
+    x = torch.istft(
+        z,
+        n_fft,
+        hop_length,
+        window=torch.hann_window(win_length).to(z.real),
+        win_length=win_length,
+        normalized=True,
+        length=length,
+        center=True,
+    )
+    _, length = x.shape
+    other.append(length)
+    return x.view(other)
+
+
+def hdemucs_low(sources: List[str]) -> HDemucs:
+    """Builds low nfft (1024) version of :class:`HDemucs`, suitable for sample rates around 8 kHz.
+
+    Args:
+        sources (List[str]): See :py:func:`HDemucs`.
+
+    Returns:
+        HDemucs:
+            HDemucs model.
+    """
+
+    return HDemucs(sources=sources, nfft=1024, depth=5)
+
+
+def hdemucs_medium(sources: List[str]) -> HDemucs:
+    r"""Builds medium nfft (2048) version of :class:`HDemucs`, suitable for sample rates of 16-32 kHz.
+
+    .. note::
+
+        Medium HDemucs has not been tested against the original Hybrid Demucs as this nfft and depth configuration is
+        not compatible with the original implementation in https://github.com/facebookresearch/demucs
+
+    Args:
+        sources (List[str]): See :py:func:`HDemucs`.
+
+    Returns:
+        HDemucs:
+            HDemucs model.
+    """
+
+    return HDemucs(sources=sources, nfft=2048, depth=6)
+
+
+def hdemucs_high(sources: List[str]) -> HDemucs:
+    r"""Builds medium nfft (4096) version of :class:`HDemucs`, suitable for sample rates of 44.1-48 kHz.
+
+    Args:
+        sources (List[str]): See :py:func:`HDemucs`.
+
+    Returns:
+        HDemucs:
+            HDemucs model.
+    """
+
+    return HDemucs(sources=sources, nfft=4096, depth=6)
diff --git a/lib/python3.11/site-packages/torchaudio/models/conformer.py b/lib/python3.11/site-packages/torchaudio/models/conformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3da0d24fac977a65cc97f4b0afae0ab64932d4b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/conformer.py
@@ -0,0 +1,293 @@
+from typing import Optional, Tuple
+
+import torch
+
+
+__all__ = ["Conformer"]
+
+
+def _lengths_to_padding_mask(lengths: torch.Tensor) -> torch.Tensor:
+    batch_size = lengths.shape[0]
+    max_length = int(torch.max(lengths).item())
+    padding_mask = torch.arange(max_length, device=lengths.device, dtype=lengths.dtype).expand(
+        batch_size, max_length
+    ) >= lengths.unsqueeze(1)
+    return padding_mask
+
+
+class _ConvolutionModule(torch.nn.Module):
+    r"""Conformer convolution module.
+
+    Args:
+        input_dim (int): input dimension.
+        num_channels (int): number of depthwise convolution layer input channels.
+        depthwise_kernel_size (int): kernel size of depthwise convolution layer.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+        bias (bool, optional): indicates whether to add bias term to each convolution layer. (Default: ``False``)
+        use_group_norm (bool, optional): use GroupNorm rather than BatchNorm. (Default: ``False``)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        num_channels: int,
+        depthwise_kernel_size: int,
+        dropout: float = 0.0,
+        bias: bool = False,
+        use_group_norm: bool = False,
+    ) -> None:
+        super().__init__()
+        if (depthwise_kernel_size - 1) % 2 != 0:
+            raise ValueError("depthwise_kernel_size must be odd to achieve 'SAME' padding.")
+        self.layer_norm = torch.nn.LayerNorm(input_dim)
+        self.sequential = torch.nn.Sequential(
+            torch.nn.Conv1d(
+                input_dim,
+                2 * num_channels,
+                1,
+                stride=1,
+                padding=0,
+                bias=bias,
+            ),
+            torch.nn.GLU(dim=1),
+            torch.nn.Conv1d(
+                num_channels,
+                num_channels,
+                depthwise_kernel_size,
+                stride=1,
+                padding=(depthwise_kernel_size - 1) // 2,
+                groups=num_channels,
+                bias=bias,
+            ),
+            torch.nn.GroupNorm(num_groups=1, num_channels=num_channels)
+            if use_group_norm
+            else torch.nn.BatchNorm1d(num_channels),
+            torch.nn.SiLU(),
+            torch.nn.Conv1d(
+                num_channels,
+                input_dim,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=bias,
+            ),
+            torch.nn.Dropout(dropout),
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        r"""
+        Args:
+            input (torch.Tensor): with shape `(B, T, D)`.
+
+        Returns:
+            torch.Tensor: output, with shape `(B, T, D)`.
+        """
+        x = self.layer_norm(input)
+        x = x.transpose(1, 2)
+        x = self.sequential(x)
+        return x.transpose(1, 2)
+
+
+class _FeedForwardModule(torch.nn.Module):
+    r"""Positionwise feed forward layer.
+
+    Args:
+        input_dim (int): input dimension.
+        hidden_dim (int): hidden dimension.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+    """
+
+    def __init__(self, input_dim: int, hidden_dim: int, dropout: float = 0.0) -> None:
+        super().__init__()
+        self.sequential = torch.nn.Sequential(
+            torch.nn.LayerNorm(input_dim),
+            torch.nn.Linear(input_dim, hidden_dim, bias=True),
+            torch.nn.SiLU(),
+            torch.nn.Dropout(dropout),
+            torch.nn.Linear(hidden_dim, input_dim, bias=True),
+            torch.nn.Dropout(dropout),
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        r"""
+        Args:
+            input (torch.Tensor): with shape `(*, D)`.
+
+        Returns:
+            torch.Tensor: output, with shape `(*, D)`.
+        """
+        return self.sequential(input)
+
+
+class ConformerLayer(torch.nn.Module):
+    r"""Conformer layer that constitutes Conformer.
+
+    Args:
+        input_dim (int): input dimension.
+        ffn_dim (int): hidden layer dimension of feedforward network.
+        num_attention_heads (int): number of attention heads.
+        depthwise_conv_kernel_size (int): kernel size of depthwise convolution layer.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+        use_group_norm (bool, optional): use ``GroupNorm`` rather than ``BatchNorm1d``
+            in the convolution module. (Default: ``False``)
+        convolution_first (bool, optional): apply the convolution module ahead of
+            the attention module. (Default: ``False``)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        ffn_dim: int,
+        num_attention_heads: int,
+        depthwise_conv_kernel_size: int,
+        dropout: float = 0.0,
+        use_group_norm: bool = False,
+        convolution_first: bool = False,
+    ) -> None:
+        super().__init__()
+
+        self.ffn1 = _FeedForwardModule(input_dim, ffn_dim, dropout=dropout)
+
+        self.self_attn_layer_norm = torch.nn.LayerNorm(input_dim)
+        self.self_attn = torch.nn.MultiheadAttention(input_dim, num_attention_heads, dropout=dropout)
+        self.self_attn_dropout = torch.nn.Dropout(dropout)
+
+        self.conv_module = _ConvolutionModule(
+            input_dim=input_dim,
+            num_channels=input_dim,
+            depthwise_kernel_size=depthwise_conv_kernel_size,
+            dropout=dropout,
+            bias=True,
+            use_group_norm=use_group_norm,
+        )
+
+        self.ffn2 = _FeedForwardModule(input_dim, ffn_dim, dropout=dropout)
+        self.final_layer_norm = torch.nn.LayerNorm(input_dim)
+        self.convolution_first = convolution_first
+
+    def _apply_convolution(self, input: torch.Tensor) -> torch.Tensor:
+        residual = input
+        input = input.transpose(0, 1)
+        input = self.conv_module(input)
+        input = input.transpose(0, 1)
+        input = residual + input
+        return input
+
+    def forward(self, input: torch.Tensor, key_padding_mask: Optional[torch.Tensor]) -> torch.Tensor:
+        r"""
+        Args:
+            input (torch.Tensor): input, with shape `(T, B, D)`.
+            key_padding_mask (torch.Tensor or None): key padding mask to use in self attention layer.
+
+        Returns:
+            torch.Tensor: output, with shape `(T, B, D)`.
+        """
+        residual = input
+        x = self.ffn1(input)
+        x = x * 0.5 + residual
+
+        if self.convolution_first:
+            x = self._apply_convolution(x)
+
+        residual = x
+        x = self.self_attn_layer_norm(x)
+        x, _ = self.self_attn(
+            query=x,
+            key=x,
+            value=x,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )
+        x = self.self_attn_dropout(x)
+        x = x + residual
+
+        if not self.convolution_first:
+            x = self._apply_convolution(x)
+
+        residual = x
+        x = self.ffn2(x)
+        x = x * 0.5 + residual
+
+        x = self.final_layer_norm(x)
+        return x
+
+
+class Conformer(torch.nn.Module):
+    r"""Conformer architecture introduced in
+    *Conformer: Convolution-augmented Transformer for Speech Recognition*
+    :cite:`gulati2020conformer`.
+
+    Args:
+        input_dim (int): input dimension.
+        num_heads (int): number of attention heads in each Conformer layer.
+        ffn_dim (int): hidden layer dimension of feedforward networks.
+        num_layers (int): number of Conformer layers to instantiate.
+        depthwise_conv_kernel_size (int): kernel size of each Conformer layer's depthwise convolution layer.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+        use_group_norm (bool, optional): use ``GroupNorm`` rather than ``BatchNorm1d``
+            in the convolution module. (Default: ``False``)
+        convolution_first (bool, optional): apply the convolution module ahead of
+            the attention module. (Default: ``False``)
+
+    Examples:
+        >>> conformer = Conformer(
+        >>>     input_dim=80,
+        >>>     num_heads=4,
+        >>>     ffn_dim=128,
+        >>>     num_layers=4,
+        >>>     depthwise_conv_kernel_size=31,
+        >>> )
+        >>> lengths = torch.randint(1, 400, (10,))  # (batch,)
+        >>> input = torch.rand(10, int(lengths.max()), input_dim)  # (batch, num_frames, input_dim)
+        >>> output = conformer(input, lengths)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        num_heads: int,
+        ffn_dim: int,
+        num_layers: int,
+        depthwise_conv_kernel_size: int,
+        dropout: float = 0.0,
+        use_group_norm: bool = False,
+        convolution_first: bool = False,
+    ):
+        super().__init__()
+
+        self.conformer_layers = torch.nn.ModuleList(
+            [
+                ConformerLayer(
+                    input_dim,
+                    ffn_dim,
+                    num_heads,
+                    depthwise_conv_kernel_size,
+                    dropout=dropout,
+                    use_group_norm=use_group_norm,
+                    convolution_first=convolution_first,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""
+        Args:
+            input (torch.Tensor): with shape `(B, T, input_dim)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``input``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor)
+                torch.Tensor
+                    output frames, with shape `(B, T, input_dim)`
+                torch.Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid frames for i-th batch element in output frames.
+        """
+        encoder_padding_mask = _lengths_to_padding_mask(lengths)
+
+        x = input.transpose(0, 1)
+        for layer in self.conformer_layers:
+            x = layer(x, encoder_padding_mask)
+        return x.transpose(0, 1), lengths
diff --git a/lib/python3.11/site-packages/torchaudio/models/conv_tasnet.py b/lib/python3.11/site-packages/torchaudio/models/conv_tasnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..770746dd46b34c47736e4607d4344672d0335ef2
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/conv_tasnet.py
@@ -0,0 +1,330 @@
+"""Implements Conv-TasNet with building blocks of it.
+
+Based on https://github.com/naplab/Conv-TasNet/tree/e66d82a8f956a69749ec8a4ae382217faa097c5c
+"""
+
+from typing import Optional, Tuple
+
+import torch
+
+
+class ConvBlock(torch.nn.Module):
+    """1D Convolutional block.
+
+    Args:
+        io_channels (int): The number of input/output channels, <B, Sc>
+        hidden_channels (int): The number of channels in the internal layers, <H>.
+        kernel_size (int): The convolution kernel size of the middle layer, <P>.
+        padding (int): Padding value of the convolution in the middle layer.
+        dilation (int, optional): Dilation value of the convolution in the middle layer.
+        no_redisual (bool, optional): Disable residual block/output.
+
+    Note:
+        This implementation corresponds to the "non-causal" setting in the paper.
+    """
+
+    def __init__(
+        self,
+        io_channels: int,
+        hidden_channels: int,
+        kernel_size: int,
+        padding: int,
+        dilation: int = 1,
+        no_residual: bool = False,
+    ):
+        super().__init__()
+
+        self.conv_layers = torch.nn.Sequential(
+            torch.nn.Conv1d(in_channels=io_channels, out_channels=hidden_channels, kernel_size=1),
+            torch.nn.PReLU(),
+            torch.nn.GroupNorm(num_groups=1, num_channels=hidden_channels, eps=1e-08),
+            torch.nn.Conv1d(
+                in_channels=hidden_channels,
+                out_channels=hidden_channels,
+                kernel_size=kernel_size,
+                padding=padding,
+                dilation=dilation,
+                groups=hidden_channels,
+            ),
+            torch.nn.PReLU(),
+            torch.nn.GroupNorm(num_groups=1, num_channels=hidden_channels, eps=1e-08),
+        )
+
+        self.res_out = (
+            None
+            if no_residual
+            else torch.nn.Conv1d(in_channels=hidden_channels, out_channels=io_channels, kernel_size=1)
+        )
+        self.skip_out = torch.nn.Conv1d(in_channels=hidden_channels, out_channels=io_channels, kernel_size=1)
+
+    def forward(self, input: torch.Tensor) -> Tuple[Optional[torch.Tensor], torch.Tensor]:
+        feature = self.conv_layers(input)
+        if self.res_out is None:
+            residual = None
+        else:
+            residual = self.res_out(feature)
+        skip_out = self.skip_out(feature)
+        return residual, skip_out
+
+
+class MaskGenerator(torch.nn.Module):
+    """TCN (Temporal Convolution Network) Separation Module
+
+    Generates masks for separation.
+
+    Args:
+        input_dim (int): Input feature dimension, <N>.
+        num_sources (int): The number of sources to separate.
+        kernel_size (int): The convolution kernel size of conv blocks, <P>.
+        num_featrs (int): Input/output feature dimenstion of conv blocks, <B, Sc>.
+        num_hidden (int): Intermediate feature dimention of conv blocks, <H>
+        num_layers (int): The number of conv blocks in one stack, <X>.
+        num_stacks (int): The number of conv block stacks, <R>.
+        msk_activate (str): The activation function of the mask output.
+
+    Note:
+        This implementation corresponds to the "non-causal" setting in the paper.
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        num_sources: int,
+        kernel_size: int,
+        num_feats: int,
+        num_hidden: int,
+        num_layers: int,
+        num_stacks: int,
+        msk_activate: str,
+    ):
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.num_sources = num_sources
+
+        self.input_norm = torch.nn.GroupNorm(num_groups=1, num_channels=input_dim, eps=1e-8)
+        self.input_conv = torch.nn.Conv1d(in_channels=input_dim, out_channels=num_feats, kernel_size=1)
+
+        self.receptive_field = 0
+        self.conv_layers = torch.nn.ModuleList([])
+        for s in range(num_stacks):
+            for l in range(num_layers):
+                multi = 2**l
+                self.conv_layers.append(
+                    ConvBlock(
+                        io_channels=num_feats,
+                        hidden_channels=num_hidden,
+                        kernel_size=kernel_size,
+                        dilation=multi,
+                        padding=multi,
+                        # The last ConvBlock does not need residual
+                        no_residual=(l == (num_layers - 1) and s == (num_stacks - 1)),
+                    )
+                )
+                self.receptive_field += kernel_size if s == 0 and l == 0 else (kernel_size - 1) * multi
+        self.output_prelu = torch.nn.PReLU()
+        self.output_conv = torch.nn.Conv1d(
+            in_channels=num_feats,
+            out_channels=input_dim * num_sources,
+            kernel_size=1,
+        )
+        if msk_activate == "sigmoid":
+            self.mask_activate = torch.nn.Sigmoid()
+        elif msk_activate == "relu":
+            self.mask_activate = torch.nn.ReLU()
+        else:
+            raise ValueError(f"Unsupported activation {msk_activate}")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        """Generate separation mask.
+
+        Args:
+            input (torch.Tensor): 3D Tensor with shape [batch, features, frames]
+
+        Returns:
+            Tensor: shape [batch, num_sources, features, frames]
+        """
+        batch_size = input.shape[0]
+        feats = self.input_norm(input)
+        feats = self.input_conv(feats)
+        output = 0.0
+        for layer in self.conv_layers:
+            residual, skip = layer(feats)
+            if residual is not None:  # the last conv layer does not produce residual
+                feats = feats + residual
+            output = output + skip
+        output = self.output_prelu(output)
+        output = self.output_conv(output)
+        output = self.mask_activate(output)
+        return output.view(batch_size, self.num_sources, self.input_dim, -1)
+
+
+class ConvTasNet(torch.nn.Module):
+    """Conv-TasNet architecture introduced in
+    *Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation*
+    :cite:`Luo_2019`.
+
+    Note:
+        This implementation corresponds to the "non-causal" setting in the paper.
+
+    See Also:
+        * :class:`torchaudio.pipelines.SourceSeparationBundle`: Source separation pipeline with pre-trained models.
+
+    Args:
+        num_sources (int, optional): The number of sources to split.
+        enc_kernel_size (int, optional): The convolution kernel size of the encoder/decoder, <L>.
+        enc_num_feats (int, optional): The feature dimensions passed to mask generator, <N>.
+        msk_kernel_size (int, optional): The convolution kernel size of the mask generator, <P>.
+        msk_num_feats (int, optional): The input/output feature dimension of conv block in the mask generator, <B, Sc>.
+        msk_num_hidden_feats (int, optional): The internal feature dimension of conv block of the mask generator, <H>.
+        msk_num_layers (int, optional): The number of layers in one conv block of the mask generator, <X>.
+        msk_num_stacks (int, optional): The numbr of conv blocks of the mask generator, <R>.
+        msk_activate (str, optional): The activation function of the mask output (Default: ``sigmoid``).
+    """
+
+    def __init__(
+        self,
+        num_sources: int = 2,
+        # encoder/decoder parameters
+        enc_kernel_size: int = 16,
+        enc_num_feats: int = 512,
+        # mask generator parameters
+        msk_kernel_size: int = 3,
+        msk_num_feats: int = 128,
+        msk_num_hidden_feats: int = 512,
+        msk_num_layers: int = 8,
+        msk_num_stacks: int = 3,
+        msk_activate: str = "sigmoid",
+    ):
+        super().__init__()
+
+        self.num_sources = num_sources
+        self.enc_num_feats = enc_num_feats
+        self.enc_kernel_size = enc_kernel_size
+        self.enc_stride = enc_kernel_size // 2
+
+        self.encoder = torch.nn.Conv1d(
+            in_channels=1,
+            out_channels=enc_num_feats,
+            kernel_size=enc_kernel_size,
+            stride=self.enc_stride,
+            padding=self.enc_stride,
+            bias=False,
+        )
+        self.mask_generator = MaskGenerator(
+            input_dim=enc_num_feats,
+            num_sources=num_sources,
+            kernel_size=msk_kernel_size,
+            num_feats=msk_num_feats,
+            num_hidden=msk_num_hidden_feats,
+            num_layers=msk_num_layers,
+            num_stacks=msk_num_stacks,
+            msk_activate=msk_activate,
+        )
+        self.decoder = torch.nn.ConvTranspose1d(
+            in_channels=enc_num_feats,
+            out_channels=1,
+            kernel_size=enc_kernel_size,
+            stride=self.enc_stride,
+            padding=self.enc_stride,
+            bias=False,
+        )
+
+    def _align_num_frames_with_strides(self, input: torch.Tensor) -> Tuple[torch.Tensor, int]:
+        """Pad input Tensor so that the end of the input tensor corresponds with
+
+        1. (if kernel size is odd) the center of the last convolution kernel
+        or 2. (if kernel size is even) the end of the first half of the last convolution kernel
+
+        Assumption:
+            The resulting Tensor will be padded with the size of stride (== kernel_width // 2)
+            on the both ends in Conv1D
+
+        |<--- k_1 --->|
+        |      |            |<-- k_n-1 -->|
+        |      |                  |  |<--- k_n --->|
+        |      |                  |         |      |
+        |      |                  |         |      |
+        |      v                  v         v      |
+        |<---->|<--- input signal --->|<--->|<---->|
+         stride                         PAD  stride
+
+        Args:
+            input (torch.Tensor): 3D Tensor with shape (batch_size, channels==1, frames)
+
+        Returns:
+            Tensor: Padded Tensor
+            int: Number of paddings performed
+        """
+        batch_size, num_channels, num_frames = input.shape
+        is_odd = self.enc_kernel_size % 2
+        num_strides = (num_frames - is_odd) // self.enc_stride
+        num_remainings = num_frames - (is_odd + num_strides * self.enc_stride)
+        if num_remainings == 0:
+            return input, 0
+
+        num_paddings = self.enc_stride - num_remainings
+        pad = torch.zeros(
+            batch_size,
+            num_channels,
+            num_paddings,
+            dtype=input.dtype,
+            device=input.device,
+        )
+        return torch.cat([input, pad], 2), num_paddings
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        """Perform source separation. Generate audio source waveforms.
+
+        Args:
+            input (torch.Tensor): 3D Tensor with shape [batch, channel==1, frames]
+
+        Returns:
+            Tensor: 3D Tensor with shape [batch, channel==num_sources, frames]
+        """
+        if input.ndim != 3 or input.shape[1] != 1:
+            raise ValueError(f"Expected 3D tensor (batch, channel==1, frames). Found: {input.shape}")
+
+        # B: batch size
+        # L: input frame length
+        # L': padded input frame length
+        # F: feature dimension
+        # M: feature frame length
+        # S: number of sources
+
+        padded, num_pads = self._align_num_frames_with_strides(input)  # B, 1, L'
+        batch_size, num_padded_frames = padded.shape[0], padded.shape[2]
+        feats = self.encoder(padded)  # B, F, M
+        masked = self.mask_generator(feats) * feats.unsqueeze(1)  # B, S, F, M
+        masked = masked.view(batch_size * self.num_sources, self.enc_num_feats, -1)  # B*S, F, M
+        decoded = self.decoder(masked)  # B*S, 1, L'
+        output = decoded.view(batch_size, self.num_sources, num_padded_frames)  # B, S, L'
+        if num_pads > 0:
+            output = output[..., :-num_pads]  # B, S, L
+        return output
+
+
+def conv_tasnet_base(num_sources: int = 2) -> ConvTasNet:
+    r"""Builds non-causal version of :class:`~torchaudio.models.ConvTasNet`.
+
+    The parameter settings follow the ones with the highest Si-SNR metirc score in the paper,
+    except the mask activation function is changed from "sigmoid" to "relu" for performance improvement.
+
+    Args:
+        num_sources (int, optional): Number of sources in the output.
+            (Default: 2)
+    Returns:
+        ConvTasNet:
+            ConvTasNet model.
+    """
+    return ConvTasNet(
+        num_sources=num_sources,
+        enc_kernel_size=16,
+        enc_num_feats=512,
+        msk_kernel_size=3,
+        msk_num_feats=128,
+        msk_num_hidden_feats=512,
+        msk_num_layers=8,
+        msk_num_stacks=3,
+        msk_activate="relu",
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/models/decoder/__init__.py b/lib/python3.11/site-packages/torchaudio/models/decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2e9b06d52ef7af302a000bb0f572b4c563e12bd
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/decoder/__init__.py
@@ -0,0 +1,46 @@
+_CTC_DECODERS = [
+    "CTCHypothesis",
+    "CTCDecoder",
+    "CTCDecoderLM",
+    "CTCDecoderLMState",
+    "ctc_decoder",
+    "download_pretrained_files",
+]
+_CUDA_CTC_DECODERS = [
+    "CUCTCDecoder",
+    "CUCTCHypothesis",
+    "cuda_ctc_decoder",
+]
+
+
+def __getattr__(name: str):
+    if name in _CTC_DECODERS:
+        try:
+            from . import _ctc_decoder
+        except Exception as err:
+            raise RuntimeError(
+                "CTC Decoder suit requires flashlight-text package and optionally KenLM. Please install them."
+            ) from err
+
+        item = getattr(_ctc_decoder, name)
+        globals()[name] = item
+        return item
+    elif name in _CUDA_CTC_DECODERS:
+        try:
+            from . import _cuda_ctc_decoder
+        except AttributeError as err:
+            raise RuntimeError(
+                "To use CUCTC decoder, please set BUILD_CUDA_CTC_DECODER=1 when building from source."
+            ) from err
+
+        item = getattr(_cuda_ctc_decoder, name)
+        globals()[name] = item
+        return item
+    raise AttributeError(f"module {__name__} has no attribute {name}")
+
+
+def __dir__():
+    return sorted(__all__)
+
+
+__all__ = _CTC_DECODERS + _CUDA_CTC_DECODERS
diff --git a/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..949944c8c836bba031536796d2bf5a474a967256
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/_ctc_decoder.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/_ctc_decoder.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a2c8b3b2916bd081957557832137e19fc3a4aaa5
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/_ctc_decoder.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/_cuda_ctc_decoder.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/_cuda_ctc_decoder.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a56a9c344acf12528242ac2bf80f5e8dd5be500e
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/decoder/__pycache__/_cuda_ctc_decoder.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/decoder/_ctc_decoder.py b/lib/python3.11/site-packages/torchaudio/models/decoder/_ctc_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d45f12f523cc7748e1552ad410557fe9a1f6664
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/decoder/_ctc_decoder.py
@@ -0,0 +1,568 @@
+from __future__ import annotations
+
+import itertools as it
+
+from abc import abstractmethod
+from collections import namedtuple
+from typing import Dict, List, NamedTuple, Optional, Tuple, Union
+
+import torch
+
+from flashlight.lib.text.decoder import (
+    CriterionType as _CriterionType,
+    LexiconDecoder as _LexiconDecoder,
+    LexiconDecoderOptions as _LexiconDecoderOptions,
+    LexiconFreeDecoder as _LexiconFreeDecoder,
+    LexiconFreeDecoderOptions as _LexiconFreeDecoderOptions,
+    LM as _LM,
+    LMState as _LMState,
+    SmearingMode as _SmearingMode,
+    Trie as _Trie,
+    ZeroLM as _ZeroLM,
+)
+from flashlight.lib.text.dictionary import (
+    create_word_dict as _create_word_dict,
+    Dictionary as _Dictionary,
+    load_words as _load_words,
+)
+from torchaudio.utils import download_asset
+
+try:
+    from flashlight.lib.text.decoder.kenlm import KenLM as _KenLM
+except Exception:
+    try:
+        from flashlight.lib.text.decoder import KenLM as _KenLM
+    except Exception:
+        _KenLM = None
+
+__all__ = [
+    "CTCHypothesis",
+    "CTCDecoder",
+    "CTCDecoderLM",
+    "CTCDecoderLMState",
+    "ctc_decoder",
+    "download_pretrained_files",
+]
+
+_PretrainedFiles = namedtuple("PretrainedFiles", ["lexicon", "tokens", "lm"])
+
+
+def _construct_trie(tokens_dict, word_dict, lexicon, lm, silence):
+    vocab_size = tokens_dict.index_size()
+    trie = _Trie(vocab_size, silence)
+    start_state = lm.start(False)
+
+    for word, spellings in lexicon.items():
+        word_idx = word_dict.get_index(word)
+        _, score = lm.score(start_state, word_idx)
+        for spelling in spellings:
+            spelling_idx = [tokens_dict.get_index(token) for token in spelling]
+            trie.insert(spelling_idx, word_idx, score)
+    trie.smear(_SmearingMode.MAX)
+    return trie
+
+
+def _get_word_dict(lexicon, lm, lm_dict, tokens_dict, unk_word):
+    word_dict = None
+    if lm_dict is not None:
+        word_dict = _Dictionary(lm_dict)
+
+    if lexicon and word_dict is None:
+        word_dict = _create_word_dict(lexicon)
+    elif not lexicon and word_dict is None and type(lm) == str:
+        d = {tokens_dict.get_entry(i): [[tokens_dict.get_entry(i)]] for i in range(tokens_dict.index_size())}
+        d[unk_word] = [[unk_word]]
+        word_dict = _create_word_dict(d)
+
+    return word_dict
+
+
+class CTCHypothesis(NamedTuple):
+    r"""Represents hypothesis generated by CTC beam search decoder :class:`CTCDecoder`."""
+    tokens: torch.LongTensor
+    """Predicted sequence of token IDs. Shape `(L, )`, where `L` is the length of the output sequence"""
+
+    words: List[str]
+    """List of predicted words.
+
+    Note:
+        This attribute is only applicable if a lexicon is provided to the decoder. If
+        decoding without a lexicon, it will be blank. Please refer to :attr:`tokens` and
+        :func:`~torchaudio.models.decoder.CTCDecoder.idxs_to_tokens` instead.
+    """
+
+    score: float
+    """Score corresponding to hypothesis"""
+
+    timesteps: torch.IntTensor
+    """Timesteps corresponding to the tokens. Shape `(L, )`, where `L` is the length of the output sequence"""
+
+
+class CTCDecoderLMState(_LMState):
+    """Language model state."""
+
+    @property
+    def children(self) -> Dict[int, CTCDecoderLMState]:
+        """Map of indices to LM states"""
+        return super().children
+
+    def child(self, usr_index: int) -> CTCDecoderLMState:
+        """Returns child corresponding to usr_index, or creates and returns a new state if input index
+        is not found.
+
+        Args:
+            usr_index (int): index corresponding to child state
+
+        Returns:
+            CTCDecoderLMState: child state corresponding to usr_index
+        """
+        return super().child(usr_index)
+
+    def compare(self, state: CTCDecoderLMState) -> CTCDecoderLMState:
+        """Compare two language model states.
+
+        Args:
+            state (CTCDecoderLMState): LM state to compare against
+
+        Returns:
+            int: 0 if the states are the same, -1 if self is less, +1 if self is greater.
+        """
+        pass
+
+
+class CTCDecoderLM(_LM):
+    """Language model base class for creating custom language models to use with the decoder."""
+
+    @abstractmethod
+    def start(self, start_with_nothing: bool) -> CTCDecoderLMState:
+        """Initialize or reset the language model.
+
+        Args:
+            start_with_nothing (bool): whether or not to start sentence with sil token.
+
+        Returns:
+            CTCDecoderLMState: starting state
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def score(self, state: CTCDecoderLMState, usr_token_idx: int) -> Tuple[CTCDecoderLMState, float]:
+        """Evaluate the language model based on the current LM state and new word.
+
+        Args:
+            state (CTCDecoderLMState): current LM state
+            usr_token_idx (int): index of the word
+
+        Returns:
+            (CTCDecoderLMState, float)
+                CTCDecoderLMState:
+                    new LM state
+                float:
+                    score
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def finish(self, state: CTCDecoderLMState) -> Tuple[CTCDecoderLMState, float]:
+        """Evaluate end for language model based on current LM state.
+
+        Args:
+            state (CTCDecoderLMState): current LM state
+
+        Returns:
+            (CTCDecoderLMState, float)
+                CTCDecoderLMState:
+                    new LM state
+                float:
+                    score
+        """
+        raise NotImplementedError
+
+
+class CTCDecoder:
+    """CTC beam search decoder from *Flashlight* :cite:`kahn2022flashlight`.
+
+    .. devices:: CPU
+
+    Note:
+        To build the decoder, please use the factory function :func:`ctc_decoder`.
+    """
+
+    def __init__(
+        self,
+        nbest: int,
+        lexicon: Optional[Dict],
+        word_dict: _Dictionary,
+        tokens_dict: _Dictionary,
+        lm: CTCDecoderLM,
+        decoder_options: Union[_LexiconDecoderOptions, _LexiconFreeDecoderOptions],
+        blank_token: str,
+        sil_token: str,
+        unk_word: str,
+    ) -> None:
+        """
+        Args:
+            nbest (int): number of best decodings to return
+            lexicon (Dict or None): lexicon mapping of words to spellings, or None for lexicon-free decoder
+            word_dict (_Dictionary): dictionary of words
+            tokens_dict (_Dictionary): dictionary of tokens
+            lm (CTCDecoderLM): language model. If using a lexicon, only word level LMs are currently supported
+            decoder_options (_LexiconDecoderOptions or _LexiconFreeDecoderOptions):
+                parameters used for beam search decoding
+            blank_token (str): token corresopnding to blank
+            sil_token (str): token corresponding to silence
+            unk_word (str): word corresponding to unknown
+        """
+
+        self.nbest = nbest
+        self.word_dict = word_dict
+        self.tokens_dict = tokens_dict
+        self.blank = self.tokens_dict.get_index(blank_token)
+        silence = self.tokens_dict.get_index(sil_token)
+        transitions = []
+
+        if lexicon:
+            trie = _construct_trie(tokens_dict, word_dict, lexicon, lm, silence)
+            unk_word = word_dict.get_index(unk_word)
+            token_lm = False  # use word level LM
+
+            self.decoder = _LexiconDecoder(
+                decoder_options,
+                trie,
+                lm,
+                silence,
+                self.blank,
+                unk_word,
+                transitions,
+                token_lm,
+            )
+        else:
+            self.decoder = _LexiconFreeDecoder(decoder_options, lm, silence, self.blank, transitions)
+        # https://github.com/pytorch/audio/issues/3218
+        # If lm is passed like rvalue reference, the lm object gets garbage collected,
+        # and later call to the lm fails.
+        # This ensures that lm object is not deleted as long as the decoder is alive.
+        # https://github.com/pybind/pybind11/discussions/4013
+        self.lm = lm
+
+    def _get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor:
+        idxs = (g[0] for g in it.groupby(idxs))
+        idxs = filter(lambda x: x != self.blank, idxs)
+        return torch.LongTensor(list(idxs))
+
+    def _get_timesteps(self, idxs: torch.IntTensor) -> torch.IntTensor:
+        """Returns frame numbers corresponding to non-blank tokens."""
+
+        timesteps = []
+        for i, idx in enumerate(idxs):
+            if idx == self.blank:
+                continue
+            if i == 0 or idx != idxs[i - 1]:
+                timesteps.append(i)
+        return torch.IntTensor(timesteps)
+
+    def decode_begin(self):
+        """Initialize the internal state of the decoder.
+
+        See :py:meth:`decode_step` for the usage.
+
+        .. note::
+
+           This method is required only when performing online decoding.
+           It is not necessary when performing batch decoding with :py:meth:`__call__`.
+        """
+        self.decoder.decode_begin()
+
+    def decode_end(self):
+        """Finalize the internal state of the decoder.
+
+        See :py:meth:`decode_step` for the usage.
+
+        .. note::
+
+           This method is required only when performing online decoding.
+           It is not necessary when performing batch decoding with :py:meth:`__call__`.
+        """
+        self.decoder.decode_end()
+
+    def decode_step(self, emissions: torch.FloatTensor):
+        """Perform incremental decoding on top of the curent internal state.
+
+        .. note::
+
+           This method is required only when performing online decoding.
+           It is not necessary when performing batch decoding with :py:meth:`__call__`.
+
+        Args:
+            emissions (torch.FloatTensor): CPU tensor of shape `(frame, num_tokens)` storing sequences of
+                probability distribution over labels; output of acoustic model.
+
+        Example:
+            >>> decoder = torchaudio.models.decoder.ctc_decoder(...)
+            >>> decoder.decode_begin()
+            >>> decoder.decode_step(emission1)
+            >>> decoder.decode_step(emission2)
+            >>> decoder.decode_end()
+            >>> result = decoder.get_final_hypothesis()
+        """
+        if emissions.dtype != torch.float32:
+            raise ValueError("emissions must be float32.")
+
+        if not emissions.is_cpu:
+            raise RuntimeError("emissions must be a CPU tensor.")
+
+        if not emissions.is_contiguous():
+            raise RuntimeError("emissions must be contiguous.")
+
+        if emissions.ndim != 2:
+            raise RuntimeError(f"emissions must be 2D. Found {emissions.shape}")
+
+        T, N = emissions.size()
+        self.decoder.decode_step(emissions.data_ptr(), T, N)
+
+    def _to_hypo(self, results) -> List[CTCHypothesis]:
+        return [
+            CTCHypothesis(
+                tokens=self._get_tokens(result.tokens),
+                words=[self.word_dict.get_entry(x) for x in result.words if x >= 0],
+                score=result.score,
+                timesteps=self._get_timesteps(result.tokens),
+            )
+            for result in results
+        ]
+
+    def get_final_hypothesis(self) -> List[CTCHypothesis]:
+        """Get the final hypothesis
+
+        Returns:
+            List[CTCHypothesis]:
+                List of sorted best hypotheses.
+
+        .. note::
+
+           This method is required only when performing online decoding.
+           It is not necessary when performing batch decoding with :py:meth:`__call__`.
+        """
+        results = self.decoder.get_all_final_hypothesis()
+        return self._to_hypo(results[: self.nbest])
+
+    def __call__(
+        self, emissions: torch.FloatTensor, lengths: Optional[torch.Tensor] = None
+    ) -> List[List[CTCHypothesis]]:
+        """
+        Performs batched offline decoding.
+
+        .. note::
+
+           This method performs offline decoding in one go. To perform incremental decoding,
+           please refer to :py:meth:`decode_step`.
+
+        Args:
+            emissions (torch.FloatTensor): CPU tensor of shape `(batch, frame, num_tokens)` storing sequences of
+                probability distribution over labels; output of acoustic model.
+            lengths (Tensor or None, optional): CPU tensor of shape `(batch, )` storing the valid length of
+                in time axis of the output Tensor in each batch.
+
+        Returns:
+            List[List[CTCHypothesis]]:
+                List of sorted best hypotheses for each audio sequence in the batch.
+        """
+
+        if emissions.dtype != torch.float32:
+            raise ValueError("emissions must be float32.")
+
+        if not emissions.is_cpu:
+            raise RuntimeError("emissions must be a CPU tensor.")
+
+        if not emissions.is_contiguous():
+            raise RuntimeError("emissions must be contiguous.")
+
+        if emissions.ndim != 3:
+            raise RuntimeError(f"emissions must be 3D. Found {emissions.shape}")
+
+        if lengths is not None and not lengths.is_cpu:
+            raise RuntimeError("lengths must be a CPU tensor.")
+
+        B, T, N = emissions.size()
+        if lengths is None:
+            lengths = torch.full((B,), T)
+
+        float_bytes = 4
+        hypos = []
+
+        for b in range(B):
+            emissions_ptr = emissions.data_ptr() + float_bytes * b * emissions.stride(0)
+            results = self.decoder.decode(emissions_ptr, lengths[b], N)
+            hypos.append(self._to_hypo(results[: self.nbest]))
+        return hypos
+
+    def idxs_to_tokens(self, idxs: torch.LongTensor) -> List:
+        """
+        Map raw token IDs into corresponding tokens
+
+        Args:
+            idxs (LongTensor): raw token IDs generated from decoder
+
+        Returns:
+            List: tokens corresponding to the input IDs
+        """
+        return [self.tokens_dict.get_entry(idx.item()) for idx in idxs]
+
+
+def ctc_decoder(
+    lexicon: Optional[str],
+    tokens: Union[str, List[str]],
+    lm: Union[str, CTCDecoderLM] = None,
+    lm_dict: Optional[str] = None,
+    nbest: int = 1,
+    beam_size: int = 50,
+    beam_size_token: Optional[int] = None,
+    beam_threshold: float = 50,
+    lm_weight: float = 2,
+    word_score: float = 0,
+    unk_score: float = float("-inf"),
+    sil_score: float = 0,
+    log_add: bool = False,
+    blank_token: str = "-",
+    sil_token: str = "|",
+    unk_word: str = "<unk>",
+) -> CTCDecoder:
+    """Builds an instance of :class:`CTCDecoder`.
+
+    Args:
+        lexicon (str or None): lexicon file containing the possible words and corresponding spellings.
+            Each line consists of a word and its space separated spelling. If `None`, uses lexicon-free
+            decoding.
+        tokens (str or List[str]): file or list containing valid tokens. If using a file, the expected
+            format is for tokens mapping to the same index to be on the same line
+        lm (str, CTCDecoderLM, or None, optional): either a path containing KenLM language model,
+            custom language model of type `CTCDecoderLM`, or `None` if not using a language model
+        lm_dict (str or None, optional): file consisting of the dictionary used for the LM, with a word
+            per line sorted by LM index. If decoding with a lexicon, entries in lm_dict must also occur
+            in the lexicon file. If `None`, dictionary for LM is constructed using the lexicon file.
+            (Default: None)
+        nbest (int, optional): number of best decodings to return (Default: 1)
+        beam_size (int, optional): max number of hypos to hold after each decode step (Default: 50)
+        beam_size_token (int, optional): max number of tokens to consider at each decode step.
+            If `None`, it is set to the total number of tokens (Default: None)
+        beam_threshold (float, optional): threshold for pruning hypothesis (Default: 50)
+        lm_weight (float, optional): weight of language model (Default: 2)
+        word_score (float, optional): word insertion score (Default: 0)
+        unk_score (float, optional): unknown word insertion score (Default: -inf)
+        sil_score (float, optional): silence insertion score (Default: 0)
+        log_add (bool, optional): whether or not to use logadd when merging hypotheses (Default: False)
+        blank_token (str, optional): token corresponding to blank (Default: "-")
+        sil_token (str, optional): token corresponding to silence (Default: "|")
+        unk_word (str, optional): word corresponding to unknown (Default: "<unk>")
+
+    Returns:
+        CTCDecoder: decoder
+
+    Example
+        >>> decoder = ctc_decoder(
+        >>>     lexicon="lexicon.txt",
+        >>>     tokens="tokens.txt",
+        >>>     lm="kenlm.bin",
+        >>> )
+        >>> results = decoder(emissions) # List of shape (B, nbest) of Hypotheses
+    """
+    if lm_dict is not None and type(lm_dict) is not str:
+        raise ValueError("lm_dict must be None or str type.")
+
+    tokens_dict = _Dictionary(tokens)
+
+    # decoder options
+    if lexicon:
+        lexicon = _load_words(lexicon)
+        decoder_options = _LexiconDecoderOptions(
+            beam_size=beam_size,
+            beam_size_token=beam_size_token or tokens_dict.index_size(),
+            beam_threshold=beam_threshold,
+            lm_weight=lm_weight,
+            word_score=word_score,
+            unk_score=unk_score,
+            sil_score=sil_score,
+            log_add=log_add,
+            criterion_type=_CriterionType.CTC,
+        )
+    else:
+        decoder_options = _LexiconFreeDecoderOptions(
+            beam_size=beam_size,
+            beam_size_token=beam_size_token or tokens_dict.index_size(),
+            beam_threshold=beam_threshold,
+            lm_weight=lm_weight,
+            sil_score=sil_score,
+            log_add=log_add,
+            criterion_type=_CriterionType.CTC,
+        )
+
+    # construct word dict and language model
+    word_dict = _get_word_dict(lexicon, lm, lm_dict, tokens_dict, unk_word)
+
+    if type(lm) == str:
+        if _KenLM is None:
+            raise RuntimeError(
+                "flashlight-text is installed, but KenLM is not installed. "
+                "Please refer to https://github.com/kpu/kenlm#python-module for how to install it."
+            )
+        lm = _KenLM(lm, word_dict)
+    elif lm is None:
+        lm = _ZeroLM()
+
+    return CTCDecoder(
+        nbest=nbest,
+        lexicon=lexicon,
+        word_dict=word_dict,
+        tokens_dict=tokens_dict,
+        lm=lm,
+        decoder_options=decoder_options,
+        blank_token=blank_token,
+        sil_token=sil_token,
+        unk_word=unk_word,
+    )
+
+
+def _get_filenames(model: str) -> _PretrainedFiles:
+    if model not in ["librispeech", "librispeech-3-gram", "librispeech-4-gram"]:
+        raise ValueError(
+            f"{model} not supported. Must be one of ['librispeech-3-gram', 'librispeech-4-gram', 'librispeech']"
+        )
+
+    prefix = f"decoder-assets/{model}"
+    return _PretrainedFiles(
+        lexicon=f"{prefix}/lexicon.txt",
+        tokens=f"{prefix}/tokens.txt",
+        lm=f"{prefix}/lm.bin" if model != "librispeech" else None,
+    )
+
+
+def download_pretrained_files(model: str) -> _PretrainedFiles:
+    """
+    Retrieves pretrained data files used for :func:`ctc_decoder`.
+
+    Args:
+        model (str): pretrained language model to download.
+            Valid values are: ``"librispeech-3-gram"``, ``"librispeech-4-gram"`` and ``"librispeech"``.
+
+    Returns:
+        Object with the following attributes
+
+            * ``lm``: path corresponding to downloaded language model,
+              or ``None`` if the model is not associated with an lm
+            * ``lexicon``: path corresponding to downloaded lexicon file
+            * ``tokens``: path corresponding to downloaded tokens file
+    """
+
+    files = _get_filenames(model)
+    lexicon_file = download_asset(files.lexicon)
+    tokens_file = download_asset(files.tokens)
+    if files.lm is not None:
+        lm_file = download_asset(files.lm)
+    else:
+        lm_file = None
+
+    return _PretrainedFiles(
+        lexicon=lexicon_file,
+        tokens=tokens_file,
+        lm=lm_file,
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/models/decoder/_cuda_ctc_decoder.py b/lib/python3.11/site-packages/torchaudio/models/decoder/_cuda_ctc_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b1f509644091e04ea3bdc4301a74c546044f31d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/decoder/_cuda_ctc_decoder.py
@@ -0,0 +1,187 @@
+from __future__ import annotations
+
+import math
+
+from typing import List, NamedTuple, Union
+
+import torch
+import torchaudio
+
+torchaudio._extension._load_lib("libctc_prefix_decoder")
+import torchaudio.lib.pybind11_prefixctc as cuctc
+
+
+__all__ = ["CUCTCHypothesis", "CUCTCDecoder", "cuda_ctc_decoder"]
+
+
+def _get_vocab_list(vocab_file):
+    vocab = []
+    with open(vocab_file, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip().split()
+            vocab.append(line[0])
+    return vocab
+
+
+class CUCTCHypothesis(NamedTuple):
+    r"""Represents hypothesis generated by CUCTC beam search decoder :class:`CUCTCDecoder`."""
+    tokens: List[int]
+    """Predicted sequence of token IDs. Shape `(L, )`, where `L` is the length of the output sequence"""
+
+    words: List[str]
+    """List of predicted tokens. Algin with modeling unit.
+    """
+
+    score: float
+    """Score corresponding to hypothesis"""
+
+
+_DEFAULT_BLANK_SKIP_THREASHOLD = 0.95
+
+
+class CUCTCDecoder:
+    """CUDA CTC beam search decoder.
+
+    .. devices:: CUDA
+
+    Note:
+        To build the decoder, please use the factory function :func:`cuda_ctc_decoder`.
+    """
+
+    def __init__(
+        self,
+        vocab_list: List[str],
+        blank_id: int = 0,
+        beam_size: int = 10,
+        nbest: int = 1,
+        blank_skip_threshold: float = _DEFAULT_BLANK_SKIP_THREASHOLD,
+        cuda_stream: torch.cuda.streams.Stream = None,
+    ):
+        """
+        Args:
+            blank_id (int): token id corresopnding to blank, only support 0 for now. (Default: 0)
+            vocab_list (List[str]): list of vocabulary tokens
+            beam_size (int, optional): max number of hypos to hold after each decode step (Default: 10)
+            nbest (int): number of best decodings to return
+            blank_skip_threshold (float):
+                skip frames if log_prob(blank) > log(blank_skip_threshold), to speed up decoding.
+                (Default: 0.95).
+            cuda_stream (torch.cuda.streams.Stream): using assigned cuda stream (Default: using default stream)
+
+        """
+        if cuda_stream:
+            if not isinstance(cuda_stream, torch.cuda.streams.Stream):
+                raise AssertionError("cuda_stream must be torch.cuda.streams.Stream")
+        cuda_stream_ = cuda_stream.cuda_stream if cuda_stream else torch.cuda.current_stream().cuda_stream
+        self.internal_data = cuctc.prefixCTC_alloc(cuda_stream_)
+        self.memory = torch.empty(0, dtype=torch.int8, device=torch.device("cuda"))
+        if blank_id != 0:
+            raise AssertionError("blank_id must be 0")
+        self.blank_id = blank_id
+        self.vocab_list = vocab_list
+        self.space_id = 0
+        self.nbest = nbest
+        if not (blank_skip_threshold >= 0 and blank_skip_threshold <= 1):
+            raise AssertionError("blank_skip_threshold must be between 0 and 1")
+        self.blank_skip_threshold = math.log(blank_skip_threshold)
+        self.beam_size = min(beam_size, len(vocab_list))  # beam size must be smaller than vocab size
+
+    def __del__(self):
+        if cuctc is not None:
+            cuctc.prefixCTC_free(self.internal_data)
+
+    def __call__(self, log_prob: torch.Tensor, encoder_out_lens: torch.Tensor):
+        """
+        Args:
+            log_prob (torch.FloatTensor): GPU tensor of shape `(batch, frame, num_tokens)` storing sequences of
+                probability distribution over labels; log_softmax(output of acoustic model).
+            lengths (dtype torch.int32): GPU tensor of shape `(batch, )` storing the valid length of
+                in time axis of the output Tensor in each batch.
+
+        Returns:
+            List[List[CUCTCHypothesis]]:
+                List of sorted best hypotheses for each audio sequence in the batch.
+        """
+        if not encoder_out_lens.dtype == torch.int32:
+            raise AssertionError("encoder_out_lens must be torch.int32")
+        if not log_prob.dtype == torch.float32:
+            raise AssertionError("log_prob must be torch.float32")
+        if not (log_prob.is_cuda and encoder_out_lens.is_cuda):
+            raise AssertionError("inputs must be cuda tensors")
+        if not (log_prob.is_contiguous() and encoder_out_lens.is_contiguous()):
+            raise AssertionError("input tensors must be contiguous")
+        required_size, score_hyps = cuctc.ctc_beam_search_decoder_batch_gpu_v2(
+            self.internal_data,
+            self.memory.data_ptr(),
+            self.memory.size(0),
+            log_prob.data_ptr(),
+            encoder_out_lens.data_ptr(),
+            log_prob.size(),
+            log_prob.stride(),
+            self.beam_size,
+            self.blank_id,
+            self.space_id,
+            self.blank_skip_threshold,
+        )
+        if required_size > 0:
+            self.memory = torch.empty(required_size, dtype=torch.int8, device=log_prob.device).contiguous()
+            _, score_hyps = cuctc.ctc_beam_search_decoder_batch_gpu_v2(
+                self.internal_data,
+                self.memory.data_ptr(),
+                self.memory.size(0),
+                log_prob.data_ptr(),
+                encoder_out_lens.data_ptr(),
+                log_prob.size(),
+                log_prob.stride(),
+                self.beam_size,
+                self.blank_id,
+                self.space_id,
+                self.blank_skip_threshold,
+            )
+        batch_size = len(score_hyps)
+        hypos = []
+        for i in range(batch_size):
+            hypos.append(
+                [
+                    CUCTCHypothesis(
+                        tokens=score_hyps[i][j][1],
+                        words=[self.vocab_list[word_id] for word_id in score_hyps[i][j][1]],
+                        score=score_hyps[i][j][0],
+                    )
+                    for j in range(self.nbest)
+                ]
+            )
+        return hypos
+
+
+def cuda_ctc_decoder(
+    tokens: Union[str, List[str]],
+    nbest: int = 1,
+    beam_size: int = 10,
+    blank_skip_threshold: float = _DEFAULT_BLANK_SKIP_THREASHOLD,
+) -> CUCTCDecoder:
+    """Builds an instance of :class:`CUCTCDecoder`.
+
+    Args:
+        tokens (str or List[str]): File or list containing valid tokens.
+            If using a file, the expected format is for tokens mapping to the same index to be on the same line
+        beam_size (int, optional): The maximum number of hypos to hold after each decode step (Default: 10)
+        nbest (int): The number of best decodings to return
+        blank_id (int): The token ID corresopnding to the blank symbol.
+        blank_skip_threshold (float): skip frames if log_prob(blank) > log(blank_skip_threshold), to speed up decoding
+            (Default: 0.95).
+
+    Returns:
+        CUCTCDecoder: decoder
+
+    Example
+        >>> decoder = cuda_ctc_decoder(
+        >>>     vocab_file="tokens.txt",
+        >>>     blank_skip_threshold=0.95,
+        >>> )
+        >>> results = decoder(log_probs, encoder_out_lens) # List of shape (B, nbest) of Hypotheses
+    """
+    if type(tokens) == str:
+        tokens = _get_vocab_list(tokens)
+
+    return CUCTCDecoder(vocab_list=tokens, beam_size=beam_size, nbest=nbest, blank_skip_threshold=blank_skip_threshold)
diff --git a/lib/python3.11/site-packages/torchaudio/models/deepspeech.py b/lib/python3.11/site-packages/torchaudio/models/deepspeech.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef23d1d351bde615cb2b1b38ffdd7782fbb5b627
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/deepspeech.py
@@ -0,0 +1,84 @@
+import torch
+
+__all__ = ["DeepSpeech"]
+
+
+class FullyConnected(torch.nn.Module):
+    """
+    Args:
+        n_feature: Number of input features
+        n_hidden: Internal hidden unit size.
+    """
+
+    def __init__(self, n_feature: int, n_hidden: int, dropout: float, relu_max_clip: int = 20) -> None:
+        super(FullyConnected, self).__init__()
+        self.fc = torch.nn.Linear(n_feature, n_hidden, bias=True)
+        self.relu_max_clip = relu_max_clip
+        self.dropout = dropout
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc(x)
+        x = torch.nn.functional.relu(x)
+        x = torch.nn.functional.hardtanh(x, 0, self.relu_max_clip)
+        if self.dropout:
+            x = torch.nn.functional.dropout(x, self.dropout, self.training)
+        return x
+
+
+class DeepSpeech(torch.nn.Module):
+    """DeepSpeech architecture introduced in
+    *Deep Speech: Scaling up end-to-end speech recognition* :cite:`hannun2014deep`.
+
+    Args:
+        n_feature: Number of input features
+        n_hidden: Internal hidden unit size.
+        n_class: Number of output classes
+    """
+
+    def __init__(
+        self,
+        n_feature: int,
+        n_hidden: int = 2048,
+        n_class: int = 40,
+        dropout: float = 0.0,
+    ) -> None:
+        super(DeepSpeech, self).__init__()
+        self.n_hidden = n_hidden
+        self.fc1 = FullyConnected(n_feature, n_hidden, dropout)
+        self.fc2 = FullyConnected(n_hidden, n_hidden, dropout)
+        self.fc3 = FullyConnected(n_hidden, n_hidden, dropout)
+        self.bi_rnn = torch.nn.RNN(n_hidden, n_hidden, num_layers=1, nonlinearity="relu", bidirectional=True)
+        self.fc4 = FullyConnected(n_hidden, n_hidden, dropout)
+        self.out = torch.nn.Linear(n_hidden, n_class)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x (torch.Tensor): Tensor of dimension (batch, channel, time, feature).
+        Returns:
+            Tensor: Predictor tensor of dimension (batch, time, class).
+        """
+        # N x C x T x F
+        x = self.fc1(x)
+        # N x C x T x H
+        x = self.fc2(x)
+        # N x C x T x H
+        x = self.fc3(x)
+        # N x C x T x H
+        x = x.squeeze(1)
+        # N x T x H
+        x = x.transpose(0, 1)
+        # T x N x H
+        x, _ = self.bi_rnn(x)
+        # The fifth (non-recurrent) layer takes both the forward and backward units as inputs
+        x = x[:, :, : self.n_hidden] + x[:, :, self.n_hidden :]
+        # T x N x H
+        x = self.fc4(x)
+        # T x N x H
+        x = self.out(x)
+        # T x N x n_class
+        x = x.permute(1, 0, 2)
+        # N x T x n_class
+        x = torch.nn.functional.log_softmax(x, dim=2)
+        # N x T x n_class
+        return x
diff --git a/lib/python3.11/site-packages/torchaudio/models/emformer.py b/lib/python3.11/site-packages/torchaudio/models/emformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ddd257552ecda94cb55bbc1eed1dae8a5382380
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/emformer.py
@@ -0,0 +1,884 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+
+
+__all__ = ["Emformer"]
+
+
+def _lengths_to_padding_mask(lengths: torch.Tensor) -> torch.Tensor:
+    batch_size = lengths.shape[0]
+    max_length = int(torch.max(lengths).item())
+    padding_mask = torch.arange(max_length, device=lengths.device, dtype=lengths.dtype).expand(
+        batch_size, max_length
+    ) >= lengths.unsqueeze(1)
+    return padding_mask
+
+
+def _gen_padding_mask(
+    utterance: torch.Tensor,
+    right_context: torch.Tensor,
+    summary: torch.Tensor,
+    lengths: torch.Tensor,
+    mems: torch.Tensor,
+    left_context_key: Optional[torch.Tensor] = None,
+) -> Optional[torch.Tensor]:
+    T = right_context.size(0) + utterance.size(0) + summary.size(0)
+    B = right_context.size(1)
+    if B == 1:
+        padding_mask = None
+    else:
+        right_context_blocks_length = T - torch.max(lengths).int() - summary.size(0)
+        left_context_blocks_length = left_context_key.size(0) if left_context_key is not None else 0
+        klengths = lengths + mems.size(0) + right_context_blocks_length + left_context_blocks_length
+        padding_mask = _lengths_to_padding_mask(lengths=klengths)
+    return padding_mask
+
+
+def _get_activation_module(activation: str) -> torch.nn.Module:
+    if activation == "relu":
+        return torch.nn.ReLU()
+    elif activation == "gelu":
+        return torch.nn.GELU()
+    elif activation == "silu":
+        return torch.nn.SiLU()
+    else:
+        raise ValueError(f"Unsupported activation {activation}")
+
+
+def _get_weight_init_gains(weight_init_scale_strategy: Optional[str], num_layers: int) -> List[Optional[float]]:
+    if weight_init_scale_strategy is None:
+        return [None for _ in range(num_layers)]
+    elif weight_init_scale_strategy == "depthwise":
+        return [1.0 / math.sqrt(layer_idx + 1) for layer_idx in range(num_layers)]
+    elif weight_init_scale_strategy == "constant":
+        return [1.0 / math.sqrt(2) for layer_idx in range(num_layers)]
+    else:
+        raise ValueError(f"Unsupported weight_init_scale_strategy value {weight_init_scale_strategy}")
+
+
+def _gen_attention_mask_block(
+    col_widths: List[int], col_mask: List[bool], num_rows: int, device: torch.device
+) -> torch.Tensor:
+    if len(col_widths) != len(col_mask):
+        raise ValueError("Length of col_widths must match that of col_mask")
+
+    mask_block = [
+        torch.ones(num_rows, col_width, device=device)
+        if is_ones_col
+        else torch.zeros(num_rows, col_width, device=device)
+        for col_width, is_ones_col in zip(col_widths, col_mask)
+    ]
+    return torch.cat(mask_block, dim=1)
+
+
+class _EmformerAttention(torch.nn.Module):
+    r"""Emformer layer attention module.
+
+    Args:
+        input_dim (int): input dimension.
+        num_heads (int): number of attention heads in each Emformer layer.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+        weight_init_gain (float or None, optional): scale factor to apply when initializing
+            attention module parameters. (Default: ``None``)
+        tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
+        negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        weight_init_gain: Optional[float] = None,
+        tanh_on_mem: bool = False,
+        negative_inf: float = -1e8,
+    ):
+        super().__init__()
+
+        if input_dim % num_heads != 0:
+            raise ValueError(f"input_dim ({input_dim}) is not a multiple of num_heads ({num_heads}).")
+
+        self.input_dim = input_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.tanh_on_mem = tanh_on_mem
+        self.negative_inf = negative_inf
+
+        self.scaling = (self.input_dim // self.num_heads) ** -0.5
+
+        self.emb_to_key_value = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
+        self.emb_to_query = torch.nn.Linear(input_dim, input_dim, bias=True)
+        self.out_proj = torch.nn.Linear(input_dim, input_dim, bias=True)
+
+        if weight_init_gain:
+            torch.nn.init.xavier_uniform_(self.emb_to_key_value.weight, gain=weight_init_gain)
+            torch.nn.init.xavier_uniform_(self.emb_to_query.weight, gain=weight_init_gain)
+
+    def _gen_key_value(self, input: torch.Tensor, mems: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        T, _, _ = input.shape
+        summary_length = mems.size(0) + 1
+        right_ctx_utterance_block = input[: T - summary_length]
+        mems_right_ctx_utterance_block = torch.cat([mems, right_ctx_utterance_block])
+        key, value = self.emb_to_key_value(mems_right_ctx_utterance_block).chunk(chunks=2, dim=2)
+        return key, value
+
+    def _gen_attention_probs(
+        self,
+        attention_weights: torch.Tensor,
+        attention_mask: torch.Tensor,
+        padding_mask: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        attention_weights_float = attention_weights.float()
+        attention_weights_float = attention_weights_float.masked_fill(attention_mask.unsqueeze(0), self.negative_inf)
+        T = attention_weights.size(1)
+        B = attention_weights.size(0) // self.num_heads
+        if padding_mask is not None:
+            attention_weights_float = attention_weights_float.view(B, self.num_heads, T, -1)
+            attention_weights_float = attention_weights_float.masked_fill(
+                padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), self.negative_inf
+            )
+            attention_weights_float = attention_weights_float.view(B * self.num_heads, T, -1)
+        attention_probs = torch.nn.functional.softmax(attention_weights_float, dim=-1).type_as(attention_weights)
+        return torch.nn.functional.dropout(attention_probs, p=float(self.dropout), training=self.training)
+
+    def _forward_impl(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        summary: torch.Tensor,
+        mems: torch.Tensor,
+        attention_mask: torch.Tensor,
+        left_context_key: Optional[torch.Tensor] = None,
+        left_context_val: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        B = utterance.size(1)
+        T = right_context.size(0) + utterance.size(0) + summary.size(0)
+
+        # Compute query with [right context, utterance, summary].
+        query = self.emb_to_query(torch.cat([right_context, utterance, summary]))
+
+        # Compute key and value with [mems, right context, utterance].
+        key, value = self.emb_to_key_value(torch.cat([mems, right_context, utterance])).chunk(chunks=2, dim=2)
+
+        if left_context_key is not None and left_context_val is not None:
+            right_context_blocks_length = T - torch.max(lengths).int() - summary.size(0)
+            key = torch.cat(
+                [
+                    key[: mems.size(0) + right_context_blocks_length],
+                    left_context_key,
+                    key[mems.size(0) + right_context_blocks_length :],
+                ],
+            )
+            value = torch.cat(
+                [
+                    value[: mems.size(0) + right_context_blocks_length],
+                    left_context_val,
+                    value[mems.size(0) + right_context_blocks_length :],
+                ],
+            )
+
+        # Compute attention weights from query, key, and value.
+        reshaped_query, reshaped_key, reshaped_value = [
+            tensor.contiguous().view(-1, B * self.num_heads, self.input_dim // self.num_heads).transpose(0, 1)
+            for tensor in [query, key, value]
+        ]
+        attention_weights = torch.bmm(reshaped_query * self.scaling, reshaped_key.transpose(1, 2))
+
+        # Compute padding mask.
+        padding_mask = _gen_padding_mask(utterance, right_context, summary, lengths, mems, left_context_key)
+
+        # Compute attention probabilities.
+        attention_probs = self._gen_attention_probs(attention_weights, attention_mask, padding_mask)
+
+        # Compute attention.
+        attention = torch.bmm(attention_probs, reshaped_value)
+        if attention.shape != (
+            B * self.num_heads,
+            T,
+            self.input_dim // self.num_heads,
+        ):
+            raise AssertionError("Computed attention has incorrect dimensions")
+        attention = attention.transpose(0, 1).contiguous().view(T, B, self.input_dim)
+
+        # Apply output projection.
+        output_right_context_mems = self.out_proj(attention)
+
+        summary_length = summary.size(0)
+        output_right_context = output_right_context_mems[: T - summary_length]
+        output_mems = output_right_context_mems[T - summary_length :]
+        if self.tanh_on_mem:
+            output_mems = torch.tanh(output_mems)
+        else:
+            output_mems = torch.clamp(output_mems, min=-10, max=10)
+
+        return output_right_context, output_mems, key, value
+
+    def forward(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        summary: torch.Tensor,
+        mems: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Forward pass for training.
+
+        B: batch size;
+        D: feature dimension of each frame;
+        T: number of utterance frames;
+        R: number of right context frames;
+        S: number of summary elements;
+        M: number of memory elements.
+
+        Args:
+            utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``utterance``.
+            right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
+            summary (torch.Tensor): summary elements, with shape `(S, B, D)`.
+            mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
+            attention_mask (torch.Tensor): attention mask for underlying attention module.
+
+        Returns:
+            (Tensor, Tensor):
+                Tensor
+                    output frames corresponding to utterance and right_context, with shape `(T + R, B, D)`.
+                Tensor
+                    updated memory elements, with shape `(M, B, D)`.
+        """
+        output, output_mems, _, _ = self._forward_impl(utterance, lengths, right_context, summary, mems, attention_mask)
+        return output, output_mems[:-1]
+
+    @torch.jit.export
+    def infer(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        summary: torch.Tensor,
+        mems: torch.Tensor,
+        left_context_key: torch.Tensor,
+        left_context_val: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""Forward pass for inference.
+
+        B: batch size;
+        D: feature dimension of each frame;
+        T: number of utterance frames;
+        R: number of right context frames;
+        S: number of summary elements;
+        M: number of memory elements.
+
+        Args:
+            utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``utterance``.
+            right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
+            summary (torch.Tensor): summary elements, with shape `(S, B, D)`.
+            mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
+            left_context_key (torch.Tensor): left context attention key computed from preceding invocation.
+            left_context_val (torch.Tensor): left context attention value computed from preceding invocation.
+
+        Returns:
+            (Tensor, Tensor, Tensor, and Tensor):
+                Tensor
+                    output frames corresponding to utterance and right_context, with shape `(T + R, B, D)`.
+                Tensor
+                    updated memory elements, with shape `(M, B, D)`.
+                Tensor
+                    attention key computed for left context and utterance.
+                Tensor
+                    attention value computed for left context and utterance.
+        """
+        query_dim = right_context.size(0) + utterance.size(0) + summary.size(0)
+        key_dim = right_context.size(0) + utterance.size(0) + mems.size(0) + left_context_key.size(0)
+        attention_mask = torch.zeros(query_dim, key_dim).to(dtype=torch.bool, device=utterance.device)
+        attention_mask[-1, : mems.size(0)] = True
+        output, output_mems, key, value = self._forward_impl(
+            utterance,
+            lengths,
+            right_context,
+            summary,
+            mems,
+            attention_mask,
+            left_context_key=left_context_key,
+            left_context_val=left_context_val,
+        )
+        return (
+            output,
+            output_mems,
+            key[mems.size(0) + right_context.size(0) :],
+            value[mems.size(0) + right_context.size(0) :],
+        )
+
+
+class _EmformerLayer(torch.nn.Module):
+    r"""Emformer layer that constitutes Emformer.
+
+    Args:
+        input_dim (int): input dimension.
+        num_heads (int): number of attention heads.
+        ffn_dim: (int): hidden layer dimension of feedforward network.
+        segment_length (int): length of each input segment.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+        activation (str, optional): activation function to use in feedforward network.
+            Must be one of ("relu", "gelu", "silu"). (Default: "relu")
+        left_context_length (int, optional): length of left context. (Default: 0)
+        max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
+        weight_init_gain (float or None, optional): scale factor to apply when initializing
+            attention module parameters. (Default: ``None``)
+        tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
+        negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        num_heads: int,
+        ffn_dim: int,
+        segment_length: int,
+        dropout: float = 0.0,
+        activation: str = "relu",
+        left_context_length: int = 0,
+        max_memory_size: int = 0,
+        weight_init_gain: Optional[float] = None,
+        tanh_on_mem: bool = False,
+        negative_inf: float = -1e8,
+    ):
+        super().__init__()
+
+        self.attention = _EmformerAttention(
+            input_dim=input_dim,
+            num_heads=num_heads,
+            dropout=dropout,
+            weight_init_gain=weight_init_gain,
+            tanh_on_mem=tanh_on_mem,
+            negative_inf=negative_inf,
+        )
+        self.dropout = torch.nn.Dropout(dropout)
+        self.memory_op = torch.nn.AvgPool1d(kernel_size=segment_length, stride=segment_length, ceil_mode=True)
+
+        activation_module = _get_activation_module(activation)
+        self.pos_ff = torch.nn.Sequential(
+            torch.nn.LayerNorm(input_dim),
+            torch.nn.Linear(input_dim, ffn_dim),
+            activation_module,
+            torch.nn.Dropout(dropout),
+            torch.nn.Linear(ffn_dim, input_dim),
+            torch.nn.Dropout(dropout),
+        )
+        self.layer_norm_input = torch.nn.LayerNorm(input_dim)
+        self.layer_norm_output = torch.nn.LayerNorm(input_dim)
+
+        self.left_context_length = left_context_length
+        self.segment_length = segment_length
+        self.max_memory_size = max_memory_size
+        self.input_dim = input_dim
+
+        self.use_mem = max_memory_size > 0
+
+    def _init_state(self, batch_size: int, device: Optional[torch.device]) -> List[torch.Tensor]:
+        empty_memory = torch.zeros(self.max_memory_size, batch_size, self.input_dim, device=device)
+        left_context_key = torch.zeros(self.left_context_length, batch_size, self.input_dim, device=device)
+        left_context_val = torch.zeros(self.left_context_length, batch_size, self.input_dim, device=device)
+        past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
+        return [empty_memory, left_context_key, left_context_val, past_length]
+
+    def _unpack_state(self, state: List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        past_length = state[3][0][0].item()
+        past_left_context_length = min(self.left_context_length, past_length)
+        past_mem_length = min(self.max_memory_size, math.ceil(past_length / self.segment_length))
+        pre_mems = state[0][self.max_memory_size - past_mem_length :]
+        lc_key = state[1][self.left_context_length - past_left_context_length :]
+        lc_val = state[2][self.left_context_length - past_left_context_length :]
+        return pre_mems, lc_key, lc_val
+
+    def _pack_state(
+        self,
+        next_k: torch.Tensor,
+        next_v: torch.Tensor,
+        update_length: int,
+        mems: torch.Tensor,
+        state: List[torch.Tensor],
+    ) -> List[torch.Tensor]:
+        new_k = torch.cat([state[1], next_k])
+        new_v = torch.cat([state[2], next_v])
+        state[0] = torch.cat([state[0], mems])[-self.max_memory_size :]
+        state[1] = new_k[new_k.shape[0] - self.left_context_length :]
+        state[2] = new_v[new_v.shape[0] - self.left_context_length :]
+        state[3] = state[3] + update_length
+        return state
+
+    def _process_attention_output(
+        self,
+        rc_output: torch.Tensor,
+        utterance: torch.Tensor,
+        right_context: torch.Tensor,
+    ) -> torch.Tensor:
+        result = self.dropout(rc_output) + torch.cat([right_context, utterance])
+        result = self.pos_ff(result) + result
+        result = self.layer_norm_output(result)
+        return result
+
+    def _apply_pre_attention_layer_norm(
+        self, utterance: torch.Tensor, right_context: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        layer_norm_input = self.layer_norm_input(torch.cat([right_context, utterance]))
+        return (
+            layer_norm_input[right_context.size(0) :],
+            layer_norm_input[: right_context.size(0)],
+        )
+
+    def _apply_post_attention_ffn(
+        self, rc_output: torch.Tensor, utterance: torch.Tensor, right_context: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        rc_output = self._process_attention_output(rc_output, utterance, right_context)
+        return rc_output[right_context.size(0) :], rc_output[: right_context.size(0)]
+
+    def _apply_attention_forward(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        mems: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if attention_mask is None:
+            raise ValueError("attention_mask must be not None when for_inference is False")
+
+        if self.use_mem:
+            summary = self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
+        else:
+            summary = torch.empty(0).to(dtype=utterance.dtype, device=utterance.device)
+        rc_output, next_m = self.attention(
+            utterance=utterance,
+            lengths=lengths,
+            right_context=right_context,
+            summary=summary,
+            mems=mems,
+            attention_mask=attention_mask,
+        )
+        return rc_output, next_m
+
+    def _apply_attention_infer(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        mems: torch.Tensor,
+        state: Optional[List[torch.Tensor]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
+        if state is None:
+            state = self._init_state(utterance.size(1), device=utterance.device)
+        pre_mems, lc_key, lc_val = self._unpack_state(state)
+        if self.use_mem:
+            summary = self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
+            summary = summary[:1]
+        else:
+            summary = torch.empty(0).to(dtype=utterance.dtype, device=utterance.device)
+        rc_output, next_m, next_k, next_v = self.attention.infer(
+            utterance=utterance,
+            lengths=lengths,
+            right_context=right_context,
+            summary=summary,
+            mems=pre_mems,
+            left_context_key=lc_key,
+            left_context_val=lc_val,
+        )
+        state = self._pack_state(next_k, next_v, utterance.size(0), mems, state)
+        return rc_output, next_m, state
+
+    def forward(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        mems: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""Forward pass for training.
+
+        B: batch size;
+        D: feature dimension of each frame;
+        T: number of utterance frames;
+        R: number of right context frames;
+        M: number of memory elements.
+
+        Args:
+            utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``utterance``.
+            right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
+            mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
+            attention_mask (torch.Tensor): attention mask for underlying attention module.
+
+        Returns:
+            (Tensor, Tensor, Tensor):
+                Tensor
+                    encoded utterance frames, with shape `(T, B, D)`.
+                Tensor
+                    updated right context frames, with shape `(R, B, D)`.
+                Tensor
+                    updated memory elements, with shape `(M, B, D)`.
+        """
+        (
+            layer_norm_utterance,
+            layer_norm_right_context,
+        ) = self._apply_pre_attention_layer_norm(utterance, right_context)
+        rc_output, output_mems = self._apply_attention_forward(
+            layer_norm_utterance,
+            lengths,
+            layer_norm_right_context,
+            mems,
+            attention_mask,
+        )
+        output_utterance, output_right_context = self._apply_post_attention_ffn(rc_output, utterance, right_context)
+        return output_utterance, output_right_context, output_mems
+
+    @torch.jit.export
+    def infer(
+        self,
+        utterance: torch.Tensor,
+        lengths: torch.Tensor,
+        right_context: torch.Tensor,
+        state: Optional[List[torch.Tensor]],
+        mems: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor], torch.Tensor]:
+        r"""Forward pass for inference.
+
+        B: batch size;
+        D: feature dimension of each frame;
+        T: number of utterance frames;
+        R: number of right context frames;
+        M: number of memory elements.
+
+        Args:
+            utterance (torch.Tensor): utterance frames, with shape `(T, B, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``utterance``.
+            right_context (torch.Tensor): right context frames, with shape `(R, B, D)`.
+            state (List[torch.Tensor] or None): list of tensors representing layer internal state
+                generated in preceding invocation of ``infer``.
+            mems (torch.Tensor): memory elements, with shape `(M, B, D)`.
+
+        Returns:
+            (Tensor, Tensor, List[torch.Tensor], Tensor):
+                Tensor
+                    encoded utterance frames, with shape `(T, B, D)`.
+                Tensor
+                    updated right context frames, with shape `(R, B, D)`.
+                List[Tensor]
+                    list of tensors representing layer internal state
+                    generated in current invocation of ``infer``.
+                Tensor
+                    updated memory elements, with shape `(M, B, D)`.
+        """
+        (
+            layer_norm_utterance,
+            layer_norm_right_context,
+        ) = self._apply_pre_attention_layer_norm(utterance, right_context)
+        rc_output, output_mems, output_state = self._apply_attention_infer(
+            layer_norm_utterance, lengths, layer_norm_right_context, mems, state
+        )
+        output_utterance, output_right_context = self._apply_post_attention_ffn(rc_output, utterance, right_context)
+        return output_utterance, output_right_context, output_state, output_mems
+
+
+class _EmformerImpl(torch.nn.Module):
+    def __init__(
+        self,
+        emformer_layers: torch.nn.ModuleList,
+        segment_length: int,
+        left_context_length: int = 0,
+        right_context_length: int = 0,
+        max_memory_size: int = 0,
+    ):
+        super().__init__()
+
+        self.use_mem = max_memory_size > 0
+        self.memory_op = torch.nn.AvgPool1d(
+            kernel_size=segment_length,
+            stride=segment_length,
+            ceil_mode=True,
+        )
+        self.emformer_layers = emformer_layers
+        self.left_context_length = left_context_length
+        self.right_context_length = right_context_length
+        self.segment_length = segment_length
+        self.max_memory_size = max_memory_size
+
+    def _gen_right_context(self, input: torch.Tensor) -> torch.Tensor:
+        T = input.shape[0]
+        num_segs = math.ceil((T - self.right_context_length) / self.segment_length)
+        right_context_blocks = []
+        for seg_idx in range(num_segs - 1):
+            start = (seg_idx + 1) * self.segment_length
+            end = start + self.right_context_length
+            right_context_blocks.append(input[start:end])
+        right_context_blocks.append(input[T - self.right_context_length :])
+        return torch.cat(right_context_blocks)
+
+    def _gen_attention_mask_col_widths(self, seg_idx: int, utterance_length: int) -> List[int]:
+        num_segs = math.ceil(utterance_length / self.segment_length)
+        rc = self.right_context_length
+        lc = self.left_context_length
+        rc_start = seg_idx * rc
+        rc_end = rc_start + rc
+        seg_start = max(seg_idx * self.segment_length - lc, 0)
+        seg_end = min((seg_idx + 1) * self.segment_length, utterance_length)
+        rc_length = self.right_context_length * num_segs
+
+        if self.use_mem:
+            m_start = max(seg_idx - self.max_memory_size, 0)
+            mem_length = num_segs - 1
+            col_widths = [
+                m_start,  # before memory
+                seg_idx - m_start,  # memory
+                mem_length - seg_idx,  # after memory
+                rc_start,  # before right context
+                rc,  # right context
+                rc_length - rc_end,  # after right context
+                seg_start,  # before query segment
+                seg_end - seg_start,  # query segment
+                utterance_length - seg_end,  # after query segment
+            ]
+        else:
+            col_widths = [
+                rc_start,  # before right context
+                rc,  # right context
+                rc_length - rc_end,  # after right context
+                seg_start,  # before query segment
+                seg_end - seg_start,  # query segment
+                utterance_length - seg_end,  # after query segment
+            ]
+
+        return col_widths
+
+    def _gen_attention_mask(self, input: torch.Tensor) -> torch.Tensor:
+        utterance_length = input.size(0)
+        num_segs = math.ceil(utterance_length / self.segment_length)
+
+        rc_mask = []
+        query_mask = []
+        summary_mask = []
+
+        if self.use_mem:
+            num_cols = 9
+            # memory, right context, query segment
+            rc_q_cols_mask = [idx in [1, 4, 7] for idx in range(num_cols)]
+            # right context, query segment
+            s_cols_mask = [idx in [4, 7] for idx in range(num_cols)]
+            masks_to_concat = [rc_mask, query_mask, summary_mask]
+        else:
+            num_cols = 6
+            # right context, query segment
+            rc_q_cols_mask = [idx in [1, 4] for idx in range(num_cols)]
+            s_cols_mask = None
+            masks_to_concat = [rc_mask, query_mask]
+
+        for seg_idx in range(num_segs):
+            col_widths = self._gen_attention_mask_col_widths(seg_idx, utterance_length)
+
+            rc_mask_block = _gen_attention_mask_block(
+                col_widths, rc_q_cols_mask, self.right_context_length, input.device
+            )
+            rc_mask.append(rc_mask_block)
+
+            query_mask_block = _gen_attention_mask_block(
+                col_widths,
+                rc_q_cols_mask,
+                min(
+                    self.segment_length,
+                    utterance_length - seg_idx * self.segment_length,
+                ),
+                input.device,
+            )
+            query_mask.append(query_mask_block)
+
+            if s_cols_mask is not None:
+                summary_mask_block = _gen_attention_mask_block(col_widths, s_cols_mask, 1, input.device)
+                summary_mask.append(summary_mask_block)
+
+        attention_mask = (1 - torch.cat([torch.cat(mask) for mask in masks_to_concat])).to(torch.bool)
+        return attention_mask
+
+    def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Forward pass for training and non-streaming inference.
+
+        B: batch size;
+        T: max number of input frames in batch;
+        D: feature dimension of each frame.
+
+        Args:
+            input (torch.Tensor): utterance frames right-padded with right context frames, with
+                shape `(B, T + right_context_length, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid utterance frames for i-th batch element in ``input``.
+
+        Returns:
+            (Tensor, Tensor):
+                Tensor
+                    output frames, with shape `(B, T, D)`.
+                Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid frames for i-th batch element in output frames.
+        """
+        input = input.permute(1, 0, 2)
+        right_context = self._gen_right_context(input)
+        utterance = input[: input.size(0) - self.right_context_length]
+        attention_mask = self._gen_attention_mask(utterance)
+        mems = (
+            self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[:-1]
+            if self.use_mem
+            else torch.empty(0).to(dtype=input.dtype, device=input.device)
+        )
+        output = utterance
+        for layer in self.emformer_layers:
+            output, right_context, mems = layer(output, lengths, right_context, mems, attention_mask)
+        return output.permute(1, 0, 2), lengths
+
+    @torch.jit.export
+    def infer(
+        self,
+        input: torch.Tensor,
+        lengths: torch.Tensor,
+        states: Optional[List[List[torch.Tensor]]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        r"""Forward pass for streaming inference.
+
+        B: batch size;
+        D: feature dimension of each frame.
+
+        Args:
+            input (torch.Tensor): utterance frames right-padded with right context frames, with
+                shape `(B, segment_length + right_context_length, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``input``.
+            states (List[List[torch.Tensor]] or None, optional): list of lists of tensors
+                representing internal state generated in preceding invocation of ``infer``. (Default: ``None``)
+
+        Returns:
+            (Tensor, Tensor, List[List[Tensor]]):
+                Tensor
+                    output frames, with shape `(B, segment_length, D)`.
+                Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid frames for i-th batch element in output frames.
+                List[List[Tensor]]
+                    output states; list of lists of tensors representing internal state
+                    generated in current invocation of ``infer``.
+        """
+        if input.size(1) != self.segment_length + self.right_context_length:
+            raise ValueError(
+                "Per configured segment_length and right_context_length"
+                f", expected size of {self.segment_length + self.right_context_length} for dimension 1 of input"
+                f", but got {input.size(1)}."
+            )
+        input = input.permute(1, 0, 2)
+        right_context_start_idx = input.size(0) - self.right_context_length
+        right_context = input[right_context_start_idx:]
+        utterance = input[:right_context_start_idx]
+        output_lengths = torch.clamp(lengths - self.right_context_length, min=0)
+        mems = (
+            self.memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
+            if self.use_mem
+            else torch.empty(0).to(dtype=input.dtype, device=input.device)
+        )
+        output = utterance
+        output_states: List[List[torch.Tensor]] = []
+        for layer_idx, layer in enumerate(self.emformer_layers):
+            output, right_context, output_state, mems = layer.infer(
+                output,
+                output_lengths,
+                right_context,
+                None if states is None else states[layer_idx],
+                mems,
+            )
+            output_states.append(output_state)
+
+        return output.permute(1, 0, 2), output_lengths, output_states
+
+
+class Emformer(_EmformerImpl):
+    r"""Emformer architecture introduced in
+    *Emformer: Efficient Memory Transformer Based Acoustic Model for Low Latency Streaming Speech Recognition*
+    :cite:`shi2021emformer`.
+
+    See Also:
+        * :func:`~torchaudio.models.emformer_rnnt_model`,
+          :func:`~torchaudio.models.emformer_rnnt_base`: factory functions.
+        * :class:`torchaudio.pipelines.RNNTBundle`: ASR pipelines with pretrained model.
+
+    Args:
+        input_dim (int): input dimension.
+        num_heads (int): number of attention heads in each Emformer layer.
+        ffn_dim (int): hidden layer dimension of each Emformer layer's feedforward network.
+        num_layers (int): number of Emformer layers to instantiate.
+        segment_length (int): length of each input segment.
+        dropout (float, optional): dropout probability. (Default: 0.0)
+        activation (str, optional): activation function to use in each Emformer layer's
+            feedforward network. Must be one of ("relu", "gelu", "silu"). (Default: "relu")
+        left_context_length (int, optional): length of left context. (Default: 0)
+        right_context_length (int, optional): length of right context. (Default: 0)
+        max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
+        weight_init_scale_strategy (str or None, optional): per-layer weight initialization scaling
+            strategy. Must be one of ("depthwise", "constant", ``None``). (Default: "depthwise")
+        tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
+        negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
+
+    Examples:
+        >>> emformer = Emformer(512, 8, 2048, 20, 4, right_context_length=1)
+        >>> input = torch.rand(128, 400, 512)  # batch, num_frames, feature_dim
+        >>> lengths = torch.randint(1, 200, (128,))  # batch
+        >>> output, lengths = emformer(input, lengths)
+        >>> input = torch.rand(128, 5, 512)
+        >>> lengths = torch.ones(128) * 5
+        >>> output, lengths, states = emformer.infer(input, lengths, None)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        num_heads: int,
+        ffn_dim: int,
+        num_layers: int,
+        segment_length: int,
+        dropout: float = 0.0,
+        activation: str = "relu",
+        left_context_length: int = 0,
+        right_context_length: int = 0,
+        max_memory_size: int = 0,
+        weight_init_scale_strategy: Optional[str] = "depthwise",
+        tanh_on_mem: bool = False,
+        negative_inf: float = -1e8,
+    ):
+        weight_init_gains = _get_weight_init_gains(weight_init_scale_strategy, num_layers)
+        emformer_layers = torch.nn.ModuleList(
+            [
+                _EmformerLayer(
+                    input_dim,
+                    num_heads,
+                    ffn_dim,
+                    segment_length,
+                    dropout=dropout,
+                    activation=activation,
+                    left_context_length=left_context_length,
+                    max_memory_size=max_memory_size,
+                    weight_init_gain=weight_init_gains[layer_idx],
+                    tanh_on_mem=tanh_on_mem,
+                    negative_inf=negative_inf,
+                )
+                for layer_idx in range(num_layers)
+            ]
+        )
+        super().__init__(
+            emformer_layers,
+            segment_length,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+        )
diff --git a/lib/python3.11/site-packages/torchaudio/models/rnnt.py b/lib/python3.11/site-packages/torchaudio/models/rnnt.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9dbe22c9fb4a97cf7f8779a953b5bd7b5bbffd9
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/rnnt.py
@@ -0,0 +1,816 @@
+from abc import ABC, abstractmethod
+from typing import List, Optional, Tuple
+
+import torch
+from torchaudio.models import Emformer
+
+
+__all__ = ["RNNT", "emformer_rnnt_base", "emformer_rnnt_model"]
+
+
+class _TimeReduction(torch.nn.Module):
+    r"""Coalesces frames along time dimension into a
+    fewer number of frames with higher feature dimensionality.
+
+    Args:
+        stride (int): number of frames to merge for each output frame.
+    """
+
+    def __init__(self, stride: int) -> None:
+        super().__init__()
+        self.stride = stride
+
+    def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Forward pass.
+
+        B: batch size;
+        T: maximum input sequence length in batch;
+        D: feature dimension of each input sequence frame.
+
+        Args:
+            input (torch.Tensor): input sequences, with shape `(B, T, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``input``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor):
+                torch.Tensor
+                    output sequences, with shape
+                    `(B, T  // stride, D * stride)`
+                torch.Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid frames for i-th batch element in output sequences.
+        """
+        B, T, D = input.shape
+        num_frames = T - (T % self.stride)
+        input = input[:, :num_frames, :]
+        lengths = lengths.div(self.stride, rounding_mode="trunc")
+        T_max = num_frames // self.stride
+
+        output = input.reshape(B, T_max, D * self.stride)
+        output = output.contiguous()
+        return output, lengths
+
+
+class _CustomLSTM(torch.nn.Module):
+    r"""Custom long-short-term memory (LSTM) block that applies layer normalization
+    to internal nodes.
+
+    Args:
+        input_dim (int): input dimension.
+        hidden_dim (int): hidden dimension.
+        layer_norm (bool, optional): if ``True``, enables layer normalization. (Default: ``False``)
+        layer_norm_epsilon (float, optional):  value of epsilon to use in
+            layer normalization layers (Default: 1e-5)
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        layer_norm: bool = False,
+        layer_norm_epsilon: float = 1e-5,
+    ) -> None:
+        super().__init__()
+        self.x2g = torch.nn.Linear(input_dim, 4 * hidden_dim, bias=(not layer_norm))
+        self.p2g = torch.nn.Linear(hidden_dim, 4 * hidden_dim, bias=False)
+        if layer_norm:
+            self.c_norm = torch.nn.LayerNorm(hidden_dim, eps=layer_norm_epsilon)
+            self.g_norm = torch.nn.LayerNorm(4 * hidden_dim, eps=layer_norm_epsilon)
+        else:
+            self.c_norm = torch.nn.Identity()
+            self.g_norm = torch.nn.Identity()
+
+        self.hidden_dim = hidden_dim
+
+    def forward(
+        self, input: torch.Tensor, state: Optional[List[torch.Tensor]]
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        r"""Forward pass.
+
+        B: batch size;
+        T: maximum sequence length in batch;
+        D: feature dimension of each input sequence element.
+
+        Args:
+            input (torch.Tensor): with shape `(T, B, D)`.
+            state (List[torch.Tensor] or None): list of tensors
+                representing internal state generated in preceding invocation
+                of ``forward``.
+
+        Returns:
+            (torch.Tensor, List[torch.Tensor]):
+                torch.Tensor
+                    output, with shape `(T, B, hidden_dim)`.
+                List[torch.Tensor]
+                    list of tensors representing internal state generated
+                    in current invocation of ``forward``.
+        """
+        if state is None:
+            B = input.size(1)
+            h = torch.zeros(B, self.hidden_dim, device=input.device, dtype=input.dtype)
+            c = torch.zeros(B, self.hidden_dim, device=input.device, dtype=input.dtype)
+        else:
+            h, c = state
+
+        gated_input = self.x2g(input)
+        outputs = []
+        for gates in gated_input.unbind(0):
+            gates = gates + self.p2g(h)
+            gates = self.g_norm(gates)
+            input_gate, forget_gate, cell_gate, output_gate = gates.chunk(4, 1)
+            input_gate = input_gate.sigmoid()
+            forget_gate = forget_gate.sigmoid()
+            cell_gate = cell_gate.tanh()
+            output_gate = output_gate.sigmoid()
+            c = forget_gate * c + input_gate * cell_gate
+            c = self.c_norm(c)
+            h = output_gate * c.tanh()
+            outputs.append(h)
+
+        output = torch.stack(outputs, dim=0)
+        state = [h, c]
+
+        return output, state
+
+
+class _Transcriber(ABC):
+    @abstractmethod
+    def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        pass
+
+    @abstractmethod
+    def infer(
+        self,
+        input: torch.Tensor,
+        lengths: torch.Tensor,
+        states: Optional[List[List[torch.Tensor]]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        pass
+
+
+class _EmformerEncoder(torch.nn.Module, _Transcriber):
+    r"""Emformer-based recurrent neural network transducer (RNN-T) encoder (transcription network).
+
+    Args:
+        input_dim (int): feature dimension of each input sequence element.
+        output_dim (int): feature dimension of each output sequence element.
+        segment_length (int): length of input segment expressed as number of frames.
+        right_context_length (int): length of right context expressed as number of frames.
+        time_reduction_input_dim (int): dimension to scale each element in input sequences to
+            prior to applying time reduction block.
+        time_reduction_stride (int): factor by which to reduce length of input sequence.
+        transformer_num_heads (int): number of attention heads in each Emformer layer.
+        transformer_ffn_dim (int): hidden layer dimension of each Emformer layer's feedforward network.
+        transformer_num_layers (int): number of Emformer layers to instantiate.
+        transformer_left_context_length (int): length of left context.
+        transformer_dropout (float, optional): transformer dropout probability. (Default: 0.0)
+        transformer_activation (str, optional): activation function to use in each Emformer layer's
+            feedforward network. Must be one of ("relu", "gelu", "silu"). (Default: "relu")
+        transformer_max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
+        transformer_weight_init_scale_strategy (str, optional): per-layer weight initialization scaling
+            strategy. Must be one of ("depthwise", "constant", ``None``). (Default: "depthwise")
+        transformer_tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
+    """
+
+    def __init__(
+        self,
+        *,
+        input_dim: int,
+        output_dim: int,
+        segment_length: int,
+        right_context_length: int,
+        time_reduction_input_dim: int,
+        time_reduction_stride: int,
+        transformer_num_heads: int,
+        transformer_ffn_dim: int,
+        transformer_num_layers: int,
+        transformer_left_context_length: int,
+        transformer_dropout: float = 0.0,
+        transformer_activation: str = "relu",
+        transformer_max_memory_size: int = 0,
+        transformer_weight_init_scale_strategy: str = "depthwise",
+        transformer_tanh_on_mem: bool = False,
+    ) -> None:
+        super().__init__()
+        self.input_linear = torch.nn.Linear(
+            input_dim,
+            time_reduction_input_dim,
+            bias=False,
+        )
+        self.time_reduction = _TimeReduction(time_reduction_stride)
+        transformer_input_dim = time_reduction_input_dim * time_reduction_stride
+        self.transformer = Emformer(
+            transformer_input_dim,
+            transformer_num_heads,
+            transformer_ffn_dim,
+            transformer_num_layers,
+            segment_length // time_reduction_stride,
+            dropout=transformer_dropout,
+            activation=transformer_activation,
+            left_context_length=transformer_left_context_length,
+            right_context_length=right_context_length // time_reduction_stride,
+            max_memory_size=transformer_max_memory_size,
+            weight_init_scale_strategy=transformer_weight_init_scale_strategy,
+            tanh_on_mem=transformer_tanh_on_mem,
+        )
+        self.output_linear = torch.nn.Linear(transformer_input_dim, output_dim)
+        self.layer_norm = torch.nn.LayerNorm(output_dim)
+
+    def forward(self, input: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Forward pass for training.
+
+        B: batch size;
+        T: maximum input sequence length in batch;
+        D: feature dimension of each input sequence frame (input_dim).
+
+        Args:
+            input (torch.Tensor): input frame sequences right-padded with right context, with
+                shape `(B, T + right context length, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``input``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor):
+                torch.Tensor
+                    output frame sequences, with
+                    shape `(B, T // time_reduction_stride, output_dim)`.
+                torch.Tensor
+                    output input lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements for i-th batch element in output frame sequences.
+        """
+        input_linear_out = self.input_linear(input)
+        time_reduction_out, time_reduction_lengths = self.time_reduction(input_linear_out, lengths)
+        transformer_out, transformer_lengths = self.transformer(time_reduction_out, time_reduction_lengths)
+        output_linear_out = self.output_linear(transformer_out)
+        layer_norm_out = self.layer_norm(output_linear_out)
+        return layer_norm_out, transformer_lengths
+
+    @torch.jit.export
+    def infer(
+        self,
+        input: torch.Tensor,
+        lengths: torch.Tensor,
+        states: Optional[List[List[torch.Tensor]]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        r"""Forward pass for inference.
+
+        B: batch size;
+        T: maximum input sequence segment length in batch;
+        D: feature dimension of each input sequence frame (input_dim).
+
+        Args:
+            input (torch.Tensor): input frame sequence segments right-padded with right context, with
+                shape `(B, T + right context length, D)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``input``.
+            state (List[List[torch.Tensor]] or None): list of lists of tensors
+                representing internal state generated in preceding invocation
+                of ``infer``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
+                torch.Tensor
+                    output frame sequences, with
+                    shape `(B, T // time_reduction_stride, output_dim)`.
+                torch.Tensor
+                    output input lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements for i-th batch element in output.
+                List[List[torch.Tensor]]
+                    output states; list of lists of tensors
+                    representing internal state generated in current invocation
+                    of ``infer``.
+        """
+        input_linear_out = self.input_linear(input)
+        time_reduction_out, time_reduction_lengths = self.time_reduction(input_linear_out, lengths)
+        (
+            transformer_out,
+            transformer_lengths,
+            transformer_states,
+        ) = self.transformer.infer(time_reduction_out, time_reduction_lengths, states)
+        output_linear_out = self.output_linear(transformer_out)
+        layer_norm_out = self.layer_norm(output_linear_out)
+        return layer_norm_out, transformer_lengths, transformer_states
+
+
+class _Predictor(torch.nn.Module):
+    r"""Recurrent neural network transducer (RNN-T) prediction network.
+
+    Args:
+        num_symbols (int): size of target token lexicon.
+        output_dim (int): feature dimension of each output sequence element.
+        symbol_embedding_dim (int): dimension of each target token embedding.
+        num_lstm_layers (int): number of LSTM layers to instantiate.
+        lstm_hidden_dim (int): output dimension of each LSTM layer.
+        lstm_layer_norm (bool, optional): if ``True``, enables layer normalization
+            for LSTM layers. (Default: ``False``)
+        lstm_layer_norm_epsilon (float, optional): value of epsilon to use in
+            LSTM layer normalization layers. (Default: 1e-5)
+        lstm_dropout (float, optional): LSTM dropout probability. (Default: 0.0)
+
+    """
+
+    def __init__(
+        self,
+        num_symbols: int,
+        output_dim: int,
+        symbol_embedding_dim: int,
+        num_lstm_layers: int,
+        lstm_hidden_dim: int,
+        lstm_layer_norm: bool = False,
+        lstm_layer_norm_epsilon: float = 1e-5,
+        lstm_dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.embedding = torch.nn.Embedding(num_symbols, symbol_embedding_dim)
+        self.input_layer_norm = torch.nn.LayerNorm(symbol_embedding_dim)
+        self.lstm_layers = torch.nn.ModuleList(
+            [
+                _CustomLSTM(
+                    symbol_embedding_dim if idx == 0 else lstm_hidden_dim,
+                    lstm_hidden_dim,
+                    layer_norm=lstm_layer_norm,
+                    layer_norm_epsilon=lstm_layer_norm_epsilon,
+                )
+                for idx in range(num_lstm_layers)
+            ]
+        )
+        self.dropout = torch.nn.Dropout(p=lstm_dropout)
+        self.linear = torch.nn.Linear(lstm_hidden_dim, output_dim)
+        self.output_layer_norm = torch.nn.LayerNorm(output_dim)
+
+        self.lstm_dropout = lstm_dropout
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        lengths: torch.Tensor,
+        state: Optional[List[List[torch.Tensor]]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        r"""Forward pass.
+
+        B: batch size;
+        U: maximum sequence length in batch;
+        D: feature dimension of each input sequence element.
+
+        Args:
+            input (torch.Tensor): target sequences, with shape `(B, U)` and each element
+                mapping to a target symbol, i.e. in range `[0, num_symbols)`.
+            lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``input``.
+            state (List[List[torch.Tensor]] or None, optional): list of lists of tensors
+                representing internal state generated in preceding invocation
+                of ``forward``. (Default: ``None``)
+
+        Returns:
+            (torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
+                torch.Tensor
+                    output encoding sequences, with shape `(B, U, output_dim)`
+                torch.Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements for i-th batch element in output encoding sequences.
+                List[List[torch.Tensor]]
+                    output states; list of lists of tensors
+                    representing internal state generated in current invocation of ``forward``.
+        """
+        input_tb = input.permute(1, 0)
+        embedding_out = self.embedding(input_tb)
+        input_layer_norm_out = self.input_layer_norm(embedding_out)
+
+        lstm_out = input_layer_norm_out
+        state_out: List[List[torch.Tensor]] = []
+        for layer_idx, lstm in enumerate(self.lstm_layers):
+            lstm_out, lstm_state_out = lstm(lstm_out, None if state is None else state[layer_idx])
+            lstm_out = self.dropout(lstm_out)
+            state_out.append(lstm_state_out)
+
+        linear_out = self.linear(lstm_out)
+        output_layer_norm_out = self.output_layer_norm(linear_out)
+        return output_layer_norm_out.permute(1, 0, 2), lengths, state_out
+
+
+class _Joiner(torch.nn.Module):
+    r"""Recurrent neural network transducer (RNN-T) joint network.
+
+    Args:
+        input_dim (int): source and target input dimension.
+        output_dim (int): output dimension.
+        activation (str, optional): activation function to use in the joiner.
+            Must be one of ("relu", "tanh"). (Default: "relu")
+
+    """
+
+    def __init__(self, input_dim: int, output_dim: int, activation: str = "relu") -> None:
+        super().__init__()
+        self.linear = torch.nn.Linear(input_dim, output_dim, bias=True)
+        if activation == "relu":
+            self.activation = torch.nn.ReLU()
+        elif activation == "tanh":
+            self.activation = torch.nn.Tanh()
+        else:
+            raise ValueError(f"Unsupported activation {activation}")
+
+    def forward(
+        self,
+        source_encodings: torch.Tensor,
+        source_lengths: torch.Tensor,
+        target_encodings: torch.Tensor,
+        target_lengths: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""Forward pass for training.
+
+        B: batch size;
+        T: maximum source sequence length in batch;
+        U: maximum target sequence length in batch;
+        D: dimension of each source and target sequence encoding.
+
+        Args:
+            source_encodings (torch.Tensor): source encoding sequences, with
+                shape `(B, T, D)`.
+            source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                valid sequence length of i-th batch element in ``source_encodings``.
+            target_encodings (torch.Tensor): target encoding sequences, with shape `(B, U, D)`.
+            target_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                valid sequence length of i-th batch element in ``target_encodings``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor, torch.Tensor):
+                torch.Tensor
+                    joint network output, with shape `(B, T, U, output_dim)`.
+                torch.Tensor
+                    output source lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements along dim 1 for i-th batch element in joint network output.
+                torch.Tensor
+                    output target lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements along dim 2 for i-th batch element in joint network output.
+        """
+        joint_encodings = source_encodings.unsqueeze(2).contiguous() + target_encodings.unsqueeze(1).contiguous()
+        activation_out = self.activation(joint_encodings)
+        output = self.linear(activation_out)
+        return output, source_lengths, target_lengths
+
+
+class RNNT(torch.nn.Module):
+    r"""torchaudio.models.RNNT()
+
+    Recurrent neural network transducer (RNN-T) model.
+
+    Note:
+        To build the model, please use one of the factory functions.
+
+    See Also:
+        :class:`torchaudio.pipelines.RNNTBundle`: ASR pipeline with pre-trained models.
+
+    Args:
+        transcriber (torch.nn.Module): transcription network.
+        predictor (torch.nn.Module): prediction network.
+        joiner (torch.nn.Module): joint network.
+    """
+
+    def __init__(self, transcriber: _Transcriber, predictor: _Predictor, joiner: _Joiner) -> None:
+        super().__init__()
+        self.transcriber = transcriber
+        self.predictor = predictor
+        self.joiner = joiner
+
+    def forward(
+        self,
+        sources: torch.Tensor,
+        source_lengths: torch.Tensor,
+        targets: torch.Tensor,
+        target_lengths: torch.Tensor,
+        predictor_state: Optional[List[List[torch.Tensor]]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        r"""Forward pass for training.
+
+        B: batch size;
+        T: maximum source sequence length in batch;
+        U: maximum target sequence length in batch;
+        D: feature dimension of each source sequence element.
+
+        Args:
+            sources (torch.Tensor): source frame sequences right-padded with right context, with
+                shape `(B, T, D)`.
+            source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``sources``.
+            targets (torch.Tensor): target sequences, with shape `(B, U)` and each element
+                mapping to a target symbol.
+            target_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``targets``.
+            predictor_state (List[List[torch.Tensor]] or None, optional): list of lists of tensors
+                representing prediction network internal state generated in preceding invocation
+                of ``forward``. (Default: ``None``)
+
+        Returns:
+            (torch.Tensor, torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
+                torch.Tensor
+                    joint network output, with shape
+                    `(B, max output source length, max output target length, output_dim (number of target symbols))`.
+                torch.Tensor
+                    output source lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements along dim 1 for i-th batch element in joint network output.
+                torch.Tensor
+                    output target lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements along dim 2 for i-th batch element in joint network output.
+                List[List[torch.Tensor]]
+                    output states; list of lists of tensors
+                    representing prediction network internal state generated in current invocation
+                    of ``forward``.
+        """
+        source_encodings, source_lengths = self.transcriber(
+            input=sources,
+            lengths=source_lengths,
+        )
+        target_encodings, target_lengths, predictor_state = self.predictor(
+            input=targets,
+            lengths=target_lengths,
+            state=predictor_state,
+        )
+        output, source_lengths, target_lengths = self.joiner(
+            source_encodings=source_encodings,
+            source_lengths=source_lengths,
+            target_encodings=target_encodings,
+            target_lengths=target_lengths,
+        )
+
+        return (
+            output,
+            source_lengths,
+            target_lengths,
+            predictor_state,
+        )
+
+    @torch.jit.export
+    def transcribe_streaming(
+        self,
+        sources: torch.Tensor,
+        source_lengths: torch.Tensor,
+        state: Optional[List[List[torch.Tensor]]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        r"""Applies transcription network to sources in streaming mode.
+
+        B: batch size;
+        T: maximum source sequence segment length in batch;
+        D: feature dimension of each source sequence frame.
+
+        Args:
+            sources (torch.Tensor): source frame sequence segments right-padded with right context, with
+                shape `(B, T + right context length, D)`.
+            source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``sources``.
+            state (List[List[torch.Tensor]] or None): list of lists of tensors
+                representing transcription network internal state generated in preceding invocation
+                of ``transcribe_streaming``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
+                torch.Tensor
+                    output frame sequences, with
+                    shape `(B, T // time_reduction_stride, output_dim)`.
+                torch.Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements for i-th batch element in output.
+                List[List[torch.Tensor]]
+                    output states; list of lists of tensors
+                    representing transcription network internal state generated in current invocation
+                    of ``transcribe_streaming``.
+        """
+        return self.transcriber.infer(sources, source_lengths, state)
+
+    @torch.jit.export
+    def transcribe(
+        self,
+        sources: torch.Tensor,
+        source_lengths: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Applies transcription network to sources in non-streaming mode.
+
+        B: batch size;
+        T: maximum source sequence length in batch;
+        D: feature dimension of each source sequence frame.
+
+        Args:
+            sources (torch.Tensor): source frame sequences right-padded with right context, with
+                shape `(B, T + right context length, D)`.
+            source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``sources``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor):
+                torch.Tensor
+                    output frame sequences, with
+                    shape `(B, T // time_reduction_stride, output_dim)`.
+                torch.Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements for i-th batch element in output frame sequences.
+        """
+        return self.transcriber(sources, source_lengths)
+
+    @torch.jit.export
+    def predict(
+        self,
+        targets: torch.Tensor,
+        target_lengths: torch.Tensor,
+        state: Optional[List[List[torch.Tensor]]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
+        r"""Applies prediction network to targets.
+
+        B: batch size;
+        U: maximum target sequence length in batch;
+        D: feature dimension of each target sequence frame.
+
+        Args:
+            targets (torch.Tensor): target sequences, with shape `(B, U)` and each element
+                mapping to a target symbol, i.e. in range `[0, num_symbols)`.
+            target_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                number of valid frames for i-th batch element in ``targets``.
+            state (List[List[torch.Tensor]] or None): list of lists of tensors
+                representing internal state generated in preceding invocation
+                of ``predict``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
+                torch.Tensor
+                    output frame sequences, with shape `(B, U, output_dim)`.
+                torch.Tensor
+                    output lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements for i-th batch element in output.
+                List[List[torch.Tensor]]
+                    output states; list of lists of tensors
+                    representing internal state generated in current invocation of ``predict``.
+        """
+        return self.predictor(input=targets, lengths=target_lengths, state=state)
+
+    @torch.jit.export
+    def join(
+        self,
+        source_encodings: torch.Tensor,
+        source_lengths: torch.Tensor,
+        target_encodings: torch.Tensor,
+        target_lengths: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""Applies joint network to source and target encodings.
+
+        B: batch size;
+        T: maximum source sequence length in batch;
+        U: maximum target sequence length in batch;
+        D: dimension of each source and target sequence encoding.
+
+        Args:
+            source_encodings (torch.Tensor): source encoding sequences, with
+                shape `(B, T, D)`.
+            source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                valid sequence length of i-th batch element in ``source_encodings``.
+            target_encodings (torch.Tensor): target encoding sequences, with shape `(B, U, D)`.
+            target_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
+                valid sequence length of i-th batch element in ``target_encodings``.
+
+        Returns:
+            (torch.Tensor, torch.Tensor, torch.Tensor):
+                torch.Tensor
+                    joint network output, with shape `(B, T, U, output_dim)`.
+                torch.Tensor
+                    output source lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements along dim 1 for i-th batch element in joint network output.
+                torch.Tensor
+                    output target lengths, with shape `(B,)` and i-th element representing
+                    number of valid elements along dim 2 for i-th batch element in joint network output.
+        """
+        output, source_lengths, target_lengths = self.joiner(
+            source_encodings=source_encodings,
+            source_lengths=source_lengths,
+            target_encodings=target_encodings,
+            target_lengths=target_lengths,
+        )
+        return output, source_lengths, target_lengths
+
+
+def emformer_rnnt_model(
+    *,
+    input_dim: int,
+    encoding_dim: int,
+    num_symbols: int,
+    segment_length: int,
+    right_context_length: int,
+    time_reduction_input_dim: int,
+    time_reduction_stride: int,
+    transformer_num_heads: int,
+    transformer_ffn_dim: int,
+    transformer_num_layers: int,
+    transformer_dropout: float,
+    transformer_activation: str,
+    transformer_left_context_length: int,
+    transformer_max_memory_size: int,
+    transformer_weight_init_scale_strategy: str,
+    transformer_tanh_on_mem: bool,
+    symbol_embedding_dim: int,
+    num_lstm_layers: int,
+    lstm_layer_norm: bool,
+    lstm_layer_norm_epsilon: float,
+    lstm_dropout: float,
+) -> RNNT:
+    r"""Builds Emformer-based :class:`~torchaudio.models.RNNT`.
+
+    Note:
+        For non-streaming inference, the expectation is for `transcribe` to be called on input
+        sequences right-concatenated with `right_context_length` frames.
+
+        For streaming inference, the expectation is for `transcribe_streaming` to be called
+        on input chunks comprising `segment_length` frames right-concatenated with `right_context_length`
+        frames.
+
+    Args:
+        input_dim (int): dimension of input sequence frames passed to transcription network.
+        encoding_dim (int): dimension of transcription- and prediction-network-generated encodings
+            passed to joint network.
+        num_symbols (int): cardinality of set of target tokens.
+        segment_length (int): length of input segment expressed as number of frames.
+        right_context_length (int): length of right context expressed as number of frames.
+        time_reduction_input_dim (int): dimension to scale each element in input sequences to
+            prior to applying time reduction block.
+        time_reduction_stride (int): factor by which to reduce length of input sequence.
+        transformer_num_heads (int): number of attention heads in each Emformer layer.
+        transformer_ffn_dim (int): hidden layer dimension of each Emformer layer's feedforward network.
+        transformer_num_layers (int): number of Emformer layers to instantiate.
+        transformer_left_context_length (int): length of left context considered by Emformer.
+        transformer_dropout (float): Emformer dropout probability.
+        transformer_activation (str): activation function to use in each Emformer layer's
+            feedforward network. Must be one of ("relu", "gelu", "silu").
+        transformer_max_memory_size (int): maximum number of memory elements to use.
+        transformer_weight_init_scale_strategy (str): per-layer weight initialization scaling
+            strategy. Must be one of ("depthwise", "constant", ``None``).
+        transformer_tanh_on_mem (bool): if ``True``, applies tanh to memory elements.
+        symbol_embedding_dim (int): dimension of each target token embedding.
+        num_lstm_layers (int): number of LSTM layers to instantiate.
+        lstm_layer_norm (bool): if ``True``, enables layer normalization for LSTM layers.
+        lstm_layer_norm_epsilon (float): value of epsilon to use in LSTM layer normalization layers.
+        lstm_dropout (float): LSTM dropout probability.
+
+    Returns:
+        RNNT:
+            Emformer RNN-T model.
+    """
+    encoder = _EmformerEncoder(
+        input_dim=input_dim,
+        output_dim=encoding_dim,
+        segment_length=segment_length,
+        right_context_length=right_context_length,
+        time_reduction_input_dim=time_reduction_input_dim,
+        time_reduction_stride=time_reduction_stride,
+        transformer_num_heads=transformer_num_heads,
+        transformer_ffn_dim=transformer_ffn_dim,
+        transformer_num_layers=transformer_num_layers,
+        transformer_dropout=transformer_dropout,
+        transformer_activation=transformer_activation,
+        transformer_left_context_length=transformer_left_context_length,
+        transformer_max_memory_size=transformer_max_memory_size,
+        transformer_weight_init_scale_strategy=transformer_weight_init_scale_strategy,
+        transformer_tanh_on_mem=transformer_tanh_on_mem,
+    )
+    predictor = _Predictor(
+        num_symbols,
+        encoding_dim,
+        symbol_embedding_dim=symbol_embedding_dim,
+        num_lstm_layers=num_lstm_layers,
+        lstm_hidden_dim=symbol_embedding_dim,
+        lstm_layer_norm=lstm_layer_norm,
+        lstm_layer_norm_epsilon=lstm_layer_norm_epsilon,
+        lstm_dropout=lstm_dropout,
+    )
+    joiner = _Joiner(encoding_dim, num_symbols)
+    return RNNT(encoder, predictor, joiner)
+
+
+def emformer_rnnt_base(num_symbols: int) -> RNNT:
+    r"""Builds basic version of Emformer-based :class:`~torchaudio.models.RNNT`.
+
+    Args:
+        num_symbols (int): The size of target token lexicon.
+
+    Returns:
+        RNNT:
+            Emformer RNN-T model.
+    """
+    return emformer_rnnt_model(
+        input_dim=80,
+        encoding_dim=1024,
+        num_symbols=num_symbols,
+        segment_length=16,
+        right_context_length=4,
+        time_reduction_input_dim=128,
+        time_reduction_stride=4,
+        transformer_num_heads=8,
+        transformer_ffn_dim=2048,
+        transformer_num_layers=20,
+        transformer_dropout=0.1,
+        transformer_activation="gelu",
+        transformer_left_context_length=30,
+        transformer_max_memory_size=0,
+        transformer_weight_init_scale_strategy="depthwise",
+        transformer_tanh_on_mem=True,
+        symbol_embedding_dim=512,
+        num_lstm_layers=3,
+        lstm_layer_norm=True,
+        lstm_layer_norm_epsilon=1e-3,
+        lstm_dropout=0.3,
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/models/rnnt_decoder.py b/lib/python3.11/site-packages/torchaudio/models/rnnt_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a02b2ca907733a8e1ab404d1107bb702e977748
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/rnnt_decoder.py
@@ -0,0 +1,339 @@
+from typing import Callable, Dict, List, Optional, Tuple
+
+import torch
+from torchaudio.models import RNNT
+
+
+__all__ = ["Hypothesis", "RNNTBeamSearch"]
+
+
+Hypothesis = Tuple[List[int], torch.Tensor, List[List[torch.Tensor]], float]
+Hypothesis.__doc__ = """Hypothesis generated by RNN-T beam search decoder,
+    represented as tuple of (tokens, prediction network output, prediction network state, score).
+    """
+
+
+def _get_hypo_tokens(hypo: Hypothesis) -> List[int]:
+    return hypo[0]
+
+
+def _get_hypo_predictor_out(hypo: Hypothesis) -> torch.Tensor:
+    return hypo[1]
+
+
+def _get_hypo_state(hypo: Hypothesis) -> List[List[torch.Tensor]]:
+    return hypo[2]
+
+
+def _get_hypo_score(hypo: Hypothesis) -> float:
+    return hypo[3]
+
+
+def _get_hypo_key(hypo: Hypothesis) -> str:
+    return str(hypo[0])
+
+
+def _batch_state(hypos: List[Hypothesis]) -> List[List[torch.Tensor]]:
+    states: List[List[torch.Tensor]] = []
+    for i in range(len(_get_hypo_state(hypos[0]))):
+        batched_state_components: List[torch.Tensor] = []
+        for j in range(len(_get_hypo_state(hypos[0])[i])):
+            batched_state_components.append(torch.cat([_get_hypo_state(hypo)[i][j] for hypo in hypos]))
+        states.append(batched_state_components)
+    return states
+
+
+def _slice_state(states: List[List[torch.Tensor]], idx: int, device: torch.device) -> List[List[torch.Tensor]]:
+    idx_tensor = torch.tensor([idx], device=device)
+    return [[state.index_select(0, idx_tensor) for state in state_tuple] for state_tuple in states]
+
+
+def _default_hypo_sort_key(hypo: Hypothesis) -> float:
+    return _get_hypo_score(hypo) / (len(_get_hypo_tokens(hypo)) + 1)
+
+
+def _compute_updated_scores(
+    hypos: List[Hypothesis],
+    next_token_probs: torch.Tensor,
+    beam_width: int,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    hypo_scores = torch.tensor([_get_hypo_score(h) for h in hypos]).unsqueeze(1)
+    nonblank_scores = hypo_scores + next_token_probs[:, :-1]  # [beam_width, num_tokens - 1]
+    nonblank_nbest_scores, nonblank_nbest_idx = nonblank_scores.reshape(-1).topk(beam_width)
+    nonblank_nbest_hypo_idx = nonblank_nbest_idx.div(nonblank_scores.shape[1], rounding_mode="trunc")
+    nonblank_nbest_token = nonblank_nbest_idx % nonblank_scores.shape[1]
+    return nonblank_nbest_scores, nonblank_nbest_hypo_idx, nonblank_nbest_token
+
+
+def _remove_hypo(hypo: Hypothesis, hypo_list: List[Hypothesis]) -> None:
+    for i, elem in enumerate(hypo_list):
+        if _get_hypo_key(hypo) == _get_hypo_key(elem):
+            del hypo_list[i]
+            break
+
+
+class RNNTBeamSearch(torch.nn.Module):
+    r"""Beam search decoder for RNN-T model.
+
+    See Also:
+        * :class:`torchaudio.pipelines.RNNTBundle`: ASR pipeline with pretrained model.
+
+    Args:
+        model (RNNT): RNN-T model to use.
+        blank (int): index of blank token in vocabulary.
+        temperature (float, optional): temperature to apply to joint network output.
+            Larger values yield more uniform samples. (Default: 1.0)
+        hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
+            for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
+            hypothesis score normalized by token sequence length. (Default: None)
+        step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
+    """
+
+    def __init__(
+        self,
+        model: RNNT,
+        blank: int,
+        temperature: float = 1.0,
+        hypo_sort_key: Optional[Callable[[Hypothesis], float]] = None,
+        step_max_tokens: int = 100,
+    ) -> None:
+        super().__init__()
+        self.model = model
+        self.blank = blank
+        self.temperature = temperature
+
+        if hypo_sort_key is None:
+            self.hypo_sort_key = _default_hypo_sort_key
+        else:
+            self.hypo_sort_key = hypo_sort_key
+
+        self.step_max_tokens = step_max_tokens
+
+    def _init_b_hypos(self, device: torch.device) -> List[Hypothesis]:
+        token = self.blank
+        state = None
+
+        one_tensor = torch.tensor([1], device=device)
+        pred_out, _, pred_state = self.model.predict(torch.tensor([[token]], device=device), one_tensor, state)
+        init_hypo = (
+            [token],
+            pred_out[0].detach(),
+            pred_state,
+            0.0,
+        )
+        return [init_hypo]
+
+    def _gen_next_token_probs(
+        self, enc_out: torch.Tensor, hypos: List[Hypothesis], device: torch.device
+    ) -> torch.Tensor:
+        one_tensor = torch.tensor([1], device=device)
+        predictor_out = torch.stack([_get_hypo_predictor_out(h) for h in hypos], dim=0)
+        joined_out, _, _ = self.model.join(
+            enc_out,
+            one_tensor,
+            predictor_out,
+            torch.tensor([1] * len(hypos), device=device),
+        )  # [beam_width, 1, 1, num_tokens]
+        joined_out = torch.nn.functional.log_softmax(joined_out / self.temperature, dim=3)
+        return joined_out[:, 0, 0]
+
+    def _gen_b_hypos(
+        self,
+        b_hypos: List[Hypothesis],
+        a_hypos: List[Hypothesis],
+        next_token_probs: torch.Tensor,
+        key_to_b_hypo: Dict[str, Hypothesis],
+    ) -> List[Hypothesis]:
+        for i in range(len(a_hypos)):
+            h_a = a_hypos[i]
+            append_blank_score = _get_hypo_score(h_a) + next_token_probs[i, -1]
+            if _get_hypo_key(h_a) in key_to_b_hypo:
+                h_b = key_to_b_hypo[_get_hypo_key(h_a)]
+                _remove_hypo(h_b, b_hypos)
+                score = float(torch.tensor(_get_hypo_score(h_b)).logaddexp(append_blank_score))
+            else:
+                score = float(append_blank_score)
+            h_b = (
+                _get_hypo_tokens(h_a),
+                _get_hypo_predictor_out(h_a),
+                _get_hypo_state(h_a),
+                score,
+            )
+            b_hypos.append(h_b)
+            key_to_b_hypo[_get_hypo_key(h_b)] = h_b
+        _, sorted_idx = torch.tensor([_get_hypo_score(hypo) for hypo in b_hypos]).sort()
+        return [b_hypos[idx] for idx in sorted_idx]
+
+    def _gen_a_hypos(
+        self,
+        a_hypos: List[Hypothesis],
+        b_hypos: List[Hypothesis],
+        next_token_probs: torch.Tensor,
+        t: int,
+        beam_width: int,
+        device: torch.device,
+    ) -> List[Hypothesis]:
+        (
+            nonblank_nbest_scores,
+            nonblank_nbest_hypo_idx,
+            nonblank_nbest_token,
+        ) = _compute_updated_scores(a_hypos, next_token_probs, beam_width)
+
+        if len(b_hypos) < beam_width:
+            b_nbest_score = -float("inf")
+        else:
+            b_nbest_score = _get_hypo_score(b_hypos[-beam_width])
+
+        base_hypos: List[Hypothesis] = []
+        new_tokens: List[int] = []
+        new_scores: List[float] = []
+        for i in range(beam_width):
+            score = float(nonblank_nbest_scores[i])
+            if score > b_nbest_score:
+                a_hypo_idx = int(nonblank_nbest_hypo_idx[i])
+                base_hypos.append(a_hypos[a_hypo_idx])
+                new_tokens.append(int(nonblank_nbest_token[i]))
+                new_scores.append(score)
+
+        if base_hypos:
+            new_hypos = self._gen_new_hypos(base_hypos, new_tokens, new_scores, t, device)
+        else:
+            new_hypos: List[Hypothesis] = []
+
+        return new_hypos
+
+    def _gen_new_hypos(
+        self,
+        base_hypos: List[Hypothesis],
+        tokens: List[int],
+        scores: List[float],
+        t: int,
+        device: torch.device,
+    ) -> List[Hypothesis]:
+        tgt_tokens = torch.tensor([[token] for token in tokens], device=device)
+        states = _batch_state(base_hypos)
+        pred_out, _, pred_states = self.model.predict(
+            tgt_tokens,
+            torch.tensor([1] * len(base_hypos), device=device),
+            states,
+        )
+        new_hypos: List[Hypothesis] = []
+        for i, h_a in enumerate(base_hypos):
+            new_tokens = _get_hypo_tokens(h_a) + [tokens[i]]
+            new_hypos.append((new_tokens, pred_out[i].detach(), _slice_state(pred_states, i, device), scores[i]))
+        return new_hypos
+
+    def _search(
+        self,
+        enc_out: torch.Tensor,
+        hypo: Optional[List[Hypothesis]],
+        beam_width: int,
+    ) -> List[Hypothesis]:
+        n_time_steps = enc_out.shape[1]
+        device = enc_out.device
+
+        a_hypos: List[Hypothesis] = []
+        b_hypos = self._init_b_hypos(device) if hypo is None else hypo
+        for t in range(n_time_steps):
+            a_hypos = b_hypos
+            b_hypos = torch.jit.annotate(List[Hypothesis], [])
+            key_to_b_hypo: Dict[str, Hypothesis] = {}
+            symbols_current_t = 0
+
+            while a_hypos:
+                next_token_probs = self._gen_next_token_probs(enc_out[:, t : t + 1], a_hypos, device)
+                next_token_probs = next_token_probs.cpu()
+                b_hypos = self._gen_b_hypos(b_hypos, a_hypos, next_token_probs, key_to_b_hypo)
+
+                if symbols_current_t == self.step_max_tokens:
+                    break
+
+                a_hypos = self._gen_a_hypos(
+                    a_hypos,
+                    b_hypos,
+                    next_token_probs,
+                    t,
+                    beam_width,
+                    device,
+                )
+                if a_hypos:
+                    symbols_current_t += 1
+
+            _, sorted_idx = torch.tensor([self.hypo_sort_key(hyp) for hyp in b_hypos]).topk(beam_width)
+            b_hypos = [b_hypos[idx] for idx in sorted_idx]
+
+        return b_hypos
+
+    def forward(self, input: torch.Tensor, length: torch.Tensor, beam_width: int) -> List[Hypothesis]:
+        r"""Performs beam search for the given input sequence.
+
+        T: number of frames;
+        D: feature dimension of each frame.
+
+        Args:
+            input (torch.Tensor): sequence of input frames, with shape (T, D) or (1, T, D).
+            length (torch.Tensor): number of valid frames in input
+                sequence, with shape () or (1,).
+            beam_width (int): beam size to use during search.
+
+        Returns:
+            List[Hypothesis]: top-``beam_width`` hypotheses found by beam search.
+        """
+        if input.dim() != 2 and not (input.dim() == 3 and input.shape[0] == 1):
+            raise ValueError("input must be of shape (T, D) or (1, T, D)")
+        if input.dim() == 2:
+            input = input.unsqueeze(0)
+
+        if length.shape != () and length.shape != (1,):
+            raise ValueError("length must be of shape () or (1,)")
+        if length.dim() == 0:
+            length = length.unsqueeze(0)
+
+        enc_out, _ = self.model.transcribe(input, length)
+        return self._search(enc_out, None, beam_width)
+
+    @torch.jit.export
+    def infer(
+        self,
+        input: torch.Tensor,
+        length: torch.Tensor,
+        beam_width: int,
+        state: Optional[List[List[torch.Tensor]]] = None,
+        hypothesis: Optional[List[Hypothesis]] = None,
+    ) -> Tuple[List[Hypothesis], List[List[torch.Tensor]]]:
+        r"""Performs beam search for the given input sequence in streaming mode.
+
+        T: number of frames;
+        D: feature dimension of each frame.
+
+        Args:
+            input (torch.Tensor): sequence of input frames, with shape (T, D) or (1, T, D).
+            length (torch.Tensor): number of valid frames in input
+                sequence, with shape () or (1,).
+            beam_width (int): beam size to use during search.
+            state (List[List[torch.Tensor]] or None, optional): list of lists of tensors
+                representing transcription network internal state generated in preceding
+                invocation. (Default: ``None``)
+            hypothesis (List[Hypothesis] or None): hypotheses from preceding invocation to seed
+                search with. (Default: ``None``)
+
+        Returns:
+            (List[Hypothesis], List[List[torch.Tensor]]):
+                List[Hypothesis]
+                    top-``beam_width`` hypotheses found by beam search.
+                List[List[torch.Tensor]]
+                    list of lists of tensors representing transcription network
+                    internal state generated in current invocation.
+        """
+        if input.dim() != 2 and not (input.dim() == 3 and input.shape[0] == 1):
+            raise ValueError("input must be of shape (T, D) or (1, T, D)")
+        if input.dim() == 2:
+            input = input.unsqueeze(0)
+
+        if length.shape != () and length.shape != (1,):
+            raise ValueError("length must be of shape () or (1,)")
+        if length.dim() == 0:
+            length = length.unsqueeze(0)
+
+        enc_out, _, state = self.model.transcribe_streaming(input, length, state)
+        return self._search(enc_out, hypothesis, beam_width), state
diff --git a/lib/python3.11/site-packages/torchaudio/models/squim/__init__.py b/lib/python3.11/site-packages/torchaudio/models/squim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..092d6eb8e36e2329c78d21bf609a8458818995e6
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/squim/__init__.py
@@ -0,0 +1,11 @@
+from .objective import squim_objective_base, squim_objective_model, SquimObjective
+from .subjective import squim_subjective_base, squim_subjective_model, SquimSubjective
+
+__all__ = [
+    "squim_objective_base",
+    "squim_objective_model",
+    "squim_subjective_base",
+    "squim_subjective_model",
+    "SquimObjective",
+    "SquimSubjective",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ae5640b042172e81f4fb99f77e7731abe0a6e9a1
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/objective.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/objective.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7df3f11d7ba1b6a5ccd3a14a67912742ec771dcb
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/objective.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/subjective.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/subjective.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cf97d8e91c03c03c178324aa234b48a310551a38
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/squim/__pycache__/subjective.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/squim/objective.py b/lib/python3.11/site-packages/torchaudio/models/squim/objective.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0a0671a4ec3828fa5dbe1613c7378fbc55d34dd
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/squim/objective.py
@@ -0,0 +1,326 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def transform_wb_pesq_range(x: float) -> float:
+    """The metric defined by ITU-T P.862 is often called 'PESQ score', which is defined
+    for narrow-band signals and has a value range of [-0.5, 4.5] exactly. Here, we use the metric
+    defined by ITU-T P.862.2, commonly known as 'wide-band PESQ' and will be referred to as "PESQ score".
+
+    Args:
+        x (float): Narrow-band PESQ score.
+
+    Returns:
+        (float): Wide-band PESQ score.
+    """
+    return 0.999 + (4.999 - 0.999) / (1 + math.exp(-1.3669 * x + 3.8224))
+
+
+PESQRange: Tuple[float, float] = (
+    1.0,  # P.862.2 uses a different input filter than P.862, and the lower bound of
+    # the raw score is not -0.5 anymore. It's hard to figure out the true lower bound.
+    # We are using 1.0 as a reasonable approximation.
+    transform_wb_pesq_range(4.5),
+)
+
+
+class RangeSigmoid(nn.Module):
+    def __init__(self, val_range: Tuple[float, float] = (0.0, 1.0)) -> None:
+        super(RangeSigmoid, self).__init__()
+        assert isinstance(val_range, tuple) and len(val_range) == 2
+        self.val_range: Tuple[float, float] = val_range
+        self.sigmoid: nn.modules.Module = nn.Sigmoid()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.sigmoid(x) * (self.val_range[1] - self.val_range[0]) + self.val_range[0]
+        return out
+
+
+class Encoder(nn.Module):
+    """Encoder module that transform 1D waveform to 2D representations.
+
+    Args:
+        feat_dim (int, optional): The feature dimension after Encoder module. (Default: 512)
+        win_len (int, optional): kernel size in the Conv1D layer. (Default: 32)
+    """
+
+    def __init__(self, feat_dim: int = 512, win_len: int = 32) -> None:
+        super(Encoder, self).__init__()
+
+        self.conv1d = nn.Conv1d(1, feat_dim, win_len, stride=win_len // 2, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply waveforms to convolutional layer and ReLU layer.
+
+        Args:
+            x (torch.Tensor): Input waveforms. Tensor with dimensions `(batch, time)`.
+
+        Returns:
+            (torch,Tensor): Feature Tensor with dimensions `(batch, channel, frame)`.
+        """
+        out = x.unsqueeze(dim=1)
+        out = F.relu(self.conv1d(out))
+        return out
+
+
+class SingleRNN(nn.Module):
+    def __init__(self, rnn_type: str, input_size: int, hidden_size: int, dropout: float = 0.0) -> None:
+        super(SingleRNN, self).__init__()
+
+        self.rnn_type = rnn_type
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+
+        self.rnn: nn.modules.Module = getattr(nn, rnn_type)(
+            input_size,
+            hidden_size,
+            1,
+            dropout=dropout,
+            batch_first=True,
+            bidirectional=True,
+        )
+
+        self.proj = nn.Linear(hidden_size * 2, input_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # input shape: batch, seq, dim
+        out, _ = self.rnn(x)
+        out = self.proj(out)
+        return out
+
+
+class DPRNN(nn.Module):
+    """*Dual-path recurrent neural networks (DPRNN)* :cite:`luo2020dual`.
+
+    Args:
+        feat_dim (int, optional): The feature dimension after Encoder module. (Default: 64)
+        hidden_dim (int, optional): Hidden dimension in the RNN layer of DPRNN. (Default: 128)
+        num_blocks (int, optional): Number of DPRNN layers. (Default: 6)
+        rnn_type (str, optional): Type of RNN in DPRNN. Valid options are ["RNN", "LSTM", "GRU"]. (Default: "LSTM")
+        d_model (int, optional): The number of expected features in the input. (Default: 256)
+        chunk_size (int, optional): Chunk size of input for DPRNN. (Default: 100)
+        chunk_stride (int, optional): Stride of chunk input for DPRNN. (Default: 50)
+    """
+
+    def __init__(
+        self,
+        feat_dim: int = 64,
+        hidden_dim: int = 128,
+        num_blocks: int = 6,
+        rnn_type: str = "LSTM",
+        d_model: int = 256,
+        chunk_size: int = 100,
+        chunk_stride: int = 50,
+    ) -> None:
+        super(DPRNN, self).__init__()
+
+        self.num_blocks = num_blocks
+
+        self.row_rnn = nn.ModuleList([])
+        self.col_rnn = nn.ModuleList([])
+        self.row_norm = nn.ModuleList([])
+        self.col_norm = nn.ModuleList([])
+        for _ in range(num_blocks):
+            self.row_rnn.append(SingleRNN(rnn_type, feat_dim, hidden_dim))
+            self.col_rnn.append(SingleRNN(rnn_type, feat_dim, hidden_dim))
+            self.row_norm.append(nn.GroupNorm(1, feat_dim, eps=1e-8))
+            self.col_norm.append(nn.GroupNorm(1, feat_dim, eps=1e-8))
+        self.conv = nn.Sequential(
+            nn.Conv2d(feat_dim, d_model, 1),
+            nn.PReLU(),
+        )
+        self.chunk_size = chunk_size
+        self.chunk_stride = chunk_stride
+
+    def pad_chunk(self, x: torch.Tensor) -> Tuple[torch.Tensor, int]:
+        # input shape: (B, N, T)
+        seq_len = x.shape[-1]
+
+        rest = self.chunk_size - (self.chunk_stride + seq_len % self.chunk_size) % self.chunk_size
+        out = F.pad(x, [self.chunk_stride, rest + self.chunk_stride])
+
+        return out, rest
+
+    def chunking(self, x: torch.Tensor) -> Tuple[torch.Tensor, int]:
+        out, rest = self.pad_chunk(x)
+        batch_size, feat_dim, seq_len = out.shape
+
+        segments1 = out[:, :, : -self.chunk_stride].contiguous().view(batch_size, feat_dim, -1, self.chunk_size)
+        segments2 = out[:, :, self.chunk_stride :].contiguous().view(batch_size, feat_dim, -1, self.chunk_size)
+        out = torch.cat([segments1, segments2], dim=3)
+        out = out.view(batch_size, feat_dim, -1, self.chunk_size).transpose(2, 3).contiguous()
+
+        return out, rest
+
+    def merging(self, x: torch.Tensor, rest: int) -> torch.Tensor:
+        batch_size, dim, _, _ = x.shape
+        out = x.transpose(2, 3).contiguous().view(batch_size, dim, -1, self.chunk_size * 2)
+        out1 = out[:, :, :, : self.chunk_size].contiguous().view(batch_size, dim, -1)[:, :, self.chunk_stride :]
+        out2 = out[:, :, :, self.chunk_size :].contiguous().view(batch_size, dim, -1)[:, :, : -self.chunk_stride]
+        out = out1 + out2
+        if rest > 0:
+            out = out[:, :, :-rest]
+        out = out.contiguous()
+        return out
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, rest = self.chunking(x)
+        batch_size, _, dim1, dim2 = x.shape
+        out = x
+        for row_rnn, row_norm, col_rnn, col_norm in zip(self.row_rnn, self.row_norm, self.col_rnn, self.col_norm):
+            row_in = out.permute(0, 3, 2, 1).contiguous().view(batch_size * dim2, dim1, -1).contiguous()
+            row_out = row_rnn(row_in)
+            row_out = row_out.view(batch_size, dim2, dim1, -1).permute(0, 3, 2, 1).contiguous()
+            row_out = row_norm(row_out)
+            out = out + row_out
+
+            col_in = out.permute(0, 2, 3, 1).contiguous().view(batch_size * dim1, dim2, -1).contiguous()
+            col_out = col_rnn(col_in)
+            col_out = col_out.view(batch_size, dim1, dim2, -1).permute(0, 3, 1, 2).contiguous()
+            col_out = col_norm(col_out)
+            out = out + col_out
+        out = self.conv(out)
+        out = self.merging(out, rest)
+        out = out.transpose(1, 2).contiguous()
+        return out
+
+
+class AutoPool(nn.Module):
+    def __init__(self, pool_dim: int = 1) -> None:
+        super(AutoPool, self).__init__()
+        self.pool_dim: int = pool_dim
+        self.softmax: nn.modules.Module = nn.Softmax(dim=pool_dim)
+        self.register_parameter("alpha", nn.Parameter(torch.ones(1)))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        weight = self.softmax(torch.mul(x, self.alpha))
+        out = torch.sum(torch.mul(x, weight), dim=self.pool_dim)
+        return out
+
+
+class SquimObjective(nn.Module):
+    """Speech Quality and Intelligibility Measures (SQUIM) model that predicts **objective** metric scores
+    for speech enhancement (e.g., STOI, PESQ, and SI-SDR).
+
+    Args:
+        encoder (torch.nn.Module): Encoder module to transform 1D waveform to 2D feature representation.
+        dprnn (torch.nn.Module): DPRNN module to model sequential feature.
+        branches (torch.nn.ModuleList): Transformer branches in which each branch estimate one objective metirc score.
+    """
+
+    def __init__(
+        self,
+        encoder: nn.Module,
+        dprnn: nn.Module,
+        branches: nn.ModuleList,
+    ):
+        super(SquimObjective, self).__init__()
+        self.encoder = encoder
+        self.dprnn = dprnn
+        self.branches = branches
+
+    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
+        """
+        Args:
+            x (torch.Tensor): Input waveforms. Tensor with dimensions `(batch, time)`.
+
+        Returns:
+            List(torch.Tensor): List of score Tenosrs. Each Tensor is with dimension `(batch,)`.
+        """
+        if x.ndim != 2:
+            raise ValueError(f"The input must be a 2D Tensor. Found dimension {x.ndim}.")
+        x = x / (torch.mean(x**2, dim=1, keepdim=True) ** 0.5 * 20)
+        out = self.encoder(x)
+        out = self.dprnn(out)
+        scores = []
+        for branch in self.branches:
+            scores.append(branch(out).squeeze(dim=1))
+        return scores
+
+
+def _create_branch(d_model: int, nhead: int, metric: str) -> nn.modules.Module:
+    """Create branch module after DPRNN model for predicting metric score.
+
+    Args:
+        d_model (int): The number of expected features in the input.
+        nhead (int): Number of heads in the multi-head attention model.
+        metric (str): The metric name to predict.
+
+    Returns:
+        (nn.Module): Returned module to predict corresponding metric score.
+    """
+    layer1 = nn.TransformerEncoderLayer(d_model, nhead, d_model * 4, dropout=0.0, batch_first=True)
+    layer2 = AutoPool()
+    if metric == "stoi":
+        layer3 = nn.Sequential(
+            nn.Linear(d_model, d_model),
+            nn.PReLU(),
+            nn.Linear(d_model, 1),
+            RangeSigmoid(),
+        )
+    elif metric == "pesq":
+        layer3 = nn.Sequential(
+            nn.Linear(d_model, d_model),
+            nn.PReLU(),
+            nn.Linear(d_model, 1),
+            RangeSigmoid(val_range=PESQRange),
+        )
+    else:
+        layer3: nn.modules.Module = nn.Sequential(nn.Linear(d_model, d_model), nn.PReLU(), nn.Linear(d_model, 1))
+    return nn.Sequential(layer1, layer2, layer3)
+
+
+def squim_objective_model(
+    feat_dim: int,
+    win_len: int,
+    d_model: int,
+    nhead: int,
+    hidden_dim: int,
+    num_blocks: int,
+    rnn_type: str,
+    chunk_size: int,
+    chunk_stride: Optional[int] = None,
+) -> SquimObjective:
+    """Build a custome :class:`torchaudio.prototype.models.SquimObjective` model.
+
+    Args:
+        feat_dim (int, optional): The feature dimension after Encoder module.
+        win_len (int): Kernel size in the Encoder module.
+        d_model (int): The number of expected features in the input.
+        nhead (int): Number of heads in the multi-head attention model.
+        hidden_dim (int): Hidden dimension in the RNN layer of DPRNN.
+        num_blocks (int): Number of DPRNN layers.
+        rnn_type (str): Type of RNN in DPRNN. Valid options are ["RNN", "LSTM", "GRU"].
+        chunk_size (int): Chunk size of input for DPRNN.
+        chunk_stride (int or None, optional): Stride of chunk input for DPRNN.
+    """
+    if chunk_stride is None:
+        chunk_stride = chunk_size // 2
+    encoder = Encoder(feat_dim, win_len)
+    dprnn = DPRNN(feat_dim, hidden_dim, num_blocks, rnn_type, d_model, chunk_size, chunk_stride)
+    branches = nn.ModuleList(
+        [
+            _create_branch(d_model, nhead, "stoi"),
+            _create_branch(d_model, nhead, "pesq"),
+            _create_branch(d_model, nhead, "sisdr"),
+        ]
+    )
+    return SquimObjective(encoder, dprnn, branches)
+
+
+def squim_objective_base() -> SquimObjective:
+    """Build :class:`torchaudio.prototype.models.SquimObjective` model with default arguments."""
+    return squim_objective_model(
+        feat_dim=256,
+        win_len=64,
+        d_model=256,
+        nhead=4,
+        hidden_dim=256,
+        num_blocks=2,
+        rnn_type="LSTM",
+        chunk_size=71,
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/models/squim/subjective.py b/lib/python3.11/site-packages/torchaudio/models/squim/subjective.py
new file mode 100644
index 0000000000000000000000000000000000000000..4be681c91c5f67a2b888b49ec8269b74762360ab
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/squim/subjective.py
@@ -0,0 +1,150 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torchaudio
+
+
+class AttPool(nn.Module):
+    """Attention-Pooling module that estimates the attention score.
+
+    Args:
+        input_dim (int): Input feature dimension.
+        att_dim (int): Attention Tensor dimension.
+    """
+
+    def __init__(self, input_dim: int, att_dim: int):
+        super(AttPool, self).__init__()
+
+        self.linear1 = nn.Linear(input_dim, 1)
+        self.linear2 = nn.Linear(input_dim, att_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply attention and pooling.
+
+        Args:
+            x (torch.Tensor): Input Tensor with dimensions `(batch, time, feature_dim)`.
+
+        Returns:
+            (torch.Tensor): Attention score with dimensions `(batch, att_dim)`.
+        """
+
+        att = self.linear1(x)  # (batch, time, 1)
+        att = att.transpose(2, 1)  # (batch, 1, time)
+        att = nn.functional.softmax(att, dim=2)
+        x = torch.matmul(att, x).squeeze(1)  # (batch, input_dim)
+        x = self.linear2(x)  # (batch, att_dim)
+        return x
+
+
+class Predictor(nn.Module):
+    """Prediction module that apply pooling and attention, then predict subjective metric scores.
+
+    Args:
+        input_dim (int): Input feature dimension.
+        att_dim (int): Attention Tensor dimension.
+    """
+
+    def __init__(self, input_dim: int, att_dim: int):
+        super(Predictor, self).__init__()
+        self.att_pool_layer = AttPool(input_dim, att_dim)
+        self.att_dim = att_dim
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Predict subjective evaluation metric score.
+
+        Args:
+            x (torch.Tensor): Input Tensor with dimensions `(batch, time, feature_dim)`.
+
+        Returns:
+            (torch.Tensor): Subjective metric score. Tensor with dimensions `(batch,)`.
+        """
+        x = self.att_pool_layer(x)
+        x = nn.functional.softmax(x, dim=1)
+        B = torch.linspace(0, 4, steps=self.att_dim, device=x.device)
+        x = (x * B).sum(dim=1)
+        return x
+
+
+class SquimSubjective(nn.Module):
+    """Speech Quality and Intelligibility Measures (SQUIM) model that predicts **subjective** metric scores
+    for speech enhancement (e.g., Mean Opinion Score (MOS)). The model is adopted from *NORESQA-MOS*
+    :cite:`manocha2022speech` which predicts MOS scores given the input speech and a non-matching reference.
+
+    Args:
+        ssl_model (torch.nn.Module): The self-supervised learning model for feature extraction.
+        projector (torch.nn.Module): Projection layer that projects SSL feature to a lower dimension.
+        predictor (torch.nn.Module): Predict the subjective scores.
+    """
+
+    def __init__(self, ssl_model: nn.Module, projector: nn.Module, predictor: nn.Module):
+        super(SquimSubjective, self).__init__()
+        self.ssl_model = ssl_model
+        self.projector = projector
+        self.predictor = predictor
+
+    def _align_shapes(self, waveform: torch.Tensor, reference: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Cut or pad the reference Tensor to make it aligned with waveform Tensor.
+
+        Args:
+            waveform (torch.Tensor): Input waveform for evaluation. Tensor with dimensions `(batch, time)`.
+            reference (torch.Tensor): Non-matching clean reference. Tensor with dimensions `(batch, time_ref)`.
+
+        Returns:
+            (torch.Tensor, torch.Tensor): The aligned waveform and reference Tensors
+                with same dimensions `(batch, time)`.
+        """
+        T_waveform = waveform.shape[-1]
+        T_reference = reference.shape[-1]
+        if T_reference < T_waveform:
+            num_padding = T_waveform // T_reference + 1
+            reference = torch.cat([reference for _ in range(num_padding)], dim=1)
+        return waveform, reference[:, :T_waveform]
+
+    def forward(self, waveform: torch.Tensor, reference: torch.Tensor):
+        """Predict subjective evaluation metric score.
+
+        Args:
+            waveform (torch.Tensor): Input waveform for evaluation. Tensor with dimensions `(batch, time)`.
+            reference (torch.Tensor): Non-matching clean reference. Tensor with dimensions `(batch, time_ref)`.
+
+        Returns:
+            (torch.Tensor): Subjective metric score. Tensor with dimensions `(batch,)`.
+        """
+        waveform, reference = self._align_shapes(waveform, reference)
+        waveform = self.projector(self.ssl_model.extract_features(waveform)[0][-1])
+        reference = self.projector(self.ssl_model.extract_features(reference)[0][-1])
+        concat = torch.cat((reference, waveform), dim=2)
+        score_diff = self.predictor(concat)  # Score difference compared to the reference
+        return 5 - score_diff
+
+
+def squim_subjective_model(
+    ssl_type: str,
+    feat_dim: int,
+    proj_dim: int,
+    att_dim: int,
+) -> SquimSubjective:
+    """Build a custome :class:`torchaudio.prototype.models.SquimSubjective` model.
+
+    Args:
+        ssl_type (str): Type of self-supervised learning (SSL) models.
+            Must be one of ["wav2vec2_base", "wav2vec2_large"].
+        feat_dim (int): Feature dimension of the SSL feature representation.
+        proj_dim (int): Output dimension of projection layer.
+        att_dim (int): Dimension of attention scores.
+    """
+    ssl_model = getattr(torchaudio.models, ssl_type)()
+    projector = nn.Linear(feat_dim, proj_dim)
+    predictor = Predictor(proj_dim * 2, att_dim)
+    return SquimSubjective(ssl_model, projector, predictor)
+
+
+def squim_subjective_base() -> SquimSubjective:
+    """Build :class:`torchaudio.prototype.models.SquimSubjective` model with default arguments."""
+    return squim_subjective_model(
+        ssl_type="wav2vec2_base",
+        feat_dim=768,
+        proj_dim=32,
+        att_dim=5,
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/models/tacotron2.py b/lib/python3.11/site-packages/torchaudio/models/tacotron2.py
new file mode 100644
index 0000000000000000000000000000000000000000..978fb97c88db9c64a9b216a340e63075e53e2295
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/tacotron2.py
@@ -0,0 +1,1046 @@
+# *****************************************************************************
+#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#      * Redistributions of source code must retain the above copyright
+#        notice, this list of conditions and the following disclaimer.
+#      * Redistributions in binary form must reproduce the above copyright
+#        notice, this list of conditions and the following disclaimer in the
+#        documentation and/or other materials provided with the distribution.
+#      * Neither the name of the NVIDIA CORPORATION nor the
+#        names of its contributors may be used to endorse or promote products
+#        derived from this software without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#  DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
+#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# *****************************************************************************
+
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+
+__all__ = [
+    "Tacotron2",
+]
+
+
+def _get_linear_layer(in_dim: int, out_dim: int, bias: bool = True, w_init_gain: str = "linear") -> torch.nn.Linear:
+    r"""Linear layer with xavier uniform initialization.
+
+    Args:
+        in_dim (int): Size of each input sample.
+        out_dim (int): Size of each output sample.
+        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias. (Default: ``True``)
+        w_init_gain (str, optional): Parameter passed to ``torch.nn.init.calculate_gain``
+            for setting the gain parameter of ``xavier_uniform_``. (Default: ``linear``)
+
+    Returns:
+        (torch.nn.Linear): The corresponding linear layer.
+    """
+    linear = torch.nn.Linear(in_dim, out_dim, bias=bias)
+    torch.nn.init.xavier_uniform_(linear.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+    return linear
+
+
+def _get_conv1d_layer(
+    in_channels: int,
+    out_channels: int,
+    kernel_size: int = 1,
+    stride: int = 1,
+    padding: Optional[Union[str, int, Tuple[int]]] = None,
+    dilation: int = 1,
+    bias: bool = True,
+    w_init_gain: str = "linear",
+) -> torch.nn.Conv1d:
+    r"""1D convolution with xavier uniform initialization.
+
+    Args:
+        in_channels (int): Number of channels in the input image.
+        out_channels (int): Number of channels produced by the convolution.
+        kernel_size (int, optional): Number of channels in the input image. (Default: ``1``)
+        stride (int, optional): Number of channels in the input image. (Default: ``1``)
+        padding (str, int or tuple, optional): Padding added to both sides of the input.
+            (Default: dilation * (kernel_size - 1) / 2)
+        dilation (int, optional): Number of channels in the input image. (Default: ``1``)
+        w_init_gain (str, optional): Parameter passed to ``torch.nn.init.calculate_gain``
+            for setting the gain parameter of ``xavier_uniform_``. (Default: ``linear``)
+
+    Returns:
+        (torch.nn.Conv1d): The corresponding Conv1D layer.
+    """
+    if padding is None:
+        if kernel_size % 2 != 1:
+            raise ValueError("kernel_size must be odd")
+        padding = int(dilation * (kernel_size - 1) / 2)
+
+    conv1d = torch.nn.Conv1d(
+        in_channels,
+        out_channels,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        dilation=dilation,
+        bias=bias,
+    )
+
+    torch.nn.init.xavier_uniform_(conv1d.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    return conv1d
+
+
+def _get_mask_from_lengths(lengths: Tensor) -> Tensor:
+    r"""Returns a binary mask based on ``lengths``. The ``i``-th row and ``j``-th column of the mask
+    is ``1`` if ``j`` is smaller than ``i``-th element of ``lengths.
+
+    Args:
+        lengths (Tensor): The length of each element in the batch, with shape (n_batch, ).
+
+    Returns:
+        mask (Tensor): The binary mask, with shape (n_batch, max of ``lengths``).
+    """
+    max_len = torch.max(lengths).item()
+    ids = torch.arange(0, max_len, device=lengths.device, dtype=lengths.dtype)
+    mask = (ids < lengths.unsqueeze(1)).byte()
+    mask = torch.le(mask, 0)
+    return mask
+
+
+class _LocationLayer(nn.Module):
+    r"""Location layer used in the Attention model.
+
+    Args:
+        attention_n_filter (int): Number of filters for attention model.
+        attention_kernel_size (int): Kernel size for attention model.
+        attention_hidden_dim (int): Dimension of attention hidden representation.
+    """
+
+    def __init__(
+        self,
+        attention_n_filter: int,
+        attention_kernel_size: int,
+        attention_hidden_dim: int,
+    ):
+        super().__init__()
+        padding = int((attention_kernel_size - 1) / 2)
+        self.location_conv = _get_conv1d_layer(
+            2,
+            attention_n_filter,
+            kernel_size=attention_kernel_size,
+            padding=padding,
+            bias=False,
+            stride=1,
+            dilation=1,
+        )
+        self.location_dense = _get_linear_layer(
+            attention_n_filter, attention_hidden_dim, bias=False, w_init_gain="tanh"
+        )
+
+    def forward(self, attention_weights_cat: Tensor) -> Tensor:
+        r"""Location layer used in the Attention model.
+
+        Args:
+            attention_weights_cat (Tensor): Cumulative and previous attention weights
+                with shape (n_batch, 2, max of ``text_lengths``).
+
+        Returns:
+            processed_attention (Tensor): Cumulative and previous attention weights
+                with shape (n_batch, ``attention_hidden_dim``).
+        """
+        # (n_batch, attention_n_filter, text_lengths.max())
+        processed_attention = self.location_conv(attention_weights_cat)
+        processed_attention = processed_attention.transpose(1, 2)
+        # (n_batch, text_lengths.max(), attention_hidden_dim)
+        processed_attention = self.location_dense(processed_attention)
+        return processed_attention
+
+
+class _Attention(nn.Module):
+    r"""Locally sensitive attention model.
+
+    Args:
+        attention_rnn_dim (int): Number of hidden units for RNN.
+        encoder_embedding_dim (int): Number of embedding dimensions in the Encoder.
+        attention_hidden_dim (int): Dimension of attention hidden representation.
+        attention_location_n_filter (int): Number of filters for Attention model.
+        attention_location_kernel_size (int): Kernel size for Attention model.
+    """
+
+    def __init__(
+        self,
+        attention_rnn_dim: int,
+        encoder_embedding_dim: int,
+        attention_hidden_dim: int,
+        attention_location_n_filter: int,
+        attention_location_kernel_size: int,
+    ) -> None:
+        super().__init__()
+        self.query_layer = _get_linear_layer(attention_rnn_dim, attention_hidden_dim, bias=False, w_init_gain="tanh")
+        self.memory_layer = _get_linear_layer(
+            encoder_embedding_dim, attention_hidden_dim, bias=False, w_init_gain="tanh"
+        )
+        self.v = _get_linear_layer(attention_hidden_dim, 1, bias=False)
+        self.location_layer = _LocationLayer(
+            attention_location_n_filter,
+            attention_location_kernel_size,
+            attention_hidden_dim,
+        )
+        self.score_mask_value = -float("inf")
+
+    def _get_alignment_energies(self, query: Tensor, processed_memory: Tensor, attention_weights_cat: Tensor) -> Tensor:
+        r"""Get the alignment vector.
+
+        Args:
+            query (Tensor): Decoder output with shape (n_batch, n_mels * n_frames_per_step).
+            processed_memory (Tensor): Processed Encoder outputs
+                with shape (n_batch, max of ``text_lengths``, attention_hidden_dim).
+            attention_weights_cat (Tensor): Cumulative and previous attention weights
+                with shape (n_batch, 2, max of ``text_lengths``).
+
+        Returns:
+            alignment (Tensor): attention weights, it is a tensor with shape (batch, max of ``text_lengths``).
+        """
+
+        processed_query = self.query_layer(query.unsqueeze(1))
+        processed_attention_weights = self.location_layer(attention_weights_cat)
+        energies = self.v(torch.tanh(processed_query + processed_attention_weights + processed_memory))
+
+        alignment = energies.squeeze(2)
+        return alignment
+
+    def forward(
+        self,
+        attention_hidden_state: Tensor,
+        memory: Tensor,
+        processed_memory: Tensor,
+        attention_weights_cat: Tensor,
+        mask: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        r"""Pass the input through the Attention model.
+
+        Args:
+            attention_hidden_state (Tensor): Attention rnn last output with shape (n_batch, ``attention_rnn_dim``).
+            memory (Tensor): Encoder outputs with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+            processed_memory (Tensor): Processed Encoder outputs
+                with shape (n_batch, max of ``text_lengths``, ``attention_hidden_dim``).
+            attention_weights_cat (Tensor): Previous and cumulative attention weights
+                with shape (n_batch, current_num_frames * 2, max of ``text_lengths``).
+            mask (Tensor): Binary mask for padded data with shape (n_batch, current_num_frames).
+
+        Returns:
+            attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
+            attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
+        """
+        alignment = self._get_alignment_energies(attention_hidden_state, processed_memory, attention_weights_cat)
+
+        alignment = alignment.masked_fill(mask, self.score_mask_value)
+
+        attention_weights = F.softmax(alignment, dim=1)
+        attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
+        attention_context = attention_context.squeeze(1)
+
+        return attention_context, attention_weights
+
+
+class _Prenet(nn.Module):
+    r"""Prenet Module. It is consists of ``len(output_size)`` linear layers.
+
+    Args:
+        in_dim (int): The size of each input sample.
+        output_sizes (list): The output dimension of each linear layers.
+    """
+
+    def __init__(self, in_dim: int, out_sizes: List[int]) -> None:
+        super().__init__()
+        in_sizes = [in_dim] + out_sizes[:-1]
+        self.layers = nn.ModuleList(
+            [_get_linear_layer(in_size, out_size, bias=False) for (in_size, out_size) in zip(in_sizes, out_sizes)]
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""Pass the input through Prenet.
+
+        Args:
+            x (Tensor): The input sequence to Prenet with shape (n_batch, in_dim).
+
+        Return:
+            x (Tensor): Tensor with shape (n_batch, sizes[-1])
+        """
+
+        for linear in self.layers:
+            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
+        return x
+
+
+class _Postnet(nn.Module):
+    r"""Postnet Module.
+
+    Args:
+        n_mels (int): Number of mel bins.
+        postnet_embedding_dim (int): Postnet embedding dimension.
+        postnet_kernel_size (int): Postnet kernel size.
+        postnet_n_convolution (int): Number of postnet convolutions.
+    """
+
+    def __init__(
+        self,
+        n_mels: int,
+        postnet_embedding_dim: int,
+        postnet_kernel_size: int,
+        postnet_n_convolution: int,
+    ):
+        super().__init__()
+        self.convolutions = nn.ModuleList()
+
+        for i in range(postnet_n_convolution):
+            in_channels = n_mels if i == 0 else postnet_embedding_dim
+            out_channels = n_mels if i == (postnet_n_convolution - 1) else postnet_embedding_dim
+            init_gain = "linear" if i == (postnet_n_convolution - 1) else "tanh"
+            num_features = n_mels if i == (postnet_n_convolution - 1) else postnet_embedding_dim
+            self.convolutions.append(
+                nn.Sequential(
+                    _get_conv1d_layer(
+                        in_channels,
+                        out_channels,
+                        kernel_size=postnet_kernel_size,
+                        stride=1,
+                        padding=int((postnet_kernel_size - 1) / 2),
+                        dilation=1,
+                        w_init_gain=init_gain,
+                    ),
+                    nn.BatchNorm1d(num_features),
+                )
+            )
+
+        self.n_convs = len(self.convolutions)
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""Pass the input through Postnet.
+
+        Args:
+            x (Tensor): The input sequence with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
+
+        Return:
+            x (Tensor): Tensor with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
+        """
+
+        for i, conv in enumerate(self.convolutions):
+            if i < self.n_convs - 1:
+                x = F.dropout(torch.tanh(conv(x)), 0.5, training=self.training)
+            else:
+                x = F.dropout(conv(x), 0.5, training=self.training)
+
+        return x
+
+
+class _Encoder(nn.Module):
+    r"""Encoder Module.
+
+    Args:
+        encoder_embedding_dim (int): Number of embedding dimensions in the encoder.
+        encoder_n_convolution (int): Number of convolution layers in the encoder.
+        encoder_kernel_size (int): The kernel size in the encoder.
+
+    Examples
+        >>> encoder = _Encoder(3, 512, 5)
+        >>> input = torch.rand(10, 20, 30)
+        >>> output = encoder(input)  # shape: (10, 30, 512)
+    """
+
+    def __init__(
+        self,
+        encoder_embedding_dim: int,
+        encoder_n_convolution: int,
+        encoder_kernel_size: int,
+    ) -> None:
+        super().__init__()
+
+        self.convolutions = nn.ModuleList()
+        for _ in range(encoder_n_convolution):
+            conv_layer = nn.Sequential(
+                _get_conv1d_layer(
+                    encoder_embedding_dim,
+                    encoder_embedding_dim,
+                    kernel_size=encoder_kernel_size,
+                    stride=1,
+                    padding=int((encoder_kernel_size - 1) / 2),
+                    dilation=1,
+                    w_init_gain="relu",
+                ),
+                nn.BatchNorm1d(encoder_embedding_dim),
+            )
+            self.convolutions.append(conv_layer)
+
+        self.lstm = nn.LSTM(
+            encoder_embedding_dim,
+            int(encoder_embedding_dim / 2),
+            1,
+            batch_first=True,
+            bidirectional=True,
+        )
+        self.lstm.flatten_parameters()
+
+    def forward(self, x: Tensor, input_lengths: Tensor) -> Tensor:
+        r"""Pass the input through the Encoder.
+
+        Args:
+            x (Tensor): The input sequences with shape (n_batch, encoder_embedding_dim, n_seq).
+            input_lengths (Tensor): The length of each input sequence with shape (n_batch, ).
+
+        Return:
+            x (Tensor): A tensor with shape (n_batch, n_seq, encoder_embedding_dim).
+        """
+
+        for conv in self.convolutions:
+            x = F.dropout(F.relu(conv(x)), 0.5, self.training)
+
+        x = x.transpose(1, 2)
+
+        input_lengths = input_lengths.cpu()
+        x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True)
+
+        outputs, _ = self.lstm(x)
+        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
+
+        return outputs
+
+
+class _Decoder(nn.Module):
+    r"""Decoder with Attention model.
+
+    Args:
+        n_mels (int): number of mel bins
+        n_frames_per_step (int): number of frames processed per step, only 1 is supported
+        encoder_embedding_dim (int): the number of embedding dimensions in the encoder.
+        decoder_rnn_dim (int): number of units in decoder LSTM
+        decoder_max_step (int): maximum number of output mel spectrograms
+        decoder_dropout (float): dropout probability for decoder LSTM
+        decoder_early_stopping (bool): stop decoding when all samples are finished
+        attention_rnn_dim (int): number of units in attention LSTM
+        attention_hidden_dim (int): dimension of attention hidden representation
+        attention_location_n_filter (int): number of filters for attention model
+        attention_location_kernel_size (int): kernel size for attention model
+        attention_dropout (float): dropout probability for attention LSTM
+        prenet_dim (int): number of ReLU units in prenet layers
+        gate_threshold (float): probability threshold for stop token
+    """
+
+    def __init__(
+        self,
+        n_mels: int,
+        n_frames_per_step: int,
+        encoder_embedding_dim: int,
+        decoder_rnn_dim: int,
+        decoder_max_step: int,
+        decoder_dropout: float,
+        decoder_early_stopping: bool,
+        attention_rnn_dim: int,
+        attention_hidden_dim: int,
+        attention_location_n_filter: int,
+        attention_location_kernel_size: int,
+        attention_dropout: float,
+        prenet_dim: int,
+        gate_threshold: float,
+    ) -> None:
+
+        super().__init__()
+        self.n_mels = n_mels
+        self.n_frames_per_step = n_frames_per_step
+        self.encoder_embedding_dim = encoder_embedding_dim
+        self.attention_rnn_dim = attention_rnn_dim
+        self.decoder_rnn_dim = decoder_rnn_dim
+        self.prenet_dim = prenet_dim
+        self.decoder_max_step = decoder_max_step
+        self.gate_threshold = gate_threshold
+        self.attention_dropout = attention_dropout
+        self.decoder_dropout = decoder_dropout
+        self.decoder_early_stopping = decoder_early_stopping
+
+        self.prenet = _Prenet(n_mels * n_frames_per_step, [prenet_dim, prenet_dim])
+
+        self.attention_rnn = nn.LSTMCell(prenet_dim + encoder_embedding_dim, attention_rnn_dim)
+
+        self.attention_layer = _Attention(
+            attention_rnn_dim,
+            encoder_embedding_dim,
+            attention_hidden_dim,
+            attention_location_n_filter,
+            attention_location_kernel_size,
+        )
+
+        self.decoder_rnn = nn.LSTMCell(attention_rnn_dim + encoder_embedding_dim, decoder_rnn_dim, True)
+
+        self.linear_projection = _get_linear_layer(decoder_rnn_dim + encoder_embedding_dim, n_mels * n_frames_per_step)
+
+        self.gate_layer = _get_linear_layer(
+            decoder_rnn_dim + encoder_embedding_dim, 1, bias=True, w_init_gain="sigmoid"
+        )
+
+    def _get_initial_frame(self, memory: Tensor) -> Tensor:
+        r"""Gets all zeros frames to use as the first decoder input.
+
+        Args:
+            memory (Tensor): Encoder outputs with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+
+        Returns:
+            decoder_input (Tensor): all zeros frames with shape
+                (n_batch, max of ``text_lengths``, ``n_mels * n_frames_per_step``).
+        """
+
+        n_batch = memory.size(0)
+        dtype = memory.dtype
+        device = memory.device
+        decoder_input = torch.zeros(n_batch, self.n_mels * self.n_frames_per_step, dtype=dtype, device=device)
+        return decoder_input
+
+    def _initialize_decoder_states(
+        self, memory: Tensor
+    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
+        r"""Initializes attention rnn states, decoder rnn states, attention
+        weights, attention cumulative weights, attention context, stores memory
+        and stores processed memory.
+
+        Args:
+            memory (Tensor): Encoder outputs with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+
+        Returns:
+            attention_hidden (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
+            attention_cell (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
+            decoder_hidden (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
+            decoder_cell (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
+            attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
+            attention_weights_cum (Tensor): Cumulated attention weights with shape (n_batch, max of ``text_lengths``).
+            attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
+            processed_memory (Tensor): Processed encoder outputs
+                with shape (n_batch, max of ``text_lengths``, ``attention_hidden_dim``).
+        """
+        n_batch = memory.size(0)
+        max_time = memory.size(1)
+        dtype = memory.dtype
+        device = memory.device
+
+        attention_hidden = torch.zeros(n_batch, self.attention_rnn_dim, dtype=dtype, device=device)
+        attention_cell = torch.zeros(n_batch, self.attention_rnn_dim, dtype=dtype, device=device)
+
+        decoder_hidden = torch.zeros(n_batch, self.decoder_rnn_dim, dtype=dtype, device=device)
+        decoder_cell = torch.zeros(n_batch, self.decoder_rnn_dim, dtype=dtype, device=device)
+
+        attention_weights = torch.zeros(n_batch, max_time, dtype=dtype, device=device)
+        attention_weights_cum = torch.zeros(n_batch, max_time, dtype=dtype, device=device)
+        attention_context = torch.zeros(n_batch, self.encoder_embedding_dim, dtype=dtype, device=device)
+
+        processed_memory = self.attention_layer.memory_layer(memory)
+
+        return (
+            attention_hidden,
+            attention_cell,
+            decoder_hidden,
+            decoder_cell,
+            attention_weights,
+            attention_weights_cum,
+            attention_context,
+            processed_memory,
+        )
+
+    def _parse_decoder_inputs(self, decoder_inputs: Tensor) -> Tensor:
+        r"""Prepares decoder inputs.
+
+        Args:
+            decoder_inputs (Tensor): Inputs used for teacher-forced training, i.e. mel-specs,
+                with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``)
+
+        Returns:
+            inputs (Tensor): Processed decoder inputs with shape (max of ``mel_specgram_lengths``, n_batch, ``n_mels``).
+        """
+        # (n_batch, n_mels, mel_specgram_lengths.max()) -> (n_batch, mel_specgram_lengths.max(), n_mels)
+        decoder_inputs = decoder_inputs.transpose(1, 2)
+        decoder_inputs = decoder_inputs.view(
+            decoder_inputs.size(0),
+            int(decoder_inputs.size(1) / self.n_frames_per_step),
+            -1,
+        )
+        # (n_batch, mel_specgram_lengths.max(), n_mels) -> (mel_specgram_lengths.max(), n_batch, n_mels)
+        decoder_inputs = decoder_inputs.transpose(0, 1)
+        return decoder_inputs
+
+    def _parse_decoder_outputs(
+        self, mel_specgram: Tensor, gate_outputs: Tensor, alignments: Tensor
+    ) -> Tuple[Tensor, Tensor, Tensor]:
+        r"""Prepares decoder outputs for output
+
+        Args:
+            mel_specgram (Tensor): mel spectrogram with shape (max of ``mel_specgram_lengths``, n_batch, ``n_mels``)
+            gate_outputs (Tensor): predicted stop token with shape (max of ``mel_specgram_lengths``, n_batch)
+            alignments (Tensor): sequence of attention weights from the decoder
+                with shape (max of ``mel_specgram_lengths``, n_batch, max of ``text_lengths``)
+
+        Returns:
+            mel_specgram (Tensor): mel spectrogram with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``)
+            gate_outputs (Tensor): predicted stop token with shape (n_batch, max of ``mel_specgram_lengths``)
+            alignments (Tensor): sequence of attention weights from the decoder
+                with shape (n_batch, max of ``mel_specgram_lengths``, max of ``text_lengths``)
+        """
+        # (mel_specgram_lengths.max(), n_batch, text_lengths.max())
+        # -> (n_batch, mel_specgram_lengths.max(), text_lengths.max())
+        alignments = alignments.transpose(0, 1).contiguous()
+        # (mel_specgram_lengths.max(), n_batch) -> (n_batch, mel_specgram_lengths.max())
+        gate_outputs = gate_outputs.transpose(0, 1).contiguous()
+        # (mel_specgram_lengths.max(), n_batch, n_mels) -> (n_batch, mel_specgram_lengths.max(), n_mels)
+        mel_specgram = mel_specgram.transpose(0, 1).contiguous()
+        # decouple frames per step
+        shape = (mel_specgram.shape[0], -1, self.n_mels)
+        mel_specgram = mel_specgram.view(*shape)
+        # (n_batch, mel_specgram_lengths.max(), n_mels) -> (n_batch, n_mels, T_out)
+        mel_specgram = mel_specgram.transpose(1, 2)
+
+        return mel_specgram, gate_outputs, alignments
+
+    def decode(
+        self,
+        decoder_input: Tensor,
+        attention_hidden: Tensor,
+        attention_cell: Tensor,
+        decoder_hidden: Tensor,
+        decoder_cell: Tensor,
+        attention_weights: Tensor,
+        attention_weights_cum: Tensor,
+        attention_context: Tensor,
+        memory: Tensor,
+        processed_memory: Tensor,
+        mask: Tensor,
+    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
+        r"""Decoder step using stored states, attention and memory
+
+        Args:
+            decoder_input (Tensor): Output of the Prenet with shape (n_batch, ``prenet_dim``).
+            attention_hidden (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
+            attention_cell (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
+            decoder_hidden (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
+            decoder_cell (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
+            attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
+            attention_weights_cum (Tensor): Cumulated attention weights with shape (n_batch, max of ``text_lengths``).
+            attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
+            memory (Tensor): Encoder output with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+            processed_memory (Tensor): Processed Encoder outputs
+                with shape (n_batch, max of ``text_lengths``, ``attention_hidden_dim``).
+            mask (Tensor): Binary mask for padded data with shape (n_batch, current_num_frames).
+
+        Returns:
+            decoder_output: Predicted mel spectrogram for the current frame with shape (n_batch, ``n_mels``).
+            gate_prediction (Tensor): Prediction of the stop token with shape (n_batch, ``1``).
+            attention_hidden (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
+            attention_cell (Tensor): Hidden state of the attention LSTM with shape (n_batch, ``attention_rnn_dim``).
+            decoder_hidden (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
+            decoder_cell (Tensor): Hidden state of the decoder LSTM with shape (n_batch, ``decoder_rnn_dim``).
+            attention_weights (Tensor): Attention weights with shape (n_batch, max of ``text_lengths``).
+            attention_weights_cum (Tensor): Cumulated attention weights with shape (n_batch, max of ``text_lengths``).
+            attention_context (Tensor): Context vector with shape (n_batch, ``encoder_embedding_dim``).
+        """
+        cell_input = torch.cat((decoder_input, attention_context), -1)
+
+        attention_hidden, attention_cell = self.attention_rnn(cell_input, (attention_hidden, attention_cell))
+        attention_hidden = F.dropout(attention_hidden, self.attention_dropout, self.training)
+
+        attention_weights_cat = torch.cat((attention_weights.unsqueeze(1), attention_weights_cum.unsqueeze(1)), dim=1)
+        attention_context, attention_weights = self.attention_layer(
+            attention_hidden, memory, processed_memory, attention_weights_cat, mask
+        )
+
+        attention_weights_cum += attention_weights
+        decoder_input = torch.cat((attention_hidden, attention_context), -1)
+
+        decoder_hidden, decoder_cell = self.decoder_rnn(decoder_input, (decoder_hidden, decoder_cell))
+        decoder_hidden = F.dropout(decoder_hidden, self.decoder_dropout, self.training)
+
+        decoder_hidden_attention_context = torch.cat((decoder_hidden, attention_context), dim=1)
+        decoder_output = self.linear_projection(decoder_hidden_attention_context)
+
+        gate_prediction = self.gate_layer(decoder_hidden_attention_context)
+
+        return (
+            decoder_output,
+            gate_prediction,
+            attention_hidden,
+            attention_cell,
+            decoder_hidden,
+            decoder_cell,
+            attention_weights,
+            attention_weights_cum,
+            attention_context,
+        )
+
+    def forward(
+        self, memory: Tensor, mel_specgram_truth: Tensor, memory_lengths: Tensor
+    ) -> Tuple[Tensor, Tensor, Tensor]:
+        r"""Decoder forward pass for training.
+
+        Args:
+            memory (Tensor): Encoder outputs
+                with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+            mel_specgram_truth (Tensor): Decoder ground-truth mel-specs for teacher forcing
+                with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
+            memory_lengths (Tensor): Encoder output lengths for attention masking
+                (the same as ``text_lengths``) with shape (n_batch, ).
+
+        Returns:
+            mel_specgram (Tensor): Predicted mel spectrogram
+                with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
+            gate_outputs (Tensor): Predicted stop token for each timestep
+                with shape (n_batch,  max of ``mel_specgram_lengths``).
+            alignments (Tensor): Sequence of attention weights from the decoder
+                with shape (n_batch,  max of ``mel_specgram_lengths``, max of ``text_lengths``).
+        """
+
+        decoder_input = self._get_initial_frame(memory).unsqueeze(0)
+        decoder_inputs = self._parse_decoder_inputs(mel_specgram_truth)
+        decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
+        decoder_inputs = self.prenet(decoder_inputs)
+
+        mask = _get_mask_from_lengths(memory_lengths)
+        (
+            attention_hidden,
+            attention_cell,
+            decoder_hidden,
+            decoder_cell,
+            attention_weights,
+            attention_weights_cum,
+            attention_context,
+            processed_memory,
+        ) = self._initialize_decoder_states(memory)
+
+        mel_outputs, gate_outputs, alignments = [], [], []
+        while len(mel_outputs) < decoder_inputs.size(0) - 1:
+            decoder_input = decoder_inputs[len(mel_outputs)]
+            (
+                mel_output,
+                gate_output,
+                attention_hidden,
+                attention_cell,
+                decoder_hidden,
+                decoder_cell,
+                attention_weights,
+                attention_weights_cum,
+                attention_context,
+            ) = self.decode(
+                decoder_input,
+                attention_hidden,
+                attention_cell,
+                decoder_hidden,
+                decoder_cell,
+                attention_weights,
+                attention_weights_cum,
+                attention_context,
+                memory,
+                processed_memory,
+                mask,
+            )
+
+            mel_outputs += [mel_output.squeeze(1)]
+            gate_outputs += [gate_output.squeeze(1)]
+            alignments += [attention_weights]
+
+        mel_specgram, gate_outputs, alignments = self._parse_decoder_outputs(
+            torch.stack(mel_outputs), torch.stack(gate_outputs), torch.stack(alignments)
+        )
+
+        return mel_specgram, gate_outputs, alignments
+
+    def _get_go_frame(self, memory: Tensor) -> Tensor:
+        """Gets all zeros frames to use as the first decoder input
+
+        args:
+            memory (Tensor): Encoder outputs
+                with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+
+        returns:
+            decoder_input (Tensor): All zeros frames with shape(n_batch, ``n_mels`` * ``n_frame_per_step``).
+        """
+
+        n_batch = memory.size(0)
+        dtype = memory.dtype
+        device = memory.device
+        decoder_input = torch.zeros(n_batch, self.n_mels * self.n_frames_per_step, dtype=dtype, device=device)
+        return decoder_input
+
+    @torch.jit.export
+    def infer(self, memory: Tensor, memory_lengths: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+        """Decoder inference
+
+        Args:
+            memory (Tensor): Encoder outputs
+                with shape (n_batch, max of ``text_lengths``, ``encoder_embedding_dim``).
+            memory_lengths (Tensor): Encoder output lengths for attention masking
+                (the same as ``text_lengths``) with shape (n_batch, ).
+
+        Returns:
+            mel_specgram (Tensor): Predicted mel spectrogram
+                with shape (n_batch, ``n_mels``, max of ``mel_specgram_lengths``).
+            mel_specgram_lengths (Tensor): the length of the predicted mel spectrogram (n_batch, ))
+            gate_outputs (Tensor): Predicted stop token for each timestep
+                with shape (n_batch,  max of ``mel_specgram_lengths``).
+            alignments (Tensor): Sequence of attention weights from the decoder
+                with shape (n_batch,  max of ``mel_specgram_lengths``, max of ``text_lengths``).
+        """
+        batch_size, device = memory.size(0), memory.device
+
+        decoder_input = self._get_go_frame(memory)
+
+        mask = _get_mask_from_lengths(memory_lengths)
+        (
+            attention_hidden,
+            attention_cell,
+            decoder_hidden,
+            decoder_cell,
+            attention_weights,
+            attention_weights_cum,
+            attention_context,
+            processed_memory,
+        ) = self._initialize_decoder_states(memory)
+
+        mel_specgram_lengths = torch.zeros([batch_size], dtype=torch.int32, device=device)
+        finished = torch.zeros([batch_size], dtype=torch.bool, device=device)
+        mel_specgrams: List[Tensor] = []
+        gate_outputs: List[Tensor] = []
+        alignments: List[Tensor] = []
+        for _ in range(self.decoder_max_step):
+            decoder_input = self.prenet(decoder_input)
+            (
+                mel_specgram,
+                gate_output,
+                attention_hidden,
+                attention_cell,
+                decoder_hidden,
+                decoder_cell,
+                attention_weights,
+                attention_weights_cum,
+                attention_context,
+            ) = self.decode(
+                decoder_input,
+                attention_hidden,
+                attention_cell,
+                decoder_hidden,
+                decoder_cell,
+                attention_weights,
+                attention_weights_cum,
+                attention_context,
+                memory,
+                processed_memory,
+                mask,
+            )
+
+            mel_specgrams.append(mel_specgram.unsqueeze(0))
+            gate_outputs.append(gate_output.transpose(0, 1))
+            alignments.append(attention_weights)
+            mel_specgram_lengths[~finished] += 1
+
+            finished |= torch.sigmoid(gate_output.squeeze(1)) > self.gate_threshold
+            if self.decoder_early_stopping and torch.all(finished):
+                break
+
+            decoder_input = mel_specgram
+
+        if len(mel_specgrams) == self.decoder_max_step:
+            warnings.warn(
+                "Reached max decoder steps. The generated spectrogram might not cover " "the whole transcript."
+            )
+
+        mel_specgrams = torch.cat(mel_specgrams, dim=0)
+        gate_outputs = torch.cat(gate_outputs, dim=0)
+        alignments = torch.cat(alignments, dim=0)
+
+        mel_specgrams, gate_outputs, alignments = self._parse_decoder_outputs(mel_specgrams, gate_outputs, alignments)
+
+        return mel_specgrams, mel_specgram_lengths, gate_outputs, alignments
+
+
+class Tacotron2(nn.Module):
+    r"""Tacotron2 model from *Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions*
+    :cite:`shen2018natural` based on the implementation from
+    `Nvidia Deep Learning Examples <https://github.com/NVIDIA/DeepLearningExamples/>`_.
+
+    See Also:
+        * :class:`torchaudio.pipelines.Tacotron2TTSBundle`: TTS pipeline with pretrained model.
+
+    Args:
+        mask_padding (bool, optional): Use mask padding (Default: ``False``).
+        n_mels (int, optional): Number of mel bins (Default: ``80``).
+        n_symbol (int, optional): Number of symbols for the input text (Default: ``148``).
+        n_frames_per_step (int, optional): Number of frames processed per step, only 1 is supported (Default: ``1``).
+        symbol_embedding_dim (int, optional): Input embedding dimension (Default: ``512``).
+        encoder_n_convolution (int, optional): Number of encoder convolutions (Default: ``3``).
+        encoder_kernel_size (int, optional): Encoder kernel size (Default: ``5``).
+        encoder_embedding_dim (int, optional): Encoder embedding dimension (Default: ``512``).
+        decoder_rnn_dim (int, optional): Number of units in decoder LSTM (Default: ``1024``).
+        decoder_max_step (int, optional): Maximum number of output mel spectrograms (Default: ``2000``).
+        decoder_dropout (float, optional): Dropout probability for decoder LSTM (Default: ``0.1``).
+        decoder_early_stopping (bool, optional): Continue decoding after all samples are finished (Default: ``True``).
+        attention_rnn_dim (int, optional): Number of units in attention LSTM (Default: ``1024``).
+        attention_hidden_dim (int, optional): Dimension of attention hidden representation (Default: ``128``).
+        attention_location_n_filter (int, optional): Number of filters for attention model (Default: ``32``).
+        attention_location_kernel_size (int, optional): Kernel size for attention model (Default: ``31``).
+        attention_dropout (float, optional): Dropout probability for attention LSTM (Default: ``0.1``).
+        prenet_dim (int, optional): Number of ReLU units in prenet layers (Default: ``256``).
+        postnet_n_convolution (int, optional): Number of postnet convolutions (Default: ``5``).
+        postnet_kernel_size (int, optional): Postnet kernel size (Default: ``5``).
+        postnet_embedding_dim (int, optional): Postnet embedding dimension (Default: ``512``).
+        gate_threshold (float, optional): Probability threshold for stop token (Default: ``0.5``).
+    """
+
+    def __init__(
+        self,
+        mask_padding: bool = False,
+        n_mels: int = 80,
+        n_symbol: int = 148,
+        n_frames_per_step: int = 1,
+        symbol_embedding_dim: int = 512,
+        encoder_embedding_dim: int = 512,
+        encoder_n_convolution: int = 3,
+        encoder_kernel_size: int = 5,
+        decoder_rnn_dim: int = 1024,
+        decoder_max_step: int = 2000,
+        decoder_dropout: float = 0.1,
+        decoder_early_stopping: bool = True,
+        attention_rnn_dim: int = 1024,
+        attention_hidden_dim: int = 128,
+        attention_location_n_filter: int = 32,
+        attention_location_kernel_size: int = 31,
+        attention_dropout: float = 0.1,
+        prenet_dim: int = 256,
+        postnet_n_convolution: int = 5,
+        postnet_kernel_size: int = 5,
+        postnet_embedding_dim: int = 512,
+        gate_threshold: float = 0.5,
+    ) -> None:
+        super().__init__()
+
+        self.mask_padding = mask_padding
+        self.n_mels = n_mels
+        self.n_frames_per_step = n_frames_per_step
+        self.embedding = nn.Embedding(n_symbol, symbol_embedding_dim)
+        torch.nn.init.xavier_uniform_(self.embedding.weight)
+        self.encoder = _Encoder(encoder_embedding_dim, encoder_n_convolution, encoder_kernel_size)
+        self.decoder = _Decoder(
+            n_mels,
+            n_frames_per_step,
+            encoder_embedding_dim,
+            decoder_rnn_dim,
+            decoder_max_step,
+            decoder_dropout,
+            decoder_early_stopping,
+            attention_rnn_dim,
+            attention_hidden_dim,
+            attention_location_n_filter,
+            attention_location_kernel_size,
+            attention_dropout,
+            prenet_dim,
+            gate_threshold,
+        )
+        self.postnet = _Postnet(n_mels, postnet_embedding_dim, postnet_kernel_size, postnet_n_convolution)
+
+    def forward(
+        self,
+        tokens: Tensor,
+        token_lengths: Tensor,
+        mel_specgram: Tensor,
+        mel_specgram_lengths: Tensor,
+    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+        r"""Pass the input through the Tacotron2 model. This is in teacher
+        forcing mode, which is generally used for training.
+
+        The input ``tokens`` should be padded with zeros to length max of ``token_lengths``.
+        The input ``mel_specgram`` should be padded with zeros to length max of ``mel_specgram_lengths``.
+
+        Args:
+            tokens (Tensor): The input tokens to Tacotron2 with shape `(n_batch, max of token_lengths)`.
+            token_lengths (Tensor): The valid length of each sample in ``tokens`` with shape `(n_batch, )`.
+            mel_specgram (Tensor): The target mel spectrogram
+                with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
+            mel_specgram_lengths (Tensor): The length of each mel spectrogram with shape `(n_batch, )`.
+
+        Returns:
+            [Tensor, Tensor, Tensor, Tensor]:
+                Tensor
+                    Mel spectrogram before Postnet with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
+                Tensor
+                    Mel spectrogram after Postnet with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
+                Tensor
+                    The output for stop token at each time step with shape `(n_batch, max of mel_specgram_lengths)`.
+                Tensor
+                    Sequence of attention weights from the decoder with
+                    shape `(n_batch, max of mel_specgram_lengths, max of token_lengths)`.
+        """
+
+        embedded_inputs = self.embedding(tokens).transpose(1, 2)
+
+        encoder_outputs = self.encoder(embedded_inputs, token_lengths)
+        mel_specgram, gate_outputs, alignments = self.decoder(
+            encoder_outputs, mel_specgram, memory_lengths=token_lengths
+        )
+
+        mel_specgram_postnet = self.postnet(mel_specgram)
+        mel_specgram_postnet = mel_specgram + mel_specgram_postnet
+
+        if self.mask_padding:
+            mask = _get_mask_from_lengths(mel_specgram_lengths)
+            mask = mask.expand(self.n_mels, mask.size(0), mask.size(1))
+            mask = mask.permute(1, 0, 2)
+
+            mel_specgram.masked_fill_(mask, 0.0)
+            mel_specgram_postnet.masked_fill_(mask, 0.0)
+            gate_outputs.masked_fill_(mask[:, 0, :], 1e3)
+
+        return mel_specgram, mel_specgram_postnet, gate_outputs, alignments
+
+    @torch.jit.export
+    def infer(self, tokens: Tensor, lengths: Optional[Tensor] = None) -> Tuple[Tensor, Tensor, Tensor]:
+        r"""Using Tacotron2 for inference. The input is a batch of encoded
+        sentences (``tokens``) and its corresponding lengths (``lengths``). The
+        output is the generated mel spectrograms, its corresponding lengths, and
+        the attention weights from the decoder.
+
+        The input `tokens` should be padded with zeros to length max of ``lengths``.
+
+        Args:
+            tokens (Tensor): The input tokens to Tacotron2 with shape `(n_batch, max of lengths)`.
+            lengths (Tensor or None, optional):
+                The valid length of each sample in ``tokens`` with shape `(n_batch, )`.
+                If ``None``, it is assumed that the all the tokens are valid. Default: ``None``
+
+        Returns:
+            (Tensor, Tensor, Tensor):
+                Tensor
+                    The predicted mel spectrogram with shape `(n_batch, n_mels, max of mel_specgram_lengths)`.
+                Tensor
+                    The length of the predicted mel spectrogram with shape `(n_batch, )`.
+                Tensor
+                    Sequence of attention weights from the decoder with shape
+                    `(n_batch, max of mel_specgram_lengths, max of lengths)`.
+        """
+        n_batch, max_length = tokens.shape
+        if lengths is None:
+            lengths = torch.tensor([max_length]).expand(n_batch).to(tokens.device, tokens.dtype)
+
+        assert lengths is not None  # For TorchScript compiler
+        embedded_inputs = self.embedding(tokens).transpose(1, 2)
+        encoder_outputs = self.encoder(embedded_inputs, lengths)
+        mel_specgram, mel_specgram_lengths, _, alignments = self.decoder.infer(encoder_outputs, lengths)
+
+        mel_outputs_postnet = self.postnet(mel_specgram)
+        mel_outputs_postnet = mel_specgram + mel_outputs_postnet
+
+        alignments = alignments.unfold(1, n_batch, n_batch).transpose(0, 2)
+
+        return mel_outputs_postnet, mel_specgram_lengths, alignments
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2letter.py b/lib/python3.11/site-packages/torchaudio/models/wav2letter.py
new file mode 100644
index 0000000000000000000000000000000000000000..d776131686d1f65982a565088e72e45e7b7c107f
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2letter.py
@@ -0,0 +1,72 @@
+from torch import nn, Tensor
+
+__all__ = [
+    "Wav2Letter",
+]
+
+
+class Wav2Letter(nn.Module):
+    r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
+    Recognition System* :cite:`collobert2016wav2letter`.
+
+    See Also:
+        * `Training example <https://github.com/pytorch/audio/tree/release/0.12/examples/pipeline_wav2letter>`__
+
+    Args:
+        num_classes (int, optional): Number of classes to be classified. (Default: ``40``)
+        input_type (str, optional): Wav2Letter can use as input: ``waveform``, ``power_spectrum``
+         or ``mfcc`` (Default: ``waveform``).
+        num_features (int, optional): Number of input features that the network will receive (Default: ``1``).
+    """
+
+    def __init__(self, num_classes: int = 40, input_type: str = "waveform", num_features: int = 1) -> None:
+        super().__init__()
+
+        acoustic_num_features = 250 if input_type == "waveform" else num_features
+        acoustic_model = nn.Sequential(
+            nn.Conv1d(in_channels=acoustic_num_features, out_channels=250, kernel_size=48, stride=2, padding=23),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=250, out_channels=2000, kernel_size=32, stride=1, padding=16),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=2000, out_channels=2000, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=2000, out_channels=num_classes, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(inplace=True),
+        )
+
+        if input_type == "waveform":
+            waveform_model = nn.Sequential(
+                nn.Conv1d(in_channels=num_features, out_channels=250, kernel_size=250, stride=160, padding=45),
+                nn.ReLU(inplace=True),
+            )
+            self.acoustic_model = nn.Sequential(waveform_model, acoustic_model)
+
+        if input_type in ["power_spectrum", "mfcc"]:
+            self.acoustic_model = acoustic_model
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""
+        Args:
+            x (torch.Tensor): Tensor of dimension (batch_size, num_features, input_length).
+
+        Returns:
+            Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length).
+        """
+
+        x = self.acoustic_model(x)
+        x = nn.functional.log_softmax(x, dim=1)
+        return x
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__init__.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb83403f5719b68c790d2f9f934f8c80acea3557
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__init__.py
@@ -0,0 +1,45 @@
+from . import utils
+from .model import (
+    hubert_base,
+    hubert_large,
+    hubert_pretrain_base,
+    hubert_pretrain_large,
+    hubert_pretrain_model,
+    hubert_pretrain_xlarge,
+    hubert_xlarge,
+    HuBERTPretrainModel,
+    wav2vec2_base,
+    wav2vec2_large,
+    wav2vec2_large_lv60k,
+    wav2vec2_model,
+    wav2vec2_xlsr_1b,
+    wav2vec2_xlsr_2b,
+    wav2vec2_xlsr_300m,
+    Wav2Vec2Model,
+    wavlm_base,
+    wavlm_large,
+    wavlm_model,
+)
+
+__all__ = [
+    "Wav2Vec2Model",
+    "HuBERTPretrainModel",
+    "wavlm_model",
+    "wavlm_base",
+    "wavlm_large",
+    "wav2vec2_model",
+    "wav2vec2_base",
+    "wav2vec2_large",
+    "wav2vec2_large_lv60k",
+    "hubert_base",
+    "hubert_large",
+    "hubert_xlarge",
+    "hubert_pretrain_model",
+    "hubert_pretrain_base",
+    "hubert_pretrain_large",
+    "hubert_pretrain_xlarge",
+    "utils",
+    "wav2vec2_xlsr_300m",
+    "wav2vec2_xlsr_1b",
+    "wav2vec2_xlsr_2b",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6c77102cd2b1ea5bd3f23ce9565e3678d006b560
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/components.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/components.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c0bbd3dda5d756d6f98cbbd023492bae34ac50a3
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/components.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/model.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/model.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b9a95940e8bf2ddd416b6fa600dfefa670ff0cdf
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/__pycache__/model.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/components.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/components.py
new file mode 100644
index 0000000000000000000000000000000000000000..480a6ae50921efebf5930dc21caaa3a1a44945dd
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/components.py
@@ -0,0 +1,1167 @@
+import logging
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn, Tensor
+from torch.nn import Module, Parameter
+
+from .wavlm_attention import WavLMSelfAttention
+
+_LG = logging.getLogger(__name__)
+
+
+def _init_transformer_params(module):
+    """
+    Initialize the weights of Transformer module in Wav2Vec2/HuBERT.
+
+    If the module is ``nn.Linear``, normalize the weight with mean 0 and standard deviation 0.02.
+    If ``bias`` is set to ``True`` in the module, set ``bias`` to 0.
+
+    If the module is ``nn.Embedding``, normalize the weight with mean 0 and standard deviation 0.02.
+    If ``padding_idx`` is not None, set the weight of padding to 0.
+
+    Note:
+        Ths method corresponds to
+        `init_bert_params
+        <https://github.com/facebookresearch/fairseq/blob/main/fairseq/modules/transformer_sentence_encoder.py#L21>`__
+        in the original ``fairseq`` implementation.
+    """
+
+    def normal_(data):
+        data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
+
+    if isinstance(module, nn.Linear):
+        normal_(module.weight.data)
+        if module.bias is not None:
+            module.bias.data.zero_()
+    if isinstance(module, nn.Embedding):
+        normal_(module.weight.data)
+        if module.padding_idx is not None:
+            module.weight.data[module.padding_idx].zero_()
+
+
+class LayerNorm(nn.LayerNorm):
+    """Layer norm with transpose"""
+
+    def forward(self, input: Tensor) -> Tensor:
+        x = input.transpose(-2, -1)
+        x = nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+        x = x.transpose(-2, -1)
+        return x
+
+
+class ConvLayerBlock(Module):
+    """Convolution unit of FeatureExtractor"""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int,
+        bias: bool,
+        layer_norm: Optional[Module],
+    ):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.layer_norm = layer_norm
+        self.conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            bias=bias,
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+        length: Optional[Tensor],
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """
+        Args:
+            x (Tensor): Shape: ``[batch, in_channels, in_frame]``.
+            length (Tensor or None, optional): Shape ``[batch, ]``.
+        Returns:
+            Tensor: Shape ``[batch, out_channels, out_frames]``.
+            Optional[Tensor]: Shape ``[batch, ]``.
+        """
+        x = self.conv(x)
+        if self.layer_norm is not None:
+            x = self.layer_norm(x)
+        x = nn.functional.gelu(x)
+
+        if length is not None:
+            length = torch.div(length - self.kernel_size, self.stride, rounding_mode="floor") + 1
+            # When input length is 0, the resulting length can be negative. So fix it here.
+            length = torch.max(torch.zeros_like(length), length)
+        return x, length
+
+
+class FeatureExtractor(Module):
+    """Extract features from audio
+
+    Args:
+        conv_layers (nn.ModuleList):
+            convolution layers
+    """
+
+    def __init__(
+        self,
+        conv_layers: nn.ModuleList,
+    ):
+        super().__init__()
+        self.conv_layers = conv_layers
+
+    def forward(
+        self,
+        x: Tensor,
+        length: Optional[Tensor],
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """
+        Args:
+            x (Tensor):
+                Input Tensor representing a batch of audio,
+                shape: ``[batch, time]``.
+            length (Tensor or None, optional):
+                Valid length of each input sample. shape: ``[batch, ]``.
+
+        Returns:
+            Tensor:
+                The resulting feature, shape: ``[batch, frame, feature]``
+            Optional[Tensor]:
+                Valid length of each output sample. shape: ``[batch, ]``.
+        """
+        if x.ndim != 2:
+            raise ValueError(f"Expected the input Tensor to be 2D (batch, time). Found: {list(x.shape)}")
+
+        x = x.unsqueeze(1)  # (batch, channel==1, frame)
+        for layer in self.conv_layers:
+            x, length = layer(x, length)  # (batch, feature, frame)
+        x = x.transpose(1, 2)  # (batch, frame, feature)
+        return x, length
+
+
+class FeatureProjection(Module):
+    """Layer that connects FeatureExtractor and Encoder
+
+    Projects features to encoder dimension.
+
+    Args:
+        in_features (int): Input feature dim.
+        out_features (int): Output feature dim.
+        dropout (float): Dropout probability.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        dropout: float,
+    ):
+        super().__init__()
+        self.layer_norm = nn.LayerNorm(in_features)
+        self.projection = nn.Linear(
+            in_features,
+            out_features,
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor):
+                Feature Tensor. shape: ``[batch, frame, in_feature]``
+        Returns:
+            Tensor: Projected features. ``[batch, frame, out_feature]``.
+        """
+        x = self.layer_norm(x)
+        x = self.projection(x)
+        x = self.dropout(x)
+        return x
+
+
+class ConvolutionalPositionalEmbedding(Module):
+    """Positional embedding which is placed at the beginning of Transformer.
+
+    Args:
+        embed_dim (int): Feature dimension of the input Tensor.
+        kernel_size (int): The number of frames to be use.
+        groups (int): The number of groups in feature dimensions.
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        kernel_size: int,
+        groups: int,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kernel_size = kernel_size
+        self.conv = nn.Conv1d(
+            in_channels=embed_dim,
+            out_channels=embed_dim,
+            kernel_size=kernel_size,
+            padding=kernel_size // 2,
+            groups=groups,
+        )
+
+        self.conv = nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2)
+        self.num_remove: int = 1 if kernel_size % 2 == 0 else 0
+
+    def __prepare_scriptable__(self):
+        if self.conv.__class__.__name__ == "ParametrizedConv1d":
+            _LG.warning("Removing weight_norm from %s", self.__class__.__name__)
+            torch.nn.utils.parametrize.remove_parametrizations(self.conv, "weight")
+        return self
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): shape ``[batch, frame, feature]``.
+
+        Returns:
+            Tensor: The resulting feature. Shape ``[batch, frame, feature]``.
+        """
+        x = x.transpose(-2, -1)
+        x = self.conv(x)
+        if self.num_remove > 0:
+            x = x[..., : -self.num_remove]
+        x = torch.nn.functional.gelu(x)
+        x = x.transpose(-2, -1)
+        return x
+
+
+class SelfAttention(Module):
+    """Multihead Self Attention module
+
+    Args:
+        embed_dim (int): Total dimension of the model.
+        num_heads (int): The number of heads.
+        dropout (float, optional):
+            Dropout probability on attn_output_weights. Default: ``0.0``
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+    ):
+        super().__init__()
+        head_dim = embed_dim // num_heads
+        if head_dim * num_heads != embed_dim:
+            raise ValueError(f"`embed_dim ({embed_dim})` is not divisible by `num_heads ({num_heads})`")
+
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = head_dim
+
+        self.scaling = self.head_dim**-0.5
+
+        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)
+
+    def forward(
+        self,
+        x: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_bias: Optional[Tensor] = None,
+        key_padding_mask: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """
+        Args:
+            x (Tensor): shape: ``[batch_size, sequence_length, embed_dim]``.
+            attention_mask (Tensor or ``None``, optional):
+                shape: ``[batch_size, 1, sequence_length, sequence_length]``
+            position_bias: Not used. Only for the compatibility with :py:class:`WavLMSelfAttention`.
+            key_padding_mask (Tensor or ``None``): Not used. Only for the compatibility with
+                :py:class:`WavLMSelfAttention`.
+        Returns:
+            (Tensor, ``None``): The resulting attention output and ``None`` (necessary for compatibility
+                with :py:class:`WavLMSelAttention`).
+                Attention output shape: ``[batch, sequence_length, embed_dim]``.
+        """
+        if x.ndim != 3 or x.shape[2] != self.embed_dim:
+            raise ValueError(
+                f"The expected input shape is (batch, sequence, embed_dim=={self.embed_dim}). " f"Found {x.shape}."
+            )
+        batch_size, length, embed_dim = x.size()
+        if attention_mask is not None:
+            shape_ = (batch_size, 1, length, length)
+            if attention_mask.size() != shape_:
+                raise ValueError(f"The expected attention mask shape is {shape_}. " f"Found {attention_mask.size()}.")
+
+        shape = (batch_size, length, self.num_heads, self.head_dim)
+        q = self.q_proj(x).view(*shape).transpose(2, 1)  # B, nH, L, Hd
+        k = self.k_proj(x).view(*shape).transpose(2, 1)  # B, nH, L, Hd
+        v = self.v_proj(x).view(*shape).transpose(2, 1)  # B, nH, L, Hd
+        dropout = self.dropout if self.training else 0.0
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v, attn_mask=attention_mask, dropout_p=dropout, is_causal=False
+        )
+        attn_output = attn_output.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        output = self.out_proj(attn_output)
+        return output, None  # Necessary for compatibility with WavLMSelAttention
+
+
+class FeedForward(Module):
+    """Layer that follows attention layer in encoder layer."""
+
+    def __init__(
+        self,
+        io_features: int,
+        intermediate_features: int,
+        intermediate_dropout: float,
+        output_dropout: float,
+    ):
+        super().__init__()
+        self.intermediate_dense = nn.Linear(io_features, intermediate_features)
+        self.intermediate_dropout = nn.Dropout(intermediate_dropout)
+        self.output_dense = nn.Linear(intermediate_features, io_features)
+        self.output_dropout = nn.Dropout(output_dropout)
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): shape: `(batch, sequence_length, io_features)`
+        Returns:
+            x (Tensor): shape: `(batch, sequence_length, io_features)`
+        """
+        x = self.intermediate_dense(x)
+        x = torch.nn.functional.gelu(x)
+        x = self.intermediate_dropout(x)
+
+        x = self.output_dense(x)
+        x = self.output_dropout(x)
+        return x
+
+
+class EncoderLayer(Module):
+    """A layer unit in encoder. Combines multihead self attention and feed forward."""
+
+    def __init__(
+        self,
+        attention: Module,
+        dropout: float,
+        layer_norm_first: bool,
+        feed_forward: Module,
+    ):
+        super().__init__()
+        self.attention = attention
+        self.dropout = nn.Dropout(dropout)
+        self.layer_norm = nn.LayerNorm(attention.embed_dim)
+        self.layer_norm_first = layer_norm_first
+        self.feed_forward = feed_forward
+        self.final_layer_norm = nn.LayerNorm(attention.embed_dim)
+
+    def forward(
+        self,
+        x: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_bias: Optional[Tensor] = None,
+        key_padding_mask: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """
+        Args:
+            x (Tensor): Input of shape ``(batch, sequence_length, embed_dim)``.
+            attention_mask (Tensor or ``None``, optional): attention mask
+                of shape ``(batch, 1, sequence_length, sequence_length)``. (Default: ``None``)
+            position_bias (Tensor or ``None``, optional): position bias of shape
+                ``(batch_size * num_heads, src_len, src_len)``.
+                Only necessary for WavLM model, ``None`` otherwise. (Default: ``None``)
+            key_padding_mask (Tensor or ``None``, optional): key padding mask of shape ``(batch_size, src_len)``.
+                Only used for WavLM model, ignored otherwise. (Default: ``None``)
+        Returns:
+            (x, position_bias): Shapes are the same as in the input. Position bias is only relevant for WaLM model,
+                ``None`` otherwise.
+        """
+        residual = x
+
+        if self.layer_norm_first:
+            x = self.layer_norm(x)
+
+        x, position_bias = self.attention(
+            x, attention_mask=attention_mask, position_bias=position_bias, key_padding_mask=key_padding_mask
+        )
+
+        x = self.dropout(x)
+        x = residual + x
+
+        if self.layer_norm_first:
+            x = x + self.feed_forward(self.final_layer_norm(x))
+        else:
+            x = self.layer_norm(x)
+            x = self.final_layer_norm(x + self.feed_forward(x))
+        return x, position_bias
+
+
+class Transformer(Module):
+    def __init__(
+        self,
+        pos_conv_embed: Module,
+        dropout: float,
+        layers: Module,
+        layer_norm_first: bool,
+        layer_drop: float,
+    ):
+        super().__init__()
+        self.pos_conv_embed = pos_conv_embed
+        self.layer_norm = nn.LayerNorm(pos_conv_embed.embed_dim)
+        self.layer_norm_first = layer_norm_first
+        self.layer_drop = layer_drop
+        self.dropout = nn.Dropout(dropout)
+        self.layers = layers
+
+    def _preprocess(self, x: Tensor):
+        x = x + self.pos_conv_embed(x)
+
+        if self.layer_norm_first:
+            x = self.layer_norm(x)
+
+        x = self.dropout(x)
+        return x
+
+    def forward(
+        self,
+        x: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_bias: Optional[Tensor] = None,
+    ) -> Tensor:
+        x = self._preprocess(x)
+        for layer in self.layers:
+            if not (self.training and torch.rand(1).item() <= self.layer_drop):
+                x, position_bias = layer(x, attention_mask, position_bias=position_bias)
+
+        if not self.layer_norm_first:
+            x = self.layer_norm(x)
+        return x
+
+    def get_intermediate_outputs(
+        self,
+        x: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        num_layers: Optional[int] = None,
+    ) -> List[Tensor]:
+        if num_layers is not None:
+            if not 0 < num_layers <= len(self.layers):
+                raise ValueError(f"`num_layers` must be between [1, {len(self.layers)}]")
+
+        ret: List[Tensor] = []
+        position_bias = None
+        x = self._preprocess(x)
+        for layer in self.layers:
+            x, position_bias = layer(x, attention_mask, position_bias=position_bias)
+            ret.append(x)
+            if num_layers is not None and len(ret) >= num_layers:
+                return ret
+        return ret
+
+
+class Encoder(Module):
+    def __init__(
+        self,
+        feature_projection: Module,
+        transformer: Module,
+    ):
+        super().__init__()
+        self.feature_projection = feature_projection
+        self.transformer = transformer
+
+    def _preprocess(
+        self,
+        features: Tensor,
+        lengths: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        x = self.feature_projection(features)
+
+        mask: Optional[Tensor] = None
+        if lengths is not None:
+            batch_size, max_len, _ = x.shape
+            # create mask for padded elements and zero-out them
+            mask = torch.arange(max_len, device=lengths.device).expand(batch_size, max_len) >= lengths[:, None]
+            x[mask] = 0.0
+            # extend the mask to attention shape and set weight
+            mask = -10000.0 * mask[:, None, None, :].to(dtype=features.dtype)
+            mask = mask.expand(batch_size, 1, max_len, max_len)
+        return x, mask
+
+    def forward(
+        self,
+        features: Tensor,
+        lengths: Optional[Tensor] = None,
+    ) -> Tensor:
+        x, mask = self._preprocess(features, lengths)
+        x = self.transformer(x, attention_mask=mask)
+        return x
+
+    def extract_features(
+        self,
+        features: Tensor,
+        lengths: Optional[Tensor] = None,
+        num_layers: Optional[int] = None,
+    ) -> List[Tensor]:
+        x, masks = self._preprocess(features, lengths)
+        return self.transformer.get_intermediate_outputs(x, attention_mask=masks, num_layers=num_layers)
+
+
+################################################################################
+def _get_feature_extractor(
+    norm_mode: str,
+    shapes: List[Tuple[int, int, int]],
+    bias: bool,
+) -> FeatureExtractor:
+    """
+    Args:
+        norm_mode (str):
+            Either "group_norm" or "layer_norm".
+            If "group_norm", then a single normalization is applied
+            in the first convolution block. Otherwise, all the convolution
+            blocks will have layer normalization.
+            This option corresponds to "extractor_mode" from fairseq.
+            Expected values are "group_norm" for Base arch, and
+            "layer_norm" for Large arch.
+        shapes (list of tuple of int):
+            Configuration of convolution layers. List of convolution configuration,
+            i.e. ``[(output_channel, kernel_size, stride), ...]``
+            This option corresponds to "conv_feature_layers" from fairseq.
+            Expected values are
+            ``[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2``
+            for all the architectures.
+        bias (bool):
+            Whether to include bias term to each convolution operation.
+            This option corresponds to "conv_bias" from fairseq.
+            Expected values are False for Base arch, and True for Large arch.
+
+    See Also:
+        * Original implementation
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L666-L733
+        * "extractor_mode"
+          - Def and base:
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L38-L45
+          - Large:
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L52
+        * "conv_feature_layers"
+          - Def, base and large:
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L94-L100
+        * "conv_bias"
+          - Def and base:
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L101-L103
+          - Large:
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L61
+    """
+    if norm_mode not in ["group_norm", "layer_norm"]:
+        raise ValueError("Invalid norm mode")
+    blocks = []
+    in_channels = 1
+    for i, (out_channels, kernel_size, stride) in enumerate(shapes):
+        normalization = None
+        if norm_mode == "group_norm" and i == 0:
+            normalization = nn.GroupNorm(
+                num_groups=out_channels,
+                num_channels=out_channels,
+                affine=True,
+            )
+        elif norm_mode == "layer_norm":
+            normalization = LayerNorm(
+                normalized_shape=out_channels,
+                elementwise_affine=True,
+            )
+        blocks.append(
+            ConvLayerBlock(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                bias=bias,
+                layer_norm=normalization,
+            )
+        )
+        in_channels = out_channels
+    return FeatureExtractor(nn.ModuleList(blocks))
+
+
+def _get_encoder(
+    in_features: int,
+    embed_dim: int,
+    dropout_input: float,
+    pos_conv_kernel: int,
+    pos_conv_groups: int,
+    num_layers: int,
+    num_heads: int,
+    attention_dropout: float,
+    ff_interm_features: int,
+    ff_interm_dropout: float,
+    dropout: float,
+    layer_norm_first: bool,
+    layer_drop: float,
+) -> Encoder:
+    """
+    Args:
+        in_features (int): The number of input features.
+        embed_dim (int):
+            The dimension of embedding.
+            This option corresponds to "encoder_embed_dim" from fairseq.
+            Expected values are 768 for Base arch, and 1024 for Large arch.
+        dropout_input (float):
+            The dropout probability applied after the input feature is projected
+            to ``embed_dim``.
+            This option corresponds to "dropout_input" from fairseq.
+            Expected values are 0.1 for both Base and Large arch.
+        pos_conv_kernel (int):
+            The kernel size of convolutional positional embeddings.
+            This option corresponds to "conv_pos" from fairseq.
+            Expected values are 128 for both Base and Large arch.
+        pos_conv_groups (int):
+            The number of groups of convolutional positional embeddings.
+            This option corresponds to "conv_pos_groups" from fairseq.
+            Expected values are 16 for both Base and Large arch.
+        num_layers (int):
+            The number of self attention layers in transformer block.
+            This option corresponds to "encoder_layers" from fairseq.
+            Expected values are 12 for Base and 24 for Large arch.
+        num_heads (int):
+            The number of heads in self attention layers.
+            This option corresponds to "encoder_attention_heads" from fairseq.
+            Expected values are 12 for Base and 16 for Large arch.
+        attention_dropout (float):
+            The dropout probability applied after softmax in self-attention layer.
+            This option corresponds to "attention_dropout" from fairseq.
+            Expected values are 0.1 for Base and 0.0 for Large arch.
+        ff_interm_features (int):
+            The dimension of hidden features in feed forward layer.
+            This option corresponds to "encoder_ffn_embed_dim" from fairseq.
+            Expected values are 3072 for Base and 4096 for Large arch.
+        ff_interm_dropout (float):
+            The dropout probability applied in feedforward layer.
+            This option correspinds to "activation_dropout" from fairseq.
+            Expected values are 0.1 for both Base and Large arch.
+        dropout (float):
+            The dropout probability applied at the end of feed forward layer.
+            This option corresponds to "dropout" from fairseq.
+            Expected values are 0.1 for Base and 0.0 for Large arch.
+        layer_norm_first (bool):
+            Control the order of layer norm in transformer layer and each encoder layer.
+            If True, in transformer layer, layer norm is applied before features are fed
+            to encoder layers. In encoder layer, two layer norms are applied before and after
+            self attention.
+            If False, in transformer layer, layer norm is applied after features are fed
+            to encoder layers. In encoder layer, two layer norms are applied after self
+            attention, before and after feed forward.
+            This option corresponds to "layer_norm_first" from fairseq.
+            Expected values are False for Base and True for Large arch.
+        layer_drop (float):
+            Probability to drop each encoder layer during training.
+            This option corresponds to "layerdrop" from fairseq.
+            Expected values are 0.1 for both Base and Large arch.
+
+    See Also:
+        * "encoder_embed_dim"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L49-L51
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L64
+        * "dropout_input"
+          - Def, base and large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L75-L78
+        * "conv_pos"
+          - Def, base and large
+            NOTE: The description is wrong.
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L204-L207
+          - Usage
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L756
+        * "conv_pos_groups"
+          - Def, base and large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L208-L211
+        * "encoder_layers"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L46-L48
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L63
+        * "encoder_attention_heads"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L55-L57
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L66
+        * "attention_dropout"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L66-L68
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L60
+        * "encoder_ffn_embed_dim"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L52-L54
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L65
+        * "activation_dropout"
+          - Def
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L69-L71
+          - Base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/base_960h.yaml#L55
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/vox_960h.yaml#L55
+        * "dropout"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L63-L65
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L59
+        * "layer_norm_first"
+          - Def and base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L91-L93
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/pretraining/wav2vec2_large_librivox.yaml#L53
+        * "layerdrop"
+          - Def
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L72-L74
+          - Base
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/base_960h.yaml#L54
+          - Large
+            https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/examples/wav2vec/config/finetuning/vox_960h.yaml#L54
+    """
+    feature_projection = FeatureProjection(in_features, embed_dim, dropout_input)
+    pos_conv = ConvolutionalPositionalEmbedding(embed_dim, pos_conv_kernel, pos_conv_groups)
+
+    # Original impl
+    # https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L768-L782
+    encoder_layers = nn.ModuleList()
+    for _ in range(num_layers):
+        attention = SelfAttention(
+            embed_dim=embed_dim,
+            num_heads=num_heads,
+            dropout=attention_dropout,
+        )
+        feed_forward = FeedForward(
+            io_features=embed_dim,
+            intermediate_features=ff_interm_features,
+            intermediate_dropout=ff_interm_dropout,
+            output_dropout=dropout,
+        )
+        encoder_layers.append(
+            EncoderLayer(
+                attention=attention,
+                dropout=dropout,
+                layer_norm_first=layer_norm_first,
+                feed_forward=feed_forward,
+            )
+        )
+    transformer = Transformer(
+        pos_conv_embed=pos_conv,
+        dropout=dropout,
+        layers=encoder_layers,
+        layer_norm_first=not layer_norm_first,
+        layer_drop=layer_drop,
+    )
+    return Encoder(feature_projection, transformer)
+
+
+def _get_wavlm_encoder(
+    in_features: int,
+    embed_dim: int,
+    dropout_input: float,
+    pos_conv_kernel: int,
+    pos_conv_groups: int,
+    num_layers: int,
+    num_heads: int,
+    num_buckets: int,
+    max_distance: int,
+    attention_dropout: float,
+    ff_interm_features: int,
+    ff_interm_dropout: float,
+    dropout: float,
+    layer_norm_first: bool,
+    layer_drop: float,
+) -> Encoder:
+    """
+    Construct encoder for WavLM model :cite:`chen2022wavlm`. The structure of the encoder and most of the argments are
+    the same as in :py:func:`_get_encoder` so refer there for documentation. The only difference from Wav2Vec2 encoder
+    is usage of `WavLMSelfAttention` instead of `SelfAttention` and two additional parameters: `num_buckets` and
+    `max_distance`.
+    Args:
+        in_features (int): See :py:func:`_get_encoder`.
+        embed_dim (int): See :py:func:`_get_encoder`.
+        dropout_input (float): See :py:func:`_get_encoder`.
+        pos_conv_kernel (int): See :py:func:`_get_encoder`.
+        pos_conv_groups (int): See :py:func:`_get_encoder`.
+        num_layers (int): See :py:func:`_get_encoder`.
+        num_heads (int): See :py:func:`_get_encoder`.
+        num_buckets (int): Number of buckets for relative position embedding.
+        max_distance (int): Maximum distance for relative position embedding.
+        attention_dropout (float): See :py:func:`_get_encoder`.
+        ff_interm_features (int): See :py:func:`_get_encoder`.
+        ff_interm_dropout (float): See :py:func:`_get_encoder`.
+        dropout (float): See :py:func:`_get_encoder`.
+        layer_norm_first (bool): See :py:func:`_get_encoder`.
+        layer_drop (float): See :py:func:`_get_encoder`.
+
+    """
+    feature_projection = FeatureProjection(in_features, embed_dim, dropout_input)
+    pos_conv = ConvolutionalPositionalEmbedding(embed_dim, pos_conv_kernel, pos_conv_groups)
+
+    # Original impl
+    # https://github.com/pytorch/fairseq/blob/425c36eafff535fe7337f8bdd5ace22ebacc78cb/fairseq/models/wav2vec/wav2vec2.py#L768-L782
+    encoder_layers = nn.ModuleList()
+    for i in range(num_layers):
+        attention = WavLMSelfAttention(
+            embed_dim=embed_dim,
+            num_heads=num_heads,
+            num_buckets=num_buckets,
+            max_distance=max_distance,
+            dropout=attention_dropout,
+            has_relative_attention_bias=(i == 0),  # Position embedding is only necessary in the first layer.
+        )
+        feed_forward = FeedForward(
+            io_features=embed_dim,
+            intermediate_features=ff_interm_features,
+            intermediate_dropout=ff_interm_dropout,
+            output_dropout=dropout,
+        )
+        encoder_layers.append(
+            EncoderLayer(
+                attention=attention,
+                dropout=dropout,
+                layer_norm_first=layer_norm_first,
+                feed_forward=feed_forward,
+            )
+        )
+    transformer = Transformer(
+        pos_conv_embed=pos_conv,
+        dropout=dropout,
+        layers=encoder_layers,
+        layer_norm_first=not layer_norm_first,
+        layer_drop=layer_drop,
+    )
+    return Encoder(feature_projection, transformer)
+
+
+def _compute_mask_indices(
+    shape: Tuple[int, int],
+    padding_mask: Optional[Tensor],
+    mask_prob: float,
+    mask_length: int,
+    mask_type: str = "static",
+    mask_other: float = 0.0,
+    min_masks: int = 0,
+    no_overlap: bool = False,
+    min_space: int = 0,
+) -> Tensor:
+    """Computes random mask spans for a given shape.
+    Args:
+        shape (int, int): The shape for which to compute masks.
+            The first element is batch size and second is the number of frames.
+        padding_mask (Tensor or None): The padding mask of the same dimension as shape,
+            which will prevent masking padded elements.
+        mask_prob (float): Probability for each token to be chosen as start of the span to be masked.
+            This will be multiplied by number of timesteps divided by length of mask span to mask
+            approximately this percentage of all elements. However due to overlaps, the actual number
+            will be smaller (unless no_overlap is True).
+        mask_type (str): How to compute mask lengths. Options: [``static``, ``uniform``, ``normal``, ``poisson``].
+            ``static``: Fixed size
+            ``uniform``: Sample from uniform distribution [mask_other, mask_length*2]
+            ``normal``: Sample from normal distribution with mean ``mask_length`` and stdev ``mask_other``.
+            ``poisson``: Sample from possion distribution with lambda = ``mask_length``.
+        min_masks (int): Minimum number of masked spans.
+        no_overlap (bool): If false, will switch to an alternative recursive algorithm
+            that prevents spans from overlapping.
+        min_space (int): How many frames to keep unmasked between spans (Only used if no_overlap is True).
+
+    Returns:
+        (Tensor): The mask indices of dimension `[batch, frame]`.
+    """
+
+    batch_size, frame = shape
+    mask = torch.full((batch_size, frame), False)
+    # add a random number for probabilistic rounding
+    all_num_mask = int(mask_prob * frame / float(mask_length) + torch.rand(1))
+
+    all_num_mask = max(min_masks, all_num_mask)
+
+    mask_idcs = []
+    for i in range(batch_size):
+        if padding_mask is not None:
+            sz = frame - padding_mask[i].long().sum().item()
+            # add a random number for probabilistic rounding
+            num_mask = int(mask_prob * sz / float(mask_length) + torch.rand(1))
+            num_mask = max(min_masks, num_mask)
+        else:
+            sz = frame
+            num_mask = all_num_mask
+
+        if mask_type == "static":
+            lengths = torch.full((num_mask,), mask_length)
+        elif mask_type == "uniform":
+            lengths = torch.randint(int(mask_other), mask_length * 2 + 1, size=(num_mask,))
+        elif mask_type == "normal":
+            lengths = torch.normal(mask_length, mask_other, size=(num_mask,))
+            lengths = torch.maximum(torch.ones(1), torch.round(lengths)).int()
+        elif mask_type == "poisson":
+            lengths = torch.poisson(mask_length, size=(num_mask,))
+            lengths = torch.round(lengths).int()
+        else:
+            raise Exception(f"unknown mask selection: {mask_type}")
+
+        if sum(lengths) == 0:
+            lengths[0] = min(mask_length, sz - 1)
+
+        if no_overlap:
+            mask_idc = []
+
+            def arrange(s, e, length, keep_length):
+                span_start = torch.randint(s, e - length, size=(1,))
+                mask_idc.extend(span_start + i for i in range(length))
+
+                new_parts = []
+                if span_start - s - min_space >= keep_length:
+                    new_parts.append((s, span_start - min_space + 1))
+                if e - span_start - keep_length - min_space > keep_length:
+                    new_parts.append((span_start + length + min_space, e))
+                return new_parts
+
+            parts = [(0, sz)]
+            min_length = min(lengths)
+            for length in sorted(lengths, reverse=True):
+                lens = torch.tensor([e - s for s, e in parts], dtype=torch.int)
+                lens[lens < length + min_space] = 0
+                l_sum = lens.sum()
+                if l_sum == 0:
+                    break
+                probs = lens / l_sum
+                c = torch.distributions.categorical.Categorical(probs).sample()
+                s, e = parts.pop(c)
+                parts.extend(arrange(s, e, length, min_length))
+            mask_idc = torch.tensor(mask_idc)
+        else:
+            min_len = min(lengths)
+            if sz - min_len <= num_mask:
+                min_len = sz - num_mask - 1
+
+            mask_idc = torch.randperm(sz - min_len)[:num_mask]
+            mask_idc = torch.tensor(
+                [mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]
+            )
+
+        mask_idcs.append(torch.unique(mask_idc[mask_idc < sz]))
+
+    min_len = min([len(m) for m in mask_idcs])
+    for i, mask_idc in enumerate(mask_idcs):
+        if len(mask_idc) > min_len:
+            mask_idc = mask_idc[torch.randperm(len(mask_idc))[:min_len].long()]
+        mask[i, mask_idc] = True
+
+    return mask
+
+
+def _get_padding_mask(input: Tensor, lengths: Tensor) -> Tensor:
+    """Generate the padding mask given the padded input and the lengths Tensors.
+    Args:
+        input (Tensor): The padded Tensor of dimension `[batch, max_len, frequency]`.
+        lengths (Tensor): The lengths Tensor of dimension `[batch,]`.
+
+    Returns:
+        (Tensor): The padding mask.
+    """
+    batch_size, max_len, _ = input.shape
+    mask = torch.arange(max_len, device=lengths.device).expand(batch_size, max_len) >= lengths[:, None]
+    return mask
+
+
+class MaskGenerator(Module):
+    """Generate the masks for masked prediction.
+    Args:
+        encoder_embed_dim (int): The dimension of the transformer embedding output.
+        mask_prob (float): Probability for each token to be chosen as start of the span to be masked.
+            This will be multiplied by number of timesteps divided by length of mask span to mask
+            approximately this percentage of all elements. However due to overlaps, the actual number
+            will be smaller (unless no_overlap is True).
+        mask_selection (str): How to choose the mask length.
+            Options: [``static``, ``uniform``, ``normal``, ``poisson``].
+        mask_other (float): Secondary mask argument (used for more complex distributions).
+        mask_length (int): The lengths of the mask.
+        no_mask_overlap (bool):  Whether to allow masks to overlap.
+        mask_min_space (int):  Minimum space between spans (if no overlap is enabled).
+        mask_channel_prob (float): The probability of replacing a feature with 0.
+        mask_channel_selection (str): How to choose the mask length for channel masking.
+            Options: [``static``, ``uniform``, ``normal``, ``poisson``].
+        mask_channel_other (float): Secondary mask argument for channel masking(used for more complex distributions).
+        mask_channel_length (int): Minimum space between spans (if no overlap is enabled) for channel masking.
+        no_mask_channel_overlap (bool):  Whether to allow channel masks to overlap.
+        mask_channel_min_space (int): Minimum space between spans for channel masking(if no overlap is enabled).
+    """
+
+    def __init__(
+        self,
+        encoder_embed_dim: int,
+        mask_prob: float,
+        mask_selection: str,
+        mask_other: float,
+        mask_length: int,
+        no_mask_overlap: bool,
+        mask_min_space: int,
+        mask_channel_prob: float,
+        mask_channel_selection: str,
+        mask_channel_other: float,
+        mask_channel_length: int,
+        no_mask_channel_overlap: bool,
+        mask_channel_min_space: int,
+    ):
+        super().__init__()
+        self.mask_prob = mask_prob
+        self.mask_selection = mask_selection
+        self.mask_other = mask_other
+        self.mask_length = mask_length
+        self.no_mask_overlap = no_mask_overlap
+        self.mask_min_space = mask_min_space
+        self.mask_channel_prob = mask_channel_prob
+        self.mask_channel_selection = mask_channel_selection
+        self.mask_channel_other = mask_channel_other
+        self.mask_channel_length = mask_channel_length
+        self.no_mask_channel_overlap = no_mask_channel_overlap
+        self.mask_channel_min_space = mask_channel_min_space
+        self.mask_embedding = Parameter(torch.FloatTensor(encoder_embed_dim))
+        torch.nn.init.uniform_(self.mask_embedding)
+
+    def forward(self, x: Tensor, padding_mask: Optional[Tensor]) -> Tensor:
+        """
+        Args:
+            x (Tensor): The encoded representations after feature extraction module.
+            padding_mask (Tensor or None): The padding mask of the same dimension as shape,
+                which will prevent masking padded elements.
+
+        Returns:
+            Tensor: The feature representations after masking.
+            Tensor: The generated mask indices.
+        """
+        B, T, C = x.shape
+        if self.mask_prob > 0:
+            mask_indices = _compute_mask_indices(
+                (B, T),
+                padding_mask,
+                self.mask_prob,
+                self.mask_length,
+                self.mask_selection,
+                self.mask_other,
+                min_masks=2,
+                no_overlap=self.no_mask_overlap,
+                min_space=self.mask_min_space,
+            )
+            mask_indices = mask_indices.to(x.device)
+            # change dtype of mask_embedding to x for mixed-precision training.
+            # see https://github.com/pytorch/audio/issues/2847 for details.
+            x[mask_indices] = self.mask_embedding.to(x.dtype)
+        else:
+            mask_indices = None
+
+        if self.mask_channel_prob > 0:
+            mask_channel_indices = _compute_mask_indices(
+                (B, C),
+                None,
+                self.mask_channel_prob,
+                self.mask_channel_length,
+                self.mask_channel_selection,
+                self.mask_channel_other,
+                no_overlap=self.no_mask_channel_overlap,
+                min_space=self.mask_channel_min_space,
+            )
+            mask_channel_indices = mask_channel_indices.to(x.device).unsqueeze(1).expand(-1, T, -1)
+            x[mask_channel_indices] = 0
+
+        return x, mask_indices
+
+
+def _compute_logits(
+    proj_x: Tensor,
+    target: Tensor,
+    label_embeddings: Parameter,
+) -> Tensor:
+    """Compute the logits of the embeddings.
+    Args:
+        proj_x (Tensor): The projected masked representations of dimension `[batch, frame, final_dim]`.
+        target (Tensor): The target Tensor of dimension `[batch, frame, final_dim]`.
+        label_embeddings (Parameter): The trainable embeddings of target of dimension `[num_class, final_dim]`.
+
+    Returns:
+        (Tensor): The logits of the inputs.
+    """
+    logit_temp = 0.1
+    pos = torch.index_select(label_embeddings, 0, target.long())
+    negs = label_embeddings.unsqueeze(1).expand(-1, proj_x.size(0), -1)
+    neg_is_pos = (pos == negs).all(-1)
+    pos = pos.unsqueeze(0)
+    targets = torch.cat([pos, negs], dim=0)
+
+    logits = torch.cosine_similarity(proj_x.float(), targets.float(), dim=-1).type_as(proj_x)
+    logits /= logit_temp
+    if neg_is_pos.any():
+        logits[1:][neg_is_pos] = float("-inf")
+    logits = logits.transpose(0, 1)  # (num_x, num_cls+1)
+    return logits
+
+
+class LogitGenerator(Module):
+    """Generate the logits of masked and unmasked inputs.
+    Args:
+        encoder_embed_dim (int): The dimension of the transformer embedding output.
+        num_classes (int): The number of classes in the labels.
+        final_dim (int): Project final representations and targets to `final_dim`.
+        skip_masked (bool): If True, skip computing losses over masked frames.
+        skip_nomask (bool): If True, skip computing losses over unmasked frames.
+    """
+
+    def __init__(
+        self,
+        encoder_embed_dim: int,
+        num_classes: int,
+        final_dim: int,
+        skip_masked: bool,
+        skip_nomask: bool,
+    ):
+        super().__init__()
+        self.label_embeddings = Parameter(torch.FloatTensor(num_classes, final_dim))
+        torch.nn.init.uniform_(self.label_embeddings)
+        self.final_proj = torch.nn.Linear(encoder_embed_dim, final_dim)
+        self.skip_masked = skip_masked
+        self.skip_nomask = skip_nomask
+
+    def forward(self, x: Tensor, label: Tensor, mask_m: Tensor, mask_u: Tensor) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            x (Tensor): The feature representation of the last transformer layer.
+            label (Tensor): The label Tensor of dimension `[batch, frame]`.
+            mask_m (Tensor): The masked indices of dimension `[batch, frame]`.
+            mask_u (Tensor): The unmasked indices of dimension `[batch, frame]`.
+
+        Returns:
+            Tensor: The logits of masked frames. Tensor of dimension `[masked_frame, final_dim]`.
+            Tensor: The logits of unmasked frames. Tensor of dimension `[unmasked_frame, final_dim]`.
+        """
+        proj_x = self.final_proj(x)
+        if self.skip_masked:
+            logit_m = None
+        else:
+            proj_x_m = proj_x[mask_m]
+            label_m = label[mask_m]
+            logit_m = _compute_logits(proj_x_m, label_m, self.label_embeddings)
+
+        if self.skip_nomask:
+            logit_u = None
+        else:
+            proj_x_u = proj_x[mask_u]
+            label_u = label[mask_u]
+            logit_u = _compute_logits(proj_x_u, label_u, self.label_embeddings)
+        return logit_m, logit_u
+
+
+class GradMultiply(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale):
+        ctx.scale = scale
+        res = x.new(x)
+        return res
+
+    @staticmethod
+    def backward(ctx, grad):
+        return grad * ctx.scale, None
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/model.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..254122f0eee21906ec50f3d4238a5b3024e74a0a
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/model.py
@@ -0,0 +1,1579 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+from torch import Tensor
+from torch.nn import Module
+
+from . import components
+
+
+class Wav2Vec2Model(Module):
+    """Acoustic model used in *wav2vec 2.0* :cite:`baevski2020wav2vec`.
+
+    Note:
+        To build the model, please use one of the factory functions.
+
+    See Also:
+        * :class:`torchaudio.pipelines.Wav2Vec2Bundle`: Pretrained models (without fine-tuning)
+        * :class:`torchaudio.pipelines.Wav2Vec2ASRBundle`: ASR pipelines with pretrained models.
+
+    Args:
+        feature_extractor (torch.nn.Module):
+            Feature extractor that extracts feature vectors from raw audio Tensor.
+
+        encoder (torch.nn.Module):
+            Encoder that converts the audio features into the sequence of probability
+            distribution (in negative log-likelihood) over labels.
+
+        aux (torch.nn.Module or None, optional):
+            Auxiliary module. If provided, the output from encoder is passed to this module.
+    """  # noqa: E501
+
+    def __init__(
+        self,
+        feature_extractor: Module,
+        encoder: Module,
+        aux: Optional[Module] = None,
+    ):
+        super().__init__()
+        self.feature_extractor = feature_extractor
+        self.encoder = encoder
+        self.aux = aux
+
+    @torch.jit.export
+    def extract_features(
+        self,
+        waveforms: Tensor,
+        lengths: Optional[Tensor] = None,
+        num_layers: Optional[int] = None,
+    ) -> Tuple[List[Tensor], Optional[Tensor]]:
+        """Extract feature vectors from raw waveforms
+
+        This returns the list of outputs from the intermediate layers of
+        transformer block in encoder.
+
+        Args:
+            waveforms (Tensor): Audio tensor of shape `(batch, frames)`.
+            lengths (Tensor or None, optional):
+                Indicates the valid length of each audio in the batch.
+                Shape: `(batch, )`.
+                When the ``waveforms`` contains audios with different durations,
+                by providing ``lengths`` argument, the model will compute
+                the corresponding valid output lengths and apply proper mask in
+                transformer attention layer.
+                If ``None``, it is assumed that the entire audio waveform
+                length is valid.
+            num_layers (int or None, optional):
+                If given, limit the number of intermediate layers to go through.
+                Providing `1` will stop the computation after going through one
+                intermediate layers. If not given, the outputs from all the
+                intermediate layers are returned.
+
+        Returns:
+            (List[Tensor], Optional[Tensor]):
+            List of Tensors
+                Features from requested layers.
+                Each Tensor is of shape: `(batch, time frame, feature dimension)`
+            Tensor or None
+                If ``lengths`` argument was provided, a Tensor of shape `(batch, )`
+                is returned.
+                It indicates the valid length in time axis of each feature Tensor.
+        """
+        x, lengths = self.feature_extractor(waveforms, lengths)
+        x = self.encoder.extract_features(x, lengths, num_layers)
+        return x, lengths
+
+    def forward(
+        self,
+        waveforms: Tensor,
+        lengths: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """Compute the sequence of probability distribution over labels.
+
+        Args:
+            waveforms (Tensor): Audio tensor of shape `(batch, frames)`.
+            lengths (Tensor or None, optional):
+                Indicates the valid length of each audio in the batch.
+                Shape: `(batch, )`.
+                When the ``waveforms`` contains audios with different durations,
+                by providing ``lengths`` argument, the model will compute
+                the corresponding valid output lengths and apply proper mask in
+                transformer attention layer.
+                If ``None``, it is assumed that all the audio in ``waveforms``
+                have valid length. Default: ``None``.
+
+        Returns:
+            (Tensor, Optional[Tensor]):
+            Tensor
+                The sequences of probability distribution (in logit) over labels.
+                Shape: `(batch, frames, num labels)`.
+            Tensor or None
+                If ``lengths`` argument was provided, a Tensor of shape `(batch, )`
+                is returned.
+                It indicates the valid length in time axis of the output Tensor.
+        """
+        x, lengths = self.feature_extractor(waveforms, lengths)
+        x = self.encoder(x, lengths)
+        if self.aux is not None:
+            x = self.aux(x)
+        return x, lengths
+
+
+class HuBERTPretrainModel(Module):
+    """HuBERTPretrainModel()
+
+    HuBERT model used for pretraining in *HuBERT* :cite:`hsu2021hubert`.
+
+    Note:
+        To build the model, please use one of the factory functions.
+
+    See Also:
+        `HuBERT Pre-training and Fine-tuning Recipes
+        <https://github.com/pytorch/audio/tree/main/examples/hubert>`__
+
+    Args:
+        wav2vec2 (Wav2Vec2Model):
+            Wav2Vec2 encoder that generates the transformer outputs.
+
+        mask_generator (torch.nn.Module):
+            Mask generator that generates the mask for masked prediction during the training.
+
+        logit_generator (torch.nn.Module):
+            Logit generator that predicts the logits of the masked and unmasked inputs.
+
+        feature_grad_mult (float or None):
+            The factor to scale the convolutional feature extraction layer gradients by.
+            If ``None``, the gradients of feature extraction layers are not affected.
+            The scale factor will not affect the forward pass.
+    """
+
+    def __init__(
+        self,
+        wav2vec2: Wav2Vec2Model,
+        mask_generator: Module,
+        logit_generator: Module,
+        feature_grad_mult: Optional[float],
+    ):
+        super().__init__()
+        self.wav2vec2 = wav2vec2
+        self.mask_generator = mask_generator
+        self.logit_generator = logit_generator
+        if feature_grad_mult is not None and not 0.0 < feature_grad_mult < 1.0:
+            raise ValueError(
+                f"The value of `feature_grad_mult` must be ``None``or between (0, 1). Found {feature_grad_mult}"
+            )
+        self.feature_grad_mult = feature_grad_mult
+
+    def forward(
+        self,
+        waveforms: Tensor,
+        labels: Tensor,
+        audio_lengths: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """Compute the sequence of probability distribution over labels.
+
+        Args:
+            waveforms (Tensor): Audio tensor of dimension `[batch, frames]`.
+            labels (Tensor): Label for pre-training. A Tensor of dimension `[batch, frames]`.
+            audio_lengths (Tensor or None, optional):
+                Indicates the valid length of each audio in the batch.
+                Shape: `[batch, ]`.
+                When the ``waveforms`` contains audios with different durations,
+                by providing ``lengths`` argument, the model will compute
+                the corresponding valid output lengths and apply proper mask in
+                transformer attention layer.
+                If ``None``, it is assumed that all the audio in ``waveforms``
+                have valid length. Default: ``None``.
+
+        Returns:
+            (Tensor, Tensor, Tensor):
+            Tensor
+                The masked sequences of probability distribution (in logit).
+                Shape: `(masked_frames, num labels)`.
+            Tensor
+                The unmasked sequence of probability distribution (in logit).
+                Shape: `(unmasked_frames, num labels)`.
+            Tensor
+                The feature mean value for additional penalty loss.
+                Shape: `(1,)`.
+        """
+        x, lengths = self.wav2vec2.feature_extractor(waveforms, audio_lengths)
+        if self.feature_grad_mult is not None and self.feature_grad_mult < 1.0:
+            x = components.GradMultiply.apply(x, self.feature_grad_mult)
+        features_pen = x.float().pow(2).mean()
+        if lengths is not None:
+            padding_mask = components._get_padding_mask(x, lengths)
+        else:
+            padding_mask = None
+        x, attention_mask = self.wav2vec2.encoder._preprocess(x, lengths)
+        x, mask = self.mask_generator(x, padding_mask)
+        x = self.wav2vec2.encoder.transformer(x, attention_mask=attention_mask)
+        if x.shape[1] != labels.shape[1]:
+            raise ValueError("The length of label must match that of HuBERT model output")
+        if padding_mask is not None:
+            mask_m = torch.logical_and(~padding_mask, mask)
+            mask_u = torch.logical_and(~padding_mask, ~mask_m)
+        else:
+            mask_m = mask
+            mask_u = ~mask_m
+
+        logit_m, logit_u = self.logit_generator(x, labels, mask_m, mask_u)
+
+        return logit_m, logit_u, features_pen
+
+
+def wav2vec2_model(
+    extractor_mode: str,
+    extractor_conv_layer_config: Optional[List[Tuple[int, int, int]]],
+    extractor_conv_bias: bool,
+    encoder_embed_dim: int,
+    encoder_projection_dropout: float,
+    encoder_pos_conv_kernel: int,
+    encoder_pos_conv_groups: int,
+    encoder_num_layers: int,
+    encoder_num_heads: int,
+    encoder_attention_dropout: float,
+    encoder_ff_interm_features: int,
+    encoder_ff_interm_dropout: float,
+    encoder_dropout: float,
+    encoder_layer_norm_first: bool,
+    encoder_layer_drop: float,
+    aux_num_out: Optional[int],
+) -> Wav2Vec2Model:
+    """Builds custom :class:`~torchaudio.models.Wav2Vec2Model`.
+
+    Note:
+        The "feature extractor" below corresponds to
+        `ConvFeatureExtractionModel <https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L736>`__
+        in the original ``fairseq`` implementation.
+        This is referred as "(convolutional) feature encoder" in the *wav2vec 2.0*
+        :cite:`baevski2020wav2vec` paper.
+
+        The "encoder" below corresponds to `TransformerEncoder <https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L817>`__,
+        and this is referred as "Transformer" in the paper.
+
+    Args:
+        extractor_mode (str): Operation mode of feature extractor.
+            Valid values are ``"group_norm"`` or ``"layer_norm"``.
+            If ``"group_norm"``, then a single normalization is applied
+            in the first convolution block. Otherwise, all the convolution
+            blocks will have layer normalization.
+
+            This option corresponds to ``extractor_mode`` from ``fairseq``.
+        extractor_conv_layer_config (list of integer tuples or None):
+            Configuration of convolution layers in feature extractor.
+            List of convolution configuration,
+            i.e. ``[(output_channel, kernel_size, stride), ...]``
+
+            If ``None`` is provided, then the following default value is used.
+
+            .. code-block:: python
+
+               [
+                 (512, 10, 5),
+                 (512, 3, 2),
+                 (512, 3, 2),
+                 (512, 3, 2),
+                 (512, 3, 2),
+                 (512, 2, 2),
+                 (512, 2, 2),
+               ]
+
+            This option corresponds to ``conv_feature_layers`` from ``fairseq``.
+
+        extractor_conv_bias (bool):
+            Whether to include bias term to each convolution operation.
+
+            This option corresponds to ``conv_bias`` from ``fairseq``.
+
+        encoder_embed_dim (int):
+            The dimension of embedding in encoder.
+
+            This option corresponds to ``encoder_embed_dim`` from ``fairseq``.
+
+        encoder_projection_dropout (float):
+            The dropout probability applied after the input feature is projected
+            to ``encoder_embed_dim``.
+
+            This option corresponds to ``dropout_input`` from ``fairseq``.
+
+        encoder_pos_conv_kernel (int):
+            The kernel size of convolutional positional embeddings.
+
+            This option corresponds to ``conv_pos`` from ``fairseq``.
+
+        encoder_pos_conv_groups (int):
+            The number of groups of convolutional positional embeddings.
+
+            This option corresponds to ``conv_pos_groups`` from ``fairseq``.
+
+        encoder_num_layers (int):
+            The number of self attention layers in transformer block.
+
+            This option corresponds to ``encoder_layers`` from ``fairseq``.
+
+        encoder_num_heads (int):
+            The number of heads in self attention layers.
+
+            This option corresponds to ``encoder_attention_heads`` from ``fairseq``.
+
+        encoder_attention_dropout (float):
+            The dropout probability applied after softmax in self-attention layer.
+
+            This option corresponds to ``attention_dropout`` from ``fairseq``.
+
+        encoder_ff_interm_features (int):
+            The dimension of hidden features in feed forward layer.
+
+            This option corresponds to ``encoder_ffn_embed_dim`` from ``fairseq``.
+
+        encoder_ff_interm_dropout (float):
+            The dropout probability applied in feedforward layer.
+
+            This option correspinds to ``activation_dropout`` from ``fairseq``.
+
+        encoder_dropout (float):
+            The dropout probability applied at the end of feed forward layer.
+
+            This option corresponds to ``dropout`` from ``fairseq``.
+
+        encoder_layer_norm_first (bool):
+            Control the order of layer norm in transformer layer and each encoder layer.
+            If True, in transformer layer, layer norm is applied before features are fed
+            to encoder layers. In encoder layer, two layer norms are applied before and after
+            self attention.
+            If False, in transformer layer, layer norm is applied after features are fed
+            to encoder layers. In encoder layer, two layer norms are applied after self
+            attention, before and after feed forward.
+
+            This option corresponds to ``layer_norm_first`` from ``fairseq``.
+
+        encoder_layer_drop (float):
+            Probability to drop each encoder layer during training.
+
+            This option corresponds to ``layerdrop`` from ``fairseq``.
+
+        aux_num_out (int or None):
+            When provided, attach an extra linear layer on top of encoder, which can be
+            used for fine-tuning.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    if extractor_conv_layer_config is None:
+        extractor_conv_layer_config = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
+
+    feature_extractor = components._get_feature_extractor(
+        extractor_mode, extractor_conv_layer_config, extractor_conv_bias
+    )
+    encoder = components._get_encoder(
+        in_features=extractor_conv_layer_config[-1][0],
+        embed_dim=encoder_embed_dim,
+        dropout_input=encoder_projection_dropout,
+        pos_conv_kernel=encoder_pos_conv_kernel,
+        pos_conv_groups=encoder_pos_conv_groups,
+        num_layers=encoder_num_layers,
+        num_heads=encoder_num_heads,
+        attention_dropout=encoder_attention_dropout,
+        ff_interm_features=encoder_ff_interm_features,
+        ff_interm_dropout=encoder_ff_interm_dropout,
+        dropout=encoder_dropout,
+        layer_norm_first=encoder_layer_norm_first,
+        layer_drop=encoder_layer_drop,
+    )
+    aux = None
+    if aux_num_out is not None:
+        aux = torch.nn.Linear(in_features=encoder_embed_dim, out_features=aux_num_out)
+    return Wav2Vec2Model(feature_extractor, encoder, aux)
+
+
+def wav2vec2_base(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.1,
+    encoder_ff_interm_dropout: float = 0.1,
+    encoder_dropout: float = 0.1,
+    encoder_layer_drop: float = 0.1,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "base" :class:`~torchaudio.models.Wav2Vec2Model` from *wav2vec 2.0* :cite:`baevski2020wav2vec`
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`wav2vec2_model`.
+        aux_num_out (int or None, optional):
+            See :py:func:`wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    return wav2vec2_model(
+        extractor_mode="group_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=768,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=12,
+        encoder_num_heads=12,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=3072,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=False,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def wav2vec2_large(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.1,
+    encoder_ff_interm_dropout: float = 0.1,
+    encoder_dropout: float = 0.1,
+    encoder_layer_drop: float = 0.1,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "large" :class:`~torchaudio.models.Wav2Vec2Model` from *wav2vec 2.0* :cite:`baevski2020wav2vec`
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`wav2vec2_model`.
+        aux_num_out (int or None, optional):
+            See :py:func:`wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    return wav2vec2_model(
+        extractor_mode="group_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=1024,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=24,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=4096,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=False,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def wav2vec2_large_lv60k(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.1,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.1,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "large lv-60k" :class:`~torchaudio.models.Wav2Vec2Model` from *wav2vec 2.0* :cite:`baevski2020wav2vec`
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`wav2vec2_model`.
+        aux_num_out (int or None, optional):
+            See :py:func:`wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    return wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=True,
+        encoder_embed_dim=1024,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=24,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=4096,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def hubert_base(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.1,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.1,
+    encoder_layer_drop: float = 0.05,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "base" :class:`HuBERT <torchaudio.models.Wav2Vec2Model>` from *HuBERT* :cite:`hsu2021hubert`
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`wav2vec2_model`.
+        aux_num_out (int or None, optional):
+            See :py:func:`wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    return wav2vec2_model(
+        extractor_mode="group_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=768,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=12,
+        encoder_num_heads=12,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=3072,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=False,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def hubert_large(
+    encoder_projection_dropout: float = 0.0,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "large" :class:`HuBERT <torchaudio.models.Wav2Vec2Model>` from *HuBERT* :cite:`hsu2021hubert`
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`wav2vec2_model`.
+        aux_num_out (int or None, optional):
+            See :py:func:`wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    return wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=1024,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=24,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=4096,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def hubert_xlarge(
+    encoder_projection_dropout: float = 0.0,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "extra large" :class:`HuBERT <torchaudio.models.Wav2Vec2Model>` from *HuBERT* :cite:`hsu2021hubert`
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`wav2vec2_model`.
+        aux_num_out (int or None, optional):
+            See :py:func:`wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """  # noqa: E501
+    return wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=1280,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=48,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=5120,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def _init_hubert_pretrain_model(module):
+    if isinstance(module, components.ConvLayerBlock):
+        torch.nn.init.kaiming_normal_(module.conv.weight)
+    elif isinstance(module, components.ConvolutionalPositionalEmbedding):
+        # normalize the weight to normal distribution.
+        std = math.sqrt(4.0 / (module.embed_dim * module.kernel_size))
+        torch.nn.init.normal_(module.conv.weight, mean=0.0, std=std)
+        torch.nn.init.constant_(module.conv.bias, 0.0)
+    elif isinstance(module, components.SelfAttention):
+        # normalize the query, key, value, and out_proj parameters in self attention module.
+        torch.nn.init.xavier_uniform_(module.k_proj.weight, gain=1 / math.sqrt(2))
+        torch.nn.init.xavier_uniform_(module.v_proj.weight, gain=1 / math.sqrt(2))
+        torch.nn.init.xavier_uniform_(module.q_proj.weight, gain=1 / math.sqrt(2))
+        torch.nn.init.xavier_uniform_(module.out_proj.weight)
+        torch.nn.init.constant_(module.out_proj.bias, 0.0)
+    elif isinstance(module, components.Transformer):
+        module.apply(components._init_transformer_params)
+    else:
+        pass
+
+
+def hubert_pretrain_model(
+    extractor_mode: str,
+    extractor_conv_layer_config: Optional[List[Tuple[int, int, int]]],
+    extractor_conv_bias: bool,
+    encoder_embed_dim: int,
+    encoder_projection_dropout: float,
+    encoder_pos_conv_kernel: int,
+    encoder_pos_conv_groups: int,
+    encoder_num_layers: int,
+    encoder_num_heads: int,
+    encoder_attention_dropout: float,
+    encoder_ff_interm_features: int,
+    encoder_ff_interm_dropout: float,
+    encoder_dropout: float,
+    encoder_layer_norm_first: bool,
+    encoder_layer_drop: float,
+    mask_prob: float,
+    mask_selection: str,
+    mask_other: float,
+    mask_length: int,
+    no_mask_overlap: bool,
+    mask_min_space: int,
+    mask_channel_prob: float,
+    mask_channel_selection: str,
+    mask_channel_other: float,
+    mask_channel_length: int,
+    no_mask_channel_overlap: bool,
+    mask_channel_min_space: int,
+    skip_masked: bool,
+    skip_nomask: bool,
+    num_classes: int,
+    final_dim: int,
+    feature_grad_mult: Optional[float],
+) -> HuBERTPretrainModel:
+    """Builds custom :class:`HuBERTPretrainModel` for training from scratch
+
+    Note:
+        The "feature extractor" below corresponds to
+        `ConvFeatureExtractionModel <https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L736>`__
+        in the original ``fairseq`` implementation.
+        This is referred as "(convolutional) feature encoder" in the *wav2vec 2.0*
+        :cite:`baevski2020wav2vec` paper.
+
+        The "encoder" below corresponds to `TransformerEncoder <https://github.com/pytorch/fairseq/blob/dd3bd3c0497ae9a7ae7364404a6b0a4c501780b3/fairseq/models/wav2vec/wav2vec2.py#L817>`__,
+        and this is referred as "Transformer" in the paper.
+
+    Args:
+        extractor_mode (str): Operation mode of feature extractor.
+            Valid values are ``"group_norm"`` or ``"layer_norm"``.
+            If ``"group_norm"``, then a single normalization is applied
+            in the first convolution block. Otherwise, all the convolution
+            blocks will have layer normalization.
+
+            This option corresponds to ``extractor_mode`` from ``fairseq``.
+
+        extractor_conv_layer_config (list of integer tuples or None):
+            Configuration of convolution layers in feature extractor.
+            List of convolution configuration,
+            i.e. ``[(output_channel, kernel_size, stride), ...]``
+
+            If ``None`` is provided, then the following default value is used.
+
+            .. code-block:: python
+
+               [
+                 (512, 10, 5),
+                 (512, 3, 2),
+                 (512, 3, 2),
+                 (512, 3, 2),
+                 (512, 3, 2),
+                 (512, 2, 2),
+                 (512, 2, 2),
+               ]
+
+            This option corresponds to ``conv_feature_layers`` from ``fairseq``.
+
+        extractor_conv_bias (bool):
+            Whether to include bias term to each convolution operation.
+
+            This option corresponds to ``conv_bias`` from ``fairseq``.
+
+        encoder_embed_dim (int):
+            The dimension of embedding in encoder.
+
+            This option corresponds to ``encoder_embed_dim`` from ``fairseq``.
+
+        encoder_projection_dropout (float):
+            The dropout probability applied after the input feature is projected
+            to ``encoder_embed_dim``.
+
+            This option corresponds to ``dropout_input`` from ``fairseq``.
+
+        encoder_pos_conv_kernel (int):
+            The kernel size of convolutional positional embeddings.
+
+            This option corresponds to ``conv_pos`` from ``fairseq``.
+
+        encoder_pos_conv_groups (int):
+            The number of groups of convolutional positional embeddings.
+
+            This option corresponds to ``conv_pos_groups`` from ``fairseq``.
+
+        encoder_num_layers (int):
+            The number of self attention layers in transformer block.
+
+            This option corresponds to ``encoder_layers`` from ``fairseq``.
+
+        encoder_num_heads (int):
+            The number of heads in self attention layers.
+
+            This option corresponds to ``encoder_attention_heads`` from ``fairseq``.
+
+        encoder_attention_dropout (float):
+            The dropout probability applied after softmax in self-attention layer.
+
+            This option corresponds to ``attention_dropout`` from ``fairseq``.
+
+        encoder_ff_interm_features (int):
+            The dimension of hidden features in feed forward layer.
+
+            This option corresponds to ``encoder_ffn_embed_dim`` from ``fairseq``.
+
+        encoder_ff_interm_dropout (float):
+            The dropout probability applied in feedforward layer.
+
+            This option correspinds to ``activation_dropout`` from ``fairseq``.
+
+        encoder_dropout (float):
+            The dropout probability applied at the end of feed forward layer.
+
+            This option corresponds to ``dropout`` from ``fairseq``.
+
+        encoder_layer_norm_first (bool):
+            Control the order of layer norm in transformer layer and each encoder layer.
+            If True, in transformer layer, layer norm is applied before features are fed
+            to encoder layers. In encoder layer, two layer norms are applied before and after
+            self attention.
+            If False, in transformer layer, layer norm is applied after features are fed
+            to encoder layers. In encoder layer, two layer norms are applied after self
+            attention, before and after feed forward.
+
+            This option corresponds to ``layer_norm_first`` from ``fairseq``.
+
+        encoder_layer_drop (float):
+            Probability to drop each encoder layer during training.
+
+            This option corresponds to ``layerdrop`` from ``fairseq``.
+
+        mask_prob (float):
+            Probability for each token to be chosen as start of the span to be masked. this will be multiplied by
+            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
+            However due to overlaps, the actual number will be smaller (unless no_overlap is True).
+
+            This option corresponds to ``mask_prob`` from ``fairseq``.
+
+        mask_selection (str):
+            How to choose the mask length. Options: [``static``, ``uniform``, ``normal``, ``poisson``].
+
+            This option corresponds to ``mask_selection`` from ``fairseq``.
+
+        mask_other (float):
+            Secondary mask argument (used for more complex distributions).
+
+            This option corresponds to ``mask_other`` from ``fairseq``.
+
+        mask_length (int):
+            The lengths of the mask.
+
+            This option corresponds to ``mask_length`` from ``fairseq``.
+
+        no_mask_overlap (bool):
+            Whether to allow masks to overlap.
+
+            This option corresponds to ``no_mask_overlap`` from ``fairseq``.
+
+        mask_min_space (int):
+            Minimum space between spans (if no overlap is enabled).
+
+            This option corresponds to ``mask_min_space`` from ``fairseq``.
+
+        mask_channel_prob: (float):
+            The probability of replacing a feature with 0.
+
+            This option corresponds to ``mask_channel_prob`` from ``fairseq``.
+
+        mask_channel_selection (str):
+            How to choose the mask length for channel masking. Options: [``static``, ``uniform``, ``normal``, ``poisson``].
+
+            This option corresponds to ``mask_channel_selection`` from ``fairseq``.
+
+        mask_channel_other (float):
+            Secondary mask argument for channel masking(used for more complex distributions).
+
+            This option corresponds to ``mask_channel_other`` from ``fairseq``.
+
+        mask_channel_length (int):
+            Minimum space between spans (if no overlap is enabled) for channel masking.
+
+            This option corresponds to ``mask_channel_length`` from ``fairseq``.
+
+        no_mask_channel_overlap (bool):
+            Whether to allow channel masks to overlap.
+
+            This option corresponds to ``no_mask_channel_overlap`` from ``fairseq``.
+
+        mask_channel_min_space (int):
+            Minimum space between spans for channel masking(if no overlap is enabled).
+
+            This option corresponds to ``mask_channel_min_space`` from ``fairseq``.
+
+        skip_masked (bool):
+            If True, skip computing losses over masked frames.
+
+            This option corresponds to ``skip_masked`` from ``fairseq``.
+
+        skip_nomask (bool):
+            If True, skip computing losses over unmasked frames.
+
+            This option corresponds to ``skip_nomask`` from ``fairseq``.
+
+        num_classes (int):
+            The number of classes in the labels.
+
+        final_dim (int):
+            Project final representations and targets to `final_dim`.
+
+            This option corresponds to ``final_dim`` from ``fairseq``.
+
+        feature_grad_mult (float or None):
+            The factor to scale the convolutional feature extraction layer gradients by.
+            The scale factor will not affect the forward pass.
+
+            This option corresponds to ``feature_grad_mult`` from ``fairseq``.
+
+    Returns:
+        HuBERTPretrainModel:
+            The resulting model.
+    """  # noqa: E501
+    if extractor_conv_layer_config is None:
+        extractor_conv_layer_config = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
+
+    feature_extractor = components._get_feature_extractor(
+        extractor_mode, extractor_conv_layer_config, extractor_conv_bias
+    )
+    encoder = components._get_encoder(
+        in_features=extractor_conv_layer_config[-1][0],
+        embed_dim=encoder_embed_dim,
+        dropout_input=encoder_projection_dropout,
+        pos_conv_kernel=encoder_pos_conv_kernel,
+        pos_conv_groups=encoder_pos_conv_groups,
+        num_layers=encoder_num_layers,
+        num_heads=encoder_num_heads,
+        attention_dropout=encoder_attention_dropout,
+        ff_interm_features=encoder_ff_interm_features,
+        ff_interm_dropout=encoder_ff_interm_dropout,
+        dropout=encoder_dropout,
+        layer_norm_first=encoder_layer_norm_first,
+        layer_drop=encoder_layer_drop,
+    )
+    wav2vec2 = Wav2Vec2Model(feature_extractor, encoder)
+    mask_generator = components.MaskGenerator(
+        encoder_embed_dim,
+        mask_prob,
+        mask_selection,
+        mask_other,
+        mask_length,
+        no_mask_overlap,
+        mask_min_space,
+        mask_channel_prob,
+        mask_channel_selection,
+        mask_channel_other,
+        mask_channel_length,
+        no_mask_channel_overlap,
+        mask_channel_min_space,
+    )
+    logit_generator = components.LogitGenerator(
+        encoder_embed_dim,
+        num_classes,
+        final_dim,
+        skip_masked,
+        skip_nomask,
+    )
+    model = HuBERTPretrainModel(
+        wav2vec2=wav2vec2,
+        mask_generator=mask_generator,
+        logit_generator=logit_generator,
+        feature_grad_mult=feature_grad_mult,
+    )
+    # initialize the model for pre-training
+    model.apply(_init_hubert_pretrain_model)
+    return model
+
+
+def hubert_pretrain_base(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.1,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.1,
+    encoder_layer_drop: float = 0.05,
+    mask_prob: float = 0.8,
+    mask_channel_prob: float = 0.0,
+    mask_channel_length: int = 10,
+    feature_grad_mult: Optional[float] = 0.1,
+    num_classes: int = 100,
+) -> HuBERTPretrainModel:
+    """Builds "base" :class:`HuBERTPretrainModel` from *HuBERT* :cite:`hsu2021hubert` for pretraining.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_layer_drop (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_prob (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_channel_prob (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_channel_length (int):
+            See :py:func:`hubert_pretrain_model`.
+        feature_grad_mult (float or None):
+            See :py:func:`hubert_pretrain_model`.
+        num_classes (int, optional):
+            See :py:func:`hubert_pretrain_model`.
+
+    Returns:
+        HuBERTPretrainModel:
+            The resulting model.
+    """  # noqa: E501
+    return hubert_pretrain_model(
+        extractor_mode="group_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=768,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=12,
+        encoder_num_heads=12,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=3072,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=False,
+        encoder_layer_drop=encoder_layer_drop,
+        mask_prob=mask_prob,
+        mask_selection="static",
+        mask_other=0.0,
+        mask_length=10,
+        no_mask_overlap=False,
+        mask_min_space=1,
+        mask_channel_prob=mask_channel_prob,
+        mask_channel_selection="static",
+        mask_channel_other=0.0,
+        mask_channel_length=mask_channel_length,
+        no_mask_channel_overlap=False,
+        mask_channel_min_space=1,
+        skip_masked=False,
+        skip_nomask=False,
+        num_classes=num_classes,
+        final_dim=256,
+        feature_grad_mult=feature_grad_mult,
+    )
+
+
+def hubert_pretrain_large(
+    encoder_projection_dropout: float = 0.0,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    mask_prob: float = 0.8,
+    mask_channel_prob: float = 0.0,
+    mask_channel_length: int = 10,
+    feature_grad_mult: Optional[float] = None,
+) -> HuBERTPretrainModel:
+    """Builds "large" :class:`HuBERTPretrainModel` from *HuBERT* :cite:`hsu2021hubert` for pretraining.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_layer_drop (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_prob (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_channel_prob (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_channel_length (int):
+            See :py:func:`hubert_pretrain_model`.
+        feature_grad_mult (float or None):
+            See :py:func:`hubert_pretrain_model`.
+
+    Returns:
+        HuBERTPretrainModel:
+            The resulting model.
+    """  # noqa: E501
+    return hubert_pretrain_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=1024,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=24,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=4096,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        mask_prob=mask_prob,
+        mask_selection="static",
+        mask_other=0.0,
+        mask_length=10,
+        no_mask_overlap=False,
+        mask_min_space=1,
+        mask_channel_prob=mask_channel_prob,
+        mask_channel_selection="static",
+        mask_channel_other=0.0,
+        mask_channel_length=mask_channel_length,
+        no_mask_channel_overlap=False,
+        mask_channel_min_space=1,
+        skip_masked=False,
+        skip_nomask=False,
+        num_classes=500,
+        final_dim=768,
+        feature_grad_mult=feature_grad_mult,
+    )
+
+
+def hubert_pretrain_xlarge(
+    encoder_projection_dropout: float = 0.0,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    mask_prob: float = 0.8,
+    mask_channel_prob: float = 0.0,
+    mask_channel_length: int = 10,
+    feature_grad_mult: Optional[float] = None,
+) -> HuBERTPretrainModel:
+    """Builds "extra large" :class:`HuBERTPretrainModel` from *HuBERT* :cite:`hsu2021hubert` for pretraining.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_dropout (float):
+            See :py:func:`hubert_pretrain_model`.
+        encoder_layer_drop (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_prob (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_channel_prob (float):
+            See :py:func:`hubert_pretrain_model`.
+        mask_channel_length (int):
+            See :py:func:`hubert_pretrain_model`.
+        feature_grad_mult (float or None):
+            See :py:func:`hubert_pretrain_model`.
+
+    Returns:
+        HuBERTPretrainModel:
+            The resulting model.
+    """  # noqa: E501
+    return hubert_pretrain_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=1280,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=48,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=5120,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        mask_prob=mask_prob,
+        mask_selection="static",
+        mask_other=0.0,
+        mask_length=10,
+        no_mask_overlap=False,
+        mask_min_space=1,
+        mask_channel_prob=mask_channel_prob,
+        mask_channel_selection="static",
+        mask_channel_other=0.0,
+        mask_channel_length=mask_channel_length,
+        no_mask_channel_overlap=False,
+        mask_channel_min_space=1,
+        skip_masked=False,
+        skip_nomask=False,
+        num_classes=500,
+        final_dim=1024,
+        feature_grad_mult=feature_grad_mult,
+    )
+
+
+def wavlm_model(
+    extractor_mode: str,
+    extractor_conv_layer_config: Optional[List[Tuple[int, int, int]]],
+    extractor_conv_bias: bool,
+    encoder_embed_dim: int,
+    encoder_projection_dropout: float,
+    encoder_pos_conv_kernel: int,
+    encoder_pos_conv_groups: int,
+    encoder_num_layers: int,
+    encoder_num_heads: int,
+    encoder_num_buckets: int,
+    encoder_max_distance: int,
+    encoder_attention_dropout: float,
+    encoder_ff_interm_features: int,
+    encoder_ff_interm_dropout: float,
+    encoder_dropout: float,
+    encoder_layer_norm_first: bool,
+    encoder_layer_drop: float,
+    aux_num_out: Optional[int],
+) -> Wav2Vec2Model:
+    """Builds custom WaveLM model :cite:`chen2022wavlm`. The architecture is compatible
+    with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output object is
+    :class:`~torchaudio.models.Wav2Vec2Model`. Most of the arguments have the same meaning
+    as in :py:func:`~torchaudio.models.wav2vec2_model` so please refer there for documentation.
+
+    Args:
+        extractor_mode (str): Operation mode of feature extractor.
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        extractor_conv_layer_config (list of integer tuples or None):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        extractor_conv_bias (bool):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_embed_dim (int):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_projection_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_pos_conv_kernel (int):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_pos_conv_groups (int):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_num_layers (int):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_num_heads (int):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_num_buckets (int):
+            Number of buckets for relative position embedding.
+        encoder_max_distance (int):
+            Maximum distance for relative position embedding.
+
+        encoder_attention_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_ff_interm_features (int):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_ff_interm_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_layer_norm_first (bool):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        encoder_layer_drop (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+        aux_num_out (int or None):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """
+    if extractor_conv_layer_config is None:
+        extractor_conv_layer_config = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
+
+    feature_extractor = components._get_feature_extractor(
+        extractor_mode, extractor_conv_layer_config, extractor_conv_bias
+    )
+    encoder = components._get_wavlm_encoder(
+        in_features=extractor_conv_layer_config[-1][0],
+        embed_dim=encoder_embed_dim,
+        dropout_input=encoder_projection_dropout,
+        pos_conv_kernel=encoder_pos_conv_kernel,
+        pos_conv_groups=encoder_pos_conv_groups,
+        num_layers=encoder_num_layers,
+        num_heads=encoder_num_heads,
+        num_buckets=encoder_num_buckets,
+        max_distance=encoder_max_distance,
+        attention_dropout=encoder_attention_dropout,
+        ff_interm_features=encoder_ff_interm_features,
+        ff_interm_dropout=encoder_ff_interm_dropout,
+        dropout=encoder_dropout,
+        layer_norm_first=encoder_layer_norm_first,
+        layer_drop=encoder_layer_drop,
+    )
+    aux = None
+    if aux_num_out is not None:
+        aux = torch.nn.Linear(in_features=encoder_embed_dim, out_features=aux_num_out)
+    return Wav2Vec2Model(feature_extractor, encoder, aux)
+
+
+def wavlm_base(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.1,
+    encoder_ff_interm_dropout: float = 0.1,
+    encoder_dropout: float = 0.1,
+    encoder_layer_drop: float = 0.1,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "base" WaveLM model :cite:`chen2022wavlm`. The architecture is compatible
+    with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is
+    :class:`~torchaudio.models.Wav2Vec2Model`.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        aux_num_out (int, optional):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """
+    return wavlm_model(
+        extractor_mode="group_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=768,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=12,
+        encoder_num_heads=12,
+        encoder_num_buckets=320,
+        encoder_max_distance=800,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=3072,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=False,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def wavlm_large(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.1,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.1,
+    encoder_layer_drop: float = 0.1,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds "large" WaveLM model :cite:`chen2022wavlm`. The architecture is compatible
+    with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is
+    :class:`~torchaudio.models.Wav2Vec2Model`.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        aux_num_out (int, optional):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """
+    return wavlm_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=False,
+        encoder_embed_dim=1024,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=24,
+        encoder_num_heads=16,
+        encoder_num_buckets=320,
+        encoder_max_distance=800,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=4096,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def wav2vec2_xlsr_300m(
+    encoder_projection_dropout: float = 0.0,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds XLS-R model :cite:`babu2021xls` with 300 millions of parameters. The architecture is compatible
+    with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is
+    :class:`~torchaudio.models.Wav2Vec2Model`.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        aux_num_out (int, optional):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """
+    return wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=True,
+        encoder_embed_dim=1024,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=24,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=4096,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def wav2vec2_xlsr_1b(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds XLS-R model :cite:`babu2021xls` with 1 billion of parameters. The architecture is compatible
+    with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is
+    :class:`~torchaudio.models.Wav2Vec2Model`.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        aux_num_out (int, optional):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """
+    return wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=True,
+        encoder_embed_dim=1280,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=48,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=5120,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
+
+
+def wav2vec2_xlsr_2b(
+    encoder_projection_dropout: float = 0.1,
+    encoder_attention_dropout: float = 0.0,
+    encoder_ff_interm_dropout: float = 0.0,
+    encoder_dropout: float = 0.0,
+    encoder_layer_drop: float = 0.0,
+    aux_num_out: Optional[int] = None,
+) -> Wav2Vec2Model:
+    """Builds XLS-R model :cite:`babu2021xls` with 2 billions of parameters. The architecture is compatible
+    with Wav2Vec2 model :cite:`baevski2020wav2vec`, and so the output class is
+    :class:`~torchaudio.models.Wav2Vec2Model`.
+
+    Args:
+        encoder_projection_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_attention_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_ff_interm_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_dropout (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        encoder_layer_drop (float):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+        aux_num_out (int, optional):
+            See :py:func:`~torchaudio.models.wav2vec2_model`.
+
+    Returns:
+        Wav2Vec2Model:
+            The resulting model.
+    """
+    return wav2vec2_model(
+        extractor_mode="layer_norm",
+        extractor_conv_layer_config=None,
+        extractor_conv_bias=True,
+        encoder_embed_dim=1920,
+        encoder_projection_dropout=encoder_projection_dropout,
+        encoder_pos_conv_kernel=128,
+        encoder_pos_conv_groups=16,
+        encoder_num_layers=48,
+        encoder_num_heads=16,
+        encoder_attention_dropout=encoder_attention_dropout,
+        encoder_ff_interm_features=7680,
+        encoder_ff_interm_dropout=encoder_ff_interm_dropout,
+        encoder_dropout=encoder_dropout,
+        encoder_layer_norm_first=True,
+        encoder_layer_drop=encoder_layer_drop,
+        aux_num_out=aux_num_out,
+    )
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__init__.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0457b5dd707f7216adc3ea919ba8e257d86f4f71
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__init__.py
@@ -0,0 +1,7 @@
+from .import_fairseq import import_fairseq_model
+from .import_huggingface import import_huggingface_model
+
+__all__ = [
+    "import_huggingface_model",
+    "import_fairseq_model",
+]
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7427e8fea0faa37868da5cc86b24b76e221879f9
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/import_fairseq.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/import_fairseq.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dc3028a7136b27a32c02c2c7cc989cb563d5e6e6
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/import_fairseq.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/import_huggingface.cpython-311.pyc b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/import_huggingface.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..efba44e2a74f37384fbd99bebc99c4b3ce7fc12f
Binary files /dev/null and b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/__pycache__/import_huggingface.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/import_fairseq.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/import_fairseq.py
new file mode 100644
index 0000000000000000000000000000000000000000..39791e9b7d75ac3c2eb1fcf4f9c3517e7483048c
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/import_fairseq.py
@@ -0,0 +1,213 @@
+"""Import fariseq's wav2vec2.0 pretrained weights to torchaudios's format.
+
+For this module to work, you need `fairseq`.
+"""
+import re
+
+from torch.nn import Module
+
+from ..model import wav2vec2_model, Wav2Vec2Model
+
+
+def _parse_config(w2v_model):
+    encoder = w2v_model.encoder
+    conv_layers = w2v_model.feature_extractor.conv_layers
+
+    extractor_mode = "layer_norm"
+    if "GroupNorm" in conv_layers[0][2].__class__.__name__:
+        extractor_mode = "group_norm"
+    else:
+        extractor_mode = "layer_norm"
+
+    conv_layer_config = [(l[0].out_channels, l[0].kernel_size[0], l[0].stride[0]) for l in conv_layers]
+
+    if all(l[0].bias is None for l in conv_layers):
+        conv_bias = False
+    elif all(l[0].bias is not None for l in conv_layers):
+        conv_bias = True
+    else:
+        raise ValueError("Either all the convolutions layers have bias term or none of them should.")
+
+    config = {
+        "extractor_mode": extractor_mode,
+        "extractor_conv_layer_config": conv_layer_config,
+        "extractor_conv_bias": conv_bias,
+        "encoder_embed_dim": w2v_model.post_extract_proj.out_features,
+        "encoder_projection_dropout": w2v_model.dropout_input.p,
+        "encoder_pos_conv_kernel": encoder.pos_conv[0].kernel_size[0],
+        "encoder_pos_conv_groups": encoder.pos_conv[0].groups,
+        "encoder_num_layers": len(encoder.layers),
+        "encoder_num_heads": encoder.layers[0].self_attn.num_heads,
+        "encoder_attention_dropout": encoder.layers[0].self_attn.dropout_module.p,
+        "encoder_ff_interm_features": encoder.layers[0].fc1.out_features,
+        "encoder_ff_interm_dropout": encoder.layers[0].dropout2.p,
+        "encoder_dropout": encoder.layers[0].dropout3.p,
+        "encoder_layer_norm_first": encoder.layer_norm_first,
+        "encoder_layer_drop": encoder.layerdrop,
+    }
+    return config
+
+
+def _map_key(key):
+    key_ = key
+    if key.startswith("w2v_model."):
+        key = key.replace("w2v_model.", "")
+    if re.match(r"(mask_emb|quantizer|project_q|final_proj|mask_emb)", key):
+        return None
+    # Feature Extractor
+    # Group norm when "extractor_mode" is "default".
+    # (Only the first layer)
+    # "conv_layers.0.2.weight" -> "conv_layers.0.layer_norm.weight"
+    # "conv_layers.0.2.bias"   -> "conv_layers.0.layer_norm.bias"
+    match = re.match(r"feature_extractor\.conv_layers\.0\.2\.(weight|bias)", key)
+    if match:
+        return f"feature_extractor.conv_layers.0.layer_norm.{match.group(1)}"
+    # Convolutions
+    # "conv_layers.X.0.weight" -> "conv_layers.X.conv.weight"
+    # "conv_layers.X.0.bias"   -> "conv_layers.X.conv.bias"
+    match = re.match(r"feature_extractor\.conv_layers\.(\d+)\.0\.(weight|bias)", key)
+    if match:
+        return f"feature_extractor.conv_layers.{match.group(1)}.conv.{match.group(2)}"
+    # Layer norm when "extractor_mode" is "layer_norm".
+    # "conv_layers.X.2.1.weight" -> "conv_layers.X.layer_norm.weight"
+    # "conv_layers.X.2.1.bias"   -> "conv_layers.X.layer_norm.bias"
+    match = re.match(r"feature_extractor\.conv_layers\.(\d+)\.2\.1\.(weight|bias)", key)
+    if match:
+        return f"feature_extractor.conv_layers.{match.group(1)}.layer_norm.{match.group(2)}"
+    match = re.match(r"post_extract_proj\.(weight|bias)", key)
+    # Encoder - Feature projection
+    if match:
+        return f"encoder.feature_projection.projection.{match.group(1)}"
+    match = re.match(r"layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.feature_projection.layer_norm.{match.group(1)}"
+    # Encoder - Transformer - Convolutional positional embedding
+    match = re.match(r"encoder\.pos_conv\.0\.(bias|weight_g|weight_v)", key)
+    if match:
+        return f"encoder.transformer.pos_conv_embed.conv.{match.group(1)}"
+    match = re.match(r"encoder\.layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layer_norm.{match.group(1)}"
+    # Encoder - Transformer - Self attention layers
+    match = re.match(r"encoder\.layers\.(\d+)\.self_attn\.((k_|v_|q_|out_)proj\.(weight|bias))", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.attention.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.self_attn_layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.layer_norm.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.fc1\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.feed_forward.intermediate_dense.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.fc2\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.feed_forward.output_dense.{match.group(2)}"
+    match = re.match(r"encoder\.layers\.(\d+)\.final_layer_norm\.(weight|bias)", key)
+    if match:
+        return f"encoder.transformer.layers.{match.group(1)}.final_layer_norm.{match.group(2)}"
+    match = re.match(r"proj\.(weight|bias)", key)
+    # Auxiliary Module
+    # Only relevant when loading fine-tuned models
+    if match:
+        return f"aux.{match.group(1)}"
+    # HuBERT Extension
+    if key in ["label_embs_concat"]:
+        return key
+    raise ValueError(f"Unexpected key: {key_}")
+
+
+def _convert_state_dict(state_dict):
+    converted = {}
+    for k, v in state_dict.items():
+        k = _map_key(k)
+        if k is not None:
+            converted[k] = v
+    return converted
+
+
+def import_fairseq_model(original: Module) -> Wav2Vec2Model:
+    """Builds :class:`Wav2Vec2Model` from the corresponding model object of
+    `fairseq <https://github.com/pytorch/fairseq>`_.
+
+    Args:
+        original (torch.nn.Module):
+            An instance of fairseq's Wav2Vec2.0 or HuBERT model.
+            One of ``fairseq.models.wav2vec.wav2vec2_asr.Wav2VecEncoder``,
+            ``fairseq.models.wav2vec.wav2vec2.Wav2Vec2Model`` or
+            ``fairseq.models.hubert.hubert_asr.HubertEncoder``.
+
+    Returns:
+        Wav2Vec2Model: Imported model.
+
+    Example - Loading pretrain-only model
+        >>> from torchaudio.models.wav2vec2.utils import import_fairseq_model
+        >>>
+        >>> # Load model using fairseq
+        >>> model_file = 'wav2vec_small.pt'
+        >>> model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_file])
+        >>> original = model[0]
+        >>> imported = import_fairseq_model(original)
+        >>>
+        >>> # Perform feature extraction
+        >>> waveform, _ = torchaudio.load('audio.wav')
+        >>> features, _ = imported.extract_features(waveform)
+        >>>
+        >>> # Compare result with the original model from fairseq
+        >>> reference = original.feature_extractor(waveform).transpose(1, 2)
+        >>> torch.testing.assert_allclose(features, reference)
+
+    Example - Fine-tuned model
+        >>> from torchaudio.models.wav2vec2.utils import import_fairseq_model
+        >>>
+        >>> # Load model using fairseq
+        >>> model_file = 'wav2vec_small_960h.pt'
+        >>> model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([model_file])
+        >>> original = model[0]
+        >>> imported = import_fairseq_model(original.w2v_encoder)
+        >>>
+        >>> # Perform encoding
+        >>> waveform, _ = torchaudio.load('audio.wav')
+        >>> emission, _ = imported(waveform)
+        >>>
+        >>> # Compare result with the original model from fairseq
+        >>> mask = torch.zeros_like(waveform)
+        >>> reference = original(waveform, mask)['encoder_out'].transpose(0, 1)
+        >>> torch.testing.assert_allclose(emission, reference)
+    """
+    class_ = original.__class__.__name__
+    if class_ == "Wav2Vec2Model":
+        return _import_wav2vec2_pretraining(original)
+    if class_ == "Wav2VecEncoder":
+        return _import_wav2vec2_finetuning(original)
+    if class_ == "HubertModel":
+        return _import_hubert_pretraining(original)
+    if class_ == "HubertEncoder":
+        return _import_hubert_finetuning(original)
+    raise ValueError(f"Expected an instance of `Wav2Vec2Model` or `Wav2VecEncoder`. Found: {class_}")
+
+
+def _import_wav2vec2_finetuning(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original.w2v_model)
+    model = wav2vec2_model(**config, aux_num_out=original.proj.out_features)
+    model.load_state_dict(_convert_state_dict(original.state_dict()))
+    return model
+
+
+def _import_wav2vec2_pretraining(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original)
+    model = wav2vec2_model(**config, aux_num_out=None)
+    model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False)
+    return model
+
+
+def _import_hubert_finetuning(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original.w2v_model)
+    model = wav2vec2_model(**config, aux_num_out=original.proj.out_features)
+    model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False)
+    return model
+
+
+def _import_hubert_pretraining(original: Module) -> Wav2Vec2Model:
+    config = _parse_config(original)
+    model = wav2vec2_model(**config, aux_num_out=None)
+    model.load_state_dict(_convert_state_dict(original.state_dict()), strict=False)
+    return model
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/import_huggingface.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/import_huggingface.py
new file mode 100644
index 0000000000000000000000000000000000000000..519d8c919f02be62b2f2e2aa0dd8db97222430d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/utils/import_huggingface.py
@@ -0,0 +1,134 @@
+"""Import Hugging Face transformers's wav2vec2.0 pretrained weights to torchaudios's format.
+"""
+import logging
+from typing import Any, Dict
+
+import torch
+from torch.nn import Module
+
+from ..model import wav2vec2_model, Wav2Vec2Model, wavlm_model
+
+_LG = logging.getLogger(__name__)
+
+
+def _get_config(cfg):
+    config = {
+        "extractor_mode": f"{cfg.feat_extract_norm}_norm",
+        "extractor_conv_layer_config": list(zip(cfg.conv_dim, cfg.conv_kernel, cfg.conv_stride)),
+        "extractor_conv_bias": cfg.conv_bias,
+        "encoder_embed_dim": cfg.hidden_size,
+        "encoder_projection_dropout": cfg.feat_proj_dropout,
+        "encoder_pos_conv_kernel": cfg.num_conv_pos_embeddings,
+        "encoder_pos_conv_groups": cfg.num_conv_pos_embedding_groups,
+        "encoder_num_layers": cfg.num_hidden_layers,
+        "encoder_num_heads": cfg.num_attention_heads,
+        "encoder_attention_dropout": cfg.attention_dropout,
+        "encoder_ff_interm_features": cfg.intermediate_size,
+        "encoder_ff_interm_dropout": cfg.activation_dropout,
+        "encoder_dropout": cfg.hidden_dropout,
+        "encoder_layer_norm_first": cfg.do_stable_layer_norm,
+        "encoder_layer_drop": cfg.layerdrop,
+    }
+    return config
+
+
+def _get_config_wavlm(cfg):
+    config = {
+        "extractor_mode": f"{cfg.feat_extract_norm}_norm",
+        "extractor_conv_layer_config": list(zip(cfg.conv_dim, cfg.conv_kernel, cfg.conv_stride)),
+        "extractor_conv_bias": cfg.conv_bias,
+        "encoder_embed_dim": cfg.hidden_size,
+        "encoder_projection_dropout": cfg.feat_proj_dropout,
+        "encoder_pos_conv_kernel": cfg.num_conv_pos_embeddings,
+        "encoder_pos_conv_groups": cfg.num_conv_pos_embedding_groups,
+        "encoder_num_layers": cfg.num_hidden_layers,
+        "encoder_num_heads": cfg.num_attention_heads,
+        "encoder_num_buckets": cfg.num_buckets,
+        "encoder_max_distance": cfg.max_bucket_distance,
+        "encoder_attention_dropout": cfg.attention_dropout,
+        "encoder_ff_interm_features": cfg.intermediate_size,
+        "encoder_ff_interm_dropout": cfg.activation_dropout,
+        "encoder_dropout": cfg.hidden_dropout,
+        "encoder_layer_norm_first": cfg.do_stable_layer_norm,
+        "encoder_layer_drop": cfg.layerdrop,
+    }
+    return config
+
+
+def _build(config, original):
+    is_for_ctc = original.__class__.__name__ in ["Wav2Vec2ForCTC", "WavLMForCTC"]
+    if is_for_ctc:
+        aux_num_out = original.config.vocab_size
+        wav2vec2 = original.wav2vec2
+    else:
+        _LG.warning(
+            "The model is not an instance of Wav2Vec2ForCTC or WavLMForCTC. " '"lm_head" module is not imported.'
+        )
+        aux_num_out = None
+        wav2vec2 = original
+    is_wavlm = original.__class__.__name__ in ["WavLMModel", "WavLMForCTC"]
+    if is_wavlm:
+        imported = wavlm_model(**config, aux_num_out=aux_num_out)
+    else:
+        imported = wav2vec2_model(**config, aux_num_out=aux_num_out)
+    imported.feature_extractor.load_state_dict(wav2vec2.feature_extractor.state_dict())
+    imported.encoder.feature_projection.load_state_dict(wav2vec2.feature_projection.state_dict())
+    encoder_state_dict = wav2vec2.encoder.state_dict()
+    if is_wavlm:  # Rename paramaters of linear transformations for compatibility with the HF model
+        transform_wavlm_encoder_state(encoder_state_dict, config["encoder_num_layers"])
+    imported.encoder.transformer.load_state_dict(encoder_state_dict)
+    if is_for_ctc:
+        imported.aux.load_state_dict(original.lm_head.state_dict())
+    return imported
+
+
+def transform_wavlm_encoder_state(state: Dict[str, Any], encoder_num_layers: int):
+    """Converts WavLM encoder state from HuggingFace format. In particular, concatenates linear projection weights and
+    biases to align with the structure of ``torch.nn.MultiheadAttention``.
+    """
+    for i in range(encoder_num_layers):
+        q_proj_bias = state.pop(f"layers.{i}.attention.q_proj.bias")
+        k_proj_bias = state.pop(f"layers.{i}.attention.k_proj.bias")
+        v_proj_bias = state.pop(f"layers.{i}.attention.v_proj.bias")
+        q_proj_weight = state.pop(f"layers.{i}.attention.q_proj.weight")
+        k_proj_weight = state.pop(f"layers.{i}.attention.k_proj.weight")
+        v_proj_weight = state.pop(f"layers.{i}.attention.v_proj.weight")
+        state[f"layers.{i}.attention.attention.in_proj_bias"] = torch.cat((q_proj_bias, k_proj_bias, v_proj_bias))
+        state[f"layers.{i}.attention.attention.in_proj_weight"] = torch.cat(
+            (q_proj_weight, k_proj_weight, v_proj_weight)
+        )
+
+        state[f"layers.{i}.attention.attention.out_proj.weight"] = state.pop(f"layers.{i}.attention.out_proj.weight")
+        state[f"layers.{i}.attention.attention.out_proj.bias"] = state.pop(f"layers.{i}.attention.out_proj.bias")
+
+
+def import_huggingface_model(original: Module) -> Wav2Vec2Model:
+    """Builds :class:`Wav2Vec2Model` from the corresponding model object of
+    `Transformers <https://huggingface.co/transformers/>`_.
+
+    Args:
+        original (torch.nn.Module): An instance of ``Wav2Vec2ForCTC`` from ``transformers``.
+
+    Returns:
+        Wav2Vec2Model: Imported model.
+
+    Example
+        >>> from torchaudio.models.wav2vec2.utils import import_huggingface_model
+        >>>
+        >>> original = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+        >>> model = import_huggingface_model(original)
+        >>>
+        >>> waveforms, _ = torchaudio.load("audio.wav")
+        >>> logits, _ = model(waveforms)
+    """
+    _LG.info("Importing model.")
+    _LG.info("Loading model configuration.")
+    is_wavlm = original.__class__.__name__ in ["WavLMModel", "WavLMForCTC"]
+    if is_wavlm:
+        config = _get_config_wavlm(original.config)
+    else:
+        config = _get_config(original.config)
+    _LG.debug("  - config: %s", config)
+    _LG.info("Building model.")
+    imported = _build(config, original)
+    return imported
diff --git a/lib/python3.11/site-packages/torchaudio/models/wav2vec2/wavlm_attention.py b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/wavlm_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..fafddfeb958cbcdfdc0a7781b49bc124fff78290
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wav2vec2/wavlm_attention.py
@@ -0,0 +1,214 @@
+"""
+The MIT License (MIT)
+
+Copyright (c) Microsoft Corporation
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import math
+from typing import Optional, Tuple
+
+import torch
+from torch import nn, Tensor
+
+
+class WavLMSelfAttention(nn.Module):
+    """Multi-headed self-attention for WavLM model :cite:`chen2022wavlm`.
+    Wraps around ``torch.nn.MultiheadAttention``, creating relaive position embeddings and passing them to multi-headed
+    attention as a mask.
+    Source: https://github.com/microsoft/unilm/blob/2d8302f09c99bca2b82e6e868d81d4281cceebc8/wavlm/modules.py#L303-L763
+
+    Args:
+        embed_dim (int): Total dimension of the model.
+        num_heads (int): The number of heads.
+        dropout (float, optional): Dropout probability on attn_output_weights. (Default: to ``0.0``)
+        bias (bool, optional): If ``True``, add bias to input / output projection layers. (Default: ``True``)
+        has_relative_attention_bias (bool, optional): If ``True``, apply relative position embedding.
+            Necessary in the first encoder layer, but not in the subsequent ones. (Default: ``False``)
+        num_buckets (int, optional): Number of buckets for relative position embedding. (Default: ``32``)
+        max_distance (int, optional): Naximum distance for relative position embedding. (Default: ``128``)
+        gru_rel_pos (bool, optional): If ``True``, apply gated relative position embedding. (Default: ``False``)
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        bias: bool = True,
+        has_relative_attention_bias: bool = False,
+        num_buckets: int = 32,
+        max_distance: int = 128,
+        gru_rel_pos: bool = True,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.num_buckets = num_buckets
+        self.max_distance = max_distance
+
+        if has_relative_attention_bias:
+            self.rel_attn_embed = nn.Embedding(num_buckets, num_heads)
+        else:
+            self.rel_attn_embed = None
+
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+
+        self.dropout = dropout
+        self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True)
+
+        self.gru_rel_pos = gru_rel_pos
+        if self.gru_rel_pos:
+            self.gru_rel_pos_linear = nn.Linear(self.head_dim, 8)
+            self.gru_rel_pos_const = nn.Parameter(torch.ones(1, num_heads, 1, 1))
+        self.has_position_bias = True
+
+    def compute_bias(self, query_length: int, key_length: int) -> Tensor:
+        """Compute relative position embeddings for WavLM model.
+        Args:
+            query_length (int): Query position can take values between 0 and ``query_length - 1``.
+            key_length (int): Key position can take values between 0 and ``key_length - 1``.
+        Returns:
+            Tensor of shape `(num_heads, query_length, key_length)`, relative positions embeddings
+        """
+        context_position = torch.arange(query_length, dtype=torch.long)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
+        relative_position = memory_position - context_position  # Shape (query_length, key_length)
+        relative_position_bucket = self._relative_positions_bucket(relative_position, bidirectional=True)
+        relative_position_bucket = relative_position_bucket.to(self.rel_attn_embed.weight.device)
+        values = self.rel_attn_embed(relative_position_bucket)  # Shape (query_length, key_length, num_heads)
+        values = values.permute([2, 0, 1])
+        return values
+
+    def _relative_positions_bucket(self, relative_positions: Tensor, bidirectional: bool = True):
+        """Compute relative position buckets for WavLM model. Computation similar to formula (5) in WavLM
+           paper :cite:`chen2022wavlm`.
+        Args:
+            relative_positions (Tensor): Relative offsets between query and key positions,
+                of shape ``(query_length, key_length)``.
+            bidirectional (bool): If ``True``, values will be filled both above and below the diagonal in the resulting
+                matrix. If ``False``, the elements above the diagonal (i.e. with negative relative offsets) will be set
+                to zero. (Default ``True``)
+        Returns:
+            Tensor of shape ``(query_length, key_length)`` filled bucketed values of with relative positions.
+        """
+        num_buckets = self.num_buckets
+        max_distance = self.max_distance
+        # Shape (query_length, key_length)
+        relative_buckets = torch.zeros_like(relative_positions, dtype=torch.long)
+
+        if bidirectional:
+            num_buckets = num_buckets // 2
+            relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
+            relative_positions = torch.abs(relative_positions)
+        else:
+            relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))
+
+        max_exact = num_buckets // 2
+        is_small = relative_positions < max_exact
+
+        relative_postion_if_large = max_exact + (
+            torch.log(relative_positions.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_postion_if_large = torch.min(
+            relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
+        return relative_buckets
+
+    def forward(
+        self,
+        query: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+        attention_mask: Optional[Tensor] = None,
+        position_bias: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        """
+        Args:
+            query (Tensor): Input of shape ``(batch_size, src_len, embed_dim)``.
+            key_padding_mask (Tensor or None, optional): Mask to exclude keys that are pads, of shape
+                `(batch, src_len)`, where padding elements are indicated by 1s. (Default: ``None``)
+            attn_mask: Needs to be ``None``. The argument exists for compatibility with
+                ``EncoderLayer``. (Default: ``None``)
+            position_bias (Tensor or None, optional): Position bias of shape
+                ``(batch_size * num_heads, src_len, src_len)``. When used inside WavLM model encoder, will be
+                generated in the first layer and then passed from each encoder layer to the next one.
+                (Default: ``None``)
+        Returns:
+            attn_output (Tensor): Attention output of shape ``(batch_size, src_len, embed_dim)``.
+            position_bias (Tensor or None): Position bias of shape ``(batch_size * num_heads, src_len, src_len)``.
+        """
+        bsz, seq_len, embed_dim = query.size()
+        assert embed_dim == self.embed_dim
+        assert attention_mask is None
+
+        if self.rel_attn_embed is not None and position_bias is None:
+            position_bias = self.compute_bias(seq_len, seq_len)
+            position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1)
+
+        attn_mask_rel_pos: Optional[Tensor] = None
+        if position_bias is not None:
+            attn_mask_rel_pos = position_bias
+            if self.gru_rel_pos:  # Apply gating on relative position bias
+                query_layer = query.view(bsz, seq_len, self.num_heads, -1)
+                query_layer = query_layer.permute(0, 2, 1, 3)
+
+                gate_a, gate_b = torch.sigmoid(
+                    self.gru_rel_pos_linear(query_layer).view(bsz, self.num_heads, seq_len, 2, 4).sum(-1, keepdim=False)
+                ).chunk(2, dim=-1)
+                gate_a_1 = gate_a * (gate_b * self.gru_rel_pos_const - 1.0) + 2.0
+                attn_mask_rel_pos = gate_a_1.view(bsz, self.num_heads, -1, 1) * position_bias
+
+            attn_mask_rel_pos = attn_mask_rel_pos.view((bsz, self.num_heads, seq_len, seq_len))
+
+        if attn_mask_rel_pos is not None and key_padding_mask is not None:
+            key_padding_mask = key_padding_mask.view(bsz, 1, 1, seq_len).expand(-1, self.num_heads, -1, -1)
+            key_padding_mask = torch.nn.functional._canonical_mask(
+                mask=key_padding_mask,
+                mask_name="key_padding_mask",
+                other_type=torch.nn.functional._none_or_dtype(attn_mask_rel_pos),
+                other_name="",
+                target_type=query.dtype,
+            )
+        if attn_mask_rel_pos is not None and key_padding_mask is not None:
+            attn_mask_rel_pos = attn_mask_rel_pos + key_padding_mask
+        query_projected = torch.nn.functional.linear(query, self.attention.in_proj_weight, self.attention.in_proj_bias)
+        query, key, value = query_projected.chunk(3, -1)
+        shape = (bsz, seq_len, self.num_heads, self.head_dim)
+        query = query.view(shape).transpose(2, 1)  # (batch, num_heads, seq_len, head_dim)
+        key = key.view(shape).transpose(2, 1)  # (batch, num_heads, seq_len, head_dim)
+        value = value.view(shape).transpose(2, 1)  # (batch, num_heads, seq_len, head_dim)
+        dropout = self.dropout if self.training else 0.0
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            attn_mask=attn_mask_rel_pos,
+            dropout_p=dropout,
+            is_causal=False,
+        )
+        attn_output = attn_output.transpose(1, 2).reshape(bsz, -1, self.num_heads * self.head_dim)
+        attn_output = self.attention.out_proj(attn_output)
+        return attn_output, position_bias
diff --git a/lib/python3.11/site-packages/torchaudio/models/wavernn.py b/lib/python3.11/site-packages/torchaudio/models/wavernn.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ae5a3e91675cd9ef7d4614f0daaec50f80dcdee
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/models/wavernn.py
@@ -0,0 +1,409 @@
+import math
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+
+__all__ = [
+    "ResBlock",
+    "MelResNet",
+    "Stretch2d",
+    "UpsampleNetwork",
+    "WaveRNN",
+]
+
+
+class ResBlock(nn.Module):
+    r"""ResNet block based on *Efficient Neural Audio Synthesis* :cite:`kalchbrenner2018efficient`.
+
+    Args:
+        n_freq: the number of bins in a spectrogram. (Default: ``128``)
+
+    Examples
+        >>> resblock = ResBlock()
+        >>> input = torch.rand(10, 128, 512)  # a random spectrogram
+        >>> output = resblock(input)  # shape: (10, 128, 512)
+    """
+
+    def __init__(self, n_freq: int = 128) -> None:
+        super().__init__()
+
+        self.resblock_model = nn.Sequential(
+            nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False),
+            nn.BatchNorm1d(n_freq),
+            nn.ReLU(inplace=True),
+            nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False),
+            nn.BatchNorm1d(n_freq),
+        )
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""Pass the input through the ResBlock layer.
+        Args:
+            specgram (Tensor): the input sequence to the ResBlock layer (n_batch, n_freq, n_time).
+
+        Return:
+            Tensor shape: (n_batch, n_freq, n_time)
+        """
+
+        return self.resblock_model(specgram) + specgram
+
+
+class MelResNet(nn.Module):
+    r"""MelResNet layer uses a stack of ResBlocks on spectrogram.
+
+    Args:
+        n_res_block: the number of ResBlock in stack. (Default: ``10``)
+        n_freq: the number of bins in a spectrogram. (Default: ``128``)
+        n_hidden: the number of hidden dimensions of resblock. (Default: ``128``)
+        n_output: the number of output dimensions of melresnet. (Default: ``128``)
+        kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``)
+
+    Examples
+        >>> melresnet = MelResNet()
+        >>> input = torch.rand(10, 128, 512)  # a random spectrogram
+        >>> output = melresnet(input)  # shape: (10, 128, 508)
+    """
+
+    def __init__(
+        self, n_res_block: int = 10, n_freq: int = 128, n_hidden: int = 128, n_output: int = 128, kernel_size: int = 5
+    ) -> None:
+        super().__init__()
+
+        ResBlocks = [ResBlock(n_hidden) for _ in range(n_res_block)]
+
+        self.melresnet_model = nn.Sequential(
+            nn.Conv1d(in_channels=n_freq, out_channels=n_hidden, kernel_size=kernel_size, bias=False),
+            nn.BatchNorm1d(n_hidden),
+            nn.ReLU(inplace=True),
+            *ResBlocks,
+            nn.Conv1d(in_channels=n_hidden, out_channels=n_output, kernel_size=1),
+        )
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""Pass the input through the MelResNet layer.
+        Args:
+            specgram (Tensor): the input sequence to the MelResNet layer (n_batch, n_freq, n_time).
+
+        Return:
+            Tensor shape: (n_batch, n_output, n_time - kernel_size + 1)
+        """
+
+        return self.melresnet_model(specgram)
+
+
+class Stretch2d(nn.Module):
+    r"""Upscale the frequency and time dimensions of a spectrogram.
+
+    Args:
+        time_scale: the scale factor in time dimension
+        freq_scale: the scale factor in frequency dimension
+
+    Examples
+        >>> stretch2d = Stretch2d(time_scale=10, freq_scale=5)
+
+        >>> input = torch.rand(10, 100, 512)  # a random spectrogram
+        >>> output = stretch2d(input)  # shape: (10, 500, 5120)
+    """
+
+    def __init__(self, time_scale: int, freq_scale: int) -> None:
+        super().__init__()
+
+        self.freq_scale = freq_scale
+        self.time_scale = time_scale
+
+    def forward(self, specgram: Tensor) -> Tensor:
+        r"""Pass the input through the Stretch2d layer.
+
+        Args:
+            specgram (Tensor): the input sequence to the Stretch2d layer (..., n_freq, n_time).
+
+        Return:
+            Tensor shape: (..., n_freq * freq_scale, n_time * time_scale)
+        """
+
+        return specgram.repeat_interleave(self.freq_scale, -2).repeat_interleave(self.time_scale, -1)
+
+
+class UpsampleNetwork(nn.Module):
+    r"""Upscale the dimensions of a spectrogram.
+
+    Args:
+        upsample_scales: the list of upsample scales.
+        n_res_block: the number of ResBlock in stack. (Default: ``10``)
+        n_freq: the number of bins in a spectrogram. (Default: ``128``)
+        n_hidden: the number of hidden dimensions of resblock. (Default: ``128``)
+        n_output: the number of output dimensions of melresnet. (Default: ``128``)
+        kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``)
+
+    Examples
+        >>> upsamplenetwork = UpsampleNetwork(upsample_scales=[4, 4, 16])
+        >>> input = torch.rand(10, 128, 10)  # a random spectrogram
+        >>> output = upsamplenetwork(input)  # shape: (10, 128, 1536), (10, 128, 1536)
+    """
+
+    def __init__(
+        self,
+        upsample_scales: List[int],
+        n_res_block: int = 10,
+        n_freq: int = 128,
+        n_hidden: int = 128,
+        n_output: int = 128,
+        kernel_size: int = 5,
+    ) -> None:
+        super().__init__()
+
+        total_scale = 1
+        for upsample_scale in upsample_scales:
+            total_scale *= upsample_scale
+        self.total_scale: int = total_scale
+
+        self.indent = (kernel_size - 1) // 2 * total_scale
+        self.resnet = MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size)
+        self.resnet_stretch = Stretch2d(total_scale, 1)
+
+        up_layers = []
+        for scale in upsample_scales:
+            stretch = Stretch2d(scale, 1)
+            conv = nn.Conv2d(
+                in_channels=1, out_channels=1, kernel_size=(1, scale * 2 + 1), padding=(0, scale), bias=False
+            )
+            torch.nn.init.constant_(conv.weight, 1.0 / (scale * 2 + 1))
+            up_layers.append(stretch)
+            up_layers.append(conv)
+        self.upsample_layers = nn.Sequential(*up_layers)
+
+    def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]:
+        r"""Pass the input through the UpsampleNetwork layer.
+
+        Args:
+            specgram (Tensor): the input sequence to the UpsampleNetwork layer (n_batch, n_freq, n_time)
+
+        Return:
+            Tensor shape: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale),
+                          (n_batch, n_output, (n_time - kernel_size + 1) * total_scale)
+        where total_scale is the product of all elements in upsample_scales.
+        """
+
+        resnet_output = self.resnet(specgram).unsqueeze(1)
+        resnet_output = self.resnet_stretch(resnet_output)
+        resnet_output = resnet_output.squeeze(1)
+
+        specgram = specgram.unsqueeze(1)
+        upsampling_output = self.upsample_layers(specgram)
+        upsampling_output = upsampling_output.squeeze(1)[:, :, self.indent : -self.indent]
+
+        return upsampling_output, resnet_output
+
+
+class WaveRNN(nn.Module):
+    r"""WaveRNN model from *Efficient Neural Audio Synthesis* :cite:`wavernn`
+    based on the implementation from `fatchord/WaveRNN <https://github.com/fatchord/WaveRNN>`_.
+
+    The original implementation was introduced in *Efficient Neural Audio Synthesis*
+    :cite:`kalchbrenner2018efficient`. The input channels of waveform and spectrogram have to be 1.
+    The product of `upsample_scales` must equal `hop_length`.
+
+    See Also:
+        * `Training example <https://github.com/pytorch/audio/tree/release/0.12/examples/pipeline_wavernn>`__
+        * :class:`torchaudio.pipelines.Tacotron2TTSBundle`: TTS pipeline with pretrained model.
+
+    Args:
+        upsample_scales: the list of upsample scales.
+        n_classes: the number of output classes.
+        hop_length: the number of samples between the starts of consecutive frames.
+        n_res_block: the number of ResBlock in stack. (Default: ``10``)
+        n_rnn: the dimension of RNN layer. (Default: ``512``)
+        n_fc: the dimension of fully connected layer. (Default: ``512``)
+        kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``)
+        n_freq: the number of bins in a spectrogram. (Default: ``128``)
+        n_hidden: the number of hidden dimensions of resblock. (Default: ``128``)
+        n_output: the number of output dimensions of melresnet. (Default: ``128``)
+
+    Example
+        >>> wavernn = WaveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200)
+        >>> waveform, sample_rate = torchaudio.load(file)
+        >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length)
+        >>> specgram = MelSpectrogram(sample_rate)(waveform)  # shape: (n_batch, n_channel, n_freq, n_time)
+        >>> output = wavernn(waveform, specgram)
+        >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, n_classes)
+    """
+
+    def __init__(
+        self,
+        upsample_scales: List[int],
+        n_classes: int,
+        hop_length: int,
+        n_res_block: int = 10,
+        n_rnn: int = 512,
+        n_fc: int = 512,
+        kernel_size: int = 5,
+        n_freq: int = 128,
+        n_hidden: int = 128,
+        n_output: int = 128,
+    ) -> None:
+        super().__init__()
+
+        self.kernel_size = kernel_size
+        self._pad = (kernel_size - 1 if kernel_size % 2 else kernel_size) // 2
+        self.n_rnn = n_rnn
+        self.n_aux = n_output // 4
+        self.hop_length = hop_length
+        self.n_classes = n_classes
+        self.n_bits: int = int(math.log2(self.n_classes))
+
+        total_scale = 1
+        for upsample_scale in upsample_scales:
+            total_scale *= upsample_scale
+        if total_scale != self.hop_length:
+            raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}")
+
+        self.upsample = UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size)
+        self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn)
+
+        self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True)
+        self.rnn2 = nn.GRU(n_rnn + self.n_aux, n_rnn, batch_first=True)
+
+        self.relu1 = nn.ReLU(inplace=True)
+        self.relu2 = nn.ReLU(inplace=True)
+
+        self.fc1 = nn.Linear(n_rnn + self.n_aux, n_fc)
+        self.fc2 = nn.Linear(n_fc + self.n_aux, n_fc)
+        self.fc3 = nn.Linear(n_fc, self.n_classes)
+
+    def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor:
+        r"""Pass the input through the WaveRNN model.
+
+        Args:
+            waveform: the input waveform to the WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length)
+            specgram: the input spectrogram to the WaveRNN layer (n_batch, 1, n_freq, n_time)
+
+        Return:
+            Tensor: shape (n_batch, 1, (n_time - kernel_size + 1) * hop_length, n_classes)
+        """
+
+        if waveform.size(1) != 1:
+            raise ValueError("Require the input channel of waveform is 1")
+        if specgram.size(1) != 1:
+            raise ValueError("Require the input channel of specgram is 1")
+        # remove channel dimension until the end
+        waveform, specgram = waveform.squeeze(1), specgram.squeeze(1)
+
+        batch_size = waveform.size(0)
+        h1 = torch.zeros(1, batch_size, self.n_rnn, dtype=waveform.dtype, device=waveform.device)
+        h2 = torch.zeros(1, batch_size, self.n_rnn, dtype=waveform.dtype, device=waveform.device)
+        # output of upsample:
+        # specgram: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale)
+        # aux: (n_batch, n_output, (n_time - kernel_size + 1) * total_scale)
+        specgram, aux = self.upsample(specgram)
+        specgram = specgram.transpose(1, 2)
+        aux = aux.transpose(1, 2)
+
+        aux_idx = [self.n_aux * i for i in range(5)]
+        a1 = aux[:, :, aux_idx[0] : aux_idx[1]]
+        a2 = aux[:, :, aux_idx[1] : aux_idx[2]]
+        a3 = aux[:, :, aux_idx[2] : aux_idx[3]]
+        a4 = aux[:, :, aux_idx[3] : aux_idx[4]]
+
+        x = torch.cat([waveform.unsqueeze(-1), specgram, a1], dim=-1)
+        x = self.fc(x)
+        res = x
+        x, _ = self.rnn1(x, h1)
+
+        x = x + res
+        res = x
+        x = torch.cat([x, a2], dim=-1)
+        x, _ = self.rnn2(x, h2)
+
+        x = x + res
+        x = torch.cat([x, a3], dim=-1)
+        x = self.fc1(x)
+        x = self.relu1(x)
+
+        x = torch.cat([x, a4], dim=-1)
+        x = self.fc2(x)
+        x = self.relu2(x)
+        x = self.fc3(x)
+
+        # bring back channel dimension
+        return x.unsqueeze(1)
+
+    @torch.jit.export
+    def infer(self, specgram: Tensor, lengths: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
+        r"""Inference method of WaveRNN.
+
+        This function currently only supports multinomial sampling, which assumes the
+        network is trained on cross entropy loss.
+
+        Args:
+            specgram (Tensor):
+                Batch of spectrograms. Shape: `(n_batch, n_freq, n_time)`.
+            lengths (Tensor or None, optional):
+                Indicates the valid length of each audio in the batch.
+                Shape: `(batch, )`.
+                When the ``specgram`` contains spectrograms with different durations,
+                by providing ``lengths`` argument, the model will compute
+                the corresponding valid output lengths.
+                If ``None``, it is assumed that all the audio in ``waveforms``
+                have valid length. Default: ``None``.
+
+        Returns:
+            (Tensor, Optional[Tensor]):
+            Tensor
+                The inferred waveform of size `(n_batch, 1, n_time)`.
+                1 stands for a single channel.
+            Tensor or None
+                If ``lengths`` argument was provided, a Tensor of shape `(batch, )`
+                is returned.
+                It indicates the valid length in time axis of the output Tensor.
+        """
+
+        device = specgram.device
+        dtype = specgram.dtype
+
+        specgram = torch.nn.functional.pad(specgram, (self._pad, self._pad))
+        specgram, aux = self.upsample(specgram)
+        if lengths is not None:
+            lengths = lengths * self.upsample.total_scale
+
+        output: List[Tensor] = []
+        b_size, _, seq_len = specgram.size()
+
+        h1 = torch.zeros((1, b_size, self.n_rnn), device=device, dtype=dtype)
+        h2 = torch.zeros((1, b_size, self.n_rnn), device=device, dtype=dtype)
+        x = torch.zeros((b_size, 1), device=device, dtype=dtype)
+
+        aux_split = [aux[:, self.n_aux * i : self.n_aux * (i + 1), :] for i in range(4)]
+
+        for i in range(seq_len):
+
+            m_t = specgram[:, :, i]
+
+            a1_t, a2_t, a3_t, a4_t = [a[:, :, i] for a in aux_split]
+
+            x = torch.cat([x, m_t, a1_t], dim=1)
+            x = self.fc(x)
+            _, h1 = self.rnn1(x.unsqueeze(1), h1)
+
+            x = x + h1[0]
+            inp = torch.cat([x, a2_t], dim=1)
+            _, h2 = self.rnn2(inp.unsqueeze(1), h2)
+
+            x = x + h2[0]
+            x = torch.cat([x, a3_t], dim=1)
+            x = F.relu(self.fc1(x))
+
+            x = torch.cat([x, a4_t], dim=1)
+            x = F.relu(self.fc2(x))
+
+            logits = self.fc3(x)
+
+            posterior = F.softmax(logits, dim=1)
+
+            x = torch.multinomial(posterior, 1).float()
+            # Transform label [0, 2 ** n_bits - 1] to waveform [-1, 1]
+            x = 2 * x / (2**self.n_bits - 1.0) - 1.0
+
+            output.append(x)
+
+        return torch.stack(output).permute(1, 2, 0), lengths
diff --git a/lib/python3.11/site-packages/torchaudio/version.py b/lib/python3.11/site-packages/torchaudio/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..497be0ddcb55299abcda5ab5c65a0605f39166d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torchaudio/version.py
@@ -0,0 +1,2 @@
+__version__ = '2.6.0+cu124'
+git_version = 'd8831425203385077a03c1d92cfbbe3bf2106008'
diff --git a/lib/python3.11/site-packages/torchgen/__init__.py b/lib/python3.11/site-packages/torchgen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d5dbf0667a022caa07ec30bb10db5b4f83159dd
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/__init__.py
@@ -0,0 +1,10 @@
+"""torchgen
+
+This module contains codegeneration utilities for PyTorch. It is used to
+build PyTorch from source, but may also be used for out-of-tree projects
+that extend PyTorch.
+
+Note well that we provide no BC guarantees for torchgen. If you're interested
+in using torchgen and want the PyTorch team to be aware, please reach out
+on GitHub.
+"""
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a31530c81692a77bb8ef398082ad718fd449798d
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/code_template.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/code_template.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a73c5202dea7eb53a0311398c44fde05800197b7
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/code_template.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/context.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/context.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..440d0b6191900665029408122f7f7d3192b4f472
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/context.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_aoti_c_shim.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_aoti_c_shim.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6f8a6e0372a6838629d1c60cd34426c4aa4063b8
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_aoti_c_shim.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_backend_stubs.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_backend_stubs.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..07ecd4de55ac7c6c51e47d83e0fa673295aaf955
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_backend_stubs.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_executorch.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_executorch.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e444f37b308a4ec3233b1d4aa87f83884f79dc75
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_executorch.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_functionalization_type.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_functionalization_type.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8cb4feca36816a466a273c816b9dabff07a55eed
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_functionalization_type.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_lazy_tensor.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_lazy_tensor.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2dce95ee726d91af4c278919b3558178e87874d6
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_lazy_tensor.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_schema_utils.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_schema_utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e65ff2c93ae505391c75181933c5e254285eed66
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_schema_utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/gen_vmap_plumbing.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/gen_vmap_plumbing.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d7fb84cb422eba130d33f2a956cfd492d7f1f59f
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/gen_vmap_plumbing.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/local.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/local.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..972bd39ca28a413b65fb45af16ff1ff607d00477
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/local.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/native_function_generation.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/native_function_generation.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f2804fa691a00a4e7c47de86b41c9dd432e943b0
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/native_function_generation.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/utils.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5ac8146cabce0dbd896e0b5e2ce67f3ac0fea580
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/__pycache__/yaml_utils.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/__pycache__/yaml_utils.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..688392bb1246fa8ab268718f90855dc9e4afe609
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/__pycache__/yaml_utils.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/aoti/__init__.py b/lib/python3.11/site-packages/torchgen/aoti/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/torchgen/aoti/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/aoti/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..999cd86d25fd7c129a5d606758b0399abd85cecf
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/aoti/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/aoti/__pycache__/fallback_ops.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/aoti/__pycache__/fallback_ops.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..87e5c6467bf256e3ef94249dd46fb2fada298994
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/aoti/__pycache__/fallback_ops.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/aoti/fallback_ops.py b/lib/python3.11/site-packages/torchgen/aoti/fallback_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e40b020ad8f890c5df1d7a24c223fe173687c55
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/aoti/fallback_ops.py
@@ -0,0 +1,156 @@
+# Be extra careful when you edit this file, because it affects AOTInductor ABI compatbility. See
+# https://github.com/pytorch/pytorch/blob/7e86a7c0155295539996e0cf422883571126073e/torchgen/gen.py#L2424-L2436
+# for details.
+#
+# The inductor_fallback_ops list is based on the fallback ops from torch/_inductor/lowering.py.
+# Generally speaking, it is ok to add a new op to the list, but you need to run
+# `python torchgen/gen.py --update-aoti-c-shim` in order to regenerate C shim header files.
+# But it is NOT ok to remove an existing fallback op from the list, since that will break
+# some existing AOTInductor-compiled models.
+inductor_fallback_ops = {
+    "aten._adaptive_avg_pool2d_backward.default",
+    "aten._adaptive_avg_pool2d.default",
+    "aten._adaptive_avg_pool3d.default",
+    "aten._adaptive_avg_pool3d_backward.default",
+    "aten.adaptive_max_pool2d_backward.default",
+    "aten.adaptive_max_pool2d.default",
+    "aten.adaptive_max_pool3d.default",
+    "aten.adaptive_max_pool3d_backward.default",
+    "aten.add.Scalar",
+    "aten.add.Tensor",
+    "aten.addbmm.default",
+    "aten._addmm_activation.default",
+    "aten.addmm.out",
+    "aten.addmv.default",
+    "aten.angle.default",
+    "aten.avg_pool2d_backward.default",
+    "aten.avg_pool2d.default",
+    "aten.avg_pool3d_backward.default",
+    "aten.avg_pool3d.default",
+    "aten.baddbmm.out",
+    "aten.bernoulli_.float",
+    "aten.bernoulli_.Tensor",
+    "aten.bmm.out",
+    "aten.bucketize.Tensor",
+    "aten.cat.default",
+    "aten._cdist_backward.default",
+    "aten._cdist_forward.default",
+    "aten.cholesky_inverse.default",
+    "aten.cholesky_solve.default",
+    "aten.convolution_backward.default",
+    "aten._cudnn_rnn.default",
+    "aten._cudnn_rnn_backward.default",
+    "aten.convolution.default",
+    "aten.cummax.default",
+    "aten.cummin.default",
+    "aten.cumprod.default",
+    "aten.cumsum.default",
+    "aten._efficient_attention_backward.default",
+    "aten._efficient_attention_forward.default",
+    "aten._efficientzerotensor.default",
+    "aten._embedding_bag.default",
+    "aten._embedding_bag_dense_backward.default",
+    "aten._embedding_bag_forward_only.default",
+    "aten._embedding_bag_per_sample_weights_backward.default",
+    "aten.exponential.default",
+    "aten._fft_c2c.default",
+    "aten._fft_r2c.default",
+    "aten._flash_attention_backward.default",
+    "aten._flash_attention_forward.default",
+    "aten.fractional_max_pool2d_backward.default",
+    "aten.fractional_max_pool2d.default",
+    "aten.fractional_max_pool3d.default",
+    "aten.fractional_max_pool3d_backward.default",
+    "aten._fused_moving_avg_obs_fq_helper.default",
+    "aten._fused_moving_avg_obs_fq_helper_functional.default",
+    "aten.gcd.default",
+    "aten.geqrf.default",
+    "aten.grid_sampler_2d_backward.default",
+    "aten.histc.default",
+    "aten.histogram.bin_ct",
+    "aten._histogramdd_bin_edges.default",
+    "aten._histogramdd_from_bin_cts.default",
+    "aten.index_put.default",
+    "aten.index_reduce.default",
+    "aten.index.Tensor",
+    "aten.kthvalue.default",
+    "aten.logcumsumexp.default",
+    "aten.lu_unpack.default",
+    "aten.masked_select.default",
+    "aten.masked_scatter.default",
+    "aten.masked_scatter_backward.default",
+    "aten.max_pool2d_with_indices_backward.default",
+    "aten.max_pool2d_with_indices.default",
+    "aten.max_pool3d_with_indices.default",
+    "aten.max_pool3d_with_indices_backward.default",
+    "aten.max_unpool2d.default",
+    "aten.max_unpool3d.default",
+    "aten.median.default",
+    "aten.mm.out",
+    "aten.mode.default",
+    "aten.mul.Scalar",
+    "aten.mul.Tensor",
+    "aten.nanmedian.default",
+    "aten.native_dropout.default",
+    "aten.normal_functional.default",
+    "aten.nonzero.default",
+    "aten.ormqr.default",
+    "aten._pdist_backward.default",
+    "aten._pdist_forward.default",
+    "aten.polar.default",
+    "aten.pow.Scalar",
+    "aten.pow.Tensor_Scalar",
+    "aten.pow.Tensor_Tensor",
+    "aten.rand.default",
+    "aten.rand.generator",
+    "aten.randint.default",
+    "aten.randint.generator",
+    "aten.randint.low",
+    "aten.randint.low_out",
+    "aten.randn.default",
+    "aten.randn.generator",
+    "aten.randperm.default",
+    "aten.repeat_interleave.Tensor",
+    "aten.replication_pad1d_backward.default",
+    "aten.replication_pad2d_backward.default",
+    "aten.reshape.default",
+    "aten.resize_.default",
+    "aten.resize_as_.default",
+    "aten._scaled_dot_product_efficient_attention_backward.default",
+    "aten._scaled_dot_product_efficient_attention.default",
+    "aten._scaled_dot_product_flash_attention_backward.default",
+    "aten._scaled_dot_product_flash_attention.default",
+    "aten._scaled_dot_product_cudnn_attention_backward.default",
+    "aten._scaled_dot_product_cudnn_attention.default",
+    "aten._scaled_dot_product_flash_attention_for_cpu_backward.default",
+    "aten._scaled_dot_product_flash_attention_for_cpu.default",
+    "aten._scaled_mm.default",
+    "aten._scaled_mm.out",
+    "aten.scatter_reduce.two_out",
+    "aten.scatter.src_out",
+    "aten.scatter.value_out",
+    "aten.searchsorted.Scalar",
+    "aten.searchsorted.Tensor",
+    "aten._segment_reduce_backward.default",
+    "aten.segment_reduce.default",
+    "aten.slice.Tensor",
+    "aten.soft_margin_loss_backward.default",
+    "aten.sort.default",
+    "aten.sort.stable",
+    "aten._sparse_coo_tensor_with_dims_and_tensors.default",
+    "aten._thnn_fused_lstm_cell.default",
+    "aten.topk.default",
+    "aten._to_sparse.default",
+    "aten.to_sparse.default",
+    "aten.triangular_solve.default",
+    "aten._trilinear.default",
+    "aten.uniform.default",
+    "aten.upsample_bicubic2d_backward.default",
+    "aten.upsample_linear1d_backward.default",
+    "aten.upsample_trilinear3d_backward.default",
+    "aten.view_as_complex.default",
+    "aten.view_as_real.default",
+    "aten.view.dtype",
+    "aten._weight_int8pack_mm.default",
+    "aten.zeros.names",
+}
diff --git a/lib/python3.11/site-packages/torchgen/api/__init__.py b/lib/python3.11/site-packages/torchgen/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/torchgen/api/autograd.py b/lib/python3.11/site-packages/torchgen/api/autograd.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f3b793825c9cbd904812eafc5a3162733a2de69
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/autograd.py
@@ -0,0 +1,874 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from typing import cast, TYPE_CHECKING
+
+from torchgen import local
+from torchgen.api import cpp
+from torchgen.api.types import BaseCType, Binding, NamedCType, tensorListT
+from torchgen.model import (
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    NativeFunctionsViewGroup,
+    SchemaKind,
+    Type,
+)
+from torchgen.utils import IDENT_REGEX
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# Represents a saved attribute involved in backward calculation.
+# Note that it can be a derived property of an input argument, e.g.:
+# we could save `other.scalar_type()` instead of the entire `other` tensor.
+@dataclass(frozen=True)
+class SavedAttribute:
+    # The NamedCType holds the updated name and cpp type of the attribute
+    # for the name, Suffix is appended if it's derived property, e.g.: `other_scalar_type`
+    nctype: NamedCType
+
+    # The expression to read the derived property at save time, e.g.:
+    # `other.scalar_type()`.
+    expr: str
+
+
+# Represents a backward formula that calculates derivatives for one
+# or more tensors.
+@dataclass(frozen=True)
+class Derivative:
+    # The formula string (legit C++ expression).
+    # Note that expressions against input arguments have been replaced with the
+    # corresponding saved attributes.
+    # E.g.:
+    #  raw formula: `mul_tensor_backward(grad, self, other.scalar_type())`
+    #         here: `mul_tensor_backward(grad, self, other_scalar_type)`
+    formula: str
+
+    # The formula string before input argument replacement
+    original_formula: str
+
+    # Names of the arguments for which this formula calculates derivatives.
+    var_names: tuple[str, ...]
+
+    # Saved inputs that are referenced by the formula.
+    saved_inputs: tuple[SavedAttribute, ...]
+
+    # Saved outputs that are referenced by the formula.
+    saved_outputs: tuple[SavedAttribute, ...]
+
+    # Gradients that are referenced by name in the formula.
+    named_gradients: set[str]
+
+
+# Represents a forward formula that calculates forward derivatives
+# for one tensor.
+@dataclass(frozen=True)
+class ForwardDerivative:
+    # The formula string (legit C++ expression).
+    # Note that special keywords such as "linear" or "element_wise" have been
+    # replaced by the automatically generated formula.
+    formula: str
+
+    # Name of the output arguments for which this formula calculates forward
+    # derivatives
+    var_names: tuple[str, ...]
+
+    # Type of the output arguments for which this formula calculates forward
+    # derivatives
+    var_types: tuple[Type, ...]
+
+    # Inputs for which the forward derivatives are required for this formula
+    required_inputs_fw_grad: tuple[str, ...] | None
+
+    # Inputs for which the primal is required for this formula
+    required_inputs_primal: tuple[str, ...] | None
+
+    # Flag to specify if this formula requires the original value of self
+    # This is only used by inplace operations
+    required_original_self_value: bool
+
+    # If this formula is specified in derivatives.yaml or if we are re-using the
+    # out of place formula for inplace
+    is_reusing_outplace_formula: bool
+
+
+# Represents differentiability info for a NativeFunction.
+@dataclass(frozen=True)
+class DifferentiabilityInfo:
+    # The base name read from derivatives.yaml.
+    name: str
+
+    # The matching native function.
+    #
+    # There can be multiple NativeFunction having the same base name:
+    #  - different overloads with different types of input arguments;
+    #  - in-place/out/functional variants of the same function;
+    #
+    # We first use the schema string (under the 'name' key) in derivatives.yaml
+    # to find the NativeFunction having the same schema string.
+    # Then we find the in-place/out/functional variants of the matching function.
+    # Among these variants, we choose the one having the same name as the
+    # derivatives.yaml entry. If there is no exact match, then we choose the
+    # in-place variant.
+    # TODO: maybe the logic to search for all variants is no longer necessary?
+    func: NativeFunction
+
+    # The name of the generated autograd function.
+    # It's set only if we will calculate a derivative, i.e.
+    # 'args_with_derivatives' is not empty.
+    op: str | None
+
+    # The derivatives formulae for this function.
+    # Note that the length of this sequence is the number of differentiable inputs
+    derivatives: Sequence[Derivative]
+
+    # The forward derivatives formulae for this function.
+    # Note that the length of this sequence is the number of differentiable outputs
+    forward_derivatives: Sequence[ForwardDerivative]
+
+    # The union of 'saved_inputs' of all 'derivatives'.
+    all_saved_inputs: Sequence[SavedAttribute]
+
+    # The union of 'saved_outputs' of all 'derivatives'.
+    all_saved_outputs: Sequence[SavedAttribute]
+
+    # All named gradients that are available for use, in the same
+    # order as in the grads vector.
+    available_named_gradients: Sequence[str]
+
+    # The named gradients that are used in any of the derivatives.
+    # Invariant: all(name in available_named_gradients for name in used_named_gradients)
+    used_named_gradients: set[str]
+
+    # The function's input arguments for which it calculates derivatives.
+    # It's the union of 'var_names' of all 'derivatives', sorted by the
+    # argument order in the function schema.
+    args_with_derivatives: Sequence[Binding]
+
+    # Names of arguments whose derivative formula is 'non_differentiable'.
+    non_differentiable_arg_names: Sequence[str]
+
+    # Raw data read from derivatives.yaml.
+    output_differentiability: list[bool] | None
+
+    # output_differentiability in derivatives.yaml can be a list of
+    # conditions that express if the output is differentiable. In this case,
+    # the number of conditions must match the number of outputs
+    # (NB: we only support one condition right now).
+    # output_differentiability gets populated with True for each condition,
+    # while output_differentiability_conditions gets populated with the conditions
+    output_differentiability_conditions: list[str] | None
+
+    @property
+    def has_derivatives(self) -> bool:
+        return len(self.args_with_derivatives) > 0
+
+    # Generates a new DifferentiabilityInfo using the exact same set of derivative information,
+    # but with a new operator name.
+    # This is used when generating "copy" variants of view ops,
+    # which are able to use the exact same derivative formula as the original view op
+    # See Note [Codegen'd {view}_copy Operators]
+    def create_view_copy_from_view_derivative(
+        self, g: NativeFunctionsViewGroup
+    ) -> DifferentiabilityInfo | None:
+        if g.view_copy is None:
+            return None
+        f = g.view_copy
+
+        name_split_by_period = self.name.split(".", maxsplit=2)
+        # Append a "_copy" to the base name of the operator (but keep the overload name the same)
+        view_copy_name = f"{name_split_by_period[0]}_copy." + ".".join(
+            name_split_by_period[1:]
+        )
+        view_copy_op_name = None if self.op is None else f"{self.op}_copy"
+
+        return DifferentiabilityInfo(
+            # Use the "_copy" version of name/func/op
+            name=view_copy_name,
+            func=f,
+            op=view_copy_op_name,
+            # But keep all derivative info the same
+            derivatives=self.derivatives,
+            forward_derivatives=self.forward_derivatives,
+            all_saved_inputs=self.all_saved_inputs,
+            all_saved_outputs=self.all_saved_outputs,
+            available_named_gradients=self.available_named_gradients,
+            used_named_gradients=self.used_named_gradients,
+            args_with_derivatives=self.args_with_derivatives,
+            non_differentiable_arg_names=self.non_differentiable_arg_names,
+            output_differentiability=self.output_differentiability,
+            output_differentiability_conditions=self.output_differentiability_conditions,
+        )
+
+
+def uses_ident(info: DifferentiabilityInfo | None, ident: str) -> bool:
+    if info is None:
+        return False
+    for derivative in info.derivatives:
+        formula = derivative.formula
+        if re.search(IDENT_REGEX.format(ident), formula):
+            return True
+    return False
+
+
+def uses_retain_variables(info: DifferentiabilityInfo | None) -> bool:
+    return uses_ident(info, "retain_variables")
+
+
+def uses_single_grad(info: DifferentiabilityInfo | None) -> bool:
+    return uses_ident(info, "grad")
+
+
+# Represents a differentiable `Argument`.
+# How is it different from the `Argument` type?
+# - It's processed Arguments which are differentiable and only used in the
+#   context of the autograd codegen;
+# - It can represent SelfArgument or regular Argument but not TensorOptionsArgument;
+@dataclass(frozen=True)
+class DifferentiableInput:
+    name: str
+    type: Type
+
+    # TODO: only to keep it byte-for-byte compatible with the old codegen, should remove.
+    cpp_type: str
+
+
+# Represents a differentiable `Return`.
+# How it it different from the `Return` type?
+# - The name in `Return` is optional. Here it is always populated using the same
+#   `cpp.return_names()` method.
+#   TODO: some cpp naming logic (e.g. resolving name conflict) might be irrelevant?
+# - It's processed Returns which are differentiable, in compliance with the
+#   `output_differentiability` field defined in derivatives.yaml (if specified),
+#   and are only used in the context of the autograd codegen;
+@dataclass(frozen=True)
+class DifferentiableOutput:
+    name: str
+    type: Type
+
+    # TODO: only to keep it byte-for-byte compatible with the old codegen, should remove.
+    cpp_type: str
+
+
+@dataclass(frozen=True)
+class NativeFunctionWithDifferentiabilityInfo:
+    func: NativeFunction
+    info: dict[str, DifferentiabilityInfo] | None
+    fw_derivatives: dict[str, Sequence[ForwardDerivative]] | None
+
+
+# TODO: Update comment below since it is out of date.
+def dispatch_strategy(fn: NativeFunctionWithDifferentiabilityInfo) -> str:
+    """How are we going to call the underlying implementation of a
+    declaration?  There are two strategies:
+        - use_derived: we want to call the implementation on CPUDoubleType
+          (or a similar, derived Type instance).  Because these derived
+          instances deal in Tensors, not Variables (it's a completely different
+          object, so it doesn't dispatch back to VariableType), code on
+          this dispatch path needs to wrap/unwrap tensors.  If the
+          derived implementation takes and returns tensors, the
+          implementation is usually differentiable (although we also use
+          the derived dispatch path for non-differentiable functions
+          that we still want to dispatch on the derived Type instance;
+          e.g., size())
+        - use_type: we want to call the implementation on Type, because
+          it is implemented concretely, and the functions it invokes will
+          get dispatched back to VariableType (which will ensure that they
+          are differentiable.)
+    """
+    # fn is derived as long as any of its per-key differentiability infos
+    # has_derivatives. dispatch_strategy() is used to guard generation of fns in VariableType
+    # and ADInplaceOrViewType. We want to generate these functions as long as a
+    # derivative is defined for ANY dispatch key.
+    if fn.func.is_abstract or (
+        fn.info is not None and any(info.has_derivatives for info in fn.info.values())
+    ):
+        # If the function is abstract (not implemented on at::Type), we must
+        # call the implementation on the derived type with unpacked tensors.
+
+        # If the function has a derivative specified and is concrete, we could
+        # call either implementation. We prefer the calling the derived
+        # type's implementation with unpacked tensors because it is more
+        # performant in some cases: any internal calls to other ATen functions
+        # won't have the history tracked.
+
+        # If the function has a type dispatched argument (i.e. is a factory),
+        # we prefer calling the derived type's implementation both because it is
+        # more performant and to ensure factory functions return tensors with _version
+        # of 0 (probably not strictly necessary, but nice to have to keeps versions simple
+        # to understand.
+
+        return "use_derived"
+    else:
+        # If the function is concrete (we don't have to override it) and we
+        # didn't declare it in derivatives.yaml, we'll assume that it is
+        # actually implemented out of differentiable functions. (This
+        # assumption might not hold, but then you'll see gradcheck fail.)
+        return "use_type"
+
+
+def is_foreach_func(f: NativeFunction) -> bool:
+    return f.func.name.name.base.startswith("_foreach_")
+
+
+# note(crcrpar): Most foreach functions can reference an out-place `torch` function whose schema kind
+# is functional for their backward derivatives (and forward derivatives in the future), i.e.,
+# they would find such one in `functional_info_by_signature`. There however are some exceptions:
+_foreach_with_inplace_ref = {"_foreach_zero_"}
+_foreach_with_tensor_overload = {
+    "_foreach_add.Tensor",
+    "_foreach_mul.Tensor",
+    "_foreach_div.Tensor",
+}
+# The following do not support the alpha kwarg, which the nonforeach versions support.
+_skip_argument_len_check = {
+    "_foreach_add.Scalar",
+    "_foreach_add_.Scalar",
+    "_foreach_add.ScalarList",
+    "_foreach_add_.ScalarList",
+    "_foreach_sub.Scalar",
+    "_foreach_sub_.Scalar",
+    "_foreach_sub.ScalarList",
+    "_foreach_sub_.ScalarList",
+}
+
+
+# Checks if `function_schema` is a native, non-foreach function which `f`, a foreach function
+# reference to generate derivatives.
+def is_reference_for_foreach(
+    f: NativeFunction,
+    function_schema: FunctionSchema,
+) -> bool:
+    return (
+        f.func.name.name.base.split("_foreach_")[-1] == function_schema.name.name.base
+        and (
+            not function_schema.name.name.inplace
+            or str(f.func.name) in _foreach_with_inplace_ref
+        )
+        and (
+            str(f.func.name) in _skip_argument_len_check
+            or len(f.func.arguments.flat_non_out)
+            == len(function_schema.arguments.flat_non_out)
+        )
+        and all(
+            ref_arg.type in (arg.type, getattr(arg.type, "elem", None))
+            for arg, ref_arg in zip(
+                f.func.arguments.flat_non_out,
+                function_schema.arguments.flat_non_out,
+            )
+        )
+    )
+
+
+# TODO(crcrpar): Avoid hard coding "Default" ideally.
+def gen_foreach_derivativeinfo(
+    foreach_function: NativeFunction,
+    functional_info_by_signature: dict[
+        FunctionSchema, dict[str, DifferentiabilityInfo]
+    ],
+    non_functional_info_by_signature: dict[
+        FunctionSchema, dict[str, DifferentiabilityInfo]
+    ],
+    dispatch_key: str = "Default",
+) -> tuple[DifferentiabilityInfo | None, bool]:
+    """Generate DifferentiabilityInfo for out-place foreach function, return the existing one for in-place.
+
+    The second return value indicates whether the info is generated in this function.
+    """
+    ref_diff_info: DifferentiabilityInfo | None = None
+
+    for function_schema, diff_info in functional_info_by_signature.items():
+        if not is_reference_for_foreach(foreach_function, function_schema):
+            continue
+        ref_diff_info = diff_info[dispatch_key]
+        if ref_diff_info is not None:
+            break
+    # note(crcrpar): It seems like `zero`'s info isn't available in functional_info_by_signature
+    # while the info of `zero_` is in non_functional_info_by_signature
+    if (
+        ref_diff_info is None
+        and foreach_function.func.kind() == SchemaKind.inplace
+        and str(foreach_function.func.name) in _foreach_with_inplace_ref
+    ):
+        for function_schema, diff_info in non_functional_info_by_signature.items():
+            if not is_reference_for_foreach(foreach_function, function_schema):
+                continue
+            ref_diff_info = diff_info[dispatch_key]
+            if ref_diff_info is not None:
+                break
+    if ref_diff_info is None:
+        return None, False
+
+    # non out-place uses the existing Derivative.
+    if foreach_function.func.kind() == SchemaKind.inplace:
+        return ref_diff_info, False
+
+    map_refarg2foreacharg, map_name2arg = {}, {}
+    for i, (arg, ref_arg) in enumerate(
+        zip(
+            foreach_function.func.arguments.flat_non_out,
+            function_schema.arguments.flat_non_out,
+        )
+    ):
+        map_refarg2foreacharg[ref_arg.name] = arg.name
+        map_name2arg[arg.name] = arg
+
+    all_saved_inputs, all_saved_outputs, all_var_names = [], [], []
+    modified_derivative_formulas = []
+    for i, derivative in enumerate(ref_diff_info.derivatives):
+        modified_formula = derivative.formula.replace("grad", "grads[i]").replace(
+            "result", "result[i]"
+        )
+        saved_inputs, saved_outputs = [], []
+        # note(crcrpar): This context seems necessary to call `cpp.argument_type`
+        with local.parametrize(
+            use_const_ref_for_mutable_tensors=foreach_function.use_const_ref_for_mutable_tensors,
+            use_ilistref_for_tensor_lists=foreach_function.part_of_structured_group,
+        ):
+            for ref_input in derivative.saved_inputs:
+                ref_input_jit_name = ref_input.expr.split(".")[0]
+                mapped_name = map_refarg2foreacharg[ref_input_jit_name]
+                if isinstance(map_name2arg[mapped_name].type, ListType):
+                    mapped_expr = mapped_name + "[i]"
+                else:
+                    mapped_expr = mapped_name
+                new_expr = ref_input.expr.replace(ref_input_jit_name, mapped_expr)
+                modified_formula = modified_formula.replace(
+                    cast(str, ref_input.nctype.name), new_expr
+                )
+
+                nctype = cpp.argument_type(map_name2arg[mapped_name], binds=mapped_name)
+                canonical_nctype = NamedCType(
+                    nctype.name, nctype.type.remove_const_ref()
+                )
+                saved_inputs.append(
+                    SavedAttribute(nctype=canonical_nctype, expr=mapped_name)
+                )
+            for ref_output in derivative.saved_outputs:
+                if ref_output.nctype.name == "result":
+                    saved_outputs.append(
+                        SavedAttribute(
+                            nctype=NamedCType(
+                                name="result", type=BaseCType(tensorListT)
+                            ),
+                            expr="result",
+                        )
+                    )
+                else:
+                    raise RuntimeError("")
+        var_names = [map_refarg2foreacharg[var] for var in derivative.var_names]
+        all_var_names.extend(var_names)
+        all_saved_inputs.extend(saved_inputs)
+        all_saved_outputs.extend(saved_outputs)
+        modified_derivative = Derivative(
+            formula=modified_formula,
+            original_formula=derivative.formula,
+            var_names=tuple(var_names),
+            saved_inputs=tuple(saved_inputs),
+            saved_outputs=tuple(saved_outputs),
+            named_gradients=set(),
+        )
+        modified_derivative_formulas.append(modified_derivative)
+
+    with local.parametrize(
+        use_const_ref_for_mutable_tensors=foreach_function.use_const_ref_for_mutable_tensors,
+        use_ilistref_for_tensor_lists=foreach_function.part_of_structured_group,
+    ):
+        args_with_derivatives = [
+            Binding(
+                name=arg.name,
+                nctype=cpp.argument_type(arg, binds=arg.name),
+                argument=arg,
+                default=None,
+            )
+            for arg in foreach_function.func.arguments.flat_non_out
+            if arg.name in all_var_names
+        ]
+
+    forward_derivatives: list[ForwardDerivative] = []
+    fw_derivative: ForwardDerivative
+    for fw_derivative in ref_diff_info.forward_derivatives:
+        var_names: list[str] = list(fw_derivative.var_names)  # type: ignore[no-redef]
+        var_types: list[Type] = list(fw_derivative.var_types)
+        required_inputs_fw_grad: list[str] = []
+        required_inputs_primal: list[str] = []
+        if fw_derivative.required_inputs_fw_grad is not None:
+            required_inputs_fw_grad = list(fw_derivative.required_inputs_fw_grad)
+        if fw_derivative.required_inputs_primal:
+            required_inputs_primal = list(fw_derivative.required_inputs_primal)
+        modified_formula = fw_derivative.formula
+
+        # Foreach's result is TensorList
+        if "result" in modified_formula:
+            modified_formula = fw_derivative.formula.replace("result", "result[i]")
+
+        for foreach_arg, ref_arg in zip(
+            foreach_function.func.arguments.flat_non_out,
+            ref_diff_info.func.func.arguments.flat_non_out,
+        ):
+            # Modify reference forward formula
+            if (
+                isinstance(foreach_arg.type, ListType)
+                and not foreach_arg.type.is_tensor_like()
+            ):
+                # Assuming ScalarList
+                modified_formula = modified_formula.replace(
+                    ref_arg.name, foreach_arg.name + "[i]"
+                )
+            elif foreach_arg.type.is_tensor_like():
+                # Assuming TensorList / Tensor
+                # assert isinstance(foreach_arg.type, ListType), f"{foreach_function.func.name}, {foreach_arg.type}"
+                assert isinstance(foreach_arg.type, ListType) or (
+                    foreach_arg.type == BaseType(BaseTy.Tensor)
+                    and str(foreach_function.func.name) in _foreach_with_tensor_overload
+                ), f"{foreach_function.func.name}, {foreach_arg.type}"
+                for suffix in ("_p", "_t"):
+                    curr_expr = ref_arg.name + suffix
+                    if curr_expr in modified_formula:
+                        new_expr = foreach_arg.name + suffix
+                        modified_formula = modified_formula.replace(curr_expr, new_expr)
+            else:
+                # Assuming Scalar
+                if foreach_arg.name != ref_arg.name:
+                    modified_formula = modified_formula.replace(
+                        ref_arg.name, foreach_arg.name
+                    )
+
+            # note(crcrpar): there should exist a cooler way...
+            for i, name in enumerate(var_names):
+                if name == ref_arg.name:
+                    var_names[i] = foreach_arg.name
+                    var_types[i] = foreach_arg.type
+            for i, name in enumerate(required_inputs_fw_grad):
+                if name == ref_arg.name:
+                    required_inputs_fw_grad[i] = foreach_arg.name
+            for i, name in enumerate(required_inputs_primal):
+                if name == ref_arg.name:
+                    required_inputs_primal[i] = foreach_arg.name
+        forward_derivatives.append(
+            ForwardDerivative(
+                formula=modified_formula,
+                var_names=tuple(var_names),
+                var_types=tuple(var_types),
+                required_inputs_fw_grad=tuple(required_inputs_fw_grad),
+                required_inputs_primal=tuple(required_inputs_primal),
+                required_original_self_value=fw_derivative.required_original_self_value,
+                is_reusing_outplace_formula=fw_derivative.is_reusing_outplace_formula,
+            )
+        )
+
+    return (
+        DifferentiabilityInfo(
+            name=foreach_function.func.name.name.base,
+            func=foreach_function,
+            op=f"Foreach{ref_diff_info.op}{foreach_function.func.name.overload_name}",
+            derivatives=modified_derivative_formulas,
+            forward_derivatives=forward_derivatives,
+            all_saved_inputs=tuple(set(all_saved_inputs)),
+            all_saved_outputs=tuple(set(all_saved_outputs)),
+            available_named_gradients=(),
+            used_named_gradients=set(),
+            args_with_derivatives=args_with_derivatives,
+            non_differentiable_arg_names=[],
+            output_differentiability=None,
+            output_differentiability_conditions=None,
+        ),
+        True,
+    )
+
+
+def match_differentiability_info(
+    native_functions: list[NativeFunction],
+    differentiability_infos: dict[FunctionSchema, dict[str, DifferentiabilityInfo]],
+) -> list[NativeFunctionWithDifferentiabilityInfo]:
+    """Sets the "derivative" key on declarations to matching autograd function
+    In-place functions will use the out-of-place derivative definition if there
+    is no in-place specific derivative.
+    """
+
+    functional_info_by_signature = {
+        schema.signature(strip_default=True): info_dict
+        for schema, info_dict in differentiability_infos.items()
+        if schema.kind() == SchemaKind.functional
+    }
+    non_functional_info_by_signature = {
+        schema.signature(strip_default=True): info_dict
+        for schema, info_dict in differentiability_infos.items()
+        if schema.kind() != SchemaKind.functional
+    }
+
+    def find_info(
+        f: NativeFunction,
+    ) -> tuple[dict[str, DifferentiabilityInfo] | None, bool]:
+        # Don't bother matching info to generated out= variants
+        if "generated" in f.tags and f.func.kind() == SchemaKind.out:
+            return None, False
+
+        # (1) Check for an exact match
+        if f.func in differentiability_infos:
+            return differentiability_infos[f.func], True
+
+        # (2) If no exact match, check if the out-of-place variant
+        # of this operator has a match.
+        # i.e mul() for mul_() or mul_out()
+        # note(crcrpar): Check foreach or not because in-place foreach functions use backward defined for the existing
+        # native functions instead of the out-place counterparts.
+        f_sig = f.func.signature(strip_default=True)
+        if f_sig in functional_info_by_signature and not is_foreach_func(f):
+            return functional_info_by_signature[f_sig], False
+
+        # (3) Some operators have a derivative explicitly defined for the mutable
+        # variant, but get a code-generated out-of-place variant which does *not*
+        # come with a derivative formula.
+        # For the generated out-of-place variant, use the mutable variant's formula
+        # if it exists.
+        if "generated" in f.tags and f_sig in non_functional_info_by_signature:
+            info_dict = non_functional_info_by_signature[f_sig]
+            # See https://github.com/pytorch/pytorch/pull/76320/files#r874816389
+            assert not any(
+                any("self" in str(inpt.nctype.name) for inpt in info.all_saved_inputs)
+                for info in info_dict.values()
+            ), f"""\
+Attempted to convert a derivative formula for a mutable operator
+ to be used by automatically by its functional variant ("{str(f.func)}").
+ this is not currently supported (we'd need to fix up the formula in the codegen)."""
+            return info_dict, False
+
+        # (4) Generate derivative information of foreach functions if none is defined in `derivatives.yaml`
+        if is_foreach_func(f):
+            assert f.func not in differentiability_infos
+            diff_info, is_generated = gen_foreach_derivativeinfo(
+                f,
+                functional_info_by_signature,
+                non_functional_info_by_signature,
+            )
+            if diff_info is None:
+                return None, False
+            # TODO(crcrpar): Avoid hard coding "Default" ideally.
+            diff_info_dict = {"Default": diff_info}
+            if is_generated:
+                differentiability_infos[f.func] = diff_info_dict
+                functional_info_by_signature[f.func] = diff_info_dict
+            return diff_info_dict, is_generated
+
+        return None, False
+
+    result: list[NativeFunctionWithDifferentiabilityInfo] = []
+    for f in native_functions:
+        info_dict, is_exact_match = find_info(f)
+
+        # Currently, the '.strides()' to 'strides_or_error' replacement does not support
+        # 'self' derivatives of an inplace function, so we must check for this case.
+        if f.func.kind() == SchemaKind.inplace and (info_dict is not None):
+            for info in info_dict.values():
+                for derivative in info.derivatives:
+                    if "self" in derivative.var_names:
+                        for saved_input in derivative.saved_inputs:
+                            assert "strides_or_error" not in saved_input.expr, (
+                                "Calling '.strides()' in the 'self' derivative formula of an "
+                                f"in-place function is not supported: {f.func}"
+                            )
+
+        if not info_dict:
+            result.append(
+                NativeFunctionWithDifferentiabilityInfo(
+                    func=f, info=None, fw_derivatives=None
+                )
+            )
+            continue
+
+        fw_derivative_dict: dict[str, Sequence[ForwardDerivative]] = {}
+        for key, info in info_dict.items():
+            if not info.forward_derivatives:
+                fw_derivative_dict[key] = []
+                continue
+
+            forward_derivatives = info.forward_derivatives
+
+            # For functions that have a single def for out-of-place and inplace (like abs())
+            if f.func.kind() == SchemaKind.inplace:
+                # For inplace functions there is a little bit of work to do:
+                #  1) Validate the formula and make sure the input that is modified in not used:
+                #    - If there is a formula for the inplace variant of the function (is_exact_match == True) then
+                #      we make sure that the original value of the input that is being modified inplace (self_p) is
+                #      not used in the formula. Note that the formula can use "original_self_p" here and that would
+                #      trigger a clone of the original input.
+                #    - If we are re-using the out of place formula (is_exact_match == False) then we replace every
+                #      occurrence of self_p and self_t by original_self_p and original_self_t. These will be
+                #      populated by cloned version of the original input (either the clone done by the backward AD
+                #      logic if self is also used in a backward formula or a special clone that we add).
+                #  2) At this point, there cannot be a self_p in the formula.
+                #  3) Change "result" into "self_p" as by design, in the inplace function codegen, the result is
+                #     simply called self (as it is modified inplace).
+                #  4) Update the required primals data in case it used to contain "result" but should now contain
+                #     "self"
+                #  5) If it is not an exact match, the user formula is not modifying the existing forward grad
+                #     inplace as it should. So add some code that makes sure that we do so if the forward grad
+                #     already exists.
+
+                assert (
+                    len(info.forward_derivatives) == 1
+                )  # Only single output inplace should exist
+                fw_info = info.forward_derivatives[0]
+                formula = fw_info.formula
+
+                def replace_self_with_original_self(formula: str, postfix: str) -> str:
+                    def repl(m: re.Match[str]) -> str:
+                        return f"{m.group(1)}original_self{postfix}{m.group(2)}"
+
+                    return re.sub(IDENT_REGEX.format(f"self{postfix}"), repl, formula)
+
+                if re.search(IDENT_REGEX.format("self_p"), formula):
+                    if is_exact_match:
+                        # For manually defined formulas, don't allow the original value to be used
+                        raise RuntimeError(
+                            f'The formula for "{f.func.name}" is using the original value of self '
+                            "that is being modified inplace. This would lead to wrong forward gradients. "
+                            'Please use "result" in the formula only.'
+                        )
+                    else:
+                        # When the original formula is out of place, we save a clone of the primal
+                        # value to be able to access this value if needed
+                        # replace "self_p"/"self_t" from the formula by "original_self_p"/"original_self_t"
+                        formula = replace_self_with_original_self(formula, "_p")
+                        formula = replace_self_with_original_self(formula, "_t")
+
+                # replace "result" from the formula by "self_p"
+                def repl(m: re.Match[str]) -> str:
+                    return f"{m.group(1)}self_p{m.group(2)}"
+
+                formula = re.sub(IDENT_REGEX.format("result"), repl, formula)
+
+                required_primals = fw_info.required_inputs_primal
+                if re.search(IDENT_REGEX.format("self_p"), formula):
+                    required_primals = (
+                        required_primals + ("self",) if required_primals else ("self",)
+                    )
+
+                if not is_exact_match:
+                    # NOTE [In-place forward AD formula Optimization]
+                    #
+                    # This optimization transforms the formula to directly do inplace, i.e.
+                    # instead of self_t.copy_(self_t.op()) we do self_t.op_() when the following are met:
+                    #
+                    # 1) the formula satisfies the pattern: "self_t.op(*args)"
+                    # 2) "op" in (1) needs to be the same as the op the derivative is for
+                    #
+                    # (2) may seem too strict, but currently the only ops that satisfy (1) also satisfy (2)
+                    # If there is a need, we can relax (2) to allow any op that has an in-place variant
+                    is_single_method_on_self_t = False
+                    directly_do_inplace = False
+                    op_name: str | None = None
+                    between_parens: str | None = None
+                    match = re.fullmatch(r"self_t.([\w]*)\((.*)\)", formula)
+                    if match:
+                        op_name, between_parens = match.group(1), match.group(2)
+
+                        # We want to...
+                        #   Match: self_t.op1(other_p.op2(arg))
+                        #   Avoid: self_t.op1(args) + self_t.op2(args)
+                        #   Avoid: self_t.op1(other_p.op2(arg)) + self_t.op2(args)
+                        def check_parens_nest_level_gt_zero(s: str) -> bool:
+                            level = 1
+                            for ch in s:
+                                if ch == ")":
+                                    level -= 1
+                                    if level == 0:
+                                        return False
+                                if ch == "(":
+                                    level += 1
+                            return True
+
+                        is_single_method_on_self_t = check_parens_nest_level_gt_zero(
+                            between_parens
+                        )
+                        directly_do_inplace = (
+                            is_single_method_on_self_t and op_name == info.name
+                        )
+
+                    if directly_do_inplace:
+                        assert op_name is not None
+                        assert between_parens is not None
+                        formula = f"self_t_raw.defined() ? self_t_raw.{op_name}_({between_parens}) : {formula}"
+                    else:
+                        # Make sure that the forward grad is modified inplace when the original formula
+                        # is out of place
+                        formula = f"self_t_raw.defined() ? self_t_raw.copy_({formula}) : {formula}"
+
+                required_original_self_value = bool(
+                    re.search(IDENT_REGEX.format("original_self_p"), formula)
+                ) or bool(re.search(IDENT_REGEX.format("original_self_t"), formula))
+
+                forward_derivatives = [
+                    ForwardDerivative(
+                        formula=formula,
+                        var_names=("self",),
+                        var_types=fw_info.var_types,
+                        required_inputs_fw_grad=fw_info.required_inputs_fw_grad,
+                        required_inputs_primal=required_primals,
+                        required_original_self_value=required_original_self_value,
+                        is_reusing_outplace_formula=not is_exact_match,
+                    ),
+                ]
+
+            fw_derivative_dict[key] = forward_derivatives
+
+        result.append(
+            NativeFunctionWithDifferentiabilityInfo(
+                func=f, info=info_dict, fw_derivatives=fw_derivative_dict
+            )
+        )
+
+    return result
+
+
+def is_differentiable(
+    name: str, type: Type, info: DifferentiabilityInfo | None
+) -> bool:
+    return type.is_tensor_like() and (
+        info is None or name not in info.non_differentiable_arg_names
+    )
+
+
+def gen_differentiable_outputs(
+    fn: NativeFunctionWithDifferentiabilityInfo, key: str = "Default"
+) -> list[DifferentiableOutput]:
+    f = fn.func
+    info = fn.info[key] if fn.info else None
+    outputs: list[DifferentiableOutput] = [
+        DifferentiableOutput(
+            name=name,
+            type=ret.type,
+            cpp_type=cpp.return_type(ret, symint=True).cpp_type(),
+        )
+        for name, ret in zip(cpp.return_names(f), f.func.returns)
+    ]
+    output_differentiability = info.output_differentiability if info else None
+    if output_differentiability is not None:
+        if len(output_differentiability) != len(outputs):
+            raise RuntimeError(
+                f"The length of output_differentiability ({len(output_differentiability)}), "
+                f"does not match the number of outputs ({len(outputs)})."
+            )
+        differentiable_outputs: list[DifferentiableOutput] = []
+        if False in output_differentiability and f.func.kind() == SchemaKind.inplace:
+            raise RuntimeError(
+                "output_differentiability=False for inplace operation (version_counter won't get updated)"
+            )
+        for differentiable, output in zip(output_differentiability, outputs):
+            if differentiable:
+                differentiable_outputs.append(output)
+        return differentiable_outputs
+    candidate_differentiable_outputs = list(
+        filter(lambda r: is_differentiable(r.name, r.type, info), outputs)
+    )
+    if uses_single_grad(info):
+        return candidate_differentiable_outputs[:1]
+    else:
+        return candidate_differentiable_outputs
diff --git a/lib/python3.11/site-packages/torchgen/api/cpp.py b/lib/python3.11/site-packages/torchgen/api/cpp.py
new file mode 100644
index 0000000000000000000000000000000000000000..c46f265b515feecb94dbb0ae07832ad8de5a64e2
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/cpp.py
@@ -0,0 +1,474 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from torchgen import local
+from torchgen.api.types import (
+    ArgName,
+    ArrayCType,
+    ArrayRefCType,
+    BaseCType,
+    BaseTypeToCppMapping,
+    Binding,
+    boolT,
+    ConstRefCType,
+    CType,
+    dimnameListT,
+    intArrayRefT,
+    iTensorListRefT,
+    ListCType,
+    longT,
+    MutRefCType,
+    NamedCType,
+    OptionalCType,
+    optionalIntArrayRefT,
+    optionalSymIntArrayRefT,
+    scalarT,
+    SpecialArgName,
+    symIntArrayRefT,
+    SymIntT,
+    tensorListT,
+    tensorOptionsT,
+    tensorT,
+    TupleCType,
+    VectorCType,
+    voidT,
+)
+from torchgen.model import (
+    Argument,
+    Arguments,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    OptionalType,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# This file describes the translation of JIT schema to the public C++
+# API, which is what people use when they call functions like at::add.
+#
+# Prominent characteristics of the C++ API:
+#
+#   - dtype, layout, device and pin_memory are collected into
+#     a single C++ type TensorOptions  (the native functions API
+#     also has this, but tensor options is really most relevant
+#     for the C++ API; it makes calling kwarg factory functions
+#     pleasant)
+#
+#   - defaulting lives here (in fact, the dispatcher is completely
+#     oblivious of defaults!)
+#
+# BTW: policy on name collisions: we try not to have types with
+# collisions, but functions are fair game to collide
+
+
+def name(
+    func: FunctionSchema,
+    *,
+    faithful_name_for_out_overloads: bool = False,
+    symint_overload: bool = False,
+) -> str:
+    name = str(func.name.name)
+    if symint_overload:
+        name += "_symint"
+    if func.is_out_fn():
+        if faithful_name_for_out_overloads:
+            name += "_outf"
+        else:
+            name += "_out"
+
+    return name
+
+
+# Translation of "value types" in JIT schema to C++ API type.  Value
+# types look the same no matter if they are argument types or return
+# types.  Returns None if the type in question is not a value type.
+def valuetype_type(
+    t: Type,
+    *,
+    binds: ArgName,
+    mutable: bool = True,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = False,
+) -> NamedCType | None:
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor or t.name == BaseTy.Scalar:
+            return None
+        elif str(t) == "SymInt":
+            if symint:
+                return NamedCType(binds, BaseCType(SymIntT))
+            else:
+                return NamedCType(binds, BaseCType(longT))
+        if remove_non_owning_ref_types:
+            if t.name == BaseTy.str:
+                raise AssertionError(
+                    "string ref->value conversion: not implemented yet"
+                )
+        # All other BaseType currently map directly to BaseCppTypes.
+        return NamedCType(binds, BaseCType(BaseTypeToCppMapping[t.name]))
+    elif isinstance(t, OptionalType):
+        elem = valuetype_type(t.elem, binds=binds, mutable=mutable, symint=symint)
+        if elem is None:
+            return None
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        if str(t.elem) == "bool":
+            assert t.size is not None
+            return NamedCType(binds, ArrayCType(BaseCType(boolT), t.size))
+        else:
+            return None
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+# Translation of types occurring in JIT arguments to a C++ argument type.
+# If remove_non_owning_ref_types is set, we'll guarantee that the outputed CType is not a non-owning reference type.
+# For example, we'll return std::vector<int> instead of IntArrayRef.
+# See Note [translation from C++ reference to value types]
+def argumenttype_type(
+    t: Type,
+    *,
+    mutable: bool,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = False,
+) -> NamedCType:
+    # If it's a value type, do the value type translation
+    r = valuetype_type(
+        t,
+        binds=binds,
+        mutable=mutable,
+        symint=symint,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+    )
+    if r is not None:
+        return r
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            if mutable and not local.use_const_ref_for_mutable_tensors():
+                return NamedCType(binds, MutRefCType(BaseCType(tensorT)))
+            else:
+                return NamedCType(binds, ConstRefCType(BaseCType(tensorT)))
+        elif t.name == BaseTy.Scalar:
+            return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+        else:
+            raise AssertionError(f"base type should have been value type {t}")
+    elif isinstance(t, OptionalType):
+        if str(t.elem) == "Tensor":
+            if mutable and not local.use_const_ref_for_mutable_tensors():
+                return NamedCType(
+                    binds, MutRefCType(BaseCType(tensorT))
+                )  # TODO: fix this discrepancy
+            else:
+                return NamedCType(
+                    binds, ConstRefCType(OptionalCType(BaseCType(tensorT)))
+                )
+        elif str(t.elem) == "Scalar":
+            return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
+        elif isinstance(t.elem, ListType) and str(t.elem.elem) == "int":
+            return NamedCType(binds, BaseCType(optionalIntArrayRefT))
+        elif isinstance(t.elem, ListType) and str(t.elem.elem) == "SymInt":
+            if symint:
+                return NamedCType(binds, BaseCType(optionalSymIntArrayRefT))
+            else:
+                return NamedCType(binds, BaseCType(optionalIntArrayRefT))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds, symint=symint)
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        # TODO: remove these special cases, ArrayRef fallthrough works fine
+        if str(t.elem) == "int":
+            if remove_non_owning_ref_types:
+                return NamedCType(binds, VectorCType(BaseCType(longT)))
+            else:
+                return NamedCType(binds, BaseCType(intArrayRefT))
+        if str(t.elem) == "SymInt":
+            if remove_non_owning_ref_types:
+                if symint:
+                    return NamedCType(binds, VectorCType(BaseCType(SymIntT)))
+                else:
+                    return NamedCType(binds, VectorCType(BaseCType(longT)))
+            else:
+                if symint:
+                    return NamedCType(binds, BaseCType(symIntArrayRefT))
+                else:
+                    return NamedCType(binds, BaseCType(intArrayRefT))
+        if str(t.elem) == "Tensor":
+            if local.use_ilistref_for_tensor_lists():
+                return NamedCType(binds, ConstRefCType(BaseCType(iTensorListRefT)))
+            else:
+                return NamedCType(binds, BaseCType(tensorListT))
+        elif str(t.elem) == "Scalar":
+            return NamedCType(binds, ArrayRefCType(BaseCType(scalarT)))
+        elif str(t.elem) == "Dimname":
+            return NamedCType(binds, BaseCType(dimnameListT))
+        elif str(t.elem) == "Tensor?":
+            return NamedCType(
+                binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT))))
+            )
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds, symint=symint)
+        return NamedCType(binds, ArrayRefCType(elem.type))
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+# Translate a JIT argument into its C++ type
+def argument_type(a: Argument, *, binds: ArgName, symint: bool = False) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, symint=symint, binds=binds)
+
+
+# Translation of a (non-multi) return type from JIT to C++
+# N.B: returntype_type returns a CType, not a NamedCType.
+# This is mostly because of the mismatch between return types and return names.
+# e.g. a function with a return type of 'void' has 0 return names,
+# and a function with a return type of 'std::tuple' has >1 return name.
+def returntype_type(t: Type, *, mutable: bool, symint: bool = False) -> CType:
+    # placeholder is ignored
+    # NB: symint is ALWAYS respected for return types.  So symint argument
+    # here is IGNORED
+    r = valuetype_type(t, binds="__placeholder__", mutable=mutable, symint=True)
+    if r is not None:
+        return r.type
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            if mutable:
+                if local.use_const_ref_for_mutable_tensors():
+                    return ConstRefCType(BaseCType(tensorT))
+                else:
+                    return MutRefCType(BaseCType(tensorT))
+            else:
+                # Note [Tensor Copy Returns]
+                # Currently, we use "Argument.is_write" to determine
+                # whether or not Tensor return types should be copies or references.
+                # If that ever changes, take a look at other locations of this note!
+                return BaseCType(tensorT)
+        elif t.name == BaseTy.Scalar:
+            return BaseCType(scalarT)
+    elif isinstance(t, ListType):
+        assert not mutable, "Native functions should never return a mutable tensor list. They should return void."
+        elem = returntype_type(t.elem, mutable=False)
+        assert t.size is None, f"fixed size list returns not supported: {t}"
+        return VectorCType(elem)
+    elif isinstance(t, OptionalType):
+        elem = returntype_type(t.elem, mutable=mutable)
+        if str(t.elem) == "Tensor":
+            return OptionalCType(elem)
+
+    raise AssertionError(f"unrecognized return type {t}")
+
+
+# Translation of a single return to its C++ type
+def return_type(r: Return, *, symint: bool = False) -> CType:
+    return returntype_type(r.type, mutable=r.is_write, symint=symint)
+
+
+# Translation of a full (possibly multi) return from JIT to its C++ type
+def returns_type(rs: Sequence[Return], *, symint: bool = False) -> CType:
+    if len(rs) == 0:
+        return BaseCType(voidT)
+    elif len(rs) == 1:
+        return return_type(rs[0], symint=symint)
+    else:
+        return TupleCType([return_type(r, symint=symint) for r in rs])
+
+
+def return_names(f: NativeFunction, *, fallback_name: str = "result") -> Sequence[str]:
+    returns: list[str] = []
+    for i, r in enumerate(f.func.returns):
+        # If we have an inplace function, the return argument is
+        # implicitly named self.
+        # TODO: Consider incorporating this into the data model
+        if f.func.name.name.inplace:
+            assert i == 0, "illegal inplace function with multiple returns"
+            name = "self"
+        # If we are out function, the name is the name of the
+        # corresponding output function (r.name will get recorded
+        # in field_name later.)
+        elif f.func.is_out_fn():
+            name = f.func.arguments.out[i].name
+        # If the return argument is explicitly named...
+        elif r.name:
+            name_conflict = any(
+                r.name == a.name for a in f.func.schema_order_arguments()
+            )
+            if name_conflict and not f.func.is_out_fn():
+                name = f"{r.name}_return"
+            else:
+                name = r.name
+        # If there is no explicit name and no fallback name was passed in, we just name the output result,
+        # unless it's a multi-return, in which case it's result0,
+        # result1, etc (zero-indexed)
+        else:
+            name = fallback_name if len(f.func.returns) == 1 else f"{fallback_name}{i}"
+        returns.append(name)
+    return returns
+
+
+JIT_TO_CPP_DEFAULT = {
+    "False": "false",
+    "True": "true",
+    "None": "::std::nullopt",  # UGH this one is type directed
+    "Mean": "at::Reduction::Mean",
+    "[]": "{}",
+    "contiguous_format": "c10::MemoryFormat::Contiguous",
+    "long": "at::kLong",
+}
+
+
+# Convert a JIT default into C++ expression representing the default
+def default_expr(d: str, t: Type, *, symint: bool) -> str:
+    if d == "None" and str(t) == "Tensor?":
+        return "{}"
+    if isinstance(t, BaseType) and t.name is BaseTy.str:
+        # Schema allows single quotes but C++ needs double
+        if len(d) >= 2 and d[0] == "'" and d[-1] == "'":
+            s = ""
+            i = 1
+            while i + 1 < len(d):
+                if d[i] != "\\":
+                    if d[i] == '"':
+                        s += '\\"'
+                    else:
+                        s += d[i]
+                    i += 1
+                else:
+                    if d[i + 1] == "'":
+                        s += "'"
+                    else:
+                        s += d[i : i + 2]
+                    i += 2
+
+            return f'"{s}"'
+
+    if isinstance(t, OptionalType):
+        if d == "None":
+            return "::std::nullopt"
+
+        return default_expr(d, t.elem, symint=symint)
+
+    if isinstance(t, ListType):
+        if d.startswith("[") and d.endswith("]"):
+            return "{" + d[1:-1] + "}"
+        elif symint and d.isdigit() and str(t.elem) == "SymInt":
+            return f"c10::SymInt({d})"
+        elif t.size is None:
+            # NOTE: Sized lists can have scalar defaults
+            raise ValueError(f"Expected a list default '[...]' but found: '{d}'")
+
+    return JIT_TO_CPP_DEFAULT.get(d, d)
+
+
+# Convert an argument into its C++ API form
+
+
+def argument(
+    a: Argument | TensorOptionsArguments | SelfArgument,
+    *,
+    cpp_no_default_args: set[str],
+    method: bool,
+    faithful: bool,
+    symint: bool = False,
+    has_tensor_options: bool,
+) -> list[Binding]:
+    def sub_argument(
+        a: Argument | TensorOptionsArguments | SelfArgument,
+    ) -> list[Binding]:
+        return argument(
+            a,
+            cpp_no_default_args=cpp_no_default_args,
+            method=method,
+            faithful=faithful,
+            symint=symint,
+            has_tensor_options=has_tensor_options,
+        )
+
+    if isinstance(a, Argument):
+        binds: ArgName
+        if a.name == "memory_format" and has_tensor_options:
+            binds = SpecialArgName.possibly_redundant_memory_format
+        else:
+            binds = a.name
+        default: str | None = None
+        if a.name not in cpp_no_default_args and a.default is not None:
+            default = default_expr(a.default, a.type, symint=symint)
+        return [
+            Binding(
+                nctype=argument_type(a, binds=binds, symint=symint),
+                name=a.name,
+                default=default,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, TensorOptionsArguments):
+        if faithful:
+            return (
+                sub_argument(a.dtype)
+                + sub_argument(a.layout)
+                + sub_argument(a.device)
+                + sub_argument(a.pin_memory)
+            )
+        else:
+            default = None
+            # Enforced by NativeFunction.__post_init__
+            assert "options" not in cpp_no_default_args
+            if all(x.default == "None" for x in a.all()):
+                default = "{}"
+            elif a.dtype.default == "long":
+                default = "at::kLong"  # TODO: this is wrong
+            return [
+                Binding(
+                    nctype=NamedCType("options", BaseCType(tensorOptionsT)),
+                    name="options",
+                    default=default,
+                    argument=a,
+                )
+            ]
+    elif isinstance(a, SelfArgument):
+        if method:
+            # Caller is responsible for installing implicit this in context!
+            return []
+        else:
+            return sub_argument(a.argument)
+    else:
+        assert_never(a)
+
+
+def arguments(
+    arguments: Arguments,
+    *,
+    faithful: bool,
+    symint: bool = False,
+    method: bool,
+    cpp_no_default_args: set[str],
+) -> list[Binding]:
+    args: list[Argument | TensorOptionsArguments | SelfArgument] = []
+    if faithful:
+        args.extend(arguments.non_out)
+        args.extend(arguments.out)
+    else:
+        args.extend(arguments.out)
+        args.extend(arguments.non_out)
+    return [
+        r.no_default() if faithful else r
+        for a in args
+        for r in argument(
+            a,
+            faithful=faithful,
+            symint=symint,
+            method=method,
+            has_tensor_options=arguments.tensor_options is not None,
+            cpp_no_default_args=cpp_no_default_args,
+        )
+    ]
diff --git a/lib/python3.11/site-packages/torchgen/api/dispatcher.py b/lib/python3.11/site-packages/torchgen/api/dispatcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cc6186d7e0e32acf0b850bc2327512e19a5a8fe
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/dispatcher.py
@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+import itertools
+from typing import TYPE_CHECKING
+
+from torchgen.api import cpp
+from torchgen.api.types import ArgName, Binding, CType, NamedCType
+from torchgen.model import (
+    Argument,
+    FunctionSchema,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never, concatMap
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# This file describes the translation of JIT schema to the dispatcher
+# API, the *unboxed* calling convention by which invocations through
+# the dispatcher are made.  Historically, the dispatcher API matched
+# the C++ API, but with the establishment of the boxed API, we've
+# made changes to the dispatcher API to so that the unboxed API
+# better aligns with the boxed API.  The dispatcher API hooks heavily
+# into our template based boxing/unboxing machinery, so changes
+# to this convention will usually need template updates too.
+#
+# Prominent characteristics of the dispatcher API:
+#
+#   - dtype, layout, device and pin_memory are represented as separate
+#     arguments.
+#
+
+
+def name(func: FunctionSchema) -> str:
+    return cpp.name(func)
+
+
+def argumenttype_type(
+    t: Type,
+    *,
+    mutable: bool,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = True,
+) -> NamedCType:
+    # This is a faux amis.  If it makes sense in the future to add
+    # more special cases here, or invert things so cpp.argument_type
+    # calls this, or just completely inline the function, please do
+    # it.
+    return cpp.argumenttype_type(
+        t,
+        mutable=mutable,
+        binds=binds,
+        symint=symint,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+    )
+
+
+def argument_type(
+    a: Argument,
+    *,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = True,
+) -> NamedCType:
+    return argumenttype_type(
+        a.type,
+        mutable=a.is_write,
+        binds=binds,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+        symint=symint,
+    )
+
+
+def returns_type(rs: Sequence[Return], *, symint: bool = True) -> CType:
+    # At present, there is no difference. But there could be!
+    return cpp.returns_type(rs, symint=symint)
+
+
+def jit_arguments(func: FunctionSchema) -> list[Argument]:
+    def to_argument(
+        a: Argument | TensorOptionsArguments | SelfArgument,
+    ) -> list[Argument]:
+        if isinstance(a, Argument):
+            return [a]
+        elif isinstance(a, SelfArgument):
+            return [a.argument]
+        elif isinstance(a, TensorOptionsArguments):
+            return [a.dtype, a.layout, a.device, a.pin_memory]
+        else:
+            assert_never(a)
+
+    return list(
+        concatMap(
+            to_argument,
+            itertools.chain(
+                func.arguments.positional, func.arguments.kwarg_only, func.arguments.out
+            ),
+        )
+    )
+
+
+def argument(
+    a: Argument, *, remove_non_owning_ref_types: bool = False, symint: bool = True
+) -> Binding:
+    return Binding(
+        nctype=argument_type(
+            a,
+            binds=a.name,
+            remove_non_owning_ref_types=remove_non_owning_ref_types,
+            symint=symint,
+        ),
+        name=a.name,
+        argument=a,
+    )
+
+
+def arguments(func: FunctionSchema, *, symint: bool = True) -> list[Binding]:
+    return [argument(a, symint=symint) for a in jit_arguments(func)]
diff --git a/lib/python3.11/site-packages/torchgen/api/functionalization.py b/lib/python3.11/site-packages/torchgen/api/functionalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..93667e39b17fa4ba82414fa92bb7200faf6f6515
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/functionalization.py
@@ -0,0 +1,199 @@
+from __future__ import annotations
+
+from torchgen.api import dispatcher
+from torchgen.api.types import (
+    BaseCppType,
+    BaseCType,
+    Binding,
+    boolT,
+    ConstRefCType,
+    CType,
+    longT,
+    NamedCType,
+    tensorT,
+)
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    NativeFunction,
+    NativeFunctionsViewGroup,
+)
+
+
+# This file describes the translation of JIT schema to API's used
+# when creating view lambdas that are used by the functionalization pass.
+# There are two types of lambdas: forward lambdas and reverse lambdas.
+# These API's mostly follow the dispatcher API, with a few quirks:
+# - The lambda capture has to convert reference types to value types
+# - While the forward lambda just directly calls into the at::_ops API
+#   (following the dispatcher convention), the logic here for the reverse lambda
+#   is responsible for generating both the call-site, and the declarations
+#   (which are implemented manually in the at::functionalization::impl namespace).
+
+# The lambdas generated for each view op in the functionalization pass are of the form
+# [capture_arguments](outer_arguments) -> returns_type {
+#     return name(inner_arguments);
+# }
+
+# Define some specific lambda input arguments.
+base_binding = Binding(
+    name="base",
+    nctype=NamedCType(name="base", type=ConstRefCType(BaseCType(tensorT))),
+    argument=Argument(
+        name="base", type=BaseType(BaseTy.Tensor), default=None, annotation=None
+    ),
+    default=None,
+)
+mutated_view_binding = Binding(
+    name="mutated_view",
+    nctype=NamedCType(name="mutated_view", type=ConstRefCType(BaseCType(tensorT))),
+    argument=Argument(
+        name="base", type=BaseType(BaseTy.Tensor), default=None, annotation=None
+    ),
+    default=None,
+)
+mutated_view_idx_binding = Binding(
+    name="mutated_view_idx",
+    nctype=NamedCType(name="mutated_view_idx", type=BaseCType(longT)),
+    argument=Argument(
+        name="base", type=BaseType(BaseTy.Tensor), default=None, annotation=None
+    ),
+    default=None,
+)
+reapply_views_binding = Binding(
+    name="reapply_views",
+    nctype=NamedCType(name="reapply_views", type=BaseCType(boolT)),
+    argument=Argument(
+        name="reapply_views", type=BaseType(BaseTy.bool), default=None, annotation=None
+    ),
+    default=None,
+)
+
+InverseReturnModeT = BaseCppType("at::functionalization", "InverseReturnMode")
+inverse_return_mode_binding = Binding(
+    name="inverse_return_mode",
+    nctype=NamedCType(name="inverse_return_mode", type=BaseCType(InverseReturnModeT)),
+    argument=Argument(
+        name="inverse_return_mode",
+        # NB: not actually a bool but it doesn't matter because this isn't used
+        type=BaseType(BaseTy.bool),
+        default=None,
+        annotation=None,
+    ),
+    default=None,
+)
+
+
+# The lambda capture itself doesn't have a name.
+# The name returned here corresponds to the name of the inner function called by the lambda.
+def name(
+    g: NativeFunctionsViewGroup,
+    *,
+    is_reverse: bool,
+    include_namespace: bool,
+    reapply_views: bool | None = None,
+) -> str:
+    if reapply_views is None:
+        # reapply_views is only important for the fwd lambda,
+        # since we always plumb the runtime "reapply_views" argument into the reverse function.
+        assert is_reverse
+    if is_reverse:
+        return reverse_name(g.view, include_namespace)
+    # in the forward case, we just directly call into the at::_ops API (so we always need the namespace)
+    assert include_namespace
+    assert g.view_copy is not None
+    api_name = (
+        g.view.func.name.unambiguous_name()
+        if reapply_views
+        else g.view_copy.func.name.unambiguous_name()
+    )
+    return f"at::_ops::{api_name}::call"
+
+
+def reverse_name(f: NativeFunction, include_namespace: bool) -> str:
+    # for the reverse: we plumb the "reapply_views" flag into that function and support
+    # both copy and non-copy variants. (We could avoid doing that, but that would require
+    # writing out twice as many view inverse functions).
+    api_name = f.func.name.unambiguous_name()
+    # in the reverse case, we codegen both the call-sites (which need the full namespace) and the declarations (which don't)
+    if include_namespace:
+        return f"at::functionalization::FunctionalInverses::{api_name}_inverse"
+    else:
+        return f"{api_name}_inverse"
+
+
+def capture_arguments(func: FunctionSchema, *, is_reverse: bool) -> list[Binding]:
+    # capture arguments include all arguments except `self`.
+    # Importantly, they don't include any C++ reference types (or else we'll get a dangling reference in the capture),
+    # So any reference types (IntArrayRef) need to be converted to value types (vector<int64_t>)
+    args = func.arguments.flat_all
+    assert args[0].type == BaseType(BaseTy.Tensor)
+    non_self_args = args[1:]
+    non_self_value_bindings = [
+        dispatcher.argument(a, remove_non_owning_ref_types=True) for a in non_self_args
+    ]
+
+    all_bindings = [
+        inverse_return_mode_binding if is_reverse else reapply_views_binding
+    ]
+    all_bindings.extend(non_self_value_bindings)
+    return all_bindings
+
+
+def returns_type(func: FunctionSchema) -> CType:
+    # Assertion: all view ops return tensor-like outputs
+    assert len(func.returns) >= 1
+    for ret in func.returns:
+        assert ret.type.is_tensor_like()
+    # However, the return type of the lambda is always an individual tensor.
+    # For multi-tensor outputs, each tensor needs to be tracked individually.
+    return BaseCType(tensorT)
+
+
+def outer_arguments(*, is_reverse: bool) -> list[Binding]:
+    if is_reverse:
+        return [base_binding, mutated_view_binding, mutated_view_idx_binding]
+    else:
+        return [base_binding, mutated_view_idx_binding]
+
+
+def inner_call_index(func: FunctionSchema) -> Binding | None:
+    # For view ops that return multiple tensors (like `split`), we generate a separate lambda for each output.
+    # When we replay a view op that returns multiple tensors, we need to index into the output appropriately
+    if len(func.returns) > 1 or (
+        len(func.returns) == 1 and func.returns[0].type.is_list_like()
+    ):
+        return mutated_view_idx_binding
+    return None
+
+
+def inner_arguments(func: FunctionSchema, is_reverse: bool) -> list[Binding]:
+    args = func.arguments.flat_all
+    assert args[0].type == BaseType(BaseTy.Tensor)
+    non_self_args = args[1:]
+    # The forward lambda calls the at::_ops API, while the reverse lambda calls the view inverse API.
+    # Both of these follow the dispatcher API.
+    non_self_bindings = [dispatcher.argument(a) for a in non_self_args]
+    if not is_reverse:
+        # the forward lambda swaps out the original tensor argument with the lambd arg "base"
+        return [base_binding] + non_self_bindings
+    else:
+        # the reverse lambda does the same, but with an additional "mutated_view" arg
+        # additionally, we have a calling convention: for view ops that return multiple tensor outputs
+        # their corresponding view_inverse function takes in an additional index argument.
+        index_binding = inner_call_index(func)
+        if index_binding is not None:
+            return [
+                base_binding,
+                mutated_view_binding,
+                inverse_return_mode_binding,
+                index_binding,
+            ] + non_self_bindings
+        else:
+            return [
+                base_binding,
+                mutated_view_binding,
+                inverse_return_mode_binding,
+            ] + non_self_bindings
diff --git a/lib/python3.11/site-packages/torchgen/api/lazy.py b/lib/python3.11/site-packages/torchgen/api/lazy.py
new file mode 100644
index 0000000000000000000000000000000000000000..1422a5d738368ede2a7350c04bef0994ff9e942a
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/lazy.py
@@ -0,0 +1,468 @@
+from __future__ import annotations
+
+from typing import Any
+
+from torchgen.api.types import (
+    BaseCppType,
+    BaseCType,
+    boolT,
+    CType,
+    deviceT,
+    doubleT,
+    generatorT,
+    layoutT,
+    ListCType,
+    longT,
+    memoryFormatT,
+    NamedCType,
+    OptionalCType,
+    scalarT,
+    scalarTypeT,
+    stringT,
+    SymIntT,
+    VectorCType,
+)
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    OperatorName,
+    OptionalType,
+    Return,
+    TensorOptionsArguments,
+    Type,
+)
+
+
+_valueT: BaseCppType | None = None
+
+
+# A ValueT is an IR type which represents the computation of a Tensor.  In other
+# words, a PyTorch user will do operations on lazy tensors, and each output lazy
+# tensor internally tracks a ValueT representing the IR node that would have
+# actually produced the value of this tensor for real.
+#
+# This is configurable because different lazy tensor backends (LTC vs XLA) will
+# have different IR representations.  (Though, arguably, after unification they
+# shouldn't!)
+def getValueT() -> BaseCppType:
+    global _valueT
+    if not _valueT:
+        raise NotImplementedError(
+            "The value type needs to be set with setValueT() in run_gen_lazy_tensor()"
+        )
+
+    return _valueT
+
+
+def setValueT(val: BaseCppType) -> None:
+    global _valueT
+    _valueT = val
+
+
+# this is a bad hack. I need to refactor the data model to represent each arg in the schema as an object,
+# making it easier to represent special properties of an arg.
+tensorListValueT = BaseCppType("torch::lazy", "Value")
+
+
+def process_ir_type(
+    typ: Type, properties: LazyIrProperties, *, symint: bool
+) -> BaseCType | VectorCType | OptionalCType | ListCType:
+    """
+    This function takes a type from NativeFunctions and converts it for use with
+    lazy tensor codegen.
+
+    Type conversion for lazy currently consists of
+     (1) changing at::Tensors into lazy::Values
+     (2) wrapping everything in a BaseCType
+     (3) making cpp-reference types into cpp-value types (e.g. vector instead of IntArrayRef)
+
+    (1) converts at::Tensors to lazy::Values (which wrap lazy::Nodes, with which Lazy IR represents tensors.)
+    There is special handling for Optional[Tensor] or list[Tensor], etc- hence 'tensor-like'
+
+    This is incomplete- there are assertions in places that it's expected to need to add
+    more types as the codegen is used with more operators.
+    """
+    if isinstance(typ, BaseType):
+        if typ.name == BaseTy.Tensor:
+            return BaseCType(getValueT())
+        elif typ.name == BaseTy.Scalar:
+            if properties.TreatScalarsAsConstants:
+                return BaseCType(scalarT)
+            # at::scalar has special handling,
+            # and is wrapped in an lazy::Value just like at::tensor
+            return BaseCType(getValueT())
+        elif typ.name == BaseTy.ScalarType:
+            return BaseCType(scalarTypeT)
+        elif typ.name == BaseTy.int:
+            return BaseCType(longT)
+        elif typ.name == BaseTy.SymInt:
+            if symint:
+                return BaseCType(getValueT())
+            else:
+                return BaseCType(longT)
+        elif typ.name == BaseTy.bool:
+            return BaseCType(boolT)
+        elif typ.name == BaseTy.float:
+            return BaseCType(doubleT)
+        elif typ.name == BaseTy.str:
+            return BaseCType(stringT)
+        elif typ.name == BaseTy.Device:
+            return BaseCType(deviceT)
+        elif typ.name == BaseTy.Generator:
+            return BaseCType(generatorT)
+        elif typ.name == BaseTy.Layout:
+            return BaseCType(layoutT)
+        elif typ.name == BaseTy.MemoryFormat:
+            return BaseCType(memoryFormatT)
+        else:
+            raise AssertionError(f"TODO add support for type {repr(typ)}")
+    elif isinstance(typ, OptionalType):
+        return OptionalCType(process_ir_type(typ.elem, properties, symint=symint))
+    elif isinstance(typ, ListType):
+        if str(typ.elem) == "Tensor?":
+            # TODO(whc) is this actually correct? or should it use a Vector like above
+            return ListCType(OptionalCType(BaseCType(getValueT())))
+        elif str(typ.elem) == "Tensor":
+            # this is a TensorList which comes in from GetTensorList as a Value
+            return BaseCType(tensorListValueT)
+        elif typ.elem == BaseType(BaseTy.SymInt):
+            # TODO: return a value type.  The problem here is analogous to
+            # the problem with tensorListValueT: if you have SymInt[] you
+            # cannot conveniently save the list of Value directly, as nodes
+            # expect to save values as a vector for ALL arguments.  So you
+            # need a separate IR node that represents all of the size nodes
+            # assembled into a list.  I'm not an LTC dev so I don't want to
+            # figure it out right now.  Y'all figure it out...
+            return VectorCType(BaseCType(longT))
+
+        else:
+            return VectorCType(process_ir_type(typ.elem, properties, symint=symint))
+    else:
+        raise AssertionError(f"unrecognized type {repr(typ)}")
+
+
+# TODO: Determining this based off of CType is bad; this should be computed
+# from Type directly; then the same logic as process_ir_type can be used
+#
+# Invariant: passed typ should be an *owning* CType (e.g., we will report
+# that ArrayRef<Value> is NOT a value type)
+def isValueType(typ: CType, properties: LazyIrProperties | None = None) -> bool:
+    """
+    Given a type, determine if it is a Value-like type.  This is equivalent to
+    being Tensor-like, but assumes the type has already been transformed.
+    """
+    if isinstance(typ, BaseCType):
+        # I am regretting my naming conventions, but now we are wrapping at::scalar in
+        # lazy value, while preserving other 'scalar' types as scalars in the IR
+        treat_scalars_as_constants = properties and properties.TreatScalarsAsConstants
+        return (
+            typ.type == getValueT()
+            or (typ.type == scalarT and not treat_scalars_as_constants)
+            or typ.type == SymIntT
+        )
+    elif typ == VectorCType(BaseCType(SymIntT)):
+        # TODO: report True for this
+        return False
+    elif isinstance(typ, (OptionalCType, ListCType, VectorCType)):
+        return isValueType(typ.elem, properties)
+    return False
+
+
+def isSymIntType(typ: Type) -> bool:
+    return isinstance(typ, BaseType) and typ.name == BaseTy.SymInt
+
+
+def isWrappedScalarType(typ: Type) -> bool:
+    """
+    Given a type, determine if it is a c10::scalar which we will wrap in a lazy Value.
+    Since we literally change the type from scalarT to valueT, information is lost.
+    This function helps build a list of wrapped scalars to save that information
+    """
+    if isinstance(typ, BaseType):
+        # I am regretting my naming conventions, but now we are wrapping at::scalar in
+        # lazy value, while preserving other 'scalar' types as scalars in the IR
+        return typ.name == BaseTy.Scalar
+    elif isinstance(typ, (OptionalType, ListType)):
+        return isWrappedScalarType(typ.elem)
+    return False
+
+
+# TODO: dedupe with Type.is_generator_like
+def isGeneratorType(typ: Type) -> bool:
+    if isinstance(typ, BaseType):
+        return typ.name == BaseTy.Generator
+    elif isinstance(typ, (OptionalType)):
+        return isGeneratorType(typ.elem)
+    return False
+
+
+# This class caches a few derived properties computed from an Argument
+# and LazyIrProperties
+class LazyArgument:
+    name: str
+    orig_type: Type
+    lazy_type_: CType | None
+    is_wrapped_scalar: bool
+    is_generator: bool
+    # TODO: this is lies, it is false for symint list
+    is_symint_or_list: bool
+
+    # Whether or not we are treating this as symint or not
+    symint: bool
+
+    # true if this argument is or contains a lazy IR value
+    is_lazy_value: bool
+
+    def __init__(
+        self, arg: Argument, properties: LazyIrProperties, *, symint: bool
+    ) -> None:
+        self.name = arg.name
+        self.orig_type = arg.type
+        self.symint = symint
+        self.is_optional = isinstance(arg.type, OptionalType)
+        self.is_generator = isGeneratorType(arg.type)
+        self.lazy_type_ = process_ir_type(arg.type, properties, symint=symint)
+        self.is_wrapped_scalar = isWrappedScalarType(arg.type)
+        self.is_symint_or_list = symint and (
+            isSymIntType(arg.type)
+            or (isinstance(arg.type, OptionalType) and isSymIntType(arg.type.elem))
+            # TODO: lists of symints are not currently treated as value types
+            # or (isinstance(arg.type, ListType) and isSymIntType(arg.type.elem))
+        )
+
+        self.is_lazy_value = isValueType(self.lazy_type, properties)
+
+    @property
+    def lazy_type(self) -> CType:
+        assert (
+            self.lazy_type_ is not None
+        ), f"Attempted to access lazy_type for invalid argument {self.name}"
+        return self.lazy_type_
+
+
+class LazyIrProperties:
+    """Collection of properties for an IR node
+
+    The property groups are listed below. Each group is mutually
+    exclusive, meaning that only one property from each group can be True
+    at any one time. The properties can be accessed as if they were normal
+    attributes. The mutual exclusivity is automatically handled.
+    """
+
+    Properties: tuple[tuple[str, ...], ...] = (
+        (
+            "ShapePrecompute",  # Assume shape has been precomputed
+            "ShapeCompute",  # Need to compute the shape on construction
+            "ShapeCache",  # Utilize the shape cache to defer computation
+        ),
+        (
+            "Lower",  # Codegen full lower function
+            "LowerDeclOnly",  # Codegen only lower function declaration
+        ),
+        (
+            "CanBeReused",  # Codegen full reuse function
+            "CanBeReusedDeclOnly",  # Codegen only reuse function declaration
+        ),
+        (
+            "CreateFn",  # Codegen full create function
+            "CreateFnDeclOnly",  # Codegen only create function declaration
+        ),
+        (
+            "TreatScalarsAsConstants",  # Treat Scalars as constants instead of handling like values
+        ),
+    )
+
+    def __init__(self, *default_properties: str) -> None:
+        properties: dict[tuple[str, ...], str | None] = dict.fromkeys(
+            LazyIrProperties.Properties
+        )
+        self.__dict__["properties"] = properties
+        for p in default_properties:
+            setattr(self, p, True)
+
+    def __getattr__(self, key: str) -> Any:
+        properties = self.__dict__["properties"]
+        for values in LazyIrProperties.Properties:
+            if key in values:
+                return properties[values] == key
+
+        return self.__getattribute__(key)
+
+    def __setattr__(self, key: str, value: Any) -> Any:
+        properties = self.__dict__["properties"]
+        for values in LazyIrProperties.Properties:
+            if key in values:
+                properties[values] = key if value else None
+                return value
+
+        raise KeyError(f"Invalid property: {key}")
+
+
+# Inspired by a FunctionSchema object, a LazyIrSchema holds the schema of a Lazy IR node.
+# Unlike a FunctionSchema, it has no round-trippable string form (relating to the YAML),
+# but carries type information from a native FunctionSchema modified for use with IR nodes,
+# and preserving original argument names.
+#
+# TODO: This is not idiomatic with how other torchgen APIs transform on schema.
+class LazyIrSchema:
+    # The name of the operator this function schema describes.
+    name: OperatorName
+
+    positional_args: tuple[LazyArgument, ...]
+    keyword_args: tuple[LazyArgument, ...]
+
+    # TODO: Need to handle collisions with argument names at some point
+    returns: tuple[Return, ...]
+
+    # if this schema has a Generator arg, list its orig ctype/name but don't
+    # build a LazyArgument since lazy IR doesn't support it
+    generator_arg: NamedCType | None = None
+
+    # original function schema
+    func: FunctionSchema
+
+    # Whether or not we are code-genning for SymInt or not
+    symint: bool
+
+    properties: LazyIrProperties = LazyIrProperties(
+        # default properties
+        "ShapePrecompute",
+        "Lower",
+        "CanBeReused",
+    )
+    opkind: str | None = None
+
+    def __init__(
+        self,
+        func: FunctionSchema,
+        properties: LazyIrProperties | None = None,
+        *,
+        symint: bool,
+    ) -> None:
+        if properties:
+            self.properties = properties
+
+        self.func = func
+        self.symint = symint
+        positional_args: list[LazyArgument] = []
+        for arg_field in ["pre_self_positional", "self_arg", "post_self_positional"]:
+            if arg_field == "self_arg" and func.arguments.self_arg is not None:
+                arg = func.arguments.self_arg.argument
+                positional_args.append(
+                    LazyArgument(arg, self.properties, symint=symint)
+                )
+            elif getattr(func.arguments, arg_field) is not None:
+                positional_args.extend(
+                    LazyArgument(arg, self.properties, symint=symint)
+                    for arg in getattr(func.arguments, arg_field)
+                )
+        self.positional_args = tuple(positional_args)
+
+        keyword_args: list[LazyArgument] = []
+        for arg_field in [
+            "pre_tensor_options_kwarg_only",
+            "tensor_options",
+            "post_tensor_options_kwarg_only",
+            "out",
+        ]:
+            curr_args = getattr(func.arguments, arg_field)
+            if curr_args is not None:
+                if isinstance(curr_args, TensorOptionsArguments):
+                    curr_args = curr_args.all()
+                for arg in curr_args:
+                    if isGeneratorType(arg.type):
+                        assert (
+                            self.generator_arg is None
+                        ), "We expect there is only one generator arg"
+                        self.generator_arg = NamedCType(
+                            arg.name,
+                            arg.type,  # type:ignore[arg-type]
+                        )
+                keyword_args.extend(
+                    LazyArgument(arg, self.properties, symint=symint)
+                    for arg in curr_args
+                )
+        self.keyword_args = tuple(keyword_args)
+        self.name = func.name
+        self.returns = func.returns
+
+    @property
+    def node_name(self) -> str:
+        """
+        Return camel-case version of op in node.
+
+        Note: This function also appends any `overload_name` in the operation.
+        For example, if the op is `bitwise_and.Tensor`, the returned name
+        will be `BitwiseAndTensor`.
+        """
+        op_name = f"{self.name.name}_{self.name.overload_name}".lower()
+        return "".join(word.capitalize() or "" for word in op_name.split("_"))
+
+    @property
+    def aten_name(self) -> str:
+        return str(self.name.name)
+
+    @property
+    def base_name(self) -> str:
+        return f"{self.name.name.base}"
+
+    def filtered_args(
+        self,
+        positional: bool = True,
+        keyword: bool = True,
+        values: bool = True,
+        scalars: bool = True,
+        generator: bool = True,
+    ) -> list[LazyArgument]:
+        # This function maintains the sorted order of arguments but provides different filtered views.
+        # Some parts of the code care about kwargs vs args (TS lowerings),
+        # other parts care about whether they need to wrap the arg in a lazy value or leave it alone.
+        # Generators are special cased, as they are needed for fallback/shape-inference but not supported
+        # in TS lowerings and therefore also omitted from lazy IR.
+        args: list[LazyArgument] = []
+        if positional:
+            args.extend(self.positional_args)
+        if keyword:
+            args.extend(self.keyword_args)
+
+        if values and scalars and generator:
+            return args
+        elif values and scalars:
+            return [a for a in args if not a.is_generator]
+        elif values:
+            return [a for a in args if a.is_lazy_value]
+        elif scalars:
+            return [
+                a
+                for a in args
+                if not a.is_lazy_value and (generator or not a.is_generator)
+            ]
+
+        return []
+
+    @property
+    def positional_values(self) -> list[LazyArgument]:
+        return self.filtered_args(
+            positional=True, keyword=False, values=True, scalars=False
+        )
+
+    @property
+    def positional_scalars(self) -> list[LazyArgument]:
+        return self.filtered_args(
+            positional=True, keyword=False, values=False, scalars=True
+        )
+
+    @property
+    def keyword_values(self) -> list[LazyArgument]:
+        return self.filtered_args(
+            positional=False, keyword=True, values=True, scalars=False
+        )
+
+    @property
+    def keyword_scalars(self) -> list[LazyArgument]:
+        return self.filtered_args(
+            positional=False, keyword=True, values=False, scalars=True
+        )
diff --git a/lib/python3.11/site-packages/torchgen/api/meta.py b/lib/python3.11/site-packages/torchgen/api/meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e99d151faeaccea7ca47f372fd26f9985ce7249
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/meta.py
@@ -0,0 +1,13 @@
+from torchgen.model import NativeFunctionsGroup
+
+
+# Follows dispatcher calling convention, but:
+#   - Mutable arguments not allowed.  Meta functions are always
+#     written in functional form.  Look at FunctionSchema.signature()
+#   - No tensor returns; instead we return a TensorMeta describing
+#     the tensor in question
+
+
+def name(g: NativeFunctionsGroup) -> str:
+    # use the overload name from the functional version
+    return str(g.functional.func.name).replace(".", "_")
diff --git a/lib/python3.11/site-packages/torchgen/api/native.py b/lib/python3.11/site-packages/torchgen/api/native.py
new file mode 100644
index 0000000000000000000000000000000000000000..82bc051a6832b74c2949fb53ba6a919320192de3
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/native.py
@@ -0,0 +1,159 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from torchgen import local
+from torchgen.api import cpp
+from torchgen.api.types import (
+    ArgName,
+    BaseCType,
+    Binding,
+    boolT,
+    ConstRefCType,
+    CType,
+    deviceT,
+    layoutT,
+    ListCType,
+    MutRefCType,
+    NamedCType,
+    OptionalCType,
+    scalarT,
+    scalarTypeT,
+    tensorT,
+)
+from torchgen.model import (
+    Argument,
+    FunctionSchema,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# This file describes the translation of JIT schema to the native functions API.
+# This looks a lot like the C++ API (which makes historical sense, because the
+# idea was you wrote native functions to implement functions in the C++ API),
+# but over time we have evolved the C++ API without actually changing our
+# native:: kernels.  The intention is to make native API and dispatcher API
+# line up as closely as possible, since this results in the least overhead
+# (no translation is needed from dispatcher API to native API).
+#
+# NB: this is symint aware, you will get the non-SymInt variant for some
+# dispatch entries and SymInt for others.
+
+
+def name(func: FunctionSchema) -> str:
+    name = str(func.name.name)
+    # TODO: delete this!
+    if func.is_out_fn():
+        name += "_out"
+    if func.name.overload_name:
+        name += f"_{func.name.overload_name}"
+    return name
+
+
+def argumenttype_type(
+    t: Type, *, mutable: bool, binds: ArgName, symint: bool
+) -> NamedCType:
+    if str(t) == "Tensor?":
+        tensor_type: OptionalCType = OptionalCType(BaseCType(tensorT))
+        if mutable and not local.use_const_ref_for_mutable_tensors():
+            return NamedCType(binds, MutRefCType(tensor_type))
+        else:
+            return NamedCType(binds, ConstRefCType(tensor_type))
+    elif str(t) == "Tensor?[]":
+        return NamedCType(
+            binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT))))
+        )
+    elif str(t) == "Scalar":
+        return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+    elif str(t) == "Scalar?":
+        return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
+    return cpp.argumenttype_type(t, mutable=mutable, binds=binds, symint=symint)
+
+
+def returns_type(rs: Sequence[Return], *, symint: bool) -> CType:
+    return cpp.returns_type(rs, symint=symint)
+
+
+def argument_type(a: Argument, *, binds: ArgName, symint: bool) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, binds=binds, symint=symint)
+
+
+def argument(
+    a: Argument | SelfArgument | TensorOptionsArguments,
+    *,
+    is_out: bool,
+    symint: bool,
+) -> list[Binding]:
+    # Ideally, we NEVER default native functions.  However, there are a number
+    # of functions that call native:: directly and rely on the defaulting
+    # existing.  So for BC, we generate defaults for non-out variants (but not
+    # for out variants, where it is impossible to generate an appropriate
+    # default)
+    should_default = not is_out
+    if isinstance(a, Argument):
+        default: str | None = None
+        if should_default and a.default is not None:
+            default = cpp.default_expr(a.default, a.type, symint=symint)
+        return [
+            Binding(
+                nctype=argument_type(a, binds=a.name, symint=symint),
+                name=a.name,
+                default=default,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, SelfArgument):
+        # Erase SelfArgument from the distinction
+        return argument(a.argument, is_out=is_out, symint=symint)
+    elif isinstance(a, TensorOptionsArguments):
+        default = None
+        if should_default:
+            default = "{}"
+        # TODO: Not sure why the arguments assigned here are for
+        # TensorOptionsArguments and not the constituent pieces.  It seems
+        # to matter
+        return [
+            Binding(
+                nctype=NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))),
+                name="dtype",
+                default=default,
+                argument=a,
+            ),
+            Binding(
+                nctype=NamedCType("layout", OptionalCType(BaseCType(layoutT))),
+                name="layout",
+                default=default,
+                argument=a,
+            ),
+            Binding(
+                nctype=NamedCType("device", OptionalCType(BaseCType(deviceT))),
+                name="device",
+                default=default,
+                argument=a,
+            ),
+            Binding(
+                nctype=NamedCType("pin_memory", OptionalCType(BaseCType(boolT))),
+                name="pin_memory",
+                default=default,
+                argument=a,
+            ),
+        ]
+    else:
+        assert_never(a)
+
+
+def arguments(func: FunctionSchema, *, symint: bool) -> list[Binding]:
+    args: list[Argument | TensorOptionsArguments | SelfArgument] = []
+    args.extend(func.arguments.non_out)
+    args.extend(func.arguments.out)
+    return [
+        r for arg in args for r in argument(arg, symint=symint, is_out=func.is_out_fn())
+    ]
diff --git a/lib/python3.11/site-packages/torchgen/api/python.py b/lib/python3.11/site-packages/torchgen/api/python.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1bfef13689fb9fa1b6c7de8b1358d8934b691c5
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/python.py
@@ -0,0 +1,1521 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from torchgen.api import cpp
+from torchgen.api.types import Binding, CppSignature, CppSignatureGroup
+from torchgen.gen import pythonify_default
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    OptionalType,
+    Return,
+    Type,
+    Variant,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                           Data Models
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+# [Notes] python binding codegen
+#
+# The Python binding codegen produces code that takes the input list of
+# PyObjects, finds the matching ATen C++ function using PythonArgParser,
+# converts the PyObjects into C++ types and calls the ATen C++ function:
+#
+# +--------+  parsing   +------------------------+  binding   +-----------------------+
+# | PyObjs | ---------> | PythonArgParser Output | ---------> | Cpp Function Dispatch |
+# +--------+            +------------------------+            +-----------------------+
+#
+# The following examples demonstrate the data models the Python binding
+# codegen needs to deal with and the tasks it needs to accomplish. It
+# helps understand the purpose of the new data types we introduced below.
+#
+#  - Function Schema (source of truth)
+#
+#      aten::empty.names(int[] size, *, Dimname[]? names,
+#                        ScalarType? dtype=None, Layout? layout=None,
+#                        Device? device=None, bool? pin_memory=None,
+#                        MemoryFormat? memory_format=None) -> Tensor
+#
+#  - Python Signature
+#
+#    It's used to generate input schema string for PythonArgParser.
+#    Note: TensorOptions fields are reordered and the additional
+#    'requires_grad' field is added:
+#
+#      empty(IntArrayRef size, *, DimnameList? names,
+#            MemoryFormat? memory_format=None, ScalarType dtype=None,
+#            Layout layout=torch.strided, Device device=None,
+#            bool pin_memory=False, bool requires_grad=False)
+#
+#  - C++ Signature
+#
+#    It's used to generate C++ lambda formals & dispatch call.
+#    Note: the scattered TensorOptions fields are packed into 'options'.
+#
+#      auto dispatch_empty =
+#          [](IntArrayRef size, std::optional<DimnameList> names,
+#             const TensorOptions & options,
+#             std::optional<MemoryFormat> memory_format) -> Tensor {
+#          pybind11::gil_scoped_release no_gil;
+#          return torch::empty(size, names, options, memory_format);
+#      };
+#
+#  - Binding between Python Arguments and C++ Arguments
+#
+#    Given a set of Python Arguments in scope, we need produce the
+#    binding expressions that translate the Python API into C++ API:
+#
+#            Python Args               Cpp Args       Binding Exprs
+#     -----------------------------------------------------------------
+#         0: size                      size           '_r.intlist(0)'
+#         1: names                     names          'names' [special init]
+#         2: memory_format -------+
+#         3: dtype         -----+-|--> options        'options' [special packing]
+#         4: layout            /  |
+#         5: device           /   +--> memory_format  '_r.memoryformatOptional(2)'
+#         6: pin_memory      /
+#         7: requires_grad -+
+#
+#    So the full dispatch expression would look like:
+#
+#      dispatch_empty(_r.intlist(0), names, options,
+#                     _r.memoryformatOptional(2))
+#
+#    Where does 'names' come from? It involves special local init:
+#
+#      auto __names = _r.toDimnameListOptional(1);
+#      std::optional<DimnameList> names =
+#          __names ? std::make_optional(DimnameList(__names.value()))
+#                  : std::nullopt;
+#
+#    Where does 'options' come from? It involves special local init
+#    for TensorOptions. Note that Python side has the additional
+#    'requires_grad' field:
+#
+#      const auto options = TensorOptions()
+#          .dtype(_r.scalartype(3))
+#          .device(_r.device(5))
+#          .layout(_r.layoutOptional(4))
+#          .requires_grad(_r.toBool(7))
+#          .pinned_memory(_r.toBool(6));
+#
+#    In some other cases one Python Argument can map to multiple C++
+#    Arguments. For example:
+#
+#     aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False)
+#       -> (Tensor values, Tensor indices)
+#
+#            Python Args               Cpp Args          Binding Exprs
+#     ---------------------------------------------------------------------
+#                               +----> max               'out[0]'
+#                              /-----> max_values        'out[1]
+#         0: input            /        self              '_r.tensor(0)'
+#         1: dim             /         dim               '_r.dimname(1)'
+#         2: keepdim        /          keepdim           '_r.toBool(2)'
+#         3: out      -----+           [local init] out  '_r.tensorlist_n<2>(3)'
+#
+#    As demonstrated above, the binding can involve reordering,
+#    packing, unpacking and special local inits.
+#
+#
+#  Let's look at a concrete example:
+#
+#      static PythonArgParser parser({
+#        "abs(Tensor input, *, Tensor out=None)",
+#        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#         ^
+#         +--- Python Schema, represented by PythonSignature and PythonArgument
+#
+#      }, /*traceable=*/true);
+#
+#      ParsedArgs<2> parsed_args;
+#      auto _r = parser.parse(nullptr, args, kwargs, parsed_args);
+#
+#      ...
+#
+#      if (_r.isNone(1)) {
+#          ~~~~~~~~~~~~  <--- Scattered PythonArgParser output (arg name = 'out')
+#                             represented by PythonArgParserOutputExpr
+#
+#        // aten::abs(Tensor self) -> Tensor
+#        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#         ^
+#         +--- NativeFunction schema, base version
+#
+#        auto dispatch_abs = [](const Tensor & self) -> Tensor {
+#                            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#                             ^
+#                             +--- dispatch_lambda_args / dispatch_lambda_return_str
+#                                  generated from NativeFunction / CppSignature
+#                                  (deprecated PythonSignature is special)
+#                                  arguments are represented by DispatchLambdaArgument
+#
+#          pybind11::gil_scoped_release no_gil;
+#          return self.abs();
+#                 ~~~~~~~~~~~  <--- cpp_dispatch_target / cpp_dispatch_exprs
+#                                   generated from NativeFunction / CppSignature
+#        };
+#        return wrap(dispatch_abs(_r.tensor(0)));
+#                                 ~~~~~~~~~~~~~
+#                                  ^
+#                                  +--- dispatch_lambda_exprs
+#                                       binding PythonArgParserOutputExpr (python args)
+#                                       and DispatchLambdaArgument (c++ args)
+#
+#      } else {
+#        // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+#        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#         ^
+#         +--- NativeFunction schema, out-variant
+#
+#        auto dispatch_abs_out = [](Tensor out, const Tensor & self) -> Tensor {
+#          pybind11::gil_scoped_release no_gil;
+#          return at::abs_out(out, self);
+#        };
+#        return wrap(dispatch_abs_out(_r.tensor(1), _r.tensor(0)));
+#      }
+#
+#
+# [Notes] python interface codegen
+# The python dataclasses below are used used to generate both python binding code
+# and pyi type hint signatures.
+# In theory these two should look very similar, but there are number of differences
+# in how pyi signatures vs. python_arg_parser signatures are generated.
+# These differences have been encapsulated in signature_str() vs. signature_str_pyi()
+# to display the full signatures, and argument_str() vs argument_str_pyi() to display arguments.
+# For examples, only pyi signatures include return types.
+
+
+@dataclass(frozen=True)
+class PythonReturns:
+    returns: tuple[Return, ...]
+
+
+@dataclass(frozen=True)
+class PythonArgument:
+    name: str
+    type: Type
+    default: str | None
+
+    # Used to generate the default init expr for some PythonArgParser outputs, e.g.:
+    #
+    #   _r.layoutWithDefault(3, layout_from_backend(self.options().backend())))
+    #                           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    #                            ^
+    #                            +--- default_init str
+    default_init: str | None
+
+    # Compute argument formal for python argument parsing.
+    # Needs to be consistent with torch/csrc/utils/python_arg_parser.h.
+    def argument_str(self, *, method: bool = False, symint: bool = True) -> str:
+        type_str = (
+            argument_type_str(self.type, symint=symint)
+            .replace("const ", "")
+            .replace(" &", "")
+        )
+
+        name = self.name
+        # s/self/input/ outside method bindings
+        # [old codegen] TODO: remove this? doesn't rename in codegen, it's just
+        # for the parse string
+        if name == "self" and type_str in ["Tensor", "Number"] and not method:
+            name = "input"
+
+        # add default
+        if self.default is not None:
+            default = {
+                "nullptr": "None",
+                "::std::nullopt": "None",
+                "std::nullopt": "None",
+                "{}": "None",
+            }.get(self.default, self.default)
+            return f"{type_str} {name}={default}"
+        else:
+            return f"{type_str} {name}"
+
+    def argument_str_pyi(
+        self, *, method: bool = False, deprecated: bool = False
+    ) -> str:
+        type_str = argument_type_str_pyi(self.type)
+
+        name = self.name
+        # s/self/input/ outside method bindings
+        # [old codegen] TODO: remove this? doesn't rename in codegen, it's just
+        # for the parse string
+        if name == "self" and type_str == "Tensor" and not method and not deprecated:
+            name = "input"
+
+        if name == "from":  # from is a Python keyword...
+            name += "_"
+
+        # pyi merges the _out and functional variants into the same signature, with an optional out arg
+        if name == "out" and type_str == "Tensor" and not deprecated:
+            type_str = "Optional[" + type_str + "]"
+
+        # pyi deprecated signatures don't get defaults for their out arg
+        treat_as_no_default = (
+            deprecated
+            and isinstance(self, PythonOutArgument)
+            and self.default == "None"
+        )
+
+        # add default
+        if self.default is not None and not treat_as_no_default:
+            if (
+                isinstance(self.type, ListType)
+                and self.type.elem == BaseType(BaseTy.int)
+                and self.default.startswith("{")
+                and self.default.endswith("}")
+            ):
+                default = (
+                    "(" + ", ".join(map(str.strip, self.default[1:-1].split(","))) + ")"
+                )
+            else:
+                default = {
+                    "nullptr": "None",
+                    "::std::nullopt": "None",
+                    "std::nullopt": "None",
+                    "{}": "None",
+                    "c10::MemoryFormat::Contiguous": "contiguous_format",
+                    "QScheme::PER_TENSOR_AFFINE": "per_tensor_affine",
+                }.get(self.default, self.default)
+            return f"{name}: {type_str} = {default}"
+        else:
+            return f"{name}: {type_str}"
+
+
+@dataclass(frozen=True)
+class PythonOutArgument(PythonArgument):
+    # In Python signature multiple output fields are packed into one 'out' argument.
+    # When binding to C++, it's first binded to a local 'out' variable:
+    #   'auto out = _r.tensorlist_n<2>(2);',
+    # then binded to scattered C++ output arguments as 'out[0]', 'out[1]', and etc.
+    # TODO: maybe don't need keep scattered out fields for python signature?
+    outputs: tuple[PythonArgument, ...]
+
+    @staticmethod
+    def from_outputs(outputs: tuple[PythonArgument, ...]) -> PythonOutArgument | None:
+        if not outputs:
+            return None
+
+        size = len(outputs)
+        if size == 1:
+            return PythonOutArgument(
+                name=outputs[0].name,
+                type=outputs[0].type,
+                default="None",
+                default_init=None,
+                outputs=outputs,
+            )
+        elif size > 1:
+            if any(not a.type.is_tensor_like() for a in outputs):
+                raise RuntimeError(f"Unsupported output type: {outputs}")
+            return PythonOutArgument(
+                name="out",
+                # TODO: shouldn't this be OptionalType[ListType[...]], since it defaults to None?
+                type=ListType(BaseType(BaseTy.Tensor), size),
+                default="None",
+                default_init=None,
+                outputs=outputs,
+            )
+        raise AssertionError(r"Unexpected PythonOutArgument size")
+
+
+@dataclass(frozen=True)
+class PythonSignature:
+    # Base operator name, without inplace/outplace suffix.
+    name: str
+
+    # Positional arguments.
+    # TODO: create a dedicated SelfArgument type for 'self'?
+    input_args: tuple[PythonArgument, ...]
+
+    # Keyword arguments excluding the 'out' argument and scattered kwargs belonging
+    # to TensorOptions (dtype, layout, device, pin_memory, requires_grad, etc).
+    input_kwargs: tuple[PythonArgument, ...]
+
+    output_args: PythonOutArgument | None
+
+    # Return types, which are only used by pyi
+    returns: PythonReturns
+
+    # These are scattered kwargs arguments belonging to TensorOptions.
+    # When binding to C++, they are packed into a TensorOptions object 'options'.
+    # It's possible that the C++ signature doesn't take TensorOptions object (e.g.
+    # for out variant), in which case they will be used as scattered fields without
+    # being packed into 'options'.
+    # TODO: maybe create a PythonTensorOptionsArgument?
+    tensor_options_args: tuple[PythonArgument, ...]
+
+    # method or function signature?
+    method: bool
+
+    @property
+    def deprecated(self) -> bool:
+        return False
+
+    def arguments(
+        self, *, skip_outputs: bool = False, skip_tensor_options: bool = False
+    ) -> tuple[PythonArgument | PythonOutArgument, ...]:
+        result: list[PythonArgument | PythonOutArgument] = []
+        result.extend(self.input_args)
+        result.extend(self.input_kwargs)
+        if self.output_args is not None and not skip_outputs:
+            result.append(self.output_args)
+        if not skip_tensor_options:
+            result.extend(self.tensor_options_args)
+        return tuple(result)
+
+    def arguments_count(self) -> int:
+        return len(self.arguments())
+
+    def output_idx(self) -> int:
+        return len(self.input_args) + len(self.input_kwargs)
+
+    # [old codegen] Compute the Python function signature for argument parsing,
+    # as specified in torch/csrc/utils/python_arg_parser.h.  WARNING:
+    # this is NOT the same type signature as specified by PEP 484
+    # as understood by mypy; our format was independently developed
+    # and has some quirks to make it more suitable specifically
+    # for error parsing.
+    #
+    # For a translation to mypy-valid type signatures, see
+    # signature_str_pyi().
+    def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str:
+        args = self.arguments(skip_outputs=skip_outputs)
+        schema_formals: list[str] = [
+            a.argument_str(method=self.method, symint=symint) for a in args
+        ]
+        positional_argc = len(self.input_args)
+        if len(schema_formals) > positional_argc:
+            schema_formals.insert(positional_argc, "*")
+
+        return f'{self.name}({", ".join(schema_formals)})'
+
+    def signature_str_pyi(self, *, skip_outputs: bool = False) -> str:
+        args = self.arguments(skip_outputs=skip_outputs)
+        schema_formals: list[str] = [
+            a.argument_str_pyi(method=self.method) for a in args
+        ]
+        positional_argc = len(self.input_args)
+        if len(schema_formals) > positional_argc:
+            schema_formals.insert(positional_argc, "*")
+
+        # only pyi signatures include returns
+        returns_str = returns_str_pyi(self)
+        # pyi also includes self (with no typing/defaults) for methods
+        if self.method:
+            schema_formals.insert(0, "self")
+        return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
+
+    def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> str | None:
+        # only pyi uses vararg signatures
+        args = self.arguments(skip_outputs=skip_outputs)
+        schema_formals: list[str] = [
+            a.argument_str_pyi(method=self.method) for a in args
+        ]
+        # vararg only applies to pyi signatures. vararg variants are not generated for all signatures
+        num_args = self.arguments_count()
+        num_positionalargs = len(self.input_args)
+
+        have_vararg_version = False
+        if num_args > 0:
+            vararg_type = args[0].type
+            if (
+                isinstance(vararg_type, ListType)
+                and str(vararg_type.elem) in ["int", "SymInt"]
+                and num_positionalargs == 1
+            ):
+                have_vararg_version = True
+
+        if not have_vararg_version:
+            return None
+
+        # Below are the major changes in vararg vs. regular pyi signatures
+        # vararg signatures also omit the asterix
+        assert isinstance(vararg_type, ListType)
+        schema_formals[0] = (
+            "*" + args[0].name + ": " + argument_type_str_pyi(vararg_type.elem)
+        )
+
+        returns_str = returns_str_pyi(self)
+        # pyi also includes self (with no typing/defaults) for methods
+        if self.method:
+            schema_formals.insert(0, "self")
+        return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
+
+
+# The deprecated python signature involves some special logic, so create a
+# dedicated data model to store these extra properties.
+@dataclass(frozen=True)
+class PythonSignatureDeprecated(PythonSignature):
+    # Schema for the deprecated function
+    deprecated_schema: FunctionSchema
+
+    # The deprecated signature might miss some arguments that the corresponding
+    # C++ signature expects. We need store the constant default values to pass in.
+    # For example:
+    #   [deprecate signature]: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2)
+    #   [func schema]: aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
+    #   [func call]: self.addmm(mat1, mat2, beta, 1)
+    # We store ['self', 'mat1', 'mat2', 'beta', '1'] in this case.
+    deprecated_args_exprs: tuple[str, ...]
+
+    @property
+    def deprecated(self) -> bool:
+        return True
+
+    def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str:
+        return (
+            PythonSignature.signature_str(
+                self, skip_outputs=skip_outputs, symint=symint
+            )
+            + "|deprecated"
+        )
+
+    def signature_str_pyi(self, *, skip_outputs: bool = False) -> str:
+        args = self.arguments(skip_outputs=skip_outputs)
+        schema_formals: list[str] = [
+            a.argument_str_pyi(method=self.method, deprecated=True) for a in args
+        ]
+        positional_argc = len(self.input_args)
+        if len(schema_formals) > positional_argc:
+            schema_formals.insert(positional_argc, "*")
+
+        returns_str = returns_str_pyi(self)
+        return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
+
+    def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> str | None:
+        # the codegen doesn't include vararg variants for deprecated signatures
+        return None
+
+
+# This struct is used to hold the PythonSignature and its corresponding
+# NativeFunction BEFORE grouping base and out-variant functions.
+# Why not store NativeFunction in PythonSignature or construct PythonSignature
+# from NativeFunction? Because they are not 1-1 mapped.
+# One native function could have both deprecated and non-deprecated python
+# signatures - NativeFunction doesn't contain information to construct the
+# deprecated python signature.
+# One python signature is used to handle both the base and the out-variant
+# function - see 'PythonSignatureGroup'.
+@dataclass(frozen=True)
+class PythonSignatureNativeFunctionPair:
+    signature: PythonSignature
+    function: NativeFunction
+
+
+# We merge pairs of functions with signatures that are equivalent mod
+# output arguments, and use a single entry in the python_arg_parser sig
+# list for both (output arguments become optional).
+@dataclass(frozen=True)
+class PythonSignatureGroup:
+    # The signature used for Python argument parsing. The outplace signature
+    # is preferred if exists, because it can be used to parse inputs for both
+    # the out-place variant and the base version (with output omitted).
+    signature: PythonSignature
+
+    # The regular ATen declaration (e.g. conv2d)
+    base: NativeFunction
+
+    # The out variant (e.g. conv2d_out)
+    outplace: NativeFunction | None
+
+    @classmethod
+    def from_pairs(
+        cls,
+        functional: PythonSignatureNativeFunctionPair,
+        out: PythonSignatureNativeFunctionPair | None,
+    ) -> PythonSignatureGroup:
+        if out is None:
+            return PythonSignatureGroup(
+                signature=functional.signature,
+                base=functional.function,
+                outplace=None,
+            )
+
+        # prefer the signature with optional out=... arguments because it's the
+        # superset that can be used to parse input for both base and outplace.
+        signature_kwargs = out.signature.__dict__.copy()
+
+        # Out overloads in C++ don't have TensorOptions arguments,
+        # so take these from the functional variant
+        signature_kwargs["tensor_options_args"] = (
+            functional.signature.tensor_options_args
+        )
+
+        return PythonSignatureGroup(
+            signature=type(out.signature)(**signature_kwargs),
+            base=functional.function,
+            outplace=out.function,
+        )
+
+
+# C++ function dispatch is wrapped in a lambda function. The lambda function
+# has almost the same signature as the C++ function, only with some small
+# variants - see details below.
+# This data model is used to represent arguments of the lambda function
+# signature.
+@dataclass(frozen=True)
+class DispatchLambdaArgument:
+    name: str
+    type_str: str
+    is_out_arg: bool
+
+
+# To pass PyObjects arguments to C++ function (via the lambda wrapper),
+# we need first convert PyObjects into simple C++ objects. This work
+# is done by PythonArgParser.
+# This data model is used to represent the output of PythonArgParser.
+# It has 1-1 mapping with PythonArgument in PythonSignature.
+@dataclass(frozen=True)
+class PythonArgParserOutputExpr:
+    # argument name
+    name: str
+
+    # RHS expression to reference PythonArgParser output.
+    expr: str
+
+    # In some special cases we need create different expr, e.g.:
+    # '_r.isNone(1)' instead of '_r.tensor(1)'.
+    index: int
+
+    # The python argument it maps to.
+    argument: PythonArgument
+
+    @property
+    def is_none_expr(self) -> str:
+        return f"_r.isNone({self.index})"
+
+
+# To pass PythonArgParser output to the lambda wrapper, we need bind
+# PythonArgParserOutputExpr to DispatchLambdaArgument.
+# They are not always 1-1 mapped, e.g. scattered TensorOptions fields
+# need be packed into a TensorOptions object, which is the argument
+# that the lambda function wrapper takes.
+@dataclass(frozen=True)
+class DispatchLambdaArgumentExprs:
+    # The exprs that provide the binding for lambda arguments, e.g.:
+    #
+    #   'self' -> '_r.tensor(0)'
+    #   'min' -> 'out[0]' / 'min_indices' -> 'out[1]'
+    #   'options' -> 'options'
+    #
+    # It has 1-1 mapping with DispatchLambdaArgument.
+    exprs: Sequence[str]
+
+    # Special local inits, which might introduce new variables that
+    # the 'exprs' above reference, e.g.:
+    #
+    #   'auto out = _r.tensorlist_n<2>(2);'
+    #
+    inits: Sequence[str]
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                          Helper Functions
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+def _cpp_signature(f: NativeFunction, *, method: bool = False) -> CppSignature:
+    return CppSignatureGroup.from_native_function(f, method=method).signature
+
+
+def has_tensor_options(f: NativeFunction) -> bool:
+    return f.func.arguments.tensor_options is not None
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                          Python Signature
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+# 'simple_type' was introduced by the old codegen, which is slightly
+# different from the python schema type, e.g.: doesn't have '?' suffix
+# for optional Tensor/TensorList; doesn't have '[size]' suffix for list type.
+def argument_type_str(
+    t: Type, *, simple_type: bool = False, symint: bool = True
+) -> str:
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            return "Tensor"
+        elif t.name == BaseTy.int:
+            return "int64_t"
+        elif t.name == BaseTy.float:
+            return "double"
+        elif t.name == BaseTy.str:
+            return "c10::string_view"
+        elif t.name in [
+            BaseTy.bool,
+            BaseTy.QScheme,
+            BaseTy.Scalar,
+            BaseTy.ScalarType,
+            BaseTy.Generator,
+            BaseTy.Storage,
+            BaseTy.Layout,
+            BaseTy.Device,
+            BaseTy.DeviceIndex,
+            BaseTy.MemoryFormat,
+            BaseTy.Dimname,
+            BaseTy.Stream,
+            BaseTy.SymInt,
+        ]:
+            # These python schema type names line up with their function schema names
+            return t.name.name
+
+    elif isinstance(t, OptionalType):
+        if str(t.elem) == "Tensor":
+            # Is it desired to keep '?' for simple_type with new style dispatcher?
+            return "Tensor?"
+        elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint)
+        return f"{elem}?"
+    elif isinstance(t, ListType):
+        size = t.size if not simple_type else None
+        if str(t.elem) == "bool":
+            assert t.size is not None
+            return f"::std::array<bool,{t.size}>"
+        elif str(t.elem) == "int":
+            return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef"
+        elif str(t.elem) == "SymInt":
+            if symint:
+                return (
+                    f"SymIntArrayRef[{size}]" if size is not None else "SymIntArrayRef"
+                )
+            else:
+                return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef"
+        elif str(t.elem) == "Tensor":
+            return f"TensorList[{size}]" if size is not None else "TensorList"
+        elif str(t.elem) == "Scalar":
+            return f"ScalarList[{size}]" if size is not None else "ScalarList"
+        elif str(t.elem) == "Tensor?":
+            if simple_type:
+                return "c10::List<::std::optional<Tensor>>"
+            else:
+                return "const c10::List<::std::optional<Tensor>> &"
+        elif str(t.elem) == "Dimname":
+            return f"DimnameList[{size}]" if size is not None else "DimnameList"
+        elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint)
+        return f"ArrayRef<{elem}>"
+
+    raise RuntimeError(f"unrecognized type {repr(t)}")
+
+
+def argument_type_size(t: Type) -> int | None:
+    l = t.is_list_like()
+    if l is not None and str(l.elem) != "bool":
+        return l.size
+    else:
+        return None
+
+
+def argument(a: Argument) -> PythonArgument:
+    return PythonArgument(
+        name=a.name,
+        type=a.type,
+        # TODO: directly translate a.default to python default
+        default=(
+            str(pythonify_default(cpp.default_expr(a.default, a.type, symint=False)))
+            if a.default is not None
+            else None
+        ),
+        default_init=None,
+    )
+
+
+# Generates a PythonSignature that can be used for either .pyi or PythonArgParser codegen
+def signature(
+    f: NativeFunction, *, method: bool = False, pyi: bool = False
+) -> PythonSignature:
+    return signature_from_schema(
+        f.func, category_override=f.category_override, method=method, pyi=pyi
+    )
+
+
+def signature_from_schema(
+    func: FunctionSchema,
+    *,
+    category_override: str | None,
+    method: bool = False,
+    pyi: bool = False,
+) -> PythonSignature:
+    args: list[Argument] = []
+    args.extend(func.arguments.pre_self_positional)
+    # Skip SelfArgument if this is method.
+    if not method and func.arguments.self_arg is not None:
+        args.append(func.arguments.self_arg.argument)
+    args.extend(func.arguments.post_self_positional)
+    args.extend(func.arguments.pre_tensor_options_kwarg_only)
+    # Skip TensorOptionsArguments. Python side TensorOptions
+    # arguments are created based on different rules - see below.
+    args.extend(func.arguments.post_tensor_options_kwarg_only)
+    args.extend(func.arguments.out)
+
+    input_arg_set = {a.name for a in func.arguments.flat_positional}
+    kwarg_only_set = {a.name for a in func.arguments.flat_kwarg_only}
+    out_arg_set = {a.name for a in func.arguments.out}
+
+    input_args = tuple(map(argument, filter(lambda a: a.name in input_arg_set, args)))
+    input_kwargs = tuple(
+        map(argument, filter(lambda a: a.name in kwarg_only_set, args))
+    )
+    outputs = tuple(map(argument, filter(lambda a: a.name in out_arg_set, args)))
+
+    # Reintroduce the scattered fields of TensorOptions for Python.
+    # Compared to the cpp counterpart, the python arguments have new property
+    # (default_init) and a new argument 'requires_grad', which require some
+    # special handlings.
+    # [old codegen] TODO: because these aren't guaranteed to be 100% faithful
+    # to the original versions in the yaml, this recreation is a potential
+    # source of drift between eager and JIT. Pull this logic out to a shared place.
+
+    has_tensor_input_arg = any(
+        a.type.is_tensor_like() for a in func.arguments.flat_non_out
+    )
+    if any(a.name == "requires_grad" for a in func.schema_order_arguments()):
+        raise ValueError(
+            "argument named requires_grad is reserved, should not explicitly add it in the schema"
+        )
+
+    # [old codegen] this probably won't work if one of the returns is not a tensor,
+    # but it will produce a compile-time error that is obvious.
+    has_tensor_return = any(r.type.is_tensor_like() for r in func.returns)
+
+    name: str = cpp.name(func)
+    is_factory_function = category_override == "factory" or (
+        has_tensor_return and not has_tensor_input_arg
+    )
+    is_like_or_new_function = (
+        category_override in ("new", "like")
+        or name.startswith("new_")
+        or name.endswith("_like")
+    )
+    is_dummy_function = category_override == "dummy"
+
+    tensor_options_args: list[PythonArgument] = []
+    if (is_factory_function or is_like_or_new_function) and not is_dummy_function:
+
+        def topt_default_init(name: str) -> str | None:
+            topt_args = func.arguments.tensor_options
+            if topt_args is None:
+                return None
+            a = getattr(topt_args, name)
+            if a.default is None or a.default == "None":
+                return None
+            return cpp.default_expr(a.default, a.type, symint=False)
+
+        tensor_options_args.append(
+            PythonArgument(
+                name="dtype",
+                type=OptionalType(BaseType(BaseTy.ScalarType)),
+                default="None",
+                default_init=(
+                    None if is_like_or_new_function else topt_default_init("dtype")
+                ),
+            )
+        )
+        tensor_options_args.append(
+            PythonArgument(
+                name="layout",
+                type=OptionalType(BaseType(BaseTy.Layout)),
+                default="None",
+                default_init=(
+                    None if is_like_or_new_function else topt_default_init("layout")
+                ),
+            )
+        )
+        tensor_options_args.append(
+            PythonArgument(
+                name="device",
+                type=OptionalType(BaseType(BaseTy.Device)),
+                default="None",
+                default_init=(
+                    None
+                    if is_like_or_new_function
+                    else (
+                        topt_default_init("device")
+                        or "torch::tensors::get_default_device()"
+                    )
+                ),
+            )
+        )
+        tensor_options_args.append(
+            PythonArgument(
+                name="pin_memory",
+                type=OptionalType(BaseType(BaseTy.bool)),
+                default="False",
+                default_init=None,
+            )
+        )
+        tensor_options_args.append(
+            PythonArgument(
+                name="requires_grad",
+                type=OptionalType(BaseType(BaseTy.bool)),
+                default="False",
+                default_init=None,
+            )
+        )
+
+    returns = PythonReturns(returns=func.returns)
+
+    return PythonSignature(
+        name=str(func.name.name),
+        input_args=input_args,
+        input_kwargs=input_kwargs,
+        output_args=PythonOutArgument.from_outputs(outputs),
+        tensor_options_args=tuple(tensor_options_args),
+        returns=returns,
+        method=method,
+    )
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                          Python Interface
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+def structseq_fieldnames(returns: tuple[Return, ...]) -> list[str]:
+    if len(returns) <= 1 or all(r.name is None for r in returns):
+        return []
+    else:
+        if any(r.name is None for r in returns):
+            # When building on Windows, `PyStructSequence_UnnamedField` could not be
+            # resolved by the linker for some reason, which cause error in building:
+            #
+            # python_nn_functions.cpp.obj : error LNK2001: unresolved external symbol
+            # PyStructSequence_UnnamedField
+            #
+            # Thus, at this point in time, we do not support unnamed
+            # fields in structseq; you must either name all fields,
+            # or none of them.
+            raise ValueError("Unnamed field is not supported by codegen")
+
+        return [str(r.name) for r in returns]
+
+
+def argument_type_str_pyi(t: Type) -> str:
+    add_optional = False
+    if isinstance(t, OptionalType):
+        t = t.elem
+        add_optional = True
+
+    if isinstance(t, BaseType):
+        if t.name in [BaseTy.int, BaseTy.DeviceIndex]:
+            ret = "_int"
+        if t.name == BaseTy.SymInt:
+            ret = "Union[_int, SymInt]"
+        elif t.name == BaseTy.float:
+            ret = "_float"
+        elif t.name == BaseTy.str:
+            ret = "str"
+        elif t.name == BaseTy.Scalar:
+            ret = "Union[Number, _complex]"
+        elif t.name == BaseTy.ScalarType:
+            ret = "_dtype"
+        elif t.name == BaseTy.bool:
+            ret = "_bool"
+        elif t.name == BaseTy.QScheme:
+            ret = "_qscheme"
+        elif t.name == BaseTy.Layout:
+            ret = "_layout"
+        elif t.name == BaseTy.Device:
+            ret = "Optional[DeviceLikeType]"
+        elif t.name == BaseTy.MemoryFormat:
+            ret = "memory_format"
+        elif t.name == BaseTy.Dimname:
+            ret = "Union[str, ellipsis, None]"
+        elif t.name == BaseTy.Storage:
+            ret = "Union[Storage, UntypedStorage]"
+        elif t.name in [BaseTy.Tensor, BaseTy.Generator, BaseTy.Stream]:
+            # These python schema type names line up with their function schema names
+            ret = t.name.name
+
+    elif isinstance(t, ListType):
+        if str(t.elem) == "int":
+            ret = "Union[_int, _size]" if t.size is not None else "_size"
+        elif t.is_tensor_like():
+            # TODO: this doesn't seem right...
+            # Tensor?[] currently translates to Optional[Union[tuple[Tensor, ...], list[Tensor]]]
+            # It should probably translate to   Union[tuple[Optional[Tensor], ...], list[Optional[Tensor]]]
+            add_optional = True
+            ret = (
+                "Union[Tensor, tuple[Tensor, ...], list[Tensor]]"
+                if t.size is not None
+                else "Union[tuple[Tensor, ...], list[Tensor]]"
+            )
+        elif str(t.elem) == "float":
+            ret = "Sequence[_float]"
+        elif str(t.elem) == "SymInt" and t.size is not None:
+            elem = argument_type_str_pyi(t.elem)
+            ret = f"Union[{elem}, Sequence[{elem}]]"
+        else:
+            elem = argument_type_str_pyi(t.elem)
+            ret = f"Sequence[{elem}]"
+
+    else:
+        raise RuntimeError(f"unrecognized type {repr(t)}")
+
+    if add_optional:
+        ret = "Optional[" + ret + "]"
+
+    return ret
+
+
+def return_type_str_pyi(t: Type) -> str:
+    # Where arguments are open to accepting Union, return types should return
+    # concrete types
+
+    if isinstance(t, OptionalType):
+        inner = return_type_str_pyi(t.elem)
+        return f"Optional[{inner}]"
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Device:
+            return "_device"
+        elif t.name == BaseTy.Dimname:
+            return "Optional[str]"
+        else:
+            return argument_type_str_pyi(t)
+
+    if isinstance(t, ListType):
+        inner = return_type_str_pyi(t.elem)
+        return f"tuple[{inner}, ...]"
+
+    return argument_type_str_pyi(t)
+
+
+def returns_structseq_pyi(signature: PythonSignature) -> tuple[str, str] | None:
+    python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
+    structseq_name = signature.name
+    field_names = structseq_fieldnames(signature.returns.returns)
+    if field_names:
+        # These types are structseq objects which act like named NamedTuples, but
+        # the constructor acts like the constructor of tuple. Using typing.NamedTuple
+        # does not allow us to override __init__.
+        seq_type = f"tuple[{', '.join(python_returns)}]"
+        structseq_def_lines = [
+            f"class {structseq_name}({seq_type}):",
+        ]
+        for name, typ in zip(field_names, python_returns):
+            structseq_def_lines.extend(
+                [
+                    "    @property",
+                    f"    def {name}(self) -> {typ}: ...",
+                ]
+            )
+        structseq_def_lines.extend(
+            [
+                f"    def __new__(cls, sequence: {seq_type}): ...",
+                f"    n_fields: _int = {len(field_names)}",
+                f"    n_sequeunce_fields: _int = {len(field_names)}",
+                "    n_unnamed_fields: _int = 0",
+                "    def __init_subclass__(cls) -> NoReturn: ...  # prohibit subclassing",
+                "",  # add an extra newline
+            ]
+        )
+        structseq_def = "\n".join(structseq_def_lines)
+        # Example:
+        # structseq_def = (
+        #     "class max(tuple[Tensor, Tensor]):\n"
+        #     "    @property\n"
+        #     "    def values(self) -> Tensor: ...\n"
+        #     "    @property\n"
+        #     "    def indices(self) -> Tensor: ...\n"
+        #     "    def __new__(cls, sequence: tuple[Tensor, Tensor]): ...\n"
+        #     "    n_fields: _int = 2",
+        #     "    n_sequeunce_fields: _int = 2",
+        #     "    n_unnamed_fields: _int = 0",
+        #     "    def __init_subclass__(cls) -> NoReturn: ...  # prohibit subclassing",
+        # )
+        return structseq_name, structseq_def
+    return None
+
+
+def returns_str_pyi(signature: PythonSignature) -> str:
+    field_names = structseq_fieldnames(signature.returns.returns)
+    if field_names:
+        return f"torch.return_types.{signature.name}"
+
+    python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
+    if len(python_returns) > 1:
+        return "tuple[" + ", ".join(python_returns) + "]"
+    if len(python_returns) == 1:
+        return python_returns[0]
+    return "None"
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                        C++ Function Dispatch
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+# This section provides APIs to generate the code that does C++ function
+# dispatch. The C++ function call is wrapped by a lambda function.
+# For example:
+#
+#    // aten::selu_(Tensor(a!) self) -> Tensor(a!)
+#    auto dispatch_selu_ = [](Tensor self) -> Tensor {
+#      pybind11::gil_scoped_release no_gil;
+#      return at::selu_(self);
+#    };
+#
+# The lambda function's signature follows the C++ signature in common
+# cases, e.g.:
+#
+#   // aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
+#   [](const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor
+#
+# For out variant the 'out' argument's type is changed from 'Tensor &'
+# to 'Tensor'. It's because when calling the lambda it passes in the
+# PythonArgParser output '_r.tensor(3)', which is stack allocated object
+# and needs to pass by value. Also see comments in 'dispatch_lambda_return_str()'.
+#
+#   // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+#   [](Tensor out, const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor
+#
+# For multi-output case it can keep using reference type because the
+# PythonArgParser output has been unpacked to local variables, e.g.:
+#
+#   // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *,
+#   //     Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
+#   [](Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim) -> std::tuple<Tensor,Tensor>
+#
+# For deprecated python signature, it should follow deprecated python arg order.
+# TODO: This is to keep same byte-for-byte result as the old codegen - maybe unnecessary?
+
+
+def dispatch_lambda_args(
+    ps: PythonSignature, f: NativeFunction, symint: bool = True
+) -> tuple[DispatchLambdaArgument, ...]:
+    if isinstance(ps, PythonSignatureDeprecated):
+        schema = ps.deprecated_schema
+    else:
+        schema = f.func
+
+    # Start with cpp arguments - dispatch lambda signature always include 'self'
+    cpp_args = cpp.arguments(
+        arguments=schema.arguments,
+        faithful=False,
+        symint=symint,
+        method=False,
+        cpp_no_default_args=f.cpp_no_default_args,
+    )
+    out_args: set[str] = {a.name for a in schema.arguments.out}
+
+    # Convert from cpp argument to lambda argument
+    def dispatch_lambda_arg(cpp_arg: Binding) -> DispatchLambdaArgument:
+        type_str = cpp_arg.type
+        is_out_arg = cpp_arg.name in out_args
+        if ps.method and cpp_arg.name == "self":
+            # For method's 'self', we can use 'const Tensor &' and simply ignore mutability!
+            type_str = "const at::Tensor &"
+        else:
+            # For other cases we need prevent dangling refs to temps (unless it's
+            # unpacked scattered output)
+            # The reason is explained in the comments above and in 'dispatch_lambda_return_str()'.
+            # TODO: avoid this special handling?
+            ensure_temp_safe = len(out_args) <= 1 or not is_out_arg
+            if ensure_temp_safe:
+                type_str = {
+                    "at::Tensor &": "at::Tensor",
+                }.get(type_str, type_str)
+        return DispatchLambdaArgument(
+            name=cpp_arg.name,
+            type_str=type_str,
+            is_out_arg=is_out_arg,
+        )
+
+    return tuple(map(dispatch_lambda_arg, cpp_args))
+
+
+# [old codegen] XXX: if you got here because of an assertion failure, it doesn't mean
+# it's enough to just extend the list here. Before you do this, make sure
+# to add an appropriate wrap() overload in torch/csrc/autograd/utils/wrap_outputs.h.
+SUPPORTED_RETURN_TYPES = {
+    "at::Tensor",
+    "::std::tuple<at::Tensor,at::Tensor>",
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor>",
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor,int64_t>",
+    "::std::tuple<at::Tensor,at::Tensor,double,int64_t>",
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t>",
+    "::std::tuple<at::Tensor,at::Tensor,double,at::Tensor,int64_t>",
+    "::std::tuple<double,int64_t>",
+    "::std::tuple<at::Tensor,::std::vector<at::Tensor>>",
+    "::std::vector<at::Tensor>",
+    # Needed for flash attention forw/backward
+    "::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor>",
+    "at::Scalar",
+    "bool",
+    "int64_t",
+    "void*",
+    "void",
+    "at::QScheme",
+    "double",
+    "at::IntArrayRef",
+    "at::ScalarType",
+    "at::Stream",
+}
+
+
+def dispatch_lambda_return_str(f: NativeFunction) -> str:
+    # [old codegen] Remove type annotation (e.g. 'Tensor' rather than 'Tensor &')
+    # because the dispatch lambdas take mutable arguments *by value*, not
+    # by reference. If you then return a reference to such an argument, you
+    # will now have a pointer to a dangling stack entry. Not good.
+    #
+    # You want:
+    #
+    #   auto dispatch_selu_ = [](Tensor self) -> Tensor { ...; return at::selu_(self); };
+    #                                            ^^^^^^
+    #
+    # *not*
+    #
+    #   auto dispatch_selu_ = [](Tensor self) -> Tensor& { ...; return at::selu_(self); };
+    #                                            ^^^^^^^
+    #
+    # (NB: We can't make dispatch_selu_ take Tensor&, because the enclosing
+    # codegen looks like dispatch_selu_(_r.tensor(0)), and you can't take a
+    # mutable reference to temporary.  Maybe we could assign it to a
+    # variable itself.)
+    returns_without_annotation = tuple(
+        Return(r.name, r.type, None) for r in f.func.returns
+    )
+    return_str = cpp.returns_type(returns_without_annotation, symint=True).cpp_type()
+    if return_str not in SUPPORTED_RETURN_TYPES:
+        raise RuntimeError(f"{f.func.name} returns unsupported type {return_str}")
+    return return_str
+
+
+def cpp_dispatch_target(f: NativeFunction) -> str:
+    symint = f.func.has_symint()
+    name = cpp.name(f.func, symint_overload=symint)
+    if Variant.method in f.variants:
+        return f"self.{name}"
+    if Variant.function in f.variants:
+        if has_tensor_options(f) or f.func.name.name.base.endswith("_like"):
+            namespace = "torch"
+        else:
+            namespace = "at"
+        return f"{namespace}::{name}"
+    raise RuntimeError(f"could not dispatch, neither function nor method: {f.func}")
+
+
+def cpp_dispatch_exprs(
+    f: NativeFunction,
+    *,
+    python_signature: PythonSignature | None = None,
+) -> tuple[str, ...]:
+    cpp_args: Sequence[Binding] = _cpp_signature(f, method=False).arguments()
+
+    exprs: tuple[str, ...] = ()
+    if not isinstance(python_signature, PythonSignatureDeprecated):
+        # By default the exprs are consistent with the C++ signature.
+        exprs = tuple(a.name for a in cpp_args)
+    else:
+        # For deprecated python signature we may need fill in some constants.
+        exprs = tuple(
+            filter(
+                lambda n: n != "out" or f.func.is_out_fn(),
+                python_signature.deprecated_args_exprs,
+            )
+        )
+
+    if Variant.method in f.variants:
+        exprs = tuple(filter("self".__ne__, exprs))
+
+    return exprs
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                     Python / C++ Args Binding
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+# We explicitly enumerate the PythonArgParser unpacking methods for all
+# supported types. This might be more verbose than necessary, partially
+# because of the irregularity of unpacking method naming, partially
+# because we want to mimic the old codegen behavior - to reject
+# unexpected and/or unsupported cases which the old codegen rejects.
+# For certain cases it is intentionally more restrictive than necessary,
+# e.g.: it doesn't accepts doublelist with definite size.
+def arg_parser_unpack_method(
+    t: Type, default: str | None, default_init: str | None, *, symint: bool = True
+) -> str:
+    has_default_init = default_init is not None
+    if has_default_init and str(t) not in (
+        "ScalarType?",
+        "ScalarType",
+        "Device",
+        "Device?",
+        "Layout",
+        "Layout?",
+        "bool",
+        "bool?",
+    ):
+        raise RuntimeError(f"type '{t}' does not supported unpacking with default")
+
+    if isinstance(t, BaseType):
+        if t.name in [
+            BaseTy.Tensor,
+            BaseTy.Stream,
+            BaseTy.Storage,
+            BaseTy.Scalar,
+            BaseTy.Dimname,
+        ]:
+            # These unpack methods line up with their schema names
+            return t.name.name.lower()
+        elif t.name == BaseTy.ScalarType:
+            return "scalartypeWithDefault" if has_default_init else "scalartype"
+        elif t.name == BaseTy.Device:
+            return "deviceWithDefault" if has_default_init else "device"
+        elif t.name == BaseTy.DeviceIndex:
+            return "toInt64"
+        elif t.name == BaseTy.int:
+            return "toInt64"
+        elif t.name == BaseTy.SymInt:
+            return "toSymInt" if symint else "toInt64"
+        elif t.name == BaseTy.bool:
+            return "toBoolWithDefault" if has_default_init else "toBool"
+        elif t.name == BaseTy.float:
+            return "toDouble"
+        elif t.name == BaseTy.str:
+            return "stringView"
+        elif t.name == BaseTy.Layout:
+            return "layoutWithDefault" if has_default_init else "layout"
+        elif t.name == BaseTy.MemoryFormat:
+            return "memoryformat"
+
+    elif isinstance(t, OptionalType):
+        if str(t.elem) == "Tensor":
+            return "optionalTensor"
+        elif str(t.elem) == "Generator":
+            return "generator"
+        elif str(t.elem) == "Dimname[]":
+            return "toDimnameListOptional"
+        elif not has_default_init and default in (
+            None,
+            "None",
+            "::std::nullopt",
+            "std::nullopt",
+        ):
+            # If default is None: append 'Optional' to elem's unpacking method
+            return (
+                arg_parser_unpack_method(t.elem, None, None, symint=symint) + "Optional"
+            )
+        else:
+            # Otherwise, load as underlying type with default
+            return arg_parser_unpack_method(
+                t.elem, default, default_init, symint=symint
+            )
+
+    elif isinstance(t, ListType):
+        if str(t.elem) == "Tensor":
+            # accept and use definite size
+            return f"tensorlist_n<{t.size}>" if t.size is not None else "tensorlist"
+        elif str(t.elem) == "Tensor?":
+            return "list_of_optional_tensors"
+        elif str(t.elem) == "Dimname":
+            # accept definite size
+            return "dimnamelist"
+        elif str(t.elem) == "int":
+            # accept definite size
+            return "intlist"
+        elif str(t.elem) == "float":
+            return "doublelist"
+        elif str(t.elem) == "SymInt":
+            # accept definite size
+            return "symintlist" if symint else "intlist"
+        elif str(t.elem) == "Scalar":
+            return "scalarlist"
+    raise RuntimeError(f"type '{t}' is not supported by PythonArgParser")
+
+
+# Return RHS expression for python argument using PythonArgParser output.
+# e.g. for arg name 'foo', arg type 'bool', arg_index = 2, returns '_r.toBool(2)'
+def arg_parser_output_expr(
+    arg_index: int, a: PythonArgument, *, symint: bool = True
+) -> PythonArgParserOutputExpr:
+    has_default = a.default_init is not None
+    unpack_method = arg_parser_unpack_method(
+        t=a.type, default=a.default, default_init=a.default_init, symint=symint
+    )
+    default = f", {a.default_init}" if has_default else ""
+    expr = f"_r.{unpack_method}({arg_index}{default})"
+
+    return PythonArgParserOutputExpr(
+        name=a.name,
+        expr=expr,
+        index=arg_index,
+        argument=a,
+    )
+
+
+# Returns a map with key = arg_name and value = PythonArgParserOutputExpr.
+def arg_parser_output_exprs(
+    ps: PythonSignature, f: NativeFunction, *, symint: bool = True
+) -> dict[str, PythonArgParserOutputExpr]:
+    return {
+        e.name: e
+        for i, a in enumerate(ps.arguments())
+        for e in (arg_parser_output_expr(i, a, symint=symint),)
+    }
+
+
+# argument name to type for scattered tensor options fields
+TENSOR_OPTIONS_FIELDS = {
+    "dtype": "ScalarType?",
+    "device": "Device?",
+    "layout": "Layout?",
+    "pin_memory": "bool?",
+    "requires_grad": "bool?",
+}
+
+
+# bind arg parser outputs (python args) with dispatch lambda arguments (c++ args).
+def dispatch_lambda_exprs(
+    ps: PythonSignature, f: NativeFunction, *, symint: bool = True
+) -> DispatchLambdaArgumentExprs:
+    # This method is to bind 'arg_parser_outputs' and 'lambda_args' by producing
+    # 'inits' and 'lambda_args_exprs' for each lambda argument using arg parser
+    # outputs.
+    arg_parser_outputs = arg_parser_output_exprs(ps, f, symint=symint)
+    lambda_args = dispatch_lambda_args(ps, f, symint=symint)
+    inits: list[str] = []
+    lambda_args_exprs: dict[str, str] = {}
+
+    has_toptions = has_tensor_options(f)
+
+    # 1. special inits/unpacking to provide binding exprs for lambda arguments.
+    for a in ps.arguments(skip_tensor_options=True):
+        name = a.name
+        arg_parser_expr = arg_parser_outputs[a.name].expr
+
+        if has_toptions and name == "self":
+            # TODO: why this needs to be special case?
+            inits.extend(
+                [
+                    f"auto self = {arg_parser_expr};",
+                ]
+            )
+            lambda_args_exprs[name] = name
+        elif (
+            isinstance(a, PythonOutArgument)
+            and len(a.outputs) > 1
+            and f.func.is_out_fn()
+        ):
+            inits.extend(
+                [
+                    f"auto out = {arg_parser_expr};",
+                ]
+            )
+            for i, out_arg in enumerate(a.outputs):
+                lambda_args_exprs[out_arg.name] = f"out[{i}]"
+        elif str(a.type) == "Dimname[]?":
+            # [old codegen]
+            # TODO: make this part of something more general, or get rid of it.
+            # optional<ArrayRef<T>> are special. The PythonArgParser returns an
+            # optional<vector<T>>, which cannot be implicitly converted to
+            # optional<ArrayRef<T>>. One needs to unwrap the optional and rewrap.
+            inits.extend(
+                [
+                    f"auto __{name} = {arg_parser_expr};",
+                    f"::std::optional<DimnameList> {name} = __{name} ? ::std::make_optional(DimnameList(__{name}.value())) : ::std::nullopt;",  # noqa: B950
+                ]
+            )
+            lambda_args_exprs[name] = name
+        else:
+            # default case - directly using PythonArgParser output expr
+            lambda_args_exprs[name] = arg_parser_expr
+
+    # method's self is passed directly to python binding, rather than parsed
+    if ps.method:
+        lambda_args_exprs["self"] = "self"
+
+    # 2. special packing/checking for TensorOptions.
+    tensor_options_args_names = [a.name for a in ps.tensor_options_args]
+    if has_toptions:
+        if f.func.is_out_fn():
+            raise RuntimeError(f"{f.func}: tensor options with output arg")
+        for a in ps.tensor_options_args:
+            if a.name not in TENSOR_OPTIONS_FIELDS:
+                raise RuntimeError(
+                    f"{f.func}: unrecognized tensor options field '{a.name}' in python binding arguments"
+                )
+            if str(a.type) != TENSOR_OPTIONS_FIELDS.get(a.name):
+                raise RuntimeError(
+                    f"{f.func}: unrecognized type '{str(a.type)}' for tensor options field '{a.name}'"
+                )
+        if not all(a in tensor_options_args_names for a in TENSOR_OPTIONS_FIELDS):
+            raise RuntimeError(
+                f"{f.func}: incomplete tensor options args: {tensor_options_args_names}"
+            )
+
+        inits.append(
+            f"""\
+const auto options = TensorOptions()
+    .dtype({arg_parser_outputs['dtype'].expr})
+    .device({arg_parser_outputs['device'].expr})
+    .layout({arg_parser_outputs['layout'].expr})
+    .requires_grad({arg_parser_outputs['requires_grad'].expr})
+    .pinned_memory({arg_parser_outputs['pin_memory'].expr});
+torch::utils::maybe_initialize_device(options);
+"""
+        )
+        lambda_args_exprs["options"] = "options"
+
+    # 3. special case - access scattered TensorOptions fields without packing
+    # TODO: maybe move to the generator side as it's not related to binding.
+    if not has_toptions and tensor_options_args_names:
+        if "dtype" in tensor_options_args_names:
+            # we're an output-arg variant, check these args against output tensor
+            if not f.func.is_out_fn():
+                raise RuntimeError(
+                    f"{f.func}: dtype in tensor_options_args without output arg, {ps} {ps.arguments}"
+                )
+            if not all(a in tensor_options_args_names for a in ("layout", "device")):
+                raise RuntimeError(
+                    f"{f.func}: incomplete tensor options for output check"
+                )
+
+            inits.append(
+                f"""\
+check_out_type_matches({arg_parser_outputs['out'].expr}, {arg_parser_outputs['dtype'].expr},
+                       {arg_parser_outputs['dtype'].is_none_expr}, {arg_parser_outputs['layout'].expr},
+                       {arg_parser_outputs['device'].expr}, {arg_parser_outputs['device'].is_none_expr});
+"""
+            )
+        # we'll set requires_grad on outgoing tensor
+        if "requires_grad" not in tensor_options_args_names:
+            raise RuntimeError(
+                f'{f.func}: expected "requires_grad" in tensor_options_args absent, but found [{tensor_options_args_names}]'
+            )
+
+    return DispatchLambdaArgumentExprs(
+        exprs=tuple(lambda_args_exprs[a.name] for a in lambda_args),
+        inits=inits,
+    )
diff --git a/lib/python3.11/site-packages/torchgen/api/structured.py b/lib/python3.11/site-packages/torchgen/api/structured.py
new file mode 100644
index 0000000000000000000000000000000000000000..93a72eb2b4a5c119ee8f60ce04f0517fe862b4d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/structured.py
@@ -0,0 +1,157 @@
+from __future__ import annotations
+
+from torchgen.api import cpp
+from torchgen.api.types import (
+    ArgName,
+    ArrayRefCType,
+    BaseCType,
+    Binding,
+    ConstRefCType,
+    dimnameListT,
+    intArrayRefT,
+    iOptTensorListRefT,
+    iTensorListRefT,
+    NamedCType,
+    OptionalCType,
+    optionalIntArrayRefT,
+    optionalScalarRefT,
+    optionalTensorRefT,
+    scalarT,
+    tensorT,
+)
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    ListType,
+    NativeFunctionsGroup,
+    OptionalType,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+
+
+# This file describes the translation of JIT schema to the structured functions API.
+# This is similar to native API, but a number of historical problems with native
+# API have been fixed.
+
+
+# Translation of types occurring in JIT arguments to a C++ argument type.
+# NB: For now, mutable doesn't do anything; but it could if we make
+# some more nominal types
+def argumenttype_type(t: Type, *, mutable: bool, binds: ArgName) -> NamedCType:
+    # If it's a value type, do the value type translation
+    # NB: structured kernels ALWAYS have symint off, since they involve actual
+    # kernels that require real ints.  The one exception is the
+    # CompositeExplicitAutograd and the meta function (which could
+    # hypothetically be SymInt), but for simplicity we plan for these to just
+    # be handled in Python
+    r = cpp.valuetype_type(t, symint=False, binds=binds, mutable=mutable)
+    if r is not None:
+        return r
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            return NamedCType(binds, ConstRefCType(BaseCType(tensorT)))
+        elif t.name == BaseTy.Scalar:
+            return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+        else:
+            raise AssertionError(f"base type should have been value type {t}")
+    elif isinstance(t, OptionalType):
+        if t.elem == BaseType(BaseTy.Tensor):
+            return NamedCType(binds, BaseCType(optionalTensorRefT))
+        elif t.elem == BaseType(BaseTy.Scalar):
+            return NamedCType(binds, BaseCType(optionalScalarRefT))
+        elif isinstance(t.elem, ListType) and str(t.elem.elem) == "int":
+            return NamedCType(binds, BaseCType(optionalIntArrayRefT))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        if t.elem == BaseType(BaseTy.Tensor):
+            return NamedCType(binds, ConstRefCType(BaseCType(iTensorListRefT)))
+        elif t.elem == OptionalType(BaseType(BaseTy.Tensor)):
+            return NamedCType(binds, BaseCType(iOptTensorListRefT))
+        # TODO: delete these special cases; see torchgen.api.cpp--these
+        # must be changed in tandem, but there are problems; see
+        # https://github.com/pytorch/pytorch/pull/51485
+        elif str(t.elem) == "int":
+            return NamedCType(binds, BaseCType(intArrayRefT))
+        elif str(t.elem) == "Dimname":
+            return NamedCType(binds, BaseCType(dimnameListT))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
+        return NamedCType(binds, ArrayRefCType(elem.type))
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+def argument_type(a: Argument, *, binds: ArgName) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, binds=binds)
+
+
+# returns_type intentionally omitted, because structured kernels never "return";
+# instead, they always indirectly report their outputs (in the case of a meta
+# function, by calling set_output; in the case of an impl function, by writing
+# directly into the provided out argument).
+
+
+# Structured kernels are never defaulted
+def argument(a: Argument | SelfArgument | TensorOptionsArguments) -> list[Binding]:
+    if isinstance(a, Argument):
+        return [
+            Binding(
+                nctype=argument_type(a, binds=a.name),
+                name=a.name,
+                default=None,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, SelfArgument):
+        return argument(a.argument)
+    elif isinstance(a, TensorOptionsArguments):
+        raise AssertionError("structured kernels don't support TensorOptions yet")
+    else:
+        assert_never(a)
+
+
+def impl_arguments(g: NativeFunctionsGroup) -> list[Binding]:
+    args: list[Argument | TensorOptionsArguments | SelfArgument] = []
+
+    if g.out.precomputed:
+        # A list of parameters for the impl function with
+        # certain parameters replaced with precomputed counterparts
+        # as specified in native_functions.yaml.
+        non_out_args_replaced: list[
+            Argument | TensorOptionsArguments | SelfArgument
+        ] = []
+        for a in g.out.func.arguments.non_out:
+            if isinstance(a, Argument) and a.name in g.out.precomputed.replace:
+                # If a is in precompute.replace, append the parameters
+                # that should replace it onto non_out_args_replaced.
+                non_out_args_replaced.extend(g.out.precomputed.replace[a.name])
+            else:
+                # If not, push a as it is.
+                non_out_args_replaced.append(a)
+
+        args.extend(non_out_args_replaced)
+        # g.out.precomputed.add is the list of parameters that are added
+        # without replacement after the non out args and just before the out args
+        args.extend(g.out.precomputed.add)
+    else:
+        args.extend(g.out.func.arguments.non_out)
+
+    args.extend(g.out.func.arguments.out)
+    return [r for arg in args for r in argument(arg)]
+
+
+def meta_arguments(g: NativeFunctionsGroup) -> list[Binding]:
+    args: list[Argument | TensorOptionsArguments | SelfArgument] = []
+    args.extend(g.functional.func.arguments.non_out)
+    return [r for arg in args for r in argument(arg)]
+
+
+def out_arguments(g: NativeFunctionsGroup) -> list[Binding]:
+    args: list[Argument | TensorOptionsArguments | SelfArgument] = []
+    args.extend(g.out.func.arguments.out)
+    return [r for arg in args for r in argument(arg)]
diff --git a/lib/python3.11/site-packages/torchgen/api/translate.py b/lib/python3.11/site-packages/torchgen/api/translate.py
new file mode 100644
index 0000000000000000000000000000000000000000..f98ce09bbfafb875a619ea01eae7b6f82d76ef71
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/api/translate.py
@@ -0,0 +1,437 @@
+from __future__ import annotations
+
+from typing import NoReturn, TYPE_CHECKING
+
+from torchgen.api.types import (
+    ArrayRefCType,
+    BaseCType,
+    Binding,
+    boolT,
+    ConstRefCType,
+    deviceT,
+    Expr,
+    intArrayRefT,
+    iOptTensorListRefT,
+    layoutT,
+    ListCType,
+    longT,
+    memoryFormatT,
+    MutRefCType,
+    NamedCType,
+    opmath_t,
+    OptionalCType,
+    optionalIntArrayRefT,
+    optionalScalarRefT,
+    optionalSymIntArrayRefT,
+    optionalTensorRefT,
+    scalar_t,
+    scalarT,
+    scalarTypeT,
+    SpecialArgName,
+    symIntArrayRefT,
+    SymIntT,
+    tensorOptionsT,
+    tensorT,
+    VectorCType,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# This file implements a small program synthesis engine that implements
+# conversions between one API to another.
+#
+# The key data type in this file in NamedCType, short for Named C++ semantic type.  A NamedCType
+# represents a C++ type, plus semantic information about what it represents.
+# For example, consider the argument "bool pin_memory"; its normal C++ type is
+# "bool", but its C++ semantic type also keeps track that this represents a
+# "pin_memory"; you can't just use a random other boolean in a context where you
+# need a "pin_memory"!
+#
+# The translator takes a list of needed NamedCTypes, and then figures out how
+# to construct expressions with these NamedCTypes from the given bindings.  Many
+# of these expressions are trivial (I need a Tensor other; there's a Tensor
+# other scope); others are more nontrivial and may require packing/unpacking.
+# Some examples of non-trivial action:
+#
+#   - Need the "dtype" binding?  Well, maybe "dtype" isn't available
+#     in the context, instead, "options" is, and you need to extract
+#     it from there.  (Gather)
+#
+#   - Need the "context" binding?  Well, maybe "context" isn't available
+#     in the context, and you need to construct it from "dtype", "device",
+#     etc.  (Scatter)
+#
+#   - Need the "memory_format" binding?  Well, actually, it's available
+#     from both "memory_format" and "options", so you had better make sure
+#     they are consistent.  (Join)
+
+options_ctype = NamedCType("options", ConstRefCType(BaseCType(tensorOptionsT)))
+
+out_tensor_ctype = NamedCType("out", ConstRefCType(BaseCType(tensorT)))
+
+longVec_ctype = VectorCType(BaseCType(longT))
+longSymVec_ctype = VectorCType(BaseCType(SymIntT))
+optionalLongVec_ctype = OptionalCType(VectorCType(BaseCType(longT)))
+optionalScalar_ctype = OptionalCType(BaseCType(scalarT))
+optionalTensor_ctype = OptionalCType(BaseCType(tensorT))
+
+
+class UnsatError(RuntimeError):
+    pass
+
+
+# Given a set of in-scope bindings and a set of target bindings, synthesize
+# a list of expressions that uses only the in-scope bindings (bindings) that
+# have all of the types of goals.  You may want to use this function if
+# you're generating code for a function like:
+#
+#   void f({args}) {
+#     g({exprs}); // g is a different API
+#   }
+#
+# and you need to generate "exprs".
+#
+# Typically, a list of Bindings is convenient to get (you usually call something
+# like arguments() to get them); but technically you only need less information:
+# for 'bindings' an (un-ordered) list of Exprs is sufficient; similarly, for
+# 'goals', an (ordered) list of NamedCType goals is sufficient.  If you are doing
+# something more complicated, e.g., tracking the set of bindings in a context,
+# you may find using these smaller types more convenient.
+def translate(
+    bindings: Sequence[Expr | Binding],
+    goals: Sequence[NamedCType | Binding],
+    *,
+    method: bool = False,
+    allow_expensive_conversions: bool = False,
+) -> list[Expr]:
+    binding_exprs: list[Expr] = []
+    for b in bindings:
+        if isinstance(b, Binding):
+            binding_exprs.append(
+                Expr(
+                    expr=b.name,
+                    type=b.nctype,
+                )
+            )
+        else:
+            binding_exprs.append(b)
+
+    goal_ctypes: list[NamedCType] = []
+    for g in goals:
+        if isinstance(g, Binding):
+            goal_ctypes.append(g.nctype)
+        else:
+            goal_ctypes.append(g)
+
+    # Add all the bindings to the context
+    ctx: dict[NamedCType, str] = {}
+    for b in binding_exprs:
+        ctx[b.type] = b.expr
+
+        # While we're at it, do some simple forward inference, looking through
+        # constructors.
+        #
+        # NB: When should you do forward inference versus backward inference?
+        # The general idea:
+        #
+        #   - Backward inference WHEN the goal gets smaller
+        #   - Forward inference WHEN the hypothesis gets smaller
+        #
+        # This helps ensure termination: backward inference starts with a goal
+        # and tries to make it simpler and simpler until it's trivial; if the
+        # goal can grow in size, we blow up to a really huge goal size.
+        # Similarly, with forward inference we take hypotheses and decompose
+        # them into simpler hypotheses; if hypotheses could expand in size,
+        # we also have potential nontermination.  (In the code below, forward
+        # inference is only ever carried out at a single step, but you could
+        # imagine repeated application of forward inference being profitable.)
+        #
+        # A good starting point in the literature for exploring more about proof
+        # search are these lecture notes
+        # https://www.cs.cmu.edu/~fp/courses/oregon-m10/04-focusing.pdf
+        #
+        # TODO: My kingdom for a pattern matcher
+        # https://www.python.org/dev/peps/pep-0634/
+        #
+        # TODO: This could get us in recomputation trouble if b.expr is nontrivial.
+        # Fix this by implementing some sort of sharing so that if multiple
+        # goals share the same expression, we only compute it once.  This seems
+        # to matter in practice as compiler is often unwilling to CSE nontrivial
+        # expressions like scalar.to<scalar_t>()
+        t = b.type
+        if (
+            isinstance(t, ConstRefCType)
+            and isinstance(t.elem, OptionalCType)
+            and isinstance(t.elem.elem, BaseCType)
+            and str(t.elem.elem.type) == "at::Tensor"
+        ):
+            ctx[NamedCType(t.elem.elem.name, ConstRefCType(BaseCType(tensorT)))] = (
+                f"({b.expr}.has_value() ? *{b.expr} : at::Tensor())"
+            )
+
+        if t.type == ConstRefCType(OptionalCType(BaseCType(tensorT))):
+            ctx[NamedCType(t.name, BaseCType(optionalTensorRefT))] = (
+                f"(({b.expr}.has_value() && (*{b.expr}).defined()) ? at::OptionalTensorRef(*{b.expr}) : at::OptionalTensorRef())"
+            )
+
+        if t.type == ConstRefCType(BaseCType(scalarT)):
+            ctx[NamedCType(t.name, BaseCType(opmath_t))] = f"({b.expr}).to<opmath_t>()"
+
+        if t.type == ConstRefCType(OptionalCType(BaseCType(scalarT))):
+            ctx[NamedCType(t.name, BaseCType(optionalScalarRefT))] = (
+                f"({b.expr}.has_value() ? at::OptionalScalarRef(&({b.expr}.value())) : at::OptionalScalarRef())"
+            )
+
+        if t.type == BaseCType(scalar_t):
+            ctx[NamedCType(t.name, BaseCType(opmath_t))] = (
+                f"static_cast<opmath_t>({b.expr})"
+            )
+
+        # [Note: IOptTensorListRef]
+        if t.type == ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT)))):
+            ctx[NamedCType(t.name, BaseCType(iOptTensorListRefT))] = (
+                f"at::IOptTensorListRef({b.expr})"
+            )
+
+    # Add implicit bindings if the generated code is inside a Tensor method
+    if method:
+        ctx[NamedCType("self", MutRefCType(BaseCType(tensorT)))] = (
+            "const_cast<Tensor&>(*this)"
+        )
+        ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = (
+            "const_cast<Tensor&>(*this)"
+        )
+        # This is better!  Byte-for-byte compat
+        # ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = "*this"
+
+    def unsat(goal: NamedCType) -> NoReturn:
+        ctx_desc = "\n".join(
+            f"  {t.cpp_type()} {t.name}; // {e}" for t, e in ctx.items()
+        )
+        raise UnsatError(
+            f"""
+Failed to synthesize the expression "{goal.cpp_type()} {goal.name}".
+When I failed, the following bindings were available in the context:
+
+{ctx_desc}
+
+This probably means there is a missing rule in the rules of torchgen.api.translate.
+Check this module for more information.
+"""
+        )
+
+    # A shitty backtracking search implementation.  It's shitty because it
+    # does backtracking via stack (bad idea!) and for the most part tries to
+    # avoid backtracking.  In particular, if
+    # direct=True, we won't try to do any fancy synthesis, just trivial
+    # conversions (e.g., "T a" is OK for "const T& a").  So all of the
+    # existing rules in this function simply try to solve immediately,
+    # and bail if things don't work out.
+    def solve(goal: NamedCType, *, direct: bool) -> str:
+        def direct_solve(goal: NamedCType) -> str:
+            return solve(goal, direct=True)
+
+        if goal in ctx:
+            # Trivial
+            return ctx[goal]
+
+        # const & is satisfied with mutable &
+        if isinstance(goal.type, ConstRefCType):
+            try:
+                # WARNING: not strictly decreasing; be careful not
+                # to add a direct conversion that goes satisfies
+                # mutable& with const&
+                return solve(
+                    NamedCType(goal.name, MutRefCType(goal.type.elem)), direct=direct
+                )
+            except UnsatError:
+                pass
+
+        # mutable & is satisfied with value
+        if isinstance(goal.type, MutRefCType):
+            try:
+                return solve(NamedCType(goal.name, goal.type.elem), direct=direct)
+            except UnsatError:
+                pass
+
+        # TODO: These are referentially equal, shouldn't have to do this;
+        # ensuring we don't use type synonym IntArrayRef in codegen would
+        # help
+        if goal.type == ArrayRefCType(BaseCType(longT)):
+            return solve(NamedCType(goal.name, BaseCType(intArrayRefT)), direct=direct)
+
+        if direct:
+            unsat(goal)
+
+        # For now, all of these rules are mutually exclusive.
+        if goal == NamedCType("memory_format", OptionalCType(BaseCType(memoryFormatT))):
+            memory_format = direct_solve(
+                NamedCType(
+                    SpecialArgName.possibly_redundant_memory_format,
+                    OptionalCType(BaseCType(memoryFormatT)),
+                )
+            )
+            # No need to join "memory_format" and "options" if the target API takes "options" directly.
+            # Otherwise it will cause the redundant memory_format error.
+            if options_ctype in goal_ctypes:
+                return memory_format
+            try:
+                options = direct_solve(options_ctype)
+                return f"c10::impl::check_tensor_options_and_extract_memory_format({options}, {memory_format})"
+            except UnsatError:
+                return memory_format
+        elif goal == NamedCType("options", BaseCType(tensorOptionsT)):
+            dtype = direct_solve(
+                NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT)))
+            )
+            pin_memory = direct_solve(
+                NamedCType("pin_memory", OptionalCType(BaseCType(boolT)))
+            )
+            device = direct_solve(
+                NamedCType("device", OptionalCType(BaseCType(deviceT)))
+            )
+            layout = direct_solve(
+                NamedCType("layout", OptionalCType(BaseCType(layoutT)))
+            )
+            return f"TensorOptions().dtype({dtype}).layout({layout}).device({device}).pinned_memory({pin_memory})"
+
+        elif goal == NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"c10::optTypeMetaToScalarType({options}.dtype_opt())"
+            except UnsatError:
+                out_tensor = direct_solve(out_tensor_ctype)
+                return f"{out_tensor}.scalar_type()"
+
+        elif goal == NamedCType("layout", OptionalCType(BaseCType(layoutT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"{options}.layout_opt()"
+            except UnsatError:
+                out_tensor = direct_solve(out_tensor_ctype)
+                return f"{out_tensor}.layout()"
+
+        elif goal == NamedCType("device", OptionalCType(BaseCType(deviceT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"{options}.device_opt()"
+            except UnsatError:
+                out_tensor = direct_solve(out_tensor_ctype)
+                return f"{out_tensor}.device()"
+
+        elif goal == NamedCType("pin_memory", OptionalCType(BaseCType(boolT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"{options}.pinned_memory_opt()"
+            except UnsatError:
+                # If we're calling a factory op from its out= variant,
+                # We don't actually care about the value of pin_memory.
+                out_tensor = direct_solve(out_tensor_ctype)
+                return "::std::nullopt"
+
+        # We can always do translations from value types to reference types, like vector<int> -> IntArrayRef
+        elif goal.type == BaseCType(intArrayRefT):
+            try:
+                return direct_solve(NamedCType(goal.name, longVec_ctype))
+            except UnsatError:
+                # We can also go SymIntArrayRef -> IntArrayRef
+                symIntArrayRef_type = direct_solve(
+                    NamedCType(goal.name, BaseCType(symIntArrayRefT))
+                )
+                return f"C10_AS_INTARRAYREF_SLOW({symIntArrayRef_type})"
+        elif goal.type == BaseCType(symIntArrayRefT):
+            try:
+                r = direct_solve(NamedCType(goal.name, BaseCType(intArrayRefT)))
+                return f"c10::fromIntArrayRefSlow({r})"
+            except UnsatError:
+                return direct_solve(NamedCType(goal.name, longSymVec_ctype))
+        elif goal.type == BaseCType(SymIntT):
+            return direct_solve(NamedCType(goal.name, BaseCType(longT)))
+        elif goal.type == OptionalCType(BaseCType(SymIntT)):
+            argname = direct_solve(
+                NamedCType(goal.name, OptionalCType(BaseCType(longT)))
+            )
+            return f"{argname}.has_value() ? ::std::make_optional(c10::SymInt(*{argname})) : ::std::nullopt"
+        elif goal.type == BaseCType(longT):
+            symInt_type = direct_solve(NamedCType(goal.name, BaseCType(SymIntT)))
+            return f"{symInt_type}.guard_int(__FILE__, __LINE__)"
+        elif goal.type == OptionalCType(BaseCType(longT)):
+            argname = direct_solve(
+                NamedCType(goal.name, OptionalCType(BaseCType(SymIntT)))
+            )
+            return f"{argname}.has_value() ? ::std::make_optional({argname}->guard_int(__FILE__, __LINE__)) : ::std::nullopt"
+        elif goal.type == BaseCType(optionalIntArrayRefT):
+            try:
+                return direct_solve(NamedCType(goal.name, optionalLongVec_ctype))
+            except UnsatError:
+                argname = direct_solve(
+                    NamedCType(goal.name, BaseCType(optionalSymIntArrayRefT))
+                )
+                return f"{argname}.has_value() ? ::std::make_optional(C10_AS_INTARRAYREF_SLOW(*{argname})) : ::std::nullopt"
+        elif goal.type == BaseCType(optionalSymIntArrayRefT):
+            # TODO: You might also want to solve this from longSymVec_ctype or
+            # an optional version of it
+            argname = direct_solve(
+                NamedCType(goal.name, BaseCType(optionalIntArrayRefT))
+            )
+            return f"{argname}.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*{argname})) : ::std::nullopt"
+        elif goal.type == BaseCType(optionalScalarRefT):
+            return direct_solve(NamedCType(goal.name, optionalScalar_ctype))
+        elif goal.type == BaseCType(optionalTensorRefT):
+            return direct_solve(NamedCType(goal.name, optionalTensor_ctype))
+
+        # Note [translation from C++ reference to value types]
+        # The below cases are all for when we have an argument with a reference type,
+        # and a corresponding goal with a value type.
+        # These are needed when we populate the inputs to a lambda capture and we need
+        # to guarantee the lifetime of each captured argument.
+        # We guard it with an explicit kwarg because converting to a value type is expensive
+        # (O(n)) to convert from IntArrayRef to vector<int>),
+        # so the caller of translate() should be explicit that they need it.
+        if allow_expensive_conversions:
+            if goal.type == VectorCType(BaseCType(longT)):
+                intArrayRef_ctype = NamedCType(goal.name, BaseCType(intArrayRefT))
+                argname = direct_solve(intArrayRef_ctype)
+                return f"{argname}.vec()"
+            if goal.type == VectorCType(BaseCType(SymIntT)):
+                symIntArrayRef_ctype = NamedCType(goal.name, BaseCType(symIntArrayRefT))
+                argname = direct_solve(symIntArrayRef_ctype)
+                return f"{argname}.vec()"
+            elif goal.type == OptionalCType(VectorCType(BaseCType(longT))):
+                optionalIntArrayRef_ctype = NamedCType(
+                    goal.name, BaseCType(optionalIntArrayRefT)
+                )
+                argname = direct_solve(optionalIntArrayRef_ctype)
+                return f"{argname}.has_value() ? ::std::make_optional({argname}->vec()) : ::std::nullopt"
+            elif goal.type == OptionalCType(BaseCType(scalarT)):
+                optionalScalarRef_ctype = NamedCType(
+                    goal.name, BaseCType(optionalScalarRefT)
+                )
+                argname = direct_solve(optionalScalarRef_ctype)
+                return f"{argname}.has_value() ? ::std::make_optional({argname}) : ::std::nullopt"
+            elif goal.type == OptionalCType(BaseCType(scalarT)):
+                optionalTensorRef_ctype = NamedCType(
+                    goal.name, BaseCType(optionalTensorRefT)
+                )
+                argname = direct_solve(optionalTensorRef_ctype)
+                return f"{argname}.has_value() ? ::std::make_optional({argname}) : ::std::nullopt"
+            # Technically, we also need to handle cases of C++ containers holding reference types.
+            # But there currently aren't any ops that require lambda capture codegen
+            # With arguments like ::std::vector<IntArrayRef>.
+            # If that changes, we'll have to add the translation here.
+
+        # We allow const casting on tensors, since const-correctness is a bit broken for at::Tensor.
+        # We could probably generalize this to non-tensor types too.
+        if goal.type == MutRefCType(BaseCType(tensorT)):
+            const_ref_tensor_ctype = NamedCType(
+                goal.name, ConstRefCType(BaseCType(tensorT))
+            )
+            argname = direct_solve(const_ref_tensor_ctype)
+            return f"const_cast<Tensor&>({argname})"
+
+        unsat(goal)
+
+    return [Expr(solve(g, direct=False), g) for g in goal_ctypes]
diff --git a/lib/python3.11/site-packages/torchgen/code_template.py b/lib/python3.11/site-packages/torchgen/code_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c33aad126f81be46606b29c329d8c5218bcc889
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/code_template.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping, Sequence
+
+
+# match $identifier or ${identifier} and replace with value in env
+# If this identifier is at the beginning of whitespace on a line
+# and its value is a list then it is treated as
+# block substitution by indenting to that depth and putting each element
+# of the list on its own line
+# if the identifier is on a line starting with non-whitespace and a list
+# then it is comma separated ${,foo} will insert a comma before the list
+# if this list is not empty and ${foo,} will insert one after.
+
+
+class CodeTemplate:
+    substitution_str = r"(^[^\n\S]*)?\$([^\d\W]\w*|\{,?[^\d\W]\w*\,?})"
+    substitution = re.compile(substitution_str, re.MULTILINE)
+
+    pattern: str
+    filename: str
+
+    @staticmethod
+    def from_file(filename: str) -> CodeTemplate:
+        with open(filename) as f:
+            return CodeTemplate(f.read(), filename)
+
+    def __init__(self, pattern: str, filename: str = "") -> None:
+        self.pattern = pattern
+        self.filename = filename
+
+    def substitute(
+        self, env: Mapping[str, object] | None = None, **kwargs: object
+    ) -> str:
+        if env is None:
+            env = {}
+
+        def lookup(v: str) -> object:
+            assert env is not None
+            return kwargs[v] if v in kwargs else env[v]
+
+        def indent_lines(indent: str, v: Sequence[object]) -> str:
+            return "".join(
+                [indent + l + "\n" for e in v for l in str(e).splitlines()]
+            ).rstrip()
+
+        def replace(match: re.Match[str]) -> str:
+            indent = match.group(1)
+            key = match.group(2)
+            comma_before = ""
+            comma_after = ""
+            if key[0] == "{":
+                key = key[1:-1]
+                if key[0] == ",":
+                    comma_before = ", "
+                    key = key[1:]
+                if key[-1] == ",":
+                    comma_after = ", "
+                    key = key[:-1]
+            v = lookup(key)
+            if indent is not None:
+                if not isinstance(v, list):
+                    v = [v]
+                return indent_lines(indent, v)
+            elif isinstance(v, list):
+                middle = ", ".join([str(x) for x in v])
+                if len(v) == 0:
+                    return middle
+                return comma_before + middle + comma_after
+            else:
+                return str(v)
+
+        return self.substitution.sub(replace, self.pattern)
+
+
+if __name__ == "__main__":
+    c = CodeTemplate(
+        """\
+    int foo($args) {
+
+        $bar
+            $bar
+        $a+$b
+    }
+    int commatest(int a${,stuff})
+    int notest(int a${,empty,})
+    """
+    )
+    print(
+        c.substitute(
+            args=["hi", 8],
+            bar=["what", 7],
+            a=3,
+            b=4,
+            stuff=["things...", "others"],
+            empty=[],
+        )
+    )
diff --git a/lib/python3.11/site-packages/torchgen/context.py b/lib/python3.11/site-packages/torchgen/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..a482a59eeb707750242554fdbddf5250d95c3bcc
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/context.py
@@ -0,0 +1,134 @@
+from __future__ import annotations
+
+import contextlib
+import functools
+from typing import Any, Callable, Optional, TYPE_CHECKING, TypeVar, Union
+
+import torchgen.local as local
+from torchgen.model import (
+    BackendIndex,
+    DispatchKey,
+    NativeFunction,
+    NativeFunctionsGroup,
+    NativeFunctionsViewGroup,
+)
+from torchgen.utils import context, S, T
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+
+# Helper functions for defining generators on things in the model
+
+F = TypeVar(
+    "F",
+    NativeFunction,
+    NativeFunctionsGroup,
+    NativeFunctionsViewGroup,
+    Union[NativeFunction, NativeFunctionsGroup],
+    Union[NativeFunction, NativeFunctionsViewGroup],
+)
+
+F2 = TypeVar(
+    "F2",
+    NativeFunction,
+    NativeFunctionsGroup,
+    Optional[NativeFunction],
+    bool,
+    str,
+)
+
+F3 = TypeVar("F3", tuple[NativeFunction, Any], list[NativeFunction])
+
+
+@contextlib.contextmanager
+def native_function_manager(
+    g: NativeFunctionsGroup | NativeFunctionsViewGroup | NativeFunction,
+) -> Iterator[None]:
+    if isinstance(g, NativeFunctionsGroup):
+        # By default, we associate all errors with structured native functions
+        # with the out variant.  In some cases, it might be better to have
+        # a more specific place to hang things; if so, use
+        # native_function_manager again on the inside
+        f = g.out
+    elif isinstance(g, NativeFunctionsViewGroup):
+        # We associate errors with the view operator
+        f = g.view
+    else:
+        f = g
+    with context(lambda: f"in native_functions.yaml line {f.loc}:\n  {f.func}"):
+        with local.parametrize(
+            use_const_ref_for_mutable_tensors=f.use_const_ref_for_mutable_tensors,
+            use_ilistref_for_tensor_lists=f.part_of_structured_group,
+        ):
+            yield
+
+
+# Given a function that operates on NativeFunction, wrap it into a new function
+# that sets some appropriate context managers for that native function.
+# YOU MUST WRAP FUNCTIONS IN THIS for calls to api modules to be sound
+# (you will get an error if we try to access the local variables without having
+# set them).
+def with_native_function(func: Callable[[F], T]) -> Callable[[F], T]:
+    @functools.wraps(func)
+    def wrapper(f: F) -> T:
+        with native_function_manager(f):
+            return func(f)
+
+    return wrapper
+
+
+def with_native_function_and(func: Callable[[F, F2], T]) -> Callable[[F, F2], T]:
+    @functools.wraps(func)
+    def wrapper(f: F, f2: F2) -> T:
+        # The first native_function is assumed to be the one with the appropriate context.
+        with native_function_manager(f):
+            return func(f, f2)
+
+    return wrapper
+
+
+def method_with_native_function(func: Callable[[S, F], T]) -> Callable[[S, F], T]:
+    @functools.wraps(func)
+    def wrapper(slf: S, f: F) -> T:
+        with native_function_manager(f):
+            return func(slf, f)
+
+    return wrapper
+
+
+def method_with_nested_native_function(
+    func: Callable[[S, F3], T],
+) -> Callable[[S, F3], T]:
+    @functools.wraps(func)
+    def wrapper(slf: S, f: F3) -> T:
+        with native_function_manager(f[0]):
+            return func(slf, f)
+
+    return wrapper
+
+
+# Convenience decorator for functions that explicitly take in a BackendIndex,
+# instead of indirectly taking one in as a closure
+def with_native_function_and_index(
+    func: Callable[[F, BackendIndex], T],
+) -> Callable[[F, BackendIndex], T]:
+    @functools.wraps(func)
+    def wrapper(f: F, backend_index: BackendIndex) -> T:
+        with native_function_manager(f):
+            return func(f, backend_index)
+
+    return wrapper
+
+
+# Convenience decorator for functions that explicitly take in a Dict of BackendIndices
+def with_native_function_and_indices(
+    func: Callable[[F, dict[DispatchKey, BackendIndex]], T],
+) -> Callable[[F, dict[DispatchKey, BackendIndex]], T]:
+    @functools.wraps(func)
+    def wrapper(f: F, backend_indices: dict[DispatchKey, BackendIndex]) -> T:
+        with native_function_manager(f):
+            return func(f, backend_indices)
+
+    return wrapper
diff --git a/lib/python3.11/site-packages/torchgen/gen.py b/lib/python3.11/site-packages/torchgen/gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..354297a9240db87750f237e1c794ba65cbf1a0bf
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen.py
@@ -0,0 +1,3064 @@
+from __future__ import annotations
+
+import argparse
+import functools
+import json
+import keyword
+import os
+from collections import defaultdict, namedtuple, OrderedDict
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Callable, Literal, TYPE_CHECKING, TypeVar
+
+import yaml
+
+import torchgen.api.dispatcher as dispatcher
+import torchgen.api.meta as meta
+import torchgen.api.native as native
+import torchgen.api.structured as structured
+import torchgen.dest as dest
+from torchgen.aoti.fallback_ops import inductor_fallback_ops
+from torchgen.api import cpp
+from torchgen.api.translate import translate
+from torchgen.api.types import (
+    Binding,
+    CppSignature,
+    CppSignatureGroup,
+    DispatcherSignature,
+    NamedCType,
+    NativeSignature,
+    SpecialArgName,
+)
+from torchgen.context import (
+    method_with_native_function,
+    native_function_manager,
+    with_native_function,
+    with_native_function_and_indices,
+)
+from torchgen.gen_aoti_c_shim import (
+    gen_aoti_c_shim,
+    gen_static_dispatch_backend_call_signature,
+    get_fallback_op_name,
+    get_header_for_aoti,
+)
+from torchgen.gen_functionalization_type import (
+    gen_functionalization_definition,
+    gen_functionalization_registration,
+    gen_functionalization_view_inverse_declaration,
+    GenCompositeViewCopyKernel,
+)
+from torchgen.gen_vmap_plumbing import gen_all_vmap_plumbing
+from torchgen.model import (
+    Argument,
+    BackendIndex,
+    BackendMetadata,
+    BaseOperatorName,
+    DEFAULT_KERNEL_NAMESPACE,
+    dispatch_device_map,
+    DispatchKey,
+    FRAGMENT_NAMESPACES,
+    FunctionSchema,
+    is_cuda_dispatch_key,
+    is_generic_dispatch_key,
+    is_ufunc_dispatch_key,
+    is_xpu_dispatch_key,
+    Location,
+    NativeFunction,
+    NativeFunctionsGroup,
+    NativeFunctionsViewGroup,
+    OperatorName,
+    OptionalType,
+    SchemaKind,
+    SelfArgument,
+    STRUCTURED_DISPATCH_KEYS,
+    TensorOptionsArguments,
+    Type,
+    Variant,
+    ViewSchemaKind,
+)
+from torchgen.native_function_generation import (
+    add_generated_native_functions,
+    gen_composite_functional_kernel,
+    gen_composite_out_kernel,
+    pre_group_native_functions,
+)
+from torchgen.selective_build.selector import SelectiveBuilder
+from torchgen.utils import (
+    assert_never,
+    concatMap,
+    context,
+    FileManager,
+    make_file_manager,
+    mapMaybe,
+    NamespaceHelper,
+    Target,
+)
+from torchgen.yaml_utils import YamlDumper, YamlLoader
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+T = TypeVar("T")
+
+# Welcome to the ATen code generator v2!  The ATen code generator is
+# responsible for parsing native_functions.yaml and then generating
+# various generated files (e.g., TypeDefault.cpp) based on the operators
+# defined in this file.  This means that the code generator knows how to
+# parse function schema, and then translate this into various C++ types
+# and boilerplate code.
+#
+# Some things to know about this file when you modify it:
+#
+# - This file has STRICT mypy typechecking.  Typecheck it with
+#   `mypy --config mypy-strict.ini` in the root source directory
+#
+# - Most of the heavy lifting lives in external modules:
+#   - 'model' has the data model for native_functions.yaml.  The classes
+#     in those file represent what you see when you look at
+#     a native_functions.yaml
+#   - 'api' has conversions for how to translate JIT schema into
+#     the various C++ APIs that the codegen interacts with.  There
+#     are in fact THREE different C++ APIs: the public C++ API,
+#     the dispatcher API, and the legacy dispatcher API.  See each
+#     of these respective files for more information
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                         HELPER FUNCTIONS
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+# A custom loader for YAML to let us also keep track of line numbers
+# of each entry in the YAML file
+class LineLoader(YamlLoader):
+    def construct_mapping(self, node, deep=False):  # type: ignore[no-untyped-def]
+        mapping = super().construct_mapping(node, deep=deep)  # type: ignore[no-untyped-call]
+        # Add 1 so line numbering starts at 1
+        mapping["__line__"] = node.start_mark.line + 1
+        return mapping
+
+
+# Parse native_functions.yaml into a sequence of NativeFunctions and Backend Indices.
+ParsedYaml = namedtuple("ParsedYaml", ["native_functions", "backend_indices"])
+
+
+_GLOBAL_PARSE_NATIVE_YAML_CACHE: dict[str, ParsedYaml] = {}
+_GLOBAL_PARSE_TAGS_YAML_CACHE: dict[str, set[str]] = {}
+
+
+def file_manager_from_dispatch_key(
+    dispatch_key: DispatchKey,
+    device_fms: dict[str, FileManager],
+    default_fm: FileManager,
+) -> FileManager:
+    fm = device_fms.get(
+        next(
+            (
+                device
+                for check, device in dispatch_device_map.items()
+                if check(dispatch_key)
+            ),
+            "",
+        ),
+        default_fm,
+    )
+    return fm
+
+
+def parse_native_yaml_struct(
+    es: object,
+    valid_tags: set[str],
+    ignore_keys: set[DispatchKey] | None = None,
+    path: str = "<stdin>",
+    skip_native_fns_gen: bool = False,
+) -> ParsedYaml:
+    assert isinstance(es, list)
+    rs: list[NativeFunction] = []
+    bs: dict[DispatchKey, dict[OperatorName, BackendMetadata]] = defaultdict(dict)
+    for e in es:
+        assert isinstance(e, dict), f"expected to be dict: {e}"
+        assert isinstance(e.get("__line__"), int), e
+        loc = Location(path, e["__line__"])
+        funcs = e.get("func")
+        assert funcs is not None, f"missed 'func' in {e}"
+        with context(lambda: f"in {loc}:\n  {funcs}"):
+            func, m = NativeFunction.from_yaml(e, loc, valid_tags, ignore_keys)
+            rs.append(func)
+            BackendIndex.grow_index(bs, m)
+    error_check_native_functions(rs)
+    # Default dict is to prevent the codegen from barfing when we have a dispatch key that has no kernels yet.
+    indices: dict[DispatchKey, BackendIndex] = defaultdict(
+        lambda: BackendIndex(
+            dispatch_key=DispatchKey.Undefined,
+            use_out_as_primary=True,
+            external=False,
+            device_guard=False,
+            # I'm actually not sure about this; undefined could be hit on
+            # empty TensorList, hypothetically that could have sizes in it
+            index={},
+        )
+    )
+    if not skip_native_fns_gen:
+        add_generated_native_functions(rs, bs)
+    for k, v in bs.items():
+        # All structured in-tree operators are implemented in terms of their out operator.
+        indices[k] = BackendIndex(
+            dispatch_key=k,
+            use_out_as_primary=True,
+            external=False,
+            # Only cuda-like devices in tree require device guards
+            device_guard=is_cuda_dispatch_key(k) or is_xpu_dispatch_key(k),
+            index=v,
+        )
+    return ParsedYaml(rs, indices)
+
+
+def parse_tags_yaml_struct(es: object, path: str = "<stdin>") -> set[str]:
+    assert isinstance(es, list)
+    rs: set[str] = set()
+    for e in es:
+        assert isinstance(e.get("__line__"), int), e
+        loc = Location(path, e["__line__"])
+        tags = e.get("tag")
+        with context(lambda: f"in {loc}:\n  {tags}"):
+            e_i = e.copy()
+            name = e_i.pop("tag")
+            desc = e_i.pop("desc", "")
+            # ensure that each tag has a non-empty description
+            assert desc != ""
+            rs.add(name)
+    return rs
+
+
+@functools.cache
+def parse_tags_yaml(path: str) -> set[str]:
+    global _GLOBAL_PARSE_TAGS_YAML_CACHE
+    if path not in _GLOBAL_PARSE_TAGS_YAML_CACHE:
+        with open(path) as f:
+            es = yaml.load(f, Loader=LineLoader)
+            _GLOBAL_PARSE_TAGS_YAML_CACHE[path] = parse_tags_yaml_struct(es, path=path)
+
+    return _GLOBAL_PARSE_TAGS_YAML_CACHE[path]
+
+
+def parse_native_yaml(
+    path: str,
+    tags_yaml_path: str,
+    ignore_keys: set[DispatchKey] | None = None,
+    *,
+    skip_native_fns_gen: bool = False,
+    loaded_yaml: object | None = None,
+) -> ParsedYaml:
+    global _GLOBAL_PARSE_NATIVE_YAML_CACHE
+    if path not in _GLOBAL_PARSE_NATIVE_YAML_CACHE:
+        valid_tags = parse_tags_yaml(tags_yaml_path)
+
+        # if a loaded yaml is provided, use that instead of reading from path
+        if loaded_yaml is None:
+            with open(path) as f:
+                es = yaml.load(f, Loader=LineLoader)
+        else:
+            es = loaded_yaml
+
+        _GLOBAL_PARSE_NATIVE_YAML_CACHE[path] = parse_native_yaml_struct(
+            es,
+            valid_tags,
+            ignore_keys,
+            path=path,
+            skip_native_fns_gen=skip_native_fns_gen,
+        )
+
+    return _GLOBAL_PARSE_NATIVE_YAML_CACHE[path]
+
+
+# Some assertions are already performed during parsing, but those are only within a single NativeFunction.
+# Assertions here are meant to be performed across NativeFunctions.
+def error_check_native_functions(funcs: Sequence[NativeFunction]) -> None:
+    func_map: dict[OperatorName, NativeFunction] = {}
+    base_func_map: dict[BaseOperatorName, list[NativeFunction]] = defaultdict(list)
+    for f in funcs:
+        func_map[f.func.name] = f
+        base_func_map[f.func.name.name].append(f)
+    for f in funcs:
+        if f.structured_delegate is not None:
+            delegate_func = func_map.get(f.structured_delegate)
+            assert delegate_func is not None, (
+                f"{f.func.name} is marked as a structured_delegate pointing to "
+                f"{f.structured_delegate}, but {f.structured_delegate} is missing."
+            )
+            assert delegate_func.structured, (
+                f"{f.func.name} is marked as a structured_delegate pointing to "
+                f"{f.structured_delegate}, but {f.structured_delegate} is not marked as structured. "
+                f"Consider adding 'structured=True' to the delegated operator"
+            )
+
+        # Check for reserved Python keywords
+        PYTHON_RESERVED_KEYWORDS = set(keyword.kwlist)
+        # List of pre-existing operators that are known to have reserved keywords
+        # Exclusion list is used to suppress the assertion for these operators
+        EXCLUSION_LIST = {
+            ("_has_compatible_shallow_copy_type", "from"),
+            ("random_.from", "from"),
+            ("uniform_", "from"),
+        }
+
+        for arg in f.func.arguments.flat_all:
+            if arg.name in PYTHON_RESERVED_KEYWORDS:
+                if (str(f.func.name), arg.name) not in EXCLUSION_LIST:
+                    raise AssertionError(
+                        f"Argument name '{arg.name}' in function '{f.func.name}' is a reserved Python keyword."
+                    )
+        # See Note [resize_ in Functionalization]
+        # resize_() is technically an inplace view op (and therefore needs the tag),
+        # but it would be overkill to add a true "view" variant of resize.
+        # Instead, resize_() gets special treatment in functionalization,
+        # and we have a resize() op that is non-aliasing + functional.
+        if (
+            "inplace_view" in f.tags
+            and str(f.func.name) != "resize_"
+            and str(f.func.name) != "resize_as_"
+            and str(f.func.name.name) != "set_"
+        ):
+            base_name = f.func.name.name
+            assert base_name.inplace, (
+                f"{f.func.name} is marked with tag: inplace_view, but it doesn't follow the naming "
+                "convention for inplace ops - the codegen expects the base name to have a trailing underscore. "
+            )
+            out_of_place_base_name = BaseOperatorName(
+                base_name.base, False, base_name.dunder_method
+            )
+            assert len(base_func_map[out_of_place_base_name]) > 0, (
+                f"{f.func.name} is marked with tag: inplace_view. The codegen expects there to be a corresponding "
+                f"out-of-place view op with the name '{base_name}' and matching schema, but it didn't find one. "
+            )
+
+
+def cpp_string(s: str) -> str:
+    """Convert a python string into a c++ string literal"""
+    s = s.replace("\\", "\\\\")
+    s = s.replace('"', '\\"')
+    s = s.replace("\a", "\\a")
+    s = s.replace("\b", "\\b")
+    s = s.replace("\f", "\\f")
+    s = s.replace("\n", "\\n")
+    s = s.replace("\v", "\\v")
+    s = s.replace("\t", "\\t")
+    return f'"{s}"'
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                        C++ CODE GENERATION
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+# Most functions in this section are curried: they consist of a function
+# that takes some parameters (e.g., what is to be generated) which itself
+# returns a function that actually maps NativeFunction to the code
+# to be generated.  This pattern makes it convenient to use map, concatMap
+# and similar functional combinators.
+
+
+def static_dispatch_keys(backends: list[BackendIndex]) -> list[DispatchKey]:
+    if len(backends) == 0:
+        return []
+    else:
+        return [backend.dispatch_key for backend in backends] + [
+            DispatchKey.CompositeImplicitAutograd,
+            DispatchKey.CompositeImplicitAutogradNestedTensor,
+            DispatchKey.CompositeExplicitAutograd,
+            DispatchKey.CompositeExplicitAutogradNonFunctional,
+        ]
+
+
+def get_static_dispatch_backend(
+    f: NativeFunction, backend_index: BackendIndex
+) -> DispatchKey | None:
+    if f.structured_delegate is not None or backend_index.has_kernel(f):
+        # TODO: for ops with structured_delegate it should check the dispatch table of
+        # the out variant instead. For now, these structured ops all have CPU/CUDA kernels
+        # so we always dispatch to the `backend`, but this could be wrong when we
+        # migrate math/default_backend ops to use structured delegate.
+        return backend_index.dispatch_key
+    elif f.has_composite_explicit_autograd_kernel:
+        return DispatchKey.CompositeExplicitAutograd
+    elif f.has_composite_explicit_autograd_non_functional_kernel:
+        return DispatchKey.CompositeExplicitAutogradNonFunctional
+    elif f.has_composite_implicit_autograd_kernel:
+        return DispatchKey.CompositeImplicitAutograd
+    elif f.has_composite_implicit_autograd_nested_tensor_kernel:
+        return DispatchKey.CompositeImplicitAutogradNestedTensor
+    return None
+
+
+def static_dispatch_ops_header(
+    f: NativeFunction, backend_index: list[BackendIndex]
+) -> str | None:
+    if backend_index is None or f.manual_kernel_registration:
+        return None
+
+    output = []
+    for index in backend_index:
+        dispatch_key = get_static_dispatch_backend(f, index)
+        if dispatch_key is not None:
+            output.append(
+                f"#include <ATen/ops/{f.root_name}_{dispatch_key.lower()}_dispatch.h>"
+            )
+    return "\n".join(output)
+
+
+def static_dispatch_extra_headers(backends: list[BackendIndex]) -> list[str]:
+    return [
+        f"#include <ATen/{dispatch_key}Functions.h>"
+        for dispatch_key in static_dispatch_keys(backends)
+    ]
+
+
+# Translates arguments of `sig` to CppSignature bindings.
+# Note that we have a special case for `memory_format` argument and this case is not covered by
+# tools.codegen.api.translate() yet as its application is limited to static dispatch.
+def translate_args(
+    sig: CppSignature | DispatcherSignature,
+    cpp_sig: CppSignature,
+) -> str:
+    # Adds SpecialArgName.possibly_redundant_memory_format NamedCType for memory_format bindings
+    def add_spl_memory_format_binding(input_bindings: list[Binding]) -> list[Binding]:
+        output_bindings: list[Binding] = []
+        for binding in input_bindings:
+            if binding.name == "memory_format":
+                spl_mem_format_binding = Binding(
+                    nctype=NamedCType(
+                        SpecialArgName.possibly_redundant_memory_format,
+                        binding.nctype.type,
+                    ),
+                    name=binding.name,
+                    default=binding.default,
+                    argument=binding.argument,
+                )
+                output_bindings.append(spl_mem_format_binding)
+            else:
+                output_bindings.append(binding)
+        return output_bindings
+
+    src_bindings = list(sig.arguments())
+    goal_bindings = list(cpp_sig.arguments())
+    # When last argument of CPP signature has SpecialArgName.possibly_redundant_memory_format NCType,
+    # get memory_format bindings of dispatcher signature to have the same NCType as well
+    for arg in goal_bindings:
+        if arg.nctype.name == SpecialArgName.possibly_redundant_memory_format:
+            src_bindings = add_spl_memory_format_binding(src_bindings)
+            break
+    exprs = translate(src_bindings, goal_bindings)
+    return ", ".join(a.expr for a in exprs)
+
+
+def generate_static_dispatch_backend_call(
+    sig: CppSignature | DispatcherSignature,
+    f: NativeFunction,
+    backend_index: BackendIndex,
+) -> str:
+    cpp_sig = gen_static_dispatch_backend_call_signature(sig, f)
+    name = cpp_sig.name()
+    exprs = translate_args(sig, cpp_sig)
+    backend_metadata = backend_index.get_kernel(f)
+    kernel_ns = (
+        backend_metadata.cpp_namespace
+        if backend_metadata and backend_metadata.cpp_namespace
+        else DEFAULT_KERNEL_NAMESPACE
+    )
+    ns = kernel_ns.replace("::native", "")
+    return f"return {ns}::{backend_index.dispatch_key.lower()}::{name}({exprs});"
+
+
+def generate_static_dispatch_fallback_call(
+    sig: CppSignature | DispatcherSignature,
+    f: NativeFunction,
+    backend_indices: list[BackendIndex],
+) -> str:
+    cpp_sigs = CppSignatureGroup.from_native_function(
+        f, method=False, fallback_binding=False
+    )
+    if sig.symint and f.func.has_symint():
+        cpp_sig = cpp_sigs.symint_signature
+    else:
+        cpp_sig = cpp_sigs.signature
+    assert cpp_sig is not None
+    name = cpp_sig.name()
+    exprs = translate_args(sig, cpp_sig)
+    ns = DEFAULT_KERNEL_NAMESPACE.replace("::native", "")
+    if f.has_composite_explicit_autograd_kernel:
+        return f"return {ns}::{DispatchKey.CompositeExplicitAutograd.lower()}::{name}({exprs});"
+    elif f.has_composite_explicit_autograd_non_functional_kernel:
+        return f"return {ns}::{DispatchKey.CompositeExplicitAutogradNonFunctional.lower()}::{name}({exprs});"
+    elif f.has_composite_implicit_autograd_kernel:
+        return f"return {ns}::{DispatchKey.CompositeImplicitAutograd.lower()}::{name}({exprs});"
+    elif f.has_composite_implicit_autograd_nested_tensor_kernel:
+        return f"return {ns}::{DispatchKey.CompositeImplicitAutogradNestedTensor.lower()}::{name}({exprs});"
+    else:
+        return f"""TORCH_CHECK(false, "Static dispatch does not support {name} for\
+{', '.join([str(index.dispatch_key)for index in backend_indices])} ");"""
+
+
+def static_dispatch(
+    sig: CppSignature | DispatcherSignature,
+    f: NativeFunction,
+    backend_indices: list[BackendIndex],
+) -> str:
+    """
+    For a given `NativeFunction`, find out the corresponding backend and dispatch to it. If more than one
+    backends exsit, fallback to static dispatch by determining dispatch key from inputs.
+    Arguments:
+        sig: A CppSignature or DispatcherSignature for this native function we want to use.
+        f: NativeFunction to generate static dispatch.
+        backend_indices: All available backends.
+    Return:
+        C++ code to call backend-specific functions, e.g., "return at::cpu::add(self, other, scale);"
+    """
+    if len(backend_indices) == 0 or f.manual_kernel_registration:
+        return ""
+
+    keys = [
+        b
+        for b in backend_indices
+        if b.has_kernel(f)
+        or (
+            f.structured_delegate is not None
+            and b.dispatch_key in STRUCTURED_DISPATCH_KEYS
+        )
+    ]
+    if len(keys) == 1:
+        return generate_static_dispatch_backend_call(sig, f, keys[0])
+    elif len(keys) == 0:
+        return generate_static_dispatch_fallback_call(sig, f, backend_indices)
+
+    native_tensor_args = [
+        a.name
+        for a in sig.arguments()
+        if isinstance(a.argument, SelfArgument)
+        or isinstance(a.argument, Argument)
+        and a.argument.type.is_tensor_like()
+    ]
+    tensor_args = ", ".join(native_tensor_args)
+    tensor_opts = f.func.arguments.tensor_options
+
+    stmts = []
+    subexprs: list[str] = []
+    if tensor_opts is not None:
+        subexprs.append(
+            "DispatchKeySet(c10::computeDispatchKey(dtype, layout, device))"
+        )
+    if tensor_args != "":
+        subexprs.append(f"c10::detail::multi_dispatch_key_set({tensor_args})")
+    stmts.append(f"""DispatchKeySet _dk_set = {' | '.join(subexprs)};""")
+    stmts.append("DispatchKey _dk = c10::highestPriorityBackendTypeId(_dk_set);")
+
+    dispatch_code = []
+    for index in keys:
+        dispatch_code.append(f"""case DispatchKey::{index.dispatch_key}:""")
+        dispatch_code.append(
+            f"""\t{generate_static_dispatch_backend_call(sig, f, index)};"""
+        )
+
+    fallback = generate_static_dispatch_fallback_call(sig, f, backend_indices)
+    connector = "\n\t\t"
+
+    return f"""
+    {connector.join(stmts)}
+    switch (_dk) {{
+        {connector.join(dispatch_code)}
+        default:
+            {fallback}
+    }}
+    """
+
+
+# Generates RegisterSchema.cpp.  Depending on the selector, either
+# all schemas are registered, or only some are (in the case of
+# selective build)
+@dataclass(frozen=True)
+class RegisterSchema:
+    selector: SelectiveBuilder
+    known_tags: dict[str, int] = field(default_factory=dict)
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        if not self.selector.is_native_function_selected(f):
+            return None
+        tags = "{" + ", ".join(f"at::Tag::{tag}" for tag in sorted(f.tags)) + "}"
+        if tags == "{}":
+            return f"m.def({cpp_string(str(f.func))}, {{}});\n"
+        maybe_tags = ""
+        if tags not in self.known_tags:
+            idx = len(self.known_tags)
+            self.known_tags[tags] = idx
+            maybe_tags = f"const std::vector<at::Tag> tags_{idx} = {tags};\n"
+        return f"{maybe_tags}m.def({cpp_string(str(f.func))}, tags_{self.known_tags[tags]});\n"
+
+
+# Generates Operators.h and Operators.cpp.
+# These provide macros that, given an operator and overload name, allow users
+# to access an "un-overloaded" function version of the operator. This
+# is useful for extension writers who want to (1) want to decltype the operator
+# and (2) don't want to worry about method-only operators.
+@dataclass(frozen=True)
+class ComputeOperators:
+    target: Literal[Target.DECLARATION, Target.DEFINITION]
+    static_dispatch_backend_indices: list[BackendIndex]
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str:
+        sig = DispatcherSignature.from_schema(f.func)
+        name = f.func.name.unambiguous_name()
+
+        if self.target is Target.DECLARATION:
+            # Note [The ATen Operators API]
+            # The ATen Operators API lives in the at::_ops namespace, and contains compile-time
+            # metadata about each operator + entry points into the Dispatcher.
+            # The C++ function, method, and redispatch API's are all implemented as wrappers
+            # into various bits of the structs defined here.
+            #
+            # Important characteristics about the Operators API:
+            # (1) It follows the Dispatcher API.
+            #     This is kind of necessary to avoid overhead.
+            #     For example: if it followed the C++ API, then all of the faithful C++ factory functions
+            #     would need to wrap their arguments into TensorOptions only to unwrap them again.
+            # (2) Overload names are disambiguated.
+            #     This is helpful for pytorch extenders who would like to decltype() an aten operator,
+            #     that has overloads, e.g. decltype(at::_ops::mul_Tensor::call)
+            # (3) No argument defaulting is allowed.
+            #     This is more of an implementation detail to avoid #include cycles,
+            #     since TensorBody.h (which defines the Tensor class) needs to include this file.
+            # (4) manual_cpp_bindings and faithful names are not included in the API.
+            #     This applies to stuff like __dispatch__is_complex(), and add_outf().
+            #     These aren't "real aten ops", they're just additional functions provided by the C++ API.
+            #     They're implemented as wrappers in Functions.h that call into the actual operators
+            #     defined here, i.e. at::_ops::is_complex::call() and at::_ops::add_out::call().
+            #     This means that ATEN_OP(is_complex) will not fastpath, and will go through the dispatcher.
+            return f"""
+struct TORCH_API {name} {{
+  using schema = {sig.type()};
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::{f.func.name.name}";
+  static constexpr const char* overload_name = "{f.func.name.overload_name}";
+  static constexpr const char* schema_str = {cpp_string(str(f.func))};
+  static {sig.defn(name="call", is_redispatching_fn=False)};
+  static {sig.defn(name="redispatch", is_redispatching_fn=True)};
+}};"""
+
+        elif self.target is Target.DEFINITION:
+            defns = f"""
+// aten::{f.func}
+static C10_NOINLINE c10::TypedOperatorHandle<{name}::schema> create_{name}_typed_handle() {{
+  return c10::Dispatcher::singleton()
+      .findSchemaOrThrow({name}::name, {name}::overload_name)
+      .typed<{name}::schema>();
+}}
+"""
+            for is_redispatching_fn in [False, True]:
+                if is_redispatching_fn:
+                    dispatcher_exprs_str = ", ".join(
+                        ["dispatchKeySet"] + [a.name for a in sig.arguments()]
+                    )
+                    method_base = "redispatch"
+                else:
+                    dispatcher_exprs_str = ", ".join([a.name for a in sig.arguments()])
+                    method_base = "call"
+
+                dispatcher_call = method_base
+                method_name = f"{name}::{method_base}"
+
+                fn_body = f"""
+    static auto op = create_{name}_typed_handle();
+    return op.{dispatcher_call}({dispatcher_exprs_str});"""
+
+                if (
+                    not is_redispatching_fn
+                    and len(self.static_dispatch_backend_indices) > 0
+                ):
+                    # call() should go through static dispatch
+                    fn_body = static_dispatch(
+                        sig, f, backend_indices=self.static_dispatch_backend_indices
+                    )
+                defns += f"""
+// aten::{f.func}
+{sig.defn(name=method_name, is_redispatching_fn=is_redispatching_fn)} {{
+    {fn_body}
+}}
+"""
+            return defns
+        else:
+            assert_never(self.target)
+
+
+# Generates Functions.h, which provides the functional public C++ API,
+# and the scaffolding to call into the dispatcher from these functions.
+@dataclass(frozen=True)
+class ComputeFunction:
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        sig_group = CppSignatureGroup.from_native_function(
+            f, method=False, fallback_binding=f.manual_cpp_binding
+        )
+        has_symint = f.func.has_symint()
+
+        result = ""
+        for sig in sig_group.signatures():
+            # See Note [The ATen Operators API]
+            target_sig = DispatcherSignature.from_schema(f.func)
+            exprs = translate(sig.arguments(), target_sig.arguments())
+            exprs_str = ", ".join([e.expr for e in exprs])
+
+            if sig.symint:
+                intlike_t = "c10::SymInt"
+            else:
+                intlike_t = "int64_t"
+
+            if Variant.function in f.variants:
+                result += f"""
+// aten::{f.func}
+inline {sig.decl()} {{
+    return at::_ops::{f.func.name.unambiguous_name()}::call({exprs_str});
+}}"""
+
+            # The template function can be used from template situations
+            # where you want to switch between the symint or not version
+            # depending on a template argument
+            #
+            # NB: we ALWAYS generate this even for methods.  But we put it in
+            # this header so it can take advantage of per-op headers
+            if has_symint:
+                result += f"""
+namespace symint {{
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, {intlike_t}>>>
+  {sig.decl(suppress_symint_suffix=True)} {{
+    return at::_ops::{f.func.name.unambiguous_name()}::call({exprs_str});
+  }}
+}}
+"""
+        return result
+
+
+# Generates TensorBody.h. This file provides the object-oriented (method-based)
+# public C++ API, and the scaffolding to call into the dispatcher from these functions.
+@dataclass(frozen=True)
+class ComputeTensorMethod:
+    target: Literal[Target.DECLARATION, Target.DEFINITION]
+    static_dispatch_backend_indices: list[BackendIndex]
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        if Variant.method not in f.variants:
+            return None
+
+        assert not f.func.is_out_fn()
+        assert f.func.arguments.self_arg is not None
+
+        sig_group = CppSignatureGroup.from_native_function(
+            f, method=True, fallback_binding=f.manual_cpp_binding
+        )
+
+        if self.target is Target.DECLARATION:
+            result = ""
+            for sig in sig_group.signatures():
+                result += f"{sig.decl()} const;\n"
+            return result
+
+        if self.target is not Target.DEFINITION:
+            assert_never(self.target)
+
+        result = ""
+
+        for sig in sig_group.signatures():
+            target_sig = DispatcherSignature.from_schema(f.func)
+            exprs = translate(sig.arguments(), target_sig.arguments(), method=True)
+            exprs_str = ", ".join([e.expr for e in exprs])
+
+            result += f"""
+// aten::{f.func}
+inline {sig.defn(prefix="Tensor::")} const {{
+    return at::_ops::{f.func.name.unambiguous_name()}::call({exprs_str});
+}}
+"""
+
+        return result
+
+
+# Generates RedispatchFunctions.h.
+# This is similar to the C++ API defined in Functions.h, but provides access
+# to the dispatcher's redispatch API.
+@dataclass(frozen=True)
+class ComputeRedispatchFunction:
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        # We unconditionally generate function variants of the redispatch API.
+        # This is mainly because we can namespace functions separately, but not methods,
+        sig_group = CppSignatureGroup.from_native_function(
+            f, method=False, fallback_binding=f.manual_cpp_binding
+        )
+
+        result = ""
+        for sig in sig_group.signatures():
+            target_sig = DispatcherSignature.from_schema(f.func)
+            exprs = translate(sig.arguments(), target_sig.arguments())
+            exprs_str = ", ".join(["dispatchKeySet"] + [a.expr for a in exprs])
+
+            result += f"""
+// aten::{f.func}
+inline {sig.decl(is_redispatching_fn=True)} {{
+    return at::_ops::{f.func.name.unambiguous_name()}::redispatch({exprs_str});
+}}
+"""
+
+        return result
+
+
+# Generates ATenOpList.cpp, a runtime accessible list of all aten
+# operators.
+# TODO: This was historically used to help some JIT interop code
+# figure out whether or not to treat aten namespace'd operators
+# one way or another, we should reevaluate if this is actually needed.
+@with_native_function
+def compute_aten_op(f: NativeFunction) -> str:
+    return f'{{"aten::{f.func.name.name}", "{f.func.name.overload_name}"}},'
+
+
+# Generates MetaFunctions.h
+def compute_meta_function_declaration(g: NativeFunctionsGroup) -> str | None:
+    if not g.structured:
+        return None
+    with native_function_manager(g.out):
+        name = meta.name(g)
+        args = structured.meta_arguments(g)
+        args_str = ", ".join(a.decl() for a in args)
+        parent_class = g.out.structured_inherits
+        if parent_class is None:
+            parent_class = "at::impl::MetaBase"
+        meta_return = "void"
+        precomputed = g.out.precomputed if g.structured else None
+
+        if precomputed:
+            # Generate the template declaration with one bool parameter for each
+            # precomputed element. Each parameter is true if the corresponding (in
+            # terms of position) precomputed element has been set.
+            precomputed_values = [*precomputed.replace.values(), precomputed.add]
+            precomputed_elements = [
+                elem for replace_list in precomputed_values for elem in replace_list
+            ]
+            precomputed_template_parameters = [
+                elem.name.upper() for elem in precomputed_elements
+            ]
+            precomputed_template_params_str = ", ".join(
+                f"bool {param} = false" for param in precomputed_template_parameters
+            )
+            precompute_template_decl = f"template <{precomputed_template_params_str}>"
+
+            # Generate a string containing declarations of all precomputed elements.
+            precomputed_elements_with_cpp_types = [
+                structured.argument_type(elem, binds=elem.name)
+                for elem in precomputed_elements
+            ]
+
+            precomputed_elements_decl = ";\n".join(
+                f"{elem.cpp_type(strip_ref=True)} {elem.name}"
+                for elem in precomputed_elements_with_cpp_types
+            )
+
+            # Generate "setter" methods for each precomputed element. Each method will return
+            # a new instance of precompute_out with the template parameter that corresponds to
+            # the member set by the method to true (to indicate that it has been set).
+            setter_methods = []
+            for i, elem in enumerate(precomputed_elements):
+                # Generate the signature. The return type will be the same
+                # as the type of `this` but with the template parameter
+                # corresponding to the element set by this method set to true.
+                # The assert generated below will ensure that this template
+                # parameter is false on the type of `this`.
+                return_ty_templates = ", ".join(
+                    precomputed_template_parameters[:i]
+                    + ["true"]
+                    + precomputed_template_parameters[i + 1 :]
+                )
+                return_ty = f"precompute_out<{return_ty_templates}>"
+                elem_cpp_ty = precomputed_elements_with_cpp_types[i].cpp_type(
+                    strip_ref=True
+                )
+                signature = f"{return_ty} set_{elem.name}({elem_cpp_ty} value)"
+
+                # Generate an assert which checks that the
+                # template parameter corresponding to the precomputed
+                # element that is set by this method is false on the
+                # class corresponding to the object that `this` points to.
+                # This ensures that each element can be set only once.
+                assert_msg = f'"{elem.name} already set"'
+                assert_stmt = f"static_assert({precomputed_template_parameters[i]} == false, {assert_msg});"
+
+                # Generate the new object construction block. All state
+                # except the element that this method sets is copied from the
+                # object that `this` points to. The value for the element that
+                # the method sets is taken from a method parameter.
+                construction_stmts = []
+                construction_stmts.append(f"{return_ty} ret;")
+
+                for j, elem in enumerate(precomputed_elements):
+                    if i == j:
+                        construction_stmts.append(f"ret.{elem.name} = value;")
+                    else:
+                        construction_stmts.append(
+                            f"ret.{elem.name} = this->{elem.name};"
+                        )
+
+                construction_stmts.append("return ret;")
+                construction_block = "\n".join(construction_stmts)
+
+                setter_methods.append(
+                    f"""
+                    {signature} {{
+                        {assert_stmt}
+                        {construction_block}
+                    }}
+                """
+                )
+            setter_methods_decl = "\n".join(setter_methods)
+
+            # Meta should return an instance of the struct containing the precomputed elements.
+            meta_return_template_params = ", ".join(
+                ["true"] * len(precomputed_template_parameters)
+            )
+            # This typedef (actually a using statement) is needed so that TORCH_META_FUNC can reuse the return
+            # type (which has a variable number of template parameters).
+            meta_return_typedef = f"using meta_return_ty = precompute_out <{meta_return_template_params}>;"
+            meta_return = "meta_return_ty"
+            precomputed_decl = f"""
+                {precompute_template_decl}
+                struct TORCH_API precompute_out {{
+                    {setter_methods_decl}
+                    {precomputed_elements_decl};
+            }};"""
+        else:
+            meta_return_typedef = ""
+            precomputed_decl = ""
+
+        return f"""\
+struct TORCH_API structured_{name} : public {parent_class} {{
+    {precomputed_decl}
+    {meta_return_typedef}
+    {meta_return} meta({args_str});
+}};
+"""
+
+
+def needs_backend_select(f: NativeFunction, selector: SelectiveBuilder) -> bool:
+    name = str(f.func.name.name)
+    if name.endswith("_like") or name.startswith("new_"):
+        return False
+    if f.func.arguments.tensor_options is None:
+        return False
+    return selector.is_native_function_selected(f)
+
+
+# Generates RegisterBackendSelect.cpp, a series of kernels which provide
+# specialized computation of dispatch key for operator signatures which cannot
+# be easily done automatically using templating.
+@dataclass(frozen=True)
+class ComputeBackendSelect:
+    target: Literal[Target.DEFINITION, Target.REGISTRATION]
+
+    # Selector object to determine which operators to generate
+    # registration code for.
+    selector: SelectiveBuilder
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        if not needs_backend_select(f, self.selector):
+            return None
+
+        name = native.name(f.func)
+        # BackendSelect can go to Meta, so it must preserve symints
+        native_sig = NativeSignature(f.func, symint=True)
+
+        native_tensor_args = [
+            a
+            for a in native_sig.arguments()
+            if isinstance(a.argument, Argument) and a.argument.type.is_tensor_like()
+        ]
+
+        dispatcher_sig = DispatcherSignature.from_schema(f.func)
+
+        sig: NativeSignature | DispatcherSignature
+        sig = dispatcher_sig
+        dispatcher_exprs = dispatcher_sig.exprs()
+        dispatch_key = "c10::computeDispatchKey(dtype, layout, device)"
+
+        if self.target is Target.DEFINITION:
+            # I don't think there's actually a good reason to generate
+            # these two cases differently
+            # The first case could probably be improved though- it calls computeDispatchKeySet(),
+            # which looks at TLS dispatch keys- there should not be any by the time we reach backend select.
+            if native_tensor_args:
+                assert f.func.arguments.has_tensor_arg()
+                tensor_args = ", ".join(a.name for a in native_tensor_args)
+                compute_dk = f"""\
+DispatchKeySet _dk_set = c10::DispatchKeySet({dispatch_key}) | c10::detail::multi_dispatch_key_set({tensor_args});
+DispatchKeySet _dk_mask = c10::DispatchKeySet(DispatchKeySet::FULL_AFTER, DispatchKey::BackendSelect);
+DispatchKeySet _dk = c10::impl::computeDispatchKeySet(_dk_set, _dk_mask);"""
+            else:
+                assert not f.func.arguments.has_tensor_arg()
+                compute_dk = (
+                    f"DispatchKeySet _dk = c10::DispatchKeySet({dispatch_key});"
+                )
+            return f"""\
+// aten::{f.func}
+C10_ALWAYS_INLINE
+{sig.defn(name)} {{
+  {compute_dk}
+  return at::_ops::{f.func.name.unambiguous_name()}::redispatch(
+      _dk, {', '.join(a.expr for a in dispatcher_exprs)});
+}}
+"""
+        elif self.target is Target.REGISTRATION:
+            return f"""m.impl("aten::{f.func.name}", TORCH_FN({name}));"""
+        else:
+            assert_never(self.target)
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                       YAML CODE GENERATION
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+def format_yaml(data: object) -> str:
+    # Ignore alias in Dumper
+    YamlDumper.ignore_aliases = lambda self, data: True  # type: ignore[assignment]
+
+    # Support serializing OrderedDict
+    def dict_representer(dumper: Any, data: Any) -> Any:
+        return dumper.represent_dict(data.items())
+
+    YamlDumper.add_representer(OrderedDict, dict_representer)  # type: ignore[no-untyped-call]
+    # Some yaml parsers (e.g. Haskell's) don't understand line breaks.
+    # width=1e9 turns off optional line breaks and improves
+    # the portability of the outputted yaml.
+    return yaml.dump(data, default_flow_style=False, Dumper=YamlDumper, width=1e9)  # type: ignore[no-any-return, call-overload]
+
+
+# For some reason, some defaults we write to YAML are written as native
+# YAML objects, rather than doing them uniformly as strings.  This
+# function detects those cases and converts them into native Python
+# objects.
+def pythonify_default(s: str) -> object:
+    if s == "true":
+        return True
+    elif s == "false":
+        return False
+
+    try:
+        return int(s)
+    except ValueError:
+        try:
+            return float(s)
+        except ValueError:
+            return s
+
+
+# What is a dynamic type?  Over time, the semantic meaning of
+# dynamic type has degraded to meaninglessness (in the old days,
+# it captured dtype-ness of types, but that has gone away with
+# the removal of TH).  These days, it's mostly the same thing as
+# the C++ API argument type, except that Tensor and Tensor?
+# arguments simply present as Tensor.
+#
+# TODO: Get rid of dynamic_type, after getting tools/autograd
+# to use the new codegen framework
+def dynamic_type(t: Type) -> str:
+    if isinstance(t, OptionalType):
+        return dynamic_type(t.elem)
+    # Note we don't use t.is_tensor_like() here because it would
+    # also include Tensor[]
+    if str(t) == "Tensor":
+        return "at::Tensor"
+    # This is a legacy concept, so never report SymInt
+    return cpp.argumenttype_type(
+        t, mutable=False, binds="__placeholder__", symint=False
+    ).cpp_type()
+
+
+def compute_method_of_yaml(variants: set[Variant]) -> list[str]:
+    # This is written out explicitly to ensure that Tensor and
+    # namespace are put into the list in the right order
+    method_of = ["Type"]
+    if Variant.method in variants:
+        method_of.append("Tensor")
+    if Variant.function in variants:
+        method_of.append("namespace")
+    return method_of
+
+
+def compute_returns_yaml(
+    f: NativeFunction,
+) -> tuple[list[dict[str, str]], dict[str, str]]:
+    # Note [name and field_name]
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # To understand name_to_field_name, we must first talk about this
+    # schema:
+    #
+    #   lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR)
+    #
+    # There is something very odd about this schema: it is an out
+    # variant of the function (that is to say, it will convert into
+    # at::lstsq_out() in the C++ API), but the names of the output
+    # return arguments don't match the keyword argument names of
+    # the inputs.  It TURNS OUT that in this situation, the historical
+    # Declarations.yaml we want to output is this (abbreviated to
+    # only show relevant fields):
+    #
+    #   arguments:
+    #     ...
+    #   - field_name: solution
+    #     name: X
+    #   - field_name: QR
+    #     name: qr
+    #     ...
+    #
+    #   returns:
+    #   - field_name: solution
+    #     name: X
+    #   - field_name: QR
+    #     name: qr
+    #
+    # The name of the return fields is stored in 'field_name', and the
+    # name of the arguments is stored in 'name'.  So when we process
+    # arguments, we need a way to get at the corresponding return.  At
+    # the moment, this is most conveniently done by constructing a
+    # mapping from name (the argument concept) to field_name (the
+    # return concept) while processing return arguments, since we don't
+    # directly maintain this correspondence in the modeling of function
+    # schema itself.
+    #
+    # See also https://github.com/pytorch/pytorch/issues/43114
+    name_to_field_name: dict[str, str] = {}
+
+    # Compute the returns field of the YAML entry
+    names = cpp.return_names(f)
+    returns = []
+    for i, (r, name) in enumerate(zip(f.func.returns, names)):
+        ret = {
+            "dynamic_type": dynamic_type(r.type),
+            "name": name,
+            # legacy, report ints
+            "type": cpp.return_type(r, symint=False).cpp_type(),
+        }
+
+        if r.name:
+            # See Note [name and field_name]
+            ret["field_name"] = r.name
+            if f.func.is_out_fn():
+                name_to_field_name[f.func.arguments.out[i].name] = r.name
+
+        returns.append(ret)
+
+    return returns, name_to_field_name
+
+
+# arguments in yaml roughly corresponds to the public C++ API
+def compute_cpp_argument_yaml(
+    cpp_a: Binding,
+    *,
+    schema_order: bool,
+    kwarg_only_set: set[str],
+    out_arg_set: set[str],
+    name_to_field_name: dict[str, str],
+) -> object:
+    if isinstance(cpp_a.argument, TensorOptionsArguments):
+        arg: dict[str, object] = {
+            "annotation": None,
+            "dynamic_type": "at::TensorOptions",
+            "is_nullable": False,
+            "name": cpp_a.name,
+            "type": cpp_a.type,
+            "kwarg_only": True,
+        }
+        if cpp_a.default is not None:
+            arg["default"] = cpp_a.default
+        return arg
+    elif isinstance(cpp_a.argument, SelfArgument):
+        raise AssertionError
+    elif isinstance(cpp_a.argument, Argument):
+        return compute_argument_yaml(
+            cpp_a.argument,
+            schema_order=schema_order,
+            kwarg_only_set=kwarg_only_set,
+            out_arg_set=out_arg_set,
+            name_to_field_name=name_to_field_name,
+        )
+
+
+def compute_argument_yaml(
+    a: Argument,
+    *,
+    schema_order: bool,
+    kwarg_only_set: set[str],
+    out_arg_set: set[str],
+    name_to_field_name: dict[str, str],
+) -> object:
+    arg: dict[str, object] = {
+        "annotation": str(a.annotation) if a.annotation else None,
+        "dynamic_type": dynamic_type(a.type),
+        "is_nullable": a.type.is_nullable(),
+        "name": a.name,
+        # legacy, report ints
+        "type": cpp.argument_type(a, binds="__placeholder__", symint=False).cpp_type(),
+    }
+    if a.default is not None:
+        arg["default"] = pythonify_default(
+            cpp.default_expr(a.default, a.type, symint=False)
+        )
+    if a.name in kwarg_only_set:
+        arg["kwarg_only"] = True
+    if a.name in out_arg_set:
+        arg["output"] = True
+        arg["allocate"] = True
+        # See Note [name and field_name]
+        if a.name in name_to_field_name:
+            arg["field_name"] = name_to_field_name[a.name]
+    # Historically, booleans don't get their size recorded, because it
+    # is already built into the cpp type (e.g., std::array<bool, 4>)
+    l = a.type.is_list_like()
+    if l is not None and l.size is not None and str(l.elem) != "bool":
+        arg["size"] = l.size
+    return arg
+
+
+@with_native_function
+def compute_declaration_yaml(f: NativeFunction) -> object:
+    returns, name_to_field_name = compute_returns_yaml(f)
+
+    # These sets are used to conveniently test if an argument is a
+    # kwarg-only or out argument
+    kwarg_only_set = {a.name for a in f.func.arguments.flat_kwarg_only}
+    out_arg_set = {a.name for a in f.func.arguments.out}
+
+    sig_group = CppSignatureGroup.from_native_function(
+        f, method=False, fallback_binding=False
+    )
+    cpp_args = sig_group.signature.arguments()
+    arguments = [
+        compute_cpp_argument_yaml(
+            cpp_a,
+            schema_order=False,
+            kwarg_only_set=kwarg_only_set,
+            out_arg_set=out_arg_set,
+            name_to_field_name=name_to_field_name,
+        )
+        for cpp_a in cpp_args
+    ]
+
+    schema_order_jit_arguments = list(f.func.schema_order_arguments())
+
+    schema_order_arguments = [
+        compute_argument_yaml(
+            a,
+            schema_order=True,
+            kwarg_only_set=kwarg_only_set,
+            out_arg_set=out_arg_set,
+            name_to_field_name=name_to_field_name,
+        )
+        for a in schema_order_jit_arguments
+    ]
+
+    cpp_schema_order_types = [
+        # NB: method here doesn't matter
+        r.type
+        for a in schema_order_jit_arguments
+        for r in cpp.argument(
+            a,
+            method=False,
+            cpp_no_default_args=set(),
+            faithful=False,
+            symint=False,
+            has_tensor_options=False,
+        )
+    ]
+
+    # legacy, report ints
+    cpp_returns = cpp.returns_type(f.func.returns, symint=False).cpp_type()
+    schema_order_cpp_signature = f"{cpp_returns} ({', '.join(cpp_schema_order_types)})"
+
+    is_factory_method = (
+        any(isinstance(a.argument, TensorOptionsArguments) for a in cpp_args)
+        and Variant.method not in f.variants
+    )
+
+    return OrderedDict(
+        [
+            ("name", cpp.name(f.func)),
+            ("operator_name", str(f.func.name.name)),
+            ("overload_name", str(f.func.name.overload_name)),
+            ("manual_kernel_registration", f.manual_kernel_registration),
+            (
+                "category_override",
+                f.category_override if f.category_override is not None else "",
+            ),
+            ("schema_string", f"aten::{f.func}"),
+            ("arguments", arguments),
+            ("schema_order_cpp_signature", schema_order_cpp_signature),
+            ("schema_order_arguments", schema_order_arguments),
+            ("method_of", compute_method_of_yaml(f.variants)),
+            ("mode", "native"),
+            ("python_module", "" if f.python_module is None else f.python_module),
+            ("returns", returns),
+            ("inplace", f.func.name.name.inplace),
+            ("is_factory_method", is_factory_method),
+            ("abstract", f.is_abstract),
+            ("device_guard", f.device_guard),
+            ("with_gil", False),
+            ("deprecated", False),
+            ("has_math_kernel", f.has_composite_implicit_autograd_kernel),
+        ]
+    )
+
+
+# See Note [Auto generated composite kernels]
+def has_autogenerated_composite_kernel(f: NativeFunction) -> bool:
+    return (f.structured or f.structured_delegate is not None) and (
+        f.func.kind() == SchemaKind.functional or f.func.kind() == SchemaKind.inplace
+    )
+
+
+@with_native_function_and_indices
+def compute_registration_declarations(
+    f: NativeFunction, backend_indices: dict[DispatchKey, BackendIndex]
+) -> str:
+    name = dispatcher.name(f.func)
+    returns_type = dispatcher.returns_type(
+        f.func.returns
+    ).cpp_type_registration_declarations()
+    args = dispatcher.arguments(f.func)
+    args_str = ", ".join(a.no_default().decl_registration_declarations() for a in args)
+    comment_data: dict[str, str] = {
+        "schema": f"aten::{f.func}",
+        # TODO: What exactly is the semantics of the 'dispatch' field?
+        "dispatch": str(
+            {k for k, v in backend_indices.items() if v.has_kernel(f)}
+            != {DispatchKey.CompositeImplicitAutograd}
+            and {k for k, v in backend_indices.items() if v.has_kernel(f)}
+            != {
+                DispatchKey.CompositeImplicitAutograd,
+                DispatchKey.CompositeImplicitAutogradNestedTensor,
+            }
+        ),
+        "default": str(f.has_composite_kernel or has_autogenerated_composite_kernel(f)),
+    }
+    return f"""{returns_type} {name}({args_str}); // {json.dumps(comment_data)}
+"""
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                           RUN IT ALL
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+def get_custom_build_selector(
+    provided_op_registration_allowlist: list[str] | None,
+    op_selection_yaml_path: str | None,
+) -> SelectiveBuilder:
+    assert not (
+        provided_op_registration_allowlist is not None
+        and op_selection_yaml_path is not None
+    ), (
+        "Both provided_op_registration_allowlist and "
+        + "op_selection_yaml_path can NOT be provided at the "
+        + "same time."
+    )
+
+    op_registration_allowlist: set[str] | None = None
+    if provided_op_registration_allowlist is not None:
+        op_registration_allowlist = set(provided_op_registration_allowlist)
+
+    if op_registration_allowlist is not None:
+        selector = SelectiveBuilder.from_legacy_op_registration_allow_list(
+            op_registration_allowlist,
+            True,
+            False,
+        )
+    elif op_selection_yaml_path is not None:
+        selector = SelectiveBuilder.from_yaml_path(op_selection_yaml_path)
+    else:
+        selector = SelectiveBuilder.get_nop_selector()
+
+    return selector
+
+
+def get_grouped_by_view_native_functions(
+    native_functions: Sequence[NativeFunction],
+) -> Sequence[NativeFunction | NativeFunctionsViewGroup]:
+    def maybe_create_view_group(
+        d: dict[ViewSchemaKind | SchemaKind, NativeFunction],
+    ) -> list[NativeFunction | NativeFunctionsViewGroup]:
+        funcs: list[NativeFunction | NativeFunctionsViewGroup] = []
+        if ViewSchemaKind.aliasing in d:
+            view = d.pop(ViewSchemaKind.aliasing)
+            view_inplace = d.pop(ViewSchemaKind.aliasing_inplace, None)
+            view_copy = d.pop(SchemaKind.functional, None)
+
+            funcs.append(
+                NativeFunctionsViewGroup(
+                    view=view,
+                    view_copy=view_copy,
+                    view_inplace=view_inplace,
+                )
+            )
+        # Take the remaining functions that weren't part of the view group
+        # and emit them separately
+        funcs.extend(d.values())
+        return funcs
+
+    grouped_by_views: dict[
+        FunctionSchema, dict[SchemaKind | ViewSchemaKind, NativeFunction]
+    ] = defaultdict(dict)
+    for f in native_functions:
+        schema = f.func.view_signature()
+        view_kind: ViewSchemaKind = f.view_schema_kind
+        # We need to group up ops relevant to the same "view", consisting of:
+        # view op (ViewSchemaKind.aliasing)
+        # view_inplace op (ViewSchemaKind.aliasing_inplace)
+        # view_copy op (SchemaKind.functional)
+        if view_kind == ViewSchemaKind.non_aliasing:
+            kind = f.func.kind()
+            assert kind not in grouped_by_views[schema]
+            grouped_by_views[schema][kind] = f
+        else:
+            assert (
+                view_kind not in grouped_by_views[schema]
+            ), f"{view_kind} already in {grouped_by_views[schema].keys()}"
+            grouped_by_views[schema][view_kind] = f
+
+    return list(concatMap(maybe_create_view_group, grouped_by_views.values()))
+
+
+def get_grouped_native_functions(
+    native_functions: Sequence[NativeFunction],
+) -> Sequence[NativeFunction | NativeFunctionsGroup]:
+    def flatten_pre_group(
+        d: dict[SchemaKind, NativeFunction],
+    ) -> Sequence[NativeFunction | NativeFunctionsGroup]:
+        r = NativeFunctionsGroup.from_dict(d)
+        if r is None:
+            # Invariant: any NativeFunctions that are code-generated
+            # should have been grouped into NativeFunctionsGroup objects
+            assert not any("generated" in f.tags for f in d.values())
+            return list(d.values())
+        else:
+            return [r]
+
+    # TODO: how come ValuesView isn't a Sequence lol
+    pre_grouped_native_functions = pre_group_native_functions(native_functions)
+    return list(
+        concatMap(flatten_pre_group, list(pre_grouped_native_functions.values()))
+    )
+
+
+def get_ns_grouped_kernels(
+    *,
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    backend_indices: dict[DispatchKey, BackendIndex],
+    native_function_decl_gen: Callable[
+        [NativeFunctionsGroup | NativeFunction, BackendIndex], list[str]
+    ] = dest.compute_native_function_declaration,
+) -> dict[str, list[str]]:
+    ns_grouped_kernels: dict[str, list[str]] = defaultdict(list)
+    for f in grouped_native_functions:
+        native_function_namespaces = set()
+        dispatch_keys = set()
+        for dispatch_key, backend_idx in backend_indices.items():
+            backend_metadata = backend_idx.get_kernel(f)
+            if backend_metadata:
+                namespace = backend_metadata.cpp_namespace
+                dispatch_keys.add(dispatch_key)
+                native_function_namespaces.add(namespace)
+            else:
+                namespace = DEFAULT_KERNEL_NAMESPACE
+            assert (
+                len(native_function_namespaces) <= 1
+            ), f"Codegen only supports one namespace per operator, got {native_function_namespaces} from {dispatch_keys}"
+            ns_grouped_kernels[namespace].extend(
+                native_function_decl_gen(f, backend_idx)
+            )
+    return ns_grouped_kernels
+
+
+def get_native_function_declarations_from_ns_grouped_kernels(
+    *,
+    ns_grouped_kernels: dict[str, list[str]],
+) -> list[str]:
+    declarations: list[str] = []
+    newline = "\n"
+    for namespace, kernels in ns_grouped_kernels.items():
+        ns_helper = NamespaceHelper(
+            namespace_str=namespace,
+            entity_name="",
+            max_level=4,
+        )
+        # Convert to a set first to remove duplicate kernel names. Backends are
+        # allowed to repeat kernel names; only generate the declaration once!
+        ordered_kernels = list(OrderedDict.fromkeys(kernels))
+        declarations.extend(
+            f"""
+{ns_helper.prologue}
+{newline.join(ordered_kernels)}
+{ns_helper.epilogue}
+        """.split(newline)
+        )
+    return declarations
+
+
+# Return native function declarations grouped by their namespaces.
+def get_native_function_declarations(
+    *,
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    backend_indices: dict[DispatchKey, BackendIndex],
+    native_function_decl_gen: Callable[
+        [NativeFunctionsGroup | NativeFunction, BackendIndex], list[str]
+    ] = dest.compute_native_function_declaration,
+) -> list[str]:
+    """
+    Generate kernel declarations, in `NativeFunction(s).h`.
+    :param grouped_native_functions: a sequence of `NativeFunction` or `NativeFunctionGroup`.
+    :param backend_indices: kernel collections grouped by dispatch key.
+    :param native_function_decl_gen: callable to generate kernel declaration for each `NativeFunction`.
+    :return: a list of string, from the string with all declarations, grouped by namespaces, split by newline.
+    """
+
+    ns_grouped_kernels = get_ns_grouped_kernels(
+        grouped_native_functions=grouped_native_functions,
+        backend_indices=backend_indices,
+        native_function_decl_gen=native_function_decl_gen,
+    )
+    return get_native_function_declarations_from_ns_grouped_kernels(
+        ns_grouped_kernels=ns_grouped_kernels
+    )
+
+
+def get_kernel_namespace(
+    *, f: NativeFunction | NativeFunctionsGroup, backend_idx: BackendIndex
+) -> str:
+    backend_metadata = backend_idx.get_kernel(f)
+    assert not backend_metadata or "::native" in backend_metadata.cpp_namespace, (
+        f"The kernel for function {f.func.name if isinstance(f, NativeFunction) else f.functional.func.name} "
+        f"with dispatch key {backend_idx.dispatch_key}"
+        f" has a namespace {backend_metadata.cpp_namespace} and it's not ending with '::native'."
+    )
+    return (
+        backend_metadata.cpp_namespace if backend_metadata else DEFAULT_KERNEL_NAMESPACE
+    )
+
+
+# Return native function definitions grouped by dispatch key and custom namespace.
+# Used in RegisterDispatchKey.cpp and etc.
+def get_native_function_definitions(
+    *,
+    fm: FileManager,
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    dispatch_key: DispatchKey,
+    backend_idx: BackendIndex,
+    selector: SelectiveBuilder,
+    rocm: bool,
+    symint: bool,
+    skip_dispatcher_op_registration: bool,
+    gen_dispatch_helpers: bool,
+) -> list[str]:
+    definitions: list[str] = []
+    ns_definitions: dict[str, list[str]] = defaultdict(list)
+    anonymous_definitions: dict[str, list[str]] = defaultdict(list)
+    registrations: dict[str, dict[str, list[str]]] = defaultdict(dict)
+    newline = "\n"
+    ns_gen = dest.RegisterDispatchKey(
+        backend_idx,
+        Target.NAMESPACED_DEFINITION,
+        selector,
+        rocm=rocm,
+        symint=symint,
+        class_method_name=None,
+        skip_dispatcher_op_registration=skip_dispatcher_op_registration,
+    )
+    anonymous_gen = dest.RegisterDispatchKey(
+        backend_idx,
+        Target.ANONYMOUS_DEFINITION,
+        selector,
+        rocm=rocm,
+        symint=symint,
+        class_method_name=None,
+        skip_dispatcher_op_registration=skip_dispatcher_op_registration,
+    )
+    reg_gen = dest.RegisterDispatchKey(
+        backend_idx,
+        Target.REGISTRATION,
+        selector,
+        rocm=rocm,
+        symint=symint,
+        class_method_name=None,
+        skip_dispatcher_op_registration=skip_dispatcher_op_registration,
+    )
+    for f in grouped_native_functions:
+        kernel_namespace = get_kernel_namespace(f=f, backend_idx=backend_idx).replace(
+            "::native", ""
+        )
+
+        ns_definitions[kernel_namespace].extend(
+            ns_gen(f),
+        )
+        anonymous_definitions[kernel_namespace].extend(
+            anonymous_gen(f),
+        )
+        namespace = (
+            f.namespace if isinstance(f, NativeFunction) else f.functional.namespace
+        )
+        if namespace not in registrations[kernel_namespace]:
+            registrations[kernel_namespace] = defaultdict(list)
+        registrations[kernel_namespace][namespace].extend(
+            reg_gen(f),
+        )
+
+    for kernel_namespace in ns_definitions:
+        if len(ns_definitions[kernel_namespace]) == 0:
+            continue
+        ns_helper = NamespaceHelper(namespace_str=kernel_namespace)
+        registration_body = ""
+        for namespace in registrations[kernel_namespace]:
+            if not registrations[kernel_namespace][namespace]:
+                continue
+            registration_body += f"""
+TORCH_LIBRARY_IMPL({namespace}, {dispatch_key}, m) {{
+    {newline.join(registrations[kernel_namespace][namespace])}
+}}"""
+        definitions.extend(
+            fm.substitute_with_template(
+                "RegisterDispatchDefinitions.ini",
+                lambda: {
+                    "ns_prologue": ns_helper.prologue,
+                    "ns_epilogue": ns_helper.epilogue,
+                    "dispatch_helpers": dest.gen_registration_helpers(backend_idx)
+                    if gen_dispatch_helpers
+                    else [],
+                    "dispatch_anonymous_definitions": anonymous_definitions[
+                        kernel_namespace
+                    ],
+                    "static_init_dispatch_registrations": ""
+                    if skip_dispatcher_op_registration
+                    else registration_body,
+                    "deferred_dispatch_registrations": "",
+                    "dispatch_namespace": dispatch_key.lower(),
+                    "dispatch_namespaced_definitions": ns_definitions[kernel_namespace],
+                },
+            ).split(newline)
+        )
+
+    return definitions
+
+
+# Return native function declarations grouped by dispatch key and custom namespace.
+# Used in CPUFunctions_inl.h and etc.
+def get_namespaced_declaration(
+    *,
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    dispatch_key: DispatchKey,
+    backend_idx: BackendIndex,
+    selector: SelectiveBuilder,
+    rocm: bool,
+    symint: bool,
+) -> list[str]:
+    declarations: list[str] = []
+    ns_grouped_kernels: dict[str, list[str]] = defaultdict(list)
+    newline = "\n"
+    func = dest.RegisterDispatchKey(
+        backend_idx,
+        Target.NAMESPACED_DECLARATION,
+        selector,
+        rocm=rocm,
+        class_method_name=None,
+        skip_dispatcher_op_registration=False,
+        symint=symint,
+    )
+    for f in grouped_native_functions:
+        namespace = get_kernel_namespace(f=f, backend_idx=backend_idx).replace(
+            "native", dispatch_key.lower()
+        )
+
+        ns_grouped_kernels[namespace].extend(
+            func(f),
+        )
+
+    for namespace, kernels in ns_grouped_kernels.items():
+        if len(kernels) == 0:
+            continue
+        ns_helper = NamespaceHelper(
+            namespace_str=namespace, entity_name="", max_level=3
+        )
+        ordered_kernels = list(OrderedDict.fromkeys(kernels))
+        declarations.extend(
+            f"""
+{ns_helper.prologue}
+{newline.join(ordered_kernels)}
+{ns_helper.epilogue}
+        """.split(newline)
+        )
+    return declarations
+
+
+# Return native function schema registration code for aten and other namespaces.
+def get_native_function_schema_registrations(
+    *,
+    native_functions: Sequence[NativeFunction],
+    schema_selector: SelectiveBuilder,
+) -> tuple[list[str], str]:
+    ns_native_functions: dict[str, list[NativeFunction]] = defaultdict(list)
+    for native_function in native_functions:
+        ns_native_functions[native_function.namespace].append(native_function)
+    schema_registrations = ""
+    aten_schema_registrations = []
+    custom_namespace = None
+    for namespace, funcs in ns_native_functions.items():
+        schema_registrations_body = list(
+            mapMaybe(RegisterSchema(schema_selector), funcs)
+        )
+        # NB: we have to separate aten namespace registration from other namespaces,
+        # because in the template we hardcoded an operator for ATen already.
+        if namespace == "aten":
+            aten_schema_registrations = schema_registrations_body
+        else:
+            custom_namespace = namespace
+            tab = "\t"
+            # if the namespace is predefined, we should use define a library fragment
+            # instead of a new library
+            torch_library_macro = (
+                "TORCH_LIBRARY_FRAGMENT"
+                if namespace in FRAGMENT_NAMESPACES
+                else "TORCH_LIBRARY"
+            )
+            schema_registrations += f"""
+{torch_library_macro}({custom_namespace}, m) {{
+  {tab.join(schema_registrations_body)}
+}};"""
+    return (aten_schema_registrations, schema_registrations)
+
+
+def gen_aggregated_headers(
+    *,
+    native_functions: Sequence[NativeFunction],
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    structured_native_functions: Sequence[NativeFunctionsGroup],
+    static_dispatch_idx: list[BackendIndex],
+    selector: SelectiveBuilder,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    cpu_fm: FileManager,
+    device_fms: dict[str, FileManager],
+    functions_keys: set[DispatchKey],
+    dispatch_keys: Sequence[DispatchKey],
+    rocm: bool,
+) -> None:
+    # Buck doesn't support dynamic output files, so we aggregate all operator
+    # headers into a single file
+    cpu_fm.write(
+        "NativeMetaFunctions.h",
+        lambda: {
+            "NativeMetaFunctions_includes": [],
+            "NativeMetaFunctions_declarations": list(
+                mapMaybe(compute_meta_function_declaration, structured_native_functions)
+            ),
+        },
+    )
+    method_native_functions = [
+        fn for fn in native_functions if Variant.method in fn.variants
+    ]
+    non_method_native_functions = [
+        fn for fn in native_functions if fn not in method_native_functions
+    ]
+    cpu_fm.write(
+        "MethodOperators.h",
+        lambda: {
+            "MethodOperators_includes": [],
+            "MethodOperators_declarations": list(
+                mapMaybe(
+                    ComputeOperators(
+                        Target.DECLARATION,
+                        static_dispatch_backend_indices=static_dispatch_idx,
+                    ),
+                    method_native_functions,
+                )
+            ),
+        },
+    )
+    cpu_fm.write(
+        "Operators.h",
+        lambda: {
+            "Operators_includes": ["#include <ATen/MethodOperators.h>"],
+            "Operators_declarations": list(
+                mapMaybe(
+                    ComputeOperators(
+                        Target.DECLARATION,
+                        static_dispatch_backend_indices=static_dispatch_idx,
+                    ),
+                    non_method_native_functions,
+                )
+            ),
+        },
+    )
+    cpu_fm.write(
+        "Functions.h",
+        lambda: {
+            "static_dispatch_extra_headers": static_dispatch_extra_headers(
+                static_dispatch_idx
+            ),
+            "Functions_includes": ["#include <ATen/Operators.h>"],
+            "Functions_declarations": list(
+                mapMaybe(
+                    ComputeFunction(),
+                    native_functions,
+                )
+            ),
+        },
+    )
+    declarations = get_native_function_declarations(
+        grouped_native_functions=grouped_native_functions,
+        backend_indices=backend_indices,
+    )
+    cpu_fm.write(
+        "NativeFunctions.h",
+        lambda: {
+            "NativeFunctions_includes": ["#include <ATen/NativeMetaFunctions.h>"],
+            "NativeFunctions_declarations": declarations,
+        },
+    )
+
+    for dispatch_key in dispatch_keys:
+        fm = file_manager_from_dispatch_key(dispatch_key, device_fms, cpu_fm)
+        if dispatch_key in functions_keys:
+            inl_headers = f"#include <ATen/{dispatch_key}Functions_inl.h>"
+
+            fm.write_with_template(
+                f"{dispatch_key}Functions.h",
+                "DispatchKeyFunctions.h",
+                lambda: {
+                    "dispatch_key": str(dispatch_key),
+                    "inline_headers": inl_headers,
+                },
+            )
+            fm.write_with_template(
+                f"{dispatch_key}Functions_inl.h",
+                "DispatchKeyFunctions_inl.h",
+                lambda: {
+                    "DispatchKeyFunctions_inl_includes": [],
+                    "dispatch_namespace": dispatch_key.lower(),
+                    "dispatch_namespaced_declarations": get_namespaced_declaration(
+                        grouped_native_functions=grouped_native_functions,
+                        dispatch_key=dispatch_key,
+                        backend_idx=backend_indices[dispatch_key],
+                        selector=selector,
+                        rocm=rocm,
+                        symint=True,
+                    ),
+                },
+            )
+
+        del fm
+
+
+def gen_per_operator_headers(
+    *,
+    native_functions: Sequence[NativeFunction],
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    static_dispatch_idx: list[BackendIndex],
+    selector: SelectiveBuilder,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    cpu_fm: FileManager,
+    device_fms: dict[str, FileManager],
+    ops_fm: FileManager,
+    functions_keys: set[DispatchKey],
+    dispatch_keys: Sequence[DispatchKey],
+    rocm: bool,
+) -> None:
+    # For CMake builds, split operator declarations into separate headers in
+    # the ATen/ops folder to split up header dependencies
+    functions_by_root_name: dict[str, list[NativeFunction]] = defaultdict(list)
+    for fn in native_functions:
+        functions_by_root_name[fn.root_name].append(fn)
+
+    grouped_functions_by_root_name: dict[
+        str, list[NativeFunction | NativeFunctionsGroup]
+    ] = defaultdict(list)
+    for group in grouped_native_functions:
+        name = group.root_name
+        grouped_functions_by_root_name[name].append(group)
+
+    for name, functions in functions_by_root_name.items():
+        ops_fm.write_with_template(
+            f"{name}_ops.h",
+            "Operator.h",
+            lambda: {
+                "declarations": list(
+                    mapMaybe(
+                        ComputeOperators(
+                            Target.DECLARATION,
+                            static_dispatch_backend_indices=static_dispatch_idx,
+                        ),
+                        functions,
+                    )
+                ),
+            },
+        )
+
+        ops_fm.write_with_template(
+            f"{name}.h",
+            "Function.h",
+            lambda: {
+                "static_dispatch_ops_headers": list(
+                    mapMaybe(
+                        lambda fn: static_dispatch_ops_header(
+                            fn, backend_index=static_dispatch_idx
+                        ),
+                        functions,
+                    )
+                ),
+                "operator_includes": f"#include <ATen/ops/{name}_ops.h>",
+                "function_definitions": list(
+                    mapMaybe(
+                        ComputeFunction(),
+                        functions,
+                    )
+                ),
+            },
+        )
+
+        grouped_functions = grouped_functions_by_root_name.get(name, [])
+        structured_functions = [
+            fn
+            for fn in grouped_functions
+            if isinstance(fn, NativeFunctionsGroup) and fn.structured
+        ]
+        is_structured = len(structured_functions) > 0
+
+        if is_structured:
+            ops_fm.write_with_template(
+                f"{name}_meta.h",
+                "NativeMetaFunction.h",
+                lambda: {
+                    "meta_function_declarations": list(
+                        mapMaybe(
+                            compute_meta_function_declaration, structured_functions
+                        )
+                    ),
+                },
+            )
+        declarations = get_native_function_declarations(
+            grouped_native_functions=grouped_functions,
+            backend_indices=backend_indices,
+            native_function_decl_gen=dest.compute_native_function_declaration,
+        )
+        ops_fm.write_with_template(
+            f"{name}_native.h",
+            "NativeFunction.h",
+            lambda: {
+                "extra_includes": (
+                    f"#include <ATen/ops/{name}_meta.h>" if is_structured else []
+                ),
+                "native_function_declarations": declarations,
+            },
+        )
+
+    for category, suffix in [
+        ("Functions", ""),
+        ("Operators", "_ops"),
+        ("NativeMetaFunctions", "_meta"),
+        ("NativeFunctions", "_native"),
+    ]:
+        cpu_fm.write(
+            f"{category}.h",
+            lambda: {
+                f"{category}_includes": [
+                    f"#include <ATen/ops/{name}{suffix}.h>"
+                    for name in sorted(functions_by_root_name.keys())
+                ],
+                f"{category}_declarations": [],
+            },
+        )
+
+    for dispatch_key in dispatch_keys:
+        if dispatch_key not in functions_keys:
+            continue
+
+        dispatch_namespace = dispatch_key.lower()
+        dispatch_names = []
+
+        for name, functions in functions_by_root_name.items():
+            grouped_functions = grouped_functions_by_root_name.get(name, [])
+            declarations = list(
+                concatMap(
+                    dest.RegisterDispatchKey(
+                        backend_indices[dispatch_key],
+                        Target.NAMESPACED_DECLARATION,
+                        selector,
+                        rocm=rocm,
+                        symint=True,
+                        class_method_name=None,
+                        skip_dispatcher_op_registration=False,
+                    ),
+                    grouped_functions,
+                )
+            )
+
+            if len(declarations) == 0:
+                continue
+
+            dispatch_names.append(name)
+            ops_fm.write_with_template(
+                f"{name}_{dispatch_namespace}_dispatch.h",
+                "DispatchKeyFunction.h",
+                lambda: {
+                    "dispatch_namespace": dispatch_namespace,
+                    "dispatch_namespaced_declarations": declarations,
+                },
+            )
+
+        fm = file_manager_from_dispatch_key(dispatch_key, device_fms, cpu_fm)
+        inl_headers = f"#include <ATen/{dispatch_key}Functions_inl.h>"
+
+        fm.write_with_template(
+            f"{dispatch_key}Functions.h",
+            "DispatchKeyFunctions.h",
+            lambda: {
+                "dispatch_key": str(dispatch_key),
+                "inline_headers": inl_headers,
+            },
+        )
+        fm.write_with_template(
+            f"{dispatch_key}Functions_inl.h",
+            "DispatchKeyFunctions_inl.h",
+            lambda: {
+                "dispatch_namespace": dispatch_namespace,
+                "DispatchKeyFunctions_inl_includes": [
+                    f"#include <ATen/ops/{name}_{dispatch_namespace}_dispatch.h>"
+                    for name in sorted(dispatch_names)
+                ],
+                "dispatch_namespaced_declarations": [],
+            },
+        )
+        del fm
+
+    cpu_fm.write(
+        "MethodOperators.h",
+        lambda: {
+            "MethodOperators_includes": sorted(
+                f"#include <ATen/ops/{name}_ops.h>"
+                for name, functions in functions_by_root_name.items()
+                if any(Variant.method in fn.variants for fn in functions)
+            ),
+            "MethodOperators_declarations": [],
+        },
+    )
+
+
+def gen_headers(
+    *,
+    native_functions: Sequence[NativeFunction],
+    valid_tags: set[str],
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    structured_native_functions: Sequence[NativeFunctionsGroup],
+    static_dispatch_idx: list[BackendIndex],
+    selector: SelectiveBuilder,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    core_fm: FileManager,
+    cpu_fm: FileManager,
+    device_fms: dict[str, FileManager],
+    ops_fm: FileManager,
+    dispatch_keys: Sequence[DispatchKey],
+    functions_keys: set[DispatchKey],
+    rocm: bool,
+    per_operator_headers: bool,
+) -> None:
+    if per_operator_headers:
+        gen_per_operator_headers(
+            native_functions=native_functions,
+            grouped_native_functions=grouped_native_functions,
+            static_dispatch_idx=static_dispatch_idx,
+            selector=selector,
+            backend_indices=backend_indices,
+            cpu_fm=cpu_fm,
+            device_fms=device_fms,
+            ops_fm=ops_fm,
+            dispatch_keys=dispatch_keys,
+            functions_keys=functions_keys,
+            rocm=rocm,
+        )
+    else:
+        gen_aggregated_headers(
+            native_functions=native_functions,
+            grouped_native_functions=grouped_native_functions,
+            structured_native_functions=structured_native_functions,
+            static_dispatch_idx=static_dispatch_idx,
+            selector=selector,
+            backend_indices=backend_indices,
+            cpu_fm=cpu_fm,
+            device_fms=device_fms,
+            dispatch_keys=dispatch_keys,
+            functions_keys=functions_keys,
+            rocm=rocm,
+        )
+
+    core_fm.write(
+        "TensorBody.h",
+        lambda: {
+            "tensor_method_declarations": list(
+                mapMaybe(
+                    ComputeTensorMethod(
+                        target=Target.DECLARATION,
+                        static_dispatch_backend_indices=static_dispatch_idx,
+                    ),
+                    native_functions,
+                )
+            ),
+            "tensor_method_definitions": list(
+                mapMaybe(
+                    ComputeTensorMethod(
+                        target=Target.DEFINITION,
+                        static_dispatch_backend_indices=static_dispatch_idx,
+                    ),
+                    native_functions,
+                )
+            ),
+        },
+    )
+
+    cpu_fm.write(
+        "RedispatchFunctions.h",
+        lambda: {
+            "function_redispatch_definitions": list(
+                mapMaybe(ComputeRedispatchFunction(), native_functions)
+            ),
+        },
+    )
+
+    cpu_fm.write(
+        "RegistrationDeclarations.h",
+        lambda: {
+            "registration_declarations": [
+                compute_registration_declarations(f, backend_indices)
+                for f in native_functions
+            ],
+        },
+    )
+
+    cpu_fm.write(
+        "VmapGeneratedPlumbing.h", lambda: gen_all_vmap_plumbing(native_functions)
+    )
+
+    def gen_aten_interned_strings() -> dict[str, str]:
+        attrs: set[str] = set()  # All function argument names
+        names = set()  # All ATen function names
+        for func in native_functions:
+            names.add(str(func.func.name.name))
+            # Some operators don't have a functional variant but we still create a
+            # symbol without the underscore
+            names.add(func.func.name.name.base)
+
+            attrs.update(arg.name for arg in func.func.schema_order_arguments())
+
+        # These are keywords in C++, so aren't valid symbol names
+        # https://en.cppreference.com/w/cpp/language/operator_alternative
+        names -= {
+            "and",
+            "and_eq",
+            "bitand",
+            "bitor",
+            "compl",
+            "not",
+            "not_eq",
+            "or",
+            "or_eq",
+            "xor",
+            "xor_eq",
+        }
+
+        return {
+            "aten_symbols": " \\\n".join(
+                [f"_(aten, {name})" for name in sorted(names)]
+            ),
+            "attr_symbols": " \\\n".join(
+                [f"_(attr, {name})" for name in sorted(attrs)]
+            ),
+        }
+
+    core_fm.write("aten_interned_strings.h", gen_aten_interned_strings)
+
+    def gen_tags_enum() -> dict[str, str]:
+        return {"enum_of_valid_tags": (",\n".join(sorted(valid_tags)))}
+
+    core_fm.write("enum_tag.h", gen_tags_enum)
+
+
+def gen_source_files(
+    *,
+    native_functions: Sequence[NativeFunction],
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    structured_native_functions: Sequence[NativeFunctionsGroup],
+    view_groups: Sequence[NativeFunctionsViewGroup],
+    selector: SelectiveBuilder,
+    static_dispatch_idx: list[BackendIndex],
+    backend_indices: dict[DispatchKey, BackendIndex],
+    aoti_fm: FileManager,
+    core_fm: FileManager,
+    cpu_vec_fm: FileManager,
+    cpu_fm: FileManager,
+    device_fms: dict[str, FileManager],
+    dispatch_keys: Sequence[DispatchKey],
+    functions_keys: set[DispatchKey],
+    rocm: bool,
+    force_schema_registration: bool,
+    per_operator_headers: bool,
+    skip_dispatcher_op_registration: bool,
+    update_aoti_c_shim: bool,
+    aoti_backends: set[DispatchKey],
+    extend_aoti_c_shim: bool,
+) -> None:
+    extra_cuda_headers = """\
+#include <c10/cuda/CUDAGuard.h>
+#include <ATen/cuda/ATenCUDAGeneral.h>
+#include <ATen/cuda/CUDADevice.h>
+#include <ATen/cuda/CUDAContext.h>"""
+    if rocm:
+        extra_cuda_headers = """\
+#include <ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h>
+#include <ATen/hip/ATenHIPGeneral.h>
+#include <ATen/hip/HIPDevice.h>
+#include <ATen/hip/HIPContext.h>"""
+
+    for dispatch_key in dispatch_keys:
+        fm = file_manager_from_dispatch_key(dispatch_key, device_fms, cpu_fm)
+        if per_operator_headers:
+
+            def operator_headers() -> list[str]:
+                headers = []
+                for g in grouped_native_functions:
+                    is_registered = False
+                    if backend_index.has_kernel(g):
+                        is_registered = True
+                    # The above has_kernel test on a group will only test for
+                    # the existence of out dispatch, because that's how
+                    # structured kernels work. But sometimes functions can be
+                    # grouped but not be structured, and then you need to check
+                    # each individual piece, as they may have manual dispatch
+                    # entries.
+                    elif isinstance(g, NativeFunctionsGroup) and any(
+                        backend_index.has_kernel(fn) for fn in g.functions()
+                    ):
+                        is_registered = True
+                    # TODO: this condition is a bit questionable
+                    # (It has to do with the fact that structured kernels get generated kernels
+                    # to the Meta + CompositeExplicitAutogradNonFunctional keys).
+                    elif g.structured and dispatch_key in (
+                        DispatchKey.Meta,
+                        DispatchKey.CompositeExplicitAutogradNonFunctional,
+                    ):
+                        is_registered = True
+                    if not is_registered:
+                        continue
+
+                    headers.append(f"#include <ATen/ops/{g.root_name}_native.h>")
+                    if (
+                        dispatch_key
+                        == DispatchKey.CompositeExplicitAutogradNonFunctional
+                    ):
+                        headers.append(f"#include <ATen/ops/{g.root_name}.h>")
+                    if dispatch_key in functions_keys:
+                        headers.append(
+                            f"#include <ATen/ops/{g.root_name}_{dispatch_namespace}_dispatch.h>"
+                        )
+
+                return sorted(set(headers))
+
+        else:
+
+            def operator_headers() -> list[str]:
+                headers = ["#include <ATen/NativeFunctions.h>"]
+                if dispatch_key == DispatchKey.CompositeExplicitAutogradNonFunctional:
+                    headers.append("#include <ATen/Functions.h>")
+                if dispatch_key in functions_keys:
+                    headers.append(f"#include <ATen/{dispatch_key!s}Functions.h>")
+                return headers
+
+        backend_index = backend_indices[dispatch_key]
+        ns_grouped_native_functions = defaultdict(list)
+        for grouped_native_function in grouped_native_functions:
+            namespace = (
+                grouped_native_function.namespace
+                if isinstance(grouped_native_function, NativeFunction)
+                else grouped_native_function.functional.namespace
+            )
+            ns_grouped_native_functions[namespace].append(grouped_native_function)
+
+        dispatch_namespace = str(dispatch_key).lower()
+
+        # CompositeImplicitAutogradNestdTensor does not currently user the helpers generated
+        # compilation will fail when `-Werror=unused-function` flag is set
+        gen_dispatch_helpers: bool = (
+            dispatch_key != DispatchKey.CompositeImplicitAutogradNestedTensor
+        )
+
+        dispatch_definitions = get_native_function_definitions(
+            fm=fm,
+            grouped_native_functions=grouped_native_functions,
+            dispatch_key=dispatch_key,
+            backend_idx=backend_index,
+            selector=selector,
+            rocm=rocm,
+            symint=True,
+            skip_dispatcher_op_registration=skip_dispatcher_op_registration,
+            gen_dispatch_helpers=gen_dispatch_helpers,
+        )
+        fm.write_with_template(
+            f"Register{dispatch_key}.cpp",
+            "RegisterDispatchKey.cpp",
+            lambda: {
+                "extra_cuda_headers": extra_cuda_headers
+                if is_cuda_dispatch_key(dispatch_key)
+                else "",
+                "external_backend_headers": "",
+                "dispatch_headers": dest.gen_registration_headers(
+                    backend_index, per_operator_headers, rocm
+                ),
+                "ops_headers": operator_headers(),
+                "dispatch_helpers": "",
+                "dispatch_definitions": dispatch_definitions,
+            },
+        )
+
+        for g in structured_native_functions:
+            if not g.out.ufunc_inner_loop or not is_ufunc_dispatch_key(dispatch_key):
+                continue
+            name = g.functional.func.name.name
+            if dispatch_key is DispatchKey.CPU:
+                assert fm is cpu_fm
+                fm.write_with_template(
+                    f"UfuncCPU_{name}.cpp",
+                    "UfuncCPU.cpp",
+                    lambda: {
+                        "meta_declaration": compute_meta_function_declaration(g),
+                        "native_declaration": dest.compute_native_function_declaration(
+                            g, backend_indices[dispatch_key]
+                        ),
+                        "native_definitions": dest.compute_ufunc_cpu(g),
+                    },
+                )
+                cpu_vec_fm.write_with_template(
+                    f"UfuncCPUKernel_{name}.cpp",
+                    "UfuncCPUKernel.cpp",
+                    lambda: {
+                        "name": name,
+                        "native_definitions": dest.compute_ufunc_cpu_kernel(g),
+                    },
+                )
+            elif dispatch_key is DispatchKey.CUDA:
+                cuda_headers = "#include <ATen/native/cuda/Loops.cuh>"
+                if rocm:
+                    cuda_headers = "#include <ATen/native/hip/Loops.cuh>"
+                fm.write_with_template(
+                    f"UfuncCUDA_{name}.cu",
+                    "UfuncCUDA.cu",
+                    lambda: {
+                        "name": name,
+                        "cuda_headers": cuda_headers,
+                        "meta_declaration": compute_meta_function_declaration(g),
+                        "native_declaration": dest.compute_native_function_declaration(
+                            g, backend_indices[dispatch_key]
+                        ),
+                        "native_definitions": dest.compute_ufunc_cuda(g),
+                    },
+                )
+            else:
+                raise AssertionError(f"unrecognized {dispatch_key} for ufunc")
+
+        structured_func_group_dict = {}
+        for func_group in structured_native_functions:
+            for func in func_group.functions():
+                if func.structured_delegate is not None:
+                    structured_func_group_dict[func.structured_delegate] = func_group
+                    break
+
+        if dispatch_key in aoti_backends:
+            fallbacks = {}
+            for func in native_functions:
+                op_name = get_fallback_op_name(func)
+                if op_name in inductor_fallback_ops:
+                    fallbacks[op_name] = func
+            fallback_native_functions = tuple(
+                value for _, value in sorted(fallbacks.items())
+            )
+
+            # header files were checked in for ABI-compatiblilty checking
+            header_file_name = f"c_shim_{dispatch_key.lower()}.h"
+            new_header = gen_aoti_c_shim(
+                fallback_native_functions,
+                structured_func_group_dict,
+                dispatch_key,
+                backend_indices,
+                header=True,
+                extend_aoti_c_shim=extend_aoti_c_shim,
+                includes="",
+            )
+            if update_aoti_c_shim:
+                aoti_fm.write(
+                    header_file_name,
+                    lambda: new_header,
+                )
+            else:
+                try:
+                    with open(
+                        os.path.join(aoti_fm.install_dir, header_file_name)
+                    ) as old_file:
+                        old_header = old_file.read()
+                        assert old_header == new_header, """
+
+WARNING: The generated AOTInductor C shim header files have unexpectedly changed. This
+indicates an AOTInductor fallback operator ABI backward compatibility breakage!!!
+Only in a limited number of situations, this is allowed:
+
+1. You added a fallback op to the inductor_fallback_ops list in torchgen/aoti/fallback_ops.py.
+If that's the case, run `python torchgen/gen.py --update-aoti-c-shim` to update the existing
+C shim header files.
+
+2. You added a new default argument to an existing fallback op. This is clearly a BC breaking
+change in the AOTInductor land. In this case, you need to keep a manual copy of that existing
+fallback op in a file, e.g. torch/csrc/inductor/aoti_torch/c/shim.h, bump up the version
+number of that fallback op in the newly generated C shim files, and update the cpp wrapper
+codegen to generate the correct cpp call for this op. Contact AOTInductor team for assistance.
+
+                        """
+                except FileNotFoundError:
+                    print(
+                        f"{os.path.join(aoti_fm.install_dir, header_file_name)} not found"
+                    )
+
+            # cpp files are always generated on-the-fly
+            def headers_for_aoti() -> str:
+                headers = []
+                for func in fallback_native_functions:
+                    header = get_header_for_aoti(
+                        func,
+                        structured_func_group_dict,
+                        dispatch_key,
+                        backend_indices,
+                        extend_aoti_c_shim=extend_aoti_c_shim,
+                    )
+                    if header is not None:
+                        headers.append(header)
+                return "\n".join(sorted(set(headers)))
+
+            extra_headers = (
+                extra_cuda_headers if is_cuda_dispatch_key(dispatch_key) else ""
+            )
+
+            aoti_fm.write(
+                f"c_shim_{dispatch_key.lower()}.cpp",
+                lambda: gen_aoti_c_shim(
+                    fallback_native_functions,
+                    structured_func_group_dict,
+                    dispatch_key,
+                    backend_indices,
+                    header=False,
+                    extend_aoti_c_shim=extend_aoti_c_shim,
+                    includes=headers_for_aoti() + "\n" + extra_headers,
+                ),
+            )
+
+        del fm
+
+    # BackendSelect is generated specially
+    def gen_backend_select() -> dict[str, list[str]]:
+        relevant_fns = [
+            fn for fn in native_functions if needs_backend_select(fn, selector)
+        ]
+        return {
+            "ops_headers": [
+                f"#include <ATen/ops/{fn.root_name}_ops.h>" for fn in relevant_fns
+            ],
+            "backend_select_method_definitions": list(
+                mapMaybe(
+                    ComputeBackendSelect(Target.DEFINITION, selector), relevant_fns
+                )
+            ),
+            "backend_select_function_registrations": list(
+                mapMaybe(
+                    ComputeBackendSelect(Target.REGISTRATION, selector), relevant_fns
+                )
+            ),
+        }
+
+    cpu_fm.write("RegisterBackendSelect.cpp", gen_backend_select)
+
+    schema_selector = selector
+    if force_schema_registration:
+        schema_selector = SelectiveBuilder.get_nop_selector()
+
+    (
+        aten_schema_registrations,
+        schema_registrations,
+    ) = get_native_function_schema_registrations(
+        native_functions=native_functions, schema_selector=schema_selector
+    )
+    cpu_fm.write(
+        "RegisterSchema.cpp",
+        lambda: {
+            "aten_schema_registrations": []
+            if skip_dispatcher_op_registration
+            else aten_schema_registrations,
+            "schema_registrations": []
+            if skip_dispatcher_op_registration
+            else schema_registrations,
+        },
+    )
+
+    def key_func(
+        fn: NativeFunction | NativeFunctionsGroup | NativeFunctionsViewGroup,
+    ) -> str:
+        return fn.root_name
+
+    cpu_fm.write_sharded(
+        "Operators.cpp",
+        native_functions,
+        key_fn=key_func,
+        env_callable=lambda fn: {
+            "operator_headers": [f"#include <ATen/ops/{fn.root_name}.h>"],
+            "definitions": [
+                ComputeOperators(
+                    Target.DEFINITION,
+                    static_dispatch_backend_indices=static_dispatch_idx,
+                )(fn)
+            ],
+        },
+        base_env={
+            "static_dispatch_extra_headers": static_dispatch_extra_headers(
+                static_dispatch_idx
+            ),
+        },
+        num_shards=5,
+        sharded_keys={
+            "operator_headers",
+            "definitions",
+            "static_dispatch_extra_headers",
+        },
+    )
+
+    cpu_fm.write("Functions.cpp", dict)
+
+    core_fm.write("TensorMethods.cpp", dict)
+
+    core_fm.write(
+        "ATenOpList.cpp",
+        lambda: {
+            "aten_ops": list(mapMaybe(compute_aten_op, native_functions)),
+        },
+    )
+
+    def functionalization_env_callable(
+        g: NativeFunction | NativeFunctionsGroup | NativeFunctionsViewGroup,
+    ) -> dict[str, list[str]]:
+        def gen_op_headers(
+            g: NativeFunction | NativeFunctionsGroup | NativeFunctionsViewGroup,
+        ) -> list[str]:
+            if isinstance(g, NativeFunctionsViewGroup):
+                # view ops always get a functionalization kernel
+                headers = [
+                    f"#include <ATen/ops/{g.view.root_name}_native.h>",
+                    f"#include <ATen/ops/{g.view.root_name}_ops.h>",
+                ]
+                if g.view_copy is not None:
+                    headers += [
+                        f"#include <ATen/ops/{g.view_copy.root_name}_native.h>",
+                        f"#include <ATen/ops/{g.view_copy.root_name}_ops.h>",
+                    ]
+                return headers
+            elif isinstance(g, NativeFunctionsGroup):
+                headers = [
+                    f"#include <ATen/ops/{g.functional.root_name}_native.h>",
+                    f"#include <ATen/ops/{g.functional.root_name}_ops.h>",
+                    f"#include <ATen/ops/{g.out.root_name}_native.h>",
+                    f"#include <ATen/ops/{g.out.root_name}_ops.h>",
+                ]
+                if g.inplace is not None:
+                    headers += [
+                        f"#include <ATen/ops/{g.inplace.root_name}_native.h>",
+                        f"#include <ATen/ops/{g.inplace.root_name}_ops.h>",
+                    ]
+                if g.mutable is not None:
+                    headers += [
+                        f"#include <ATen/ops/{g.mutable.root_name}_native.h>",
+                        f"#include <ATen/ops/{g.mutable.root_name}_ops.h>",
+                    ]
+                return headers
+            else:
+                return [
+                    f"#include <ATen/ops/{g.root_name}_native.h>",
+                    f"#include <ATen/ops/{g.root_name}_ops.h>",
+                ]
+
+        return {
+            "ops_headers": gen_op_headers(g),
+            "func_definitions": gen_functionalization_definition(
+                selector,
+                g,
+            ),
+            "func_registrations": gen_functionalization_registration(
+                selector,
+                g,
+                backend_indices[DispatchKey.CompositeImplicitAutograd],
+            ),
+        }
+
+    all_groups: list[
+        NativeFunction | NativeFunctionsGroup | NativeFunctionsViewGroup
+    ] = list(structured_native_functions) + list(
+        view_groups  # type: ignore[assignment, arg-type, operator]
+    )
+    # Note: all operators that functionalization needs to handle (mutable and aliasing ops) should be grouped properly.
+    # The only reason we really need to deal with direct NativeFunctions here (instead of the groups) is because:
+    # (1) We can provide better error checking (error out if someone introduces a mutable op that doesn't obey the grouping logic)
+    # (2) functionalization needs to manually register CompositeImplicitAutograd kernels, which might not be grouped.
+    #     Although this could go away long-term if we add a dedicated dispatch key for decompositions.
+    structured_map: dict[OperatorName, NativeFunction] = {
+        f.func.name: f
+        for f in concatMap(lambda g: list(g.functions()), structured_native_functions)
+    }
+    view_map: dict[OperatorName, NativeFunction] = {
+        f.func.name: f for f in concatMap(lambda g: list(g.functions()), view_groups)
+    }
+    all_groups.extend(
+        f
+        for f in native_functions
+        if f.func.name not in structured_map and f.func.name not in view_map
+    )
+
+    cpu_fm.write_sharded(
+        "RegisterFunctionalization.cpp",
+        all_groups,
+        key_fn=key_func,
+        env_callable=functionalization_env_callable,
+        num_shards=4,
+        sharded_keys={
+            "ops_headers",
+            "func_definitions",
+            "func_registrations",
+            "func_add_back_views_definitions",
+            "func_add_back_views_registrations",
+        },
+    )
+
+    cpu_fm.write(
+        "FunctionalInverses.h",
+        lambda: {
+            "view_inverse_declarations": list(
+                mapMaybe(
+                    lambda g: gen_functionalization_view_inverse_declaration(
+                        selector, g
+                    ),
+                    view_groups,
+                )
+            )
+        },
+    )
+
+    # Note [view_copy NativeFunctions]
+    # Every view operator in native_functions.yaml that is not CompositeImplicitAutograd
+    # needs to have a corresponding non-aliasing {view}_copy variant.
+    # Backends that use functionalization and don't know how to handle aliasing ops
+    # are expected to implement kernels for these {view}_copy kernels instead.
+    # The code for {view}_copy operators in core is pretty boilerplate-heavy however,
+    # so we codegen the following:
+    # (1) A CompositeExplicitAutogradNonFunctional kernel for every {view}_copy operator.
+    #     These are never explicitly invoked by the functionalization pass,
+    #     but they could theoretically be called from user code (I added these kernels for completeness,
+    #     since the ops are part of the public API).
+    # (2) A derivative formula for every {view}_copy operator
+    #     {view}_copy operators can re-use the same derivative formulas as their {view} op counterparts,
+    #     so rather than stamping all of the entries out in derivatives.yaml,
+    #     we codegen them in.
+    #     This is similar to how autograd codegen doesn't require inplace ops to have a derivatives.yaml entry.
+    cpu_fm.write(
+        "CompositeViewCopyKernels.cpp",
+        lambda: {
+            "ops_headers": [
+                "\n".join(
+                    f"#include <ATen/ops/{f.root_name}_ops.h>\n"
+                    # NB: this include is important as it ensures we
+                    # set the visibility on generated view_copy kernels
+                    # correctly
+                    f"#include <ATen/ops/{f.root_name}_native.h>"
+                    for f in (
+                        [g.view] if g.view_copy is None else [g.view, g.view_copy]
+                    )
+                )
+                for g in view_groups
+            ]
+            + [
+                "\n".join(
+                    f"#include <ATen/ops/{f.root_name}_ops.h>\n"
+                    # NB: this include is also important for correct visibility
+                    f"#include <ATen/ops/{f.root_name}_native.h>"
+                    for f in [g.inplace, g.mutable, g.functional]
+                    if f is not None and "generated" not in f.tags
+                )
+                for g in structured_native_functions
+            ],
+            "CompositeViewCopyKernel_Definitions": list(
+                mapMaybe(
+                    GenCompositeViewCopyKernel(
+                        backend_indices[
+                            DispatchKey.CompositeExplicitAutogradNonFunctional
+                        ]
+                    ),
+                    view_groups,
+                )
+            ),
+            "GeneratedCompositeFunctional_Definitions": list(
+                mapMaybe(
+                    gen_composite_functional_kernel,
+                    structured_native_functions,
+                )
+            ),
+            "GeneratedCompositeOut_Definitions": list(
+                mapMaybe(
+                    gen_composite_out_kernel,
+                    structured_native_functions,
+                )
+            ),
+        },
+    )
+
+
+def gen_declarations_yaml(
+    cpu_fm: FileManager, native_functions: Sequence[NativeFunction]
+) -> None:
+    cpu_fm.write(
+        "Declarations.yaml",
+        lambda: format_yaml([compute_declaration_yaml(f) for f in native_functions]),
+    )
+
+
+def get_torchgen_root() -> Path:
+    """
+    If you're depending on torchgen out-of-tree, you can use the root to figure
+    out the path to native_functions.yaml
+    """
+    return Path(__file__).parent.resolve()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate ATen source files")
+    parser.add_argument(
+        "-s",
+        "--source-path",
+        help="path to source directory for ATen",
+        default="aten/src/ATen",
+    )
+    parser.add_argument(
+        "-o",
+        "--output-dependencies",
+        help="output a list of dependencies into the given file and exit",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="run without writing any files (still updates outputs)",
+    )
+    parser.add_argument(
+        "--per-operator-headers",
+        action="store_true",
+        help="generate separate headers per operator in ATen/ops",
+    )
+    parser.add_argument(
+        "-d",
+        "--install-dir",
+        "--install_dir",
+        help="output directory",
+        default="build/aten/src/ATen",
+    )
+    parser.add_argument(
+        "--aoti-install-dir",
+        "--aoti_install_dir",
+        help="output directory for AOTInductor shim",
+        default="torch/csrc/inductor/aoti_torch/generated",
+    )
+    parser.add_argument(
+        "--rocm",
+        action="store_true",
+        help="reinterpret CUDA as ROCm/HIP and adjust filepaths accordingly",
+    )
+    parser.add_argument(
+        "--mps",
+        action="store_true",
+        help="Generate MPS registration code when set",
+    )
+    parser.add_argument(
+        "--xpu",
+        action="store_true",
+        help="Generate XPU registration code when set",
+    )
+
+    # TODO: --op-registration-whitelist will be removed when all call-sites
+    # for gen.py are moved over to using the operator YAML file for mobile
+    # custom build.
+    parser.add_argument(
+        "--op-registration-whitelist",
+        "--op_registration_whitelist",
+        nargs="*",
+        help="filter op registrations by the whitelist (if set); "
+        "each item is `namespace`::`operator name` without overload name; "
+        "e.g.: aten::empty aten::conv2d ...",
+    )
+    parser.add_argument(
+        "--op-selection-yaml-path",
+        "--op_selection_yaml_path",
+        help="Provide a path to the operator selection (for custom build) YAML "
+        "that contains the information about the set of selected operators "
+        "and their categories (training, ...). Each operator is either a "
+        "full operator name with overload or just a bare operator name. "
+        "The operator names also contain the namespace prefix (e.g. aten::)",
+    )
+    parser.add_argument(
+        "--backend-whitelist",
+        "--backend_whitelist",
+        nargs="*",
+        help="filter dispatch backend by the whitelist (if set), "
+        "e.g.: CPU CUDA QuantizedCPU ...",
+    )
+    parser.add_argument(
+        "--static-dispatch-backend",
+        "--static_dispatch_backend",
+        nargs="*",
+        help="generate static dispatch code for the specific backend (if set)",
+    )
+    parser.add_argument(
+        "--skip-dispatcher-op-registration",
+        "--skip_dispatcher_op_registration",
+        action="store_true",
+        help="Avoid registering operators into the dispatcher.",
+    )
+    parser.add_argument(
+        "--force-schema-registration",
+        "--force_schema_registration",
+        action="store_true",
+        help="force it to generate schema-only registrations for all ops, including"
+        "those that are not listed on --op-registration-whitelist",
+    )
+    parser.add_argument(
+        "--generate",
+        type=str,
+        nargs="*",
+        choices=["headers", "sources", "declarations_yaml"],
+        default=["headers", "sources", "declarations_yaml"],
+        help="Generate only a subset of files",
+    )
+    parser.add_argument(
+        "--update-aoti-c-shim",
+        action="store_true",
+        help="Update AOTInductor C shim after adding an entry to inductor_fallback_ops in torchgen/aoti/fallback_ops.py. "
+        "WARNING: Do not use this unless you are sure what you are doing!!!",
+    )
+    parser.add_argument(
+        "--extend-aoti-c-shim",
+        action="store_true",
+        help="This Flag indicates the generation of c shims for out-of-tree ATen ops,"
+        "which is an extension to the In-tree ATen op c shims. This flag needs to be combined with"
+        "---source-path=<out-of-tree native_functions.yaml>"
+        "--aoti-install-dir=<in-tree aoti_install_dir>/extend"
+        "   default is torch/csrc/inductor/aoti_torch/generated/extend"
+        "WARNING: Do not use this unless you are sure what you are doing!!!",
+    )
+
+    options = parser.parse_args()
+
+    selector = get_custom_build_selector(
+        options.op_registration_whitelist,
+        options.op_selection_yaml_path,
+    )
+
+    native_yaml_path = os.path.join(options.source_path, "native/native_functions.yaml")
+    tags_yaml_path = os.path.join(options.source_path, "native/tags.yaml")
+
+    from torchgen.model import dispatch_keys
+
+    # TODO: stop generating CUDA kernels for non-CUDA builds
+    ignore_keys = set()
+    if not options.mps:
+        ignore_keys.add(DispatchKey.MPS)
+
+        if DispatchKey.MPS in dispatch_keys:
+            del dispatch_keys[dispatch_keys.index(DispatchKey.MPS)]
+
+    if not options.xpu:
+        ignore_keys.add(DispatchKey.XPU)
+
+        if DispatchKey.XPU in dispatch_keys:
+            del dispatch_keys[dispatch_keys.index(DispatchKey.XPU)]
+
+    parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path, ignore_keys)
+    valid_tags = _GLOBAL_PARSE_TAGS_YAML_CACHE[tags_yaml_path]
+    native_functions, backend_indices = (
+        parsed_yaml.native_functions,
+        parsed_yaml.backend_indices,
+    )
+
+    grouped_native_functions = get_grouped_native_functions(native_functions)
+
+    structured_native_functions = [
+        g for g in grouped_native_functions if isinstance(g, NativeFunctionsGroup)
+    ]
+    native_functions_with_view_groups = get_grouped_by_view_native_functions(
+        native_functions
+    )
+    view_groups = [
+        g
+        for g in native_functions_with_view_groups
+        if isinstance(g, NativeFunctionsViewGroup)
+    ]
+
+    # NB: It is mandatory to NOT use os.path.join here, as the install directory
+    # will eventually be ingested by cmake, which does not respect Windows style
+    # path slashes.  If you switch this to use os.path.join, you'll get an error
+    # like:
+    #
+    #   Syntax error in cmake code when parsing string
+    #
+    #     C:/Jenkins/workspace/pytorch-builds/pytorch-win-ws2016-cuda9-cudnn7-py3-build/build/aten/src/ATen\core/TensorMethods.h
+    #
+    #   Invalid character escape '\c'.
+    core_install_dir = f"{options.install_dir}/core"
+    Path(core_install_dir).mkdir(parents=True, exist_ok=True)
+    ops_install_dir = f"{options.install_dir}/ops"
+    Path(ops_install_dir).mkdir(parents=True, exist_ok=True)
+
+    aoti_install_dir = f"{options.aoti_install_dir}"
+    Path(aoti_install_dir).mkdir(parents=True, exist_ok=True)
+
+    core_fm = make_file_manager(options=options, install_dir=core_install_dir)
+    cpu_fm = make_file_manager(options=options)
+    cpu_vec_fm = make_file_manager(options=options)
+    cuda_fm = make_file_manager(options=options)
+    ops_fm = make_file_manager(options=options, install_dir=ops_install_dir)
+    aoti_fm = make_file_manager(options=options, install_dir=aoti_install_dir)
+    device_fms = {"cuda": cuda_fm}
+    if options.xpu:
+        device_fms["xpu"] = make_file_manager(options=options)
+
+    # Only a limited set of dispatch keys get CPUFunctions.h headers generated
+    # for them; this is the set
+    functions_keys = {
+        DispatchKey.CPU,
+        DispatchKey.CUDA,
+        DispatchKey.CompositeImplicitAutograd,
+        DispatchKey.CompositeImplicitAutogradNestedTensor,
+        DispatchKey.CompositeExplicitAutograd,
+        DispatchKey.CompositeExplicitAutogradNonFunctional,
+        DispatchKey.Meta,
+    }
+
+    aoti_backends = {
+        DispatchKey.CPU,
+        DispatchKey.CUDA,
+    }
+
+    if options.mps:
+        functions_keys.add(DispatchKey.MPS)
+
+    if options.xpu:
+        functions_keys.add(DispatchKey.XPU)
+        aoti_backends.add(DispatchKey.XPU)
+
+    if options.backend_whitelist:
+        dispatch_keys = [
+            k
+            for k in dispatch_keys
+            if is_generic_dispatch_key(k) or str(k) in options.backend_whitelist
+        ]
+
+    static_dispatch_idx: list[BackendIndex] = []
+    if options.static_dispatch_backend:
+        static_dispatch_idx = [
+            backend_indices[DispatchKey.parse(key)]
+            for key in options.static_dispatch_backend
+        ]
+        for key in options.static_dispatch_backend:
+            dp_key = DispatchKey.parse(key)
+            if dp_key not in functions_keys:
+                functions_keys.add(dp_key)
+
+    if "sources" in options.generate:
+        gen_source_files(
+            native_functions=native_functions,
+            grouped_native_functions=grouped_native_functions,
+            structured_native_functions=structured_native_functions,
+            view_groups=view_groups,
+            selector=selector,
+            static_dispatch_idx=static_dispatch_idx,
+            backend_indices=backend_indices,
+            aoti_fm=aoti_fm,
+            core_fm=core_fm,
+            cpu_vec_fm=cpu_vec_fm,
+            cpu_fm=cpu_fm,
+            device_fms=device_fms,
+            dispatch_keys=dispatch_keys,
+            functions_keys=functions_keys,
+            rocm=options.rocm,
+            force_schema_registration=options.force_schema_registration,
+            per_operator_headers=options.per_operator_headers,
+            skip_dispatcher_op_registration=options.skip_dispatcher_op_registration,
+            update_aoti_c_shim=options.update_aoti_c_shim,
+            aoti_backends=aoti_backends,
+            extend_aoti_c_shim=options.extend_aoti_c_shim,
+        )
+
+    if "headers" in options.generate:
+        gen_headers(
+            native_functions=native_functions,
+            valid_tags=valid_tags,
+            grouped_native_functions=grouped_native_functions,
+            structured_native_functions=structured_native_functions,
+            static_dispatch_idx=static_dispatch_idx,
+            selector=selector,
+            backend_indices=backend_indices,
+            core_fm=core_fm,
+            cpu_fm=cpu_fm,
+            device_fms=device_fms,
+            ops_fm=ops_fm,
+            dispatch_keys=dispatch_keys,
+            functions_keys=functions_keys,
+            rocm=options.rocm,
+            per_operator_headers=options.per_operator_headers,
+        )
+
+    if "declarations_yaml" in options.generate:
+        gen_declarations_yaml(native_functions=native_functions, cpu_fm=cpu_fm)
+
+    if options.output_dependencies:
+        depfile_path = Path(options.output_dependencies).resolve()
+        depfile_name = depfile_path.name
+        depfile_stem = depfile_path.stem
+
+        for fm, prefix in [
+            (cpu_fm, ""),
+            (cpu_vec_fm, "cpu_vec_"),
+            (core_fm, "core_"),
+            (ops_fm, "ops_"),
+        ] + [(device_fm, f"{device}_") for device, device_fm in device_fms.items()]:
+            varname = prefix + depfile_stem
+            path = depfile_path.parent / (prefix + depfile_name)
+            fm.write_outputs(varname, str(path))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/lib/python3.11/site-packages/torchgen/gen_aoti_c_shim.py b/lib/python3.11/site-packages/torchgen/gen_aoti_c_shim.py
new file mode 100644
index 0000000000000000000000000000000000000000..9846de77b43011137f29811bef74bb2c39121859
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_aoti_c_shim.py
@@ -0,0 +1,508 @@
+from __future__ import annotations
+
+import textwrap
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from torchgen.api.types import DispatcherSignature
+from torchgen.api.types.signatures import CppSignature, CppSignatureGroup
+from torchgen.context import method_with_native_function
+from torchgen.model import (
+    Argument,
+    BackendIndex,
+    BaseTy,
+    BaseType,
+    DispatchKey,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    NativeFunctionsGroup,
+    OperatorName,
+    OptionalType,
+    Type,
+)
+from torchgen.utils import mapMaybe
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+base_type_to_c_type = {
+    BaseTy.Tensor: "AtenTensorHandle",
+    BaseTy.bool: "int32_t",  # Use int to pass bool
+    BaseTy.int: "int64_t",
+    BaseTy.SymInt: "int64_t",  # Inductor-generated code won't see a SymInt
+    BaseTy.Scalar: "double",  # Use double to pass both integer and floating point
+    BaseTy.float: "double",  # TODO: how about other floating point types?
+    BaseTy.str: "const char*",
+    BaseTy.DeviceIndex: "int32_t",
+    BaseTy.Layout: "int32_t",  # Represent enum as int
+    BaseTy.MemoryFormat: "int32_t",  # Represent enum as int
+    BaseTy.ScalarType: "int32_t",  # Represent enum as int
+    BaseTy.Generator: "AtenGeneratorHandle",
+}
+
+base_type_to_aten_type = {
+    BaseTy.Tensor: "at::Tensor",
+    BaseTy.bool: "bool",
+    BaseTy.int: "int64_t",
+    BaseTy.SymInt: "c10::SymInt",
+    BaseTy.Scalar: "c10::Scalar",
+    BaseTy.float: "double",
+    BaseTy.str: "c10::string_view",
+    BaseTy.DeviceIndex: "c10::DeviceIndex",
+    BaseTy.Layout: "c10::Layout",
+    BaseTy.MemoryFormat: "c10::MemoryFormat",
+    BaseTy.ScalarType: "c10::ScalarType",
+    BaseTy.Generator: "at::Generator",
+}
+
+base_type_to_callsite_expr = {
+    BaseTy.Tensor: "*tensor_handle_to_tensor_pointer",
+    BaseTy.bool: "",
+    BaseTy.int: "",
+    BaseTy.SymInt: "",
+    BaseTy.Scalar: "",
+    BaseTy.float: "",
+    BaseTy.str: "",
+    BaseTy.DeviceIndex: "static_cast<c10::DeviceIndex>",
+    BaseTy.Layout: "static_cast<c10::Layout>",
+    BaseTy.MemoryFormat: "static_cast<c10::MemoryFormat>",
+    BaseTy.ScalarType: "static_cast<c10::ScalarType>",
+    BaseTy.Generator: "*generator_handle_to_generator_pointer",
+}
+
+
+# convert args to C types, names in declarations, and expressions in function bodies
+def convert_arg_type_and_name(  # type: ignore[return]
+    typ: Type,
+    name: str,
+) -> tuple[list[str], list[str], list[str], list[str]]:
+    if isinstance(typ, BaseType):
+        if typ.name in base_type_to_c_type:
+            return (
+                [base_type_to_c_type[typ.name]],
+                [name],
+                [base_type_to_aten_type[typ.name]],
+                [
+                    f"{base_type_to_callsite_expr[typ.name]}({name})"
+                    if base_type_to_callsite_expr[typ.name]
+                    else name
+                ],
+            )
+        elif typ.name == BaseTy.Device:
+            return (
+                ["int32_t", "int32_t"],
+                [name, name + "_index_"],
+                ["c10::Device"],
+                [
+                    f"c10::Device(static_cast<c10::DeviceType>({name}), static_cast<c10::DeviceIndex>({name}_index_))"
+                ],
+            )
+        else:
+            # TODO: BaseTy.Dimname, etc.
+            raise NotImplementedError(f"TODO: add support for arg type {repr(typ)}")
+    elif isinstance(typ, OptionalType):
+        c_types, names, aten_types, callsite_exprs = convert_arg_type_and_name(
+            typ.elem, name
+        )
+        j = 0  # index for names
+        new_aten_types = []
+        new_callsite_exprs = []
+        for aten_type in aten_types:
+            # Use pointer to denote optional type
+            c_types[j] = c_types[j] + "*"
+            if aten_type.startswith("c10::ArrayRef<"):
+                # ArrayRef is passed as pointer + size, but no need to add "*" to the size argument
+                new_aten_types.append(f"::std::optional<{aten_type}>")
+                base_type = aten_type[len("c10::ArrayRef<") : -1]
+                new_callsite_exprs.append(
+                    f"pointer_to_optional_list<{base_type}>({names[j]}, {names[j + 1]})"
+                )
+                j += 2
+            elif aten_type == "c10::Device":
+                # Device is passed as device_type + device_index
+                new_aten_types.append("::std::optional<c10::Device>")
+                new_callsite_exprs.append(
+                    f"pointer_to_optional_device({names[j]}, {names[j + 1]})"
+                )
+                j += 2
+            else:
+                new_aten_types.append(f"::std::optional<{aten_type}>")
+                new_callsite_exprs.append(
+                    f"pointer_to_optional<{aten_type}>({names[j]})"
+                )
+                j += 1
+
+        return (
+            c_types,
+            names,
+            new_aten_types,
+            new_callsite_exprs,
+        )
+    elif isinstance(typ, ListType):
+        # Need to explictly pass the list as pointer + length
+        c_types, names, aten_types, _ = convert_arg_type_and_name(typ.elem, name)
+        assert len(c_types) == 1, "ListType with unsupported element type " + repr(typ)
+
+        # The list content should never be modified
+        c_types[0] = f"const {c_types[0]}*"
+        c_types.append("int64_t")
+        name = names[0]
+        names.append(name + "_len_")
+
+        atype = aten_types[0]
+        callsite_exprs = []
+        if atype == "bool":
+            # no converter from std::vector<bool> to c10::ArrayRef<bool>
+            # construct std::array<bool, N> instead
+            assert typ.size is not None
+            callsite_exprs.append(f"pointer_to_list<{typ.size}>({name})")
+        elif atype == "::std::optional<at::Tensor>":
+            # convert from std::vector<::std::optional<at::Tensor>> to c10::List<::std::optional<at::Tensor>>
+            callsite_exprs.append(
+                f"c10::List<{atype}>(c10::ArrayRef<{atype}>(pointer_to_list<{atype}>({name}, {name}_len_)))"
+            )
+        else:
+            callsite_exprs.append(f"pointer_to_list<{atype}>({name}, {name}_len_)")
+
+        aten_types = [f"c10::ArrayRef<{t}>" for t in aten_types]
+        return (
+            c_types,
+            names,
+            aten_types,
+            callsite_exprs,
+        )
+
+
+def zip_type_and_name(types: list[str], names: list[str]) -> list[str]:
+    return [typ + " " + name for typ, name in zip(types, names)]
+
+
+# Generate argument declarations and callsite expressions
+def gen_arguments(flat_arguments: Sequence[Argument]) -> tuple[list[str], list[str]]:
+    types = []
+    new_names = []
+    callsite_exprs = []
+    for arg in flat_arguments:
+        new_types, names, _, new_callsite_exprs = convert_arg_type_and_name(
+            arg.type, arg.name
+        )
+        types.extend(new_types)
+        new_names.extend(names)
+        callsite_exprs.extend(new_callsite_exprs)
+    return zip_type_and_name(types, new_names), callsite_exprs
+
+
+# Return values are passed out as pointer arguments because all the C shim functions
+# are expected to return AOTITorchError.
+# Generate returns as declarations and callsite expressions
+def gen_returns(schema: FunctionSchema) -> tuple[list[str], list[str]]:
+    types = []
+    names = []
+    for idx, ret in enumerate(schema.returns):
+        names.append(f"ret{idx}")
+        if isinstance(ret.type, BaseType) and ret.type.name in base_type_to_c_type:
+            types.append(base_type_to_c_type[ret.type.name] + "*")
+        else:
+            raise NotImplementedError(
+                f"TODO: add support for return type {repr(ret.type)}"
+            )
+
+    def convert_return(typ: BaseType, val: str) -> str:
+        if typ.name == BaseTy.Tensor:
+            return f"new_tensor_handle(std::move({val}));"
+        elif typ.name == BaseTy.SymInt:
+            return f"{val}.expect_int()"
+        elif typ.name == BaseTy.Scalar:
+            return f"{val}.toDouble()"
+        else:
+            return val
+
+    ret_pointer_can_be_null = False
+    unambiguous_name = schema.name.unambiguous_name()
+    for name in [
+        "_scaled_dot_product_flash_attention",
+        "_scaled_dot_product_efficient_attention",
+        "_scaled_dot_product_cudnn_attention",
+        "convolution_backward",
+    ]:
+        if name in unambiguous_name:
+            ret_pointer_can_be_null = True
+            break
+
+    callsite_exprs: list[str] = []
+    for idx, ret in enumerate(schema.returns):
+        tmp = "tmp_result" if len(names) == 1 else f"std::get<{idx}>(tmp_result)"
+        assert isinstance(ret.type, BaseType)
+        rval = convert_return(ret.type, tmp)
+        if ret_pointer_can_be_null:
+            callsite_exprs.append(f"if ({names[idx]}) {{ *{names[idx]} = {rval}; }}")
+        else:
+            callsite_exprs.append(f"*{names[idx]} = {rval};")
+
+    return zip_type_and_name(types, names), callsite_exprs
+
+
+# gen.py generates header first and then src, so caching the result here to avoid duplicate work
+declaration_definition_cache: dict[tuple[str, str, str], tuple[str, str]] = {}
+
+
+def gen_declaration_and_definition(
+    schema: FunctionSchema, device: str, backend_call: str
+) -> tuple[str, str]:
+    func_name = schema.name.unambiguous_name()
+
+    global declaration_definition_cache
+    if (func_name, device, backend_call) in declaration_definition_cache:
+        return declaration_definition_cache[(func_name, device, backend_call)]
+
+    if schema.is_out_fn():
+        # out_variant has out arguments in the front, and it's ok to ignore return values
+        # because C shim functions only return AOTITorchError
+        args, callsite_exprs = gen_arguments(
+            [*schema.arguments.out, *schema.arguments.flat_non_out]
+        )
+        ret_assignments: list[str] = []
+    else:
+        args, callsite_exprs = gen_arguments(schema.arguments.flat_all)
+        # ignore return values for inplace ops
+        ret_declarations, ret_assignments = (
+            ([], []) if schema.name.name.inplace else gen_returns(schema)
+        )
+        args.extend(ret_declarations)
+
+    declaration = f"AOTITorchError aoti_torch_{device}_{func_name}({', '.join(args)})"
+
+    tmp_result = "auto tmp_result = " if ret_assignments else ""
+    ret_assignments_str = "\n" + "\n".join(ret_assignments) if ret_assignments else ""
+    definition = f"""
+{declaration} {{
+    AOTI_TORCH_CONVERT_EXCEPTION_TO_ERROR_CODE({{
+        {tmp_result}{backend_call}(
+{textwrap.indent(', '.join(callsite_exprs), "            ")}
+        );{textwrap.indent(ret_assignments_str, "        ")}
+    }});
+}}
+"""
+    declaration_definition_cache[(func_name, device, backend_call)] = (
+        declaration,
+        definition,
+    )
+    return declaration, definition
+
+
+def gen_static_dispatch_backend_call_signature(
+    sig: CppSignature | DispatcherSignature,
+    f: NativeFunction,
+) -> CppSignature:
+    sig = DispatcherSignature.from_schema(f.func)
+    cpp_sigs = CppSignatureGroup.from_native_function(
+        f, method=False, fallback_binding=False
+    )
+    if sig.symint and f.func.has_symint():
+        cpp_sig = cpp_sigs.symint_signature
+    else:
+        cpp_sig = cpp_sigs.signature
+    assert cpp_sig is not None
+    return cpp_sig
+
+
+def gen_static_dispatch_backend_call(
+    f: NativeFunction,
+    backend_index: BackendIndex,
+) -> str:
+    sig = DispatcherSignature.from_schema(f.func)
+    cpp_sig = gen_static_dispatch_backend_call_signature(sig, f)
+    return f"at::{backend_index.dispatch_key.lower()}::{cpp_sig.name()}"
+
+
+def get_backend_index_for_aoti(
+    func: NativeFunction,
+    func_group_mapping: dict[OperatorName, NativeFunctionsGroup],
+    dispatch_key: DispatchKey,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    extend_aoti_c_shim: bool,
+) -> BackendIndex | None:
+    backend_index = None
+    if backend_indices[dispatch_key].has_kernel(func) or (
+        func.structured_delegate is not None
+        and func.structured_delegate in func_group_mapping
+        and backend_indices[dispatch_key].has_kernel(
+            func_group_mapping[func.structured_delegate]
+        )
+    ):
+        backend_index = backend_indices[dispatch_key]
+    else:
+        # for the extend out-of-tree kernels, we don't need to
+        # duplicatly create C shim wrappers for other dispatch keys
+        if extend_aoti_c_shim:
+            return backend_index
+
+        elif backend_indices[DispatchKey.CompositeExplicitAutograd].has_kernel(func):
+            # We need to create C shim wrappers for CompositeExplicitAutograd kernels
+            backend_index = backend_indices[DispatchKey.CompositeExplicitAutograd]
+        elif backend_indices[
+            DispatchKey.CompositeExplicitAutogradNonFunctional
+        ].has_kernel(func):
+            # We need to create C shim wrappers for CompositeExplicitAutogradNonFunctional kernels
+            backend_index = backend_indices[
+                DispatchKey.CompositeExplicitAutogradNonFunctional
+            ]
+        elif backend_indices[DispatchKey.CompositeImplicitAutograd].has_kernel(func):
+            backend_index = backend_indices[DispatchKey.CompositeImplicitAutograd]
+
+    return backend_index
+
+
+def get_header_for_aoti(
+    func: NativeFunction,
+    func_group_mapping: dict[OperatorName, NativeFunctionsGroup],
+    dispatch_key: DispatchKey,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    extend_aoti_c_shim: bool,
+) -> str | None:
+    backend_index = get_backend_index_for_aoti(
+        func, func_group_mapping, dispatch_key, backend_indices, extend_aoti_c_shim
+    )
+    return (
+        None
+        if backend_index is None
+        else f"#include <ATen/ops/{func.root_name}_{backend_index.dispatch_key.lower()}_dispatch.h>"
+    )
+
+
+def get_fallback_op_name(func: NativeFunction) -> str:
+    return (
+        f"{func.namespace}.{func.func.name.name}.{func.func.name.overload_name}"
+        if func.func.name.overload_name
+        else f"{func.namespace}.{func.func.name.name}.default"
+    )
+
+
+def gen_c_shim(
+    func: NativeFunction,
+    func_group_mapping: dict[OperatorName, NativeFunctionsGroup],
+    dispatch_key: DispatchKey,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    header: bool,
+    extend_aoti_c_shim: bool,
+) -> str | None:
+    backend_index = get_backend_index_for_aoti(
+        func, func_group_mapping, dispatch_key, backend_indices, extend_aoti_c_shim
+    )
+    if backend_index is None:
+        return None
+
+    schema = func.func
+    device = dispatch_key.lower()
+    backend_call = gen_static_dispatch_backend_call(
+        func,
+        backend_index,
+    )
+
+    try:
+        if header:
+            declaration, _ = gen_declaration_and_definition(
+                schema, device, backend_call
+            )
+            return f"AOTI_TORCH_EXPORT {declaration};"
+        else:
+            _, definition = gen_declaration_and_definition(schema, device, backend_call)
+            return definition
+
+    except NotImplementedError:
+        return None
+
+
+@dataclass(frozen=True)
+class ShimGenerator:
+    func_group_mapping: dict[OperatorName, NativeFunctionsGroup]
+    dispatch_key: DispatchKey
+    backend_indices: dict[DispatchKey, BackendIndex]
+    header: bool  # True to generate .h and False to generate .cpp
+    extend_aoti_c_shim: bool
+
+    @method_with_native_function
+    def __call__(
+        self,
+        func: NativeFunction,
+    ) -> str | None:
+        result = gen_c_shim(
+            func,
+            self.func_group_mapping,
+            self.dispatch_key,
+            self.backend_indices,
+            self.header,
+            self.extend_aoti_c_shim,
+        )
+        return result
+
+
+def gen_aoti_c_shim(
+    native_functions: Sequence[NativeFunction],
+    func_group_mapping: dict[OperatorName, NativeFunctionsGroup],
+    dispatch_key: DispatchKey,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    header: bool,
+    extend_aoti_c_shim: bool,
+    includes: str = "",
+) -> str:
+    body = "\n".join(
+        list(
+            mapMaybe(
+                ShimGenerator(
+                    func_group_mapping,
+                    dispatch_key,
+                    backend_indices,
+                    header,
+                    extend_aoti_c_shim,
+                ),
+                native_functions,
+            )
+        )
+    )
+    device = dispatch_key.lower()
+    warning = """
+// WARNING: THIS FILE IS AUTOGENERATED BY torchgen. DO NOT MODIFY BY HAND.
+// See https://github.com/pytorch/pytorch/blob/7e86a7c0155295539996e0cf422883571126073e/torchgen/gen.py#L2424-L2436 for details"""
+
+    if header:
+        return f"""
+{warning}
+
+#pragma once
+
+#include <torch/csrc/inductor/aoti_torch/c/shim.h>
+
+#ifdef __cplusplus
+extern "C" {{
+#endif
+
+{body}
+
+#ifdef __cplusplus
+}} // extern "C"
+#endif
+"""
+
+    else:
+        return f"""
+{warning}
+
+#include <torch/csrc/inductor/aoti_torch/generated/{"extend/" if extend_aoti_c_shim else ""}c_shim_{device}.h>
+#include <torch/csrc/inductor/aoti_torch/utils.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/{str(dispatch_key)}Functions.h>
+#include <ATen/CompositeExplicitAutogradFunctions.h>
+#include <ATen/CompositeExplicitAutogradNonFunctionalFunctions.h>
+#include <ATen/CompositeImplicitAutogradFunctions.h>
+#else
+{includes}
+#endif
+
+using namespace torch::aot_inductor;
+
+{body}"""
diff --git a/lib/python3.11/site-packages/torchgen/gen_backend_stubs.py b/lib/python3.11/site-packages/torchgen/gen_backend_stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..b891c17671fca35e0111aa9d281819af04fa4bf9
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_backend_stubs.py
@@ -0,0 +1,615 @@
+from __future__ import annotations
+
+import argparse
+import os
+import re
+from collections import Counter, defaultdict, namedtuple
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import yaml
+
+import torchgen.api.dispatcher as dispatcher
+import torchgen.dest as dest
+from torchgen.api.types import DispatcherSignature
+from torchgen.code_template import CodeTemplate
+from torchgen.context import native_function_manager
+from torchgen.gen import get_grouped_native_functions, parse_native_yaml
+from torchgen.model import (
+    BackendIndex,
+    BackendMetadata,
+    DispatchKey,
+    NativeFunction,
+    NativeFunctionsGroup,
+    OperatorName,
+)
+from torchgen.selective_build.selector import SelectiveBuilder
+from torchgen.utils import concatMap, context, FileManager, NamespaceHelper, Target
+from torchgen.yaml_utils import YamlLoader
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# Parses the external backend's yaml, and adds a new BackendIndex for the backend's dispatch key.
+# Returns a Tuple of (backend_key, autograd_key, cpp_namespace, updated BackendIndex mapping)
+ParsedExternalYaml = namedtuple(
+    "ParsedExternalYaml",
+    ["backend_key", "autograd_key", "class_name", "cpp_namespace", "backend_indices"],
+)
+
+
+def parse_backend_yaml(
+    backend_yaml_path: str,
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    backend_indices: dict[DispatchKey, BackendIndex],
+) -> ParsedExternalYaml:
+    native_functions_map: dict[OperatorName, NativeFunction] = {
+        f.func.name: f
+        for f in concatMap(
+            lambda f: [f] if isinstance(f, NativeFunction) else list(f.functions()),
+            grouped_native_functions,
+        )
+    }
+
+    with open(backend_yaml_path) as f:
+        yaml_values = yaml.load(f, Loader=YamlLoader)
+    assert isinstance(yaml_values, dict)
+
+    valid_keys = [
+        "backend",
+        "class_name",
+        "cpp_namespace",
+        "extra_headers",
+        "supported",
+        "autograd",
+        "full_codegen",
+        "non_native",
+        "ir_gen",
+        "symint",
+    ]
+
+    backend = yaml_values.pop("backend", None)
+    assert backend is not None, 'You must provide a value for "backend"'
+
+    class_name = yaml_values.pop("class_name", None)
+
+    cpp_namespace = yaml_values.pop("cpp_namespace", None)
+    assert cpp_namespace is not None, 'You must provide a value for "cpp_namespace"'
+
+    # Mostly just defaulting to false to stick with LazyTensor convention.
+    use_out_as_primary = yaml_values.pop("use_out_as_primary", False)
+    assert isinstance(
+        use_out_as_primary, bool
+    ), f"You must provide either True or False for use_out_as_primary. Provided: {use_out_as_primary}"
+
+    use_device_guard = yaml_values.pop("device_guard", False)
+    assert isinstance(
+        use_device_guard, bool
+    ), f"You must provide either True or False for device_guard. Provided: {use_device_guard}"
+
+    supported = yaml_values.pop("supported", [])
+    if supported is None:
+        supported = []  # Allow an empty list of supported ops
+    assert isinstance(
+        supported, list
+    ), f'expected "supported" to be a list, but got: {supported} (of type {type(supported)})'
+
+    symint = yaml_values.pop("symint", [])
+    if symint is None:
+        symint = []  # Allow an empty list of symint ops
+    assert isinstance(
+        symint, list
+    ), f'expected "symint" to be a list, but got: {supported} (of type {type(supported)})'
+    symint_set = set(symint)
+
+    supported_autograd = yaml_values.pop("autograd", [])
+    assert isinstance(
+        supported_autograd, list
+    ), f'expected "autograd" to be a list, but got: {supported_autograd}'
+
+    # full_codegen is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
+    full_codegen = yaml_values.pop("full_codegen", [])
+    supported.extend(full_codegen)
+
+    # non_native is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
+    yaml_values.pop("non_native", {})
+
+    # ir_gen is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
+    yaml_values.pop("ir_gen", {})
+
+    assert (
+        len(yaml_values.keys()) == 0
+    ), f'{backend_yaml_path} contains unexpected keys: {", ".join(yaml_values.keys())}. \
+Only the following keys are supported: {", ".join(valid_keys)}'
+
+    def create_backend_index(
+        backend_ops: list[str],
+        symint_ops: set[str],
+        dispatch_key: DispatchKey,
+        *,
+        use_out_as_primary: bool,
+        use_device_guard: bool,
+    ) -> BackendIndex:
+        metadata: dict[OperatorName, BackendMetadata] = {}
+        for op in backend_ops:
+            op_name = OperatorName.parse(op)
+            assert (
+                op_name in native_functions_map
+            ), f"Found an invalid operator name: {op_name}"
+            # See Note [External Backends Follow Dispatcher API]
+            kernel_name = dispatcher.name(native_functions_map[op_name].func)
+            if op in symint_ops:
+                kernel_name += "_symint"
+            # TODO: allow structured external backends later.
+            m = BackendMetadata(
+                kernel=kernel_name, structured=False, cpp_namespace=cpp_namespace
+            )
+            metadata[op_name] = m
+        return BackendIndex(
+            dispatch_key=dispatch_key,
+            use_out_as_primary=use_out_as_primary,
+            external=True,
+            device_guard=use_device_guard,
+            index=metadata,
+        )
+
+    backend_key: DispatchKey | None = None
+    if len(supported) > 0:
+        with context(
+            lambda: f'The provided value for "backend" must be a valid DispatchKey, but got {backend}.'
+        ):
+            backend_key = DispatchKey.parse(backend)
+
+        backend_idx = create_backend_index(
+            supported,
+            symint_set,
+            backend_key,
+            use_out_as_primary=use_out_as_primary,
+            use_device_guard=use_device_guard,
+        )
+        assert backend_key not in backend_indices
+        backend_indices[backend_key] = backend_idx
+
+    autograd_key: DispatchKey | None = None
+    if len(supported_autograd) > 0:
+        with context(
+            lambda: f'The "autograd" key was specified, which indicates that you would like to override \
+the behavior of autograd for some operators on your backend. However "Autograd{backend}" is not a valid DispatchKey.'
+        ):
+            autograd_key = DispatchKey.parse(f"Autograd{backend}")
+
+        autograd_idx = create_backend_index(
+            supported_autograd,
+            symint_set,
+            autograd_key,
+            use_out_as_primary=use_out_as_primary,
+            use_device_guard=use_device_guard,
+        )
+        assert autograd_key not in backend_indices
+        backend_indices[autograd_key] = autograd_idx
+
+    for g in grouped_native_functions:
+        if isinstance(g, NativeFunction):
+            forward_kernels = (
+                []
+                if backend_key is None
+                else [
+                    m
+                    for m in [backend_indices[backend_key].get_kernel(g)]
+                    if m is not None
+                ]
+            )
+            backward_kernels = (
+                []
+                if autograd_key is None
+                else [
+                    m
+                    for m in [backend_indices[autograd_key].get_kernel(g)]
+                    if m is not None
+                ]
+            )
+        else:
+            forward_kernels = (
+                []
+                if backend_key is None
+                else [
+                    m
+                    for m in [
+                        backend_indices[backend_key].get_kernel(f)
+                        for f in g.functions()
+                    ]
+                    if m is not None
+                ]
+            )
+            backward_kernels = (
+                []
+                if autograd_key is None
+                else [
+                    m
+                    for m in [
+                        backend_indices[autograd_key].get_kernel(f)
+                        for f in g.functions()
+                    ]
+                    if m is not None
+                ]
+            )
+
+        forward_kernels = [f for f in forward_kernels if f is not None]
+        backward_kernels = [f for f in backward_kernels if f is not None]
+        assert (
+            len(forward_kernels) == 0 or len(backward_kernels) == 0
+        ), f'Currently, all variants of an op must either be registered to a backend key, or to a backend\'s \
+autograd key. They cannot be mix and matched. If this is something you need, feel free to create an issue! \
+{forward_kernels[0].kernel} is listed under "supported", but {backward_kernels[0].kernel} is listed under "autograd".'
+
+    return ParsedExternalYaml(
+        backend_key, autograd_key, class_name, cpp_namespace, backend_indices
+    )
+
+
+def error_on_missing_kernels(
+    native_functions: Sequence[NativeFunction],
+    backend_indices: dict[DispatchKey, BackendIndex],
+    backend_key: DispatchKey,
+    autograd_key: DispatchKey | None,
+    class_name: str,
+    kernel_defn_file_path: str,
+    full_codegen: list[OperatorName] | None = None,
+) -> None:
+    try:
+        with open(kernel_defn_file_path) as f:
+            backend_defns = f.read()
+    except OSError as e:
+        raise AssertionError(
+            f"Unable to read from the specified impl_path file: {kernel_defn_file_path}"
+        ) from e
+
+    if full_codegen is None:
+        full_codegen = []
+
+    indices = [backend_indices[backend_key].index] + (
+        [] if autograd_key is None else [backend_indices[autograd_key].index]
+    )
+    # Quick mapping from each OperatorName used by the external backend
+    # to its backend kernel name
+    expected_backend_op_names: dict[OperatorName, str] = dict(
+        list(
+            concatMap(
+                lambda index: [
+                    (op_name, metadata.kernel) for op_name, metadata in index.items()
+                ],
+                indices,
+            )
+        )
+    )
+    expected_backend_native_funcs: list[NativeFunction] = [
+        f
+        for f in native_functions
+        if f.func.name in expected_backend_op_names.keys()
+        and f.func.name not in full_codegen
+    ]
+    expected_backend_kernel_name_counts: dict[str, list[NativeFunction]] = defaultdict(
+        list
+    )
+    for native_f in expected_backend_native_funcs:
+        expected_backend_kernel_name_counts[
+            expected_backend_op_names[native_f.func.name]
+        ].append(native_f)
+
+    # This just looks for lines containing "foo(", and assumes that the kernel foo has been implemented.
+    # It might cause false negatives (we won't catch all cases), but that's ok - if we catch a missing kernel
+    # here, then we get a nicer error message. If we miss it, you get a linker error.
+    kernel_defn_regex = rf"(.*){class_name}::\s*([\w\d]*)\("
+    actual_backend_kernel_name_counts = Counter(
+        # A bit unwieldy (this could probably be moved into regex),
+        # but we don't want to include kernel names that come from function calls,
+        # like "return torch_xla::XLANativeFunctions::empty_strided_symint(...)".
+        # Easy check is to ignore any lines with colons before the class name.
+        [
+            y
+            for (x, y) in re.findall(kernel_defn_regex, backend_defns)
+            if not x.endswith(":")
+        ]
+    )
+
+    missing_kernels_err_msg = ""
+    for expected_name, funcs in expected_backend_kernel_name_counts.items():
+        expected_overload_count = len(funcs)
+        actual_overload_count = actual_backend_kernel_name_counts[expected_name]
+        if expected_overload_count != actual_overload_count:
+
+            def create_decl(f: NativeFunction) -> str:
+                with native_function_manager(f):
+                    return DispatcherSignature.from_schema(f.func).decl()
+
+            expected_schemas_str = "\n".join([create_decl(f) for f in funcs])
+            missing_kernels_err_msg += f"""
+{class_name} is missing a kernel definition for {expected_name}. We found {actual_overload_count} kernel(s) with that name,
+but expected {expected_overload_count} kernel(s). The expected function schemas for the missing operator are:
+{expected_schemas_str}
+
+"""
+    assert missing_kernels_err_msg == "", missing_kernels_err_msg
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate backend stub files")
+    parser.add_argument(
+        "-s",
+        "--source-yaml",
+        "--source_yaml",
+        help="path to source yaml file containing operator external definitions",
+    )
+    parser.add_argument("-o", "--output-dir", "--output_dir", help="output directory")
+    parser.add_argument(
+        "--dry-run", "--dry_run", type=bool, default=False, help="output directory"
+    )
+    parser.add_argument(
+        "--impl-path",
+        "--impl_path",
+        type=str,
+        default=None,
+        help="path to the source C++ file containing kernel definitions",
+    )
+    options = parser.parse_args()
+
+    run(options.source_yaml, options.output_dir, options.dry_run, options.impl_path)
+
+
+def gen_dispatchkey_nativefunc_headers(
+    fm: FileManager,
+    class_name: str,
+    cpp_namespace: str,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    backend_dispatch_key: DispatchKey,
+    autograd_dispatch_key: DispatchKey | None,
+    backend_name: str = "",
+) -> None:
+    assert class_name is not None
+    generated_comment = (
+        "Autogenerated file by gen_backend_stubs.py. Do not edit directly!"
+    )
+
+    # Convert to a set first to remove duplicate kernel names.
+    # Backends are allowed to repeat kernel names; only generate the declaration once!
+    # Sort for deterministic output.
+    backend_declarations = sorted(
+        set(
+            concatMap(
+                lambda f: dest.compute_native_function_declaration(
+                    f, backend_indices[backend_dispatch_key]
+                ),
+                grouped_native_functions,
+            )
+        )
+    )
+    autograd_declarations = sorted(
+        set(
+            concatMap(
+                lambda f: []
+                if autograd_dispatch_key is None
+                else dest.compute_native_function_declaration(
+                    f, backend_indices[autograd_dispatch_key]
+                ),
+                grouped_native_functions,
+            )
+        )
+    )
+
+    ns_helper = NamespaceHelper(cpp_namespace)
+    fm.write_with_template(
+        f"{backend_dispatch_key}NativeFunctions.h",
+        "DispatchKeyNativeFunctions.h",
+        lambda: {
+            "generated_comment": generated_comment,
+            "namespace_prologue": ns_helper.prologue,
+            "class_name": class_name,
+            "namespace_epilogue": ns_helper.epilogue,
+            "dispatch_declarations": backend_declarations + autograd_declarations,
+            "BackendName": backend_name,
+            "DispatchKey": backend_dispatch_key,
+        },
+    )
+
+
+def gen_dispatcher_registrations(
+    fm: FileManager,
+    output_dir: str,
+    class_name: str,
+    backend_indices: dict[DispatchKey, BackendIndex],
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+    backend_dispatch_key: DispatchKey,
+    dispatch_key: DispatchKey,
+    selector: SelectiveBuilder,
+    # build_in_tree is true for lazy TS backend and affects include paths, not used for external backends
+    build_in_tree: bool = False,
+    per_operator_headers: bool = False,
+    backend_name: str = "",
+    eager_registration: bool = True,
+) -> None:
+    headers = [
+        f"{output_dir}/{backend_dispatch_key}NativeFunctions.h",
+    ]
+    if build_in_tree:
+        external_backend_headers_str = "\n".join(f"#include <{h}>" for h in headers)
+    else:
+        external_backend_headers_str = "\n".join(f'#include "{h}"' for h in headers)
+
+    assert class_name is not None
+    backend_index = backend_indices[dispatch_key]
+
+    dispatch_registrations_body = list(
+        concatMap(
+            dest.RegisterDispatchKey(
+                backend_index,
+                Target.REGISTRATION,
+                selector,
+                rocm=False,
+                symint=True,
+                class_method_name=f"{class_name}",
+                skip_dispatcher_op_registration=False,
+            ),
+            grouped_native_functions,
+        )
+    )
+    newline = "\n"
+    ns_helper = NamespaceHelper(namespace_str="at")
+    deferred_dispatch_registrations = ""
+    static_init_dispatch_registrations = ""
+    if eager_registration:
+        static_template = CodeTemplate(
+            """\
+TORCH_LIBRARY_IMPL(aten, $dispatch_key, m) {
+    $dispatch_registrations_body
+}"""
+        )
+        static_init_dispatch_registrations = static_template.substitute(
+            dispatch_key=dispatch_key,
+            dispatch_registrations_body=dispatch_registrations_body,
+        )
+    else:
+        deferred_template = CodeTemplate(
+            """\
+TORCH_API void Register${backend_name}${dispatch_key}NativeFunctions();
+TORCH_API void Register${backend_name}${dispatch_key}NativeFunctions() {
+    static auto m = MAKE_TORCH_LIBRARY_IMPL(aten, $dispatch_key);
+    $dispatch_registrations_body
+}"""
+        )
+        deferred_dispatch_registrations = deferred_template.substitute(
+            backend_name=backend_name,
+            dispatch_key=dispatch_key,
+            dispatch_registrations_body=dispatch_registrations_body,
+        )
+
+    fm.write_with_template(
+        f"Register{dispatch_key}.cpp",
+        "RegisterDispatchKey.cpp",
+        lambda: {
+            "extra_cuda_headers": "",
+            "external_backend_headers": external_backend_headers_str,
+            "ops_headers": "#include <ATen/Functions.h>"
+            if not per_operator_headers
+            else "",
+            "DispatchKey": dispatch_key,
+            "dispatch_namespace": dispatch_key.lower(),
+            "dispatch_headers": dest.gen_registration_headers(
+                backend_index, per_operator_headers=per_operator_headers, rocm=False
+            ),
+            "dispatch_definitions": fm.substitute_with_template(
+                "RegisterDispatchDefinitions.ini",
+                lambda: {
+                    "ns_prologue": ns_helper.prologue,
+                    "ns_epilogue": ns_helper.epilogue,
+                    "static_init_dispatch_registrations": static_init_dispatch_registrations,
+                    "deferred_dispatch_registrations": deferred_dispatch_registrations,
+                    "dispatch_helpers": dest.gen_registration_helpers(backend_index),
+                    "dispatch_namespace": dispatch_key.lower(),
+                    "dispatch_namespaced_definitions": "",
+                    "dispatch_anonymous_definitions": list(
+                        concatMap(
+                            dest.RegisterDispatchKey(
+                                backend_index,
+                                Target.ANONYMOUS_DEFINITION,
+                                selector,
+                                rocm=False,
+                                symint=True,
+                                class_method_name=f"{class_name}",
+                                skip_dispatcher_op_registration=False,
+                            ),
+                            grouped_native_functions,
+                        )
+                    ),
+                },
+            ).split(newline),
+        },
+    )
+
+
+def run(
+    source_yaml: str, output_dir: str, dry_run: bool, impl_path: str | None = None
+) -> None:
+    # Assumes that this file lives at PYTORCH_ROOT/torchgen/gen_backend_stubs.py
+    pytorch_root = Path(__file__).parent.parent.absolute()
+    template_dir = os.path.join(pytorch_root, "aten/src/ATen/templates")
+
+    def make_file_manager(install_dir: str) -> FileManager:
+        return FileManager(
+            install_dir=install_dir, template_dir=template_dir, dry_run=dry_run
+        )
+
+    fm = make_file_manager(output_dir)
+
+    native_yaml_path = os.path.join(
+        pytorch_root, "aten/src/ATen/native/native_functions.yaml"
+    )
+    tags_yaml_path = os.path.join(pytorch_root, "aten/src/ATen/native/tags.yaml")
+    parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path)
+    native_functions, backend_indices = (
+        parsed_yaml.native_functions,
+        parsed_yaml.backend_indices,
+    )
+    grouped_native_functions = get_grouped_native_functions(native_functions)
+    parsed_backend_yaml = parse_backend_yaml(
+        source_yaml, grouped_native_functions, backend_indices
+    )
+    backend_key = parsed_backend_yaml.backend_key
+    autograd_key = parsed_backend_yaml.autograd_key
+    cpp_namespace = parsed_backend_yaml.cpp_namespace
+    class_name = parsed_backend_yaml.class_name
+    backend_indices = parsed_backend_yaml.backend_indices
+
+    selector = SelectiveBuilder.get_nop_selector()
+
+    if backend_key is None:
+        # This could be useful if a backend wants to quickly set up a noop yaml file but doesn't have any kernels ready yet.
+        return
+
+    if class_name is None:
+        # class_name is an optional argument to backend yaml file.
+        # if specified it allows an external backend to override
+        # the name of the class that all generated kernel definitions live under.
+        # if not specified, its value is given as native_function_class_name.
+        class_name = backend_indices[backend_key].native_function_class_name()
+    assert class_name is not None
+
+    if impl_path is not None:
+        error_on_missing_kernels(
+            native_functions,
+            backend_indices,
+            backend_key,
+            autograd_key,
+            class_name,
+            impl_path,
+        )
+
+    gen_dispatchkey_nativefunc_headers(
+        fm,
+        class_name,
+        cpp_namespace,
+        backend_indices,
+        grouped_native_functions,
+        backend_key,
+        autograd_key,
+    )
+
+    for dispatch_key in (
+        [backend_key] if autograd_key is None else [backend_key, autograd_key]
+    ):
+        gen_dispatcher_registrations(
+            fm,
+            output_dir,
+            class_name,
+            backend_indices,
+            grouped_native_functions,
+            backend_key,
+            dispatch_key,
+            selector,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/lib/python3.11/site-packages/torchgen/gen_executorch.py b/lib/python3.11/site-packages/torchgen/gen_executorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d3cf4edb05f99babfba02a057c361c6113847dd
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_executorch.py
@@ -0,0 +1,1000 @@
+from __future__ import annotations
+
+import argparse
+import os
+from collections import defaultdict
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Callable, TextIO, TYPE_CHECKING
+
+import yaml
+
+# Parse native_functions.yaml into a sequence of NativeFunctions and Backend Indices.
+from torchgen import dest
+from torchgen.api import cpp as aten_cpp
+from torchgen.api.types import CppSignature, CppSignatureGroup, CType, NamedCType
+from torchgen.context import (
+    method_with_native_function,
+    method_with_nested_native_function,
+    with_native_function_and_index,
+)
+from torchgen.executorch.api import et_cpp
+from torchgen.executorch.api.custom_ops import (
+    ComputeNativeFunctionStub,
+    gen_custom_ops_registration,
+)
+from torchgen.executorch.api.types import contextArg, ExecutorchCppSignature
+from torchgen.executorch.api.unboxing import Unboxing
+from torchgen.executorch.model import ETKernelIndex, ETKernelKey, ETParsedYaml
+from torchgen.executorch.parse import ET_FIELDS, parse_et_yaml, parse_et_yaml_struct
+from torchgen.gen import (
+    get_custom_build_selector,
+    get_native_function_declarations,
+    get_native_function_declarations_from_ns_grouped_kernels,
+    get_native_function_schema_registrations,
+    LineLoader,
+    parse_native_yaml,
+)
+from torchgen.model import (
+    BackendIndex,
+    BackendMetadata,
+    DEFAULT_KERNEL_NAMESPACE,
+    DispatchKey,
+    FunctionSchema,
+    Location,
+    NativeFunction,
+    NativeFunctionsGroup,
+    OperatorName,
+    Variant,
+)
+from torchgen.utils import (
+    context,
+    FileManager,
+    make_file_manager,
+    mapMaybe,
+    NamespaceHelper,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from torchgen.selective_build.selector import SelectiveBuilder
+
+
+def _sig_decl_wrapper(sig: CppSignature | ExecutorchCppSignature) -> str:
+    """
+    A wrapper function to basically get `sig.decl(include_context=True)`.
+    For ATen kernel, the codegen has no idea about ET contextArg, so we
+    use this wrapper to add it.
+    """
+    if isinstance(sig, ExecutorchCppSignature):
+        return sig.decl()
+
+    returns_type = aten_cpp.returns_type(sig.func.returns).cpp_type()
+    cpp_args = [a.decl() for a in sig.arguments()]
+    cpp_args_str = ", ".join([contextArg.decl()] + cpp_args)
+    sig_decl = f"{returns_type} {sig.name()}({cpp_args_str})"
+    return sig_decl
+
+
+def static_dispatch(
+    sig: CppSignature | ExecutorchCppSignature,
+    f: NativeFunction,
+    backend_indices: list[BackendIndex],
+) -> str:
+    """
+    For a given `NativeFunction`, find out the corresponding native function and dispatch to it. If zero or more than one
+    native function exists, error out. A simplified version of register_dispatch_key.py
+    Arguments:
+        sig: A CppSignature for this native function we want to use.
+        f: NativeFunction to generate static dispatch.
+        backend_indices: All available backends.
+    Return:
+        C++ code to call backend-specific functions, e.g., "return at::native::add(self, other, scale);"
+    """
+    if len(backend_indices) == 0 or f.manual_kernel_registration:
+        return ""
+
+    backends = [b for b in backend_indices if b.has_kernel(f)]
+    static_block = None
+    if len(backends) == 1:
+        backend_metadata = backends[0].get_kernel(f)
+        if backend_metadata:
+            args = ", ".join(a.name for a in sig.arguments())
+            # Here we are assuming there's no difference between CppSignature and NativeSignature for Executorch.
+            static_block = f"return ::{backend_metadata.cpp_namespace}::{backend_metadata.kernel}({args});"
+    else:
+        static_block = f"""
+ET_ASSERT_UNREACHABLE_MSG("The number of native function(s) binding to {f.func.name} is {len(backends)}.");
+    """
+    return f"""
+// {f.namespace}::{f.func}
+TORCH_API inline {_sig_decl_wrapper(sig)} {{
+    {static_block}
+}}
+"""
+
+
+# Generates Functions.h, which provides the functional public C++ API,
+# and the scaffolding to call into the dispatcher from these functions.
+@dataclass(frozen=True)
+class ComputeFunction:
+    static_dispatch_backend_indices: list[BackendIndex]
+
+    selector: SelectiveBuilder
+
+    use_aten_lib: bool
+
+    is_custom_op: Callable[[NativeFunction], bool]
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        is_method_variant = False
+        if not self.selector.is_root_operator(f"{f.namespace}::{f.func.name}"):
+            return None
+
+        if Variant.function not in f.variants and Variant.method in f.variants:
+            is_method_variant = True
+
+        # only valid remaining case is only function is in f.variants
+        elif not (Variant.function in f.variants and Variant.method not in f.variants):
+            raise Exception(  # noqa: TRY002
+                f"Can't handle native function {f.func} with the following variant specification {f.variants}."
+            )
+
+        sig: CppSignature | ExecutorchCppSignature = (
+            CppSignatureGroup.from_native_function(
+                f, method=False, fallback_binding=f.manual_cpp_binding
+            ).most_faithful_signature()
+            if self.use_aten_lib
+            else ExecutorchCppSignature.from_native_function(f)
+        )
+        if self.use_aten_lib and not self.is_custom_op(f):
+            comma = ", "
+
+            if is_method_variant:
+                return f"""
+// {f.namespace}::{f.func}
+TORCH_API inline {_sig_decl_wrapper(sig)} {{
+    return {sig.arguments()[0].name}.{sig.name()}({comma.join(e.name for e in sig.arguments()[1:])});
+}}
+"""
+            else:
+                return f"""
+// {f.namespace}::{f.func}
+TORCH_API inline {_sig_decl_wrapper(sig)} {{
+    return at::{sig.name()}({comma.join(e.name for e in sig.arguments())});
+}}
+"""
+
+        else:
+            return static_dispatch(
+                sig,
+                f,
+                backend_indices=self.static_dispatch_backend_indices,
+            )
+
+
+# Generates RegisterCodegenUnboxedKernels.cpp.
+@dataclass(frozen=True)
+class ComputeCodegenUnboxedKernels:
+    selector: SelectiveBuilder
+
+    use_aten_lib: bool
+
+    @method_with_nested_native_function
+    def __call__(
+        self,
+        unbox_kernel_entry: tuple[NativeFunction, tuple[ETKernelKey, BackendMetadata]],
+    ) -> str:
+        f: NativeFunction = unbox_kernel_entry[0]
+        kernel_key: ETKernelKey | list[ETKernelKey] = unbox_kernel_entry[1][0]
+        kernel_meta: BackendMetadata = unbox_kernel_entry[1][1]
+
+        op_name = f"{f.namespace}::{f.func.name}"
+        if not self.selector.is_root_operator(op_name):
+            return ""
+
+        if not isinstance(kernel_key, list):
+            kernel_key = [kernel_key]
+        used_kernel_keys = self.selector.et_get_selected_kernels(
+            op_name, [k.to_native_string() for k in kernel_key]
+        )
+        if not used_kernel_keys:
+            return ""
+        sig: CppSignature | ExecutorchCppSignature
+        argument_type_gen: Callable[..., NamedCType]
+        return_type_gen: Callable[..., CType]
+        if self.use_aten_lib:
+            sig = CppSignatureGroup.from_native_function(
+                f, method=False, fallback_binding=f.manual_cpp_binding
+            ).most_faithful_signature()
+            argument_type_gen = aten_cpp.argumenttype_type
+            return_type_gen = aten_cpp.returns_type
+            arguments = sig.arguments()
+            kernel_call = f"torch::executor::{f.namespace}::{sig.name()}"
+        else:
+            sig = ExecutorchCppSignature.from_native_function(f)
+            argument_type_gen = et_cpp.argumenttype_type
+            return_type_gen = et_cpp.returns_type
+            arguments = sig.arguments(include_context=False)
+            kernel_call = f"{kernel_meta.cpp_namespace}::{kernel_meta.kernel}"
+        # parse arguments into C++ code
+        binding_list, code_list = Unboxing(
+            argument_type_gen=argument_type_gen
+        ).convert_arguments(arguments)
+
+        # for each C++ argument, generate the conversion code
+        code_connector = "\n\t"
+        arg_connector = ", "
+
+        args_str = f"{arg_connector.join(e.name for e in binding_list)}"
+        event_tracer_output_logging = ""
+        output_ids = []
+
+        if len(f.func.returns) == 0:
+            if len(f.func.arguments.out) == 0:
+                raise Exception(  # noqa: TRY002
+                    f"Can't handle native function {f.func} with no returns and no out yet."
+                )
+            out = f.func.arguments.out[0]
+            return_assignment = f"""stack[{len(binding_list)}] = &{out.name};"""
+            ret_prefix = ""
+            output_ids = [len(binding_list)]
+        else:
+            if len(f.func.arguments.out) == 0:
+                return_assignment = (
+                    f"""*stack[{len(binding_list)}] = EValue(result_);"""
+                )
+                ret_prefix = return_type_gen(f.func.returns).cpp_type() + " result_ = "
+                output_ids = [len(binding_list)]
+            else:
+                return_assignment = ""
+                ret_prefix = ""
+                output_ids = [
+                    len(binding_list) - (i + 1)
+                    for i in reversed(range(len(f.func.arguments.out)))
+                ]
+
+        for output_id in output_ids:
+            event_tracer_output_logging += (
+                f"internal::event_tracer_log_evalue("
+                f"context.internal_event_tracer(), "
+                f"*stack[{output_id}]);\n"
+            )
+
+        newline = "\n    "
+        return "\n".join(
+            [
+                f"""
+Kernel(
+    "{f.namespace}::{f.func.name}",{newline + '"' + (k + '",') if k != 'default' else ''}
+    []({contextArg.defn()}, EValue** stack) {{
+        {code_connector.join(code_list)}
+
+        internal::EventTracerProfileOpScope event_tracer_op_scope(context.internal_event_tracer(), "native_call_{f.func.name}");
+        EXECUTORCH_SCOPE_PROF("native_call_{f.func.name}");
+        {ret_prefix}{kernel_call}(context, {args_str});
+        {event_tracer_output_logging}
+        {return_assignment}
+    }}
+),
+"""
+                for k in used_kernel_keys
+            ]
+        )
+
+
+def gen_unboxing(
+    *,
+    native_functions: Sequence[NativeFunction],
+    cpu_fm: FileManager,
+    selector: SelectiveBuilder,
+    use_aten_lib: bool,
+    kernel_index: ETKernelIndex,
+    manual_registration: bool,
+) -> None:
+    # Iterable type for write_sharded is a Tuple of (native_function, (kernel_key, metadata))
+    def key_func(
+        item: tuple[NativeFunction, tuple[ETKernelKey, BackendMetadata]],
+    ) -> str:
+        return item[0].root_name + ":" + item[1][0].to_native_string()
+
+    items: list[tuple[NativeFunction, tuple[ETKernelKey, BackendMetadata]]] = [
+        (native_function, (kernel_key, metadata))
+        for native_function in native_functions
+        for kernel_key, metadata in kernel_index.get_kernels(native_function).items()
+    ]
+
+    header = ["Functions.h" if use_aten_lib else "NativeFunctions.h"]
+    filename = (
+        "RegisterKernels.cpp"
+        if manual_registration
+        else "RegisterCodegenUnboxedKernels.cpp"
+    )
+    cpu_fm.write_sharded(
+        filename,
+        items,
+        key_fn=key_func,
+        env_callable=lambda unbox_kernel_entry: {
+            "unboxed_kernels": [
+                ComputeCodegenUnboxedKernels(selector, use_aten_lib)(unbox_kernel_entry)
+            ],
+            "fn_header": header
+            if unbox_kernel_entry == items[0]
+            else [],  # Only write header once
+        },
+        num_shards=1,
+        sharded_keys={"unboxed_kernels", "fn_header"},
+    )
+
+
+@with_native_function_and_index  # type: ignore[arg-type]
+def compute_native_function_declaration(
+    g: NativeFunctionsGroup | NativeFunction, kernel_index: ETKernelIndex
+) -> list[str]:
+    assert isinstance(g, NativeFunction)
+    sig = ExecutorchCppSignature.from_native_function(f=g)
+    metadata_list = kernel_index.get_kernels(g).values()
+    if metadata_list is None:
+        return []
+
+    # for kernels in lean mode, we declare two versions, one with context and one without.
+    # In the end we will cleanup the unused one.
+    def gen_decl(metadata: BackendMetadata, include_context: bool) -> str:
+        return f"{sig.decl(name=metadata.kernel, include_context=include_context)};"
+
+    return [
+        gen_decl(metadata, include_context)
+        for include_context in [False, True]
+        for metadata in metadata_list
+    ]
+
+
+def gen_functions_declarations(
+    *,
+    native_functions: Sequence[NativeFunction],
+    kernel_index: ETKernelIndex,
+    selector: SelectiveBuilder,
+    use_aten_lib: bool,
+    custom_ops_native_functions: Sequence[NativeFunction] | None = None,
+) -> str:
+    """
+    Generates namespace separated C++ function API inline declaration/definitions.
+    Native functions are grouped by namespaces and the generated code is wrapped inside
+    namespace blocks.
+
+    E.g., for `custom_1::foo.out` in yaml file we will generate a C++ API as a symbol
+    in `torch::executor::custom_1::foo_out`. This way we avoid symbol conflict when
+    the other `custom_2::foo.out` is available.
+    """
+
+    # convert kernel index to BackendIndex. This is because we can't handle ETKernelIndex yet.
+    # TODO larryliu: evaluate if this code is still needed. If yes let it handle ETKernelIndex.
+
+    backend_index = kernel_index._to_backend_index()
+
+    ns_grouped_functions = defaultdict(list)
+    for native_function in native_functions:
+        ns_grouped_functions[native_function.namespace].append(native_function)
+    functions_declarations = ""
+    newline = "\n"
+    for namespace in ns_grouped_functions:
+        ns_helper = NamespaceHelper(
+            namespace_str=namespace,
+            entity_name="",
+            max_level=3,
+        )
+        declarations = list(
+            mapMaybe(
+                ComputeFunction(
+                    static_dispatch_backend_indices=[backend_index],
+                    selector=selector,
+                    use_aten_lib=use_aten_lib,
+                    is_custom_op=lambda f: custom_ops_native_functions is not None
+                    and f in custom_ops_native_functions,
+                ),
+                ns_grouped_functions[namespace],
+            )
+        )
+        functions_declarations += f"""
+{ns_helper.prologue}
+{newline.join(declarations)}
+{ns_helper.epilogue}
+        """
+    return functions_declarations
+
+
+def get_ns_grouped_kernels(
+    *,
+    native_functions: Sequence[NativeFunction],
+    kernel_index: ETKernelIndex,
+    native_function_decl_gen: Callable[
+        [
+            NativeFunctionsGroup | NativeFunction,
+            ETKernelIndex,
+        ],
+        list[str],
+    ],
+) -> dict[str, list[str]]:
+    ns_grouped_kernels: dict[str, list[str]] = defaultdict(list)
+    for f in native_functions:
+        native_function_namespaces = set()
+        op_kernels = kernel_index.get_kernels(f)
+        for backend_metadata in op_kernels.values():
+            if backend_metadata:
+                namespace = backend_metadata.cpp_namespace
+                native_function_namespaces.add(namespace)
+            else:
+                namespace = DEFAULT_KERNEL_NAMESPACE
+            assert (
+                len(native_function_namespaces) <= 1
+            ), f"Codegen only supports one namespace per operator, got {native_function_namespaces}"
+            ns_grouped_kernels[namespace].extend(
+                native_function_decl_gen(f, kernel_index)
+            )
+    return ns_grouped_kernels
+
+
+def gen_headers(
+    *,
+    native_functions: Sequence[NativeFunction],
+    gen_custom_ops_header: bool,
+    custom_ops_native_functions: Sequence[NativeFunction],
+    selector: SelectiveBuilder,
+    kernel_index: ETKernelIndex,
+    cpu_fm: FileManager,
+    use_aten_lib: bool,
+) -> None:
+    """Generate headers.
+
+    Args:
+        native_functions (Sequence[NativeFunction]): a collection of NativeFunction for ATen ops.
+        gen_custom_ops_header (bool): whether we should generate CustomOpsNativeFunctions.h
+        custom_ops_native_functions (Sequence[NativeFunction]): a collection of NativeFunction for custom ops.
+        kernel_index (ETKernelIndex): kernel collection
+        cpu_fm (FileManager): file manager manages output stream
+        use_aten_lib (bool): whether we are generating for PyTorch types or Executorch types.
+    """
+    aten_headers = ["#include <ATen/Functions.h>"]
+    backend_indices = {DispatchKey.CPU: kernel_index._to_backend_index()}
+    if gen_custom_ops_header:
+        cpu_fm.write_with_template(
+            "CustomOpsNativeFunctions.h",
+            "NativeFunctions.h",
+            lambda: {
+                "nativeFunctions_declarations": get_native_function_declarations(
+                    grouped_native_functions=custom_ops_native_functions,
+                    backend_indices=backend_indices,
+                    native_function_decl_gen=dest.compute_native_function_declaration,
+                ),
+                "headers": [
+                    "#include <ATen/ATen.h>",
+                    "#include <torch/torch.h>",
+                ],
+            },
+        )
+        aten_headers.append('#include "CustomOpsNativeFunctions.h"')
+    cpu_fm.write(
+        "Functions.h",
+        lambda: {
+            "static_dispatch_extra_headers": aten_headers
+            if use_aten_lib
+            else ['#include "NativeFunctions.h"'],
+            "Functions_declarations": gen_functions_declarations(
+                native_functions=native_functions,
+                kernel_index=kernel_index,
+                selector=selector,
+                use_aten_lib=use_aten_lib,
+                custom_ops_native_functions=custom_ops_native_functions,
+            ),
+        },
+    )
+    cpu_fm.write(
+        "RegisterKernels.h",
+        lambda: {
+            "generated_comment": "@" + "generated by torchgen/gen_executorch.py",
+        },
+    )
+    headers = {
+        "headers": [
+            "#include <executorch/runtime/core/exec_aten/exec_aten.h> // at::Tensor etc.",
+            "#include <executorch/runtime/kernel/kernel_runtime_context.h>",
+        ],
+    }
+    if use_aten_lib:
+        headers["headers"].append("#include <executorch/codegen/macros.h> // TORCH_API")
+        cpu_fm.write(
+            "NativeFunctions.h",
+            lambda: dict(
+                {
+                    "nativeFunctions_declarations": get_native_function_declarations(
+                        grouped_native_functions=native_functions,
+                        backend_indices=backend_indices,
+                        native_function_decl_gen=dest.compute_native_function_declaration,
+                    ),
+                },
+                **headers,
+            ),
+        )
+    else:
+        ns_grouped_kernels = get_ns_grouped_kernels(
+            native_functions=native_functions,
+            kernel_index=kernel_index,
+            native_function_decl_gen=compute_native_function_declaration,  # type: ignore[arg-type]
+        )
+        cpu_fm.write(
+            "NativeFunctions.h",
+            lambda: dict(
+                {
+                    "nativeFunctions_declarations": get_native_function_declarations_from_ns_grouped_kernels(
+                        ns_grouped_kernels=ns_grouped_kernels,
+                    ),
+                },
+                **headers,
+            ),
+        )
+
+
+def gen_custom_ops(
+    *,
+    native_functions: Sequence[NativeFunction],
+    selector: SelectiveBuilder,
+    kernel_index: ETKernelIndex,
+    cpu_fm: FileManager,
+    rocm: bool,
+) -> None:
+    dispatch_key = DispatchKey.CPU
+    (
+        anonymous_definition,
+        static_init_dispatch_registrations,
+    ) = gen_custom_ops_registration(
+        native_functions=native_functions,
+        selector=selector,
+        kernel_index=kernel_index,
+        rocm=rocm,
+    )
+    cpu_fm.write_with_template(
+        f"Register{dispatch_key}CustomOps.cpp",
+        "RegisterDispatchKeyCustomOps.cpp",
+        lambda: {
+            "ops_headers": '#include "CustomOpsNativeFunctions.h"',
+            "DispatchKey": dispatch_key,
+            "dispatch_namespace": dispatch_key.lower(),
+            "dispatch_namespaced_definitions": "",
+            "dispatch_anonymous_definitions": anonymous_definition,
+            "static_init_dispatch_registrations": static_init_dispatch_registrations,
+        },
+    )
+    cpu_fm.write_with_template(
+        f"Register{dispatch_key}Stub.cpp",
+        "RegisterDispatchKeyCustomOps.cpp",
+        lambda: {
+            "ops_headers": "",
+            "DispatchKey": dispatch_key,
+            "dispatch_namespace": dispatch_key.lower(),
+            "dispatch_namespaced_definitions": "",
+            "dispatch_anonymous_definitions": list(
+                mapMaybe(ComputeNativeFunctionStub(), native_functions)
+            ),
+            "static_init_dispatch_registrations": static_init_dispatch_registrations,
+        },
+    )
+
+    (
+        aten_schema_registrations,
+        schema_registrations,
+    ) = get_native_function_schema_registrations(
+        native_functions=native_functions,
+        schema_selector=selector,
+    )
+    cpu_fm.write(
+        "RegisterSchema.cpp",
+        lambda: {
+            "schema_registrations": schema_registrations,
+            "aten_schema_registrations": aten_schema_registrations,
+        },
+    )
+
+
+def translate_native_yaml(
+    tags_yaml_path: str,
+    aten_yaml_path: str,
+    native_yaml_path: str | None,
+    use_aten_lib: bool,
+    out_file: TextIO,
+) -> None:
+    """Translates Executorch DSL dialect to use the same syntax as
+    native_functions.yaml. The major difference is that Executorch DSL dialect
+    supports "op" key, where it refers to the operator name in native_functions.yaml.
+
+    For example, a functions.yaml may have the following entry:
+
+    - op: add.out
+      ...
+
+    It needs to be translated to the following:
+
+    - func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+      ...
+
+    We go in aten_yaml_path and find the operator schema for "add.out" and add it
+    to the original functions.yaml. We also add required field "variants", where for
+    Executorch it will always be "function".
+
+    For ATen mode we don't have to do the translation because native_yaml_path is
+    the same as native_functions.yaml.
+
+    Args:
+        tags_yaml_path: Path to a tags.yaml file to satisfy codegen parsing.
+            It is not optional.
+        aten_yaml_path: Path to ATen operator yaml file native_functions.yaml.
+        native_yaml_path: Path to a functions.yaml file to parse.
+            If the path does not exist in the filesystem, it is treated as an
+            empty file. If `custom_ops_yaml_path` exists, the contents of that
+            file are appended to the yaml input to be parsed.
+        use_aten_lib: We use this flag to determine if we want to generate native
+            functions. In ATen mode we should generate out= variants.
+        out_file: The IO object that we are writing into.
+    Returns:
+        None
+    """
+    if use_aten_lib:
+        with open(aten_yaml_path) as aten_yaml:
+            out_file.writelines(aten_yaml.readlines())
+        return
+
+    native_functions, persisted_fields = parse_et_yaml(
+        aten_yaml_path,
+        tags_yaml_path,
+        None,
+        skip_native_fns_gen=False,
+    )
+
+    func_to_scoped_name: dict[FunctionSchema, str] = {
+        f.func: f"{f.namespace}::{f.func.name}" for f in native_functions
+    }
+    op_to_scoped_name: dict[OperatorName, str] = {
+        func.name: name for func, name in func_to_scoped_name.items()
+    }
+
+    schema_dict = {name: str(func) for func, name in func_to_scoped_name.items()}
+    kernel_persist_dict: dict[str, dict[str, Any]] = {
+        op_to_scoped_name[op]: v for op, v in persisted_fields.items()
+    }
+
+    if (
+        not native_yaml_path
+        or not os.path.exists(native_yaml_path)
+        or os.stat(native_yaml_path).st_size == 0
+    ):
+        return
+    with open(native_yaml_path) as native_yaml:
+        native_es = yaml.load(native_yaml, Loader=LineLoader)
+        if not native_es:
+            return
+        for e in native_es:
+            assert isinstance(e.get("__line__"), int), e
+            loc = Location(native_yaml_path, e.pop("__line__"))
+            with context(lambda: f"in {loc}:\n  "):
+                if "variants" not in e:
+                    e["variants"] = "function"
+                if "func" in e:
+                    continue
+                assert isinstance(e.get("op"), str), e
+                opname = e.pop("op")
+                if "::" not in opname:
+                    opname = "aten::" + opname
+                assert opname in schema_dict
+                e["func"] = schema_dict.get(opname)
+
+                # Write out persisted kernel information
+                if opname in kernel_persist_dict:
+                    for k, v in kernel_persist_dict[opname].items():
+                        e[k] = v
+
+        yaml.dump(native_es, out_file, width=1000)
+
+
+def parse_yaml(
+    path: str | None,
+    tags_yaml_path: str,
+    function_filter: Callable[[NativeFunction], bool],
+    skip_native_fns_gen: bool = False,
+) -> tuple[
+    list[NativeFunction],
+    dict[DispatchKey, dict[OperatorName, BackendMetadata]] | ETKernelIndex,
+]:
+    if path and os.path.exists(path) and os.stat(path).st_size > 0:
+        with open(path) as f:
+            es = yaml.load(f, Loader=LineLoader)
+
+        # Check for kernel index structure
+        kernel_index = (
+            parse_et_yaml_struct(es) if any("kernels" in e for e in es) else None
+        )
+
+        # Remove ET specific fields from entries for BC compatibility
+        for entry in es:
+            for field in ET_FIELDS:
+                entry.pop(field, None)
+
+        parsed_yaml = parse_native_yaml(
+            path,
+            tags_yaml_path,
+            None,
+            skip_native_fns_gen=skip_native_fns_gen,
+            loaded_yaml=es,
+        )
+        native_functions = list(filter(function_filter, parsed_yaml.native_functions))
+        op_names = [f.func.name for f in native_functions]
+
+        # (1) Return ETKernelIndex if kernel index is present
+        if kernel_index is not None:
+            filtered_index = {
+                op_name: kernel_mapping
+                for op_name, kernel_mapping in kernel_index.index.items()
+                if op_name in op_names
+            }
+            return native_functions, ETKernelIndex(index=filtered_index)
+
+        # (2) Return BackendIndices if kernel index is absent
+        def map_index(
+            m: dict[OperatorName, BackendMetadata],
+        ) -> dict[OperatorName, BackendMetadata]:
+            return {op: m[op] for op in m if op in op_names}
+
+        backend_indices = {
+            k: map_index(b.index) for (k, b) in parsed_yaml.backend_indices.items()
+        }
+
+        return native_functions, backend_indices
+    else:
+        return [], {}
+
+
+def parse_yaml_files(
+    tags_yaml_path: str,
+    aten_yaml_path: str,
+    native_yaml_path: str | None,
+    custom_ops_yaml_path: str | None,
+    selector: SelectiveBuilder,
+    use_aten_lib: bool,
+) -> tuple[ETParsedYaml, ETParsedYaml | None]:
+    """Parses functions.yaml and custom_ops.yaml files.
+
+    Args:
+        tags_yaml_path: Path to a tags.yaml file to satisfy codegen parsing.
+            It is not optional.
+        aten_yaml_path: Path to ATen operator yaml file native_functions.yaml.
+        native_yaml_path: Path to a functions.yaml file to parse.
+            If the path does not exist in the filesystem, it is treated as an
+            empty file. If `custom_ops_yaml_path` exists, the contents of that
+            file are appended to the yaml input to be parsed.
+        custom_ops_yaml_path: Path to a custom_ops.yaml file to parse. If
+            the path does not exist in the filesystem, it is ignored.
+        selector: For selective build.
+        use_aten_lib: We use this flag to determine if we want to generate native
+            functions. In ATen mode we should generate out= variants.
+    Returns:
+        A tuple with two elements:
+        [0]: The parsed results of concatenating the contents of
+             `native_yaml_path` and `custom_ops_yaml_path`.
+        [1]: The parsed results of the contents of `custom_ops_yaml_path`, if
+             present. If not present, None.
+    """
+    import tempfile
+
+    # only include selected ops, this is because we want to avoid
+    def function_filter(f: NativeFunction) -> bool:
+        return selector.is_native_function_selected(f)
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        translated_yaml_path = os.path.join(tmpdirname, "translated.yaml")
+        with open(translated_yaml_path, "w") as translated:
+            translate_native_yaml(
+                tags_yaml_path,
+                aten_yaml_path,
+                native_yaml_path,
+                use_aten_lib,
+                translated,
+            )
+
+        translated_functions, translated_indices = parse_yaml(
+            translated_yaml_path, tags_yaml_path, function_filter, not use_aten_lib
+        )
+        custom_ops_functions, custom_ops_indices = parse_yaml(
+            custom_ops_yaml_path, tags_yaml_path, function_filter, True
+        )
+
+        # Convert BackendIndices to ETKernelIndex
+        if not isinstance(translated_indices, ETKernelIndex):
+            translated_indices = ETKernelIndex.from_backend_indices(translated_indices)
+        if not isinstance(custom_ops_indices, ETKernelIndex):
+            custom_ops_indices = ETKernelIndex.from_backend_indices(custom_ops_indices)
+
+        combined_functions = translated_functions + custom_ops_functions
+        combined_kernel_index = ETKernelIndex.merge_indices(
+            translated_indices, custom_ops_indices
+        )
+        combined_yaml = ETParsedYaml(combined_functions, combined_kernel_index)
+        custom_ops_parsed_yaml = ETParsedYaml(custom_ops_functions, custom_ops_indices)
+
+    return combined_yaml, custom_ops_parsed_yaml
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate operator source files")
+    # Although we don't refer to --source-path directly, make_file_manager()
+    # expects it to point to a directory that contains a templates/ subdirectory
+    # containing the file templates.
+    parser.add_argument(
+        "-s",
+        "--source-path",
+        help="path to source directory for kernel templates",
+    )
+    parser.add_argument(
+        "--functions-yaml-path",
+        "--functions_yaml_path",
+        help="path to the functions.yaml file to use. Optional, but at least "
+        "one of --functions-yaml-path and --custom-ops-yaml-path must be "
+        "specified.",
+    )
+    parser.add_argument(
+        "--custom-ops-yaml-path",
+        "--custom_ops_yaml_path",
+        help="path to the custom_ops.yaml file to use. Optional, but at least "
+        "one of --functions-yaml-path and --custom-ops-yaml-path must be "
+        "specified.",
+    )
+    parser.add_argument(
+        "--aten-yaml-path",
+        "--aten_yaml_path",
+        help="path to native_functions.yaml file.",
+    )
+    # Note that make_file_manager() also looks at --install-dir.
+    parser.add_argument(
+        "-d",
+        "--install-dir",
+        "--install_dir",
+        help="output directory",
+        default="build/generated",
+    )
+    parser.add_argument(
+        "-o",
+        "--output-dependencies",
+        help="output a list of dependencies into the given file and exit",
+    )
+    # Although we don't refer to --dry-run directly, make_file_manager() looks
+    # for it.
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="run without writing any files (still updates outputs)",
+    )
+    parser.add_argument(
+        "--static-dispatch-backend",
+        "--static_dispatch_backend",
+        nargs="*",
+        help="generate static dispatch code for the specific backend (if set)",
+    )
+    parser.add_argument(
+        "--op-registration-whitelist",
+        "--op_registration_whitelist",
+        nargs="*",
+        help="filter op registrations by the whitelist (if set); "
+        "each item is `namespace`::`operator name` without overload name; "
+        "e.g.: aten::empty aten::conv2d ...",
+    )
+    parser.add_argument(
+        "--op-selection-yaml-path",
+        "--op_selection_yaml_path",
+        help="Provide a path to the operator selection (for custom build) YAML "
+        "that contains the information about the set of selected operators "
+        "and their categories (training, ...). Each operator is either a "
+        "full operator name with overload or just a bare operator name. "
+        "The operator names also contain the namespace prefix (e.g. aten::)",
+    )
+    parser.add_argument(
+        "--tags-path",
+        help="Path to tags.yaml. Required by yaml parsing in codegen system.",
+    )
+    parser.add_argument(
+        "--rocm",
+        action="store_true",
+        help="reinterpret CUDA as ROCm/HIP and adjust filepaths accordingly",
+    )
+    parser.add_argument(
+        "--use-aten-lib",
+        "--use_aten_lib",
+        action="store_true",
+        help="a boolean flag to indicate whether we use ATen kernels or not, in the future this flag will be per "
+        "operator",
+    )
+    parser.add_argument(
+        "--manual_registration",
+        "--manual-registration",
+        action="store_true",
+        help="a boolean flag to indicate whether we want to manually call"
+        "register_kernels() or rely on static init. ",
+    )
+    parser.add_argument(
+        "--generate",
+        type=str,
+        nargs="*",
+        choices=["headers", "sources"],
+        default=["headers", "sources"],
+        help="Generate only a subset of files",
+    )
+    options = parser.parse_args()
+    assert options.tags_path, "tags.yaml is required by codegen yaml parsing."
+
+    selector = get_custom_build_selector(
+        options.op_registration_whitelist,
+        options.op_selection_yaml_path,
+    )
+
+    parsed_yaml, custom_ops_parsed_yaml = parse_yaml_files(
+        aten_yaml_path=options.aten_yaml_path,
+        tags_yaml_path=options.tags_path,
+        native_yaml_path=options.functions_yaml_path,
+        custom_ops_yaml_path=options.custom_ops_yaml_path,
+        selector=selector,
+        use_aten_lib=options.use_aten_lib,
+    )
+    native_functions, kernel_index = (
+        parsed_yaml.native_functions,
+        parsed_yaml.kernel_index,
+    )
+    custom_ops_native_functions = (
+        custom_ops_parsed_yaml.native_functions if custom_ops_parsed_yaml else []
+    )
+
+    cpu_fm = make_file_manager(options=options)
+
+    if "headers" in options.generate:
+        # generate CustomOpsNativeFunctions.h when custom_ops.yaml is present, to match the build system.
+        gen_headers(
+            native_functions=native_functions,
+            gen_custom_ops_header=options.custom_ops_yaml_path,
+            custom_ops_native_functions=custom_ops_native_functions,
+            selector=selector,
+            kernel_index=kernel_index,
+            cpu_fm=cpu_fm,
+            use_aten_lib=options.use_aten_lib,
+        )
+
+    if "sources" in options.generate:
+        gen_unboxing(
+            native_functions=native_functions,
+            cpu_fm=cpu_fm,
+            selector=selector,
+            use_aten_lib=options.use_aten_lib,
+            kernel_index=kernel_index,
+            manual_registration=options.manual_registration,
+        )
+        if custom_ops_native_functions:
+            gen_custom_ops(
+                native_functions=custom_ops_native_functions,
+                selector=selector,
+                kernel_index=kernel_index,
+                cpu_fm=cpu_fm,
+                rocm=options.rocm,
+            )
+
+    if options.output_dependencies:
+        depfile_path = Path(options.output_dependencies).resolve()
+        depfile_name = depfile_path.name
+        depfile_stem = depfile_path.stem
+
+        for fm, prefix in [
+            (cpu_fm, ""),
+        ]:
+            varname = prefix + depfile_stem
+            path = depfile_path.parent / (prefix + depfile_name)
+            fm.write_outputs(varname, str(path))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/lib/python3.11/site-packages/torchgen/gen_functionalization_type.py b/lib/python3.11/site-packages/torchgen/gen_functionalization_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f9865d6d3ebad225ba506759afff527c1da9e47
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_functionalization_type.py
@@ -0,0 +1,882 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, TYPE_CHECKING
+
+from torchgen.api import cpp, dispatcher
+from torchgen.api.translate import translate
+from torchgen.api.types import (
+    BaseCType,
+    Binding,
+    CType,
+    DispatcherSignature,
+    FunctionalizationLambda,
+    iTensorListRefT,
+    NativeSignature,
+    OptionalCType,
+    optionalSymIntArrayRefT,
+    symIntArrayRefT,
+    SymIntT,
+    tensorListT,
+    tensorT,
+    VectorCType,
+    ViewInverseSignature,
+)
+from torchgen.context import (
+    method_with_native_function,
+    native_function_manager,
+    with_native_function,
+    with_native_function_and,
+)
+from torchgen.model import (
+    Argument,
+    BackendIndex,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    NativeFunctionsGroup,
+    NativeFunctionsViewGroup,
+    Return,
+    SchemaKind,
+    SelfArgument,
+    TensorOptionsArguments,
+)
+from torchgen.native_function_generation import (
+    INPLACE_OPS_THAT_DONT_GET_GROUPED_PROPERLY,
+    MUTABLE_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT,
+    OUT_OPS_THAT_DONT_GET_GROUPED_PROPERLY,
+)
+from torchgen.utils import dataclass_repr
+
+
+if TYPE_CHECKING:
+    from torchgen.selective_build.selector import SelectiveBuilder
+
+
+# Note: [Mutable Ops Not Using Functionalization]
+# Ops in this list currently do not work with functionalization and should be fixed.
+MUTABLE_OPS_NOT_USING_FUNCTIONALIZATION = (
+    OUT_OPS_THAT_DONT_GET_GROUPED_PROPERLY
+    + MUTABLE_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT
+    + INPLACE_OPS_THAT_DONT_GET_GROUPED_PROPERLY
+    + [
+        # It will be BC-breaking, but we should fix their schemas.
+        # should be inplace?
+        "record_stream",
+        # See Note [resize_ in Functionalization]
+        "resize_",
+        "resize_as_",
+        # This function is used as for testing purposes only.
+        "_fill_mem_eff_dropout_mask_",
+    ]
+)
+
+# This file contains codegen that relates to the functionalization pass.
+# It includes:
+# - gen_functionalization_definition
+#     Generates dispatcher kernel definitions for the functionalization pass.
+# - gen_functionalization_registration
+#     Generates dispatcher kernel registrations for the functionalization pass.
+# - gen_functionalization_view_inverse_declaration
+#     Generates a declaration for an "inverse view", for every view op
+#     that is needed in functionalization. We manually implement their definitions.
+# - gen_composite_view_copy_kernel
+#     Generates view_copy() composite kernels for all view_copy operators.
+
+
+# Generates the body of the default composite C++ kernel for a {view}_copy NativeFunction
+# See Note [view_copy NativeFunctions]
+@dataclass(frozen=True)
+class GenCompositeViewCopyKernel:
+    backend_index: BackendIndex
+
+    @method_with_native_function
+    def __call__(self, g: NativeFunctionsViewGroup) -> str | None:
+        if g.view_copy is None:
+            return None
+        elif g.view_copy.func.name.name.base != f"{g.view.func.name.name}_copy":
+            # If the view_copy doesn't match the standard naming scheme of <op>_copy,
+            # assume it already exists and doesn't need to be generated.
+            # Example: slice_inverse() with the copy variant named slice_scatter()
+            # instead of slice_inverse_copy()
+            return None
+
+        metadata = self.backend_index.get_kernel(g.view_copy)
+        assert metadata is not None
+
+        # We can make view_copy work in more cases by using reshape()
+        # when a normal view call would ordinarily fail.
+        # This also makes LTC more efficient, because they don't need to include
+        # clone() calls in their graph (which is normally needed by reshape).
+        if str(g.view_copy.func.name) == "view_copy":
+            assert metadata.kernel == "view_copy_symint"
+            return """\
+at::Tensor view_copy_symint(const at::Tensor & self, at::SymIntArrayRef size) {
+  c10::SymDimVector shape = infer_size_dv(size, self.sym_numel());
+  if (!at::detail::computeStride(self.sym_sizes(), self.sym_strides(), shape).has_value()) {
+    return self.reshape_symint(size);
+  } else {
+    auto output = at::_ops::view::call(self, size);
+    return output.clone(/*memory_format=*/at::MemoryFormat::Contiguous);
+  }
+}
+"""
+        # view_copy is a native signature, since we're generating an at::native:: kernel
+        # Functionalization always operates on symints though
+        view_copy_sig = NativeSignature(
+            g.view_copy.func, symint=metadata.supports_symint()
+        )
+
+        # view is a dispatcher signature, since we're calling into the at::_ops API
+        view_sig = DispatcherSignature(g.view.func)
+
+        view_api_name = g.view.func.name.unambiguous_name()
+        exprs = ", ".join(
+            [e.expr for e in translate(view_copy_sig.arguments(), view_sig.arguments())]
+        )
+
+        # view ops today always return either a Tensor or a list of Tensors
+        assert len(g.view.func.returns) == 1
+        assert g.view.func.returns[0].type == BaseType(
+            BaseTy.Tensor
+        ) or g.view.func.returns[0].type == ListType(BaseType(BaseTy.Tensor), None)
+
+        if g.view.func.returns[0].type == BaseType(BaseTy.Tensor):
+            return_cloned_output = """\
+  return output.clone(/*memory_format=*/at::MemoryFormat::Contiguous);"""
+        else:
+            # If the return type is a list, we need to clone each tensor in the list.
+            return_cloned_output = f"""\
+  {view_copy_sig.returns_type().cpp_type()} out_clone;
+  for (const auto i : c10::irange(output.size())) {{
+    out_clone.push_back(output[i].clone(/*memory_format=*/at::MemoryFormat::Contiguous));
+  }}
+  return out_clone;"""
+
+        # The default generated composite kernel for {view}_copy() operators just clones
+        # the input tensor, and runs the underlying view on the clone.
+        return f"""
+{view_copy_sig.defn(name=metadata.kernel)} {{
+  auto output = at::_ops::{view_api_name}::call({exprs});
+  {return_cloned_output}
+}}
+"""
+
+
+def return_str(rets: tuple[Return, ...], names: list[str]) -> str:
+    assert len(rets) == len(names)
+    if len(rets) == 0:
+        return ""
+    elif len(rets) == 1:
+        return f"return {names[0]};"
+    else:
+        return f"return {dispatcher.returns_type(rets).cpp_type()}({', '.join(names)});"
+
+
+def modifies_arguments(f: NativeFunction) -> bool:
+    return any(
+        a.annotation is not None and a.annotation.is_write
+        for a in f.func.arguments.flat_all
+    )
+
+
+def wrapper_name(func: FunctionSchema) -> str:
+    if func.name.overload_name:
+        return f"{cpp.name(func)}_{func.name.overload_name}"
+    else:
+        return cpp.name(func)
+
+
+def is_tensor_like(a: Argument | TensorOptionsArguments | SelfArgument) -> bool:
+    return isinstance(a, SelfArgument) or (
+        isinstance(a, Argument) and a.type.is_tensor_like()
+    )
+
+
+# We need to wrap / unwrap various arguments from the op in the functionalization kernels.
+# Some op schemas include non-owning types though (like TensorList),
+# and when we unwrap them we expect to get out an owning type!.
+# We also return a lambda that tells you how to conver the non-owning type argument into the owning type.
+def get_owning_type(t: CType) -> tuple[CType, Callable[[str], str]]:
+    if t == BaseCType(tensorListT):
+        return VectorCType(BaseCType(tensorT)), lambda x: f"{x}.vec()"
+    if t == BaseCType(iTensorListRefT):
+        return VectorCType(BaseCType(tensorT)), lambda x: f"{{{x}.begin(), {x}.end()}}"
+    # There are technically other non-owning types out there (like IntArrayRef),
+    # but functionalization only actually cares about the ones involving tensors.
+    return t, lambda x: x
+
+
+# unwraps all tensor-like arguments, returning:
+# (1) a string containing all of the logic that does the unwrapping
+# (2) a context, to be used by translate(), with all of the relevant bindings.
+def unwrap_tensor_args(
+    sig: DispatcherSignature, *, is_view_op: bool
+) -> tuple[str, list[Binding]]:
+    context: list[Binding] = []
+    unwrapped_tensor_args: list[str] = []
+    for arg in sig.arguments():
+        if is_tensor_like(arg.argument):
+            # for tensor inputs, we want to unwrap them before passing them into the redispatch calls.
+            unwrapped_name = f"{arg.name}_"
+            # For most ops, the functionalization needs to sync any pending updates on the input tensors
+            # before calling the operator, since otherwise the operator will act on stale data.
+            # For view ops though, we can continue to defer syncing until the tensor is used by
+            # a non-view operator.
+            maybe_sync_input = (
+                "" if is_view_op else f"at::functionalization::impl::sync({arg.name});"
+            )
+            unwrapped_type, conversion_fn = get_owning_type(
+                arg.nctype.remove_const_ref().type
+            )
+            unwrapped_tensor_args.append(
+                f"""
+      {unwrapped_type.cpp_type()} {unwrapped_name};
+      if (at::functionalization::impl::isFunctionalTensor({arg.name})) {{
+        {maybe_sync_input}
+        {unwrapped_name} = at::functionalization::impl::from_functional_tensor({arg.name});
+      }} else {{
+        {unwrapped_name} = {conversion_fn(arg.name)};
+      }}"""
+            )
+            context.append(arg.with_name(unwrapped_name))
+        else:
+            # for non-tensor inputs, we want to pass them directly into the redispatch calls.
+            context.append(arg)
+    unwrap_tensor_args_str = "\n      ".join(unwrapped_tensor_args)
+    return unwrap_tensor_args_str, context
+
+
+# converts  all tensor-like arguments to meta tensors, which are used to compute stride info. Returns:
+# (1) a string containing all of the logic that does the conversions.
+# (2) a context, to be used by translate(), with all of the relevant bindings.
+def convert_to_meta_tensors(sig: DispatcherSignature) -> tuple[str, list[Binding]]:
+    context: list[Binding] = []
+    unwrapped_tensor_args: list[str] = []
+    for arg in sig.arguments():
+        if is_tensor_like(arg.argument):
+            # for tensor inputs, we want to unwrap them before passing them into the redispatch calls.
+            a_ = arg.name
+            unwrapped_name = f"{arg.name}_meta"
+            unwrapped_tensor_args.append(f"auto {unwrapped_name} = to_meta({a_});")
+            context.append(arg.with_name(unwrapped_name))
+        else:
+            # for non-tensor inputs, we want to pass them directly into the redispatch calls.
+            context.append(arg)
+    unwrap_tensor_args_str = "\n        ".join(unwrapped_tensor_args)
+    return unwrap_tensor_args_str, context
+
+
+# The functionalization codegen currently expects view op schemas to have this form:
+# foo(Tensor(a), ...) -> Tensor(a) (e.g. transpose)
+# foo(Tensor(a!), ...) -> Tensor(a!) (e.g. transpose_)
+def assert_view_op_properties(func: FunctionSchema) -> None:
+    def is_alias(a: Argument) -> bool:
+        return a.annotation is not None
+
+    args = func.arguments.flat_non_out
+    # The first argument is a tensor with an alias semantics (annotations)
+    assert (
+        len(args) > 0 and args[0].type == BaseType(BaseTy.Tensor)
+    ), f"""In the functionalization codegen, we expect the first argument of every view operator to be a tensor,
+but found an argument of type {str(args[0].type)} for operator: {str(func.name)}."""
+    # No other arguments have aliasing semantics
+    assert (
+        is_alias(args[0]) and not any(is_alias(a) for a in args[1:])
+    ), """In the functionalization codegen, we expect the first argument of every view operator to alias the output.
+View operators with multiple aliasing inputs aren't supported yet. Found an operator that doesn't satisfy this constraint"""
+
+
+# One-liner expression for checking if an expression expr of type type has any
+# symbolic values.
+def emit_expr_has_symbolic_values(expr: str, type: CType) -> str:
+    if type == BaseCType(SymIntT):
+        return f"{expr}.is_symbolic()"
+
+    if isinstance(type, OptionalCType):
+        innerexpr = f"(*{expr})"
+        return f"{expr}.has_value() ? {emit_expr_has_symbolic_values(innerexpr, type.elem)} : false"
+
+    if type == BaseCType(optionalSymIntArrayRefT):
+        return emit_expr_has_symbolic_values(
+            expr, OptionalCType(BaseCType(symIntArrayRefT))
+        )
+
+    if type in (BaseCType(symIntArrayRefT), VectorCType(BaseCType(SymIntT))):
+        argname = "arg"
+        lambda_check = emit_expr_has_symbolic_values(argname, BaseCType(SymIntT))
+        return (
+            "std::any_of("
+            f"{expr}.begin(), {expr}.end(), "
+            f"[=](auto& {argname}) {{ return {lambda_check}; }})"
+        )
+
+    raise ValueError(
+        "unsupported type for has_symbolic_values check. "
+        "It should be a SymInt or a collection of those. "
+        f"Got: {type.cpp_type()}"
+    )
+
+
+# Detects whether any of the SymInt arguments are, in fact, symbolic values.
+# This is used in the constructor of ViewMeta.
+def emit_has_symbolic_inputs(sig: DispatcherSignature) -> tuple[str, str]:
+    name = "has_symbolic_inputs"
+    statements = [
+        f"{name} = {name} | ({emit_expr_has_symbolic_values(binding.name, binding.nctype.type)});"
+        for binding in sig.arguments()
+        if (
+            isinstance(binding.argument, Argument)
+            and binding.argument.type.is_symint_like()
+        )
+    ]
+    body = "\n      ".join(statements)
+    return (
+        name,
+        f"""
+      bool {name} = false;
+      {body}""",
+    )
+
+
+# Generates the Functionalization kernel for:
+# - ops that create aliases (e.g. transpose())
+# - ops that are views AND mutations (e.g. transpose_())
+def emit_view_functionalization_body(
+    g: NativeFunctionsViewGroup, *, view_inplace: bool
+) -> str:
+    if view_inplace:
+        # This op is both an inplace op AND a view op.
+        # See Note [Functionalization Pass - Inplace View Ops] for details.
+        # I currently have the view meta call into the out-of-place variant of the view, to avoid
+        # having to define an extra ~20 inplace {view}_inverse_ functions.
+        # Most view ops don't have NativeFunctionGroup's both, because we don't define out= variants for view ops.
+        # I'm assuming that every inplace-view op has a corresponding out-of-place view op,
+        # with the same name but the trailing underscore removed.
+        # This is currently asserted at parse time in gen.py (see error_check_native_functions).
+        assert g.view_inplace is not None
+        f = g.view_inplace
+    else:
+        f = g.view
+
+    assert g.view_copy is not None
+    with native_function_manager(f):
+        call_sig = DispatcherSignature.from_schema(g.view_copy.func)
+
+        # the "view_copy" op name that the functionalization kernels need to call
+        api_name = g.view_copy.func.name.unambiguous_name()
+        # Sometimes the functionalization pass needs to no-op (e.g. if it was passed non-functional tensors)
+        # "no-op"ing in this context is just redispatching to the original op.
+        noop_api_name = f.func.name.unambiguous_name()
+
+        dispatcher_sig = DispatcherSignature.from_schema(f.func)
+        assert_view_op_properties(f.func)
+        view_tensor_name = dispatcher_sig.arguments()[0].name
+
+        return_type = dispatcher_sig.returns_type().remove_const_ref().cpp_type()
+
+        unwrap_tensor_args_str, unwrapped_args_ctx = unwrap_tensor_args(
+            dispatcher_sig, is_view_op=True
+        )
+        view_redispatch_args = [
+            e.expr
+            for e in translate(unwrapped_args_ctx, call_sig.arguments(), method=False)
+        ]
+
+        forward_lambda = FunctionalizationLambda.from_func(g, is_reverse=False)
+        reverse_lambda = FunctionalizationLambda.from_func(g, is_reverse=True)
+
+        # The meta API call should use the same arguments, but convert all tensors to meta tensors first.
+        meta_conversion_str, meta_call_ctx = convert_to_meta_tensors(dispatcher_sig)
+        meta_call_args = [
+            e.expr for e in translate(meta_call_ctx, call_sig.arguments(), method=False)
+        ]
+
+        (
+            symbolic_inputs_varname,
+            symbolic_inputs_check,
+        ) = emit_has_symbolic_inputs(call_sig)
+
+        if "inplace_view" in f.tags:
+            # See Note [Functionalization Pass - Inplace View Ops] for more details
+            return f"""
+    {dispatcher_sig.defn(name=wrapper_name(f.func), is_redispatching_fn=True)} {{
+      if (!at::functionalization::impl::isFunctionalTensor({view_tensor_name})) {{
+        // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper.
+        {unwrap_tensor_args_str}
+        at::AutoDispatchSkipFunctionalize guard;
+        return at::_ops::{noop_api_name}::call({', '.join(view_redispatch_args)});
+      }}
+      auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS();
+      auto inverse_return_mode = (
+          reapply_views ? at::functionalization::InverseReturnMode::ViewOrScatterInverse
+            : at::functionalization::InverseReturnMode::NeverView
+      );
+      {symbolic_inputs_check}
+      at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta(
+        {forward_lambda.decl()} {{
+          if (reapply_views) {{
+            return {forward_lambda.inner_call(reapply_views=True)}
+          }} else {{
+            return {forward_lambda.inner_call(reapply_views=False)}
+          }}
+        }},
+        {reverse_lambda.decl()} {{
+          return {reverse_lambda.inner_call()}
+        }},
+        /*has_symbolic_inputs=*/{symbolic_inputs_varname}
+      );
+      auto compute_reference_meta =
+        {view_tensor_name}.key_set().has_backend(c10::BackendComponent::XLABit) ||
+        {view_tensor_name}.key_set().has_backend(c10::BackendComponent::LazyBit);
+      {return_type} reference_tensor_output;
+      if (compute_reference_meta) {{
+        {meta_conversion_str}
+        at::AutoDispatchSkipFunctionalize func_guard;
+        c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch);
+        reference_tensor_output = at::_ops::{noop_api_name}::call({', '.join(meta_call_args)});
+      }}
+      // This function adds the above view meta to the current tensor and replays them off the base,
+      // mutating the size/stride info of the current FunctionalTensorWrapper.
+      // Because of this, we need to make sure to run the reference shape function above,
+      // BEFORE doing this (otherwise we'll end up runnin the reference function using the wrong sizes/strides)
+      at::functionalization::impl::mutate_view_meta({view_tensor_name}, view_meta);
+      // See  Note [Propagating strides in the functionalization pass]
+      // XLA/LTC don't implement the logic to propagate strides correctly, so we need to rely
+      // on a reference implementation here (instead of relying on the output from the forward lambda
+      // having the correct stride info)
+      if (compute_reference_meta) {{
+        at::functionalization::impl::set_sizes_strides_offset({view_tensor_name}, reference_tensor_output);
+      }}
+      return {view_tensor_name};
+    }}
+"""
+
+        else:
+            is_multi_output_view = isinstance(f.func.returns[0].type, ListType)
+            return f"""
+    {dispatcher_sig.defn(name=wrapper_name(f.func), is_redispatching_fn=True)} {{
+      {unwrap_tensor_args_str}
+      if (!at::functionalization::impl::isFunctionalTensor({view_tensor_name})) {{
+        // functionalization is re-entrant, but will no-op if it wasn't passed a FunctionalTensorWrapper.
+        at::AutoDispatchSkipFunctionalize guard;
+        return at::_ops::{noop_api_name}::call({', '.join(view_redispatch_args)});
+      }}
+      auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS();
+      auto inverse_return_mode = (
+          reapply_views ? at::functionalization::InverseReturnMode::ViewOrScatterInverse
+            : at::functionalization::InverseReturnMode::NeverView
+      );
+      auto compute_reference_meta =
+        {view_tensor_name}.key_set().has_backend(c10::BackendComponent::XLABit) ||
+        {view_tensor_name}.key_set().has_backend(c10::BackendComponent::LazyBit);
+      {return_type} reference_tensor_output;
+      if (compute_reference_meta) {{
+        {meta_conversion_str}
+        at::AutoDispatchSkipFunctionalize func_guard;
+        c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch);
+        reference_tensor_output = at::_ops::{noop_api_name}::call({', '.join(meta_call_args)});
+      }}
+      {return_type} tmp_output;
+      {{
+        at::AutoDispatchSkipFunctionalize guard;
+        if (reapply_views) {{
+          tmp_output = at::_ops::{noop_api_name}::call({', '.join(view_redispatch_args)});
+        }} else {{
+          tmp_output = at::_ops::{api_name}::call({', '.join(view_redispatch_args)});
+        }}
+      }}
+      {symbolic_inputs_check}
+      at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta(
+        {forward_lambda.decl()} {{
+          if (reapply_views) {{
+            return {forward_lambda.inner_call(reapply_views=True)}
+          }} else {{
+            return {forward_lambda.inner_call(reapply_views=False)}
+          }}
+        }},
+        {reverse_lambda.decl()} {{
+          return {reverse_lambda.inner_call()}
+        }},
+        /*has_symbolic_inputs=*/{symbolic_inputs_varname},
+        /*is_multi_output=*/{str(is_multi_output_view).lower()},
+        /*is_as_strided=*/{str(str(f.func.name) == 'as_strided').lower()}
+      );
+      auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, {view_tensor_name}, view_meta);
+      // See  Note [Propagating strides in the functionalization pass]
+      if (compute_reference_meta) {{
+        at::functionalization::impl::set_sizes_strides_offset(out, reference_tensor_output);
+      }}
+      return out;
+    }}
+"""
+
+
+def maybe_create_output(f: NativeFunction, var_name: str) -> str:
+    if len(f.func.returns) == 0:
+        return ""
+    return_type = dispatcher.returns_type(f.func.returns).remove_const_ref().cpp_type()
+    return f"{return_type} {var_name} = "
+
+
+# Given a NativeFunction, and a variable name corresponding to the output of redispatching on the function,
+# this returns two lists of names, consisting of:
+# - the names of returns corresponding to the original (mutable) inputs of the outer function
+# - the names of returns corresponding to the (immutable) outputs of the inner redispatched function
+def get_mutable_redispatch_return_names(
+    f: NativeFunction, inner_return_var: str
+) -> tuple[list[str], list[str]]:
+    aliased_returns = []
+    non_aliased_returns = []
+    for i, name in enumerate(f.func.aliased_return_names()):
+        if name is not None:
+            aliased_returns.append(name)
+        else:
+            non_aliased_returns.append(
+                inner_return_var
+                if len(f.func.returns) == 1
+                else f"std::get<{i}>({inner_return_var})"
+            )
+    return aliased_returns, non_aliased_returns
+
+
+# When functionalization "no-op's" and redispatches on a mutable operator, we need to take care so that:
+#  - For fresh outputs, we return the result of the redispatch (without wrapping outputs)
+#  - For outputs that were aliased to inputs, we return the inputs directly (since some of them might have been wrapped)
+def return_from_mutable_noop_redispatch(
+    f: NativeFunction, inner_return_var: str
+) -> str:
+    aliased, non_aliased = get_mutable_redispatch_return_names(f, inner_return_var)
+    # Just get all of the return names, and immediately return them
+    return return_str(f.func.returns, aliased + non_aliased)
+
+
+def wrap_propagate_mutations_and_return(
+    f: NativeFunction, functional_op: NativeFunction, inner_return_var: str
+) -> str:
+    mutable_arg_names = f.func.arguments.mutable_arg_names()
+    (
+        aliased_outer_rets,
+        non_aliased_outer_rets,
+    ) = get_mutable_redispatch_return_names(f, inner_return_var)
+    _, non_aliased_inner_rets = get_mutable_redispatch_return_names(
+        functional_op, inner_return_var
+    )
+    # The outer function may have a mix of aliased and non-aliased outputs,
+    # But the inner functional op that we're transforming to should only have non-aliased outputs
+    assert len(mutable_arg_names) + len(non_aliased_outer_rets) == len(
+        non_aliased_inner_rets
+    )
+
+    # First, take all of the newly created outputs from the inner call and wrap them into functional tensors
+    updates = []
+    non_aliased_wrapped_ret_names = []
+    for i, inner_ret in enumerate(
+        non_aliased_inner_rets[: len(non_aliased_outer_rets)]
+    ):
+        ret_name = f"output_{i}"
+        updates.append(
+            f"""\
+  auto output_{i} = at::functionalization::impl::to_functional_tensor({inner_ret});"""
+        )
+        non_aliased_wrapped_ret_names.append(ret_name)
+
+    # Next, take all of the mutated outputs from the inner call corresponding to mutated inputs,
+    # and propagate the mutations
+    for outer_arg, inner_ret in zip(
+        mutable_arg_names, non_aliased_inner_rets[len(non_aliased_outer_rets) :]
+    ):
+        updates.append(
+            f"""\
+  auto {outer_arg}_inner = at::functionalization::impl::from_functional_tensor({outer_arg});
+  at::functionalization::impl::replace_({outer_arg}, {inner_ret});
+  at::functionalization::impl::commit_update({outer_arg});
+  at::functionalization::impl::sync({outer_arg});
+  auto {outer_arg}_inner_updated = at::functionalization::impl::from_functional_tensor({outer_arg});
+  at::functionalization::impl::propagate_xla_data_direct({outer_arg}_inner, {outer_arg}_inner_updated);"""
+        )
+
+    # Finally, we return:
+    # - Any mutable arguments that also returns
+    # - Any immutable returns that were created wrapping the output from the inner call
+    returns_str = return_str(
+        f.func.returns, aliased_outer_rets + non_aliased_wrapped_ret_names
+    )
+    updates_str = "\n".join(updates)
+    return f"""\
+{updates_str}
+    {returns_str}"""
+
+
+# Generates the Functionalization kernel for:
+# - mutation ops (inplace and out= ops)
+@with_native_function_and
+def emit_inplace_functionalization_body(
+    f: NativeFunction, g: NativeFunctionsGroup
+) -> str:
+    # mutation case
+    assert modifies_arguments(f)
+
+    dispatcher_sig = DispatcherSignature.from_schema(f.func)
+
+    unwrap_tensor_args_str, unwrapped_args_ctx = unwrap_tensor_args(
+        dispatcher_sig, is_view_op=False
+    )
+
+    mutated_names = [
+        a.name
+        for a in f.func.arguments.flat_all
+        if a.type.is_tensor_like() and a.annotation is not None
+    ]
+    non_mutated_names = [
+        a.name
+        for a in f.func.arguments.flat_all
+        if a.type.is_tensor_like() and a.annotation is None
+    ]
+    non_mutated_tensor_names = [
+        a.name
+        for a in f.func.arguments.flat_all
+        if a.type == BaseType(BaseTy.Tensor) and a.annotation is None
+    ]
+    # all mutable inputs must be functional tensors in order to participate in functionalization
+    check_all_mutated_args_are_functional = " && ".join(
+        ["true"]
+        + [
+            f"at::functionalization::impl::isFunctionalTensor({a})"
+            for a in mutated_names
+        ]
+    )
+    check_any_non_mutated_args_are_functional = " || ".join(
+        ["false"]
+        + [
+            f"at::functionalization::impl::isFunctionalTensor({a})"
+            for a in non_mutated_names
+        ]
+    )
+
+    check_any_non_mutated_tensors_are_xla = " || ".join(
+        ["false"]
+        + [
+            f"{a}.device().type() == c10::DeviceType::XLA"
+            for a in non_mutated_tensor_names
+        ]
+    )
+    # These are used in the cases where we don't functionalize and redispatch to the inplace op
+    # case 1: we hit an inplace op that doesn't have an out-of-place equivalent
+    # case 2: we hit an inplace ops but our inputs are not functional tensors (in which case our kernel just no-ops)
+    inplace_exprs = [
+        e.expr
+        for e in translate(unwrapped_args_ctx, dispatcher_sig.arguments(), method=False)
+    ]
+
+    # call the out-of-place variant of the op
+    return_type = (
+        dispatcher.returns_type(g.functional.func.returns).remove_const_ref().cpp_type()
+    )
+    functional_sig = DispatcherSignature.from_schema(g.functional.func)
+    functional_exprs = [
+        e.expr
+        for e in translate(unwrapped_args_ctx, functional_sig.arguments(), method=False)
+    ]
+
+    if f.func.is_out_fn():
+        mutable_input_post_processing = "\n".join(
+            [
+                f"""
+      at::functionalization::impl::replace_(
+        {a.name}, {'std::get<' + str(i) + '>(tmp_output)' if len(f.func.returns) > 1 else 'tmp_output'});
+      at::functionalization::impl::commit_update({a.name});"""
+                for (i, a) in enumerate(f.func.arguments.out)
+                if a.annotation and a.annotation.is_write and a.type.is_tensor_like()
+            ]
+        )
+    else:
+        mutable_input_post_processing = "\n".join(  # noqa: F841
+            [
+                f"""
+      at::functionalization::impl::replace_({a.name}, tmp_output);
+      at::functionalization::impl::commit_update({a.name});"""
+                for a in f.func.arguments.flat_all
+                if a.annotation and a.annotation.is_write and a.type.is_tensor_like()
+            ]
+        )
+
+    meta_conversion_str, meta_call_ctx = convert_to_meta_tensors(dispatcher_sig)
+    # We don't want to run the inplace meta func for ops like .set_(), because:
+    # (1) they're unnecessary: inplace meta checks are only useful for ops like add_(),
+    #     where broadcasting will work for the out-of-place case but should fail on the inplace call
+    # (2) They'll also fail without adding extra infra: we'd need to convert the input storage argument
+    #     into a meta storage
+    any_storage_args = any(
+        a.type == BaseType(BaseTy.Storage) for a in f.func.arguments.flat_all
+    )
+
+    return f"""
+    {dispatcher_sig.defn(name=wrapper_name(f.func), is_redispatching_fn=True)} {{
+      if ({str(not any_storage_args and f.func.kind() == SchemaKind.inplace).lower()}) {{
+        // Before converting the mutable op to its functional variant, run meta tensors through the original op.
+        // This will help us catch shape errors that apply to inplace ops that wouldn't apply to their functional variants.
+        // (We can only do this for inplace ops today though, because they technically all support meta tensors).
+        {meta_conversion_str}
+        at::AutoDispatchSkipFunctionalize func_guard;
+        c10::impl::ExcludeDispatchKeyGuard guard(exclude_keys_for_meta_dispatch);
+        at::_ops::{f.func.name.unambiguous_name()}::call({', '.join(a.name for a in meta_call_ctx)});
+      }}
+      {unwrap_tensor_args_str}
+      if (!({check_all_mutated_args_are_functional})) {{
+        // We want to disable this check if there are any XLA tensors.
+        // cpu_tensor.copy_(xla_tensor) is valid code.
+        if (!({check_any_non_mutated_tensors_are_xla}) && ({check_any_non_mutated_args_are_functional})) {{
+         // case 1: trying to mutate a non functional tensor with a functional tensor is an error
+         TORCH_INTERNAL_ASSERT(false,
+           "mutating a non-functional tensor with a functional tensor is not allowed.",
+           " Please ensure that all of your inputs are wrapped inside of a functionalize() call.");
+        }} else {{
+         // case 2: arguments are not functional tensors, so we no-op and redispatch.
+         at::AutoDispatchSkipFunctionalize guard;
+         {maybe_create_output(f, 'tmp_output')}at::_ops::{f.func.name.unambiguous_name()}::call({', '.join(inplace_exprs)});
+         {return_from_mutable_noop_redispatch(f, 'tmp_output')}
+        }}
+      }} else {{
+        {return_type} tmp_output;
+        {{
+          at::AutoDispatchSkipFunctionalize guard;
+          tmp_output = at::_ops::{g.functional.func.name.unambiguous_name()}::call({', '.join(functional_exprs)});
+        }}
+        {wrap_propagate_mutations_and_return(f, g.functional, 'tmp_output')}
+      }}
+    }}"""
+
+
+# The below functions generate RegisterFunctionalization.cpp
+# These files provide the kernels that run the functionalization pass, which can be opted into
+# per backend (e.g. XLA or Vulkan), or as a composable transform (functionalize() in functorch).
+
+
+# See Note [Functionalization Pass: View Inverses].
+def gen_functionalization_view_inverse_declaration(
+    selector: SelectiveBuilder, g: NativeFunctionsViewGroup
+) -> str | None:
+    # For every (non-composite) view op, we need a corresponding "inverse view" function.
+    # This generates the declarations so we get a good compiler error when someone adds a new view.
+    @with_native_function
+    def emit_decl_helper(g: NativeFunctionsViewGroup) -> str | None:
+        if g.view.has_composite_implicit_autograd_kernel:
+            return None
+        view_inverse_sig = ViewInverseSignature(g)
+        return view_inverse_sig.decl()
+
+    return emit_decl_helper(g)
+
+
+def gen_functionalization_registration(
+    selector: SelectiveBuilder,
+    g: NativeFunction | NativeFunctionsGroup | NativeFunctionsViewGroup,
+    composite_implicit_autograd_index: BackendIndex,
+) -> list[str]:
+    @with_native_function
+    def emit_registration_helper(f: NativeFunction) -> str:
+        assert not f.has_composite_implicit_autograd_kernel
+        registration_str = f"TORCH_FN(functionalization::{wrapper_name(f.func)})"
+        return f'm.impl("{f.func.name}", {registration_str});'
+
+    # Don't generate kernels in mobile build
+    if not selector.include_all_operators:
+        return []
+
+    if isinstance(g, NativeFunctionsViewGroup):
+        # functionalization needs to register kernels for view + view_inplace ops
+        # See Note [Functionalization <> torch.Tensor constructor]
+        if str(g.view.func.name) == "lift_fresh":
+            return []
+        view_str = []
+        if not g.view.has_composite_implicit_autograd_kernel:
+            view_str.append(emit_registration_helper(g.view))
+        if (
+            g.view_inplace is not None
+            and not g.view_inplace.has_composite_implicit_autograd_kernel
+        ):
+            assert g.view_inplace.is_view_op
+            view_str.append(emit_registration_helper(g.view_inplace))
+        return view_str
+
+    elif isinstance(g, NativeFunctionsGroup):
+        # Gets a hand-written functionalization kernel
+        if g.inplace is not None and str(g.inplace.func.name) == "set_.source_Tensor":
+            fns = []
+        else:
+            fns = list(g.functions())
+    else:
+        if str(g.func.name) in MUTABLE_OPS_NOT_USING_FUNCTIONALIZATION:
+            return []
+        fns = [g]
+
+    registrations = []
+    for f in fns:
+        if f.has_composite_implicit_autograd_kernel:
+            continue
+        if str(f.func.name) == "lift":
+            # See Note [Functionalization <> torch.Tensor constructor]
+            return []
+        if str(f.func.name) == "resize_":
+            # See Note [resize_ in Functionalization]
+            return []
+        if str(f.func.name.name) != "set_":
+            assert not f.is_view_op
+        # functionalization needs to generate and register kernels for inplace ops.
+        # We *also* need to directly register CompositeImplicitAUtograd kernels
+        # so that they decompose properly before functioanlization.
+        if modifies_arguments(f):
+            registrations.append(emit_registration_helper(f))
+    return registrations
+
+
+def gen_functionalization_definition(
+    selector: SelectiveBuilder,
+    # Note: Ideally this code should never have to look at NativeFunction
+    # (and instead only need to operate on grouped NativeFunctions).
+    # The only reason currently is because we need to emit direct dispatch registrations
+    # For CompositeImplicitAutograd operators, which are potentially ungrouped.
+    g: NativeFunction | NativeFunctionsGroup | NativeFunctionsViewGroup,
+) -> list[str]:
+    # Don't generate kernels in mobile build
+    if not selector.include_all_operators:
+        return []
+
+    if isinstance(g, NativeFunctionsViewGroup):
+        # Case 1: emit view -> view_copy kernels for the functionalization pass
+        view_defs = []
+        if not g.composite:
+            # invariant: NativeFunctionsViewGroup's always have a view_copy operator
+            # if the view is not composite (implicit autograd)
+            assert g.view_copy is not None, dataclass_repr(g, indent=1)
+            view_defs.append(emit_view_functionalization_body(g, view_inplace=False))
+            if g.view_inplace is not None:
+                view_defs.append(emit_view_functionalization_body(g, view_inplace=True))
+        return view_defs
+    elif isinstance(g, NativeFunction):
+        # Invariant: all mutable operators that we need to handle in functionalization
+        # should have been properly grouped up.
+        # TODO: The below ops all have "problematic" schemas that prevent them from
+        # getting functionalized. Instead of bending over backwards to get things to work,
+        # I think we should either:
+        # (1) fix their schemas (BC-breaking)
+        # (2) hand-write their functionalization kernels
+        if (
+            str(g.func.name) not in MUTABLE_OPS_NOT_USING_FUNCTIONALIZATION
+            and str(g.func.name.name) not in MUTABLE_OPS_NOT_USING_FUNCTIONALIZATION
+        ):
+            assert g.has_composite_implicit_autograd_kernel or not modifies_arguments(g)
+        return []
+    else:
+        # Case 2: emit inplace -> out-of-place kernels for the functionalization pass
+        mutation_defs = []
+        mutation_defs.append(emit_inplace_functionalization_body(g.out, g))
+        if g.inplace is not None:
+            mutation_defs.append(emit_inplace_functionalization_body(g.inplace, g))
+        if g.mutable is not None:
+            mutation_defs.append(emit_inplace_functionalization_body(g.mutable, g))
+        return mutation_defs
+    return []
diff --git a/lib/python3.11/site-packages/torchgen/gen_lazy_tensor.py b/lib/python3.11/site-packages/torchgen/gen_lazy_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..a15fa62fd1ee4e7bf0d4499e222deb6bb18c7b99
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_lazy_tensor.py
@@ -0,0 +1,585 @@
+from __future__ import annotations
+
+import argparse
+import os
+from collections import namedtuple
+from pathlib import Path
+from typing import Any, Callable, TYPE_CHECKING
+
+import yaml
+
+import torchgen.dest as dest
+from torchgen.api.lazy import setValueT
+from torchgen.api.types import BaseCppType
+from torchgen.dest.lazy_ir import GenLazyIR, GenLazyNativeFuncDefinition, GenTSLazyIR
+from torchgen.gen import get_grouped_native_functions, parse_native_yaml
+from torchgen.gen_backend_stubs import (
+    error_on_missing_kernels,
+    gen_dispatcher_registrations,
+    gen_dispatchkey_nativefunc_headers,
+    parse_backend_yaml,
+)
+from torchgen.model import NativeFunction, NativeFunctionsGroup, OperatorName
+from torchgen.selective_build.selector import SelectiveBuilder
+from torchgen.utils import FileManager, NamespaceHelper
+from torchgen.yaml_utils import YamlLoader
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Iterator, Sequence
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                        Lazy Tensor Codegen
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+# Overview
+# ~~~~~~~~
+#
+# This codegen script builds on existing data models and helpers used
+# by all ATen backends, and adds new functionality specific to lazy
+# tensor backends.
+#
+# Inputs:
+# - <backend>_native_functions.yaml: controls which operators are
+#   supported by the backend.
+#
+# Outputs:
+# (for all backends)
+# <DispatchKey>Ir.h defines Lazy IR classes to be constructed during tracing
+# - opt-in: also generate 'lowering' methods for the TorchScript backend only
+# <DispatchKey>NativeFunctions.cpp defines implementations of native functions which perform lazy tracing
+# - opt-in: 'full_codegen' section of backend yaml; 'supported' section omits these implementations
+# <DispatchKey>NativeFunctions.h declares implementations of native functions for both 'supported' and 'full_codegen'
+# ops
+#
+# Register<DispatchKey>.cpp registers all op implementations with the dispatcher
+# RegisterAutograd<DispatchKey>.cpp registers all autograd implementations with the dispatcher
+#
+# Validation Helpers:
+# - Shape Inference: errs if any ops in backend yaml require shape inference not provided by meta kernels or
+#   implementations in torch/csrc/lazy/core/shape_inference.*
+# - native function impls: errs if any 'supported' ops do not have an implementation defined in the backend
+#   (non-codegen) implementation file
+#
+#
+# About the Data Model
+# ~~~~~~~~~~~~~~~~~~~~
+#
+# Modeled after ATen codegen, the first step is to parse yaml and build a data model for the operators
+# we care about.  In this case, the <backend>_native_functions yaml defines a subset of the core operators
+# (defined in more detail in the main native_functions.yaml), which will be supported by your backend.
+# Backends can list ops in two categories:
+#  - `supported` ops require hand-implementations but still get codegenned declarations and registrations
+#  - `full_codegen` ops get implementations (and IR classes) generated too
+#
+# Each native function is modeled as an object with a schema, and each schema has objects representing their
+# arguments.  Much of the codegen is manipulation of the arguments and their types.  For example, lazy tensor
+# backends need to transform 'at::Tensor' arguments into 'lazy::Value' objects, as well as replacing reference
+# types (stringref) with actual string objects, and this is done by manipulating the data model objects.
+# - see api/lazy.py for the lazy data model
+#
+# Once the data model is set up, the rest of this script processes a number of templates for output CPP file
+# and fills in the template values using helpers in `dest/lazy_ir.py` and `dest/lazy_ts_lowering.py`.  These
+# helpers mostly iterate over functions and their arguments, outputting different c++ snippets.
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+# Parses the external backend's yaml, and adds a new BackendIndex for the backend's dispatch key.
+# Returns a Tuple of (backend_key, autograd_key, cpp_namespace, updated BackendIndex mapping, full_codegen)
+ParsedExternalYaml = namedtuple(
+    "ParsedExternalYaml",
+    ["backend_key", "autograd_key", "cpp_namespace", "backend_indices", "full_codegen"],
+)
+
+
+def parse_native_functions_keys(
+    backend_yaml_path: str,
+    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
+) -> tuple[list[OperatorName], list[Any], list[OperatorName]]:
+    with open(backend_yaml_path) as f:
+        yaml_values = yaml.load(f, Loader=YamlLoader)
+    assert isinstance(yaml_values, dict)
+
+    full_codegen = yaml_values.pop("full_codegen", [])
+    non_native = yaml_values.pop("non_native", [])
+    ir_gen = yaml_values.pop("ir_gen", [])
+    assert isinstance(full_codegen, list)
+    assert isinstance(non_native, list)
+    assert isinstance(ir_gen, list)
+    full_codegen_opnames = [OperatorName.parse(name) for name in full_codegen]
+    ir_gen_opnames = [OperatorName.parse(name) for name in ir_gen]
+    return full_codegen_opnames, non_native, ir_gen_opnames
+
+
+def validate_shape_inference_header(
+    shape_inference_hdr: str, expected_shape_infr_decls: list[str]
+) -> None:
+    try:
+        with open(shape_inference_hdr) as f:
+            shape_infr_decls = f.read()
+            shape_infr_decl_lines = set(shape_infr_decls.split("\n"))
+    except OSError as e:
+        raise AssertionError(
+            f"Unable to read from the specified shape_inference_hdr file: {shape_inference_hdr}"
+        ) from e
+
+    # TODO(whc) add a check for shape inference functions that have meta kernels implement and should be retired.
+
+    missing_decls = [
+        decl for decl in expected_shape_infr_decls if decl not in shape_infr_decl_lines
+    ]
+    if missing_decls:
+        raise Exception(  # noqa: TRY002
+            f"""Missing shape inference function.\n
+Please add declare this function in {shape_inference_hdr}:\n
+and implement it in the corresponding shape_inference.cpp file.\n
+{os.linesep.join(missing_decls)}"""
+        )
+
+
+# Some helper functions for the codegen.
+def get_ltc_helper_fns() -> str:
+    return """\
+at::Tensor to_meta(const at::Tensor& tensor) {
+  // undefined tensors can't be converted to the meta device, since they don't have sizes/strides
+  if (!tensor.defined()) return tensor;
+  auto out = at::native::empty_strided_meta_symint(tensor.sym_sizes(), tensor.sym_strides(), \
+/*dtype=*/tensor.scalar_type(), /*layout=*/tensor.layout(), \
+/*device=*/c10::Device(c10::kMeta), /*pin_memory=*/std::nullopt);
+  // needs to handle wrapped numbers, so dtype promotion works properly.
+  if (tensor.unsafeGetTensorImpl()->is_wrapped_number()) {
+    out.unsafeGetTensorImpl()->set_wrapped_number(true);
+  }
+  return out;
+}
+std::optional<at::Tensor> to_meta(const std::optional<at::Tensor>& tensor) {
+  if (tensor.has_value()) {
+    return to_meta(*tensor);
+  }
+  return std::nullopt;
+}
+
+std::vector<at::Tensor> to_meta(at::ITensorListRef t_list) {
+  std::vector<at::Tensor> outs;
+  outs.reserve(t_list.size());
+  for (const auto& tensor : t_list) {
+    outs.push_back(to_meta(tensor));
+  }
+  return outs;
+}
+"""
+
+
+class default_args:
+    node_base: str = "Node"
+    node_base_hdr: str | None = None
+    shape_inference_hdr: str = "torch/csrc/lazy/core/shape_inference.h"
+    tensor_class: str = "torch::lazy::LazyTensor"
+    tensor_class_hdr: str = "torch/csrc/lazy/core/tensor.h"
+    lazy_ir_generator: type[GenLazyIR] = GenLazyIR
+    native_func_definition_generator: type[GenLazyNativeFuncDefinition] = (
+        GenLazyNativeFuncDefinition
+    )
+    backend_name: str = "TorchScript"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate Lazy Tensor backend files")
+    parser.add_argument(
+        "-s",
+        "--source-yaml",
+        "--source_yaml",
+        help="path to source yaml file containing operator external definitions",
+    )
+    parser.add_argument("-o", "--output-dir", "--output_dir", help="output directory")
+    parser.add_argument(
+        "--dry-run", "--dry_run", type=bool, default=False, help="output directory"
+    )
+    parser.add_argument(
+        "--impl-path",
+        "--impl_path",
+        type=str,
+        default=None,
+        help="path to the source C++ file containing kernel definitions",
+    )
+    parser.add_argument(
+        "--gen-ts-lowerings",
+        "--gen_ts_lowerings",
+        action="store_true",
+        help="Generate TorchScript lowerings in addition to Lazy IR and NativeFunctions",
+    )
+    parser.add_argument(
+        "--node-base",
+        "--node_base",
+        type=str,
+        default=default_args.node_base,
+        help="Name of backend specific custom Lazy IR Node base class",
+    )
+    parser.add_argument(
+        "--node-base-hdr",
+        "--node_base_hdr",
+        type=str,
+        default=default_args.node_base_hdr,
+        help="Path to header file defining custom Lazy IR Node base class",
+    )
+    parser.add_argument(
+        "--shape-inference-hdr",
+        "--shape_inference_hdr",
+        type=str,
+        default=default_args.shape_inference_hdr,
+        help="Path to header file defining custom Lazy shape inference functions",
+    )
+    parser.add_argument(
+        "--tensor-class",
+        "--tensor_class",
+        type=str,
+        default=default_args.tensor_class,
+        help="Name of backend specific custom Lazy Tensor class",
+    )
+    parser.add_argument(
+        "--tensor-class-hdr",
+        "--tensor_class_hdr",
+        type=str,
+        default=default_args.tensor_class_hdr,
+        help="Path to header file defining custom Lazy Tensor class",
+    )
+    parser.add_argument(
+        "--backend-name",
+        "--backend_name",
+        type=str,
+        default=default_args.backend_name,
+        help="Name of the backend to generate",
+    )
+    options = parser.parse_args()
+
+    # Assumes that this file lives at PYTORCH_ROOT/torchgen/gen_backend_stubs.py
+    torch_root = Path(__file__).parent.parent.parent.absolute()
+    aten_path = str(torch_root / "aten" / "src" / "ATen")
+    lazy_ir_generator: type[GenLazyIR] = default_args.lazy_ir_generator
+    if options.gen_ts_lowerings:
+        lazy_ir_generator = GenTSLazyIR
+    native_func_definition_generator: type[GenLazyNativeFuncDefinition] = (
+        default_args.native_func_definition_generator
+    )
+
+    run_gen_lazy_tensor(
+        aten_path,
+        options.source_yaml,
+        options.output_dir,
+        options.dry_run,
+        options.impl_path,
+        options.node_base,
+        options.node_base_hdr,
+        options.tensor_class,
+        options.tensor_class_hdr,
+        options.shape_inference_hdr,
+        lazy_ir_generator,
+        native_func_definition_generator,
+        options.backend_name,
+    )
+
+
+def run_gen_lazy_tensor(
+    aten_path: str,
+    source_yaml: str,
+    output_dir: str,
+    dry_run: bool,
+    impl_path: str | None,
+    node_base: str = default_args.node_base,
+    node_base_hdr: str | None = default_args.node_base_hdr,
+    tensor_class: str = default_args.tensor_class,
+    tensor_class_hdr: str = default_args.tensor_class_hdr,
+    shape_inference_hdr: str = default_args.shape_inference_hdr,
+    lazy_ir_generator: type[GenLazyIR] = default_args.lazy_ir_generator,
+    native_func_definition_generator: type[
+        GenLazyNativeFuncDefinition
+    ] = default_args.native_func_definition_generator,
+    # build_in_tree is true for TS backend and affects include paths
+    build_in_tree: bool = False,
+    # per_operator_headers changes whether ATen/Functions.h or individual operator headers are used
+    # it must match how ATen was built
+    per_operator_headers: bool = False,
+    backend_name: str = default_args.backend_name,
+    gen_forced_fallback_code: bool = False,
+    use_lazy_shape: bool = True,
+    # the following arguments are temporary customization points for xla backend migration.
+    # do not rely on them otherwise, they should be removed once migration is complete
+    backend_namespace: str = "torch::lazy",
+    get_tensorlist: str = "GetTensorList",
+    get_tensor_or_wrap_number: str = "GetLtcTensorOrCreateForWrappedNumber",
+    try_get_tensor: str = "TryGetLtcTensor",
+    metrics_counter: str = 'TORCH_LAZY_FN_COUNTER("lazy::")',
+    create_tensor: str = "LazyTensor::Create",
+    create_from_first_tensor: bool = False,
+    create_aten_from_ltc_tensor: str = "torch::lazy::CreateAtenFromLtcTensor",
+    tuple_aten_from_ltc_tensors: str = "torch::lazy::TupleAtenFromLtcTensors",
+    lazy_value_class: str = "torch::lazy::Value",
+    lazy_tensor_ptr: str = "LazyTensorPtr",
+    get_device_fn: str = "torch::lazy::GetBackendDevice",
+) -> None:
+    lv_tokens = lazy_value_class.split("::")
+    lv_class = lv_tokens[-1]
+    lv_ns = "::".join(lv_tokens[:-1])
+    setValueT(BaseCppType(lv_ns, lv_class))
+    template_dir = os.path.join(aten_path, "templates")
+
+    def make_file_manager(install_dir: str) -> FileManager:
+        return FileManager(
+            install_dir=install_dir, template_dir=template_dir, dry_run=dry_run
+        )
+
+    fm = make_file_manager(output_dir)
+
+    native_yaml_path = os.path.join(aten_path, "native/native_functions.yaml")
+    tags_yaml_path = os.path.join(aten_path, "native/tags.yaml")
+    parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path)
+    native_functions, backend_indices = (
+        parsed_yaml.native_functions,
+        parsed_yaml.backend_indices,
+    )
+    grouped_native_functions = get_grouped_native_functions(native_functions)
+
+    def sort_native_function(f: NativeFunctionsGroup | NativeFunction) -> str:
+        """
+        We sort the native function because of the note in concat_map_codegen.
+        TODO(alanwaketan): Remove this sorting hack once all ops are grouped properly.
+        """
+        func = f.functional.func if isinstance(f, NativeFunctionsGroup) else f.func
+        return str(func.name.name)
+
+    grouped_native_functions = sorted(
+        grouped_native_functions, key=sort_native_function
+    )
+
+    parsed_backend_yaml = parse_backend_yaml(
+        source_yaml, grouped_native_functions, backend_indices
+    )
+    backend_key = parsed_backend_yaml.backend_key
+    autograd_key = parsed_backend_yaml.autograd_key
+    cpp_namespace = parsed_backend_yaml.cpp_namespace
+    backend_indices = parsed_backend_yaml.backend_indices
+    # the following 3 keys are all processed differently
+    # for full_codegen, we generate IR, kernels, etc
+    # for ir_gen, we generate only IR
+    # non_native is used to register kernels not declared in
+    # native_functions.yaml
+    full_codegen, non_native, ir_gen = parse_native_functions_keys(
+        source_yaml, grouped_native_functions
+    )
+
+    def concat_map_codegen(
+        func: Callable[[NativeFunction], Sequence[str]],
+        xs: Iterable[NativeFunctionsGroup | NativeFunction],
+        ops_list: list[OperatorName] = full_codegen,
+    ) -> Iterator[str]:
+        """
+        We code-gen for the functional variant, which is all we need for IR classes/lowerings/shape inferences, but we
+        only code-gen additional entries for the inplace variant for the native functions.
+        """
+
+        for x in xs:
+            fs = list(x.functions()) if isinstance(x, NativeFunctionsGroup) else [x]
+            for f in fs:
+                if f.func.name in ops_list:
+                    yield from func(f)
+
+    selector = SelectiveBuilder.get_nop_selector()
+
+    assert backend_key is not None
+    class_name = backend_indices[backend_key].native_function_class_name()
+
+    if impl_path is not None:
+        error_on_missing_kernels(
+            native_functions,
+            backend_indices,
+            backend_key,
+            autograd_key,
+            class_name,
+            impl_path,
+            full_codegen,
+        )
+
+    """ Validate Shape Inference Definitions
+
+    Generated lazy native functions all perform shape inference, by first using a meta:: kernel
+    if available for that op, and otherwise using a 'compute_shape_{op}' function instead.  The generator
+    knows the call signature for compute_shape_{op} because it matches the nativefunction (and meta::) signature,
+    so it just has to check whether the op is structured and generate a call for one or the other.  It's up to the dev
+    to supply the missing compute_shape_{op} function, but the codegen at least warns you about this and provides
+    the expected signature which can be copy-pasted into shape_inference.h.
+
+    compute_shape_{op} functions are handwritten and should be replaced over time as ops get ported
+    to structured kernels.
+
+    See torch/csrc/lazy/core/shape_inference.cpp #READ THIS! for more information.
+    """
+    if shape_inference_hdr is not None:
+        expected_shape_infr_decls = list(
+            concat_map_codegen(
+                dest.GenLazyShapeInferenceDefinition(
+                    backend_indices[backend_key], tensor_class
+                ),
+                grouped_native_functions,
+            )
+        )
+
+        validate_shape_inference_header(shape_inference_hdr, expected_shape_infr_decls)
+    assert class_name is not None
+
+    # Generate nativefunction declarations
+    # Note, eager registrations is set to False for the lazy TS backend as another LTC backend
+    # may want to register their own lazy kernels instead of registering the TS ones.
+    # The registration will lazily happen when init_ts_backend is called.
+    gen_dispatchkey_nativefunc_headers(
+        fm,
+        class_name,
+        cpp_namespace,
+        backend_indices,
+        grouped_native_functions,
+        backend_key,
+        autograd_key,
+        backend_name,
+    )
+
+    # Generate Dispatcher registrations which hook up the nativefunctions
+    for dispatch_key in (
+        [backend_key] if autograd_key is None else [backend_key, autograd_key]
+    ):
+        gen_dispatcher_registrations(
+            fm,
+            output_dir,
+            class_name,
+            backend_indices,
+            grouped_native_functions,
+            backend_key,
+            dispatch_key,
+            selector,
+            build_in_tree=build_in_tree,
+            per_operator_headers=per_operator_headers,
+            backend_name=backend_name,
+            eager_registration=False,
+        )
+
+    # Generate native function impls that build IR nodes
+    ns_helper = NamespaceHelper(cpp_namespace)
+    fm.write_with_template(
+        f"{backend_key}NativeFunctions.cpp",
+        "DispatchKeyNativeFunctions.cpp",
+        lambda: {
+            "includes": [
+                f"#include <{path}>"
+                for path in [
+                    tensor_class_hdr,
+                    shape_inference_hdr,
+                    "ATen/Functions.h",
+                    "ATen/native/TensorConversions.h",
+                    "ATen/NativeFunctions.h",
+                    "ATen/CompositeExplicitAutogradNonFunctionalFunctions.h",
+                    "ATen/MetaFunctions.h",
+                    "ATen/Operators.h",
+                    "ATen/native/CPUFallback.h",
+                    "torch/csrc/lazy/core/ir_builder.h",
+                    "torch/csrc/lazy/core/lazy_graph_executor.h",
+                    "torch/csrc/lazy/core/metrics.h",
+                    "torch/csrc/lazy/core/shape.h",
+                    f"{output_dir}/{backend_key}NativeFunctions.h",
+                    f"{output_dir}/LazyIr.h",
+                ]
+                + (
+                    ["torch/csrc/lazy/ts_backend/ts_eager_fallback.h"]
+                    if gen_forced_fallback_code
+                    else []
+                )
+            ],
+            "helper_fns": get_ltc_helper_fns(),
+            "native_functions_include": "",
+            "namespace_prologue": ns_helper.prologue,
+            "namespace_epilogue": ns_helper.epilogue,
+            "native_function_definitions": list(
+                concat_map_codegen(
+                    native_func_definition_generator(
+                        f"{backend_key}NativeFunctions",
+                        backend_indices[backend_key],
+                        tensor_class,
+                        gen_forced_fallback_code,
+                        backend_namespace,
+                        get_tensorlist,
+                        get_tensor_or_wrap_number,
+                        try_get_tensor,
+                        metrics_counter,
+                        create_tensor,
+                        create_from_first_tensor,
+                        create_aten_from_ltc_tensor,
+                        tuple_aten_from_ltc_tensors,
+                        lazy_tensor_ptr,
+                        get_device_fn,
+                    ),
+                    grouped_native_functions,
+                )
+            ),
+        },
+    )
+    # Generate IR node classes
+    lazy_ir_obj = lazy_ir_generator(
+        backend_indices[backend_key], backend_name, node_base, use_lazy_shape
+    )
+
+    fm.write_with_template(
+        "LazyIr.h",
+        "LazyIr.h",
+        lambda: {
+            "lazy_ir_sysinc": [
+                f"#include <{path}>"
+                for path in [
+                    "ATen/core/Formatting.h",
+                    "c10/core/ScalarType.h",
+                    "torch/csrc/lazy/core/hash.h",
+                    "torch/csrc/lazy/core/ir.h",
+                    "torch/csrc/lazy/core/shape.h",
+                    "optional",
+                    "vector",
+                ]
+            ],
+            "lazy_ir_inc": [f'#include "{node_base_hdr}"']
+            if node_base_hdr is not None
+            else [],
+            "ir_declarations": list(
+                concat_map_codegen(
+                    lazy_ir_obj, grouped_native_functions, full_codegen + ir_gen
+                )
+            ),
+            "namespace_prologue": ns_helper.prologue,
+            "namespace_epilogue": ns_helper.epilogue,
+        },
+    )
+
+    # Generate Non Native IR Node classes
+    fm.write_with_template(
+        "LazyNonNativeIr.h",
+        "LazyNonNativeIr.h",
+        lambda: {
+            "lazy_non_native_ir_inc": [
+                f"#include <{path}>"
+                for path in [
+                    "torch/csrc/lazy/core/ir.h",
+                    "torch/csrc/lazy/core/ir_builder.h",
+                    "torch/csrc/lazy/core/internal_ops/ltc_ops.h",
+                    "torch/csrc/lazy/core/shape_inference.h",
+                ]
+                + ([node_base_hdr] if node_base_hdr else [])
+                if path
+            ],
+            "non_native_ir_nodes": dest.generate_non_native_lazy_ir_nodes(
+                non_native, lazy_ir_obj
+            ),
+            "namespace_prologue": ns_helper.prologue,
+            "namespace_epilogue": ns_helper.epilogue,
+        },
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/lib/python3.11/site-packages/torchgen/gen_schema_utils.py b/lib/python3.11/site-packages/torchgen/gen_schema_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1095d2e7e4313b381044f8734000854415b0e574
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_schema_utils.py
@@ -0,0 +1,97 @@
+from typing import Any, Optional, Union
+
+from torchgen.model import (
+    Annotation,
+    Argument,
+    Arguments,
+    BaseOperatorName,
+    BaseTy,
+    BaseType,
+    CustomClassType,
+    FunctionSchema,
+    ListType,
+    OperatorName,
+    Return,
+)
+
+
+# Note: These aren't actually used in torchgen, they're some utilities for generating a schema
+# from real arguments. For example, this is used to generate HigherOrderOperators' schema since
+# their schemas can vary for different instances of the same HOP.
+
+
+class TypeGen:
+    convert_to_base_ty = {
+        int: BaseTy.int,
+        float: BaseTy.float,
+        str: BaseTy.str,
+        bool: BaseTy.bool,
+    }
+
+    @staticmethod
+    def from_example(obj: Any) -> Union[BaseType, ListType, CustomClassType]:
+        import torch
+
+        if isinstance(obj, torch.fx.GraphModule):
+            return BaseType(BaseTy.GraphModule)
+        elif isinstance(obj, torch.Tensor):
+            return BaseType(BaseTy.Tensor)
+        elif isinstance(obj, torch.SymInt):
+            return BaseType(BaseTy.SymInt)
+        elif isinstance(obj, torch.SymBool):
+            return BaseType(BaseTy.SymBool)
+        elif isinstance(obj, torch.ScriptObject):
+            return CustomClassType(obj._type().name())  # type: ignore[attr-defined]
+        elif isinstance(obj, (list, tuple)):
+            assert len(obj) > 0
+            all_base_tys = [TypeGen.from_example(x) for x in obj]
+            if len(set(all_base_tys)) > 1:
+                raise RuntimeError(
+                    f"Cannot generate schema for a seqeunce of args of heterogeneous types: {all_base_tys}. "
+                    "Consider unpacking the argument and give proper names to them if possible "
+                    "instead of using *args."
+                )
+            return ListType(all_base_tys[0], len(obj))
+        tp = type(obj)
+        if tp not in TypeGen.convert_to_base_ty:
+            raise RuntimeError(f"unsupported type {tp}")
+        return BaseType(TypeGen.convert_to_base_ty[tp])
+
+
+class ReturnGen:
+    @staticmethod
+    def from_example(
+        name: Optional[str], obj: Any, annotation: Optional[Annotation]
+    ) -> Return:
+        return Return(name, TypeGen.from_example(obj), annotation)
+
+
+class ArgumentGen:
+    @staticmethod
+    def from_example(
+        name: str, obj: Any, default: Optional[str], annotation: Optional[Annotation]
+    ) -> Argument:
+        return Argument(
+            name, TypeGen.from_example(obj), default=default, annotation=annotation
+        )
+
+
+class FunctionSchemaGen:
+    @staticmethod
+    def from_example(
+        op_name: str,
+        example_inputs: tuple[tuple[str, Any], ...],
+        example_outputs: tuple[Any, ...],
+    ) -> FunctionSchema:
+        args = []
+        for name, inp in example_inputs:
+            args.append(ArgumentGen.from_example(name, inp, None, None))
+        # ignore the annotations and other attributes for now, we could add more when needed.
+        arguments = Arguments(
+            tuple(), None, tuple(args), tuple(), None, tuple(), tuple()
+        )
+        returns = tuple(
+            ReturnGen.from_example(None, out, None) for out in example_outputs
+        )
+        op_name = OperatorName(BaseOperatorName(op_name, False, False, False), "")
+        return FunctionSchema(op_name, arguments, returns)
diff --git a/lib/python3.11/site-packages/torchgen/gen_vmap_plumbing.py b/lib/python3.11/site-packages/torchgen/gen_vmap_plumbing.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f1f14d45749f211db49e4623b96f3a48885439d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/gen_vmap_plumbing.py
@@ -0,0 +1,275 @@
+from __future__ import annotations
+
+import textwrap
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from torchgen.api.translate import translate
+from torchgen.api.types import DispatcherSignature
+from torchgen.context import method_with_native_function
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    OptionalType,
+    Return,
+    SchemaKind,
+    Type,
+)
+from torchgen.utils import mapMaybe
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+def is_tensor(typ: Type) -> bool:
+    return isinstance(typ, BaseType) and typ.name == BaseTy.Tensor
+
+
+def is_optional_tensor(typ: Type) -> bool:
+    return isinstance(typ, OptionalType) and is_tensor(typ.elem)
+
+
+def is_tensor_list(typ: Type) -> bool:
+    return isinstance(typ, ListType) and is_tensor(typ.elem)
+
+
+def unwrap_tensor(name: str, cur_level_var: str) -> list[str]:
+    result = f"""\
+    auto [{name}_value, {name}_bdim] = unwrapTensorAtLevel({name}, {cur_level_var});"""
+    return textwrap.dedent(result).split("\n")
+
+
+def unwrap_optional_tensor(name: str, cur_level_var: str) -> list[str]:
+    result = f"""\
+    std::optional<Tensor> {name}_value;
+    std::optional<int64_t> {name}_bdim;
+    if ({name}) {{
+        std::tie({name}_value, {name}_bdim) = unwrapTensorAtLevel({name}.value(), {cur_level_var});
+    }}"""
+    return textwrap.dedent(result).split("\n")
+
+
+def gen_unwraps(
+    flat_arguments: Sequence[Argument], cur_level_var: str
+) -> tuple[str, list[str]]:
+    arg_names = [a.name for a in flat_arguments]
+    arg_types = [a.type for a in flat_arguments]
+
+    tensors = [name for typ, name in zip(arg_types, arg_names) if is_tensor(typ)]
+    optional_tensors = [
+        name for typ, name in zip(arg_types, arg_names) if is_optional_tensor(typ)
+    ]
+
+    unwraps = []
+    for tensor in tensors:
+        unwraps += unwrap_tensor(tensor, cur_level_var)
+
+    for opt_tensor in optional_tensors:
+        unwraps += unwrap_optional_tensor(opt_tensor, cur_level_var)
+    unwrap_code = "\n".join(unwraps)
+
+    unwrapped_arg_list = []
+    for arg in arg_names:
+        if arg in tensors or arg in optional_tensors:
+            unwrapped_arg_list += [f"{arg}_value", f"{arg}_bdim"]
+        else:
+            unwrapped_arg_list.append(arg)
+    return unwrap_code, unwrapped_arg_list
+
+
+def gen_case_where_all_bdims_are_none(
+    outer_sig: DispatcherSignature, schema: FunctionSchema, cur_level_var: str
+) -> str:
+    conditions = []
+    flat_args = schema.arguments.flat_all
+    for arg in flat_args:
+        if not arg.type.is_tensor_like():
+            continue
+        conditions.append(f"!isBatchedAtLevel({arg.name}, {cur_level_var})")
+
+    sig = DispatcherSignature.from_schema(schema)
+    translated_args = ", ".join(
+        e.expr for e in translate(outer_sig.arguments(), sig.arguments())
+    )
+    return f"""\
+if ({' && '.join(conditions)}) {{
+  return at::_ops::{sig.func.name.unambiguous_name()}::call({translated_args});
+}}"""
+
+
+def gen_returns(
+    returns: tuple[Return, ...], cur_level_var: str, results_var: str
+) -> str:
+    idx = 0
+    wrapped_returns = []
+    for ret in returns:
+        if is_tensor(ret.type):
+            wrapped_returns.append(
+                f"makeBatched(std::get<{idx}>({results_var}), std::get<{idx + 1}>({results_var}), {cur_level_var})"
+            )
+            idx += 2
+        elif is_tensor_list(ret.type):
+            wrapped_returns.append(
+                f"makeBatchedVector(std::get<{idx}>({results_var}), std::get<{idx + 1}>({results_var}), {cur_level_var})"
+            )
+            idx += 2
+        else:
+            wrapped_returns.append(f"std::get<{idx}>({results_var})")
+            idx += 1
+    if len(wrapped_returns) == 1:
+        result = f"return {wrapped_returns[0]};"
+    else:
+        result = f'return std::make_tuple({", ".join(wrapped_returns)});'
+    return result
+
+
+def accepts_at_least_one_tensor_input(schema: FunctionSchema) -> bool:
+    return any(a.type.is_tensor_like() for a in schema.arguments.flat_all)
+
+
+def is_mutated_arg(argument: Argument) -> bool:
+    return argument.annotation is not None and argument.annotation.is_write
+
+
+def gen_vmap_inplace_plumbing(native_function: NativeFunction) -> str | None:
+    # Assumptions:
+    # - only one argument is being modified in-place
+    # - the argument that is being modified in-place is the first argument
+    # - all returns are either Tensor, tuple of Tensor, or TensorList
+    schema = native_function.func
+    sig = DispatcherSignature.from_schema(schema)
+    returns = schema.returns
+
+    # Check assumptions. If these are invalid we return None
+    # and punt the work to handle them to the future.
+    assert schema.kind() == SchemaKind.inplace
+    if not is_mutated_arg(schema.arguments.flat_all[0]):
+        return None
+    if not len([arg for arg in schema.arguments.flat_all if is_mutated_arg(arg)]) == 1:
+        return None
+
+    # Only support cases where all returns are Tensors or vector<Tensor>
+    if len(returns) == 0:
+        return None
+    if not all(is_tensor(ret.type) or is_tensor_list(ret.type) for ret in returns):
+        return None
+    if not accepts_at_least_one_tensor_input(schema):
+        return None
+
+    cur_level_var = "cur_level"
+
+    unwraps, unwrapped_arg_list = gen_unwraps(schema.arguments.flat_all, cur_level_var)
+    bdims_all_none_case = gen_case_where_all_bdims_are_none(sig, schema, cur_level_var)
+
+    return f"""\
+template <typename batch_rule_t, batch_rule_t batch_rule>
+{sig.decl(name=schema.name.unambiguous_name() + '_generated_plumbing')} {{
+  c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched);
+  auto maybe_layer = maybeCurrentDynamicLayer();
+  vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing");
+  int64_t {cur_level_var} = maybe_layer->layerId();
+{textwrap.indent(bdims_all_none_case, "  ")}
+{textwrap.indent(unwraps, "  ")}
+  batch_rule({', '.join(unwrapped_arg_list)});
+  return {schema.arguments.flat_all[0].name};
+}}"""
+
+
+def gen_vmap_plumbing_no_returns(native_function: NativeFunction) -> str:
+    schema = native_function.func
+    sig = DispatcherSignature.from_schema(schema)
+    cur_level_var = "cur_level"
+
+    unwraps, unwrapped_arg_list = gen_unwraps(schema.arguments.flat_all, cur_level_var)
+    bdims_all_none_case = gen_case_where_all_bdims_are_none(sig, schema, cur_level_var)
+
+    return f"""\
+template <typename batch_rule_t, batch_rule_t batch_rule>
+{sig.decl(name=schema.name.unambiguous_name() + '_generated_plumbing')} {{
+  c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched);
+  auto maybe_layer = maybeCurrentDynamicLayer();
+  vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns");
+  int64_t {cur_level_var} = maybe_layer->layerId();
+{textwrap.indent(bdims_all_none_case, "  ")}
+{textwrap.indent(unwraps, "  ")}
+  batch_rule({', '.join(unwrapped_arg_list)});
+}}"""
+
+
+def gen_vmap_plumbing(native_function: NativeFunction) -> str | None:
+    schema = native_function.func
+    sig = DispatcherSignature.from_schema(schema)
+    returns = schema.returns
+
+    # Only support cases where all returns are Tensors or vector<Tensor>
+    if not accepts_at_least_one_tensor_input(schema):
+        return None
+    if len(returns) == 0:
+        return gen_vmap_plumbing_no_returns(native_function)
+    return_symint_overrides = [
+        "_scaled_dot_product_flash_attention",
+        "_scaled_dot_product_cudnn_attention",
+    ]
+    if (
+        not all(ret.type.is_tensor_like() for ret in returns)
+        and schema.name.unambiguous_name() not in return_symint_overrides
+    ):
+        return None
+    # in-place views need special handling
+    if "inplace_view" in native_function.tags:
+        return None
+
+    if schema.kind() == SchemaKind.inplace:
+        return gen_vmap_inplace_plumbing(native_function)
+
+    # Don't support these (mutable, out, scratch)
+    if schema.kind() != SchemaKind.functional:
+        return None
+
+    results_var = "results"
+    cur_level_var = "cur_level"
+
+    unwraps, unwrapped_arg_list = gen_unwraps(schema.arguments.flat_all, cur_level_var)
+    bdims_all_none_case = gen_case_where_all_bdims_are_none(sig, schema, cur_level_var)
+
+    wrapped_returns = gen_returns(returns, cur_level_var, results_var)
+    return f"""\
+template <typename batch_rule_t, batch_rule_t batch_rule>
+{sig.decl(name=schema.name.unambiguous_name() + '_generated_plumbing')} {{
+  c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched);
+  auto maybe_layer = maybeCurrentDynamicLayer();
+  vmap_check_escaped(maybe_layer, "gen_vmap_plumbing");
+  int64_t {cur_level_var} = maybe_layer->layerId();
+{textwrap.indent(bdims_all_none_case, "  ")}
+{textwrap.indent(unwraps, "  ")}
+  auto {results_var} = batch_rule({', '.join(unwrapped_arg_list)});
+  {wrapped_returns}
+}}"""
+
+
+@dataclass(frozen=True)
+class ComputeBatchRulePlumbing:
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> str | None:
+        result = gen_vmap_plumbing(f)
+        return result
+
+
+def gen_all_vmap_plumbing(native_functions: Sequence[NativeFunction]) -> str:
+    body = "\n".join(list(mapMaybe(ComputeBatchRulePlumbing(), native_functions)))
+    return f"""
+#pragma once
+#include <ATen/Operators.h>
+#include <ATen/functorch/PlumbingHelper.h>
+
+namespace at {{ namespace functorch {{
+
+{body}
+
+}}}} // namespace at::functorch
+"""
diff --git a/lib/python3.11/site-packages/torchgen/local.py b/lib/python3.11/site-packages/torchgen/local.py
new file mode 100644
index 0000000000000000000000000000000000000000..19045f4a94879130f64bcafbdc6424146bde48b4
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/local.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+import threading
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
+
+# Simple dynamic scoping implementation.  The name "parametrize" comes
+# from Racket.
+#
+# WARNING WARNING: LOOKING TO EDIT THIS FILE?  Think carefully about
+# why you need to add a toggle to the global behavior of code
+# generation.  The parameters here should really only be used
+# for "temporary" situations, where we need to temporarily change
+# the codegen in some cases because we cannot conveniently update
+# all call sites, and are slated to be eliminated once all call
+# sites are eliminated.  If you don't have a plan for how to get there,
+# DON'T add a new entry here.
+
+
+class Locals(threading.local):
+    use_const_ref_for_mutable_tensors: bool | None = None
+    use_ilistref_for_tensor_lists: bool | None = None
+
+
+_locals = Locals()
+
+
+def use_const_ref_for_mutable_tensors() -> bool:
+    assert _locals.use_const_ref_for_mutable_tensors is not None, (
+        "need to initialize local.use_const_ref_for_mutable_tensors with "
+        "local.parametrize"
+    )
+    return _locals.use_const_ref_for_mutable_tensors
+
+
+def use_ilistref_for_tensor_lists() -> bool:
+    assert _locals.use_ilistref_for_tensor_lists is not None, (
+        "need to initialize local.use_ilistref_for_tensor_lists with "
+        "local.parametrize"
+    )
+    return _locals.use_ilistref_for_tensor_lists
+
+
+@contextmanager
+def parametrize(
+    *, use_const_ref_for_mutable_tensors: bool, use_ilistref_for_tensor_lists: bool
+) -> Iterator[None]:
+    old_use_const_ref_for_mutable_tensors = _locals.use_const_ref_for_mutable_tensors
+    old_use_ilistref_for_tensor_lists = _locals.use_ilistref_for_tensor_lists
+    try:
+        _locals.use_const_ref_for_mutable_tensors = use_const_ref_for_mutable_tensors
+        _locals.use_ilistref_for_tensor_lists = use_ilistref_for_tensor_lists
+        yield
+    finally:
+        _locals.use_const_ref_for_mutable_tensors = (
+            old_use_const_ref_for_mutable_tensors
+        )
+        _locals.use_ilistref_for_tensor_lists = old_use_ilistref_for_tensor_lists
diff --git a/lib/python3.11/site-packages/torchgen/model.py b/lib/python3.11/site-packages/torchgen/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b78ab638e70fc01b0b155762a21d9ec135570ff2
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/model.py
@@ -0,0 +1,2855 @@
+from __future__ import annotations
+
+import dataclasses
+import itertools
+import re
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Callable, TYPE_CHECKING
+
+from torchgen.utils import assert_never, NamespaceHelper, OrderedSet
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator, Sequence
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                           DATA MODEL
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+# Some general principles for our data model.
+#
+# - Stop using C++ data types as the internal data representation
+#   format.  Instead, the internal data structures are centered
+#   around JIT schema representation.  This avoid a big problem
+#   with the old codegen where we read in all the types from
+#   native_functions.yaml and then immediately had to retranslate
+#   them into C++ types.
+#
+# - More semantic data representation.  Instead of representing
+#   everything as dicts and strings, we define dataclasses for
+#   every interesting entity the code generation has to deal with.
+#   These dataclasses have strong semantic invariants: for example,
+#   we generally require them to roundtrip losslessly into the
+#   form they were parsed from.  These structures are immutable
+#   and you're expected to populate information once during
+#   construction.
+
+
+# Represent a source location; used for better error reporting
+@dataclass(frozen=True)
+class Location:
+    file: str
+    line: int
+
+    def __str__(self) -> str:
+        return f"{self.file}:{self.line}"
+
+
+# Valid values of the 'variants' field in native_functions.yaml
+class Variant(Enum):
+    function = auto()
+    method = auto()
+
+
+# Default kernel namespace
+DEFAULT_KERNEL_NAMESPACE = "at::native"
+
+# NOTE: Keep the list in sync with `DispatchKey` in c10/core/DispatchKey.h
+BACKEND_COMPONENTS = "CPU CUDA HIP XLA MTIA MPS IPU XPU HPU VE Lazy Meta PrivateUse1 PrivateUse2 PrivateUse3".split()
+FUNCTIONALITY_KEYS = [
+    "",
+    "Quantized",
+    "Sparse",
+    "SparseCsr",
+    "NestedTensor",
+    "Autograd",
+]
+
+# This list guards dispatches that can be used in derivatives.yaml
+# For now we omit AutogradFunctionality and AutogradOther
+AUTOGRAD_KEYS = ["AutogradNestedTensor"] + [
+    "Autograd" + component for component in BACKEND_COMPONENTS
+]
+
+FRAGMENT_NAMESPACES = {"quantized", "quantized_decomposed"}
+
+
+# This doesn't have to be in sync with the header, it only needs to contain
+# entries that we actually use in the codegen or want pyi entries for
+class DispatchKey(Enum):
+    Undefined = 0
+    CatchAll = Undefined
+
+    FPGA = auto()
+    MAIA = auto()
+    Vulkan = auto()
+    Metal = auto()
+    MKLDNN = auto()
+    OpenGL = auto()
+    OpenCL = auto()
+    IDEEP = auto()
+    CustomRNGKeyId = auto()
+    MkldnnCPU = auto()
+    Sparse = auto()
+    SparseCsr = auto()
+    NestedTensor = auto()
+    Dense = auto()
+
+    PythonTLSSnapshot = auto()
+    PreDispatch = auto()
+    PythonDispatcher = auto()
+    Python = auto()
+    FuncTorchDynamicLayerBackMode = auto()
+    ZeroTensor = auto()
+    Conjugate = auto()
+    Negative = auto()
+    BackendSelect = auto()
+    Named = auto()
+    AutogradOther = auto()
+    AutogradFunctionality = auto()
+    AutogradNestedTensor = auto()
+    Tracer = auto()
+    Autocast = auto()
+    AutocastCPU = auto()
+    AutocastCUDA = auto()
+    Batched = auto()
+    VmapMode = auto()
+    FuncTorchGradWrapper = auto()
+    FuncTorchBatched = auto()
+    BatchedNestedTensor = auto()
+    FuncTorchVmapMode = auto()
+    FuncTorchDynamicLayerFrontMode = auto()
+    Functionalize = auto()
+    TESTING_ONLY_GenericWrapper = auto()
+    TESTING_ONLY_GenericMode = auto()
+
+    ADInplaceOrView = auto()
+    Autograd = auto()
+    CompositeImplicitAutograd = auto()
+    CompositeImplicitAutogradNestedTensor = auto()
+    CompositeExplicitAutograd = auto()
+    CompositeExplicitAutogradNonFunctional = auto()
+    FuncTorchBatchedDecomposition = auto()
+
+    # BEGIN autogenerated
+    CPU = auto()
+    CUDA = auto()
+    HIP = auto()
+    XLA = auto()
+    MTIA = auto()
+    MPS = auto()
+    IPU = auto()
+    XPU = auto()
+    HPU = auto()
+    VE = auto()
+    Lazy = auto()
+    Meta = auto()
+    PrivateUse1 = auto()
+    PrivateUse2 = auto()
+    PrivateUse3 = auto()
+    QuantizedCPU = auto()
+    QuantizedCUDA = auto()
+    QuantizedHIP = auto()
+    QuantizedXLA = auto()
+    QuantizedMTIA = auto()
+    QuantizedMPS = auto()
+    QuantizedIPU = auto()
+    QuantizedXPU = auto()
+    QuantizedHPU = auto()
+    QuantizedVE = auto()
+    QuantizedLazy = auto()
+    QuantizedMeta = auto()
+    QuantizedPrivateUse1 = auto()
+    QuantizedPrivateUse2 = auto()
+    QuantizedPrivateUse3 = auto()
+    SparseCPU = auto()
+    SparseCUDA = auto()
+    SparseHIP = auto()
+    SparseXLA = auto()
+    SparseMTIA = auto()
+    SparseMPS = auto()
+    SparseIPU = auto()
+    SparseXPU = auto()
+    SparseHPU = auto()
+    SparseVE = auto()
+    SparseLazy = auto()
+    SparseMeta = auto()
+    SparsePrivateUse1 = auto()
+    SparsePrivateUse2 = auto()
+    SparsePrivateUse3 = auto()
+    SparseCsrCPU = auto()
+    SparseCsrCUDA = auto()
+    SparseCsrHIP = auto()
+    SparseCsrXLA = auto()
+    SparseCsrMTIA = auto()
+    SparseCsrMPS = auto()
+    SparseCsrIPU = auto()
+    SparseCsrXPU = auto()
+    SparseCsrHPU = auto()
+    SparseCsrVE = auto()
+    SparseCsrLazy = auto()
+    SparseCsrMeta = auto()
+    SparseCsrPrivateUse1 = auto()
+    SparseCsrPrivateUse2 = auto()
+    SparseCsrPrivateUse3 = auto()
+    NestedTensorCPU = auto()
+    NestedTensorCUDA = auto()
+    NestedTensorHIP = auto()
+    NestedTensorXLA = auto()
+    NestedTensorMTIA = auto()
+    NestedTensorMPS = auto()
+    NestedTensorIPU = auto()
+    NestedTensorXPU = auto()
+    NestedTensorHPU = auto()
+    NestedTensorVE = auto()
+    NestedTensorLazy = auto()
+    NestedTensorMeta = auto()
+    NestedTensorPrivateUse1 = auto()
+    NestedTensorPrivateUse2 = auto()
+    NestedTensorPrivateUse3 = auto()
+    AutogradCPU = auto()
+    AutogradCUDA = auto()
+    AutogradHIP = auto()
+    AutogradXLA = auto()
+    AutogradMTIA = auto()
+    AutogradMPS = auto()
+    AutogradIPU = auto()
+    AutogradXPU = auto()
+    AutogradHPU = auto()
+    AutogradVE = auto()
+    AutogradLazy = auto()
+    AutogradMeta = auto()
+    AutogradPrivateUse1 = auto()
+    AutogradPrivateUse2 = auto()
+    AutogradPrivateUse3 = auto()
+    # END autogenerated
+
+    def __str__(self) -> str:
+        return self.name
+
+    def lower(self) -> str:
+        return str(self).lower()
+
+    @staticmethod
+    def parse(value: str) -> DispatchKey:
+        for k, v in DispatchKey.__members__.items():
+            if k == value:
+                return v
+        raise AssertionError(f"unknown dispatch key {value}")
+
+
+class _TorchDispatchModeKey(Enum):
+    FAKE = auto()
+    PROXY = auto()
+    FUNCTIONAL = auto()
+
+
+def codegen_per_backend_entries() -> str:
+    r: list[str] = []
+    for fk in FUNCTIONALITY_KEYS:
+        r.extend(f"    {fk}{bc} = auto()" for bc in BACKEND_COMPONENTS)
+    return "\n".join(r)
+
+
+for fk in FUNCTIONALITY_KEYS:
+    for bc in BACKEND_COMPONENTS:
+        if not hasattr(DispatchKey, fk + bc):
+            r = codegen_per_backend_entries()
+            print(r)
+            raise RuntimeError(
+                f"Missing {fk}{bc} from DispatchKey enum.  Here is the autogenerated list we expect to have:\n\n{r}"
+            )
+
+
+STRUCTURED_DISPATCH_KEYS = {
+    DispatchKey.MPS,
+    DispatchKey.CUDA,
+    DispatchKey.CPU,
+    DispatchKey.XPU,
+}
+UFUNC_DISPATCH_KEYS = {DispatchKey.CUDA, DispatchKey.CPU}
+
+# Set of supported dispatch keys
+dispatch_keys = [
+    DispatchKey.CPU,
+    DispatchKey.SparseCPU,
+    DispatchKey.SparseCsrCPU,
+    DispatchKey.MkldnnCPU,
+    DispatchKey.CUDA,
+    DispatchKey.MPS,
+    DispatchKey.XPU,
+    DispatchKey.SparseXPU,
+    DispatchKey.SparseCUDA,
+    DispatchKey.SparseCsrCUDA,
+    DispatchKey.QuantizedCPU,
+    DispatchKey.QuantizedCUDA,
+    DispatchKey.CompositeImplicitAutograd,
+    DispatchKey.CompositeImplicitAutogradNestedTensor,
+    DispatchKey.CompositeExplicitAutograd,
+    DispatchKey.CompositeExplicitAutogradNonFunctional,
+    DispatchKey.NestedTensorCPU,
+    DispatchKey.NestedTensorCUDA,
+    DispatchKey.NestedTensorXPU,
+    # Meta is a magic key: it is automatically generated for structured
+    # kernels
+    DispatchKey.Meta,
+    DispatchKey.SparseMeta,
+    DispatchKey.SparseCsrMeta,
+    DispatchKey.QuantizedMeta,
+    DispatchKey.NestedTensorMeta,
+    DispatchKey.ZeroTensor,
+]
+
+
+# Dispatch keys that "support all backends".  These codegen slightly differently
+# then backend specific keys.
+def is_generic_dispatch_key(dk: DispatchKey) -> bool:
+    return dk in {
+        DispatchKey.CompositeExplicitAutograd,
+        DispatchKey.CompositeExplicitAutogradNonFunctional,
+        DispatchKey.CompositeImplicitAutograd,
+        DispatchKey.CompositeImplicitAutogradNestedTensor,
+    }
+
+
+# CUDA specific dispatch keys
+def is_cuda_dispatch_key(dk: DispatchKey) -> bool:
+    return dk in {
+        DispatchKey.CUDA,
+        DispatchKey.QuantizedCUDA,
+        DispatchKey.SparseCUDA,
+        DispatchKey.SparseCsrCUDA,
+        DispatchKey.NestedTensorCUDA,
+        DispatchKey.AutogradCUDA,
+    }
+
+
+# XPU specific dispatcy keys
+def is_xpu_dispatch_key(dk: DispatchKey) -> bool:
+    return dk in {
+        DispatchKey.XPU,
+        DispatchKey.QuantizedXPU,
+        DispatchKey.SparseXPU,
+        DispatchKey.SparseCsrXPU,
+        DispatchKey.NestedTensorXPU,
+        DispatchKey.AutogradXPU,
+    }
+
+
+# Structured kernel generation is only supported for certain key types;
+# otherwise use old-style
+def is_structured_dispatch_key(dk: DispatchKey) -> bool:
+    return dk in STRUCTURED_DISPATCH_KEYS
+
+
+def is_ufunc_dispatch_key(dk: DispatchKey) -> bool:
+    # For now, ufunc dispatch keys coincide with structured keys
+    return dk in UFUNC_DISPATCH_KEYS
+
+
+dispatch_device_map = {is_cuda_dispatch_key: "cuda", is_xpu_dispatch_key: "xpu"}
+
+
+# This is oddly named ScalarType and not DType for symmetry with C++
+class ScalarType(Enum):
+    Byte = auto()
+    Char = auto()
+    Short = auto()
+    Int = auto()
+    Long = auto()
+    Half = auto()
+    Float = auto()
+    Double = auto()
+    ComplexHalf = auto()
+    ComplexFloat = auto()
+    ComplexDouble = auto()
+    Bool = auto()
+    BFloat16 = auto()
+    Float8_e5m2 = auto()
+    Float8_e5m2fnuz = auto()
+    Float8_e4m3fn = auto()
+    Float8_e4m3fnuz = auto()
+
+    def __str__(self) -> str:
+        return self.name
+
+    @staticmethod
+    def maybe_parse(value: str) -> ScalarType | None:
+        for k, v in ScalarType.__members__.items():
+            if k == value:
+                return v
+        return None
+
+    @staticmethod
+    def parse(value: str) -> ScalarType:
+        mb_r = ScalarType.maybe_parse(value)
+        assert mb_r is not None, f"unknown dtype {value}"
+        return mb_r
+
+    @staticmethod
+    def parse_set(values: str) -> OrderedSet[ScalarType]:
+        dtypes: OrderedSet[ScalarType] = OrderedSet()
+        for value in values.split(", "):
+            if value in DTYPE_CLASSES:
+                dtypes.update(DTYPE_CLASSES[value])
+            else:
+                dtypes.add(ScalarType.parse(value))
+        return dtypes
+
+
+DTYPE_CLASSES: dict[str, OrderedSet[ScalarType]] = {}
+# NB: Integral doesn't include boolean
+DTYPE_CLASSES["Integral"] = OrderedSet(
+    [
+        ScalarType.Byte,
+        ScalarType.Char,
+        ScalarType.Int,
+        ScalarType.Long,
+        ScalarType.Short,
+    ]
+)
+# NB: Floating doesn't include low precision types
+DTYPE_CLASSES["Floating"] = OrderedSet([ScalarType.Float, ScalarType.Double])
+DTYPE_CLASSES["Complex"] = OrderedSet(
+    [ScalarType.ComplexFloat, ScalarType.ComplexDouble]
+)
+DTYPE_CLASSES["All"] = DTYPE_CLASSES["Integral"] | DTYPE_CLASSES["Floating"]
+DTYPE_CLASSES["AllAndComplex"] = DTYPE_CLASSES["All"] | DTYPE_CLASSES["Complex"]
+DTYPE_CLASSES["FloatingAndComplex"] = (
+    DTYPE_CLASSES["Floating"] | DTYPE_CLASSES["Complex"]
+)
+
+
+# Represents the valid entries for ufunc_inner_loop in native_functions.yaml.
+# NB: if you add a new UfuncKey, you will teach torchgen.dest.ufunc how
+# to process it.  Most logic will ignore keys they don't understand, so your
+# new key will get silently ignored until you hook in logic to deal with it.
+class UfuncKey(Enum):
+    # These are low level keys that represent exactly one particular
+    # instantiation of the kernel produced by codegen
+    CUDAFunctor = auto()
+    CUDAFunctorOnOther = auto()
+    CUDAFunctorOnSelf = auto()
+
+    CPUScalar = auto()
+    CPUVector = auto()
+
+    # These are the ones users will usually specify, and
+    # implicitly "fill in" the low level keys
+    ScalarOnly = auto()  # CUDA*, CPUScalar
+    Generic = auto()  # CUDA*, CPU*
+
+    def __str__(self) -> str:
+        return self.name
+
+    @staticmethod
+    def parse(value: str) -> UfuncKey:
+        for k, v in UfuncKey.__members__.items():
+            if k == value:
+                return v
+        raise AssertionError(f"unknown ufunc key {value}")
+
+
+class DeviceCheckType(Enum):
+    NoCheck = 0
+    ExactSame = 1
+
+
+class ViewSchemaKind(Enum):
+    aliasing = auto()
+    aliasing_inplace = auto()
+    non_aliasing = auto()
+
+
+# The basic input to the code generation is native_functions.yaml.
+# The name "native", BTW, comes from the distinction between native
+# functions and legacy TH functions.  The legacy TH functions are gone,
+# but the "native" descriptor has stuck.
+#
+# NativeFunction models a single entry in native_functions.yaml.  Its
+# fields roughly correspond to what you would see in the YAML itself,
+# but after canonicalization and parsing has occurred.
+#
+# You can see some of the overall design patterns for how we setup
+# dataclasses in this class, but we will defer a complete discussion
+# of this at FunctionSchema.
+@dataclass(frozen=True)
+class NativeFunction:
+    # The namespace for this operator. For example, if we have "at::add"
+    # then the namespace would be "at". This enables ops to be registered
+    # through the same DSL with a custom namespace. If not specified, the
+    # default namespace would be "at".
+    namespace: str
+
+    # The function schema of the operator in question.  This schema
+    # has been parsed; see FunctionSchema for more about its structure.
+    # (This type is quoted as we are forward referencing a type
+    # defined later in the file.  I opted for this ordering of the
+    # classes for expository clarity.)
+    func: FunctionSchema
+
+    # Whether or not to generate mutable tensor arguments like regular
+    # ones
+    use_const_ref_for_mutable_tensors: bool
+
+    # Whether or not to omit automatic generation of a DeviceGuard
+    device_guard: bool
+
+    # How to emit automatic generation of device check
+    device_check: DeviceCheckType
+
+    # What python module to put the function in
+    python_module: str | None
+
+    # TODO: figure out what this does
+    category_override: str | None
+
+    # If no variants are specified in native_functions.yaml, this is
+    # assumed to be {'function'}.
+    variants: set[Variant]
+
+    # Whether or not we should skip generating registrations for
+    # this kernel.  This is a bit of a double-edged sword, as manual
+    # registrations don't participate in codegen-based selective build!
+    manual_kernel_registration: bool
+
+    # Whether or not to skip generating TensorMethod/Functions bindings
+    # for this kernel.  Technically, this doesn't actually skip generating
+    # the binding; instead, the binding gets generated to __dispatch_{funcname}
+    # so you can make use of the normal binding if you need it.
+    manual_cpp_binding: bool
+
+    # The location in the YAML file were this native function entry was
+    # defined.  This is for conveniently reporting error messages!
+    loc: Location
+
+    # A list of operators that are expected to be auto-generated for this NativeFunction.
+    # Note: This list isn't actually directly used by the codegen to generate anything.
+    # Instead, the codegen figures out what operators to generate purely based off of
+    # function schema, and uses the autogen declarations to error check.
+    # We expect every NativeFunction that gets auto-generated be explicitly called out
+    # in native_functions.yaml
+    autogen: list[OperatorName]
+
+    # If non-empty, this kernel is subject to ufunc codegen.
+    # Sorted by ufunc_key
+    ufunc_inner_loop: dict[UfuncKey, UfuncInnerLoop]
+
+    # Whether or not this out functions is a "structured kernel".  Structured
+    # kernels are defined a little differently from normal kernels; in
+    # particular, their shape checking logic is defined separately from
+    # the kernel.  Only out functions can be structured; other functions
+    # delegate to the out function using the structured_delegate keyword.
+    # Every structured kernel must have at least an out and a functional
+    # variant.
+    structured: bool
+
+    # Whether or not this non-out function is a structured kernel, defined
+    # in terms of the out kernel referenced by the string here.
+    structured_delegate: OperatorName | None
+
+    # Only valid for structured kernels.  Specifies alternative of what
+    # to inherit from when defining the meta class for the structured
+    # operator.  This will usually be TensorIteratorBase.  This also
+    # changes the semantics of set_output to call the parent class.
+    structured_inherits: str | None
+
+    # Structured kernels can declare elements as "precomputed". These elements
+    # are returned by the meta function in one struct and passed to the impl
+    # function in lieu of certain kernel arguments that these precomputed
+    # elements supersede. Information about the names and types of these
+    # precomputed elements and how they correspond to kernel arguments is stored
+    # in this member, if applicable.
+    precomputed: Precompute | None
+
+    # Argument names whose default  should be excluded from the C++ interface.
+    # Intended for resolving overload ambiguities between signatures.
+    cpp_no_default_args: set[str]
+
+    # Note [Abstract ATen methods]
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # An abstract ATen method is one whose dispatch differs between
+    # types.  These are implemented in derived types (with a
+    # standard (throwing) definition in Type).  A concrete ATen
+    # method is one which has the same dispatch for all types;
+    # we just implement it in the base Type.  This is exposed
+    # in Declarations.yaml via a field named 'abstract'.
+    is_abstract: bool
+
+    # Whether or not the NativeFunction contains a backend-agnostic kernel
+    has_composite_implicit_autograd_kernel: bool
+    has_composite_implicit_autograd_nested_tensor_kernel: bool
+    has_composite_explicit_autograd_kernel: bool
+    has_composite_explicit_autograd_non_functional_kernel: bool
+
+    # Tags are used to describe semantic information about (groups of) operators,
+    # That aren't easily inferrable directly from the operator's schema.
+    tags: set[str]
+
+    # NB: The benefit of defining a dataclass is that we automatically get
+    # a constructor defined for all the fields we specify.  No need
+    # to explicitly write it out.
+
+    # We parse both the NativeFunction + backend-specific information about it, which it stored in a corresponding BackendIndex.
+    @staticmethod
+    def from_yaml(
+        ei: dict[str, object],
+        loc: Location,
+        valid_tags: set[str],
+        ignore_keys: set[DispatchKey] | None = None,
+    ) -> tuple[NativeFunction, dict[DispatchKey, dict[OperatorName, BackendMetadata]]]:
+        """
+        Parse a NativeFunction from a dictionary as directly parsed
+        from native_functions.yaml
+        """
+        e = ei.copy()
+
+        funcs = e.pop("func")
+        assert isinstance(funcs, str), f"not a str: {funcs}"
+        # only support one level of namespace. E.g., aten::add
+        namespace_helper = NamespaceHelper.from_namespaced_entity(
+            namespaced_entity=funcs, max_level=1
+        )
+        namespace = namespace_helper.get_cpp_namespace(default="aten")
+        func = FunctionSchema.parse(namespace_helper.entity_name)
+
+        cpp_no_default_args_list = e.pop("cpp_no_default_args", [])
+        assert isinstance(cpp_no_default_args_list, list)
+        cpp_no_default_args = set(cpp_no_default_args_list)
+
+        use_const_ref_for_mutable_tensors = e.pop(
+            "use_const_ref_for_mutable_tensors", False
+        )
+        assert isinstance(use_const_ref_for_mutable_tensors, bool)
+
+        variants_s = e.pop("variants", "function")
+        assert isinstance(variants_s, str)
+        variants: set[Variant] = set()
+        for v in variants_s.split(", "):
+            if v == "function":
+                variants.add(Variant.function)
+            elif v == "method":
+                variants.add(Variant.method)
+            else:
+                raise AssertionError(f"illegal variant {v}")
+
+        manual_kernel_registration = e.pop("manual_kernel_registration", False)
+        assert isinstance(
+            manual_kernel_registration, bool
+        ), f"not a bool: {manual_kernel_registration}"
+
+        manual_cpp_binding = e.pop("manual_cpp_binding", False)
+        assert isinstance(manual_cpp_binding, bool), f"not a bool: {manual_cpp_binding}"
+
+        device_guard = e.pop("device_guard", True)
+        assert isinstance(device_guard, bool), f"not a bool: {device_guard}"
+
+        device_check_s = e.pop("device_check", None)
+        assert device_check_s is None or isinstance(
+            device_check_s, str
+        ), f"not a str: {device_check_s}"
+        assert (
+            device_check_s is None or device_check_s in DeviceCheckType.__members__
+        ), f"illegal device_check: {device_check_s}"
+        device_check: DeviceCheckType
+        if device_check_s is None:
+            device_check = DeviceCheckType.ExactSame
+        else:
+            device_check = DeviceCheckType[device_check_s]
+
+        structured = e.pop("structured", False)
+        assert isinstance(structured, bool), f"not a bool: {structured}"
+
+        structured_delegate_s = e.pop("structured_delegate", None)
+        assert structured_delegate_s is None or isinstance(
+            structured_delegate_s, str
+        ), f"not a str: {structured_delegate_s}"
+        assert structured_delegate_s is None or "::" not in structured_delegate_s, (
+            "namespace is not supported in structured delegate,"
+            " using the same namespace as the native function"
+        )
+        structured_delegate: OperatorName | None = None
+        if structured_delegate_s is not None:
+            structured_delegate = OperatorName.parse(structured_delegate_s)
+
+        structured_inherits = e.pop("structured_inherits", None)
+        assert structured_inherits is None or isinstance(
+            structured_inherits, str
+        ), f"not a str: {structured_inherits}"
+        assert structured_inherits is None or "::" not in structured_inherits, (
+            "namespace is not supported in structured inherits,"
+            " using the same namespace as the native function"
+        )
+
+        python_module = e.pop("python_module", None)
+        assert python_module is None or isinstance(
+            python_module, str
+        ), f"not a str: {python_module}"
+        assert (
+            python_module is None or Variant.method not in variants
+        ), "functions in modules cannot be methods"
+
+        category_override = e.pop("category_override", None)
+        assert category_override is None or isinstance(
+            category_override, str
+        ), f"not a str: {category_override}"
+
+        precomputed_dict = e.pop("precomputed", None)
+        assert precomputed_dict is None or structured is True
+        precomputed = Precompute.parse(precomputed_dict) if precomputed_dict else None
+
+        tags_inp = e.pop("tags", [])
+        if isinstance(tags_inp, str):
+            tags_inp = [tags_inp]
+        assert isinstance(tags_inp, list)
+
+        # All aten ops generated by torchgen receive the pt2_compliant tag.
+        if namespace == "aten" and "pt2_compliant_tag" in valid_tags:
+            tags_inp.append("pt2_compliant_tag")
+
+        tags: set[str] = set()
+        for t in tags_inp:
+            assert len(valid_tags) > 0
+            # TODO: verify that the tag is valid and has an entry in tags.yaml
+            if t in valid_tags:
+                tags.add(t)
+            else:
+                raise AssertionError(f"illegal tag {t}")
+
+        from torchgen.api import cpp
+
+        raw_dispatch = e.pop("dispatch", None)
+        assert raw_dispatch is None or isinstance(raw_dispatch, dict), e
+        dispatch: dict[DispatchKey, BackendMetadata] = {}
+        num_dispatch_keys: int = 0
+        if raw_dispatch is not None:
+            assert not manual_kernel_registration, (
+                "cannot specify both manual_kernel_registration and dispatch; with "
+                "manual registration, dispatch has no effect!"
+            )
+            redundant_composite_implicit_autograd = False
+            for ks, v in raw_dispatch.items():
+                if ks == "__line__":
+                    continue  # not worth tracking line numbers for dispatch entries
+                assert isinstance(
+                    ks, str
+                ), f"illegal dispatch key '{ks}' in {raw_dispatch}"
+                assert isinstance(
+                    v, str
+                ), f"illegal dispatch value '{v}' in {raw_dispatch}"
+                for k in ks.split(","):
+                    dispatch_key = DispatchKey.parse(k.strip())
+                    num_dispatch_keys += 1
+
+                    if ignore_keys and dispatch_key in ignore_keys:
+                        continue
+                    assert dispatch_key in dispatch_keys, (
+                        f"Dispatch key {dispatch_key} of kernel {v} "
+                        "is not a supported dispatch key."
+                    )
+                    # We only allow at most 3 levels of namespace for kernels.
+                    # We will append "native" to a custom kernel namespace.
+                    namespace_helper = NamespaceHelper.from_namespaced_entity(
+                        v, max_level=3
+                    )
+                    kernel_namespace = namespace_helper.get_cpp_namespace(default="at")
+                    # Why is 'structured' included? External backends (e.g.
+                    # XLA) opt into which ops are structured independently
+                    # of which in-tree ops are structured
+                    dispatch[dispatch_key] = BackendMetadata(
+                        kernel=namespace_helper.entity_name,
+                        structured=structured
+                        and is_structured_dispatch_key(dispatch_key),
+                        cpp_namespace=(kernel_namespace + "::native"),
+                    )
+                    if (
+                        dispatch_key is DispatchKey.CompositeImplicitAutograd
+                        and v == cpp.name(func)
+                    ):
+                        redundant_composite_implicit_autograd = True
+
+            # We count the number of dispatch keys which have not been ignored to prevent a dispatch table
+            # in which all backend keys are ignored but necessarily kept, remaining compositeimplicit,
+            # from being treated as redundant.
+            assert not (
+                num_dispatch_keys == 1 and redundant_composite_implicit_autograd
+            ), (
+                "unnecessary dispatch table for this function; just delete the dispatch "
+                "key entirely"
+            )
+            # if a function is a structured delegate, deleting the dispatch
+            # table is NOT semantics preserving
+            assert (
+                structured_delegate
+                or dispatch.keys() != {DispatchKey.CompositeImplicitAutograd}
+                or dispatch[DispatchKey.CompositeImplicitAutograd].supports_symint()
+                or num_dispatch_keys != 1
+            ), (
+                f"unexpected name for singleton CompositeImplicitAutograd dispatch entry: expected {cpp.name(func)} "
+                f"but got {dispatch[DispatchKey.CompositeImplicitAutograd]}.  Rename your implementation to the expected "
+                "name, then delete the dispatch table"
+            )
+        elif not structured and structured_delegate is None:
+            name = str(func.name.name)
+            assert not (
+                name.startswith("new_")
+                or name.endswith("_like")
+                # TODO: maybe it's better to test the return
+                or (
+                    func.arguments.tensor_options
+                    and not func.arguments.has_tensor_arg()
+                )
+            ), (
+                f"expected {name} to have a CompositeExplicitAutograd "
+                "dispatch entry, but there was no dispatch table.  Factory functions "
+                "should not have implicit dispatch as they should not be decomposed "
+                "for __torch_dispatch__"
+            )
+            dispatch[DispatchKey.CompositeImplicitAutograd] = BackendMetadata(
+                cpp.name(func), structured=False, cpp_namespace=DEFAULT_KERNEL_NAMESPACE
+            )
+
+        composites_in_dispatch = [
+            d
+            for d in dispatch
+            if d == DispatchKey.CompositeExplicitAutograd
+            or d == DispatchKey.CompositeExplicitAutogradNonFunctional
+            or d == DispatchKey.CompositeImplicitAutograd
+            or d == DispatchKey.CompositeImplicitAutogradNestedTensor
+        ]
+
+        assert len(composites_in_dispatch) <= 1 or (
+            len(composites_in_dispatch) == 2
+            and (
+                DispatchKey.CompositeExplicitAutogradNonFunctional
+                not in composites_in_dispatch
+            )
+            and (
+                DispatchKey.CompositeImplicitAutogradNestedTensor
+                in composites_in_dispatch
+            )
+        ), (
+            "cannot specify more than one of CompositeExplicitAutograd, CompositeExplicitAutogradNonFunctional, "
+            "or CompositeImplicitAutograd on a single kernel; each "
+            "strictly subsumes the other.  If you wanted to provide an explicit autograd "
+            "implementation, specify CompositeExplicitAutograd; otherwise specify CompositeImplicitAutograd only"
+        )
+
+        autogen_str = e.pop("autogen", "")
+        assert isinstance(autogen_str, str)
+        autogen = (
+            []
+            if autogen_str == ""
+            else [OperatorName.parse(x) for x in autogen_str.split(", ")]
+        )
+
+        raw_ufunc_inner_loop = e.pop("ufunc_inner_loop", {})
+        ufunc_inner_loop = {}
+        if isinstance(raw_ufunc_inner_loop, str):
+            ufunc_inner_loop[UfuncKey.Generic] = UfuncInnerLoop.parse(
+                raw_ufunc_inner_loop, UfuncKey.Generic
+            )
+        elif isinstance(raw_ufunc_inner_loop, dict):
+            for k, vo in raw_ufunc_inner_loop.items():
+                if k == "__line__":
+                    continue
+                assert isinstance(k, str), f"ufunc_inner_loop key is not a str: {k}"
+                assert isinstance(vo, str), f"ufunc_inner_loop value is not a str: {v}"
+                ufunc_key = UfuncKey.parse(k)
+                ufunc_inner_loop[ufunc_key] = UfuncInnerLoop.parse(vo, ufunc_key)
+        else:
+            raise AssertionError(
+                f"ufunc_inner_loop not str or dict: {raw_ufunc_inner_loop}"
+            )
+        # Program the BackendIndex for the implicit dispatch entry from ufunc
+        if ufunc_inner_loop:
+            assert structured, "ufunc must be structured"
+
+            # Delay import ufunc here to avoid circular import issue
+            # See: https://github.com/pytorch/pytorch/issues/81294
+            import torchgen.api.ufunc as ufunc
+
+            for dispatch_key in UFUNC_DISPATCH_KEYS:
+                assert (
+                    dispatch_key not in dispatch
+                ), f"ufunc should not have explicit dispatch entry for {dispatch_key}"
+                dispatch[dispatch_key] = BackendMetadata(
+                    kernel=ufunc.schema_kernel_name(func, dispatch_key),
+                    structured=True,
+                    cpp_namespace=DEFAULT_KERNEL_NAMESPACE,
+                )
+
+        if structured_delegate:
+            # Structured functions MUST have a dispatch table
+            is_abstract = True
+        else:
+            is_abstract = (
+                dispatch.keys() != {DispatchKey.CompositeImplicitAutograd}
+                and dispatch.keys()
+                != {DispatchKey.CompositeImplicitAutogradNestedTensor}
+                and dispatch.keys()
+                != {
+                    DispatchKey.CompositeImplicitAutograd,
+                    DispatchKey.CompositeImplicitAutogradNestedTensor,
+                }
+            )
+
+        has_composite_implicit_autograd_kernel = (
+            DispatchKey.CompositeImplicitAutograd in dispatch
+        )
+        has_composite_implicit_autograd_nested_tensor_kernel = (
+            DispatchKey.CompositeImplicitAutogradNestedTensor in dispatch
+        )
+        has_composite_explicit_autograd_kernel = (
+            DispatchKey.CompositeExplicitAutograd in dispatch
+        )
+        has_composite_explicit_autograd_non_functional_kernel = (
+            DispatchKey.CompositeExplicitAutogradNonFunctional in dispatch
+        )
+
+        # We aren't going to store dispatch metadata inline in NativeFunctions;
+        # instead it is separately indexed by backend (so other backends can
+        # add more dispatch entries after the fact).  Reindex the individual
+        # metadata by OperatorName!
+        backend_metadata = {k: {func.name: v} for k, v in dispatch.items()}
+
+        # don't care if it exists or not; make it easier to use this function
+        # with other yaml parsers that aren't setting __line__ in the dict
+        e.pop("__line__", None)
+        assert not e, f"leftover entries: {e}"
+
+        # Asserts that we can't do in post_init, because they rely on backend-specific info
+        if structured_delegate is not None:
+            for key in STRUCTURED_DISPATCH_KEYS:
+                assert key not in dispatch, (
+                    f"if structured_delegate, then must not have {key} in dispatch dictionary "
+                    "(it is delegated!)"
+                )
+
+        return (
+            NativeFunction(
+                func=func,
+                use_const_ref_for_mutable_tensors=use_const_ref_for_mutable_tensors,
+                variants=variants,
+                structured=structured,
+                structured_delegate=structured_delegate,
+                structured_inherits=structured_inherits,
+                precomputed=precomputed,
+                autogen=autogen,
+                ufunc_inner_loop=ufunc_inner_loop,
+                manual_kernel_registration=manual_kernel_registration,
+                manual_cpp_binding=manual_cpp_binding,
+                python_module=python_module,
+                category_override=category_override,
+                device_guard=device_guard,
+                device_check=device_check,
+                loc=loc,
+                cpp_no_default_args=cpp_no_default_args,
+                is_abstract=is_abstract,
+                has_composite_implicit_autograd_kernel=has_composite_implicit_autograd_kernel,
+                has_composite_implicit_autograd_nested_tensor_kernel=has_composite_implicit_autograd_nested_tensor_kernel,
+                has_composite_explicit_autograd_kernel=has_composite_explicit_autograd_kernel,
+                has_composite_explicit_autograd_non_functional_kernel=has_composite_explicit_autograd_non_functional_kernel,
+                tags=tags,
+                namespace=namespace,
+            ),
+            backend_metadata,
+        )
+
+    def validate_unstructured(self) -> None:
+        # TODO: probably better to accumulate these errors and report them all
+        # at once
+        assert not self.structured, (
+            "This function is structured, but there was "
+            "no valid functional variant of it."
+        )
+        assert self.structured_delegate, (
+            "This function delegates to another structured out function, "
+            "but no valid function was found (the delegate may not exist, or it has the wrong type)"
+        )
+
+    # __post_init__ functions in dataclasses can be used to do extra
+    # validation after construction.
+    #
+    # Notice that we don't do any type validation here.  In fact, we
+    # rely exclusively on mypy to check if you've done types correctly!
+    # Validation is for nontrivial invariants that cannot be (conveniently)
+    # encoded in the type system.
+    def __post_init__(self) -> None:
+        if self.func.arguments.out:
+            assert self.variants == {Variant.function}, (
+                "Native functions with out arguments MUST "
+                "be declared with only function variant; e.g., variants: function; "
+                "otherwise you will tickle a Python argument binding bug "
+                "(which usually manifests itself as the result variable being undefined.)"
+            )
+        if self.structured:
+            assert self.func.kind() == SchemaKind.out, (
+                "Put structured field on the out= "
+                "variant of a function; did you mean structured_delegate?"
+            )
+            assert (
+                self.device_guard
+            ), "device_guard: False is not respected by structured kernels"
+        if self.structured_delegate:
+            assert self.func.kind() != SchemaKind.out, (
+                "structured_delegate field not allowed "
+                "on out= functions; did you mean structured?"
+            )
+            assert (
+                self.device_guard
+            ), "device_guard: False is not respected by structured kernels"
+        # Technically, with the asserts above, this assert is impossible to
+        # happen
+        assert not (
+            self.structured and self.structured_delegate
+        ), "Cannot have both structured and structured_delegate on function"
+        defaulted_arguments = {
+            a.name for a in self.func.schema_order_arguments() if a.default is not None
+        }
+        invalid_args = set.difference(self.cpp_no_default_args, defaulted_arguments)
+        assert len(invalid_args) == 0, f"Invalid cpp_no_default_args: {invalid_args}"
+        if self.structured_inherits is not None:
+            assert (
+                self.structured
+            ), "structured_inherits must also imply structured: True"
+        if str(self.func.name).startswith("_foreach"):
+            assert self.device_check == DeviceCheckType.NoCheck, (
+                "foreach kernels fall back to slow path when tensor are on different devices, "
+                "device_check not allowed to be enabled"
+            )
+
+        # NB: if your function accidentally has rand/dropout/... in its name
+        # but is not actually random, feel free to amend this to special case
+        if (
+            "rand" in str(self.func.name)
+            or (
+                (
+                    "dropout" in str(self.func.name)
+                    or any(
+                        "dropout" in arg.name for arg in self.func.arguments.flat_all
+                    )
+                )
+                # Backwards of dropout is typically deterministic
+                and "backward" not in str(self.func.name)
+                and str(self.func.name.name) not in ["_cudnn_init_dropout_state"]
+            )
+            or self.func.arguments.has_generator_arg()
+        ):
+            assert "nondeterministic_seeded" in self.tags, str(self.func.name)
+
+    @property
+    def has_composite_kernel(self) -> bool:
+        return (
+            self.has_composite_implicit_autograd_kernel
+            or self.has_composite_explicit_autograd_kernel
+            or self.has_composite_explicit_autograd_non_functional_kernel
+        ) or (
+            self.has_composite_implicit_autograd_kernel
+            and self.has_composite_implicit_autograd_nested_tensor_kernel
+        )
+
+    @property
+    def is_view_op(self) -> bool:
+        rets = self.func.returns
+        is_non_mutating_view = len(rets) > 0 and any(
+            r.annotation is not None and not r.annotation.is_write for r in rets
+        )
+        # See Note [resize_ in Functionalization] for more dtails
+        is_inplace_view = (
+            "inplace_view" in self.tags
+            and str(self.func.name) != "resize_"
+            and str(self.func.name) != "resize_as_"
+        )
+        is_wildcard_view = any(
+            inp.annotation is not None and "*" in inp.annotation.alias_set_after
+            for inp in self.func.schema_order_arguments()
+        )
+        return is_non_mutating_view or is_inplace_view or is_wildcard_view
+
+    @property
+    def view_schema_kind(self) -> ViewSchemaKind:
+        if self.is_view_op and self.func.name.name.inplace:
+            assert "inplace_view" in self.tags
+            return ViewSchemaKind.aliasing_inplace
+        if self.is_view_op:
+            return ViewSchemaKind.aliasing
+        else:
+            return ViewSchemaKind.non_aliasing
+
+    @property
+    def root_name(self) -> str:
+        return self.func.name.name.base
+
+    @property
+    def part_of_structured_group(self) -> bool:
+        return self.structured or self.structured_delegate is not None
+
+
+class SchemaKind(Enum):
+    functional = auto()
+    inplace = auto()
+    out = auto()
+    mutable = auto()
+    scratch = auto()
+
+
+# A structured kernel is guaranteed to have a functional and out variant, and
+# optionally an inplace variant.
+#
+# NB: we create NativeFunctionsGroup *even if* the function is not
+# actually annotated structured.  Test the structured boolean to see if it
+# actually is structured or not.
+@dataclass(frozen=True)
+class NativeFunctionsGroup:
+    functional: NativeFunction
+    inplace: NativeFunction | None
+    mutable: NativeFunction | None
+    out: NativeFunction
+
+    @property
+    def structured(self) -> bool:
+        # Whether or not the operator has a meta() function. This information is backend-agnostic.
+        return self.out.structured
+
+    def __post_init__(self) -> None:
+        test_sig: FunctionSchema = self.functional.func.signature()
+        for f in self.functions():
+            if test_sig != f.func.signature():
+                raise AssertionError(
+                    "NativeFunctionsGroup constructed from two NativeFunctions "
+                    f"that don't have matching signatures: {test_sig} != {f.func.signature()}"
+                )
+
+            if self.structured != f.part_of_structured_group:
+                raise AssertionError(
+                    "NativeFunctionsGroup constructed from structured and unstructured "
+                    f"functions: {self.out.func.name} and {f.func.name}"
+                )
+        assert self.functional.func.kind() == SchemaKind.functional
+        assert self.out.func.kind() == SchemaKind.out
+        assert self.functional.namespace == self.out.namespace
+        if self.inplace is not None:
+            assert self.inplace.func.kind() == SchemaKind.inplace
+            assert self.inplace.namespace == self.functional.namespace
+
+        if self.mutable is not None:
+            assert self.mutable.func.kind() == SchemaKind.mutable
+            assert self.mutable.namespace == self.functional.namespace
+            # See Note [Overload Ambiguity With Functional Variants]
+            assert self.functional.func.name.name.functional_overload
+
+        if self.structured:
+            # For now, structured composite kernels are not supported (need some
+            # design work to figure out how to make the composite case work)
+            assert (
+                not self.out.has_composite_implicit_autograd_kernel
+                and not self.out.has_composite_implicit_autograd_nested_tensor_kernel
+            )
+
+            assert self.functional.structured_delegate == self.out.func.name, (
+                f"{self.functional.func.name} delegates to {self.functional.structured_delegate} "
+                f"but its actual delegate is {self.out.func.name}"
+            )
+            if self.inplace is not None:
+                assert self.inplace.structured_delegate == self.out.func.name
+
+        generated_fns = sorted(
+            [str(f.func.name) for f in self.functions() if "generated" in f.tags]
+        )
+        generated_fns_str = ", ".join(str(x) for x in generated_fns)
+        expected_generated_fns: set[str] = set()
+        for f in self.functions():
+            expected_generated_fns.update(str(op) for op in f.autogen)
+        expected_generated_fns_str = ", ".join(
+            str(x) for x in sorted(expected_generated_fns)
+        )
+        if len(expected_generated_fns) == 0 and len(generated_fns) > 0:
+            raise RuntimeError(
+                f"The codegen expects to be able to generate '{generated_fns_str}'."
+                " In order to generate them however, we expect them to be called out explicitly in the yaml."
+                f" Please add an 'autogen: {generated_fns_str}' line to the entry for {str(f.func.name)}"
+            )
+        if expected_generated_fns_str != generated_fns_str:
+            raise RuntimeError(
+                f"The codegen expects to be able to generate '{generated_fns_str}'."
+                f" To do so, it expects a line: 'autogen: {generated_fns_str}'."
+                f" Instead, it found 'autogen: {expected_generated_fns_str}'"
+            )
+
+    def signature(self) -> FunctionSchema:
+        return self.out.func.signature()
+
+    def functions(self) -> Iterator[NativeFunction]:
+        yield self.functional
+        yield self.out
+        if self.inplace is not None:
+            yield self.inplace
+        if self.mutable is not None:
+            yield self.mutable
+
+    @property
+    def root_name(self) -> str:
+        return self.functional.root_name
+
+    @staticmethod
+    def from_dict(d: dict[SchemaKind, NativeFunction]) -> NativeFunctionsGroup | None:
+        assert d
+        if len(d) == 1:
+            return None
+        d = dict(d)  # non-destructive updates please
+        functional = d.pop(SchemaKind.functional, None)
+        inplace = d.pop(SchemaKind.inplace, None)
+        mutable = d.pop(SchemaKind.mutable, None)
+        out = d.pop(SchemaKind.out, None)
+        assert not d
+        assert functional is not None
+        # There are a few operators which only have functional/inplace variants;
+        # these don't count as structured for our purposes here
+        if out is None:
+            return None
+        # assuming all variants have the same namespace
+        return NativeFunctionsGroup(
+            functional=functional,
+            inplace=inplace,
+            mutable=mutable,
+            out=out,
+        )
+
+
+@dataclass(frozen=True)
+class BackendMetadata:
+    # The name of the backend kernel, for a given operator
+    # for in-tree backends. These names come directly from the 'dispatch" field
+    # in native_functions.yaml. The dispatch entry is optional; in that
+    # case, that is equivalent to having written:
+    #
+    #   dispatch:
+    #       CompositeImplicitAutograd: $operator_name
+    kernel: str
+    # Whether or not the operator has a structured kernel implemented, for this particular backend.
+    # For in-tree backends, they all have the same value for structured- this is listed
+    # in native_functions.yaml.
+    # However, external backends like XLA can indendently toggle which ops are structured.
+    structured: bool
+
+    # The namespace for kernels, default value: DEFAULT_KERNEL_NAMESPACE
+    cpp_namespace: str
+
+    def supports_symint(self) -> bool:
+        return "_symint" in self.kernel
+
+
+@dataclass(frozen=True)
+class UfuncInnerLoop:
+    name: str
+    supported_dtypes: OrderedSet[ScalarType]
+    # key is stored here because it affects the semantics of name,
+    # so its helpful to have them together for further processing
+    ufunc_key: UfuncKey
+
+    @staticmethod
+    def parse(value: str, ufunc_key: UfuncKey) -> UfuncInnerLoop:
+        name, supported_dtypes_str = value.split(" ", 1)
+        assert supported_dtypes_str[0] == "("
+        assert supported_dtypes_str[-1] == ")"
+        supported_dtypes: OrderedSet[ScalarType] = OrderedSet()
+        for k in supported_dtypes_str[1:-1].split(", "):
+            supported_dtypes |= ScalarType.parse_set(k)
+        return UfuncInnerLoop(
+            name=name, supported_dtypes=supported_dtypes, ufunc_key=ufunc_key
+        )
+
+
+# BackendIndex represents a backend.
+# The BackendIndex encodes per-operator information that is potentially different
+# for each backend. The most obvious example is the name of the kernel
+# (the 'dispatch' entry in native_functions.yaml).
+# However, there can be other examples of different backends having different information.
+# External backends can choose to opt their kernels to be structured independently from in-tree backends,
+# which means that this information isn't inherently tied to a NativeFunction- it's different per backend.
+@dataclass(frozen=True)
+class BackendIndex:
+    dispatch_key: DispatchKey
+    # Mainly important for structured kernels, this determines which variant in the operator group is used to implement the others.
+    # All in-tree ops use out kernels, while XLA uses functional kernels.
+    use_out_as_primary: bool
+    # Whether the backend requires a device guard, and device checks.
+    # For in-tree backends, this is currently just CUDA/HIP
+    # For out-of-tree backends, this is currently just Intel XPU
+    device_guard: bool
+    # Whether the backend is in-tree (CPU/CUDA) or out-of-tree (XLA)
+    external: bool
+    # Other backend-specific information that is on a per-operator basis
+    index: dict[OperatorName, BackendMetadata]
+
+    @staticmethod
+    def grow_index(
+        parent_index: dict[DispatchKey, dict[OperatorName, BackendMetadata]],
+        child_index: dict[DispatchKey, dict[OperatorName, BackendMetadata]],
+    ) -> None:
+        for k, v in child_index.items():
+            for op_name, metadata in v.items():
+                assert (
+                    op_name not in parent_index[k]
+                ), f"duplicate operator {op_name} for dispatch key {k}"
+                parent_index[k][op_name] = metadata
+
+    def primary(self, g: NativeFunctionsGroup) -> NativeFunction:
+        if self.use_out_as_primary:
+            return g.out
+        else:
+            return g.functional
+
+    def has_kernel(self, g: NativeFunction | NativeFunctionsGroup) -> bool:
+        m = self.get_kernel(g)
+        return m is not None
+
+    def get_kernel(
+        self, g: NativeFunction | NativeFunctionsGroup
+    ) -> BackendMetadata | None:
+        if isinstance(g, NativeFunction):
+            f = g
+        elif isinstance(g, NativeFunctionsGroup):
+            f = self.primary(g)
+        else:
+            assert_never(g)
+        if f.func.name not in self.index:
+            return None
+        return self.index[f.func.name]
+
+    def native_function_class_name(self) -> str | None:
+        if self.external:
+            return f"{str(self.dispatch_key)}NativeFunctions"
+        else:
+            # TODO: This discrepancy isn't required; we could also generated
+            # a class for in-tree kernels. It'll just require carefully
+            # updating every kernel definition + callsite of every in-tree aten kernel.
+            return None
+
+
+# The function schema is undoubtedly the most important data structure
+# in all of the codegen, as it defines the type signature for operators,
+# and most of the code generation we do is type directed (e.g., look at
+# the types, decide what to do.  Think about how we code generate
+# C++ function stubs!)
+#
+# We will also see in this class the general structure for how we model
+# data in this code generation.  A few notable properties to point out
+# ahead of time:
+#
+#   - These dataclasses are a *lossless* representation of the strings
+#     they are parsed from.  In fact, we assert that given the
+#     information stored in the dataclass, we can exactly reconstruct
+#     the string we parsed from (and assert this inside the parse
+#     definition).  There are a few reasons for this:
+#
+#       - If you find that it is difficult to reconstruct the string
+#         given a dataclass, that is a clue that you are data
+#         representation is wrong.
+#
+#       - It helps ensure that all relevant information is present
+#         in the dataclass, so that downstream users aren't tempted
+#         to reparse the original string to get some information
+#         that was omitted.
+#
+#       - It forces you to represent the data in-memory in the same way
+#         it is recorded textually, which makes the dataclasses easier
+#         to understand for someone who is familiar with the
+#         textual format.  (As a tradeoff, it means you have to model
+#         the syntax, even when it is inconvenient.  But maybe that means
+#         the syntax is bad!)  If you don't understand the internal
+#         representation, go look at the printing code to see how
+#         it maps onto the surface syntax!
+#
+#       - It makes it easy to test the parsing code, as parsing code
+#         that is inconsistent with the string code will fail early
+#         and loudly.  (As a tradeoff, it makes the parsing code a bit
+#         brittle (in particular, with trivial whitespace changes you
+#         are likely to trigger an assert error).
+#
+#     In general, try to make the __str__ code as simple as possible
+#     (even at the cost of more complex parsing logic.)  Additionally,
+#     try to minimize redundancy in data representation.  (Precomputed
+#     fields are OK though: they are defined as a simple function on
+#     the canonical representation in question.)
+#
+#   - These dataclasses are all frozen; once constructed their
+#     values never change.  This makes it easy to tell where any
+#     given data came from: just look to the constructor.  As a
+#     tradeoff, you can't easily "decorate" a schema with extra
+#     information from a post-facto analysis.  We impose this
+#     restriction to make these structures more understandable.
+#
+@dataclass(frozen=True)
+class FunctionSchema:
+    # The name of the operator this function schema describes.
+    name: OperatorName
+
+    arguments: Arguments
+
+    # TODO: Need to handle collisions with argument names at some point
+    returns: tuple[Return, ...]
+
+    @property
+    def is_mutable(self) -> bool:
+        def is_write(arg: Argument) -> bool:
+            if arg.annotation is None:
+                return False
+            return arg.annotation.is_write
+
+        # Corresponds to torch._C._FunctionSchema.is_mutable
+        # See aten/src/ATen/core/function_schema.h (keep these in sync)
+        return any(is_write(a) for a in self.arguments.flat_all)
+
+    def schema_order_arguments(self) -> Iterator[Argument]:
+        return itertools.chain(
+            self.arguments.flat_positional,
+            self.arguments.flat_kwarg_only,
+            self.arguments.out,
+        )
+
+    decl_re = re.compile(r"(?P<name>[^\(]+)\((?P<args>.*)\) -> (?P<returns>.*)")
+
+    @staticmethod
+    def parse(func: str) -> FunctionSchema:
+        # We should probably get a proper parser here
+        decls = FunctionSchema.decl_re.findall(func)
+        assert len(decls) == 1, f"Invalid function schema: {func}"
+        ops, args, return_decl = decls[0]
+        name = OperatorName.parse(ops)
+        arguments = Arguments.parse(args)
+        returns = parse_returns(return_decl)
+        r = FunctionSchema(name=name, arguments=arguments, returns=returns)
+        assert str(r) == func, f"{str(r)} != {func}"
+        return r
+
+    def returns_are_aliased(self) -> bool:
+        # We assert earlier that schemas can't have a mix of aliased and non-aliased returns
+        return any(
+            r
+            for r in self.returns
+            if r.annotation is not None and r.annotation.is_write
+        )
+
+    def __post_init__(self) -> None:
+        for arg, ret in zip(self.arguments.out, self.returns):
+            assert arg.annotation == ret.annotation, (
+                "Out arguments must have matching return Tensor; furthermore, "
+                "the ith-argument needs to correspond to the ith return"
+            )
+        # We also enforce that if you have any mutable, positional args, then they are not returned.
+        # This makes it easier to group these functions properly with their functional/out= counterparts.
+        for a in self.arguments.post_self_positional_mutable:
+            assert not any(
+                a.annotation == r.annotation for r in self.returns
+            ), f"If you have a schema with mutable positional args, we expect them to not be returned. schema: {str(self)}"
+        # Invariant: we expect out arguments to appear as keyword arguments in the schema.
+        # This means that all mutable returns should be aliased to a keyword argument
+        # (except for "self", which we explicitly don't treat as an out argument because of its use in methods)
+        # See Note [is_out_fn]
+        out_and_self = list(self.arguments.out) + [
+            arg for arg in self.arguments.flat_positional if arg.name == "self"
+        ]
+        mutable_returns = [
+            ret
+            for ret in self.returns
+            if ret.annotation is not None and ret.annotation.is_write
+        ]
+        immutable_returns = [
+            ret
+            for ret in self.returns
+            if ret.annotation is None or not ret.annotation.is_write
+        ]
+        # Some assertions: We don't want any functions with a return type of "-> (Tensor(a!), Tensor)",
+        # because:
+        # (1) It's more annoying to handle properly
+        # (2) It's unnecessary - you can't method-chain on the first (mutated) output because it's part of a tuple.
+        # Instead, we expect the (a!) argument to not be returned.
+        assert (
+            len(mutable_returns) == 0 or len(immutable_returns) == 0
+        ), f"NativeFunctions must have either only mutable returns, or only immutable returns. Found: {str(self)}"
+        for ret in mutable_returns:
+            assert any(ret.annotation == arg.annotation for arg in out_and_self), (
+                'All mutable returns must be aliased either to a keyword argument, or to "self". '
+                "Did you forget to mark an out argument as keyword-only?"
+            )
+        if self.arguments.out:
+            # out= ops that return their mutable inputs are only really useful for method chaining.
+            # And method chaining is only really useful if the thing you're returning is a plain Tensor.
+            # So ideally, we'd enforce that out= ops with a single plain mutable tensor should return the tensor,
+            # and all other types of out= op schemas should return void.
+            # There are a bunch of existing out= ops that return tuples of tensors though, so we're stuck with allowing that.
+            if any(a.type != BaseType(BaseTy.Tensor) for a in self.arguments.out):
+                assert (
+                    len(self.returns) == 0
+                ), "out= ops that accept tensor lists as out arguments "
+                "are expected to have no return type (since you can't do method chaining on them)"
+            else:
+                # mutable keyword arguments whose name has _scratch_ prefix are
+                # scratch tensors for memory planning and should not be returned
+                assert (
+                    len(
+                        [
+                            arg
+                            for arg in self.arguments.out
+                            if not arg.name.startswith("_scratch_")
+                        ]
+                    )
+                    == len(self.returns)
+                ), "Must return as many arguments as there are out arguments, or no return at all"
+
+        if self.name.name.inplace:
+            self_a = self.arguments.self_arg
+            assert (
+                self_a
+                and self_a.argument.annotation
+                and self_a.argument.annotation.is_write
+            )
+            if self_a.argument.type == BaseType(BaseTy.Tensor):
+                # All inplace ops with an ordinary `Tensor self` argument should return self,
+                # to allow for method chaining.
+                assert (
+                    len(self.returns) == 1
+                    and self.returns[0].annotation == self_a.argument.annotation
+                )
+            else:
+                # You can't method chain on non-tensor self arguments though (like a list[Tensor])
+                # so in all other cases we expect the return type to be none.
+                assert len(self.returns) == 0
+
+        if self.arguments.tensor_options is not None:
+            assert self.kind() == SchemaKind.functional, (
+                "Found an operator that is not functional or out variant, but has tensor options arguments."
+                "This is not allowed- tensor options arguments are only allowed for factory functions."
+                f"schema: {str(self)}"
+            )
+        if self.is_functional_fn():
+            assert self.kind() == SchemaKind.functional, (
+                "Found an operator that is not functional, but its overload contains the string 'functional'."
+                "This is a special keyword in the codegen, please use a different overload name."
+                f"schema: {str(self)}"
+            )
+
+    def is_functional_fn(self) -> bool:
+        return "functional" in self.name.overload_name
+
+    def is_out_fn(self) -> bool:
+        # Note [is_out_fn]
+        #
+        # out functions are the variants which take an explicit out= argument
+        # to populate into.  We need to know if a schema corresponds to an
+        # out function for several reasons:
+        #
+        #   - They codegen differently in C++ API
+        #       - codegen to at::add_out rather than at::add
+        #       - out argument is moved to front of C++ argument list
+        #
+        # out functions are DEFINED to be any function with a keyword-only
+        # argument that is mutable.  In principle, this could lead to a
+        # false positive if you define a function that mutates a
+        # kwarg only argument, but this isn't the "true" output of this
+        # function.  A more robust definition that would work in this
+        # case would also look at:
+        #
+        #   - The output types.  Out functions take in the arguments
+        #     they mutate and then return them again; this is sort
+        #     of "definitionally" what makes something an out function.
+        #     Historically, we DO check this for consistency.
+        #   - Correspondence with pure variant.  An out function
+        #     should have a signature equivalent to its pure variant,
+        #     but just with extra kwargs for the output elements.  This
+        #     is difficult to actually check for and historically
+        #     we only do this check in tools/
+        return bool(self.arguments.out)
+
+    def kind(self) -> SchemaKind:
+        """
+        What kind of schema is this?  A functional schema is one
+        that returns a newly allocated output; an inplace schema
+        modifies the self argument inplace; an out schema writes
+        the result into an explicitly provided out argument.
+        """
+        is_out = bool(self.arguments.out)
+        is_scratch = bool(
+            [arg for arg in self.arguments.out if arg.name.startswith("_scratch_")]
+        )
+        is_inplace = self.name.name.inplace
+        is_mutable = any(
+            a.annotation is not None and a.annotation.is_write
+            for a in self.arguments.post_self_positional
+        )
+        assert not (is_out and is_inplace)
+        # out= and inplace schemas can also have post_self_positional mutable args,
+        # but we give precedence to out= and inplace when deciding the schema kind.
+        # Tradeoff: we probably don't want to have to teach codegen that looks at inplace ops
+        # to also worry about mutable post_self_positional arguments,
+        # but it seems like a much bigger lift to classify them has having a new schema kind.
+        # The number of ops that fit in this strange category is small enough that
+        # we can probably manually write code for them instead of forcing the codegen to handle them.
+        if is_inplace:
+            return SchemaKind.inplace
+        elif is_scratch:
+            assert (
+                is_out
+            ), "invariant: all scratch operators are expected to be out= operators too"
+            return SchemaKind.scratch
+        elif is_out:
+            assert not is_scratch, "We should not categorize a scratch op as an out variant. Check if the order of if statements are expected!"  # noqa: B950
+            return SchemaKind.out
+        elif is_mutable:
+            return SchemaKind.mutable
+        else:
+            return SchemaKind.functional
+
+    # For every return:
+    # - If the return aliases an input, we return the input name
+    # - Otherwise, we return None.
+    # If return names were enforced to be consistent with aliasing information, then we wouldn't need this.
+    def aliased_return_names(self) -> list[str | None]:
+        outs: list[str | None] = []
+        for r in self.returns:
+            aliased_args = [
+                a
+                for a in self.arguments.flat_all
+                if a.annotation is not None and a.annotation == r.annotation
+            ]
+            if len(aliased_args) == 0:
+                outs.append(None)
+            elif len(aliased_args) == 1:
+                outs.append(aliased_args[0].name)
+            else:
+                aliased_names = ", ".join(a.name for a in aliased_args)
+                raise AssertionError(
+                    f"Found a return ({r.name})that aliases multiple inputs ({aliased_names})"
+                )
+        return outs
+
+    def signature(
+        self,
+        *,
+        strip_default: bool = False,
+        strip_view_copy_name: bool = False,
+        keep_return_names: bool = False,
+    ) -> FunctionSchema:
+        """
+                Certain schemas are 'related', in that they are simply
+                inplace/out/functional versions of the same function.  This method
+                factors these schemas into the "core" functional signature which
+                is equal across all versions.
+
+                Here is what normalization happens to the schema to convert
+                it to a signature:
+                - The overload name is stripped (name is retained, since
+                  it expresses semantic content about what the function does)
+                - Inplace is set False
+                - Out arguments are stripped
+                - Mutable post_self_positional args are converted to returns
+                - Mutability annotations are stripped  (this is sound
+                  because you cannot overload on mutability annotation)
+                - Return names are stripped since they are not overloadable and
+                  some variants have return names but some not
+                - TensorOptions are dropped
+                  because out= variants of factory functions don't include them
+                  (and we want to be able to pair up factory functions with their out variants)
+
+                Finally, we want to be able to pair up related "view" and their
+                corresponding "view_copy" operators. We do this by optionally
+                stripping the trailing "_copy" from the base name.
+
+                Example of a mutable op before and after:
+
+                f.func (Mutable operator):
+        _fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask)  # noqa: B950
+
+                f.func (Corresponding functional operator):
+        _fused_moving_avg_obs_fq_helper.functional(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask, Tensor running_min_out, Tensor running_max_out, Tensor scale_out, Tensor zero_point_out)  # noqa: B950
+
+                f.func.signature() output:
+        _fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)  # noqa: B950
+        """
+
+        def strip_ret_annotation(r: Return) -> Return:
+            return Return(
+                name=r.name if keep_return_names else None,
+                type=r.type,
+                annotation=None,
+            )
+
+        base_name = self.name.name.base
+        if strip_view_copy_name:
+            if base_name.endswith("_copy"):
+                base_name = base_name.replace("_copy", "")
+            elif base_name.endswith("_scatter"):
+                base_name = base_name.replace("scatter", "inverse")
+
+        # find mutable inputs that are not originally returned, and convert them to returns
+        returns_from_mutable_inputs = tuple(
+            # When we're grouping functions we strip the return names,
+            # but when we're generating the actual functional variants then we follow
+            # a convention for what to name the returns
+            Return(
+                name=f"{a.name}_out" if keep_return_names else None,
+                type=a.type,
+                annotation=None,
+            )
+            for a in itertools.chain(
+                # Order is important here (otherwise e.g. inplace with mutable args
+                # and out= with mutable args won't have the same signature)
+                [self.arguments.self_arg.argument]
+                if self.arguments.self_arg is not None
+                else [],
+                self.arguments.out,
+                self.arguments.post_self_positional,
+            )
+            if a.annotation is not None
+            and a.annotation.is_write
+            and not any(a.annotation == r.annotation for r in self.returns)
+        )
+        original_returns = tuple(map(strip_ret_annotation, self.returns))
+        # Ordering is important here. We expect the "mutable input" returns to come last.
+        returns = original_returns + returns_from_mutable_inputs
+
+        args_sig = self.arguments.signature(strip_default=strip_default)
+        # See Note [bernoulli.p schema]
+        if str(self.name) == "bernoulli.p":
+            args_sig = Arguments.parse(str(args_sig).replace("float p", "float p=0.5"))
+
+        return FunctionSchema(
+            name=OperatorName(
+                name=BaseOperatorName(
+                    base=base_name,
+                    inplace=False,
+                    dunder_method=self.name.name.dunder_method,
+                ),
+                overload_name="",  # stripped
+            ),
+            arguments=args_sig,
+            returns=returns,
+        )
+
+    def view_signature(self) -> FunctionSchema:
+        return self.signature(strip_view_copy_name=True)
+
+    def with_name(self, name: OperatorName) -> FunctionSchema:
+        return FunctionSchema(
+            name=name,
+            arguments=self.arguments,
+            returns=self.returns,
+        )
+
+    @property
+    def modifies_arguments(self) -> bool:
+        return self.kind() in [SchemaKind.inplace, SchemaKind.out, SchemaKind.mutable]
+
+    def has_symint(self) -> bool:
+        return self.arguments.has_symint_arg()
+
+    def __str__(self) -> str:
+        all_arguments_str = str(self.arguments)
+        if len(self.returns) == 1:
+            returns = str(self.returns[0])  # omit parentheses
+        else:
+            returns = "(" + ", ".join(map(str, self.returns)) + ")"
+        return f"{self.name}({all_arguments_str}) -> {returns}"
+
+
+# Here is the rest of the data model, described more briefly.
+
+
+# Simplified version for what actually shows up in built-ins.
+# Look at alias_info.h for expanded syntax.  If you need the structure,
+# you also need to make this structure recursive so it can be lined
+# up with the type components too.  For primitives this isn't really
+# necessary
+@dataclass(frozen=True)
+class Annotation:
+    # Typically only has one element.  Not actually a set so
+    # we can conveniently assume it is canonically ordered
+    alias_set: tuple[str, ...]
+    is_write: bool
+    alias_set_after: tuple[str, ...]
+
+    @staticmethod
+    def parse(ann: str) -> Annotation:
+        # TODO: implement a proper parser if this gets more ugly
+        # Regex Explanation:
+        # Example: "a! -> a|b"
+        # Group #1: alias before optional '|', required. Matches the first
+        #   character 'a' in the example
+        # Group #2: optional alias set after optional '|', matches empty string
+        #   in the example
+        # Group #3: optional "is write" flag, matches '!' in the example.
+        # Group #4: optional section containing arrow, matches " -> a|b" in the
+        #   example.
+        # Group #5: optional alias after set, supports wildcard, matches "a|b"
+        #   in the example.
+        # Group #6: optional sub-section of alias after set, matches "|b" in the
+        #   example.
+        m = re.match(r"^([a-z])(\|[a-z])*(!?)( -> (\*|[a-z](\|[a-z])*))?$", ann)
+
+        assert m is not None, f"unrecognized alias annotation {ann}"
+        before_alias = m.group(1) + (m.group(2) if m.group(2) else "")
+        alias_set = tuple(before_alias.split("|"))
+        is_write = m.group(3) == "!"
+        assert not (
+            is_write and len(alias_set) > 1
+        ), f"alias set larger than 1 is not mutable, got {ann} instead."
+        after_set = tuple(m.group(5).split("|")) if m.group(5) else ()
+        assert not (
+            len(before_alias) > 1 and len(after_set) > 1
+        ), f"before alias set and after alias set cannot be larger than 1 at the same time, got {ann} instead."
+        r = Annotation(
+            alias_set=alias_set, is_write=is_write, alias_set_after=after_set
+        )
+        assert str(r) == ann, f"{r} != {ann}"
+        return r
+
+    def __str__(self) -> str:
+        alias_set = "|".join(self.alias_set)
+        if self.is_write:
+            alias_set = f"{alias_set}!"
+        alias_set_after = "|".join(self.alias_set_after)
+        if alias_set_after:
+            alias_set = f'{alias_set}{" -> "}{alias_set_after}'
+        return alias_set
+
+
+# The base class for the type system.  This is also loosely modeled
+# off of jit_type.h, but we've simplified the hierarchy to focus
+# in on the aspects of the type system that matter for code generation
+# (for example, there's no SingleElementType subclass anymore).
+# You never actually construct a Type; usually it's going to be one
+# of the subclasses.  If Python had ADTs this would be one!
+@dataclass(frozen=True)
+class Type:
+    @staticmethod
+    def parse(t: str) -> Type:
+        r = Type._parse(t)
+        assert str(r) == t, f"{r} != {t}"
+        return r
+
+    @staticmethod
+    def _parse(t: str) -> Type:
+        m = re.match(r"^(.+)\?$", t)
+        if m is not None:
+            return OptionalType(Type.parse(m.group(1)))
+        m = re.match(r"^(.+)\[([0-9]+)?\]$", t)
+        if m is not None:
+            size = int(m.group(2)) if m.group(2) is not None else None
+            return ListType(elem=Type.parse(m.group(1)), size=size)
+
+        # '__torch__.torch.classes.' is the prefix for custom class
+        m = re.match(r"^__torch__\.torch\.classes\.([a-zA-Z0-9_.]+)$", t)
+        if m is not None:
+            return CustomClassType(m.group(1))
+        try:
+            return BaseType(BaseTy[t])
+        except KeyError as e:
+            raise RuntimeError(f"unrecognized type {t}") from e
+
+    def __str__(self) -> str:
+        raise NotImplementedError
+
+    # WARNING: These concepts are not very well-defined.  For example,
+    # is "int?" nullable? How about "int?[]".  They are defined
+    # so we can conveniently generate legacy Declarations.yaml but
+    # really we should probably just remove these at some point
+
+    def is_base_ty_like(self, base_ty: BaseTy) -> bool:
+        raise NotImplementedError
+
+    def is_tensor_like(self) -> bool:
+        return self.is_base_ty_like(BaseTy.Tensor)
+
+    def is_generator_like(self) -> bool:
+        return self.is_base_ty_like(BaseTy.Generator)
+
+    def is_symint_like(self) -> bool:
+        return self.is_base_ty_like(BaseTy.SymInt)
+
+    def is_nullable(self) -> bool:
+        raise NotImplementedError
+
+    def is_list_like(self) -> ListType | None:
+        raise NotImplementedError
+
+
+# Base types are simple, atomic types with no further structure
+class BaseTy(Enum):
+    Generator = auto()
+    ScalarType = auto()
+    Tensor = auto()
+    int = auto()
+    Dimname = auto()
+    DimVector = auto()
+    float = auto()
+    str = auto()
+    bool = auto()
+    Layout = auto()
+    Device = auto()
+    DeviceIndex = auto()
+    Scalar = auto()
+    MemoryFormat = auto()
+    QScheme = auto()
+    Storage = auto()
+    Stream = auto()
+    SymInt = auto()
+    SymBool = auto()
+    GraphModule = auto()
+
+
+@dataclass(frozen=True)
+class BaseType(Type):
+    name: BaseTy
+
+    def __str__(self) -> str:
+        return f"{self.name.name}"
+
+    def is_base_ty_like(self, base_ty: BaseTy) -> bool:
+        return self.name == base_ty
+
+    def is_nullable(self) -> bool:
+        return False
+
+    def is_list_like(self) -> ListType | None:
+        return None
+
+    def is_symint_like(self) -> bool:
+        return self.name == BaseTy.SymInt
+
+
+# Optional types may be specified, or may also be validly given None
+@dataclass(frozen=True)
+class OptionalType(Type):
+    elem: Type
+
+    def __str__(self) -> str:
+        return f"{self.elem}?"
+
+    def is_base_ty_like(self, base_ty: BaseTy) -> bool:
+        return self.elem.is_base_ty_like(base_ty)
+
+    def is_symint_like(self) -> bool:
+        return self.elem.is_symint_like()
+
+    def is_nullable(self) -> bool:
+        return True
+
+    def is_list_like(self) -> ListType | None:
+        return self.elem.is_list_like()
+
+
+# A type representing a PyTorch custom class
+@dataclass(frozen=True)
+class CustomClassType(Type):
+    class_name: str
+
+    def __str__(self) -> str:
+        """
+        Return the class name will prefix __torch__.torch.classes
+        """
+        return f"__torch__.torch.classes.{self.class_name}"
+
+    def is_base_ty_like(self, base_ty: BaseTy) -> bool:
+        return False
+
+    def is_symint_like(self) -> bool:
+        return False
+
+    def is_nullable(self) -> bool:
+        """
+        Assume a custom class is not nullable.
+        """
+        return False
+
+    def is_list_like(self) -> ListType | None:
+        return None
+
+
+# List types specify that we may have multiples of an element.  We
+# also support explicit sizes on list types, but these have
+# some nontrivial semantics!  (However, for C++ API purposes, explicit
+# sizes are mostly erased from the type system.)
+#
+# DANGER WILL ROBINSON: C++ elaboration depends on elem type; e.g.,
+# int[] elaborates differently than bool[3]!
+@dataclass(frozen=True)
+class ListType(Type):
+    elem: Type
+    size: int | None
+
+    def __str__(self) -> str:
+        size = f"{self.size}" if self.size else ""
+        return f"{self.elem}[{size}]"
+
+    def is_base_ty_like(self, base_ty: BaseTy) -> bool:
+        return self.elem.is_base_ty_like(base_ty)
+
+    def is_symint_like(self) -> bool:
+        return self.elem.is_symint_like()
+
+    def is_nullable(self) -> bool:
+        return self.elem.is_nullable()
+
+    def is_list_like(self) -> ListType | None:
+        return self
+
+
+@dataclass(frozen=True)
+class Argument:
+    # NB: I didn't put kwarg_only as a boolean field here, unlike
+    # c10::Argument, so that printing works correctly
+
+    name: str
+    type: Type
+    default: str | None
+
+    # The semantics of the annotation field are a little strange.
+    #
+    # Alias annotations parametrize Tensors (since Tensors are the only things
+    # that can alias.)  This motivates why I write Tensor(a!)?  (and not, for
+    # example, Tensor?(a!)), because the (a!) describes aliasing on the tensor,
+    # which may be optional (i.e., the alias annotation should bind first to
+    # Tensor, before the optional postfix annotation).
+    #
+    # However, despite being a property of Tensor, we (and c10::Argument)
+    # store the annotation at the top level of the Argument, rather than
+    # inside the embedded Tensor type.  In the C++ version of this
+    # class, we then go through great lengths to mimic the type
+    # structure in the annotation structure so we can correlate
+    # annotations with types.
+    #
+    # Now, it turns out, in all applications in code generation, the
+    # structure of annotated types is very simple.  So we just hard
+    # code it here.  But if we ever do get anything more complex, this
+    # model will have to change!
+    annotation: Annotation | None
+
+    @property
+    def alias_info(self) -> Annotation | None:
+        return self.annotation
+
+    @staticmethod
+    def parse(arg: str) -> Argument:
+        name: str
+        default: str | None
+        assert " " in arg, f"illegal argument '{arg}'"
+        if "=" in arg:
+            assert arg.count("=") == 1, f"illegal argument with default value: '{arg}'"
+            type_and_annot_and_name, default = arg.split("=")
+            type_and_annot, name = type_and_annot_and_name.rsplit(" ", 1)
+            name_and_default = f"{name}={default}"
+        else:
+            type_and_annot, name_and_default = arg.rsplit(" ", 1)
+            name = name_and_default
+            default = None
+        # TODO: deduplicate annotation matching with Return
+        match = re.match(r"Tensor\((.+)\)(.*)", type_and_annot)
+        annotation: Annotation | None
+        if match:
+            # If you update this, make sure the __str__ still works too
+            assert match.group(2) in [
+                "",
+                "?",
+                "[]",
+            ], "unrecognized alias analysis form with Tensor"
+            type_s = "Tensor" + match.group(2)
+            annotation = Annotation.parse(match.group(1))
+        else:
+            type_s = type_and_annot
+            annotation = None
+        type = Type.parse(type_s)
+        r = Argument(
+            name=name,
+            type=type,
+            default=default,
+            annotation=annotation,
+        )
+        assert str(r) == arg, f"{str(r)} != {arg}"
+        return r
+
+    @property
+    def is_write(self) -> bool:
+        return self.annotation is not None and self.annotation.is_write
+
+    def __str__(self) -> str:
+        type = f"{self.type}"
+        if self.annotation:
+            assert type in ["Tensor", "Tensor?", "Tensor[]"]
+            type = type.replace("Tensor", f"Tensor({self.annotation})")
+        if self.name is None:
+            return type
+        else:
+            mb_default = ""
+            if self.default:
+                mb_default = f"={self.default}"
+            return f"{type} {self.name}{mb_default}"
+
+
+@dataclass(frozen=True)
+class Return:
+    name: str | None
+    type: Type
+    annotation: Annotation | None
+
+    @property
+    def alias_info(self) -> Annotation | None:
+        return self.annotation
+
+    @staticmethod
+    def parse(arg: str) -> Return:
+        name: str | None
+        if " " in arg:
+            type_and_annot, name = arg.rsplit(" ", 1)
+        else:
+            type_and_annot = arg
+            name = None
+        match = re.match(r"Tensor\((.+)\)(.*)", type_and_annot)
+        annotation: Annotation | None
+        if match:
+            # If you update this, make sure the __str__ still works too
+            assert match.group(2) in [
+                "",
+                "?",
+                "[]",
+            ], "unrecognized alias analysis form with Tensor"
+            type_s = "Tensor" + match.group(2)
+            annotation = Annotation.parse(match.group(1))
+        else:
+            type_s = type_and_annot
+            annotation = None
+        type = Type.parse(type_s)
+        r = Return(
+            name=name,
+            type=type,
+            annotation=annotation,
+        )
+        assert str(r) == arg, f"{str(r)} != {arg}"
+        return r
+
+    @property
+    def is_write(self) -> bool:
+        return self.annotation is not None and self.annotation.is_write
+
+    def __str__(self) -> str:
+        type = f"{self.type}"
+        if self.annotation:
+            assert type in ["Tensor", "Tensor?", "Tensor[]"]
+            type = type.replace("Tensor", f"Tensor({self.annotation})")
+        if self.name is None:
+            return type
+        else:
+            return f"{type} {self.name}"
+
+
+# Represents the self argument for functions that may be methods
+@dataclass(frozen=True)
+class SelfArgument:
+    argument: Argument
+
+
+# Bundle of arguments that represent a TensorOptions.  This is mostly
+# relevant for the public C++ API but we bake it into the core data
+# model because other APIs often have to interact with it
+@dataclass(frozen=True)
+class TensorOptionsArguments:
+    dtype: Argument
+    layout: Argument
+    device: Argument
+    pin_memory: Argument
+
+    def all(self) -> Sequence[Argument]:
+        return [self.dtype, self.layout, self.device, self.pin_memory]
+
+
+@dataclass(frozen=True)
+class Arguments:
+    # pre_self_positional is usually empty, but is notably non-empty
+    # for where.self, where the condition argument comes before the
+    # self argument
+    pre_self_positional: tuple[Argument, ...]
+    self_arg: SelfArgument | None
+    post_self_positional: tuple[Argument, ...]
+
+    pre_tensor_options_kwarg_only: tuple[Argument, ...]
+    tensor_options: TensorOptionsArguments | None
+    # post_tensor_options is typically memory format, which should be
+    # part of tensor options but isn't right now, and is usually
+    # placed after the tensor options arguments
+    post_tensor_options_kwarg_only: tuple[Argument, ...]
+
+    # Unlike in the previous codegen, we have factored out 'out' arguments
+    # in the canonical representation, removing them from kwarg
+    # arguments.  This choice is justified by numerous downstream
+    # transformations which treat out arguments specially; additionally,
+    # you can see that canonicity is not violated!
+    out: tuple[Argument, ...]  # these are also kwarg-only
+
+    @property
+    def flat_non_out(self) -> Sequence[Argument]:
+        ret: list[Argument] = []
+        ret.extend(self.flat_positional)
+        ret.extend(self.flat_kwarg_only)
+        return ret
+
+    @property
+    def flat_positional(self) -> Sequence[Argument]:
+        ret: list[Argument] = []
+        ret.extend(self.pre_self_positional)
+        if self.self_arg is not None:
+            ret.append(self.self_arg.argument)
+        ret.extend(self.post_self_positional)
+        return ret
+
+    @property
+    def post_self_positional_mutable(self) -> Sequence[Argument]:
+        return [a for a in self.post_self_positional if a.is_write]
+
+    # NB: doesn't contain out arguments
+    @property
+    def flat_kwarg_only(self) -> Sequence[Argument]:
+        ret: list[Argument] = []
+        ret.extend(self.pre_tensor_options_kwarg_only)
+        if self.tensor_options is not None:
+            ret.extend(self.tensor_options.all())
+        ret.extend(self.post_tensor_options_kwarg_only)
+        return ret
+
+    @property
+    def flat_all(self) -> Sequence[Argument]:
+        ret: list[Argument] = []
+        ret.extend(self.flat_positional)
+        ret.extend(self.flat_kwarg_only)
+        ret.extend(self.out)
+        return ret
+
+    @property
+    def non_out(
+        self,
+    ) -> Sequence[Argument | SelfArgument | TensorOptionsArguments]:
+        ret: list[Argument | SelfArgument | TensorOptionsArguments] = []
+        ret.extend(self.positional)
+        ret.extend(self.kwarg_only)
+        return ret
+
+    @property
+    def positional(self) -> Sequence[Argument | SelfArgument]:
+        ret: list[Argument | SelfArgument] = []
+        ret.extend(self.pre_self_positional)
+        if self.self_arg is not None:
+            ret.append(self.self_arg)
+        ret.extend(self.post_self_positional)
+        return ret
+
+    @property
+    def kwarg_only(self) -> Sequence[Argument | TensorOptionsArguments]:
+        ret: list[Argument | TensorOptionsArguments] = []
+        ret.extend(self.pre_tensor_options_kwarg_only)
+        if self.tensor_options is not None:
+            ret.append(self.tensor_options)
+        ret.extend(self.post_tensor_options_kwarg_only)
+        return ret
+
+    @property
+    def all(self) -> Sequence[Argument | SelfArgument | TensorOptionsArguments]:
+        ret: list[Argument | SelfArgument | TensorOptionsArguments] = []
+        ret.extend(self.positional)
+        ret.extend(self.kwarg_only)
+        ret.extend(self.out)
+        return ret
+
+    def mutable_arg_names(self) -> list[str]:
+        return [
+            a.name
+            for a in self.flat_all
+            if a.annotation is not None and a.annotation.is_write
+        ]
+
+    def has_tensor_arg(self) -> bool:
+        return any(a.type.is_tensor_like() for a in self.flat_non_out)
+
+    def has_symint_arg(self) -> bool:
+        return any(a.type.is_symint_like() for a in self.flat_non_out)
+
+    def has_generator_arg(self) -> bool:
+        return any(a.type.is_generator_like() for a in self.flat_non_out)
+
+    def signature(self, *, strip_default: bool = False) -> Arguments:
+        # dataclasses.replace could be used here, but it is less
+        # type safe so for now I've opted to type everything out
+        def strip_arg_annotation(a: Argument) -> Argument:
+            return Argument(
+                name=a.name,
+                type=a.type,
+                default=a.default if not strip_default else None,
+                annotation=None,
+            )
+
+        return Arguments(
+            pre_self_positional=tuple(
+                map(strip_arg_annotation, self.pre_self_positional)
+            ),
+            self_arg=SelfArgument(strip_arg_annotation(self.self_arg.argument))
+            if self.self_arg is not None
+            else None,
+            post_self_positional=tuple(
+                map(strip_arg_annotation, self.post_self_positional)
+            ),
+            # Since TensorOptions are dropped, the post_tensor_options_kwargs are
+            # converted to pre_tensor_options_kwargs
+            pre_tensor_options_kwarg_only=tuple(
+                map(strip_arg_annotation, self.pre_tensor_options_kwarg_only)
+            )
+            + tuple(map(strip_arg_annotation, self.post_tensor_options_kwarg_only)),
+            # TensorOptions are dropped in signature,
+            # so we can pair factory functions with their out= variants.
+            tensor_options=None,
+            post_tensor_options_kwarg_only=(),
+            # out arguments are dropped in signature
+            out=(),
+        )
+
+    def remove_self_annotation(self) -> Arguments:
+        assert self.self_arg is not None
+        return dataclasses.replace(
+            self,
+            self_arg=SelfArgument(
+                dataclasses.replace(self.self_arg.argument, annotation=None)
+            ),
+        )
+
+    def with_out_args(self, outs: list[Argument]) -> Arguments:
+        assert len(self.out) == 0
+        return dataclasses.replace(
+            self,
+            out=tuple(outs),
+        )
+
+    @staticmethod
+    def _preparse(args: str) -> tuple[list[Argument], list[Argument], list[Argument]]:
+        positional: list[Argument] = []
+        kwarg_only: list[Argument] = []
+        out: list[Argument] = []
+        arguments_acc = positional
+
+        # TODO: Use a real parser here; this will get bamboozled
+        # by signatures that contain things like std::array<bool, 2> (note the space)
+        for arg in args.split(", "):
+            if not arg:
+                continue
+            if arg == "*":
+                assert (
+                    arguments_acc is positional
+                ), "invalid syntax: kwarg-only specifier * can only occur once"
+                arguments_acc = kwarg_only
+                continue
+            parg = Argument.parse(arg)
+            # Currently, we rely directly on the invariant that there are NO
+            # kwarg-only mutating arguments.  If you want to relax this,
+            # we will need a more semantic way of matching that takes
+            # into account return arguments.  In that case, you will have
+            # to manage out computation a level up, in FunctionSchema.  See Note
+            # [is_out_fn]
+            if parg.annotation is not None and parg.annotation.is_write:
+                if arguments_acc is positional:
+                    pass  # do nothing
+                elif arguments_acc is kwarg_only:
+                    arguments_acc = out
+            else:
+                assert arguments_acc is not out
+            arguments_acc.append(parg)
+
+        return positional, kwarg_only, out
+
+    @staticmethod
+    def parse(args: str) -> Arguments:
+        """
+        Input: 'int x, int y, int z'
+        """
+
+        # We do this in two phases.  First we parse into three
+        # main categories: positional, kwarg_only, out.
+        # Then, we reparse positional and kwarg_only to separate
+        # out the self argument and tensor options arguments.
+
+        positional, kwarg_only, out = Arguments._preparse(args)
+
+        # Split self argument
+        self_ix = None
+        for i, a in enumerate(positional):
+            if a.name == "self":
+                self_ix = i
+                break
+        pre_self_positional: list[Argument]
+        self_arg: SelfArgument | None
+        post_self_positional: list[Argument]
+        if self_ix is not None:
+            pre_self_positional = positional[:self_ix]
+            self_arg = SelfArgument(positional[self_ix])
+            post_self_positional = positional[self_ix + 1 :]
+        else:
+            pre_self_positional = []
+            self_arg = None
+            post_self_positional = positional
+
+        # Group tensor options arguments
+        pre_tensor_options_kwarg_only: list[Argument] = []
+        tensor_options: TensorOptionsArguments | None = None
+        post_tensor_options_kwarg_only: list[Argument] = []
+        kwarg_only_acc = pre_tensor_options_kwarg_only
+
+        def pred(name: str, ty: Type) -> Callable[[Argument], bool]:
+            return lambda a: a.name == name and a.type in [ty, OptionalType(ty)]
+
+        predicates = [  # order matters
+            pred("dtype", Type.parse("ScalarType")),
+            pred("layout", Type.parse("Layout")),
+            pred("device", Type.parse("Device")),
+            pred("pin_memory", Type.parse("bool")),
+        ]
+
+        i = 0
+        while i < len(kwarg_only):
+            # If there is enough space...
+            if i <= len(kwarg_only) - len(predicates):
+                # And the next len(predicates) arguments look like TensorOptions arguments
+                if all(
+                    p(a)
+                    for p, a in zip(predicates, kwarg_only[i : i + len(predicates)])
+                ):
+                    assert kwarg_only_acc is pre_tensor_options_kwarg_only
+                    # Group them together as one argument
+                    tensor_options = TensorOptionsArguments(
+                        dtype=kwarg_only[i],
+                        layout=kwarg_only[i + 1],
+                        device=kwarg_only[i + 2],
+                        pin_memory=kwarg_only[i + 3],
+                    )
+                    i += len(predicates)
+                    kwarg_only_acc = post_tensor_options_kwarg_only
+                    continue
+            kwarg_only_acc.append(kwarg_only[i])
+            i += 1
+
+        return Arguments(
+            pre_self_positional=tuple(pre_self_positional),
+            self_arg=self_arg,
+            post_self_positional=tuple(post_self_positional),
+            pre_tensor_options_kwarg_only=tuple(pre_tensor_options_kwarg_only),
+            tensor_options=tensor_options,
+            post_tensor_options_kwarg_only=tuple(post_tensor_options_kwarg_only),
+            out=tuple(out),
+        )
+
+    def __str__(self) -> str:
+        all_arguments: list[str] = []
+        all_arguments.extend(map(str, self.flat_positional))
+        if self.flat_kwarg_only or self.out:
+            all_arguments.append("*")
+        all_arguments.extend(map(str, self.flat_kwarg_only))
+        all_arguments.extend(map(str, self.out))
+        return ", ".join(all_arguments)
+
+    def __post_init__(self) -> None:
+        # TODO: These invariants are weirdly asymmetric?
+        # TODO: Fancier types?
+        if self.self_arg is None:
+            assert not self.pre_self_positional
+        if self.tensor_options is None:
+            assert not self.post_tensor_options_kwarg_only
+
+        # We don't allow any of the following to have argument annotations,
+        # to keep things simple.
+        mutable_pre_self_positionals = [
+            a
+            for a in self.pre_self_positional
+            if a.annotation is not None and a.annotation.is_write
+        ]
+        assert (
+            len(mutable_pre_self_positionals) == 0
+        ), "mutable pre_self_positional arguments are not currently supported in the schema"
+
+
+# Names that validly are __iXXX__ indicating inplace operations.
+# Taken from https://www.python.org/dev/peps/pep-0203/#new-methods
+# NB: PyTorch hasn't actually implemented all of these
+AUGMENTED_ASSIGNMENT_NAMES = [
+    "add",
+    "sub",
+    "mul",
+    "div",
+    "mod",
+    "pow",
+    "lshift",
+    "rshift",
+    "and",
+    "xor",
+    "or",
+]
+
+
+# A BaseOperatorName is what we think of the operator name, without
+# the overload name.  Unusually, we don't represent this as just a
+# string; instead, we directly represent a few important semantic
+# bits of information we derive from the string: namely whether
+# or not it's inplace (add_) and whether or not it's a double-underscore
+# method (__add__)
+@dataclass(frozen=True)
+class BaseOperatorName:
+    base: str
+    inplace: bool
+    dunder_method: bool
+    # Note [Overload Ambiguity With Functional Variants]
+    # A handful of operators have both a "mutable" and a "functional" variant.
+    # (native_batch_norm is a good example, although this isn't the case today).
+    # For those operators, the mutable and functional variant take in the same set of
+    # arguments, but have different alias annotations.
+    # this makes it ambiguous when you try to resolve an OverloadPacket into an overload,
+    # given a set of input arguments.
+    #
+    # So instead of making the "functional" variant in this case a real overload, e.g:
+    #   native_batch_norm (mutable variant)
+    #   native_batch_norm.functional (functional variant)
+    # we make it a new base operator,
+    #   native_batch_norm_functional (functional variant)
+    #
+    # In an ideal world, we would probably invert this so the operators were:
+    #   native_batch_norm.mutable (mutable variant)
+    #   native_batch_norm (functional variant)
+    #
+    # Doing that is BC-breaking though, so we're stuck with the above modeling.
+    functional_overload: bool = False
+
+    @staticmethod
+    def parse(op: str) -> BaseOperatorName:
+        assert op != ""
+        assert not op.endswith("_out"), (
+            "_out suffix is reserved and not permitted for operator names; "
+            "did you mean to specify an out overload name instead?"
+        )
+        m = re.match(r"^__([^_]+)__$", op)
+        if m is not None:
+            dunder_method = True
+            base = m.group(1)
+            if any(base == f"i{n}" for n in AUGMENTED_ASSIGNMENT_NAMES):
+                inplace = True
+                base = base[1:]
+            else:
+                inplace = False
+                # temporary, this is not intrinsically true but
+                # has been historically true for dunder methods
+                # we support  (but, if we ever got, say, __int__, this would
+                # be wrong!)
+                assert base[0] != "i"
+        else:
+            dunder_method = False
+            base = op
+            if base[-1] == "_":
+                inplace = True
+                base = base[:-1]
+            else:
+                inplace = False
+
+        # See Note [Overload Ambiguity With Functional Variants]
+        functional_suffix = "_functional"
+        if base.endswith(functional_suffix):
+            functional_overload = True
+            base = base[: -len(functional_suffix)]
+            # This seems complicated and unnecessary, so banning dunder methods
+            # for now on ops that have a functional + mutable variant (like native_batch_norm).
+            assert not dunder_method and not inplace
+        else:
+            functional_overload = False
+
+        r = BaseOperatorName(
+            base=base,
+            inplace=inplace,
+            dunder_method=dunder_method,
+            functional_overload=functional_overload,
+        )
+        assert str(r) == op, f"{str(r)} != {op}"
+        return r
+
+    def __str__(self) -> str:
+        if self.dunder_method:
+            i = "i" if self.inplace else ""
+            return f"__{i}{self.base}__"
+        else:
+            i = (
+                "_"
+                if self.inplace
+                else "_functional"
+                if self.functional_overload
+                else ""
+            )
+            return f"{self.base}{i}"
+
+
+# Operator name is the base operator name along with the (typically not
+# user visible) overload string.
+@dataclass(frozen=True)
+class OperatorName:
+    name: BaseOperatorName
+    overload_name: str
+
+    @staticmethod
+    def parse(op_name: str) -> OperatorName:
+        if "." in op_name:
+            name, overload_name = op_name.split(".", 1)
+        else:
+            name = op_name
+            overload_name = ""
+        r = OperatorName(name=BaseOperatorName.parse(name), overload_name=overload_name)
+        assert str(r) == op_name, f"{str(r)} != {op_name}"
+        return r
+
+    def __str__(self) -> str:
+        if self.overload_name:
+            return f"{self.name}.{self.overload_name}"
+        else:
+            return f"{self.name}"
+
+    # NB: This must be synchronized with the naming scheme in
+    # aten/src/ATen/templates/Operators.h
+    # Given a function schema "aten::op.overload(...)",
+    # If there is no overload name, this returns f"{op}"
+    # If there is an overload name, this returns f"{op}_{overload}"
+    def unambiguous_name(self) -> str:
+        if self.overload_name:
+            return f"{self.name}_{self.overload_name}"
+        else:
+            return f"{self.name}"
+
+    def remove_inplace(self) -> OperatorName:
+        return OperatorName(
+            name=BaseOperatorName(
+                base=self.name.base,
+                inplace=False,
+                dunder_method=self.name.dunder_method,
+            ),
+            overload_name=self.overload_name,
+        )
+
+    def with_overload(self, overload: str) -> OperatorName:
+        return OperatorName(
+            name=BaseOperatorName(
+                base=self.name.base,
+                inplace=False,
+                dunder_method=self.name.dunder_method,
+            ),
+            overload_name=overload,
+        )
+
+
+def gets_generated_out_inplace_wrapper(
+    f: NativeFunction, g: NativeFunctionsGroup, b: BackendIndex
+) -> bool:
+    return (
+        f.func.kind() is not SchemaKind.functional
+        and not b.has_kernel(f)
+        and b.has_kernel(g.functional)
+    )
+
+
+# NativeFunction objects that are views (f.is_view_op returns True)
+# are added into a `NativeFunctionsViewGroup`, which we can use to
+# easily access the generated (optional) view_copy NativeFunction.
+# It's convenient to group them together, so we pair them up in NativeFunctionsViewGroup.
+# See Note [Codegen'd {view}_copy Operators]
+#
+# One property of this representation is that in order for a view-like op to be part of
+# a NativeFunctionsViewGroup, the "aliasing" version of that view op must exist.
+# There's one case where that doesn't happen: we have a non-aliasing `narrow_copy.out` op,
+# but don't have corresponding aliasing `narrow.out` op.
+# This means that `narrow_copy.out` won't appear as a NativeFunctionsViewGroup.
+@dataclass(frozen=True)
+class NativeFunctionsViewGroup:
+    view: NativeFunction
+    # Note: the {view}_copy operator is optional because we currently don't generate copy variants
+    # for all view ops. Notably, we don't generate them for CompositeImplicitAutograd views
+    # (we already get them "for free" through decomposition)
+    view_copy: NativeFunction | None
+    # view_inplace ops are also optional, but every view_inplace op should have out-of-place variant.
+    view_inplace: NativeFunction | None
+
+    def __post_init__(self) -> None:
+        assert self.view.is_view_op
+        if self.view_copy is None:
+            assert not gets_generated_view_copy(self.view), (
+                f"{str(self.view.func.name)} appears to be a new operator that aliases its inputs."
+                " The codegen expects you to add a corresponding operator to native_functions.yaml:"
+                f" {get_view_copy_name(self.view)!s}."
+                " See Note [view_copy NativeFunctions] for details."
+            )
+        else:
+            assert self.view_copy.func.name.name.base.endswith(("_copy", "_scatter"))
+            assert self.view.func.signature() == self.view_copy.func.signature(
+                strip_view_copy_name=True,
+            )
+            assert "view_copy" in self.view_copy.tags, (
+                f"{str(self.view_copy.func.name), str(self.view.tags)} appears to be a view_copy operator. The codegen expects"
+                " view_copy operators to be annotated with the 'view_copy' tag in native_functions.yaml."
+                " See Note [view_copy NativeFunction] for details."
+            )
+        if self.view_inplace is not None:
+            assert self.view.func.signature() == self.view_inplace.func.signature()
+
+        if self.view.has_composite_implicit_autograd_kernel:
+            if self.view_inplace is not None:
+                assert self.view_inplace.has_composite_implicit_autograd_kernel, (
+                    f"{str(self.view.func.name)} and {str(self.view_inplace.func.name)} must either"
+                    " both have CompositeImplicitAutograd kernels, or both not have composite kernels."
+                )
+        if self.view.has_composite_implicit_autograd_nested_tensor_kernel:
+            if self.view_inplace is not None:
+                assert self.view_inplace.has_composite_implicit_autograd_nested_tensor_kernel, (
+                    f"{str(self.view.func.name)} and {str(self.view_inplace.func.name)} must either"
+                    " both have CompositeImplicitAutogradNestedTensor kernels, or both not have composite kernels."
+                )
+
+    def functions(self, *, include_copy: bool = True) -> Iterator[NativeFunction]:
+        yield self.view
+        if self.view_inplace is not None:
+            yield self.view_inplace
+        if self.view_copy is not None and include_copy:
+            yield self.view_copy
+
+    @property
+    def root_name(self) -> str:
+        return self.view.root_name
+
+    @property
+    def composite(self) -> bool:
+        # We currently assert that the "group" is consistent.
+        # If the view op is composite, then its view_inplace op is too.
+        return self.view.has_composite_implicit_autograd_kernel
+
+
+def gets_generated_view_copy(f: NativeFunction) -> bool:
+    # Only aliasing (view) operators get a copy variant.
+    if not f.is_view_op:
+        return False
+    # We don't need to bother generating copy variants for CompositeImplicitAutograd ops,
+    # because we can let them decompose into base view ops.
+    if f.has_composite_implicit_autograd_kernel:
+        return False
+    # We also don't need to generate copy variants for inplace views.
+    if "inplace_view" in f.tags:
+        return False
+    # Assume ops ending in _inverse have manually-defined copy variants
+    # (e.g. slice_inverse() has the copy variant slice_scatter()).
+    # We -could- probably generate these as well, but the codegen will be
+    # slightly different, and hand-writing these few kernels keeps codegen
+    # complexity lower.
+    if f.func.name.name.base.endswith("_inverse"):
+        return False
+    return True
+
+
+# Given a NativeFunction that corresponds to a view op,
+# returns the OperatorName of the corresponding "copy" variant of the op.
+def get_view_copy_name(f: NativeFunction) -> OperatorName:
+    # Right now, when asking for a view op's corresponding "view_copy" name
+    # we assert for sanity that the op is allowed to have a generated view_copy variant.
+    # (We can do this because "gets_generated_view_copy()" tell us which ops get a generated view_copy op).
+    # However, narrow_copy() already exists as an op directly in native_functions.yaml.
+    # I'm hardcoding narrow_copy here for now to maintain the assert,
+    # But we could also just get rid of the assert.
+    list_of_ops_with_explicit_view_copy_operators = ["narrow"]
+    if str(f.func.name) not in list_of_ops_with_explicit_view_copy_operators:
+        assert gets_generated_view_copy(f)
+
+    base_name = f"{f.func.name.name.base}_copy"
+    view_copy_name = OperatorName(
+        name=BaseOperatorName(
+            base=base_name, inplace=False, dunder_method=f.func.name.name.dunder_method
+        ),
+        overload_name=f.func.name.overload_name,
+    )
+    return view_copy_name
+
+
+# Helper functions for parsing argument lists (both inputs and returns)
+
+
+def parse_returns(return_decl: str) -> tuple[Return, ...]:
+    """
+    Input: '()'
+    Output: []
+    """
+    if return_decl == "()":
+        return ()
+    if return_decl[0] == "(" and return_decl[-1] == ")":
+        return_decl = return_decl[1:-1]
+    return tuple(Return.parse(arg) for arg in return_decl.split(", "))
+
+
+# A Precompute instance consists of a map from kernel argument name
+# to the list of Argument instances that should replace that
+# kernel argument in the impl function.
+@dataclass(frozen=True)
+class Precompute:
+    # A map from kernel argument name -> a list of precomputed
+    # elements that replaces/supersedes it.
+    replace: dict[str, list[Argument]]
+    # List of precomputed args added without replacement
+    add: list[Argument]
+
+    @staticmethod
+    def parse(src: object) -> Precompute:
+        assert isinstance(src, list)
+
+        # src is a list of strings of the format:
+        #   {kernel param name} -> {replacement decl}[, {replacement decl}, ...]
+        #   [{add decl}[, {add decl}, ...]]
+        # The last line is optional and contains the precomputed parameters that are
+        # added without replacement.
+        # The other lines are parsed to get the names of which precomputed elements
+        # should replace which kernel arguments.
+        add_args = []
+        if " -> " not in src[-1]:
+            add_list = src[-1].split(",")
+            add_args = [Argument.parse(name.strip()) for name in add_list]
+            src = src[:-1]
+
+        replace = {}
+        for raw_replace_item in src:
+            assert isinstance(raw_replace_item, str)
+            assert " -> " in raw_replace_item, (
+                "precomputed parameters without replacement"
+                " are allowed only in the last line"
+            )
+
+            arg, with_list_raw = raw_replace_item.split(" -> ")
+            assert (
+                " " not in arg
+            ), f"illegal kernel param name '{arg}' in precomputed parameters'"
+            with_list = with_list_raw.split(",")
+            with_list_args = [Argument.parse(name.strip()) for name in with_list]
+            replace[arg] = with_list_args
+
+        r = Precompute(replace=replace, add=add_args)
+        assert r.to_list() == src, "r.to_list() != src"
+        return r
+
+    def __post_init__(self) -> None:
+        # the template parameters are upper so if these are the
+        # same then it is ambiguous
+        for a in self.add:
+            assert a.name.upper() != a.name
+        for args in self.replace.values():
+            for a in args:
+                assert a.name.upper() != a.name
+
+    def to_list(self) -> list[str]:
+        replace_list = []
+        for kernel_param, replacement_params in self.replace.items():
+            replacements = ", ".join(str(param) for param in replacement_params)
+            replace_list.append(f"{kernel_param} -> {replacements}")
+
+        return replace_list
diff --git a/lib/python3.11/site-packages/torchgen/native_function_generation.py b/lib/python3.11/site-packages/torchgen/native_function_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..f986c77f8faaaeb5d961082a044ca5f595851136
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/native_function_generation.py
@@ -0,0 +1,651 @@
+from __future__ import annotations
+
+import string
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+import torchgen.api.dispatcher as dispatcher
+from torchgen.api.translate import translate
+from torchgen.api.types import Binding, DispatcherSignature, Expr
+from torchgen.context import with_native_function
+from torchgen.model import (
+    Annotation,
+    Argument,
+    BackendIndex,
+    BackendMetadata,
+    BaseOperatorName,
+    BaseTy,
+    BaseType,
+    DEFAULT_KERNEL_NAMESPACE,
+    DeviceCheckType,
+    DispatchKey,
+    FunctionSchema,
+    NativeFunction,
+    NativeFunctionsGroup,
+    OperatorName,
+    Return,
+    SchemaKind,
+    Variant,
+)
+from torchgen.utils import concatMap
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# See Note: [Out ops with functional variants that don't get grouped properly]
+OUT_OPS_THAT_DONT_GET_GROUPED_PROPERLY = [
+    # This has a functional variant, but it's currently marked private.
+    # This function should be marked private as well (*_backward ops aren't exposed to python anyway).
+    "adaptive_avg_pool3d_backward.grad_input",
+    # There's a functional variant, _slow_conv2d_backward.output_mask, that isn't grouped properly.
+    # Maybe we can kill this operator in favor of convolution_backward?
+    "_slow_conv2d_backward.grad_input",
+]
+
+
+# See Note: [Mutable ops that cannot get an out variant]
+MUTABLE_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT = [
+    # should be out=?
+    "_cummax_helper",
+    # should be out=?
+    "_cummin_helper",
+]
+
+# All of these operators don't have any tensor like returns
+FUNCTIONAL_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT = [
+    "_assert_async",  # no return
+    "_assert_async.msg",  # no return
+    "_assert_tensor_metadata",  # no return
+    "_cslt_sparse_mm_search",  # returns an int
+    "_assert_scalar",  # no return
+    "_dimI",  # returns an int
+    "_dimV",  # returns an int
+    "_has_same_storage_numel",  # returns a boolean
+    "_linalg_check_errors",  # no return
+    "_local_scalar_dense",  # returns a Scalar
+    "_nested_tensor_from_mask_left_aligned",  # returns a boolean
+    "_nnz",  # returns an int
+    "_use_cudnn_ctc_loss",  # returns a boolean
+    "_use_cudnn_ctc_loss.Tensor",  # returns a boolean
+    "_validate_compressed_sparse_indices",  # no return
+    "allclose",  # returns a boolean
+    "dense_dim",  # returns an int
+    "equal",  # returns a boolean
+    "is_coalesced",  # returns an boolean
+    "is_pinned",  # returns a boolean
+    "is_same_size",  # returns a boolean
+    "is_set_to",  # returns a boolean
+    "q_per_channel_axis",  # returns an int
+    "q_scale",  # returns a float
+    "q_zero_point",  # returns an int
+    "qscheme",  # returns a QScheme
+    "record_stream",  # no return
+    "sparse_dim",  # returns an int
+    "sym_constrain_range",  # no return
+    "sym_constrain_range_for_size",  # no return
+    "_nested_tensor_storage_offsets",  # returns a vector of ints
+    "_chunk_grad_outputs_efficient_attention",  # returns a bool
+    "_fused_sdp_choice",  # returns an int
+    "_print",  # no return
+    "_sink_tokens",  # no return
+    "_nested_get_ragged_idx",  # returns an int
+]
+
+INPLACE_OPS_THAT_DONT_GET_GROUPED_PROPERLY = [
+    # polygamma and polygamma.out both exist, but have a
+    # pre-self arg (while polygamma_ does not)
+    # We should either fix this schema so it can be grouped properly,
+    # or allow the codegen to generate new functional/out= NativeFunctions for this op
+    # (which would require changing its overload name to prevent overload ambiguity).
+    "polygamma_"
+]
+
+
+# Groups "similar" NativeFunctions together
+# example add.Tensor, add_.Tensor, add.out
+# "similar" NativeFunctions are all expected to have an identical `signature()`,
+# But have differing SchemaKinds.
+def pre_group_native_functions(
+    native_functions: Sequence[NativeFunction],
+) -> dict[FunctionSchema, dict[SchemaKind, NativeFunction]]:
+    pre_grouped_native_functions: dict[
+        FunctionSchema, dict[SchemaKind, NativeFunction]
+    ] = defaultdict(dict)
+    for f in native_functions:
+        d = pre_grouped_native_functions[f.func.signature()]
+        assert f.func.kind() not in d
+        d[f.func.kind()] = f
+    return pre_grouped_native_functions
+
+
+# Returns the out variant overload name given a base function overload name
+def get_expected_out_variant_overload_name(overload_name: str | None) -> str:
+    return "out" if not overload_name else f"{overload_name}_out"
+
+
+# Helper function: given an inplace FunctionSchema, generate its corresponding out= variant
+# Example before:
+#   _add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
+# Example after:
+#   _add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out)
+def self_to_out_signature(func: FunctionSchema) -> FunctionSchema:
+    # Generating an out= schema from an inplace schema.
+    assert func.kind() == SchemaKind.inplace
+    assert func.arguments.self_arg is not None
+    # The new out= schema has:
+    # - a new out argument with the same type as "func" (but with a mutable annotation)
+    # - The returns (if any) now alias the out= argument instead of "func"
+    # - an "out" overload name
+    return FunctionSchema(
+        name=func.name.remove_inplace().with_overload(
+            get_expected_out_variant_overload_name(func.name.overload_name)
+        ),
+        arguments=func.arguments.remove_self_annotation().with_out_args(
+            [
+                Argument(
+                    name="out",
+                    type=func.arguments.self_arg.argument.type,
+                    default=None,
+                    annotation=func.arguments.self_arg.argument.annotation,
+                )
+            ]
+        ),
+        returns=func.returns,
+    )
+
+
+# Helper function: given a functional FunctionSchema, generate its corresponding out= variant
+# Example before:
+#   _to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None,
+#       bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor
+# Example after:
+#   _to_copy._out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None,
+#       Tensor(a!) out) -> Tensor(a!)
+def functional_to_out_signature(func: FunctionSchema) -> FunctionSchema:
+    # Generating an out= schema from a functional schema.
+    assert func.kind() == SchemaKind.functional
+
+    new_returns, new_out_args = generate_out_args_from_schema(func)
+    # The new out= schema has:
+    # - one or more new out argument(s) with the same type as returns (but with a mutable annotation)
+    # - The returns now alias the out= arguments
+    # - an "_out" overload name
+    return FunctionSchema(
+        name=func.name.with_overload(
+            get_expected_out_variant_overload_name(func.name.overload_name)
+        ),
+        arguments=func.arguments.signature().with_out_args(
+            new_out_args,
+        ),
+        returns=tuple(new_returns),
+    )
+
+
+# Helper function: given a function schema, generate corresponding out arguments, also the updated return annotations.
+def generate_out_args_from_schema(
+    func: FunctionSchema,
+) -> tuple[list[Return], list[Argument]]:
+    # More of a sanity check - our existing restrictions on schemas should enforce that
+    # mutable schema kinds never return their mutable arguments.
+    assert not any(
+        r.annotation is not None and r.annotation.is_write for r in func.returns
+    )
+
+    tensorlike_rets = [r for r in func.returns if r.type.is_tensor_like()]
+    assert len(tensorlike_rets) > 0
+
+    used_annotations = concatMap(
+        lambda a: [] if a.annotation is None else a.annotation.alias_set,
+        func.arguments.flat_all,
+    )
+    valid_annotations = [x for x in string.ascii_lowercase if x not in used_annotations]
+
+    all_rets_are_tensors = all(r.type == BaseType(BaseTy.Tensor) for r in func.returns)
+
+    new_out_args: list[Argument] = []
+    # The end result of new_returns is that:
+    # - If every return is a plain tensor, then the new returns == the old returns, but with the out= alias annotations added.
+    # - Otherwise, none of the out arguments show up in the returns (and we're only left with non-tensor-like returns, if any).
+    new_returns: list[Return] = []
+    for i, r in enumerate(func.returns):
+        if r.type.is_tensor_like():
+            new_out = Argument(
+                name="out" if len(func.returns) == 1 else f"out{i}",
+                type=r.type,
+                default=None,
+                annotation=Annotation.parse(f"{valid_annotations[i]}!"),
+            )
+            new_out_args.append(new_out)
+            if all_rets_are_tensors:
+                # The convention for out= schemas is that they only return their out arguments
+                # if the return is a plain Tensor (or if it's a tuple of plain Tensors)
+                new_ret = Return(
+                    name=None, type=new_out.type, annotation=new_out.annotation
+                )
+                new_returns.append(new_ret)
+        else:
+            new_returns.append(r)
+    return new_returns, new_out_args
+
+
+# Helper function: given a mutable FunctionSchema, generate its corresponding out= variant
+# Example before:
+#   _fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask)  # noqa: B950
+# Example after:
+#   _fused_moving_avg_obs_fq_helper._out(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False, *, Tensor(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!))  # noqa: B950
+def mutable_to_out_signature(func: FunctionSchema) -> FunctionSchema:
+    # Generating an out= schema from a mutable schema.
+    assert func.kind() == SchemaKind.mutable
+    # The new out= schema has:
+    # - Any non-aliased tensor-like returns are converted to mutable, aliased out= arguments
+    #   (if the argument is a tensor then we also return it for method chaining,
+    #   otherwise we return nothing)
+    # - an "out" overload name
+    #
+    # Note that:
+    # (1) This also means that we can *only* generate an out= variant from a mutable schema
+    #     if the mutable schema has at least one tensor-like non-aliasing return.
+    # (2) The generated out= variant still has mutable positional arguments,
+    #     but if necessary we could probably add another out= variant that also
+    #     functionalizes the mutable arguments (a functional_out variant)
+
+    new_returns, new_out_args = generate_out_args_from_schema(func)
+
+    return FunctionSchema(
+        name=func.name.remove_inplace().with_overload(
+            get_expected_out_variant_overload_name(func.name.overload_name)
+        ),
+        arguments=func.arguments.with_out_args(new_out_args),
+        returns=tuple(new_returns),
+    )
+
+
+# This function, given function of one SchemaKind, as well as a target SchemaKind,
+# generates a new NativeFunction with the same properties, but using the target SchemaKind.
+# We only actually generate functions for either functional or out= SchemaKinds.
+# This function returns a tuple, with:
+# - The generated NativeFunction
+# - a dictionary of `BackendIndex` objects, describing which dispatch keys
+#   we will generate kernels for, for the new NativeFunction.
+#   Details are in the function, but we only generate composite kernels (in some cases) today.
+def generate_function(
+    f: NativeFunction, k: SchemaKind
+) -> tuple[NativeFunction, dict[DispatchKey, dict[OperatorName, BackendMetadata]]]:
+    from torchgen.api import cpp
+
+    if k == SchemaKind.functional:
+        assert f.func.kind() != SchemaKind.functional
+        # The new "functional" NativeFunction has:
+        # - any mutable arguments have been converted into (immutable) returns.
+        #   (if a mutable argument was not also a return, it gets converted to one)
+        # - "_functional" appended to the base name, ONLY IF this op has a mutable variant.
+        #   See Note [Overload Ambiguity With Functional Variants]
+        # The default grouping logic in signature() actually already does this,
+        # so we can piggy-back off it (but we still want return names)
+        func = f.func.signature(keep_return_names=True).with_name(
+            OperatorName(
+                name=BaseOperatorName(
+                    base=f.func.name.name.base,
+                    inplace=False,
+                    dunder_method=f.func.name.name.dunder_method,
+                    # See Note [Overload Ambiguity With Functional Variants]
+                    functional_overload=f.func.kind() == SchemaKind.mutable,
+                ),
+                overload_name=f.func.name.overload_name,
+            )
+        )
+    elif k == SchemaKind.out:
+        # We generate out= ops mostly just so that we can pair up NativeFunctions into groups easily,
+        # but at least today, there is no good reason to actually use them.
+        # we'll generate a dispatcher entry for them, but won't actually register any kernels for them.
+        if f.func.kind() == SchemaKind.inplace:
+            func = self_to_out_signature(f.func)
+        elif f.func.kind() == SchemaKind.mutable:
+            func = mutable_to_out_signature(f.func)
+        elif f.func.kind() == SchemaKind.functional:
+            func = functional_to_out_signature(f.func)
+        else:
+            raise AssertionError(
+                "We only bother generating out= functions from either inplace or mutable or functional variants"
+            )
+    else:
+        raise AssertionError(
+            "We currently only generate either functional or out= NativeFunctions"
+        )
+
+    # Generated kernel naming convention for out: <op_name>_<overload_name>. The reason for this is to
+    # disambiguate operator with the same name but different overload name, e.g., `randn.names_out` and
+    # `randn.generator_with_names_out`.
+    kernel_name = (
+        func.name.unambiguous_name()
+        if func.kind() == SchemaKind.out
+        else cpp.name(func)
+    )
+    if f.func.has_symint():
+        kernel_name += "_symint"
+    backend_metadata = {
+        DispatchKey.CompositeExplicitAutograd: {
+            func.name: BackendMetadata(
+                kernel=kernel_name,
+                structured=False,
+                cpp_namespace=DEFAULT_KERNEL_NAMESPACE,
+            )
+        }
+    }
+    tags = {"generated"} | set(
+        f.tags & {"nondeterministic_seeded", "view_copy", "pt2_compliant_tag"}
+    )
+
+    return (
+        NativeFunction(
+            func=func,
+            use_const_ref_for_mutable_tensors=f.use_const_ref_for_mutable_tensors,
+            # These generated fn's aren't meant to be user friendly- don't generate methods.
+            variants={Variant.function},
+            structured=False,
+            structured_delegate=None,
+            structured_inherits=None,
+            precomputed=None,
+            autogen=[],
+            ufunc_inner_loop={},
+            manual_kernel_registration=False,
+            manual_cpp_binding=False,
+            python_module=None,
+            category_override=None,
+            device_guard=False,
+            device_check=DeviceCheckType.NoCheck,
+            loc=f.loc,
+            cpp_no_default_args=set(),
+            is_abstract=f.is_abstract,
+            has_composite_implicit_autograd_kernel=False,
+            has_composite_implicit_autograd_nested_tensor_kernel=False,
+            has_composite_explicit_autograd_kernel=True,
+            has_composite_explicit_autograd_non_functional_kernel=False,
+            # Every generated NativeFunction gets a "generated" tag, so it's easy to tell
+            # which NativeFunction objects did not come directly from native_functions.yaml.
+            tags=tags,
+            namespace=f.namespace,
+        ),
+        backend_metadata,
+    )
+
+
+# This function is responsible for adding generated NativeFunctions which don't appear
+# explicitly in the codegen.
+# You can inspect the full list of NativeFunctions yourself with the torchgen package, by running
+# torchgen.parse_native_yaml("aten/src/ATen/native/native_functions.yaml", "aten/src/ATen/native/tags.yaml")
+# (Maybe we should make a friendly API for this)
+#
+# Note: this function *mutates* its two inputs,
+# adding the new NativeFunctions / BackendMetadata to them
+def add_generated_native_functions(
+    rs: list[NativeFunction],
+    indices: dict[DispatchKey, dict[OperatorName, BackendMetadata]],
+) -> None:
+    # The main code for generating new NativeFunctions
+    # First we group of NativeFunctions by schema kind,
+    # then we detect which ones are missing and generate them.
+    pre_grouped_native_functions = pre_group_native_functions(rs)
+    for d in pre_grouped_native_functions.values():
+        has_functional = SchemaKind.functional in d
+        has_inplace = SchemaKind.inplace in d
+        has_mutable = SchemaKind.mutable in d
+        has_out = SchemaKind.out in d
+        is_core = any("core" in variant.tags for variant in d.values())
+
+        # We automatically generate a few native functions that don't exist in the yaml, for a few reasons:
+        # (1) If an operator has an inplace/out= variant but no functional variant, we can generate
+        #     a simple functional variant that the functionalization pass can consume.
+        # (2) If an operator has an inplace or functional but no out= variant, we generate an out=
+        #     variant, mostly so we can easily pair up functions into NativeFunctionsGroup,
+        #     while maintaining the constraint that the out= variant is "required".
+        if has_mutable or has_inplace or has_out or has_functional:
+            # Don't bother generating functions trio's for native functions that bypass the dispatcher.
+            are_manual = all(f.manual_cpp_binding for f in d.values())
+            # Don't bother generating functional + out= variants for view operators
+            # set_ is technically an inplace_view, but for now it is treated
+            # as a normal inplace op in the codegen
+            has_view_ops = any(
+                f.is_view_op and str(f.func.name.name) != "set_" for f in d.values()
+            )
+            # Don't generate the other variants for non-core CompositeImplicitAutograd operators.
+            # We could probably do this, but the main benefit of generating the function triplets
+            # is for transforms that need them, and transforms don't need to act directly
+            # on CompositeImplicitAutograd operators (since we let them decompose).
+            are_composite_implicit = all(
+                f.has_composite_implicit_autograd_kernel for f in d.values()
+            )
+            if are_manual or has_view_ops or are_composite_implicit and not is_core:
+                continue
+            if has_out and len(d.values()) == 1:
+                # Note: [Out ops with functional variants that don't get grouped properly]
+                # In theory we could validly have an out= operator in native_functions.yaml
+                # that has no other variants.
+                # But today, all of the operators where that's the case actually do have
+                # functional variants, that we are just unable to pair up properly.
+                # I think banning this all together is probably safer
+                # (you can always add a functional variant yourself if you want to add a new out= operator).
+                #
+                # We should probably fix the existing cases; this check is to prevent us from adding more over time.
+                if (
+                    str(d[SchemaKind.out].func.name)
+                    not in OUT_OPS_THAT_DONT_GET_GROUPED_PROPERLY
+                ):
+                    raise AssertionError(
+                        f"Found an out= operator that we could not find any other variants of: {str(d[SchemaKind.out].func)}"
+                    )
+                continue
+
+            # Some inplace ops that have problematic schemas (that we should fix), which prevent us
+            # from generating out= and functional variants
+            if (
+                has_inplace
+                and str(d[SchemaKind.inplace].func.name)
+                in INPLACE_OPS_THAT_DONT_GET_GROUPED_PROPERLY
+            ):
+                continue
+
+            base_fn = (
+                d[SchemaKind.mutable]
+                if has_mutable
+                else d[SchemaKind.inplace]
+                if has_inplace
+                else d[SchemaKind.out]
+                if has_out
+                else d[SchemaKind.functional]
+            )
+
+            # Note: [Mutable ops that cannot get an out variant]
+            # We can only generate an out= variant if either:
+            # - the original function has tensor-like returns (since we can convert them to out kwargs)
+            # - or it's inplace (since we can convert `self` to an out kwarg)
+            # There are only two functions that don't fit this criteria today though,
+            # and they both look like they should be fixed to be out= variants,
+            # so if feels safer to ban this schema all-together
+            base_fn_valid = base_fn.func.kind() == SchemaKind.inplace or any(
+                r.type.is_tensor_like() for r in base_fn.func.returns
+            )
+            # Note: [Loosen the assertion that all functional should have out variant]
+            # By design all functional operators should have our variants. The needs_out check
+            # is loosening this requirement, changing it to only generate out variant if there's
+            # an `autogen` block in the native function, in the long run it should be removed.
+            # FIXME: Remove this after figuring out CI job failures related to min, max, mean
+            needs_out = any("out" in str(op_name) for op_name in base_fn.autogen)
+            gets_out_variant = not has_out and base_fn_valid and needs_out
+            if not has_out and not base_fn_valid:
+                if (
+                    str(base_fn.func.name)
+                    not in MUTABLE_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT
+                    and str(base_fn.func.name)
+                    not in FUNCTIONAL_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT
+                ):
+                    raise AssertionError(
+                        f"""Found an operator that we could not generate an out= variant for: {str(base_fn.func)}.
+This type of operators don't have tensor-like return, making it difficult to generate a proper out= variant. If
+out= variant is not needed, please add the function name into FUNCTIONAL_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT list."""
+                    )
+
+            # Generate an out= variant
+            if gets_out_variant:
+                fn, metadata = generate_function(base_fn, SchemaKind.out)
+                d[SchemaKind.out] = fn
+                BackendIndex.grow_index(indices, metadata)
+                rs.append(fn)
+
+            # Generate a functional variant, but only do it if the operator got an out= variant
+            # (Functional variants are only useful if we can group up the variants,
+            # which we can only do if they have an out= variant)
+            if not has_functional and (has_out or gets_out_variant):
+                fn, metadata = generate_function(base_fn, SchemaKind.functional)
+                d[SchemaKind.functional] = fn
+                BackendIndex.grow_index(indices, metadata)
+                rs.append(fn)
+
+
+def return_str(rets: tuple[Return, ...], names: list[str]) -> str:
+    assert len(rets) == len(names)
+    if len(rets) == 0:
+        return ""
+    elif len(rets) == 1:
+        return f"return {names[0]};"
+    else:
+        return f"return {dispatcher.returns_type(rets).cpp_type()}({', '.join(names)});"
+
+
+# Given a function, and the name of a variable corresponding to the output of that function,
+# gather up all of the individual returns that are not aliased
+def gather_nonaliased_inner_rets(func: FunctionSchema, out_var: str) -> list[str]:
+    aliased_rets = func.aliased_return_names()
+    non_aliased_names = []
+    is_out_var_a_tuple = len(func.returns) > 1
+    for i, r in enumerate(aliased_rets):
+        if r is None:
+            non_aliased_names.append(
+                f"std::get<{i}>({out_var})" if is_out_var_a_tuple else out_var
+            )
+    return non_aliased_names
+
+
+# Generates functional kernels in terms of their inplace.mutable counterparts.
+# We only do this for "generated" NativeFunctions
+@with_native_function
+def gen_composite_functional_kernel(g: NativeFunctionsGroup) -> str | None:
+    # We should only be generating these for code-generated NativeFunctions
+    if "generated" not in g.functional.tags:
+        return None
+    # And we always write the kernel for a generated op in terms of a non-generated op.
+    if g.inplace is not None and "generated" not in g.inplace.tags:
+        target_f = g.inplace
+    elif g.mutable is not None and "generated" not in g.mutable.tags:
+        target_f = g.mutable
+    else:
+        # We should be guaranteed to have a valid inplace/mutable variant to call into.
+        # See Note: [Mutable Ops Not Using Functionalization]
+        raise AssertionError(str(g.functional.func))
+
+    sig = DispatcherSignature(g.functional.func)
+    target_sig = DispatcherSignature(target_f.func)
+
+    context: list[Binding | Expr] = []
+    clone_mutable_inputs = []
+    cloned_return_names = []
+    # We can't just directly pass all of the arguments from the functional op into the mutating op.
+    # We need to check for which inputs to the mutating operator are mutable,
+    # and clone those inputs first.
+    for a_curr, a_tgt in zip(
+        dispatcher.jit_arguments(g.functional.func),
+        dispatcher.jit_arguments(target_f.func),
+    ):
+        if a_tgt.annotation is not None and a_tgt.annotation.is_write:
+            clone_mutable_inputs.append(
+                f"auto {a_curr.name}_clone = clone_arg({a_curr.name});"
+            )
+            context.append(
+                Expr(
+                    expr=f"{a_curr.name}_clone",
+                    type=dispatcher.argument_type(a_curr, binds=a_curr.name),
+                )
+            )
+            # Invariant: mutable arguments on the inner mutable op are always returns on the functional op.
+            cloned_return_names.append(f"{a_curr.name}_clone")
+        else:
+            context.append(dispatcher.argument(a_curr))
+    exprs = ", ".join([e.expr for e in translate(context, target_sig.arguments())])
+
+    out_name = "output"
+    maybe_assign = f"auto {out_name} = " if len(target_f.func.returns) > 0 else ""
+    inner_return_names = gather_nonaliased_inner_rets(target_f.func, out_name)
+    ret_str = return_str(
+        g.functional.func.returns, inner_return_names + cloned_return_names
+    )
+
+    clone_mutable_inputs_str = "\n".join(clone_mutable_inputs)
+    return f"""
+{sig.defn(name=sig.name() + ("_symint" if g.out.func.has_symint() else ""))} {{
+  {clone_mutable_inputs_str}
+  {maybe_assign}at::_ops::{target_f.func.name.unambiguous_name()}::call({exprs});
+  {ret_str}
+}}
+"""
+
+
+# Generates out= kernels in terms of their functional counterparts.
+# We only do this for "generated" NativeFunctions
+@with_native_function
+def gen_composite_out_kernel(g: NativeFunctionsGroup) -> str | None:
+    # We should only be generating these for code-generated NativeFunctions
+    if "generated" not in g.out.tags:
+        return None
+    # And we always write the kernel for the out= op in terms of the functional.
+    # Note that the functional op might have also been generated, but we don't have to
+    # worry about cycles, because the generated functional kernels are always implemented
+    # in terms of non-generated kernels (see gen_composite_functional_kernel).
+
+    sig = DispatcherSignature(g.out.func)
+    target_sig = DispatcherSignature(g.functional.func)
+
+    exprs = ", ".join(
+        [e.expr for e in translate(sig.arguments(), target_sig.arguments())]
+    )
+
+    copy_outs = []
+    out_name = "tmp_output"
+    for i, out_arg in enumerate(g.out.func.arguments.out):
+        functional_return_name = (
+            out_name
+            if len(g.functional.func.returns) == 1
+            else f"std::get<{i}>({out_name})"
+        )
+        copy_outs.append(
+            f"""\
+  resize_out_helper({out_arg.name}, {functional_return_name});
+  copy_arg({out_arg.name}, {functional_return_name});"""
+        )
+
+    rets = []
+    # For each return arg in the calling (out=) operator,
+    # If it corresponds to an aliased input, return the input.
+    # Otherwise, return the corresponding output from calling the functional operator.
+    for i, ret_name in enumerate(g.out.func.aliased_return_names()):
+        if ret_name is not None:
+            rets.append(ret_name)
+        else:
+            functional_return_name = (
+                out_name
+                if len(g.functional.func.returns) == 1
+                else f"std::get<{i}>({out_name})"
+            )
+            rets.append(functional_return_name)
+
+    copy_outs_str = "\n".join(copy_outs)
+
+    # Kernel name needs to follow the naming convention defined in `generate_function()`
+    return f"""
+{sig.defn(name=g.out.func.name.unambiguous_name() + ("_symint" if g.out.func.has_symint() else ""))} {{
+  auto {out_name} = at::_ops::{g.functional.func.name.unambiguous_name()}::call({exprs});
+  {copy_outs_str}
+  {return_str(g.out.func.returns, rets)}
+}}
+"""
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e07c01ecc3c789c46ec1905812f39b42910ff6d4
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/context.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/context.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..995fbb1309aa1632dd9acbef4404d734a7d60a81
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/context.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_annotated_fn_args.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_annotated_fn_args.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4ca103a9ed1cdc296c24ab11b7095ae7e981e851
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_annotated_fn_args.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e9b1fd7b696709dc7ddb285e53b474879933c223
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd_functions.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..00637b31fd7c43cb7a2d36382bd0b916535516e2
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_autograd_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_inplace_or_view_type.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_inplace_or_view_type.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b7b98d26d9c8b81f017b82e211ab35a8375f3870
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_inplace_or_view_type.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_python_functions.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_python_functions.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..41d197a23f031023338575c71aedc61c6d8425d4
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_python_functions.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_trace_type.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_trace_type.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..039d207c338a3fc03fc62ab1a3b13b1b3feba349
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_trace_type.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_factories.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_factories.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..082260690d855466ff07e085da4719be4d538b00
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_factories.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_type.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_type.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1d3b49e820909b7ea91c25640a8813ec72eb072c
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_variable_type.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_view_funcs.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_view_funcs.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ae44a6dfe8b4608baa29133bae9e774bd93140ad
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/gen_view_funcs.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/load_derivatives.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/load_derivatives.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f16a8cf6f5bf4a8054ed075c6f7411b0bc8a09d7
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/packaged/autograd/__pycache__/load_derivatives.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_nested_functions.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_nested_functions.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..3acb5128cee1e180de887080106e7cf5559f15ee
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_nested_functions.cpp
@@ -0,0 +1,81 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+#include "torch/csrc/Device.h"
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/autograd/python_nested_functions.h"
+#include "torch/csrc/autograd/generated/python_return_types.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/autograd/utils/python_arg_parsing.h"
+#include "torch/csrc/autograd/generated/variable_factories.h"
+#include "torch/csrc/utils/out_types.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/structseq.h"
+#include "torch/csrc/utils/device_lazy_init.h"
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#endif
+
+using at::Tensor;
+using at::Device;
+using at::Layout;
+using at::Scalar;
+using at::ScalarType;
+using at::Backend;
+using at::OptionalDeviceGuard;
+using at::DeviceGuard;
+using at::TensorOptions;
+using at::IntArrayRef;
+using at::OptionalIntArrayRef;
+using at::Generator;
+using at::TensorList;
+using at::Dimname;
+using at::DimnameList;
+
+using namespace torch::autograd::utils;
+
+namespace torch::autograd {
+
+// generated forward declarations start here
+
+${py_forwards}
+
+static PyMethodDef nested_functions[] = {
+  {NULL, NULL, 0, NULL},
+  ${py_method_defs}
+  {NULL}
+};
+
+static PyObject* THPNestedVariableFunctionsModule = NULL;
+
+void initNestedFunctions(PyObject* module) {
+  nested_functions[0] = get_nested_functions_manual()[0];
+  static struct PyModuleDef def = {
+     PyModuleDef_HEAD_INIT,
+     "torch._C._nested",
+     NULL,
+     -1,
+     nested_functions
+  };
+  PyObject* nested = PyModule_Create(&def);
+  THPNestedVariableFunctionsModule = nested;
+  if (!nested) {
+    throw python_error();
+  }
+  // steals a reference to nested
+  if (PyModule_AddObject(module, "_nested", nested) != 0) {
+    throw python_error();
+  }
+}
+
+// generated methods start here
+
+${py_methods}
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_nn_functions.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_nn_functions.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..8eabb0da2332283a02e98e54dd0a277a83a55ad6
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_nn_functions.cpp
@@ -0,0 +1,113 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+#include "torch/csrc/Device.h"
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/autograd/python_nn_functions.h"
+#include "torch/csrc/autograd/generated/python_return_types.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/autograd/utils/python_arg_parsing.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/structseq.h"
+#include "torch/csrc/utils/tensor_memoryformats.h"
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#endif
+
+using at::Tensor;
+using at::Scalar;
+using at::MemoryFormat;
+using at::Generator;
+using at::IntArrayRef;
+using at::ArrayRef;
+
+using namespace torch::autograd::utils;
+
+namespace torch::autograd {
+
+static PyObject* THPNNVariableFunctionsModule = nullptr;
+
+static PyObject * THPVariable__parse_to(PyObject* module, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "to(Device device=None, ScalarType dtype=None, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
+    "to(ScalarType dtype, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
+    "to(Tensor tensor, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
+  });
+  ParsedArgs<5> parsed_args;
+  auto r = parser.parse(args, kwargs, parsed_args);
+  if (r.has_torch_function()) {
+    return handle_torch_function(r, args, kwargs, THPNNVariableFunctionsModule, "torch.nn", "_parse_to");
+  }
+  auto parsed = parse_to_conversion(r, /*allow_copy*/ false); // we don't want copy for nn.Module.to
+  auto& device = std::get<0>(parsed);
+  auto& scalarType = std::get<1>(parsed);
+  auto non_blocking = std::get<2>(parsed);
+  auto opt_memory_format = std::get<4>(parsed);
+  auto tuple = THPObjectPtr{PyTuple_New(4)};
+  if (!tuple) throw python_error();
+  if (device) {
+    PyTuple_SET_ITEM(tuple.get(), 0, THPDevice_New(*device));
+  } else {
+    Py_INCREF(Py_None);
+    PyTuple_SET_ITEM(tuple.get(), 0, Py_None);
+  }
+  if (scalarType) {
+    PyTuple_SET_ITEM(tuple.get(), 1, Py_NewRef(torch::getTHPDtype(*scalarType)));
+  } else {
+    Py_INCREF(Py_None);
+    PyTuple_SET_ITEM(tuple.get(), 1, Py_None);
+  }
+  PyTuple_SET_ITEM(tuple.get(), 2, torch::autograd::utils::wrap(non_blocking));
+  if (opt_memory_format.has_value()) {
+    PyTuple_SET_ITEM(tuple.get(), 3, Py_NewRef(torch::utils::getTHPMemoryFormat(opt_memory_format.value())));
+  } else {
+    Py_INCREF(Py_None);
+    PyTuple_SET_ITEM(tuple.get(), 3, Py_None);
+  }
+  return tuple.release();
+  END_HANDLE_TH_ERRORS
+}
+
+// generated forward declarations start here
+
+${py_forwards}
+
+static PyMethodDef nn_functions[] = {
+  {"_parse_to", castPyCFunctionWithKeywords(THPVariable__parse_to),
+    METH_VARARGS | METH_KEYWORDS, nullptr},
+  ${py_method_defs}
+  {nullptr}
+};
+
+void initNNFunctions(PyObject* module) {
+  static struct PyModuleDef def = {
+     PyModuleDef_HEAD_INIT,
+     "torch._C._nn",
+     nullptr,
+     -1,
+     nn_functions
+  };
+  PyObject* nn = PyModule_Create(&def);
+  THPNNVariableFunctionsModule = nn;
+  if (!nn) {
+    throw python_error();
+  }
+  // steals a reference to nn
+  if (PyModule_AddObject(module, "_nn", nn) != 0) {
+    throw python_error();
+  }
+}
+
+// generated methods start here
+
+${py_methods}
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_return_types.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_return_types.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..139e6b8958336cfcc8328fa33581e9f1ab6d5532
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_return_types.cpp
@@ -0,0 +1,52 @@
+#include <Python.h>
+
+#include <vector>
+#include <map>
+#include <string>
+
+#include "torch/csrc/autograd/generated/python_return_types.h"
+#include "torch/csrc/utils/structseq.h"
+#include "torch/csrc/Exceptions.h"
+
+namespace torch { namespace autograd { namespace generated {
+
+${py_return_types}
+
+}}}
+
+namespace torch::autograd {
+
+static void addReturnType(
+    PyObject* module,
+    const char* name,
+    PyTypeObject* type) {
+  // hold onto the TypeObject for the unlikely case of user
+  // deleting or overriding it.
+  Py_INCREF(type);
+  if (PyModule_AddObject(
+          module,
+          name,
+          (PyObject*)type) != 0) {
+    Py_DECREF(type);
+    throw python_error();
+  }
+}
+
+void initReturnTypes(PyObject* module) {
+  static struct PyModuleDef def = {
+      PyModuleDef_HEAD_INIT, "torch._C._return_types", nullptr, -1, {}};
+  PyObject* return_types_module = PyModule_Create(&def);
+  if (!return_types_module) {
+    throw python_error();
+  }
+
+  ${py_return_types_registrations}
+
+  // steals a reference to return_types on success
+  if (PyModule_AddObject(module, "_return_types", return_types_module) != 0) {
+    Py_DECREF(return_types_module);
+    throw python_error();
+  }
+}
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_return_types.h b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_return_types.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce6c355ea146a272709255b898603764112168b9
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_return_types.h
@@ -0,0 +1,14 @@
+#pragma once
+
+namespace torch {
+namespace autograd {
+namespace generated {
+
+${py_return_types_declarations}
+
+}
+
+void initReturnTypes(PyObject* module);
+
+} // namespace autograd
+} // namespace torch
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_sparse_functions.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_sparse_functions.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..648d91442102e9b950cb2ddb8db545c4b4e1100e
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_sparse_functions.cpp
@@ -0,0 +1,67 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+#include "torch/csrc/Device.h"
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/autograd/python_sparse_functions.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/autograd/utils/python_arg_parsing.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/structseq.h"
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#endif
+
+using at::Tensor;
+using at::Scalar;
+using at::ScalarType;
+using at::MemoryFormat;
+using at::Generator;
+using at::IntArrayRef;
+using at::TensorList;
+
+using namespace torch::autograd::utils;
+
+namespace torch::autograd {
+
+// generated forward declarations start here
+
+${py_forwards}
+
+static PyMethodDef sparse_functions[] = {
+  ${py_method_defs}
+  {NULL}
+};
+
+static PyObject* THPSparseVariableFunctionsModule = NULL;
+
+void initSparseFunctions(PyObject* module) {
+  static struct PyModuleDef def = {
+     PyModuleDef_HEAD_INIT,
+     "torch._C._sparse",
+     NULL,
+     -1,
+     sparse_functions
+  };
+  PyObject* sparse = PyModule_Create(&def);
+  THPSparseVariableFunctionsModule = sparse;
+  if (!sparse) {
+    throw python_error();
+  }
+  // steals a reference to sparse
+  if (PyModule_AddObject(module, "_sparse", sparse) != 0) {
+    throw python_error();
+  }
+}
+
+// generated methods start here
+
+${py_methods}
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_special_functions.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_special_functions.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..bf9e109b4a77352cd85ba828b97d67d329543867
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_special_functions.cpp
@@ -0,0 +1,79 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+#include "torch/csrc/Device.h"
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/autograd/python_special_functions.h"
+#include "torch/csrc/autograd/generated/python_return_types.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/autograd/utils/python_arg_parsing.h"
+#include "torch/csrc/autograd/generated/variable_factories.h"
+#include "torch/csrc/utils/out_types.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/structseq.h"
+#include "torch/csrc/utils/device_lazy_init.h"
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#endif
+
+using at::Tensor;
+using at::Device;
+using at::Layout;
+using at::Scalar;
+using at::ScalarType;
+using at::Backend;
+using at::OptionalDeviceGuard;
+using at::DeviceGuard;
+using at::TensorOptions;
+using at::IntArrayRef;
+using at::Generator;
+using at::TensorList;
+using at::Dimname;
+using at::DimnameList;
+
+using torch::utils::check_out_type_matches;
+using namespace torch::autograd::utils;
+
+namespace torch::autograd {
+
+// generated forward declarations start here
+
+${py_forwards}
+
+static PyMethodDef special_functions[] = {
+  ${py_method_defs}
+  {NULL}
+};
+
+static PyObject* THPSpecialVariableFunctionsModule = NULL;
+
+void initSpecialFunctions(PyObject* module) {
+  static struct PyModuleDef def = {
+     PyModuleDef_HEAD_INIT,
+     "torch._C._special",
+     NULL,
+     -1,
+     special_functions
+  };
+  PyObject* special = PyModule_Create(&def);
+  THPSpecialVariableFunctionsModule = special;
+  if (!special) {
+    throw python_error();
+  }
+  // steals a reference to special
+  if (PyModule_AddObject(module, "_special", special) != 0) {
+    throw python_error();
+  }
+}
+
+// generated methods start here
+
+${py_methods}
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_torch_functions.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_torch_functions.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..c17d1040e1892b6a215a8c4264fe5a5345265bc7
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_torch_functions.cpp
@@ -0,0 +1,93 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+// Python bindings for torch.* functions implemented through ATen.
+//
+// The functions are bound as static methods on a class
+// torch._C._VariableFunctions which is also aliased as Variable._torch
+// and also copied into 'torch' module.
+
+#include <Python.h>
+
+// Undefine the copysign macro so that at::copysign works as intended with MSVC
+// https://github.com/python/cpython/blob/c60394c7fc9cc09b16e9675a3eeb5844b6d8523f/PC/pyconfig.h#L196
+#ifdef _MSC_VER
+#undef copysign
+#endif // _MSC_VER
+
+#include "torch/csrc/autograd/python_torch_functions.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/Dtype.h"
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/utils/out_types.h"
+#include "torch/csrc/utils/pybind.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/tensor_layouts.h"
+#include "torch/csrc/utils/tensor_new.h"
+#include "torch/csrc/utils/tensor_numpy.h"
+#include "torch/csrc/jit/frontend/tracer.h"
+#include "torch/csrc/autograd/generated/variable_factories.h"
+#include "torch/csrc/utils/structseq.h"
+#include "torch/csrc/utils/device_lazy_init.h"
+#include "torch/csrc/autograd/generated/python_return_types.h"
+
+#include <ATen/core/Tensor.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#endif
+
+#include <functional>
+#include <initializer_list>
+#include <stdexcept>
+#include <utility>
+
+using at::Tensor;
+using at::Device;
+using at::Layout;
+using at::Scalar;
+using at::ScalarType;
+using at::Backend;
+using at::OptionalDeviceGuard;
+using at::DeviceGuard;
+using at::TensorOptions;
+using at::IntArrayRef;
+using at::Generator;
+using at::TensorList;
+using at::Dimname;
+using at::DimnameList;
+using at::ArrayRef;
+
+using torch::utils::check_out_type_matches;
+using namespace torch::autograd::utils;
+
+// NOTE: See [Sharded File] comment in VariableType
+
+namespace torch::autograd {
+
+// generated forward declarations start here
+
+${py_forwards}
+
+static PyMethodDef torch_functions_shard[] = {
+  ${py_method_defs}
+};
+
+void gatherTorchFunctions${shard_id}(std::vector<PyMethodDef> &torch_functions) {
+  constexpr size_t num_functions = sizeof(torch_functions_shard) / sizeof(torch_functions_shard[0]);
+  torch_functions.insert(
+    torch_functions.end(),
+    torch_functions_shard,
+    torch_functions_shard + num_functions);
+}
+
+// generated methods start here
+
+${py_methods}
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_variable_methods.cpp b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_variable_methods.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..505ccc6d2f6de40f226a0fc9acda0a6723a37528
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/python_variable_methods.cpp
@@ -0,0 +1,1328 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+// ${generated_comment}
+
+#include <Python.h>
+
+// Undefine the copysign macro so that at::copysign works as intended with MSVC
+// https://github.com/python/cpython/blob/c60394c7fc9cc09b16e9675a3eeb5844b6d8523f/PC/pyconfig.h#L196
+#ifdef _MSC_VER
+#undef copysign
+#endif // _MSC_VER
+
+#include "torch/csrc/DynamicTypes.h"
+#include "torch/csrc/Exceptions.h"
+#include "torch/csrc/Size.h"
+#include "torch/csrc/autograd/generated/VariableType.h"
+#include "torch/csrc/autograd/python_variable.h"
+#include "torch/csrc/autograd/utils/python_arg_parsing.h"
+#include "torch/csrc/autograd/utils/error_messages.h"
+#include "torch/csrc/autograd/utils/wrap_outputs.h"
+#include "torch/csrc/jit/frontend/tracer.h"
+#ifdef USE_CUDA
+#include "torch/csrc/cuda/Event.h"
+#endif
+#include "torch/csrc/utils/device_lazy_init.h"
+#include <torch/csrc/utils/numpy_stub.h>
+#include "torch/csrc/utils/object_ptr.h"
+#include "torch/csrc/utils/pycfunction_helpers.h"
+#include "torch/csrc/utils/python_arg_parser.h"
+#include "torch/csrc/utils/python_numbers.h"
+#include "torch/csrc/utils/python_strings.h"
+#include "torch/csrc/utils/tensor_apply.h"
+#include "torch/csrc/utils/tensor_list.h"
+#include "torch/csrc/utils/tensor_new.h"
+#include "torch/csrc/utils/tensor_numpy.h"
+#include "torch/csrc/utils/tensor_types.h"
+#include "torch/csrc/autograd/generated/python_return_types.h"
+
+#include <ATen/core/Tensor.h>
+#include <ATen/FuncTorchTLS.h>
+#include "c10/core/Stream.h"
+
+#include <optional>
+#include <stdexcept>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+$ops_headers
+#include <ATen/ops/_local_scalar_dense.h>
+#endif
+
+using at::device_of;
+using at::OptionalDeviceGuard;
+using at::Scalar;
+using at::ScalarType;
+using at::Tensor;
+using c10::Stream;
+using namespace torch::autograd::utils;
+
+namespace torch::autograd {
+
+static PyObject * THPVariable__is_view(PyObject *self, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "_is_view", args);
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  if (self_.is_view()) {
+    Py_RETURN_TRUE;
+  } else {
+    Py_RETURN_FALSE;
+  }
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object bc no support for first-class functions in native_functions.yaml
+// See: ATen/native/README.md for more context
+static PyObject * THPVariable_apply_(PyObject* self, PyObject* arg)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    auto args = py::make_tuple(py::handle(arg));
+    return handle_torch_function(self, "apply_", args.ptr());
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  if (self_.requires_grad()) {
+    throw std::runtime_error(
+        "Can't call apply_() on Variable that requires grad. Use "
+        "var.detach().apply_() instead.");
+  }
+  return THPVariable_Wrap(torch::utils::apply_(self_, arg));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_size(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "size(int64_t? dim=None)",
+    "size(Dimname dim)",
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+  if (r.idx == 0) {
+    if (!r.toInt64Optional(0).has_value()) {
+      return THPSize_NewFromSymSizes(self_);
+    }
+    if (jit::tracer::isTracing()) {
+      // will error out if a tensor has symints
+      return wrap(jit::tracer::getSizeOf(self_, r.toInt64(0)));
+    } else {
+      return torch::toPyObject(self_.sym_size(r.toInt64(0)));
+    }
+  } else if (r.idx == 1) {
+    if (jit::tracer::isTracing()) {
+      TORCH_INTERNAL_ASSERT(false, "NYI: Named tensors w/ JIT");
+    }
+    return wrap(self_.size(r.dimname(0)));
+  }
+  Py_RETURN_NONE;
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_stride(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "stride(int64_t? dim=None)",
+    "stride(Dimname dim)",
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  if (r.idx == 0) {
+    if (r.toInt64Optional(0).has_value()) {
+      return torch::toPyObject(self_.sym_stride(r.toInt64(0)));
+    }
+    // yes, this is called strides in ATen.
+    at::SymIntArrayRef strides = self_.sym_strides();
+    // we can't do the normal wrapping here because IntArrayRef maps to both
+    // torch.Size and tuple in python
+    // TODO: consider factoring this out
+    THPObjectPtr tuple(PyTuple_New(static_cast<Py_ssize_t>(strides.size())));
+    if (!tuple) throw python_error();
+    for (size_t i = 0; i != strides.size(); i++) {
+      PyObject* s = torch::toPyObject(strides[i]);
+      if (!s) throw python_error();
+      PyTuple_SET_ITEM(tuple.get(), i, s);
+    }
+    return tuple.release();
+  } else if (r.idx == 1) {
+    return wrap(self_.stride(r.dimname(0)));
+  }
+  Py_RETURN_NONE;
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_get_device(PyObject* self_, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self_)) {
+    return handle_torch_function(self_, "get_device", args, nullptr);
+  }
+  auto& self = THPVariable_Unpack(self_);
+  return wrap(self.get_device());
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_has_names(PyObject* self_, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self_)) {
+    return handle_torch_function(self_, "has_names", args);
+  }
+  auto& self = THPVariable_Unpack(self_);
+  return wrap(self.has_names());
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_data_ptr(PyObject* self_, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self_)) {
+    return handle_torch_function(self_, "data_ptr", args);
+  }
+  auto& self = THPVariable_Unpack(self_);
+  return wrap(self.data_ptr());
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_storage_offset(PyObject* self_, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self_)) {
+    return handle_torch_function(self_, "storage_offset");
+  }
+  auto& self = THPVariable_Unpack(self_);
+  return py::cast(self.sym_storage_offset()).release().ptr();
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_dim(PyObject* self, PyObject* args)
+{
+   HANDLE_TH_ERRORS
+   if (check_has_torch_function(self)) {
+     return handle_torch_function(self, "dim", args);
+   }
+   auto& self_ = THPVariable_Unpack(self);
+   return THPUtils_packInt64(self_.dim());
+   END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_numel(PyObject* self, PyObject* args)
+{
+   HANDLE_TH_ERRORS
+   if (check_has_torch_function(self)) {
+     return handle_torch_function(self, "numel", args);
+   }
+   auto& self_ = THPVariable_Unpack(self);
+   if (jit::tracer::isTracing()) {
+     return wrap(jit::tracer::getNumelOf(self_));
+   } else {
+     return py::cast(self_.sym_numel()).release().ptr();
+   }
+   END_HANDLE_TH_ERRORS
+}
+
+static Tensor dispatch_contiguous(const Tensor & self, at::MemoryFormat memory_format) {
+  pybind11::gil_scoped_release no_gil;
+  OptionalDeviceGuard device_guard(device_of(self));
+  return self.contiguous(memory_format);
+}
+
+static PyObject * THPVariable_contiguous(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "contiguous(*, MemoryFormat memory_format=contiguous_format)",
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto& self_ = THPVariable_Unpack(self);
+  auto memory_format = r.memoryformat(0);
+  // avoids touching the GIL or current device if self is already contiguous
+  if (self_.is_contiguous(memory_format)) {
+    // NOTE: this logic is duplicated from VariableType.cpp. Since we need to
+    // record this call to contiguous() in the trace regardless of whether
+    // we actually call contiguous here, we need to record this information
+    // manually.
+    if (jit::tracer::isTracing()) {
+      const auto& tracer_state = jit::tracer::getTracingState();
+      auto op_name = c10::Symbol::fromQualString("aten::contiguous");
+      auto node = tracer_state->createNode(op_name, /*num_outputs=*/0);
+      jit::tracer::recordSourceLocation(node);
+      jit::tracer::addInputs(node, "self", self_);
+      jit::tracer::addInputs(node, "memory_format", memory_format);
+      tracer_state->insertNode(node);
+      jit::tracer::addOutput(node, self_);
+    }
+    Py_INCREF(self);
+    return self;
+  }
+  return THPVariable_Wrap(dispatch_contiguous(self_, memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+static Tensor dispatch_copy_(const Tensor & self, const Tensor & other, bool non_blocking) {
+  pybind11::gil_scoped_release no_gil;
+  OptionalDeviceGuard device_guard(device_of(self));
+  return self.copy_(other, non_blocking);
+}
+
+ static PyObject * THPVariable_copy_(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "copy_(Tensor other, bool non_blocking=False)",
+    "copy_(Tensor other, bool async=False)|deprecated"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<2> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  return THPVariable_Wrap(dispatch_copy_(self_, r.tensor(0), r.toBool(1)));
+  END_HANDLE_TH_ERRORS
+}
+
+template<typename T>
+static T dispatch_to(const Tensor & self) {
+  pybind11::gil_scoped_release no_gil;
+  OptionalDeviceGuard device_guard(device_of(self));
+  TORCH_CHECK_VALUE(self.sym_numel() == 1, "only one element tensors can be converted to Python scalars");
+  return self.template item<T>();
+}
+
+static PyObject * THPVariable_float_scalar(PyObject* self, PyObject* args) {
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "__float__", args);
+  }
+  jit::tracer::warn("Converting a tensor to a Python float", jit::tracer::WARN_PYTHON_DATAFLOW);
+  auto& self_ = THPVariable_Unpack(self);
+  return wrap(dispatch_to<double>(self_));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_complex_scalar(PyObject* self, PyObject* args) {
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "__complex__", args);
+  }
+  jit::tracer::warn("Converting a tensor to a Python complex", jit::tracer::WARN_PYTHON_DATAFLOW);
+  auto& self_ = THPVariable_Unpack(self);
+  return wrap(dispatch_to<c10::complex<double>>(self_));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_integral_scalar(PyObject* self, PyObject* args) {
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "__int__", args);
+  }
+  jit::tracer::warn("Converting a tensor to a Python integer", jit::tracer::WARN_PYTHON_DATAFLOW);
+  auto& self_ = THPVariable_Unpack(self);
+  if (isFloatingType(self_.scalar_type())) {
+    // we can't dispatch to item<int64_t> here because we want to avoid ATen overflow checks;
+    // the python integral type (long in python2) can't overflow.
+    return THPUtils_packDoubleAsInt(dispatch_to<double>(self_));
+  } else {
+    return wrap(dispatch_to<int64_t>(self_));
+  }
+  END_HANDLE_TH_ERRORS
+}
+
+// This is the __index__ function in Python which is similar to __int__, but
+// called when used as a slice.
+static PyObject * THPVariable_index_scalar(PyObject* self, PyObject* args) {
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "__index__", args);
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  // TODO: change the condition to `self_.dim() != 0` once we expose scalars
+  // in PyTorch.
+  if (!isIntegralType(self_.scalar_type(), /*includeBool=*/true) || self_.sym_numel() != 1) {
+    throw TypeError("only integer tensors of a single element can be converted to an index");
+  }
+  return wrap(dispatch_to<int64_t>(self_));
+  END_HANDLE_TH_ERRORS
+}
+
+static Tensor dispatch_invert(const Tensor & self) {
+  pybind11::gil_scoped_release no_gil;
+  OptionalDeviceGuard device_guard(device_of(self));
+  return self.bitwise_not();
+}
+
+static PyObject * THPVariable_invert(PyObject* self, PyObject* args) {
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "__invert__", args);
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  if (!isIntegralType(self_.scalar_type(), /*includeBool=*/true)) {
+    throw TypeError("~ (operator.invert) is only implemented on integer and Boolean-type tensors");
+  }
+  return THPVariable_Wrap(dispatch_invert(self_));
+  END_HANDLE_TH_ERRORS
+}
+
+static Tensor dispatch_to(const Tensor & self, Device device, bool non_blocking, bool copy, std::optional<c10::MemoryFormat> optional_memory_format) {
+  pybind11::gil_scoped_release no_gil;
+  // NOTE: this is where we record aten::to in the graph during tracing. However, the behavior of aten::to
+  // is different with respect to TensorOptions fields that are not present: aten::to inherits fields that
+  // are missing from the self argument while the tracer assumes that they should be populated with the
+  // default values (eg. float for scalar type). By explicitly copying over the tensor options here we fully
+  // specify all tensor options and thus record the proper trace
+  return self.to(self.options().device(device).memory_format(optional_memory_format), non_blocking, copy);
+}
+
+static Tensor dispatch_to(const Tensor & self, bool non_blocking, bool copy, std::optional<c10::MemoryFormat> optional_memory_format) {
+  pybind11::gil_scoped_release no_gil;
+  return self.to(self.options().memory_format(optional_memory_format), non_blocking, copy);
+}
+
+static Tensor dispatch_to(const Tensor & self, ScalarType dtype, bool non_blocking, bool copy, std::optional<c10::MemoryFormat> optional_memory_format) {
+  pybind11::gil_scoped_release no_gil;
+  // TODO: Make this call the TensorOptions version, maybe?
+  return self.to(dtype, non_blocking, copy, optional_memory_format);
+}
+
+static Tensor dispatch_to(const Tensor & self, Device device, ScalarType dtype, bool non_blocking, bool copy, std::optional<c10::MemoryFormat> optional_memory_format) {
+  pybind11::gil_scoped_release no_gil;
+  // TODO: Make this call the TensorOptions version, maybe?
+  return self.to(device, dtype, non_blocking, copy, optional_memory_format);
+}
+
+static PyObject * THPVariable_cpu(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+   HANDLE_TH_ERRORS
+   static PythonArgParser parser({
+     "cpu(*, MemoryFormat? memory_format=None)"
+   });
+   auto& self_ = THPVariable_Unpack(self);
+   ParsedArgs<1> parsed_args;
+   auto r = parser.parse(self, args, kwargs, parsed_args);
+
+   if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+    }
+
+   auto opt_memory_format = r.memoryformatOptional(0);
+   return THPVariable_Wrap(dispatch_to(self_, at::Device(at::DeviceType::CPU), false, false, opt_memory_format));
+   END_HANDLE_TH_ERRORS
+}
+
+static Tensor dispatch_nonzero(const Tensor & self) {
+  pybind11::gil_scoped_release no_gil;
+  OptionalDeviceGuard device_guard(device_of(self));
+  return self.nonzero();
+}
+
+static std::vector<Tensor> dispatch_nonzero_numpy(const Tensor & self) {
+  pybind11::gil_scoped_release no_gil;
+  OptionalDeviceGuard device_guard(device_of(self));
+  return self.nonzero_numpy();
+}
+
+static PyObject * THPVariable_nonzero(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "nonzero()",
+    "nonzero(*, bool as_tuple)",
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<2> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  if (r.idx == 0 || (r.idx == 1 && !r.toBool(0))) {
+    return wrap(dispatch_nonzero(self_));
+  } else {
+    return wrap(dispatch_nonzero_numpy(self_));
+  }
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_cuda(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "cuda(Device? device=None, bool non_blocking=False, *, MemoryFormat? memory_format=None)",
+    "cuda(Device? device=None, bool async=False, *, MemoryFormat? memory_format=None)|deprecated"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto device = r.isNone(0) ? at::Device(at::DeviceType::CUDA) : r.device(0);
+  auto opt_memory_format = r.memoryformatOptional(2);
+  TORCH_CHECK(device.is_cuda(), "Invalid device, must be cuda device");
+  torch::utils::device_lazy_init(at::kCUDA);
+  return THPVariable_Wrap(dispatch_to(self_, device, r.toBool(1), false, opt_memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_mtia(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "mtia(Device? device=None, bool non_blocking=False, *, MemoryFormat? memory_format=None)",
+    "mtia(Device? device=None, bool async=False, *, MemoryFormat? memory_format=None)|deprecated"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if (r.has_torch_function()) {
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto device = r.isNone(0) ? at::Device(at::DeviceType::MTIA) : r.device(0);
+  auto opt_memory_format = r.memoryformatOptional(2);
+  TORCH_CHECK(device.is_mtia(), "Invalid device, must be MTIA device");
+  torch::utils::device_lazy_init(at::kMTIA);
+  return THPVariable_Wrap(dispatch_to(self_, device, r.toBool(1), false, opt_memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_xpu(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "xpu(Device? device=None, bool non_blocking=False, *, MemoryFormat? memory_format=None)",
+    "xpu(Device? device=None, bool async=False, *, MemoryFormat? memory_format=None)|deprecated"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if (r.has_torch_function()) {
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto device = r.isNone(0) ? at::Device(at::DeviceType::XPU) : r.device(0);
+  auto opt_memory_format = r.memoryformatOptional(2);
+  TORCH_CHECK(device.is_xpu(), "Invalid device, must be xpu device");
+  torch::utils::device_lazy_init(at::kXPU);
+  return THPVariable_Wrap(dispatch_to(self_, device, r.toBool(1), false, opt_memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_ipu(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "ipu(Device? device=None, bool non_blocking=False, *, MemoryFormat? memory_format=None)",
+    "ipu(Device? device=None, bool async=False, *, MemoryFormat? memory_format=None)|deprecated"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if (r.has_torch_function()) {
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto device = r.isNone(0) ? at::Device(at::DeviceType::IPU) : r.device(0);
+  auto opt_memory_format = r.memoryformatOptional(2);
+  TORCH_CHECK(device.is_ipu(), "Invalid device, must be ipu device");
+  return THPVariable_Wrap(dispatch_to(self_, device, r.toBool(1), false, opt_memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_to_type(PyObject* self, ScalarType scalarType, std::optional<c10::MemoryFormat> optional_memory_format) {
+  HANDLE_TH_ERRORS
+  auto& self_ = THPVariable_Unpack(self);
+  return THPVariable_Wrap(dispatch_to(self_, scalarType, false, false, optional_memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_byte(PyObject* self, PyObject* args, PyObject* kwargs)  {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "byte(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Byte, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_char(PyObject* self, PyObject* args, PyObject* kwargs)  {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "char(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Char, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_double(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "double(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Double, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_float(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "float(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Float, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_cdouble(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "cdouble(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::ComplexDouble, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_cfloat(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "cfloat(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::ComplexFloat, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_half(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "half(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Half, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_int(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "int(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Int, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_long(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "long(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Long, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_short(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "short(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Short, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_bool(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "bool(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::Bool, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_bfloat16(PyObject* self, PyObject* args, PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "bfloat16(*, MemoryFormat? memory_format=None)"
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  auto opt_memory_format = r.memoryformatOptional(0);
+  return THPVariable_to_type(self, ScalarType::BFloat16, opt_memory_format);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_element_size(PyObject* self, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "element_size", args);
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  return THPUtils_packInt64(self_.element_size());
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object bc PyObjects not declarable in native_functions.yaml
+// See: ATen/native/README.md for more context
+static PyObject * THPVariable_numpy(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "numpy(*, bool force=False)"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if (r.has_torch_function()) {
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  jit::tracer::warn("Converting a tensor to a NumPy array", jit::tracer::WARN_PYTHON_DATAFLOW);
+  return torch::utils::tensor_to_numpy(self_, r.toBool(0));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_requires_grad_(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "requires_grad_(bool requires_grad=True)",
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  // temporary hack to improve functorch UX.
+  const auto& functorch_tls = at::functorch::functorchTLSAccessor();
+  if (functorch_tls) {
+    functorch_tls->checkSupportsInplaceRequiresGrad();
+  }
+
+  auto requires_grad = r.toBool(0);
+  // should we throw if requires_grad is true?  var.requires_grad = True throws here
+  // but it's nice to let this be a no-op.
+  if (!self_.is_leaf() && !requires_grad) {
+    throw std::runtime_error(autograd::utils::requires_grad_leaf_error(requires_grad));
+  }
+  if (requires_grad && ! isDifferentiableType(at::typeMetaToScalarType(self_.dtype()))) {
+    throw std::runtime_error("only Tensors of floating point dtype can require gradients");
+  }
+  self_.set_requires_grad(requires_grad);
+  return THPVariable_Wrap(self_);
+  END_HANDLE_TH_ERRORS
+}
+
+inline bool dispatch_is_contiguous(const Tensor & self, MemoryFormat memory_format) {
+  return self.is_contiguous(memory_format);
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_is_contiguous(PyObject* self_, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "is_contiguous(*, MemoryFormat memory_format=contiguous_format)",
+  });
+  ParsedArgs<1> parsed_args;
+  auto r = parser.parse(self_, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self_, args, kwargs, PyObject_Type(self_), "torch.Tensor");
+  }
+
+  auto memory_format = r.memoryformat(0);
+  auto& self = THPVariable_Unpack(self_);
+  return wrap(dispatch_is_contiguous(self, memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object to avoid dispatch overhead
+static PyObject * THPVariable_item(PyObject* self, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "item", args);
+  }
+  jit::tracer::warn("Converting a tensor to a Python number", jit::tracer::WARN_PYTHON_DATAFLOW);
+  auto& self_ = THPVariable_Unpack(self);
+  auto dispatch_item_ = [](const Tensor& self) -> at::Scalar {
+    pybind11::gil_scoped_release no_gil;
+    return self.item();
+  };
+  return py::cast(dispatch_item_(self_)).release().ptr();
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object bc no support for first class functions in native_functions.yaml
+// See: ATen/native/README.md for more context
+static PyObject * THPVariable_map_(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({ "map_(Tensor other, PyObject* callable)" });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<2> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  Variable other = r.tensor(0);
+  if (self_.requires_grad() || other.requires_grad()) {
+    throw std::runtime_error(
+        "Can't call map_() on Variable that requires grad. Use "
+        "var.detach().map_() instead.");
+  }
+  TORCH_CHECK(
+      !self_.unsafeGetTensorImpl()->is_python_dispatch() && !other.unsafeGetTensorImpl()->is_python_dispatch(),
+      ".map_ is not supported for tensor subclasses.");
+
+  return THPVariable_Wrap(torch::utils::map_(self_, other, r.pyobject(1)));
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object bc no support for first class functions in native_functions.yaml
+// See: ATen/native/README.md for more context
+static PyObject * THPVariable_map2_(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({ "map2_(Tensor x, Tensor y, PyObject* callable)" });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  Variable x = r.tensor(0);
+  Variable y = r.tensor(1);
+  if (self_.requires_grad() || x.requires_grad() || y.requires_grad()) {
+    throw std::runtime_error(
+        "Can't call map2_() on Variable that requires grad. Use "
+        "var.detach().map2_() instead.");
+  }
+  TORCH_CHECK(
+      !x.unsafeGetTensorImpl()->is_python_dispatch() && !y.unsafeGetTensorImpl()->is_python_dispatch(),
+      ".map2_ is not supported for tensor subclasses.");
+  return THPVariable_Wrap(torch::utils::map2_(self_, x, y, r.pyobject(2)));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_new(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "new", args, kwargs);
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  OptionalDeviceGuard device_guard(device_of(self_));
+  return THPVariable_Wrap(torch::utils::legacy_tensor_new(legacyExtractDispatchKey(self_), self_.scalar_type(), args, kwargs));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_new_tensor(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "new_tensor", args, kwargs);
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  OptionalDeviceGuard device_guard(device_of(self_));
+  return THPVariable_Wrap(torch::utils::new_tensor(legacyExtractDispatchKey(self_), self_.scalar_type(), args, kwargs));
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_storage(PyObject* self, PyObject* arg)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "untyped_storage");
+  }
+  auto& self_ = THPVariable_Unpack(self);
+  return createPyObject(self_.storage());
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_to(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "to(Device device=None, ScalarType dtype=None, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
+    "to(ScalarType dtype, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
+    "to(Tensor tensor, bool non_blocking=False, bool copy=False, *, MemoryFormat? memory_format=None)",
+  });
+  ParsedArgs<5> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+  if (r.has_torch_function()) {
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+  auto parsed = parse_to_conversion(r, /*allow_copy*/ true);
+  auto& device = std::get<0>(parsed);
+  auto& scalarType = std::get<1>(parsed);
+  auto non_blocking = std::get<2>(parsed);
+  auto copy = std::get<3>(parsed);
+  auto opt_memory_format = std::get<4>(parsed);
+  auto& self_ = THPVariable_Unpack(self);
+  torch::utils::maybe_initialize_device(device);
+  if (!device && !scalarType && !copy && !opt_memory_format.has_value()) {
+    Py_INCREF(self);
+    return self;
+  } else if (!device && !scalarType) {
+    return THPVariable_Wrap(
+        dispatch_to(self_, non_blocking, copy, opt_memory_format));
+  } else if (!device) {
+    return THPVariable_Wrap(dispatch_to(self_, *scalarType, non_blocking, copy, opt_memory_format));
+  } else if (!scalarType) {
+    return THPVariable_Wrap(dispatch_to(self_, *device, non_blocking, copy, opt_memory_format));
+  } else {
+    return THPVariable_Wrap(dispatch_to(self_, *device, *scalarType, non_blocking, copy, opt_memory_format));
+  }
+  Py_RETURN_NONE;
+  END_HANDLE_TH_ERRORS
+}
+
+// implemented on the python object b/c arbitrarily nested list not declarable in native_functions.yaml
+// See: ATen/native/README.md for more context
+static PyObject * THPVariable_tolist(PyObject* self, PyObject* args)
+{
+  HANDLE_TH_ERRORS
+  if (check_has_torch_function(self)) {
+    return handle_torch_function(self, "tolist", args);
+  }
+  jit::tracer::warn("Converting a tensor to a Python list", jit::tracer::WARN_PYTHON_DATAFLOW);
+  auto self_ = THPVariable_Unpack(self);
+  return torch::utils::tensor_to_list(self_);
+  END_HANDLE_TH_ERRORS
+}
+
+static PyObject * THPVariable_type(PyObject* self, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+  static PythonArgParser parser({
+    "type(PyObject* dtype=None, bool non_blocking=False, *, MemoryFormat? memory_format=None)",
+    "type(PyObject* dtype=None, bool async=False, *, MemoryFormat? memory_format=None)|deprecated"
+  });
+  auto& self_ = THPVariable_Unpack(self);
+  ParsedArgs<3> parsed_args;
+  auto r = parser.parse(self, args, kwargs, parsed_args);
+
+  if(r.has_torch_function()){
+    return handle_torch_function(r, self, args, kwargs, THPVariableClass, "torch.Tensor");
+  }
+
+  if (r.isNone(0)) {
+    return THPUtils_packString(torch::utils::options_to_string(self_.options()));
+  }
+  auto obj = r.pyobject(0);
+  auto opt_memory_format = r.memoryformatOptional(2);
+  std::string type_name;
+  bool is_dtype = false;
+  if (PyType_Check(obj)) {
+    if (obj == THPVariableClass) {
+      type_name = "torch.Tensor";
+    } else {
+      type_name = ((PyTypeObject*)obj)->tp_name;
+    }
+  } else if (THPUtils_checkString(obj)) {
+    type_name = THPUtils_unpackString(obj);
+  } else if (THPDtype_Check(obj)) {
+    is_dtype = true;
+  } else {
+    throw TypeError("dtype must be a type, str, or dtype object");
+  }
+  Device device = self_.device();
+  if (is_dtype) {
+    auto scalar_type = r.scalartype(0);
+    return THPVariable_Wrap(dispatch_to(self_, scalar_type, /*non_blocking=*/ r.toBool(1), /*copy=*/ false, opt_memory_format));
+  }
+  at::TensorOptions options = torch::utils::options_from_string(type_name);
+  auto scalar_type = at::typeMetaToScalarType(options.dtype());
+  auto device_type = options.device().type();
+  if (device_type != device.type()) {
+    device = at::Device(device_type);
+  }
+  torch::utils::maybe_initialize_device(device);
+  return THPVariable_Wrap(dispatch_to(self_, device, scalar_type, /*non_blocking=*/ r.toBool(1), /*copy=*/ false, opt_memory_format));
+  END_HANDLE_TH_ERRORS
+}
+
+// generated methods start here
+
+${py_methods}
+
+static PyObject * THPVariable_bool_scalar(PyObject* self, PyObject* args) {
+  if (check_has_torch_function(self)) {
+    HANDLE_TH_ERRORS
+    return handle_torch_function(self, "__bool__", args);
+    END_HANDLE_TH_ERRORS
+  }
+  jit::tracer::warn("Converting a tensor to a Python boolean", jit::tracer::WARN_PYTHON_DATAFLOW);
+  return THPVariable_is_nonzero(self, args);
+}
+
+static PyObject * THPVariable___eq__(PyObject* self_, PyObject* args, PyObject* kwargs)
+{
+  HANDLE_TH_ERRORS
+#ifdef USE_NUMPY
+  if (torch::utils::is_numpy_available()) {
+    static PythonArgParser parser({
+      "__eq__(PyObject* other)",
+    }, /*traceable=*/true);
+
+    ParsedArgs<1> parsed_args;
+    auto _r = parser.parse(self_, args, kwargs, parsed_args);
+    if(_r.has_torch_function()) {
+      return handle_torch_function(_r, self_, args, kwargs, THPVariableClass, "torch.Tensor");
+    }
+    switch (_r.idx) {
+      case 0: {
+        auto other = _r.pyobject(0);
+        if (PyArray_Check(other)) {
+          auto other_tensor = torch::utils::tensor_from_numpy(other);
+          auto dispatch_eq = [](const at::Tensor & self, const at::Tensor & other) -> at::Tensor {
+            pybind11::gil_scoped_release no_gil;
+            return self.eq(other);
+          };
+          const Tensor& self = THPVariable_Unpack(self_);
+          return wrap(dispatch_eq(self, other_tensor));
+        }
+      }
+    }
+  }
+#endif
+  return THPVariable_eq(self_, args, kwargs);
+  Py_RETURN_NONE;
+  END_HANDLE_TH_ERRORS
+}
+
+// Wrapper converts a raised TypeError into returning NotImplemented
+// Used to implement binary arithmetic operators
+template <PyObject* (*Func)(PyObject*, PyObject*, PyObject*)>
+static PyObject * TypeError_to_NotImplemented_(PyObject* self, PyObject* args, PyObject* kwargs) {
+
+  PyObject* ret = Func(self, args, kwargs);
+  if (!ret && PyErr_ExceptionMatches(PyExc_TypeError)) {
+    PyErr_Clear();
+    Py_INCREF(Py_NotImplemented);
+    ret = Py_NotImplemented;
+  }
+  return ret;
+}
+
+// set_ has to be defined in the template because the c10::Storage object
+// does not have a type, and we need to make sure the Python storage object's
+// type matches the tensor's type
+static PyObject* THPVariable_set_(
+    PyObject* self_,
+    PyObject* args,
+    PyObject* kwargs) {
+  HANDLE_TH_ERRORS
+  const Tensor& self = THPVariable_Unpack(self_);
+  static PythonArgParser parser(
+      {
+          "set_()",
+          "set_(Storage source)",
+          "set_(Storage source, SymInt storage_offset, SymIntArrayRef size, SymIntArrayRef stride=None)",
+          "set_(Tensor source)",
+          "set_(Tensor source, SymInt storage_offset, SymIntArrayRef size, SymIntArrayRef stride=None)",
+      },
+      /*traceable=*/false);
+
+  ParsedArgs<4> parsed_args;
+  auto _r = parser.parse(args, kwargs, parsed_args);
+
+  switch (_r.idx) {
+    case 0: {
+      // aten::set_(Tensor(a!) self) -> Tensor(a!)
+      auto dispatch_set_ = [](const Tensor& self) -> Tensor {
+        pybind11::gil_scoped_release no_gil;
+        return self.set_();
+      };
+      return wrap(dispatch_set_(self));
+    }
+    case 1: {
+      // aten::set_.source_Storage(Tensor(a!) self, Storage source) ->
+      // Tensor(a!)
+      at::ScalarType storage_scalar_type{};
+      bool is_typed_storage = true;
+      at::Storage storage = _r.storage(0, storage_scalar_type, is_typed_storage);
+      TORCH_CHECK(storage_scalar_type == self.dtype() || !is_typed_storage,
+        "Expected a Storage of type ", self.dtype(),
+        " or an UntypedStorage, but got type ", storage_scalar_type,
+        " for argument 1 'storage'");
+      auto dispatch_set_ = [](const Tensor& self, Storage source) -> Tensor {
+        pybind11::gil_scoped_release no_gil;
+        return self.set_(std::move(source));
+      };
+      return wrap(dispatch_set_(self, storage));
+    }
+    case 2: {
+      // aten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage
+      // source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!)
+      at::ScalarType storage_scalar_type{};
+      bool is_typed_storage = true;
+      at::Storage storage = _r.storage(0, storage_scalar_type, is_typed_storage);
+      TORCH_CHECK(storage_scalar_type == self.dtype() || !is_typed_storage,
+        "Expected a Storage of type ", self.dtype(),
+        " or an UntypedStorage, but got type ", storage_scalar_type,
+        " for argument 1 'storage'");
+      auto dispatch_set_ = [](const Tensor& self,
+                              Storage source,
+                              c10::SymInt storage_offset,
+                              c10::SymIntArrayRef size,
+                              c10::SymIntArrayRef stride) -> Tensor {
+        pybind11::gil_scoped_release no_gil;
+        return self.set__symint(std::move(source), std::move(storage_offset), size, stride);
+      };
+      return wrap(dispatch_set_(
+          self, storage, _r.toSymInt(1), _r.symintlist(2), _r.symintlist(3)));
+    }
+    case 3: {
+      // aten::set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!)
+      auto dispatch_set_ = [](const Tensor& self, const Tensor& source) -> Tensor {
+        TORCH_CHECK(source.dtype() == self.dtype(), "Could not set tensor of type ", source.dtype(), " to a tensor of type ", self.dtype());
+        pybind11::gil_scoped_release no_gil;
+        return self.set_(source);
+      };
+      return wrap(dispatch_set_(self, _r.tensor(0)));
+    }
+    case 4: {
+      // aten::set_.source_Tensor_storage_offset(Tensor(a!) self, Tensor
+      // source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!)
+      at::Tensor storage = _r.tensor(0);
+      auto dispatch_set_ = [](const Tensor& self,
+                              const Tensor& source,
+                              c10::SymInt storage_offset,
+                              c10::SymIntArrayRef size,
+                              c10::SymIntArrayRef stride) -> Tensor {
+        pybind11::gil_scoped_release no_gil;
+        return self.set__symint(source, std::move(storage_offset), size, stride);
+      };
+      return wrap(dispatch_set_(
+          self, storage, _r.toSymInt(1), _r.symintlist(2), _r.symintlist(3)));
+    }
+  }
+  Py_RETURN_NONE;
+  END_HANDLE_TH_ERRORS
+}
+
+// XXX: ops that are bound here are not exposed to the C++ api nor the JIT.
+// Any new ops added here should be accompanied with a comment why they are not
+// being registered through native_functions.yaml, and be tagged cpp / JIT
+PyMethodDef variable_methods[] = {
+  // These magic methods are all implemented on python object to wrap NotImplementedError
+  {"__add__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_add>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__radd__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_add>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__iadd__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_add_>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__rmul__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_mul>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__mul__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_mul>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__imul__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_mul_>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__sub__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_sub>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__isub__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_sub_>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__div__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_div>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__truediv__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_div>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__floordiv__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_floor_divide>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__idiv__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_div_>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__ifloordiv__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_floor_divide_>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__mod__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_remainder>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__imod__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_remainder_>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__eq__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable___eq__>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__ne__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_ne>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__lt__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_lt>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__le__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_le>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__gt__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_gt>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__ge__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_ge>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__rand__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_bitwise_and>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__ror__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_bitwise_or>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__rxor__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_bitwise_xor>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"__bool__", THPVariable_bool_scalar, METH_NOARGS, nullptr},
+  {"__float__", THPVariable_float_scalar, METH_NOARGS, nullptr},
+  {"__complex__", THPVariable_complex_scalar, METH_NOARGS, nullptr},
+  {"__int__", THPVariable_integral_scalar, METH_NOARGS, nullptr},
+  {"__long__", THPVariable_integral_scalar, METH_NOARGS, nullptr},
+  {"__index__", THPVariable_index_scalar, METH_NOARGS, nullptr},
+  {"__nonzero__", THPVariable_bool_scalar, METH_NOARGS, nullptr},
+  {"__invert__", THPVariable_invert, METH_NOARGS, nullptr},
+  {"__matmul__", castPyCFunctionWithKeywords(TypeError_to_NotImplemented_<THPVariable_matmul>), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"_is_view", THPVariable__is_view, METH_NOARGS, nullptr},
+  {"apply_", THPVariable_apply_, METH_O, nullptr},
+  {"bfloat16", castPyCFunctionWithKeywords(THPVariable_bfloat16), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"byte", castPyCFunctionWithKeywords(THPVariable_byte), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"char", castPyCFunctionWithKeywords(THPVariable_char), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"contiguous", castPyCFunctionWithKeywords(THPVariable_contiguous), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"copy_", castPyCFunctionWithKeywords(THPVariable_copy_), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"cpu", castPyCFunctionWithKeywords(THPVariable_cpu), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"cuda", castPyCFunctionWithKeywords(THPVariable_cuda), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"mtia", castPyCFunctionWithKeywords(THPVariable_mtia), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"xpu", castPyCFunctionWithKeywords(THPVariable_xpu), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"ipu", castPyCFunctionWithKeywords(THPVariable_ipu), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"data_ptr", THPVariable_data_ptr, METH_NOARGS, nullptr},
+  {"dim", THPVariable_dim, METH_NOARGS, nullptr},
+  {"has_names", THPVariable_has_names, METH_NOARGS, nullptr},
+  {"double", castPyCFunctionWithKeywords(THPVariable_double), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"cdouble", castPyCFunctionWithKeywords(THPVariable_cdouble), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"element_size", THPVariable_element_size, METH_NOARGS, nullptr},
+  {"float", castPyCFunctionWithKeywords(THPVariable_float), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"cfloat", castPyCFunctionWithKeywords(THPVariable_cfloat), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"get_device", THPVariable_get_device, METH_NOARGS, nullptr},
+  {"bool", castPyCFunctionWithKeywords(THPVariable_bool), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"half", castPyCFunctionWithKeywords(THPVariable_half), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"int", castPyCFunctionWithKeywords(THPVariable_int), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"is_contiguous", castPyCFunctionWithKeywords(THPVariable_is_contiguous), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"item", THPVariable_item, METH_NOARGS, nullptr},
+  {"long", castPyCFunctionWithKeywords(THPVariable_long), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"map_", castPyCFunctionWithKeywords(THPVariable_map_), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"map2_", castPyCFunctionWithKeywords(THPVariable_map2_), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"ndimension", THPVariable_dim, METH_NOARGS, nullptr},
+  {"nelement", THPVariable_numel, METH_NOARGS, nullptr},
+  {"new", castPyCFunctionWithKeywords(THPVariable_new), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"new_tensor", castPyCFunctionWithKeywords(THPVariable_new_tensor), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"nonzero", castPyCFunctionWithKeywords(THPVariable_nonzero), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"numel", THPVariable_numel, METH_NOARGS, nullptr},
+  {"numpy", castPyCFunctionWithKeywords(THPVariable_numpy), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"requires_grad_", castPyCFunctionWithKeywords(THPVariable_requires_grad_), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"set_", castPyCFunctionWithKeywords(THPVariable_set_), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"short", castPyCFunctionWithKeywords(THPVariable_short), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"size", castPyCFunctionWithKeywords(THPVariable_size), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"untyped_storage", THPVariable_storage, METH_NOARGS, nullptr},
+  {"storage_offset", THPVariable_storage_offset, METH_NOARGS, nullptr},
+  {"stride", castPyCFunctionWithKeywords(THPVariable_stride), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"to", castPyCFunctionWithKeywords(THPVariable_to), METH_VARARGS | METH_KEYWORDS, nullptr},
+  {"tolist", THPVariable_tolist, METH_NOARGS, nullptr},
+  {"type", castPyCFunctionWithKeywords(THPVariable_type), METH_VARARGS | METH_KEYWORDS, nullptr},
+  ${py_method_defs}
+  {nullptr}
+};
+
+} // namespace torch::autograd
diff --git a/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/variable_factories.h b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/variable_factories.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b55f441ab6249cb7963c5e4a15070f626f775b7
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/packaged/autograd/templates/variable_factories.h
@@ -0,0 +1,135 @@
+#pragma once
+
+// ${generated_comment}
+
+#include <ATen/core/Tensor.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/grad_mode.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/core/MemoryFormat.h>
+#include <torch/csrc/api/include/torch/detail/TensorDataContainer.h>
+#include <torch/csrc/autograd/variable.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/from_blob.h>
+$ops_headers
+#endif
+
+#include <functional>
+#include <initializer_list>
+#include <utility>
+
+namespace torch {
+
+/// NOTE: Currently `torch::tensor(...)` doesn't support mixed data types
+/// (i.e. `torch::tensor({{bool, 2.0}})` doesn't work). We might be able to
+/// support it in the future by iterating over all sub-lists to find
+/// the largest data type that can represent all of the elements, or by using
+/// variadic templates.
+///
+/// NOTE: C++ `torch::tensor` with a floating-point type or an `at::ArrayRef` / `std::vector` /
+/// (nested) braced-init-list of floating-point types always produces a tensor of dtype
+/// `torch::get_default_dtype()`, matching Python `torch.tensor` behavior.
+///
+/// NOTE: C++ `torch::tensor` with an integer type or an `at::ArrayRef` / `std::vector` /
+/// (nested) braced-init-list of integer types always produces a tensor of dtype `at::kLong`
+/// (aka. int64_t), matching Python `torch.tensor` behavior.
+///
+/// NOTE: The following dtypes are not supported by `torch::tensor` currently:
+/// - `unsigned int`
+/// - `unsigned long int`
+/// - `unsigned long long int`
+/// - `long long int`
+inline at::Tensor tensor(detail::TensorDataContainer tensor_data_container, const at::TensorOptions& options = {}) {
+  return autograd::make_variable(
+    // note: we remove the requires_grad setting from the TensorOptions because
+    // it is ignored anyways (and we actually have an assertion that it isn't set
+    // which would fail otherwise). We handle requires_grad explicitly here
+    // instead of passing it through to the kernel.
+    tensor_data_container.convert_to_tensor(options.requires_grad(::std::nullopt)),
+    options.requires_grad());
+}
+
+/// A generic deleter function.
+using Deleter = std::function<void(void*)>;
+using at::MemoryFormat;
+
+/// Exposes the given `data` as a `Tensor` without taking ownership of the
+/// original data. `sizes` should specify the shape of the tensor, `strides` the
+/// stride in each dimension. The `deleter` function (a
+/// `std::function<void(void*)>`) will be called on the `data` when the Tensor
+/// data would normally be deallocated. The `TensorOptions` specify additional
+/// configuration options for the returned tensor, such as what type to
+/// interpret the `data` as.
+inline at::Tensor from_blob(
+    void* data,
+    at::IntArrayRef sizes,
+    at::IntArrayRef strides,
+    const Deleter& deleter,
+    const at::TensorOptions& options = at::TensorOptions()) {
+  at::Tensor tensor = ([&]() {
+    at::AutoDispatchBelowAutograd guard;  // TODO: remove
+    at::tracer::impl::NoTracerDispatchMode tracer_guard;
+    return at::from_blob(data, sizes, strides, deleter, options.requires_grad(::std::nullopt));
+  })();
+  return autograd::make_variable(tensor, options.requires_grad());
+}
+
+/// Exposes the given `data` as a `Tensor` without taking ownership of the
+/// original data. `sizes` should specify the shape of the tensor, `strides` the
+/// stride in each dimension. The `TensorOptions`
+/// specify additional configuration options for the returned tensor, such as
+/// what type to interpret the `data` as.
+inline at::Tensor from_blob(
+    void* data,
+    at::IntArrayRef sizes,
+    at::IntArrayRef strides,
+    const at::TensorOptions& options = at::TensorOptions()) {
+  at::Tensor tensor = ([&]() {
+    at::AutoDispatchBelowAutograd guard;  // TODO: remove
+    at::tracer::impl::NoTracerDispatchMode tracer_guard;
+    return at::from_blob(data, sizes, strides, options.requires_grad(::std::nullopt));
+  })();
+  return autograd::make_variable(tensor, options.requires_grad());
+}
+
+/// Exposes the given `data` as a `Tensor` without taking ownership of the
+/// original data. `sizes` should specify the shape of the tensor. The `deleter`
+/// (a `std::function<void(void*)>`) function will be called on the `data` when
+/// the Tensor data would normally be deallocated. The `TensorOptions` specify
+/// additional configuration options for the returned tensor, such as what type
+/// to interpret the `data` as.
+inline at::Tensor from_blob(
+    void* data,
+    at::IntArrayRef sizes,
+    const Deleter& deleter,
+    const at::TensorOptions& options = at::TensorOptions()) {
+  at::Tensor tensor = ([&]() {
+    at::AutoDispatchBelowAutograd guard;  // TODO: remove
+    at::tracer::impl::NoTracerDispatchMode tracer_guard;
+    return at::from_blob(data, sizes, deleter, options.requires_grad(::std::nullopt));
+  })();
+  return autograd::make_variable(tensor, options.requires_grad());
+}
+
+/// Exposes the given `data` as a `Tensor` without taking ownership of the
+/// original data. `sizes` should specify the shape of the tensor. The
+/// `TensorOptions` specify additional configuration options for the returned
+/// tensor, such as what type to interpret the `data` as.
+inline at::Tensor from_blob(
+    void* data,
+    at::IntArrayRef sizes,
+    const at::TensorOptions& options = at::TensorOptions()) {
+  at::Tensor tensor = ([&]() {
+    at::AutoDispatchBelowAutograd guard;  // TODO: remove
+    at::tracer::impl::NoTracerDispatchMode tracer_guard;
+    return at::from_blob(data, sizes, options.requires_grad(::std::nullopt));
+  })();
+  return autograd::make_variable(tensor, options.requires_grad());
+}
+
+${function_definitions}
+
+} // namespace torch
diff --git a/lib/python3.11/site-packages/torchgen/selective_build/__init__.py b/lib/python3.11/site-packages/torchgen/selective_build/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8d27cfe23dd28c418f6ed0bd1a6f0fa793b2e3b6
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/operator.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/operator.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..17f2c8a3f83df7b62839a71b41f34296790c5243
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/operator.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/selector.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/selector.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3d4ffdc13569ca101fd930f2558a10b218002279
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/selective_build/__pycache__/selector.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/selective_build/operator.py b/lib/python3.11/site-packages/torchgen/selective_build/operator.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cb92dfc09e28c7c98ab7230af362c363a30d621
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/selective_build/operator.py
@@ -0,0 +1,171 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+# This class holds information about a single operator used to determine
+# the outcome of a selective/custom PyTorch build that doesn't include
+# registration code for all the supported operators. This is done to
+# reduce the size of the generated binary so that it can be deployed in
+# situations where binary size comes at a premium.
+#
+@dataclass(frozen=True)
+class SelectiveBuildOperator:
+    # The name of the operator. This includes the aten::, etc... prefix
+    # The operator name may or may not have the overload name. If this
+    # operator name does not specify an overload name, the way to determine
+    # if this entry refers to the family of operators with this base name
+    # or just the operator with this name is to look at the value of the
+    # 'include_all_overloads' flag in this class.
+    name: str
+
+    # True if this is a root operator (i.e. called directly from a
+    # TorchScript model, etc...). An operator is considered to be a
+    # root operator if it is called directly from any one of the models
+    # that this instance of the pytorch library was built for. Hence, it
+    # may not be a root operator in all of the models that are used in
+    # this instance of the pytorch library.
+    is_root_operator: bool
+
+    # Is this operator used for on-device training? If True, then we need to
+    # use the information to generate code in VariableType_N.cpp for registration
+    # of training related operators. Again, this is True if this operator
+    # is used for training in one or more models used by this instance of the
+    # pytorch library.
+    is_used_for_training: bool
+
+    # If True, it indicates that this operator instance (object) refers to an
+    # operator without the overload name and should apply to all overloads
+    # which have this operator name as the base name. This flag is applicable
+    # only for objects that have operator names without a DOT (period) character
+    # in them.
+    #
+    # Note: This flag is a temporary workaround to grandfather in the current
+    # static selective (custom) build mechanism, which largely ignores overload
+    # names when determining whether to select operators for registration
+    # purposes.
+    include_all_overloads: bool
+
+    # Debug Information at the operator level
+    _debug_info: tuple[str, ...] | None
+
+    @staticmethod
+    def from_yaml_dict(
+        op_name: str, op_info: dict[str, object]
+    ) -> SelectiveBuildOperator:
+        allowed_keys = {
+            "name",
+            "is_root_operator",
+            "is_used_for_training",
+            "include_all_overloads",
+            "debug_info",
+        }
+
+        if len(set(op_info.keys()) - allowed_keys) > 0:
+            raise Exception(  # noqa: TRY002
+                "Got unexpected top level keys: {}".format(
+                    ",".join(set(op_info.keys()) - allowed_keys),
+                )
+            )
+
+        if "name" in op_info:
+            assert op_name == op_info["name"]
+
+        is_root_operator = op_info.get("is_root_operator", True)
+        assert isinstance(is_root_operator, bool)
+
+        is_used_for_training = op_info.get("is_used_for_training", True)
+        assert isinstance(is_used_for_training, bool)
+
+        include_all_overloads = op_info.get("include_all_overloads", True)
+        assert isinstance(include_all_overloads, bool)
+
+        debug_info: tuple[str, ...] | None = None
+        if "debug_info" in op_info:
+            di_list = op_info["debug_info"]
+            assert isinstance(di_list, list)
+            debug_info = tuple(str(x) for x in di_list)
+
+        return SelectiveBuildOperator(
+            name=op_name,
+            is_root_operator=is_root_operator,
+            is_used_for_training=is_used_for_training,
+            include_all_overloads=include_all_overloads,
+            _debug_info=debug_info,
+        )
+
+    @staticmethod
+    def from_legacy_operator_name_without_overload(
+        name: str,
+    ) -> SelectiveBuildOperator:
+        return SelectiveBuildOperator(
+            name=name,
+            is_root_operator=True,
+            is_used_for_training=True,
+            include_all_overloads=True,
+            _debug_info=None,
+        )
+
+    def to_dict(self) -> dict[str, object]:
+        ret: dict[str, object] = {
+            "is_root_operator": self.is_root_operator,
+            "is_used_for_training": self.is_used_for_training,
+            "include_all_overloads": self.include_all_overloads,
+        }
+        if self._debug_info is not None:
+            ret["debug_info"] = self._debug_info
+
+        return ret
+
+
+def merge_debug_info(
+    lhs: tuple[str, ...] | None,
+    rhs: tuple[str, ...] | None,
+) -> tuple[str, ...] | None:
+    # Ensure that when merging, each entry shows up just once.
+    if lhs is None and rhs is None:
+        return None
+
+    return tuple(set((lhs or ()) + (rhs or ())))
+
+
+def combine_operators(
+    lhs: SelectiveBuildOperator, rhs: SelectiveBuildOperator
+) -> SelectiveBuildOperator:
+    if str(lhs.name) != str(rhs.name):
+        raise Exception(  # noqa: TRY002
+            f"Expected both arguments to have the same name, but got '{str(lhs.name)}' and '{str(rhs.name)}' instead"
+        )
+
+    return SelectiveBuildOperator(
+        name=lhs.name,
+        # Consider this operator to be a root operator if it is a
+        # root operator in any of the models used in this instance of
+        # the pytorch library.
+        is_root_operator=lhs.is_root_operator or rhs.is_root_operator,
+        # Consider this operator to be a training operator if it is
+        # an operator used for training in any of the models used
+        # in this instance of the pytorch library.
+        is_used_for_training=lhs.is_used_for_training or rhs.is_used_for_training,
+        include_all_overloads=lhs.include_all_overloads or rhs.include_all_overloads,
+        _debug_info=merge_debug_info(lhs._debug_info, rhs._debug_info),
+    )
+
+
+def merge_operator_dicts(
+    lhs: dict[str, SelectiveBuildOperator],
+    rhs: dict[str, SelectiveBuildOperator],
+) -> dict[str, SelectiveBuildOperator]:
+    operators: dict[str, SelectiveBuildOperator] = {}
+    for op_name, op in list(lhs.items()) + list(rhs.items()):
+        new_op = op
+        if op_name in operators:
+            new_op = combine_operators(operators[op_name], op)
+
+        operators[op_name] = new_op
+
+    return operators
+
+
+def strip_operator_overload_name(op_name: str) -> str:
+    return op_name.split(".")[0]
diff --git a/lib/python3.11/site-packages/torchgen/selective_build/selector.py b/lib/python3.11/site-packages/torchgen/selective_build/selector.py
new file mode 100644
index 0000000000000000000000000000000000000000..04acc354203ade2f48dcef56fd9d9ef70c82ad1d
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/selective_build/selector.py
@@ -0,0 +1,352 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import yaml
+
+from torchgen.selective_build.operator import (
+    merge_debug_info,
+    merge_operator_dicts,
+    SelectiveBuildOperator,
+    strip_operator_overload_name,
+)
+
+
+if TYPE_CHECKING:
+    from torchgen.model import NativeFunction
+
+
+# A SelectiveBuilder holds information extracted from the selective build
+# YAML specification.
+#
+# It includes information about the build's selectivity, the debug_info
+# associated with this selective build (opaque string), and the set of
+# operators that should be included in the build.
+#
+@dataclass(frozen=True)
+class SelectiveBuilder:
+    # If true, then the build is not selective, and includes all
+    # operators.
+    include_all_operators: bool
+
+    # Debug Information at the selective/custom build level.
+    _debug_info: tuple[str, ...] | None
+
+    # A dictionary of operator -> operator metadata.
+    operators: dict[str, SelectiveBuildOperator]
+
+    # A dictionary of selected kernel tags and dtypes. Typically a
+    # PyTorch Operator Kernel (function) may have many code paths
+    # that are specialized for many many Tensor dtypes, so it's not
+    # one per kernel function, but there could be many per kernel
+    # function. The tag isn't a kernel function name, but some fragment
+    # of the kernel function implementation itself.
+    kernel_metadata: dict[str, list[str]]
+
+    # ExecuTorch only. A dictionary of kernel tag -> list of (list of input
+    # dtypes for tensor-like input args).
+    # This is from selective.yaml
+    et_kernel_metadata: dict[str, list[str]]
+
+    # A set of all the custom torch bind classes used by the selected models
+    # Stored as a set internally to remove duplicates proactively, but written
+    # as a list to yamls
+    custom_classes: set[str]
+
+    # A set of all the build features used by the selected models
+    # Stored as a set internally to remove duplicates proactively, but written
+    # as a list to yamls
+    build_features: set[str]
+
+    # If true, then fragments for all dtypes for all kernel functions
+    # are included as well as all custom classes. This is typically set when any one of the
+    # operator lists is generated from a mechanism other than
+    # tracing based selective build.
+    include_all_non_op_selectives: bool
+
+    @staticmethod
+    def get_nop_selector() -> SelectiveBuilder:
+        return SelectiveBuilder.from_yaml_dict({"include_all_operators": True})
+
+    @staticmethod
+    def from_yaml_dict(data: dict[str, object]) -> SelectiveBuilder:
+        valid_top_level_keys = {
+            "include_all_non_op_selectives",
+            "include_all_operators",
+            "debug_info",
+            "operators",
+            "kernel_metadata",
+            "et_kernel_metadata",
+            "custom_classes",
+            "build_features",
+        }
+        top_level_keys = set(data.keys())
+        if len(top_level_keys - valid_top_level_keys) > 0:
+            raise Exception(  # noqa: TRY002
+                "Got unexpected top level keys: {}".format(
+                    ",".join(top_level_keys - valid_top_level_keys),
+                )
+            )
+        include_all_operators = data.get("include_all_operators", False)
+        assert isinstance(include_all_operators, bool)
+
+        debug_info = None
+        if "debug_info" in data:
+            di_list = data["debug_info"]
+            assert isinstance(di_list, list)
+
+            debug_info = tuple(str(x) for x in di_list)
+
+        operators = {}
+        operators_dict = data.get("operators", {})
+        assert isinstance(operators_dict, dict)
+
+        for k, v in operators_dict.items():
+            operators[k] = SelectiveBuildOperator.from_yaml_dict(k, v)
+
+        kernel_metadata = {}
+        kernel_metadata_dict = data.get("kernel_metadata", {})
+        assert isinstance(kernel_metadata_dict, dict)
+
+        for k, v in kernel_metadata_dict.items():
+            kernel_metadata[str(k)] = [str(dtype) for dtype in v]
+
+        et_kernel_metadata = data.get("et_kernel_metadata", {})
+        assert isinstance(et_kernel_metadata, dict)
+
+        custom_classes = data.get("custom_classes", [])
+        assert isinstance(custom_classes, Iterable)
+        custom_classes = set(custom_classes)
+
+        build_features = data.get("build_features", [])
+        assert isinstance(build_features, Iterable)
+        build_features = set(build_features)
+
+        include_all_non_op_selectives = data.get("include_all_non_op_selectives", False)
+        assert isinstance(include_all_non_op_selectives, bool)
+
+        return SelectiveBuilder(
+            include_all_operators,
+            debug_info,
+            operators,
+            kernel_metadata,
+            et_kernel_metadata,
+            custom_classes,  # type: ignore[arg-type]
+            build_features,  # type: ignore[arg-type]
+            include_all_non_op_selectives,
+        )
+
+    @staticmethod
+    def from_yaml_str(config_contents: str) -> SelectiveBuilder:
+        contents = yaml.safe_load(config_contents)
+        return SelectiveBuilder.from_yaml_dict(contents)
+
+    @staticmethod
+    def from_yaml_path(config_path: str) -> SelectiveBuilder:
+        with open(config_path) as f:
+            contents = yaml.safe_load(f)
+            return SelectiveBuilder.from_yaml_dict(contents)
+
+    @staticmethod
+    def from_legacy_op_registration_allow_list(
+        allow_list: set[str], is_root_operator: bool, is_used_for_training: bool
+    ) -> SelectiveBuilder:
+        operators = {}
+        for op in allow_list:
+            operators[op] = {
+                "name": op,
+                "is_root_operator": is_root_operator,
+                "is_used_for_training": is_used_for_training,
+                "include_all_overloads": True,
+            }
+        return SelectiveBuilder.from_yaml_dict(
+            {
+                "operators": operators,
+                "include_all_non_op_selectives": True,
+            }
+        )
+
+    def is_operator_selected(self, name: str) -> bool:
+        if self.include_all_operators:
+            return True
+
+        if name in self.operators:
+            return True
+        name = strip_operator_overload_name(name)
+        return name in self.operators and self.operators[name].include_all_overloads
+
+    def is_native_function_selected(self, func: NativeFunction) -> bool:
+        op_name = op_name_from_native_function(func)
+        return self.is_operator_selected(op_name)
+
+    def is_operator_selected_for_training(self, name: str) -> bool:
+        if not self.is_operator_selected(name):
+            return False
+        if self.include_all_operators:
+            return True
+
+        not_training_op = SelectiveBuildOperator(
+            name="",
+            is_root_operator=False,
+            is_used_for_training=False,
+            include_all_overloads=False,
+            _debug_info=None,
+        )
+        op = not_training_op
+        if name in self.operators:
+            op = self.operators[name]
+
+        name = strip_operator_overload_name(name)
+        base_op = not_training_op
+        if name in self.operators:
+            base_op = self.operators[name]
+
+        return op.is_used_for_training or (
+            base_op.include_all_overloads and base_op.is_used_for_training
+        )
+
+    def is_native_function_selected_for_training(self, func: NativeFunction) -> bool:
+        op_name = op_name_from_native_function(func)
+        return self.is_operator_selected_for_training(op_name)
+
+    def is_root_operator(self, name: str) -> bool:
+        if not self.is_operator_selected(name):
+            return False
+        if self.include_all_operators:
+            return True
+
+        if name in self.operators:
+            op: SelectiveBuildOperator = self.operators[name]
+            return op.is_root_operator
+        name = strip_operator_overload_name(name)
+        if name not in self.operators:
+            return False
+        base_op: SelectiveBuildOperator = self.operators[name]
+        return base_op.include_all_overloads and base_op.is_root_operator
+
+    def is_kernel_dtype_selected(self, kernel_tag: str, dtype: str) -> bool:
+        if self.include_all_operators or self.include_all_non_op_selectives:
+            return True
+
+        return (
+            kernel_tag in self.kernel_metadata
+            and dtype in self.kernel_metadata[kernel_tag]
+        )
+
+    def et_get_selected_kernels(self, op_name: str, kernel_key: list[str]) -> list[str]:
+        """
+        Return a list of kernel keys that cover the used ops
+        """
+        # If no kernel metadata, either it's implied by include_all_operators=True or the op is not used.
+        if op_name not in self.et_kernel_metadata:
+            return kernel_key if self.include_all_operators else []
+        # Otherwise, only return the specific kernel keys.
+
+        result_set = set()
+
+        for model_kernel_keys in self.et_kernel_metadata[op_name]:
+            key_found = False
+            for key in kernel_key:
+                # Don't compare the version for now
+                if (
+                    key != "default"
+                    and key.split("/")[1] == model_kernel_keys.split("/")[1]
+                ):
+                    result_set.add(key)
+                    key_found = True
+                    break
+            if not key_found:
+                if "default" not in kernel_key:
+                    raise Exception("Missing kernel for the model")  # noqa: TRY002
+                else:
+                    result_set.add("default")
+
+        return list(result_set)
+
+    def to_dict(self) -> dict[str, object]:
+        ret: dict[str, object] = {
+            "include_all_non_op_selectives": self.include_all_non_op_selectives,
+            "include_all_operators": self.include_all_operators,
+        }
+        operators = {}
+        for op_name, op in self.operators.items():
+            operators[op_name] = op.to_dict()
+        ret["operators"] = operators
+
+        if self._debug_info is not None:
+            ret["debug_info"] = sorted(self._debug_info)
+
+        ret["kernel_metadata"] = {
+            k: sorted(v) for (k, v) in self.kernel_metadata.items()
+        }
+
+        ret["et_kernel_metadata"] = self.et_kernel_metadata
+
+        ret["custom_classes"] = sorted(self.custom_classes)
+
+        ret["build_features"] = sorted(self.build_features)
+
+        return ret
+
+
+def merge_kernel_metadata(
+    lhs: dict[str, list[str]],
+    rhs: dict[str, list[str]],
+) -> dict[str, list[str]]:
+    kernel_metadata: dict[str, list[str]] = {}
+    for tag_name, dtypes in list(lhs.items()) + list(rhs.items()):
+        dtypes_copy = set(dtypes)
+        if tag_name in kernel_metadata:
+            dtypes_copy |= set(kernel_metadata[tag_name])
+
+        kernel_metadata[tag_name] = list(dtypes_copy)
+
+    return kernel_metadata
+
+
+def merge_et_kernel_metadata(
+    lhs: dict[str, list[str]],
+    rhs: dict[str, list[str]],
+) -> dict[str, list[str]]:
+    merge_et_kernel_metadata: dict[str, set[str]] = defaultdict(set)
+    for op in list(lhs.keys()) + list(rhs.keys()):
+        merge_et_kernel_metadata[op].update(lhs.get(op, []))
+        merge_et_kernel_metadata[op].update(rhs.get(op, []))
+
+    return {op: sorted(val) for op, val in merge_et_kernel_metadata.items()}
+
+
+def combine_selective_builders(
+    lhs: SelectiveBuilder, rhs: SelectiveBuilder
+) -> SelectiveBuilder:
+    include_all_operators = lhs.include_all_operators or rhs.include_all_operators
+    debug_info = merge_debug_info(lhs._debug_info, rhs._debug_info)
+    operators = merge_operator_dicts(lhs.operators, rhs.operators)
+    kernel_metadata = merge_kernel_metadata(lhs.kernel_metadata, rhs.kernel_metadata)
+    et_kernel_metadata = merge_et_kernel_metadata(
+        lhs.et_kernel_metadata, rhs.et_kernel_metadata
+    )
+    include_all_non_op_selectives = (
+        lhs.include_all_non_op_selectives or rhs.include_all_non_op_selectives
+    )
+    custom_classes = lhs.custom_classes.union(rhs.custom_classes)
+    build_features = lhs.build_features.union(rhs.build_features)
+    return SelectiveBuilder(
+        include_all_operators,
+        debug_info,
+        operators,
+        kernel_metadata,
+        et_kernel_metadata,
+        custom_classes,
+        build_features,
+        include_all_non_op_selectives,
+    )
+
+
+def op_name_from_native_function(f: NativeFunction) -> str:
+    # This was originally read from the 'operator_name_with_overload' field in the
+    # declaration dict, which was the part before the first '(' in 'schema_string'.
+    return f"{f.namespace}::{f.func.name}"
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/__init__.py b/lib/python3.11/site-packages/torchgen/static_runtime/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..68ff0e03b2e0793be0bbc65731f86c0868afe6e4
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/__init__.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/config.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/config.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f2ed5abd289a8ee1f507054aebd0a1a18bb16ccd
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/config.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/gen_static_runtime_ops.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/gen_static_runtime_ops.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..99613a982ddf904e49360792b5826b70b48de057
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/gen_static_runtime_ops.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/generator.cpython-311.pyc b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/generator.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d31bcf9118128ba9f8c8339ed3bc1dd59cb6e441
Binary files /dev/null and b/lib/python3.11/site-packages/torchgen/static_runtime/__pycache__/generator.cpython-311.pyc differ
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/config.py b/lib/python3.11/site-packages/torchgen/static_runtime/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fe129f9754dd83a136fbf9dc4478e04a2242efa
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/static_runtime/config.py
@@ -0,0 +1,388 @@
+from __future__ import annotations
+
+from torchgen.model import NativeFunctionsGroup, NativeFunctionsViewGroup
+
+
+def func_name_base_str(g: NativeFunctionsGroup | NativeFunctionsViewGroup) -> str:
+    if isinstance(g, NativeFunctionsGroup):
+        return str(g.functional.func.name.name.base)
+    else:
+        return str(g.view.root_name)
+
+
+is_hand_written_ops_ = frozenset(
+    (
+        "abs",
+        "add",
+        "addmm",
+        "all",
+        "any",
+        "argmin",
+        "bmm",
+        "clamp",
+        "clamp_min",
+        "cumsum",
+        "div",
+        "fmod",
+        "index_select",
+        "leaky_relu",
+        "linear",
+        "log",
+        "matmul",
+        "mul",
+        "narrow_copy",
+        "nonzero",
+        "pow",
+        "remainder",
+        "sigmoid",
+        "sign",
+        "sub",
+        "tanh",
+        "detach",
+        "expand_as",
+        "flatten",
+        "narrow",
+        "reshape_as",
+        "select",
+        "slice",
+        "softmax",
+        "split",
+        "squeeze",
+        "transpose",
+        "view",
+        "where",
+    )
+)
+
+
+def is_hand_written(g: NativeFunctionsGroup | NativeFunctionsViewGroup) -> bool:
+    name_base = func_name_base_str(g)
+    return name_base in is_hand_written_ops_
+
+
+def override_test_values(arg_map: dict[str, str], op_name: str, index: int) -> None:
+    assert index == 0 or index == 1
+    if op_name == "addr":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["vec1"] = "at::rand({6})"
+            arg_map["vec2"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["vec1"] = "at::rand({22})"
+            arg_map["vec2"] = "at::rand({22})"
+        return
+    if op_name == "mv":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["vec"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["vec"] = "at::rand({22})"
+        return
+    if op_name == "addbmm":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+        return
+    if op_name == "cross":
+        if index == 0:
+            arg_map["self"] = "at::rand({3, 3, 3})"
+            arg_map["other"] = "at::rand({3, 3, 3})"
+        else:
+            arg_map["self"] = "at::rand({22, 3, 22})"
+            arg_map["other"] = "at::rand({22, 3, 22})"
+        return
+    if op_name == "take":
+        if index == 0:
+            arg_map["index"] = "at::randint(0, 216, {20}, torch::kInt64)"
+        else:
+            arg_map["index"] = "at::randint(0, 1000, {100}, torch::kInt64)"
+        return
+    if op_name == "take_along_dim":
+        if index == 0:
+            arg_map["indices"] = "at::argsort(self0, 1, true)"
+        else:
+            arg_map["indices"] = "at::argsort(self1, 1, true)"
+        return
+    if op_name == "masked_select":
+        if index == 0:
+            arg_map["mask"] = "at::randn({6, 6, 6}) > 0.5"
+        else:
+            arg_map["mask"] = "at::rand({22, 22, 22}) > 0.5"
+        return
+    if op_name == "orgqr":
+        if index == 0:
+            arg_map["input2"] = "at::rand({6, 6})"
+        else:
+            arg_map["input2"] = "at::rand({22, 22})"
+        return
+    if op_name == "ormqr":
+        if index == 0:
+            arg_map["input2"] = "at::rand({6, 6})"
+        else:
+            arg_map["input2"] = "at::rand({22, 22})"
+        return
+    if op_name == "quantile":
+        if index == 0:
+            arg_map["q"] = "at::rand({6})"
+            arg_map["interpolation"] = '"linear"'
+        else:
+            arg_map["q"] = "at::rand({22})"
+            arg_map["interpolation"] = '"linear"'
+        return
+    if op_name == "nanquantile":
+        if index == 0:
+            arg_map["q"] = "at::rand({6})"
+            arg_map["interpolation"] = '"linear"'
+        else:
+            arg_map["q"] = "at::rand({22})"
+            arg_map["interpolation"] = '"linear"'
+        return
+    if op_name == "multi_margin_loss":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({22})"
+        return
+    if op_name == "multilabel_margin_loss":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["target"] = "at::randint(6, {6, 6}, torch::kInt64)"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["target"] = "at::randint(22, {22, 22}, torch::kInt64)"
+        return
+    if op_name == "nll_loss":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({22})"
+        return
+    if op_name == "nll_loss2d":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6, 6, 6})"
+            arg_map["target"] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22, 22, 22})"
+            arg_map["target"] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({22})"
+        return
+    if op_name in (
+        "fft_fft",
+        "fft_ifft",
+        "fft_rfft",
+        "fft_irfft",
+        "fft_hfft",
+        "fft_ihfft",
+    ):
+        arg_map["norm"] = '"forward"'
+        return
+    if op_name == "linalg_tensorinv":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6, 6, 6})"
+            arg_map["ind"] = "2"
+        else:
+            arg_map["self"] = "at::rand({22, 22, 22, 22})"
+            arg_map["ind"] = "2"
+        return
+    if op_name == "addmv":
+        if index == 0:
+            arg_map["self"] = "at::rand({2})"
+            arg_map["mat"] = "at::rand({2, 2})"
+            arg_map["vec"] = "at::rand({2})"
+        else:
+            arg_map["self"] = "at::rand({35})"
+            arg_map["mat"] = "at::rand({35, 35})"
+            arg_map["vec"] = "at::rand({35})"
+        return
+    if op_name == "acosh":
+        if index == 0:
+            arg_map["self"] = "at::rand({2, 2, 2}) + at::ones({2, 2, 2})"
+        else:
+            arg_map["self"] = "at::rand({5, 5, 5}) + at::ones({5, 5, 5})"
+        return
+    if op_name == "adaptive_max_pool2d_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({2, 2, 2}, at::kFloat)"
+            arg_map["self"] = "at::rand({2, 2, 2}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {2, 2, 2}, at::kLong)"
+        else:
+            arg_map["grad_output"] = "at::rand({3, 3, 3}, at::kFloat)"
+            arg_map["self"] = "at::rand({3, 3, 3}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {3, 3, 3}, at::kLong)"
+        return
+    if op_name == "adaptive_max_pool3d_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
+            arg_map["self"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {2, 2, 2, 2}, at::kLong)"
+        else:
+            arg_map["grad_output"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
+            arg_map["self"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {3, 3, 3, 3}, at::kLong)"
+        return
+    if op_name == "bitwise_left_shift":
+        if index == 0:
+            arg_map["self"] = "at::randint(1, 1 << 4, {6, 6, 6}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 26, {6, 6, 6}, at::kInt)"
+        else:
+            arg_map["self"] = "at::randint(1, 1 << 4, {22, 22, 22}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 26, {22, 22, 22}, at::kInt)"
+        return
+    if op_name == "bitwise_right_shift":
+        if index == 0:
+            arg_map["self"] = "at::randint(1 << 21, 1 << 30, {6, 6, 6}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 22, {6, 6, 6}, at::kInt)"
+        else:
+            arg_map["self"] = "at::randint(1 << 21, 1 << 30, {22, 22, 22}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 22, {22, 22, 22}, at::kInt)"
+        return
+    if op_name == "gather":
+        if index == 0:
+            arg_map["self"] = "at::randint(1, 100, {2,2,2}, at::kInt)"
+            arg_map["dim"] = "1"
+            arg_map["index"] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
+            arg_map["sparse_grad"] = "false"
+        else:
+            arg_map["self"] = "at::randint(1, 100, {5,5,5}, at::kInt)"
+            arg_map["dim"] = "1"
+            arg_map["index"] = "at::randint(0, 4, {5,5,5}, torch::kInt64)"
+            arg_map["sparse_grad"] = "false"
+        return
+    if op_name == "gelu":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6, 6})"
+            arg_map["approximate"] = '"tanh"'
+        else:
+            arg_map["self"] = "at::rand({22, 22, 22})"
+            arg_map["approximate"] = '"tanh"'
+        return
+    if op_name == "gelu_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({6, 6, 6})"
+            arg_map["self"] = "at::rand({6, 6, 6})"
+            arg_map["approximate"] = '"tanh"'
+        else:
+            arg_map["grad_output"] = "at::rand({22, 22, 22})"
+            arg_map["self"] = "at::rand({22, 22, 22})"
+            arg_map["approximate"] = '"tanh"'
+        return
+    if op_name == "index_add":
+        if index == 0:
+            arg_map["self"] = "at::rand({2})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 1, {2}, at::kInt)"
+            arg_map["source"] = "at::rand({2})"
+            arg_map["alpha"] = "2"
+        else:
+            arg_map["self"] = "at::rand({16})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 10, {16}, at::kInt)"
+            arg_map["source"] = "at::rand({16})"
+            arg_map["alpha"] = "2"
+        return
+    if op_name == "index_copy":
+        if index == 0:
+            arg_map["self"] = "at::rand({2})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 1, {2}, at::kLong)"
+            arg_map["source"] = "at::rand({2})"
+        else:
+            arg_map["self"] = "at::rand({32})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 10, {32}, at::kLong)"
+            arg_map["source"] = "at::rand({32})"
+        return
+    if op_name == "linalg_cross":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 3, 6})"
+            arg_map["other"] = "at::rand({6, 3, 6})"
+            arg_map["dim"] = "1"
+        else:
+            arg_map["self"] = "at::rand({22, 3, 22})"
+            arg_map["other"] = "at::rand({22, 3, 22})"
+            arg_map["dim"] = "1"
+        return
+    if op_name == "nll_loss_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({})"
+            arg_map["self"] = "at::rand({6})"
+            arg_map["target"] = "at::randint(0, 5, {6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+            arg_map["reduction"] = "1"
+            arg_map["ignore_index"] = "1"
+            arg_map["total_weight"] = "at::rand({})"
+        else:
+            arg_map["grad_output"] = "at::rand({})"
+            arg_map["self"] = "at::rand({36})"
+            arg_map["target"] = "at::randint(0, 11, {36}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({36})"
+            arg_map["reduction"] = "1"
+            arg_map["ignore_index"] = "1"
+            arg_map["total_weight"] = "at::rand({})"
+        return
+    if op_name in ["scatter", "scatter_add", "_scatter_reduce"]:
+        if index == 0:
+            arg_map["self"] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
+            arg_map["index"] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
+            arg_map["src"] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
+        else:
+            arg_map["self"] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
+            arg_map["index"] = "at::randint(0, 1, {5,5,5}, torch::kInt64)"
+            arg_map["src"] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
+        if "reduce" in arg_map:
+            arg_map["reduce"] = '"sum"' if op_name == "_scatter_reduce" else '"add"'
+        return
+    if op_name == "scatter_reduce":
+        arg_map["reduce"] = '"mean"'
+        if index == 0:
+            arg_map["index"] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
+        else:
+            arg_map["index"] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
+        return
+    if op_name == "special_zeta":
+        if index == 0:
+            arg_map["self"] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
+            arg_map["other"] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
+        else:
+            arg_map["self"] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
+            arg_map["other"] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
+        return
+    if op_name == "_convert_indices_from_csr_to_coo":
+        if index == 0:
+            arg_map["crow_indices"] = "torch::tensor({1}, torch::kInt32)"
+            arg_map["col_indices"] = "torch::tensor({0, 1, 0}, torch::kInt32)"
+            arg_map["out_int32"] = "false"
+        else:
+            arg_map["crow_indices"] = "torch::tensor({0}, torch::kInt32)"
+            arg_map["col_indices"] = (
+                "torch::tensor({0, 1, 0, 2, 1, 2, 0, 1, 0, 2, 1, 2}, torch::kInt32)"
+            )
+            arg_map["out_int32"] = "false"
+        return
+    if op_name == "_convert_indices_from_coo_to_csr":
+        if index == 0:
+            arg_map["self"] = "at::randint(0, 3, {2}, at::kInt)"
+            arg_map["size"] = "10"
+            arg_map["out_int32"] = "false"
+        else:
+            arg_map["self"] = "at::randint(0, 3, {12}, at::kInt)"
+            arg_map["size"] = "24"
+            arg_map["out_int32"] = "false"
+        return
+    if op_name in ("diagonal", "linalg_diagonal"):
+        arg_map["offset"] = "0"
+        arg_map["dim1"] = "2"
+        arg_map["dim2"] = "1"
+        return
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/gen_static_runtime_ops.py b/lib/python3.11/site-packages/torchgen/static_runtime/gen_static_runtime_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..81faef4f1094fa8852cca3ed3c73e9534f4845bc
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/static_runtime/gen_static_runtime_ops.py
@@ -0,0 +1,233 @@
+from __future__ import annotations
+
+import argparse
+import itertools
+import os
+from typing import TYPE_CHECKING, TypeVar, Union
+
+from libfb.py.log import set_simple_logging  # type: ignore[import]
+
+from torchgen import gen
+from torchgen.context import native_function_manager
+from torchgen.model import DispatchKey, NativeFunctionsGroup, NativeFunctionsViewGroup
+from torchgen.static_runtime import config, generator
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+# Given a list of `grouped_native_functions` sorted by their op names, return a list of
+# lists each of which groups ops that share the base name. For example, `mean` and
+# `mean.dim` are grouped together by this function.
+
+NativeGroupT = TypeVar(
+    "NativeGroupT",
+    bound=Union[NativeFunctionsGroup, NativeFunctionsViewGroup],
+)
+
+
+def group_functions_by_op_name(
+    grouped_native_functions: Sequence[NativeGroupT],
+) -> Sequence[Sequence[NativeGroupT]]:
+    if not grouped_native_functions:
+        return []
+    groups = []
+
+    def is_supported(g: NativeFunctionsGroup | NativeFunctionsViewGroup) -> bool:
+        with native_function_manager(g):
+            return generator.is_supported(g)
+
+    eligible_ops = (g for g in grouped_native_functions if is_supported(g))
+    groups = [
+        list(group)
+        for k, group in (
+            itertools.groupby(
+                eligible_ops,
+                key=config.func_name_base_str,
+            )
+        )
+    ]
+
+    return groups
+
+
+def clang_format(cpp_file_path: str) -> None:
+    import subprocess
+
+    subprocess.check_call(["clang-format", "-i", cpp_file_path])
+
+
+def write_cpp(cpp_ops: Sequence[str], file_path: str) -> None:
+    code = "\n".join(cpp_ops)
+    generated = f"""// @lint-ignore-every CLANGTIDY HOWTOEVEN
+// AUTO-GENERATED FROM: torchgen/static_runtime/gen_static_runtime_ops.py
+#include <torch/csrc/jit/runtime/static/ops.h>
+
+#include <ATen/CPUFunctions.h>
+#include <ATen/InferSize.h>
+#include <ATen/NativeFunctions.h>
+#include <ATen/Parallel.h>
+#include <ATen/ScalarOps.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/cpu/vec/functional.h>
+#include <ATen/cpu/vec/vec.h>
+#include <ATen/native/EmbeddingBag.h>
+#include <ATen/native/Fill.h>
+#include <ATen/native/IndexingUtils.h>
+#include <ATen/native/NonSymbolicBC.h>
+#include <ATen/native/Resize.h>
+#include <ATen/native/SharedReduceOps.h>
+#include <ATen/native/TensorAdvancedIndexing.h>
+#include <ATen/native/cpu/SerialStackImpl.h>
+#include <ATen/native/layer_norm.h>
+#include <ATen/native/quantized/cpu/fbgemm_utils.h>
+#include <ATen/native/quantized/cpu/qembeddingbag.h>
+#include <ATen/native/quantized/cpu/qembeddingbag_prepack.h>
+#include <ATen/quantized/QTensorImpl.h>
+#include <ATen/quantized/Quantizer.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/WrapDimMinimal.h>
+#include <c10/util/irange.h>
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/runtime/static/impl.h>
+#include <torch/csrc/jit/runtime/static/te_wrapper.h>
+#include <torch/csrc/jit/runtime/vararg_functions.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
+#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
+#include <torch/csrc/jit/tensorexpr/loopnest.h>
+
+namespace torch {{
+namespace jit {{
+
+{code}
+
+}} // namespace jit
+}} // namespace torch
+"""
+    with open(file_path, "w") as f:
+        f.write(generated)
+    clang_format(file_path)
+
+
+def write_test_cpp(cpp_ops: Sequence[str], file_path: str) -> None:
+    code = "\n".join(cpp_ops)
+    generated = f"""// @lint-ignore-every CLANGTIDY HOWTOEVEN
+// AUTO-GENERATED FROM: torchgen/static_runtime/gen_static_runtime_ops.py
+#include <gtest/gtest.h>
+#include <torch/csrc/jit/runtime/static/impl.h>
+#include <torch/torch.h>
+
+#include "test_utils.h"
+
+using namespace caffe2;
+using namespace torch;
+using namespace torch::jit;
+using namespace torch::jit::test;
+using c10::IValue;
+
+{code}
+
+"""
+    with open(file_path, "w") as f:
+        f.write(generated)
+    clang_format(file_path)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate ATen source files")
+    parser.add_argument(
+        "-s",
+        "--source-path",
+        help="path to source directory for ATen",
+        default="caffe2/aten/src/ATen",
+    )
+    parser.add_argument(
+        "-p",
+        "--generated-ops-cpp-path",
+        help="path to directory to generate op dispatcher .cpp file",
+        default="caffe2/torch/csrc/jit/runtime/static/generated_ops.cpp",
+    )
+    parser.add_argument(
+        "-t",
+        "--generated-ops-test-cpp-path",
+        help="path to directory to generate op dispatcher .cpp file",
+        default="caffe2/benchmarks/static_runtime/test_generated_ops.cc",
+    )
+    options = parser.parse_args()
+    native_yaml_path = os.path.join(options.source_path, "native/native_functions.yaml")
+    tags_yaml_path = os.path.join(options.source_path, "native/tags.yaml")
+    parsed_yaml = gen.parse_native_yaml(native_yaml_path, tags_yaml_path)
+    native_functions, backend_indices = (
+        parsed_yaml.native_functions,
+        parsed_yaml.backend_indices,
+    )
+
+    op_generator = generator.GenOpDispatcher()
+    test_case_generator = generator.GenOpTestCase()
+
+    native_functions_groups = [
+        g
+        for g in gen.get_grouped_native_functions(native_functions)
+        if isinstance(g, NativeFunctionsGroup)
+    ]
+
+    supported_functions_groups = group_functions_by_op_name(native_functions_groups)
+
+    out_variant_op_result = [
+        op_generator.out_variant(groups, backend_indices[DispatchKey.CPU])
+        for groups in supported_functions_groups
+    ]
+    out_variant_test_result = [
+        test_case_generator.out_variant(groups) for groups in supported_functions_groups
+    ]
+
+    native_functions_view_groups = [
+        g
+        for g in gen.get_grouped_by_view_native_functions(native_functions)
+        if isinstance(g, NativeFunctionsViewGroup)
+    ]
+
+    supported_functions_view_groups = group_functions_by_op_name(
+        native_functions_view_groups
+    )
+
+    view_op_result = [
+        op_generator.view(groups, backend_indices[DispatchKey.CPU])
+        for groups in supported_functions_view_groups
+    ]
+    view_test_result = [
+        test_case_generator.view(groups) for groups in supported_functions_view_groups
+    ]
+
+    op_result = out_variant_op_result + ["\n\n"] + view_op_result
+    test_result = out_variant_test_result + ["\n\n"] + view_test_result
+
+    write_cpp(op_result, options.generated_ops_cpp_path)
+    write_test_cpp(test_result, options.generated_ops_test_cpp_path)
+
+    print(
+        "\ntotal grouped native ops: %d"
+        % len(gen.get_grouped_native_functions(native_functions))
+    )
+
+    print("grouped native ops with out variant: %d" % len(native_functions_groups))
+    supported_functions_num = sum(len(groups) for groups in supported_functions_groups)
+    print("generated functions groups with out variant: %d" % supported_functions_num)
+
+    print("\nview grouped native ops: %d" % len(native_functions_view_groups))
+    supported_view_functions_num = sum(
+        len(groups) for groups in supported_functions_view_groups
+    )
+    print("generated functions view groups: %d" % supported_view_functions_num)
+
+    print(
+        "\noverall generated : %d"
+        % (supported_functions_num + supported_view_functions_num)
+    )
+
+
+if __name__ == "__main__":
+    set_simple_logging(escape_newlines=False)
+    main()
diff --git a/lib/python3.11/site-packages/torchgen/static_runtime/generator.py b/lib/python3.11/site-packages/torchgen/static_runtime/generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ed70ec5200f5be99a10854d64f9888e1552e7ae
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/static_runtime/generator.py
@@ -0,0 +1,813 @@
+from __future__ import annotations
+
+import json
+import logging
+import math
+from typing import TYPE_CHECKING
+
+import torchgen.api.cpp as cpp
+from torchgen.context import native_function_manager
+from torchgen.model import (
+    Argument,
+    BackendIndex,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    NativeFunctionsGroup,
+    NativeFunctionsViewGroup,
+    OptionalType,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.static_runtime import config
+
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+
+logger: logging.Logger = logging.getLogger()
+
+
+def has_alias(
+    arguments: Sequence[Argument | SelfArgument | TensorOptionsArguments],
+) -> bool:
+    for arg in arguments:
+        annotation = getattr(arg, "annotation", None)
+        if not annotation:
+            continue
+        alias_set = getattr(annotation, "alias_set", ())
+        if alias_set:
+            return True
+    return False
+
+
+BLOCKED_OPS = frozenset(
+    (
+        # non cpu ops
+        "sparse_sampled_addmm",
+        "hspmm",
+        "linalg_svdvals",
+        # sparse ops
+        "sspaddmm",
+        "coalesce",
+        "_indices",
+        "indices",
+        "_values",
+        "values",
+        "crow_indices",
+        "col_indices",
+        # deprecated ops
+        "floor_divide",
+        "ger",
+        # buggy ops
+        "conj_physical",  # P495807361
+        "binary_cross_entropy",  # P496394764
+        "arccosh",
+        # uncommon ops
+        "cholesky",
+        "lu_solve",
+        "linalg_cholesky",
+        "linalg_householder_product",
+        "linalg_ldl_solve",
+        "_compute_linear_combination",
+        # training related ops
+        "_make_dual",
+        # cannot call directly
+        "_fw_primal",
+        # no documentation
+        "_index_reduce",
+        # TODO: these ones got added recently and need manual inspection
+        "_new_zeros_with_same_feature_meta",
+        "_conj_physical",
+        "binary_cross_entropy_with_logits",
+        "bincount",
+        "conv_tbc",
+        "copy",
+        "_copy_from",
+        "_copy_from_and_resize",
+        "count_nonzero",
+        "cudnn_affine_grid_generator",
+        "cudnn_affine_grid_generator_backward",
+        "cudnn_grid_sampler",
+        "diag_embed",
+        "embedding",
+        "embedding_dense_backward",
+        "_embedding_bag_dense_backward",
+        "_embedding_bag_per_sample_weights_backward",
+        "grid_sampler_2d",
+        "_grid_sampler_2d_cpu_fallback",
+        "grid_sampler_3d",
+        "isnan",
+        "mkldnn_linear",
+        "median",
+        "nanmedian",
+        "_sparse_sparse_matmul",
+        "batch_norm_backward_elemt",
+        "_euclidean_dist",
+        "pixel_shuffle",
+        "pixel_unshuffle",
+        "channel_shuffle",
+        "_reshape_nested_backward",
+        "relu",
+        "prelu",
+        "celu",
+        "slice_scatter",
+        "select_scatter",
+        "diagonal_scatter",
+        "sum",
+        "_mkldnn_transpose",
+        "_nested_tensor_from_mask",
+        "_nested_from_padded",
+        "_nested_tensor_size",
+        "_nested_from_padded_and_nested_example",
+        "_standard_gamma_grad",
+        "_dirichlet_grad",
+        "native_norm",
+        "_sparse_softmax",
+        "_sparse_softmax_backward_data",
+        "_sparse_log_softmax",
+        "_sparse_log_softmax_backward_data",
+        "zero",
+        "_sparse_addmm",
+        "sparse_mask",
+        "_sparse_mask_projection",
+        "_to_dense",
+        "_coalesce",
+        "_coalesced",
+        "copy_sparse_to_sparse",
+        "to_sparse",
+        "to_sparse_csr",
+        "to_sparse_csc",
+        "to_mkldnn",
+        "quantize_per_tensor_dynamic",
+        "quantize_per_channel",
+        "q_per_channel_scales",
+        "q_per_channel_zero_points",
+        "int_repr",
+        "_make_per_channel_quantized_tensor",
+        "set",
+        "lift",
+        "lift_fresh",
+        "lift_fresh_copy",
+        "masked_scatter",
+        "_masked_softmax",
+        "_masked_softmax_backward",
+        "put",
+        "index_reduce",
+        "trace",
+        "_cholesky_solve_helper",
+        "dist",
+        "max",
+        "_torch_cuda_cu_linker_symbol_op",
+        "glu_jvp",
+        "glu_backward_jvp",
+        "hardswish_backward",
+        "rrelu_with_noise_backward",
+        "mkldnn_adaptive_avg_pool2d_backward",
+        "_adaptive_avg_pool2d_backward",
+        "_adaptive_avg_pool3d_backward",
+        "isinf",
+        "linalg_lu_solve",
+        "linalg_vecdot",
+        "linalg_matrix_exp",
+        "linalg_eigvalsh",
+        "_test_warn_in_autograd",
+        "_test_autograd_multiple_dispatch_view",
+        "_test_autograd_multiple_dispatch_view_copy",
+        "_segment_reduce",
+        "_segment_reduce_backward",
+        "_fw_primal_copy",
+        "_make_dual_copy",
+        "view_as_real_copy",
+        "view_as_complex_copy",
+        "_conj_copy",
+        "_neg_view_copy",
+        "diagonal_copy",
+        "detach_copy",
+        "squeeze_copy",
+        "t_copy",
+        "unsqueeze_copy",
+        "_indices_copy",
+        "_values_copy",
+        "indices_copy",
+        "values_copy",
+        "crow_indices_copy",
+        "col_indices_copy",
+        "ccol_indices",
+        "ccol_indices_copy",
+        "row_indices",
+        "row_indices_copy",
+        "unfold_copy",
+        "alias_copy",
+        "_triton_multi_head_attention",
+        "special_airy_ai",
+        "special_bessel_j0",
+        "special_bessel_j1",
+        "special_bessel_y0",
+        "special_bessel_y1",
+        "special_chebyshev_polynomial_t",
+        "special_chebyshev_polynomial_u",
+        "special_chebyshev_polynomial_v",
+        "special_chebyshev_polynomial_w",
+        "special_hermite_polynomial_h",
+        "special_hermite_polynomial_he",
+        "special_laguerre_polynomial_l",
+        "special_legendre_polynomial_p",
+        "special_modified_bessel_i0",
+        "special_modified_bessel_i1",
+        "special_modified_bessel_k0",
+        "special_modified_bessel_k1",
+        "special_scaled_modified_bessel_k0",
+        "special_scaled_modified_bessel_k1",
+        "special_shifted_chebyshev_polynomial_t",
+        "special_shifted_chebyshev_polynomial_u",
+        "special_shifted_chebyshev_polynomial_v",
+        "special_shifted_chebyshev_polynomial_w",
+        "special_spherical_bessel_j0",
+        "_foobar",
+        "_nested_tensor_strides",
+        "_nested_tensor_storage_offsets",
+        "_nested_get_values",  # no CPU backend
+        "_nested_get_values_copy",  # no CPU backend
+        "_nested_view_from_jagged",  # testing needs to be patched
+        "_nested_view_from_jagged_copy",  # testing needs to be patched
+        "_nested_view_from_buffer",  # testing needs to be patched
+        "_nested_view_from_buffer_copy",  # testing needs to be patched
+        "_int_mm",  # testing needs to be patched
+        "_to_sparse_csc",  # testing needs to be patched
+        "_to_sparse_csr",  # testing needs to be patched
+        "segment_reduce",  # testing needs to be patched
+    )
+)
+
+
+def is_supported(g: NativeFunctionsGroup | NativeFunctionsViewGroup) -> bool:
+    base_op_name = ""
+    func = None
+    if isinstance(g, NativeFunctionsViewGroup):
+        base_op_name = g.view.root_name
+        func = g.view.func
+    else:
+        base_op_name = g.out.func.name.name.base
+        func = g.out.func
+    if config.is_hand_written(g):
+        logger.info("HAND WRITTEN: %s", base_op_name)
+        return False
+    if base_op_name in BLOCKED_OPS:
+        logger.info("BLOCKED: %s", base_op_name)
+        return False
+    for arg in func.schema_order_arguments():
+        maybe_method = ivalue_type_conversion_method(arg.type)
+        if not maybe_method:
+            # Type converting is unsupported yet.
+            logger.info("NOT SUPPORTED TYPE CONVERTING: %s", func)
+            return False
+
+    if isinstance(g, NativeFunctionsViewGroup):
+        # TODO: stop doing type tests by converting to C++ and then testing
+        # the string, just test the dang thing directly
+        if "at::Tensor" != cpp.returns_type(func.returns, symint=False).cpp_type():
+            # Returns a non-Tensor value.
+            logger.info("NON-TENSOR RET TYPE: %s", str(func))
+            return False
+        return True
+
+    # For out variant ops, we need to check the arguments of its functional func.
+    for arg in g.functional.func.schema_order_arguments():
+        maybe_method = ivalue_type_conversion_method(arg.type)
+        if not maybe_method:
+            # Type converting is unsupported yet.
+            logger.info("NOT SUPPORTED TYPE CONVERTING: %s", g.functional.func)
+            return False
+
+    if not g.structured:
+        # In case of unstructured op, we check if it has out variant implementation.
+        # The out variant implementation satisfies the minimum requirement that it has the output tensor as the last
+        # parameter.
+        if (
+            not hasattr(g, "out")
+            or not str(func).endswith("Tensor(a!) out) -> Tensor(a!)")
+            or not str(func.name).endswith(".out")
+        ):
+            return False
+    # TODO: stop type testing by converting to C++
+    if "at::Tensor &" != cpp.returns_type(func.returns, symint=False).cpp_type():
+        logger.info("NON_TENSOR RET TYPE: %s", func)
+        return False
+    if has_alias(func.arguments.non_out):
+        # This op may create an alias of inputs.
+        logger.info("INPUTS ALIAS: %s", base_op_name)
+        return False
+    return True
+
+
+def ivalue_type_conversion_method(
+    arg_type: BaseType | OptionalType | Type,
+) -> tuple[bool, str] | None:
+    """
+    Return the method call expression of `c10::ivalue' to convert its contained value to
+    the expected value of `arg_type` type. For example, for `arg_type` == BaseTy.Tensor,
+    this function returns ".toTensor()", so that it can be appended to the ivalue's
+    variable name to get the value of the expected type.
+    """
+    type_conversion_methods = {
+        BaseTy.Tensor: ((True, "toTensor()"), (False, "toOptional<at::Tensor>()")),
+        BaseTy.int: ((False, "toInt()"), (False, "toOptional<int64_t>()")),
+        BaseTy.bool: ((False, "toBool()"), (False, "toOptional<bool>()")),
+        BaseTy.Scalar: ((False, "toScalar()"), (False, "toOptional<at::Scalar>()")),
+        BaseTy.ScalarType: (
+            (False, "toScalarType()"),
+            (False, "toOptional<at::ScalarType>()"),
+        ),
+        BaseTy.str: (
+            (False, "toStringView()"),
+            (False, "toOptional<c10::string_view>()"),
+        ),
+    }
+
+    base_ty_object = None
+    if isinstance(arg_type, BaseType):
+        base_ty_object = arg_type.name
+    elif isinstance(arg_type, OptionalType):
+        if not isinstance(arg_type.elem, BaseType):
+            # ListType is currently unsupported.
+            return None
+        base_ty_object = arg_type.elem.name
+    else:
+        return None
+
+    if base_ty_object not in type_conversion_methods:
+        return None
+    methods = type_conversion_methods[base_ty_object]
+    if isinstance(arg_type, BaseType):
+        return methods[0]
+    return methods[1]
+
+
+should_use_int_tensor_ops_ = frozenset(
+    (
+        "bitwise_not",
+        "bitwise_and",
+        "bitwise_or",
+        "bitwise_xor",
+        "bitwise_left_shift",
+        "bitwise_right_shift",
+        "gcd",
+        "lcm",
+        "scatter",
+        "gather",
+        "_convert_indices_from_coo_to_csr",
+        "_convert_indices_from_csr_to_coo",
+    )
+)
+should_use_complex_tensor_ops_ = frozenset(("view_as_real", "imag", "_conj"))
+
+
+def should_use_int_tensor(op_name: str) -> bool:
+    return op_name in should_use_int_tensor_ops_
+
+
+def should_use_complex_tensor(op_name: str) -> bool:
+    return op_name in should_use_complex_tensor_ops_
+
+
+test_tensor_dim_ops_1_ = frozenset(
+    (
+        "addmv",
+        "index_add",
+        "_convert_indices_from_coo_to_csr",
+        "_convert_indices_from_csr_to_coo",
+        "nll_loss_backward",
+        "dot",
+        "vdot",
+        "outer",
+        "ger",
+    )
+)
+test_tensor_dim_ops_2_ = frozenset(
+    ("addmm", "mm", "nuclear_norm", "diag", "_addmm_activation", "matrix_H", "t")
+)
+
+
+def test_tensor_dim(op_name: str) -> int:
+    if op_name in test_tensor_dim_ops_1_:
+        return 1
+    if op_name in test_tensor_dim_ops_2_:
+        return 2
+    return 3
+
+
+test_tensor_shapes_string = '{"view_as_complex": "{2, 2}"}'
+test_tensor_shape_json: dict[str, str] = json.loads(test_tensor_shapes_string)
+
+
+def test_tensor_shape(op_name: str) -> str:
+    if op_name in test_tensor_shape_json:
+        return test_tensor_shape_json[op_name]
+    else:
+        return ""
+
+
+def test_value_expression(
+    arg_type: BaseType | OptionalType | Type, index: int, op_name: str
+) -> str:
+    tensor_size_ex = test_tensor_shape(op_name)
+    if tensor_size_ex == "":
+        num_tensors = 16 if index == 0 else 64
+        num_dim = test_tensor_dim(op_name)
+        size_per_dim = math.ceil(num_tensors / float(num_dim))
+        size_per_dim += size_per_dim % 2
+        tensor_size_ex = "{{{}}}".format(",".join([f"{size_per_dim}"] * num_dim))
+    if should_use_int_tensor(op_name):
+        tensor_expression = f"at::randint(1, 100, {tensor_size_ex}, at::kInt)"
+    elif should_use_complex_tensor(op_name):
+        tensor_expression = f"at::randn({tensor_size_ex}, at::kComplexFloat)"
+    else:
+        tensor_expression = f"at::rand({tensor_size_ex})"
+
+    value_expressions = {
+        BaseTy.Tensor: tensor_expression,
+        BaseTy.int: "1",
+        BaseTy.bool: "false",
+        BaseTy.Scalar: "2",
+        BaseTy.ScalarType: "at::ScalarType::Float",
+        BaseTy.str: '"floor"',
+    }
+
+    base_ty_object = None
+    if isinstance(arg_type, BaseType):
+        base_ty_object = arg_type.name
+    else:
+        assert isinstance(arg_type, OptionalType) and isinstance(
+            arg_type.elem, BaseType
+        )
+        base_ty_object = arg_type.elem.name
+    assert base_ty_object in value_expressions, "not expected type"
+    value_expression = value_expressions[base_ty_object]
+    return value_expression
+
+
+def generate_test_value_definitions(schema: FunctionSchema, index: int) -> str:
+    assert not schema.is_out_fn()
+    schema_name = schema.name.name.base
+    arg_map = {}
+    for arg in schema.schema_order_arguments():
+        test_value_exp = test_value_expression(arg.type, index, schema_name)
+        arg_map[arg.name] = test_value_exp
+    config.override_test_values(arg_map, schema_name, index)
+    arg_populations = []
+    for arg_name, arg_value in arg_map.items():
+        arg_populations.append(f"auto {arg_name}{index} = {arg_value}")
+    return ";\n    ".join(arg_populations) + ";"
+
+
+def generate_test_value_names(schema: FunctionSchema, index: int) -> str:
+    assert not schema.is_out_fn()
+    return ",".join(f"{arg.name}{index}" for arg in schema.schema_order_arguments())
+
+
+generate_test_ir_arguments_base_ty_to_type_str_ = {
+    BaseTy.Tensor: "Tensor",
+    BaseTy.int: "int",
+    BaseTy.float: "float",
+    BaseTy.str: "str",
+    BaseTy.Scalar: "int",
+    BaseTy.ScalarType: "int",
+    BaseTy.bool: "bool",
+}
+
+
+def generate_test_ir_arguments(
+    schema: FunctionSchema,
+) -> list[tuple[str, str | None]]:
+    def ir_argument(arg: Argument) -> tuple[str, str | None]:
+        t = arg.type
+        add_optional = False
+        if isinstance(t, OptionalType):
+            t = t.elem
+            add_optional = True
+        assert isinstance(t, BaseType)
+        type_str = None
+        if t.name in generate_test_ir_arguments_base_ty_to_type_str_:
+            type_str = generate_test_ir_arguments_base_ty_to_type_str_[t.name]
+        if type_str and add_optional:
+            type_str = f"{type_str}?"
+        return ("%" + arg.name, type_str)
+
+    return [ir_argument(arg) for arg in schema.schema_order_arguments()]
+
+
+def generate_arg_extraction(schema: FunctionSchema) -> str:
+    arg_populations = []
+    for i, arg in enumerate(schema.schema_order_arguments()):
+        maybe_method = ivalue_type_conversion_method(arg.type)
+        assert maybe_method
+        is_reference, type_conversion_method = maybe_method
+        reference = "&" if is_reference else ""
+        arg_populations.append(
+            f"const auto{reference} {arg.name} = p_node->Input({i}).{type_conversion_method}"
+        )
+    return ";\n    ".join(arg_populations) + ";"
+
+
+def get_kernel_name(g: NativeFunctionsGroup, backend_index: BackendIndex) -> str:
+    kernel = backend_index.get_kernel(g.functional)
+    if g.structured or kernel is None:
+        return cpp.name(g.functional.func)
+    return kernel.kernel
+
+
+def get_out_kernel_name(g: NativeFunctionsGroup, backend_index: BackendIndex) -> str:
+    kernel = backend_index.get_kernel(g.out)
+    if g.structured or kernel is None:
+        return cpp.name(g.out.func)
+    return kernel.kernel
+
+
+def generate_non_out_variant_call(
+    g: NativeFunctionsGroup, backend_index: BackendIndex
+) -> str:
+    schema = g.functional.func
+    assert not schema.is_out_fn()
+    kernel_name = get_kernel_name(g, backend_index)
+    arg_names = (arg.name for arg in schema.schema_order_arguments())
+    namespace_name = "cpu" if g.structured else "native"
+    return f'at::{namespace_name}::{kernel_name}({",".join(arg_names)})'
+
+
+def generate_call_to_view_ops(
+    g: NativeFunctionsViewGroup, backend_index: BackendIndex
+) -> str:
+    schema = g.view.func
+    kernel_name = cpp.name(schema)
+    kernel = backend_index.get_kernel(g.view)
+    if kernel:
+        kernel_name = kernel.kernel
+    arg_names = (arg.name for arg in schema.schema_order_arguments())
+    namespace_name = "native"
+    return f'at::{namespace_name}::{kernel_name}({",".join(arg_names)})'
+
+
+def generate_out_variant_call(
+    g: NativeFunctionsGroup, backend_index: BackendIndex
+) -> str:
+    schema = g.out.func
+    assert schema.is_out_fn()
+    arg_names = []
+    kernel_name = get_out_kernel_name(g, backend_index)
+    if g.structured:
+        # structured op starts with the output tensor argument.
+        arg_names = [out_arg.name for out_arg in schema.arguments.out]
+    else:
+        arg_names = []
+    for arg in schema.arguments.non_out:
+        if isinstance(arg, SelfArgument):
+            arg_names.append(arg.argument.name)
+        else:
+            assert isinstance(arg, Argument)
+            arg_names.append(arg.name)
+    if not g.structured:
+        assert len(schema.arguments.out) == 1
+        arg_names.append(schema.arguments.out[0].name)
+    cpp_arg_names = ",".join(arg_names)
+    namespace_name = "cpu" if g.structured else "native"
+    return f"at::{namespace_name}::{kernel_name}({cpp_arg_names})"
+
+
+no_memory_resize_ops = frozenset(
+    (
+        "isin.Scalar_Tensor",
+        "index_add",
+        "dot",
+        "vdot",
+        "nuclear_norm",
+        "histc",
+        "l1_loss",
+        "multi_margin_loss",
+        "multilabel_margin_loss",
+        "nll_loss",
+        "nll_loss2d",
+        "prod",
+    )
+)
+
+
+def should_check_resize(schema: FunctionSchema) -> bool:
+    schema_str = str(schema)
+    type_variant_op_name = schema_str[: schema_str.find("(")]
+    return type_variant_op_name not in no_memory_resize_ops
+
+
+def op_name_from_group(g: NativeFunctionsGroup) -> str:
+    return g.functional.func.name.name.base
+
+
+class GenOpDispatcher:
+    def out_variant(
+        self, groups: Sequence[NativeFunctionsGroup], backend_index: BackendIndex
+    ) -> str:
+        if not groups:
+            return ""
+        generated_type_variants = []
+        for g in groups:
+            with native_function_manager(g):
+                assert is_supported(g)
+                assert isinstance(g, NativeFunctionsGroup)
+                generated_type_variant = self.out_variant_op_generator(g, backend_index)
+                generated_type_variants.append(generated_type_variant)
+        op_name = op_name_from_group(groups[0])
+        body = "\n".join(generated_type_variants)
+        generated = f"""
+REGISTER_OPERATOR_FUNCTOR(
+    aten::{op_name},
+    aten_{op_name},
+    [](Node* n) -> SROperator {{
+      {body}
+      LogAndDumpSchema(n);
+      return nullptr;
+    }})
+"""
+        return generated
+
+    def view(
+        self, groups: Sequence[NativeFunctionsViewGroup], backend_index: BackendIndex
+    ) -> str:
+        if not groups:
+            return ""
+        generated_type_variants = []
+        for g in groups:
+            with native_function_manager(g):
+                assert is_supported(g)
+                assert isinstance(g, NativeFunctionsViewGroup)
+                generated_type_variant = self.view_op_generator(g, backend_index)
+                generated_type_variants.append(generated_type_variant)
+        op_name = config.func_name_base_str(groups[0])
+        body = "\n".join(generated_type_variants)
+        generated = f"""
+REGISTER_NATIVE_OPERATOR_FUNCTOR(
+    aten::{op_name},
+    aten_{op_name},
+    [](Node* n) -> SROperator {{
+      {body}
+      LogAndDumpSchema(n);
+      return nullptr;
+    }});
+"""
+        return generated
+
+    def out_variant_op_generator(
+        self, g: NativeFunctionsGroup, backend_index: BackendIndex
+    ) -> str:
+        functional = g.functional
+        schema = str(functional.func)
+        populated_argument = generate_arg_extraction(g.functional.func)
+        functional_variant_call = generate_non_out_variant_call(g, backend_index)
+        assert len(g.out.func.arguments.out) == 1
+        out_variable_name = str(g.out.func.arguments.out[0].name)
+        out_variant_call = generate_out_variant_call(g, backend_index)
+        generated = f"""
+      if (n->matches(torch::schema("aten::{schema}"))) {{
+        return [](ProcessedNode* p_node) {{
+          {populated_argument}
+          if (p_node->Output(0).isNone()) {{
+            p_node->Output(0) = {functional_variant_call};
+            return;
+          }}
+          auto& {out_variable_name} = p_node->Output(0).toTensor();
+          fastResizeToZero({out_variable_name});
+          {out_variant_call};
+        }};
+      }}"""
+        return generated
+
+    def view_op_generator(
+        self, g: NativeFunctionsViewGroup, backend_index: BackendIndex
+    ) -> str:
+        schema = str(g.view.func)
+        populated_argument = generate_arg_extraction(g.view.func)
+        functional_variant_call = generate_call_to_view_ops(g, backend_index)
+        generated = f"""
+      if (n->matches(torch::schema("aten::{schema}"))) {{
+        return [](ProcessedNode* p_node) {{
+          {populated_argument}
+            p_node->Output(0) = {functional_variant_call};
+        }};
+      }}"""
+        return generated
+
+
+class GenOpTestCase:
+    def out_variant(self, groups: Sequence[NativeFunctionsGroup]) -> str:
+        if not groups:
+            return ""
+        generated_type_variants = []
+        for g in groups:
+            with native_function_manager(g):
+                assert is_supported(g)
+                assert isinstance(g, NativeFunctionsGroup)
+                generated_type_variant = self.out_variant_op_test_case_generator(g)
+                generated_type_variants.append(generated_type_variant)
+        return "\n".join(generated_type_variants)
+
+    def view(self, groups: Sequence[NativeFunctionsViewGroup]) -> str:
+        if not groups:
+            return ""
+        generated_type_variants = []
+        for g in groups:
+            with native_function_manager(g):
+                assert is_supported(g)
+                assert isinstance(g, NativeFunctionsViewGroup)
+                generated_type_variant = self.view_op_test_case_generator(g)
+                generated_type_variants.append(generated_type_variant)
+        return "\n".join(generated_type_variants)
+
+    def out_variant_op_test_case_generator(self, g: NativeFunctionsGroup) -> str:
+        schema = g.functional.func
+        schema_str = str(schema)
+        assert schema_str.find("(") > 0
+        type_variant_op_name = schema_str[: schema_str.find("(")].replace(".", "_")
+        op_name = op_name_from_group(g)
+        assert type_variant_op_name.startswith(op_name)
+
+        arg_types = generate_test_ir_arguments(schema)
+        arg_declarations = ", ".join(
+            (
+                arg_name if arg_type is None else f"{arg_name}: {arg_type}"
+                for arg_name, arg_type in arg_types
+            )
+        )
+        arg_names = ", ".join((arg_name for arg_name, _ in arg_types))
+        assert (
+            len(schema.returns) == 1
+            and isinstance(schema.returns[0].type, BaseType)
+            and schema.returns[0].type.name is BaseTy.Tensor
+        )
+        test_value_definitions = generate_test_value_definitions(schema, 0)
+        test_value_names = generate_test_value_names(schema, 0)
+        test_value_definitions2 = generate_test_value_definitions(schema, 1)
+        test_value_names2 = generate_test_value_names(schema, 1)
+        check_resize = "true" if should_check_resize(schema) else "false"
+        generated = f"""
+TEST(StaticRuntime, autogen_{type_variant_op_name}) {{
+  const std::string script = R"IR(
+    graph({arg_declarations}):
+        %bias: None = prim::Constant()
+        %ret = aten::{op_name}({arg_names})
+        %cloned = aten::clone(%ret, %bias)
+        return (%cloned)
+  )IR";
+
+  {test_value_definitions}
+  std::vector<IValue> args{{{test_value_names}}};
+  testStaticRuntime(script, args, {{}}, /*use_allclose=*/false, /*use_equalnan=*/false, /*check_resize=*/{check_resize});
+
+  {test_value_definitions2}
+  std::vector<IValue> args2{{{test_value_names2}}};
+  testStaticRuntime(script, args, args2, /*use_allclose=*/false, /*use_equalnan=*/false, /*check_resize=*/{check_resize});
+
+}}
+"""
+        return generated
+
+    def view_op_test_case_generator(self, g: NativeFunctionsViewGroup) -> str:
+        schema = g.view.func
+        schema_str = str(schema)
+        assert schema_str.find("(") > 0
+        type_variant_op_name = schema_str[: schema_str.find("(")].replace(".", "_")
+        op_name = g.view.root_name
+        assert type_variant_op_name.startswith(op_name)
+
+        arg_types = generate_test_ir_arguments(schema)
+        arg_declarations = ", ".join(
+            (
+                arg_name if arg_type is None else f"{arg_name}: {arg_type}"
+                for arg_name, arg_type in arg_types
+            )
+        )
+        arg_names = ", ".join((arg_name for arg_name, _ in arg_types))
+        assert (
+            len(schema.returns) == 1
+            and isinstance(schema.returns[0].type, BaseType)
+            and schema.returns[0].type.name is BaseTy.Tensor
+        )
+        test_value_definitions = generate_test_value_definitions(schema, 0)
+        test_value_names = generate_test_value_names(schema, 0)
+        generated = f"""
+TEST(StaticRuntime, autogen_{type_variant_op_name}) {{
+  const std::string script = R"IR(
+    graph({arg_declarations}):
+        %bias: None = prim::Constant()
+        %ret = aten::{op_name}({arg_names})
+        %cloned = aten::clone(%ret, %bias)
+        return (%cloned)
+  )IR";
+
+  {test_value_definitions}
+  std::vector<IValue> args{{{test_value_names}}};
+  testStaticRuntime(script, args);
+}}
+"""
+
+        return generated
diff --git a/lib/python3.11/site-packages/torchgen/utils.py b/lib/python3.11/site-packages/torchgen/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..17b8146b672b3b79ce5fbb8f7316d7047f0f5800
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/utils.py
@@ -0,0 +1,509 @@
+from __future__ import annotations
+
+import contextlib
+import functools
+import hashlib
+import os
+import re
+import sys
+import textwrap
+from dataclasses import fields, is_dataclass
+from enum import auto, Enum
+from pathlib import Path
+from typing import Any, Callable, Generic, Literal, NoReturn, TYPE_CHECKING, TypeVar
+from typing_extensions import Self
+
+from torchgen.code_template import CodeTemplate
+
+
+if TYPE_CHECKING:
+    from argparse import Namespace
+    from collections.abc import Iterable, Iterator, Sequence
+
+
+REPO_ROOT = Path(__file__).absolute().parent.parent
+
+
+# Many of these functions share logic for defining both the definition
+# and declaration (for example, the function signature is the same), so
+# we organize them into one function that takes a Target to say which
+# code we want.
+#
+# This is an OPEN enum (we may add more cases to it in the future), so be sure
+# to explicitly specify with Literal[Target.XXX] or Literal[Target.XXX, Target.YYY]
+# what targets are valid for your use.
+class Target(Enum):
+    # top level namespace (not including at)
+    DEFINITION = auto()
+    DECLARATION = auto()
+    # TORCH_LIBRARY(...) { ... }
+    REGISTRATION = auto()
+    # namespace { ... }
+    ANONYMOUS_DEFINITION = auto()
+    # namespace cpu { ... }
+    NAMESPACED_DEFINITION = auto()
+    NAMESPACED_DECLARATION = auto()
+
+
+# Matches "foo" in "foo, bar" but not "foobar". Used to search for the
+# occurrence of a parameter in the derivative formula
+IDENT_REGEX = r"(^|\W){}($|\W)"
+
+
+# TODO: Use a real parser here; this will get bamboozled
+def split_name_params(schema: str) -> tuple[str, list[str]]:
+    m = re.match(r"(\w+)(\.\w+)?\((.*)\)", schema)
+    if m is None:
+        raise RuntimeError(f"Unsupported function schema: {schema}")
+    name, _, params = m.groups()
+    return name, params.split(", ")
+
+
+T = TypeVar("T")
+S = TypeVar("S")
+
+# These two functions purposely return generators in analogy to map()
+# so that you don't mix up when you need to list() them
+
+
+# Map over function that may return None; omit Nones from output sequence
+def mapMaybe(func: Callable[[T], S | None], xs: Iterable[T]) -> Iterator[S]:
+    for x in xs:
+        r = func(x)
+        if r is not None:
+            yield r
+
+
+# Map over function that returns sequences and cat them all together
+def concatMap(func: Callable[[T], Sequence[S]], xs: Iterable[T]) -> Iterator[S]:
+    for x in xs:
+        yield from func(x)
+
+
+# Conveniently add error context to exceptions raised.  Lets us
+# easily say that an error occurred while processing a specific
+# context.
+@contextlib.contextmanager
+def context(msg_fn: Callable[[], str]) -> Iterator[None]:
+    try:
+        yield
+    except Exception as e:
+        # TODO: this does the wrong thing with KeyError
+        msg = msg_fn()
+        msg = textwrap.indent(msg, "  ")
+        msg = f"{e.args[0]}\n{msg}" if e.args else msg
+        e.args = (msg,) + e.args[1:]
+        raise
+
+
+# A little trick from https://github.com/python/mypy/issues/6366
+# for getting mypy to do exhaustiveness checking
+# TODO: put this somewhere else, maybe
+def assert_never(x: NoReturn) -> NoReturn:
+    raise AssertionError(f"Unhandled type: {type(x).__name__}")
+
+
+@functools.cache
+def _read_template(template_fn: str) -> CodeTemplate:
+    return CodeTemplate.from_file(template_fn)
+
+
+# String hash that's stable across different executions, unlike builtin hash
+def string_stable_hash(s: str) -> int:
+    sha1 = hashlib.sha1(s.encode("latin1")).digest()
+    return int.from_bytes(sha1, byteorder="little")
+
+
+# A small abstraction for writing out generated files and keeping track
+# of what files have been written (so you can write out a list of output
+# files)
+class FileManager:
+    install_dir: str
+    template_dir: str
+    dry_run: bool
+    filenames: set[str]
+
+    def __init__(self, install_dir: str, template_dir: str, dry_run: bool) -> None:
+        self.install_dir = install_dir
+        self.template_dir = template_dir
+        self.filenames = set()
+        self.dry_run = dry_run
+
+    def _write_if_changed(self, filename: str, contents: str) -> None:
+        old_contents: str | None
+        try:
+            with open(filename) as f:
+                old_contents = f.read()
+        except OSError:
+            old_contents = None
+        if contents != old_contents:
+            # Create output directory if it doesn't exist
+            os.makedirs(os.path.dirname(filename), exist_ok=True)
+            with open(filename, "w") as f:
+                f.write(contents)
+
+    # Read from template file and replace pattern with callable (type could be dict or str).
+    def substitute_with_template(
+        self, template_fn: str, env_callable: Callable[[], str | dict[str, Any]]
+    ) -> str:
+        template_path = os.path.join(self.template_dir, template_fn)
+        env = env_callable()
+        if isinstance(env, dict):
+            if "generated_comment" not in env:
+                generator_default = REPO_ROOT / "torchgen" / "gen.py"
+                try:
+                    generator = Path(
+                        sys.modules["__main__"].__file__ or generator_default
+                    ).absolute()
+                except (KeyError, AttributeError):
+                    generator = generator_default.absolute()
+
+                try:
+                    generator_path = generator.relative_to(REPO_ROOT).as_posix()
+                except ValueError:
+                    generator_path = generator.name
+
+                env = {
+                    **env,  # copy the original dict instead of mutating it
+                    "generated_comment": (
+                        "@" + f"generated by {generator_path} from {template_fn}"
+                    ),
+                }
+            template = _read_template(template_path)
+            return template.substitute(env)
+        elif isinstance(env, str):
+            return env
+        else:
+            assert_never(env)
+
+    def write_with_template(
+        self,
+        filename: str,
+        template_fn: str,
+        env_callable: Callable[[], str | dict[str, Any]],
+    ) -> None:
+        filename = f"{self.install_dir}/{filename}"
+        assert filename not in self.filenames, "duplicate file write {filename}"
+        self.filenames.add(filename)
+        if not self.dry_run:
+            substitute_out = self.substitute_with_template(
+                template_fn=template_fn,
+                env_callable=env_callable,
+            )
+            self._write_if_changed(filename=filename, contents=substitute_out)
+
+    def write(
+        self,
+        filename: str,
+        env_callable: Callable[[], str | dict[str, Any]],
+    ) -> None:
+        self.write_with_template(filename, filename, env_callable)
+
+    def write_sharded(
+        self,
+        filename: str,
+        items: Iterable[T],
+        *,
+        key_fn: Callable[[T], str],
+        env_callable: Callable[[T], dict[str, list[str]]],
+        num_shards: int,
+        base_env: dict[str, Any] | None = None,
+        sharded_keys: set[str],
+    ) -> None:
+        everything: dict[str, Any] = {"shard_id": "Everything"}
+        shards: list[dict[str, Any]] = [
+            {"shard_id": f"_{i}"} for i in range(num_shards)
+        ]
+        all_shards = [everything] + shards
+
+        if base_env is not None:
+            for shard in all_shards:
+                shard.update(base_env)
+
+        for key in sharded_keys:
+            for shard in all_shards:
+                if key in shard:
+                    assert isinstance(
+                        shard[key], list
+                    ), "sharded keys in base_env must be a list"
+                    shard[key] = shard[key].copy()
+                else:
+                    shard[key] = []
+
+        def merge_env(into: dict[str, list[str]], from_: dict[str, list[str]]) -> None:
+            for k, v in from_.items():
+                assert k in sharded_keys, f"undeclared sharded key {k}"
+                into[k] += v
+
+        if self.dry_run:
+            # Dry runs don't write any templates, so incomplete environments are fine
+            items = ()
+
+        for item in items:
+            key = key_fn(item)
+            sid = string_stable_hash(key) % num_shards
+            env = env_callable(item)
+
+            merge_env(shards[sid], env)
+            merge_env(everything, env)
+
+        dot_pos = filename.rfind(".")
+        if dot_pos == -1:
+            dot_pos = len(filename)
+        base_filename = filename[:dot_pos]
+        extension = filename[dot_pos:]
+
+        for shard in all_shards:
+            shard_id = shard["shard_id"]
+            self.write_with_template(
+                f"{base_filename}{shard_id}{extension}", filename, lambda: shard
+            )
+
+        # filenames is used to track compiled files, but FooEverything.cpp isn't meant to be compiled
+        self.filenames.discard(
+            f"{self.install_dir}/{base_filename}Everything{extension}"
+        )
+
+    def write_outputs(self, variable_name: str, filename: str) -> None:
+        """Write a file containing the list of all outputs which are
+        generated by this script."""
+        content = "set({}\n    {})".format(
+            variable_name,
+            "\n    ".join('"' + name + '"' for name in sorted(self.filenames)),
+        )
+        self._write_if_changed(filename, content)
+
+    def template_dir_for_comments(self) -> str:
+        """
+        This needs to be deterministic. The template dir is an absolute path
+        that varies across builds. So, just use the path relative to this file,
+        which will point to the codegen source but will be stable.
+        """
+        return os.path.relpath(self.template_dir, os.path.dirname(__file__))
+
+
+# Helper function to generate file manager
+def make_file_manager(
+    options: Namespace, install_dir: str | None = None
+) -> FileManager:
+    template_dir = os.path.join(options.source_path, "templates")
+    install_dir = install_dir if install_dir else options.install_dir
+    return FileManager(
+        install_dir=install_dir, template_dir=template_dir, dry_run=options.dry_run
+    )
+
+
+# Helper function to create a pretty representation for dataclasses
+def dataclass_repr(
+    obj: Any,
+    indent: int = 0,
+    width: int = 80,
+) -> str:
+    # built-in pprint module support dataclasses from python 3.10
+    if sys.version_info >= (3, 10):
+        from pprint import pformat
+
+        return pformat(obj, indent, width)
+
+    return _pformat(obj, indent=indent, width=width)
+
+
+def _pformat(
+    obj: Any,
+    indent: int,
+    width: int,
+    curr_indent: int = 0,
+) -> str:
+    assert is_dataclass(obj), f"obj should be a dataclass, received: {type(obj)}"
+
+    class_name = obj.__class__.__name__
+    # update current indentation level with class name
+    curr_indent += len(class_name) + 1
+
+    fields_list = [(f.name, getattr(obj, f.name)) for f in fields(obj) if f.repr]
+
+    fields_str = []
+    for name, attr in fields_list:
+        # update the current indent level with the field name
+        # dict, list, set and tuple also add indent as done in pprint
+        _curr_indent = curr_indent + len(name) + 1
+        if is_dataclass(attr):
+            str_repr = _pformat(attr, indent, width, _curr_indent)
+        elif isinstance(attr, dict):
+            str_repr = _format_dict(attr, indent, width, _curr_indent)
+        elif isinstance(attr, (list, set, tuple)):
+            str_repr = _format_list(attr, indent, width, _curr_indent)
+        else:
+            str_repr = repr(attr)
+
+        fields_str.append(f"{name}={str_repr}")
+
+    indent_str = curr_indent * " "
+    body = f",\n{indent_str}".join(fields_str)
+    return f"{class_name}({body})"
+
+
+def _format_dict(
+    attr: dict[Any, Any],
+    indent: int,
+    width: int,
+    curr_indent: int,
+) -> str:
+    curr_indent += indent + 3
+    dict_repr = []
+    for k, v in attr.items():
+        k_repr = repr(k)
+        v_str = (
+            _pformat(v, indent, width, curr_indent + len(k_repr))
+            if is_dataclass(v)
+            else repr(v)
+        )
+        dict_repr.append(f"{k_repr}: {v_str}")
+
+    return _format(dict_repr, indent, width, curr_indent, "{", "}")
+
+
+def _format_list(
+    attr: list[Any] | set[Any] | tuple[Any, ...],
+    indent: int,
+    width: int,
+    curr_indent: int,
+) -> str:
+    curr_indent += indent + 1
+    list_repr = [
+        _pformat(l, indent, width, curr_indent) if is_dataclass(l) else repr(l)
+        for l in attr
+    ]
+    start, end = ("[", "]") if isinstance(attr, list) else ("(", ")")
+    return _format(list_repr, indent, width, curr_indent, start, end)
+
+
+def _format(
+    fields_str: list[str],
+    indent: int,
+    width: int,
+    curr_indent: int,
+    start: str,
+    end: str,
+) -> str:
+    delimiter, curr_indent_str = "", ""
+    # if it exceed the max width then we place one element per line
+    if len(repr(fields_str)) >= width:
+        delimiter = "\n"
+        curr_indent_str = " " * curr_indent
+
+    indent_str = " " * indent
+    body = f", {delimiter}{curr_indent_str}".join(fields_str)
+    return f"{start}{indent_str}{body}{end}"
+
+
+class NamespaceHelper:
+    """A helper for constructing the namespace open and close strings for a nested set of namespaces.
+
+    e.g. for namespace_str torch::lazy,
+
+    prologue:
+    namespace torch {
+    namespace lazy {
+
+    epilogue:
+    } // namespace lazy
+    } // namespace torch
+    """
+
+    def __init__(
+        self, namespace_str: str, entity_name: str = "", max_level: int = 2
+    ) -> None:
+        # cpp_namespace can be a colon joined string such as torch::lazy
+        cpp_namespaces = namespace_str.split("::")
+        assert (
+            len(cpp_namespaces) <= max_level
+        ), f"Codegen doesn't support more than {max_level} level(s) of custom namespace. Got {namespace_str}."
+        self.cpp_namespace_ = namespace_str
+        self.prologue_ = "\n".join([f"namespace {n} {{" for n in cpp_namespaces])
+        self.epilogue_ = "\n".join(
+            [f"}} // namespace {n}" for n in reversed(cpp_namespaces)]
+        )
+        self.namespaces_ = cpp_namespaces
+        self.entity_name_ = entity_name
+
+    @staticmethod
+    def from_namespaced_entity(
+        namespaced_entity: str, max_level: int = 2
+    ) -> NamespaceHelper:
+        """
+        Generate helper from nested namespaces as long as class/function name. E.g.: "torch::lazy::add"
+        """
+        names = namespaced_entity.split("::")
+        entity_name = names[-1]
+        namespace_str = "::".join(names[:-1])
+        return NamespaceHelper(
+            namespace_str=namespace_str, entity_name=entity_name, max_level=max_level
+        )
+
+    @property
+    def prologue(self) -> str:
+        return self.prologue_
+
+    @property
+    def epilogue(self) -> str:
+        return self.epilogue_
+
+    @property
+    def entity_name(self) -> str:
+        return self.entity_name_
+
+    # Only allow certain level of namespaces
+    def get_cpp_namespace(self, default: str = "") -> str:
+        """
+        Return the namespace string from joining all the namespaces by "::" (hence no leading "::").
+        Return default if namespace string is empty.
+        """
+        return self.cpp_namespace_ if self.cpp_namespace_ else default
+
+
+class OrderedSet(Generic[T]):
+    storage: dict[T, Literal[None]]
+
+    def __init__(self, iterable: Iterable[T] | None = None) -> None:
+        if iterable is None:
+            self.storage = {}
+        else:
+            self.storage = dict.fromkeys(iterable)
+
+    def __contains__(self, item: T) -> bool:
+        return item in self.storage
+
+    def __iter__(self) -> Iterator[T]:
+        return iter(self.storage.keys())
+
+    def update(self, items: OrderedSet[T]) -> None:
+        self.storage.update(items.storage)
+
+    def add(self, item: T) -> None:
+        self.storage[item] = None
+
+    def copy(self) -> OrderedSet[T]:
+        ret: OrderedSet[T] = OrderedSet()
+        ret.storage = self.storage.copy()
+        return ret
+
+    @staticmethod
+    def union(*args: OrderedSet[T]) -> OrderedSet[T]:
+        ret = args[0].copy()
+        for s in args[1:]:
+            ret.update(s)
+        return ret
+
+    def __or__(self, other: OrderedSet[T]) -> OrderedSet[T]:
+        return OrderedSet.union(self, other)
+
+    def __ior__(self, other: OrderedSet[T]) -> Self:
+        self.update(other)
+        return self
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, OrderedSet):
+            return self.storage == other.storage
+        else:
+            return set(self.storage.keys()) == other
diff --git a/lib/python3.11/site-packages/torchgen/yaml_utils.py b/lib/python3.11/site-packages/torchgen/yaml_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0278af84bf633a85a857b6bd7798dd64cb8259dc
--- /dev/null
+++ b/lib/python3.11/site-packages/torchgen/yaml_utils.py
@@ -0,0 +1,26 @@
+# Safely load fast C Yaml loader/dumper if they are available
+try:
+    from yaml import CSafeLoader as Loader
+except ImportError:
+    from yaml import SafeLoader as Loader  # type: ignore[assignment, misc]
+
+try:
+    from yaml import CSafeDumper as Dumper
+except ImportError:
+    from yaml import SafeDumper as Dumper  # type: ignore[assignment, misc]
+YamlDumper = Dumper
+
+
+# A custom loader for YAML that errors on duplicate keys.
+# This doesn't happen by default: see https://github.com/yaml/pyyaml/issues/165
+class YamlLoader(Loader):
+    def construct_mapping(self, node, deep=False):  # type: ignore[no-untyped-def]
+        mapping = []
+        for key_node, value_node in node.value:
+            key = self.construct_object(key_node, deep=deep)  # type: ignore[no-untyped-call]
+            assert (
+                key not in mapping
+            ), f"Found a duplicate key in the yaml. key={key}, line={node.start_mark.line}"
+            mapping.append(key)
+        mapping = super().construct_mapping(node, deep=deep)  # type: ignore[no-untyped-call]
+        return mapping